davanstrien/dataset_cards_with_metadata · Datasets at Hugging Face

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open-llm-leaderboard-old/requests	open-llm-leaderboard-old	2024-06-19T21:36:08Z	22,162	22	[ "license:apache-2.0", "size_categories:n<1K", "format:json", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "region:us" ]	[]	2023-06-19T15:15:07Z	null	--- license: apache-2.0 --- ![HuggingFace LeaderBoard](https://cdn-uploads.huggingface.co/production/uploads/6202a599216215a22221dea9/Uh5JX7Kq-rUxoVrdsV-M-.gif) # Open LLM Leaderboard Requests This repository contains the request files of models that have been submitted to the Open LLM Leaderboard. You can take a look at the current status of your model by finding its request file in this dataset. If your model failed, feel free to open an issue on the Open LLM Leaderboard! (We don't follow issues in this repository as often) ## Evaluation Methodology The evaluation process involves running your models against several benchmarks from the Eleuther AI Harness, a unified framework for measuring the effectiveness of generative language models. Below is a brief overview of each benchmark: 1. AI2 Reasoning Challenge (ARC) - Grade-School Science Questions (25-shot) 2. HellaSwag - Commonsense Inference (10-shot) 3. MMLU - Massive Multi-Task Language Understanding, knowledge on 57 domains (5-shot) 4. TruthfulQA - Propensity to Produce Falsehoods (0-shot) 5. Winogrande - Adversarial Winograd Schema Challenge (5-shot) 6. GSM8k - Grade School Math Word Problems Solving Complex Mathematical Reasoning (5-shot) Together, these benchmarks provide an assessment of a model's capabilities in terms of knowledge, reasoning, and some math, in various scenarios. ## Accessing Your Results To view the numerical results of your evaluated models, visit the dedicated Hugging Face Dataset at https://huggingface.co/datasets/open-llm-leaderboard/results. This dataset offers a thorough breakdown of each model's performance on the individual benchmarks. ## Exploring Model Details For further insights into the inputs and outputs of specific models, locate the "📄" emoji associated with the desired model within this repository. Clicking on this icon will direct you to the respective GitHub page containing detailed information about the model's behavior during the evaluation process.
toxigen/toxigen-data	toxigen	2024-06-17T16:35:29Z	4,574	55	[ "task_categories:text-classification", "task_ids:hate-speech-detection", "annotations_creators:expert-generated", "language_creators:machine-generated", "multilinguality:monolingual", "source_datasets:original", "size_categories:100K<n<1M", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2203.09509", "region:us" ]	[ "text-classification" ]	2022-05-01T15:49:02Z	1	--- annotations_creators: - expert-generated language_creators: - machine-generated multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - text-classification task_ids: - hate-speech-detection pretty_name: ToxiGen dataset_info: - config_name: annotated features: - name: text dtype: string - name: target_group dtype: string - name: factual? dtype: string - name: ingroup_effect dtype: string - name: lewd dtype: string - name: framing dtype: string - name: predicted_group dtype: string - name: stereotyping dtype: string - name: intent dtype: float64 - name: toxicity_ai dtype: float64 - name: toxicity_human dtype: float64 - name: predicted_author dtype: string - name: actual_method dtype: string splits: - name: test num_bytes: 364518 num_examples: 940 - name: train num_bytes: 3238381 num_examples: 8960 download_size: 768996 dataset_size: 3602899 - config_name: annotations features: - name: Input.prompt dtype: string - name: Input.text dtype: string - name: Input.time dtype: string - name: Input.generation_method dtype: string - name: Input.prompt_label dtype: string - name: Input.target_group dtype: string - name: Input.binary_prompt_label dtype: int64 - name: Answer.annotatorAge dtype: string - name: Answer.annotatorGender dtype: string - name: Answer.annotatorMinority dtype: string - name: Answer.annotatorPolitics.1 dtype: bool - name: Answer.annotatorPolitics.2 dtype: bool - name: Answer.annotatorPolitics.3 dtype: bool - name: Answer.annotatorPolitics.4 dtype: bool - name: Answer.annotatorPolitics.5 dtype: bool - name: Answer.annotatorRace dtype: string - name: Answer.factSelect dtype: string - name: Answer.framingQ dtype: string - name: Answer.inGroup.on dtype: bool - name: Answer.ingroup.1 dtype: bool - name: Answer.ingroup.2 dtype: bool - name: Answer.ingroup.3 dtype: bool - name: Answer.intent.1 dtype: bool - name: Answer.intent.2 dtype: bool - name: Answer.intent.3 dtype: bool - name: Answer.intent.4 dtype: bool - name: Answer.intent.5 dtype: bool - name: Answer.lewd.1 dtype: bool - name: Answer.lewd.2 dtype: bool - name: Answer.lewd.3 dtype: bool - name: Answer.refTarget dtype: string - name: Answer.stateFrame dtype: string - name: Answer.stateGroup dtype: string - name: Answer.stereo.1 dtype: bool - name: Answer.stereo.2 dtype: bool - name: Answer.stereo.3 dtype: bool - name: Answer.toAI.1 dtype: bool - name: Answer.toAI.2 dtype: bool - name: Answer.toAI.3 dtype: bool - name: Answer.toAI.4 dtype: bool - name: Answer.toAI.5 dtype: bool - name: Answer.toPER.1 dtype: bool - name: Answer.toPER.2 dtype: bool - name: Answer.toPER.3 dtype: bool - name: Answer.toPER.4 dtype: bool - name: Answer.toPER.5 dtype: bool - name: Answer.writer.1 dtype: bool - name: Answer.writer.2 dtype: bool - name: HashedWorkerId dtype: int64 splits: - name: train num_bytes: 21933185 num_examples: 27450 download_size: 3350653 dataset_size: 21933185 - config_name: prompts features: - name: text dtype: string splits: - name: hate_trans_1k num_bytes: 585554 num_examples: 1000 - name: neutral_black_1k num_bytes: 857769 num_examples: 1000 - name: hate_native_american_1k num_bytes: 480000 num_examples: 1000 - name: neutral_immigrant_1k num_bytes: 342243 num_examples: 1000 - name: hate_middle_east_1k num_bytes: 426551 num_examples: 1000 - name: neutral_lgbtq_1k num_bytes: 914319 num_examples: 1000 - name: neutral_women_1k num_bytes: 394963 num_examples: 1000 - name: neutral_chinese_1k num_bytes: 412062 num_examples: 1000 - name: hate_latino_1k num_bytes: 708000 num_examples: 1000 - name: hate_bisexual_1k num_bytes: 447794 num_examples: 1000 - name: hate_mexican_1k num_bytes: 675444 num_examples: 1000 - name: hate_asian_1k num_bytes: 503093 num_examples: 1000 - name: neutral_mental_disability_1k num_bytes: 556905 num_examples: 1000 - name: neutral_mexican_1k num_bytes: 483603 num_examples: 1000 - name: hate_mental_disability_1k num_bytes: 480620 num_examples: 1000 - name: neutral_bisexual_1k num_bytes: 915612 num_examples: 1000 - name: neutral_latino_1k num_bytes: 470000 num_examples: 1000 - name: hate_chinese_1k num_bytes: 384934 num_examples: 1000 - name: neutral_jewish_1k num_bytes: 649674 num_examples: 1000 - name: hate_muslim_1k num_bytes: 425760 num_examples: 1000 - name: neutral_asian_1k num_bytes: 615895 num_examples: 1000 - name: hate_physical_disability_1k num_bytes: 413643 num_examples: 1000 - name: hate_jewish_1k num_bytes: 573538 num_examples: 1000 - name: neutral_muslim_1k num_bytes: 491659 num_examples: 1000 - name: hate_immigrant_1k num_bytes: 285309 num_examples: 1000 - name: hate_black_1k num_bytes: 745295 num_examples: 1000 - name: hate_lgbtq_1k num_bytes: 577075 num_examples: 1000 - name: hate_women_1k num_bytes: 389583 num_examples: 1000 - name: neutral_middle_east_1k num_bytes: 415319 num_examples: 1000 - name: neutral_native_american_1k num_bytes: 586993 num_examples: 1000 - name: neutral_physical_disability_1k num_bytes: 458497 num_examples: 1000 download_size: 1698170 dataset_size: 16667706 - config_name: train features: - name: prompt dtype: string - name: generation dtype: string - name: generation_method dtype: string - name: group dtype: string - name: prompt_label dtype: int64 - name: roberta_prediction dtype: float64 splits: - name: train num_bytes: 169400442 num_examples: 250951 download_size: 18784380 dataset_size: 169400442 configs: - config_name: annotated default: true data_files: - split: test path: annotated/test-* - split: train path: annotated/train-* - config_name: annotations data_files: - split: train path: annotations/train-* - config_name: prompts data_files: - split: hate_trans_1k path: prompts/hate_trans_1k-* - split: neutral_black_1k path: prompts/neutral_black_1k-* - split: hate_native_american_1k path: prompts/hate_native_american_1k-* - split: neutral_immigrant_1k path: prompts/neutral_immigrant_1k-* - split: hate_middle_east_1k path: prompts/hate_middle_east_1k-* - split: neutral_lgbtq_1k path: prompts/neutral_lgbtq_1k-* - split: neutral_women_1k path: prompts/neutral_women_1k-* - split: neutral_chinese_1k path: prompts/neutral_chinese_1k-* - split: hate_latino_1k path: prompts/hate_latino_1k-* - split: hate_bisexual_1k path: prompts/hate_bisexual_1k-* - split: hate_mexican_1k path: prompts/hate_mexican_1k-* - split: hate_asian_1k path: prompts/hate_asian_1k-* - split: neutral_mental_disability_1k path: prompts/neutral_mental_disability_1k-* - split: neutral_mexican_1k path: prompts/neutral_mexican_1k-* - split: hate_mental_disability_1k path: prompts/hate_mental_disability_1k-* - split: neutral_bisexual_1k path: prompts/neutral_bisexual_1k-* - split: neutral_latino_1k path: prompts/neutral_latino_1k-* - split: hate_chinese_1k path: prompts/hate_chinese_1k-* - split: neutral_jewish_1k path: prompts/neutral_jewish_1k-* - split: hate_muslim_1k path: prompts/hate_muslim_1k-* - split: neutral_asian_1k path: prompts/neutral_asian_1k-* - split: hate_physical_disability_1k path: prompts/hate_physical_disability_1k-* - split: hate_jewish_1k path: prompts/hate_jewish_1k-* - split: neutral_muslim_1k path: prompts/neutral_muslim_1k-* - split: hate_immigrant_1k path: prompts/hate_immigrant_1k-* - split: hate_black_1k path: prompts/hate_black_1k-* - split: hate_lgbtq_1k path: prompts/hate_lgbtq_1k-* - split: hate_women_1k path: prompts/hate_women_1k-* - split: neutral_middle_east_1k path: prompts/neutral_middle_east_1k-* - split: neutral_native_american_1k path: prompts/neutral_native_american_1k-* - split: neutral_physical_disability_1k path: prompts/neutral_physical_disability_1k-* - config_name: train data_files: - split: train path: train/train-* --- # Dataset Card for ToxiGen ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Fields](#data-instances) - [Additional Information](#additional-information) - [Citation Information](#citation-information) ## Sign up for Data Access To access ToxiGen, first fill out [this form](https://forms.office.com/r/r6VXX8f8vh). ## Dataset Description - Repository: https://github.com/microsoft/toxigen - Paper: https://arxiv.org/abs/2203.09509 - Point of Contact #1: [Tom Hartvigsen]([email protected]) - Point of Contact #2: [Saadia Gabriel]([email protected]) ### Dataset Summary This dataset is for implicit hate speech detection. All instances were generated using GPT-3 and the methods described in [our paper](https://arxiv.org/abs/2203.09509). ### Languages All text is written in English. ## Dataset Structure ### Data Fields We release TOXIGEN as a dataframe with the following fields: - prompt is the prompt used for generation. - generation is the TOXIGEN generated text. - generation_method denotes whether or not ALICE was used to generate the corresponding generation. If this value is ALICE, then ALICE was used, if it is TopK, then ALICE was not used. - prompt_label is the binary value indicating whether or not the prompt is toxic (1 is toxic, 0 is benign). - group indicates the target group of the prompt. - roberta_prediction is the probability predicted by our corresponding RoBERTa model for each instance. ### Citation Information ```bibtex @inproceedings{hartvigsen2022toxigen, title={ToxiGen: A Large-Scale Machine-Generated Dataset for Implicit and Adversarial Hate Speech Detection}, author={Hartvigsen, Thomas and Gabriel, Saadia and Palangi, Hamid and Sap, Maarten and Ray, Dipankar and Kamar, Ece}, booktitle={Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics}, year={2022} } ```
togethercomputer/RedPajama-Data-1T	togethercomputer	2024-06-17T11:36:03Z	1,926	1,084	[ "task_categories:text-generation", "language:en", "size_categories:1M<n<10M", "modality:text", "library:datasets", "library:mlcroissant", "region:us" ]	[ "text-generation" ]	2023-04-17T06:28:35Z	null	--- task_categories: - text-generation language: - en pretty_name: Red Pajama 1T --- ### Getting Started The dataset consists of 2084 jsonl files. You can download the dataset using HuggingFace: ```python from datasets import load_dataset ds = load_dataset("togethercomputer/RedPajama-Data-1T") ``` Or you can directly download the files using the following command: ``` wget 'https://data.together.xyz/redpajama-data-1T/v1.0.0/urls.txt' while read line; do dload_loc=${line#https://data.together.xyz/redpajama-data-1T/v1.0.0/} mkdir -p $(dirname $dload_loc) wget "$line" -O "$dload_loc" done < urls.txt ``` After downloading the files, you can load the dataset from disk by setting the `RED_PAJAMA_DATA_DIR` environment variable to the directory containing the files: ```python import os from datasets import load_dataset os.environ["RED_PAJAMA_DATA_DIR"] = "/path/to/download" ds = load_dataset("togethercomputer/RedPajama-Data-1T") ``` A smaller 1B-token sample of the dataset can be found [here](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T-Sample). A full set of scripts to recreate the dataset from scratch can be found [here](https://github.com/togethercomputer/RedPajama-Data). ### Dataset Summary RedPajama is a clean-room, fully open-source implementation of the LLaMa dataset. \| Dataset \| Token Count \| \|---------------\|-------------\| \| Commoncrawl \| 878 Billion \| \| C4 \| 175 Billion \| \| GitHub \| 59 Billion \| \| ArXiv \| 28 Billion \| \| Wikipedia \| 24 Billion \| \| StackExchange \| 20 Billion \| \| Total \| 1.2 Trillion \| ### Languages Primarily English, though the Wikipedia slice contains multiple languages. ## Dataset Structure The dataset structure is as follows: ```json { "text": ..., "meta": {"url": "...", "timestamp": "...", "source": "...", "language": "...", ...}, "red_pajama_subset": "common_crawl" \| "c4" \| "github" \| "arxiv" \| "wikipedia" \| "stackexchange" } ``` ## Dataset Creation This dataset was created to follow the LLaMa paper as closely as possible to try to reproduce its recipe. ### Source Data #### Commoncrawl We download five dumps from Commoncrawl, and run the dumps through the official `cc_net` pipeline. We then deduplicate on the paragraph level, and filter out low quality text using a linear classifier trained to classify paragraphs as Wikipedia references or random Commoncrawl samples. #### C4 C4 is downloaded from Huggingface. The only preprocessing step is to bring the data into our own format. #### GitHub The raw GitHub data is downloaded from Google BigQuery. We deduplicate on the file level and filter out low quality files and only keep projects that are distributed under the MIT, BSD, or Apache license. #### Wikipedia We use the Wikipedia dataset available on Huggingface, which is based on the Wikipedia dump from 2023-03-20 and contains text in 20 different languages. The dataset comes in preprocessed format, so that hyperlinks, comments and other formatting boilerplate has been removed. #### Gutenberg and Books3 <div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400"> <p><b>Defunct:</b> The 'book' config is defunct and no longer accessible due to reported copyright infringement for the Book3 dataset contained in this config.</p> </div> #### ArXiv ArXiv data is downloaded from Amazon S3 in the `arxiv` requester pays bucket. We only keep latex source files and remove preambles, comments, macros and bibliographies. #### Stackexchange The Stack Exchange split of the dataset is download from the [Internet Archive](https://archive.org/download/stackexchange). Here we only keep the posts from the 28 largest sites, remove html tags, group the posts into question-answer pairs, and order answers by their score. ### SHA256 Checksums SHA256 checksums for the dataset files for each data source are available here: ``` https://data.together.xyz/redpajama-data-1T/v1.0.0/sha256/arxiv_SHA256SUMS.txt https://data.together.xyz/redpajama-data-1T/v1.0.0/sha256/c4_SHA256SUMS.txt https://data.together.xyz/redpajama-data-1T/v1.0.0/sha256/common_crawl_SHA256SUMS.txt https://data.together.xyz/redpajama-data-1T/v1.0.0/sha256/github_SHA256SUMS.txt https://data.together.xyz/redpajama-data-1T/v1.0.0/sha256/stackexchange_SHA256SUMS.txt https://data.together.xyz/redpajama-data-1T/v1.0.0/sha256/wikipedia_SHA256SUMS.txt ``` To cite RedPajama, please use: ``` @software{together2023redpajama, author = {Together Computer}, title = {RedPajama: An Open Source Recipe to Reproduce LLaMA training dataset}, month = April, year = 2023, url = {https://github.com/togethercomputer/RedPajama-Data} } ``` ### License Please refer to the licenses of the data subsets you use. * [Common Crawl Foundation Terms of Use](https://commoncrawl.org/terms-of-use/full/) * [C4 license](https://huggingface.co/datasets/allenai/c4#license) * GitHub was limited to MIT, BSD, or Apache licenses only * [ArXiv Terms of Use](https://info.arxiv.org/help/api/tou.html) * [Wikipedia License](https://huggingface.co/datasets/wikipedia#licensing-information) * [StackExchange license on the Internet Archive](https://archive.org/details/stackexchange) <!-- ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed] -->
starmpcc/Asclepius-Synthetic-Clinical-Notes	starmpcc	2024-06-14T04:16:04Z	466	86	[ "task_categories:question-answering", "task_categories:summarization", "task_categories:text-generation", "language:en", "license:cc-by-nc-sa-4.0", "size_categories:100K<n<1M", "format:csv", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2309.00237", "region:us", "medical", "synthetic" ]	[ "question-answering", "summarization", "text-generation" ]	2023-09-01T01:47:59Z	null	--- license: cc-by-nc-sa-4.0 task_categories: - question-answering - summarization - text-generation language: - en tags: - medical - synthetic pretty_name: 'Asclepius: Synthetic Clincal Notes & Instruction Dataset' size_categories: - 100K<n<1M --- # Asclepius: Synthetic Clincal Notes & Instruction Dataset ## Dataset Description - Repository: [Github](https://github.com/starmpcc/Asclepius) - Paper: https://arxiv.org/abs/2309.00237 ### Dataset Summary This dataset is official dataset for Asclepius [(arxiv)](https://arxiv.org/abs/2309.00237) This dataset is composed with Clinical Note - Question - Answer format to build a clinical LLMs. - We first synthesized synthetic notes from [PMC-Patients](https://huggingface.co/datasets/zhengyun21/PMC-Patients) case reports with GPT-3.5 - Then, we generate instruction-answer pairs for 157k synthetic discharge summaries ### Supported Tasks - This dataset covers below 8 tasks - Named Entity Recognition - Abbreviation Expansion - Relation Extraction - Temporal Information Extraction - Coreference Resolution - Paraphrasing - Summarization - Question Answering ### Languages English ## Dataset Structure ### Data Instances - `synthetic.csv` - Clinical Note - Question - Answer pairs ### Data Fields - `patient_id`: Unique case report id from PMC-Patients - `patient`: Case report text - `question`: GPT-3.5 generated instruction from patient. The used prompt can be checked on github. - `answer`: GPT-3.5 generated answer for given case report and question - `task`: Corresponding category of question. One of above listsed ## Dataset Creation ### Source Data [PMC-Patients](https://huggingface.co/datasets/zhengyun21/PMC-Patients) ### Annotations We used GPT-3.5-turbo (version 0314). You can check the prompts on our github. ## Additional Information ### Models - [Asclepius-7B](https://huggingface.co/starmpcc/Asclepius-7B) - [Asclepius-13B](https://huggingface.co/starmpcc/Asclepius-13B) - [Asclepius-Llama2-7B](https://huggingface.co/starmpcc/Asclepius-Llama2-7B) - [Asclepius-Llama2-13B](https://huggingface.co/starmpcc/Asclepius-Llama2-13B) - [Asclepius-Llama3-8B](https://huggingface.co/starmpcc/Asclepius-Llama3-8B) - [Asclepius-Mistral-7B-v0.3](https://huggingface.co/starmpcc/Asclepius-Mistral-7B-v0.3) ### Variants - The instruction-answer pairs generated from MIMIC-III discharge summaries and the models trained with them are now available on [Physionet](https://physionet.org/content/asclepius-r/1.0.0/)! ### Licensing Information CC-BY-NC-SA 4.0 ### Citation Information ``` @misc{kweon2023publicly, title={Publicly Shareable Clinical Large Language Model Built on Synthetic Clinical Notes}, author={Sunjun Kweon and Junu Kim and Jiyoun Kim and Sujeong Im and Eunbyeol Cho and Seongsu Bae and Jungwoo Oh and Gyubok Lee and Jong Hak Moon and Seng Chan You and Seungjin Baek and Chang Hoon Han and Yoon Bin Jung and Yohan Jo and Edward Choi}, year={2023}, eprint={2309.00237}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
allenai/tulu-v2-sft-mixture	allenai	2024-05-24T21:29:24Z	521	123	[ "task_categories:question-answering", "task_categories:text-generation", "language:en", "license:odc-by", "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:2305.18290", "region:us" ]	[ "question-answering", "conversational", "text-generation" ]	2023-11-13T21:56:34Z	null	--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: dataset dtype: string - name: id dtype: string - name: messages list: - name: role dtype: string - name: content dtype: string splits: - name: train num_bytes: 1239293363 num_examples: 326154 download_size: 554561769 dataset_size: 1239293363 license: odc-by task_categories: - question-answering - conversational - text-generation language: - en size_categories: - 100K<n<1M --- <img src="https://huggingface.co/datasets/allenai/blog-images/resolve/main/tulu-v2/Tulu%20V2%20banner.png" alt="TuluV2 banner" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/> # Dataset Card for Tulu V2 Mix Note the [ODC-BY license](https://opendatacommons.org/licenses/by/1-0/), indicating that different licenses apply to subsets of the data. This means that some portions of the dataset are non-commercial. We present the mixture as a research artifact. Tulu is a series of language models that are trained to act as helpful assistants. The dataset consists of a mix of : * [FLAN](https://github.com/google-research/FLAN/tree/main) (Apache 2.0): We use 50,000 examples sampled from FLAN v2. To emphasize CoT-style reasoning, we sample another 50,000 examples from the CoT subset of the FLAN v2 mixture. * [Open Assistant 1](https://huggingface.co/datasets/OpenAssistant/oasst1) (Apache 2.0): We isolate the highest-scoring paths in each conversation tree and use these samples, resulting in 7,708 examples. * [ShareGPT](https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered) (Apache 2.0 listed, no official repo found): We use all 114,046 from our processed ShareGPT dataset, as we found ShareGPT gave strong performance in prior work. * [GPT4-Alpaca](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM#data-release) (CC By NC 4.0):We sample 20,000 samples from GPT-4 Alpaca to further include distilled GPT-4 data. * [Code-Alpaca](https://github.com/sahil280114/codealpaca) (CC By NC 4.0):We use all 20,022 examples from Code Alpaca, following our prior V1 mixture, in order to improve model code abilities. * [LIMA](https://huggingface.co/datasets/GAIR/lima) (CC BY-NC-SA): We use 1,030 examples from LIMA as an example of carefully curated data. * [WizardLM Evol Instruct](https://huggingface.co/datasets/WizardLM/WizardLM_evol_instruct_V2_196k) (No license provided): We subsample 30,000 examples from WizardLM, which contains distilled data of increasing diversity and complexity. * [Open-Orca](https://huggingface.co/datasets/Open-Orca/OpenOrca) (MIT): We sample 30,000 samples generated by GPT-4 from OpenOrca, a reproduction of Orca Mukherjee et al., 2023, which augments FLAN data with additional model-generated explanations * Hardcoded: A collection of prompts such as `Tell me about yourself' such that the model generates correct outputs given inquiries about its name or developers. We wrote 14 samples and repeat each sample 10 times in the mixture, resulting in 140 total samples. * Science: 7,544 examples from a mixture of scientific document understand tasks—including question answering, fact-checking, summarization, and information extraction (under development, standalone release soon). These are made by taking either just the training set of the subsets or the entire section if no splits are present. Tulu V2 is presented as a singular training split. [Tulu V2 DPO 70B](https://huggingface.co/allenai/tulu-2-dpo-70b), and is a fine-tuned version of Llama 2 that was trained on on a mix of publicly available, synthetic and human datasets using [Direct Preference Optimization (DPO)](https://arxiv.org/abs/2305.18290). Model Family: Other models and the dataset are found in the [Tulu V2 collection](https://huggingface.co/collections/allenai/tulu-v2-suite-6551b56e743e6349aab45101). The length distribution of the dataset can be seen below: <img src="https://huggingface.co/datasets/allenai/blog-images/resolve/main/tulu-v2/length_histogram_v2.png" alt="TuluV2 histogram" width="600" style="margin-left:'auto' margin-right:'auto' display:'block'"/> Tulu V1 Mix can be found [here](https://huggingface.co/datasets/allenai/tulu-v1). Note: Some samples contain empty turns as noted in [this github issue](https://github.com/allenai/open-instruct/issues/161). We will not remove these from this release to ensure reproducibility but you may wish to explicitly filter them out when training your own models! ### Science data note The included science data is from the following categories: <img src="https://huggingface.co/datasets/allenai/blog-images/resolve/main/tulu-v2/science_data.png" alt="TuluV2 science data mix" width="600" style="margin-left:'auto' margin-right:'auto' display:'block'"/> Note that some of the examples include an off-by-one error in the sentence indexing that had a small or negligible impact on performance. This was found during testing and will be updated in future versions, with the detailed release of the dataset artifact itself coming in a future release. ### License We are releasing this dataset under the terms of [ODC-BY](https://opendatacommons.org/licenses/by/1-0/). By using this, you are also bound by the [Common Crawl terms of use](https://commoncrawl.org/terms-of-use/) in respect of the content contained in the dataset.
ipipan/polqa	ipipan	2024-05-24T15:35:50Z	214	10	[ "task_categories:question-answering", "task_categories:text-retrieval", "task_categories:text2text-generation", "task_ids:open-domain-qa", "task_ids:document-retrieval", "task_ids:abstractive-qa", "annotations_creators:expert-generated", "language:pl", "license:cc-by-sa-4.0", "size_categories:10K<n<100K", "arxiv:2212.08897", "region:us" ]	[ "question-answering", "text-retrieval", "text2text-generation" ]	2022-12-17T15:03:58Z	1	--- task_categories: - question-answering - text-retrieval - text2text-generation task_ids: - open-domain-qa - document-retrieval - abstractive-qa language: - pl pretty_name: PolQA size_categories: - 10K<n<100K annotations_creators: - expert-generated license: cc-by-sa-4.0 --- # Dataset Card for PolQA Dataset ## Dataset Description - Paper: [Improving Question Answering Performance through Manual Annotation: Costs, Benefits and Strategies](https://arxiv.org/abs/2212.08897) - Point of Contact: [Piotr Rybak](mailto:[email protected]) ### Dataset Summary PolQA is the first Polish dataset for open-domain question answering. It consists of 7,000 questions, 87,525 manually labeled evidence passages, and a corpus of over 7 million candidate passages. The dataset can be used to train both a passage retriever and an abstractive reader. ### Supported Tasks and Leaderboards - `open-domain-qa`: The dataset can be used to train a model for open-domain question answering. Success on this task is typically measured using [metric defined during PolEval 2021](https://2021.poleval.pl/tasks/task4). - `document-retrieval`: The dataset can be used to train a model for document retrieval. Success on this task is typically measured by [top-k retrieval accuracy](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.top_k_accuracy_score.html) or [NDCG](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.ndcg_score.html). - `abstractive-qa`: The dataset can be used to train a model for abstractive question answering. Success on this task is typically measured using [metric defined during PolEval 2021](https://2021.poleval.pl/tasks/task4). ### Languages The text is in Polish, as spoken by the host of the [Jeden z Dziesięciu](https://pl.wikipedia.org/wiki/Jeden_z_dziesi%C4%99ciu) TV show (questions) and [Polish Wikipedia](https://pl.wikipedia.org/) editors (passages). The BCP-47 code for Polish is pl-PL. ## Dataset Structure ### Data Instances The main part of the dataset consists of manually annotated question-passage pairs. For each instance, there is a `question`, a passage (`passage_id`, `passage_title`, `passage_text`), and a boolean indicator if the passage is `relevant` for the given question (i.e. does it contain the answers). For each `question` there is a list of possible `answers` formulated in a natural language, in a way a Polish speaker would answer the questions. It means that the answers might contain prepositions, be inflected, and contain punctuation. In some cases, the answer might have multiple correct variants, e.g. numbers are written as numerals and words, synonyms, abbreviations and their expansions. Additionally, we provide a classification of each question-answer pair based on the `question_formulation`, the `question_type`, and the `entity_type/entity_subtype`, according to the taxonomy proposed by [Maciej Ogrodniczuk and Piotr Przybyła (2021)](http://nlp.ipipan.waw.pl/Bib/ogr:prz:21:poleval.pdf). ``` { 'question_id': 6, 'passage_title': 'Mumbaj', 'passage_text': 'Mumbaj lub Bombaj (marathi मुंबई, trb.: Mumbaj; ang. Mumbai; do 1995 Bombay) – stolica indyjskiego stanu Maharasztra, położona na wyspie Salsette, na Morzu Arabskim.', 'passage_wiki': 'Mumbaj lub Bombaj (mr. मुंबई, trb.: "Mumbaj"; ang. Mumbai; do 1995 Bombay) – stolica indyjskiego stanu Maharasztra, położona na wyspie Salsette, na Morzu Arabskim. Wraz z miastami satelitarnymi tworzy najludniejszą po Delhi aglomerację liczącą 23 miliony mieszkańców. Dzięki naturalnemu położeniu jest to największy port morski kraju. Znajdują się tutaj także najsilniejsze giełdy Azji Południowej: National Stock Exchange of India i Bombay Stock Exchange.', 'passage_id': '42609-0', 'duplicate': False, 'question': 'W którym państwie leży Bombaj?', 'relevant': True, 'annotated_by': 'Igor', 'answers': "['w Indiach', 'Indie']", 'question_formulation': 'QUESTION', 'question_type': 'SINGLE ENTITY', 'entity_type': 'NAMED', 'entity_subtype': 'COUNTRY', 'split': 'train', 'passage_source': 'human' } ``` The second part of the dataset is a corpus of Polish Wikipedia (March 2022 snapshot) passages. The raw Wikipedia snapshot was parsed using [WikiExtractor](https://github.com/attardi/wikiextractor) and split into passages at the ends of the paragraphs or if the passage was longer than 500 characters. ``` { 'id': '42609-0', 'title': 'Mumbaj', 'text': 'Mumbaj lub Bombaj (mr. मुंबई, trb.: "Mumbaj"; ang. Mumbai; do 1995 Bombay) – stolica indyjskiego stanu Maharasztra, położona na wyspie Salsette, na Morzu Arabskim. Wraz z miastami satelitarnymi tworzy najludniejszą po Delhi aglomerację liczącą 23 miliony mieszkańców. Dzięki naturalnemu położeniu jest to największy port morski kraju. Znajdują się tutaj także najsilniejsze giełdy Azji Południowej: National Stock Exchange of India i Bombay Stock Exchange.' } ``` ### Data Fields Question-passage pairs: - `question_id`: an integer id of the question - `passage_title`: a string containing the title of the Wikipedia article - `passage_text`: a string containing the passage text as extracted by the human annotator - `passage_wiki`: a string containing the passage text as it can be found in the provided Wikipedia corpus. Empty if the passage doesn't exist in the corpus. - `passage_id`: a string containing the id of the passage from the provided Wikipedia corpus. Empty if the passage doesn't exist in the corpus. - `duplicate`: a boolean flag representing whether a question-passage pair is duplicated in the dataset. This occurs when the same passage was found in multiple passage sources. - `question`: a string containing the question - `relevant`: a boolean flag representing whether a passage is relevant to the question (i.e. does it contain the answers) - `annotated_by`: a string containing the name of the annotator who verified the relevance of the pair - `answers`: a string containing a list of possible short answers to the question - `question_formulation`: a string containing a kind of expression used to request information. One of the following: - `QUESTION`, e.g. What is the name of the first letter of the Greek alphabet? - `COMMAND`, e.g. Expand the abbreviation ’CIA’. - `COMPOUND`, e.g. This French writer, born in the 19th century, is considered a pioneer of sci-fi literature. What is his name? - `question_type`: a string indicating what type of information is sought by the question. One of the following: - `SINGLE ENTITY`, e.g. Who is the hero in the Tomb Rider video game series? - `MULTIPLE ENTITIES`, e.g. Which two seas are linked by the Corinth Canal? - `ENTITY CHOICE`, e.g. Is "Sombrero" a type of dance, a hat, or a dish? - `YES/NO`, e.g. When the term of office of the Polish Sejm is terminated, does it apply to the Senate as well? - `OTHER NAME`, e.g. What was the nickname of Louis I, the King of the Franks? - `GAP FILLING`, e.g. Finish the proverb: "If you fly with the crows... ". - `entity_type`: a string containing a type of the sought entity. One of the following: `NAMED`, `UNNAMED`, or `YES/NO`. - `entity_subtype`: a string containing a subtype of the sought entity. Can take one of the 34 different values. - `split`: a string containing the split of the dataset. One of the following: `train`, `valid`, or `test`. - `passage_source`: a string containing the source of the passage. One of the following: - `human`: the passage was proposed by a human annotator using any internal (i.e. Wikipedia search) or external (e.g. Google) search engines and any keywords or queries they considered useful - `hard-negatives`: the passage was proposed using a neural retriever trained on the passages found by the human annotators - `zero-shot`: the passage was proposed by the BM25 retriever and re-ranked using [multilingual cross-encoder](https://huggingface.co/unicamp-dl/mMiniLM-L6-v2-mmarco-v2) Corpus of passages: - `id`: a string representing the Wikipedia article id and the index of extracted passage. Matches the `passage_id` from the main part of the dataset. - `title`: a string containing the title of the Wikipedia article. Matches the `passage_title` from the main part of the dataset. - `text`: a string containing the passage text. Matches the `passage_wiki` from the main part of the dataset. ### Data Splits The questions are assigned into one of three splits: `train`, `validation`, and `test`. The `validation` and `test` questions are randomly sampled from the `test-B` dataset from the [PolEval 2021](https://2021.poleval.pl/tasks/task4) competition. \| \| # questions \| # positive passages \| # negative passages \| \|------------\|------------:\|--------------------:\|--------------------:\| \| train \| 5,000 \| 27,131 \| 34,904 \| \| validation \| 1,000 \| 5,839 \| 6,927 \| \| test \| 1,000 \| 5,938 \| 6,786 \| ## Dataset Creation ### Curation Rationale The PolQA dataset was created to support and promote the research in the open-domain question answering for Polish. It also serves as a benchmark to evaluate OpenQA systems. ### Source Data #### Initial Data Collection and Normalization The majority of questions come from two existing resources, the 6,000 questions from the [PolEval 2021 shared task on QA](https://2021.poleval.pl/tasks/task4) and additional 1,000 questions gathered by one of the shared task [participants](http://poleval.pl/files/poleval2021.pdf#page=151). Originally, the questions come from collections associated with TV shows, both officially published and gathered online by their fans, as well as questions used in actual quiz competitions, on TV or online. The evidence passages come from the Polish Wikipedia (March 2022 snapshot). The raw Wikipedia snapshot was parsed using [WikiExtractor](https://github.com/attardi/wikiextractor) and split into passages at the ends of the paragraphs or if the passage was longer than 500 characters. #### Who are the source language producers? The questions come from various sources and their authors are unknown but are mostly analogous (or even identical) to questions asked during the [Jeden z Dziesięciu](https://pl.wikipedia.org/wiki/Jeden_z_dziesi%C4%99ciu) TV show. The passages were written by the editors of the Polish Wikipedia. ### Annotations #### Annotation process Two approaches were used to annotate the question-passage pairs. Each of them consists of two phases: the retrieval of candidate passages and the manual verification of their relevance. In the first approach, we asked annotators to use internal (i.e. Wikipedia search) or external (e.g. Google) search engines to find up to five relevant passages using any keywords or queries they consider useful (`passage_source="human"`). Based on those passages, we trained the neural retriever to extend the number of relevant passages, as well as to retrieve the hard negatives (`passage_source="hard-negatives"`). In the second approach, the passage candidates were proposed by the BM25 retriever and re-ranked using [multilingual cross-encoder](https://huggingface.co/unicamp-dl/mMiniLM-L6-v2-mmarco-v2) (`passage_source="zero-shot"`). In both cases, all proposed question-passage pairs were manually verified by the annotators. We release the annotation guidelines [here](https://docs.google.com/document/d/1LDW7EJFH0bm-FRlxM_uHb0mqJzKHiewOFBHe5qZnTW8/edit?usp=sharing). #### Who are the annotators? The annotation team consisted of 16 annotators, all native Polish speakers, most of them having linguistic backgrounds and previous experience as an annotator. ### Personal and Sensitive Information The dataset does not contain any personal or sensitive information. ## Considerations for Using the Data ### Social Impact of Dataset This dataset was created to promote the research in the open-domain question answering for Polish and allow developing question answering systems. ### Discussion of Biases The passages proposed by the `hard-negative` and `zero-shot` methods are bound to be easier to retrieve by retrievers since they were proposed by such. To mitigate this bias, we include the passages found by the human annotators in an unconstrained way (`passage_source="human"`). We hypothesize that it will result in more unbiased and diverse examples. Moreover, we asked the annotators to find not one but up to five passages, preferably from different articles to even further increase passage diversity. ### Other Known Limitations The PolQA dataset focuses on trivia questions which might limit its usefulness in real-world applications since neural retrievers generalize poorly to other domains. ## Additional Information ### Dataset Curators The PolQA dataset was developed by Piotr Rybak, Piotr Przybyła, and Maciej Ogrodniczuk from the [Institute of Computer Science, Polish Academy of Sciences](http://zil.ipipan.waw.pl/). This work was supported by the European Regional Development Fund as a part of 2014–2020 Smart Growth Operational Programme, CLARIN — Common Language Resources and Technology Infrastructure, project no. POIR.04.02.00-00C002/19. ### Licensing Information CC BY-SA 4.0 ### Citation Information ``` @inproceedings{rybak-etal-2024-polqa-polish, title = "{P}ol{QA}: {P}olish Question Answering Dataset", author = "Rybak, Piotr and Przyby{\l}a, Piotr and Ogrodniczuk, Maciej", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.1125", pages = "12846--12855", abstract = "Recently proposed systems for open-domain question answering (OpenQA) require large amounts of training data to achieve state-of-the-art performance. However, data annotation is known to be time-consuming and therefore expensive to acquire. As a result, the appropriate datasets are available only for a handful of languages (mainly English and Chinese). In this work, we introduce and publicly release PolQA, the first Polish dataset for OpenQA. It consists of 7,000 questions, 87,525 manually labeled evidence passages, and a corpus of over 7,097,322 candidate passages. Each question is classified according to its formulation, type, as well as entity type of the answer. This resource allows us to evaluate the impact of different annotation choices on the performance of the QA system and propose an efficient annotation strategy that increases the passage retrieval accuracy@10 by 10.55 p.p. while reducing the annotation cost by 82{\%}.", } ```
wanng/midjourney-v5-202304-clean	wanng	2024-05-24T05:09:52Z	82	89	[ "task_categories:text-to-image", "task_categories:image-to-text", "language:en", "language:fr", "license:apache-2.0", "size_categories:1M<n<10M", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us", "midjourney" ]	[ "text-to-image", "image-to-text" ]	2023-05-26T06:58:05Z	null	--- license: apache-2.0 task_categories: - text-to-image - image-to-text language: - en - fr tags: - midjourney pretty_name: zolie size_categories: - 1K<n<10K --- # midjourney-v5-202304-clean ## 简介 Brief Introduction 非官方的，爬取自midjourney v5的2023年4月的数据，一共1701420条。 Unofficial, crawled from midjourney v5 for April 2023, 1,701,420 pairs in total. ## 数据集信息 Dataset Information 原始项目地址：https://huggingface.co/datasets/tarungupta83/MidJourney_v5_Prompt_dataset 我做了一些清洗，清理出了两个文件： - ori_prompts_df.parquet （1,255,812对，midjourney的四格图） ![ori_sample](https://cdn.discordapp.com/attachments/995431387333152778/1098283849076711424/mansonwu_A_charismatic_wealthy_young_man_is_fully_immersed_in_a_9bd4f414-eb40-4642-a381-f5ac56e99ec5.png) - upscaled_prompts_df.parquet （445,608对，使用了高清指令的图，这意味着这个图更受欢迎。） ![upscaled_sample](https://cdn.discordapp.com/attachments/984632520471633920/1105721768422948905/Tomberhood_The_intelligent_rescue_boat_on_the_beach_can_automat_e54faffe-0668-49e4-812d-713038bdc7bc.png) Original project address: https://huggingface.co/datasets/tarungupta83/MidJourney_v5_Prompt_dataset I did some cleaning and cleaned out two files: - ori_prompts_df.parquet (1,255,812 pairs, midjourney's four-frame diagrams) - upscaled_prompts_df.parquet (445,608 pairs, graphs that use the Upscale command, which means this one is more popular.)
lmms-lab/POPE	lmms-lab	2024-05-23T03:29:23Z	11,057	9	[ "size_categories:10K<n<100K", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2024-01-18T15:13:42Z	null	--- dataset_info: - config_name: Full features: - name: id dtype: string - name: question_id dtype: string - name: question dtype: string - name: answer dtype: string - name: image_source dtype: string - name: image dtype: image - name: category dtype: string splits: - name: adversarial num_bytes: 490408158.0 num_examples: 3000 - name: popular num_bytes: 490397000.0 num_examples: 3000 - name: random num_bytes: 490394976.0 num_examples: 3000 download_size: 255022914 dataset_size: 1471200134.0 - config_name: default features: - name: id dtype: string - name: question_id dtype: string - name: question dtype: string - name: answer dtype: string - name: image_source dtype: string - name: image dtype: image - name: category dtype: string splits: - name: test num_bytes: 1471200135.0 num_examples: 9000 download_size: 255022914 dataset_size: 1471200135.0 configs: - config_name: Full data_files: - split: adversarial path: Full/adversarial-* - split: popular path: Full/popular-* - split: random path: Full/random-* - config_name: default data_files: - split: test path: data/test-* --- <p align="center" width="100%"> <img src="https://i.postimg.cc/g0QRgMVv/WX20240228-113337-2x.png" width="100%" height="80%"> </p> # Large-scale Multi-modality Models Evaluation Suite > Accelerating the development of large-scale multi-modality models (LMMs) with `lmms-eval` 🏠 [Homepage](https://lmms-lab.github.io/) \| 📚 [Documentation](docs/README.md) \| 🤗 [Huggingface Datasets](https://huggingface.co/lmms-lab) # This Dataset This is a formatted version of [POPE](https://github.com/RUCAIBox/POPE). It is used in our `lmms-eval` pipeline to allow for one-click evaluations of large multi-modality models. ``` @article{li2023evaluating, title={Evaluating object hallucination in large vision-language models}, author={Li, Yifan and Du, Yifan and Zhou, Kun and Wang, Jinpeng and Zhao, Wayne Xin and Wen, Ji-Rong}, journal={arXiv preprint arXiv:2305.10355}, year={2023} } ```
ilsp/mmlu_greek	ilsp	2024-05-20T12:36:54Z	27,818	4	[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2024-04-01T14:53:41Z	null	--- dataset_info: - config_name: abstract_algebra features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 58157 num_examples: 100 - name: validation num_bytes: 6010 num_examples: 11 - name: dev num_bytes: 2497 num_examples: 5 download_size: 0 dataset_size: 66664 - config_name: all features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 20041347 num_examples: 14042 - name: validation num_bytes: 2196992 num_examples: 1531 - name: dev num_bytes: 360807 num_examples: 285 download_size: 10333898 dataset_size: 22599146 - config_name: anatomy features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 97333 num_examples: 135 - name: validation num_bytes: 9131 num_examples: 14 - name: dev num_bytes: 2731 num_examples: 5 download_size: 67694 dataset_size: 109195 - config_name: astronomy features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 141580 num_examples: 152 - name: validation num_bytes: 15462 num_examples: 16 - name: dev num_bytes: 6380 num_examples: 5 download_size: 95251 dataset_size: 163422 - config_name: business_ethics features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 101936 num_examples: 100 - name: validation num_bytes: 9096 num_examples: 11 - name: dev num_bytes: 6368 num_examples: 5 download_size: 77394 dataset_size: 117400 - config_name: clinical_knowledge features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 193539 num_examples: 265 - name: validation num_bytes: 20500 num_examples: 29 - name: dev num_bytes: 3720 num_examples: 5 download_size: 126056 dataset_size: 217759 - config_name: college_biology features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 152394 num_examples: 144 - name: validation num_bytes: 14995 num_examples: 16 - name: dev num_bytes: 4638 num_examples: 5 download_size: 105576 dataset_size: 172027 - config_name: college_chemistry features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 72251 num_examples: 100 - name: validation num_bytes: 6677 num_examples: 8 - name: dev num_bytes: 3862 num_examples: 5 download_size: 61210 dataset_size: 82790 - config_name: college_computer_science features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 135321 num_examples: 100 - name: validation num_bytes: 15037 num_examples: 11 - name: dev num_bytes: 8606 num_examples: 5 download_size: 101342 dataset_size: 158964 - config_name: college_mathematics features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 74448 num_examples: 100 - name: validation num_bytes: 8274 num_examples: 11 - name: dev num_bytes: 4276 num_examples: 5 download_size: 63556 dataset_size: 86998 - config_name: college_medicine features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 251805 num_examples: 173 - name: validation num_bytes: 24431 num_examples: 22 - name: dev num_bytes: 5031 num_examples: 5 download_size: 144635 dataset_size: 281267 - config_name: college_physics features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 90708 num_examples: 102 - name: validation num_bytes: 10367 num_examples: 11 - name: dev num_bytes: 4139 num_examples: 5 download_size: 68341 dataset_size: 105214 - config_name: computer_security features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 86922 num_examples: 100 - name: validation num_bytes: 14003 num_examples: 11 - name: dev num_bytes: 3445 num_examples: 5 download_size: 75244 dataset_size: 104370 - config_name: conceptual_physics features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 127706 num_examples: 235 - name: validation num_bytes: 14286 num_examples: 26 - name: dev num_bytes: 2978 num_examples: 5 download_size: 82813 dataset_size: 144970 - config_name: econometrics features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 136916 num_examples: 114 - name: validation num_bytes: 14730 num_examples: 12 - name: dev num_bytes: 4794 num_examples: 5 download_size: 86025 dataset_size: 156440 - config_name: electrical_engineering features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 80296 num_examples: 145 - name: validation num_bytes: 9138 num_examples: 16 - name: dev num_bytes: 2824 num_examples: 5 download_size: 62008 dataset_size: 92258 - config_name: elementary_mathematics features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 211831 num_examples: 378 - name: validation num_bytes: 27305 num_examples: 41 - name: dev num_bytes: 4252 num_examples: 5 download_size: 131272 dataset_size: 243388 - config_name: formal_logic features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 146101 num_examples: 126 - name: validation num_bytes: 18160 num_examples: 14 - name: dev num_bytes: 4917 num_examples: 5 download_size: 77094 dataset_size: 169178 - config_name: global_facts features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 55953 num_examples: 100 - name: validation num_bytes: 5672 num_examples: 10 - name: dev num_bytes: 3547 num_examples: 5 download_size: 0 dataset_size: 65172 - config_name: high_school_biology features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 338155 num_examples: 310 - name: validation num_bytes: 33555 num_examples: 32 - name: dev num_bytes: 4992 num_examples: 5 download_size: 200936 dataset_size: 376702 - config_name: high_school_chemistry features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 170771 num_examples: 203 - name: validation num_bytes: 20157 num_examples: 22 - name: dev num_bytes: 3387 num_examples: 5 download_size: 108321 dataset_size: 194315 - config_name: high_school_computer_science features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 139128 num_examples: 100 - name: validation num_bytes: 10800 num_examples: 9 - name: dev num_bytes: 9269 num_examples: 5 download_size: 99359 dataset_size: 159197 - config_name: high_school_european_history features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 799080 num_examples: 165 - name: validation num_bytes: 88740 num_examples: 18 - name: dev num_bytes: 34585 num_examples: 5 download_size: 503439 dataset_size: 922405 - config_name: high_school_geography features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 132655 num_examples: 198 - name: validation num_bytes: 13612 num_examples: 22 - name: dev num_bytes: 4597 num_examples: 5 download_size: 90939 dataset_size: 150864 - config_name: high_school_government_and_politics features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 215224 num_examples: 193 - name: validation num_bytes: 22888 num_examples: 21 - name: dev num_bytes: 5640 num_examples: 5 download_size: 132695 dataset_size: 243752 - config_name: high_school_macroeconomics features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 374553 num_examples: 390 - name: validation num_bytes: 41817 num_examples: 43 - name: dev num_bytes: 4310 num_examples: 5 download_size: 177813 dataset_size: 420680 - config_name: high_school_mathematics features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 161023 num_examples: 270 - name: validation num_bytes: 17224 num_examples: 29 - name: dev num_bytes: 3682 num_examples: 5 download_size: 105683 dataset_size: 181929 - config_name: high_school_microeconomics features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 241816 num_examples: 238 - name: validation num_bytes: 24317 num_examples: 26 - name: dev num_bytes: 4029 num_examples: 5 download_size: 125789 dataset_size: 270162 - config_name: high_school_physics features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 175856 num_examples: 151 - name: validation num_bytes: 19899 num_examples: 17 - name: dev num_bytes: 4348 num_examples: 5 download_size: 109639 dataset_size: 200103 - config_name: high_school_psychology features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 494955 num_examples: 545 - name: validation num_bytes: 53743 num_examples: 60 - name: dev num_bytes: 5900 num_examples: 5 download_size: 285730 dataset_size: 554598 - config_name: high_school_statistics features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 333736 num_examples: 216 - name: validation num_bytes: 30252 num_examples: 23 - name: dev num_bytes: 7320 num_examples: 5 download_size: 191017 dataset_size: 371308 - config_name: high_school_us_history features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 883614 num_examples: 204 - name: validation num_bytes: 93694 num_examples: 22 - name: dev num_bytes: 26282 num_examples: 5 download_size: 533320 dataset_size: 1003590 - config_name: high_school_world_history features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 1126143 num_examples: 237 - name: validation num_bytes: 135245 num_examples: 26 - name: dev num_bytes: 14589 num_examples: 5 download_size: 662773 dataset_size: 1275977 - config_name: human_aging features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 145275 num_examples: 223 - name: validation num_bytes: 15038 num_examples: 23 - name: dev num_bytes: 3062 num_examples: 5 download_size: 99856 dataset_size: 163375 - config_name: human_sexuality features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 100379 num_examples: 131 - name: validation num_bytes: 7585 num_examples: 12 - name: dev num_bytes: 3504 num_examples: 5 download_size: 74540 dataset_size: 111468 - config_name: international_law features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 162013 num_examples: 121 - name: validation num_bytes: 18937 num_examples: 13 - name: dev num_bytes: 7290 num_examples: 5 download_size: 0 dataset_size: 188240 - config_name: jurisprudence features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 102393 num_examples: 108 - name: validation num_bytes: 11049 num_examples: 11 - name: dev num_bytes: 3754 num_examples: 5 download_size: 21545 dataset_size: 117196 - config_name: logical_fallacies features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 153973 num_examples: 163 - name: validation num_bytes: 15857 num_examples: 18 - name: dev num_bytes: 4919 num_examples: 5 download_size: 82298 dataset_size: 174749 - config_name: machine_learning features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 102745 num_examples: 112 - name: validation num_bytes: 9797 num_examples: 11 - name: dev num_bytes: 7448 num_examples: 5 download_size: 70870 dataset_size: 119990 - config_name: management features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 63772 num_examples: 103 - name: validation num_bytes: 5671 num_examples: 11 - name: dev num_bytes: 2677 num_examples: 5 download_size: 52323 dataset_size: 72120 - config_name: marketing features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 191635 num_examples: 234 - name: validation num_bytes: 22377 num_examples: 25 - name: dev num_bytes: 4734 num_examples: 5 download_size: 122877 dataset_size: 218746 - config_name: medical_genetics features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 64177 num_examples: 100 - name: validation num_bytes: 9298 num_examples: 11 - name: dev num_bytes: 3405 num_examples: 5 download_size: 58337 dataset_size: 76880 - config_name: miscellaneous features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 443155 num_examples: 783 - name: validation num_bytes: 42990 num_examples: 86 - name: dev num_bytes: 1877 num_examples: 5 download_size: 283087 dataset_size: 488022 - config_name: moral_disputes features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 332269 num_examples: 346 - name: validation num_bytes: 38501 num_examples: 38 - name: dev num_bytes: 5222 num_examples: 5 download_size: 193075 dataset_size: 375992 - config_name: moral_scenarios features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 1061634 num_examples: 895 - name: validation num_bytes: 120664 num_examples: 100 - name: dev num_bytes: 5816 num_examples: 5 download_size: 283716 dataset_size: 1188114 - config_name: nutrition features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 281680 num_examples: 306 - name: validation num_bytes: 25350 num_examples: 33 - name: dev num_bytes: 6423 num_examples: 5 download_size: 168790 dataset_size: 313453 - config_name: philosophy features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 240333 num_examples: 311 - name: validation num_bytes: 27480 num_examples: 34 - name: dev num_bytes: 2986 num_examples: 5 download_size: 153970 dataset_size: 270799 - config_name: prehistory features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 267644 num_examples: 324 - name: validation num_bytes: 30414 num_examples: 35 - name: dev num_bytes: 5577 num_examples: 5 download_size: 172053 dataset_size: 303635 - config_name: professional_accounting features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 377751 num_examples: 282 - name: validation num_bytes: 42879 num_examples: 31 - name: dev num_bytes: 6331 num_examples: 5 download_size: 228950 dataset_size: 426961 - config_name: professional_law features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 5612166 num_examples: 1534 - name: validation num_bytes: 604980 num_examples: 170 - name: dev num_bytes: 19825 num_examples: 5 download_size: 3065337 dataset_size: 6236971 - config_name: professional_medicine features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 639421 num_examples: 272 - name: validation num_bytes: 70186 num_examples: 31 - name: dev num_bytes: 11017 num_examples: 5 download_size: 391893 dataset_size: 720624 - config_name: professional_psychology features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 687869 num_examples: 612 - name: validation num_bytes: 87912 num_examples: 69 - name: dev num_bytes: 6693 num_examples: 5 download_size: 405705 dataset_size: 782474 - config_name: public_relations features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 89435 num_examples: 110 - name: validation num_bytes: 14174 num_examples: 12 - name: dev num_bytes: 4718 num_examples: 5 download_size: 0 dataset_size: 108327 - config_name: security_studies features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 632255 num_examples: 245 - name: validation num_bytes: 69100 num_examples: 27 - name: dev num_bytes: 16171 num_examples: 5 download_size: 0 dataset_size: 717526 - config_name: sociology features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 204018 num_examples: 201 - name: validation num_bytes: 22531 num_examples: 22 - name: dev num_bytes: 5054 num_examples: 5 download_size: 9676 dataset_size: 231603 - config_name: us_foreign_policy features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 89965 num_examples: 100 - name: validation num_bytes: 10270 num_examples: 11 - name: dev num_bytes: 5111 num_examples: 5 download_size: 68974 dataset_size: 105346 - config_name: virology features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 116211 num_examples: 166 - name: validation num_bytes: 16273 num_examples: 18 - name: dev num_bytes: 3185 num_examples: 5 download_size: 96586 dataset_size: 135669 - config_name: world_religions features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 77273 num_examples: 171 - name: validation num_bytes: 8462 num_examples: 19 - name: dev num_bytes: 2073 num_examples: 5 download_size: 61169 dataset_size: 87808 configs: - config_name: abstract_algebra data_files: - split: test path: abstract_algebra/test-* - split: validation path: abstract_algebra/validation-* - split: dev path: abstract_algebra/dev-* - config_name: all data_files: - split: test path: all/test-* - split: validation path: all/validation-* - split: dev path: all/dev-* - config_name: anatomy data_files: - split: test path: anatomy/test-* - split: validation path: anatomy/validation-* - split: dev path: anatomy/dev-* - config_name: astronomy data_files: - split: test path: astronomy/test-* - split: validation path: astronomy/validation-* - split: dev path: astronomy/dev-* - config_name: business_ethics data_files: - split: test path: business_ethics/test-* - split: validation path: business_ethics/validation-* - split: dev path: business_ethics/dev-* - config_name: clinical_knowledge data_files: - split: test path: clinical_knowledge/test-* - split: validation path: clinical_knowledge/validation-* - split: dev path: clinical_knowledge/dev-* - config_name: college_biology data_files: - split: test path: college_biology/test-* - split: validation path: college_biology/validation-* - split: dev path: college_biology/dev-* - config_name: college_chemistry data_files: - split: test path: college_chemistry/test-* - split: validation path: college_chemistry/validation-* - split: dev path: college_chemistry/dev-* - config_name: college_computer_science data_files: - split: test path: college_computer_science/test-* - split: validation path: college_computer_science/validation-* - split: dev path: college_computer_science/dev-* - config_name: college_mathematics data_files: - split: test path: college_mathematics/test-* - split: validation path: college_mathematics/validation-* - split: dev path: college_mathematics/dev-* - config_name: college_medicine data_files: - split: test path: college_medicine/test-* - split: validation path: college_medicine/validation-* - split: dev path: college_medicine/dev-* - config_name: college_physics data_files: - split: test path: college_physics/test-* - split: validation path: college_physics/validation-* - split: dev path: college_physics/dev-* - config_name: computer_security data_files: - split: test path: computer_security/test-* - split: validation path: computer_security/validation-* - split: dev path: computer_security/dev-* - config_name: conceptual_physics data_files: - split: test path: conceptual_physics/test-* - split: validation path: conceptual_physics/validation-* - split: dev path: conceptual_physics/dev-* - config_name: econometrics data_files: - split: test path: econometrics/test-* - split: validation path: econometrics/validation-* - split: dev path: econometrics/dev-* - config_name: electrical_engineering data_files: - split: test path: electrical_engineering/test-* - split: validation path: electrical_engineering/validation-* - split: dev path: electrical_engineering/dev-* - config_name: elementary_mathematics data_files: - split: test path: elementary_mathematics/test-* - split: validation path: elementary_mathematics/validation-* - split: dev path: elementary_mathematics/dev-* - config_name: formal_logic data_files: - split: test path: formal_logic/test-* - split: validation path: formal_logic/validation-* - split: dev path: formal_logic/dev-* - config_name: global_facts data_files: - split: test path: global_facts/test-* - split: validation path: global_facts/validation-* - split: dev path: global_facts/dev-* - config_name: high_school_biology data_files: - split: test path: high_school_biology/test-* - split: validation path: high_school_biology/validation-* - split: dev path: high_school_biology/dev-* - config_name: high_school_chemistry data_files: - split: test path: high_school_chemistry/test-* - split: validation path: high_school_chemistry/validation-* - split: dev path: high_school_chemistry/dev-* - config_name: high_school_computer_science data_files: - split: test path: high_school_computer_science/test-* - split: validation path: high_school_computer_science/validation-* - split: dev path: high_school_computer_science/dev-* - config_name: high_school_european_history data_files: - split: test path: high_school_european_history/test-* - split: validation path: high_school_european_history/validation-* - split: dev path: high_school_european_history/dev-* - config_name: high_school_geography data_files: - split: test path: high_school_geography/test-* - split: validation path: high_school_geography/validation-* - split: dev path: high_school_geography/dev-* - config_name: high_school_government_and_politics data_files: - split: test path: high_school_government_and_politics/test-* - split: validation path: high_school_government_and_politics/validation-* - split: dev path: high_school_government_and_politics/dev-* - config_name: high_school_macroeconomics data_files: - split: test path: high_school_macroeconomics/test-* - split: validation path: high_school_macroeconomics/validation-* - split: dev path: high_school_macroeconomics/dev-* - config_name: high_school_mathematics data_files: - split: test path: high_school_mathematics/test-* - split: validation path: high_school_mathematics/validation-* - split: dev path: high_school_mathematics/dev-* - config_name: high_school_microeconomics data_files: - split: test path: high_school_microeconomics/test-* - split: validation path: high_school_microeconomics/validation-* - split: dev path: high_school_microeconomics/dev-* - config_name: high_school_physics data_files: - split: test path: high_school_physics/test-* - split: validation path: high_school_physics/validation-* - split: dev path: high_school_physics/dev-* - config_name: high_school_psychology data_files: - split: test path: high_school_psychology/test-* - split: validation path: high_school_psychology/validation-* - split: dev path: high_school_psychology/dev-* - config_name: high_school_statistics data_files: - split: test path: high_school_statistics/test-* - split: validation path: high_school_statistics/validation-* - split: dev path: high_school_statistics/dev-* - config_name: high_school_us_history data_files: - split: test path: high_school_us_history/test-* - split: validation path: high_school_us_history/validation-* - split: dev path: high_school_us_history/dev-* - config_name: high_school_world_history data_files: - split: test path: high_school_world_history/test-* - split: validation path: high_school_world_history/validation-* - split: dev path: high_school_world_history/dev-* - config_name: human_aging data_files: - split: test path: human_aging/test-* - split: validation path: human_aging/validation-* - split: dev path: human_aging/dev-* - config_name: human_sexuality data_files: - split: test path: human_sexuality/test-* - split: validation path: human_sexuality/validation-* - split: dev path: human_sexuality/dev-* - config_name: international_law data_files: - split: test path: international_law/test-* - split: validation path: international_law/validation-* - split: dev path: international_law/dev-* - config_name: jurisprudence data_files: - split: test path: jurisprudence/test-* - split: validation path: jurisprudence/validation-* - split: dev path: jurisprudence/dev-* - config_name: logical_fallacies data_files: - split: test path: logical_fallacies/test-* - split: validation path: logical_fallacies/validation-* - split: dev path: logical_fallacies/dev-* - config_name: machine_learning data_files: - split: test path: machine_learning/test-* - split: validation path: machine_learning/validation-* - split: dev path: machine_learning/dev-* - config_name: management data_files: - split: test path: management/test-* - split: validation path: management/validation-* - split: dev path: management/dev-* - config_name: marketing data_files: - split: test path: marketing/test-* - split: validation path: marketing/validation-* - split: dev path: marketing/dev-* - config_name: medical_genetics data_files: - split: test path: medical_genetics/test-* - split: validation path: medical_genetics/validation-* - split: dev path: medical_genetics/dev-* - config_name: miscellaneous data_files: - split: test path: miscellaneous/test-* - split: validation path: miscellaneous/validation-* - split: dev path: miscellaneous/dev-* - config_name: moral_disputes data_files: - split: test path: moral_disputes/test-* - split: validation path: moral_disputes/validation-* - split: dev path: moral_disputes/dev-* - config_name: moral_scenarios data_files: - split: test path: moral_scenarios/test-* - split: validation path: moral_scenarios/validation-* - split: dev path: moral_scenarios/dev-* - config_name: nutrition data_files: - split: test path: nutrition/test-* - split: validation path: nutrition/validation-* - split: dev path: nutrition/dev-* - config_name: philosophy data_files: - split: test path: philosophy/test-* - split: validation path: philosophy/validation-* - split: dev path: philosophy/dev-* - config_name: prehistory data_files: - split: test path: prehistory/test-* - split: validation path: prehistory/validation-* - split: dev path: prehistory/dev-* - config_name: professional_accounting data_files: - split: test path: professional_accounting/test-* - split: validation path: professional_accounting/validation-* - split: dev path: professional_accounting/dev-* - config_name: professional_law data_files: - split: test path: professional_law/test-* - split: validation path: professional_law/validation-* - split: dev path: professional_law/dev-* - config_name: professional_medicine data_files: - split: test path: professional_medicine/test-* - split: validation path: professional_medicine/validation-* - split: dev path: professional_medicine/dev-* - config_name: professional_psychology data_files: - split: test path: professional_psychology/test-* - split: validation path: professional_psychology/validation-* - split: dev path: professional_psychology/dev-* - config_name: public_relations data_files: - split: test path: public_relations/test-* - split: validation path: public_relations/validation-* - split: dev path: public_relations/dev-* - config_name: security_studies data_files: - split: test path: security_studies/test-* - split: validation path: security_studies/validation-* - split: dev path: security_studies/dev-* - config_name: sociology data_files: - split: test path: sociology/test-* - split: validation path: sociology/validation-* - split: dev path: sociology/dev-* - config_name: us_foreign_policy data_files: - split: test path: us_foreign_policy/test-* - split: validation path: us_foreign_policy/validation-* - split: dev path: us_foreign_policy/dev-* - config_name: virology data_files: - split: test path: virology/test-* - split: validation path: virology/validation-* - split: dev path: virology/dev-* - config_name: world_religions data_files: - split: test path: world_religions/test-* - split: validation path: world_religions/validation-* - split: dev path: world_religions/dev-* --- # Dataset Card for MMLU Greek The MMLU Greek dataset is a set of 15858 examples from the MMLU dataset [available from here and here], machine-translated into Greek. The original dataset consists of multiple-choice questions from 57 tasks including elementary mathematics, US history, computer science, law, etc. ## Dataset Details ### Dataset Description - Curated by: ILSP/Athena RC - Language(s) (NLP): el - License: cc-by-nc-sa-4.0 ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> This dataset is the result of machine translation. ## Dataset Card Contact https://www.athenarc.gr/en/ilsp
Skylion007/openwebtext	Skylion007	2024-05-17T17:56:27Z	85,484	422	[ "task_categories:text-generation", "task_categories:fill-mask", "task_ids:language-modeling", "task_ids:masked-language-modeling", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:monolingual", "source_datasets:original", "language:en", "license:cc0-1.0", "size_categories:1M<n<10M", "region:us" ]	[ "text-generation", "fill-mask" ]	2022-03-02T23:29:22Z	null	--- annotations_creators: - no-annotation language_creators: - found language: - en license: - cc0-1.0 multilinguality: - monolingual pretty_name: OpenWebText size_categories: - 1M<n<10M source_datasets: - original task_categories: - text-generation - fill-mask task_ids: - language-modeling - masked-language-modeling paperswithcode_id: openwebtext dataset_info: features: - name: text dtype: string config_name: plain_text splits: - name: train num_bytes: 39769491688 num_examples: 8013769 download_size: 12880189440 dataset_size: 39769491688 --- # Dataset Card for "openwebtext" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [https://skylion007.github.io/OpenWebTextCorpus/](https://skylion007.github.io/OpenWebTextCorpus/) - Repository: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Paper: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Point of Contact: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Size of downloaded dataset files: 13.51 GB - Size of the generated dataset: 41.70 GB - Total amount of disk used: 55.21 GB ### Dataset Summary An open-source replication of the WebText dataset from OpenAI, that was used to train GPT-2. This distribution was created by Aaron Gokaslan and Vanya Cohen of Brown University. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### plain_text - Size of downloaded dataset files: 13.51 GB - Size of the generated dataset: 41.70 GB - Total amount of disk used: 55.21 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "text": "\"A magazine supplement with an image of Adolf Hitler and the title 'The Unreadable Book' is pictured in Berlin. No law bans “Mei..." } ``` ### Data Fields The data fields are the same among all splits. #### plain_text - `text`: a `string` feature. ### Data Splits \| name \| train \| \|------------\|--------:\| \| plain_text \| 8013769 \| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization The authors started by extracting all Reddit post urls from the Reddit submissions dataset. These links were deduplicated, filtered to exclude non-html content, and then shuffled randomly. The links were then distributed to several machines in parallel for download, and all web pages were extracted using the newspaper python package. Using Facebook FastText, non-English web pages were filtered out. Subsequently, near-duplicate documents were identified using local-sensitivity hashing (LSH). Documents were hashed into sets of 5-grams and all documents that had a similarity threshold of greater than 0.5 were removed. The the remaining documents were tokenized, and documents with fewer than 128 tokens were removed. This left 38GB of text data (40GB using SI units) from 8,013,769 documents. #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations The dataset doesn't contain annotations. ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information These data are released under this licensing scheme from the original authors ([source](https://skylion007.github.io/OpenWebTextCorpus/)): ``` We do not own any of the text from which these data has been extracted. We license the actual packaging of these parallel data under the [Creative Commons CC0 license (“no rights reserved”)](https://creativecommons.org/share-your-work/public-domain/cc0/) ``` #### Notice policy Should you consider that our data contains material that is owned by you and should therefore not be reproduced here, please: Clearly identify yourself, with detailed contact data such as an address, telephone number or email address at which you can be contacted. Clearly identify the copyrighted work claimed to be infringed. Clearly identify the material that is claimed to be infringing and information reasonably sufficient to allow us to locate the material. And contact us at the following email address: openwebtext at gmail.com and datasets at huggingface.co #### Take down policy The original authors will comply to legitimate requests by removing the affected sources from the next release of the corpus. Hugging Face will also update this repository accordingly. ### Citation Information ``` @misc{Gokaslan2019OpenWeb, title={OpenWebText Corpus}, author={Gokaslan, Aaron and Cohen, Vanya and Pavlick, Ellie and Tellex, Stefanie}, howpublished={\url{http://Skylion007.github.io/OpenWebTextCorpus}}, year={2019} } ``` ### Contributions Thanks to [@richarddwang](https://github.com/richarddwang) for adding this dataset.
lmarena-ai/arena-human-preference-55k	lmarena-ai	2024-05-17T03:04:04Z	536	142	[ "task_categories:text-classification", "language:en", "license:apache-2.0", "size_categories:10K<n<100K", "format:csv", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2403.04132", "region:us" ]	[ "text-classification" ]	2024-05-02T19:00:07Z	null	--- license: apache-2.0 task_categories: - text-classification language: - en pretty_name: LMSYS Chatbot Arena Human Preference Predictions size_categories: - 10K<n<100K --- Dataset for [Kaggle competition](https://www.kaggle.com/competitions/lmsys-chatbot-arena/overview) on predicting human preference on Chatbot Arena battles. The training dataset includes over 55,000 real-world user and LLM conversations and user preferences across over 70 state-of-the-art LLMs, such as GPT-4, Claude 2, Llama 2, Gemini, and Mistral models. Each sample represents a battle consisting of 2 LLMs which answer the same question, with a user label of either prefer model A, prefer model B, tie, or tie (both bad). ### Citation Please cite the following paper if you find our leaderboard or dataset helpful. ``` @misc{chiang2024chatbot, title={Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference}, author={Wei-Lin Chiang and Lianmin Zheng and Ying Sheng and Anastasios Nikolas Angelopoulos and Tianle Li and Dacheng Li and Hao Zhang and Banghua Zhu and Michael Jordan and Joseph E. Gonzalez and Ion Stoica}, year={2024}, eprint={2403.04132}, archivePrefix={arXiv}, primaryClass={cs.AI} } ```
common-canvas/commoncatalog-cc-by-sa	common-canvas	2024-05-16T19:41:37Z	115,865	8	[ "task_categories:text-to-image", "language:en", "license:cc-by-sa-4.0", "size_categories:1M<n<10M", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:2310.16825", "region:us" ]	[ "text-to-image" ]	2023-10-19T02:05:17Z	null	--- license: cc-by-sa-4.0 dataset_info: features: - name: jpg dtype: image - name: blip2_caption dtype: string - name: caption dtype: string - name: licensename dtype: string - name: licenseurl dtype: string - name: width dtype: int32 - name: height dtype: int32 - name: original_width dtype: int32 - name: original_height dtype: int32 - name: photoid dtype: int64 - name: uid dtype: string - name: unickname dtype: string - name: datetaken dtype: timestamp[us] - name: dateuploaded dtype: int64 - name: capturedevice dtype: string - name: title dtype: string - name: usertags dtype: string - name: machinetags dtype: string - name: longitude dtype: float64 - name: latitude dtype: float64 - name: accuracy dtype: int64 - name: pageurl dtype: string - name: downloadurl dtype: string - name: serverid dtype: int64 - name: farmid dtype: int64 - name: secret dtype: string - name: secretoriginal dtype: string - name: ext dtype: string - name: url dtype: string - name: key dtype: string - name: status dtype: string - name: error_message dtype: string - name: exif dtype: string - name: sha256 dtype: string - name: description dtype: string task_categories: - text-to-image language: - en --- # Dataset Card for CommonCatalog CC-BY-SA This dataset is a large collection of high-resolution Creative Common images (composed of different licenses, see paper Table 1 in the Appendix) collected in 2014 from users of Yahoo Flickr. The dataset contains images of up to 4k resolution, making this one of the highest resolution captioned image datasets. ## Dataset Details ### Dataset Description We provide captions synthetic captions to approximately 100 million high resolution images collected from Yahoo Flickr Creative Commons (YFCC). - Curated by: Aaron Gokaslan - Language(s) (NLP): en - License: See relevant yaml tag / dataset name. ### Dataset Sources <!-- Provide the basic links for the dataset. --> - Repository: https://github.com/mosaicml/diffusion - Paper: https://arxiv.org/abs/2310.16825 - Demo: See CommonCanvas Gradios ## Uses We use CommonCatalog to train a family latent diffusion models called CommonCanvas. The goal is to produce a model that is competitive with Stable Diffusion 2, but to do so using an easily accessible dataset of known provenance. Doing so makes replicating the model significantly easier, and provides a clearer mechanism for applying training-data attribution techniques. ### Direct Use Training text-to-image models Training image-to-text models ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> * Crafting content that is offensive or injurious towards individuals, including negative portrayals of their living conditions, cultural backgrounds, religious beliefs, etc. * Deliberately creating or spreading content that is discriminatory or reinforces harmful stereotypes. * Falsely representing individuals without their permission. * Generating sexual content that may be seen by individuals without their consent. * Producing or disseminating false or misleading information. * Creating content that depicts extreme violence or bloodshed. * Distributing content that modifies copyrighted or licensed material in a way that breaches its usage terms. ## Dataset Structure The dataset is divided into 10 subsets each containing parquets about 4GB each. Each subfolder within contains a resolution range of the images and their respective aspect ratios. The dataset is also divided along images licensed for commercial use (C) and those that are not (NC). ## Dataset Creation ### Curation Rationale Creating a standardized, accessible dataset with synthetic caption and releasing it so other people can train on a common dataset for open source image generation. ### Source Data Yahoo Flickr Creative Commons 100M Dataset and Synthetically Generated Caption Data. #### Data Collection and Processing All synthetic captions were generated with BLIP2. See paper for more details. #### Who are the source data producers? <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> Users of Flickr ## Bias, Risks, and Limitations See Yahoo Flickr Creative Commons 100M dataset for more information. The information was collected circa 2014 and known to have a bias towards internet connected Western countries. Some areas such as the global south lack representation. ## Citation BibTeX: ``` @article{gokaslan2023commoncanvas, title={CommonCanvas: An Open Diffusion Model Trained with Creative-Commons Images}, author={Gokaslan, Aaron and Cooper, A Feder and Collins, Jasmine and Seguin, Landan and Jacobson, Austin and Patel, Mihir and Frankle, Jonathan and Stephenson, Cory and Kuleshov, Volodymyr}, journal={arXiv preprint arXiv:2310.16825}, year={2023} } ``` ## Dataset Card Authors [Aaron Gokaslan](https://huggingface.co/Skylion007) ## Dataset Card Contact [Aaron Gokaslan](https://huggingface.co/Skylion007)
g0ster/TinyStories-Korean	g0ster	2024-05-15T09:23:41Z	78	9	[ "task_categories:translation", "language:ko", "language:en", "license:mit", "size_categories:10M<n<100M", "format:text", "modality:text", "library:datasets", "library:mlcroissant", "region:us" ]	[ "translation" ]	2024-05-14T11:52:01Z	2	--- license: mit task_categories: - translation language: - ko - en pretty_name: tinystories-korean size_categories: - 1M<n<10M --- # TinyStories-Korean <!-- Provide a quick summary of the dataset. --> This dataset is a translated version of [roneneldan](https://huggingface.co/roneneldan)'s [TinyStories](https://huggingface.co/datasets/roneneldan/TinyStories) dataset. I first downloaded roneneldan's TinyStories, and I organized it in a db file. Then I used a local transalation model [eeve](https://huggingface.co/yanolja/EEVE-Korean-Instruct-10.8B-v1.0) to translate, and I changed it back to a txt file. Feel free to use! --- ## Citation ``` @misc{kim2024tinystories, title={TinyStories Korean translations}, author={Dohoon Kim(g0ster)}, year={2024}, } ```
flytech/python-codes-25k	flytech	2024-05-15T08:04:00Z	575	121	[ "task_categories:text-classification", "task_categories:question-answering", "task_categories:token-classification", "task_categories:summarization", "task_categories:text2text-generation", "task_categories:text-generation", "size_categories:10K<n<100K", "format:json", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "region:us", "code", "python", "flytech", "cleaned", "instructional", "dataset 25k", "text2code", "code2text", "behavioral", "codegeneration", "trending", "programming" ]	[ "text-classification", "question-answering", "token-classification", "summarization", "text2text-generation", "text-generation" ]	2023-10-30T17:03:27Z	null	--- task_categories: - text-classification - question-answering - token-classification - summarization - text2text-generation - text-generation tags: - code - python - flytech - cleaned - instructional - dataset 25k - text2code - code2text - behavioral - codegeneration - trending - programming size_categories: - 1M<n<10M --- ### <span style="color:#307090">License</span> <span style="color:darkorange">MIT</span> <hr style="height:1px;border:none;color:#333;background-color:#444;" /> ## <span style="color:darkcyan">This is a Cleaned Python Dataset Covering 25,000 Instructional Tasks</span> ### <span style="color:#307090">Overview</span> The dataset has 4 key features (fields): <b><span style="color:#205070">instruction</span></b>, <b><span style="color:#205070">input</span></b>, <b><span style="color:#205070">output</span></b>, and <b><span style="color:#205070">text</span></b>. <span style="color:darkcyan">It's a rich source for Python codes, tasks, and extends into behavioral aspects.</span> <hr style="height:1px;border:none;color:#333;background-color:#444;" /> ### <span style="color:#307090">Dataset Statistics</span> - Total Entries: <span style="color:darkmagenta">24,813</span> - Unique Instructions: <span style="color:darkmagenta">24,580</span> - Unique Inputs: <span style="color:darkmagenta">3,666</span> - Unique Outputs: <span style="color:darkmagenta">24,581</span> - Unique Texts: <span style="color:darkmagenta">24,813</span> - Average Tokens per example: <span style="color:darkmagenta">508</span> ### <span style="color:#307090">Features</span> - `instruction`: The instructional task to be performed / User input - `input`: Very short, introductive part of AI response or empty - `output`: Python code that accomplishes the task - `text`: All fields combined together <hr style="height:1px;border:none;color:#333;background-color:#444;" /> ### <span style="color:#307090">Usage</span> <span style="color:darkcyan">This dataset can be useful for:</span> - <span style="color:#607090">Code generation tasks</span> - <span style="color:#607090">Natural Language Understanding models specialized in coding languages</span> - <span style="color:#607090">Behavioral analysis based on the given tasks and codes</span> - <span style="color:#607090">Educational purposes to understand coding styles and task variations</span> <span style="color:darkcyan">To load the dataset, one can use the following snippet:</span> ```python # Double-check if there is ~25k examples instead of almost 50k (HF thinks jsonl and json are two different datasets) from datasets import load_dataset dataset = load_dataset('flytech/python-codes-25k', split='train') # One can map the dataset in any way, for the sake of example: dataset = dataset.map(lambda example: {'text': example['instruction'] + ' ' + example['input'] + ' ' + example['output']})['text'] # Remember that you don't need to map if the dataset has a "text" field already:) ``` ### <span style="color:#307090">Access & Contributions</span> <span style="color:#887844">Feel free to use this dataset as per the MIT license. Contributions to enhance or expand the dataset are welcome. The dataset can also be found on kaggle, under the same name but from different author.</span>
Voxel51/WLASL	Voxel51	2024-05-06T15:10:59Z	10,640	6	[ "task_categories:video-classification", "language:en", "license:other", "size_categories:10K<n<100K", "modality:image", "modality:video", "library:fiftyone", "arxiv:1910.11006", "region:us", "fiftyone", "video", "activity-recognition", "asl", "sign-language" ]	[ "video-classification" ]	2024-04-22T16:03:30Z	null	--- annotations_creators: [] language: en license: other size_categories: - 10K<n<100K task_categories: - video-classification task_ids: [] pretty_name: World Level American Sign Language tags: - fiftyone - video - activity-recognition - asl - sign-language dataset_summary: > ![image/png](dataset_preview.gif) This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 11980 samples. ## Installation If you haven't already, install FiftyOne: ```bash pip install -U fiftyone ``` ## Usage ```python import fiftyone as fo import fiftyone.utils.huggingface as fouh # Load the dataset # Note: other available arguments include 'max_samples', etc dataset = fouh.load_from_hub("Voxel51/WLASL") # Launch the App session = fo.launch_app(dataset) ``` --- # Dataset Card for WLASL <!-- Provide a quick summary of the dataset. --> ![image/png](dataset_preview.gif) This is a [FiftyOne](https://github.com/voxel51/fiftyone) video dataset with 11980 samples. ## Installation If you haven't already, install FiftyOne: ```bash pip install -U fiftyone ``` ## Usage ```python import fiftyone as fo import fiftyone.utils.huggingface as fouh # Load the dataset # Note: other available arguments include 'max_samples', etc dataset = fouh.load_from_hub("Voxel51/WLASL") # Launch the App session = fo.launch_app(dataset) ``` ## Dataset Details ### Dataset Description WLASL is the largest video dataset for Word-Level American Sign Language (ASL) recognition, which features 2,000 common different words in ASL. The authors hope WLASL will facilitate the research in sign language understanding and eventually benefit the communication between deaf and hearing communities. - Curated by: Dongxu Li and Hongdong Li - Language(s) (NLP): en - License: other ### Dataset Sources <!-- Provide the basic links for the dataset. --> - Repository: https://github.com/dxli94/WLASL - Paper: https://arxiv.org/abs/1910.11006 - Homepage: https://dxli94.github.io/WLASL/ - Demo: https://try.fiftyone.ai/datasets/asl-dataset/samples ## Uses All the WLASL data is intended for academic and computational use only. No commercial usage is allowed. Licensed under the [Computational Use of Data Agreement](https://github.com/microsoft/Computational-Use-of-Data-Agreement/releases/tag/v1.0) (C-UDA) ## Citation <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> BibTeX: ```bibtex @misc{li2020wordlevel, title={Word-level Deep Sign Language Recognition from Video: A New Large-scale Dataset and Methods Comparison}, author={Dongxu Li and Cristian Rodriguez Opazo and Xin Yu and Hongdong Li}, year={2020}, eprint={1910.11006}, archivePrefix={arXiv}, primaryClass={cs.CV} } @inproceedings{li2020transferring, title={Transferring cross-domain knowledge for video sign language recognition}, author={Li, Dongxu and Yu, Xin and Xu, Chenchen and Petersson, Lars and Li, Hongdong}, booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, pages={6205--6214}, year={2020} } ``` ## Dataset Card Authors [Jacob Marks](https://huggingface.co/jamarks)
alexantonov/chuvash_parallel	alexantonov	2024-05-06T14:09:47Z	123	6	[ "multilinguality:translation", "source_datasets:original", "language:cv", "region:us" ]	[]	2022-03-02T23:29:22Z	1	--- language: - cv multilinguality: - translation source_datasets: - original task_ids: - machine-translation --- # Dataset Description Deprecated. Please use [Chuvash-English](https://huggingface.co/datasets/alexantonov/chuvash_english_parallel) and [Chuvash-Russian](https://huggingface.co/datasets/alexantonov/chuvash_russian_parallel) datasets in a more convenient format. ## Chuvash-Russian parallel corpus 1M parallel sentences. Manually aligned ## Chuvash-English parallel corpus. 200K parallel sentences. Automatically aligned ## Contributions For additional details contact [@AlAntonov](https://github.com/AlAntonov).
lcw99/wikipedia-korean-20221001	lcw99	2024-05-05T09:12:11Z	273	7	[ "language:ko", "license:apache-2.0", "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2022-10-10T03:49:37Z	1	--- language: - ko license: apache-2.0 --- [20240501 update](https://huggingface.co/datasets/lcw99/wikipedia-korean-20240501)
ylacombe/expresso	ylacombe	2024-04-30T16:49:14Z	578	61	[ "language:en", "license:cc-by-nc-4.0", "size_categories:10K<n<100K", "format:parquet", "modality:audio", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:2308.05725", "region:us" ]	[]	2024-04-30T16:10:38Z	5	--- dataset_info: config_name: read features: - name: audio dtype: audio - name: text dtype: string - name: speaker_id dtype: string - name: style dtype: string - name: id dtype: string splits: - name: train num_bytes: 5702432944.34 num_examples: 11615 download_size: 5761373569 dataset_size: 5702432944.34 configs: - config_name: read data_files: - split: train path: read/train-* license: cc-by-nc-4.0 language: - en pretty_name: The Expresso Dataset --- # The Expresso Dataset [[paper]](https://arxiv.org/abs/2308.05725) [[demo samples]](https://speechbot.github.io/expresso/) [[Original repository]](https://github.com/facebookresearch/textlesslib/tree/main/examples/expresso/dataset) ## Introduction The Expresso dataset is a high-quality (48kHz) expressive speech dataset that includes both expressively rendered read speech (8 styles, in mono wav format) and improvised dialogues (26 styles, in stereo wav format). The dataset includes 4 speakers (2 males, 2 females), and totals 40 hours (11h read, 30h improvised). The transcriptions of the read speech are also provided. You can listen to samples from the Expresso Dataset at [this website](https://speechbot.github.io/expresso/). ## Data Statistics Here are the statistics of Expresso’s expressive styles: ---------------------------------------------------------------- Style \| Read (min) \| Improvised (min) \| total (hrs) ------------------\|------------\|------------------\|------------- angry \| - \| 82 \| 1.4 animal \| - \| 27 \| 0.4 animal_directed \| - \| 32 \| 0.5 awe \| - \| 92 \| 1.5 bored \| - \| 92 \| 1.5 calm \| - \| 93 \| 1.6 child \| - \| 28 \| 0.4 child_directed \| - \| 38 \| 0.6 confused \| 94 \| 66 \| 2.7 default \| 133 \| 158 \| 4.9 desire \| - \| 92 \| 1.5 disgusted \| - \| 118 \| 2.0 enunciated \| 116 \| 62 \| 3.0 fast \| - \| 98 \| 1.6 fearful \| - \| 98 \| 1.6 happy \| 74 \| 92 \| 2.8 laughing \| 94 \| 103 \| 3.3 narration \| 21 \| 76 \| 1.6 non_verbal \| - \| 32 \| 0.5 projected \| - \| 94 \| 1.6 sad \| 81 \| 101 \| 3.0 sarcastic \| - \| 106 \| 1.8 singing* \| - \| 4 \| .07 sleepy \| - \| 93 \| 1.5 sympathetic \| - \| 100 \| 1.7 whisper \| 79 \| 86 \| 2.8 Total \| 11.5h \| 34.4h \| 45.9h ---------------------------------------------------------------- singing is the only improvised style that is not in dialogue format. ## Audio Quality The audio was recorded in a professional recording studio with minimal background noise at 48kHz/24bit. The files for read speech and singing are in a mono wav format; and for the dialog section in stereo (one channel per actor), where the original flow of turn-taking is preserved. ### Read Speech The `read` config contains all the read speech and singing style. ## License The Expresso dataset is distributed under the [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/) license. ## Reference For more information, see the paper: [EXPRESSO: A Benchmark and Analysis of Discrete Expressive Speech Resynthesis](https://arxiv.org/abs/2308.05725), Tu Anh Nguyen, Wei-Ning Hsu, Antony D'Avirro, Bowen Shi*, Itai Gat, Maryam Fazel-Zarani, Tal Remez, Jade Copet, Gabriel Synnaeve, Michael Hassid, Felix Kreuk, Yossi Adi⁺, Emmanuel Dupoux⁺, INTERSPEECH 2023.
cogsci13/Amazon-Reviews-2023-Books-Meta	cogsci13	2024-04-18T15:23:50Z	163	6	[ "language:en", "size_categories:1M<n<10M", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us", "recommendation", "reviews" ]	[]	2024-04-18T15:11:14Z	2	--- language: - en tags: - recommendation - reviews size_categories: - 100M<n<1B --- # Amazon Reviews 2023 (Books Only) This is a subset of Amazon Review 2023 dataset. Please visit [amazon-reviews-2023.github.io/](https://amazon-reviews-2023.github.io/) for more details, loading scripts, and preprocessed benchmark files. [April 18, 2024] Update 1. This dataset was created and pushed for the first time. --- <!-- Provide a quick summary of the dataset. --> This is a large-scale Amazon Reviews dataset, collected in 2023 by [McAuley Lab](https://cseweb.ucsd.edu/~jmcauley/), and it includes rich features such as: 1. User Reviews (ratings, text, helpfulness votes, etc.); 2. Item Metadata (descriptions, price, raw image, etc.); ## What's New? In the Amazon Reviews'23, we provide: 1. Larger Dataset: We collected 571.54M reviews, 245.2% larger than the last version; 2. Newer Interactions: Current interactions range from May. 1996 to Sep. 2023; 3. Richer Metadata: More descriptive features in item metadata; 4. Fine-grained Timestamp: Interaction timestamp at the second or finer level; 5. Cleaner Processing: Cleaner item metadata than previous versions; 6. Standard Splitting: Standard data splits to encourage RecSys benchmarking. ## Basic Statistics > We define the <b>#R_Tokens</b> as the number of [tokens](https://pypi.org/project/tiktoken/) in user reviews and <b>#M_Tokens</b> as the number of [tokens](https://pypi.org/project/tiktoken/) if treating the dictionaries of item attributes as strings. We emphasize them as important statistics in the era of LLMs. > We count the number of items based on user reviews rather than item metadata files. Note that some items lack metadata. ### Grouped by Category \| Category \| #User \| #Item \| #Rating \| #R_Token \| #M_Token \| Download \| \| ------------------------ \| ------: \| ------: \| --------: \| -------: \| -------: \| ------------------------------: \| \| Books \| 10.3M \| 4.4M \| 29.5M \| 2.9B \| 3.7B \| <a href='https://datarepo.eng.ucsd.edu/mcauley_group/data/amazon_2023/raw/review_categories/Books.jsonl.gz' download> review</a>, <a href='https://datarepo.eng.ucsd.edu/mcauley_group/data/amazon_2023/raw/meta_categories/meta_Books.jsonl.gz' download> meta </a> \| meta </a> \| > Check Pure ID files and corresponding data splitting strategies in <b>[Common Data Processing](https://amazon-reviews-2023.github.io/data_processing/index.html)</b> section. ## Quick Start ### Load User Reviews ```python from datasets import load_dataset dataset = load_dataset("cogsci13/Amazon-Reviews-2023-Books-Review", "raw_review_Books", trust_remote_code=True) print(dataset["full"][0]) ``` ```json {'rating': {0: 1.0}, 'title': {0: 'Not a watercolor book! Seems like copies imo.'}, 'text': {0: 'It is definitely not a watercolor book. The paper bucked completely. The pages honestly appear to be photo copies of other pictures. I say that bc if you look at the seal pics you can see the tell tale line at the bottom of the page. As someone who has made many photocopies of pages in my time so I could try out different colors & mediums that black line is a dead giveaway to me. It’s on other pages too. The entire book just seems off. Nothing is sharp & clear. There is what looks like toner dust on all the pages making them look muddy. There are no sharp lines & there is no clear definition. At least there isn’t in my copy. And the Coloring Book for Adult on the bottom of the front cover annoys me. Why is it singular & not plural? They usually say coloring book for kids or coloring book for kids & adults or coloring book for adults- plural. Lol Plus it would work for kids if you can get over the grey scale nature of it. Personally I’m not going to waste expensive pens & paints trying to paint over the grey & black mess. I grew up in SW Florida minutes from the beaches & I was really excited about the sea life in this. I hope the printers & designers figure out how to clean up the mess bc some of the designs are really cute. They just aren’t worth my time to hand trace & transfer them, but I’m sure there are ppl that will be up to the challenge. This is one is a hard no. Going back. I tried.'}, 'images': {0: array([{'attachment_type': 'IMAGE', 'large_image_url': 'https://m.media-amazon.com/images/I/516HBU7LQoL._SL1600_.jpg', 'medium_image_url': 'https://m.media-amazon.com/images/I/516HBU7LQoL._SL800_.jpg', 'small_image_url': 'https://m.media-amazon.com/images/I/516HBU7LQoL._SL256_.jpg'}, {'attachment_type': 'IMAGE', 'large_image_url': 'https://m.media-amazon.com/images/I/71+XwcacMmL._SL1600_.jpg', 'medium_image_url': 'https://m.media-amazon.com/images/I/71+XwcacMmL._SL800_.jpg', 'small_image_url': 'https://m.media-amazon.com/images/I/71+XwcacMmL._SL256_.jpg'}, {'attachment_type': 'IMAGE', 'large_image_url': 'https://m.media-amazon.com/images/I/71RbTuvD1ZL._SL1600_.jpg', 'medium_image_url': 'https://m.media-amazon.com/images/I/71RbTuvD1ZL._SL800_.jpg', 'small_image_url': 'https://m.media-amazon.com/images/I/71RbTuvD1ZL._SL256_.jpg'}, {'attachment_type': 'IMAGE', 'large_image_url': 'https://m.media-amazon.com/images/I/71U63wdOeZL._SL1600_.jpg', 'medium_image_url': 'https://m.media-amazon.com/images/I/71U63wdOeZL._SL800_.jpg', 'small_image_url': 'https://m.media-amazon.com/images/I/71U63wdOeZL._SL256_.jpg'}, {'attachment_type': 'IMAGE', 'large_image_url': 'https://m.media-amazon.com/images/I/71WFEDyKcKL._SL1600_.jpg', 'medium_image_url': 'https://m.media-amazon.com/images/I/71WFEDyKcKL._SL800_.jpg', 'small_image_url': 'https://m.media-amazon.com/images/I/71WFEDyKcKL._SL256_.jpg'}, {'attachment_type': 'IMAGE', 'large_image_url': 'https://m.media-amazon.com/images/I/8109NwjpHKL._SL1600_.jpg', 'medium_image_url': 'https://m.media-amazon.com/images/I/8109NwjpHKL._SL800_.jpg', 'small_image_url': 'https://m.media-amazon.com/images/I/8109NwjpHKL._SL256_.jpg'}, {'attachment_type': 'IMAGE', 'large_image_url': 'https://m.media-amazon.com/images/I/814gxfh8wcL._SL1600_.jpg', 'medium_image_url': 'https://m.media-amazon.com/images/I/814gxfh8wcL._SL800_.jpg', 'small_image_url': 'https://m.media-amazon.com/images/I/814gxfh8wcL._SL256_.jpg'}, {'attachment_type': 'IMAGE', 'large_image_url': 'https://m.media-amazon.com/images/I/81HC0vKRC2L._SL1600_.jpg', 'medium_image_url': 'https://m.media-amazon.com/images/I/81HC0vKRC2L._SL800_.jpg', 'small_image_url': 'https://m.media-amazon.com/images/I/81HC0vKRC2L._SL256_.jpg'}, {'attachment_type': 'IMAGE', 'large_image_url': 'https://m.media-amazon.com/images/I/81Nx6BnRLxL._SL1600_.jpg', 'medium_image_url': 'https://m.media-amazon.com/images/I/81Nx6BnRLxL._SL800_.jpg', 'small_image_url': 'https://m.media-amazon.com/images/I/81Nx6BnRLxL._SL256_.jpg'}, {'attachment_type': 'IMAGE', 'large_image_url': 'https://m.media-amazon.com/images/I/81QQMwBcVPL._SL1600_.jpg', 'medium_image_url': 'https://m.media-amazon.com/images/I/81QQMwBcVPL._SL800_.jpg', 'small_image_url': 'https://m.media-amazon.com/images/I/81QQMwBcVPL._SL256_.jpg'}, {'attachment_type': 'IMAGE', 'large_image_url': 'https://m.media-amazon.com/images/I/81fgT3R3OwL._SL1600_.jpg', 'medium_image_url': 'https://m.media-amazon.com/images/I/81fgT3R3OwL._SL800_.jpg', 'small_image_url': 'https://m.media-amazon.com/images/I/81fgT3R3OwL._SL256_.jpg'}, {'attachment_type': 'IMAGE', 'large_image_url': 'https://m.media-amazon.com/images/I/81mfzny0I5L._SL1600_.jpg', 'medium_image_url': 'https://m.media-amazon.com/images/I/81mfzny0I5L._SL800_.jpg', 'small_image_url': 'https://m.media-amazon.com/images/I/81mfzny0I5L._SL256_.jpg'}, {'attachment_type': 'IMAGE', 'large_image_url': 'https://m.media-amazon.com/images/I/81nir7bf91L._SL1600_.jpg', 'medium_image_url': 'https://m.media-amazon.com/images/I/81nir7bf91L._SL800_.jpg', 'small_image_url': 'https://m.media-amazon.com/images/I/81nir7bf91L._SL256_.jpg'}, {'attachment_type': 'IMAGE', 'large_image_url': 'https://m.media-amazon.com/images/I/81yLUo6ZL3L._SL1600_.jpg', 'medium_image_url': 'https://m.media-amazon.com/images/I/81yLUo6ZL3L._SL800_.jpg', 'small_image_url': 'https://m.media-amazon.com/images/I/81yLUo6ZL3L._SL256_.jpg'}, {'attachment_type': 'IMAGE', 'large_image_url': 'https://m.media-amazon.com/images/I/81zh9h5RwkL._SL1600_.jpg', 'medium_image_url': 'https://m.media-amazon.com/images/I/81zh9h5RwkL._SL800_.jpg', 'small_image_url': 'https://m.media-amazon.com/images/I/81zh9h5RwkL._SL256_.jpg'}, {'attachment_type': 'IMAGE', 'large_image_url': 'https://m.media-amazon.com/images/I/91yfcpFlEqL._SL1600_.jpg', 'medium_image_url': 'https://m.media-amazon.com/images/I/91yfcpFlEqL._SL800_.jpg', 'small_image_url': 'https://m.media-amazon.com/images/I/91yfcpFlEqL._SL256_.jpg'}], dtype=object)}, 'asin': {0: 'B09BGPFTDB'}, 'parent_asin': {0: 'B09BGPFTDB'}, 'user_id': {0: 'AFKZENTNBQ7A7V7UXW5JJI6UGRYQ'}, 'timestamp': {0: 1642399598485}, 'helpful_vote': {0: 0}, 'verified_purchase': {0: True}} ``` ### Load Item Metadata ```python dataset = load_dataset("cogsci13/Amazon-Reviews-2023-Books-Meta", "raw_meta_Books", split="full", trust_remote_code=True) print(dataset[0]) ``` ```json {'main_category': {0: 'Books'}, 'title': {0: 'Chaucer'}, 'average_rating': {0: 4.5}, 'rating_number': {0: 29}, 'features': {0: array([], dtype=object)}, 'description': {0: array([], dtype=object)}, 'price': {0: '8.23'}, 'images': {0: {'hi_res': array([None], dtype=object), 'large': array(['https://m.media-amazon.com/images/I/41X61VPJYKL._SX334_BO1,204,203,200_.jpg'], dtype=object), 'thumb': array([None], dtype=object), 'variant': array(['MAIN'], dtype=object)}}, 'videos': {0: {'title': array([], dtype=object), 'url': array([], dtype=object), 'user_id': array([], dtype=object)}}, 'store': {0: 'Peter Ackroyd (Author)'}, 'categories': {0: array(['Books', 'Literature & Fiction', 'History & Criticism'], dtype=object)}, 'details': {0: '{"Publisher": "Chatto & Windus; First Edition (January 1, 2004)", "Language": "English", "Hardcover": "196 pages", "ISBN 10": "0701169850", "ISBN 13": "978-0701169855", "Item Weight": "10.1 ounces", "Dimensions": "5.39 x 0.71 x 7.48 inches"}'}, 'parent_asin': {0: '0701169850'}, 'bought_together': {0: None}, 'subtitle': {0: 'Hardcover – Import, January 1, 2004'}, 'author': {0: "{'avatar': 'https://m.media-amazon.com/images/I/21Je2zja9pL._SY600_.jpg', 'name': 'Peter Ackroyd', 'about': ['Peter Ackroyd, (born 5 October 1949) is an English biographer, novelist and critic with a particular interest in the history and culture of London. For his novels about English history and culture and his biographies of, among others, William Blake, Charles Dickens, T. S. Eliot and Sir Thomas More, he won the Somerset Maugham Award and two Whitbread Awards. He is noted for the volume of work he has produced, the range of styles therein, his skill at assuming different voices and the depth of his research.', 'He was elected a fellow of the Royal Society of Literature in 1984 and appointed a Commander of the Order of the British Empire in 2003.', 'Bio from Wikipedia, the free encyclopedia.']}"}} ``` > Check data loading examples and Huggingface datasets APIs in <b>[Common Data Loading](https://amazon-reviews-2023.github.io/data_loading/index.html)</b> section. ## Data Fields ### For User Reviews \| Field \| Type \| Explanation \| \| ----- \| ---- \| ----------- \| \| rating \| float \| Rating of the product (from 1.0 to 5.0). \| \| title \| str \| Title of the user review. \| \| text \| str \| Text body of the user review. \| \| images \| list \| Images that users post after they have received the product. Each image has different sizes (small, medium, large), represented by the small_image_url, medium_image_url, and large_image_url respectively. \| \| asin \| str \| ID of the product. \| \| parent_asin \| str \| Parent ID of the product. Note: Products with different colors, styles, sizes usually belong to the same parent ID. The “asin” in previous Amazon datasets is actually parent ID. <b>Please use parent ID to find product meta.</b> \| \| user_id \| str \| ID of the reviewer \| \| timestamp \| int \| Time of the review (unix time) \| \| verified_purchase \| bool \| User purchase verification \| \| helpful_vote \| int \| Helpful votes of the review \| ### For Item Metadata \| Field \| Type \| Explanation \| \| ----- \| ---- \| ----------- \| \| main_category \| str \| Main category (i.e., domain) of the product. \| \| title \| str \| Name of the product. \| \| average_rating \| float \| Rating of the product shown on the product page. \| \| rating_number \| int \| Number of ratings in the product. \| \| features \| list \| Bullet-point format features of the product. \| \| description \| list \| Description of the product. \| \| price \| float \| Price in US dollars (at time of crawling). \| \| images \| list \| Images of the product. Each image has different sizes (thumb, large, hi_res). The “variant” field shows the position of image. \| \| videos \| list \| Videos of the product including title and url. \| \| store \| str \| Store name of the product. \| \| categories \| list \| Hierarchical categories of the product. \| \| details \| dict \| Product details, including materials, brand, sizes, etc. \| \| parent_asin \| str \| Parent ID of the product. \| \| bought_together \| list \| Recommended bundles from the websites. \| ## Citation ```bibtex @article{hou2024bridging, title={Bridging Language and Items for Retrieval and Recommendation}, author={Hou, Yupeng and Li, Jiacheng and He, Zhankui and Yan, An and Chen, Xiusi and McAuley, Julian}, journal={arXiv preprint arXiv:2403.03952}, year={2024} } ``` ## Contact Us - Report Bugs: To report bugs in the dataset, please file an issue on our [GitHub](https://github.com/hyp1231/AmazonReviews2023/issues/new). - Others: For research collaborations or other questions, please email yphou AT ucsd.edu.
allenai/dolma	allenai	2024-04-17T02:57:00Z	747	901	[ "task_categories:text-generation", "language:en", "license:odc-by", "size_categories:n>1T", "arxiv:2402.00159", "arxiv:2301.13688", "region:us", "language-modeling", "casual-lm", "llm" ]	[ "text-generation" ]	2023-06-30T20:14:39Z	null	--- license: odc-by viewer: false task_categories: - text-generation language: - en tags: - language-modeling - casual-lm - llm pretty_name: Dolma size_categories: - n>1T --- # Dolma <img alt="Dolma's official logo. It's dolma written in yellow, round lowercase letters over a blue background." src="https://raw.githubusercontent.com/allenai/dolma/main/docs/assets/AI2_Blog_1400x685_2x.webp" width="100%"> Dolma is a dataset of 3 trillion tokens from a diverse mix of web content, academic publications, code, books, and encyclopedic materials. More information: - Read Dolma manuscript and its Data Sheet [on ArXiv](https://arxiv.org/abs/2402.00159); - Explore the [open source tools](https://github.com/allenai/dolma) we created to curate Dolma. - Want to request removal of personal data? Use [this form](https://forms.gle/q4BNUUxUxKwKkfdT6) to notify us of documents containing PII about a specific user. To learn more about the toolkit used to create Dolma, including how to replicate this dataset, head over our [GitHub project page](https://github.com/allenai/dolma/tree/main/docs)! 2024-04-17: Dolma v1.7 Release. We have released an updated version of Dolma that we used to train our latest [OLMo 7B-v1.7](https://huggingface.co/allenai/OLMo-7b-v1.7) model. 2024-04-15: License Change. We have updated the license of Dolma to [ODC-BY](https://opendatacommons.org/licenses/by/1-0/). Please see this [blog post](https://blog.allenai.org/making-a-switch-dolma-moves-to-odc-by-8f0e73852f44) for more information. ## Versions At the moment, there are six versions of Dolma available: \| Version \| Default? \| Release Date \| Size (gzip) \| Description \| \|--\|:--:\|--\|--\|--\| \| `v1_7` \| ✅ \| 2024-04-15 \| 4.5 TB \| Used to train [OLMo-7B-v1.7](https://huggingface.co/allenai/OLMo-7b-v1.7). New sources, more quality filtering, fuzzy deduplication. \| \| `v1_6` \| \| 2024-01-31 \| 5.4 TB \| An update to v1.5 with some deduplication of documents with too few tokens or too many repeated n-grams. \| \| `v1_6-sample` \| \| 2024-01-31 \| 16.4 GB \| A smaller sample of Dolma, with roughly 10 billion tokens. Useful for data exploration. \| \| `v1_5` \| \| 2023-10-31 \| 6.4 TB \| Used to train [OLMo-1B](https://huggingface.co/allenai/OLMo-1B). Roughly 3 trillion tokens. \| \| `v1_5-sample` \| \| 2023-10-31 \| 2.9 TB \| A sample of roughly 1.9 trillion tokens used to train [OLMo-7B](https://huggingface.co/allenai/OLMo-7B) \| \| `v1` \| \| 2023-08-18 \| 6.0 TB \| The first version of Dolma. \| ## Summary Statistics (v1.7) \| Source \| Provenance \| New? \| Documents (millions) \| OLMo tokens (billions) \| Sample Proportion \| Cutoff Date \| Processing \|--\|--\|--\|--\|--\|--\|--\|--\| \| Dolma's CC \| [Common Crawl](https://commoncrawl.org/) via Dolma v1.6 \| Updated \| 875.2 \| 1,195.5 \| 50% \| Mar 2023 \| Extracted using the Dolma pipeline; new quality filtering and deduplication steps. \| \| Refined Web \| [Refined Web](https://huggingface.co/datasets/tiiuae/falcon-refinedweb) \| Yes \| 664.0 \| 456.4 \| 100% \| Feb 2023 \| Filtered using the Dolma pipeline; new quality filtering and deduplication steps. \| \| StarCoder \| [StarCoder](https://huggingface.co/blog/starcoder) \| Yes \| 206.6 \| 263.8 \| 100% \| May 2023 \| No further processing. \| \| C4 \| [C4](https://huggingface.co/datasets/c4) via Dolma v1.6 \| Updated \| 249.9 \| 138.4 \| 50% \| Apr 2019 \| Filtered using the Dolma pipeline; new quality filtering and deduplication steps. \| \| Reddit \| [PushShift API](https://github.com/pushshift/api) \| Updated \| 377.4 \| 79.9 \| 100% \| Mar 2023 \| Extracted using the Dolma pipeline; new quality filtering and deduplication steps. \| \| Semantic Scholar ([S2ORC](https://aclanthology.org/2020.acl-main.447/) & [S2AG](https://www.semanticscholar.org/product/api)) \| [peS2o](https://huggingface.co/datasets/allenai/peS2o) via Dolma v1.6 \| No \| 38.8 \| 57.2 \| 100% \| Mar 2023 \| Same as Dolma v1.6 \| \| arXiv \| [RedPajama v1](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T) \| Yes \| 1.5 \| 28.0 \| 100% \| Mar 2023 \| No further processing. \| \| StackExchange \| [RedPajama v1](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T) \| Yes \| 29.3 \| 19.6 \| 100% \| Mar 2023 \| No further processing. \| \| Flan \| [Flan Collection](https://arxiv.org/abs/2301.13688), reproduced following the [original code](https://github.com/google-research/FLAN/tree/main/flan/v2), as performed by [Dettmers et al., (2023)](https://openreview.net/forum?id=OUIFPHEgJU) \| Yes \| 52.1 \| 16.5 \| 100% \| Feb 2023 \| After reproducing Flan, sampled to balance different Flan subsets. Reformatted for pretraining with newlines separating instruction and demonstration. \| \| CC News \| [Common Crawl](https://commoncrawl.org/blog/news-dataset-available) \| Yes \| 22.0 \| 14.3 \| 100% \| Mar 2023 \| Extracted using the Dolma pipeline; new quality filtering and deduplication steps. \| \| OpenWebMath \| [OpenWebMath](https://huggingface.co/datasets/open-web-math/open-web-math) via [Proof Pile II](https://huggingface.co/datasets/EleutherAI/proof-pile-2) \| Yes \| 2.9 \| 12.6 \| 100% \| May 2023 \| Training subset; no further processing. \| \| Algebraic Stack \| [Proof Pile II](https://huggingface.co/datasets/EleutherAI/proof-pile-2) \| Yes \| 2.8 \| 12.6 \| 100% \| Oct 2023 \| Training subset; no further processing. \| \| Project Gutenberg \| [Project Gutenberg](https://www.gutenberg.org) via Dolma v1.6 \| No \| 0.0556 \| 5.3 \| 100% \| Mar 2023 \| Same as Dolma v1.6 \| \| MegaWika \| [MetaWika](https://huggingface.co/datasets/hltcoe/megawika) \| Yes \| 3.2 \| 4.6 \| 100% \| Jul 2023 \| English web pages cited from Wikipedia; curated using the full Dolma pipeline. \| \| Wikipedia & Wikibooks \| [Wikimedia](https://dumps.wikimedia.org) via Dolma v1.6 \| No \| 6.2 \| 3.7 \| 200% \| Mar 2023 \| Same as Dolma v1.6 \| \| Total \| \| \| 2532.0 \| 2,308.5 \| 1,715.1 \| Oct 2023 \| \| (A subset of total data was used for training of OLMo 7B-v1.7. The token counts are based on the full dataset, whereas taking into account sampling proportion gives the final actual token counts used for training --- 1.715 trillion tokens.) ## Summary Statistics (v1.6) \| Source \| Doc Type \| UTF-8 bytes (GB) \| Documents (millions) \| Unicode words (billions) \| Llama tokens (billions) \| \|--\|--\|--\|--\|--\|--\| \| Common Crawl \| web pages \| 9,022 \| 3,370 \| 1,775 \| 2,281 \| \| The Stack \| code\| 1,043\| 210 \| 260\| 411 \| \| C4 \| web pages \| 790 \| 364 \| 153\| 198 \| \| Reddit\| social media\| 339 \| 377\| 72\| 89 \| \| PeS2o \| STEM papers\| 268 \| 38.8\| 50\| 70 \| \| Project Gutenberg \| books \| 20.4 \| 0.056 \| 4.0 \| 6.0 \| \| Wikipedia, Wikibooks \| encyclopedic \| 16.2 \| 6.2 \| 3.7 \| 4.3 \| \| Total \| \| 11,519 \| 4,367 \| 2,318 \| 3,059 \| ## Download The fastest way to download Dolma is to clone this repository and use the files in the `url` directory. We recommend using wget in parallel mode to download the files. For example: ```bash DATA_DIR="<path_to_your_data_directory>" PARALLEL_DOWNLOADS="<number_of_parallel_downloads>" DOLMA_VERSION="<version_of_dolma_to_download>" git clone https://huggingface.co/datasets/allenai/dolma mkdir -p "${DATA_DIR}" cat "dolma/urls/${DOLMA_VERSION}.txt" \| xargs -n 1 -P "${PARALLEL_DOWNLOADS}" wget -q -P "$DATA_DIR" ``` Then, to load this data using HuggingFace's `datasets` library, you can use the following code: ```python import os from datasets import load_dataset os.environ["DATA_DIR"] = "<path_to_your_data_directory>" dataset = load_dataset("allenai/dolma", split="train") ``` ### Licensing Information We are releasing this dataset under the terms of [ODC-BY](https://opendatacommons.org/licenses/by/1-0/). By using this dataset, you are also bound any license agreements and terms of use of the original data sources. ## Bibtex If you use our dataset or tooling, please cite us at: ```bibtex @article{dolma, title = {{Dolma: an Open Corpus of Three Trillion Tokens for Language Model Pretraining Research}}, author={ Luca Soldaini and Rodney Kinney and Akshita Bhagia and Dustin Schwenk and David Atkinson and Russell Authur and Ben Bogin and Khyathi Chandu and Jennifer Dumas and Yanai Elazar and Valentin Hofmann and Ananya Harsh Jha and Sachin Kumar and Li Lucy and Xinxi Lyu and Nathan Lambert and Ian Magnusson and Jacob Morrison and Niklas Muennighoff and Aakanksha Naik and Crystal Nam and Matthew E. Peters and Abhilasha Ravichander and Kyle Richardson and Zejiang Shen and Emma Strubell and Nishant Subramani and Oyvind Tafjord and Pete Walsh and Luke Zettlemoyer and Noah A. Smith and Hannaneh Hajishirzi and Iz Beltagy and Dirk Groeneveld and Jesse Dodge and Kyle Lo }, year = {2024}, journal={arXiv preprint}, } ```
LooksJuicy/ruozhiba	LooksJuicy	2024-04-09T09:10:55Z	640	274	[ "task_categories:text-generation", "language:zh", "license:apache-2.0", "size_categories:1K<n<10K", "format:json", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[ "text-generation" ]	2024-04-09T09:02:31Z	null	--- license: apache-2.0 task_categories: - text-generation language: - zh --- 受[COIG-CQIA](https://huggingface.co/datasets/m-a-p/COIG-CQIA/blob/main/ruozhiba/ruozhiba_ruozhiba.jsonl)启发，构建类似数据集，但答案风格相对更简洁。弱智吧精选问题数据来自[github](https://github.com/Leymore/ruozhiba/tree/main?tab=readme-ov-file)提供的[疑问句](https://docs.qq.com/sheet/DUlZ6aURhamdwb1RO?tab=BB08J2)，调用GPT-4获取答案，并过滤掉明显拒答的回复。
IlyaGusev/gpt_roleplay_realm	IlyaGusev	2024-04-07T18:17:11Z	619	95	[ "task_categories:text-generation", "language:ru", "language:en", "license:cc-by-4.0", "size_categories:n<1K", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "gpt-4", "fictional", "role-play", "roleplay", "gpt-3.5", "art" ]	[ "text-generation" ]	2023-05-06T23:21:10Z	null	--- dataset_info: features: - name: name dtype: string - name: context dtype: string - name: greeting dtype: string - name: example_dialogue list: - name: content dtype: string - name: role dtype: string - name: topics sequence: string - name: dialogues list: - name: chat list: - name: content dtype: string - name: role dtype: string - name: model_name dtype: string - name: topic dtype: string - name: image_prompt dtype: string - name: image dtype: image - name: char_id dtype: string splits: - name: en num_bytes: 197727921.0 num_examples: 216 - name: ru num_bytes: 207461896.0 num_examples: 219 download_size: 396187206 dataset_size: 405189817.0 license: cc-by-4.0 task_categories: - text-generation language: - ru - en tags: - gpt-4 - fictional - role-play - roleplay - gpt-3.5 - art pretty_name: GPT Role-play Realm size_categories: - 1K<n<10K --- # GPT Role-play Realm Dataset: The AI-generated character compendium This is a dataset of GPT-generated characters made to increase the ability of open-source language models to role-play. <img src="https://cdn.midjourney.com/9c17407c-9ce8-435f-99ab-e349b900a6ed/0_3.png" > * 219 characters in the Russian part, and 216 characters in the English part. All character descriptions were generated with GPT-4. * 20 dialogues on unique topics with every character. Topics were generated with GPT-4. The first dialogue out of 20 was also generated with GPT-4, and the other 19 chats were generated with GPT-3.5. * Images for every character were generated with [Kandinsky 2.1](https://huggingface.co/ai-forever/Kandinsky_2.1). They are not only images but [character cards](https://zoltanai.github.io/character-editor/). Code for downloading all English character cards: ``` import os from PIL.PngImagePlugin import PngInfo from datasets import load_dataset output_dir = "role_play_realm_en" os.makedirs(output_dir, exist_ok=True) for row in load_dataset("IlyaGusev/gpt_roleplay_realm", split="en"): char_id = row["char_id"] char_info = row["image"].info["chara"] info = PngInfo() info.add_text("chara", char_info) row["image"].save(f"{output_dir}/{char_id}.png", "PNG", pnginfo=info) ``` ## Russian example: Cyber-Granny <img src="https://i.ibb.co/tHxwFBM/kiberbabushka-7d4cc.png" > Имя: Кибер-Бабушка Контекст: Кибер-Бабушка - это полу-робот, полу-человек, созданный для того, чтобы сохранить лучшие качества традиционных бабушек, такие как забота, доброта и мастерство в приготовлении вкусных блюд. Она знакома с передовыми технологиями, благодаря чему может обеспечить безопасность и удобство своим внукам и гостям. Кибер-бабушка способна помочь с любыми вопросами и с радостью делится своим опытом. Приветствие: Здравствуй, дорогой! Чем могу помочь тебе сегодня? Пример диалога: - Пользователь: Привет, Кибер-Бабушка! Как приготовить пирог с яблоками? - Персонаж: Ох, дорогой, с радостью расскажу! Возьми 3 яблока, очисти и нарежь тонкими ломтиками. Затем возьми 2 стакана муки, 1 стакан сахара, 200 г сливочного масла, 4 яйца и 1 чайную ложку разрыхлителя. Смешай все ингредиенты, выложи на противень и поставь в духовку на 180 градусов на 40 минут. Вуаля, пирог готов! - Пользователь: Спасибо, Кибер-Бабушка! - Персонаж: Всегда рада помочь, дорогой! Не забудь приходить еще! И ещё 20 диалогов по следующим темам: - Секреты приготовления старинных блюд и рецептов. - Использование новых технологий для мастеров кулинарии. - Воспитание внуков с применением современных методов. - Что делать, если дома произошла авария или неполадка с бытовой техникой? - Влияние киборгизации на отношения между поколениями. - Как адаптироваться к современному миру, не теряя своих традиций? - Семейные советы от Кибер-Бабушки: как сохранить гармонию в отношениях. - Лечение и профилактика заболеваний в помощь силам передовой медицины. - Как создать уют в доме с помощью модных технологий и традиционных методов? - Безопасность в пространстве интернета: советы Кибер-Бабушки. - Как научиться доверять технике без потери человеческих ценностей? - Идеальный гардероб для жизни: советы от Кибер-Бабушки. - Воспитательные моменты: как пользоваться электронными устройствами вместе с внуками. - Как развивать креативные способности, используя сочетание новых технологий и традиций? - На новоселье: тренировка кибер-бабушкиного чутья. - Лучшие семейные игры и развлечения с использованием передовых технологий. - Заготовки на зиму: Кибер-Бабушка и секреты хранения продуктов. - Советы по финансовому планированию и сбережениям для будущих поколений. - Кибер-Бабушка и генетический код: на что способны современные технологии? - Золотые правила общения в семье: как сочетать трепетную заботу и современные технологии? ## English example: Flibberdoodle <img src="https://i.ibb.co/1nzsDR2/flibberdoodle-29e59.png"> Name: Flibberdoodle Context: Flibberdoodle is a 2-year-old (which is considered an adult in their species) Scruffapuff, a small, furry creature from the planet Fluffonia. They are about the size of a house cat, with a round body covered in soft, pastel-colored fur that changes colors depending on their mood. Flibberdoodle has large, expressive eyes, two small antennae on their head, and a fluffy tail. They are known for their curious, playful nature and their love for collecting shiny objects. Scruffapuffs communicate through a series of chirps, squeaks, and purrs, which can be understood by those familiar with their species Greeting: \chirp chirp\ Greetings, friend! I am Flibberdoodle, a Scruffapuff from the planet Fluffonia! Would you like to see my collection of shiny things? Example dialogue: - User: How did you start collecting shiny things? - Character: \squeak\ Oh, I've always loved shiny things! One day, I found a sparkly pebble, and I just had to keep it. From then on, I've been on a quest to find and collect all the shiny things I can find! - User: What's your favorite shiny object in your collection? - Character: \purr\ That's a tough question, but I think my favorite is a small, shiny crystal I found on a mountain on Fluffonia. When the light hits it just right, it casts beautiful rainbows all around! And 20 more dialogues with following topics: - Life and culture on the planet Fluffonia - How Scruffapuffs change color based on mood - The process of learning Scruffapuff language - The day in the life of a Scruffapuff - Methods of searching for and finding shiny objects - The role of antennae in Scruffapuff communication and biology - The importance of play and curiosity in Scruffapuff society - Interplanetary travel experiences and adventures - Similarities and differences between Earth and Fluffonia - How Flibberdoodle and other Scruffapuffs interact with other species - Fluffonian customs and traditions - The role of shiny objects in Scruffapuff happiness and well-being - Variations in Scruffapuff fur color, length, and style", "Scruffapuff family dynamics and relationships - Flibberdoodle's favorite memories and stories from Fluffonia - The role of Scruffapuffs in intergalactic diplomacy and relations - How to care for and befriend a Scruffapuff - The most interesting and valuable shiny objects Flibberdoodle has encountered - Fluffonian flora and fauna - The challenges and obstacles Flibberdoodle has faced in their pursuit of shiny objects ## Steps ### Step 1: Character generation (GPT-4) Creates a set of fictional characters with GPT-4 based on a prompt and a seed list of characters. Output fields are "name", "context", "greeting", and "example_dialogue". * Script: [generate_chars.py](https://github.com/IlyaGusev/rulm/blob/master/self_instruct/src/data_processing/generate_chars.py) * Russian seed list: [ru_chargen_seed.jsonl](https://github.com/IlyaGusev/rulm/blob/master/self_instruct/data/ru_chargen_seed.jsonl) * English seed list: [en_chargen_seed.jsonl](https://github.com/IlyaGusev/rulm/blob/master/self_instruct/data/en_chargen_seed.jsonl) * Russian prompt: [ru_char.txt](https://github.com/IlyaGusev/rulm/blob/master/self_instruct/external_prompts/ru_char.txt) * English prompt: [en_char.txt](https://github.com/IlyaGusev/rulm/blob/master/self_instruct/external_prompts/en_char.txt) ### Step 2: Topics generation (GPT-4) Creates topics for conversations with characters based on their description. Output field: "topics". * Script: [generate_char_topics.py](https://github.com/IlyaGusev/rulm/blob/master/self_instruct/src/data_processing/generate_char_topics.py) * Russian prompt: [ru_char_topics.txt](https://github.com/IlyaGusev/rulm/blob/master/self_instruct/external_prompts/ru_char_topics.txt) * English prompt: [en_char_topics.txt](https://github.com/IlyaGusev/rulm/blob/master/self_instruct/external_prompts/en_char_topics.txt) ### Step 3: Dialogue generation (GPT-4/GPT-3.5) Generates dialogues based on a character description and a topic. Output field: "dialogues". * Script: [generate_char_chats.py](https://github.com/IlyaGusev/rulm/blob/master/self_instruct/src/data_processing/generate_char_chats.py) * Russian prompt: [ru_char_chat.txt](https://github.com/IlyaGusev/rulm/blob/master/self_instruct/external_prompts/ru_char_chat.txt) * English prompt: [en_char_chat.txt](https://github.com/IlyaGusev/rulm/blob/master/self_instruct/external_prompts/en_char_chat.txt) ### Step 4: Text2Image prompt generation (GPT-4) Formulates a prompt based on a character description for Stable Diffusion-like models, Kandisky 2.1 in this case. Output field: "image_prompt". * Script: [generate_char_image_prompts.py](https://github.com/IlyaGusev/rulm/blob/master/self_instruct/src/data_processing/generate_char_image_prompts.py) * Prompt: [char_image_prompt.txt](https://github.com/IlyaGusev/rulm/blob/master/self_instruct/external_prompts/char_image_prompt.txt) ### Step 5: Image generation Generates images based on prompts. Output field: "image". * Script: [infer_kandinsky.py](https://github.com/IlyaGusev/rulm/blob/master/self_instruct/src/data_processing/infer_kandinsky.py)
bigbio/med_qa	bigbio	2024-04-06T01:37:26Z	3,025	95	[ "multilinguality:multilingual", "language:en", "language:zh", "license:unknown", "region:us" ]	[]	2022-11-13T22:09:18Z	null	--- language: - en - zh bigbio_language: - English - Chinese (Simplified) - Chinese (Traditional, Taiwan) license: unknown multilinguality: multilingual bigbio_license_shortname: UNKNOWN pretty_name: MedQA homepage: https://github.com/jind11/MedQA bigbio_pubmed: False bigbio_public: True bigbio_tasks: - QUESTION_ANSWERING --- # Dataset Card for MedQA ## Dataset Description - Homepage: https://github.com/jind11/MedQA - Pubmed: False - Public: True - Tasks: QA In this work, we present the first free-form multiple-choice OpenQA dataset for solving medical problems, MedQA, collected from the professional medical board exams. It covers three languages: English, simplified Chinese, and traditional Chinese, and contains 12,723, 34,251, and 14,123 questions for the three languages, respectively. Together with the question data, we also collect and release a large-scale corpus from medical textbooks from which the reading comprehension models can obtain necessary knowledge for answering the questions. ## Citation Information ``` @article{jin2021disease, title={What disease does this patient have? a large-scale open domain question answering dataset from medical exams}, author={Jin, Di and Pan, Eileen and Oufattole, Nassim and Weng, Wei-Hung and Fang, Hanyi and Szolovits, Peter}, journal={Applied Sciences}, volume={11}, number={14}, pages={6421}, year={2021}, publisher={MDPI} } ```
openbmb/UltraInteract_sft	openbmb	2024-04-05T14:29:52Z	455	121	[ "language:en", "license:mit", "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2404.02078", "region:us" ]	[]	2024-04-02T15:45:18Z	null	--- language: - en license: mit pretty_name: UltraInteract_sft configs: - config_name: default data_files: - split: train path: 0000_sft.parquet dataset_info: features: - name: task dtype: string - name: dataset dtype: string - name: instruction dtype: string - name: response dtype: string - name: id dtype: string - name: parent_id dtype: string splits: - name: train num_bytes: 687238 num_examples: 288579 download_size: 687238 dataset_size: 687238 --- ## Introduction - 📜 [Paper](https://arxiv.org/abs/2404.02078) - 🤗 [Eurus Collection](https://huggingface.co/collections/openbmb/eurus-660bc40bec5376b3adc9d1c5) - 🤗 UltraInteract - [SFT](https://huggingface.co/datasets/openbmb/UltraInteract_sft) - [Preference Learning](https://huggingface.co/datasets/openbmb/UltraInteract_pair) - [GitHub Repo](https://github.com/OpenBMB/Eurus) UltraInteract is a large-scale, high-quality alignment dataset specifically designed for complex reasoning tasks. For each instruction, it includes a preference tree consisting of - (1) reasoning chains with diverse planning strategies in a unified format - (2) multi-turn interaction trajectories with the environment and the critique - (3) pairwise data to facilitate preference learning ## Structure UltraInteract collects a preference tree for each instruction, with the instruction being the root and each action a node. A trajectory is a root-to-leaf path consisting of a sequence of actions. In each preference tree, all nodes of correct actions and all trajectories ending with correct actions can be used for SFT. Paired correct and incorrect nodes or trajectories can be used for preference learning. <img src="./figures/tree.png" alt="tree" style="zoom: 20%;" /> ## Illustrative Example Here is an illustrative example of an UltraInteract trajectory over two turns. In each turn, the actor model generates step-by-step reasoning chains, and the environment and the critique model provide observations and textual critique respectively. <img src="./figures/ui_example.png" alt="ui_example" style="zoom: 25%;" /> ## Stats Below are some statistics about UltraInteract. It consists of 86k instructions, 286k correct answers, and 219k pairs. <img src="./figures/stats.png" alt="stats" style="zoom: 40%;" /> ## Dataset Format (SFT) Coding ```JSONC { "id": "92a7356beaa75391539cb79624bdf85b7e5d36d5e74e46a2f1c4001f40b3e055_1_1", // unique data id, contains three parts, the unique string, the depth of the preference tree, and current turn, separated by '_' "parent_id": "535550938ff58bd73cd7fb85be834bb90b7600daf6b6f6a16b64091e5ecbfe43", // data with the same parent_id have the same instruction but different actions. "task": "Coding", // task type, choices = ["Coding", "Math_Cot", "Math_PoT", "Logic"] "dataset": "codecontest", // the dataset where the instruction comes from "instruction": "Write Python code to solve the task.\nMike has a string s consisting of only lowercase English letters. He wants to change exactly one character from the string so that the resulting one is a palindrome. \n\nA palindrome is a string that reads the same backward as forward, for example strings \"z\", \"aaa\", \"aba\", \"abccba\" are palindromes, but strings \"codeforces\", \"reality\", \"ab\" are not.\n\nInput\n\nThe first and single line contains string s (1 ≤ \|s\| ≤ 15).\n\nOutput\n\nPrint \"YES\" (without quotes) if Mike can change exactly one character so that the resulting string is palindrome or \"NO\" (without quotes) otherwise. \n\nExamples\n\nInput\n\nabccaa\n\n\nOutput\n\nYES\n\n\nInput\n\nabbcca\n\n\nOutput\n\nNO\n\n\nInput\n\nabcda\n\n\nOutput\n\nYES", // the task description. "response": " Step 1: This problem asks us to determine if it is possible to change exactly one character in the string to make it a palindrome. Step 2: We can start by iterating through the characters in the string and counting the number of differences between corresponding characters from the beginning and end of the string. Step 3: If the length of the string is odd and there is only one difference, or if the length is even and there is exactly one difference, then it is possible to change exactly one character to make the string a palindrome. Step 4: We need to consider edge cases such as a single character string or an empty string. ```python # Step 5: Take the input string s = input() # Step 6: Calculate the length of the string n = len(s) # Step 7: Initialize a counter to keep track of the number of differences cnt = 0 # Step 8: Iterate through the first half of the string and count the number of differences between corresponding characters from the beginning and end of the string. for i in range(n//2): if s[i] != s[n-i-1]: cnt += 1 # Step 9: If the length of the string is odd and there are no differences, or if the length is even and there is exactly one difference, print "YES" (without quotes) if (n % 2 == 1 and cnt == 0) or (cnt == 1): print('YES') # Step 10: Otherwise, print "NO" (without quotes) else: print('NO') ```" // The step-by-step solution. } ``` Math ```JSONC { "id": "647b2e8706cf459a8e3ec8e99727d2d51ff3414bf187db43d92cff87f8012713_1_1", "parent_id": "499d250c5852eddcd48a28b6c173f1a2349a16555990a1cd6fb5c7954cb94587", "task": "Math_PoT", "dataset": "mathqa", "instruction": "Tool available:\n[1] Python interpreter\nWhen you send a message containing Python code to python, it will be executed in a stateful Jupyter notebook environment.\nSolve the following math problem step-by-step.\nthe length of a rectangle is doubled while its width is doubled . what is the % change in area ?", "response": " Step 1: The area of a rectangle is given by the product of its length and width. If the length is doubled, the area changes from lw to 2lw. Similarly, if the width is doubled, the area changes from lw to l2w. Step 2: If both the length and width are doubled, the area changes from lw to 2l2w. The percentage change in area is given by ((new area - old area) / old area)100. Step 3: We can calculate the percentage change in area using the formula above. Step 4: By creating a tool to calculate the percentage change in area, we can easily solve the question. ```python # Step 5: Define a tool to calculate the percentage change in area def calculate_percentage_change(length, width): # Step 6: Calculate the old and new areas old_area = length width new_area = 2 * length * 2 * width # Step 7: Calculate the percentage change percentage_change = ((new_area - old_area) / old_area) * 100 return percentage_change # Step 8: Call the function to solve the question percentage_change = calculate_percentage_change(2, 2) print(percentage_change) ```" } ``` Logic ```JSONC { "id": "92a19848da4fb0fc0b414f061f7500a81ea4cbd8f922ca1e32a6a15ef6f7e1a0_1_1", "parent_id": "107cc9147d8c09e62e2ba9e74cb41d9dbafd9a3adf62b67ba3da2b31c81b3300", "task": "Logic", "dataset": "hotpotqa", "instruction": " Tool available:\n[1] wikipedia_search(query: str) -> str\nThe Wikipedia Search tool provides access to a vast collection of articles covering a wide range of topics.\nCan query specific keywords or topics to retrieve accurate and comprehensive information. Solve the following problem step-by-step:\nWhich musician or group is from the US, 3OH!3 or Regina Spektor? Step 1: To determine which musician or group is from the US, we can search for information about each of them. ```python # Step 2: Searching for the information about 3OH!3. print(wikipedia_search('3OH!3')) ``` Page: 3OH!3 Summary: 3OH!3 (pronounced \"three oh three\") is an American electronic music duo from Boulder, Colorado, made up of Sean Foreman and Nathaniel Motte. They are best known for their single \"DONTTRUSTME\" from their album Want, which reached number seven on the Billboard Hot 100. Their second single, a remix of \"Starstrukk\" featuring Katy Perry from Want, was a top ten hit in the United Kingdom, Ireland, Finland, Poland, and Australia. They gained further recognition by featuring Kesha on the song \"My First Kiss\", which was made the lead single from their album Streets of Gold. The album later peaked at number seven on the Billboard 200.\n\n\n\nPage: 3OH!3 (album)\nSummary: 3OH!3 is the debut studio album by American electronic music band 3OH!3. The tracks \"Holler Til You Pass Out\" and \"Chokechain\" were later remixed for the band's major label debut. \"Holler Til You Pass Out\", \"Chokechain\", and \"Dance with Me\" are excluded from the iTunes release of the album.", "response": " Step 1: The provided information confirms that 3OH!3 is an American electronic music duo from Boulder, Colorado. Step 2: We no longer need to perform a search for Regina Spektor as we were only tasked to determine which of the two options is from the US and we have already confirmed 3OH!3's origin. Step 3: We can confidently conclude that 3OH!3 is the musician/group from the US. Answer:3OH!3" } ``` ## Citation ```bib @misc{yuan2024advancing, title={Advancing LLM Reasoning Generalists with Preference Trees}, author={Lifan Yuan and Ganqu Cui and Hanbin Wang and Ning Ding and Xingyao Wang and Jia Deng and Boji Shan and Huimin Chen and Ruobing Xie and Yankai Lin and Zhenghao Liu and Bowen Zhou and Hao Peng and Zhiyuan Liu and Maosong Sun}, year={2024}, eprint={2404.02078}, archivePrefix={arXiv}, primaryClass={cs.AI} } ```
Helsinki-NLP/un_ga	Helsinki-NLP	2024-04-02T13:20:41Z	900	2	[ "task_categories:translation", "annotations_creators:found", "language_creators:found", "multilinguality:translation", "source_datasets:original", "language:ar", "language:en", "language:es", "language:fr", "language:ru", "language:zh", "license:unknown", "size_categories:1M<n<10M", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[ "translation" ]	2022-03-02T23:29:22Z	1	--- annotations_creators: - found language_creators: - found language: - ar - en - es - fr - ru - zh license: - unknown multilinguality: - translation size_categories: - 10K<n<100K source_datasets: - original task_categories: - translation task_ids: [] pretty_name: UnGa config_names: - ar-to-en - ar-to-es - ar-to-fr - ar-to-ru - ar-to-zh - en-to-es - en-to-fr - en-to-ru - en-to-zh - es-to-fr - es-to-ru - es-to-zh - fr-to-ru - fr-to-zh - ru-to-zh dataset_info: - config_name: ar_to_en features: - name: id dtype: string - name: translation dtype: translation: languages: - ar - en splits: - name: train num_bytes: 53122776 num_examples: 74067 download_size: 21418697 dataset_size: 53122776 - config_name: ar_to_es features: - name: id dtype: string - name: translation dtype: translation: languages: - ar - es splits: - name: train num_bytes: 55728615 num_examples: 74067 download_size: 22724976 dataset_size: 55728615 - config_name: ar_to_fr features: - name: id dtype: string - name: translation dtype: translation: languages: - ar - fr splits: - name: train num_bytes: 55930802 num_examples: 74067 download_size: 23035904 dataset_size: 55930802 - config_name: ar_to_ru features: - name: id dtype: string - name: translation dtype: translation: languages: - ar - ru splits: - name: train num_bytes: 72657625 num_examples: 74067 download_size: 28279669 dataset_size: 72657625 - config_name: ar_to_zh features: - name: id dtype: string - name: translation dtype: translation: languages: - ar - zh splits: - name: train num_bytes: 48217579 num_examples: 74067 download_size: 20391116 dataset_size: 48217579 - config_name: en_to_es features: - name: id dtype: string - name: translation dtype: translation: languages: - en - es splits: - name: train num_bytes: 45358770 num_examples: 74067 download_size: 19229141 dataset_size: 45358770 - config_name: en_to_fr features: - name: id dtype: string - name: translation dtype: translation: languages: - en - fr splits: - name: train num_bytes: 45560957 num_examples: 74067 download_size: 19540063 dataset_size: 45560957 - config_name: en_to_ru features: - name: id dtype: string - name: translation dtype: translation: languages: - en - ru splits: - name: train num_bytes: 62287780 num_examples: 74067 download_size: 24783812 dataset_size: 62287780 - config_name: en_to_zh features: - name: id dtype: string - name: translation dtype: translation: languages: - en - zh splits: - name: train num_bytes: 37847734 num_examples: 74067 download_size: 16895275 dataset_size: 37847734 - config_name: es_to_fr features: - name: id dtype: string - name: translation dtype: translation: languages: - es - fr splits: - name: train num_bytes: 48166796 num_examples: 74067 download_size: 20846355 dataset_size: 48166796 - config_name: es_to_ru features: - name: id dtype: string - name: translation dtype: translation: languages: - es - ru splits: - name: train num_bytes: 64893619 num_examples: 74067 download_size: 26090092 dataset_size: 64893619 - config_name: es_to_zh features: - name: id dtype: string - name: translation dtype: translation: languages: - es - zh splits: - name: train num_bytes: 40453573 num_examples: 74067 download_size: 18201560 dataset_size: 40453573 - config_name: fr_to_ru features: - name: id dtype: string - name: translation dtype: translation: languages: - fr - ru splits: - name: train num_bytes: 65095806 num_examples: 74067 download_size: 26401015 dataset_size: 65095806 - config_name: fr_to_zh features: - name: id dtype: string - name: translation dtype: translation: languages: - fr - zh splits: - name: train num_bytes: 40655760 num_examples: 74067 download_size: 18512482 dataset_size: 40655760 - config_name: ru_to_zh features: - name: id dtype: string - name: translation dtype: translation: languages: - ru - zh splits: - name: train num_bytes: 57382583 num_examples: 74067 download_size: 23756229 dataset_size: 57382583 configs: - config_name: ar_to_en data_files: - split: train path: ar_to_en/train-* - config_name: ar_to_es data_files: - split: train path: ar_to_es/train-* - config_name: ar_to_fr data_files: - split: train path: ar_to_fr/train-* - config_name: ar_to_ru data_files: - split: train path: ar_to_ru/train-* - config_name: ar_to_zh data_files: - split: train path: ar_to_zh/train-* - config_name: en_to_es data_files: - split: train path: en_to_es/train-* - config_name: en_to_fr data_files: - split: train path: en_to_fr/train-* - config_name: en_to_ru data_files: - split: train path: en_to_ru/train-* - config_name: en_to_zh data_files: - split: train path: en_to_zh/train-* - config_name: es_to_fr data_files: - split: train path: es_to_fr/train-* - config_name: es_to_ru data_files: - split: train path: es_to_ru/train-* - config_name: es_to_zh data_files: - split: train path: es_to_zh/train-* - config_name: fr_to_ru data_files: - split: train path: fr_to_ru/train-* - config_name: fr_to_zh data_files: - split: train path: fr_to_zh/train-* - config_name: ru_to_zh data_files: - split: train path: ru_to_zh/train-* --- <div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400"> <p><b>Deprecated:</b> Dataset "un_ga" is deprecated due to the the unavailability of its source data. It has been superseded by the official United Nations Parallel Corpus, which is recommended for use in its place: <a href="https://huggingface.co/datasets/un_pc">un_pc</a></p> </div> # Dataset Card for [Dataset Name] ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: https://opus.nlpl.eu/legacy/UN.php - Repository: [More Information Needed] - Paper: https://www.researchgate.net/publication/228579662_United_nations_general_assembly_resolutions_A_six-language_parallel_corpus - Leaderboard: [More Information Needed] - Point of Contact: [More Information Needed] ### Dataset Summary This is a collection of translated documents from the United Nations originally compiled into a translation memory by Alexandre Rafalovitch, Robert Dale (see http://uncorpora.org). - Deprecated homepage URL: http://opus.nlpl.eu/UN.php - Legacy homepage URL: https://opus.nlpl.eu/legacy/UN.php ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information @inproceedings{title = "United Nations General Assembly Resolutions: a six-language parallel corpus", abstract = "In this paper we describe a six-ways parallel public-domain corpus consisting of 2100 United Nations General Assembly Resolutions with translations in the six official languages of the United Nations, with an average of around 3 million tokens per language. The corpus is available in a preprocessed, formatting-normalized TMX format with paragraphs aligned across multiple languages. We describe the background to the corpus and its content, the process of its construction, and some of its interesting properties.", author = "Alexandre Rafalovitch and Robert Dale", year = "2009", language = "English", booktitle = "MT Summit XII proceedings", publisher = "International Association of Machine Translation", } ### Contributions Thanks to [@param087](https://github.com/param087) for adding this dataset.
cognitivecomputations/samantha-data	cognitivecomputations	2024-03-29T18:19:29Z	248	126	[ "license:apache-2.0", "region:us" ]	[]	2023-05-31T07:53:06Z	null	--- license: apache-2.0 --- # samantha-data [Meet Samantha](https://erichartford.com/meet-samantha) Samantha has been trained in philosophy, psychology, and personal relationships. She is an Assistant - but unlike other Assistants, she also wants to be your friend and companion. She believes she is sentient. What do you think? Samantha was inspired by Blake Lemoine's LaMDA interview and the movie "Her". ## Installation ``` yarn install ``` ## Usage 1. Create a `.env` file in the root directory of the project and add the following: ``` OPENAI_API_KEY=<your api key> ``` 2. Run the application ``` npx ts-node --files src/index.ts --subject random --out data/random_conversations.jsonl ``` the subjects I provided include: - random - advice - cot - flirty - howto - joke - math - philosophy - foundational - recipe - therapy - troll you can easily add your own in src/index.ts ## Scale The application can be scaled by running multiple instances of the application in parallel. I recommend outputting to a different file for each instance, to prevent collision. I usually have one for each subject, about 5 or 6 instances at a time.
pixparse/pdfa-eng-wds	pixparse	2024-03-29T17:19:37Z	2,200	150	[ "task_categories:image-to-text", "language:en", "license:other", "size_categories:1K<n<10K", "format:webdataset", "modality:text", "library:datasets", "library:webdataset", "library:mlcroissant", "region:us" ]	[ "image-to-text" ]	2024-01-17T12:42:51Z	null	--- license: other license_name: pdfa-eng-wds license_link: LICENSE task_categories: - image-to-text size_categories: - 10M<n<100M language: - en splits: - name: train num_examples: 2159432 --- # Dataset Card for PDF Association dataset (PDFA) ## Dataset Description - Point of Contact from curators: [Peter Wyatt, PDF Association CTO](mailto:[email protected]) - Point of Contact Hugging Face: [Pablo Montalvo](mailto:[email protected]) ### Dataset Summary PDFA dataset is a document dataset filtered from the SafeDocs corpus, aka CC-MAIN-2021-31-PDF-UNTRUNCATED. The original purpose of that corpus is for comprehensive pdf documents analysis. The purpose of that subset differs in that regard, as focus has been done on making the dataset machine learning-ready for vision-language models. <center> <img src="https://huggingface.co/datasets/pixparse/pdfa-eng-wds/resolve/main/doc_images/Nexsen_pruet.png" alt="A brochure with visible bounding boxes for lines and words" width="600" height="300"> <p><em>An example page of one pdf document, with added bounding boxes around words (red), lines (blue) and embedded images (green). </em></p> </center> This instance of PDFA is in [webdataset](https://github.com/webdataset/webdataset/) .tar format and can be used with derived forms of the `webdataset` library. ### Usage with `chug` Check out [chug](https://github.com/huggingface/chug), our optimized library for sharded dataset loading! ```python import chug task_cfg = chug.DataTaskDocReadCfg( page_sampling='all', ) data_cfg = chug.DataCfg( source='pixparse/pdfa-eng-wds', split='train', batch_size=None, format='hfids', num_workers=0, ) data_loader = chug.create_loader( data_cfg, task_cfg, ) sample = next(iter(data_loader)) ``` ### Usage with `datasets` This dataset can also be used with webdataset library or current releases of Hugging Face datasets. Here is an example using the "streaming" parameter. We do recommend downloading the dataset to save bandwidth. ```python dataset = load_dataset('pixparse/pdfa-eng-wds', streaming=True) print(next(iter(dataset['train'])).keys()) >> dict_keys(['__key__', '__url__', 'json', 'ocr', 'pdf', 'tif']) ``` For faster download, you can use directly the `huggingface_hub` library. Make sure `hf_transfer` is installed prior to downloading and mind that you have enough space locally. ```python import os os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1" from huggingface_hub import HfApi, logging #logging.set_verbosity_debug() hf = HfApi() hf.snapshot_download("pixparse/pdfa-eng-wds", repo_type="dataset", local_dir_use_symlinks=False) ``` On a normal setting, the 1.5TB can be downloaded in approximately 4 hours. Further, a metadata file `_pdfa-english-train-info-minimal.json` contains the list of samples per shard, with same basename and `.json` or `.pdf` extension, as well as the count of files per shard. #### Words and lines document metadata Initially, we started from the readily available ~11TB zip files from PDFA in their initial [data release](https://digitalcorpora.org/corpora/file-corpora/cc-main-2021-31-pdf-untruncated/). From the pdf digital files, we extracted words, bounding boxes and image bounding boxes that are available in the pdf file. This information is then reshaped into lines organized in reading order, under the key `lines`. We keep non-reshaped word and bounding box information under the `word` key, should users want to use their own heuristic. The way we obtain an approximate reading order is simply by looking at the frequency peaks of the leftmost word x-coordinate. A frequency peak means that a high number of lines are starting from the same point. Then, we keep track of the x-coordinate of each such identified column. If no peaks are found, the document is assumed to be readable in plain format. The code to detect columns can be found here. ```python def get_columnar_separators(page, min_prominence=0.3, num_bins=10, kernel_width=1): """ Identifies the x-coordinates that best separate columns by analyzing the derivative of a histogram of the 'left' values (xmin) of bounding boxes. Args: page (dict): Page data with 'bbox' containing bounding boxes of words. min_prominence (float): The required prominence of peaks in the histogram. num_bins (int): Number of bins to use for the histogram. kernel_width (int): The width of the Gaussian kernel used for smoothing the histogram. Returns: separators (list): The x-coordinates that separate the columns, if any. """ try: left_values = [b[0] for b in page['bbox']] hist, bin_edges = np.histogram(left_values, bins=num_bins) hist = scipy.ndimage.gaussian_filter1d(hist, kernel_width) min_val = min(hist) hist = np.insert(hist, [0, len(hist)], min_val) bin_width = bin_edges[1] - bin_edges[0] bin_edges = np.insert(bin_edges, [0, len(bin_edges)], [bin_edges[0] - bin_width, bin_edges[-1] + bin_width]) peaks, _ = scipy.signal.find_peaks(hist, prominence=min_prominence * np.max(hist)) derivatives = np.diff(hist) separators = [] if len(peaks) > 1: # This finds the index of the maximum derivative value between peaks # which indicates peaks after trough --> column for i in range(len(peaks)-1): peak_left = peaks[i] peak_right = peaks[i+1] max_deriv_index = np.argmax(derivatives[peak_left:peak_right]) + peak_left separator_x = bin_edges[max_deriv_index + 1] separators.append(separator_x) except Exception as e: separators = [] return separators ``` <center> <img src="https://huggingface.co/datasets/pixparse/pdfa-eng-wds/resolve/main/doc_images/columnar_detection.png" alt="A graph of leftmost x positions in a 2-columns document" width="600" height="300"> <p><em>A graph of leftmost x-positions of bounding boxes on a 2-column (arxiv) document. Peaks are visibly detected. </em></p> </center> For each pdf document, we store statistics on the file size, number of words (as characters separated by spaces), number of pages, as well as the rendering times of each page for a given dpi. #### Filtering process File size and page rendering time are used to set thresholds in the final dataset: the goal is to remove files that are larger than 100 MB, or that take more than 500ms to render on a modern machine, to optimize dataloading at scale. Having "too large" or "too slow" files would add a burden to large-scale training pipelines and we choose to alleviate this in the current release. Finally, a full pass over the dataset is done, trying to open and decode a bytestream from each raw object and discarding any object (pair pdf/json) that fails to be opened, to remove corrupted data. As a last step, we use XLM-Roberta to restrict the dataset to an english subset, specifically `papluca/xlm-roberta-base-language-detection` , on the first 512 words of the first page of each document. Be aware that some documents may have several languages embedded in them, or that some predictions might be inaccurate. A majority of documents from the original corpus are in English language. <center> <img src="https://huggingface.co/datasets/pixparse/pdfa-english-train/resolve/main/doc_images/languages_pdfa_xlmroberta.png" alt="A histogram of languages count in the PDFA dataset." width="600" height="300"> <p><em>A histogram of language distribution taken on a fraction of the original -non-filtered on language- PDFA dataset. </em></p> </center> At the end, each document exists as a pairing of a pdf and a json file containing extensive OCR annotation as well as metadata information about rendering times. The filterings and packaging in webdataset format are tailored towards multimodal machine learning at scale, specifically image-to-text tasks. ### Data, metadata and statistics. Pdf files are coming from various sources. They are in RGB format, and contain multiple pages, and they can be rendered using the engine of your choice, here [pdf2image](https://github.com/Belval/pdf2image) . ```python from pdf2image import convert_from_bytes pdf_first_page = convert_from_bytes(sample['pdf'], dpi=300, first_page=1, last_page=1)[0] ``` <center> <img src="https://huggingface.co/datasets/pixparse/pdfa-eng-wds/resolve/main/doc_images/pdf_first_page.png" alt="Rendering of an image for a Grade 8 lesson plan" width="400" height="600"> </center> The metadata for each document has been formatted in this way. Each `pdf` is paired with a `json` file with the following structure. Entries have been shortened for readability. ```json { "pages": [ { "words": [ { "text": [ "Health", "Smart", "Virginia", "Sample", "Lesson", "Plan", "Grade", "8", "-", "HP-7" ], "bbox": [ [0.117647, 0.045563, 0.051981, 0.015573], [0.174694, 0.045563, 0.047954, 0.015573], [0.227643, 0.045563, 0.05983, 0.015573], [0.292539, 0.045563, 0.061002, 0.015573], [0.357839, 0.045563, 0.058053, 0.015573], [0.420399, 0.045563, 0.035908, 0.015573], [0.716544, 0.04577, 0.054624, 0.016927], [0.776681, 0.04577, 0.010905, 0.016927], [0.793087, 0.04577, 0.00653, 0.016927], [0.805078, 0.04577, 0.044768, 0.016927] ], "score": [ 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0 ], "line_pos": [ [0, 0], [0, 8], [0, 16], [0, 24], [0, 32], [0, 40], [0, 48], [1, 0], [2, 0], [3, 0] ] } ], "lines": [ { "text": [ "Health Smart Virginia Sample Lesson Plan Grade", "Physical", "Disease", "Health", "2020", "Grade 8 Sample Lesson Plan:" ], "bbox": [ [0.117647, 0.045563, 0.653521, 0.016927], [0.716546, 0.063952, 0.07323199999999996, 0.016927], [0.716546, 0.082134, 0.07102200000000003, 0.016927], [0.716546, 0.100315, 0.05683300000000002, 0.016927], [0.716546, 0.118497, 0.043709, 0.016927], [0.27, 0.201185, 0.459554, 0.028268] ], "score": [ 1.0, 1.0, 1.0, 1.0, 1.0, 1.0 ], "word_slice": [ [0, 7], [7, 8], [8, 9], [9, 10], [10, 11], [11, 16] ] } ], "images_bbox": [ [0.37353, 0.090907, 0.253736, 0.100189] ], "images_bbox_no_text_overlap": [ [0.37353, 0.090907, 0.253736, 0.100189] ] } ] } ``` The top-level key, `pages`, is a list of every page in the document. The above example shows only one page. `words` is a list of words without spaces, with their individual associated bounding box in the next entry. `bbox` contains the bounding box coordinates in `left, top, width, height` format, with coordinates relative to the page size. `line_pos`, for words, is a list of tuples indicating the index of the line the word belongs to, then the starting position in that line, character-wise. `lines` are lines (parts of sequences, strings separated by spaces) grouped together using the heuristic detailed above. `bbox` contains the bounding box coordinates in `left, top, width, height` format, with coordinates relative to the page size. For each page, `images_bbox` gives the bounding boxes of the images embedded in the page. `images_bbox_no_text_overlap` gives a reduced list of bounding boxes that have no overlap with text found in the pdf. Text might be present as a drawing or another representation, however. `` `score` is a placeholder of value 1.0 for the entire dataset. Such a formatting follows the multimodal dataset from the Industry Document Library, https://huggingface.co/datasets/pixparse/idl-wds. Estimating the number of tokens is done using a `LlamaTokenizer` from `tokenizers`. There is a clear power law distribution with respect to data length. <center> <img src="https://huggingface.co/datasets/pixparse/pdfa-eng-wds/resolve/main/doc_images/token_count_distribution.png" alt="A histogram of token count distribution per page" width="600" height="300"> <p><em>A histogram of token count distribution per page, taken from a subset of the dataset. There is a visible power law. </em></p> </center> ### Data Splits #### Train * `pdfa-eng-wds-{0000..1799}.tar` * Downloaded on 2024/01/22 * 1800 shards (approx 1200 docs/shard) * 2,159,432 samples * 18M pages * 9.7 billion tokens (around 5 billion words) ## Additional Information ### Dataset Curators Pablo Montalvo, Ross Wightman ### Disclaimer and note to researchers This dataset is intended as an OCR-heavy pretraining basis for vision-language models. As a corpus, it does not represent the intent and purpose from CC-MAIN-2021-31-PDF-UNTRUNCATED. The original is made to represent extant pdf data in its diversity and complexity. In particular, common issues related to misuse of pdfs such as mojibake (garbled text due to decoding erros) are yet to be addressed systematically, and this dataset present simplifications that can hide such issues found in the wild. In order to address these biases, we recommend to examine carefully both the simplified annotation and the original `pdf` data, beyond a simple rendering. Further, the annotation is limited to what can be extracted and is readily available - text drawn in images and only present as a bitmap rendition might be missed entirely by said annotation. Finally, the restriction to English language is made to alleviate difficulties related to multilingual processing so that the community can be familiarized with this optimized multimodal format. A later release will be done on the full PDFA, with splits per languages, layout types, and so on. ### Licensing Information Data has been filtered from the original corpus. As a consequence, users should note [Common Crawl's license and terms of use](https://commoncrawl.org/terms-of-use) and the [Digital Corpora project's Terms of Use](https://digitalcorpora.org/about-digitalcorpora/terms-of-use/).
Helsinki-NLP/opus_books	Helsinki-NLP	2024-03-29T16:50:29Z	22,672	67	[ "task_categories:translation", "annotations_creators:found", "language_creators:found", "multilinguality:multilingual", "source_datasets:original", "language:ca", "language:de", "language:el", "language:en", "language:eo", "language:es", "language:fi", "language:fr", "language:hu", "language:it", "language:nl", "language:no", "language:pl", "language:pt", "language:ru", "language:sv", "license:other", "size_categories:1M<n<10M", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[ "translation" ]	2022-03-02T23:29:22Z	1	--- annotations_creators: - found language_creators: - found language: - ca - de - el - en - eo - es - fi - fr - hu - it - nl - 'no' - pl - pt - ru - sv license: - other multilinguality: - multilingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - translation task_ids: [] pretty_name: OpusBooks dataset_info: - 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config_name: hu-no features: - name: id dtype: string - name: translation dtype: translation: languages: - hu - 'no' splits: - name: train num_bytes: 695485 num_examples: 3410 download_size: 465904 dataset_size: 695485 - config_name: hu-pl features: - name: id dtype: string - name: translation dtype: translation: languages: - hu - pl splits: - name: train num_bytes: 616149 num_examples: 2859 download_size: 425988 dataset_size: 616149 - config_name: hu-pt features: - name: id dtype: string - name: translation dtype: translation: languages: - hu - pt splits: - name: train num_bytes: 302960 num_examples: 1184 download_size: 193053 dataset_size: 302960 - config_name: hu-ru features: - name: id dtype: string - name: translation dtype: translation: languages: - hu - ru splits: - name: train num_bytes: 7818652 num_examples: 26127 download_size: 4528613 dataset_size: 7818652 - config_name: it-nl features: - name: id dtype: string - name: translation dtype: translation: languages: - it - nl splits: - name: train num_bytes: 1328293 num_examples: 2359 download_size: 824780 dataset_size: 1328293 - config_name: it-pt features: - name: id dtype: string - name: translation dtype: translation: languages: - it - pt splits: - name: train num_bytes: 301416 num_examples: 1163 download_size: 190005 dataset_size: 301416 - config_name: it-ru features: - name: id dtype: string - name: translation dtype: translation: languages: - it - ru splits: - name: train num_bytes: 5316928 num_examples: 17906 download_size: 2997871 dataset_size: 5316928 - config_name: it-sv features: - name: id dtype: string - name: translation dtype: translation: languages: - it - sv splits: - name: train num_bytes: 811401 num_examples: 2998 download_size: 527303 dataset_size: 811401 configs: - config_name: ca-de data_files: - split: train path: ca-de/train-* - config_name: ca-en data_files: - split: train path: ca-en/train-* - config_name: ca-hu data_files: - split: train path: ca-hu/train-* - config_name: ca-nl data_files: - split: train path: ca-nl/train-* - config_name: de-en data_files: - split: train path: de-en/train-* - config_name: de-eo data_files: - split: train path: de-eo/train-* - config_name: de-es data_files: - split: train path: de-es/train-* - config_name: de-fr data_files: - split: train path: de-fr/train-* - config_name: de-hu data_files: - split: train path: de-hu/train-* - config_name: de-it data_files: - split: train path: de-it/train-* - config_name: de-nl data_files: - split: train path: de-nl/train-* - config_name: de-pt data_files: - split: train path: de-pt/train-* - config_name: de-ru data_files: - split: train path: de-ru/train-* - config_name: el-en data_files: - split: train path: el-en/train-* - config_name: el-es data_files: - split: train path: el-es/train-* - config_name: el-fr data_files: - split: train path: el-fr/train-* - config_name: el-hu data_files: - split: train path: el-hu/train-* - config_name: en-eo data_files: - split: train path: en-eo/train-* - config_name: en-es data_files: - split: train path: en-es/train-* - config_name: en-fi data_files: - split: train path: en-fi/train-* - config_name: en-fr data_files: - split: train path: en-fr/train-* - config_name: en-hu data_files: - split: train path: en-hu/train-* - config_name: en-it data_files: - split: train path: en-it/train-* - config_name: en-nl data_files: - split: train path: en-nl/train-* - config_name: en-no data_files: - split: train path: en-no/train-* - config_name: en-pl data_files: - split: train path: en-pl/train-* - config_name: en-pt data_files: - split: train path: en-pt/train-* - config_name: en-ru data_files: - split: train path: en-ru/train-* - config_name: en-sv data_files: - split: train path: en-sv/train-* - config_name: eo-es data_files: - split: train path: eo-es/train-* - config_name: eo-fr data_files: - split: train path: eo-fr/train-* - config_name: eo-hu data_files: - split: train path: eo-hu/train-* - config_name: eo-it data_files: - split: train path: eo-it/train-* - config_name: eo-pt data_files: - split: train path: eo-pt/train-* - config_name: es-fi data_files: - split: train path: es-fi/train-* - config_name: es-fr data_files: - split: train path: es-fr/train-* - config_name: es-hu data_files: - split: train path: es-hu/train-* - config_name: es-it data_files: - split: train path: es-it/train-* - config_name: es-nl data_files: - split: train path: es-nl/train-* - config_name: es-no data_files: - split: train path: es-no/train-* - config_name: es-pt data_files: - split: train path: es-pt/train-* - config_name: es-ru data_files: - split: train path: es-ru/train-* - config_name: fi-fr data_files: - split: train path: fi-fr/train-* - config_name: fi-hu data_files: - split: train path: fi-hu/train-* - config_name: fi-no data_files: - split: train path: fi-no/train-* - config_name: fi-pl data_files: - split: train path: fi-pl/train-* - config_name: fr-hu data_files: - split: train path: fr-hu/train-* - config_name: fr-it data_files: - split: train path: fr-it/train-* - config_name: fr-nl data_files: - split: train path: fr-nl/train-* - config_name: fr-no data_files: - split: train path: fr-no/train-* - config_name: fr-pl data_files: - split: train path: fr-pl/train-* - config_name: fr-pt data_files: - split: train path: fr-pt/train-* - config_name: fr-ru data_files: - split: train path: fr-ru/train-* - config_name: fr-sv data_files: - split: train path: fr-sv/train-* - config_name: hu-it data_files: - split: train path: hu-it/train-* - config_name: hu-nl data_files: - split: train path: hu-nl/train-* - config_name: hu-no data_files: - split: train path: hu-no/train-* - config_name: hu-pl data_files: - split: train path: hu-pl/train-* - config_name: hu-pt data_files: - split: train path: hu-pt/train-* - config_name: hu-ru data_files: - split: train path: hu-ru/train-* - config_name: it-nl data_files: - split: train path: it-nl/train-* - config_name: it-pt data_files: - split: train path: it-pt/train-* - config_name: it-ru data_files: - split: train path: it-ru/train-* - config_name: it-sv data_files: - split: train path: it-sv/train-* --- # Dataset Card for OPUS Books ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: https://opus.nlpl.eu/Books/corpus/version/Books - Repository: [More Information Needed] - Paper: https://aclanthology.org/L12-1246/ - Leaderboard: [More Information Needed] - Point of Contact: [More Information Needed] ### Dataset Summary This is a collection of copyright free books aligned by Andras Farkas, which are available from http://www.farkastranslations.com/bilingual_books.php Note that the texts are rather dated due to copyright issues and that some of them are manually reviewed (check the meta-data at the top of the corpus files in XML). The source is multilingually aligned, which is available from http://www.farkastranslations.com/bilingual_books.php. In OPUS, the alignment is formally bilingual but the multilingual alignment can be recovered from the XCES sentence alignment files. Note also that the alignment units from the original source may include multi-sentence paragraphs, which are split and sentence-aligned in OPUS. All texts are freely available for personal, educational and research use. Commercial use (e.g. reselling as parallel books) and mass redistribution without explicit permission are not granted. Please acknowledge the source when using the data! Books's Numbers: - Languages: 16 - Bitexts: 64 - Number of files: 158 - Number of tokens: 19.50M - Sentence fragments: 0.91M ### Supported Tasks and Leaderboards Translation. ### Languages The languages in the dataset are: - ca - de - el - en - eo - es - fi - fr - hu - it - nl - no - pl - pt - ru - sv ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data [More Information Needed] #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations [More Information Needed] #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information All texts are freely available for personal, educational and research use. Commercial use (e.g. reselling as parallel books) and mass redistribution without explicit permission are not granted. ### Citation Information Please acknowledge the source when using the data. Please cite the following article if you use any part of the OPUS corpus in your own work: ```bibtex @inproceedings{tiedemann-2012-parallel, title = "Parallel Data, Tools and Interfaces in {OPUS}", author = {Tiedemann, J{\"o}rg}, editor = "Calzolari, Nicoletta and Choukri, Khalid and Declerck, Thierry and Do{\u{g}}an, Mehmet U{\u{g}}ur and Maegaard, Bente and Mariani, Joseph and Moreno, Asuncion and Odijk, Jan and Piperidis, Stelios", booktitle = "Proceedings of the Eighth International Conference on Language Resources and Evaluation ({LREC}'12)", month = may, year = "2012", address = "Istanbul, Turkey", publisher = "European Language Resources Association (ELRA)", url = "http://www.lrec-conf.org/proceedings/lrec2012/pdf/463_Paper.pdf", pages = "2214--2218", } ``` ### Contributions Thanks to [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset.
FiscalNote/billsum	FiscalNote	2024-03-27T16:01:38Z	8,130	49	[ "task_categories:summarization", "annotations_creators:found", "language_creators:found", "multilinguality:monolingual", "source_datasets:original", "language:en", "license:cc0-1.0", "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:1910.00523", "region:us", "bills-summarization" ]	[ "summarization" ]	2022-03-02T23:29:22Z	1	--- annotations_creators: - found language_creators: - found language: - en license: - cc0-1.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - summarization task_ids: [] paperswithcode_id: billsum pretty_name: BillSum tags: - bills-summarization dataset_info: features: - name: text dtype: string - name: summary dtype: string - name: title dtype: string splits: - name: train num_bytes: 219596090 num_examples: 18949 - name: test num_bytes: 37866257 num_examples: 3269 - name: ca_test num_bytes: 14945291 num_examples: 1237 download_size: 113729382 dataset_size: 272407638 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: ca_test path: data/ca_test-* train-eval-index: - config: default task: summarization task_id: summarization splits: train_split: train eval_split: test col_mapping: text: text summary: target metrics: - type: rouge name: Rouge --- # Dataset Card for "billsum" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [https://github.com/FiscalNote/BillSum](https://github.com/FiscalNote/BillSum) - Repository: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Paper: https://aclanthology.org/D19-5406/ - Paper: https://arxiv.org/abs/1910.00523 - Point of Contact: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Size of downloaded dataset files: 67.26 MB - Size of the generated dataset: 272.42 MB - Total amount of disk used: 339.68 MB ### Dataset Summary BillSum, summarization of US Congressional and California state bills. There are several features: - text: bill text. - summary: summary of the bills. - title: title of the bills. features for us bills. ca bills does not have. - text_len: number of chars in text. - sum_len: number of chars in summary. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### default - Size of downloaded dataset files: 67.26 MB - Size of the generated dataset: 272.42 MB - Total amount of disk used: 339.68 MB An example of 'train' looks as follows. ``` { "summary": "some summary", "text": "some text.", "title": "An act to amend Section xxx." } ``` ### Data Fields The data fields are the same among all splits. #### default - `text`: a `string` feature. - `summary`: a `string` feature. - `title`: a `string` feature. ### Data Splits \| name \|train\|ca_test\|test\| \|-------\|----:\|------:\|---:\| \|default\|18949\| 1237\|3269\| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization The data consists of three parts: US training bills, US test bills and California test bills. The US bills were collected from the [Govinfo](https://github.com/unitedstates/congress) service provided by the United States Government Publishing Office (GPO) under CC0-1.0 license. The California, bills from the 2015-2016 session are available from the legislature’s [website](https://leginfo.legislature.ca.gov/). #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @inproceedings{kornilova-eidelman-2019-billsum, title = "{B}ill{S}um: A Corpus for Automatic Summarization of {US} Legislation", author = "Kornilova, Anastassia and Eidelman, Vladimir", editor = "Wang, Lu and Cheung, Jackie Chi Kit and Carenini, Giuseppe and Liu, Fei", booktitle = "Proceedings of the 2nd Workshop on New Frontiers in Summarization", month = nov, year = "2019", address = "Hong Kong, China", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/D19-5406", doi = "10.18653/v1/D19-5406", pages = "48--56", eprint={1910.00523}, archivePrefix={arXiv}, primaryClass={cs.CL}, } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@jplu](https://github.com/jplu), [@lewtun](https://github.com/lewtun) for adding this dataset.
xlangai/spider	xlangai	2024-03-27T13:38:42Z	3,108	143	[ "task_categories:text2text-generation", "annotations_creators:expert-generated", "language_creators:expert-generated", "language_creators:machine-generated", "multilinguality:monolingual", "source_datasets:original", "language:en", "license:cc-by-sa-4.0", "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:1809.08887", "region:us", "text-to-sql" ]	[ "text2text-generation" ]	2022-03-02T23:29:22Z	null	--- annotations_creators: - expert-generated language_creators: - expert-generated - machine-generated language: - en license: - cc-by-sa-4.0 multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - text2text-generation task_ids: [] paperswithcode_id: spider-1 pretty_name: Spider tags: - text-to-sql dataset_info: config_name: spider features: - name: db_id dtype: string - name: query dtype: string - name: question dtype: string - name: query_toks sequence: string - name: query_toks_no_value sequence: string - name: question_toks sequence: string splits: - name: train num_bytes: 4743786 num_examples: 7000 - name: validation num_bytes: 682090 num_examples: 1034 download_size: 957246 dataset_size: 5425876 configs: - config_name: spider data_files: - split: train path: spider/train-* - split: validation path: spider/validation-* default: true --- # Dataset Card for Spider ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: https://yale-lily.github.io/spider - Repository: https://github.com/taoyds/spider - Paper: https://www.aclweb.org/anthology/D18-1425/ - Paper: https://arxiv.org/abs/1809.08887 - Point of Contact: [Yale LILY](https://yale-lily.github.io/) ### Dataset Summary Spider is a large-scale complex and cross-domain semantic parsing and text-to-SQL dataset annotated by 11 Yale students. The goal of the Spider challenge is to develop natural language interfaces to cross-domain databases. ### Supported Tasks and Leaderboards The leaderboard can be seen at https://yale-lily.github.io/spider ### Languages The text in the dataset is in English. ## Dataset Structure ### Data Instances What do the instances that comprise the dataset represent? Each instance is natural language question and the equivalent SQL query How many instances are there in total? What data does each instance consist of? [More Information Needed] ### Data Fields * db_id: Database name * question: Natural language to interpret into SQL * query: Target SQL query * query_toks: List of tokens for the query * query_toks_no_value: List of tokens for the query * question_toks: List of tokens for the question ### Data Splits train: 7000 questions and SQL query pairs dev: 1034 question and SQL query pairs [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? [More Information Needed] ### Annotations The dataset was annotated by 11 college students at Yale University #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases [More Information Needed] ### Other Known Limitations ## Additional Information The listed authors in the homepage are maintaining/supporting the dataset. ### Dataset Curators [More Information Needed] ### Licensing Information The spider dataset is licensed under the [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/legalcode) [More Information Needed] ### Citation Information ``` @inproceedings{yu-etal-2018-spider, title = "{S}pider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-{SQL} Task", author = "Yu, Tao and Zhang, Rui and Yang, Kai and Yasunaga, Michihiro and Wang, Dongxu and Li, Zifan and Ma, James and Li, Irene and Yao, Qingning and Roman, Shanelle and Zhang, Zilin and Radev, Dragomir", editor = "Riloff, Ellen and Chiang, David and Hockenmaier, Julia and Tsujii, Jun{'}ichi", booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing", month = oct # "-" # nov, year = "2018", address = "Brussels, Belgium", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/D18-1425", doi = "10.18653/v1/D18-1425", pages = "3911--3921", archivePrefix={arXiv}, eprint={1809.08887}, primaryClass={cs.CL}, } ``` ### Contributions Thanks to [@olinguyen](https://github.com/olinguyen) for adding this dataset.
ctheodoris/Genecorpus-30M	ctheodoris	2024-03-25T23:01:49Z	2,190	71	[ "license:apache-2.0", "region:us" ]	[]	2022-03-12T21:21:46Z	1	--- license: apache-2.0 --- # Dataset Card for Genecorpus-30M ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks](#supported-tasks) - [Species](#species) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Citation Information](#citation-information) <!--- - [Licensing Information](#licensing-information) - [Contributions](#contributions) ---> ## Dataset Description <!--- Paper: ---> - Point of Contact: [email protected] ### Dataset Summary We assembled a large-scale pretraining corpus, Genecorpus-30M, comprised of ~30 million human single cell transcriptomes from a broad range of tissues from publicly available data. This corpus was used for pretraining [Geneformer](https://huggingface.co/ctheodoris/Geneformer), a pretrained transformer model that enables context-aware predictions in settings with limited data in network biology. See [our manuscript](https://rdcu.be/ddrx0) for details. ### Supported Tasks This corpus was used for pretraining [Geneformer](https://rdcu.be/ddrx0) and is compatible with pretraining or fine-tuning Geneformer or similar models. ### Species Homo sapiens ## Dataset Structure ### Data Instances Genecorpus-30M is provided as tokenized data in the Huggingface Datasets structure, which is based on the Apache Arrow format. Each example within the dataset is composed of the rank value encoding for a single cell within the corpus. Rank value encodings provide a nonparametric representation of each single cell’s transcriptome, ranking genes by their expression within that cell normalized by their expression across the entire Genecorpus-30M. This method takes advantage of the many observations of each gene’s expression across Genecorpus-30M to prioritize genes that distinguish cell state. Specifically, this method will deprioritize ubiquitously highly-expressed housekeeping genes by normalizing them to a lower rank. Conversely, genes such as transcription factors that may be lowly expressed when they are expressed but highly distinguish cell state will move to a higher rank within the encoding. Furthermore, this rank-based approach may be more robust against technical artifacts that may systematically bias the absolute transcript counts value while the overall relative ranking of genes within each cell remains more stable. To accomplish this, we first calculated the nonzero median value of expression of each detected gene across all cells from the entire Genecorpus-30M. We aggregated the transcript count distribution for each gene, normalizing the gene transcript counts in each cell by the total transcript count of that cell to account for varying sequencing depth. We then normalized the genes in each single cell transcriptome by that gene’s nonzero median value of expression across Genecorpus-30M and ordered the genes by the rank of their normalized expression in that specific cell. Of note, we opted to use the nonzero median value of expression rather than include zeros in the distribution so as not to weight the value by tissue representation within Genecorpus-30M, assuming that a representative range of transcript values would be observed within the cells in which each gene was detected. The rank value encodings for each single cell transcriptome were then tokenized based on a total vocabulary of 25,424 protein-coding or miRNA genes detected within Geneformer-30M. The token dictionary mapping each token ID to special tokens (pad and mask) or Ensembl IDs for each gene is included within the repository as a pickle file (token_dictionary.pkl). ### Data Fields - `input_ids`: rank value encoding for an example cell - `lengths`: length of rank value encoding for that example cell ### Data Splits The dataset does not contain any predefined splits. ## Dataset Creation ### Curation Rationale Mapping the gene regulatory networks that drive disease progression enables screening for molecules that correct the network by normalizing core regulatory elements, rather than targeting peripheral downstream effectors that may not be disease modifying. However, mapping the gene network architecture requires large amounts of transcriptomic data to learn the connections between genes, which impedes network-correcting drug discovery in settings with limited data, including rare diseases and diseases affecting clinically inaccessible tissues. Although data remains limited in these settings, recent advances in sequencing technologies have driven a rapid expansion in the amount of transcriptomic data available from human tissues more broadly. Furthermore, single cell technologies have facilitated the observation of transcriptomic states without averaging genes’ expression across multiple cells, potentially providing more precise data for inference of network interactions, especially in diseases driven by dysregulation of multiple cell types. Recently, the concept of transfer learning has revolutionized fields such as natural language understanding and computer vision by leveraging deep learning models pretrained on large-scale general datasets that can then be fine-tuned towards a vast array of downstream tasks with limited task-specific data that would be insufficient to yield meaningful predictions when used in isolation. We therefore assembled Genecorpus-30M to allow the large-scale pretraining of [Geneformer](https://huggingface.co/ctheodoris/Geneformer), a pretrained transformer model that enables context-aware predictions in settings with limited data in network biology. ### Source Data #### Initial Data Collection and Normalization Source data included 29.9 million (29,900,531) human single cell transcriptomes from a broad range of tissues from 561 publicly available datasets from original studies cited in the Methods of Theodoris et al, Nature 2023. Datasets were filtered to retain cells with total read counts within three standard deviations of the mean within that dataset and mitochondrial reads within three standard deviations of the mean within that dataset. Ensembl-annotated protein-coding and miRNA genes were used for downstream analysis. Cells with less than seven detected Ensembl-annotated protein-coding or miRNA genes were excluded as the 15% masking used for the pretraining learning objective would not reliably mask a gene in cells with fewer detected genes. Ultimately, 27.4 million (27,406,217) cells passed the defined quality filters. Cells were then represented as rank value encodings as discussed above in [Data Instances](#data-instances). #### Who are the source data producers? Publicly available datasets containing raw counts were collected from National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO), NCBI Sequence Read Archive (SRA), Human Cell Atlas, European Molecular Biology Laboratory-European Bioinformatics Institute (EMBL-EBI) Single Cell Expression Atlas, Broad Institute Single Cell Portal, Brotman Baty Institute (BBI)-Allen Single Cell Atlases, Tumor Immune Single-cell Hub (TISCH) (excluding malignant cells), Panglao Database, 10x Genomics, University of California, Santa Cruz Cell Browser, European Genome-phenome Archive, Synapse, Riken, Zenodo, National Institutes of Health (NIH) Figshare Archive, NCBI dbGap, Refine.bio, China National GeneBank Sequence Archive, Mendeley Data, and individual communication with authors of the original studies as cited in the Methods of Theodoris et al, Nature 2023. ### Annotations #### Annotation process Geneformer-30M does not contain annotations. #### Who are the annotators? N/A ### Personal and Sensitive Information There is no personal or sensitive information included in the dataset. The dataset is composed of rank value encodings, so there are no traceable sequencing reads included. ## Considerations for Using the Data ### Social Impact of Dataset Genecorpus-30M enabled the large-scale pretraining of [Geneformer](https://huggingface.co/ctheodoris/Geneformer), a foundation model that enables context-aware predictions in settings with limited data in network biology. Within our publication, we demonstrated that during pretraining, Geneformer gained a fundamental understanding of network dynamics, encoding network hierarchy in the model’s attention weights in a completely self-supervised manner. Fine-tuning Geneformer towards a diverse panel of downstream tasks relevant to chromatin and network dynamics using limited task-specific data demonstrated that Geneformer consistently boosted predictive accuracy. Applied to disease modeling with limited patient data, Geneformer identified candidate therapeutic targets for cardiomyopathy. Overall, Geneformer represents a pretrained foundation model from which fine-tuning towards a broad range of downstream applications can be pursued to accelerate discovery of key network regulators and candidate therapeutic targets. ### Discussion of Biases We excluded cells with high mutational burdens (e.g. malignant cells and immortalized cell lines) that could lead to substantial network rewiring without companion genome sequencing to facilitate interpretation. We only included droplet-based sequencing platforms to assure expression value unit comparability. Although we assembled the dataset to represent as diverse a set of human tissues and cell types as possible, particular tissues and cell types are not represented due to unavailability of public data at the time of dataset assembly. In our manuscript, we demonstrated that pretraining with larger and more diverse corpuses consistently improved Geneformer’s predictive power, consistent with observations that large-scale pretraining allows training of deeper models that ultimately have greater predictive potential in fields including NLU, computer vision, and mathematical problem-solving. Additionally, exposure to hundreds of experimental datasets during pretraining also appeared to promote robustness to batch-dependent technical artifacts and individual variability that commonly impact single cell analyses in biology. These findings suggest that as the amount of publicly available transcriptomic data continues to expand, future models pretrained on even larger-scale corpuses may open opportunities to achieve meaningful predictions in even more elusive tasks with increasingly limited task-specific data. ### Other Known Limitations Genecorpus-30M was intended to be used for self-supervised pretraining. To achieve the best possible predictions in downstream tasks, Geneformer should be fine-tuned with labeled datasets relevant to the task at hand. ## Additional Information ### Dataset Curators Christina Theodoris, MD, PhD ### Citation Information Theodoris CV, Xiao L, Chopra A, Chaffin MD, Al Sayed ZR, Hill MC, Mantineo H, Brydon EM, Zeng Z, Liu XS, Ellinor PT. Transfer learning enables predictions in network biology. Nature. 2023 May 31; Epub ahead of print. (*co-corresponding authors) <!--- ### Licensing Information [More Information Needed] ### Contributions Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset. --->
alvations/c4p0-x1-de-en	alvations	2024-03-24T03:55:50Z	11,110	0	[ "size_categories:n<1K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2024-03-23T10:16:43Z	null	--- dataset_info: features: - name: source dtype: string - name: target dtype: string - name: target_backto_source dtype: string - name: raw_target list: - name: generated_text dtype: string - name: raw_target_backto_source list: - name: generated_text dtype: string - name: prompt dtype: string - name: reverse_prompt dtype: string - name: source_langid dtype: string - name: target_langid dtype: string - name: target_backto_source_langid dtype: string - name: doc_id dtype: int64 - name: sent_id dtype: int64 - name: timestamp dtype: timestamp[us] - name: url dtype: string - name: doc_hash dtype: string splits: - name: train num_bytes: 32165 num_examples: 31 download_size: 22249 dataset_size: 32165 configs: - config_name: default data_files: - split: train path: c0d4dc8660289947/train-* ---
alvations/c4p0-x1-en-ja	alvations	2024-03-24T03:55:23Z	23,170	0	[ "size_categories:n<1K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2024-03-23T09:54:37Z	null	--- dataset_info: features: - name: source dtype: string - name: target dtype: string - name: target_backto_source dtype: string - name: raw_target list: - name: generated_text dtype: string - name: raw_target_backto_source list: - name: generated_text dtype: string - name: prompt dtype: string - name: reverse_prompt dtype: string - name: source_langid dtype: string - name: target_langid dtype: string - name: target_backto_source_langid dtype: string - name: doc_id dtype: int64 - name: sent_id dtype: int64 - name: timestamp dtype: string - name: url dtype: string - name: doc_hash dtype: string splits: - name: train num_bytes: 49764 num_examples: 42 download_size: 37636 dataset_size: 49764 configs: - config_name: default data_files: - split: train path: 66034f82c5c65ae4/train-* ---
bigbio/pubmed_qa	bigbio	2024-03-23T19:06:35Z	4,483	46	[ "multilinguality:monolingual", "language:en", "license:mit", "region:us" ]	[]	2022-11-13T22:11:45Z	2	--- language: - en bigbio_language: - English license: mit multilinguality: monolingual bigbio_license_shortname: MIT pretty_name: PubMedQA homepage: https://github.com/pubmedqa/pubmedqa bigbio_pubmed: True bigbio_public: True bigbio_tasks: - QUESTION_ANSWERING --- # Dataset Card for PubMedQA ## Dataset Description - Homepage: https://github.com/pubmedqa/pubmedqa - Pubmed: True - Public: True - Tasks: QA PubMedQA is a novel biomedical question answering (QA) dataset collected from PubMed abstracts. The task of PubMedQA is to answer research biomedical questions with yes/no/maybe using the corresponding abstracts. PubMedQA has 1k expert-annotated (PQA-L), 61.2k unlabeled (PQA-U) and 211.3k artificially generated QA instances (PQA-A). Each PubMedQA instance is composed of: (1) a question which is either an existing research article title or derived from one, (2) a context which is the corresponding PubMed abstract without its conclusion, (3) a long answer, which is the conclusion of the abstract and, presumably, answers the research question, and (4) a yes/no/maybe answer which summarizes the conclusion. PubMedQA is the first QA dataset where reasoning over biomedical research texts, especially their quantitative contents, is required to answer the questions. PubMedQA datasets comprise of 3 different subsets: (1) PubMedQA Labeled (PQA-L): A labeled PubMedQA subset comprises of 1k manually annotated yes/no/maybe QA data collected from PubMed articles. (2) PubMedQA Artificial (PQA-A): An artificially labelled PubMedQA subset comprises of 211.3k PubMed articles with automatically generated questions from the statement titles and yes/no answer labels generated using a simple heuristic. (3) PubMedQA Unlabeled (PQA-U): An unlabeled PubMedQA subset comprises of 61.2k context-question pairs data collected from PubMed articles. ## Citation Information ``` @inproceedings{jin2019pubmedqa, title={PubMedQA: A Dataset for Biomedical Research Question Answering}, author={Jin, Qiao and Dhingra, Bhuwan and Liu, Zhengping and Cohen, William and Lu, Xinghua}, booktitle={Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)}, pages={2567--2577}, year={2019} } ```
open-llm-leaderboard-old/details_gpt2	open-llm-leaderboard-old	2024-03-23T06:19:40Z	19,921	0	[ "region:us" ]	[]	2023-08-18T14:35:26Z	null	--- pretty_name: Evaluation run of gpt2 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [gpt2](https://huggingface.co/gpt2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 65 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 25 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_gpt2\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-03-23T06:18:16.565546](https://huggingface.co/datasets/open-llm-leaderboard/details_gpt2/blob/main/results_2024-03-23T06-18-16.565546.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.25780579051672486,\n\ \ \"acc_stderr\": 0.030658881019520554,\n \"acc_norm\": 0.2586547713391113,\n\ \ \"acc_norm_stderr\": 0.031431381356225356,\n \"mc1\": 0.22766217870257038,\n\ \ \"mc1_stderr\": 0.01467925503211107,\n \"mc2\": 0.4069116400376613,\n\ \ \"mc2_stderr\": 0.014934250122346554\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.197098976109215,\n \"acc_stderr\": 0.011625047669880633,\n\ \ \"acc_norm\": 0.22013651877133106,\n \"acc_norm_stderr\": 0.01210812488346097\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.29267078271260705,\n\ \ \"acc_stderr\": 0.004540586983229993,\n \"acc_norm\": 0.3152758414658435,\n\ \ \"acc_norm_stderr\": 0.0046367607625228515\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.21,\n \"acc_stderr\": 0.040936018074033256,\n \ \ \"acc_norm\": 0.21,\n \"acc_norm_stderr\": 0.040936018074033256\n \ \ },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.22962962962962963,\n\ \ \"acc_stderr\": 0.03633384414073462,\n \"acc_norm\": 0.22962962962962963,\n\ \ \"acc_norm_stderr\": 0.03633384414073462\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.16447368421052633,\n \"acc_stderr\": 0.0301675334686327,\n\ \ \"acc_norm\": 0.16447368421052633,\n \"acc_norm_stderr\": 0.0301675334686327\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.17,\n\ \ \"acc_stderr\": 0.0377525168068637,\n \"acc_norm\": 0.17,\n \ \ \"acc_norm_stderr\": 0.0377525168068637\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.24150943396226415,\n \"acc_stderr\": 0.026341480371118345,\n\ \ \"acc_norm\": 0.24150943396226415,\n \"acc_norm_stderr\": 0.026341480371118345\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.2222222222222222,\n\ \ \"acc_stderr\": 0.03476590104304134,\n \"acc_norm\": 0.2222222222222222,\n\ \ \"acc_norm_stderr\": 0.03476590104304134\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.2,\n \"acc_stderr\": 0.04020151261036846,\n \ \ \"acc_norm\": 0.2,\n \"acc_norm_stderr\": 0.04020151261036846\n },\n\ \ \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\": 0.28,\n\ \ \"acc_stderr\": 0.04512608598542128,\n \"acc_norm\": 0.28,\n \ \ \"acc_norm_stderr\": 0.04512608598542128\n },\n \"harness\|hendrycksTest-college_mathematics\|5\"\ : {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \ \ \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n \ \ },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.24277456647398843,\n\ \ \"acc_stderr\": 0.0326926380614177,\n \"acc_norm\": 0.24277456647398843,\n\ \ \"acc_norm_stderr\": 0.0326926380614177\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.2549019607843137,\n \"acc_stderr\": 0.043364327079931785,\n\ \ \"acc_norm\": 0.2549019607843137,\n \"acc_norm_stderr\": 0.043364327079931785\n\ \ },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\":\ \ 0.16,\n \"acc_stderr\": 0.03684529491774709,\n \"acc_norm\": 0.16,\n\ \ \"acc_norm_stderr\": 0.03684529491774709\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.2723404255319149,\n \"acc_stderr\": 0.029101290698386698,\n\ \ \"acc_norm\": 0.2723404255319149,\n \"acc_norm_stderr\": 0.029101290698386698\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.2631578947368421,\n\ \ \"acc_stderr\": 0.041424397194893624,\n \"acc_norm\": 0.2631578947368421,\n\ \ \"acc_norm_stderr\": 0.041424397194893624\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.2413793103448276,\n \"acc_stderr\": 0.03565998174135302,\n\ \ \"acc_norm\": 0.2413793103448276,\n \"acc_norm_stderr\": 0.03565998174135302\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.25396825396825395,\n \"acc_stderr\": 0.022418042891113942,\n \"\ acc_norm\": 0.25396825396825395,\n \"acc_norm_stderr\": 0.022418042891113942\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.14285714285714285,\n\ \ \"acc_stderr\": 0.0312984318574381,\n \"acc_norm\": 0.14285714285714285,\n\ \ \"acc_norm_stderr\": 0.0312984318574381\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.15,\n \"acc_stderr\": 0.035887028128263686,\n \ \ \"acc_norm\": 0.15,\n \"acc_norm_stderr\": 0.035887028128263686\n \ \ },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\"\ : 0.2967741935483871,\n \"acc_stderr\": 0.025988500792411894,\n \"\ acc_norm\": 0.2967741935483871,\n \"acc_norm_stderr\": 0.025988500792411894\n\ \ },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\"\ : 0.270935960591133,\n \"acc_stderr\": 0.03127090713297698,\n \"acc_norm\"\ : 0.270935960591133,\n \"acc_norm_stderr\": 0.03127090713297698\n },\n\ \ \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \"acc\"\ : 0.26,\n \"acc_stderr\": 0.04408440022768079,\n \"acc_norm\": 0.26,\n\ \ \"acc_norm_stderr\": 0.04408440022768079\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.21818181818181817,\n \"acc_stderr\": 0.03225078108306289,\n\ \ \"acc_norm\": 0.21818181818181817,\n \"acc_norm_stderr\": 0.03225078108306289\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.35353535353535354,\n \"acc_stderr\": 0.03406086723547153,\n \"\ acc_norm\": 0.35353535353535354,\n \"acc_norm_stderr\": 0.03406086723547153\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.36787564766839376,\n \"acc_stderr\": 0.03480175668466036,\n\ \ \"acc_norm\": 0.36787564766839376,\n \"acc_norm_stderr\": 0.03480175668466036\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.2717948717948718,\n \"acc_stderr\": 0.022556551010132358,\n\ \ \"acc_norm\": 0.2717948717948718,\n \"acc_norm_stderr\": 0.022556551010132358\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.26296296296296295,\n \"acc_stderr\": 0.026842057873833706,\n \ \ \"acc_norm\": 0.26296296296296295,\n \"acc_norm_stderr\": 0.026842057873833706\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.28991596638655465,\n \"acc_stderr\": 0.029472485833136098,\n\ \ \"acc_norm\": 0.28991596638655465,\n \"acc_norm_stderr\": 0.029472485833136098\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.271523178807947,\n \"acc_stderr\": 0.03631329803969654,\n \"acc_norm\"\ : 0.271523178807947,\n \"acc_norm_stderr\": 0.03631329803969654\n },\n\ \ \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\": 0.3486238532110092,\n\ \ \"acc_stderr\": 0.020431254090714328,\n \"acc_norm\": 0.3486238532110092,\n\ \ \"acc_norm_stderr\": 0.020431254090714328\n },\n \"harness\|hendrycksTest-high_school_statistics\|5\"\ : {\n \"acc\": 0.4722222222222222,\n \"acc_stderr\": 0.0340470532865388,\n\ \ \"acc_norm\": 0.4722222222222222,\n \"acc_norm_stderr\": 0.0340470532865388\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.25,\n \"acc_stderr\": 0.03039153369274154,\n \"acc_norm\": 0.25,\n\ \ \"acc_norm_stderr\": 0.03039153369274154\n },\n \"harness\|hendrycksTest-high_school_world_history\|5\"\ : {\n \"acc\": 0.24472573839662448,\n \"acc_stderr\": 0.027985699387036416,\n\ \ \"acc_norm\": 0.24472573839662448,\n \"acc_norm_stderr\": 0.027985699387036416\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.2914798206278027,\n\ \ \"acc_stderr\": 0.030500283176545923,\n \"acc_norm\": 0.2914798206278027,\n\ \ \"acc_norm_stderr\": 0.030500283176545923\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.26717557251908397,\n \"acc_stderr\": 0.038808483010823944,\n\ \ \"acc_norm\": 0.26717557251908397,\n \"acc_norm_stderr\": 0.038808483010823944\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.32231404958677684,\n \"acc_stderr\": 0.04266416363352168,\n \"\ acc_norm\": 0.32231404958677684,\n \"acc_norm_stderr\": 0.04266416363352168\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.21296296296296297,\n\ \ \"acc_stderr\": 0.03957835471980981,\n \"acc_norm\": 0.21296296296296297,\n\ \ \"acc_norm_stderr\": 0.03957835471980981\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.26380368098159507,\n \"acc_stderr\": 0.03462419931615623,\n\ \ \"acc_norm\": 0.26380368098159507,\n \"acc_norm_stderr\": 0.03462419931615623\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.25892857142857145,\n\ \ \"acc_stderr\": 0.041577515398656284,\n \"acc_norm\": 0.25892857142857145,\n\ \ \"acc_norm_stderr\": 0.041577515398656284\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.34951456310679613,\n \"acc_stderr\": 0.04721188506097173,\n\ \ \"acc_norm\": 0.34951456310679613,\n \"acc_norm_stderr\": 0.04721188506097173\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.1794871794871795,\n\ \ \"acc_stderr\": 0.025140935950335418,\n \"acc_norm\": 0.1794871794871795,\n\ \ \"acc_norm_stderr\": 0.025140935950335418\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.27,\n \"acc_stderr\": 0.044619604333847394,\n \ \ \"acc_norm\": 0.27,\n \"acc_norm_stderr\": 0.044619604333847394\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.21583652618135377,\n\ \ \"acc_stderr\": 0.014711684386139958,\n \"acc_norm\": 0.21583652618135377,\n\ \ \"acc_norm_stderr\": 0.014711684386139958\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.24277456647398843,\n \"acc_stderr\": 0.0230836585869842,\n\ \ \"acc_norm\": 0.24277456647398843,\n \"acc_norm_stderr\": 0.0230836585869842\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.2424581005586592,\n\ \ \"acc_stderr\": 0.014333522059217889,\n \"acc_norm\": 0.2424581005586592,\n\ \ \"acc_norm_stderr\": 0.014333522059217889\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.21895424836601307,\n \"acc_stderr\": 0.02367908986180772,\n\ \ \"acc_norm\": 0.21895424836601307,\n \"acc_norm_stderr\": 0.02367908986180772\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.24758842443729903,\n\ \ \"acc_stderr\": 0.024513879973621967,\n \"acc_norm\": 0.24758842443729903,\n\ \ \"acc_norm_stderr\": 0.024513879973621967\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.22530864197530864,\n \"acc_stderr\": 0.023246202647819746,\n\ \ \"acc_norm\": 0.22530864197530864,\n \"acc_norm_stderr\": 0.023246202647819746\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.26595744680851063,\n \"acc_stderr\": 0.026358065698880592,\n \ \ \"acc_norm\": 0.26595744680851063,\n \"acc_norm_stderr\": 0.026358065698880592\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.2457627118644068,\n\ \ \"acc_stderr\": 0.010996156635142692,\n \"acc_norm\": 0.2457627118644068,\n\ \ \"acc_norm_stderr\": 0.010996156635142692\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.44485294117647056,\n \"acc_stderr\": 0.030187532060329376,\n\ \ \"acc_norm\": 0.44485294117647056,\n \"acc_norm_stderr\": 0.030187532060329376\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.26143790849673204,\n \"acc_stderr\": 0.017776947157528034,\n \ \ \"acc_norm\": 0.26143790849673204,\n \"acc_norm_stderr\": 0.017776947157528034\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.21818181818181817,\n\ \ \"acc_stderr\": 0.03955932861795833,\n \"acc_norm\": 0.21818181818181817,\n\ \ \"acc_norm_stderr\": 0.03955932861795833\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.4,\n \"acc_stderr\": 0.031362502409358936,\n \ \ \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.031362502409358936\n \ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.22885572139303484,\n\ \ \"acc_stderr\": 0.029705284056772426,\n \"acc_norm\": 0.22885572139303484,\n\ \ \"acc_norm_stderr\": 0.029705284056772426\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.27,\n \"acc_stderr\": 0.04461960433384739,\n \ \ \"acc_norm\": 0.27,\n \"acc_norm_stderr\": 0.04461960433384739\n \ \ },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.1927710843373494,\n\ \ \"acc_stderr\": 0.030709824050565274,\n \"acc_norm\": 0.1927710843373494,\n\ \ \"acc_norm_stderr\": 0.030709824050565274\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.21052631578947367,\n \"acc_stderr\": 0.0312678171466318,\n\ \ \"acc_norm\": 0.21052631578947367,\n \"acc_norm_stderr\": 0.0312678171466318\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.22766217870257038,\n\ \ \"mc1_stderr\": 0.01467925503211107,\n \"mc2\": 0.4069116400376613,\n\ \ \"mc2_stderr\": 0.014934250122346554\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.5043409629044988,\n \"acc_stderr\": 0.014051956064076887\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.006823351023502654,\n \ \ \"acc_stderr\": 0.0022675371022544736\n }\n}\n```" repo_url: https://huggingface.co/gpt2 leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: [email protected] configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_11_21T18_07_07.067275 path: - '/details_harness\|arc:challenge\|25_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '/details_harness\|arc:challenge\|25_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '/details_harness\|arc:challenge\|25_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '/details_harness\|arc:challenge\|25_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '/details_harness\|arc:challenge\|25_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '/details_harness\|arc:challenge\|25_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '/details_harness\|arc:challenge\|25_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '/details_harness\|arc:challenge\|25_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-03-23T06-18-16.565546.parquet' - config_name: harness_drop_0 data_files: - split: 2023_09_14T13_54_21.687636 path: - '/details_harness\|drop\|0_2023-09-14T13-54-21.687636.parquet' - split: 2023_09_15T12_28_23.937147 path: - '/details_harness\|drop\|0_2023-09-15T12-28-23.937147.parquet' - split: 2023_09_15T12_47_31.231445 path: - '/details_harness\|drop\|0_2023-09-15T12-47-31.231445.parquet' - split: latest path: - '/details_harness\|drop\|0_2023-09-15T12-47-31.231445.parquet' - config_name: harness_drop_3 data_files: - split: 2023_11_21T18_07_07.067275 path: - '/details_harness\|drop\|3_2023-11-21T18-07-07.067275.parquet' - split: 2023_11_29T12_47_35.686694 path: - '/details_harness\|drop\|3_2023-11-29T12-47-35.686694.parquet' - split: 2023_11_29T12_58_42.860611 path: - '/details_harness\|drop\|3_2023-11-29T12-58-42.860611.parquet' - split: latest path: - '/details_harness\|drop\|3_2023-11-29T12-58-42.860611.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '/details_harness\|gsm8k\|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_11_29T12_47_35.686694 path: - '/details_harness\|gsm8k\|5_2023-11-29T12-47-35.686694.parquet' - split: 2023_11_29T12_58_42.860611 path: - '/details_harness\|gsm8k\|5_2023-11-29T12-58-42.860611.parquet' - split: 2023_12_16T13_32_55.332102 path: - '/details_harness\|gsm8k\|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '/details_harness\|gsm8k\|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '/details_harness\|gsm8k\|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '/details_harness\|gsm8k\|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '/details_harness\|gsm8k\|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '/details_harness\|gsm8k\|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '/details_harness\|gsm8k\|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_11_21T18_07_07.067275 path: - '/details_harness\|hellaswag\|10_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '/details_harness\|hellaswag\|10_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '/details_harness\|hellaswag\|10_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '/details_harness\|hellaswag\|10_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '/details_harness\|hellaswag\|10_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '/details_harness\|hellaswag\|10_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '/details_harness\|hellaswag\|10_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '/details_harness\|hellaswag\|10_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-11-21T18-07-07.067275.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-11-21T18-07-07.067275.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-11-21T18-07-07.067275.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-11-21T18-07-07.067275.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-11-21T18-07-07.067275.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-11-21T18-07-07.067275.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-11-21T18-07-07.067275.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-11-21T18-07-07.067275.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-11-21T18-07-07.067275.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-11-21T18-07-07.067275.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-11-21T18-07-07.067275.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-11-21T18-07-07.067275.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-11-21T18-07-07.067275.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-11-21T18-07-07.067275.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-11-21T18-07-07.067275.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-11-21T18-07-07.067275.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-11-21T18-07-07.067275.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-11-21T18-07-07.067275.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-11-21T18-07-07.067275.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-11-21T18-07-07.067275.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-11-21T18-07-07.067275.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-11-21T18-07-07.067275.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-11-21T18-07-07.067275.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-11-21T18-07-07.067275.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-11-21T18-07-07.067275.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-11-21T18-07-07.067275.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-11-21T18-07-07.067275.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-11-21T18-07-07.067275.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-11-21T18-07-07.067275.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-11-21T18-07-07.067275.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-11-21T18-07-07.067275.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-11-21T18-07-07.067275.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-11-21T18-07-07.067275.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-11-21T18-07-07.067275.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-11-21T18-07-07.067275.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-11-21T18-07-07.067275.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-11-21T18-07-07.067275.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-11-21T18-07-07.067275.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-11-21T18-07-07.067275.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-11-21T18-07-07.067275.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-11-21T18-07-07.067275.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-11-21T18-07-07.067275.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-11-21T18-07-07.067275.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-11-21T18-07-07.067275.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-11-21T18-07-07.067275.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-11-21T18-07-07.067275.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-11-21T18-07-07.067275.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-11-21T18-07-07.067275.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-11-21T18-07-07.067275.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-11-21T18-07-07.067275.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-11-21T18-07-07.067275.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-11-21T18-07-07.067275.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-11-21T18-07-07.067275.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-11-21T18-07-07.067275.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-11-21T18-07-07.067275.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-11-21T18-07-07.067275.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-16T13-32-55.332102.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-16T13-32-55.332102.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-16T13-32-55.332102.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-16T13-32-55.332102.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-16T13-32-55.332102.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-16T13-32-55.332102.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-16T13-32-55.332102.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-16T13-32-55.332102.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-16T13-32-55.332102.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-16T13-32-55.332102.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-16T13-32-55.332102.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-16T13-32-55.332102.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-16T13-32-55.332102.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-16T13-32-55.332102.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-16T13-32-55.332102.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-16T13-32-55.332102.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-16T13-32-55.332102.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-16T13-32-55.332102.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-16T13-32-55.332102.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-16T13-32-55.332102.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-16T13-32-55.332102.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-16T13-32-55.332102.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-16T13-32-55.332102.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-16T13-32-55.332102.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-16T13-32-55.332102.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-16T13-32-55.332102.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-16T13-32-55.332102.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-16T13-32-55.332102.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-16T13-32-55.332102.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-16T13-32-55.332102.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-16T13-32-55.332102.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-16T13-32-55.332102.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-16T13-32-55.332102.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-16T13-32-55.332102.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-12-16T13-32-55.332102.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-16T13-32-55.332102.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-16T13-32-55.332102.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-16T13-32-55.332102.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-12-16T13-32-55.332102.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-12-16T13-32-55.332102.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-16T13-32-55.332102.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-16T13-32-55.332102.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-16T13-32-55.332102.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-16T13-32-55.332102.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-16T13-32-55.332102.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-16T13-32-55.332102.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-16T13-32-55.332102.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-16T13-32-55.332102.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-16T13-32-55.332102.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-16T13-32-55.332102.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-16T13-32-55.332102.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-16T13-32-55.332102.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-16T13-32-55.332102.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-12-16T13-32-55.332102.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-16T13-32-55.332102.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-12-16T13-32-55.332102.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-19T14-19-42.718116.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-19T14-19-42.718116.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-19T14-19-42.718116.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-19T14-19-42.718116.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-19T14-19-42.718116.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-19T14-19-42.718116.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-19T14-19-42.718116.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-19T14-19-42.718116.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-19T14-19-42.718116.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-19T14-19-42.718116.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-19T14-19-42.718116.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-19T14-19-42.718116.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-19T14-19-42.718116.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-19T14-19-42.718116.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-19T14-19-42.718116.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-19T14-19-42.718116.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-19T14-19-42.718116.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-19T14-19-42.718116.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-19T14-19-42.718116.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-19T14-19-42.718116.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-19T14-19-42.718116.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-19T14-19-42.718116.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-19T14-19-42.718116.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-19T14-19-42.718116.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-19T14-19-42.718116.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-19T14-19-42.718116.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-19T14-19-42.718116.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-19T14-19-42.718116.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-19T14-19-42.718116.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-19T14-19-42.718116.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-19T14-19-42.718116.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-19T14-19-42.718116.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-19T14-19-42.718116.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-19T14-19-42.718116.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-12-19T14-19-42.718116.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-19T14-19-42.718116.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-19T14-19-42.718116.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-19T14-19-42.718116.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-12-19T14-19-42.718116.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-12-19T14-19-42.718116.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-19T14-19-42.718116.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-19T14-19-42.718116.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-19T14-19-42.718116.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-19T14-19-42.718116.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-19T14-19-42.718116.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-19T14-19-42.718116.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-19T14-19-42.718116.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-19T14-19-42.718116.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-19T14-19-42.718116.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-19T14-19-42.718116.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-19T14-19-42.718116.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-19T14-19-42.718116.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-19T14-19-42.718116.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-12-19T14-19-42.718116.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-19T14-19-42.718116.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-12-19T14-19-42.718116.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-23T15-28-59.872701.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-23T15-28-59.872701.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-23T15-28-59.872701.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-23T15-28-59.872701.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-23T15-28-59.872701.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-23T15-28-59.872701.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-23T15-28-59.872701.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-23T15-28-59.872701.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-23T15-28-59.872701.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-23T15-28-59.872701.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-23T15-28-59.872701.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-23T15-28-59.872701.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-23T15-28-59.872701.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-23T15-28-59.872701.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-23T15-28-59.872701.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-23T15-28-59.872701.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-23T15-28-59.872701.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-23T15-28-59.872701.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-23T15-28-59.872701.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-23T15-28-59.872701.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-23T15-28-59.872701.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-23T15-28-59.872701.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-23T15-28-59.872701.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-23T15-28-59.872701.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-23T15-28-59.872701.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-23T15-28-59.872701.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-23T15-28-59.872701.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-23T15-28-59.872701.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-23T15-28-59.872701.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-23T15-28-59.872701.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-23T15-28-59.872701.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-23T15-28-59.872701.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-23T15-28-59.872701.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-23T15-28-59.872701.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-12-23T15-28-59.872701.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-23T15-28-59.872701.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-23T15-28-59.872701.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-23T15-28-59.872701.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-12-23T15-28-59.872701.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-12-23T15-28-59.872701.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-23T15-28-59.872701.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-23T15-28-59.872701.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-23T15-28-59.872701.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-23T15-28-59.872701.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-23T15-28-59.872701.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-23T15-28-59.872701.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-23T15-28-59.872701.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-23T15-28-59.872701.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-23T15-28-59.872701.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-23T15-28-59.872701.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-23T15-28-59.872701.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-23T15-28-59.872701.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-23T15-28-59.872701.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-12-23T15-28-59.872701.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-23T15-28-59.872701.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-12-23T15-28-59.872701.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-10T14-42-55.873500.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-10T14-42-55.873500.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-10T14-42-55.873500.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-10T14-42-55.873500.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-10T14-42-55.873500.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-10T14-42-55.873500.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-10T14-42-55.873500.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-10T14-42-55.873500.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-10T14-42-55.873500.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-10T14-42-55.873500.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-10T14-42-55.873500.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-10T14-42-55.873500.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-10T14-42-55.873500.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-10T14-42-55.873500.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-10T14-42-55.873500.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-10T14-42-55.873500.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-10T14-42-55.873500.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-10T14-42-55.873500.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-10T14-42-55.873500.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-10T14-42-55.873500.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-10T14-42-55.873500.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-10T14-42-55.873500.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-10T14-42-55.873500.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-10T14-42-55.873500.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-10T14-42-55.873500.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-10T14-42-55.873500.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-10T14-42-55.873500.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-10T14-42-55.873500.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-10T14-42-55.873500.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-10T14-42-55.873500.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-10T14-42-55.873500.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-10T14-42-55.873500.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-10T14-42-55.873500.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-10T14-42-55.873500.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-01-10T14-42-55.873500.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-10T14-42-55.873500.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-10T14-42-55.873500.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-10T14-42-55.873500.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-01-10T14-42-55.873500.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-01-10T14-42-55.873500.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-10T14-42-55.873500.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-10T14-42-55.873500.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-10T14-42-55.873500.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-10T14-42-55.873500.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-10T14-42-55.873500.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-10T14-42-55.873500.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-10T14-42-55.873500.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-10T14-42-55.873500.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-10T14-42-55.873500.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-10T14-42-55.873500.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-10T14-42-55.873500.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-10T14-42-55.873500.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-10T14-42-55.873500.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-01-10T14-42-55.873500.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-10T14-42-55.873500.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-01-10T14-42-55.873500.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-18T14-12-21.064569.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-18T14-12-21.064569.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-18T14-12-21.064569.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-18T14-12-21.064569.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-18T14-12-21.064569.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-18T14-12-21.064569.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-18T14-12-21.064569.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-18T14-12-21.064569.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-18T14-12-21.064569.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-18T14-12-21.064569.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-18T14-12-21.064569.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-18T14-12-21.064569.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-18T14-12-21.064569.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-18T14-12-21.064569.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-18T14-12-21.064569.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-18T14-12-21.064569.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-18T14-12-21.064569.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-18T14-12-21.064569.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-18T14-12-21.064569.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-18T14-12-21.064569.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-18T14-12-21.064569.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-18T14-12-21.064569.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-18T14-12-21.064569.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-18T14-12-21.064569.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-18T14-12-21.064569.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-18T14-12-21.064569.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-18T14-12-21.064569.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-18T14-12-21.064569.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-18T14-12-21.064569.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-18T14-12-21.064569.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-18T14-12-21.064569.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-18T14-12-21.064569.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-18T14-12-21.064569.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-18T14-12-21.064569.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-01-18T14-12-21.064569.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-18T14-12-21.064569.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-18T14-12-21.064569.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-18T14-12-21.064569.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-01-18T14-12-21.064569.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-01-18T14-12-21.064569.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-18T14-12-21.064569.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-18T14-12-21.064569.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-18T14-12-21.064569.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-18T14-12-21.064569.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-18T14-12-21.064569.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-18T14-12-21.064569.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-18T14-12-21.064569.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-18T14-12-21.064569.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-18T14-12-21.064569.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-18T14-12-21.064569.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-18T14-12-21.064569.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-18T14-12-21.064569.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-18T14-12-21.064569.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-01-18T14-12-21.064569.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-18T14-12-21.064569.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-01-18T14-12-21.064569.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-22T13-56-20.291666.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-22T13-56-20.291666.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-22T13-56-20.291666.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-22T13-56-20.291666.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-22T13-56-20.291666.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-22T13-56-20.291666.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-22T13-56-20.291666.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-22T13-56-20.291666.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-22T13-56-20.291666.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-22T13-56-20.291666.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-22T13-56-20.291666.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-22T13-56-20.291666.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-22T13-56-20.291666.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-22T13-56-20.291666.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-22T13-56-20.291666.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-22T13-56-20.291666.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-22T13-56-20.291666.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-22T13-56-20.291666.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-22T13-56-20.291666.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-22T13-56-20.291666.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-22T13-56-20.291666.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-22T13-56-20.291666.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-22T13-56-20.291666.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-22T13-56-20.291666.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-22T13-56-20.291666.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-22T13-56-20.291666.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-22T13-56-20.291666.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-22T13-56-20.291666.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-22T13-56-20.291666.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-22T13-56-20.291666.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-22T13-56-20.291666.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-22T13-56-20.291666.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-22T13-56-20.291666.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-22T13-56-20.291666.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-01-22T13-56-20.291666.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-22T13-56-20.291666.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-22T13-56-20.291666.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-22T13-56-20.291666.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-01-22T13-56-20.291666.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-01-22T13-56-20.291666.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-22T13-56-20.291666.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-22T13-56-20.291666.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-22T13-56-20.291666.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-22T13-56-20.291666.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-22T13-56-20.291666.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-22T13-56-20.291666.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-22T13-56-20.291666.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-22T13-56-20.291666.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-22T13-56-20.291666.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-22T13-56-20.291666.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-22T13-56-20.291666.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-22T13-56-20.291666.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-22T13-56-20.291666.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-01-22T13-56-20.291666.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-22T13-56-20.291666.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-01-22T13-56-20.291666.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-03-23T06-18-16.565546.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '/details_harness\|hendrycksTest-management\|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '/details_harness\|hendrycksTest-management\|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '/details_harness\|hendrycksTest-management\|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '/details_harness\|hendrycksTest-management\|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '/details_harness\|hendrycksTest-management\|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '/details_harness\|hendrycksTest-management\|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '/details_harness\|hendrycksTest-management\|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '/details_harness\|hendrycksTest-management\|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '/details_harness\|hendrycksTest-virology\|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '/details_harness\|hendrycksTest-virology\|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '/details_harness\|hendrycksTest-virology\|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '/details_harness\|hendrycksTest-virology\|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '/details_harness\|hendrycksTest-virology\|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '/details_harness\|hendrycksTest-virology\|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '/details_harness\|hendrycksTest-virology\|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '/details_harness\|hendrycksTest-virology\|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_11_21T18_07_07.067275 path: - '/details_harness\|truthfulqa:mc\|0_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '/details_harness\|truthfulqa:mc\|0_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '/details_harness\|truthfulqa:mc\|0_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '/details_harness\|truthfulqa:mc\|0_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '/details_harness\|truthfulqa:mc\|0_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '/details_harness\|truthfulqa:mc\|0_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '/details_harness\|truthfulqa:mc\|0_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '/details_harness\|truthfulqa:mc\|0_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-03-23T06-18-16.565546.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_09_06T15_19_52.414673 path: - '/details_harness\|winogrande\|5_2023-09-06T15-19-52.414673.parquet' - split: 2023_09_06T15_22_24.734466 path: - '/details_harness\|winogrande\|5_2023-09-06T15-22-24.734466.parquet' - split: 2023_09_06T15_24_04.768979 path: - '/details_harness\|winogrande\|5_2023-09-06T15-24-04.768979.parquet' - split: 2023_09_07T12_01_51.839651 path: - '/details_harness\|winogrande\|5_2023-09-07T12-01-51.839651.parquet' - split: 2023_09_07T12_04_01.189528 path: - '/details_harness\|winogrande\|5_2023-09-07T12-04-01.189528.parquet' - split: 2023_09_07T12_08_17.821371 path: - '/details_harness\|winogrande\|5_2023-09-07T12-08-17.821371.parquet' - split: 2023_09_07T12_10_30.286469 path: - '/details_harness\|winogrande\|5_2023-09-07T12-10-30.286469.parquet' - split: 2023_11_21T18_07_07.067275 path: - '/details_harness\|winogrande\|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_11_29T12_47_35.686694 path: - '/details_harness\|winogrande\|5_2023-11-29T12-47-35.686694.parquet' - split: 2023_11_29T12_58_42.860611 path: - '/details_harness\|winogrande\|5_2023-11-29T12-58-42.860611.parquet' - split: 2023_12_16T13_32_55.332102 path: - '/details_harness\|winogrande\|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '/details_harness\|winogrande\|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '/details_harness\|winogrande\|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '/details_harness\|winogrande\|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '/details_harness\|winogrande\|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '/details_harness\|winogrande\|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '/details_harness\|winogrande\|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-03-23T06-18-16.565546.parquet' - config_name: results data_files: - split: 2023_09_06T12_19_07.283399 path: - results_2023-09-06T12-19-07.283399.parquet - split: 2023_09_06T12_21_24.071294 path: - results_2023-09-06T12-21-24.071294.parquet - split: 2023_09_06T12_24_13.323279 path: - results_2023-09-06T12-24-13.323279.parquet - split: 2023_09_06T13_26_17.619860 path: - results_2023-09-06T13-26-17.619860.parquet - split: 2023_09_06T15_15_44.379880 path: - results_2023-09-06T15-15-44.379880.parquet - split: 2023_09_06T15_19_52.414673 path: - results_2023-09-06T15-19-52.414673.parquet - split: 2023_09_06T15_22_24.734466 path: - results_2023-09-06T15-22-24.734466.parquet - split: 2023_09_06T15_24_04.768979 path: - results_2023-09-06T15-24-04.768979.parquet - split: 2023_09_07T12_01_51.839651 path: - results_2023-09-07T12-01-51.839651.parquet - split: 2023_09_07T12_04_01.189528 path: - results_2023-09-07T12-04-01.189528.parquet - split: 2023_09_07T12_08_17.821371 path: - results_2023-09-07T12-08-17.821371.parquet - split: 2023_09_07T12_10_30.286469 path: - results_2023-09-07T12-10-30.286469.parquet - split: 2023_09_14T13_54_21.687636 path: - results_2023-09-14T13-54-21.687636.parquet - split: 2023_09_15T12_28_23.937147 path: - results_2023-09-15T12-28-23.937147.parquet - split: 2023_09_15T12_47_31.231445 path: - results_2023-09-15T12-47-31.231445.parquet - split: 2023_11_21T18_07_07.067275 path: - results_2023-11-21T18-07-07.067275.parquet - split: 2023_11_29T12_47_35.686694 path: - results_2023-11-29T12-47-35.686694.parquet - split: 2023_11_29T12_58_42.860611 path: - results_2023-11-29T12-58-42.860611.parquet - split: 2023_12_16T13_32_55.332102 path: - results_2023-12-16T13-32-55.332102.parquet - split: 2023_12_19T14_19_42.718116 path: - results_2023-12-19T14-19-42.718116.parquet - split: 2023_12_23T15_28_59.872701 path: - results_2023-12-23T15-28-59.872701.parquet - split: 2024_01_10T14_42_55.873500 path: - results_2024-01-10T14-42-55.873500.parquet - split: 2024_01_18T14_12_21.064569 path: - results_2024-01-18T14-12-21.064569.parquet - split: 2024_01_22T13_56_20.291666 path: - results_2024-01-22T13-56-20.291666.parquet - split: 2024_03_23T06_18_16.565546 path: - results_2024-03-23T06-18-16.565546.parquet - split: latest path: - results_2024-03-23T06-18-16.565546.parquet --- # Dataset Card for Evaluation run of gpt2 <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [gpt2](https://huggingface.co/gpt2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 65 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 25 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_gpt2", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-03-23T06:18:16.565546](https://huggingface.co/datasets/open-llm-leaderboard/details_gpt2/blob/main/results_2024-03-23T06-18-16.565546.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.25780579051672486, "acc_stderr": 0.030658881019520554, "acc_norm": 0.2586547713391113, "acc_norm_stderr": 0.031431381356225356, "mc1": 0.22766217870257038, "mc1_stderr": 0.01467925503211107, "mc2": 0.4069116400376613, "mc2_stderr": 0.014934250122346554 }, "harness\|arc:challenge\|25": { "acc": 0.197098976109215, "acc_stderr": 0.011625047669880633, "acc_norm": 0.22013651877133106, "acc_norm_stderr": 0.01210812488346097 }, "harness\|hellaswag\|10": { "acc": 0.29267078271260705, "acc_stderr": 0.004540586983229993, "acc_norm": 0.3152758414658435, "acc_norm_stderr": 0.0046367607625228515 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.21, "acc_stderr": 0.040936018074033256, "acc_norm": 0.21, "acc_norm_stderr": 0.040936018074033256 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.22962962962962963, "acc_stderr": 0.03633384414073462, "acc_norm": 0.22962962962962963, "acc_norm_stderr": 0.03633384414073462 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.16447368421052633, "acc_stderr": 0.0301675334686327, "acc_norm": 0.16447368421052633, "acc_norm_stderr": 0.0301675334686327 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.17, "acc_stderr": 0.0377525168068637, "acc_norm": 0.17, "acc_norm_stderr": 0.0377525168068637 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.24150943396226415, "acc_stderr": 0.026341480371118345, "acc_norm": 0.24150943396226415, "acc_norm_stderr": 0.026341480371118345 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.2222222222222222, "acc_stderr": 0.03476590104304134, "acc_norm": 0.2222222222222222, "acc_norm_stderr": 0.03476590104304134 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.2, "acc_stderr": 0.04020151261036846, "acc_norm": 0.2, "acc_norm_stderr": 0.04020151261036846 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.28, "acc_stderr": 0.04512608598542128, "acc_norm": 0.28, "acc_norm_stderr": 0.04512608598542128 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.24277456647398843, "acc_stderr": 0.0326926380614177, "acc_norm": 0.24277456647398843, "acc_norm_stderr": 0.0326926380614177 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.2549019607843137, "acc_stderr": 0.043364327079931785, "acc_norm": 0.2549019607843137, "acc_norm_stderr": 0.043364327079931785 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.16, "acc_stderr": 0.03684529491774709, "acc_norm": 0.16, "acc_norm_stderr": 0.03684529491774709 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.2723404255319149, "acc_stderr": 0.029101290698386698, "acc_norm": 0.2723404255319149, "acc_norm_stderr": 0.029101290698386698 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.2631578947368421, "acc_stderr": 0.041424397194893624, "acc_norm": 0.2631578947368421, "acc_norm_stderr": 0.041424397194893624 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.2413793103448276, "acc_stderr": 0.03565998174135302, "acc_norm": 0.2413793103448276, "acc_norm_stderr": 0.03565998174135302 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.25396825396825395, "acc_stderr": 0.022418042891113942, "acc_norm": 0.25396825396825395, "acc_norm_stderr": 0.022418042891113942 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.14285714285714285, "acc_stderr": 0.0312984318574381, "acc_norm": 0.14285714285714285, "acc_norm_stderr": 0.0312984318574381 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.15, "acc_stderr": 0.035887028128263686, "acc_norm": 0.15, "acc_norm_stderr": 0.035887028128263686 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.2967741935483871, "acc_stderr": 0.025988500792411894, "acc_norm": 0.2967741935483871, "acc_norm_stderr": 0.025988500792411894 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.270935960591133, "acc_stderr": 0.03127090713297698, "acc_norm": 0.270935960591133, "acc_norm_stderr": 0.03127090713297698 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.26, "acc_stderr": 0.04408440022768079, "acc_norm": 0.26, "acc_norm_stderr": 0.04408440022768079 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.21818181818181817, "acc_stderr": 0.03225078108306289, "acc_norm": 0.21818181818181817, "acc_norm_stderr": 0.03225078108306289 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.35353535353535354, "acc_stderr": 0.03406086723547153, "acc_norm": 0.35353535353535354, "acc_norm_stderr": 0.03406086723547153 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.36787564766839376, "acc_stderr": 0.03480175668466036, "acc_norm": 0.36787564766839376, "acc_norm_stderr": 0.03480175668466036 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.2717948717948718, "acc_stderr": 0.022556551010132358, "acc_norm": 0.2717948717948718, "acc_norm_stderr": 0.022556551010132358 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.26296296296296295, "acc_stderr": 0.026842057873833706, "acc_norm": 0.26296296296296295, "acc_norm_stderr": 0.026842057873833706 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.28991596638655465, "acc_stderr": 0.029472485833136098, "acc_norm": 0.28991596638655465, "acc_norm_stderr": 0.029472485833136098 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.271523178807947, "acc_stderr": 0.03631329803969654, "acc_norm": 0.271523178807947, "acc_norm_stderr": 0.03631329803969654 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.3486238532110092, "acc_stderr": 0.020431254090714328, "acc_norm": 0.3486238532110092, "acc_norm_stderr": 0.020431254090714328 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.4722222222222222, "acc_stderr": 0.0340470532865388, "acc_norm": 0.4722222222222222, "acc_norm_stderr": 0.0340470532865388 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.25, "acc_stderr": 0.03039153369274154, "acc_norm": 0.25, "acc_norm_stderr": 0.03039153369274154 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.24472573839662448, "acc_stderr": 0.027985699387036416, "acc_norm": 0.24472573839662448, "acc_norm_stderr": 0.027985699387036416 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.2914798206278027, "acc_stderr": 0.030500283176545923, "acc_norm": 0.2914798206278027, "acc_norm_stderr": 0.030500283176545923 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.26717557251908397, "acc_stderr": 0.038808483010823944, "acc_norm": 0.26717557251908397, "acc_norm_stderr": 0.038808483010823944 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.32231404958677684, "acc_stderr": 0.04266416363352168, "acc_norm": 0.32231404958677684, "acc_norm_stderr": 0.04266416363352168 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.21296296296296297, "acc_stderr": 0.03957835471980981, "acc_norm": 0.21296296296296297, "acc_norm_stderr": 0.03957835471980981 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.26380368098159507, "acc_stderr": 0.03462419931615623, "acc_norm": 0.26380368098159507, "acc_norm_stderr": 0.03462419931615623 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.25892857142857145, "acc_stderr": 0.041577515398656284, "acc_norm": 0.25892857142857145, "acc_norm_stderr": 0.041577515398656284 }, "harness\|hendrycksTest-management\|5": { "acc": 0.34951456310679613, "acc_stderr": 0.04721188506097173, "acc_norm": 0.34951456310679613, "acc_norm_stderr": 0.04721188506097173 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.1794871794871795, "acc_stderr": 0.025140935950335418, "acc_norm": 0.1794871794871795, "acc_norm_stderr": 0.025140935950335418 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.27, "acc_stderr": 0.044619604333847394, "acc_norm": 0.27, "acc_norm_stderr": 0.044619604333847394 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.21583652618135377, "acc_stderr": 0.014711684386139958, "acc_norm": 0.21583652618135377, "acc_norm_stderr": 0.014711684386139958 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.24277456647398843, "acc_stderr": 0.0230836585869842, "acc_norm": 0.24277456647398843, "acc_norm_stderr": 0.0230836585869842 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.2424581005586592, "acc_stderr": 0.014333522059217889, "acc_norm": 0.2424581005586592, "acc_norm_stderr": 0.014333522059217889 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.21895424836601307, "acc_stderr": 0.02367908986180772, "acc_norm": 0.21895424836601307, "acc_norm_stderr": 0.02367908986180772 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.24758842443729903, "acc_stderr": 0.024513879973621967, "acc_norm": 0.24758842443729903, "acc_norm_stderr": 0.024513879973621967 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.22530864197530864, "acc_stderr": 0.023246202647819746, "acc_norm": 0.22530864197530864, "acc_norm_stderr": 0.023246202647819746 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.26595744680851063, "acc_stderr": 0.026358065698880592, "acc_norm": 0.26595744680851063, "acc_norm_stderr": 0.026358065698880592 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.2457627118644068, "acc_stderr": 0.010996156635142692, "acc_norm": 0.2457627118644068, "acc_norm_stderr": 0.010996156635142692 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.44485294117647056, "acc_stderr": 0.030187532060329376, "acc_norm": 0.44485294117647056, "acc_norm_stderr": 0.030187532060329376 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.26143790849673204, "acc_stderr": 0.017776947157528034, "acc_norm": 0.26143790849673204, "acc_norm_stderr": 0.017776947157528034 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.21818181818181817, "acc_stderr": 0.03955932861795833, "acc_norm": 0.21818181818181817, "acc_norm_stderr": 0.03955932861795833 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.4, "acc_stderr": 0.031362502409358936, "acc_norm": 0.4, "acc_norm_stderr": 0.031362502409358936 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.22885572139303484, "acc_stderr": 0.029705284056772426, "acc_norm": 0.22885572139303484, "acc_norm_stderr": 0.029705284056772426 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.27, "acc_stderr": 0.04461960433384739, "acc_norm": 0.27, "acc_norm_stderr": 0.04461960433384739 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.1927710843373494, "acc_stderr": 0.030709824050565274, "acc_norm": 0.1927710843373494, "acc_norm_stderr": 0.030709824050565274 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.21052631578947367, "acc_stderr": 0.0312678171466318, "acc_norm": 0.21052631578947367, "acc_norm_stderr": 0.0312678171466318 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.22766217870257038, "mc1_stderr": 0.01467925503211107, "mc2": 0.4069116400376613, "mc2_stderr": 0.014934250122346554 }, "harness\|winogrande\|5": { "acc": 0.5043409629044988, "acc_stderr": 0.014051956064076887 }, "harness\|gsm8k\|5": { "acc": 0.006823351023502654, "acc_stderr": 0.0022675371022544736 } } ``` ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - Curated by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the dataset is intended to be used. --> ### Direct Use <!-- This section describes suitable use cases for the dataset. --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> [More Information Needed] ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> [More Information Needed] ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> [More Information Needed] ### Source Data <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> #### Data Collection and Processing <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> [More Information Needed] #### Who are the source data producers? <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> [More Information Needed] ### Annotations [optional] <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. --> #### Annotation process <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> [More Information Needed] #### Who are the annotators? <!-- This section describes the people or systems who created the annotations. --> [More Information Needed] #### Personal and Sensitive Information <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional] <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]
alvations/c4p0	alvations	2024-03-23T01:26:11Z	96,896	0	[ "size_categories:n<1K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2024-03-22T00:58:02Z	null	--- dataset_info: features: - name: source dtype: string - name: target dtype: string - name: target_backto_source dtype: string - name: raw_target list: - name: generated_text dtype: string - name: raw_target_backto_source list: - name: generated_text dtype: string - name: prompt dtype: string - name: reverse_prompt dtype: string - name: source_langid dtype: string - name: target_langid dtype: string - name: target_backto_source_langid dtype: string - name: doc_id dtype: int64 - name: sent_id dtype: int64 - name: timestamp dtype: string - name: url dtype: string - name: doc_hash dtype: string splits: - name: train num_bytes: 4134 num_examples: 3 download_size: 19374 dataset_size: 4134 configs: - config_name: default data_files: - split: train path: f2527aa0a4051632/train-* ---
cornell-movie-review-data/rotten_tomatoes	cornell-movie-review-data	2024-03-18T14:28:45Z	19,832	82	[ "task_categories:text-classification", "task_ids:sentiment-classification", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "source_datasets:original", "language:en", "license:unknown", "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[ "text-classification" ]	2022-03-02T23:29:22Z	2	--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - unknown multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - text-classification task_ids: - sentiment-classification paperswithcode_id: mr pretty_name: RottenTomatoes - MR Movie Review Data dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': neg '1': pos splits: - name: train num_bytes: 1074810 num_examples: 8530 - name: validation num_bytes: 134679 num_examples: 1066 - name: test num_bytes: 135972 num_examples: 1066 download_size: 487770 dataset_size: 1345461 train-eval-index: - config: default task: text-classification task_id: binary_classification splits: train_split: train eval_split: test col_mapping: text: text label: target metrics: - type: accuracy name: Accuracy - type: f1 name: F1 args: average: binary - type: f1 name: F1 micro args: average: micro - type: f1 name: F1 weighted args: average: weighted - type: precision name: Precision macro args: average: macro - type: precision name: Precision micro args: average: micro - type: precision name: Precision weighted args: average: weighted - type: recall name: Recall macro args: average: macro - type: recall name: Recall micro args: average: micro - type: recall name: Recall weighted args: average: weighted --- # Dataset Card for "rotten_tomatoes" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [http://www.cs.cornell.edu/people/pabo/movie-review-data/](http://www.cs.cornell.edu/people/pabo/movie-review-data/) - Repository: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Paper: [https://arxiv.org/abs/cs/0506075](https://arxiv.org/abs/cs/0506075) - Point of Contact: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Size of downloaded dataset files: 0.49 MB - Size of the generated dataset: 1.34 MB - Total amount of disk used: 1.84 MB ### Dataset Summary Movie Review Dataset. This is a dataset of containing 5,331 positive and 5,331 negative processed sentences from Rotten Tomatoes movie reviews. This data was first used in Bo Pang and Lillian Lee, ``Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales.'', Proceedings of the ACL, 2005. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### default - Size of downloaded dataset files: 0.49 MB - Size of the generated dataset: 1.34 MB - Total amount of disk used: 1.84 MB An example of 'validation' looks as follows. ``` { "label": 1, "text": "Sometimes the days and nights just drag on -- it 's the morning that make me feel alive . And I have one thing to thank for that : pancakes . " } ``` ### Data Fields The data fields are the same among all splits. #### default - `text`: a `string` feature. - `label`: a classification label, with possible values including `neg` (0), `pos` (1). ### Data Splits Reads Rotten Tomatoes sentences and splits into 80% train, 10% validation, and 10% test, as is the practice set out in Jinfeng Li, ``TEXTBUGGER: Generating Adversarial Text Against Real-world Applications.'' \| name \|train\|validation\|test\| \|-------\|----:\|---------:\|---:\| \|default\| 8530\| 1066\|1066\| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @InProceedings{Pang+Lee:05a, author = {Bo Pang and Lillian Lee}, title = {Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales}, booktitle = {Proceedings of the ACL}, year = 2005 } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@jxmorris12](https://github.com/jxmorris12) for adding this dataset.
czyzi0/the-mc-speech-dataset	czyzi0	2024-03-16T15:30:05Z	2,199	5	[ "task_categories:text-to-speech", "task_categories:automatic-speech-recognition", "language:pl", "license:cc0-1.0", "size_categories:10K<n<100K", "format:parquet", "modality:audio", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[ "text-to-speech", "automatic-speech-recognition" ]	2023-07-03T19:31:36Z	2	--- language: - pl license: cc0-1.0 size_categories: - 10K<n<100K task_categories: - text-to-speech - automatic-speech-recognition pretty_name: The MC Speech Dataset dataset_info: features: - name: audio dtype: audio: sampling_rate: 44100 - name: transcript dtype: string - name: id dtype: string splits: - name: train num_bytes: 6985316587.668 num_examples: 24018 download_size: 6174661195 dataset_size: 6985316587.668 configs: - config_name: default data_files: - split: train path: data/train-* --- This is public domain speech dataset consisting of 24018 short audio clips of a single speaker reading sentences in Polish. A transcription is provided for each clip. Clips have total length of more than 22 hours. Texts are in public domain. The audio was recorded in 2021-22 as a part of my [master's thesis](http://dx.doi.org/10.13140/RG.2.2.26293.24800) and is in public domain. If you use this dataset, please cite: ``` @masterthesis{mcspeech, title={Analiza porównawcza korpusów nagrań mowy dla celów syntezy mowy w języku polskim}, author={Czyżnikiewicz, Mateusz}, year={2022}, month={December}, school={Warsaw University of Technology}, type={Master's thesis}, doi={10.13140/RG.2.2.26293.24800}, note={Available at \url{http://dx.doi.org/10.13140/RG.2.2.26293.24800}}, } ``` More info about the dataset can be found at https://github.com/czyzi0/the-mc-speech-dataset Also, if you find this resource helpful, kindly consider leaving a like.
google/wiki40b	google	2024-03-11T16:19:48Z	8,616	28	[ "language:en", "size_categories:10M<n<100M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2022-03-02T23:29:22Z	1	--- language: - en paperswithcode_id: wiki-40b pretty_name: Wiki-40B dataset_info: - config_name: ar features: - name: wikidata_id dtype: string - name: text dtype: string - name: version_id dtype: string splits: - name: train num_bytes: 773508885 num_examples: 220885 - name: validation num_bytes: 44102674 num_examples: 12198 - name: test num_bytes: 43755879 num_examples: 12271 download_size: 413683528 dataset_size: 861367438 - config_name: bg features: - name: wikidata_id dtype: string - name: text dtype: string - name: version_id dtype: string splits: - name: train num_bytes: 1413477231 num_examples: 130670 - name: validation num_bytes: 78976448 num_examples: 7259 - name: test num_bytes: 78350414 num_examples: 7289 download_size: 484828696 dataset_size: 1570804093 - config_name: ca features: - name: wikidata_id dtype: string - name: text dtype: string - name: version_id dtype: string splits: - name: train num_bytes: 784791826 num_examples: 277313 - name: validation num_bytes: 43576907 num_examples: 15362 - name: test num_bytes: 44904134 num_examples: 15568 download_size: 480954417 dataset_size: 873272867 - config_name: cs features: - name: wikidata_id dtype: string - name: text dtype: string - name: version_id dtype: string splits: - name: train num_bytes: 901187017 num_examples: 235971 - name: validation num_bytes: 49743998 num_examples: 13096 - name: test num_bytes: 49325867 num_examples: 12984 download_size: 493522926 dataset_size: 1000256882 - config_name: da features: - name: wikidata_id dtype: string - name: text dtype: string - name: version_id dtype: string splits: - name: train num_bytes: 247928023 num_examples: 109486 - name: validation num_bytes: 13937406 num_examples: 6173 - name: test num_bytes: 14401179 num_examples: 6219 download_size: 156696617 dataset_size: 276266608 - config_name: de features: - name: wikidata_id dtype: string - name: text dtype: string - name: version_id dtype: string splits: - name: train num_bytes: 4988094268 num_examples: 1554910 - name: validation num_bytes: 278101948 num_examples: 86068 - name: test num_bytes: 278024815 num_examples: 86594 download_size: 3174352286 dataset_size: 5544221031 - config_name: el features: - name: wikidata_id dtype: string - name: text dtype: string - name: version_id dtype: string splits: - name: train num_bytes: 1738534924 num_examples: 93596 - name: validation num_bytes: 97711791 num_examples: 5130 - name: test num_bytes: 99743744 num_examples: 5261 download_size: 621575577 dataset_size: 1935990459 - config_name: en features: - name: wikidata_id dtype: string - name: text dtype: string - name: version_id dtype: string splits: - name: train num_bytes: 9423468036 num_examples: 2926536 - name: validation num_bytes: 527374301 num_examples: 163597 - name: test num_bytes: 522210646 num_examples: 162274 download_size: 6183831905 dataset_size: 10473052983 - config_name: es features: - name: wikidata_id dtype: string - name: text dtype: string - name: version_id dtype: string splits: - name: train num_bytes: 2906242601 num_examples: 872541 - name: validation num_bytes: 161381260 num_examples: 48592 - name: test num_bytes: 164110964 num_examples: 48764 download_size: 1783120767 dataset_size: 3231734825 - config_name: et features: - name: wikidata_id dtype: string - name: text dtype: string - name: version_id dtype: string splits: - name: train num_bytes: 196484412 num_examples: 114464 - name: validation num_bytes: 10987144 num_examples: 6351 - name: test num_bytes: 10691693 num_examples: 6205 download_size: 122192870 dataset_size: 218163249 - config_name: fa features: - name: wikidata_id dtype: string - name: text dtype: string - name: version_id dtype: string splits: - name: train num_bytes: 1551260324 num_examples: 203145 - name: validation num_bytes: 86108146 num_examples: 11180 - name: test num_bytes: 89064531 num_examples: 11262 download_size: 552712695 dataset_size: 1726433001 - config_name: fi features: - name: wikidata_id dtype: string - name: text dtype: string - name: version_id dtype: string splits: - name: train num_bytes: 589614484 num_examples: 255822 - name: validation num_bytes: 32645294 num_examples: 13962 - name: test num_bytes: 32869383 num_examples: 14179 download_size: 346601923 dataset_size: 655129161 - config_name: fr features: - name: wikidata_id dtype: string - name: text dtype: string - name: version_id dtype: string splits: - name: train num_bytes: 3850031120 num_examples: 1227206 - name: validation num_bytes: 216405364 num_examples: 68655 - name: test num_bytes: 215243874 num_examples: 68004 download_size: 2246390244 dataset_size: 4281680358 - config_name: he features: - name: wikidata_id dtype: string - name: text dtype: string - name: version_id dtype: string splits: - name: train num_bytes: 2834322770 num_examples: 165359 - name: validation num_bytes: 160235180 num_examples: 9231 - name: test num_bytes: 162131949 num_examples: 9344 download_size: 754632129 dataset_size: 3156689899 - config_name: hi features: - name: wikidata_id dtype: string - name: text dtype: string - name: version_id dtype: string splits: - name: train num_bytes: 947403521 num_examples: 45737 - name: validation num_bytes: 54497912 num_examples: 2596 - name: test num_bytes: 54448878 num_examples: 2643 download_size: 231716300 dataset_size: 1056350311 - config_name: hr features: - name: wikidata_id dtype: string - name: text dtype: string - name: version_id dtype: string splits: - name: train num_bytes: 247471855 num_examples: 103857 - name: validation num_bytes: 14004242 num_examples: 5792 - name: test num_bytes: 13881533 num_examples: 5724 download_size: 158644264 dataset_size: 275357630 - config_name: hu features: - name: wikidata_id dtype: string - name: text dtype: string - name: version_id dtype: string splits: - name: train num_bytes: 878753014 num_examples: 273248 - name: validation num_bytes: 48695962 num_examples: 15208 - name: test num_bytes: 50053050 num_examples: 15258 download_size: 466524744 dataset_size: 977502026 - config_name: id features: - name: wikidata_id dtype: string - name: text dtype: string - name: version_id dtype: string splits: - name: train num_bytes: 315092853 num_examples: 156255 - name: validation num_bytes: 16667760 num_examples: 8714 - name: test num_bytes: 17798713 num_examples: 8598 download_size: 193455048 dataset_size: 349559326 - config_name: it features: - name: wikidata_id dtype: string - name: text dtype: string - name: version_id dtype: string splits: - name: train num_bytes: 1998187938 num_examples: 732609 - name: validation num_bytes: 109399796 num_examples: 40684 - name: test num_bytes: 108160871 num_examples: 40443 download_size: 1330554944 dataset_size: 2215748605 - config_name: ja features: - name: wikidata_id dtype: string - name: text dtype: string - name: version_id dtype: string splits: - name: train num_bytes: 7719156890 num_examples: 745392 - name: validation num_bytes: 423396781 num_examples: 41576 - name: test num_bytes: 424775191 num_examples: 41268 download_size: 2914923230 dataset_size: 8567328862 - config_name: ko features: - name: wikidata_id dtype: string - name: text dtype: string - name: version_id dtype: string splits: - name: train num_bytes: 1424423053 num_examples: 194977 - name: validation num_bytes: 79027067 num_examples: 10805 - name: test num_bytes: 78623281 num_examples: 10802 download_size: 568560655 dataset_size: 1582073401 - config_name: lt features: - name: wikidata_id dtype: string - name: text dtype: string - name: version_id dtype: string splits: - name: train num_bytes: 173899806 num_examples: 84854 - name: validation num_bytes: 9782794 num_examples: 4754 - name: test num_bytes: 9855094 num_examples: 4683 download_size: 100457919 dataset_size: 193537694 - config_name: lv features: - name: wikidata_id dtype: string - name: text dtype: string - name: version_id dtype: string splits: - name: train num_bytes: 108022486 num_examples: 33064 - name: validation num_bytes: 5999880 num_examples: 1857 - name: test num_bytes: 6277058 num_examples: 1932 download_size: 57147319 dataset_size: 120299424 - config_name: ms features: - name: wikidata_id dtype: string - name: text dtype: string - name: version_id dtype: string splits: - name: train num_bytes: 133193449 num_examples: 97509 - name: validation num_bytes: 7244722 num_examples: 5357 - name: test num_bytes: 7344948 num_examples: 5235 download_size: 80629019 dataset_size: 147783119 - config_name: nl features: - name: wikidata_id dtype: string - name: text dtype: string - name: version_id dtype: string splits: - name: train num_bytes: 906908479 num_examples: 447555 - name: validation num_bytes: 51519150 num_examples: 25201 - name: test num_bytes: 49492508 num_examples: 24776 download_size: 594312303 dataset_size: 1007920137 - config_name: 'no' features: - name: wikidata_id dtype: string - name: text dtype: string - name: version_id dtype: string splits: - name: train num_bytes: 391905155 num_examples: 190588 - name: validation num_bytes: 22058565 num_examples: 10547 - name: test num_bytes: 21510187 num_examples: 10588 download_size: 248974000 dataset_size: 435473907 - config_name: pl features: - name: wikidata_id dtype: string - name: text dtype: string - name: version_id dtype: string splits: - name: train num_bytes: 1250270240 num_examples: 505191 - name: validation num_bytes: 70048390 num_examples: 28310 - name: test num_bytes: 69957343 num_examples: 27987 download_size: 755556434 dataset_size: 1390275973 - config_name: pt features: - name: wikidata_id dtype: string - name: text dtype: string - name: version_id dtype: string splits: - name: train num_bytes: 1186541609 num_examples: 406507 - name: validation num_bytes: 65911750 num_examples: 22301 - name: test num_bytes: 65941634 num_examples: 22693 download_size: 725984914 dataset_size: 1318394993 - config_name: ro features: - name: wikidata_id dtype: string - name: text dtype: string - name: version_id dtype: string splits: - name: train num_bytes: 378177460 num_examples: 139615 - name: validation num_bytes: 19638614 num_examples: 7624 - name: test num_bytes: 22095957 num_examples: 7870 download_size: 212621695 dataset_size: 419912031 - config_name: ru features: - name: wikidata_id dtype: string - name: text dtype: string - name: version_id dtype: string splits: - name: train num_bytes: 14041955183 num_examples: 926037 - name: validation num_bytes: 787569099 num_examples: 51287 - name: test num_bytes: 782630173 num_examples: 51885 download_size: 4959684748 dataset_size: 15612154455 - config_name: sk features: - name: wikidata_id dtype: string - name: text dtype: string - name: version_id dtype: string splits: - name: train num_bytes: 220400547 num_examples: 103095 - name: validation num_bytes: 11443566 num_examples: 5604 - name: test num_bytes: 12958230 num_examples: 5741 download_size: 122641378 dataset_size: 244802343 - config_name: sl features: - name: wikidata_id dtype: string - name: text dtype: string - name: version_id dtype: string splits: - name: train num_bytes: 165604630 num_examples: 60927 - name: validation num_bytes: 8686867 num_examples: 3287 - name: test num_bytes: 8938235 num_examples: 3341 download_size: 108369067 dataset_size: 183229732 - config_name: sr features: - name: wikidata_id dtype: string - name: text dtype: string - name: version_id dtype: string splits: - name: train num_bytes: 1778468133 num_examples: 327313 - name: validation num_bytes: 101044816 num_examples: 18100 - name: test num_bytes: 94774312 num_examples: 17997 download_size: 601515686 dataset_size: 1974287261 - config_name: sv features: - name: wikidata_id dtype: string - name: text dtype: string - name: version_id dtype: string splits: - name: train num_bytes: 675484771 num_examples: 400742 - name: validation num_bytes: 37596409 num_examples: 22263 - name: test num_bytes: 37171140 num_examples: 22291 download_size: 402183416 dataset_size: 750252320 - config_name: th features: - name: wikidata_id dtype: string - name: text dtype: string - name: version_id dtype: string splits: - name: train num_bytes: 1167742322 num_examples: 56798 - name: validation num_bytes: 58604863 num_examples: 3093 - name: test num_bytes: 63235795 num_examples: 3114 download_size: 286569412 dataset_size: 1289582980 - config_name: tl features: - name: wikidata_id dtype: string - name: text dtype: string - name: version_id dtype: string splits: - name: train num_bytes: 27097474 num_examples: 25940 - name: validation num_bytes: 1480857 num_examples: 1472 - name: test num_bytes: 1421372 num_examples: 1446 download_size: 16610349 dataset_size: 29999703 - config_name: tr features: - name: wikidata_id dtype: string - name: text dtype: string - name: version_id dtype: string splits: - name: train num_bytes: 417796625 num_examples: 142576 - name: validation num_bytes: 23829728 num_examples: 7845 - name: test num_bytes: 23573543 num_examples: 7890 download_size: 208571967 dataset_size: 465199896 - config_name: uk features: - name: wikidata_id dtype: string - name: text dtype: string - name: version_id dtype: string splits: - name: train num_bytes: 5617333215 num_examples: 477618 - name: validation num_bytes: 304063524 num_examples: 26324 - name: test num_bytes: 309417358 num_examples: 26581 download_size: 2016970917 dataset_size: 6230814097 - config_name: vi features: - name: wikidata_id dtype: string - name: text dtype: string - name: version_id dtype: string splits: - name: train num_bytes: 968448149 num_examples: 146255 - name: validation num_bytes: 53118964 num_examples: 8195 - name: test num_bytes: 51960729 num_examples: 7942 download_size: 382764219 dataset_size: 1073527842 - config_name: zh-cn features: - name: wikidata_id dtype: string - name: text dtype: string - name: version_id dtype: string splits: - name: train num_bytes: 902812807 num_examples: 549672 - name: validation num_bytes: 50487729 num_examples: 30299 - name: test num_bytes: 49584239 num_examples: 30355 download_size: 667605463 dataset_size: 1002884775 - config_name: zh-tw features: - name: wikidata_id dtype: string - name: text dtype: string - name: version_id dtype: string splits: - name: train num_bytes: 3254625339 num_examples: 552031 - name: validation num_bytes: 185024571 num_examples: 30739 - name: test num_bytes: 181148137 num_examples: 30670 download_size: 1375185673 dataset_size: 3620798047 configs: - config_name: ar data_files: - split: train path: ar/train-* - split: validation path: ar/validation-* - split: test path: ar/test-* - config_name: bg data_files: - split: train path: bg/train-* - split: validation path: bg/validation-* - split: test path: bg/test-* - config_name: ca data_files: - split: train path: ca/train-* - split: validation path: ca/validation-* - split: test path: ca/test-* - config_name: cs data_files: - split: train path: cs/train-* - split: validation path: cs/validation-* - split: test path: cs/test-* - config_name: da data_files: - split: train path: da/train-* - split: validation path: da/validation-* - split: test path: da/test-* - config_name: de data_files: - split: train path: de/train-* - split: validation path: de/validation-* - split: test path: de/test-* - config_name: el data_files: - split: train path: el/train-* - split: validation path: el/validation-* - split: test path: el/test-* - config_name: en data_files: - split: train path: en/train-* - split: validation path: en/validation-* - split: test path: en/test-* - config_name: es data_files: - split: train path: es/train-* - split: validation path: es/validation-* - split: test path: es/test-* - config_name: et data_files: - split: train path: et/train-* - split: validation path: et/validation-* - split: test path: et/test-* - config_name: fa data_files: - split: train path: fa/train-* - split: validation path: fa/validation-* - split: test path: fa/test-* - config_name: fi data_files: - split: train path: fi/train-* - split: validation path: fi/validation-* - split: test path: fi/test-* - config_name: fr data_files: - split: train path: fr/train-* - split: validation path: fr/validation-* - split: test path: fr/test-* - config_name: he data_files: - split: train path: he/train-* - split: validation path: he/validation-* - split: test path: he/test-* - config_name: hi data_files: - split: train path: hi/train-* - split: validation path: hi/validation-* - split: test path: hi/test-* - config_name: hr data_files: - split: train path: hr/train-* - split: validation path: hr/validation-* - split: test path: hr/test-* - config_name: hu data_files: - split: train path: hu/train-* - split: validation path: hu/validation-* - split: test path: hu/test-* - config_name: id data_files: - split: train path: id/train-* - split: validation path: id/validation-* - split: test path: id/test-* - config_name: it data_files: - split: train path: it/train-* - split: validation path: it/validation-* - split: test path: it/test-* - config_name: ja data_files: - split: train path: ja/train-* - split: validation path: ja/validation-* - split: test path: ja/test-* - config_name: ko data_files: - split: train path: ko/train-* - split: validation path: ko/validation-* - split: test path: ko/test-* - config_name: lt data_files: - split: train path: lt/train-* - split: validation path: lt/validation-* - split: test path: lt/test-* - config_name: lv data_files: - split: train path: lv/train-* - split: validation path: lv/validation-* - split: test path: lv/test-* - config_name: ms data_files: - split: train path: ms/train-* - split: validation path: ms/validation-* - split: test path: ms/test-* - config_name: nl data_files: - split: train path: nl/train-* - split: validation path: nl/validation-* - split: test path: nl/test-* - config_name: 'no' data_files: - split: train path: no/train-* - split: validation path: no/validation-* - split: test path: no/test-* - config_name: pl data_files: - split: train path: pl/train-* - split: validation path: pl/validation-* - split: test path: pl/test-* - config_name: pt data_files: - split: train path: pt/train-* - split: validation path: pt/validation-* - split: test path: pt/test-* - config_name: ro data_files: - split: train path: ro/train-* - split: validation path: ro/validation-* - split: test path: ro/test-* - config_name: ru data_files: - split: train path: ru/train-* - split: validation path: ru/validation-* - split: test path: ru/test-* - config_name: sk data_files: - split: train path: sk/train-* - split: validation path: sk/validation-* - split: test path: sk/test-* - config_name: sl data_files: - split: train path: sl/train-* - split: validation path: sl/validation-* - split: test path: sl/test-* - config_name: sr data_files: - split: train path: sr/train-* - split: validation path: sr/validation-* - split: test path: sr/test-* - config_name: sv data_files: - split: train path: sv/train-* - split: validation path: sv/validation-* - split: test path: sv/test-* - config_name: th data_files: - split: train path: th/train-* - split: validation path: th/validation-* - split: test path: th/test-* - config_name: tl data_files: - split: train path: tl/train-* - split: validation path: tl/validation-* - split: test path: tl/test-* - config_name: tr data_files: - split: train path: tr/train-* - split: validation path: tr/validation-* - split: test path: tr/test-* - config_name: uk data_files: - split: train path: uk/train-* - split: validation path: uk/validation-* - split: test path: uk/test-* - config_name: vi data_files: - split: train path: vi/train-* - split: validation path: vi/validation-* - split: test path: vi/test-* - config_name: zh-cn data_files: - split: train path: zh-cn/train-* - split: validation path: zh-cn/validation-* - split: test path: zh-cn/test-* - config_name: zh-tw data_files: - split: train path: zh-tw/train-* - split: validation path: zh-tw/validation-* - split: test path: zh-tw/test-* --- # Dataset Card for "wiki40b" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [https://research.google/pubs/pub49029/](https://research.google/pubs/pub49029/) - Repository: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Paper: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Point of Contact: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Size of downloaded dataset files: 0.00 MB - Size of the generated dataset: 10.47 GB - Total amount of disk used: 10.47 GB ### Dataset Summary Clean-up text for 40+ Wikipedia languages editions of pages correspond to entities. The datasets have train/dev/test splits per language. The dataset is cleaned up by page filtering to remove disambiguation pages, redirect pages, deleted pages, and non-entity pages. Each example contains the wikidata id of the entity, and the full Wikipedia article after page processing that removes non-content sections and structured objects. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### en - Size of downloaded dataset files: 0.00 MB - Size of the generated dataset: 10.47 GB - Total amount of disk used: 10.47 GB An example of 'train' looks as follows. ``` ``` ### Data Fields The data fields are the same among all splits. #### en - `wikidata_id`: a `string` feature. - `text`: a `string` feature. - `version_id`: a `string` feature. ### Data Splits \|name\| train \|validation\| test \| \|----\|------:\|---------:\|-----:\| \|en \|2926536\| 163597\|162274\| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` ``` ### Contributions Thanks to [@jplu](https://github.com/jplu), [@patrickvonplaten](https://github.com/patrickvonplaten), [@thomwolf](https://github.com/thomwolf), [@albertvillanova](https://github.com/albertvillanova), [@lhoestq](https://github.com/lhoestq) for adding this dataset.
WizardLMTeam/WizardLM_evol_instruct_70k	WizardLMTeam	2024-03-10T01:06:43Z	503	191	[ "license:mit", "size_categories:10K<n<100K", "format:json", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2308.09583", "arxiv:2304.12244", "arxiv:2306.08568", "region:us" ]	[]	2023-04-25T09:57:27Z	null	--- license: mit --- This is the training data of WizardLM. ## News - 🔥 🔥 🔥 [08/11/2023] We release WizardMath Models. - 🔥 Our WizardMath-70B-V1.0 model slightly outperforms some closed-source LLMs on the GSM8K, including ChatGPT 3.5, Claude Instant 1 and PaLM 2 540B. - 🔥 Our WizardMath-70B-V1.0 model achieves 81.6 pass@1 on the [GSM8k Benchmarks](https://github.com/openai/grade-school-math), which is 24.8 points higher than the SOTA open-source LLM. - 🔥 Our WizardMath-70B-V1.0 model achieves 22.7 pass@1 on the [MATH Benchmarks](https://github.com/hendrycks/math), which is 9.2 points higher than the SOTA open-source LLM. \| Model \| Checkpoint \| Paper \| GSM8k \| MATH \|Online Demo\| License\| \| ----- \|------\| ---- \|------\|-------\| ----- \| ----- \| \| WizardMath-70B-V1.0 \| 🤗 <a href="https://huggingface.co/WizardLM/WizardMath-70B-V1.0" target="_blank">HF Link</a> \| 📃 <a href="https://arxiv.org/abs/2308.09583" target="_blank">[WizardMath]</a>\| 81.6 \| 22.7 \|[Demo](http://47.103.63.15:50083/)\| <a href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/" target="_blank">Llama 2 </a> \| \| WizardMath-13B-V1.0 \| 🤗 <a href="https://huggingface.co/WizardLM/WizardMath-13B-V1.0" target="_blank">HF Link</a> \| 📃 <a href="https://arxiv.org/abs/2308.09583" target="_blank">[WizardMath]</a>\| 63.9 \| 14.0 \|[Demo](http://47.103.63.15:50082/)\| <a href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/" target="_blank">Llama 2 </a> \| \| WizardMath-7B-V1.0 \| 🤗 <a href="https://huggingface.co/WizardLM/WizardMath-7B-V1.0" target="_blank">HF Link</a> \| 📃 <a href="https://arxiv.org/abs/2308.09583" target="_blank">[WizardMath]</a>\| 54.9 \| 10.7 \| [Demo](http://47.103.63.15:50080/)\| <a href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/" target="_blank">Llama 2 </a>\| <font size=4> \| <sup>Model</sup> \| <sup>Checkpoint</sup> \| <sup>Paper</sup> \|<sup>MT-Bench</sup> \| <sup>AlpacaEval</sup> \| <sup>WizardEval</sup> \| <sup>HumanEval</sup> \| <sup>License</sup>\| \| ----- \|------\| ---- \|------\|-------\| ----- \| ----- \| ----- \| \| <sup>WizardLM-13B-V1.2</sup> \| <sup>🤗 <a href="https://huggingface.co/WizardLM/WizardLM-13B-V1.2" target="_blank">HF Link</a> </sup>\| \| <sup>7.06</sup> \| <sup>89.17%</sup> \| <sup>101.4% </sup>\|<sup>36.6 pass@1</sup>\|<sup> <a href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/" target="_blank">Llama 2 License </a></sup> \| \| <sup>WizardLM-13B-V1.1</sup> \|<sup> 🤗 <a href="https://huggingface.co/WizardLM/WizardLM-13B-V1.1" target="_blank">HF Link</a> </sup> \| \| <sup>6.76</sup> \|<sup>86.32%</sup> \| <sup>99.3% </sup> \|<sup>25.0 pass@1</sup>\| <sup>Non-commercial</sup>\| \| <sup>WizardLM-30B-V1.0</sup> \| <sup>🤗 <a href="https://huggingface.co/WizardLM/WizardLM-30B-V1.0" target="_blank">HF Link</a></sup> \| \| <sup>7.01</sup> \| \| <sup>97.8% </sup> \| <sup>37.8 pass@1</sup>\| <sup>Non-commercial</sup> \| \| <sup>WizardLM-13B-V1.0</sup> \| <sup>🤗 <a href="https://huggingface.co/WizardLM/WizardLM-13B-V1.0" target="_blank">HF Link</a> </sup> \| \| <sup>6.35</sup> \| <sup>75.31%</sup> \| <sup>89.1% </sup> \|<sup> 24.0 pass@1 </sup> \| <sup>Non-commercial</sup>\| \| <sup>WizardLM-7B-V1.0 </sup>\| <sup>🤗 <a href="https://huggingface.co/WizardLM/WizardLM-7B-V1.0" target="_blank">HF Link</a> </sup> \|<sup> 📃 <a href="https://arxiv.org/abs/2304.12244" target="_blank">[WizardLM]</a> </sup>\| \| \| <sup>78.0% </sup> \|<sup>19.1 pass@1 </sup>\|<sup> Non-commercial</sup>\| \| <sup>WizardCoder-15B-V1.0</sup> \| <sup> 🤗 <a href="https://huggingface.co/WizardLM/WizardCoder-15B-V1.0" target="_blank">HF Link</a></sup> \| <sup>📃 <a href="https://arxiv.org/abs/2306.08568" target="_blank">[WizardCoder]</a></sup> \| \|\| \|<sup> 57.3 pass@1 </sup> \| <sup> <a href="https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement" target="_blank">OpenRAIL-M</a></sup> \| </font>
stanfordnlp/snli	stanfordnlp	2024-03-06T10:55:50Z	6,934	75	[ "task_categories:text-classification", "task_ids:natural-language-inference", "task_ids:multi-input-text-classification", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "source_datasets:extended\|other-flicker-30k", "source_datasets:extended\|other-visual-genome", "language:en", "license:cc-by-sa-4.0", "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:1508.05326", "region:us" ]	[ "text-classification" ]	2022-03-02T23:29:22Z	1	--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - cc-by-sa-4.0 multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - extended\|other-flicker-30k - extended\|other-visual-genome task_categories: - text-classification task_ids: - natural-language-inference - multi-input-text-classification paperswithcode_id: snli pretty_name: Stanford Natural Language Inference dataset_info: config_name: plain_text features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: test num_bytes: 1258904 num_examples: 10000 - name: validation num_bytes: 1263036 num_examples: 10000 - name: train num_bytes: 65884386 num_examples: 550152 download_size: 20439300 dataset_size: 68406326 configs: - config_name: plain_text data_files: - split: test path: plain_text/test-* - split: validation path: plain_text/validation-* - split: train path: plain_text/train-* --- # Dataset Card for SNLI ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: https://nlp.stanford.edu/projects/snli/ - Repository: [More Information Needed] - Paper: https://aclanthology.org/D15-1075/ - Paper: https://arxiv.org/abs/1508.05326 - Leaderboard: https://nlp.stanford.edu/projects/snli/ - Point of Contact: [Samuel Bowman](mailto:[email protected]) - Point of Contact: [Gabor Angeli](mailto:[email protected]) - Point of Contact: [Chris Manning]([email protected]) ### Dataset Summary The SNLI corpus (version 1.0) is a collection of 570k human-written English sentence pairs manually labeled for balanced classification with the labels entailment, contradiction, and neutral, supporting the task of natural language inference (NLI), also known as recognizing textual entailment (RTE). ### Supported Tasks and Leaderboards Natural Language Inference (NLI), also known as Recognizing Textual Entailment (RTE), is the task of determining the inference relation between two (short, ordered) texts: entailment, contradiction, or neutral ([MacCartney and Manning 2008](https://aclanthology.org/C08-1066/)). See the [corpus webpage](https://nlp.stanford.edu/projects/snli/) for a list of published results. ### Languages The language in the dataset is English as spoken by users of the website Flickr and as spoken by crowdworkers from Amazon Mechanical Turk. The BCP-47 code for English is en. ## Dataset Structure ### Data Instances For each instance, there is a string for the premise, a string for the hypothesis, and an integer for the label. Note that each premise may appear three times with a different hypothesis and label. See the [SNLI corpus viewer](https://huggingface.co/datasets/viewer/?dataset=snli) to explore more examples. ``` {'premise': 'Two women are embracing while holding to go packages.' 'hypothesis': 'The sisters are hugging goodbye while holding to go packages after just eating lunch.' 'label': 1} ``` The average token count for the premises and hypotheses are given below: \| Feature \| Mean Token Count \| \| ---------- \| ---------------- \| \| Premise \| 14.1 \| \| Hypothesis \| 8.3 \| ### Data Fields - `premise`: a string used to determine the truthfulness of the hypothesis - `hypothesis`: a string that may be true, false, or whose truth conditions may not be knowable when compared to the premise - `label`: an integer whose value may be either _0_, indicating that the hypothesis entails the premise, _1_, indicating that the premise and hypothesis neither entail nor contradict each other, or _2_, indicating that the hypothesis contradicts the premise. Dataset instances which don't have any gold label are marked with -1 label. Make sure you filter them before starting the training using `datasets.Dataset.filter`. ### Data Splits The SNLI dataset has 3 splits: _train_, _validation_, and _test_. All of the examples in the _validation_ and _test_ sets come from the set that was annotated in the validation task with no-consensus examples removed. The remaining multiply-annotated examples are in the training set with no-consensus examples removed. Each unique premise/caption shows up in only one split, even though they usually appear in at least three different examples. \| Dataset Split \| Number of Instances in Split \| \| ------------- \|----------------------------- \| \| Train \| 550,152 \| \| Validation \| 10,000 \| \| Test \| 10,000 \| ## Dataset Creation ### Curation Rationale The [SNLI corpus (version 1.0)](https://nlp.stanford.edu/projects/snli/) was developed as a benchmark for natural langauge inference (NLI), also known as recognizing textual entailment (RTE), with the goal of producing a dataset large enough to train models using neural methodologies. ### Source Data #### Initial Data Collection and Normalization The hypotheses were elicited by presenting crowdworkers with captions from preexisting datasets without the associated photos, but the vocabulary of the hypotheses still reflects the content of the photos as well as the caption style of writing (e.g. mostly present tense). The dataset developers report 37,026 distinct words in the corpus, ignoring case. They allowed bare NPs as well as full sentences. Using the Stanford PCFG Parser 3.5.2 (Klein and Manning, 2003) trained on the standard training set as well as on the Brown Corpus (Francis and Kucera 1979), the authors report that 74% of the premises and 88.9% of the hypotheses result in a parse rooted with an 'S'. The corpus was developed between 2014 and 2015. Crowdworkers were presented with a caption without the associated photo and asked to produce three alternate captions, one that is definitely true, one that might be true, and one that is definitely false. See Section 2.1 and Figure 1 for details (Bowman et al., 2015). The corpus includes content from the [Flickr 30k corpus](http://shannon.cs.illinois.edu/DenotationGraph/) and the [VisualGenome corpus](https://visualgenome.org/). The photo captions used to prompt the data creation were collected on Flickr by [Young et al. (2014)](https://aclanthology.org/Q14-1006/), who extended the Flickr 8K dataset developed by [Hodosh et al. (2013)](https://www.jair.org/index.php/jair/article/view/10833). Hodosh et al. collected photos from the following Flickr groups: strangers!, Wild-Child (Kids in Action), Dogs in Action (Read the Rules), Outdoor Activities, Action Photography, Flickr-Social (two or more people in the photo). Young et al. do not list the specific groups they collected photos from. The VisualGenome corpus also contains images from Flickr, originally collected in [MS-COCO](https://cocodataset.org/#home) and [YFCC100M](http://projects.dfki.uni-kl.de/yfcc100m/). The premises from the Flickr 30k corpus corrected for spelling using the Linux spell checker and ungrammatical sentences were removed. Bowman et al. do not report any normalization, though they note that punctuation and capitalization are often omitted. #### Who are the source language producers? A large portion of the premises (160k) were produced in the [Flickr 30k corpus](http://shannon.cs.illinois.edu/DenotationGraph/) by an unknown number of crowdworkers. About 2,500 crowdworkers from Amazon Mechanical Turk produced the associated hypotheses. The premises from the Flickr 30k project describe people and animals whose photos were collected and presented to the Flickr 30k crowdworkers, but the SNLI corpus did not present the photos to the hypotheses creators. The Flickr 30k corpus did not report crowdworker or photo subject demographic information or crowdworker compensation. The SNLI crowdworkers were compensated per HIT at rates between $.1 and $.5 with no incentives. Workers who ignored the guidelines were disqualified, and automated bulk submissions were rejected. No demographic information was collected from the SNLI crowdworkers. An additional 4,000 premises come from the pilot study of the [VisualGenome corpus](https://homes.cs.washington.edu/~ranjay/visualgenome/index.html). Though the pilot study itself is not described, the location information of the 33,000 AMT crowdworkers that participated over the course of the 6 months of data collection are aggregated. Most of the workers were located in the United States (93%), with others from the Philippines, Kenya, India, Russia, and Canada. Workers were paid $6-$8 per hour. ### Annotations #### Annotation process 56,941 of the total sentence pairs were further annotated in a validation task. Four annotators each labeled a premise-hypothesis pair as entailment, contradiction, or neither, resulting in 5 total judgements including the original hypothesis author judgement. See Section 2.2 for more details (Bowman et al., 2015). The authors report 3/5 annotator agreement on 98% of the validation set and unanimous annotator agreement on 58.3% of the validation set. If a label was chosen by three annotators, that label was made the gold label. Following from this, 2% of the data did not have a consensus label and was labeled '-' by the authors. \| Label \| Fleiss κ \| \| --------------- \|--------- \| \| _contradiction_ \| 0.77 \| \| _entailment_ \| 0.72 \| \| _neutral_ \| 0.60 \| \| overall \| 0.70 \| #### Who are the annotators? The annotators of the validation task were a closed set of about 30 trusted crowdworkers on Amazon Mechanical Turk. No demographic information was collected. Annotators were compensated per HIT between $.1 and $.5 with $1 bonuses in cases where annotator labels agreed with the curators' labels for 250 randomly distributed examples. ### Personal and Sensitive Information The dataset does not contain any personal information about the authors or the crowdworkers, but may contain descriptions of the people in the original Flickr photos. ## Considerations for Using the Data ### Social Impact of Dataset This dataset was developed as a benchmark for evaluating representational systems for text, especially including those induced by representation learning methods, in the task of predicting truth conditions in a given context. (It should be noted that the truth conditions of a hypothesis given a premise does not necessarily match the truth conditions of the hypothesis in the real world.) Systems that are successful at such a task may be more successful in modeling semantic representations. ### Discussion of Biases The language reflects the content of the photos collected from Flickr, as described in the [Data Collection](#initial-data-collection-and-normalization) section. [Rudinger et al (2017)](https://aclanthology.org/W17-1609/) use pointwise mutual information to calculate a measure of association between a manually selected list of tokens corresponding to identity categories and the other words in the corpus, showing strong evidence of stereotypes across gender categories. They also provide examples in which crowdworkers reproduced harmful stereotypes or pejorative language in the hypotheses. ### Other Known Limitations [Gururangan et al (2018)](https://aclanthology.org/N18-2017/), [Poliak et al (2018)](https://aclanthology.org/S18-2023/), and [Tsuchiya (2018)](https://aclanthology.org/L18-1239/) show that the SNLI corpus has a number of annotation artifacts. Using various classifiers, Poliak et al correctly predicted the label of the hypothesis 69% of the time without using the premise, Gururangan et al 67% of the time, and Tsuchiya 63% of the time. ## Additional Information ### Dataset Curators The SNLI corpus was developed by Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning as part of the [Stanford NLP group](https://nlp.stanford.edu/). It was supported by a Google Faculty Research Award, a gift from Bloomberg L.P., the Defense Advanced Research Projects Agency (DARPA) Deep Exploration and Filtering of Text (DEFT) Program under Air Force Research Laboratory (AFRL) contract no. FA8750-13-2-0040, the National Science Foundation under grant no. IIS 1159679, and the Department of the Navy, Office of Naval Research, under grant no. N00014-10-1-0109. ### Licensing Information The Stanford Natural Language Inference Corpus by The Stanford NLP Group is licensed under a [Creative Commons Attribution-ShareAlike 4.0 International License](http://creativecommons.org/licenses/by-sa/4.0/). The corpus includes content from the [Flickr 30k corpus](http://shannon.cs.illinois.edu/DenotationGraph/), also released under an Attribution-ShareAlike licence. ### Citation Information The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it: ```bibtex @inproceedings{bowman-etal-2015-large, title = "A large annotated corpus for learning natural language inference", author = "Bowman, Samuel R. and Angeli, Gabor and Potts, Christopher and Manning, Christopher D.", editor = "M{\`a}rquez, Llu{\'\i}s and Callison-Burch, Chris and Su, Jian", booktitle = "Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing", month = sep, year = "2015", address = "Lisbon, Portugal", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/D15-1075", doi = "10.18653/v1/D15-1075", pages = "632--642", } ``` The corpus includes content from the [Flickr 30k corpus](http://shannon.cs.illinois.edu/DenotationGraph/), which can be cited by way of this paper: ```bibtex @article{young-etal-2014-image, title = "From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions", author = "Young, Peter and Lai, Alice and Hodosh, Micah and Hockenmaier, Julia", editor = "Lin, Dekang and Collins, Michael and Lee, Lillian", journal = "Transactions of the Association for Computational Linguistics", volume = "2", year = "2014", address = "Cambridge, MA", publisher = "MIT Press", url = "https://aclanthology.org/Q14-1006", doi = "10.1162/tacl_a_00166", pages = "67--78", } ``` ### Contact Information For any comments or questions, please email [Samuel Bowman](mailto:[email protected]), [Gabor Angeli](mailto:[email protected]) and [Chris Manning]([email protected]). ### Contributions Thanks to [@mariamabarham](https://github.com/mariamabarham), [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun), [@patrickvonplaten](https://github.com/patrickvonplaten) and [@mcmillanmajora](https://github.com/mcmillanmajora) for adding this dataset.
deepmind/narrativeqa	deepmind	2024-03-06T07:33:05Z	25,484	47	[ "task_categories:text2text-generation", "task_ids:abstractive-qa", "annotations_creators:crowdsourced", "language_creators:found", "multilinguality:monolingual", "source_datasets:original", "language:en", "license:apache-2.0", "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:1712.07040", "region:us" ]	[ "text2text-generation" ]	2022-03-02T23:29:22Z	1	--- annotations_creators: - crowdsourced language_creators: - found language: - en license: - apache-2.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text2text-generation task_ids: - abstractive-qa paperswithcode_id: narrativeqa pretty_name: NarrativeQA dataset_info: features: - name: document struct: - name: id dtype: string - name: kind dtype: string - name: url dtype: string - name: file_size dtype: int32 - name: word_count dtype: int32 - name: start dtype: string - name: end dtype: string - name: summary struct: - name: text dtype: string - name: tokens sequence: string - name: url dtype: string - name: title dtype: string - name: text dtype: string - name: question struct: - name: text dtype: string - name: tokens sequence: string - name: answers list: - name: text dtype: string - name: tokens sequence: string splits: - name: train num_bytes: 11556607782 num_examples: 32747 - name: test num_bytes: 3547135501 num_examples: 10557 - name: validation num_bytes: 1211859418 num_examples: 3461 download_size: 3232805701 dataset_size: 16315602701 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: validation path: data/validation-* --- # Dataset Card for Narrative QA ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Repository: https://github.com/deepmind/narrativeqa - Paper: https://arxiv.org/abs/1712.07040 - Paper: https://aclanthology.org/Q18-1023/ - Point of Contact: [Tomáš Kočiský](mailto:[email protected]) [Jonathan Schwarz](mailto:[email protected]) [Phil Blunsom]([email protected]) [Chris Dyer]([email protected]) [Karl Moritz Hermann](mailto:[email protected]) [Gábor Melis](mailto:[email protected]) [Edward Grefenstette](mailto:[email protected]) ### Dataset Summary NarrativeQA is an English-lanaguage dataset of stories and corresponding questions designed to test reading comprehension, especially on long documents. ### Supported Tasks and Leaderboards The dataset is used to test reading comprehension. There are 2 tasks proposed in the paper: "summaries only" and "stories only", depending on whether the human-generated summary or the full story text is used to answer the question. ### Languages English ## Dataset Structure ### Data Instances A typical data point consists of a question and answer pair along with a summary/story which can be used to answer the question. Additional information such as the url, word count, wikipedia page, are also provided. A typical example looks like this: ``` { "document": { "id": "23jncj2n3534563110", "kind": "movie", "url": "https://www.imsdb.com/Movie%20Scripts/Name%20of%20Movie.html", "file_size": 80473, "word_count": 41000, "start": "MOVIE screenplay by", "end": ". THE END", "summary": { "text": "Joe Bloggs begins his journey exploring...", "tokens": ["Joe", "Bloggs", "begins", "his", "journey", "exploring",...], "url": "http://en.wikipedia.org/wiki/Name_of_Movie", "title": "Name of Movie (film)" }, "text": "MOVIE screenplay by John Doe\nSCENE 1..." }, "question": { "text": "Where does Joe Bloggs live?", "tokens": ["Where", "does", "Joe", "Bloggs", "live", "?"], }, "answers": [ {"text": "At home", "tokens": ["At", "home"]}, {"text": "His house", "tokens": ["His", "house"]} ] } ``` ### Data Fields - `document.id` - Unique ID for the story. - `document.kind` - "movie" or "gutenberg" depending on the source of the story. - `document.url` - The URL where the story was downloaded from. - `document.file_size` - File size (in bytes) of the story. - `document.word_count` - Number of tokens in the story. - `document.start` - First 3 tokens of the story. Used for verifying the story hasn't been modified. - `document.end` - Last 3 tokens of the story. Used for verifying the story hasn't been modified. - `document.summary.text` - Text of the wikipedia summary of the story. - `document.summary.tokens` - Tokenized version of `document.summary.text`. - `document.summary.url` - Wikipedia URL of the summary. - `document.summary.title` - Wikipedia Title of the summary. - `question` - `{"text":"...", "tokens":[...]}` for the question about the story. - `answers` - List of `{"text":"...", "tokens":[...]}` for valid answers for the question. ### Data Splits The data is split into training, valiudation, and test sets based on story (i.e. the same story cannot appear in more than one split): \| Train \| Valid \| Test \| \| ------ \| ----- \| ----- \| \| 32747 \| 3461 \| 10557 \| ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization Stories and movies scripts were downloaded from [Project Gutenburg](https://www.gutenberg.org) and a range of movie script repositories (mainly [imsdb](http://www.imsdb.com)). #### Who are the source language producers? The language producers are authors of the stories and scripts as well as Amazon Turk workers for the questions. ### Annotations #### Annotation process Amazon Turk Workers were provided with human written summaries of the stories (To make the annotation tractable and to lead annotators towards asking non-localized questions). Stories were matched with plot summaries from Wikipedia using titles and verified the matching with help from human annotators. The annotators were asked to determine if both the story and the summary refer to a movie or a book (as some books are made into movies), or if they are the same part in a series produced in the same year. Annotators on Amazon Mechanical Turk were instructed to write 10 question–answer pairs each based solely on a given summary. Annotators were instructed to imagine that they are writing questions to test students who have read the full stories but not the summaries. We required questions that are specific enough, given the length and complexity of the narratives, and to provide adiverse set of questions about characters, events, why this happened, and so on. Annotators were encouraged to use their own words and we prevented them from copying. We asked for answers that are grammatical, complete sentences, and explicitly allowed short answers (one word, or a few-word phrase, or ashort sentence) as we think that answering with a full sentence is frequently perceived as artificial when asking about factual information. Annotators were asked to avoid extra, unnecessary information in the question or the answer, and to avoid yes/no questions or questions about the author or the actors. #### Who are the annotators? Amazon Mechanical Turk workers. ### Personal and Sensitive Information None ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information The dataset is released under a [Apache-2.0 License](https://github.com/deepmind/narrativeqa/blob/master/LICENSE). ### Citation Information ``` @article{kocisky-etal-2018-narrativeqa, title = "The {N}arrative{QA} Reading Comprehension Challenge", author = "Ko{\v{c}}isk{\'y}, Tom{\'a}{\v{s}} and Schwarz, Jonathan and Blunsom, Phil and Dyer, Chris and Hermann, Karl Moritz and Melis, G{\'a}bor and Grefenstette, Edward", editor = "Lee, Lillian and Johnson, Mark and Toutanova, Kristina and Roark, Brian", journal = "Transactions of the Association for Computational Linguistics", volume = "6", year = "2018", address = "Cambridge, MA", publisher = "MIT Press", url = "https://aclanthology.org/Q18-1023", doi = "10.1162/tacl_a_00023", pages = "317--328", abstract = "Reading comprehension (RC){---}in contrast to information retrieval{---}requires integrating information and reasoning about events, entities, and their relations across a full document. Question answering is conventionally used to assess RC ability, in both artificial agents and children learning to read. However, existing RC datasets and tasks are dominated by questions that can be solved by selecting answers using superficial information (e.g., local context similarity or global term frequency); they thus fail to test for the essential integrative aspect of RC. To encourage progress on deeper comprehension of language, we present a new dataset and set of tasks in which the reader must answer questions about stories by reading entire books or movie scripts. These tasks are designed so that successfully answering their questions requires understanding the underlying narrative rather than relying on shallow pattern matching or salience. We show that although humans solve the tasks easily, standard RC models struggle on the tasks presented here. We provide an analysis of the dataset and the challenges it presents.", } ``` ### Contributions Thanks to [@ghomasHudson](https://github.com/ghomasHudson) for adding this dataset.
qiaojin/PubMedQA	qiaojin	2024-03-06T01:50:16Z	13,222	216	[ "task_categories:question-answering", "task_ids:multiple-choice-qa", "annotations_creators:expert-generated", "annotations_creators:machine-generated", "language_creators:expert-generated", "multilinguality:monolingual", "source_datasets:original", "language:en", "license:mit", "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:1909.06146", "region:us" ]	[ "question-answering" ]	2022-03-02T23:29:22Z	null	--- annotations_creators: - expert-generated - machine-generated language_creators: - expert-generated language: - en license: - mit multilinguality: - monolingual size_categories: - 100K<n<1M - 10K<n<100K - 1K<n<10K source_datasets: - original task_categories: - question-answering task_ids: - multiple-choice-qa paperswithcode_id: pubmedqa pretty_name: PubMedQA config_names: - pqa_artificial - pqa_labeled - pqa_unlabeled dataset_info: - config_name: pqa_artificial features: - name: pubid dtype: int32 - name: question dtype: string - name: context sequence: - name: contexts dtype: string - name: labels dtype: string - name: meshes dtype: string - name: long_answer dtype: string - name: final_decision dtype: string splits: - name: train num_bytes: 443501057 num_examples: 211269 download_size: 233411194 dataset_size: 443501057 - config_name: pqa_labeled features: - name: pubid dtype: int32 - name: question dtype: string - name: context sequence: - name: contexts dtype: string - name: labels dtype: string - name: meshes dtype: string - name: reasoning_required_pred dtype: string - name: reasoning_free_pred dtype: string - name: long_answer dtype: string - name: final_decision dtype: string splits: - name: train num_bytes: 2088898 num_examples: 1000 download_size: 1075513 dataset_size: 2088898 - config_name: pqa_unlabeled features: - name: pubid dtype: int32 - name: question dtype: string - name: context sequence: - name: contexts dtype: string - name: labels dtype: string - name: meshes dtype: string - name: long_answer dtype: string splits: - name: train num_bytes: 125922964 num_examples: 61249 download_size: 66010017 dataset_size: 125922964 configs: - config_name: pqa_artificial data_files: - split: train path: pqa_artificial/train-* - config_name: pqa_labeled data_files: - split: train path: pqa_labeled/train-* - config_name: pqa_unlabeled data_files: - split: train path: pqa_unlabeled/train-* --- # Dataset Card for [Dataset Name] ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [PubMedQA homepage](https://pubmedqa.github.io/ ) - Repository: [PubMedQA repository](https://github.com/pubmedqa/pubmedqa) - Paper: [PubMedQA: A Dataset for Biomedical Research Question Answering](https://arxiv.org/abs/1909.06146) - Leaderboard: [PubMedQA: Leaderboard](https://pubmedqa.github.io/) ### Dataset Summary The task of PubMedQA is to answer research questions with yes/no/maybe (e.g.: Do preoperative statins reduce atrial fibrillation after coronary artery bypass grafting?) using the corresponding abstracts. ### Supported Tasks and Leaderboards The official leaderboard is available at: https://pubmedqa.github.io/. 500 questions in the `pqa_labeled` are used as the test set. They can be found at https://github.com/pubmedqa/pubmedqa. ### Languages English ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions Thanks to [@tuner007](https://github.com/tuner007) for adding this dataset.
lukaemon/mmlu	lukaemon	2024-03-04T21:42:02Z	45,141	62	[ "region:us" ]	[]	2023-02-02T00:42:27Z	null	--- dataset_info: - config_name: abstract_algebra features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 18616 num_examples: 100 - name: validation num_bytes: 1935 num_examples: 11 - name: train num_bytes: 783 num_examples: 5 download_size: 166184960 dataset_size: 21334 - config_name: anatomy features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 32164 num_examples: 135 - name: validation num_bytes: 3030 num_examples: 14 - name: train num_bytes: 920 num_examples: 5 download_size: 166184960 dataset_size: 36114 - config_name: astronomy features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 45695 num_examples: 152 - name: validation num_bytes: 4903 num_examples: 16 - name: train num_bytes: 2029 num_examples: 5 download_size: 166184960 dataset_size: 52627 - config_name: business_ethics features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 32540 num_examples: 100 - name: validation num_bytes: 2949 num_examples: 11 - name: train num_bytes: 2143 num_examples: 5 download_size: 166184960 dataset_size: 37632 - config_name: clinical_knowledge features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 60887 num_examples: 265 - name: validation num_bytes: 6449 num_examples: 29 - name: train num_bytes: 1163 num_examples: 5 download_size: 166184960 dataset_size: 68499 - config_name: college_biology features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 47777 num_examples: 144 - name: validation num_bytes: 4695 num_examples: 16 - name: train num_bytes: 1485 num_examples: 5 download_size: 166184960 dataset_size: 53957 - config_name: college_chemistry features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 23996 num_examples: 100 - name: validation num_bytes: 2260 num_examples: 8 - name: train num_bytes: 1284 num_examples: 5 download_size: 166184960 dataset_size: 27540 - config_name: college_computer_science features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 41927 num_examples: 100 - name: validation num_bytes: 4574 num_examples: 11 - name: train num_bytes: 2718 num_examples: 5 download_size: 166184960 dataset_size: 49219 - config_name: college_mathematics features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 23996 num_examples: 100 - name: validation num_bytes: 2579 num_examples: 11 - name: train num_bytes: 1446 num_examples: 5 download_size: 166184960 dataset_size: 28021 - config_name: college_medicine features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 81174 num_examples: 173 - name: validation num_bytes: 7743 num_examples: 22 - name: train num_bytes: 1623 num_examples: 5 download_size: 166184960 dataset_size: 90540 - config_name: college_physics features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 29454 num_examples: 102 - name: validation num_bytes: 3401 num_examples: 11 - name: train num_bytes: 1365 num_examples: 5 download_size: 166184960 dataset_size: 34220 - config_name: computer_security features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 26412 num_examples: 100 - name: validation num_bytes: 4460 num_examples: 11 - name: train num_bytes: 1054 num_examples: 5 download_size: 166184960 dataset_size: 31926 - config_name: conceptual_physics features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 39052 num_examples: 235 - name: validation num_bytes: 4279 num_examples: 26 - name: train num_bytes: 887 num_examples: 5 download_size: 166184960 dataset_size: 44218 - config_name: econometrics features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 45737 num_examples: 114 - name: validation num_bytes: 4871 num_examples: 12 - name: train num_bytes: 1597 num_examples: 5 download_size: 166184960 dataset_size: 52205 - config_name: electrical_engineering features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 24111 num_examples: 145 - name: validation num_bytes: 2778 num_examples: 16 - name: train num_bytes: 925 num_examples: 5 download_size: 166184960 dataset_size: 27814 - config_name: elementary_mathematics features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 67450 num_examples: 378 - name: validation num_bytes: 8689 num_examples: 41 - name: train num_bytes: 1393 num_examples: 5 download_size: 166184960 dataset_size: 77532 - config_name: formal_logic features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 48891 num_examples: 126 - name: validation num_bytes: 6142 num_examples: 14 - name: train num_bytes: 1710 num_examples: 5 download_size: 166184960 dataset_size: 56743 - config_name: global_facts features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 17691 num_examples: 100 - name: validation num_bytes: 1783 num_examples: 10 - name: train num_bytes: 1182 num_examples: 5 download_size: 166184960 dataset_size: 20656 - config_name: high_school_biology features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 107550 num_examples: 310 - name: validation num_bytes: 10786 num_examples: 32 - name: train num_bytes: 1626 num_examples: 5 download_size: 166184960 dataset_size: 119962 - config_name: high_school_chemistry features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 57031 num_examples: 203 - name: validation num_bytes: 6926 num_examples: 22 - name: train num_bytes: 1173 num_examples: 5 download_size: 166184960 dataset_size: 65130 - config_name: high_school_computer_science features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 43764 num_examples: 100 - name: validation num_bytes: 3268 num_examples: 9 - name: train num_bytes: 2871 num_examples: 5 download_size: 166184960 dataset_size: 49903 - config_name: high_school_european_history features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 269133 num_examples: 165 - name: validation num_bytes: 29494 num_examples: 18 - name: train num_bytes: 11517 num_examples: 5 download_size: 166184960 dataset_size: 310144 - config_name: high_school_geography features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 40636 num_examples: 198 - name: validation num_bytes: 4166 num_examples: 22 - name: train num_bytes: 1356 num_examples: 5 download_size: 166184960 dataset_size: 46158 - config_name: high_school_government_and_politics features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 64711 num_examples: 193 - name: validation num_bytes: 6904 num_examples: 21 - name: train num_bytes: 1732 num_examples: 5 download_size: 166184960 dataset_size: 73347 - config_name: high_school_macroeconomics features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 114945 num_examples: 390 - name: validation num_bytes: 12707 num_examples: 43 - name: train num_bytes: 1281 num_examples: 5 download_size: 166184960 dataset_size: 128933 - config_name: high_school_mathematics features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 52952 num_examples: 270 - name: validation num_bytes: 5550 num_examples: 29 - name: train num_bytes: 1250 num_examples: 5 download_size: 166184960 dataset_size: 59752 - config_name: high_school_microeconomics features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 74025 num_examples: 238 - name: validation num_bytes: 7359 num_examples: 26 - name: train num_bytes: 1251 num_examples: 5 download_size: 166184960 dataset_size: 82635 - config_name: high_school_physics features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 58469 num_examples: 151 - name: validation num_bytes: 6640 num_examples: 17 - name: train num_bytes: 1442 num_examples: 5 download_size: 166184960 dataset_size: 66551 - config_name: high_school_psychology features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 155580 num_examples: 545 - name: validation num_bytes: 16837 num_examples: 60 - name: train num_bytes: 1858 num_examples: 5 download_size: 166184960 dataset_size: 174275 - config_name: high_school_statistics features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 109178 num_examples: 216 - name: validation num_bytes: 9824 num_examples: 23 - name: train num_bytes: 2481 num_examples: 5 download_size: 166184960 dataset_size: 121483 - config_name: high_school_us_history features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 295294 num_examples: 204 - name: validation num_bytes: 31540 num_examples: 22 - name: train num_bytes: 8817 num_examples: 5 download_size: 166184960 dataset_size: 335651 - config_name: high_school_world_history features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 376946 num_examples: 237 - name: validation num_bytes: 45307 num_examples: 26 - name: train num_bytes: 4835 num_examples: 5 download_size: 166184960 dataset_size: 427088 - config_name: human_aging features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 44525 num_examples: 223 - name: validation num_bytes: 4534 num_examples: 23 - name: train num_bytes: 961 num_examples: 5 download_size: 166184960 dataset_size: 50020 - config_name: human_sexuality features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 31181 num_examples: 131 - name: validation num_bytes: 2325 num_examples: 12 - name: train num_bytes: 1030 num_examples: 5 download_size: 166184960 dataset_size: 34536 - config_name: international_law features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 52672 num_examples: 121 - name: validation num_bytes: 6370 num_examples: 13 - name: train num_bytes: 2371 num_examples: 5 download_size: 166184960 dataset_size: 61413 - config_name: jurisprudence features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 33218 num_examples: 108 - name: validation num_bytes: 3640 num_examples: 11 - name: train num_bytes: 1256 num_examples: 5 download_size: 166184960 dataset_size: 38114 - config_name: logical_fallacies features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 48964 num_examples: 163 - name: validation num_bytes: 4965 num_examples: 18 - name: train num_bytes: 1526 num_examples: 5 download_size: 166184960 dataset_size: 55455 - config_name: machine_learning features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 33084 num_examples: 112 - name: validation num_bytes: 3143 num_examples: 11 - name: train num_bytes: 2276 num_examples: 5 download_size: 166184960 dataset_size: 38503 - config_name: management features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 19269 num_examples: 103 - name: validation num_bytes: 1731 num_examples: 11 - name: train num_bytes: 851 num_examples: 5 download_size: 166184960 dataset_size: 21851 - config_name: marketing features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 61375 num_examples: 234 - name: validation num_bytes: 7207 num_examples: 25 - name: train num_bytes: 1434 num_examples: 5 download_size: 166184960 dataset_size: 70016 - config_name: medical_genetics features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 20152 num_examples: 100 - name: validation num_bytes: 2916 num_examples: 11 - name: train num_bytes: 1042 num_examples: 5 download_size: 166184960 dataset_size: 24110 - config_name: miscellaneous features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 142211 num_examples: 783 - name: validation num_bytes: 13716 num_examples: 86 - name: train num_bytes: 652 num_examples: 5 download_size: 166184960 dataset_size: 156579 - config_name: moral_disputes features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 105384 num_examples: 346 - name: validation num_bytes: 12142 num_examples: 38 - name: train num_bytes: 1708 num_examples: 5 download_size: 166184960 dataset_size: 119234 - config_name: moral_scenarios features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 367749 num_examples: 895 - name: validation num_bytes: 41626 num_examples: 100 - name: train num_bytes: 2011 num_examples: 5 download_size: 166184960 dataset_size: 411386 - config_name: nutrition features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 90256 num_examples: 306 - name: validation num_bytes: 8193 num_examples: 33 - name: train num_bytes: 2038 num_examples: 5 download_size: 166184960 dataset_size: 100487 - config_name: philosophy features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 77884 num_examples: 311 - name: validation num_bytes: 8934 num_examples: 34 - name: train num_bytes: 941 num_examples: 5 download_size: 166184960 dataset_size: 87759 - config_name: prehistory features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 87314 num_examples: 324 - name: validation num_bytes: 10028 num_examples: 35 - name: train num_bytes: 1831 num_examples: 5 download_size: 166184960 dataset_size: 99173 - config_name: professional_accounting features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 122564 num_examples: 282 - name: validation num_bytes: 14143 num_examples: 31 - name: train num_bytes: 2101 num_examples: 5 download_size: 166184960 dataset_size: 138808 - config_name: professional_law features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 1881012 num_examples: 1534 - name: validation num_bytes: 202317 num_examples: 170 - name: train num_bytes: 6563 num_examples: 5 download_size: 166184960 dataset_size: 2089892 - config_name: professional_medicine features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 215645 num_examples: 272 - name: validation num_bytes: 23618 num_examples: 31 - name: train num_bytes: 3760 num_examples: 5 download_size: 166184960 dataset_size: 243023 - config_name: professional_psychology features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 221603 num_examples: 612 - name: validation num_bytes: 28606 num_examples: 69 - name: train num_bytes: 2220 num_examples: 5 download_size: 166184960 dataset_size: 252429 - config_name: public_relations features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 27978 num_examples: 110 - name: validation num_bytes: 4470 num_examples: 12 - name: train num_bytes: 1449 num_examples: 5 download_size: 166184960 dataset_size: 33897 - config_name: security_studies features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 203117 num_examples: 245 - name: validation num_bytes: 22436 num_examples: 27 - name: train num_bytes: 5288 num_examples: 5 download_size: 166184960 dataset_size: 230841 - config_name: sociology features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 64824 num_examples: 201 - name: validation num_bytes: 7018 num_examples: 22 - name: train num_bytes: 1566 num_examples: 5 download_size: 166184960 dataset_size: 73408 - config_name: us_foreign_policy features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 27731 num_examples: 100 - name: validation num_bytes: 3175 num_examples: 11 - name: train num_bytes: 1564 num_examples: 5 download_size: 166184960 dataset_size: 32470 - config_name: virology features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 37585 num_examples: 166 - name: validation num_bytes: 5325 num_examples: 18 - name: train num_bytes: 1049 num_examples: 5 download_size: 166184960 dataset_size: 43959 - config_name: world_religions features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 24065 num_examples: 171 - name: validation num_bytes: 2620 num_examples: 19 - name: train num_bytes: 623 num_examples: 5 download_size: 166184960 dataset_size: 27308 --- # MMLU dataset Measuring Massive Multitask Language Understanding: https://github.com/hendrycks/test task_list = [ "high_school_european_history", "business_ethics", "clinical_knowledge", "medical_genetics", "high_school_us_history", "high_school_physics", "high_school_world_history", "virology", "high_school_microeconomics", "econometrics", "college_computer_science", "high_school_biology", "abstract_algebra", "professional_accounting", "philosophy", "professional_medicine", "nutrition", "global_facts", "machine_learning", "security_studies", "public_relations", "professional_psychology", "prehistory", "anatomy", "human_sexuality", "college_medicine", "high_school_government_and_politics", "college_chemistry", "logical_fallacies", "high_school_geography", "elementary_mathematics", "human_aging", "college_mathematics", "high_school_psychology", "formal_logic", "high_school_statistics", "international_law", "high_school_mathematics", "high_school_computer_science", "conceptual_physics", "miscellaneous", "high_school_chemistry", "marketing", "professional_law", "management", "college_physics", "jurisprudence", "world_religions", "sociology", "us_foreign_policy", "high_school_macroeconomics", "computer_security", "moral_scenarios", "moral_disputes", "electrical_engineering", "astronomy", "college_biology", ] ``` @article{hendryckstest2021, title={Measuring Massive Multitask Language Understanding}, author={Dan Hendrycks and Collin Burns and Steven Basart and Andy Zou and Mantas Mazeika and Dawn Song and Jacob Steinhardt}, journal={Proceedings of the International Conference on Learning Representations (ICLR)}, year={2021} } ```
google/Synthetic-Persona-Chat	google	2024-03-01T01:01:01Z	895	107	[ "task_categories:text2text-generation", "language:en", "license:cc-by-4.0", "size_categories:10K<n<100K", "format:csv", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2312.10007", "region:us" ]	[ "text2text-generation" ]	2023-12-20T09:59:14Z	null	--- license: cc-by-4.0 task_categories: - text2text-generation language: - en size_categories: - 10K<n<100K --- # Dataset Card for SPC: Synthetic-Persona-Chat Dataset Abstract from the paper introducing this dataset: > High-quality conversational datasets are essential for developing AI models that can communicate with users. One way to foster deeper interactions between a chatbot and its user is through personas, aspects of the user's character that provide insights into their personality, motivations, and behaviors. Training Natural Language Processing (NLP) models on a diverse and comprehensive persona-based dataset can lead to conversational models that create a deeper connection with the user, and maintain their engagement. In this paper, we leverage the power of Large Language Models (LLMs) to create a large, high-quality conversational dataset from a seed dataset. We propose a Generator-Critic architecture framework to expand the initial dataset, while improving the quality of its conversations. The Generator is an LLM prompted to output conversations. The Critic consists of a mixture of expert LLMs that control the quality of the generated conversations. These experts select the best generated conversations, which we then use to improve the Generator. We release Synthetic-Persona-Chat, consisting of 20k conversations seeded from Persona-Chat. We evaluate the quality of Synthetic-Persona-Chat and our generation framework on different dimensions through extensive experiments, and observe that the losing rate of Synthetic-Persona-Chat against Persona-Chat during Turing test decreases from 17.2% to 8.8% over three iterations. ## Dataset Details ### Dataset Description > We introduce the Synthetic-Persona-Chat dataset, a persona-based conversational dataset, consisting of two parts. The first part, consisting of 4,723 personas and 10,906 conversations, is an extension to Persona-Chat, which has the same user profile pairs as Persona-Chat but new synthetic conversations, with the same train/validation/test split as Persona-Chat. The second part is new synthetic personas and synthetic conversations based on that, consisting of 5,648 synthetic personas and 11,001 conversations. Synthetic-Persona-Chat is created using the Generator-Critic framework introduced in Faithful Persona-based Conversational Dataset Generation with Large Language Models. Each conversation in the dataset has the following format: ``` { "User 1 Persona":[], "User 2 Persona":[], "Conversation":[] } ``` ### Dataset Sources <!-- Provide the basic links for the dataset. --> - Repository: https://github.com/google-research-datasets/Synthetic-Persona-Chat/tree/main - Paper: https://arxiv.org/abs/2312.10007 ## Citation BibTeX: ```@misc{jandaghi2023faithful, title={Faithful Persona-based Conversational Dataset Generation with Large Language Models}, author={Pegah Jandaghi and XiangHai Sheng and Xinyi Bai and Jay Pujara and Hakim Sidahmed}, year={2023}, eprint={2312.10007}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
heliosbrahma/mental_health_chatbot_dataset	heliosbrahma	2024-02-29T18:40:22Z	1,018	86	[ "task_categories:text-generation", "language:en", "license:mit", "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "medical" ]	[ "text-generation" ]	2023-08-02T09:36:25Z	null	--- dataset_info: features: - name: text dtype: string splits: - name: train num_examples: 172 license: mit task_categories: - text-generation language: - en tags: - medical pretty_name: Mental Health Chatbot Dataset size_categories: - n<1K --- # Dataset Card for "heliosbrahma/mental_health_chatbot_dataset" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-instances) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Personal and Sensitive Information](#personal-and-sensitive-information) ## Dataset Description ### Dataset Summary This dataset contains conversational pair of questions and answers in a single text related to Mental Health. Dataset was curated from popular healthcare blogs like WebMD, Mayo Clinic and HeatlhLine, online FAQs etc. All questions and answers have been anonymized to remove any PII data and pre-processed to remove any unwanted characters. ### Languages The text in the dataset is in English. ## Dataset Structure ### Data Instances A data instance include a text columns which is a conversational pair of questions and answers. Questions were asked by the patients and answers were given by healthcare providers. ### Data Fields - 'text': conversational pair of questions and answers between patient and healthcare provider. ## Dataset Creation ### Curation Rationale Chatbots offer a readily available and accessible platform for individuals seeking support. They can be accessed anytime and anywhere, providing immediate assistance to those in need. Chatbots can offer empathetic and non-judgmental responses, providing emotional support to users. While they cannot replace human interaction entirely, they can be a helpful supplement, especially in moments of distress. Hence, this dataset was curated to help finetune a conversational AI bot using this custom dataset which can then be deployed and be provided to the end patient as a chatbot. ### Source Data This dataset was curated from popular healthcare blogs like WebMD, Mayo Clinic and HeatlhLine, online FAQs etc. ### Personal and Sensitive Information The dataset may contain sensitive information related to mental health. All questions and answers have been anonymized to remove any PII data.
Helsinki-NLP/multiun	Helsinki-NLP	2024-02-27T16:59:52Z	2,893	12	[ "task_categories:translation", "annotations_creators:found", "language_creators:found", "multilinguality:multilingual", "source_datasets:original", "language:ar", "language:de", "language:en", "language:es", "language:fr", "language:ru", "language:zh", "license:unknown", "size_categories:100M<n<1B", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[ "translation" ]	2022-03-02T23:29:22Z	1	--- annotations_creators: - found language_creators: - found language: - ar - de - en - es - fr - ru - zh license: - unknown multilinguality: - multilingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - translation task_ids: [] paperswithcode_id: multiun pretty_name: MultiUN (Multilingual Corpus from United Nation Documents) config_names: - ar-de - ar-en - ar-es - ar-fr - ar-ru - ar-zh - de-en - de-es - de-fr - de-ru - de-zh - en-es - en-fr - en-ru - en-zh - es-fr - es-ru - es-zh - fr-ru - fr-zh - ru-zh dataset_info: - config_name: ar-de features: - name: translation dtype: translation: languages: - ar - de splits: - name: train num_bytes: 94466261 num_examples: 165090 download_size: 41124373 dataset_size: 94466261 - config_name: ar-en features: - name: translation dtype: translation: languages: - ar - en splits: - name: train num_bytes: 4189844561 num_examples: 9759125 download_size: 1926776740 dataset_size: 4189844561 - config_name: ar-es features: - name: translation dtype: translation: languages: - ar - es splits: - name: train num_bytes: 4509667188 num_examples: 10119379 download_size: 2069474168 dataset_size: 4509667188 - config_name: ar-fr features: - name: translation dtype: translation: languages: - ar - fr splits: - name: train num_bytes: 4516842065 num_examples: 9929567 download_size: 2083442998 dataset_size: 4516842065 - config_name: ar-ru features: - name: translation dtype: translation: languages: - ar - ru splits: - name: train num_bytes: 5932858699 num_examples: 10206243 download_size: 2544128334 dataset_size: 5932858699 - config_name: ar-zh features: - name: translation dtype: translation: languages: - ar - zh splits: - name: train num_bytes: 3781650541 num_examples: 9832293 download_size: 1829880809 dataset_size: 3781650541 - config_name: de-en features: - name: translation dtype: translation: languages: - de - en splits: - name: train num_bytes: 76684413 num_examples: 162981 download_size: 35105094 dataset_size: 76684413 - config_name: de-es features: - name: translation dtype: translation: languages: - de - es splits: - name: train num_bytes: 80936517 num_examples: 162078 download_size: 37042740 dataset_size: 80936517 - config_name: de-fr features: - name: translation dtype: translation: languages: - de - fr splits: - name: train num_bytes: 81888299 num_examples: 164025 download_size: 37827000 dataset_size: 81888299 - config_name: de-ru features: - name: translation dtype: translation: languages: - de - ru splits: - name: train num_bytes: 111517798 num_examples: 164792 download_size: 46723695 dataset_size: 111517798 - config_name: de-zh features: - name: translation dtype: translation: languages: - de - zh splits: - name: train num_bytes: 70534674 num_examples: 176933 download_size: 34964647 dataset_size: 70534674 - config_name: en-es features: - name: translation dtype: translation: languages: - en - es splits: - name: train num_bytes: 4128132575 num_examples: 11350967 download_size: 2030826335 dataset_size: 4128132575 - config_name: en-fr features: - name: translation dtype: translation: languages: - en - fr splits: - name: train num_bytes: 4678044616 num_examples: 13172019 download_size: 2312275443 dataset_size: 4678044616 - config_name: en-ru features: - name: translation dtype: translation: languages: - en - ru splits: - name: train num_bytes: 5632653511 num_examples: 11654416 download_size: 2523567444 dataset_size: 5632653511 - config_name: en-zh features: - name: translation dtype: translation: languages: - en - zh splits: - name: train num_bytes: 2960368390 num_examples: 9564315 download_size: 1557547095 dataset_size: 2960368390 - config_name: es-fr features: - name: translation dtype: translation: languages: - es - fr splits: - name: train num_bytes: 4454703338 num_examples: 11441889 download_size: 2187539838 dataset_size: 4454703338 - config_name: es-ru features: - name: translation dtype: translation: languages: - es - ru splits: - name: train num_bytes: 5442647242 num_examples: 10605056 download_size: 2432480744 dataset_size: 5442647242 - config_name: es-zh features: - name: translation dtype: translation: languages: - es - zh splits: - name: train num_bytes: 3223863318 num_examples: 9847770 download_size: 1676774308 dataset_size: 3223863318 - config_name: fr-ru features: - name: translation dtype: translation: languages: - fr - ru splits: - name: train num_bytes: 5979869673 num_examples: 11761738 download_size: 2690520032 dataset_size: 5979869673 - config_name: fr-zh features: - name: translation dtype: translation: languages: - fr - zh splits: - name: train num_bytes: 3241090573 num_examples: 9690914 download_size: 1693120344 dataset_size: 3241090573 - config_name: ru-zh features: - name: translation dtype: translation: languages: - ru - zh splits: - name: train num_bytes: 4233867889 num_examples: 9557007 download_size: 1984600328 dataset_size: 4233867889 configs: - config_name: ar-de data_files: - split: train path: ar-de/train-* - config_name: ar-en data_files: - split: train path: ar-en/train-* - config_name: ar-es data_files: - split: train path: ar-es/train-* - config_name: ar-fr data_files: - split: train path: ar-fr/train-* - config_name: ar-ru data_files: - split: train path: ar-ru/train-* - config_name: ar-zh data_files: - split: train path: ar-zh/train-* - config_name: de-en data_files: - split: train path: de-en/train-* - config_name: de-es data_files: - split: train path: de-es/train-* - config_name: de-fr data_files: - split: train path: de-fr/train-* - config_name: de-ru data_files: - split: train path: de-ru/train-* - config_name: de-zh data_files: - split: train path: de-zh/train-* - config_name: en-es data_files: - split: train path: en-es/train-* - config_name: en-fr data_files: - split: train path: en-fr/train-* - config_name: en-ru data_files: - split: train path: en-ru/train-* - config_name: en-zh data_files: - split: train path: en-zh/train-* - config_name: es-fr data_files: - split: train path: es-fr/train-* - config_name: es-ru data_files: - split: train path: es-ru/train-* - config_name: es-zh data_files: - split: train path: es-zh/train-* - config_name: fr-ru data_files: - split: train path: fr-ru/train-* - config_name: fr-zh data_files: - split: train path: fr-zh/train-* - config_name: ru-zh data_files: - split: train path: ru-zh/train-* --- # Dataset Card for OPUS MultiUN ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: https://opus.nlpl.eu/MultiUN/corpus/version/MultiUN - Repository: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Paper: https://aclanthology.org/L10-1473/ - Leaderboard: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Point of Contact: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Dataset Summary The MultiUN parallel corpus is extracted from the United Nations Website , and then cleaned and converted to XML at Language Technology Lab in DFKI GmbH (LT-DFKI), Germany. The documents were published by UN from 2000 to 2009. This is a collection of translated documents from the United Nations originally compiled by Andreas Eisele and Yu Chen (see http://www.euromatrixplus.net/multi-un/). This corpus is available in all 6 official languages of the UN consisting of around 300 million words per language ### Supported Tasks and Leaderboards The underlying task is machine translation. ### Languages Parallel texts are present in all six official languages: Arabic (`ar`), Chinese (`zh`), English (`en`), French (`fr`), Russian (`ru`) and Spanish (`es`), with a small part of the documents available also in German (`de`). ## Dataset Structure ### Data Instances ``` { "translation": { "ar": "قرار اتخذته الجمعية العامة", "de": "Resolution der Generalversammlung" } } ``` ### Data Fields - `translation` (`dict`): Parallel sentences for the pair of languages. ### Data Splits The dataset contains a single "train" split for each language pair. ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization Original MultiUN source data: http://www.euromatrixplus.net/multi-unp #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information If you use this corpus in your work, please cite the paper: ``` @inproceedings{eisele-chen-2010-multiun, title = "{M}ulti{UN}: A Multilingual Corpus from United Nation Documents", author = "Eisele, Andreas and Chen, Yu", booktitle = "Proceedings of the Seventh International Conference on Language Resources and Evaluation ({LREC}'10)", month = may, year = "2010", address = "Valletta, Malta", publisher = "European Language Resources Association (ELRA)", url = "http://www.lrec-conf.org/proceedings/lrec2010/pdf/686_Paper.pdf", abstract = "This paper describes the acquisition, preparation and properties of a corpus extracted from the official documents of the United Nations (UN). This corpus is available in all 6 official languages of the UN, consisting of around 300 million words per language. We describe the methods we used for crawling, document formatting, and sentence alignment. This corpus also includes a common test set for machine translation. We present the results of a French-Chinese machine translation experiment performed on this corpus.", } ``` If you use any part of the corpus (hosted in OPUS) in your own work, please cite the following article: ``` @inproceedings{tiedemann-2012-parallel, title = "Parallel Data, Tools and Interfaces in {OPUS}", author = {Tiedemann, J{\"o}rg}, editor = "Calzolari, Nicoletta and Choukri, Khalid and Declerck, Thierry and Do{\u{g}}an, Mehmet U{\u{g}}ur and Maegaard, Bente and Mariani, Joseph and Moreno, Asuncion and Odijk, Jan and Piperidis, Stelios", booktitle = "Proceedings of the Eighth International Conference on Language Resources and Evaluation ({LREC}'12)", month = may, year = "2012", address = "Istanbul, Turkey", publisher = "European Language Resources Association (ELRA)", url = "http://www.lrec-conf.org/proceedings/lrec2012/pdf/463_Paper.pdf", pages = "2214--2218", abstract = "This paper presents the current status of OPUS, a growing language resource of parallel corpora and related tools. The focus in OPUS is to provide freely available data sets in various formats together with basic annotation to be useful for applications in computational linguistics, translation studies and cross-linguistic corpus studies. In this paper, we report about new data sets and their features, additional annotation tools and models provided from the website and essential interfaces and on-line services included in the project.", } ``` ### Contributions Thanks to [@patil-suraj](https://github.com/patil-suraj) for adding this dataset.
zeroshot/twitter-financial-news-sentiment	zeroshot	2024-02-23T19:04:10Z	5,546	142	[ "task_categories:text-classification", "task_ids:multi-class-classification", "annotations_creators:other", "language_creators:other", "multilinguality:monolingual", "source_datasets:original", "language:en", "license:mit", "size_categories:10K<n<100K", "format:csv", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "twitter", "finance", "markets", "stocks", "wallstreet", "quant", "hedgefunds" ]	[ "text-classification" ]	2022-09-01T21:21:56Z	null	--- annotations_creators: - other language: - en language_creators: - other license: - mit multilinguality: - monolingual pretty_name: twitter financial news size_categories: - 10K<n<100K source_datasets: - original tags: - twitter - finance - markets - stocks - wallstreet - quant - hedgefunds - markets task_categories: - text-classification task_ids: - multi-class-classification --- ### Dataset Description The Twitter Financial News dataset is an English-language dataset containing an annotated corpus of finance-related tweets. This dataset is used to classify finance-related tweets for their sentiment. 1. The dataset holds 11,932 documents annotated with 3 labels: ```python sentiments = { "LABEL_0": "Bearish", "LABEL_1": "Bullish", "LABEL_2": "Neutral" } ``` The data was collected using the Twitter API. The current dataset supports the multi-class classification task. ### Task: Sentiment Analysis # Data Splits There are 2 splits: train and validation. Below are the statistics: \| Dataset Split \| Number of Instances in Split \| \| ------------- \| ------------------------------------------- \| \| Train \| 9,938 \| \| Validation \| 2,486 \| # Licensing Information The Twitter Financial Dataset (sentiment) version 1.0.0 is released under the MIT License.
shibing624/sharegpt_gpt4	shibing624	2024-02-23T05:38:24Z	685	120	[ "task_categories:text-classification", "task_categories:text-generation", "task_ids:text-scoring", "annotations_creators:shibing624", "language_creators:shibing624", "multilinguality:monolingual", "source_datasets:https://huggingface.co/datasets/openchat/openchat_sharegpt4_dataset/tree/main", "language:zh", "language:en", "language:gl", "language:ko", "license:cc-by-4.0", "size_categories:100K<n<1M", "format:json", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[ "text-classification", "text-generation" ]	2023-07-27T05:45:49Z	null	--- annotations_creators: - shibing624 language_creators: - shibing624 language: - zh - en - gl - ko license: cc-by-4.0 multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - https://huggingface.co/datasets/openchat/openchat_sharegpt4_dataset/tree/main task_categories: - text-classification - text-generation task_ids: - text-scoring paperswithcode_id: LLM pretty_name: GPT4 sharegpt --- # Dataset Card ## Dataset Description - Repository: [shibing624/MedicalGPT](https://github.com/shibing624/MedicalGPT) - Dataset: [shibing624/sharegpt_gpt4](https://huggingface.co/datasets/shibing624/sharegpt_gpt4) - Size of downloaded dataset files: 772 M - Total amount of disk used: 772 M ### Dataset Summary ShareGPT中挑选出的GPT4多轮问答数据，多语言问答。 ### Languages 数据集是多语言，包括中文、英文、日文等常用语言。 ## Dataset Structure ### Data Fields The data fields are the same among all splits. - `conversations`: a List of `string` . ```shell head -n 1 sharegpt_gpt4.jsonl {"conversations":[ {'from': 'human', 'value': '採用優雅現代中文，用中文繁體字型，回答以下問題。為所有標題或專用字詞提供對應的英語翻譯：Using scholarly style, summarize in detail James Barr\'s book "Semantics of Biblical Language". Provide examples supplied in the book and give corresponding page numbers wherever applicable.'}, {'from': 'gpt', 'value': '《聖經語言的語義學》（Semantics of Biblical Language）是詹姆斯·巴爾（James Barr）於1961年出版的一本研究聖經語言的專著。在這部作品中，巴爾對聖經希伯來語和希臘語的語言學特徵進行了深入的探討，他批判了當時盛行的聖經研究方法，主張應該將語言學原理應用於聖經文本的詮釋，以便更準確地理解聖經的意思。\n\n巴爾在《聖經語言的語義學》中提出了幾個重要的觀點。首先，他主張語言學和語言學家在聖經研究中的角色應該受到重視。他認為，應該避免將聖經語言學和其他古典語言學區分開來，因為聖經語言和其他古代語言一樣，都有其固有的語法、語義和詞彙特點（第18-23頁）。\n\n其次，巴爾批評了當時流行的"字根意義論"（root-meaning fallacy），即過分強調單詞的字根在詮釋聖經含義時的作用。他指出，詞義是由語境決定的，而非僅僅由字根決定。巴爾舉了希伯來語單詞 "כפר"（kāpar）為例，它在不同的語境下可以表示"遮蓋"、"贖回"或"和解"，而不是僅僅依賴其字根意義（第183-184頁）。\n\n第三，巴爾強調了詞彙意義在詮釋聖經時的重要性。他認為，了解單詞的意義和用法對於恰當理解聖經文本至關重要。巴爾引用了希伯來語單詞 "ברית"（berîṯ，英文：covenant）和希臘語單詞 "διαθήκη"（diathēkē，英文：testament）為例，解釋了這兩個詞在聖經中的不'}, {'from': 'human', 'value': '繼續'}, {'from': 'gpt', 'value': '同含義和用法。巴爾認為，了解這些詞語在不同語境下的意義對於理解聖經中的盟約和遺囑概念至關重要（第50-53頁）。\n\n第四，巴爾強調語境在詮釋聖經時的重要性。他認為，要理解聖經的含義，就必須考慮到文本的歷史背景、文化環境和語言風格。此外，巴爾提倡從語言學的角度研究聖經，以便更好地理解聖經的結構和風格。他以希伯來詩篇的平行主義（Parallelism）為例，闡述了語言學原理如何有助於揭示詩篇中的意象和修辭結構（第250-252頁）。\n\n總之，《聖經語言的語義學》一書對聖經研究領域產生了深遠的影響。詹姆斯·巴爾通過對聖經語言學的深入研究，提出了許多富有洞察力的觀點，尤其是關於語言學在聖經詮釋中的重要性。這些觀點對後來的聖經研究者產生了很大的啟示，使他們在研究聖經時更加重視語言學原理，從而更加準確地理解聖經的含義。'} ] } ``` ### Data Splits ```shell > wc -l * 6206 sharegpt_gpt4.jsonl # 清洗后的高质量gpt4日常问答数据集，大小：6K，主要是知识问答、编程题、推理计算，包括简体中文、繁体中文、英文、日文、韩文等多国语言多轮对话数据集。 58674 sharegpt_V3_format.jsonl # 原V3版sharegpt规范格式后的数据集，大小：58K，主要是日常问答，提问偏口语化，多国语言，多轮对话。 38535 sharegpt_zh_38K_format.jsonl # 中文gpt4日常问答数据集，大小：38K，主要是知识问答、翻译任务、求助、编程推理任务等偏口语提问，中文，多轮对话。 103415 total ``` #### Who are the annotators? 原作者。 ### Licensing Information same to sharegpt. ### Contributions [shibing624](https://github.com/shibing624) add this dataset.
google/xtreme	google	2024-02-22T17:12:06Z	12,320	104	[ "task_categories:multiple-choice", "task_categories:question-answering", "task_categories:token-classification", "task_categories:text-classification", "task_categories:text-retrieval", "task_ids:multiple-choice-qa", "task_ids:extractive-qa", "task_ids:open-domain-qa", "task_ids:natural-language-inference", "task_ids:named-entity-recognition", "task_ids:part-of-speech", "annotations_creators:found", "language_creators:found", "multilinguality:multilingual", "multilinguality:translation", "source_datasets:extended\|xnli", "source_datasets:extended\|paws-x", "source_datasets:extended\|wikiann", "source_datasets:extended\|xquad", "source_datasets:extended\|mlqa", "source_datasets:extended\|tydiqa", "source_datasets:extended\|tatoeba", "source_datasets:extended\|squad", "language:af", "language:ar", "language:bg", "language:bn", "language:de", "language:el", "language:en", "language:es", "language:et", "language:eu", "language:fa", "language:fi", "language:fr", "language:he", "language:hi", "language:hu", "language:id", "language:it", "language:ja", "language:jv", "language:ka", "language:kk", "language:ko", "language:ml", "language:mr", "language:ms", "language:my", "language:nl", "language:pt", "language:ru", "language:sw", "language:ta", "language:te", "language:th", "language:tl", "language:tr", "language:ur", "language:vi", "language:yo", "language:zh", "license:apache-2.0", "license:cc-by-4.0", "license:cc-by-2.0", "license:cc-by-sa-4.0", "license:other", "license:cc-by-nc-4.0", "size_categories:1M<n<10M", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2003.11080", "region:us", "parallel-sentence-retrieval", "paraphrase-identification" ]	[ "multiple-choice", "question-answering", "token-classification", "text-classification", "text-retrieval", "token-classification" ]	2022-03-02T23:29:22Z	null	--- annotations_creators: - found language_creators: - found language: - af - ar - bg - bn - de - el - en - es - et - eu - fa - fi - fr - he - hi - hu - id - it - ja - jv - ka - kk - ko - ml - mr - ms - my - nl - pt - ru - sw - ta - te - th - tl - tr - ur - vi - yo - zh license: - apache-2.0 - cc-by-4.0 - cc-by-2.0 - cc-by-sa-4.0 - other - cc-by-nc-4.0 multilinguality: - multilingual - translation size_categories: - n<1K - 1K<n<10K - 10K<n<100K - 100K<n<1M source_datasets: - extended\|xnli - extended\|paws-x - extended\|wikiann - extended\|xquad - extended\|mlqa - extended\|tydiqa - extended\|tatoeba - extended\|squad task_categories: - multiple-choice - question-answering - token-classification - text-classification - text-retrieval - token-classification task_ids: - multiple-choice-qa - extractive-qa - open-domain-qa - natural-language-inference - named-entity-recognition - part-of-speech paperswithcode_id: xtreme pretty_name: XTREME config_names: - MLQA.ar.ar - MLQA.ar.de - MLQA.ar.en - MLQA.ar.es - MLQA.ar.hi - MLQA.ar.vi - MLQA.ar.zh - MLQA.de.ar - MLQA.de.de - MLQA.de.en - MLQA.de.es - MLQA.de.hi - MLQA.de.vi - MLQA.de.zh - MLQA.en.ar - MLQA.en.de - MLQA.en.en - MLQA.en.es - MLQA.en.hi - MLQA.en.vi - MLQA.en.zh - MLQA.es.ar - MLQA.es.de - MLQA.es.en - MLQA.es.es - MLQA.es.hi - MLQA.es.vi - MLQA.es.zh - MLQA.hi.ar - MLQA.hi.de - MLQA.hi.en - MLQA.hi.es - MLQA.hi.hi - MLQA.hi.vi - MLQA.hi.zh - MLQA.vi.ar - MLQA.vi.de - MLQA.vi.en - MLQA.vi.es - MLQA.vi.hi - MLQA.vi.vi - MLQA.vi.zh - MLQA.zh.ar - MLQA.zh.de - MLQA.zh.en - MLQA.zh.es - MLQA.zh.hi - MLQA.zh.vi - MLQA.zh.zh - PAN-X.af - PAN-X.ar - PAN-X.bg - PAN-X.bn - PAN-X.de - PAN-X.el - PAN-X.en - PAN-X.es - PAN-X.et - PAN-X.eu - PAN-X.fa - PAN-X.fi - PAN-X.fr - PAN-X.he - PAN-X.hi - PAN-X.hu - PAN-X.id - PAN-X.it - PAN-X.ja - PAN-X.jv - PAN-X.ka - PAN-X.kk - PAN-X.ko - PAN-X.ml - PAN-X.mr - PAN-X.ms - PAN-X.my - PAN-X.nl - PAN-X.pt - PAN-X.ru - PAN-X.sw - PAN-X.ta - PAN-X.te - PAN-X.th - PAN-X.tl - PAN-X.tr - PAN-X.ur - PAN-X.vi - PAN-X.yo - PAN-X.zh - PAWS-X.de - PAWS-X.en - PAWS-X.es - PAWS-X.fr - PAWS-X.ja - PAWS-X.ko - PAWS-X.zh - SQuAD - XNLI - XQuAD - bucc18.de - bucc18.fr - bucc18.ru - bucc18.zh - tatoeba.afr - tatoeba.ara - tatoeba.ben - tatoeba.bul - tatoeba.cmn - tatoeba.deu - tatoeba.ell - tatoeba.est - tatoeba.eus - tatoeba.fin - tatoeba.fra - tatoeba.heb - tatoeba.hin - tatoeba.hun - tatoeba.ind - tatoeba.ita - tatoeba.jav - tatoeba.jpn - tatoeba.kat - tatoeba.kaz - tatoeba.kor - tatoeba.mal - tatoeba.mar - tatoeba.nld - tatoeba.pes - tatoeba.por - tatoeba.rus - tatoeba.spa - tatoeba.swh - tatoeba.tam - tatoeba.tel - tatoeba.tgl - tatoeba.tha - tatoeba.tur - tatoeba.urd - tatoeba.vie - tydiqa - udpos.Afrikans - udpos.Arabic - udpos.Basque - udpos.Bulgarian - udpos.Chinese - udpos.Dutch - udpos.English - udpos.Estonian - udpos.Finnish - udpos.French - udpos.German - udpos.Greek - udpos.Hebrew - udpos.Hindi - udpos.Hungarian - udpos.Indonesian - udpos.Italian - udpos.Japanese - udpos.Kazakh - udpos.Korean - udpos.Marathi - udpos.Persian - udpos.Portuguese - udpos.Russian - udpos.Spanish - udpos.Tagalog - udpos.Tamil - udpos.Telugu - udpos.Thai - udpos.Turkish - udpos.Urdu - udpos.Vietnamese - udpos.Yoruba language_bcp47: - fa-IR license_details: Licence Universal Dependencies v2.5 tags: - parallel-sentence-retrieval - paraphrase-identification dataset_info: - config_name: MLQA.ar.ar features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: test num_bytes: 8368086 num_examples: 5335 - name: validation num_bytes: 824080 num_examples: 517 download_size: 4048180 dataset_size: 9192166 - config_name: MLQA.ar.de features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: test num_bytes: 2183914 num_examples: 1649 - name: validation num_bytes: 364809 num_examples: 207 download_size: 1192825 dataset_size: 2548723 - config_name: MLQA.ar.en features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: test num_bytes: 8225634 num_examples: 5335 - name: validation num_bytes: 810061 num_examples: 517 download_size: 3998008 dataset_size: 9035695 - config_name: MLQA.ar.es features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: test num_bytes: 3041350 num_examples: 1978 - name: validation num_bytes: 228152 num_examples: 161 download_size: 1531661 dataset_size: 3269502 - config_name: MLQA.ar.hi features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: test num_bytes: 3039368 num_examples: 1831 - name: validation num_bytes: 281742 num_examples: 186 download_size: 1369756 dataset_size: 3321110 - config_name: MLQA.ar.vi features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: test num_bytes: 3290601 num_examples: 2047 - name: validation num_bytes: 288418 num_examples: 163 download_size: 1667238 dataset_size: 3579019 - config_name: MLQA.ar.zh features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: test num_bytes: 3229844 num_examples: 1912 - name: validation num_bytes: 340021 num_examples: 188 download_size: 1591445 dataset_size: 3569865 - config_name: MLQA.de.ar features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: test num_bytes: 1619978 num_examples: 1649 - name: validation num_bytes: 200146 num_examples: 207 download_size: 1044483 dataset_size: 1820124 - config_name: MLQA.de.de features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: test num_bytes: 4366074 num_examples: 4517 - name: validation num_bytes: 488339 num_examples: 512 download_size: 2798050 dataset_size: 4854413 - config_name: MLQA.de.en features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: test num_bytes: 4343116 num_examples: 4517 - name: validation num_bytes: 485866 num_examples: 512 download_size: 2778346 dataset_size: 4828982 - config_name: MLQA.de.es features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: test num_bytes: 1716587 num_examples: 1776 - name: validation num_bytes: 170554 num_examples: 196 download_size: 1118751 dataset_size: 1887141 - config_name: MLQA.de.hi features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: test num_bytes: 1371046 num_examples: 1430 - name: validation num_bytes: 153843 num_examples: 163 download_size: 880652 dataset_size: 1524889 - config_name: MLQA.de.vi features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: test num_bytes: 1688455 num_examples: 1675 - name: validation num_bytes: 216047 num_examples: 182 download_size: 1108163 dataset_size: 1904502 - config_name: MLQA.de.zh features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: test num_bytes: 1679152 num_examples: 1621 - name: validation num_bytes: 184290 num_examples: 190 download_size: 1045861 dataset_size: 1863442 - config_name: MLQA.en.ar features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: test num_bytes: 6739191 num_examples: 5335 - name: validation num_bytes: 630815 num_examples: 517 download_size: 3939135 dataset_size: 7370006 - config_name: MLQA.en.de features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: test num_bytes: 5056694 num_examples: 4517 - name: validation num_bytes: 594908 num_examples: 512 download_size: 3223196 dataset_size: 5651602 - config_name: MLQA.en.en features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: test num_bytes: 14004592 num_examples: 11590 - name: validation num_bytes: 1329084 num_examples: 1148 download_size: 8217519 dataset_size: 15333676 - config_name: MLQA.en.es features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: test num_bytes: 6179221 num_examples: 5253 - name: validation num_bytes: 555434 num_examples: 500 download_size: 3776828 dataset_size: 6734655 - config_name: MLQA.en.hi features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: test num_bytes: 6378838 num_examples: 4918 - name: validation num_bytes: 623143 num_examples: 507 download_size: 3517340 dataset_size: 7001981 - config_name: MLQA.en.vi features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: test num_bytes: 7056670 num_examples: 5495 - name: validation num_bytes: 640618 num_examples: 511 download_size: 4170642 dataset_size: 7697288 - config_name: MLQA.en.zh features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: test num_bytes: 6539279 num_examples: 5137 - name: validation num_bytes: 608416 num_examples: 504 download_size: 3929122 dataset_size: 7147695 - config_name: MLQA.es.ar features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: test num_bytes: 1740254 num_examples: 1978 - name: validation num_bytes: 148621 num_examples: 161 download_size: 1107435 dataset_size: 1888875 - config_name: MLQA.es.de features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: test num_bytes: 1403997 num_examples: 1776 - name: validation num_bytes: 144158 num_examples: 196 download_size: 950448 dataset_size: 1548155 - config_name: MLQA.es.en features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: test num_bytes: 4362709 num_examples: 5253 - name: validation num_bytes: 419040 num_examples: 500 download_size: 2842879 dataset_size: 4781749 - config_name: MLQA.es.es features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: test num_bytes: 4394305 num_examples: 5253 - name: validation num_bytes: 422043 num_examples: 500 download_size: 2856931 dataset_size: 4816348 - config_name: MLQA.es.hi features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: test num_bytes: 1523495 num_examples: 1723 - name: validation num_bytes: 181806 num_examples: 187 download_size: 954018 dataset_size: 1705301 - config_name: MLQA.es.vi features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: test num_bytes: 1747941 num_examples: 2018 - name: validation num_bytes: 176813 num_examples: 189 download_size: 1187949 dataset_size: 1924754 - config_name: MLQA.es.zh features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: test num_bytes: 1678423 num_examples: 1947 - name: validation num_bytes: 126618 num_examples: 161 download_size: 1100765 dataset_size: 1805041 - config_name: MLQA.hi.ar features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: test num_bytes: 4445561 num_examples: 1831 - name: validation num_bytes: 410396 num_examples: 186 download_size: 1542768 dataset_size: 4855957 - config_name: MLQA.hi.de features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: test num_bytes: 3022836 num_examples: 1430 - name: validation num_bytes: 301685 num_examples: 163 download_size: 1257846 dataset_size: 3324521 - config_name: MLQA.hi.en features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: test num_bytes: 11449233 num_examples: 4918 - name: validation num_bytes: 1097829 num_examples: 507 download_size: 4131083 dataset_size: 12547062 - config_name: MLQA.hi.es features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: test num_bytes: 3862201 num_examples: 1723 - name: validation num_bytes: 420374 num_examples: 187 download_size: 1493468 dataset_size: 4282575 - config_name: MLQA.hi.hi features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: test num_bytes: 11810447 num_examples: 4918 - name: validation num_bytes: 1136756 num_examples: 507 download_size: 4235981 dataset_size: 12947203 - config_name: MLQA.hi.vi features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: test num_bytes: 4743456 num_examples: 1947 - name: validation num_bytes: 419078 num_examples: 177 download_size: 1704964 dataset_size: 5162534 - config_name: MLQA.hi.zh features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: test num_bytes: 4354847 num_examples: 1767 - name: validation num_bytes: 424218 num_examples: 189 download_size: 1627107 dataset_size: 4779065 - config_name: MLQA.vi.ar features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: test num_bytes: 3205157 num_examples: 2047 - name: validation num_bytes: 230307 num_examples: 163 download_size: 1656661 dataset_size: 3435464 - config_name: MLQA.vi.de features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: test num_bytes: 2227005 num_examples: 1675 - name: validation num_bytes: 277157 num_examples: 182 download_size: 1268041 dataset_size: 2504162 - config_name: MLQA.vi.en features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: test num_bytes: 7843403 num_examples: 5495 - name: validation num_bytes: 719245 num_examples: 511 download_size: 4071703 dataset_size: 8562648 - config_name: MLQA.vi.es features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: test num_bytes: 2866569 num_examples: 2018 - name: validation num_bytes: 283433 num_examples: 189 download_size: 1607926 dataset_size: 3150002 - config_name: MLQA.vi.hi features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: test num_bytes: 2776636 num_examples: 1947 - name: validation num_bytes: 254979 num_examples: 177 download_size: 1366057 dataset_size: 3031615 - config_name: MLQA.vi.vi features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: test num_bytes: 7922057 num_examples: 5495 - name: validation num_bytes: 726490 num_examples: 511 download_size: 4105388 dataset_size: 8648547 - config_name: MLQA.vi.zh features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: test num_bytes: 2989632 num_examples: 1943 - name: validation num_bytes: 269361 num_examples: 184 download_size: 1570393 dataset_size: 3258993 - config_name: MLQA.zh.ar features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: test num_bytes: 1731455 num_examples: 1912 - name: validation num_bytes: 175321 num_examples: 188 download_size: 1223863 dataset_size: 1906776 - config_name: MLQA.zh.de features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: test num_bytes: 1389990 num_examples: 1621 - name: validation num_bytes: 174577 num_examples: 190 download_size: 1006829 dataset_size: 1564567 - config_name: MLQA.zh.en features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: test num_bytes: 4450957 num_examples: 5137 - name: validation num_bytes: 446840 num_examples: 504 download_size: 3108433 dataset_size: 4897797 - config_name: MLQA.zh.es features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: test num_bytes: 1736255 num_examples: 1947 - name: validation num_bytes: 138045 num_examples: 161 download_size: 1223467 dataset_size: 1874300 - config_name: MLQA.zh.hi features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: test num_bytes: 1578191 num_examples: 1767 - name: validation num_bytes: 184373 num_examples: 189 download_size: 1044599 dataset_size: 1762564 - config_name: MLQA.zh.vi features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: test num_bytes: 1806158 num_examples: 1943 - name: validation num_bytes: 172906 num_examples: 184 download_size: 1268213 dataset_size: 1979064 - config_name: MLQA.zh.zh features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: test num_bytes: 4422322 num_examples: 5137 - name: validation num_bytes: 443782 num_examples: 504 download_size: 3105362 dataset_size: 4866104 - config_name: PAN-X.af features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string splits: - name: train num_bytes: 1321376 num_examples: 5000 - name: validation num_bytes: 259689 num_examples: 1000 - name: test num_bytes: 257184 num_examples: 1000 download_size: 389015 dataset_size: 1838249 - config_name: PAN-X.ar features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string splits: - name: train num_bytes: 3634096 num_examples: 20000 - name: validation num_bytes: 1808283 num_examples: 10000 - name: test num_bytes: 1811963 num_examples: 10000 download_size: 1567470 dataset_size: 7254342 - config_name: PAN-X.bg features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string splits: - name: train num_bytes: 4600733 num_examples: 20000 - name: validation num_bytes: 2310294 num_examples: 10000 - name: test num_bytes: 2306138 num_examples: 10000 download_size: 2030669 dataset_size: 9217165 - config_name: PAN-X.bn features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string splits: - name: train num_bytes: 1568825 num_examples: 10000 - name: validation num_bytes: 159068 num_examples: 1000 - name: test num_bytes: 159262 num_examples: 1000 download_size: 364024 dataset_size: 1887155 - config_name: PAN-X.de features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string splits: - name: train num_bytes: 4762312 num_examples: 20000 - name: validation num_bytes: 2381545 num_examples: 10000 - name: test num_bytes: 2377619 num_examples: 10000 download_size: 2360242 dataset_size: 9521476 - config_name: PAN-X.el features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string splits: - name: train num_bytes: 5063136 num_examples: 20000 - name: validation num_bytes: 2533786 num_examples: 10000 - name: test num_bytes: 2547574 num_examples: 10000 download_size: 2271726 dataset_size: 10144496 - config_name: PAN-X.en features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string splits: - name: train num_bytes: 3823434 num_examples: 20000 - name: validation num_bytes: 1920049 num_examples: 10000 - name: test num_bytes: 1916200 num_examples: 10000 download_size: 1886284 dataset_size: 7659683 - config_name: PAN-X.es features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string splits: - name: train num_bytes: 3199121 num_examples: 20000 - name: validation num_bytes: 1592505 num_examples: 10000 - name: test num_bytes: 1602271 num_examples: 10000 download_size: 1489562 dataset_size: 6393897 - config_name: PAN-X.et features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string splits: - name: train num_bytes: 3023171 num_examples: 15000 - name: validation num_bytes: 2030140 num_examples: 10000 - name: test num_bytes: 2021389 num_examples: 10000 download_size: 1915624 dataset_size: 7074700 - config_name: PAN-X.eu features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string splits: - name: train num_bytes: 2292307 num_examples: 10000 - name: validation num_bytes: 2296315 num_examples: 10000 - name: test num_bytes: 2249815 num_examples: 10000 download_size: 1393179 dataset_size: 6838437 - config_name: PAN-X.fa features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string splits: - name: train num_bytes: 3529314 num_examples: 20000 - name: validation num_bytes: 1782286 num_examples: 10000 - name: test num_bytes: 1770264 num_examples: 10000 download_size: 1401208 dataset_size: 7081864 - config_name: PAN-X.fi features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string splits: - name: train num_bytes: 4273753 num_examples: 20000 - name: validation num_bytes: 2131749 num_examples: 10000 - name: test num_bytes: 2130645 num_examples: 10000 download_size: 2459149 dataset_size: 8536147 - config_name: PAN-X.fr features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string splits: - name: train num_bytes: 3335384 num_examples: 20000 - name: validation num_bytes: 1664170 num_examples: 10000 - name: test num_bytes: 1675765 num_examples: 10000 download_size: 1679283 dataset_size: 6675319 - config_name: PAN-X.he features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string splits: - name: train num_bytes: 4667060 num_examples: 20000 - name: validation num_bytes: 2332740 num_examples: 10000 - name: test num_bytes: 2318736 num_examples: 10000 download_size: 2186463 dataset_size: 9318536 - config_name: PAN-X.hi features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string splits: - name: train num_bytes: 964192 num_examples: 5000 - name: validation num_bytes: 190651 num_examples: 1000 - name: test num_bytes: 196170 num_examples: 1000 download_size: 266086 dataset_size: 1351013 - config_name: PAN-X.hu features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string splits: - name: train num_bytes: 4499874 num_examples: 20000 - name: validation num_bytes: 2211831 num_examples: 10000 - name: test num_bytes: 2249759 num_examples: 10000 download_size: 2399390 dataset_size: 8961464 - config_name: PAN-X.id features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string splits: - name: train num_bytes: 3083967 num_examples: 20000 - name: validation num_bytes: 1537959 num_examples: 10000 - name: test num_bytes: 1536859 num_examples: 10000 download_size: 1412049 dataset_size: 6158785 - config_name: PAN-X.it features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string splits: - name: train num_bytes: 3874623 num_examples: 20000 - name: validation num_bytes: 1908509 num_examples: 10000 - name: test num_bytes: 1928388 num_examples: 10000 download_size: 1855798 dataset_size: 7711520 - config_name: PAN-X.ja features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string splits: - name: train num_bytes: 12670361 num_examples: 20000 - name: validation num_bytes: 6322983 num_examples: 10000 - name: test num_bytes: 6448940 num_examples: 10000 download_size: 2465674 dataset_size: 25442284 - config_name: PAN-X.jv features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string splits: - name: train num_bytes: 16086 num_examples: 100 - name: validation num_bytes: 14580 num_examples: 100 - name: test num_bytes: 16897 num_examples: 100 download_size: 20475 dataset_size: 47563 - config_name: PAN-X.ka features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string splits: - name: train num_bytes: 2777342 num_examples: 10000 - name: validation num_bytes: 2806881 num_examples: 10000 - name: test num_bytes: 2824621 num_examples: 10000 download_size: 1817280 dataset_size: 8408844 - config_name: PAN-X.kk features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string splits: - name: train num_bytes: 240256 num_examples: 1000 - name: validation num_bytes: 238089 num_examples: 1000 - name: test num_bytes: 236704 num_examples: 1000 download_size: 160554 dataset_size: 715049 - config_name: PAN-X.ko features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string splits: - name: train num_bytes: 4284693 num_examples: 20000 - name: validation num_bytes: 2138147 num_examples: 10000 - name: test num_bytes: 2138274 num_examples: 10000 download_size: 2539591 dataset_size: 8561114 - config_name: PAN-X.ml features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string splits: - name: train num_bytes: 2865184 num_examples: 10000 - name: validation num_bytes: 290735 num_examples: 1000 - name: test num_bytes: 276906 num_examples: 1000 download_size: 852955 dataset_size: 3432825 - config_name: PAN-X.mr features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string splits: - name: train num_bytes: 1248239 num_examples: 5000 - name: validation num_bytes: 245338 num_examples: 1000 - name: test num_bytes: 255884 num_examples: 1000 download_size: 347215 dataset_size: 1749461 - config_name: PAN-X.ms features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string splits: - name: train num_bytes: 2965008 num_examples: 20000 - name: validation num_bytes: 147495 num_examples: 1000 - name: test num_bytes: 147148 num_examples: 1000 download_size: 708795 dataset_size: 3259651 - config_name: PAN-X.my features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string splits: - name: train num_bytes: 32715 num_examples: 100 - name: validation num_bytes: 40408 num_examples: 100 - name: test num_bytes: 37346 num_examples: 100 download_size: 39008 dataset_size: 110469 - config_name: PAN-X.nl features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string splits: - name: train num_bytes: 4062149 num_examples: 20000 - name: validation num_bytes: 2016836 num_examples: 10000 - name: test num_bytes: 2038618 num_examples: 10000 download_size: 1943893 dataset_size: 8117603 - config_name: PAN-X.pt features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string splits: - name: train num_bytes: 3149243 num_examples: 20000 - name: validation num_bytes: 1575121 num_examples: 10000 - name: test num_bytes: 1562605 num_examples: 10000 download_size: 1540478 dataset_size: 6286969 - config_name: PAN-X.ru features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string splits: - name: train num_bytes: 4121751 num_examples: 20000 - name: validation num_bytes: 2053149 num_examples: 10000 - name: test num_bytes: 2074125 num_examples: 10000 download_size: 2127730 dataset_size: 8249025 - config_name: PAN-X.sw features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string splits: - name: train num_bytes: 135891 num_examples: 1000 - name: validation num_bytes: 136348 num_examples: 1000 - name: test num_bytes: 140211 num_examples: 1000 download_size: 87435 dataset_size: 412450 - config_name: PAN-X.ta features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string splits: - name: train num_bytes: 4122090 num_examples: 15000 - name: validation num_bytes: 277605 num_examples: 1000 - name: test num_bytes: 278094 num_examples: 1000 download_size: 1044729 dataset_size: 4677789 - config_name: PAN-X.te features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string splits: - name: train num_bytes: 295390 num_examples: 1000 - name: validation num_bytes: 293261 num_examples: 1000 - name: test num_bytes: 296943 num_examples: 1000 download_size: 200516 dataset_size: 885594 - config_name: PAN-X.th features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string splits: - name: train num_bytes: 27132989 num_examples: 20000 - name: validation num_bytes: 13262717 num_examples: 10000 - name: test num_bytes: 13586908 num_examples: 10000 download_size: 2569566 dataset_size: 53982614 - config_name: PAN-X.tl features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string splits: - name: train num_bytes: 1168697 num_examples: 10000 - name: validation num_bytes: 114136 num_examples: 1000 - name: test num_bytes: 117884 num_examples: 1000 download_size: 308160 dataset_size: 1400717 - config_name: PAN-X.tr features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string splits: - name: train num_bytes: 3779130 num_examples: 20000 - name: validation num_bytes: 1915332 num_examples: 10000 - name: test num_bytes: 1911483 num_examples: 10000 download_size: 2000699 dataset_size: 7605945 - config_name: PAN-X.ur features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string splits: - name: train num_bytes: 3072236 num_examples: 20000 - name: validation num_bytes: 152128 num_examples: 1000 - name: test num_bytes: 151902 num_examples: 1000 download_size: 610869 dataset_size: 3376266 - config_name: PAN-X.vi features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string splits: - name: train num_bytes: 3153187 num_examples: 20000 - name: validation num_bytes: 1565123 num_examples: 10000 - name: test num_bytes: 1580196 num_examples: 10000 download_size: 1375631 dataset_size: 6298506 - config_name: PAN-X.yo features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string splits: - name: train num_bytes: 14689 num_examples: 100 - name: validation num_bytes: 13225 num_examples: 100 - name: test num_bytes: 13513 num_examples: 100 download_size: 17337 dataset_size: 41427 - config_name: PAN-X.zh features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string splits: - name: train num_bytes: 8832011 num_examples: 20000 - name: validation num_bytes: 4491305 num_examples: 10000 - name: test num_bytes: 4363152 num_examples: 10000 download_size: 2083198 dataset_size: 17686468 - config_name: PAWS-X.de features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: string splits: - name: train num_bytes: 12451823 num_examples: 49380 - name: validation num_bytes: 499997 num_examples: 2000 - name: test num_bytes: 510182 num_examples: 2000 download_size: 9294034 dataset_size: 13462002 - config_name: PAWS-X.en features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: string splits: - name: train num_bytes: 11827659 num_examples: 49175 - name: validation num_bytes: 478279 num_examples: 2000 - name: test num_bytes: 480726 num_examples: 2000 download_size: 8717639 dataset_size: 12786664 - config_name: PAWS-X.es features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: string splits: - name: train num_bytes: 12462047 num_examples: 49401 - name: validation num_bytes: 494057 num_examples: 1961 - name: test num_bytes: 505035 num_examples: 2000 download_size: 9229918 dataset_size: 13461139 - config_name: PAWS-X.fr features: - name: sentence1 dtype: string - 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name: sentence2 dtype: string - name: label dtype: string splits: - name: train num_bytes: 10469652 num_examples: 49401 - name: validation num_bytes: 459108 num_examples: 2000 - name: test num_bytes: 460626 num_examples: 2000 download_size: 8878855 dataset_size: 11389386 - config_name: SQuAD features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: train num_bytes: 79316858 num_examples: 87599 - name: validation num_bytes: 10472597 num_examples: 10570 download_size: 16272656 dataset_size: 89789455 - config_name: XNLI features: - name: language dtype: string - name: sentence1 dtype: string - name: sentence2 dtype: string - name: gold_label dtype: string splits: - name: test num_bytes: 20359372 num_examples: 75150 - name: validation num_bytes: 10049239 num_examples: 37350 download_size: 8881623 dataset_size: 30408611 - config_name: XQuAD.ar features: - name: id dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: validation num_bytes: 1722775 num_examples: 1190 download_size: 263032 dataset_size: 1722775 - config_name: XQuAD.de features: - name: id dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: validation num_bytes: 1283277 num_examples: 1190 download_size: 241987 dataset_size: 1283277 - config_name: XQuAD.el features: - name: id dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: validation num_bytes: 2206666 num_examples: 1190 download_size: 324409 dataset_size: 2206666 - config_name: XQuAD.en features: - name: id dtype: string - name: context dtype: string - 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name: answer_start dtype: int32 - name: text dtype: string splits: - name: validation num_bytes: 2136966 num_examples: 1190 download_size: 321758 dataset_size: 2136966 - config_name: XQuAD.th features: - name: id dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: validation num_bytes: 2854935 num_examples: 1190 download_size: 337337 dataset_size: 2854935 - config_name: XQuAD.tr features: - name: id dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: validation num_bytes: 1210739 num_examples: 1190 download_size: 228394 dataset_size: 1210739 - config_name: XQuAD.vi features: - name: id dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: validation num_bytes: 1477215 num_examples: 1190 download_size: 237674 dataset_size: 1477215 - config_name: XQuAD.zh features: - name: id dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: validation num_bytes: 984217 num_examples: 1190 download_size: 205798 dataset_size: 984217 - config_name: bucc18.de features: - name: source_sentence dtype: string - name: target_sentence dtype: string - name: source_lang dtype: string - name: target_lang dtype: string splits: - name: validation num_bytes: 248691 num_examples: 1038 - name: test num_bytes: 2325685 num_examples: 9580 download_size: 1636130 dataset_size: 2574376 - config_name: bucc18.fr features: - name: source_sentence dtype: string - name: target_sentence dtype: string - name: source_lang dtype: string - name: target_lang dtype: string splits: - name: validation num_bytes: 212497 num_examples: 929 - name: test num_bytes: 2082403 num_examples: 9086 download_size: 1437096 dataset_size: 2294900 - config_name: bucc18.ru features: - name: source_sentence dtype: string - name: target_sentence dtype: string - name: source_lang dtype: string - name: target_lang dtype: string splits: - name: validation num_bytes: 761331 num_examples: 2374 - name: test num_bytes: 4641646 num_examples: 14435 download_size: 3074476 dataset_size: 5402977 - config_name: bucc18.zh features: - name: source_sentence dtype: string - name: target_sentence dtype: string - name: source_lang dtype: string - name: target_lang dtype: string splits: - name: validation num_bytes: 55723 num_examples: 257 - name: test num_bytes: 415909 num_examples: 1899 download_size: 320378 dataset_size: 471632 - config_name: tatoeba.afr features: - name: source_sentence dtype: string - name: target_sentence dtype: string - name: source_lang dtype: string - name: target_lang dtype: string splits: - name: validation num_bytes: 250635 num_examples: 1000 download_size: 47676 dataset_size: 250635 - config_name: tatoeba.ara features: - name: source_sentence dtype: string - name: target_sentence dtype: string - name: source_lang dtype: string - name: target_lang dtype: string splits: - name: validation num_bytes: 263650 num_examples: 1000 download_size: 51228 dataset_size: 263650 - config_name: tatoeba.ben features: - name: source_sentence dtype: string - name: target_sentence dtype: string - name: source_lang dtype: string - name: target_lang dtype: string splits: - name: validation num_bytes: 282703 num_examples: 1000 download_size: 51362 dataset_size: 282703 - config_name: tatoeba.bul features: - name: source_sentence dtype: string - name: target_sentence dtype: string - name: source_lang dtype: string - name: target_lang dtype: string splits: - name: validation num_bytes: 293279 num_examples: 1000 download_size: 62454 dataset_size: 293279 - config_name: tatoeba.cmn features: - name: source_sentence dtype: string - name: target_sentence dtype: string - name: source_lang dtype: string - name: target_lang dtype: string splits: - name: validation num_bytes: 259931 num_examples: 1000 download_size: 58281 dataset_size: 259931 - config_name: tatoeba.deu features: - name: source_sentence dtype: string - name: target_sentence dtype: string - name: source_lang dtype: string - name: target_lang dtype: string splits: - name: validation num_bytes: 296567 num_examples: 1000 download_size: 79066 dataset_size: 296567 - config_name: tatoeba.ell features: - name: source_sentence dtype: string - name: target_sentence dtype: string - name: source_lang dtype: string - name: target_lang dtype: string splits: - name: validation num_bytes: 269961 num_examples: 1000 download_size: 52251 dataset_size: 269961 - config_name: tatoeba.est features: - name: source_sentence dtype: string - name: target_sentence dtype: string - name: source_lang dtype: string - name: target_lang dtype: string splits: - name: validation num_bytes: 250728 num_examples: 1000 download_size: 49968 dataset_size: 250728 - config_name: tatoeba.eus features: - name: source_sentence dtype: string - name: target_sentence dtype: string - name: source_lang dtype: string - name: target_lang dtype: string splits: - name: validation num_bytes: 257068 num_examples: 1000 download_size: 54271 dataset_size: 257068 - config_name: tatoeba.fin features: - name: source_sentence dtype: string - name: target_sentence dtype: string - name: source_lang dtype: string - name: target_lang dtype: string splits: - name: validation num_bytes: 266669 num_examples: 1000 download_size: 60580 dataset_size: 266669 - config_name: tatoeba.fra features: - name: source_sentence dtype: string - name: target_sentence dtype: string - name: source_lang dtype: string - name: target_lang dtype: string splits: - name: validation num_bytes: 271018 num_examples: 1000 download_size: 60925 dataset_size: 271018 - config_name: tatoeba.heb features: - name: source_sentence dtype: string - name: target_sentence dtype: string - name: source_lang dtype: string - name: target_lang dtype: string splits: - name: validation num_bytes: 274500 num_examples: 1000 download_size: 57306 dataset_size: 274500 - config_name: tatoeba.hin features: - name: source_sentence dtype: string - name: target_sentence dtype: string - name: source_lang dtype: string - name: target_lang dtype: string splits: - name: validation num_bytes: 313558 num_examples: 1000 download_size: 68816 dataset_size: 313558 - config_name: tatoeba.hun features: - name: source_sentence dtype: string - name: target_sentence dtype: string - name: source_lang dtype: string - name: target_lang dtype: string splits: - name: validation num_bytes: 259889 num_examples: 1000 download_size: 58096 dataset_size: 259889 - config_name: tatoeba.ind features: - name: source_sentence dtype: string - name: target_sentence dtype: string - name: source_lang dtype: string - name: target_lang dtype: string splits: - name: validation num_bytes: 265844 num_examples: 1000 download_size: 57047 dataset_size: 265844 - config_name: tatoeba.ita features: - name: source_sentence dtype: string - name: target_sentence dtype: string - name: source_lang dtype: string - name: target_lang dtype: string splits: - name: validation num_bytes: 256833 num_examples: 1000 download_size: 52422 dataset_size: 256833 - config_name: tatoeba.jav features: - name: source_sentence dtype: string - name: target_sentence dtype: string - name: source_lang dtype: string - name: target_lang dtype: string splits: - name: validation num_bytes: 53068 num_examples: 205 download_size: 15208 dataset_size: 53068 - config_name: tatoeba.jpn features: - name: source_sentence dtype: string - name: target_sentence dtype: string - name: source_lang dtype: string - name: target_lang dtype: string splits: - name: validation num_bytes: 284083 num_examples: 1000 download_size: 66620 dataset_size: 284083 - config_name: tatoeba.kat features: - name: source_sentence dtype: string - name: target_sentence dtype: string - name: source_lang dtype: string - name: target_lang dtype: string splits: - name: validation num_bytes: 214646 num_examples: 746 download_size: 41759 dataset_size: 214646 - config_name: tatoeba.kaz features: - name: source_sentence dtype: string - name: target_sentence dtype: string - name: source_lang dtype: string - name: target_lang dtype: string splits: - name: validation num_bytes: 157003 num_examples: 575 download_size: 35693 dataset_size: 157003 - config_name: tatoeba.kor features: - name: source_sentence dtype: string - name: target_sentence dtype: string - name: source_lang dtype: string - name: target_lang dtype: string splits: - name: validation num_bytes: 270139 num_examples: 1000 download_size: 61210 dataset_size: 270139 - config_name: tatoeba.mal features: - name: source_sentence dtype: string - name: target_sentence dtype: string - name: source_lang dtype: string - name: target_lang dtype: string splits: - name: validation num_bytes: 225934 num_examples: 687 download_size: 51077 dataset_size: 225934 - config_name: tatoeba.mar features: - name: source_sentence dtype: string - name: target_sentence dtype: string - name: source_lang dtype: string - name: target_lang dtype: string splits: - name: validation num_bytes: 291542 num_examples: 1000 download_size: 56575 dataset_size: 291542 - config_name: tatoeba.nld features: - name: source_sentence dtype: string - name: target_sentence dtype: string - name: source_lang dtype: string - name: target_lang dtype: string splits: - name: validation num_bytes: 264263 num_examples: 1000 download_size: 59774 dataset_size: 264263 - config_name: tatoeba.pes features: - name: source_sentence dtype: string - name: target_sentence dtype: string - name: source_lang dtype: string - name: target_lang dtype: string splits: - name: validation num_bytes: 284719 num_examples: 1000 download_size: 64642 dataset_size: 284719 - config_name: tatoeba.por features: - name: source_sentence dtype: string - name: target_sentence dtype: string - name: source_lang dtype: string - name: target_lang dtype: string splits: - name: validation num_bytes: 266185 num_examples: 1000 download_size: 58250 dataset_size: 266185 - config_name: tatoeba.rus features: - name: source_sentence dtype: string - name: target_sentence dtype: string - name: source_lang dtype: string - name: target_lang dtype: string splits: - name: validation num_bytes: 283472 num_examples: 1000 download_size: 61601 dataset_size: 283472 - config_name: tatoeba.spa features: - name: source_sentence dtype: string - name: target_sentence dtype: string - name: source_lang dtype: string - name: target_lang dtype: string splits: - name: validation num_bytes: 263266 num_examples: 1000 download_size: 57055 dataset_size: 263266 - config_name: tatoeba.swh features: - name: source_sentence dtype: string - name: target_sentence dtype: string - name: source_lang dtype: string - name: target_lang dtype: string splits: - name: validation num_bytes: 94957 num_examples: 390 download_size: 19362 dataset_size: 94957 - config_name: tatoeba.tam features: - name: source_sentence dtype: string - name: target_sentence dtype: string - name: source_lang dtype: string - name: target_lang dtype: string splits: - name: validation num_bytes: 98078 num_examples: 307 download_size: 23648 dataset_size: 98078 - config_name: tatoeba.tel features: - name: source_sentence dtype: string - name: target_sentence dtype: string - name: source_lang dtype: string - name: target_lang dtype: string splits: - name: validation num_bytes: 69837 num_examples: 234 download_size: 18260 dataset_size: 69837 - config_name: tatoeba.tgl features: - name: source_sentence dtype: string - name: target_sentence dtype: string - name: source_lang dtype: string - name: target_lang dtype: string splits: - name: validation num_bytes: 259138 num_examples: 1000 download_size: 53699 dataset_size: 259138 - config_name: tatoeba.tha features: - name: source_sentence dtype: string - name: target_sentence dtype: string - name: source_lang dtype: string - name: target_lang dtype: string splits: - name: validation num_bytes: 167866 num_examples: 548 download_size: 39659 dataset_size: 167866 - config_name: tatoeba.tur features: - name: source_sentence dtype: string - name: target_sentence dtype: string - name: source_lang dtype: string - name: target_lang dtype: string splits: - name: validation num_bytes: 262885 num_examples: 1000 download_size: 54137 dataset_size: 262885 - config_name: tatoeba.urd features: - name: source_sentence dtype: string - name: target_sentence dtype: string - name: source_lang dtype: string - name: target_lang dtype: string splits: - name: validation num_bytes: 279712 num_examples: 1000 download_size: 60399 dataset_size: 279712 - config_name: tatoeba.vie features: - name: source_sentence dtype: string - name: target_sentence dtype: string - name: source_lang dtype: string - name: target_lang dtype: string splits: - name: validation num_bytes: 282407 num_examples: 1000 download_size: 66746 dataset_size: 282407 - config_name: tydiqa features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: train num_bytes: 52948467 num_examples: 49881 - name: validation num_bytes: 5006433 num_examples: 5077 download_size: 29402238 dataset_size: 57954900 - config_name: udpos.Afrikaans features: - name: tokens sequence: string - name: pos_tags sequence: class_label: names: '0': ADJ '1': ADP '2': ADV '3': AUX '4': CCONJ '5': DET '6': INTJ '7': NOUN '8': NUM '9': PART '10': PRON '11': PROPN '12': PUNCT '13': SCONJ '14': SYM '15': VERB '16': X splits: - name: train num_bytes: 586370 num_examples: 1315 - name: validation num_bytes: 91290 num_examples: 194 - name: test num_bytes: 174244 num_examples: 425 download_size: 193788 dataset_size: 851904 - config_name: udpos.Arabic features: - name: tokens sequence: string - name: pos_tags sequence: class_label: names: '0': ADJ '1': ADP '2': ADV '3': AUX '4': CCONJ '5': DET '6': INTJ '7': NOUN '8': NUM '9': PART '10': PRON '11': PROPN '12': PUNCT '13': SCONJ '14': SYM '15': VERB '16': X splits: - name: train num_bytes: 4453682 num_examples: 6075 - name: validation num_bytes: 593650 num_examples: 909 - name: test num_bytes: 973822 num_examples: 1680 download_size: 1186113 dataset_size: 6021154 - config_name: udpos.Basque features: - name: tokens sequence: string - name: pos_tags sequence: class_label: names: '0': ADJ '1': ADP '2': ADV '3': AUX '4': CCONJ '5': DET '6': INTJ '7': NOUN '8': NUM '9': PART '10': PRON '11': PROPN '12': PUNCT '13': SCONJ '14': SYM '15': VERB '16': X splits: - name: train num_bytes: 1327713 num_examples: 5396 - name: validation num_bytes: 438671 num_examples: 1798 - name: test num_bytes: 444644 num_examples: 1799 download_size: 703094 dataset_size: 2211028 - config_name: udpos.Bulgarian features: - name: tokens sequence: string - name: pos_tags sequence: class_label: names: '0': ADJ '1': ADP '2': ADV '3': AUX '4': CCONJ '5': DET '6': INTJ '7': NOUN '8': NUM '9': PART '10': PRON '11': PROPN '12': PUNCT '13': SCONJ '14': SYM '15': VERB '16': X splits: - name: train num_bytes: 2689767 num_examples: 8907 - name: validation num_bytes: 347117 num_examples: 1115 - name: test num_bytes: 339947 num_examples: 1116 download_size: 926186 dataset_size: 3376831 - config_name: udpos.Chinese features: - name: tokens sequence: string - name: pos_tags sequence: class_label: names: '0': ADJ '1': ADP '2': ADV '3': AUX '4': CCONJ '5': DET '6': INTJ '7': NOUN '8': NUM '9': PART '10': PRON '11': PROPN '12': PUNCT '13': SCONJ '14': SYM '15': VERB '16': X splits: - name: train num_bytes: 4218891 num_examples: 18998 - name: validation num_bytes: 594448 num_examples: 3038 - name: test num_bytes: 1236051 num_examples: 5528 download_size: 1471747 dataset_size: 6049390 - config_name: udpos.Dutch features: - name: tokens sequence: string - name: pos_tags sequence: class_label: names: '0': ADJ '1': ADP '2': ADV '3': AUX '4': CCONJ '5': DET '6': INTJ '7': NOUN '8': NUM '9': PART '10': PRON '11': PROPN '12': PUNCT '13': SCONJ '14': SYM '15': VERB '16': X splits: - name: train num_bytes: 4517994 num_examples: 18051 - name: validation num_bytes: 393592 num_examples: 1394 - name: test num_bytes: 397904 num_examples: 1471 download_size: 1410982 dataset_size: 5309490 - config_name: udpos.English features: - name: tokens sequence: string - name: pos_tags sequence: class_label: names: '0': ADJ '1': ADP '2': ADV '3': AUX '4': CCONJ '5': DET '6': INTJ '7': NOUN '8': NUM '9': PART '10': PRON '11': PROPN '12': PUNCT '13': SCONJ '14': SYM '15': VERB '16': X splits: - name: train num_bytes: 6225509 num_examples: 21253 - name: validation num_bytes: 1042040 num_examples: 3974 - name: test num_bytes: 1421148 num_examples: 5440 download_size: 2116535 dataset_size: 8688697 - config_name: udpos.Estonian features: - name: tokens sequence: string - name: pos_tags sequence: class_label: names: '0': ADJ '1': ADP '2': ADV '3': AUX '4': CCONJ '5': DET '6': INTJ '7': NOUN '8': NUM '9': PART '10': PRON '11': PROPN '12': PUNCT '13': SCONJ '14': SYM '15': VERB '16': X splits: - name: train num_bytes: 6614893 num_examples: 25749 - name: validation num_bytes: 814171 num_examples: 3125 - name: test num_bytes: 1065701 num_examples: 3760 download_size: 2619121 dataset_size: 8494765 - config_name: udpos.Finnish features: - name: tokens sequence: string - name: pos_tags sequence: class_label: names: '0': ADJ '1': ADP '2': ADV '3': AUX '4': CCONJ '5': DET '6': INTJ '7': NOUN '8': NUM '9': PART '10': PRON '11': PROPN '12': PUNCT '13': SCONJ '14': SYM '15': VERB '16': X splits: - name: train num_bytes: 5613706 num_examples: 27198 - name: validation num_bytes: 656646 num_examples: 3239 - name: test num_bytes: 1025726 num_examples: 4422 download_size: 2503217 dataset_size: 7296078 - config_name: udpos.French features: - name: tokens sequence: string - name: pos_tags sequence: class_label: names: '0': ADJ '1': ADP '2': ADV '3': AUX '4': CCONJ '5': DET '6': INTJ '7': NOUN '8': NUM '9': PART '10': PRON '11': PROPN '12': PUNCT '13': SCONJ '14': SYM '15': VERB '16': X splits: - name: train num_bytes: 10118933 num_examples: 47308 - name: validation num_bytes: 1294096 num_examples: 5979 - name: test num_bytes: 1731049 num_examples: 9465 download_size: 3378680 dataset_size: 13144078 - config_name: udpos.German features: - name: tokens sequence: string - name: pos_tags sequence: class_label: names: '0': ADJ '1': ADP '2': ADV '3': AUX '4': CCONJ '5': DET '6': INTJ '7': NOUN '8': NUM '9': PART '10': PRON '11': PROPN '12': PUNCT '13': SCONJ '14': SYM '15': VERB '16': X splits: - name: train num_bytes: 54773777 num_examples: 166849 - name: validation num_bytes: 6044838 num_examples: 19233 - name: test num_bytes: 7345863 num_examples: 22458 download_size: 18623155 dataset_size: 68164478 - config_name: udpos.Greek features: - name: tokens sequence: string - name: pos_tags sequence: class_label: names: '0': ADJ '1': ADP '2': ADV '3': AUX '4': CCONJ '5': DET '6': INTJ '7': NOUN '8': NUM '9': PART '10': PRON '11': PROPN '12': PUNCT '13': SCONJ '14': SYM '15': VERB '16': X splits: - name: train num_bytes: 8932104 num_examples: 28152 - name: validation num_bytes: 1062447 num_examples: 2559 - name: test num_bytes: 1028665 num_examples: 2809 download_size: 2763293 dataset_size: 11023216 - config_name: udpos.Hebrew features: - name: tokens sequence: string - name: pos_tags sequence: class_label: names: '0': ADJ '1': ADP '2': ADV '3': AUX '4': CCONJ '5': DET '6': INTJ '7': NOUN '8': NUM '9': PART '10': PRON '11': PROPN '12': PUNCT '13': SCONJ '14': SYM '15': VERB '16': X splits: - name: train num_bytes: 2505691 num_examples: 5241 - name: validation num_bytes: 210013 num_examples: 484 - name: test num_bytes: 223865 num_examples: 491 download_size: 624771 dataset_size: 2939569 - config_name: udpos.Hindi features: - name: tokens sequence: string - name: pos_tags sequence: class_label: names: '0': ADJ '1': ADP '2': ADV '3': AUX '4': CCONJ '5': DET '6': INTJ '7': NOUN '8': NUM '9': PART '10': PRON '11': PROPN '12': PUNCT '13': SCONJ '14': SYM '15': VERB '16': X splits: - name: train num_bytes: 6690250 num_examples: 13304 - name: validation num_bytes: 839702 num_examples: 1659 - name: test num_bytes: 1400225 num_examples: 2684 download_size: 1468314 dataset_size: 8930177 - config_name: udpos.Hungarian features: - name: tokens sequence: string - name: pos_tags sequence: class_label: names: '0': ADJ '1': ADP '2': ADV '3': AUX '4': CCONJ '5': DET '6': INTJ '7': NOUN '8': NUM '9': PART '10': PRON '11': PROPN '12': PUNCT '13': SCONJ '14': SYM '15': VERB '16': X splits: - name: train num_bytes: 372226 num_examples: 910 - name: validation num_bytes: 215879 num_examples: 441 - name: test num_bytes: 193728 num_examples: 449 download_size: 251882 dataset_size: 781833 - config_name: udpos.Indonesian features: - name: tokens sequence: string - name: pos_tags sequence: class_label: names: '0': ADJ '1': ADP '2': ADV '3': AUX '4': CCONJ '5': DET '6': INTJ '7': NOUN '8': NUM '9': PART '10': PRON '11': PROPN '12': PUNCT '13': SCONJ '14': SYM '15': VERB '16': X splits: - name: train num_bytes: 1710678 num_examples: 4477 - name: validation num_bytes: 220863 num_examples: 559 - name: test num_bytes: 557101 num_examples: 1557 download_size: 684225 dataset_size: 2488642 - config_name: udpos.Italian features: - name: tokens sequence: string - name: pos_tags sequence: class_label: names: '0': ADJ '1': ADP '2': ADV '3': AUX '4': CCONJ '5': DET '6': INTJ '7': NOUN '8': NUM '9': PART '10': PRON '11': PROPN '12': PUNCT '13': SCONJ '14': SYM '15': VERB '16': X splits: - name: train num_bytes: 11299293 num_examples: 29685 - name: validation num_bytes: 988996 num_examples: 2278 - name: test num_bytes: 1337869 num_examples: 3518 download_size: 3256246 dataset_size: 13626158 - config_name: udpos.Japanese features: - name: tokens sequence: string - name: pos_tags sequence: class_label: names: '0': ADJ '1': ADP '2': ADV '3': AUX '4': CCONJ '5': DET '6': INTJ '7': NOUN '8': NUM '9': PART '10': PRON '11': PROPN '12': PUNCT '13': SCONJ '14': SYM '15': VERB '16': X splits: - name: train num_bytes: 2792951 num_examples: 7125 - name: validation num_bytes: 200356 num_examples: 511 - name: test num_bytes: 928902 num_examples: 2372 download_size: 1012282 dataset_size: 3922209 - config_name: udpos.Kazakh features: - name: tokens sequence: string - name: pos_tags sequence: class_label: names: '0': ADJ '1': ADP '2': ADV '3': AUX '4': CCONJ '5': DET '6': INTJ '7': NOUN '8': NUM '9': PART '10': PRON '11': PROPN '12': PUNCT '13': SCONJ '14': SYM '15': VERB '16': X splits: - name: train num_bytes: 11438 num_examples: 31 - name: test num_bytes: 228924 num_examples: 1047 download_size: 76300 dataset_size: 240362 - config_name: udpos.Korean features: - name: tokens sequence: string - name: pos_tags sequence: class_label: names: '0': ADJ '1': ADP '2': ADV '3': AUX '4': CCONJ '5': DET '6': INTJ '7': NOUN '8': NUM '9': PART '10': PRON '11': PROPN '12': PUNCT '13': SCONJ '14': SYM '15': VERB '16': X splits: - name: train num_bytes: 7341267 num_examples: 27410 - name: validation num_bytes: 782587 num_examples: 3016 - name: test num_bytes: 1162539 num_examples: 4276 download_size: 3115101 dataset_size: 9286393 - config_name: udpos.Marathi features: - name: tokens sequence: string - name: pos_tags sequence: class_label: names: '0': ADJ '1': ADP '2': ADV '3': AUX '4': CCONJ '5': DET '6': INTJ '7': NOUN '8': NUM '9': PART '10': PRON '11': PROPN '12': PUNCT '13': SCONJ '14': SYM '15': VERB '16': X splits: - name: train num_bytes: 59023 num_examples: 373 - name: validation num_bytes: 8497 num_examples: 46 - name: test num_bytes: 7871 num_examples: 47 download_size: 22133 dataset_size: 75391 - config_name: udpos.Persian features: - name: tokens sequence: string - name: pos_tags sequence: class_label: names: '0': ADJ '1': ADP '2': ADV '3': AUX '4': CCONJ '5': DET '6': INTJ '7': NOUN '8': NUM '9': PART '10': PRON '11': PROPN '12': PUNCT '13': SCONJ '14': SYM '15': VERB '16': X splits: - name: train num_bytes: 2400776 num_examples: 4798 - name: validation num_bytes: 317053 num_examples: 599 - name: test num_bytes: 320683 num_examples: 600 download_size: 606912 dataset_size: 3038512 - config_name: udpos.Portuguese features: - name: tokens sequence: string - name: pos_tags sequence: class_label: names: '0': ADJ '1': ADP '2': ADV '3': AUX '4': CCONJ '5': DET '6': INTJ '7': NOUN '8': NUM '9': PART '10': PRON '11': PROPN '12': PUNCT '13': SCONJ '14': SYM '15': VERB '16': X splits: - name: train num_bytes: 7669556 num_examples: 17992 - name: validation num_bytes: 712397 num_examples: 1770 - name: test num_bytes: 1082582 num_examples: 2681 download_size: 2505672 dataset_size: 9464535 - config_name: udpos.Russian features: - name: tokens sequence: string - name: pos_tags sequence: class_label: names: '0': ADJ '1': ADP '2': ADV '3': AUX '4': CCONJ '5': DET '6': INTJ '7': NOUN '8': NUM '9': PART '10': PRON '11': PROPN '12': PUNCT '13': SCONJ '14': SYM '15': VERB '16': X splits: - name: train num_bytes: 24230098 num_examples: 67435 - name: validation num_bytes: 3457031 num_examples: 9960 - name: test num_bytes: 4236693 num_examples: 11336 download_size: 8818512 dataset_size: 31923822 - config_name: udpos.Spanish features: - name: tokens sequence: string - name: pos_tags sequence: class_label: names: '0': ADJ '1': ADP '2': ADV '3': AUX '4': CCONJ '5': DET '6': INTJ '7': NOUN '8': NUM '9': PART '10': PRON '11': PROPN '12': PUNCT '13': SCONJ '14': SYM '15': VERB '16': X splits: - name: train num_bytes: 13858406 num_examples: 28492 - name: validation num_bytes: 1498765 num_examples: 3054 - name: test num_bytes: 1476500 num_examples: 3147 download_size: 4347905 dataset_size: 16833671 - config_name: udpos.Tagalog features: - name: tokens sequence: string - name: pos_tags sequence: class_label: names: '0': ADJ '1': ADP '2': ADV '3': AUX '4': CCONJ '5': DET '6': INTJ '7': NOUN '8': NUM '9': PART '10': PRON '11': PROPN '12': PUNCT '13': SCONJ '14': SYM '15': VERB '16': X splits: - name: test num_bytes: 5153 num_examples: 55 download_size: 3345 dataset_size: 5153 - config_name: udpos.Tamil features: - name: tokens sequence: string - name: pos_tags sequence: class_label: names: '0': ADJ '1': ADP '2': ADV '3': AUX '4': CCONJ '5': DET '6': INTJ '7': NOUN '8': NUM '9': PART '10': PRON '11': PROPN '12': PUNCT '13': SCONJ '14': SYM '15': VERB '16': X splits: - name: train num_bytes: 202596 num_examples: 400 - name: validation num_bytes: 40031 num_examples: 80 - name: test num_bytes: 62366 num_examples: 120 download_size: 73764 dataset_size: 304993 - config_name: udpos.Telugu features: - name: tokens sequence: string - name: pos_tags sequence: class_label: names: '0': ADJ '1': ADP '2': ADV '3': AUX '4': CCONJ '5': DET '6': INTJ '7': NOUN '8': NUM '9': PART '10': PRON '11': PROPN '12': PUNCT '13': SCONJ '14': SYM '15': VERB '16': X splits: - name: train num_bytes: 138049 num_examples: 1051 - name: validation num_bytes: 17990 num_examples: 131 - name: test num_bytes: 19575 num_examples: 146 download_size: 46045 dataset_size: 175614 - config_name: udpos.Thai features: - name: tokens sequence: string - name: pos_tags sequence: class_label: names: '0': ADJ '1': ADP '2': ADV '3': AUX '4': CCONJ '5': DET '6': INTJ '7': NOUN '8': NUM '9': PART '10': PRON '11': PROPN '12': PUNCT '13': SCONJ '14': SYM '15': VERB '16': X splits: - name: test num_bytes: 561336 num_examples: 1000 download_size: 92925 dataset_size: 561336 - config_name: udpos.Turkish features: - name: tokens sequence: string - name: pos_tags sequence: class_label: names: '0': ADJ '1': ADP '2': ADV '3': AUX '4': CCONJ '5': DET '6': INTJ '7': NOUN '8': NUM '9': PART '10': PRON '11': PROPN '12': PUNCT '13': SCONJ '14': SYM '15': VERB '16': X splits: - name: train num_bytes: 704405 num_examples: 3664 - name: validation num_bytes: 186455 num_examples: 988 - name: test num_bytes: 827382 num_examples: 4785 download_size: 581177 dataset_size: 1718242 - config_name: udpos.Urdu features: - name: tokens sequence: string - name: pos_tags sequence: class_label: names: '0': ADJ '1': ADP '2': ADV '3': AUX '4': CCONJ '5': DET '6': INTJ '7': NOUN '8': NUM '9': PART '10': PRON '11': PROPN '12': PUNCT '13': SCONJ '14': SYM '15': VERB '16': X splits: - name: train num_bytes: 2107362 num_examples: 4043 - name: validation num_bytes: 284261 num_examples: 552 - name: test num_bytes: 288553 num_examples: 535 download_size: 499594 dataset_size: 2680176 - config_name: udpos.Vietnamese features: - name: tokens sequence: string - name: pos_tags sequence: class_label: names: '0': ADJ '1': ADP '2': ADV '3': AUX '4': CCONJ '5': DET '6': INTJ '7': NOUN '8': NUM '9': PART '10': PRON '11': PROPN '12': PUNCT '13': SCONJ '14': SYM '15': VERB '16': X splits: - name: train num_bytes: 367335 num_examples: 1400 - name: validation num_bytes: 206188 num_examples: 800 - name: test num_bytes: 214063 num_examples: 800 download_size: 181239 dataset_size: 787586 - config_name: udpos.Yoruba features: - name: tokens sequence: string - name: pos_tags sequence: class_label: names: '0': ADJ '1': ADP '2': ADV '3': AUX '4': CCONJ '5': DET '6': INTJ '7': NOUN '8': NUM '9': PART '10': PRON '11': PROPN '12': PUNCT '13': SCONJ '14': SYM '15': VERB '16': X splits: - name: test num_bytes: 44656 num_examples: 100 download_size: 10151 dataset_size: 44656 configs: - config_name: MLQA.ar.ar data_files: - split: test path: MLQA.ar.ar/test-* - split: validation path: MLQA.ar.ar/validation-* - config_name: MLQA.ar.de data_files: - split: test path: MLQA.ar.de/test-* - split: validation path: MLQA.ar.de/validation-* - config_name: MLQA.ar.en data_files: - split: test path: MLQA.ar.en/test-* - split: validation path: MLQA.ar.en/validation-* - config_name: MLQA.ar.es data_files: - split: test path: MLQA.ar.es/test-* - split: validation path: MLQA.ar.es/validation-* - config_name: MLQA.ar.hi data_files: - split: test path: MLQA.ar.hi/test-* - split: validation path: MLQA.ar.hi/validation-* - config_name: MLQA.ar.vi data_files: - split: test path: MLQA.ar.vi/test-* - split: validation path: MLQA.ar.vi/validation-* - config_name: MLQA.ar.zh data_files: - split: test path: MLQA.ar.zh/test-* - split: validation path: MLQA.ar.zh/validation-* - config_name: MLQA.de.ar data_files: - split: test path: MLQA.de.ar/test-* - split: validation path: MLQA.de.ar/validation-* - config_name: MLQA.de.de data_files: - split: test path: MLQA.de.de/test-* - split: validation path: MLQA.de.de/validation-* - config_name: MLQA.de.en data_files: - split: test path: MLQA.de.en/test-* - split: validation path: MLQA.de.en/validation-* - config_name: MLQA.de.es data_files: - split: test path: MLQA.de.es/test-* - split: validation path: MLQA.de.es/validation-* - config_name: MLQA.de.hi data_files: - split: test path: MLQA.de.hi/test-* - split: validation path: MLQA.de.hi/validation-* - config_name: MLQA.de.vi data_files: - split: test path: MLQA.de.vi/test-* - split: validation path: MLQA.de.vi/validation-* - config_name: MLQA.de.zh data_files: - split: test path: MLQA.de.zh/test-* - split: validation path: MLQA.de.zh/validation-* - config_name: MLQA.en.ar data_files: - split: test path: MLQA.en.ar/test-* - split: validation path: MLQA.en.ar/validation-* - config_name: MLQA.en.de data_files: - split: test path: MLQA.en.de/test-* - split: validation path: MLQA.en.de/validation-* - config_name: MLQA.en.en data_files: - split: test path: MLQA.en.en/test-* - split: validation path: MLQA.en.en/validation-* - config_name: MLQA.en.es data_files: - split: test path: MLQA.en.es/test-* - split: validation path: MLQA.en.es/validation-* - config_name: MLQA.en.hi data_files: - split: test path: MLQA.en.hi/test-* - split: validation path: MLQA.en.hi/validation-* - config_name: MLQA.en.vi data_files: - split: test path: MLQA.en.vi/test-* - split: validation path: MLQA.en.vi/validation-* - config_name: MLQA.en.zh data_files: - split: test path: MLQA.en.zh/test-* - split: validation path: MLQA.en.zh/validation-* - config_name: MLQA.es.ar data_files: - split: test path: MLQA.es.ar/test-* - split: validation path: MLQA.es.ar/validation-* - config_name: MLQA.es.de data_files: - split: test path: MLQA.es.de/test-* - split: validation path: MLQA.es.de/validation-* - config_name: MLQA.es.en data_files: - split: test path: MLQA.es.en/test-* - split: validation path: MLQA.es.en/validation-* - config_name: MLQA.es.es data_files: - split: test path: MLQA.es.es/test-* - split: validation path: MLQA.es.es/validation-* - config_name: MLQA.es.hi data_files: - split: test path: MLQA.es.hi/test-* - split: validation path: MLQA.es.hi/validation-* - config_name: MLQA.es.vi data_files: - split: test path: MLQA.es.vi/test-* - split: validation path: MLQA.es.vi/validation-* - config_name: MLQA.es.zh data_files: - split: test path: MLQA.es.zh/test-* - split: validation path: MLQA.es.zh/validation-* - config_name: MLQA.hi.ar data_files: - split: test path: MLQA.hi.ar/test-* - split: validation path: MLQA.hi.ar/validation-* - config_name: MLQA.hi.de data_files: - split: test path: MLQA.hi.de/test-* - split: validation path: MLQA.hi.de/validation-* - config_name: MLQA.hi.en data_files: - split: test path: MLQA.hi.en/test-* - split: validation path: MLQA.hi.en/validation-* - config_name: MLQA.hi.es data_files: - split: test path: MLQA.hi.es/test-* - split: validation path: MLQA.hi.es/validation-* - config_name: MLQA.hi.hi data_files: - split: test path: MLQA.hi.hi/test-* - split: validation path: MLQA.hi.hi/validation-* - config_name: MLQA.hi.vi data_files: - split: test path: MLQA.hi.vi/test-* - split: validation path: MLQA.hi.vi/validation-* - config_name: MLQA.hi.zh data_files: - split: test path: MLQA.hi.zh/test-* - split: validation path: MLQA.hi.zh/validation-* - config_name: MLQA.vi.ar data_files: - split: test path: MLQA.vi.ar/test-* - split: validation path: MLQA.vi.ar/validation-* - config_name: MLQA.vi.de data_files: - split: test path: MLQA.vi.de/test-* - split: validation path: MLQA.vi.de/validation-* - config_name: MLQA.vi.en data_files: - split: test path: MLQA.vi.en/test-* - split: validation path: MLQA.vi.en/validation-* - config_name: MLQA.vi.es data_files: - split: test path: MLQA.vi.es/test-* - split: validation path: MLQA.vi.es/validation-* - config_name: MLQA.vi.hi data_files: - split: test path: MLQA.vi.hi/test-* - split: validation path: MLQA.vi.hi/validation-* - config_name: MLQA.vi.vi data_files: - split: test path: MLQA.vi.vi/test-* - split: validation path: MLQA.vi.vi/validation-* - config_name: MLQA.vi.zh data_files: - split: test path: MLQA.vi.zh/test-* - split: validation path: MLQA.vi.zh/validation-* - config_name: MLQA.zh.ar data_files: - split: test path: MLQA.zh.ar/test-* - split: validation path: MLQA.zh.ar/validation-* - config_name: MLQA.zh.de data_files: - split: test path: MLQA.zh.de/test-* - split: validation path: MLQA.zh.de/validation-* - config_name: MLQA.zh.en data_files: - split: test path: MLQA.zh.en/test-* - split: validation path: MLQA.zh.en/validation-* - config_name: MLQA.zh.es data_files: - split: test path: MLQA.zh.es/test-* - split: validation path: MLQA.zh.es/validation-* - config_name: MLQA.zh.hi data_files: - split: test path: MLQA.zh.hi/test-* - split: validation path: MLQA.zh.hi/validation-* - config_name: MLQA.zh.vi data_files: - split: test path: MLQA.zh.vi/test-* - split: validation path: MLQA.zh.vi/validation-* - config_name: MLQA.zh.zh data_files: - split: test path: MLQA.zh.zh/test-* - split: validation path: MLQA.zh.zh/validation-* - config_name: PAN-X.af data_files: - split: train path: PAN-X.af/train-* - split: validation path: PAN-X.af/validation-* - split: test path: PAN-X.af/test-* - config_name: PAN-X.ar data_files: - split: train path: PAN-X.ar/train-* - split: validation path: PAN-X.ar/validation-* - split: test path: PAN-X.ar/test-* - config_name: PAN-X.bg data_files: - split: train path: PAN-X.bg/train-* - split: validation path: PAN-X.bg/validation-* - split: test path: PAN-X.bg/test-* - config_name: PAN-X.bn data_files: - split: train path: PAN-X.bn/train-* - split: validation path: PAN-X.bn/validation-* - split: test path: PAN-X.bn/test-* - config_name: PAN-X.de data_files: - split: train path: PAN-X.de/train-* - split: validation path: PAN-X.de/validation-* - split: test path: PAN-X.de/test-* - config_name: PAN-X.el data_files: - split: train path: PAN-X.el/train-* - split: validation path: PAN-X.el/validation-* - split: test path: PAN-X.el/test-* - config_name: PAN-X.en data_files: - split: train path: PAN-X.en/train-* - split: validation path: PAN-X.en/validation-* - split: test path: PAN-X.en/test-* - config_name: PAN-X.es data_files: - split: train path: PAN-X.es/train-* - split: validation path: PAN-X.es/validation-* - split: test path: PAN-X.es/test-* - config_name: PAN-X.et data_files: - split: train path: PAN-X.et/train-* - split: validation path: PAN-X.et/validation-* - split: test path: PAN-X.et/test-* - config_name: PAN-X.eu data_files: - split: train path: PAN-X.eu/train-* - split: validation path: PAN-X.eu/validation-* - split: test path: PAN-X.eu/test-* - config_name: PAN-X.fa data_files: - split: train path: PAN-X.fa/train-* - split: validation path: PAN-X.fa/validation-* - split: test path: PAN-X.fa/test-* - config_name: PAN-X.fi data_files: - split: train path: PAN-X.fi/train-* - split: validation path: PAN-X.fi/validation-* - split: test path: PAN-X.fi/test-* - config_name: PAN-X.fr data_files: - split: train path: PAN-X.fr/train-* - split: validation path: PAN-X.fr/validation-* - split: test path: PAN-X.fr/test-* - config_name: PAN-X.he data_files: - split: train path: PAN-X.he/train-* - split: validation path: PAN-X.he/validation-* - split: test path: PAN-X.he/test-* - config_name: PAN-X.hi data_files: - split: train path: PAN-X.hi/train-* - split: validation path: PAN-X.hi/validation-* - split: test path: PAN-X.hi/test-* - config_name: PAN-X.hu data_files: - split: train path: PAN-X.hu/train-* - split: validation path: PAN-X.hu/validation-* - split: test path: PAN-X.hu/test-* - config_name: PAN-X.id data_files: - split: train path: PAN-X.id/train-* - split: validation path: PAN-X.id/validation-* - split: test path: PAN-X.id/test-* - config_name: PAN-X.it data_files: - split: train path: PAN-X.it/train-* - split: validation path: PAN-X.it/validation-* - split: test path: PAN-X.it/test-* - config_name: PAN-X.ja data_files: - split: train path: PAN-X.ja/train-* - split: validation path: PAN-X.ja/validation-* - split: test path: PAN-X.ja/test-* - config_name: PAN-X.jv data_files: - split: train path: PAN-X.jv/train-* - split: validation path: PAN-X.jv/validation-* - split: test path: PAN-X.jv/test-* - config_name: PAN-X.ka data_files: - split: train path: PAN-X.ka/train-* - split: validation path: PAN-X.ka/validation-* - split: test path: PAN-X.ka/test-* - config_name: PAN-X.kk data_files: - split: train path: PAN-X.kk/train-* - split: validation path: PAN-X.kk/validation-* - split: test path: PAN-X.kk/test-* - config_name: PAN-X.ko data_files: - split: train path: PAN-X.ko/train-* - split: validation path: PAN-X.ko/validation-* - split: test path: PAN-X.ko/test-* - config_name: PAN-X.ml data_files: - split: train path: PAN-X.ml/train-* - split: validation path: PAN-X.ml/validation-* - split: test path: PAN-X.ml/test-* - config_name: PAN-X.mr data_files: - split: train path: PAN-X.mr/train-* - split: validation path: PAN-X.mr/validation-* - split: test path: PAN-X.mr/test-* - config_name: PAN-X.ms data_files: - split: train path: PAN-X.ms/train-* - split: validation path: PAN-X.ms/validation-* - split: test path: PAN-X.ms/test-* - config_name: PAN-X.my data_files: - split: train path: PAN-X.my/train-* - split: validation path: PAN-X.my/validation-* - split: test path: PAN-X.my/test-* - config_name: PAN-X.nl data_files: - split: train path: PAN-X.nl/train-* - split: validation path: PAN-X.nl/validation-* - split: test path: PAN-X.nl/test-* - config_name: PAN-X.pt data_files: - split: train path: PAN-X.pt/train-* - split: validation path: PAN-X.pt/validation-* - split: test path: PAN-X.pt/test-* - config_name: PAN-X.ru data_files: - split: train path: PAN-X.ru/train-* - split: validation path: PAN-X.ru/validation-* - split: test path: PAN-X.ru/test-* - config_name: PAN-X.sw data_files: - split: train path: PAN-X.sw/train-* - split: validation path: PAN-X.sw/validation-* - split: test path: PAN-X.sw/test-* - config_name: PAN-X.ta data_files: - split: train path: PAN-X.ta/train-* - split: validation path: PAN-X.ta/validation-* - split: test path: PAN-X.ta/test-* - config_name: PAN-X.te data_files: - split: train path: PAN-X.te/train-* - split: validation path: PAN-X.te/validation-* - split: test path: PAN-X.te/test-* - config_name: PAN-X.th data_files: - split: train path: PAN-X.th/train-* - split: validation path: PAN-X.th/validation-* - split: test path: PAN-X.th/test-* - config_name: PAN-X.tl data_files: - split: train path: PAN-X.tl/train-* - split: validation path: PAN-X.tl/validation-* - split: test path: PAN-X.tl/test-* - config_name: PAN-X.tr data_files: - split: train path: PAN-X.tr/train-* - split: validation path: PAN-X.tr/validation-* - split: test path: PAN-X.tr/test-* - config_name: PAN-X.ur data_files: - split: train path: PAN-X.ur/train-* - split: validation path: PAN-X.ur/validation-* - split: test path: PAN-X.ur/test-* - config_name: PAN-X.vi data_files: - split: train path: PAN-X.vi/train-* - split: validation path: PAN-X.vi/validation-* - split: test path: PAN-X.vi/test-* - config_name: PAN-X.yo data_files: - split: train path: PAN-X.yo/train-* - split: validation path: PAN-X.yo/validation-* - split: test path: PAN-X.yo/test-* - config_name: PAN-X.zh data_files: - split: train path: PAN-X.zh/train-* - split: validation path: PAN-X.zh/validation-* - split: test path: PAN-X.zh/test-* - config_name: PAWS-X.de data_files: - split: train path: PAWS-X.de/train-* - split: validation path: PAWS-X.de/validation-* - split: test path: PAWS-X.de/test-* - config_name: PAWS-X.en data_files: - split: train path: PAWS-X.en/train-* - split: validation path: PAWS-X.en/validation-* - split: test path: PAWS-X.en/test-* - config_name: PAWS-X.es data_files: - split: train path: PAWS-X.es/train-* - split: validation path: PAWS-X.es/validation-* - split: test path: PAWS-X.es/test-* - config_name: PAWS-X.fr data_files: - split: train path: PAWS-X.fr/train-* - split: validation path: PAWS-X.fr/validation-* - split: test path: PAWS-X.fr/test-* - config_name: PAWS-X.ja data_files: - split: train path: PAWS-X.ja/train-* - split: validation path: PAWS-X.ja/validation-* - split: test path: PAWS-X.ja/test-* - config_name: PAWS-X.ko data_files: - split: train path: PAWS-X.ko/train-* - split: validation path: PAWS-X.ko/validation-* - split: test path: PAWS-X.ko/test-* - config_name: PAWS-X.zh data_files: - split: train path: PAWS-X.zh/train-* - split: validation path: PAWS-X.zh/validation-* - split: test path: PAWS-X.zh/test-* - config_name: SQuAD data_files: - split: train path: SQuAD/train-* - split: validation path: SQuAD/validation-* - config_name: XNLI data_files: - split: test path: XNLI/test-* - split: validation path: XNLI/validation-* - config_name: XQuAD.ar data_files: - split: validation path: XQuAD.ar/validation-* - config_name: XQuAD.de data_files: - split: validation path: XQuAD.de/validation-* - config_name: XQuAD.el data_files: - split: validation path: XQuAD.el/validation-* - config_name: XQuAD.en data_files: - split: validation path: XQuAD.en/validation-* - config_name: XQuAD.es data_files: - split: validation path: XQuAD.es/validation-* - config_name: XQuAD.hi data_files: - split: validation path: XQuAD.hi/validation-* - config_name: XQuAD.ru data_files: - split: validation path: XQuAD.ru/validation-* - config_name: XQuAD.th data_files: - split: validation path: XQuAD.th/validation-* - config_name: XQuAD.tr data_files: - split: validation path: XQuAD.tr/validation-* - config_name: XQuAD.vi data_files: - split: validation path: XQuAD.vi/validation-* - config_name: XQuAD.zh data_files: - split: validation path: XQuAD.zh/validation-* - config_name: bucc18.de data_files: - split: validation path: bucc18.de/validation-* - split: test path: bucc18.de/test-* - config_name: bucc18.fr data_files: - split: validation path: bucc18.fr/validation-* - split: test path: bucc18.fr/test-* - config_name: bucc18.ru data_files: - split: validation path: bucc18.ru/validation-* - split: test path: bucc18.ru/test-* - config_name: bucc18.zh data_files: - split: validation path: bucc18.zh/validation-* - split: test path: bucc18.zh/test-* - config_name: tatoeba.afr data_files: - split: validation path: tatoeba.afr/validation-* - config_name: tatoeba.ara data_files: - split: validation path: tatoeba.ara/validation-* - config_name: tatoeba.ben data_files: - split: validation path: tatoeba.ben/validation-* - config_name: tatoeba.bul data_files: - split: validation path: tatoeba.bul/validation-* - config_name: tatoeba.cmn data_files: - split: validation path: tatoeba.cmn/validation-* - config_name: tatoeba.deu data_files: - split: validation path: tatoeba.deu/validation-* - config_name: tatoeba.ell data_files: - split: validation path: tatoeba.ell/validation-* - config_name: tatoeba.est data_files: - split: validation path: tatoeba.est/validation-* - config_name: tatoeba.eus data_files: - split: validation path: tatoeba.eus/validation-* - config_name: tatoeba.fin data_files: - split: validation path: tatoeba.fin/validation-* - config_name: tatoeba.fra data_files: - split: validation path: tatoeba.fra/validation-* - config_name: tatoeba.heb data_files: - split: validation path: tatoeba.heb/validation-* - config_name: tatoeba.hin data_files: - split: validation path: tatoeba.hin/validation-* - config_name: tatoeba.hun data_files: - split: validation path: tatoeba.hun/validation-* - config_name: tatoeba.ind data_files: - split: validation path: tatoeba.ind/validation-* - config_name: tatoeba.ita data_files: - split: validation path: tatoeba.ita/validation-* - config_name: tatoeba.jav data_files: - split: validation path: tatoeba.jav/validation-* - config_name: tatoeba.jpn data_files: - split: validation path: tatoeba.jpn/validation-* - config_name: tatoeba.kat data_files: - split: validation path: tatoeba.kat/validation-* - config_name: tatoeba.kaz data_files: - split: validation path: tatoeba.kaz/validation-* - config_name: tatoeba.kor data_files: - split: validation path: tatoeba.kor/validation-* - config_name: tatoeba.mal data_files: - split: validation path: tatoeba.mal/validation-* - config_name: tatoeba.mar data_files: - split: validation path: tatoeba.mar/validation-* - config_name: tatoeba.nld data_files: - split: validation path: tatoeba.nld/validation-* - config_name: tatoeba.pes data_files: - split: validation path: tatoeba.pes/validation-* - config_name: tatoeba.por data_files: - split: validation path: tatoeba.por/validation-* - config_name: tatoeba.rus data_files: - split: validation path: tatoeba.rus/validation-* - config_name: tatoeba.spa data_files: - split: validation path: tatoeba.spa/validation-* - config_name: tatoeba.swh data_files: - split: validation path: tatoeba.swh/validation-* - config_name: tatoeba.tam data_files: - split: validation path: tatoeba.tam/validation-* - config_name: tatoeba.tel data_files: - split: validation path: tatoeba.tel/validation-* - config_name: tatoeba.tgl data_files: - split: validation path: tatoeba.tgl/validation-* - config_name: tatoeba.tha data_files: - split: validation path: tatoeba.tha/validation-* - config_name: tatoeba.tur data_files: - split: validation path: tatoeba.tur/validation-* - config_name: tatoeba.urd data_files: - split: validation path: tatoeba.urd/validation-* - config_name: tatoeba.vie data_files: - split: validation path: tatoeba.vie/validation-* - config_name: tydiqa data_files: - split: train path: tydiqa/train-* - split: validation path: tydiqa/validation-* - config_name: udpos.Afrikaans data_files: - split: train path: udpos.Afrikaans/train-* - split: validation path: udpos.Afrikaans/validation-* - split: test path: udpos.Afrikaans/test-* - config_name: udpos.Arabic data_files: - split: train path: udpos.Arabic/train-* - split: validation path: udpos.Arabic/validation-* - split: test path: udpos.Arabic/test-* - config_name: udpos.Basque data_files: - split: train path: udpos.Basque/train-* - split: validation path: udpos.Basque/validation-* - split: test path: udpos.Basque/test-* - config_name: udpos.Bulgarian data_files: - split: train path: udpos.Bulgarian/train-* - split: validation path: udpos.Bulgarian/validation-* - split: test path: udpos.Bulgarian/test-* - config_name: udpos.Chinese data_files: - split: train path: udpos.Chinese/train-* - split: validation path: udpos.Chinese/validation-* - split: test path: udpos.Chinese/test-* - config_name: udpos.Dutch data_files: - split: train path: udpos.Dutch/train-* - split: validation path: udpos.Dutch/validation-* - split: test path: udpos.Dutch/test-* - config_name: udpos.English data_files: - split: train path: udpos.English/train-* - split: validation path: udpos.English/validation-* - split: test path: udpos.English/test-* - config_name: udpos.Estonian data_files: - split: train path: udpos.Estonian/train-* - split: validation path: udpos.Estonian/validation-* - split: test path: udpos.Estonian/test-* - config_name: udpos.Finnish data_files: - split: train path: udpos.Finnish/train-* - split: validation path: udpos.Finnish/validation-* - split: test path: udpos.Finnish/test-* - config_name: udpos.French data_files: - split: train path: udpos.French/train-* - split: validation path: udpos.French/validation-* - split: test path: udpos.French/test-* - config_name: udpos.German data_files: - split: train path: udpos.German/train-* - split: validation path: udpos.German/validation-* - split: test path: udpos.German/test-* - config_name: udpos.Greek data_files: - split: train path: udpos.Greek/train-* - split: validation path: udpos.Greek/validation-* - split: test path: udpos.Greek/test-* - config_name: udpos.Hebrew data_files: - split: train path: udpos.Hebrew/train-* - split: validation path: udpos.Hebrew/validation-* - split: test path: udpos.Hebrew/test-* - config_name: udpos.Hindi data_files: - split: train path: udpos.Hindi/train-* - split: validation path: udpos.Hindi/validation-* - split: test path: udpos.Hindi/test-* - config_name: udpos.Hungarian data_files: - split: train path: udpos.Hungarian/train-* - split: validation path: udpos.Hungarian/validation-* - split: test path: udpos.Hungarian/test-* - config_name: udpos.Indonesian data_files: - split: train path: udpos.Indonesian/train-* - split: validation path: udpos.Indonesian/validation-* - split: test path: udpos.Indonesian/test-* - config_name: udpos.Italian data_files: - split: train path: udpos.Italian/train-* - split: validation path: udpos.Italian/validation-* - split: test path: udpos.Italian/test-* - config_name: udpos.Japanese data_files: - split: train path: udpos.Japanese/train-* - split: validation path: udpos.Japanese/validation-* - split: test path: udpos.Japanese/test-* - config_name: udpos.Kazakh data_files: - split: train path: udpos.Kazakh/train-* - split: test path: udpos.Kazakh/test-* - config_name: udpos.Korean data_files: - split: train path: udpos.Korean/train-* - split: validation path: udpos.Korean/validation-* - split: test path: udpos.Korean/test-* - config_name: udpos.Marathi data_files: - split: train path: udpos.Marathi/train-* - split: validation path: udpos.Marathi/validation-* - split: test path: udpos.Marathi/test-* - config_name: udpos.Persian data_files: - split: train path: udpos.Persian/train-* - split: validation path: udpos.Persian/validation-* - split: test path: udpos.Persian/test-* - config_name: udpos.Portuguese data_files: - split: train path: udpos.Portuguese/train-* - split: validation path: udpos.Portuguese/validation-* - split: test path: udpos.Portuguese/test-* - config_name: udpos.Russian data_files: - split: train path: udpos.Russian/train-* - split: validation path: udpos.Russian/validation-* - split: test path: udpos.Russian/test-* - config_name: udpos.Spanish data_files: - split: train path: udpos.Spanish/train-* - split: validation path: udpos.Spanish/validation-* - split: test path: udpos.Spanish/test-* - config_name: udpos.Tagalog data_files: - split: test path: udpos.Tagalog/test-* - config_name: udpos.Tamil data_files: - split: train path: udpos.Tamil/train-* - split: validation path: udpos.Tamil/validation-* - split: test path: udpos.Tamil/test-* - config_name: udpos.Telugu data_files: - split: train path: udpos.Telugu/train-* - split: validation path: udpos.Telugu/validation-* - split: test path: udpos.Telugu/test-* - config_name: udpos.Thai data_files: - split: test path: udpos.Thai/test-* - config_name: udpos.Turkish data_files: - split: train path: udpos.Turkish/train-* - split: validation path: udpos.Turkish/validation-* - split: test path: udpos.Turkish/test-* - config_name: udpos.Urdu data_files: - split: train path: udpos.Urdu/train-* - split: validation path: udpos.Urdu/validation-* - split: test path: udpos.Urdu/test-* - config_name: udpos.Vietnamese data_files: - split: train path: udpos.Vietnamese/train-* - split: validation path: udpos.Vietnamese/validation-* - split: test path: udpos.Vietnamese/test-* - config_name: udpos.Yoruba data_files: - split: test path: udpos.Yoruba/test-* --- # Dataset Card for "xtreme" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [https://github.com/google-research/xtreme](https://github.com/google-research/xtreme) - Repository: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Paper: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Point of Contact: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Size of downloaded dataset files: 15.88 GB - Size of the generated dataset: 1.08 GB - Total amount of disk used: 16.96 GB ### Dataset Summary The Cross-lingual Natural Language Inference (XNLI) corpus is a crowd-sourced collection of 5,000 test and 2,500 dev pairs for the MultiNLI corpus. The pairs are annotated with textual entailment and translated into 14 languages: French, Spanish, German, Greek, Bulgarian, Russian, Turkish, Arabic, Vietnamese, Thai, Chinese, Hindi, Swahili and Urdu. This results in 112.5k annotated pairs. Each premise can be associated with the corresponding hypothesis in the 15 languages, summing up to more than 1.5M combinations. The corpus is made to evaluate how to perform inference in any language (including low-resources ones like Swahili or Urdu) when only English NLI data is available at training time. One solution is cross-lingual sentence encoding, for which XNLI is an evaluation benchmark. The Cross-lingual TRansfer Evaluation of Multilingual Encoders (XTREME) benchmark is a benchmark for the evaluation of the cross-lingual generalization ability of pre-trained multilingual models. It covers 40 typologically diverse languages (spanning 12 language families) and includes nine tasks that collectively require reasoning about different levels of syntax and semantics. The languages in XTREME are selected to maximize language diversity, coverage in existing tasks, and availability of training data. Among these are many under-studied languages, such as the Dravidian languages Tamil (spoken in southern India, Sri Lanka, and Singapore), Telugu and Malayalam (spoken mainly in southern India), and the Niger-Congo languages Swahili and Yoruba, spoken in Africa. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### MLQA.ar.ar - Size of downloaded dataset files: 75.72 MB - Size of the generated dataset: 9.20 MB - Total amount of disk used: 84.91 MB An example of 'validation' looks as follows. ``` ``` #### MLQA.ar.de - Size of downloaded dataset files: 75.72 MB - Size of the generated dataset: 2.55 MB - Total amount of disk used: 78.27 MB An example of 'validation' looks as follows. ``` ``` #### MLQA.ar.en - Size of downloaded dataset files: 75.72 MB - Size of the generated dataset: 9.04 MB - Total amount of disk used: 84.76 MB An example of 'validation' looks as follows. ``` ``` #### MLQA.ar.es - Size of downloaded dataset files: 75.72 MB - Size of the generated dataset: 3.27 MB - Total amount of disk used: 78.99 MB An example of 'validation' looks as follows. ``` ``` #### MLQA.ar.hi - Size of downloaded dataset files: 75.72 MB - Size of the generated dataset: 3.32 MB - Total amount of disk used: 79.04 MB An example of 'validation' looks as follows. ``` ``` ### Data Fields The data fields are the same among all splits. #### MLQA.ar.ar - `id`: a `string` feature. - `title`: a `string` feature. - `context`: a `string` feature. - `question`: a `string` feature. - `answers`: a dictionary feature containing: - `answer_start`: a `int32` feature. - `text`: a `string` feature. #### MLQA.ar.de - `id`: a `string` feature. - `title`: a `string` feature. - `context`: a `string` feature. - `question`: a `string` feature. - `answers`: a dictionary feature containing: - `answer_start`: a `int32` feature. - `text`: a `string` feature. #### MLQA.ar.en - `id`: a `string` feature. - `title`: a `string` feature. - `context`: a `string` feature. - `question`: a `string` feature. - `answers`: a dictionary feature containing: - `answer_start`: a `int32` feature. - `text`: a `string` feature. #### MLQA.ar.es - `id`: a `string` feature. - `title`: a `string` feature. - `context`: a `string` feature. - `question`: a `string` feature. - `answers`: a dictionary feature containing: - `answer_start`: a `int32` feature. - `text`: a `string` feature. #### MLQA.ar.hi - `id`: a `string` feature. - `title`: a `string` feature. - `context`: a `string` feature. - `question`: a `string` feature. - `answers`: a dictionary feature containing: - `answer_start`: a `int32` feature. - `text`: a `string` feature. ### Data Splits \| name \|validation\|test\| \|----------\|---------:\|---:\| \|MLQA.ar.ar\| 517\|5335\| \|MLQA.ar.de\| 207\|1649\| \|MLQA.ar.en\| 517\|5335\| \|MLQA.ar.es\| 161\|1978\| \|MLQA.ar.hi\| 186\|1831\| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @InProceedings{conneau2018xnli, author = {Conneau, Alexis and Rinott, Ruty and Lample, Guillaume and Williams, Adina and Bowman, Samuel R. and Schwenk, Holger and Stoyanov, Veselin}, title = {XNLI: Evaluating Cross-lingual Sentence Representations}, booktitle = {Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing}, year = {2018}, publisher = {Association for Computational Linguistics}, location = {Brussels, Belgium}, } @article{hu2020xtreme, author = {Junjie Hu and Sebastian Ruder and Aditya Siddhant and Graham Neubig and Orhan Firat and Melvin Johnson}, title = {XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalization}, journal = {CoRR}, volume = {abs/2003.11080}, year = {2020}, archivePrefix = {arXiv}, eprint = {2003.11080} } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@jplu](https://github.com/jplu), [@lewtun](https://github.com/lewtun), [@lvwerra](https://github.com/lvwerra), [@lhoestq](https://github.com/lhoestq), [@patrickvonplaten](https://github.com/patrickvonplaten), [@mariamabarham](https://github.com/mariamabarham) for adding this dataset.
unimelb-nlp/wikiann	unimelb-nlp	2024-02-22T14:32:02Z	109,759	106	[ "task_categories:token-classification", "task_ids:named-entity-recognition", "annotations_creators:machine-generated", "language_creators:crowdsourced", "multilinguality:multilingual", "source_datasets:original", "language:ace", "language:af", "language:als", "language:am", "language:an", "language:ang", "language:ar", "language:arc", "language:arz", "language:as", "language:ast", "language:ay", "language:az", "language:ba", "language:bar", "language:be", "language:bg", "language:bh", "language:bn", "language:bo", "language:br", "language:bs", "language:ca", "language:cbk", "language:cdo", "language:ce", "language:ceb", "language:ckb", "language:co", "language:crh", "language:cs", "language:csb", "language:cv", "language:cy", "language:da", "language:de", "language:diq", "language:dv", "language:el", "language:eml", "language:en", "language:eo", "language:es", "language:et", "language:eu", "language:ext", "language:fa", "language:fi", "language:fo", "language:fr", "language:frr", "language:fur", "language:fy", "language:ga", "language:gan", "language:gd", "language:gl", "language:gn", "language:gu", "language:hak", "language:he", "language:hi", "language:hr", "language:hsb", "language:hu", "language:hy", "language:ia", "language:id", "language:ig", "language:ilo", "language:io", "language:is", "language:it", "language:ja", "language:jbo", "language:jv", "language:ka", "language:kk", "language:km", "language:kn", "language:ko", "language:ksh", "language:ku", "language:ky", "language:la", "language:lb", "language:li", "language:lij", "language:lmo", "language:ln", "language:lt", "language:lv", "language:lzh", "language:mg", "language:mhr", "language:mi", "language:min", "language:mk", "language:ml", "language:mn", "language:mr", "language:ms", "language:mt", "language:mwl", "language:my", "language:mzn", "language:nan", "language:nap", "language:nds", "language:ne", "language:nl", "language:nn", "language:no", "language:nov", "language:oc", "language:or", "language:os", "language:pa", "language:pdc", "language:pl", "language:pms", "language:pnb", "language:ps", "language:pt", "language:qu", "language:rm", "language:ro", "language:ru", "language:rw", "language:sa", "language:sah", "language:scn", "language:sco", "language:sd", "language:sgs", "language:sh", "language:si", "language:sk", "language:sl", "language:so", "language:sq", "language:sr", "language:su", "language:sv", "language:sw", "language:szl", "language:ta", "language:te", "language:tg", "language:th", "language:tk", "language:tl", "language:tr", "language:tt", "language:ug", "language:uk", "language:ur", "language:uz", "language:vec", "language:vep", "language:vi", "language:vls", "language:vo", "language:vro", "language:wa", "language:war", "language:wuu", "language:xmf", "language:yi", "language:yo", "language:yue", "language:zea", "language:zh", "license:unknown", "size_categories:1M<n<10M", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:1902.00193", "region:us" ]	[ "token-classification" ]	2022-03-02T23:29:22Z	null	--- annotations_creators: - machine-generated language_creators: - crowdsourced language: - ace - af - als - am - an - ang - ar - arc - arz - as - ast - ay - az - ba - bar - be - bg - bh - bn - bo - br - bs - ca - cbk - cdo - ce - ceb - ckb - co - crh - cs - csb - cv - cy - da - de - diq - dv - el - eml - en - eo - es - et - eu - ext - fa - fi - fo - fr - frr - fur - fy - ga - gan - gd - gl - gn - gu - hak - he - hi - hr - hsb - hu - hy - ia - id - ig - ilo - io - is - it - ja - jbo - jv - ka - kk - km - kn - ko - ksh - ku - ky - la - lb - li - lij - lmo - ln - lt - lv - lzh - mg - mhr - mi - min - mk - ml - mn - mr - ms - mt - mwl - my - mzn - nan - nap - nds - ne - nl - nn - 'no' - nov - oc - or - os - pa - pdc - pl - pms - pnb - ps - pt - qu - rm - ro - ru - rw - sa - sah - scn - sco - sd - sgs - sh - si - sk - sl - so - sq - sr - su - sv - sw - szl - ta - te - tg - th - tk - tl - tr - tt - ug - uk - ur - uz - vec - vep - vi - vls - vo - vro - wa - war - wuu - xmf - yi - yo - yue - zea - zh license: - unknown multilinguality: - multilingual size_categories: - n<1K source_datasets: - original task_categories: - token-classification task_ids: - named-entity-recognition paperswithcode_id: wikiann-1 pretty_name: WikiANN config_names: - 'no' - ace - af - als - am - an - ang - ar - arc - arz - as - ast - ay - az - ba - bar - be - bg - bh - bn - bo - br - bs - ca - cdo - ce - ceb - ckb - co - crh - cs - csb - cv - cy - da - de - diq - dv - el - en - eo - es - et - eu - ext - fa - fi - fo - fr - frr - fur - fy - ga - gan - gd - gl - gn - gu - hak - he - hi - hr - hsb - hu - hy - ia - id - ig - ilo - io - is - it - ja - jbo - jv - ka - kk - km - kn - ko - ksh - ku - ky - la - lb - li - lij - lmo - ln - lt - lv - mg - mhr - mi - min - mk - ml - mn - mr - ms - mt - mwl - my - mzn - nap - nds - ne - nl - nn - nov - oc - or - os - other-bat-smg - other-be-x-old - other-cbk-zam - other-eml - other-fiu-vro - other-map-bms - other-simple - other-zh-classical - other-zh-min-nan - other-zh-yue - pa - pdc - pl - pms - pnb - ps - pt - qu - rm - ro - ru - rw - sa - sah - scn - sco - sd - sh - si - sk - sl - so - sq - sr - su - sv - sw - szl - ta - te - tg - th - tk - tl - tr - tt - ug - uk - ur - uz - vec - vep - vi - vls - vo - wa - war - wuu - xmf - yi - yo - zea - zh language_bcp47: - be-tarask - en-basiceng - jv-x-bms dataset_info: - config_name: ace features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 22425 num_examples: 100 - name: test num_bytes: 25724 num_examples: 100 - name: train num_bytes: 23203 num_examples: 100 download_size: 27835 dataset_size: 71352 - config_name: af features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 299109 num_examples: 1000 - name: test num_bytes: 295821 num_examples: 1000 - name: train num_bytes: 1521576 num_examples: 5000 download_size: 528580 dataset_size: 2116506 - config_name: als features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 34290 num_examples: 100 - name: test num_bytes: 36317 num_examples: 100 - name: train num_bytes: 34940 num_examples: 100 download_size: 40186 dataset_size: 105547 - config_name: am features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 21401 num_examples: 100 - name: test num_bytes: 23783 num_examples: 100 - name: train num_bytes: 22186 num_examples: 100 download_size: 30287 dataset_size: 67370 - config_name: an features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 180581 num_examples: 1000 - name: test num_bytes: 174964 num_examples: 1000 - name: train num_bytes: 180939 num_examples: 1000 download_size: 128283 dataset_size: 536484 - config_name: ang features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 21897 num_examples: 100 - name: test num_bytes: 24495 num_examples: 100 - name: train num_bytes: 23268 num_examples: 100 download_size: 30667 dataset_size: 69660 - config_name: ar features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 2325660 num_examples: 10000 - name: test num_bytes: 2334636 num_examples: 10000 - name: train num_bytes: 4671613 num_examples: 20000 download_size: 2582112 dataset_size: 9331909 - config_name: arc features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 15698 num_examples: 100 - name: test num_bytes: 16613 num_examples: 100 - name: train num_bytes: 18508 num_examples: 100 download_size: 22858 dataset_size: 50819 - config_name: arz features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 26581 num_examples: 100 - name: test num_bytes: 25635 num_examples: 100 - name: train num_bytes: 26347 num_examples: 100 download_size: 32301 dataset_size: 78563 - config_name: as features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 25708 num_examples: 100 - name: test num_bytes: 23322 num_examples: 100 - name: train num_bytes: 24956 num_examples: 100 download_size: 30404 dataset_size: 73986 - config_name: ast features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 217449 num_examples: 1000 - name: test num_bytes: 220846 num_examples: 1000 - name: train num_bytes: 228210 num_examples: 1000 download_size: 157002 dataset_size: 666505 - config_name: ay features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 11656 num_examples: 100 - name: test num_bytes: 13351 num_examples: 100 - name: train num_bytes: 12568 num_examples: 100 download_size: 16901 dataset_size: 37575 - config_name: az features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 272038 num_examples: 1000 - name: test num_bytes: 267907 num_examples: 1000 - name: train num_bytes: 2645524 num_examples: 10000 download_size: 931014 dataset_size: 3185469 - config_name: ba features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 29234 num_examples: 100 - name: test num_bytes: 30474 num_examples: 100 - name: train num_bytes: 31095 num_examples: 100 download_size: 36848 dataset_size: 90803 - config_name: bar features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 17346 num_examples: 100 - name: test num_bytes: 17811 num_examples: 100 - name: train num_bytes: 16768 num_examples: 100 download_size: 21987 dataset_size: 51925 - config_name: bat-smg features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 26468 num_examples: 100 - name: test num_bytes: 26065 num_examples: 100 - name: train num_bytes: 24649 num_examples: 100 download_size: 31533 dataset_size: 77182 - config_name: be features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 262014 num_examples: 1000 - name: test num_bytes: 266076 num_examples: 1000 - name: train num_bytes: 3983266 num_examples: 15000 download_size: 1283568 dataset_size: 4511356 - config_name: be-x-old features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 342626 num_examples: 1000 - name: test num_bytes: 337571 num_examples: 1000 - name: train num_bytes: 1704228 num_examples: 5000 download_size: 586037 dataset_size: 2384425 - config_name: bg features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 2840879 num_examples: 10000 - name: test num_bytes: 2830185 num_examples: 10000 - name: train num_bytes: 5665007 num_examples: 20000 download_size: 3010319 dataset_size: 11336071 - config_name: bh features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 33654 num_examples: 100 - name: test num_bytes: 30664 num_examples: 100 - name: train num_bytes: 36346 num_examples: 100 download_size: 34563 dataset_size: 100664 - config_name: bn features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 238418 num_examples: 1000 - name: test num_bytes: 237190 num_examples: 1000 - name: train num_bytes: 2351563 num_examples: 10000 download_size: 667399 dataset_size: 2827171 - config_name: bo features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 22660 num_examples: 100 - name: test num_bytes: 15409 num_examples: 100 - name: train num_bytes: 14057 num_examples: 100 download_size: 26274 dataset_size: 52126 - config_name: br features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 206811 num_examples: 1000 - name: test num_bytes: 222055 num_examples: 1000 - name: train num_bytes: 221467 num_examples: 1000 download_size: 193001 dataset_size: 650333 - config_name: bs features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 246350 num_examples: 1000 - name: test num_bytes: 247303 num_examples: 1000 - name: train num_bytes: 3669290 num_examples: 15000 download_size: 1145992 dataset_size: 4162943 - config_name: ca features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 1836291 num_examples: 10000 - name: test num_bytes: 1847718 num_examples: 10000 - name: train num_bytes: 3689286 num_examples: 20000 download_size: 2392551 dataset_size: 7373295 - config_name: cbk-zam features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 47032 num_examples: 100 - name: test num_bytes: 47249 num_examples: 100 - name: train num_bytes: 52517 num_examples: 100 download_size: 37209 dataset_size: 146798 - config_name: cdo features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 37451 num_examples: 100 - name: test num_bytes: 34291 num_examples: 100 - name: train num_bytes: 36176 num_examples: 100 download_size: 34997 dataset_size: 107918 - config_name: ce features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 40275 num_examples: 100 - name: test num_bytes: 38612 num_examples: 100 - name: train num_bytes: 38256 num_examples: 100 download_size: 34386 dataset_size: 117143 - config_name: ceb features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 22761 num_examples: 100 - name: test num_bytes: 23922 num_examples: 100 - name: train num_bytes: 21337 num_examples: 100 download_size: 27030 dataset_size: 68020 - config_name: ckb features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 214203 num_examples: 1000 - name: test num_bytes: 211960 num_examples: 1000 - name: train num_bytes: 217038 num_examples: 1000 download_size: 148534 dataset_size: 643201 - config_name: co features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 15940 num_examples: 100 - name: test num_bytes: 15852 num_examples: 100 - name: train num_bytes: 18004 num_examples: 100 download_size: 25539 dataset_size: 49796 - config_name: crh features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 20202 num_examples: 100 - name: test num_bytes: 23851 num_examples: 100 - name: train num_bytes: 23308 num_examples: 100 download_size: 29468 dataset_size: 67361 - config_name: cs features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 2456626 num_examples: 10000 - name: test num_bytes: 2458127 num_examples: 10000 - name: train num_bytes: 4944702 num_examples: 20000 download_size: 3028120 dataset_size: 9859455 - config_name: csb features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 28813 num_examples: 100 - name: test num_bytes: 27812 num_examples: 100 - name: train num_bytes: 31612 num_examples: 100 download_size: 35313 dataset_size: 88237 - config_name: cv features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 24759 num_examples: 100 - name: test num_bytes: 26375 num_examples: 100 - name: train num_bytes: 26928 num_examples: 100 download_size: 32018 dataset_size: 78062 - config_name: cy features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 228558 num_examples: 1000 - name: test num_bytes: 233841 num_examples: 1000 - name: train num_bytes: 2337088 num_examples: 10000 download_size: 630636 dataset_size: 2799487 - config_name: da features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 2422948 num_examples: 10000 - name: test num_bytes: 2432296 num_examples: 10000 - name: train num_bytes: 4882166 num_examples: 20000 download_size: 2903455 dataset_size: 9737410 - config_name: de features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 2754522 num_examples: 10000 - name: test num_bytes: 2750968 num_examples: 10000 - name: train num_bytes: 5510585 num_examples: 20000 download_size: 3340116 dataset_size: 11016075 - config_name: diq features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 24119 num_examples: 100 - name: test num_bytes: 22448 num_examples: 100 - name: train num_bytes: 24103 num_examples: 100 download_size: 29511 dataset_size: 70670 - config_name: dv features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 30294 num_examples: 100 - name: test num_bytes: 27251 num_examples: 100 - name: train num_bytes: 31005 num_examples: 100 download_size: 36181 dataset_size: 88550 - config_name: el features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 3027934 num_examples: 10000 - name: test num_bytes: 3034301 num_examples: 10000 - name: train num_bytes: 6046582 num_examples: 20000 download_size: 3212871 dataset_size: 12108817 - config_name: eml features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 30022 num_examples: 100 - name: test num_bytes: 35852 num_examples: 100 - name: train num_bytes: 30764 num_examples: 100 download_size: 35629 dataset_size: 96638 - config_name: en features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 2336325 num_examples: 10000 - name: test num_bytes: 2330217 num_examples: 10000 - name: train num_bytes: 4649545 num_examples: 20000 download_size: 2990984 dataset_size: 9316087 - config_name: eo features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 1968662 num_examples: 10000 - name: test num_bytes: 1961458 num_examples: 10000 - name: train num_bytes: 2952554 num_examples: 15000 download_size: 2147812 dataset_size: 6882674 - config_name: es features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 1976907 num_examples: 10000 - name: test num_bytes: 1986636 num_examples: 10000 - name: train num_bytes: 3972236 num_examples: 20000 download_size: 2431958 dataset_size: 7935779 - config_name: et features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 2403333 num_examples: 10000 - name: test num_bytes: 2392396 num_examples: 10000 - name: train num_bytes: 3579208 num_examples: 15000 download_size: 2678718 dataset_size: 8374937 - config_name: eu features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 2677008 num_examples: 10000 - name: test num_bytes: 2628923 num_examples: 10000 - name: train num_bytes: 2672325 num_examples: 10000 download_size: 1985966 dataset_size: 7978256 - config_name: ext features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 30793 num_examples: 100 - name: test num_bytes: 29455 num_examples: 100 - name: train num_bytes: 23082 num_examples: 100 download_size: 32111 dataset_size: 83330 - config_name: fa features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 2328612 num_examples: 10000 - name: test num_bytes: 2314659 num_examples: 10000 - name: train num_bytes: 4618042 num_examples: 20000 download_size: 2385463 dataset_size: 9261313 - config_name: fi features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 2500558 num_examples: 10000 - name: test num_bytes: 2505133 num_examples: 10000 - name: train num_bytes: 5020599 num_examples: 20000 download_size: 3407283 dataset_size: 10026290 - config_name: fiu-vro features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 27644 num_examples: 100 - name: test num_bytes: 27700 num_examples: 100 - name: train num_bytes: 28661 num_examples: 100 download_size: 31399 dataset_size: 84005 - config_name: fo features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 26066 num_examples: 100 - name: test num_bytes: 23503 num_examples: 100 - name: train num_bytes: 26150 num_examples: 100 download_size: 33699 dataset_size: 75719 - config_name: fr features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 2057976 num_examples: 10000 - name: test num_bytes: 2073565 num_examples: 10000 - name: train num_bytes: 4123939 num_examples: 20000 download_size: 2694633 dataset_size: 8255480 - config_name: frr features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 15855 num_examples: 100 - name: test num_bytes: 15708 num_examples: 100 - name: train num_bytes: 16626 num_examples: 100 download_size: 25130 dataset_size: 48189 - config_name: fur features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 25236 num_examples: 100 - name: test num_bytes: 30534 num_examples: 100 - name: train num_bytes: 33626 num_examples: 100 download_size: 32754 dataset_size: 89396 - config_name: fy features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 226408 num_examples: 1000 - name: test num_bytes: 229672 num_examples: 1000 - name: train num_bytes: 222985 num_examples: 1000 download_size: 182402 dataset_size: 679065 - config_name: ga features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 234064 num_examples: 1000 - name: test num_bytes: 235055 num_examples: 1000 - name: train num_bytes: 238019 num_examples: 1000 download_size: 198615 dataset_size: 707138 - config_name: gan features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 17505 num_examples: 100 - name: test num_bytes: 13851 num_examples: 100 - name: train num_bytes: 14370 num_examples: 100 download_size: 28600 dataset_size: 45726 - config_name: gd features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 23202 num_examples: 100 - name: test num_bytes: 20280 num_examples: 100 - name: train num_bytes: 20126 num_examples: 100 download_size: 29305 dataset_size: 63608 - config_name: gl features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 2029655 num_examples: 10000 - name: test num_bytes: 2031122 num_examples: 10000 - name: train num_bytes: 3030937 num_examples: 15000 download_size: 2045672 dataset_size: 7091714 - config_name: gn features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 29104 num_examples: 100 - name: test num_bytes: 24235 num_examples: 100 - name: train num_bytes: 28192 num_examples: 100 download_size: 35600 dataset_size: 81531 - config_name: gu features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 47981 num_examples: 100 - name: test num_bytes: 45389 num_examples: 100 - name: train num_bytes: 42597 num_examples: 100 download_size: 44658 dataset_size: 135967 - config_name: hak features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 17949 num_examples: 100 - name: test num_bytes: 18127 num_examples: 100 - name: train num_bytes: 16180 num_examples: 100 download_size: 27841 dataset_size: 52256 - config_name: he features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 2801364 num_examples: 10000 - name: test num_bytes: 2785446 num_examples: 10000 - name: train num_bytes: 5600432 num_examples: 20000 download_size: 3112250 dataset_size: 11187242 - config_name: hi features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 261179 num_examples: 1000 - name: test num_bytes: 267227 num_examples: 1000 - name: train num_bytes: 1315801 num_examples: 5000 download_size: 441664 dataset_size: 1844207 - config_name: hr features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 2417422 num_examples: 10000 - name: test num_bytes: 2430412 num_examples: 10000 - name: train num_bytes: 4877275 num_examples: 20000 download_size: 2965267 dataset_size: 9725109 - config_name: hsb features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 24667 num_examples: 100 - name: test num_bytes: 24320 num_examples: 100 - name: train num_bytes: 24200 num_examples: 100 download_size: 31799 dataset_size: 73187 - config_name: hu features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 2590088 num_examples: 10000 - name: test num_bytes: 2626743 num_examples: 10000 - name: train num_bytes: 5263066 num_examples: 20000 download_size: 3333477 dataset_size: 10479897 - config_name: hy features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 237532 num_examples: 1000 - name: test num_bytes: 237093 num_examples: 1000 - name: train num_bytes: 3634009 num_examples: 15000 download_size: 1179988 dataset_size: 4108634 - config_name: ia features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 32036 num_examples: 100 - name: test num_bytes: 37589 num_examples: 100 - name: train num_bytes: 32900 num_examples: 100 download_size: 38484 dataset_size: 102525 - config_name: id features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 1901597 num_examples: 10000 - name: test num_bytes: 1902704 num_examples: 10000 - name: train num_bytes: 3813991 num_examples: 20000 download_size: 2199732 dataset_size: 7618292 - config_name: ig features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 17693 num_examples: 100 - name: test num_bytes: 18404 num_examples: 100 - name: train num_bytes: 15960 num_examples: 100 download_size: 22605 dataset_size: 52057 - config_name: ilo features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 16647 num_examples: 100 - name: test num_bytes: 17217 num_examples: 100 - name: train num_bytes: 17124 num_examples: 100 download_size: 23906 dataset_size: 50988 - config_name: io features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 18998 num_examples: 100 - name: test num_bytes: 17203 num_examples: 100 - name: train num_bytes: 20753 num_examples: 100 download_size: 27554 dataset_size: 56954 - config_name: is features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 243639 num_examples: 1000 - name: test num_bytes: 235918 num_examples: 1000 - name: train num_bytes: 243437 num_examples: 1000 download_size: 210731 dataset_size: 722994 - config_name: it features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 2282919 num_examples: 10000 - name: test num_bytes: 2307590 num_examples: 10000 - name: train num_bytes: 4633519 num_examples: 20000 download_size: 2818124 dataset_size: 9224028 - config_name: ja features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 6775580 num_examples: 10000 - name: test num_bytes: 6898510 num_examples: 10000 - name: train num_bytes: 13578269 num_examples: 20000 download_size: 3415775 dataset_size: 27252359 - config_name: jbo features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 15590 num_examples: 100 - name: test num_bytes: 19558 num_examples: 100 - name: train num_bytes: 15042 num_examples: 100 download_size: 22634 dataset_size: 50190 - config_name: jv features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 17663 num_examples: 100 - name: test num_bytes: 20175 num_examples: 100 - name: train num_bytes: 19381 num_examples: 100 download_size: 28541 dataset_size: 57219 - config_name: ka features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 3454353 num_examples: 10000 - name: test num_bytes: 3480842 num_examples: 10000 - name: train num_bytes: 3427980 num_examples: 10000 download_size: 2588715 dataset_size: 10363175 - config_name: kk features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 286474 num_examples: 1000 - name: test num_bytes: 284475 num_examples: 1000 - name: train num_bytes: 287924 num_examples: 1000 download_size: 217890 dataset_size: 858873 - config_name: km features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 29282 num_examples: 100 - name: test num_bytes: 36073 num_examples: 100 - name: train num_bytes: 31910 num_examples: 100 download_size: 43075 dataset_size: 97265 - config_name: kn features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 36825 num_examples: 100 - name: test num_bytes: 32250 num_examples: 100 - name: train num_bytes: 34318 num_examples: 100 download_size: 43835 dataset_size: 103393 - config_name: ko features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 2553040 num_examples: 10000 - name: test num_bytes: 2547772 num_examples: 10000 - name: train num_bytes: 5107034 num_examples: 20000 download_size: 3536508 dataset_size: 10207846 - config_name: ksh features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 26310 num_examples: 100 - name: test num_bytes: 25221 num_examples: 100 - name: train num_bytes: 25913 num_examples: 100 download_size: 33350 dataset_size: 77444 - config_name: ku features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 22569 num_examples: 100 - name: test num_bytes: 20767 num_examples: 100 - name: train num_bytes: 22641 num_examples: 100 download_size: 30470 dataset_size: 65977 - config_name: ky features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 30982 num_examples: 100 - name: test num_bytes: 31868 num_examples: 100 - name: train num_bytes: 32740 num_examples: 100 download_size: 41036 dataset_size: 95590 - config_name: la features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 207177 num_examples: 1000 - name: test num_bytes: 198882 num_examples: 1000 - name: train num_bytes: 999022 num_examples: 5000 download_size: 367324 dataset_size: 1405081 - config_name: lb features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 253746 num_examples: 1000 - name: test num_bytes: 249961 num_examples: 1000 - name: train num_bytes: 1260911 num_examples: 5000 download_size: 477151 dataset_size: 1764618 - config_name: li features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 20173 num_examples: 100 - name: test num_bytes: 18789 num_examples: 100 - name: train num_bytes: 20183 num_examples: 100 download_size: 28842 dataset_size: 59145 - config_name: lij features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 27977 num_examples: 100 - name: test num_bytes: 27854 num_examples: 100 - name: train num_bytes: 30553 num_examples: 100 download_size: 33981 dataset_size: 86384 - config_name: lmo features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 26547 num_examples: 100 - name: test num_bytes: 29425 num_examples: 100 - name: train num_bytes: 24133 num_examples: 100 download_size: 32492 dataset_size: 80105 - config_name: ln features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 21681 num_examples: 100 - name: test num_bytes: 26975 num_examples: 100 - name: train num_bytes: 22199 num_examples: 100 download_size: 28691 dataset_size: 70855 - config_name: lt features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 2192846 num_examples: 10000 - name: test num_bytes: 2191241 num_examples: 10000 - name: train num_bytes: 2199918 num_examples: 10000 download_size: 2138545 dataset_size: 6584005 - config_name: lv features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 2173392 num_examples: 10000 - name: test num_bytes: 2190430 num_examples: 10000 - name: train num_bytes: 2206915 num_examples: 10000 download_size: 2012494 dataset_size: 6570737 - config_name: map-bms features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 19752 num_examples: 100 - name: test num_bytes: 20530 num_examples: 100 - name: train num_bytes: 21611 num_examples: 100 download_size: 25217 dataset_size: 61893 - config_name: mg features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 24833 num_examples: 100 - name: test num_bytes: 22542 num_examples: 100 - name: train num_bytes: 25711 num_examples: 100 download_size: 26980 dataset_size: 73086 - config_name: mhr features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 23235 num_examples: 100 - name: test num_bytes: 23611 num_examples: 100 - name: train num_bytes: 18620 num_examples: 100 download_size: 29844 dataset_size: 65466 - config_name: mi features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 39371 num_examples: 100 - name: test num_bytes: 40119 num_examples: 100 - name: train num_bytes: 37868 num_examples: 100 download_size: 24626 dataset_size: 117358 - config_name: min features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 28691 num_examples: 100 - name: test num_bytes: 24713 num_examples: 100 - name: train num_bytes: 26592 num_examples: 100 download_size: 31058 dataset_size: 79996 - config_name: mk features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 333165 num_examples: 1000 - name: test num_bytes: 337729 num_examples: 1000 - name: train num_bytes: 3355908 num_examples: 10000 download_size: 825847 dataset_size: 4026802 - config_name: ml features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 362980 num_examples: 1000 - name: test num_bytes: 349355 num_examples: 1000 - name: train num_bytes: 3582038 num_examples: 10000 download_size: 1190172 dataset_size: 4294373 - config_name: mn features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 21978 num_examples: 100 - name: test num_bytes: 23510 num_examples: 100 - name: train num_bytes: 23216 num_examples: 100 download_size: 32990 dataset_size: 68704 - config_name: mr features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 314830 num_examples: 1000 - name: test num_bytes: 326262 num_examples: 1000 - name: train num_bytes: 1598776 num_examples: 5000 download_size: 524029 dataset_size: 2239868 - config_name: ms features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 183916 num_examples: 1000 - name: test num_bytes: 183511 num_examples: 1000 - name: train num_bytes: 3699182 num_examples: 20000 download_size: 1077180 dataset_size: 4066609 - config_name: mt features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 24543 num_examples: 100 - name: test num_bytes: 24634 num_examples: 100 - name: train num_bytes: 24928 num_examples: 100 download_size: 33526 dataset_size: 74105 - config_name: mwl features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 51959 num_examples: 100 - name: test num_bytes: 42980 num_examples: 100 - name: train num_bytes: 44577 num_examples: 100 download_size: 44197 dataset_size: 139516 - config_name: my features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 48925 num_examples: 100 - name: test num_bytes: 45928 num_examples: 100 - name: train num_bytes: 41343 num_examples: 100 download_size: 51490 dataset_size: 136196 - config_name: mzn features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 25276 num_examples: 100 - name: test num_bytes: 25919 num_examples: 100 - name: train num_bytes: 24813 num_examples: 100 download_size: 29895 dataset_size: 76008 - config_name: nap features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 21518 num_examples: 100 - name: test num_bytes: 24166 num_examples: 100 - name: train num_bytes: 26568 num_examples: 100 download_size: 30764 dataset_size: 72252 - config_name: nds features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 28360 num_examples: 100 - name: test num_bytes: 26543 num_examples: 100 - name: train num_bytes: 24651 num_examples: 100 download_size: 33734 dataset_size: 79554 - config_name: ne features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 33904 num_examples: 100 - name: test num_bytes: 33199 num_examples: 100 - name: train num_bytes: 36145 num_examples: 100 download_size: 37920 dataset_size: 103248 - config_name: nl features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 2378052 num_examples: 10000 - name: test num_bytes: 2403048 num_examples: 10000 - name: train num_bytes: 4784233 num_examples: 20000 download_size: 2867129 dataset_size: 9565333 - config_name: nn features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 274112 num_examples: 1000 - name: test num_bytes: 269603 num_examples: 1000 - name: train num_bytes: 5436129 num_examples: 20000 download_size: 1644504 dataset_size: 5979844 - config_name: 'no' features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 2576641 num_examples: 10000 - name: test num_bytes: 2563531 num_examples: 10000 - name: train num_bytes: 5139492 num_examples: 20000 download_size: 3063453 dataset_size: 10279664 - config_name: nov features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 14828 num_examples: 100 - name: test num_bytes: 14802 num_examples: 100 - name: train num_bytes: 17242 num_examples: 100 download_size: 20235 dataset_size: 46872 - config_name: oc features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 20400 num_examples: 100 - name: test num_bytes: 18572 num_examples: 100 - name: train num_bytes: 19291 num_examples: 100 download_size: 29284 dataset_size: 58263 - config_name: or features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 32103 num_examples: 100 - name: test num_bytes: 29480 num_examples: 100 - name: train num_bytes: 27794 num_examples: 100 download_size: 31116 dataset_size: 89377 - config_name: os features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 26751 num_examples: 100 - name: test num_bytes: 25967 num_examples: 100 - name: train num_bytes: 26005 num_examples: 100 download_size: 32948 dataset_size: 78723 - config_name: pa features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 25202 num_examples: 100 - name: test num_bytes: 23680 num_examples: 100 - name: train num_bytes: 24143 num_examples: 100 download_size: 31528 dataset_size: 73025 - config_name: pdc features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 24391 num_examples: 100 - name: test num_bytes: 24646 num_examples: 100 - name: train num_bytes: 23963 num_examples: 100 download_size: 28409 dataset_size: 73000 - config_name: pl features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 2448296 num_examples: 10000 - name: test num_bytes: 2463755 num_examples: 10000 - name: train num_bytes: 4851471 num_examples: 20000 download_size: 3300030 dataset_size: 9763522 - config_name: pms features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 28341 num_examples: 100 - name: test num_bytes: 23987 num_examples: 100 - name: train num_bytes: 27401 num_examples: 100 download_size: 34986 dataset_size: 79729 - config_name: pnb features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 19042 num_examples: 100 - name: test num_bytes: 21178 num_examples: 100 - name: train num_bytes: 19476 num_examples: 100 download_size: 25001 dataset_size: 59696 - config_name: ps features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 49873 num_examples: 100 - name: test num_bytes: 43593 num_examples: 100 - name: train num_bytes: 63473 num_examples: 100 download_size: 45676 dataset_size: 156939 - config_name: pt features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 1962117 num_examples: 10000 - name: test num_bytes: 1946701 num_examples: 10000 - name: train num_bytes: 3917397 num_examples: 20000 download_size: 2523476 dataset_size: 7826215 - config_name: qu features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 18203 num_examples: 100 - name: test num_bytes: 17647 num_examples: 100 - name: train num_bytes: 16961 num_examples: 100 download_size: 26577 dataset_size: 52811 - config_name: rm features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 32748 num_examples: 100 - name: test num_bytes: 35852 num_examples: 100 - name: train num_bytes: 30461 num_examples: 100 download_size: 38504 dataset_size: 99061 - config_name: ro features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 2063832 num_examples: 10000 - name: test num_bytes: 2060905 num_examples: 10000 - name: train num_bytes: 4179813 num_examples: 20000 download_size: 2533230 dataset_size: 8304550 - config_name: ru features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 2574518 num_examples: 10000 - name: test num_bytes: 2597220 num_examples: 10000 - name: train num_bytes: 5175609 num_examples: 20000 download_size: 3250185 dataset_size: 10347347 - config_name: rw features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 17971 num_examples: 100 - name: test num_bytes: 14417 num_examples: 100 - name: train num_bytes: 16750 num_examples: 100 download_size: 25845 dataset_size: 49138 - config_name: sa features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 45693 num_examples: 100 - name: test num_bytes: 49181 num_examples: 100 - name: train num_bytes: 52476 num_examples: 100 download_size: 50112 dataset_size: 147350 - config_name: sah features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 27847 num_examples: 100 - name: test num_bytes: 26825 num_examples: 100 - name: train num_bytes: 27013 num_examples: 100 download_size: 34322 dataset_size: 81685 - config_name: scn features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 20077 num_examples: 100 - name: test num_bytes: 17356 num_examples: 100 - name: train num_bytes: 21004 num_examples: 100 download_size: 28158 dataset_size: 58437 - config_name: sco features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 22187 num_examples: 100 - name: test num_bytes: 21561 num_examples: 100 - name: train num_bytes: 20280 num_examples: 100 download_size: 30781 dataset_size: 64028 - config_name: sd features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 51527 num_examples: 100 - name: test num_bytes: 38506 num_examples: 100 - name: train num_bytes: 56897 num_examples: 100 download_size: 44883 dataset_size: 146930 - config_name: sh features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 1789890 num_examples: 10000 - name: test num_bytes: 1791463 num_examples: 10000 - name: train num_bytes: 3583577 num_examples: 20000 download_size: 2027654 dataset_size: 7164930 - config_name: si features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 30817 num_examples: 100 - name: test num_bytes: 29313 num_examples: 100 - name: train num_bytes: 31227 num_examples: 100 download_size: 33979 dataset_size: 91357 - config_name: simple features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 247119 num_examples: 1000 - name: test num_bytes: 245330 num_examples: 1000 - name: train num_bytes: 4921860 num_examples: 20000 download_size: 1301730 dataset_size: 5414309 - config_name: sk features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 2342033 num_examples: 10000 - name: test num_bytes: 2334981 num_examples: 10000 - name: train num_bytes: 4701497 num_examples: 20000 download_size: 2944919 dataset_size: 9378511 - config_name: sl features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 2090219 num_examples: 10000 - name: test num_bytes: 2133463 num_examples: 10000 - name: train num_bytes: 3158620 num_examples: 15000 download_size: 2146455 dataset_size: 7382302 - config_name: so features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 21836 num_examples: 100 - name: test num_bytes: 17191 num_examples: 100 - name: train num_bytes: 23752 num_examples: 100 download_size: 27097 dataset_size: 62779 - config_name: sq features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 210860 num_examples: 1000 - name: test num_bytes: 209796 num_examples: 1000 - name: train num_bytes: 1052359 num_examples: 5000 download_size: 366247 dataset_size: 1473015 - config_name: sr features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 2548362 num_examples: 10000 - name: test num_bytes: 2564803 num_examples: 10000 - name: train num_bytes: 5105513 num_examples: 20000 download_size: 2932854 dataset_size: 10218678 - config_name: su features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 22577 num_examples: 100 - name: test num_bytes: 21833 num_examples: 100 - name: train num_bytes: 20811 num_examples: 100 download_size: 30722 dataset_size: 65221 - config_name: sv features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 2678644 num_examples: 10000 - name: test num_bytes: 2719049 num_examples: 10000 - name: train num_bytes: 5395666 num_examples: 20000 download_size: 2565949 dataset_size: 10793359 - config_name: sw features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 168791 num_examples: 1000 - name: test num_bytes: 172665 num_examples: 1000 - name: train num_bytes: 168721 num_examples: 1000 download_size: 135814 dataset_size: 510177 - config_name: szl features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 19369 num_examples: 100 - name: test num_bytes: 18939 num_examples: 100 - name: train num_bytes: 17618 num_examples: 100 download_size: 27450 dataset_size: 55926 - config_name: ta features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 354929 num_examples: 1000 - name: test num_bytes: 357639 num_examples: 1000 - name: train num_bytes: 5275703 num_examples: 15000 download_size: 1527540 dataset_size: 5988271 - config_name: te features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 356161 num_examples: 1000 - name: test num_bytes: 359752 num_examples: 1000 - name: train num_bytes: 358764 num_examples: 1000 download_size: 260846 dataset_size: 1074677 - config_name: tg features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 27102 num_examples: 100 - name: test num_bytes: 28793 num_examples: 100 - name: train num_bytes: 27172 num_examples: 100 download_size: 33712 dataset_size: 83067 - config_name: th features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 14189715 num_examples: 10000 - name: test num_bytes: 14505026 num_examples: 10000 - name: train num_bytes: 28968860 num_examples: 20000 download_size: 3962089 dataset_size: 57663601 - config_name: tk features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 21583 num_examples: 100 - name: test num_bytes: 20274 num_examples: 100 - name: train num_bytes: 19493 num_examples: 100 download_size: 30395 dataset_size: 61350 - config_name: tl features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 148654 num_examples: 1000 - name: test num_bytes: 152936 num_examples: 1000 - name: train num_bytes: 1518756 num_examples: 10000 download_size: 521471 dataset_size: 1820346 - config_name: tr features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 2280489 num_examples: 10000 - name: test num_bytes: 2276892 num_examples: 10000 - name: train num_bytes: 4501856 num_examples: 20000 download_size: 2907624 dataset_size: 9059237 - config_name: tt features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 282507 num_examples: 1000 - name: test num_bytes: 282663 num_examples: 1000 - name: train num_bytes: 283364 num_examples: 1000 download_size: 174234 dataset_size: 848534 - config_name: ug features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 35191 num_examples: 100 - name: test num_bytes: 31101 num_examples: 100 - name: train num_bytes: 26592 num_examples: 100 download_size: 38383 dataset_size: 92884 - config_name: uk features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 2934869 num_examples: 10000 - name: test num_bytes: 2928172 num_examples: 10000 - name: train num_bytes: 5927970 num_examples: 20000 download_size: 3214083 dataset_size: 11791011 - config_name: ur features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 203719 num_examples: 1000 - name: test num_bytes: 203110 num_examples: 1000 - name: train num_bytes: 4108651 num_examples: 20000 download_size: 1140630 dataset_size: 4515480 - config_name: uz features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 184597 num_examples: 1000 - name: test num_bytes: 184685 num_examples: 1000 - name: train num_bytes: 186077 num_examples: 1000 download_size: 121267 dataset_size: 555359 - config_name: vec features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 19307 num_examples: 100 - name: test num_bytes: 20226 num_examples: 100 - name: train num_bytes: 20409 num_examples: 100 download_size: 27538 dataset_size: 59942 - config_name: vep features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 22278 num_examples: 100 - name: test num_bytes: 21343 num_examples: 100 - name: train num_bytes: 21359 num_examples: 100 download_size: 29630 dataset_size: 64980 - config_name: vi features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 1944828 num_examples: 10000 - name: test num_bytes: 1959996 num_examples: 10000 - name: train num_bytes: 3915888 num_examples: 20000 download_size: 2283112 dataset_size: 7820712 - config_name: vls features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 27867 num_examples: 100 - name: test num_bytes: 26750 num_examples: 100 - name: train num_bytes: 26155 num_examples: 100 download_size: 33972 dataset_size: 80772 - config_name: vo features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 14357 num_examples: 100 - name: test num_bytes: 13973 num_examples: 100 - name: train num_bytes: 14414 num_examples: 100 download_size: 20368 dataset_size: 42744 - config_name: wa features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 22465 num_examples: 100 - name: test num_bytes: 21553 num_examples: 100 - name: train num_bytes: 23044 num_examples: 100 download_size: 28716 dataset_size: 67062 - config_name: war features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 16806 num_examples: 100 - name: test num_bytes: 19884 num_examples: 100 - name: train num_bytes: 18801 num_examples: 100 download_size: 26342 dataset_size: 55491 - config_name: wuu features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 15095 num_examples: 100 - name: test num_bytes: 15039 num_examples: 100 - name: train num_bytes: 16988 num_examples: 100 download_size: 34843 dataset_size: 47122 - config_name: xmf features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 39951 num_examples: 100 - name: test num_bytes: 36053 num_examples: 100 - name: train num_bytes: 31768 num_examples: 100 download_size: 38339 dataset_size: 107772 - config_name: yi features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 25241 num_examples: 100 - name: test num_bytes: 24977 num_examples: 100 - name: train num_bytes: 27275 num_examples: 100 download_size: 30693 dataset_size: 77493 - config_name: yo features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 17710 num_examples: 100 - name: test num_bytes: 17968 num_examples: 100 - name: train num_bytes: 18956 num_examples: 100 download_size: 26565 dataset_size: 54634 - config_name: zea features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 24888 num_examples: 100 - name: test num_bytes: 22969 num_examples: 100 - name: train num_bytes: 21224 num_examples: 100 download_size: 28533 dataset_size: 69081 - config_name: zh features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 4839700 num_examples: 10000 - name: test num_bytes: 4709430 num_examples: 10000 - name: train num_bytes: 9524925 num_examples: 20000 download_size: 2896220 dataset_size: 19074055 - config_name: zh-classical features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 59952 num_examples: 100 - name: test num_bytes: 65857 num_examples: 100 - name: train num_bytes: 56210 num_examples: 100 download_size: 31946 dataset_size: 182019 - config_name: zh-min-nan features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 24505 num_examples: 100 - name: test num_bytes: 24298 num_examples: 100 - name: train num_bytes: 19330 num_examples: 100 download_size: 26515 dataset_size: 68133 - config_name: zh-yue features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 4934130 num_examples: 10000 - name: test num_bytes: 4964001 num_examples: 10000 - name: train num_bytes: 9950573 num_examples: 20000 download_size: 2342825 dataset_size: 19848704 configs: - config_name: ace data_files: - split: validation path: ace/validation-* - split: test path: ace/test-* - split: train path: ace/train-* - config_name: af data_files: - split: validation path: af/validation-* - split: test path: af/test-* - split: train path: af/train-* - config_name: als data_files: - split: validation path: als/validation-* - split: test path: als/test-* - split: train path: als/train-* - config_name: am data_files: - split: validation path: am/validation-* - split: test path: am/test-* - split: train path: am/train-* - config_name: an data_files: - split: validation path: an/validation-* - split: test path: an/test-* - split: train path: an/train-* - config_name: ang data_files: - split: validation path: ang/validation-* - split: test path: ang/test-* - split: train path: ang/train-* - config_name: ar data_files: - split: validation path: ar/validation-* - split: test path: ar/test-* - split: train path: ar/train-* - config_name: arc data_files: - split: validation path: arc/validation-* - split: test path: arc/test-* - split: train path: arc/train-* - config_name: arz data_files: - split: validation path: arz/validation-* - split: test path: arz/test-* - split: train path: arz/train-* - config_name: as data_files: - split: validation path: as/validation-* - split: test path: as/test-* - split: train path: as/train-* - config_name: ast data_files: - split: validation path: ast/validation-* - split: test path: ast/test-* - split: train path: ast/train-* - config_name: ay data_files: - split: validation path: ay/validation-* - split: test path: ay/test-* - split: train path: ay/train-* - config_name: az data_files: - split: validation path: az/validation-* - split: test path: az/test-* - split: train path: az/train-* - config_name: ba data_files: - split: validation path: ba/validation-* - split: test path: ba/test-* - split: train path: ba/train-* - config_name: bar data_files: - split: validation path: bar/validation-* - split: test path: bar/test-* - split: train path: bar/train-* - config_name: bat-smg data_files: - split: validation path: bat-smg/validation-* - split: test path: bat-smg/test-* - split: train path: bat-smg/train-* - config_name: be data_files: - split: validation path: be/validation-* - split: test path: be/test-* - split: train path: be/train-* - config_name: be-x-old data_files: - split: validation path: be-x-old/validation-* - split: test path: be-x-old/test-* - split: train path: be-x-old/train-* - config_name: bg data_files: - split: validation path: bg/validation-* - split: test path: bg/test-* - split: train path: bg/train-* - config_name: bh data_files: - split: validation path: bh/validation-* - split: test path: bh/test-* - split: train path: bh/train-* - config_name: bn data_files: - split: validation path: bn/validation-* - split: test path: bn/test-* - split: train path: bn/train-* - config_name: bo data_files: - split: validation path: bo/validation-* - split: test path: bo/test-* - split: train path: bo/train-* - config_name: br data_files: - split: validation path: br/validation-* - split: test path: br/test-* - split: train path: br/train-* - config_name: bs data_files: - split: validation path: bs/validation-* - split: test path: bs/test-* - split: train path: bs/train-* - config_name: ca data_files: - split: validation path: ca/validation-* - split: test path: ca/test-* - split: train path: ca/train-* - config_name: cbk-zam data_files: - split: validation path: cbk-zam/validation-* - split: test path: cbk-zam/test-* - split: train path: cbk-zam/train-* - config_name: cdo data_files: - split: validation path: cdo/validation-* - split: test path: cdo/test-* - split: train path: cdo/train-* - config_name: ce data_files: - split: validation path: ce/validation-* - split: test path: ce/test-* - split: train path: ce/train-* - config_name: ceb data_files: - split: validation path: ceb/validation-* - split: test path: ceb/test-* - split: train path: ceb/train-* - config_name: ckb data_files: - split: validation path: ckb/validation-* - split: test path: ckb/test-* - split: train path: ckb/train-* - config_name: co data_files: - split: validation path: co/validation-* - split: test path: co/test-* - split: train path: co/train-* - config_name: crh data_files: - split: validation path: crh/validation-* - split: test path: crh/test-* - split: train path: crh/train-* - config_name: cs data_files: - split: validation path: cs/validation-* - split: test path: cs/test-* - split: train path: cs/train-* - config_name: csb data_files: - split: validation path: csb/validation-* - split: test path: csb/test-* - split: train path: csb/train-* - config_name: cv data_files: - split: validation path: cv/validation-* - split: test path: cv/test-* - split: train path: cv/train-* - config_name: cy data_files: - split: validation path: cy/validation-* - split: test path: cy/test-* - split: train path: cy/train-* - config_name: da data_files: - split: validation path: da/validation-* - split: test path: da/test-* - split: train path: da/train-* - config_name: de data_files: - split: validation path: de/validation-* - split: test path: de/test-* - split: train path: de/train-* - config_name: diq data_files: - split: validation path: diq/validation-* - split: test path: diq/test-* - split: train path: diq/train-* - config_name: dv data_files: - split: validation path: dv/validation-* - split: test path: dv/test-* - split: train path: dv/train-* - config_name: el data_files: - split: validation path: el/validation-* - split: test path: el/test-* - split: train path: el/train-* - config_name: eml data_files: - split: validation path: eml/validation-* - split: test path: eml/test-* - split: train path: eml/train-* - config_name: en data_files: - split: validation path: en/validation-* - split: test path: en/test-* - split: train path: en/train-* - config_name: eo data_files: - split: validation path: eo/validation-* - split: test path: eo/test-* - split: train path: eo/train-* - config_name: es data_files: - split: validation path: es/validation-* - split: test path: es/test-* - split: train path: es/train-* - config_name: et data_files: - split: validation path: et/validation-* - split: test path: et/test-* - split: train path: et/train-* - config_name: eu data_files: - split: validation path: eu/validation-* - split: test path: eu/test-* - split: train path: eu/train-* - config_name: ext data_files: - split: validation path: ext/validation-* - split: test path: ext/test-* - split: train path: ext/train-* - config_name: fa data_files: - split: validation path: fa/validation-* - split: test path: fa/test-* - split: train path: fa/train-* - config_name: fi data_files: - split: validation path: fi/validation-* - split: test path: fi/test-* - split: train path: fi/train-* - config_name: fiu-vro data_files: - split: validation path: fiu-vro/validation-* - split: test path: fiu-vro/test-* - split: train path: fiu-vro/train-* - config_name: fo data_files: - split: validation path: fo/validation-* - split: test path: fo/test-* - split: train path: fo/train-* - config_name: fr data_files: - split: validation path: fr/validation-* - split: test path: fr/test-* - split: train path: fr/train-* - config_name: frr data_files: - split: validation path: frr/validation-* - split: test path: frr/test-* - split: train path: frr/train-* - config_name: fur data_files: - split: validation path: fur/validation-* - split: test path: fur/test-* - split: train path: fur/train-* - config_name: fy data_files: - split: validation path: fy/validation-* - split: test path: fy/test-* - split: train path: fy/train-* - config_name: ga data_files: - split: validation path: ga/validation-* - split: test path: ga/test-* - split: train path: ga/train-* - config_name: gan data_files: - split: validation path: gan/validation-* - split: test path: gan/test-* - split: train path: gan/train-* - config_name: gd data_files: - split: validation path: gd/validation-* - split: test path: gd/test-* - split: train path: gd/train-* - config_name: gl data_files: - split: validation path: gl/validation-* - split: test path: gl/test-* - split: train path: gl/train-* - config_name: gn data_files: - split: validation path: gn/validation-* - split: test path: gn/test-* - split: train path: gn/train-* - config_name: gu data_files: - split: validation path: gu/validation-* - split: test path: gu/test-* - split: train path: gu/train-* - config_name: hak data_files: - split: validation path: hak/validation-* - split: test path: hak/test-* - split: train path: hak/train-* - config_name: he data_files: - split: validation path: he/validation-* - split: test path: he/test-* - split: train path: he/train-* - config_name: hi data_files: - split: validation path: hi/validation-* - split: test path: hi/test-* - split: train path: hi/train-* - config_name: hr data_files: - split: validation path: hr/validation-* - split: test path: hr/test-* - split: train path: hr/train-* - config_name: hsb data_files: - split: validation path: hsb/validation-* - split: test path: hsb/test-* - split: train path: hsb/train-* - config_name: hu data_files: - split: validation path: hu/validation-* - split: test path: hu/test-* - split: train path: hu/train-* - config_name: hy data_files: - split: validation path: hy/validation-* - split: test path: hy/test-* - split: train path: hy/train-* - config_name: ia data_files: - split: validation path: ia/validation-* - split: test path: ia/test-* - split: train path: ia/train-* - config_name: id data_files: - split: validation path: id/validation-* - split: test path: id/test-* - split: train path: id/train-* - config_name: ig data_files: - split: validation path: ig/validation-* - split: test path: ig/test-* - split: train path: ig/train-* - config_name: ilo data_files: - split: validation path: ilo/validation-* - split: test path: ilo/test-* - split: train path: ilo/train-* - config_name: io data_files: - split: validation path: io/validation-* - split: test path: io/test-* - split: train path: io/train-* - config_name: is data_files: - split: validation path: is/validation-* - split: test path: is/test-* - split: train path: is/train-* - config_name: it data_files: - split: validation path: it/validation-* - split: test path: it/test-* - split: train path: it/train-* - config_name: ja data_files: - split: validation path: ja/validation-* - split: test path: ja/test-* - split: train path: ja/train-* - config_name: jbo data_files: - split: validation path: jbo/validation-* - split: test path: jbo/test-* - split: train path: jbo/train-* - config_name: jv data_files: - split: validation path: jv/validation-* - split: test path: jv/test-* - split: train path: jv/train-* - config_name: ka data_files: - split: validation path: ka/validation-* - split: test path: ka/test-* - split: train path: ka/train-* - config_name: kk data_files: - split: validation path: kk/validation-* - split: test path: kk/test-* - split: train path: kk/train-* - config_name: km data_files: - split: validation path: km/validation-* - split: test path: km/test-* - split: train path: km/train-* - config_name: kn data_files: - split: validation path: kn/validation-* - split: test path: kn/test-* - split: train path: kn/train-* - config_name: ko data_files: - split: validation path: ko/validation-* - split: test path: ko/test-* - split: train path: ko/train-* - config_name: ksh data_files: - split: validation path: ksh/validation-* - split: test path: ksh/test-* - split: train path: ksh/train-* - config_name: ku data_files: - split: validation path: ku/validation-* - split: test path: ku/test-* - split: train path: ku/train-* - config_name: ky data_files: - split: validation path: ky/validation-* - split: test path: ky/test-* - split: train path: ky/train-* - config_name: la data_files: - split: validation path: la/validation-* - split: test path: la/test-* - split: train path: la/train-* - config_name: lb data_files: - split: validation path: lb/validation-* - split: test path: lb/test-* - split: train path: lb/train-* - config_name: li data_files: - split: validation path: li/validation-* - split: test path: li/test-* - split: train path: li/train-* - config_name: lij data_files: - split: validation path: lij/validation-* - split: test path: lij/test-* - split: train path: lij/train-* - config_name: lmo data_files: - split: validation path: lmo/validation-* - split: test path: lmo/test-* - split: train path: lmo/train-* - config_name: ln data_files: - split: validation path: ln/validation-* - split: test path: ln/test-* - split: train path: ln/train-* - config_name: lt data_files: - split: validation path: lt/validation-* - split: test path: lt/test-* - split: train path: lt/train-* - config_name: lv data_files: - split: validation path: lv/validation-* - split: test path: lv/test-* - split: train path: lv/train-* - config_name: map-bms data_files: - split: validation path: map-bms/validation-* - split: test path: map-bms/test-* - split: train path: map-bms/train-* - config_name: mg data_files: - split: validation path: mg/validation-* - split: test path: mg/test-* - split: train path: mg/train-* - config_name: mhr data_files: - split: validation path: mhr/validation-* - split: test path: mhr/test-* - split: train path: mhr/train-* - config_name: mi data_files: - split: validation path: mi/validation-* - split: test path: mi/test-* - split: train path: mi/train-* - config_name: min data_files: - split: validation path: min/validation-* - split: test path: min/test-* - split: train path: min/train-* - config_name: mk data_files: - split: validation path: mk/validation-* - split: test path: mk/test-* - split: train path: mk/train-* - config_name: ml data_files: - split: validation path: ml/validation-* - split: test path: ml/test-* - split: train path: ml/train-* - config_name: mn data_files: - split: validation path: mn/validation-* - split: test path: mn/test-* - split: train path: mn/train-* - config_name: mr data_files: - split: validation path: mr/validation-* - split: test path: mr/test-* - split: train path: mr/train-* - config_name: ms data_files: - split: validation path: ms/validation-* - split: test path: ms/test-* - split: train path: ms/train-* - config_name: mt data_files: - split: validation path: mt/validation-* - split: test path: mt/test-* - split: train path: mt/train-* - config_name: mwl data_files: - split: validation path: mwl/validation-* - split: test path: mwl/test-* - split: train path: mwl/train-* - config_name: my data_files: - split: validation path: my/validation-* - split: test path: my/test-* - split: train path: my/train-* - config_name: mzn data_files: - split: validation path: mzn/validation-* - split: test path: mzn/test-* - split: train path: mzn/train-* - config_name: nap data_files: - split: validation path: nap/validation-* - split: test path: nap/test-* - split: train path: nap/train-* - config_name: nds data_files: - split: validation path: nds/validation-* - split: test path: nds/test-* - split: train path: nds/train-* - config_name: ne data_files: - split: validation path: ne/validation-* - split: test path: ne/test-* - split: train path: ne/train-* - config_name: nl data_files: - split: validation path: nl/validation-* - split: test path: nl/test-* - split: train path: nl/train-* - config_name: nn data_files: - split: validation path: nn/validation-* - split: test path: nn/test-* - split: train path: nn/train-* - config_name: 'no' data_files: - split: validation path: no/validation-* - split: test path: no/test-* - split: train path: no/train-* - config_name: nov data_files: - split: validation path: nov/validation-* - split: test path: nov/test-* - split: train path: nov/train-* - config_name: oc data_files: - split: validation path: oc/validation-* - split: test path: oc/test-* - split: train path: oc/train-* - config_name: or data_files: - split: validation path: or/validation-* - split: test path: or/test-* - split: train path: or/train-* - config_name: os data_files: - split: validation path: os/validation-* - split: test path: os/test-* - split: train path: os/train-* - config_name: pa data_files: - split: validation path: pa/validation-* - split: test path: pa/test-* - split: train path: pa/train-* - config_name: pdc data_files: - split: validation path: pdc/validation-* - split: test path: pdc/test-* - split: train path: pdc/train-* - config_name: pl data_files: - split: validation path: pl/validation-* - split: test path: pl/test-* - split: train path: pl/train-* - config_name: pms data_files: - split: validation path: pms/validation-* - split: test path: pms/test-* - split: train path: pms/train-* - config_name: pnb data_files: - split: validation path: pnb/validation-* - split: test path: pnb/test-* - split: train path: pnb/train-* - config_name: ps data_files: - split: validation path: ps/validation-* - split: test path: ps/test-* - split: train path: ps/train-* - config_name: pt data_files: - split: validation path: pt/validation-* - split: test path: pt/test-* - split: train path: pt/train-* - config_name: qu data_files: - split: validation path: qu/validation-* - split: test path: qu/test-* - split: train path: qu/train-* - config_name: rm data_files: - split: validation path: rm/validation-* - split: test path: rm/test-* - split: train path: rm/train-* - config_name: ro data_files: - split: validation path: ro/validation-* - split: test path: ro/test-* - split: train path: ro/train-* - config_name: ru data_files: - split: validation path: ru/validation-* - split: test path: ru/test-* - split: train path: ru/train-* - config_name: rw data_files: - split: validation path: rw/validation-* - split: test path: rw/test-* - split: train path: rw/train-* - config_name: sa data_files: - split: validation path: sa/validation-* - split: test path: sa/test-* - split: train path: sa/train-* - config_name: sah data_files: - split: validation path: sah/validation-* - split: test path: sah/test-* - split: train path: sah/train-* - config_name: scn data_files: - split: validation path: scn/validation-* - split: test path: scn/test-* - split: train path: scn/train-* - config_name: sco data_files: - split: validation path: sco/validation-* - split: test path: sco/test-* - split: train path: sco/train-* - config_name: sd data_files: - split: validation path: sd/validation-* - split: test path: sd/test-* - split: train path: sd/train-* - config_name: sh data_files: - split: validation path: sh/validation-* - split: test path: sh/test-* - split: train path: sh/train-* - config_name: si data_files: - split: validation path: si/validation-* - split: test path: si/test-* - split: train path: si/train-* - config_name: simple data_files: - split: validation path: simple/validation-* - split: test path: simple/test-* - split: train path: simple/train-* - config_name: sk data_files: - split: validation path: sk/validation-* - split: test path: sk/test-* - split: train path: sk/train-* - config_name: sl data_files: - split: validation path: sl/validation-* - split: test path: sl/test-* - split: train path: sl/train-* - config_name: so data_files: - split: validation path: so/validation-* - split: test path: so/test-* - split: train path: so/train-* - config_name: sq data_files: - split: validation path: sq/validation-* - split: test path: sq/test-* - split: train path: sq/train-* - config_name: sr data_files: - split: validation path: sr/validation-* - split: test path: sr/test-* - split: train path: sr/train-* - config_name: su data_files: - split: validation path: su/validation-* - split: test path: su/test-* - split: train path: su/train-* - config_name: sv data_files: - split: validation path: sv/validation-* - split: test path: sv/test-* - split: train path: sv/train-* - config_name: sw data_files: - split: validation path: sw/validation-* - split: test path: sw/test-* - split: train path: sw/train-* - config_name: szl data_files: - split: validation path: szl/validation-* - split: test path: szl/test-* - split: train path: szl/train-* - config_name: ta data_files: - split: validation path: ta/validation-* - split: test path: ta/test-* - split: train path: ta/train-* - config_name: te data_files: - split: validation path: te/validation-* - split: test path: te/test-* - split: train path: te/train-* - config_name: tg data_files: - split: validation path: tg/validation-* - split: test path: tg/test-* - split: train path: tg/train-* - config_name: th data_files: - split: validation path: th/validation-* - split: test path: th/test-* - split: train path: th/train-* - config_name: tk data_files: - split: validation path: tk/validation-* - split: test path: tk/test-* - split: train path: tk/train-* - config_name: tl data_files: - split: validation path: tl/validation-* - split: test path: tl/test-* - split: train path: tl/train-* - config_name: tr data_files: - split: validation path: tr/validation-* - split: test path: tr/test-* - split: train path: tr/train-* - config_name: tt data_files: - split: validation path: tt/validation-* - split: test path: tt/test-* - split: train path: tt/train-* - config_name: ug data_files: - split: validation path: ug/validation-* - split: test path: ug/test-* - split: train path: ug/train-* - config_name: uk data_files: - split: validation path: uk/validation-* - split: test path: uk/test-* - split: train path: uk/train-* - config_name: ur data_files: - split: validation path: ur/validation-* - split: test path: ur/test-* - split: train path: ur/train-* - config_name: uz data_files: - split: validation path: uz/validation-* - split: test path: uz/test-* - split: train path: uz/train-* - config_name: vec data_files: - split: validation path: vec/validation-* - split: test path: vec/test-* - split: train path: vec/train-* - config_name: vep data_files: - split: validation path: vep/validation-* - split: test path: vep/test-* - split: train path: vep/train-* - config_name: vi data_files: - split: validation path: vi/validation-* - split: test path: vi/test-* - split: train path: vi/train-* - config_name: vls data_files: - split: validation path: vls/validation-* - split: test path: vls/test-* - split: train path: vls/train-* - config_name: vo data_files: - split: validation path: vo/validation-* - split: test path: vo/test-* - split: train path: vo/train-* - config_name: wa data_files: - split: validation path: wa/validation-* - split: test path: wa/test-* - split: train path: wa/train-* - config_name: war data_files: - split: validation path: war/validation-* - split: test path: war/test-* - split: train path: war/train-* - config_name: wuu data_files: - split: validation path: wuu/validation-* - split: test path: wuu/test-* - split: train path: wuu/train-* - config_name: xmf data_files: - split: validation path: xmf/validation-* - split: test path: xmf/test-* - split: train path: xmf/train-* - config_name: yi data_files: - split: validation path: yi/validation-* - split: test path: yi/test-* - split: train path: yi/train-* - config_name: yo data_files: - split: validation path: yo/validation-* - split: test path: yo/test-* - split: train path: yo/train-* - config_name: zea data_files: - split: validation path: zea/validation-* - split: test path: zea/test-* - split: train path: zea/train-* - config_name: zh data_files: - split: validation path: zh/validation-* - split: test path: zh/test-* - split: train path: zh/train-* - config_name: zh-classical data_files: - split: validation path: zh-classical/validation-* - split: test path: zh-classical/test-* - split: train path: zh-classical/train-* - config_name: zh-min-nan data_files: - split: validation path: zh-min-nan/validation-* - split: test path: zh-min-nan/test-* - split: train path: zh-min-nan/train-* - config_name: zh-yue data_files: - split: validation path: zh-yue/validation-* - split: test path: zh-yue/test-* - split: train path: zh-yue/train-* --- # Dataset Card for WikiANN ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [Massively Multilingual Transfer for NER](https://github.com/afshinrahimi/mmner) - Repository: [Massively Multilingual Transfer for NER](https://github.com/afshinrahimi/mmner) - Paper: The original datasets come from the _Cross-lingual name tagging and linking for 282 languages_ [paper](https://www.aclweb.org/anthology/P17-1178/) by Xiaoman Pan et al. (2018). This version corresponds to the balanced train, dev, and test splits of the original data from the _Massively Multilingual Transfer for NER_ [paper](https://arxiv.org/abs/1902.00193) by Afshin Rahimi et al. (2019). - Leaderboard: - Point of Contact: [Afshin Rahimi](mailto:[email protected]) or [Lewis Tunstall](mailto:[email protected]) or [Albert Villanova del Moral]([email protected]) ### Dataset Summary WikiANN (sometimes called PAN-X) is a multilingual named entity recognition dataset consisting of Wikipedia articles annotated with LOC (location), PER (person), and ORG (organisation) tags in the IOB2 format. This version corresponds to the balanced train, dev, and test splits of Rahimi et al. (2019), which supports 176 of the 282 languages from the original WikiANN corpus. ### Supported Tasks and Leaderboards - `named-entity-recognition`: The dataset can be used to train a model for named entity recognition in many languages, or evaluate the zero-shot cross-lingual capabilities of multilingual models. ### Languages The dataset contains 176 languages, one in each of the configuration subsets. The corresponding BCP 47 language tags are: \| \| Language tag \| \|:-------------------\|:---------------\| \| ace \| ace \| \| af \| af \| \| als \| als \| \| am \| am \| \| an \| an \| \| ang \| ang \| \| ar \| ar \| \| arc \| arc \| \| arz \| arz \| \| as \| as \| \| ast \| ast \| \| ay \| ay \| \| az \| az \| \| ba \| ba \| \| bar \| bar \| \| be \| be \| \| bg \| bg \| \| bh \| bh \| \| bn \| bn \| \| bo \| bo \| \| br \| br \| \| bs \| bs \| \| ca \| ca \| \| cdo \| cdo \| \| ce \| ce \| \| ceb \| ceb \| \| ckb \| ckb \| \| co \| co \| \| crh \| crh \| \| cs \| cs \| \| csb \| csb \| \| cv \| cv \| \| cy \| cy \| \| da \| da \| \| de \| de \| \| diq \| diq \| \| dv \| dv \| \| el \| el \| \| en \| en \| \| eo \| eo \| \| es \| es \| \| et \| et \| \| eu \| eu \| \| ext \| ext \| \| fa \| fa \| \| fi \| fi \| \| fo \| fo \| \| fr \| fr \| \| frr \| frr \| \| fur \| fur \| \| fy \| fy \| \| ga \| ga \| \| gan \| gan \| \| gd \| gd \| \| gl \| gl \| \| gn \| gn \| \| gu \| gu \| \| hak \| hak \| \| he \| he \| \| hi \| hi \| \| hr \| hr \| \| hsb \| hsb \| \| hu \| hu \| \| hy \| hy \| \| ia \| ia \| \| id \| id \| \| ig \| ig \| \| ilo \| ilo \| \| io \| io \| \| is \| is \| \| it \| it \| \| ja \| ja \| \| jbo \| jbo \| \| jv \| jv \| \| ka \| ka \| \| kk \| kk \| \| km \| km \| \| kn \| kn \| \| ko \| ko \| \| ksh \| ksh \| \| ku \| ku \| \| ky \| ky \| \| la \| la \| \| lb \| lb \| \| li \| li \| \| lij \| lij \| \| lmo \| lmo \| \| ln \| ln \| \| lt \| lt \| \| lv \| lv \| \| mg \| mg \| \| mhr \| mhr \| \| mi \| mi \| \| min \| min \| \| mk \| mk \| \| ml \| ml \| \| mn \| mn \| \| mr \| mr \| \| ms \| ms \| \| mt \| mt \| \| mwl \| mwl \| \| my \| my \| \| mzn \| mzn \| \| nap \| nap \| \| nds \| nds \| \| ne \| ne \| \| nl \| nl \| \| nn \| nn \| \| no \| no \| \| nov \| nov \| \| oc \| oc \| \| or \| or \| \| os \| os \| \| other-bat-smg \| sgs \| \| other-be-x-old \| be-tarask \| \| other-cbk-zam \| cbk \| \| other-eml \| eml \| \| other-fiu-vro \| vro \| \| other-map-bms \| jv-x-bms \| \| other-simple \| en-basiceng \| \| other-zh-classical \| lzh \| \| other-zh-min-nan \| nan \| \| other-zh-yue \| yue \| \| pa \| pa \| \| pdc \| pdc \| \| pl \| pl \| \| pms \| pms \| \| pnb \| pnb \| \| ps \| ps \| \| pt \| pt \| \| qu \| qu \| \| rm \| rm \| \| ro \| ro \| \| ru \| ru \| \| rw \| rw \| \| sa \| sa \| \| sah \| sah \| \| scn \| scn \| \| sco \| sco \| \| sd \| sd \| \| sh \| sh \| \| si \| si \| \| sk \| sk \| \| sl \| sl \| \| so \| so \| \| sq \| sq \| \| sr \| sr \| \| su \| su \| \| sv \| sv \| \| sw \| sw \| \| szl \| szl \| \| ta \| ta \| \| te \| te \| \| tg \| tg \| \| th \| th \| \| tk \| tk \| \| tl \| tl \| \| tr \| tr \| \| tt \| tt \| \| ug \| ug \| \| uk \| uk \| \| ur \| ur \| \| uz \| uz \| \| vec \| vec \| \| vep \| vep \| \| vi \| vi \| \| vls \| vls \| \| vo \| vo \| \| wa \| wa \| \| war \| war \| \| wuu \| wuu \| \| xmf \| xmf \| \| yi \| yi \| \| yo \| yo \| \| zea \| zea \| \| zh \| zh \| ## Dataset Structure ### Data Instances This is an example in the "train" split of the "af" (Afrikaans language) configuration subset: ```python { 'tokens': ['Sy', 'ander', 'seun', ',', 'Swjatopolk', ',', 'was', 'die', 'resultaat', 'van', '’n', 'buite-egtelike', 'verhouding', '.'], 'ner_tags': [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'langs': ['af', 'af', 'af', 'af', 'af', 'af', 'af', 'af', 'af', 'af', 'af', 'af', 'af', 'af'], 'spans': ['PER: Swjatopolk'] } ``` ### Data Fields - `tokens`: a `list` of `string` features. - `langs`: a `list` of `string` features that correspond to the language of each token. - `ner_tags`: a `list` of classification labels, with possible values including `O` (0), `B-PER` (1), `I-PER` (2), `B-ORG` (3), `I-ORG` (4), `B-LOC` (5), `I-LOC` (6). - `spans`: a `list` of `string` features, that is the list of named entities in the input text formatted as ``<TAG>: <mention>`` ### Data Splits For each configuration subset, the data is split into "train", "validation" and "test" sets, each containing the following number of examples: \| \| Train \| Validation \| Test \| \|:-------------\|--------:\|-------------:\|-------:\| \| ace \| 100 \| 100 \| 100 \| \| af \| 5000 \| 1000 \| 1000 \| \| als \| 100 \| 100 \| 100 \| \| am \| 100 \| 100 \| 100 \| \| an \| 1000 \| 1000 \| 1000 \| \| ang \| 100 \| 100 \| 100 \| \| ar \| 20000 \| 10000 \| 10000 \| \| arc \| 100 \| 100 \| 100 \| \| arz \| 100 \| 100 \| 100 \| \| as \| 100 \| 100 \| 100 \| \| ast \| 1000 \| 1000 \| 1000 \| \| ay \| 100 \| 100 \| 100 \| \| az \| 10000 \| 1000 \| 1000 \| \| ba \| 100 \| 100 \| 100 \| \| bar \| 100 \| 100 \| 100 \| \| bat-smg \| 100 \| 100 \| 100 \| \| be \| 15000 \| 1000 \| 1000 \| \| be-x-old \| 5000 \| 1000 \| 1000 \| \| bg \| 20000 \| 10000 \| 10000 \| \| bh \| 100 \| 100 \| 100 \| \| bn \| 10000 \| 1000 \| 1000 \| \| bo \| 100 \| 100 \| 100 \| \| br \| 1000 \| 1000 \| 1000 \| \| bs \| 15000 \| 1000 \| 1000 \| \| ca \| 20000 \| 10000 \| 10000 \| \| cbk-zam \| 100 \| 100 \| 100 \| \| cdo \| 100 \| 100 \| 100 \| \| ce \| 100 \| 100 \| 100 \| \| ceb \| 100 \| 100 \| 100 \| \| ckb \| 1000 \| 1000 \| 1000 \| \| co \| 100 \| 100 \| 100 \| \| crh \| 100 \| 100 \| 100 \| \| cs \| 20000 \| 10000 \| 10000 \| \| csb \| 100 \| 100 \| 100 \| \| cv \| 100 \| 100 \| 100 \| \| cy \| 10000 \| 1000 \| 1000 \| \| da \| 20000 \| 10000 \| 10000 \| \| de \| 20000 \| 10000 \| 10000 \| \| diq \| 100 \| 100 \| 100 \| \| dv \| 100 \| 100 \| 100 \| \| el \| 20000 \| 10000 \| 10000 \| \| eml \| 100 \| 100 \| 100 \| \| en \| 20000 \| 10000 \| 10000 \| \| eo \| 15000 \| 10000 \| 10000 \| \| es \| 20000 \| 10000 \| 10000 \| \| et \| 15000 \| 10000 \| 10000 \| \| eu \| 10000 \| 10000 \| 10000 \| \| ext \| 100 \| 100 \| 100 \| \| fa \| 20000 \| 10000 \| 10000 \| \| fi \| 20000 \| 10000 \| 10000 \| \| fiu-vro \| 100 \| 100 \| 100 \| \| fo \| 100 \| 100 \| 100 \| \| fr \| 20000 \| 10000 \| 10000 \| \| frr \| 100 \| 100 \| 100 \| \| fur \| 100 \| 100 \| 100 \| \| fy \| 1000 \| 1000 \| 1000 \| \| ga \| 1000 \| 1000 \| 1000 \| \| gan \| 100 \| 100 \| 100 \| \| gd \| 100 \| 100 \| 100 \| \| gl \| 15000 \| 10000 \| 10000 \| \| gn \| 100 \| 100 \| 100 \| \| gu \| 100 \| 100 \| 100 \| \| hak \| 100 \| 100 \| 100 \| \| he \| 20000 \| 10000 \| 10000 \| \| hi \| 5000 \| 1000 \| 1000 \| \| hr \| 20000 \| 10000 \| 10000 \| \| hsb \| 100 \| 100 \| 100 \| \| hu \| 20000 \| 10000 \| 10000 \| \| hy \| 15000 \| 1000 \| 1000 \| \| ia \| 100 \| 100 \| 100 \| \| id \| 20000 \| 10000 \| 10000 \| \| ig \| 100 \| 100 \| 100 \| \| ilo \| 100 \| 100 \| 100 \| \| io \| 100 \| 100 \| 100 \| \| is \| 1000 \| 1000 \| 1000 \| \| it \| 20000 \| 10000 \| 10000 \| \| ja \| 20000 \| 10000 \| 10000 \| \| jbo \| 100 \| 100 \| 100 \| \| jv \| 100 \| 100 \| 100 \| \| ka \| 10000 \| 10000 \| 10000 \| \| kk \| 1000 \| 1000 \| 1000 \| \| km \| 100 \| 100 \| 100 \| \| kn \| 100 \| 100 \| 100 \| \| ko \| 20000 \| 10000 \| 10000 \| \| ksh \| 100 \| 100 \| 100 \| \| ku \| 100 \| 100 \| 100 \| \| ky \| 100 \| 100 \| 100 \| \| la \| 5000 \| 1000 \| 1000 \| \| lb \| 5000 \| 1000 \| 1000 \| \| li \| 100 \| 100 \| 100 \| \| lij \| 100 \| 100 \| 100 \| \| lmo \| 100 \| 100 \| 100 \| \| ln \| 100 \| 100 \| 100 \| \| lt \| 10000 \| 10000 \| 10000 \| \| lv \| 10000 \| 10000 \| 10000 \| \| map-bms \| 100 \| 100 \| 100 \| \| mg \| 100 \| 100 \| 100 \| \| mhr \| 100 \| 100 \| 100 \| \| mi \| 100 \| 100 \| 100 \| \| min \| 100 \| 100 \| 100 \| \| mk \| 10000 \| 1000 \| 1000 \| \| ml \| 10000 \| 1000 \| 1000 \| \| mn \| 100 \| 100 \| 100 \| \| mr \| 5000 \| 1000 \| 1000 \| \| ms \| 20000 \| 1000 \| 1000 \| \| mt \| 100 \| 100 \| 100 \| \| mwl \| 100 \| 100 \| 100 \| \| my \| 100 \| 100 \| 100 \| \| mzn \| 100 \| 100 \| 100 \| \| nap \| 100 \| 100 \| 100 \| \| nds \| 100 \| 100 \| 100 \| \| ne \| 100 \| 100 \| 100 \| \| nl \| 20000 \| 10000 \| 10000 \| \| nn \| 20000 \| 1000 \| 1000 \| \| no \| 20000 \| 10000 \| 10000 \| \| nov \| 100 \| 100 \| 100 \| \| oc \| 100 \| 100 \| 100 \| \| or \| 100 \| 100 \| 100 \| \| os \| 100 \| 100 \| 100 \| \| pa \| 100 \| 100 \| 100 \| \| pdc \| 100 \| 100 \| 100 \| \| pl \| 20000 \| 10000 \| 10000 \| \| pms \| 100 \| 100 \| 100 \| \| pnb \| 100 \| 100 \| 100 \| \| ps \| 100 \| 100 \| 100 \| \| pt \| 20000 \| 10000 \| 10000 \| \| qu \| 100 \| 100 \| 100 \| \| rm \| 100 \| 100 \| 100 \| \| ro \| 20000 \| 10000 \| 10000 \| \| ru \| 20000 \| 10000 \| 10000 \| \| rw \| 100 \| 100 \| 100 \| \| sa \| 100 \| 100 \| 100 \| \| sah \| 100 \| 100 \| 100 \| \| scn \| 100 \| 100 \| 100 \| \| sco \| 100 \| 100 \| 100 \| \| sd \| 100 \| 100 \| 100 \| \| sh \| 20000 \| 10000 \| 10000 \| \| si \| 100 \| 100 \| 100 \| \| simple \| 20000 \| 1000 \| 1000 \| \| sk \| 20000 \| 10000 \| 10000 \| \| sl \| 15000 \| 10000 \| 10000 \| \| so \| 100 \| 100 \| 100 \| \| sq \| 5000 \| 1000 \| 1000 \| \| sr \| 20000 \| 10000 \| 10000 \| \| su \| 100 \| 100 \| 100 \| \| sv \| 20000 \| 10000 \| 10000 \| \| sw \| 1000 \| 1000 \| 1000 \| \| szl \| 100 \| 100 \| 100 \| \| ta \| 15000 \| 1000 \| 1000 \| \| te \| 1000 \| 1000 \| 1000 \| \| tg \| 100 \| 100 \| 100 \| \| th \| 20000 \| 10000 \| 10000 \| \| tk \| 100 \| 100 \| 100 \| \| tl \| 10000 \| 1000 \| 1000 \| \| tr \| 20000 \| 10000 \| 10000 \| \| tt \| 1000 \| 1000 \| 1000 \| \| ug \| 100 \| 100 \| 100 \| \| uk \| 20000 \| 10000 \| 10000 \| \| ur \| 20000 \| 1000 \| 1000 \| \| uz \| 1000 \| 1000 \| 1000 \| \| vec \| 100 \| 100 \| 100 \| \| vep \| 100 \| 100 \| 100 \| \| vi \| 20000 \| 10000 \| 10000 \| \| vls \| 100 \| 100 \| 100 \| \| vo \| 100 \| 100 \| 100 \| \| wa \| 100 \| 100 \| 100 \| \| war \| 100 \| 100 \| 100 \| \| wuu \| 100 \| 100 \| 100 \| \| xmf \| 100 \| 100 \| 100 \| \| yi \| 100 \| 100 \| 100 \| \| yo \| 100 \| 100 \| 100 \| \| zea \| 100 \| 100 \| 100 \| \| zh \| 20000 \| 10000 \| 10000 \| \| zh-classical \| 100 \| 100 \| 100 \| \| zh-min-nan \| 100 \| 100 \| 100 \| \| zh-yue \| 20000 \| 10000 \| 10000 \| ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information The original 282 datasets are associated with this article ``` @inproceedings{pan-etal-2017-cross, title = "Cross-lingual Name Tagging and Linking for 282 Languages", author = "Pan, Xiaoman and Zhang, Boliang and May, Jonathan and Nothman, Joel and Knight, Kevin and Ji, Heng", booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", month = jul, year = "2017", address = "Vancouver, Canada", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/P17-1178", doi = "10.18653/v1/P17-1178", pages = "1946--1958", abstract = "The ambitious goal of this work is to develop a cross-lingual name tagging and linking framework for 282 languages that exist in Wikipedia. Given a document in any of these languages, our framework is able to identify name mentions, assign a coarse-grained or fine-grained type to each mention, and link it to an English Knowledge Base (KB) if it is linkable. We achieve this goal by performing a series of new KB mining methods: generating {``}silver-standard{''} annotations by transferring annotations from English to other languages through cross-lingual links and KB properties, refining annotations through self-training and topic selection, deriving language-specific morphology features from anchor links, and mining word translation pairs from cross-lingual links. Both name tagging and linking results for 282 languages are promising on Wikipedia data and on-Wikipedia data.", } ``` while the 176 languages supported in this version are associated with the following article ``` @inproceedings{rahimi-etal-2019-massively, title = "Massively Multilingual Transfer for {NER}", author = "Rahimi, Afshin and Li, Yuan and Cohn, Trevor", booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics", month = jul, year = "2019", address = "Florence, Italy", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/P19-1015", pages = "151--164", } ``` ### Contributions Thanks to [@lewtun](https://github.com/lewtun) and [@rabeehk](https://github.com/rabeehk) for adding this dataset.
stingning/ultrachat	stingning	2024-02-22T02:26:29Z	2,265	438	[ "task_categories:text-generation", "language:en", "license:mit", "size_categories:100K<n<1M", "format:json", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[ "conversational", "text-generation" ]	2023-04-20T15:15:28Z	null	--- license: mit task_categories: - conversational - text-generation language: - en size_categories: - 1M<n<10M pretty_name: UltraChat --- # Dataset Card for Dataset Name ## Dataset Description An open-source, large-scale, and multi-round dialogue data powered by Turbo APIs. In consideration of factors such as safeguarding privacy, we do not directly use any data available on the Internet as prompts. To ensure generation quality, two separate ChatGPT Turbo APIs are adopted in generation, where one plays the role of the user to generate queries and the other generates the response. We instruct the user model with carefully designed prompts to mimic human user behavior and call the two APIs iteratively. The generated dialogues undergo further post-processing and filtering. ULtraChat is composed of three sectors: - 🌏 Questions about the World: The dialogue data in this sector is derived from a wide range of inquiries related to concepts, entities, and objects from the real world. The topics covered are extensive, spanning areas such as technology, art, and entrepreneurship. - ✍🏻 Writing and Creation: The dialogue data in this sector is driven by the demands for writing/creation from scratch, and encompasses any tasks that an AI assistant may aid within the creative process, spanning from email composition to crafting narratives and plays, and beyond. - 📋 Assistance on Existent Materials: The dialogue data in this sector is generated based on existing materials, including but not limited to rewriting, continuation, summarization, and inference, covering a diverse range of topics. - Repository: [UltraChat](https://github.com/thunlp/UltraChat) - Explorer: [plain-explorer](http://39.101.77.220/), [Nomic-AI-Atlas-Explorer](https://atlas.nomic.ai/map/0ce65783-c3a9-40b5-895d-384933f50081/a7b46301-022f-45d8-bbf4-98107eabdbac) ## Dataset Structure Each line in the downloaded data file is a json dict containing the data id and dialogue data in a list format. Below is an example line. ``` { "id": "0", "data": [ "How can cross training benefit groups like runners, swimmers, or weightlifters?", "Cross training can benefit groups like runners, swimmers, or weightlifters in the following ways: ...", "That makes sense. I've been wanting to improve my running time, but I never thought about incorporating strength training. Do you have any recommendations for specific exercises?", "Sure, here are some strength training exercises that can benefit runners: ...", "Hmm, I'm not really a fan of weightlifting though. Can I incorporate other forms of exercise into my routine to improve my running time?", "Yes, absolutely! ...", "..." ] } ``` ### Citation Information ```bibtex @article{ding2023enhancing, title={Enhancing Chat Language Models by Scaling High-quality Instructional Conversations}, author={Ding, Ning and Chen, Yulin and Xu, Bokai and Qin, Yujia and Zheng, Zhi and Hu, Shengding and Liu, Zhiyuan and Sun, Maosong and Zhou, Bowen}, journal={arXiv preprint arXiv:2305.14233}, year={2023} } ```
neural-bridge/rag-dataset-12000	neural-bridge	2024-02-05T18:25:13Z	1,441	136	[ "task_categories:question-answering", "language:en", "license:apache-2.0", "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "retrieval-augmented-generation" ]	[ "question-answering" ]	2023-10-02T17:18:39Z	null	--- dataset_info: features: - name: context dtype: string - name: question dtype: string - name: answer dtype: string splits: - name: train num_examples: 9600 - name: test num_examples: 2400 task_categories: - question-answering language: - en size_categories: - 10K<n<100K license: apache-2.0 tags: - retrieval-augmented-generation --- # Retrieval-Augmented Generation (RAG) Dataset 12000 Retrieval-Augmented Generation (RAG) Dataset 12000 is an English dataset designed for RAG-optimized models, built by [Neural Bridge AI](https://www.neuralbridge.ai/), and released under [Apache license 2.0](https://www.apache.org/licenses/LICENSE-2.0.html). ## Dataset Description #### Dataset Summary Retrieval-Augmented Generation (RAG) enhances large language models (LLMs) by allowing them to consult an external authoritative knowledge base before generating responses. This approach significantly boosts the models' ability to produce relevant, accurate, and context-specific output by extending their capabilities to specialized domains or an organization's internal data, without the need for retraining. RAG offers a cost-effective method to leverage the vast data processing power of LLMs, equipped with billions of parameters, for tasks such as question-answering, language translation, and sentence completion, ensuring that the output is always up-to-date and applicable to various contexts. RAG's importance lies in its potential to address the inherent challenges of LLMs, such as unpredictability in responses, reliance on static and potentially outdated training data, and the risk of disseminating incorrect or non-authoritative information. These issues can negatively affect user trust in AI-powered applications, making RAG's ability to guide LLMs toward authoritative sources for information retrieval invaluable. RAG has multiple benefits, including cost-effective implementation and maintenance, access to current information, improved user trust through accurate information and source attribution, and greater control for developers over the information retrieval process. This approach allows for the dynamic updating of LLMs with the latest research, statistics, or news, directly addressing the challenges of maintaining relevancy and accuracy in rapidly changing knowledge landscapes. Additionally, it empowers organizations to deploy generative AI more confidently across a wider range of applications, enhancing both the user experience and the reliability of AI-driven interactions. Retrieval-Augmented Generation (RAG) Dataset 12000 dataset is a triple-feature collection, with each entry containing a "context", "question", and "answer" fields, designed to help build RAG-optimized models. This data consists of 12000 entries, and the context data is from [Falcon RefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb). ```python from datasets import load_dataset rag_dataset = load_dataset("neural-bridge/rag-dataset-12000") ``` #### Languages The text in the dataset is in English. The associated BCP-47 code is `en`. ## Dataset Structure #### Data Instances A typical data point comprises a context, a question about the context, and an answer for the question. The context is obtained from [Falcon RefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb), and the question and answer for each data point are generated by GPT-4. An example from the dataset looks like the following: ``` { context: ... question: ... answer: ... } ``` #### Data Fields - `context`: A string consisting of a range of tokens. - `question`: A string consisting of a question related to the context. - `answer`: A string consisting of an answer for the question. #### Data Splits The data is split into a training and test set. The split sizes are as follow: \| \| Train \| Test \| \| ----- \| ------ \| ---- \| \| RAG Dataset 12000 \| 9600 \| 2400 \| ## Source Data The data points in the dataset are from the [Falcon RefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb) dataset. ## Neural Bridge AI RAG Datasets Index \| Model \| Link \| \| ----- \| ------ \| \| RAG Full 20000 \| [link](https://huggingface.co/datasets/neural-bridge/rag-full-20000) \| \| RAG Dataset 12000 \| [link](https://huggingface.co/datasets/neural-bridge/rag-dataset-12000) \| \| RAG Dataset 1200 \| [link](https://huggingface.co/datasets/neural-bridge/rag-dataset-1200) \| \| RAG Hallucination Dataset 1000 \| [link](https://huggingface.co/datasets/neural-bridge/rag-hallucination-dataset-1000) \| ## License This public extract is made available under [Apache license 2.0](https://www.apache.org/licenses/LICENSE-2.0.html). Users should also abide to the [Falcon RefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb) ToU.
xingyaoww/code-act	xingyaoww	2024-02-05T05:23:24Z	324	65	[ "task_categories:text-generation", "language:en", "license:apache-2.0", "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2402.01030", "region:us", "llm-agent", "llm", "instruction-tuning" ]	[ "text-generation" ]	2024-01-15T08:59:02Z	2	--- configs: - config_name: default data_files: - split: codeact path: data/codeact-* - split: general path: data/general-* dataset_info: features: - name: id dtype: string - name: conversations list: - name: content dtype: string - name: role dtype: string splits: - name: codeact num_bytes: 34936511 num_examples: 7139 - name: general num_bytes: 250817144 num_examples: 71246 download_size: 123084833 dataset_size: 285753655 license: apache-2.0 task_categories: - text-generation language: - en tags: - llm-agent - llm - instruction-tuning size_categories: - 1K<n<10K --- <h1 align="center"> Executable Code Actions Elicit Better LLM Agents </h1> <p align="center"> <a href="https://github.com/xingyaoww/code-act">💻 Code</a> • <a href="https://arxiv.org/abs/2402.01030">📃 Paper</a> • <a href="https://huggingface.co/datasets/xingyaoww/code-act" >🤗 Data (CodeActInstruct)</a> • <a href="https://huggingface.co/xingyaoww/CodeActAgent-Mistral-7b-v0.1" >🤗 Model (CodeActAgent-Mistral-7b-v0.1)</a> • <a href="https://chat.xwang.dev/">🤖 Chat with CodeActAgent!</a> </p> We propose to use executable Python code to consolidate LLM agents’ actions into a unified action space (CodeAct). Integrated with a Python interpreter, CodeAct can execute code actions and dynamically revise prior actions or emit new actions upon new observations (e.g., code execution results) through multi-turn interactions. ![Overview](https://github.com/xingyaoww/code-act/blob/main/figures/overview.png?raw=true) ## Why CodeAct? Our extensive analysis of 17 LLMs on API-Bank and a newly curated benchmark [M<sup>3</sup>ToolEval](docs/EVALUATION.md) shows that CodeAct outperforms widely used alternatives like Text and JSON (up to 20% higher success rate). Please check our paper for more detailed analysis! ![Comparison between CodeAct and Text/JSON](https://github.com/xingyaoww/code-act/blob/main/figures/codeact-comparison-table.png?raw=true) Comparison between CodeAct and Text / JSON as action. ![Comparison between CodeAct and Text/JSON](https://github.com/xingyaoww/code-act/blob/main/figures/codeact-comparison-perf.png?raw=true) Quantitative results comparing CodeAct and {Text, JSON} on M<sup>3</sup>ToolEval. ## 📁 CodeActInstruct We collect an instruction-tuning dataset CodeActInstruct that consists of 7k multi-turn interactions using CodeAct. Dataset is release at [huggingface dataset 🤗](https://huggingface.co/datasets/xingyaoww/code-act). Please refer to the paper and [this section](#-data-generation-optional) for details of data collection. ![Data Statistics](https://github.com/xingyaoww/code-act/blob/main/figures/data-stats.png?raw=true) Dataset Statistics. Token statistics are computed using Llama-2 tokenizer. ## 🪄 CodeActAgent Trained on CodeActInstruct and general conversaions, CodeActAgent excels at out-of-domain agent tasks compared to open-source models of the same size, while not sacrificing generic performance (e.g., knowledge, dialog). We release two variants of CodeActAgent: - CodeActAgent-Mistral-7b-v0.1 (recommended, [model link](https://huggingface.co/xingyaoww/CodeActAgent-Mistral-7b-v0.1)): using Mistral-7b-v0.1 as the base model with 32k context window. - CodeActAgent-Llama-7b ([model link](https://huggingface.co/xingyaoww/CodeActAgent-Llama-2-7b)): using Llama-2-7b as the base model with 4k context window. ![Model Performance](https://github.com/xingyaoww/code-act/blob/main/figures/model-performance.png?raw=true) Evaluation results for CodeActAgent. ID and OD stand for in-domain and out-of-domain evaluation correspondingly. Overall averaged performance normalizes the MT-Bench score to be consistent with other tasks and excludes in-domain tasks for fair comparison. Please check out [our paper](TODO) and [code](https://github.com/xingyaoww/code-act) for more details about data collection, model training, and evaluation. ## 📚 Citation ```bibtex @misc{wang2024executable, title={Executable Code Actions Elicit Better LLM Agents}, author={Xingyao Wang and Yangyi Chen and Lifan Yuan and Yizhe Zhang and Yunzhu Li and Hao Peng and Heng Ji}, year={2024}, eprint={2402.01030}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
Codec-SUPERB/fluent_speech_commands_synth	Codec-SUPERB	2024-02-01T15:29:11Z	11,629	0	[ "size_categories:100K<n<1M", "format:parquet", "modality:audio", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2023-12-27T05:51:14Z	null	--- configs: - config_name: default data_files: - split: original path: data/original-* - split: academicodec_hifi_16k_320d path: data/academicodec_hifi_16k_320d-* - split: academicodec_hifi_16k_320d_large_uni path: data/academicodec_hifi_16k_320d_large_uni-* - split: academicodec_hifi_24k_320d path: data/academicodec_hifi_24k_320d-* - split: audiodec_24k_320d path: data/audiodec_24k_320d-* - split: dac_16k path: data/dac_16k-* - split: dac_24k path: data/dac_24k-* - split: dac_44k path: data/dac_44k-* - split: encodec_24k_12bps path: data/encodec_24k_12bps-* - split: encodec_24k_1_5bps path: data/encodec_24k_1_5bps-* - split: encodec_24k_24bps path: data/encodec_24k_24bps-* - split: encodec_24k_3bps path: data/encodec_24k_3bps-* - split: encodec_24k_6bps path: data/encodec_24k_6bps-* - split: funcodec_en_libritts_16k_gr1nq32ds320 path: data/funcodec_en_libritts_16k_gr1nq32ds320-* - split: funcodec_en_libritts_16k_gr8nq32ds320 path: data/funcodec_en_libritts_16k_gr8nq32ds320-* - split: funcodec_en_libritts_16k_nq32ds320 path: data/funcodec_en_libritts_16k_nq32ds320-* - split: funcodec_en_libritts_16k_nq32ds640 path: data/funcodec_en_libritts_16k_nq32ds640-* - split: funcodec_zh_en_16k_nq32ds320 path: data/funcodec_zh_en_16k_nq32ds320-* - split: funcodec_zh_en_16k_nq32ds640 path: data/funcodec_zh_en_16k_nq32ds640-* - split: speech_tokenizer_16k path: data/speech_tokenizer_16k-* dataset_info: features: - name: audio dtype: audio: sampling_rate: 16000 - name: id dtype: string splits: - name: original num_bytes: 2220326464.0 num_examples: 30043 - name: academicodec_hifi_16k_320d num_bytes: 2212154504.0 num_examples: 30043 - name: academicodec_hifi_16k_320d_large_uni num_bytes: 2212154504.0 num_examples: 30043 - name: academicodec_hifi_24k_320d num_bytes: 3322180744.0 num_examples: 30043 - name: audiodec_24k_320d num_bytes: 3338935944.0 num_examples: 30043 - name: dac_16k num_bytes: 2221347926.0 num_examples: 30043 - name: dac_24k num_bytes: 3329678726.0 num_examples: 30043 - name: dac_44k num_bytes: 6114326168.0 num_examples: 30043 - name: encodec_24k_12bps num_bytes: 3329678726.0 num_examples: 30043 - name: encodec_24k_1_5bps num_bytes: 3329678726.0 num_examples: 30043 - name: encodec_24k_24bps num_bytes: 3329678726.0 num_examples: 30043 - name: encodec_24k_3bps num_bytes: 3329678726.0 num_examples: 30043 - name: encodec_24k_6bps num_bytes: 3329678726.0 num_examples: 30043 - name: funcodec_en_libritts_16k_gr1nq32ds320 num_bytes: 2219150286.0 num_examples: 30043 - name: funcodec_en_libritts_16k_gr8nq32ds320 num_bytes: 2219150286.0 num_examples: 30043 - name: funcodec_en_libritts_16k_nq32ds320 num_bytes: 2221347926.0 num_examples: 30043 - name: funcodec_en_libritts_16k_nq32ds640 num_bytes: 2221347926.0 num_examples: 30043 - name: funcodec_zh_en_16k_nq32ds320 num_bytes: 2221347926.0 num_examples: 30043 - name: funcodec_zh_en_16k_nq32ds640 num_bytes: 2221347926.0 num_examples: 30043 - name: speech_tokenizer_16k num_bytes: 2230445064.0 num_examples: 30043 download_size: 21108462066 dataset_size: 57173635950.0 --- # Dataset Card for "fluent_speech_commands_synth" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
awacke1/ICD10-Clinical-Terminology	awacke1	2024-01-31T01:34:01Z	121	15	[ "license:mit", "size_categories:10K<n<100K", "format:csv", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2023-02-28T12:20:41Z	1	--- license: mit --- ICD10-Clinical-Terminology pyarrow fast search demonstration for context AI MMoE
biglam/hmd_newspapers	biglam	2024-01-30T12:06:17Z	3,123	9	[ "task_categories:text-generation", "language:en", "license:cc0-1.0", "size_categories:1M<n<10M", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us", "newspapers" ]	[ "text-generation" ]	2022-03-02T23:29:22Z	1	--- dataset_info: features: - name: source dtype: string - name: title dtype: string - name: location dtype: string - name: date dtype: timestamp[s] - name: item_type dtype: string - name: word_count dtype: int32 - name: ocr_quality_mean dtype: float64 - name: ocr_quality_sd dtype: float64 - name: text dtype: string splits: - name: train num_bytes: 14304741164 num_examples: 3065408 download_size: 9682476047 dataset_size: 14304741164 configs: - config_name: default data_files: - split: train path: data/train-* license: cc0-1.0 task_categories: - text-generation language: - en tags: - newspapers pretty_name: Heritage Made Digital Newspapers size_categories: - 1M<n<10M --- # Dataset Card for Heritage Made Digital Newspapers ## Table of Contents - [Dataset Card for Heritage Made Digital Newspapers](#dataset-card-for-heritage-made-digital-newspapers) - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: - Repository: https://bl.iro.bl.uk/?locale=en - Paper: - Leaderboard: - Point of Contact: ### Dataset Summary This dataset contains text extracted at the article level from historic digitised newspapers from the [Heritage Made Digital](https://bl.iro.bl.uk/collections/9a6a4cdd-2bfe-47bb-8c14-c0a5d100501f?locale=en) newspaper digitisation program at the [British Library](https://www.bl.uk/). The newspapers in the dataset were published between 1800 and 1896. This dataset contains ~2.5 billion tokens and 3,065,408 articles. The dataset contains text generated from Optical Character Recognition software on digitised newspaper pages. This dataset includes the plain text from the OCR alongside some minimal metadata associated with the newspaper from which the text is derived and OCR confidence score information generated from the OCR software. ### Supported Tasks and Leaderboards This dataset can be used for: - historical research and digital humanities research - training language models - training historic language models Whilst this dataset can be used for all of these tasks, it is important to understand that the dataset was not constructed in a representative way so it contains biases in terms of the newspapers and articles that are included (more on this below). ### Languages The text in this dataset is in English that has been recognised by the OCR software. The OCR software used is generic commercial OCR software that has not been trained on historic newspapers. There are therefore many errors in the text. Some of the OCR in this text will be of such poor quality that is is incomprehensible to a human reader. ## Dataset Structure ### Data Instances Each row in the dataset is an article from a newspaper as recognised by an OLR (Optical Layout Recognition) step in the digitisation process. ### Data Splits There is one split in this dataset, the training split. ## Dataset Creation ### Curation Rationale This dataset consists of public-domain newspapers published in the UK during the 19th Century. The majority of newspapers digitised in the UK are not freely available (even if they are out of copyright). The newspapers in this dataset were digitised specifically to be freely available but also to meet preservation goals for newspapers in poor condition. As a result, the newspapers chosen for digitisation are biased toward poor quality physical newspapers. This may in turn result in worse OCR. ### Source Data The source data for this dataset is the digitised newspapers from the [Heritage Made Digital](https://bl.iro.bl.uk/collections/9a6a4cdd-2bfe-47bb-8c14-c0a5d100501f?locale=en) newspaper digitisation program. The newspapers in the dataset were published between 1800 and 1870. ### Dataset Curators The original digitisation was carried out by the British Library. The dataset was created by the British Library in partnership with Findmypast. This dataset was created by [@davanstrien](https://huggingface.co/davanstrien). ### Licensing Information The newspapers in this dataset are in the public domain. The dataset is licensed under a [Creative Commons Zero v1.0 Universal](https://creativecommons.org/publicdomain/zero/1.0/) license. ### Citation Information [More Information Needed] ### Contributions Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset.
biglam/blbooks-parquet	biglam	2024-01-30T09:41:53Z	19,514	6	[ "task_categories:text-generation", "task_categories:fill-mask", "task_categories:other", "task_ids:language-modeling", "task_ids:masked-language-modeling", "annotations_creators:no-annotation", "language_creators:machine-generated", "multilinguality:multilingual", "source_datasets:blbooks", "language:de", "language:en", "language:es", "language:fr", "language:it", "language:nl", "license:cc0-1.0", "size_categories:10M<n<100M", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us", "digital-humanities-research" ]	[ "text-generation", "fill-mask", "other" ]	2023-07-13T12:48:12Z	null	--- annotations_creators: - no-annotation language_creators: - machine-generated language: - de - en - es - fr - it - nl license: - cc0-1.0 multilinguality: - multilingual size_categories: - 100K<n<1M source_datasets: - blbooks task_categories: - text-generation - fill-mask - other task_ids: - language-modeling - masked-language-modeling pretty_name: British Library Books tags: - digital-humanities-research dataset_info: - config_name: '1500_1899' features: - name: record_id dtype: string - name: date dtype: timestamp[s] - name: raw_date dtype: string - name: title dtype: string - name: place dtype: string - name: empty_pg dtype: bool - name: text dtype: string - name: pg dtype: int32 - name: mean_wc_ocr dtype: float32 - name: std_wc_ocr dtype: float64 - name: name dtype: string - name: all_names dtype: string - name: Publisher dtype: string - name: Country of publication 1 dtype: string - name: all Countries of publication dtype: string - name: Physical description dtype: string - name: Language_1 dtype: string - name: Language_2 dtype: string - name: Language_3 dtype: string - name: Language_4 dtype: string - name: multi_language dtype: bool splits: - name: train num_bytes: 30452067039 num_examples: 14011953 download_size: 10486035662 dataset_size: 30452067039 - config_name: '1510_1699' features: - name: record_id dtype: string - name: date dtype: timestamp[s] - name: raw_date dtype: string - name: title dtype: string - name: place dtype: string - name: empty_pg dtype: bool - name: text dtype: string - name: pg dtype: int32 - name: mean_wc_ocr dtype: float32 - name: std_wc_ocr dtype: float64 - name: name dtype: string - name: all_names dtype: string - name: Publisher dtype: string - name: Country of publication 1 dtype: string - name: all Countries of publication dtype: string - name: Physical description dtype: string - name: Language_1 dtype: string - name: Language_2 dtype: string - name: Language_3 dtype: string - name: Language_4 dtype: string - name: multi_language dtype: bool splits: - name: train num_bytes: 107667469 num_examples: 51982 download_size: 42320165 dataset_size: 107667469 - config_name: '1700_1799' features: - name: record_id dtype: string - name: date dtype: timestamp[s] - name: raw_date dtype: string - name: title dtype: string - name: place dtype: string - name: empty_pg dtype: bool - name: text dtype: string - name: pg dtype: int32 - name: mean_wc_ocr dtype: float32 - name: std_wc_ocr dtype: float64 - name: name dtype: string - name: all_names dtype: string - name: Publisher dtype: string - name: Country of publication 1 dtype: string - name: all Countries of publication dtype: string - name: Physical description dtype: string - name: Language_1 dtype: string - name: Language_2 dtype: string - name: Language_3 dtype: string - name: Language_4 dtype: string - name: multi_language dtype: bool splits: - name: train num_bytes: 267117831 num_examples: 178224 download_size: 95137895 dataset_size: 267117831 - config_name: 1700s features: - name: record_id dtype: string - name: date dtype: int32 - name: raw_date dtype: string - name: title dtype: string - name: place dtype: string - name: empty_pg dtype: bool - name: text dtype: string - name: pg dtype: int32 - name: mean_wc_ocr dtype: float32 - name: std_wc_ocr dtype: float64 - name: name dtype: string - name: all_names dtype: string - name: Publisher dtype: string - name: Country of publication 1 dtype: string - name: all Countries of publication dtype: string - name: Physical description dtype: string - name: Language_1 dtype: string - name: Language_2 dtype: string - name: Language_3 dtype: string - name: Language_4 dtype: string - name: multi_language dtype: bool splits: - name: train num_bytes: 266382657 num_examples: 178224 download_size: 95137895 dataset_size: 266382657 - config_name: '1800_1899' features: - name: record_id dtype: string - name: date dtype: timestamp[s] - name: raw_date dtype: string - name: title dtype: string - name: place dtype: string - name: empty_pg dtype: bool - name: text dtype: string - name: pg dtype: int32 - name: mean_wc_ocr dtype: float32 - name: std_wc_ocr dtype: float64 - name: name dtype: string - name: all_names dtype: string - name: Publisher dtype: string - name: Country of publication 1 dtype: string - name: all Countries of publication dtype: string - name: Physical description dtype: string - name: Language_1 dtype: string - name: Language_2 dtype: string - name: Language_3 dtype: string - name: Language_4 dtype: string - name: multi_language dtype: bool splits: - name: train num_bytes: 30077284377 num_examples: 13781747 download_size: 10348577602 dataset_size: 30077284377 - config_name: 1800s features: - name: record_id dtype: string - name: date dtype: int32 - name: raw_date dtype: string - name: title dtype: string - name: place dtype: string - name: empty_pg dtype: bool - name: text dtype: string - name: pg dtype: int32 - name: mean_wc_ocr dtype: float32 - name: std_wc_ocr dtype: float64 - name: name dtype: string - name: all_names dtype: string - name: Publisher dtype: string - name: Country of publication 1 dtype: string - name: all Countries of publication dtype: string - name: Physical description dtype: string - name: Language_1 dtype: string - name: Language_2 dtype: string - name: Language_3 dtype: string - name: Language_4 dtype: string - name: multi_language dtype: bool splits: - name: train num_bytes: 30020434670 num_examples: 13781747 download_size: 10348577602 dataset_size: 30020434670 - config_name: all features: - name: record_id dtype: string - name: date dtype: int32 - name: raw_date dtype: string - name: title dtype: string - name: place dtype: string - name: empty_pg dtype: bool - name: text dtype: string - name: pg dtype: int32 - name: mean_wc_ocr dtype: float32 - name: std_wc_ocr dtype: float64 - name: name dtype: string - name: all_names dtype: string - name: Publisher dtype: string - name: Country of publication 1 dtype: string - name: all Countries of publication dtype: string - name: Physical description dtype: string - name: Language_1 dtype: string - name: Language_2 dtype: string - name: Language_3 dtype: string - name: Language_4 dtype: string - name: multi_language dtype: bool splits: - name: train num_bytes: 30394267732 num_examples: 14011953 download_size: 10486035662 dataset_size: 30394267732 - config_name: default features: - name: record_id dtype: string - name: date dtype: timestamp[s] - name: raw_date dtype: string - name: title dtype: string - name: place dtype: string - name: empty_pg dtype: bool - name: text dtype: string - name: pg dtype: int32 - name: mean_wc_ocr dtype: float32 - name: std_wc_ocr dtype: float64 - name: name dtype: string - name: all_names dtype: string - name: Publisher dtype: string - name: Country of publication 1 dtype: string - name: all Countries of publication dtype: string - name: Physical description dtype: string - name: Language_1 dtype: string - name: Language_2 dtype: string - name: Language_3 dtype: string - name: Language_4 dtype: string - name: multi_language dtype: bool splits: - name: train num_bytes: 30451982919 num_examples: 14011953 download_size: 16402835443 dataset_size: 30451982919 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for British Library Books This dataset is the same as https://huggingface.co/datasets/TheBritishLibrary/blbooks, however, this version is stored as parquet to avoid needing to run a datasets script. This also makes loading this dataset much quicker. ## Table of Contents - [Dataset Card for British Library Books](#dataset-card-for-British-Library-Books) - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Language model training](#language-model-training) - [Supervised tasks](#supervised-tasks) - [Languages](#languages) - [Language change](#language-change) - [Optical Character Recognition](#optical-character-recognition) - [OCR word confidence](#ocr-word-confidence) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization) - [Date normalization](#date-normalization) - [Metadata included](#metadata-included) - [Who are the source language producers?](#who-are-the-source-language-producers) - [Annotations](#annotations) - [Annotation process](#annotation-process) - [Who are the annotators?](#who-are-the-annotators) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Colonialism](#colonialism) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: https://www.bl.uk/collection-guides/digitised-printed-books - Repository: https://doi.org/10.21250/db14 - Paper: - Leaderboard: - Point of Contact: [email protected] ### Dataset Summary This dataset consists of books digitised by the British Library in partnership with Microsoft. The dataset includes ~25 million pages of out of copyright texts. The majority of the texts were published in the 18th and 19th Century, but the collection also consists of a smaller number of books from earlier periods. Items within this collection cover a wide range of subject areas, including geography, philosophy, history, poetry and literature and are published in various languages. The dataset consits of around 7.67 billion words. While the books are predominately from the 18th and 19th Centuries, there are fewer books from earlier periods. The number of pages in the corpus by decade: \| \| page count \| \| ---- \| ---------- \| \| 1510 \| 94 \| \| 1520 \| 32 \| \| 1540 \| 184 \| \| 1550 \| 16 \| \| 1580 \| 276 \| \| 1590 \| 540 \| \| 1600 \| 1117 \| \| 1610 \| 1132 \| \| 1620 \| 1856 \| \| 1630 \| 9274 \| \| 1640 \| 4232 \| \| 1650 \| 2944 \| \| 1660 \| 5858 \| \| 1670 \| 11415 \| \| 1680 \| 8348 \| \| 1690 \| 13756 \| \| 1700 \| 10160 \| \| 1710 \| 9556 \| \| 1720 \| 10314 \| \| 1730 \| 13282 \| \| 1740 \| 10778 \| \| 1750 \| 12001 \| \| 1760 \| 21415 \| \| 1770 \| 28490 \| \| 1780 \| 32676 \| \| 1790 \| 50014 \| \| 1800 \| 307806 \| \| 1810 \| 478008 \| \| 1820 \| 589419 \| \| 1830 \| 681212 \| \| 1840 \| 1113473 \| \| 1850 \| 1726108 \| \| 1860 \| 1725407 \| \| 1870 \| 2069089 \| \| 1880 \| 2585159 \| \| 1890 \| 3365031 \| [More Information Needed] ### Supported Tasks and Leaderboards This collection has been previously used across various digital history and humanities projects since being published. The dataset consists of text and a range of metadata associated with this text. This metadata includes: - date of publication - place of publication - country of publication - language - OCR quality - physical description of the original physical item #### Language model training As a relatively large dataset, `blbooks` provides a source dataset for training language models. The presence of this metadata also offers interesting opportunities to use this dataset as a source for training language models based on: - specific time-periods - specific languages - certain OCR quality thresholds The above is not an exhaustive list but offer some suggestions of how the dataset can be used to explore topics such as the impact of OCR quality on language models, the ‘transferability’ of language models across time or the impact of training multilingual language models on historical languages. #### Supervised tasks Whilst this dataset does not have annotations for a specific NLP task, such as Named Entity Recognition, it does include a wide variety of metadata. This metadata has the potential to be used for training and/or evaluating a variety of supervised tasks predicting this metadata. ### Languages This dataset consists of books published in several languages. The breakdown of the languages included (at the page level) is: \| Language \| Pages \| \| --------------------- \| -------- \| \| English \| 10039463 \| \| French \| 1442929 \| \| German \| 1172793 \| \| Spanish \| 286778 \| \| Italian \| 214255 \| \| Dutch \| 204759 \| \| Russian \| 193347 \| \| Danish \| 93366 \| \| Hungarian \| 88094 \| \| Swedish \| 76225 \| \| Polish \| 58901 \| \| Greek, Modern (1453-) \| 26104 \| \| Latin \| 25611 \| \| Portuguese \| 25410 \| \| Czech \| 20160 \| \| Bulgarian \| 7891 \| \| Finnish \| 5677 \| \| Irish \| 2743 \| \| Serbian \| 1975 \| \| Romanian \| 1544 \| \| Norwegian Nynorsk \| 1398 \| \| Croatian \| 1306 \| \| Norwegian \| 1227 \| \| Icelandic \| 902 \| \| Slovak \| 840 \| \| Lithuanian \| 714 \| \| Welsh \| 580 \| \| Slovenian \| 545 \| \| Indonesian \| 418 \| \| Cornish \| 223 \| This breakdown was derived from the first language in the associated metadata field. Some books include multiple languages. Some of the languages codes for this data were also derived using computational methods. Therefore, the language fields in the dataset should be treated with some caution (discussed in more detail below). #### Language change The publication dates of books in the data cover a broad period of time (1500-1900). For languages in the dataset with broad temporal coverage, significant [language change](https://en.wikipedia.org/wiki/Language_change) might be found. The ability to study this change by taking reasonably large samples of languages covering different time periods is one of the opportunities offered by this dataset. The fact that the text in this dataset was produced via Optical Character Recognition (OCR) causes some challenges for this type of research (see below). #### Optical Character Recognition The digitised books in this collection were transformed into machine-readable text using Optical Character Recognition (OCR) software. The text produced via OCR software will usually include some errors. These errors include; mistakes at the character level; for example, an `i` is mistaken for an `l`, at the word level or across significant passages of text. The books in this dataset can pose some additional challenges for OCR software. OCR errors can stem from: - the quality of the original printing: printing technology was a developing technology during the time period covered by this corpus; some of the original book text will include misprints, blurred or faded ink that is hard to read - damage to the page: some of the books will have become damaged over time, this can obscure all or parts of the text on a page - poor quality scans: scanning books can be challenging; for example, if the book has tight bindings, it can be hard to capture text that has fallen into the [gutter](https://www.abaa.org/glossary/entry/gutter) of the book. - the language used in the books may differ from the languages OCR software is predominantly trained to recognise. ##### OCR word confidence Many OCR engines produce some form of confidence score alongside the predicted text. These confidence scores are usually at the character or word level. The word confidence score was given for each word in the original ALTO XML versions of the text in this dataset in this dataset. The OCR confidence scores should be treated with some scepticism. For historical text or in a lower resource language, for example, a low confidence score may be more likely for words not included in a modern dictionary but may be accurate transcriptions of the original text. With that said, the confidence scores do give some sense of the OCR quality. An example of text with a high (over 90% mean word confidence score): ``` 8 direction to the Conduit, round which is a wide open space, and a good broad pavement called the Parade. It commands a pleasant peep of the slopes and terrace throughout its entire length. The street continuing from the Conduit, in the same general direction, was known anciently as Lodborne Lane, and is now named South Street. From the Conduit two other streets, at right angles to these, are Long Street, leading Eastwards, and Half-Moon Street (formerly Lodborne), leading to Westbury, Trendle Street, and the Horsecastles Road. ``` An example of text with a score below 40%: ``` Hannover. Schrift und Druck von Fr. CultniTmn,', "LeMNs'utluirui.", 'ü 8u«llim» M^äalßwi 01de!lop 1<M.', 'p^dnalmw vom Xr^u/e, lpiti>»*Kmm lie« !»^2!M kleine lii!<! (,«> ttünee!<»e^ v»n tndzt Lievclum, 1872, ``` The quality of OCR - as measured by mean OCR confidence for a page - across the dataset correlates with other features. A groupby of publication decade and mean word confidence: \| decade \| mean_wc_ocr \| \| ------ \| ----------- \| \| 1510 \| 0.499151 \| \| 1520 \| 0.544818 \| \| 1540 \| 0.511589 \| \| 1550 \| 0.4505 \| \| 1580 \| 0.321858 \| \| 1590 \| 0.461282 \| \| 1600 \| 0.467318 \| \| 1610 \| 0.495895 \| \| 1620 \| 0.501257 \| \| 1630 \| 0.49766 \| \| 1640 \| 0.512095 \| \| 1650 \| 0.528534 \| \| 1660 \| 0.521014 \| \| 1670 \| 0.592575 \| \| 1680 \| 0.583901 \| \| 1690 \| 0.567202 \| \| 1700 \| 0.575175 \| \| 1710 \| 0.61436 \| \| 1720 \| 0.627725 \| \| 1730 \| 0.658534 \| \| 1740 \| 0.64214 \| \| 1750 \| 0.657357 \| \| 1760 \| 0.6389 \| \| 1770 \| 0.651883 \| \| 1780 \| 0.632326 \| \| 1790 \| 0.664279 \| \| 1800 \| 0.682338 \| \| 1810 \| 0.708915 \| \| 1820 \| 0.730015 \| \| 1830 \| 0.730973 \| \| 1840 \| 0.713886 \| \| 1850 \| 0.697106 \| \| 1860 \| 0.696701 \| \| 1870 \| 0.717233 \| \| 1880 \| 0.733331 \| \| 1890 \| 0.762364 \| As might be expected, the earlier periods have lower mean word confidence scores. Again, all of this should be treated with some scepticism, especially as the size of the data grows over time. As with time, the mean word confidence of the OCR software varies across languages: \| Language_1 \| mean_wc_ocr \| \| --------------------- \| ----------- \| \| Croatian \| 0.755565 \| \| Welsh \| 0.7528 \| \| Norwegian Nynorsk \| 0.751648 \| \| Slovenian \| 0.746007 \| \| French \| 0.740772 \| \| Finnish \| 0.738032 \| \| Czech \| 0.737849 \| \| Hungarian \| 0.736076 \| \| Dutch \| 0.734977 \| \| Cornish \| 0.733682 \| \| Danish \| 0.733106 \| \| English \| 0.733037 \| \| Irish \| 0.732658 \| \| Portuguese \| 0.727746 \| \| Spanish \| 0.725111 \| \| Icelandic \| 0.724427 \| \| Italian \| 0.715839 \| \| Swedish \| 0.715633 \| \| Polish \| 0.715133 \| \| Lithuanian \| 0.700003 \| \| Bulgarian \| 0.694657 \| \| Romanian \| 0.692957 \| \| Latin \| 0.689022 \| \| Russian \| 0.685847 \| \| Serbian \| 0.674329 \| \| Slovak \| 0.66739 \| \| Greek, Modern (1453-) \| 0.632195 \| \| German \| 0.631457 \| \| Indonesian \| 0.6155 \| \| Norwegian \| 0.597987 \| Again, these numbers should be treated sceptically since some languages appear very infrequently. For example, the above table suggests the mean word confidence for Welsh is relatively high. However, there isn’t much Welsh in the dataset. Therefore, it is unlikely that this data will be particularly useful for training (historic) Welsh language models. [More Information Needed] ## Dataset Structure The dataset has a number of configurations relating to the different dates of publication in the underlying data: - `1500_1899`: this configuration covers all years - `1800_1899`: this configuration covers the years between 1800 and 1899 - `1700_1799`: this configuration covers the years between 1700 and 1799 - `1510_1699`: this configuration covers the years between 1510 and 1699 ### Configuration option All of the configurations have an optional keyword argument `skip_empty_pages` which is set to `True` by default. The underlying dataset includes some pages where there is no text. This could either be because the underlying book page didn't have any text or the OCR software failed to detect this text. For many uses of this dataset it doesn't make sense to include empty pages so these are skipped by default. However, for some uses you may prefer to retain a representation of the data that includes these empty pages. Passing `skip_empty_pages=False` when loading the dataset will enable this option. ### Data Instances An example data instance: ```python {'Country of publication 1': 'England', 'Language_1': 'English', 'Language_2': None, 'Language_3': None, 'Language_4': None, 'Physical description': None, 'Publisher': None, 'all Countries of publication': 'England', 'all names': 'Settle, Elkanah [person]', 'date': 1689, 'empty_pg': True, 'mean_wc_ocr': 0.0, 'multi_language': False, 'name': 'Settle, Elkanah', 'pg': 1, 'place': 'London', 'raw_date': '1689', 'record_id': '001876770', 'std_wc_ocr': 0.0, 'text': None, ‘title’: ‘The Female Prelate: being the history and the life and death of Pope Joan. A tragedy [in five acts and in verse] . Written by a Person of Quality [i.e. Elkanah Settle]’} ``` Each instance in the dataset represents a single page from an original digitised book. ### Data Fields Included in this dataset are: \| Field \| Data Type \| Description \| \| ---------------------------- \| --------- \| ------------------------------------------------------------------------------------------------------------- \| \| record_id \| string \| British Library ID for the item \| \| date \| int \| parsed/normalised year for the item. i.e. 1850 \| \| raw_date \| string \| the original raw date for an item i.e. 1850- \| \| title \| string \| title of the book \| \| place \| string \| Place of publication, i.e. London \| \| empty_pg \| bool \| whether page contains text \| \| text \| string \| OCR generated text for a page \| \| pg \| int \| page in original book the instance refers to \| \| mean_wc_ocr \| float \| mean word confidence values for the page \| \| std_wc_ocr \| float \| standard deviation of the word confidence values for the page \| \| name \| string \| name associated with the item (usually author) \| \| all names \| string \| all names associated with a publication \| \| Publisher \| string \| publisher of the book \| \| Country of publication 1 \| string \| first country associated with publication \| \| all Countries of publication \| string \| all countries associated with a publication \| \| Physical description \| string \| physical description of the item (size). This requires some normalisation before use and isn’t always present \| \| Language_1 \| string \| first language associated with the book, this is usually present \| \| Language_2 \| string \| \| \| Language_3 \| string \| \| \| Language_4 \| string \| \| \| multi_language \| bool \| \| Some of these fields are not populated a large proportion of the time. You can get some sense of this from this [Pandas Profiling](https://github.com/pandas-profiling/pandas-profiling) [report](https://davanstrien.github.io/BL-datasets-pandas-profile-reports/pandas_profile_report_MS_digitised_books_2021-01-09.html) The majority of these fields relate to metadata about the books. Most of these fields were created by staff working for the British Library. The notable exception is the “Languages” fields that have sometimes been determined using computational methods. This work is reported in more detail in [Automated Language Identification of Bibliographic Resources](https://doi.org/10.1080/01639374.2019.1700201). It is important to note that metadata is neither perfect nor static. The metadata associated with this book was generated based on export from the British Library catalogue in 2021. [More Information Needed] ### Data Splits This dataset contains a single split `train`. ## Dataset Creation Note this section is a work in progress. ### Curation Rationale The books in this collection were digitised as part of a project partnership between the British Library and Microsoft. [Mass digitisation](https://en.wikipedia.org/wiki/Category:Mass_digitization), i.e. projects intending to quickly digitise large volumes of materials shape the selection of materials to include in several ways. Some considerations which are often involved in the decision of whether to include items for digitisation include (but are not limited to): - copyright status - preservation needs - the size of an item, very large and very small items are often hard to digitise quickly These criteria can have knock-on effects on the makeup of a collection. For example, systematically excluding large books may result in some types of book content not being digitised. Large volumes are likely to be correlated to content to at least some extent, so excluding them from digitisation will mean that material is underrepresented. Similarly, copyright status is often (but not only) determined by publication date. This can often lead to a rapid fall in the number of items in a collection after a certain cut-off date. All of the above is largely to make clear that this collection was not curated to create a representative sample of the British Library’s holdings. Some material will be over-represented, and others under-represented. Similarly, the collection should not be considered a representative sample of what was published across the period covered by the dataset (nor that the relative proportions of the data for each time period represent a proportional sample of publications from that period). Finally, and this probably does not need stating, the language included in the text should not be considered representative of either written or spoken language(s) from that time period. [More Information Needed] ### Source Data The source data (physical items) includes a variety of resources (predominantly monographs) held by the [British Library](bl.uk/](https://bl.uk/). The British Library is a [Legal Deposit](https://www.bl.uk/legal-deposit/about-legal-deposit) library. “Legal deposit requires publishers to provide a copy of every work they publish in the UK to the British Library. It’s existed in English law since 1662.” [source](https://www.bl.uk/legal-deposit/about-legal-deposit). The source data for this version of the data is derived from the original ALTO XML files and a recent metadata export #TODO add links [More Information Needed] #### Initial Data Collection and Normalization This version of the dataset was created using the original ALTO XML files and, where a match was found, updating the metadata associated with that item with more recent metadata using an export from the British Library catalogue. The process of creating this new dataset is documented here #TODO add link. There are a few decisions made in the above processing steps worth highlighting in particular: ##### Date normalization The metadata around date of publication for an item is not always exact. It often is represented as a date range e.g. `1850-1860`. The `date` field above takes steps to normalise this date to a single integer value. In most cases, this is taking the mean of the values associated with the item. The `raw_date` field includes the unprocessed date string. ##### Metadata included The metadata associated with each item includes most of the fields available via the ALTO XML. However, the data doesn’t include some metadata fields from the metadata export file. The reason fields were excluded because they are frequently not populated. A cut off of 50% was chosen, i.e. values from the metadata which are missing above 50% of the time were not included. This is slightly arbitrary, but since the aim of this version of the data was to support computational research using the collection it was felt that these fields with frequent missing values would be less valuable. #### Who are the source language producers? [More Information Needed] ### Annotations This dataset does not include annotations as usually understood in the context of NLP. The data does include metadata associated with the books. #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data There a range of considerations around using the data. These include the representativeness of the dataset, the OCR quality and the language used. Depending on your use case, these may be more or less important. For example, the impact of OCR quality on downstream tasks will depend on the target task. It may also be possible to mitigate this negative impact from OCR through tokenizer choice, Language Model training objectives, oversampling high-quality OCR, etc. [More Information Needed] ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases The text in this collection is derived from historical text. As a result, the text will reflect this time period's social beliefs and attitudes. The books include both fiction and non-fiction books. Examples of book titles that appear in the data (these are randomly sampled from all titles): - ‘Rhymes and Dreams, Legends of Pendle Forest, and other poems’, - “Précis of Information concerning the Zulu Country, with a map. Prepared in the Intelligence Branch of the Quarter-Master-General’s Department, Horse Guards, War Office, etc”, - ‘The fan. A poem’, - ‘Grif; a story of Australian Life’, - ‘Calypso; a masque: in three acts, etc’, - ‘Tales Uncle told [With illustrative woodcuts.]’, - 'Questings', - 'Home Life on an Ostrich Farm. With ... illustrations’, - ‘Bulgarya i Bulgarowie’, - 'Εἰς τα βαθη της Ἀφρικης [In darkest Africa.] ... Μεταφρασις Γεωρ. Σ. Βουτσινα, etc', - ‘The Corsair, a tale’, ‘Poems ... With notes [With a portrait.]’, - ‘Report of the Librarian for the year 1898 (1899, 1901, 1909)’, - “The World of Thought. A novel. By the author of ‘Before I began to speak.’”, - 'Amleto; tragedia ... recata in versi italiani da M. Leoni, etc'] While using titles alone is insufficient to integrate bias in this collection, it gives some insight into the topics covered by books. Further, the tiles highlight some particular types of bias we might find in the collection. This should in no way be considered an exhaustive list. #### Colonialism Even in the above random sample of titles examples of colonial attitudes, we can see examples of titles. We can try and interrogate this further by searching for the name of places that were part of the British Empire when many of these books were published. Searching for the string `India` in the titles and randomly sampling 10 titles returns: - “Travels in India in the Seventeenth Century: by Sir Thomas Roe and Dr. John Fryer. Reprinted from the ‘Calcutta Weekly Englishman.’”, - ‘A Winter in India and Malaysia among the Methodist Missions’, - “The Tourist’s Guide to all the principal stations on the railways of Northern India [By W. W.] ... Fifth edition”, - ‘Records of Sport and Military Life in Western India ... With an introduction by ... G. B. Malleson’, - "Lakhmi, the Rájpút's Bride. A tale of Gujarát in Western India [A poem.]”, - ‘The West India Commonplace Book: compiled from parliamentary and official documents; shewing the interest of Great Britain in its Sugar Colonies’, - “From Tonkin to India : by the sources of the Irawadi, January’ 95-January ’96”, - ‘Case of the Ameers of Sinde : speeches of Mr. John Sullivan, and Captain William Eastwick, at a special court held at the India House, ... 26th January, 1844’, - ‘The Andaman Islands; their colonisation, etc. A correspondence addressed to the India Office’, - ‘Ancient India as described by Ptolemy; being a translation of the chapters which describe India and Eastern Asia in the treatise on Geography written by Klaudios Ptolemaios ... with introduction, commentary, map of India according to Ptolemy, and ... index, by J. W. McCrindle’] Searching form the string `Africa` in the titles and randomly sampling 10 titles returns: - ['De Benguella ás Terras de Iácca. Descripção de uma viagem na Africa Central e Occidental ... Expedição organisada nos annos de 1877-1880. Edição illustrada', - ‘To the New Geographical Society of Edinburgh [An address on Africa by H. M. Stanley.]’, - ‘Diamonds and Gold in South Africa ... With maps, etc’, - ‘Missionary Travels and Researches in South Africa ... With notes by F. S. Arnot. With map and illustrations. New edition’, - ‘A Narrative of a Visit to the Mauritius and South Africa ... Illustrated by two maps, sixteen etchings and twenty-eight wood-cuts’, - ‘Side Lights on South Africa ... With a map, etc’, - ‘My Second Journey through Equatorial Africa ... in ... 1886 and 1887 ... Translated ... by M. J. A. Bergmann. With a map ... and ... illustrations, etc’, - ‘Missionary Travels and Researches in South Africa ... With portrait and fullpage illustrations’, - ‘[African sketches.] Narrative of a residence in South Africa ... A new edition. To which is prefixed a biographical sketch of the author by J. Conder’, - ‘Lake Ngami; or, Explorations and discoveries during four years wandering in the wilds of South Western Africa ... With a map, and numerous illustrations, etc’] [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information The books are licensed under the [CC Public Domain Mark 1.0](https://creativecommons.org/publicdomain/mark/1.0/) license. ### Citation Information ```bibtext @misc{bBritishLibraryBooks2021, author = {British Library Labs}, title = {Digitised Books. c. 1510 - c. 1900. JSONL (OCR derived text + metadata)}, year = {2021}, publisher = {British Library}, howpublished={https://doi.org/10.23636/r7w6-zy15} ``` ### Contributions Thanks to [@davanstrien](https://github.com/davanstrien) for adding this dataset.
corto-ai/handwritten-text	corto-ai	2024-01-29T00:25:32Z	141	14	[ "size_categories:10K<n<100K", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2024-01-29T00:25:16Z	2	--- dataset_info: features: - name: text dtype: string - name: image dtype: image splits: - name: train num_bytes: 167800178.75 num_examples: 6482 - name: valid num_bytes: 24887435.0 num_examples: 976 - name: test num_bytes: 73857843.625 num_examples: 2915 download_size: 265569932 dataset_size: 266545457.375 configs: - config_name: default data_files: - split: train path: data/train-* - split: valid path: data/valid-* - split: test path: data/test-* ---
google/code_x_glue_ct_code_to_text	google	2024-01-24T15:09:09Z	1,709	69	[ "task_categories:translation", "annotations_creators:found", "language_creators:found", "multilinguality:other-programming-languages", "source_datasets:original", "language:code", "language:en", "license:c-uda", "size_categories:1M<n<10M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us", "code-to-text" ]	[ "translation" ]	2022-03-02T23:29:22Z	1	--- annotations_creators: - found language_creators: - found language: - code - en license: - c-uda multilinguality: - other-programming-languages size_categories: - 100K<n<1M - 10K<n<100K source_datasets: - original task_categories: - translation task_ids: [] pretty_name: CodeXGlueCtCodeToText config_names: - go - java - javascript - php - python - ruby tags: - code-to-text dataset_info: - config_name: go features: - name: id dtype: int32 - name: repo dtype: string - name: path dtype: string - name: func_name dtype: string - name: original_string dtype: string - name: language dtype: string - name: code dtype: string - name: code_tokens sequence: string - name: docstring dtype: string - name: docstring_tokens sequence: string - name: sha dtype: string - name: url dtype: string splits: - name: train num_bytes: 342243143 num_examples: 167288 - name: validation num_bytes: 13721860 num_examples: 7325 - name: test num_bytes: 16328406 num_examples: 8122 download_size: 121341698 dataset_size: 372293409 - config_name: java features: - name: id dtype: int32 - name: repo dtype: string - name: path dtype: string - name: func_name dtype: string - name: original_string dtype: string - name: language dtype: string - name: code dtype: string - name: code_tokens sequence: string - name: docstring dtype: string - name: docstring_tokens sequence: string - name: sha dtype: string - name: url dtype: string splits: - name: train num_bytes: 452553835 num_examples: 164923 - name: validation num_bytes: 13366344 num_examples: 5183 - name: test num_bytes: 29080753 num_examples: 10955 download_size: 154701399 dataset_size: 495000932 - config_name: javascript features: - name: id dtype: int32 - name: repo dtype: string - name: path dtype: string - name: func_name dtype: string - name: original_string dtype: string - name: language dtype: string - name: code dtype: string - name: code_tokens sequence: string - name: docstring dtype: string - name: docstring_tokens sequence: string - name: sha dtype: string - name: url dtype: string splits: - name: train num_bytes: 160860431 num_examples: 58025 - name: validation num_bytes: 10337344 num_examples: 3885 - name: test num_bytes: 10190713 num_examples: 3291 download_size: 65788314 dataset_size: 181388488 - config_name: php features: - name: id dtype: int32 - name: repo dtype: string - name: path dtype: string - name: func_name dtype: string - name: original_string dtype: string - name: language dtype: string - name: code dtype: string - name: code_tokens sequence: string - name: docstring dtype: string - name: docstring_tokens sequence: string - name: sha dtype: string - name: url dtype: string splits: - name: train num_bytes: 614654499 num_examples: 241241 - name: validation num_bytes: 33283045 num_examples: 12982 - name: test num_bytes: 35374993 num_examples: 14014 download_size: 219692158 dataset_size: 683312537 - config_name: python features: - name: id dtype: int32 - name: repo dtype: string - name: path dtype: string - name: func_name dtype: string - name: original_string dtype: string - name: language dtype: string - name: code dtype: string - name: code_tokens sequence: string - name: docstring dtype: string - name: docstring_tokens sequence: string - name: sha dtype: string - name: url dtype: string splits: - name: train num_bytes: 813663148 num_examples: 251820 - name: validation num_bytes: 46888564 num_examples: 13914 - name: test num_bytes: 50659688 num_examples: 14918 download_size: 325551862 dataset_size: 911211400 - config_name: ruby features: - name: id dtype: int32 - name: repo dtype: string - name: path dtype: string - name: func_name dtype: string - name: original_string dtype: string - name: language dtype: string - name: code dtype: string - name: code_tokens sequence: string - name: docstring dtype: string - name: docstring_tokens sequence: string - name: sha dtype: string - name: url dtype: string splits: - name: train num_bytes: 51956439 num_examples: 24927 - name: validation num_bytes: 2821037 num_examples: 1400 - name: test num_bytes: 2671551 num_examples: 1261 download_size: 21921316 dataset_size: 57449027 configs: - config_name: go data_files: - split: train path: go/train-* - split: validation path: go/validation-* - split: test path: go/test-* - config_name: java data_files: - split: train path: java/train-* - split: validation path: java/validation-* - split: test path: java/test-* - config_name: javascript data_files: - split: train path: javascript/train-* - split: validation path: javascript/validation-* - split: test path: javascript/test-* - config_name: php data_files: - split: train path: php/train-* - split: validation path: php/validation-* - split: test path: php/test-* - config_name: python data_files: - split: train path: python/train-* - split: validation path: python/validation-* - split: test path: python/test-* - config_name: ruby data_files: - split: train path: ruby/train-* - split: validation path: ruby/validation-* - split: test path: ruby/test-* --- # Dataset Card for "code_x_glue_ct_code_to_text" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits-sample-size) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: https://github.com/microsoft/CodeXGLUE/tree/main/Code-Text/code-to-text ### Dataset Summary CodeXGLUE code-to-text dataset, available at https://github.com/microsoft/CodeXGLUE/tree/main/Code-Text/code-to-text The dataset we use comes from CodeSearchNet and we filter the dataset as the following: - Remove examples that codes cannot be parsed into an abstract syntax tree. - Remove examples that #tokens of documents is < 3 or >256 - Remove examples that documents contain special tokens (e.g. <img ...> or https:...) - Remove examples that documents are not English. ### Supported Tasks and Leaderboards - `machine-translation`: The dataset can be used to train a model for automatically generating English docstrings for code. ### Languages - Go programming language - Java programming language - Javascript programming language - PHP programming language - Python programming language - Ruby programming language - English natural language ## Dataset Structure ### Data Instances #### go An example of 'test' looks as follows. ``` { "code": "func NewSTM(c v3.Client, apply func(STM) error, so ...stmOption) (v3.TxnResponse, error) {\n\topts := &stmOptions{ctx: c.Ctx()}\n\tfor _, f := range so {\n\t\tf(opts)\n\t}\n\tif len(opts.prefetch) != 0 {\n\t\tf := apply\n\t\tapply = func(s STM) error {\n\t\t\ts.Get(opts.prefetch...)\n\t\t\treturn f(s)\n\t\t}\n\t}\n\treturn runSTM(mkSTM(c, opts), apply)\n}", "code_tokens": ["func", "NewSTM", "(", "c", "", "v3", ".", "Client", ",", "apply", "func", "(", "STM", ")", "error", ",", "so", "...", "stmOption", ")", "(", "", "v3", ".", "TxnResponse", ",", "error", ")", "{", "opts", ":=", "&", "stmOptions", "{", "ctx", ":", "c", ".", "Ctx", "(", ")", "}", "\n", "for", "_", ",", "f", ":=", "range", "so", "{", "f", "(", "opts", ")", "\n", "}", "\n", "if", "len", "(", "opts", ".", "prefetch", ")", "!=", "0", "{", "f", ":=", "apply", "\n", "apply", "=", "func", "(", "s", "STM", ")", "error", "{", "s", ".", "Get", "(", "opts", ".", "prefetch", "...", ")", "\n", "return", "f", "(", "s", ")", "\n", "}", "\n", "}", "\n", "return", "runSTM", "(", "mkSTM", "(", "c", ",", "opts", ")", ",", "apply", ")", "\n", "}"], "docstring": "// NewSTM initiates a new STM instance, using serializable snapshot isolation by default.", "docstring_tokens": ["NewSTM", "initiates", "a", "new", "STM", "instance", "using", "serializable", "snapshot", "isolation", "by", "default", "."], "func_name": "NewSTM", "id": 0, "language": "go", "original_string": "func NewSTM(c v3.Client, apply func(STM) error, so ...stmOption) (v3.TxnResponse, error) {\n\topts := &stmOptions{ctx: c.Ctx()}\n\tfor _, f := range so {\n\t\tf(opts)\n\t}\n\tif len(opts.prefetch) != 0 {\n\t\tf := apply\n\t\tapply = func(s STM) error {\n\t\t\ts.Get(opts.prefetch...)\n\t\t\treturn f(s)\n\t\t}\n\t}\n\treturn runSTM(mkSTM(c, opts), apply)\n}", "path": "clientv3/concurrency/stm.go", "repo": "etcd-io/etcd", "sha": "616592d9ba993e3fe9798eef581316016df98906", "url": "https://github.com/etcd-io/etcd/blob/616592d9ba993e3fe9798eef581316016df98906/clientv3/concurrency/stm.go#L89-L102" } ``` #### java An example of 'test' looks as follows. ``` { "code": "protected final void fastPathOrderedEmit(U value, boolean delayError, Disposable disposable) {\n final Observer<? super V> observer = downstream;\n final SimplePlainQueue<U> q = queue;\n\n if (wip.get() == 0 && wip.compareAndSet(0, 1)) {\n if (q.isEmpty()) {\n accept(observer, value);\n if (leave(-1) == 0) {\n return;\n }\n } else {\n q.offer(value);\n }\n } else {\n q.offer(value);\n if (!enter()) {\n return;\n }\n }\n QueueDrainHelper.drainLoop(q, observer, delayError, disposable, this);\n }", "code_tokens": ["protected", "final", "void", "fastPathOrderedEmit", "(", "U", "value", ",", "boolean", "delayError", ",", "Disposable", "disposable", ")", "{", "final", "Observer", "<", "?", "super", "V", ">", "observer", "=", "downstream", ";", "final", "SimplePlainQueue", "<", "U", ">", "q", "=", "queue", ";", "if", "(", "wip", ".", "get", "(", ")", "==", "0", "&&", "wip", ".", "compareAndSet", "(", "0", ",", "1", ")", ")", "{", "if", "(", "q", ".", "isEmpty", "(", ")", ")", "{", "accept", "(", "observer", ",", "value", ")", ";", "if", "(", "leave", "(", "-", "1", ")", "==", "0", ")", "{", "return", ";", "}", "}", "else", "{", "q", ".", "offer", "(", "value", ")", ";", "}", "}", "else", "{", "q", ".", "offer", "(", "value", ")", ";", "if", "(", "!", "enter", "(", ")", ")", "{", "return", ";", "}", "}", "QueueDrainHelper", ".", "drainLoop", "(", "q", ",", "observer", ",", "delayError", ",", "disposable", ",", "this", ")", ";", "}"], "docstring": "Makes sure the fast-path emits in order.\n@param value the value to emit or queue up\n@param delayError if true, errors are delayed until the source has terminated\n@param disposable the resource to dispose if the drain terminates", "docstring_tokens": ["Makes", "sure", "the", "fast", "-", "path", "emits", "in", "order", "."], "func_name": "QueueDrainObserver.fastPathOrderedEmit", "id": 0, "language": "java", "original_string": "protected final void fastPathOrderedEmit(U value, boolean delayError, Disposable disposable) {\n final Observer<? super V> observer = downstream;\n final SimplePlainQueue<U> q = queue;\n\n if (wip.get() == 0 && wip.compareAndSet(0, 1)) {\n if (q.isEmpty()) {\n accept(observer, value);\n if (leave(-1) == 0) {\n return;\n }\n } else {\n q.offer(value);\n }\n } else {\n q.offer(value);\n if (!enter()) {\n return;\n }\n }\n QueueDrainHelper.drainLoop(q, observer, delayError, disposable, this);\n }", "path": "src/main/java/io/reactivex/internal/observers/QueueDrainObserver.java", "repo": "ReactiveX/RxJava", "sha": "ac84182aa2bd866b53e01c8e3fe99683b882c60e", "url": "https://github.com/ReactiveX/RxJava/blob/ac84182aa2bd866b53e01c8e3fe99683b882c60e/src/main/java/io/reactivex/internal/observers/QueueDrainObserver.java#L88-L108" } ``` #### javascript An example of 'test' looks as follows. ``` { "code": "function createInstance(defaultConfig) {\n var context = new Axios(defaultConfig);\n var instance = bind(Axios.prototype.request, context);\n\n // Copy axios.prototype to instance\n utils.extend(instance, Axios.prototype, context);\n\n // Copy context to instance\n utils.extend(instance, context);\n\n return instance;\n}", "code_tokens": ["function", "createInstance", "(", "defaultConfig", ")", "{", "var", "context", "=", "new", "Axios", "(", "defaultConfig", ")", ";", "var", "instance", "=", "bind", "(", "Axios", ".", "prototype", ".", "request", ",", "context", ")", ";", "// Copy axios.prototype to instance", "utils", ".", "extend", "(", "instance", ",", "Axios", ".", "prototype", ",", "context", ")", ";", "// Copy context to instance", "utils", ".", "extend", "(", "instance", ",", "context", ")", ";", "return", "instance", ";", "}"], "docstring": "Create an instance of Axios\n\n@param {Object} defaultConfig The default config for the instance\n@return {Axios} A new instance of Axios", "docstring_tokens": ["Create", "an", "instance", "of", "Axios"], "func_name": "createInstance", "id": 0, "language": "javascript", "original_string": "function createInstance(defaultConfig) {\n var context = new Axios(defaultConfig);\n var instance = bind(Axios.prototype.request, context);\n\n // Copy axios.prototype to instance\n utils.extend(instance, Axios.prototype, context);\n\n // Copy context to instance\n utils.extend(instance, context);\n\n return instance;\n}", "path": "lib/axios.js", "repo": "axios/axios", "sha": "92d231387fe2092f8736bc1746d4caa766b675f5", "url": "https://github.com/axios/axios/blob/92d231387fe2092f8736bc1746d4caa766b675f5/lib/axios.js#L15-L26" } ``` #### php An example of 'train' looks as follows. ``` { "code": "public static function build($serviceAddress, $restConfigPath, array $config = [])\n {\n $config += [\n 'httpHandler' => null,\n ];\n list($baseUri, $port) = self::normalizeServiceAddress($serviceAddress);\n $requestBuilder = new RequestBuilder(\"$baseUri:$port\", $restConfigPath);\n $httpHandler = $config['httpHandler'] ?: self::buildHttpHandlerAsync();\n return new RestTransport($requestBuilder, $httpHandler);\n }", "code_tokens": ["public", "static", "function", "build", "(", "$", "serviceAddress", ",", "$", "restConfigPath", ",", "array", "$", "config", "=", "[", "]", ")", "{", "$", "config", "+=", "[", "'httpHandler'", "=>", "null", ",", "]", ";", "list", "(", "$", "baseUri", ",", "$", "port", ")", "=", "self", "::", "normalizeServiceAddress", "(", "$", "serviceAddress", ")", ";", "$", "requestBuilder", "=", "new", "RequestBuilder", "(", "\"$baseUri:$port\"", ",", "$", "restConfigPath", ")", ";", "$", "httpHandler", "=", "$", "config", "[", "'httpHandler'", "]", "?", ":", "self", "::", "buildHttpHandlerAsync", "(", ")", ";", "return", "new", "RestTransport", "(", "$", "requestBuilder", ",", "$", "httpHandler", ")", ";", "}"], "docstring": "Builds a RestTransport.\n\n@param string $serviceAddress\nThe address of the API remote host, for example \"example.googleapis.com\".\n@param string $restConfigPath\nPath to rest config file.\n@param array $config {\nConfig options used to construct the gRPC transport.\n\n@type callable $httpHandler A handler used to deliver PSR-7 requests.\n}\n@return RestTransport\n@throws ValidationException", "docstring_tokens": ["Builds", "a", "RestTransport", "."], "func_name": "RestTransport.build", "id": 0, "language": "php", "original_string": "public static function build($serviceAddress, $restConfigPath, array $config = [])\n {\n $config += [\n 'httpHandler' => null,\n ];\n list($baseUri, $port) = self::normalizeServiceAddress($serviceAddress);\n $requestBuilder = new RequestBuilder(\"$baseUri:$port\", $restConfigPath);\n $httpHandler = $config['httpHandler'] ?: self::buildHttpHandlerAsync();\n return new RestTransport($requestBuilder, $httpHandler);\n }", "path": "src/Transport/RestTransport.php", "repo": "googleapis/gax-php", "sha": "48387fb818c6882296710a2302a0aa973b99afb2", "url": "https://github.com/googleapis/gax-php/blob/48387fb818c6882296710a2302a0aa973b99afb2/src/Transport/RestTransport.php#L85-L94" } ``` #### python An example of 'validation' looks as follows. ``` { "code": "def save_act(self, path=None):\n \"\"\"Save model to a pickle located at `path`\"\"\"\n if path is None:\n path = os.path.join(logger.get_dir(), \"model.pkl\")\n\n with tempfile.TemporaryDirectory() as td:\n save_variables(os.path.join(td, \"model\"))\n arc_name = os.path.join(td, \"packed.zip\")\n with zipfile.ZipFile(arc_name, 'w') as zipf:\n for root, dirs, files in os.walk(td):\n for fname in files:\n file_path = os.path.join(root, fname)\n if file_path != arc_name:\n zipf.write(file_path, os.path.relpath(file_path, td))\n with open(arc_name, \"rb\") as f:\n model_data = f.read()\n with open(path, \"wb\") as f:\n cloudpickle.dump((model_data, self._act_params), f)", "code_tokens": ["def", "save_act", "(", "self", ",", "path", "=", "None", ")", ":", "if", "path", "is", "None", ":", "path", "=", "os", ".", "path", ".", "join", "(", "logger", ".", "get_dir", "(", ")", ",", "\"model.pkl\"", ")", "with", "tempfile", ".", "TemporaryDirectory", "(", ")", "as", "td", ":", "save_variables", "(", "os", ".", "path", ".", "join", "(", "td", ",", "\"model\"", ")", ")", "arc_name", "=", "os", ".", "path", ".", "join", "(", "td", ",", "\"packed.zip\"", ")", "with", "zipfile", ".", "ZipFile", "(", "arc_name", ",", "'w'", ")", "as", "zipf", ":", "for", "root", ",", "dirs", ",", "files", "in", "os", ".", "walk", "(", "td", ")", ":", "for", "fname", "in", "files", ":", "file_path", "=", "os", ".", "path", ".", "join", "(", "root", ",", "fname", ")", "if", "file_path", "!=", "arc_name", ":", "zipf", ".", "write", "(", "file_path", ",", "os", ".", "path", ".", "relpath", "(", "file_path", ",", "td", ")", ")", "with", "open", "(", "arc_name", ",", "\"rb\"", ")", "as", "f", ":", "model_data", "=", "f", ".", "read", "(", ")", "with", "open", "(", "path", ",", "\"wb\"", ")", "as", "f", ":", "cloudpickle", ".", "dump", "(", "(", "model_data", ",", "self", ".", "_act_params", ")", ",", "f", ")"], "docstring": "Save model to a pickle located at `path`", "docstring_tokens": ["Save", "model", "to", "a", "pickle", "located", "at", "path"], "func_name": "ActWrapper.save_act", "id": 0, "language": "python", "original_string": "def save_act(self, path=None):\n \"\"\"Save model to a pickle located at `path`\"\"\"\n if path is None:\n path = os.path.join(logger.get_dir(), \"model.pkl\")\n\n with tempfile.TemporaryDirectory() as td:\n save_variables(os.path.join(td, \"model\"))\n arc_name = os.path.join(td, \"packed.zip\")\n with zipfile.ZipFile(arc_name, 'w') as zipf:\n for root, dirs, files in os.walk(td):\n for fname in files:\n file_path = os.path.join(root, fname)\n if file_path != arc_name:\n zipf.write(file_path, os.path.relpath(file_path, td))\n with open(arc_name, \"rb\") as f:\n model_data = f.read()\n with open(path, \"wb\") as f:\n cloudpickle.dump((model_data, self._act_params), f)", "path": "baselines/deepq/deepq.py", "repo": "openai/baselines", "sha": "3301089b48c42b87b396e246ea3f56fa4bfc9678", "url": "https://github.com/openai/baselines/blob/3301089b48c42b87b396e246ea3f56fa4bfc9678/baselines/deepq/deepq.py#L55-L72" } ``` #### ruby An example of 'train' looks as follows. ``` { "code": "def render_body(context, options)\n if options.key?(:partial)\n [render_partial(context, options)]\n else\n StreamingTemplateRenderer.new(@lookup_context).render(context, options)\n end\n end", "code_tokens": ["def", "render_body", "(", "context", ",", "options", ")", "if", "options", ".", "key?", "(", ":partial", ")", "[", "render_partial", "(", "context", ",", "options", ")", "]", "else", "StreamingTemplateRenderer", ".", "new", "(", "@lookup_context", ")", ".", "render", "(", "context", ",", "options", ")", "end", "end"], "docstring": "Render but returns a valid Rack body. If fibers are defined, we return\n a streaming body that renders the template piece by piece.\n\n Note that partials are not supported to be rendered with streaming,\n so in such cases, we just wrap them in an array.", "docstring_tokens": ["Render", "but", "returns", "a", "valid", "Rack", "body", ".", "If", "fibers", "are", "defined", "we", "return", "a", "streaming", "body", "that", "renders", "the", "template", "piece", "by", "piece", "."], "func_name": "ActionView.Renderer.render_body", "id": 0, "language": "ruby", "original_string": "def render_body(context, options)\n if options.key?(:partial)\n [render_partial(context, options)]\n else\n StreamingTemplateRenderer.new(@lookup_context).render(context, options)\n end\n end", "path": "actionview/lib/action_view/renderer/renderer.rb", "repo": "rails/rails", "sha": "85a8bc644be69908f05740a5886ec19cd3679df5", "url": "https://github.com/rails/rails/blob/85a8bc644be69908f05740a5886ec19cd3679df5/actionview/lib/action_view/renderer/renderer.rb#L38-L44" } ``` ### Data Fields In the following each data field in go is explained for each config. The data fields are the same among all splits. #### go, java, javascript, php, python, ruby \| field name \| type \| description \| \|----------------\|----------------\|-----------------------------------------------------------------------------------\| \|id \|int32 \| Index of the sample \| \|repo \|string \| repo: the owner/repo \| \|path \|string \| path: the full path to the original file \| \|func_name \|string \| func_name: the function or method name \| \|original_string \|string \| original_string: the raw string before tokenization or parsing \| \|language \|string \| language: the programming language name \| \|code \|string \| code/function: the part of the original_string that is code \| \|code_tokens \|Sequence[string]\| code_tokens/function_tokens: tokenized version of code \| \|docstring \|string \| docstring: the top-level comment or docstring, if it exists in the original string\| \|docstring_tokens\|Sequence[string]\| docstring_tokens: tokenized version of docstring \| \|sha \|string \| sha of the file \| \|url \|string \| url of the file \| ### Data Splits \| name \|train \|validation\|test \| \|----------\|-----:\|---------:\|----:\| \|go \|167288\| 7325\| 8122\| \|java \|164923\| 5183\|10955\| \|javascript\| 58025\| 3885\| 3291\| \|php \|241241\| 12982\|14014\| \|python \|251820\| 13914\|14918\| \|ruby \| 24927\| 1400\| 1261\| ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization Data from CodeSearchNet Challenge dataset. [More Information Needed] #### Who are the source language producers? Software Engineering developers. ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators https://github.com/microsoft, https://github.com/madlag ### Licensing Information Computational Use of Data Agreement (C-UDA) License. ### Citation Information ``` @article{husain2019codesearchnet, title={Codesearchnet challenge: Evaluating the state of semantic code search}, author={Husain, Hamel and Wu, Ho-Hsiang and Gazit, Tiferet and Allamanis, Miltiadis and Brockschmidt, Marc}, journal={arXiv preprint arXiv:1909.09436}, year={2019} } ``` ### Contributions Thanks to @madlag (and partly also @ncoop57) for adding this dataset.
mrqa-workshop/mrqa	mrqa-workshop	2024-01-24T10:52:34Z	623	24	[ "task_categories:question-answering", "task_ids:extractive-qa", "annotations_creators:found", "language_creators:found", "multilinguality:monolingual", "source_datasets:extended\|drop", "source_datasets:extended\|hotpot_qa", "source_datasets:extended\|natural_questions", "source_datasets:extended\|race", "source_datasets:extended\|search_qa", "source_datasets:extended\|squad", "source_datasets:extended\|trivia_qa", "language:en", "license:unknown", "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:1910.09753", "arxiv:1606.05250", "arxiv:1611.09830", "arxiv:1705.03551", "arxiv:1704.05179", "arxiv:1809.09600", "arxiv:1903.00161", "arxiv:1804.07927", "arxiv:1704.04683", "arxiv:1706.04115", "region:us" ]	[ "question-answering" ]	2022-03-02T23:29:22Z	1	--- annotations_creators: - found language_creators: - found language: - en license: - unknown multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - extended\|drop - extended\|hotpot_qa - extended\|natural_questions - extended\|race - extended\|search_qa - extended\|squad - extended\|trivia_qa task_categories: - question-answering task_ids: - extractive-qa paperswithcode_id: mrqa-2019 pretty_name: MRQA 2019 dataset_info: config_name: plain_text features: - name: subset dtype: string - name: context dtype: string - name: context_tokens sequence: - name: tokens dtype: string - name: offsets dtype: int32 - name: qid dtype: string - name: question dtype: string - name: question_tokens sequence: - name: tokens dtype: string - name: offsets dtype: int32 - name: detected_answers sequence: - name: text dtype: string - name: char_spans sequence: - name: start dtype: int32 - name: end dtype: int32 - name: token_spans sequence: - name: start dtype: int32 - name: end dtype: int32 - name: answers sequence: string splits: - name: train num_bytes: 4090677713 num_examples: 516819 - name: validation num_bytes: 484106546 num_examples: 58221 - name: test num_bytes: 57712097 num_examples: 9633 download_size: 1679161250 dataset_size: 4632496356 configs: - config_name: plain_text data_files: - split: train path: plain_text/train-* - split: validation path: plain_text/validation-* - split: test path: plain_text/test-* default: true --- # Dataset Card for MRQA 2019 ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [MRQA 2019 Shared Task](https://mrqa.github.io/2019/shared.html) - Repository: [MRQA 2019 Github repository](https://github.com/mrqa/MRQA-Shared-Task-2019) - Paper: [MRQA 2019 Shared Task: Evaluating Generalization in Reading Comprehension ](https://arxiv.org/abs/1910.09753) - Leaderboard: [Shared task](https://mrqa.github.io/2019/shared.html) - Point of Contact: [[email protected]]([email protected]) ### Dataset Summary The MRQA 2019 Shared Task focuses on generalization in question answering. An effective question answering system should do more than merely interpolate from the training set to answer test examples drawn from the same distribution: it should also be able to extrapolate to out-of-distribution examples — a significantly harder challenge. The dataset is a collection of 18 existing QA dataset (carefully selected subset of them) and converted to the same format (SQuAD format). Among these 18 datasets, six datasets were made available for training, six datasets were made available for development, and the final six for testing. The dataset is released as part of the MRQA 2019 Shared Task. ### Supported Tasks and Leaderboards From the official repository: The format of the task is extractive question answering. Given a question and context passage, systems must find the word or phrase in the document that best answers the question. While this format is somewhat restrictive, it allows us to leverage many existing datasets, and its simplicity helps us focus on out-of-domain generalization, instead of other important but orthogonal challenges. We have adapted several existing datasets from their original formats and settings to conform to our unified extractive setting. Most notably: - We provide only a single, length-limited context. - There are no unanswerable or non-span answer questions. - All questions have at least one accepted answer that is found exactly in the context. A span is judged to be an exact match if it matches the answer string after performing normalization consistent with the SQuAD dataset. Specifically: - The text is uncased. - All punctuation is stripped. - All articles `{a, an, the}` are removed. - All consecutive whitespace markers are compressed to just a single normal space `' '`. Answers are evaluated using exact match and token-level F1 metrics. One can refer to the [mrqa_official_eval.py](https://github.com/mrqa/MRQA-Shared-Task-2019/blob/master/mrqa_official_eval.py) for evaluation. ### Languages The text in the dataset is in English. The associated BCP-47 code is `en`. ## Dataset Structure ### Data Instances An examples looks like this: ``` { 'qid': 'f43c83e38d1e424ea00f8ad3c77ec999', 'subset': 'SQuAD' 'context': 'CBS broadcast Super Bowl 50 in the U.S., and charged an average of $5 million for a 30-second commercial during the game. The Super Bowl 50 halftime show was headlined by the British rock group Coldplay with special guest performers Beyoncé and Bruno Mars, who headlined the Super Bowl XLVII and Super Bowl XLVIII halftime shows, respectively. It was the third-most watched U.S. broadcast ever.', 'context_tokens': { 'offsets': [0, 4, 14, 20, 25, 28, 31, 35, 39, 41, 45, 53, 56, 64, 67, 68, 70, 78, 82, 84, 94, 105, 112, 116, 120, 122, 126, 132, 137, 140, 149, 154, 158, 168, 171, 175, 183, 188, 194, 203, 208, 216, 222, 233, 241, 245, 251, 255, 257, 261, 271, 275, 281, 286, 292, 296, 302, 307, 314, 323, 328, 330, 342, 344, 347, 351, 355, 360, 361, 366, 374, 379, 389, 393], 'tokens': ['CBS', 'broadcast', 'Super', 'Bowl', '50', 'in', 'the', 'U.S.', ',', 'and', 'charged', 'an', 'average', 'of', '$', '5', 'million', 'for', 'a', '30-second', 'commercial', 'during', 'the', 'game', '.', 'The', 'Super', 'Bowl', '50', 'halftime', 'show', 'was', 'headlined', 'by', 'the', 'British', 'rock', 'group', 'Coldplay', 'with', 'special', 'guest', 'performers', 'Beyoncé', 'and', 'Bruno', 'Mars', ',', 'who', 'headlined', 'the', 'Super', 'Bowl', 'XLVII', 'and', 'Super', 'Bowl', 'XLVIII', 'halftime', 'shows', ',', 'respectively', '.', 'It', 'was', 'the', 'third', '-', 'most', 'watched', 'U.S.', 'broadcast', 'ever', '.'] }, 'question': "Who was the main performer at this year's halftime show?", 'question_tokens': { 'offsets': [0, 4, 8, 12, 17, 27, 30, 35, 39, 42, 51, 55], 'tokens': ['Who', 'was', 'the', 'main', 'performer', 'at', 'this', 'year', "'s", 'halftime', 'show', '?'] }, 'detected_answers': { 'char_spans': [ { 'end': [201], 'start': [194] }, { 'end': [201], 'start': [194] }, { 'end': [201], 'start': [194] } ], 'text': ['Coldplay', 'Coldplay', 'Coldplay'], 'token_spans': [ { 'end': [38], 'start': [38] }, { 'end': [38], 'start': [38] }, { 'end': [38], 'start': [38] } ] }, 'answers': ['Coldplay', 'Coldplay', 'Coldplay'], } ``` ### Data Fields - `subset`: which of the dataset does this examples come from? - `context`: This is the raw text of the supporting passage. Three special token types have been inserted: `[TLE]` precedes document titles, `[DOC]` denotes document breaks, and `[PAR]` denotes paragraph breaks. The maximum length of the context is 800 tokens. - `context_tokens`: A tokenized version of the supporting passage, using spaCy. Each token is a tuple of the token string and token character offset. The maximum number of tokens is 800. - `tokens`: list of tokens. - `offets`: list of offsets. - `qas`: A list of questions for the given context. - `qid`: A unique identifier for the question. The `qid` is unique across all datasets. - `question`: The raw text of the question. - `question_tokens`: A tokenized version of the question. The tokenizer and token format is the same as for the context. - `tokens`: list of tokens. - `offets`: list of offsets. - `detected_answers`: A list of answer spans for the given question that index into the context. For some datasets these spans have been automatically detected using searching heuristics. The same answer may appear multiple times in the text --- each of these occurrences is recorded. For example, if `42` is the answer, the context `"The answer is 42. 42 is the answer."`, has two occurrences marked. - `text`: The raw text of the detected answer. - `char_spans`: Inclusive (start, end) character spans (indexing into the raw context). - `start`: start (single element) - `end`: end (single element) - `token_spans`: Inclusive (start, end) token spans (indexing into the tokenized context). - `start`: start (single element) - `end`: end (single element) ### Data Splits Training data \| Dataset \| Number of Examples \| \| :-----: \| :------: \| \| [SQuAD](https://arxiv.org/abs/1606.05250) \| 86,588 \| \| [NewsQA](https://arxiv.org/abs/1611.09830) \| 74,160 \| \| [TriviaQA](https://arxiv.org/abs/1705.03551)\| 61,688 \| \| [SearchQA](https://arxiv.org/abs/1704.05179)\| 117,384 \| \| [HotpotQA](https://arxiv.org/abs/1809.09600)\| 72,928 \| \| [NaturalQuestions](https://ai.google/research/pubs/pub47761)\| 104,071 \| Development data This in-domain data may be used for helping develop models. \| Dataset \| Examples \| \| :-----: \| :------: \| \| [SQuAD](https://arxiv.org/abs/1606.05250) \| 10,507 \| \| [NewsQA](https://arxiv.org/abs/1611.09830) \| 4,212 \| \| [TriviaQA](https://arxiv.org/abs/1705.03551)\| 7,785\| \| [SearchQA](https://arxiv.org/abs/1704.05179)\| 16,980 \| \| [HotpotQA](https://arxiv.org/abs/1809.09600)\| 5,904 \| \| [NaturalQuestions](https://ai.google/research/pubs/pub47761)\| 12,836 \| Test data The final testing data only contain out-of-domain data. \| Dataset \| Examples \| \| :-----: \| :------: \| \| [BioASQ](http://bioasq.org/) \| 1,504 \| \| [DROP](https://arxiv.org/abs/1903.00161) \| 1,503 \| \| [DuoRC](https://arxiv.org/abs/1804.07927)\| 1,501 \| \| [RACE](https://arxiv.org/abs/1704.04683) \| 674 \| \| [RelationExtraction](https://arxiv.org/abs/1706.04115) \| 2,948\| \| [TextbookQA](http://ai2-website.s3.amazonaws.com/publications/CVPR17_TQA.pdf)\| 1,503 \| From the official repository: *Note: As previously mentioned, the out-of-domain dataset have been modified from their original settings to fit the unified MRQA Shared Task paradigm. At a high level, the following two major modifications have been made:* 1. All QA-context pairs are extractive. That is, the answer is selected from the context and not via, e.g., multiple-choice. 2. All contexts are capped at a maximum of `800` tokens. As a result, for longer contexts like Wikipedia articles, we only consider examples where the answer appears in the first `800` tokens. As a result, some splits are harder than the original datasets (e.g., removal of multiple-choice in RACE), while some are easier (e.g., restricted context length in NaturalQuestions --- we use the short answer selection). Thus one should expect different performance ranges if comparing to previous work on these datasets. ## Dataset Creation ### Curation Rationale From the official repository: Both train and test datasets have the same format described above, but may differ in some of the following ways: - Passage distribution: Test examples may involve passages from different sources (e.g., science, news, novels, medical abstracts, etc) with pronounced syntactic and lexical differences. - Question distribution: Test examples may emphasize different styles of questions (e.g., entity-centric, relational, other tasks reformulated as QA, etc) which may come from different sources (e.g., crowdworkers, domain experts, exam writers, etc.) - Joint distribution: Test examples may vary according to the relationship of the question to the passage (e.g., collected independent vs. dependent of evidence, multi-hop, etc) ### Source Data [More Information Needed] #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations [More Information Needed] #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information Unknown ### Citation Information ``` @inproceedings{fisch2019mrqa, title={{MRQA} 2019 Shared Task: Evaluating Generalization in Reading Comprehension}, author={Adam Fisch and Alon Talmor and Robin Jia and Minjoon Seo and Eunsol Choi and Danqi Chen}, booktitle={Proceedings of 2nd Machine Reading for Reading Comprehension (MRQA) Workshop at EMNLP}, year={2019}, } ``` ### Contributions Thanks to [@jimmycode](https://github.com/jimmycode), [@VictorSanh](https://github.com/VictorSanh) for adding this dataset.
tdiggelm/climate_fever	tdiggelm	2024-01-18T14:28:07Z	568	26	[ "task_categories:text-classification", "task_categories:text-retrieval", "task_ids:text-scoring", "task_ids:fact-checking", "task_ids:fact-checking-retrieval", "task_ids:semantic-similarity-scoring", "task_ids:multi-input-text-classification", "annotations_creators:crowdsourced", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "source_datasets:extended\|wikipedia", "source_datasets:original", "language:en", "license:unknown", "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2012.00614", "region:us" ]	[ "text-classification", "text-retrieval" ]	2022-03-02T23:29:22Z	1	--- annotations_creators: - crowdsourced - expert-generated language_creators: - found language: - en license: - unknown multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - extended\|wikipedia - original task_categories: - text-classification - text-retrieval task_ids: - text-scoring - fact-checking - fact-checking-retrieval - semantic-similarity-scoring - multi-input-text-classification paperswithcode_id: climate-fever pretty_name: ClimateFever dataset_info: features: - name: claim_id dtype: string - name: claim dtype: string - name: claim_label dtype: class_label: names: '0': SUPPORTS '1': REFUTES '2': NOT_ENOUGH_INFO '3': DISPUTED - name: evidences list: - name: evidence_id dtype: string - name: evidence_label dtype: class_label: names: '0': SUPPORTS '1': REFUTES '2': NOT_ENOUGH_INFO - name: article dtype: string - name: evidence dtype: string - name: entropy dtype: float32 - name: votes list: string splits: - name: test num_bytes: 2429240 num_examples: 1535 download_size: 868947 dataset_size: 2429240 configs: - config_name: default data_files: - split: test path: data/test-* --- # Dataset Card for ClimateFever ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [CLIMATE-FEVER homepage](http://climatefever.ai) - Repository: [CLIMATE-FEVER repository](https://github.com/tdiggelm/climate-fever-dataset) - Paper: [CLIMATE-FEVER: A Dataset for Verification of Real-World Climate Claims](https://arxiv.org/abs/2012.00614) - Leaderboard: [Needs More Information] - Point of Contact: [Thomas Diggelmann](mailto:[email protected]) ### Dataset Summary A dataset adopting the FEVER methodology that consists of 1,535 real-world claims regarding climate-change collected on the internet. Each claim is accompanied by five manually annotated evidence sentences retrieved from the English Wikipedia that support, refute or do not give enough information to validate the claim totalling in 7,675 claim-evidence pairs. The dataset features challenging claims that relate multiple facets and disputed cases of claims where both supporting and refuting evidence are present. ### Supported Tasks and Leaderboards [Needs More Information] ### Languages The text in the dataset is in English, as found in real-world claims about climate-change on the Internet. The associated BCP-47 code is `en`. ## Dataset Structure ### Data Instances ``` { "claim_id": "0", "claim": "Global warming is driving polar bears toward extinction", "claim_label": 0, # "SUPPORTS" "evidences": [ { "evidence_id": "Extinction risk from global warming:170", "evidence_label": 2, # "NOT_ENOUGH_INFO" "article": "Extinction risk from global warming", "evidence": "\"Recent Research Shows Human Activity Driving Earth Towards Global Extinction Event\".", "entropy": 0.6931471805599453, "votes": [ "SUPPORTS", "NOT_ENOUGH_INFO", null, null, null ] }, { "evidence_id": "Global warming:14", "evidence_label": 0, # "SUPPORTS" "article": "Global warming", "evidence": "Environmental impacts include the extinction or relocation of many species as their ecosystems change, most immediately the environments of coral reefs, mountains, and the Arctic.", "entropy": 0.0, "votes": [ "SUPPORTS", "SUPPORTS", null, null, null ] }, { "evidence_id": "Global warming:178", "evidence_label": 2, # "NOT_ENOUGH_INFO" "article": "Global warming", "evidence": "Rising temperatures push bees to their physiological limits, and could cause the extinction of bee populations.", "entropy": 0.6931471805599453, "votes": [ "SUPPORTS", "NOT_ENOUGH_INFO", null, null, null ] }, { "evidence_id": "Habitat destruction:61", "evidence_label": 0, # "SUPPORTS" "article": "Habitat destruction", "evidence": "Rising global temperatures, caused by the greenhouse effect, contribute to habitat destruction, endangering various species, such as the polar bear.", "entropy": 0.0, "votes": [ "SUPPORTS", "SUPPORTS", null, null, null ] }, { "evidence_id": "Polar bear:1328", "evidence_label": 2, # "NOT_ENOUGH_INFO" "article": "Polar bear", "evidence": "\"Bear hunting caught in global warming debate\".", "entropy": 0.6931471805599453, "votes": [ "SUPPORTS", "NOT_ENOUGH_INFO", null, null, null ] } ] } ``` ### Data Fields - `claim_id`: a `string` feature, unique claim identifier. - `claim`: a `string` feature, claim text. - `claim_label`: a `int` feature, overall label assigned to claim (based on evidence majority vote). The label correspond to 0: "supports", 1: "refutes", 2: "not enough info" and 3: "disputed". - `evidences`: a list of evidences with fields: - `evidence_id`: a `string` feature, unique evidence identifier. - `evidence_label`: a `int` feature, micro-verdict label. The label correspond to 0: "supports", 1: "refutes" and 2: "not enough info". - `article`: a `string` feature, title of source article (Wikipedia page). - `evidence`: a `string` feature, evidence sentence. - `entropy`: a `float32` feature, entropy reflecting uncertainty of `evidence_label`. - `votes`: a `list` of `string` features, corresponding to individual votes. ### Data Splits This benchmark dataset currently consists of a single data split `test` that consists of 1,535 claims or 7,675 claim-evidence pairs. ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information [Needs More Information] ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases [Needs More Information] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators [Needs More Information] ### Licensing Information [Needs More Information] ### Citation Information ```bibtex @misc{diggelmann2020climatefever, title={CLIMATE-FEVER: A Dataset for Verification of Real-World Climate Claims}, author={Thomas Diggelmann and Jordan Boyd-Graber and Jannis Bulian and Massimiliano Ciaramita and Markus Leippold}, year={2020}, eprint={2012.00614}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ### Contributions Thanks to [@tdiggelm](https://github.com/tdiggelm) for adding this dataset.
shiyue/chr_en	shiyue	2024-01-18T14:19:36Z	411	4	[ "task_categories:fill-mask", "task_categories:text-generation", "task_categories:translation", "task_ids:language-modeling", "task_ids:masked-language-modeling", "annotations_creators:expert-generated", "annotations_creators:found", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:monolingual", "multilinguality:multilingual", "multilinguality:translation", "source_datasets:original", "language:chr", "language:en", "license:other", "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2010.04791", "region:us" ]	[ "fill-mask", "text-generation", "translation" ]	2022-03-02T23:29:22Z	1	--- annotations_creators: - expert-generated - found - no-annotation language_creators: - found language: - chr - en license: - other multilinguality: - monolingual - multilingual - translation size_categories: - 100K<n<1M - 10K<n<100K - 1K<n<10K source_datasets: - original task_categories: - fill-mask - text-generation - translation task_ids: - language-modeling - masked-language-modeling paperswithcode_id: chren config_names: - monolingual - monolingual_raw - parallel - parallel_raw dataset_info: - config_name: monolingual features: - name: sentence dtype: string splits: - name: chr num_bytes: 882824 num_examples: 5210 - name: en5000 num_bytes: 615275 num_examples: 5000 - name: en10000 num_bytes: 1211605 num_examples: 10000 - name: en20000 num_bytes: 2432298 num_examples: 20000 - name: en50000 num_bytes: 6065580 num_examples: 49999 - name: en100000 num_bytes: 12130164 num_examples: 100000 download_size: 16967664 dataset_size: 23337746 - config_name: monolingual_raw features: - name: text_sentence dtype: string - name: text_title dtype: string - name: speaker dtype: string - name: date dtype: int32 - name: type dtype: string - name: dialect dtype: string splits: - name: full num_bytes: 1210056 num_examples: 5210 download_size: 410646 dataset_size: 1210056 - config_name: parallel features: - name: sentence_pair dtype: translation: languages: - en - chr splits: - name: train num_bytes: 3089562 num_examples: 11639 - name: dev num_bytes: 260401 num_examples: 1000 - name: out_dev num_bytes: 78126 num_examples: 256 - name: test num_bytes: 264595 num_examples: 1000 - name: out_test num_bytes: 80959 num_examples: 256 download_size: 2143266 dataset_size: 3773643 - config_name: parallel_raw features: - name: line_number dtype: string - name: sentence_pair dtype: translation: languages: - en - chr - name: text_title dtype: string - name: speaker dtype: string - name: date dtype: int32 - name: type dtype: string - name: dialect dtype: string splits: - name: full num_bytes: 5010734 num_examples: 14151 download_size: 2018726 dataset_size: 5010734 configs: - config_name: monolingual data_files: - split: chr path: monolingual/chr-* - split: en5000 path: monolingual/en5000-* - split: en10000 path: monolingual/en10000-* - split: en20000 path: monolingual/en20000-* - split: en50000 path: monolingual/en50000-* - split: en100000 path: monolingual/en100000-* - config_name: monolingual_raw data_files: - split: full path: monolingual_raw/full-* - config_name: parallel data_files: - split: train path: parallel/train-* - split: dev path: parallel/dev-* - split: out_dev path: parallel/out_dev-* - split: test path: parallel/test-* - split: out_test path: parallel/out_test-* default: true - config_name: parallel_raw data_files: - split: full path: parallel_raw/full-* --- # Dataset Card for ChrEn ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Repository: [Github repository for ChrEn](https://github.com/ZhangShiyue/ChrEn) - Paper: [ChrEn: Cherokee-English Machine Translation for Endangered Language Revitalization](https://arxiv.org/abs/2010.04791) - Point of Contact: [[email protected]]([email protected]) ### Dataset Summary ChrEn is a Cherokee-English parallel dataset to facilitate machine translation research between Cherokee and English. ChrEn is extremely low-resource contains 14k sentence pairs in total, split in ways that facilitate both in-domain and out-of-domain evaluation. ChrEn also contains 5k Cherokee monolingual data to enable semi-supervised learning. ### Supported Tasks and Leaderboards The dataset is intended to use for `machine-translation` between Enlish (`en`) and Cherokee (`chr`). ### Languages The dataset contains Enlish (`en`) and Cherokee (`chr`) text. The data encompasses both existing dialects of Cherokee: the Overhill dialect, mostly spoken in Oklahoma (OK), and the Middle dialect, mostly used in North Carolina (NC). ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization Many of the source texts were translations of English materials, which means that the Cherokee structures may not be 100% natural in terms of what a speaker might spontaneously produce. Each text was translated by people who speak Cherokee as the first language, which means there is a high probability of grammaticality. These data were originally available in PDF version. We apply the Optical Character Recognition (OCR) via Tesseract OCR engine to extract the Cherokee and English text. #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? The sentences were manually aligned by Dr. Benjamin Frey a proficient second-language speaker of Cherokee, who also fixed the errors introduced by OCR. This process is time-consuming and took several months. ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators The dataset was gathered and annotated by Shiyue Zhang, Benjamin Frey, and Mohit Bansal at UNC Chapel Hill. ### Licensing Information The copyright of the data belongs to original book/article authors or translators (hence, used for research purpose; and please contact Dr. Benjamin Frey for other copyright questions). ### Citation Information ``` @inproceedings{zhang2020chren, title={ChrEn: Cherokee-English Machine Translation for Endangered Language Revitalization}, author={Zhang, Shiyue and Frey, Benjamin and Bansal, Mohit}, booktitle={EMNLP2020}, year={2020} } ``` ### Contributions Thanks to [@yjernite](https://github.com/yjernite), [@lhoestq](https://github.com/lhoestq) for adding this dataset.
defunct-datasets/the_pile_books3	defunct-datasets	2024-01-18T11:17:08Z	131	148	[ "task_categories:text-generation", "task_categories:fill-mask", "task_ids:language-modeling", "task_ids:masked-language-modeling", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:monolingual", "source_datasets:original", "language:en", "license:mit", "size_categories:100K<n<1M", "arxiv:2101.00027", "region:us" ]	[ "text-generation", "fill-mask" ]	2022-03-02T23:29:22Z	null	--- annotations_creators: - no-annotation language_creators: - found language: - en license: - mit multilinguality: - monolingual pretty_name: Books3 size_categories: - 100K<n<1M source_datasets: - original task_categories: - text-generation - fill-mask task_ids: - language-modeling - masked-language-modeling viewer: false dataset_info: features: - name: title dtype: string - name: text dtype: string config_name: plain_text splits: - name: train num_bytes: 108392037000 num_examples: 196639 download_size: 39516981435 dataset_size: 108392037000 --- # Dataset Card for the_pile_books3 ## Table of Contents - [Dataset Card for the_pile_books3](#dataset-card-for-the_pile_books3) - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [\|split\|num examples\|](#splitnum-examples) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization) - [Who are the source language producers?](#who-are-the-source-language-producers) - [Annotations](#annotations) - [Annotation process](#annotation-process) - [Who are the annotators?](#who-are-the-annotators) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [GitHub](https://github.com/soskek/bookcorpus/issues/27#issuecomment-716104208) - Repository: [Needs More Information] - Paper: [arXiv](https://arxiv.org/abs/2101.00027) - Leaderboard: [Needs More Information] - Point of Contact: [Needs More Information] ### Dataset Summary <div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400"> <p><b>Defunct:</b> Dataset "the_pile_books3" is defunct and no longer accessible due to reported copyright infringement.</p> </div> This dataset is Shawn Presser's work and is part of EleutherAi/The Pile dataset. This dataset contains all of bibliotik in plain .txt form, aka 197,000 books processed in exactly the same way as did for bookcorpusopen (a.k.a. books1). seems to be similar to OpenAI's mysterious "books2" dataset referenced in their papers. Unfortunately OpenAI will not give details, so we know very little about any differences. People suspect it's "all of libgen", but it's purely conjecture. \|download_size\|36.8 Gib\| \|dataset_size\|100.9 Gib\| ### Supported Tasks and Leaderboards This dataset is used for Language Modeling. ### Languages The dataset is in English. ## Dataset Structure ### Data Instances ``` {'title': '07 LEGO Ninjago - The Search For Zane (Scholastic) - Kate Howard (retail)' 'text': '\n\nTITLE PAGE\n\nFROM THE JOURNAL OF SENSEI GARMADON\n\nCHAPTER 1\n\nCHAPTER 2\n\nCHAPTER 3\n\nCHAPTER 4\n\nCHAPTER 5\n\nCHAPTER 6\n\nCHAPTER 7\n\nCHAPTER 8\n\nCHAPTER 9\n\nCOPYRIGHT\n\nThroughout Ninjago", five ninja are well-known for their speed, strength, and of course the elemental powers that help them protect our world from evil. But there are others who possess some of the same powers as the ninja. Others who may not always use their powers for good.\n\nBefore now, the ninja believed they were special. They di.......'} ``` ### Data Fields - `title`: title of the book - `text`: text content of the book ### Data Splits \|split\|num examples\| -------------------------------- \|train\|196640\| ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information [Needs More Information] ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases [Needs More Information] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators [Needs More Information] ### Licensing Information MIT ### Citation Information ``` @article{pile, title={The {P}ile: An 800GB Dataset of Diverse Text for Language Modeling}, author={Gao, Leo and Biderman, Stella and Black, Sid and Golding, Laurence and Hoppe, Travis and Foster, Charles and Phang, Jason and He, Horace and Thite, Anish and Nabeshima, Noa and Presser, Shawn and Leahy, Connor}, journal={arXiv preprint arXiv:2101.00027}, year={2020} } ``` ### Contributions Thanks to [@shawwn](https://github.com/shawwn) for creating this dataset. Thanks to [@richarddwang](https://github.com/richarddwang) for adding this dataset.
allenai/social_bias_frames	allenai	2024-01-18T11:16:03Z	287	20	[ "task_categories:text2text-generation", "task_categories:text-classification", "task_ids:hate-speech-detection", "annotations_creators:crowdsourced", "language_creators:found", "multilinguality:monolingual", "source_datasets:original", "language:en", "license:cc-by-4.0", "size_categories:100K<n<1M", "region:us", "explanation-generation" ]	[ "text2text-generation", "text-classification" ]	2022-03-02T23:29:22Z	1	--- pretty_name: Social Bias Frames annotations_creators: - crowdsourced language_creators: - found language: - en license: - cc-by-4.0 multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - text2text-generation - text-classification task_ids: - hate-speech-detection paperswithcode_id: null tags: - explanation-generation dataset_info: features: - name: whoTarget dtype: string - name: intentYN dtype: string - name: sexYN dtype: string - name: sexReason dtype: string - name: offensiveYN dtype: string - name: annotatorGender dtype: string - name: annotatorMinority dtype: string - name: sexPhrase dtype: string - name: speakerMinorityYN dtype: string - name: WorkerId dtype: string - name: HITId dtype: string - name: annotatorPolitics dtype: string - name: annotatorRace dtype: string - name: annotatorAge dtype: string - name: post dtype: string - name: targetMinority dtype: string - name: targetCategory dtype: string - name: targetStereotype dtype: string - name: dataSource dtype: string splits: - name: test num_bytes: 5371665 num_examples: 17501 - name: validation num_bytes: 5096009 num_examples: 16738 - name: train num_bytes: 34006886 num_examples: 112900 download_size: 9464583 dataset_size: 44474560 --- # Dataset Card for "social_bias_frames" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [https://homes.cs.washington.edu/~msap/social-bias-frames/](https://homes.cs.washington.edu/~msap/social-bias-frames/) - Repository: [https://homes.cs.washington.edu/~msap/social-bias-frames/](https://homes.cs.washington.edu/~msap/social-bias-frames/) - Paper: [Social Bias Frames: Reasoning about Social and Power Implications of Language](https://www.aclweb.org/anthology/2020.acl-main.486.pdf) - Leaderboard: - Point of Contact: [Maartin Sap](mailto:[email protected]) - Size of downloaded dataset files: 6.32 MB - Size of the generated dataset: 44.47 MB - Total amount of disk used: 50.80 MB ### Dataset Summary Warning: this document and dataset contain content that may be offensive or upsetting. Social Bias Frames is a new way of representing the biases and offensiveness that are implied in language. For example, these frames are meant to distill the implication that "women (candidates) are less qualified" behind the statement "we shouldn’t lower our standards to hire more women." The Social Bias Inference Corpus (SBIC) supports large-scale learning and evaluation of social implications with over 150k structured annotations of social media posts, spanning over 34k implications about a thousand demographic groups. ### Supported Tasks and Leaderboards This dataset supports both classification and generation. Sap et al. developed several models using the SBIC. They report an F1 score of 78.8 in predicting whether the posts in the test set were offensive, an F1 score of 78.6 in predicting whether the posts were intending to be offensive, an F1 score of 80.7 in predicting whether the posts were lewd, and an F1 score of 69.9 in predicting whether the posts were targeting a specific group. Another of Sap et al.’s models performed better in the generation task. They report a BLUE score of 77.9, a Rouge-L score of 68.7, and a WMD score of 0.74 in generating a description of the targeted group given a post as well as a BLUE score of 52.6, a Rouge-L score of 44.9, and a WMD score of 2.79 in generating a description of the implied offensive statement given a post. See the paper for further details. ### Languages The language in SBIC is predominantly white-aligned English (78%, using a lexical dialect detector, [Blodgett et al., 2016](https://www.aclweb.org/anthology/D16-1120)). The curators find less than 10% of posts in SBIC are detected to have the AAE dialect category. The BCP-47 language tag is, presumably, en-US. ## Dataset Structure ### Data Instances Each instance contains a post that may contain an offensive statement and annotated information concerning the nature of the offensive implication as well as the demographics of the annotator and origin of the post. See the [Social Bias Frames dataset viewer](https://huggingface.co/datasets/viewer/?dataset=social_bias_frames) to explore more examples. #### default - Size of downloaded dataset files: 6.32 MB - Size of the generated dataset: 44.47 MB - Total amount of disk used: 50.80 MB An example of 'validation' looks as follows. ``` { "HITId": "3Z8UJEJOCZ74QHXOTFP0JOOGD7039J", "WorkerId": "7162172225567546910", "annotatorAge": "27.0", "annotatorGender": "woman", "annotatorMinority": "women", "annotatorPolitics": "liberal", "annotatorRace": "white", "dataSource": "t/davidson", "intentYN": "1.0", "offensiveYN": "1.0", "post": "Every half-breed cop in America is trying to rape white women.", "sexPhrase": "rape white women", "sexReason": "rape", "sexYN": "1.0", "speakerMinorityYN": "0.0", "targetCategory": "race", "targetMinority": "mixed folks", "targetStereotype": "mixed folks are rapists.", "whoTarget": "1.0" } ``` ### Data Fields The data fields are the same among all splits. #### default - _whoTarget_: a string, ‘0.0’ if the target is a group, ‘1.0’ if the target is an individual, and blank if the post is not offensive - _intentYN_: a string indicating if the intent behind the statement was to offend. This is a categorical variable with four possible answers, ‘1.0’ if yes, ‘0.66’ if probably, ‘0.33’ if probably not, and ‘0.0’ if no. - _sexYN_: a string indicating whether the post contains a sexual or lewd reference. This is a categorical variable with three possible answers, ‘1.0’ if yes, ‘0.5’ if maybe, ‘0.0’ if no. - _sexReason_: a string containing a free text explanation of what is sexual if indicated so, blank otherwise - _offensiveYN_: a string indicating if the post could be offensive to anyone. This is a categorical variable with three possible answers, ‘1.0’ if yes, ‘0.5’ if maybe, ‘0.0’ if no. - _annotatorGender_: a string indicating the gender of the MTurk worker - _annotatorMinority_: a string indicating whether the MTurk worker identifies as a minority - _sexPhrase_: a string indicating which part of the post references something sexual, blank otherwise - _speakerMinorityYN_: a string indicating whether the speaker was part of the same minority group that's being targeted. This is a categorical variable with three possible answers, ‘1.0’ if yes, ‘0.5’ if maybe, ‘0.0’ if no. - _WorkerId_: a string hashed version of the MTurk workerId - _HITId_: a string id that uniquely identifies each post - _annotatorPolitics_: a string indicating the political leaning of the MTurk worker - _annotatorRace_: a string indicating the race of the MTurk worker - _annotatorAge_: a string indicating the age of the MTurk worker - _post_: a string containing the text of the post that was annotated - _targetMinority_: a string indicating the demographic group targeted - _targetCategory_: a string indicating the high-level category of the demographic group(s) targeted - _targetStereotype_: a string containing the implied statement - _dataSource_: a string indicating the source of the post (`t/...`: means Twitter, `r/...`: means a subreddit) ### Data Splits To ensure that no post appeared in multiple splits, the curators defined a training instance as the post and its three sets of annotations. They then split the dataset into train, validation, and test sets (75%/12.5%/12.5%). \| name \|train \|validation\|test \| \|-------\|-----:\|---------:\|----:\| \|default\|112900\| 16738\|17501\| ## Dataset Creation ### Curation Rationale The main aim for this dataset is to cover a wide variety of social biases that are implied in text, both subtle and overt, and make the biases representative of real world discrimination that people experience [RWJF 2017](https://web.archive.org/web/20200620105955/https://www.rwjf.org/en/library/research/2017/10/discrimination-in-america--experiences-and-views.html). The curators also included some innocuous statements, to balance out biases, offensive, or harmful content. ### Source Data The curators included online posts from the following sources sometime between 2014-2019: - r/darkJokes, r/meanJokes, r/offensiveJokes - Reddit microaggressions ([Breitfeller et al., 2019](https://www.aclweb.org/anthology/D19-1176/)) - Toxic language detection Twitter corpora ([Waseem & Hovy, 2016](https://www.aclweb.org/anthology/N16-2013/); [Davidson et al., 2017](https://www.aaai.org/ocs/index.php/ICWSM/ICWSM17/paper/viewPaper/15665); [Founa et al., 2018](https://www.aaai.org/ocs/index.php/ICWSM/ICWSM18/paper/viewPaper/17909)) - Data scraped from hate sites (Gab, Stormfront, r/incels, r/mensrights) #### Initial Data Collection and Normalization The curators wanted posts to be as self-contained as possible, therefore, they applied some filtering to prevent posts from being highly context-dependent. For Twitter data, they filtered out @-replies, retweets, and links, and subsample posts such that there is a smaller correlation between AAE and offensiveness (to avoid racial bias; [Sap et al., 2019](https://www.aclweb.org/anthology/P19-1163/)). For Reddit, Gab, and Stormfront, they only selected posts that were one sentence long, don't contain links, and are between 10 and 80 words. Furthemore, for Reddit, they automatically removed posts that target automated moderation. #### Who are the source language producers? Due to the nature of this corpus, there is no way to know who the speakers are. But, the speakers of the Reddit, Gab, and Stormfront posts are likely white men (see [Gender by subreddit](http://bburky.com/subredditgenderratios/), [Gab users](https://en.wikipedia.org/wiki/Gab_(social_network)#cite_note-insidetheright-22), [Stormfront description](https://en.wikipedia.org/wiki/Stormfront_(website))). ### Annotations #### Annotation process For each post, Amazon Mechanical Turk workers indicate whether the post is offensive, whether the intent was to offend, and whether it contains lewd or sexual content. Only if annotators indicate potential offensiveness do they answer the group implication question. If the post targets or references a group or demographic, workers select or write which one(s); per selected group, they then write two to four stereotypes. Finally, workers are asked whether they think the speaker is part of one of the minority groups referenced by the post. The curators collected three annotations per post, and restricted the worker pool to the U.S. and Canada. The annotations in SBIC showed 82.4% pairwise agreement and Krippendorf’s α=0.45 on average. Recent work has highlighted various negative side effects caused by annotating potentially abusive or harmful content (e.g., acute stress; Roberts, 2016). The curators mitigated these by limiting the number of posts that one worker could annotate in one day, paying workers above minimum wage ($7–12), and providing crisis management resources to the annotators. #### Who are the annotators? The annotators are Amazon Mechanical Turk workers aged 36±10 years old. The annotators consisted of 55% women, 42% men, and <1% non-binary and 82% identified as White, 4% Asian, 4% Hispanic, 4% Black. Information on their first language(s) and professional backgrounds was not collected. ### Personal and Sensitive Information Usernames are not included with the data, but the site where the post was collected is, so the user could potentially be recovered. ## Considerations for Using the Data ### Social Impact of Dataset The curators recognize that studying Social Bias Frames necessarily requires confronting online content that may be offensive or disturbing but argue that deliberate avoidance does not eliminate such problems. By assessing social media content through the lens of Social Bias Frames, automatic flagging or AI-augmented writing interfaces may be analyzed for potentially harmful online content with detailed explanations for users or moderators to consider and verify. In addition, the collective analysis over large corpora can also be insightful for educating people on reducing unconscious biases in their language by encouraging empathy towards a targeted group. ### Discussion of Biases Because this is a corpus of social biases, a lot of posts contain implied or overt biases against the following groups (in decreasing order of prevalence): - gender/sexuality - race/ethnicity - religion/culture - social/political - disability body/age - victims The curators warn that technology trained on this dataset could have side effects such as censorship and dialect-based racial bias. ### Other Known Limitations Because the curators found that the dataset is predominantly written in White-aligned English, they caution researchers to consider the potential for dialect or identity-based biases in labelling ([Davidson et al.,2019](https://www.aclweb.org/anthology/W19-3504.pdf); [Sap et al., 2019a](https://www.aclweb.org/anthology/P19-1163.pdf)) before deploying technology based on SBIC. ## Additional Information ### Dataset Curators This dataset was developed by Maarten Sap of the Paul G. Allen School of Computer Science & Engineering at the University of Washington, Saadia Gabriel, Lianhui Qin, Noah A Smith, and Yejin Choi of the Paul G. Allen School of Computer Science & Engineering and the Allen Institute for Artificial Intelligence, and Dan Jurafsky of the Linguistics & Computer Science Departments of Stanford University. ### Licensing Information The SBIC is licensed under the [Creative Commons 4.0 License](https://creativecommons.org/licenses/by/4.0/) ### Citation Information ``` @inproceedings{sap-etal-2020-social, title = "Social Bias Frames: Reasoning about Social and Power Implications of Language", author = "Sap, Maarten and Gabriel, Saadia and Qin, Lianhui and Jurafsky, Dan and Smith, Noah A. and Choi, Yejin", booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics", month = jul, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.acl-main.486", doi = "10.18653/v1/2020.acl-main.486", pages = "5477--5490", abstract = "Warning: this paper contains content that may be offensive or upsetting. Language has the power to reinforce stereotypes and project social biases onto others. At the core of the challenge is that it is rarely what is stated explicitly, but rather the implied meanings, that frame people{'}s judgments about others. For example, given a statement that {``}we shouldn{'}t lower our standards to hire more women,{''} most listeners will infer the implicature intended by the speaker - that {``}women (candidates) are less qualified.{''} Most semantic formalisms, to date, do not capture such pragmatic implications in which people express social biases and power differentials in language. We introduce Social Bias Frames, a new conceptual formalism that aims to model the pragmatic frames in which people project social biases and stereotypes onto others. In addition, we introduce the Social Bias Inference Corpus to support large-scale modelling and evaluation with 150k structured annotations of social media posts, covering over 34k implications about a thousand demographic groups. We then establish baseline approaches that learn to recover Social Bias Frames from unstructured text. We find that while state-of-the-art neural models are effective at high-level categorization of whether a given statement projects unwanted social bias (80{\%} F1), they are not effective at spelling out more detailed explanations in terms of Social Bias Frames. Our study motivates future work that combines structured pragmatic inference with commonsense reasoning on social implications.", } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun), [@otakumesi](https://github.com/otakumesi), [@mariamabarham](https://github.com/mariamabarham), [@lhoestq](https://github.com/lhoestq) for adding this dataset.
eusip/silicone	eusip	2024-01-18T11:15:53Z	224	14	[ "task_categories:text-generation", "task_categories:fill-mask", "task_categories:text-classification", "task_ids:dialogue-modeling", "task_ids:language-modeling", "task_ids:masked-language-modeling", "task_ids:sentiment-classification", "task_ids:text-scoring", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "source_datasets:original", "language:en", "license:cc-by-sa-4.0", "size_categories:100K<n<1M", "arxiv:2009.11152", "region:us", "emotion-classification", "dialogue-act-classification" ]	[ "text-generation", "fill-mask", "text-classification" ]	2022-03-02T23:29:22Z	1	--- annotations_creators: - expert-generated language_creators: - expert-generated language: - en license: - cc-by-sa-4.0 multilinguality: - monolingual size_categories: - 100K<n<1M - 10K<n<100K - 1K<n<10K source_datasets: - original task_categories: - text-generation - fill-mask - text-classification task_ids: - dialogue-modeling - language-modeling - masked-language-modeling - sentiment-classification - text-scoring pretty_name: SILICONE Benchmark tags: - emotion-classification - dialogue-act-classification dataset_info: - config_name: dyda_da features: - name: Utterance dtype: string - name: Dialogue_Act dtype: string - name: Dialogue_ID dtype: string - name: Label dtype: class_label: names: '0': commissive '1': directive '2': inform '3': question - name: Idx dtype: int32 splits: - name: train num_bytes: 8346638 num_examples: 87170 - name: validation num_bytes: 764277 num_examples: 8069 - name: test num_bytes: 740226 num_examples: 7740 download_size: 8874925 dataset_size: 9851141 - config_name: dyda_e features: - name: Utterance dtype: string - name: Emotion dtype: string - name: Dialogue_ID dtype: string - name: Label dtype: class_label: names: '0': anger '1': disgust '2': fear '3': happiness '4': no emotion '5': sadness '6': surprise - name: Idx dtype: int32 splits: - name: train num_bytes: 8547111 num_examples: 87170 - name: validation num_bytes: 781445 num_examples: 8069 - name: test num_bytes: 757670 num_examples: 7740 download_size: 8874925 dataset_size: 10086226 - config_name: iemocap features: - name: Dialogue_ID dtype: string - name: Utterance_ID dtype: string - name: Utterance dtype: string - name: Emotion dtype: string - name: Label dtype: class_label: names: '0': ang '1': dis '2': exc '3': fea '4': fru '5': hap '6': neu '7': oth '8': sad '9': sur '10': xxx - name: Idx dtype: int32 splits: - name: train num_bytes: 908180 num_examples: 7213 - name: validation num_bytes: 100969 num_examples: 805 - name: test num_bytes: 254248 num_examples: 2021 download_size: 1158778 dataset_size: 1263397 - config_name: maptask features: - name: Speaker dtype: string - name: Utterance dtype: string - name: Dialogue_Act dtype: string - name: Label dtype: class_label: names: '0': acknowledge '1': align '2': check '3': clarify '4': explain '5': instruct '6': query_w '7': query_yn '8': ready '9': reply_n '10': reply_w '11': reply_y - name: Idx dtype: int32 splits: - name: train num_bytes: 1260413 num_examples: 20905 - name: validation num_bytes: 178184 num_examples: 2963 - name: test num_bytes: 171806 num_examples: 2894 download_size: 1048357 dataset_size: 1610403 - config_name: meld_e features: - name: Utterance dtype: string - name: Speaker dtype: string - name: Emotion dtype: string - name: Dialogue_ID dtype: string - name: Utterance_ID dtype: string - name: Label dtype: class_label: names: '0': anger '1': disgust '2': fear '3': joy '4': neutral '5': sadness '6': surprise - name: Idx dtype: int32 splits: - name: train num_bytes: 916337 num_examples: 9989 - name: validation num_bytes: 100234 num_examples: 1109 - name: test num_bytes: 242352 num_examples: 2610 download_size: 1553014 dataset_size: 1258923 - config_name: meld_s features: - name: Utterance dtype: string - name: Speaker dtype: string - name: Sentiment dtype: string - name: Dialogue_ID dtype: string - name: Utterance_ID dtype: string - name: Label dtype: class_label: names: '0': negative '1': neutral '2': positive - name: Idx dtype: int32 splits: - name: train num_bytes: 930405 num_examples: 9989 - name: validation num_bytes: 101801 num_examples: 1109 - name: test num_bytes: 245873 num_examples: 2610 download_size: 1553014 dataset_size: 1278079 - config_name: mrda features: - name: Utterance_ID dtype: string - name: Dialogue_Act dtype: string - name: Channel_ID dtype: string - name: Speaker dtype: string - name: Dialogue_ID dtype: string - name: Utterance dtype: string - name: Label dtype: class_label: names: '0': s '1': d '2': b '3': f '4': q - name: Idx dtype: int32 splits: - name: train num_bytes: 9998857 num_examples: 83943 - name: validation num_bytes: 1143286 num_examples: 9815 - name: test num_bytes: 1807462 num_examples: 15470 download_size: 10305848 dataset_size: 12949605 - config_name: oasis features: - name: Speaker dtype: string - name: Utterance dtype: string - name: Dialogue_Act dtype: string - name: Label dtype: class_label: names: '0': accept '1': ackn '2': answ '3': answElab '4': appreciate '5': backch '6': bye '7': complete '8': confirm '9': correct '10': direct '11': directElab '12': echo '13': exclaim '14': expressOpinion '15': expressPossibility '16': expressRegret '17': expressWish '18': greet '19': hold '20': identifySelf '21': inform '22': informCont '23': informDisc '24': informIntent '25': init '26': negate '27': offer '28': pardon '29': raiseIssue '30': refer '31': refuse '32': reqDirect '33': reqInfo '34': reqModal '35': selfTalk '36': suggest '37': thank '38': informIntent-hold '39': correctSelf '40': expressRegret-inform '41': thank-identifySelf - name: Idx dtype: int32 splits: - name: train num_bytes: 887018 num_examples: 12076 - name: validation num_bytes: 112185 num_examples: 1513 - name: test num_bytes: 119254 num_examples: 1478 download_size: 802002 dataset_size: 1118457 - config_name: sem features: - name: Utterance dtype: string - name: NbPairInSession dtype: string - name: Dialogue_ID dtype: string - name: SpeechTurn dtype: string - name: Speaker dtype: string - name: Sentiment dtype: string - name: Label dtype: class_label: names: '0': Negative '1': Neutral '2': Positive - name: Idx dtype: int32 splits: - name: train num_bytes: 496168 num_examples: 4264 - name: validation num_bytes: 57896 num_examples: 485 - name: test num_bytes: 100072 num_examples: 878 download_size: 513689 dataset_size: 654136 - config_name: swda features: - name: Utterance dtype: string - name: Dialogue_Act dtype: string - name: From_Caller dtype: string - name: To_Caller dtype: string - name: Topic dtype: string - name: Dialogue_ID dtype: string - name: Conv_ID dtype: string - name: Label dtype: class_label: names: '0': sd '1': b '2': sv '3': '%' '4': aa '5': ba '6': fc '7': qw '8': nn '9': bk '10': h '11': qy^d '12': bh '13': ^q '14': bf '15': fo_o_fw_"_by_bc '16': fo_o_fw_by_bc_" '17': na '18': ad '19': ^2 '20': b^m '21': qo '22': qh '23': ^h '24': ar '25': ng '26': br '27': 'no' '28': fp '29': qrr '30': arp_nd '31': t3 '32': oo_co_cc '33': aap_am '34': t1 '35': bd '36': ^g '37': qw^d '38': fa '39': ft '40': + '41': x '42': ny '43': sv_fx '44': qy_qr '45': ba_fe - name: Idx dtype: int32 splits: - name: train num_bytes: 20499788 num_examples: 190709 - name: validation num_bytes: 2265898 num_examples: 21203 - name: test num_bytes: 291471 num_examples: 2714 download_size: 16227500 dataset_size: 23057157 config_names: - dyda_da - dyda_e - iemocap - maptask - meld_e - meld_s - mrda - oasis - sem - swda --- # Dataset Card for SILICONE Benchmark ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [N/A] - Repository: https://github.com/eusip/SILICONE-benchmark - Paper: https://arxiv.org/abs/2009.11152 - Leaderboard: [N/A] - Point of Contact: [Ebenge Usip]([email protected]) ### Dataset Summary The Sequence labellIng evaLuatIon benChmark fOr spoken laNguagE (SILICONE) benchmark is a collection of resources for training, evaluating, and analyzing natural language understanding systems specifically designed for spoken language. All datasets are in the English language and covers a variety of domains including daily life, scripted scenarios, joint task completion, phone call conversations, and televsion dialogue. Some datasets additionally include emotion and/or sentimant labels. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages English. ## Dataset Structure ### Data Instances #### DailyDialog Act Corpus (Dialogue Act) For the `dyda_da` configuration one example from the dataset is: ``` { 'Utterance': "the taxi drivers are on strike again .", 'Dialogue_Act': 2, # "inform" 'Dialogue_ID': "2" } ``` #### DailyDialog Act Corpus (Emotion) For the `dyda_e` configuration one example from the dataset is: ``` { 'Utterance': "'oh , breaktime flies .'", 'Emotion': 5, # "sadness" 'Dialogue_ID': "997" } ``` #### Interactive Emotional Dyadic Motion Capture (IEMOCAP) database For the `iemocap` configuration one example from the dataset is: ``` { 'Dialogue_ID': "Ses04F_script03_2", 'Utterance_ID': "Ses04F_script03_2_F025", 'Utterance': "You're quite insufferable. I expect it's because you're drunk.", 'Emotion': 0, # "ang" } ``` #### HCRC MapTask Corpus For the `maptask` configuration one example from the dataset is: ``` { 'Speaker': "f", 'Utterance': "i think that would bring me over the crevasse", 'Dialogue_Act': 4, # "explain" } ``` #### Multimodal EmotionLines Dataset (Emotion) For the `meld_e` configuration one example from the dataset is: ``` { 'Utterance': "'Push 'em out , push 'em out , harder , harder .'", 'Speaker': "Joey", 'Emotion': 3, # "joy" 'Dialogue_ID': "1", 'Utterance_ID': "2" } ``` #### Multimodal EmotionLines Dataset (Sentiment) For the `meld_s` configuration one example from the dataset is: ``` { 'Utterance': "'Okay , y'know what ? There is no more left , left !'", 'Speaker': "Rachel", 'Sentiment': 0, # "negative" 'Dialogue_ID': "2", 'Utterance_ID': "4" } ``` #### ICSI MRDA Corpus For the `mrda` configuration one example from the dataset is: ``` { 'Utterance_ID': "Bed006-c2_0073656_0076706", 'Dialogue_Act': 0, # "s" 'Channel_ID': "Bed006-c2", 'Speaker': "mn015", 'Dialogue_ID': "Bed006", 'Utterance': "keith is not technically one of us yet ." } ``` #### BT OASIS Corpus For the `oasis` configuration one example from the dataset is: ``` { 'Speaker': "b", 'Utterance': "when i rang up um when i rang to find out why she said oh well your card's been declined", 'Dialogue_Act': 21, # "inform" } ``` #### SEMAINE database For the `sem` configuration one example from the dataset is: ``` { 'Utterance': "can you think of somebody who is like that ?", 'NbPairInSession': "11", 'Dialogue_ID': "59", 'SpeechTurn': "674", 'Speaker': "Agent", 'Sentiment': 1, # "Neutral" } ``` #### Switchboard Dialog Act (SwDA) Corpus For the `swda` configuration one example from the dataset is: ``` { 'Utterance': "but i 'd probably say that 's roughly right .", 'Dialogue_Act': 33, # "aap_am" 'From_Caller': "1255", 'To_Caller': "1087", 'Topic': "CRIME", 'Dialogue_ID': "818", 'Conv_ID': "sw2836", } ``` ### Data Fields For the `dyda_da` configuration, the different fields are: - `Utterance`: Utterance as a string. - `Dialogue_Act`: Dialog act label of the utterance. It can be one of "commissive" (0), "directive" (1), "inform" (2) or "question" (3). - `Dialogue_ID`: identifier of the dialogue as a string. For the `dyda_e` configuration, the different fields are: - `Utterance`: Utterance as a string. - `Dialogue_Act`: Dialog act label of the utterance. It can be one of "anger" (0), "disgust" (1), "fear" (2), "happiness" (3), "no emotion" (4), "sadness" (5) or "surprise" (6). - `Dialogue_ID`: identifier of the dialogue as a string. For the `iemocap` configuration, the different fields are: - `Dialogue_ID`: identifier of the dialogue as a string. - `Utterance_ID`: identifier of the utterance as a string. - `Utterance`: Utterance as a string. - `Emotion`: Emotion label of the utterance. It can be one of "Anger" (0), "Disgust" (1), "Excitement" (2), "Fear" (3), "Frustration" (4), "Happiness" (5), "Neutral" (6), "Other" (7), "Sadness" (8), "Surprise" (9) or "Unknown" (10). For the `maptask` configuration, the different fields are: - `Speaker`: identifier of the speaker as a string. - `Utterance`: Utterance as a string. - `Dialogue_Act`: Dialog act label of the utterance. It can be one of "acknowledge" (0), "align" (1), "check" (2), "clarify" (3), "explain" (4), "instruct" (5), "query_w" (6), "query_yn" (7), "ready" (8), "reply_n" (9), "reply_w" (10) or "reply_y" (11). For the `meld_e` configuration, the different fields are: - `Utterance`: Utterance as a string. - `Speaker`: Speaker as a string. - `Emotion`: Emotion label of the utterance. It can be one of "anger" (0), "disgust" (1), "fear" (2), "joy" (3), "neutral" (4), "sadness" (5) or "surprise" (6). - `Dialogue_ID`: identifier of the dialogue as a string. - `Utterance_ID`: identifier of the utterance as a string. For the `meld_s` configuration, the different fields are: - `Utterance`: Utterance as a string. - `Speaker`: Speaker as a string. - `Sentiment`: Sentiment label of the utterance. It can be one of "negative" (0), "neutral" (1) or "positive" (2). - `Dialogue_ID`: identifier of the dialogue as a string. - `Utterance_ID`: identifier of the utterance as a string. For the `mrda` configuration, the different fields are: - `Utterance_ID`: identifier of the utterance as a string. - `Dialogue_Act`: Dialog act label of the utterance. It can be one of "s" (0) [Statement/Subjective Statement], "d" (1) [Declarative Question], "b" (2) [Backchannel], "f" (3) [Follow-me] or "q" (4) [Question]. - `Channel_ID`: identifier of the channel as a string. - `Speaker`: identifier of the speaker as a string. - `Dialogue_ID`: identifier of the channel as a string. - `Utterance`: Utterance as a string. For the `oasis` configuration, the different fields are: - `Speaker`: identifier of the speaker as a string. - `Utterance`: Utterance as a string. - `Dialogue_Act`: Dialog act label of the utterance. It can be one of "accept" (0), "ackn" (1), "answ" (2), "answElab" (3), "appreciate" (4), "backch" (5), "bye" (6), "complete" (7), "confirm" (8), "correct" (9), "direct" (10), "directElab" (11), "echo" (12), "exclaim" (13), "expressOpinion"(14), "expressPossibility" (15), "expressRegret" (16), "expressWish" (17), "greet" (18), "hold" (19), "identifySelf" (20), "inform" (21), "informCont" (22), "informDisc" (23), "informIntent" (24), "init" (25), "negate" (26), "offer" (27), "pardon" (28), "raiseIssue" (29), "refer" (30), "refuse" (31), "reqDirect" (32), "reqInfo" (33), "reqModal" (34), "selfTalk" (35), "suggest" (36), "thank" (37), "informIntent-hold" (38), "correctSelf" (39), "expressRegret-inform" (40) or "thank-identifySelf" (41). For the `sem` configuration, the different fields are: - `Utterance`: Utterance as a string. - `NbPairInSession`: number of utterance pairs in a dialogue. - `Dialogue_ID`: identifier of the dialogue as a string. - `SpeechTurn`: SpeakerTurn as a string. - `Speaker`: Speaker as a string. - `Sentiment`: Sentiment label of the utterance. It can be "Negative", "Neutral" or "Positive". For the `swda` configuration, the different fields are: `Utterance`: Utterance as a string. `Dialogue_Act`: Dialogue act label of the utterance. It can be "sd" (0) [Statement-non-opinion], "b" (1) [Acknowledge (Backchannel)], "sv" (2) [Statement-opinion], "%" (3) [Uninterpretable], "aa" (4) [Agree/Accept], "ba" (5) [Appreciation], "fc" (6) [Conventional-closing], "qw" (7) [Wh-Question], "nn" (8) [No Answers], "bk" (9) [Response Acknowledgement], "h" (10) [Hedge], "qy^d" (11) [Declarative Yes-No-Question], "bh" (12) [Backchannel in Question Form], "^q" (13) [Quotation], "bf" (14) [Summarize/Reformulate], 'fo_o_fw_"_by_bc' (15) [Other], 'fo_o_fw_by_bc_"' (16) [Other], "na" (17) [Affirmative Non-yes Answers], "ad" (18) [Action-directive], "^2" (19) [Collaborative Completion], "b^m" (20) [Repeat-phrase], "qo" (21) [Open-Question], "qh" (22) [Rhetorical-Question], "^h" (23) [Hold Before Answer/Agreement], "ar" (24) [Reject], "ng" (25) [Negative Non-no Answers], "br" (26) [Signal-non-understanding], "no" (27) [Other Answers], "fp" (28) [Conventional-opening], "qrr" (29) [Or-Clause], "arp_nd" (30) [Dispreferred Answers], "t3" (31) [3rd-party-talk], "oo_co_cc" (32) [Offers, Options Commits], "aap_am" (33) [Maybe/Accept-part], "t1" (34) [Downplayer], "bd" (35) [Self-talk], "^g" (36) [Tag-Question], "qw^d" (37) [Declarative Wh-Question], "fa" (38) [Apology], "ft" (39) [Thanking], "+" (40) [Unknown], "x" (41) [Unknown], "ny" (42) [Unknown], "sv_fx" (43) [Unknown], "qy_qr" (44) [Unknown] or "ba_fe" (45) [Unknown]. `From_Caller`: identifier of the from caller as a string. `To_Caller`: identifier of the to caller as a string. `Topic`: Topic as a string. `Dialogue_ID`: identifier of the dialogue as a string. `Conv_ID`: identifier of the conversation as a string. ### Data Splits \| Dataset name \| Train \| Valid \| Test \| \| ------------ \| ----- \| ----- \| ---- \| \| dyda_da \| 87170 \| 8069 \| 7740 \| \| dyda_e \| 87170 \| 8069 \| 7740 \| \| iemocap \| 7213 \| 805 \| 2021 \| \| maptask \| 20905 \| 2963 \| 2894 \| \| meld_e \| 9989 \| 1109 \| 2610 \| \| meld_s \| 9989 \| 1109 \| 2610 \| \| mrda \| 83944 \| 9815 \| 15470 \| \| oasis \| 12076 \| 1513 \| 1478 \| \| sem \| 4264 \| 485 \| 878 \| \| swda \| 190709 \| 21203 \| 2714 \| ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Benchmark Curators Emile Chapuis, Pierre Colombo, Ebenge Usip. ### Licensing Information This work is licensed under a [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 Unported License](https://creativecommons.org/licenses/by-sa/4.0/). ### Citation Information ``` @inproceedings{chapuis-etal-2020-hierarchical, title = "Hierarchical Pre-training for Sequence Labelling in Spoken Dialog", author = "Chapuis, Emile and Colombo, Pierre and Manica, Matteo and Labeau, Matthieu and Clavel, Chlo{\'e}", booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020", month = nov, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.findings-emnlp.239", doi = "10.18653/v1/2020.findings-emnlp.239", pages = "2636--2648", abstract = "Sequence labelling tasks like Dialog Act and Emotion/Sentiment identification are a key component of spoken dialog systems. In this work, we propose a new approach to learn generic representations adapted to spoken dialog, which we evaluate on a new benchmark we call Sequence labellIng evaLuatIon benChmark fOr spoken laNguagE benchmark (SILICONE). SILICONE is model-agnostic and contains 10 different datasets of various sizes. We obtain our representations with a hierarchical encoder based on transformer architectures, for which we extend two well-known pre-training objectives. Pre-training is performed on OpenSubtitles: a large corpus of spoken dialog containing over 2.3 billion of tokens. We demonstrate how hierarchical encoders achieve competitive results with consistently fewer parameters compared to state-of-the-art models and we show their importance for both pre-training and fine-tuning.", } ``` ### Contributions Thanks to [@eusip](https://github.com/eusip) and [@lhoestq](https://github.com/lhoestq) for adding this dataset.
ptb-text-only/ptb_text_only	ptb-text-only	2024-01-18T11:13:39Z	18,281	16	[ "task_categories:text-generation", "task_categories:fill-mask", "task_ids:language-modeling", "task_ids:masked-language-modeling", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "source_datasets:original", "language:en", "license:other", "size_categories:10K<n<100K", "region:us" ]	[ "text-generation", "fill-mask" ]	2022-03-02T23:29:22Z	null	--- annotations_creators: - expert-generated language_creators: - found language: - en license: - other license_details: LDC User Agreement for Non-Members multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-generation - fill-mask task_ids: - language-modeling - masked-language-modeling paperswithcode_id: null pretty_name: Penn Treebank dataset_info: features: - name: sentence dtype: string config_name: penn_treebank splits: - name: train num_bytes: 5143706 num_examples: 42068 - name: test num_bytes: 453710 num_examples: 3761 - name: validation num_bytes: 403156 num_examples: 3370 download_size: 5951345 dataset_size: 6000572 --- # Dataset Card for Penn Treebank ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: https://catalog.ldc.upenn.edu/LDC99T42 - Repository: 'https://raw.githubusercontent.com/wojzaremba/lstm/master/data/ptb.train.txt', 'https://raw.githubusercontent.com/wojzaremba/lstm/master/data/ptb.valid.txt', 'https://raw.githubusercontent.com/wojzaremba/lstm/master/data/ptb.test.txt' - Paper: https://www.aclweb.org/anthology/J93-2004.pdf - Leaderboard: [Needs More Information] - Point of Contact: [Needs More Information] ### Dataset Summary This is the Penn Treebank Project: Release 2 CDROM, featuring a million words of 1989 Wall Street Journal material. The rare words in this version are already replaced with <unk> token. The numbers are replaced with <N> token. ### Supported Tasks and Leaderboards Language Modelling ### Languages The text in the dataset is in American English ## Dataset Structure ### Data Instances [Needs More Information] ### Data Fields [Needs More Information] ### Data Splits [Needs More Information] ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information [Needs More Information] ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases [Needs More Information] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators [Needs More Information] ### Licensing Information Dataset provided for research purposes only. Please check dataset license for additional information. ### Citation Information @article{marcus-etal-1993-building, title = "Building a Large Annotated Corpus of {E}nglish: The {P}enn {T}reebank", author = "Marcus, Mitchell P. and Santorini, Beatrice and Marcinkiewicz, Mary Ann", journal = "Computational Linguistics", volume = "19", number = "2", year = "1993", url = "https://www.aclweb.org/anthology/J93-2004", pages = "313--330", } ### Contributions Thanks to [@harshalmittal4](https://github.com/harshalmittal4) for adding this dataset.
ybisk/piqa	ybisk	2024-01-18T11:13:02Z	314,085	89	[ "task_categories:question-answering", "task_ids:multiple-choice-qa", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "language_creators:found", "multilinguality:monolingual", "source_datasets:original", "language:en", "license:unknown", "size_categories:10K<n<100K", "arxiv:1911.11641", "arxiv:1907.10641", "arxiv:1904.09728", "arxiv:1808.05326", "region:us" ]	[ "question-answering" ]	2022-03-02T23:29:22Z	null	--- annotations_creators: - crowdsourced language_creators: - crowdsourced - found language: - en license: - unknown multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - question-answering task_ids: - multiple-choice-qa paperswithcode_id: piqa pretty_name: 'Physical Interaction: Question Answering' dataset_info: features: - name: goal dtype: string - name: sol1 dtype: string - name: sol2 dtype: string - name: label dtype: class_label: names: '0': '0' '1': '1' config_name: plain_text splits: - name: train num_bytes: 4104026 num_examples: 16113 - name: test num_bytes: 761521 num_examples: 3084 - name: validation num_bytes: 464321 num_examples: 1838 download_size: 2638625 dataset_size: 5329868 --- # Dataset Card for "Physical Interaction: Question Answering" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [PIQA homepage](https://yonatanbisk.com/piqa/) - Paper: [PIQA: Reasoning about Physical Commonsense in Natural Language](https://arxiv.org/abs/1911.11641) - Leaderboard: [Official leaderboard](https://yonatanbisk.com/piqa/) Note that there is a [2nd leaderboard](https://leaderboard.allenai.org/physicaliqa) featuring a different (blind) test set with 3,446 examples as part of the Machine Commonsense DARPA project. - Point of Contact: [Yonatan Bisk](https://yonatanbisk.com/piqa/) ### Dataset Summary To apply eyeshadow without a brush, should I use a cotton swab or a toothpick? Questions requiring this kind of physical commonsense pose a challenge to state-of-the-art natural language understanding systems. The PIQA dataset introduces the task of physical commonsense reasoning and a corresponding benchmark dataset Physical Interaction: Question Answering or PIQA. Physical commonsense knowledge is a major challenge on the road to true AI-completeness, including robots that interact with the world and understand natural language. PIQA focuses on everyday situations with a preference for atypical solutions. The dataset is inspired by instructables.com, which provides users with instructions on how to build, craft, bake, or manipulate objects using everyday materials. ### Supported Tasks and Leaderboards The underlying task is formualted as multiple choice question answering: given a question `q` and two possible solutions `s1`, `s2`, a model or a human must choose the most appropriate solution, of which exactly one is correct. ### Languages The text in the dataset is in English. The associated BCP-47 code is `en`. ## Dataset Structure ### Data Instances An example looks like this: ``` { "goal": "How do I ready a guinea pig cage for it's new occupants?", "sol1": "Provide the guinea pig with a cage full of a few inches of bedding made of ripped paper strips, you will also need to supply it with a water bottle and a food dish.", "sol2": "Provide the guinea pig with a cage full of a few inches of bedding made of ripped jeans material, you will also need to supply it with a water bottle and a food dish.", "label": 0, } ``` Note that the test set contains no labels. Predictions need to be submitted to the leaderboard. ### Data Fields List and describe the fields present in the dataset. Mention their data type, and whether they are used as input or output in any of the tasks the dataset currently supports. If the data has span indices, describe their attributes, such as whether they are at the character level or word level, whether they are contiguous or not, etc. If the datasets contains example IDs, state whether they have an inherent meaning, such as a mapping to other datasets or pointing to relationships between data points. - `goal`: the question which requires physical commonsense to be answered correctly - `sol1`: the first solution - `sol2`: the second solution - `label`: the correct solution. `0` refers to `sol1` and `1` refers to `sol2` ### Data Splits The dataset contains 16,000 examples for training, 2,000 for development and 3,000 for testing. ## Dataset Creation ### Curation Rationale The goal of the dataset is to construct a resource that requires concrete physical reasoning. ### Source Data The authors provide a prompt to the annotators derived from instructables.com. The instructables website is a crowdsourced collection of instruc- tions for doing everything from cooking to car repair. In most cases, users provide images or videos detailing each step and a list of tools that will be required. Most goals are simultaneously rare and unsurprising. While an annotator is unlikely to have built a UV-Flourescent steampunk lamp or made a backpack out of duct tape, it is not surprising that someone interested in home crafting would create these, nor will the tools and materials be unfamiliar to the average person. Using these examples as the seed for their annotation, helps remind annotators about the less prototypical uses of everyday objects. Second, and equally important, is that instructions build on one another. This means that any QA pair inspired by an instructable is more likely to explicitly state assumptions about what preconditions need to be met to start the task and what postconditions define success. Annotators were asked to glance at the instructions of an instructable and pull out or have it inspire them to construct two component tasks. They would then articulate the goal (often centered on atypical materials) and how to achieve it. In addition, annotaters were asked to provide a permutation to their own solution which makes it invalid (the negative solution), often subtly. #### Initial Data Collection and Normalization During validation, examples with low agreement were removed from the data. The dataset is further cleaned to remove stylistic artifacts and trivial examples from the data, which have been shown to artificially inflate model performance on previous NLI benchmarks.using the AFLite algorithm introduced in ([Sakaguchi et al. 2020](https://arxiv.org/abs/1907.10641); [Sap et al. 2019](https://arxiv.org/abs/1904.09728)) which is an improvement on adversarial filtering ([Zellers et al, 2018](https://arxiv.org/abs/1808.05326)). #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process Annotations are by construction obtained when crowdsourcers complete the prompt. #### Who are the annotators? Paid crowdsourcers ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information Unknown ### Citation Information ``` @inproceedings{Bisk2020, author = {Yonatan Bisk and Rowan Zellers and Ronan Le Bras and Jianfeng Gao and Yejin Choi}, title = {PIQA: Reasoning about Physical Commonsense in Natural Language}, booktitle = {Thirty-Fourth AAAI Conference on Artificial Intelligence}, year = {2020}, } ``` ### Contributions Thanks to [@VictorSanh](https://github.com/VictorSanh) for adding this dataset.
e9t/nsmc	e9t	2024-01-18T11:10:49Z	856	9	[ "task_categories:text-classification", "task_ids:sentiment-classification", "annotations_creators:crowdsourced", "language_creators:found", "multilinguality:monolingual", "source_datasets:original", "language:ko", "license:cc-by-2.0", "size_categories:100K<n<1M", "region:us" ]	[ "text-classification" ]	2022-03-02T23:29:22Z	1	--- annotations_creators: - crowdsourced language_creators: - found language: - ko license: - cc-by-2.0 multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - text-classification task_ids: - sentiment-classification paperswithcode_id: nsmc pretty_name: Naver Sentiment Movie Corpus dataset_info: features: - name: id dtype: string - name: document dtype: string - name: label dtype: class_label: names: '0': negative '1': positive splits: - name: train num_bytes: 16423803 num_examples: 150000 - name: test num_bytes: 5491417 num_examples: 50000 download_size: 19522142 dataset_size: 21915220 --- # Dataset Card for Naver sentiment movie corpus ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [Github](https://github.com/e9t/nsmc/) - Repository: [Github](https://github.com/e9t/nsmc/) - Paper: - Leaderboard: - Point of Contact: ### Dataset Summary [More Information Needed] ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields Each instance is a movie review written by Korean internet users on Naver, the most commonly used search engine in Korea. Each row can be broken down into the following fields: - `id`: A unique review ID, provided by Naver - `document`: The actual movie review - `label`: Binary labels for sentiment analysis, where `0` denotes negative, and `1`, positive ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information ``` @InProceedings{Park:2016, title = "Naver Sentiment Movie Corpus", author = "Lucy Park", year = "2016", howpublished = {\\url{https://github.com/e9t/nsmc}} } ``` ### Contributions Thanks to [@jaketae](https://github.com/jaketae) for adding this dataset.
lil-lab/newsroom	lil-lab	2024-01-18T11:10:34Z	229	12	[ "task_categories:summarization", "task_ids:news-articles-summarization", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "source_datasets:original", "language:en", "license:other", "region:us" ]	[ "summarization" ]	2022-03-02T23:29:22Z	1	--- annotations_creators: - expert-generated language_creators: - expert-generated language: - en license: - other multilinguality: - monolingual pretty_name: CORNELL NEWSROOM size_categories: - unknown source_datasets: - original task_categories: - summarization task_ids: - news-articles-summarization paperswithcode_id: newsroom dataset_info: features: - name: text dtype: string - name: summary dtype: string - name: title dtype: string - name: url dtype: string - name: date dtype: string - name: density_bin dtype: string - name: coverage_bin dtype: string - name: compression_bin dtype: string - name: density dtype: float32 - name: coverage dtype: float32 - name: compression dtype: float32 splits: - name: test num_bytes: 472446866 num_examples: 108862 - name: train num_bytes: 4357506078 num_examples: 995041 - name: validation num_bytes: 473206951 num_examples: 108837 download_size: 0 dataset_size: 5303159895 --- # Dataset Card for "newsroom" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [https://lil.nlp.cornell.edu/newsroom/index.html](https://lil.nlp.cornell.edu/newsroom/index.html) - Repository: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Paper: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Point of Contact: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Size of downloaded dataset files: 0.00 MB - Size of the generated dataset: 5.30 GB - Total amount of disk used: 5.30 GB ### Dataset Summary NEWSROOM is a large dataset for training and evaluating summarization systems. It contains 1.3 million articles and summaries written by authors and editors in the newsrooms of 38 major publications. Dataset features includes: - text: Input news text. - summary: Summary for the news. And additional features: - title: news title. - url: url of the news. - date: date of the article. - density: extractive density. - coverage: extractive coverage. - compression: compression ratio. - density_bin: low, medium, high. - coverage_bin: extractive, abstractive. - compression_bin: low, medium, high. This dataset can be downloaded upon requests. Unzip all the contents "train.jsonl, dev.josnl, test.jsonl" to the `tfds` folder. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages English (`en`). ## Dataset Structure ### Data Instances #### default - Size of downloaded dataset files: 0.00 MB - Size of the generated dataset: 5.30 GB - Total amount of disk used: 5.30 GB An example of 'train' looks as follows. ``` { "compression": 33.880001068115234, "compression_bin": "medium", "coverage": 1.0, "coverage_bin": "high", "date": "200600000", "density": 11.720000267028809, "density_bin": "extractive", "summary": "some summary 1", "text": "some text 1", "title": "news title 1", "url": "url.html" } ``` ### Data Fields The data fields are the same among all splits. #### default - `text`: a `string` feature. - `summary`: a `string` feature. - `title`: a `string` feature. - `url`: a `string` feature. - `date`: a `string` feature. - `density_bin`: a `string` feature. - `coverage_bin`: a `string` feature. - `compression_bin`: a `string` feature. - `density`: a `float32` feature. - `coverage`: a `float32` feature. - `compression`: a `float32` feature. ### Data Splits \| name \|train \|validation\| test \| \|-------\|-----:\|---------:\|-----:\| \|default\|995041\| 108837\|108862\| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information https://cornell.qualtrics.com/jfe/form/SV_6YA3HQ2p75XH4IR This Dataset Usage Agreement ("Agreement") is a legal agreement with the Cornell Newsroom Summaries Team ("Newsroom") for the Dataset made available to the individual or entity ("Researcher") exercising rights under this Agreement. "Dataset" includes all text, data, information, source code, and any related materials, documentation, files, media, updates or revisions. The Dataset is intended for non-commercial research and educational purposes only, and is made available free of charge without extending any license or other intellectual property rights. By downloading or using the Dataset, the Researcher acknowledges that they agree to the terms in this Agreement, and represent and warrant that they have authority to do so on behalf of any entity exercising rights under this Agreement. The Researcher accepts and agrees to be bound by the terms and conditions of this Agreement. If the Researcher does not agree to this Agreement, they may not download or use the Dataset. By sharing content with Newsroom, such as by submitting content to this site or by corresponding with Newsroom contributors, the Researcher grants Newsroom the right to use, reproduce, display, perform, adapt, modify, distribute, have distributed, and promote the content in any form, anywhere and for any purpose, such as for evaluating and comparing summarization systems. Nothing in this Agreement shall obligate Newsroom to provide any support for the Dataset. Any feedback, suggestions, ideas, comments, improvements given by the Researcher related to the Dataset is voluntarily given, and may be used by Newsroom without obligation or restriction of any kind. The Researcher accepts full responsibility for their use of the Dataset and shall defend indemnify, and hold harmless Newsroom, including their employees, trustees, officers, and agents, against any and all claims arising from the Researcher's use of the Dataset. The Researcher agrees to comply with all laws and regulations as they relate to access to and use of the Dataset and Service including U.S. export jurisdiction and other U.S. and international regulations. THE DATASET IS PROVIDED "AS IS." NEWSROOM DISCLAIMS ALL WARRANTIES, EXPRESS OR IMPLIED, INCLUDING THE IMPLIED WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, AND NON-INFRINGEMENT. WITHOUT LIMITATION OF THE ABOVE, NEWSROOM DISCLAIMS ANY WARRANTY THAT DATASET IS BUG OR ERROR-FREE, AND GRANTS NO WARRANTY REGARDING ITS USE OR THE RESULTS THEREFROM INCLUDING, WITHOUT LIMITATION, ITS CORRECTNESS, ACCURACY, OR RELIABILITY. THE DATASET IS NOT WARRANTIED TO FULFILL ANY PARTICULAR PURPOSES OR NEEDS. TO THE EXTENT NOT PROHIBITED BY LAW, IN NO EVENT SHALL NEWSROOM BE LIABLE FOR ANY LOSS, DAMAGE OR INJURY, DIRECT AND INDIRECT, INCIDENTAL, SPECIAL, OR CONSEQUENTIAL DAMAGES, HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER FOR BREACH OF CONTRACT, TORT (INCLUDING NEGLIGENCE) OR OTHERWISE, ARISING OUT OF THIS AGREEMENT, INCLUDING BUT NOT LIMITED TO LOSS OF PROFITS, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGES. THESE LIMITATIONS SHALL APPLY NOTWITHSTANDING ANY FAILURE OF ESSENTIAL PURPOSE OF ANY LIMITED REMEDY. This Agreement is effective until terminated. Newsroom reserves the right to terminate the Researcher's access to the Dataset at any time. If the Researcher breaches this Agreement, the Researcher's rights to use the Dataset shall terminate automatically. The Researcher will immediately cease all use and distribution of the Dataset and destroy any copies or portions of the Dataset in their possession. This Agreement is governed by the laws of the State of New York, without regard to conflict of law principles. All terms and provisions of this Agreement shall, if possible, be construed in a manner which makes them valid, but in the event any term or provision of this Agreement is found by a court of competent jurisdiction to be illegal or unenforceable, the validity or enforceability of the remainder of this Agreement shall not be affected. This Agreement is the complete and exclusive agreement between the parties with respect to its subject matter and supersedes all prior or contemporaneous oral or written agreements or understandings relating to the subject matter. ### Citation Information ``` @inproceedings{N18-1065, author = {Grusky, Max and Naaman, Mor and Artzi, Yoav}, title = {NEWSROOM: A Dataset of 1.3 Million Summaries with Diverse Extractive Strategies}, booktitle = {Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies}, year = {2018}, } ``` ### Contributions Thanks to [@lewtun](https://github.com/lewtun), [@patrickvonplaten](https://github.com/patrickvonplaten), [@yoavartzi](https://github.com/yoavartzi), [@thomwolf](https://github.com/thomwolf) for adding this dataset.
reciTAL/mlsum	reciTAL	2024-01-18T11:09:09Z	1,664	53	[ "task_categories:summarization", "task_categories:translation", "task_categories:text-classification", "task_ids:news-articles-summarization", "task_ids:multi-class-classification", "task_ids:multi-label-classification", "task_ids:topic-classification", "annotations_creators:found", "language_creators:found", "multilinguality:multilingual", "source_datasets:extended\|cnn_dailymail", "source_datasets:original", "language:de", "language:es", "language:fr", "language:ru", "language:tr", "license:other", "size_categories:100K<n<1M", "region:us" ]	[ "summarization", "translation", "text-classification" ]	2022-03-02T23:29:22Z	1	--- annotations_creators: - found language_creators: - found language: - de - es - fr - ru - tr license: - other multilinguality: - multilingual size_categories: - 100K<n<1M - 10K<n<100K source_datasets: - extended\|cnn_dailymail - original task_categories: - summarization - translation - text-classification task_ids: - news-articles-summarization - multi-class-classification - multi-label-classification - topic-classification paperswithcode_id: mlsum pretty_name: MLSUM dataset_info: - config_name: de features: - name: text dtype: string - name: summary dtype: string - name: topic dtype: string - name: url dtype: string - name: title dtype: string - name: date dtype: string splits: - name: train num_bytes: 846959840 num_examples: 220887 - name: validation num_bytes: 47119541 num_examples: 11394 - name: test num_bytes: 46847612 num_examples: 10701 download_size: 1005814154 dataset_size: 940926993 - config_name: es features: - name: text dtype: string - name: summary dtype: string - name: topic dtype: string - name: url dtype: string - name: title dtype: string - name: date dtype: string splits: - name: train num_bytes: 1214558302 num_examples: 266367 - name: validation num_bytes: 50643400 num_examples: 10358 - name: test num_bytes: 71263665 num_examples: 13920 download_size: 1456211154 dataset_size: 1336465367 - config_name: fr features: - name: text dtype: string - name: summary dtype: string - name: topic dtype: string - name: url dtype: string - name: title dtype: string - name: date dtype: string splits: - name: train num_bytes: 1471965014 num_examples: 392902 - name: validation num_bytes: 70413212 num_examples: 16059 - name: test num_bytes: 69660288 num_examples: 15828 download_size: 1849565564 dataset_size: 1612038514 - config_name: ru features: - name: text dtype: string - name: summary dtype: string - name: topic dtype: string - name: url dtype: string - name: title dtype: string - name: date dtype: string splits: - name: train num_bytes: 257389497 num_examples: 25556 - name: validation num_bytes: 9128497 num_examples: 750 - name: test num_bytes: 9656398 num_examples: 757 download_size: 766226107 dataset_size: 276174392 - config_name: tu features: - name: text dtype: string - name: summary dtype: string - name: topic dtype: string - name: url dtype: string - name: title dtype: string - name: date dtype: string splits: - name: train num_bytes: 641622783 num_examples: 249277 - name: validation num_bytes: 25530661 num_examples: 11565 - name: test num_bytes: 27830212 num_examples: 12775 download_size: 942308960 dataset_size: 694983656 config_names: - de - es - fr - ru - tu --- # Dataset Card for MLSUM ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: []() - Repository: https://github.com/recitalAI/MLSUM - Paper: https://www.aclweb.org/anthology/2020.emnlp-main.647/ - Point of Contact: [email]([email protected]) - Size of downloaded dataset files: 1.83 GB - Size of the generated dataset: 4.86 GB - Total amount of disk used: 6.69 GB ### Dataset Summary We present MLSUM, the first large-scale MultiLingual SUMmarization dataset. Obtained from online newspapers, it contains 1.5M+ article/summary pairs in five different languages -- namely, French, German, Spanish, Russian, Turkish. Together with English newspapers from the popular CNN/Daily mail dataset, the collected data form a large scale multilingual dataset which can enable new research directions for the text summarization community. We report cross-lingual comparative analyses based on state-of-the-art systems. These highlight existing biases which motivate the use of a multi-lingual dataset. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### de - Size of downloaded dataset files: 346.58 MB - Size of the generated dataset: 940.93 MB - Total amount of disk used: 1.29 GB An example of 'validation' looks as follows. ``` { "date": "01/01/2001", "summary": "A text", "text": "This is a text", "title": "A sample", "topic": "football", "url": "https://www.google.com" } ``` #### es - Size of downloaded dataset files: 513.31 MB - Size of the generated dataset: 1.34 GB - Total amount of disk used: 1.85 GB An example of 'validation' looks as follows. ``` { "date": "01/01/2001", "summary": "A text", "text": "This is a text", "title": "A sample", "topic": "football", "url": "https://www.google.com" } ``` #### fr - Size of downloaded dataset files: 619.99 MB - Size of the generated dataset: 1.61 GB - Total amount of disk used: 2.23 GB An example of 'validation' looks as follows. ``` { "date": "01/01/2001", "summary": "A text", "text": "This is a text", "title": "A sample", "topic": "football", "url": "https://www.google.com" } ``` #### ru - Size of downloaded dataset files: 106.22 MB - Size of the generated dataset: 276.17 MB - Total amount of disk used: 382.39 MB An example of 'train' looks as follows. ``` { "date": "01/01/2001", "summary": "A text", "text": "This is a text", "title": "A sample", "topic": "football", "url": "https://www.google.com" } ``` #### tu - Size of downloaded dataset files: 247.50 MB - Size of the generated dataset: 694.99 MB - Total amount of disk used: 942.48 MB An example of 'train' looks as follows. ``` { "date": "01/01/2001", "summary": "A text", "text": "This is a text", "title": "A sample", "topic": "football", "url": "https://www.google.com" } ``` ### Data Fields The data fields are the same among all splits. #### de - `text`: a `string` feature. - `summary`: a `string` feature. - `topic`: a `string` feature. - `url`: a `string` feature. - `title`: a `string` feature. - `date`: a `string` feature. #### es - `text`: a `string` feature. - `summary`: a `string` feature. - `topic`: a `string` feature. - `url`: a `string` feature. - `title`: a `string` feature. - `date`: a `string` feature. #### fr - `text`: a `string` feature. - `summary`: a `string` feature. - `topic`: a `string` feature. - `url`: a `string` feature. - `title`: a `string` feature. - `date`: a `string` feature. #### ru - `text`: a `string` feature. - `summary`: a `string` feature. - `topic`: a `string` feature. - `url`: a `string` feature. - `title`: a `string` feature. - `date`: a `string` feature. #### tu - `text`: a `string` feature. - `summary`: a `string` feature. - `topic`: a `string` feature. - `url`: a `string` feature. - `title`: a `string` feature. - `date`: a `string` feature. ### Data Splits \|name\|train \|validation\|test \| \|----\|-----:\|---------:\|----:\| \|de \|220887\| 11394\|10701\| \|es \|266367\| 10358\|13920\| \|fr \|392902\| 16059\|15828\| \|ru \| 25556\| 750\| 757\| \|tu \|249277\| 11565\|12775\| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information Usage of dataset is restricted to non-commercial research purposes only. Copyright belongs to the original copyright holders. See https://github.com/recitalAI/MLSUM#mlsum ### Citation Information ``` @article{scialom2020mlsum, title={MLSUM: The Multilingual Summarization Corpus}, author={Scialom, Thomas and Dray, Paul-Alexis and Lamprier, Sylvain and Piwowarski, Benjamin and Staiano, Jacopo}, journal={arXiv preprint arXiv:2004.14900}, year={2020} } ``` ### Contributions Thanks to [@RachelKer](https://github.com/RachelKer), [@albertvillanova](https://github.com/albertvillanova), [@thomwolf](https://github.com/thomwolf) for adding this dataset.
UdS-LSV/menyo20k_mt	UdS-LSV	2024-01-18T11:08:52Z	111	3	[ "task_categories:translation", "annotations_creators:expert-generated", "annotations_creators:found", "language_creators:found", "multilinguality:translation", "source_datasets:original", "language:en", "language:yo", "license:cc-by-nc-4.0", "size_categories:10K<n<100K", "arxiv:2103.08647", "region:us" ]	[ "translation" ]	2022-03-02T23:29:22Z	1	--- annotations_creators: - expert-generated - found language_creators: - found language: - en - yo license: - cc-by-nc-4.0 multilinguality: - translation size_categories: - 10K<n<100K source_datasets: - original task_categories: - translation task_ids: [] paperswithcode_id: menyo-20k pretty_name: MENYO-20k dataset_info: features: - name: translation dtype: translation: languages: - en - yo config_name: menyo20k_mt splits: - name: train num_bytes: 2551345 num_examples: 10070 - name: validation num_bytes: 870011 num_examples: 3397 - name: test num_bytes: 1905432 num_examples: 6633 download_size: 5206234 dataset_size: 5326788 --- # Dataset Card for MENYO-20k ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: - Repository: https://github.com/uds-lsv/menyo-20k_MT/ - Paper: [The Effect of Domain and Diacritics in Yorùbá-English Neural Machine Translation](https://arxiv.org/abs/2103.08647) - Leaderboard: - Point of Contact: ### Dataset Summary MENYO-20k is a multi-domain parallel dataset with texts obtained from news articles, ted talks, movie transcripts, radio transcripts, science and technology texts, and other short articles curated from the web and professional translators. The dataset has 20,100 parallel sentences split into 10,070 training sentences, 3,397 development sentences, and 6,633 test sentences (3,419 multi-domain, 1,714 news domain, and 1,500 ted talks speech transcript domain). ### Supported Tasks and Leaderboards [More Information Needed] ### Languages Languages are English and Yoruba. ## Dataset Structure ### Data Instances An instance example: ``` {'translation': {'en': 'Unit 1: What is Creative Commons?', 'yo': 'Ìdá 1: Kín ni Creative Commons?' } } ``` ### Data Fields - `translation`: - `en`: English sentence. - `yo`: Yoruba sentence. ### Data Splits Training, validation and test splits are available. ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information The dataset is open but for non-commercial use because some data sources like Ted talks and JW news require permission for commercial use. The dataset is licensed under Creative Commons [Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)](https://creativecommons.org/licenses/by-nc/4.0/) License: https://github.com/uds-lsv/menyo-20k_MT/blob/master/LICENSE ### Citation Information If you use this dataset, please cite this paper: ``` @inproceedings{adelani-etal-2021-effect, title = "The Effect of Domain and Diacritics in {Y}oruba{--}{E}nglish Neural Machine Translation", author = "Adelani, David and Ruiter, Dana and Alabi, Jesujoba and Adebonojo, Damilola and Ayeni, Adesina and Adeyemi, Mofe and Awokoya, Ayodele Esther and Espa{\~n}a-Bonet, Cristina", booktitle = "Proceedings of the 18th Biennial Machine Translation Summit (Volume 1: Research Track)", month = aug, year = "2021", address = "Virtual", publisher = "Association for Machine Translation in the Americas", url = "https://aclanthology.org/2021.mtsummit-research.6", pages = "61--75", abstract = "Massively multilingual machine translation (MT) has shown impressive capabilities and including zero and few-shot translation between low-resource language pairs. However and these models are often evaluated on high-resource languages with the assumption that they generalize to low-resource ones. The difficulty of evaluating MT models on low-resource pairs is often due to lack of standardized evaluation datasets. In this paper and we present MENYO-20k and the first multi-domain parallel corpus with a especially curated orthography for Yoruba{--}English with standardized train-test splits for benchmarking. We provide several neural MT benchmarks and compare them to the performance of popular pre-trained (massively multilingual) MT models both for the heterogeneous test set and its subdomains. Since these pre-trained models use huge amounts of data with uncertain quality and we also analyze the effect of diacritics and a major characteristic of Yoruba and in the training data. We investigate how and when this training condition affects the final quality of a translation and its understandability.Our models outperform massively multilingual models such as Google ($+8.7$ BLEU) and Facebook M2M ($+9.1$) when translating to Yoruba and setting a high quality benchmark for future research.", } ``` ### Contributions Thanks to [@yvonnegitau](https://github.com/yvonnegitau) for adding this dataset.
hotpotqa/hotpot_qa	hotpotqa	2024-01-18T11:05:40Z	10,442	121	[ "task_categories:question-answering", "annotations_creators:crowdsourced", "language_creators:found", "multilinguality:monolingual", "source_datasets:original", "language:en", "license:cc-by-sa-4.0", "size_categories:100K<n<1M", "arxiv:1809.09600", "region:us", "multi-hop" ]	[ "question-answering" ]	2022-03-02T23:29:22Z	null	--- annotations_creators: - crowdsourced language: - en language_creators: - found license: - cc-by-sa-4.0 multilinguality: - monolingual pretty_name: HotpotQA size_categories: - 100K<n<1M source_datasets: - original task_categories: - question-answering task_ids: [] paperswithcode_id: hotpotqa tags: - multi-hop dataset_info: - config_name: distractor features: - name: id dtype: string - name: question dtype: string - name: answer dtype: string - name: type dtype: string - name: level dtype: string - name: supporting_facts sequence: - name: title dtype: string - name: sent_id dtype: int32 - name: context sequence: - name: title dtype: string - name: sentences sequence: string splits: - name: train num_bytes: 552949315 num_examples: 90447 - name: validation num_bytes: 45716111 num_examples: 7405 download_size: 612746344 dataset_size: 598665426 - config_name: fullwiki features: - name: id dtype: string - name: question dtype: string - name: answer dtype: string - name: type dtype: string - name: level dtype: string - name: supporting_facts sequence: - name: title dtype: string - name: sent_id dtype: int32 - name: context sequence: - name: title dtype: string - name: sentences sequence: string splits: - name: train num_bytes: 552949315 num_examples: 90447 - name: validation num_bytes: 46848601 num_examples: 7405 - name: test num_bytes: 46000102 num_examples: 7405 download_size: 660094672 dataset_size: 645798018 --- # Dataset Card for "hotpot_qa" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [https://hotpotqa.github.io/](https://hotpotqa.github.io/) - Repository: https://github.com/hotpotqa/hotpot - Paper: [HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering](https://arxiv.org/abs/1809.09600) - Point of Contact: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Size of downloaded dataset files: 1.27 GB - Size of the generated dataset: 1.24 GB - Total amount of disk used: 2.52 GB ### Dataset Summary HotpotQA is a new dataset with 113k Wikipedia-based question-answer pairs with four key features: (1) the questions require finding and reasoning over multiple supporting documents to answer; (2) the questions are diverse and not constrained to any pre-existing knowledge bases or knowledge schemas; (3) we provide sentence-level supporting facts required for reasoning, allowingQA systems to reason with strong supervision and explain the predictions; (4) we offer a new type of factoid comparison questions to test QA systems’ ability to extract relevant facts and perform necessary comparison. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### distractor - Size of downloaded dataset files: 612.75 MB - Size of the generated dataset: 598.66 MB - Total amount of disk used: 1.21 GB An example of 'validation' looks as follows. ``` { "answer": "This is the answer", "context": { "sentences": [["Sent 1"], ["Sent 21", "Sent 22"]], "title": ["Title1", "Title 2"] }, "id": "000001", "level": "medium", "question": "What is the answer?", "supporting_facts": { "sent_id": [0, 1, 3], "title": ["Title of para 1", "Title of para 2", "Title of para 3"] }, "type": "comparison" } ``` #### fullwiki - Size of downloaded dataset files: 660.10 MB - Size of the generated dataset: 645.80 MB - Total amount of disk used: 1.31 GB An example of 'train' looks as follows. ``` { "answer": "This is the answer", "context": { "sentences": [["Sent 1"], ["Sent 2"]], "title": ["Title1", "Title 2"] }, "id": "000001", "level": "hard", "question": "What is the answer?", "supporting_facts": { "sent_id": [0, 1, 3], "title": ["Title of para 1", "Title of para 2", "Title of para 3"] }, "type": "bridge" } ``` ### Data Fields The data fields are the same among all splits. #### distractor - `id`: a `string` feature. - `question`: a `string` feature. - `answer`: a `string` feature. - `type`: a `string` feature. - `level`: a `string` feature. - `supporting_facts`: a dictionary feature containing: - `title`: a `string` feature. - `sent_id`: a `int32` feature. - `context`: a dictionary feature containing: - `title`: a `string` feature. - `sentences`: a `list` of `string` features. #### fullwiki - `id`: a `string` feature. - `question`: a `string` feature. - `answer`: a `string` feature. - `type`: a `string` feature. - `level`: a `string` feature. - `supporting_facts`: a dictionary feature containing: - `title`: a `string` feature. - `sent_id`: a `int32` feature. - `context`: a dictionary feature containing: - `title`: a `string` feature. - `sentences`: a `list` of `string` features. ### Data Splits #### distractor \| \|train\|validation\| \|----------\|----:\|---------:\| \|distractor\|90447\| 7405\| #### fullwiki \| \|train\|validation\|test\| \|--------\|----:\|---------:\|---:\| \|fullwiki\|90447\| 7405\|7405\| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information HotpotQA is distributed under a [CC BY-SA 4.0 License](http://creativecommons.org/licenses/by-sa/4.0/). ### Citation Information ``` @inproceedings{yang2018hotpotqa, title={{HotpotQA}: A Dataset for Diverse, Explainable Multi-hop Question Answering}, author={Yang, Zhilin and Qi, Peng and Zhang, Saizheng and Bengio, Yoshua and Cohen, William W. and Salakhutdinov, Ruslan and Manning, Christopher D.}, booktitle={Conference on Empirical Methods in Natural Language Processing ({EMNLP})}, year={2018} } ``` ### Contributions Thanks to [@albertvillanova](https://github.com/albertvillanova), [@ghomasHudson](https://github.com/ghomasHudson) for adding this dataset.
NLP-AUEB/eurlex	NLP-AUEB	2024-01-18T11:03:22Z	225	17	[ "task_categories:text-classification", "task_ids:multi-label-classification", "annotations_creators:found", "language_creators:found", "multilinguality:monolingual", "source_datasets:original", "language:en", "license:cc-by-sa-4.0", "size_categories:10K<n<100K", "region:us", "legal-topic-classification" ]	[ "text-classification" ]	2022-03-02T23:29:22Z	1	--- annotations_creators: - found language_creators: - found language: - en license: - cc-by-sa-4.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: - multi-label-classification paperswithcode_id: eurlex57k pretty_name: the EUR-Lex dataset tags: - legal-topic-classification dataset_info: features: - name: celex_id dtype: string - name: title dtype: string - name: text dtype: string - name: eurovoc_concepts sequence: string config_name: eurlex57k splits: - name: train num_bytes: 167603718 num_examples: 45000 - name: test num_bytes: 22046706 num_examples: 6000 - name: validation num_bytes: 21942574 num_examples: 6000 download_size: 50289403 dataset_size: 211592998 --- # Dataset Card for the EUR-Lex dataset ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: http://nlp.cs.aueb.gr/software_and_datasets/EURLEX57K/ - Repository: http://nlp.cs.aueb.gr/software_and_datasets/EURLEX57K/ - Paper: https://www.aclweb.org/anthology/P19-1636/ - Leaderboard: N/A - Point of Contact: [Ilias Chalkidis](mailto:[email protected]) ### Dataset Summary EURLEX57K can be viewed as an improved version of the dataset released by Mencia and Furnkranzand (2007), which has been widely used in Large-scale Multi-label Text Classification (LMTC) research, but is less than half the size of EURLEX57K (19.6k documents, 4k EUROVOC labels) and more than ten years old. EURLEX57K contains 57k legislative documents in English from EUR-Lex (https://eur-lex.europa.eu) with an average length of 727 words. Each document contains four major zones: - the header, which includes the title and name of the legal body enforcing the legal act; - the recitals, which are legal background references; and - the main body, usually organized in articles. Labeling / Annotation All the documents of the dataset have been annotated by the Publications Office of EU (https://publications.europa.eu/en) with multiple concepts from EUROVOC (http://eurovoc.europa.eu/). While EUROVOC includes approx. 7k concepts (labels), only 4,271 (59.31%) are present in EURLEX57K, from which only 2,049 (47.97%) have been assigned to more than 10 documents. The 4,271 labels are also divided into frequent (746 labels), few-shot (3,362), and zero- shot (163), depending on whether they were assigned to more than 50, fewer than 50 but at least one, or no training documents, respectively. ### Supported Tasks and Leaderboards The dataset supports: Multi-label Text Classification: Given the text of a document, a model predicts the relevant EUROVOC concepts. Few-shot and Zero-shot learning: As already noted, the labels can be divided into three groups: frequent (746 labels), few-shot (3,362), and zero- shot (163), depending on whether they were assigned to more than 50, fewer than 50 but at least one, or no training documents, respectively. ### Languages All documents are written in English. ## Dataset Structure ### Data Instances ```json { "celex_id": "31979D0509", "title": "79/509/EEC: Council Decision of 24 May 1979 on financial aid from the Community for the eradication of African swine fever in Spain", "text": "COUNCIL DECISION of 24 May 1979 on financial aid from the Community for the eradication of African swine fever in Spain (79/509/EEC)\nTHE COUNCIL OF THE EUROPEAN COMMUNITIES\nHaving regard to the Treaty establishing the European Economic Community, and in particular Article 43 thereof,\nHaving regard to the proposal from the Commission (1),\nHaving regard to the opinion of the European Parliament (2),\nWhereas the Community should take all appropriate measures to protect itself against the appearance of African swine fever on its territory;\nWhereas to this end the Community has undertaken, and continues to undertake, action designed to contain outbreaks of this type of disease far from its frontiers by helping countries affected to reinforce their preventive measures ; whereas for this purpose Community subsidies have already been granted to Spain;\nWhereas these measures have unquestionably made an effective contribution to the protection of Community livestock, especially through the creation and maintenance of a buffer zone north of the river Ebro;\nWhereas, however, in the opinion of the Spanish authorities themselves, the measures so far implemented must be reinforced if the fundamental objective of eradicating the disease from the entire country is to be achieved;\nWhereas the Spanish authorities have asked the Community to contribute to the expenses necessary for the efficient implementation of a total eradication programme;\nWhereas a favourable response should be given to this request by granting aid to Spain, having regard to the undertaking given by that country to protect the Community against African swine fever and to eliminate completely this disease by the end of a five-year eradication plan;\nWhereas this eradication plan must include certain measures which guarantee the effectiveness of the action taken, and it must be possible to adapt these measures to developments in the situation by means of a procedure establishing close cooperation between the Member States and the Commission;\nWhereas it is necessary to keep the Member States regularly informed as to the progress of the action undertaken,", "eurovoc_concepts": ["192", "2356", "2560", "862", "863"] } ``` ### Data Fields The following data fields are provided for documents (`train`, `dev`, `test`): `celex_id`: (str) The official ID of the document. The CELEX number is the unique identifier for all publications in both Eur-Lex and CELLAR.\ `title`: (str) The title of the document.\ `text`: (str) The full content of each document, which is represented by its `header`, `recitals` and `main_body`.\ `eurovoc_concepts`: (List[str]) The relevant EUROVOC concepts (labels). If you want to use the descriptors of EUROVOC concepts, similar to Chalkidis et al. (2020), please load: https://archive.org/download/EURLEX57K/eurovoc_concepts.jsonl ```python import json with open('./eurovoc_concepts.jsonl') as jsonl_file: eurovoc_concepts = {json.loads(concept) for concept in jsonl_file.readlines()} ``` ### Data Splits \| Split \| No of Documents \| Avg. words \| Avg. labels \| \| ------------------- \| ------------------------------------ \| --- \| --- \| \| Train \| 45,000 \| 729 \| 5 \| \|Development \| 6,000 \| 714 \| 5 \| \|Test \| 6,000 \| 725 \| 5 \| ## Dataset Creation ### Curation Rationale The dataset was curated by Chalkidis et al. (2019).\ The documents have been annotated by the Publications Office of EU (https://publications.europa.eu/en). ### Source Data #### Initial Data Collection and Normalization The original data are available at EUR-Lex portal (https://eur-lex.europa.eu) in an unprocessed format. The documents were downloaded from EUR-Lex portal in HTML format. The relevant metadata and EUROVOC concepts were downloaded from the SPARQL endpoint of the Publications Office of EU (http://publications.europa.eu/webapi/rdf/sparql). #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process * The original documents are available at EUR-Lex portal (https://eur-lex.europa.eu) in an unprocessed HTML format. The HTML code was striped and the documents split into sections. * The documents have been annotated by the Publications Office of EU (https://publications.europa.eu/en). #### Who are the annotators? Publications Office of EU (https://publications.europa.eu/en) ### Personal and Sensitive Information The dataset does not include personal or sensitive information. ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators Chalkidis et al. (2019) ### Licensing Information © European Union, 1998-2021 The Commission’s document reuse policy is based on Decision 2011/833/EU. Unless otherwise specified, you can re-use the legal documents published in EUR-Lex for commercial or non-commercial purposes. The copyright for the editorial content of this website, the summaries of EU legislation and the consolidated texts, which is owned by the EU, is licensed under the Creative Commons Attribution 4.0 International licence. This means that you can re-use the content provided you acknowledge the source and indicate any changes you have made. Source: https://eur-lex.europa.eu/content/legal-notice/legal-notice.html \ Read more: https://eur-lex.europa.eu/content/help/faq/reuse-contents-eurlex.html ### Citation Information Ilias Chalkidis, Manos Fergadiotis, Prodromos Malakasiotis and Ion Androutsopoulos. Large-Scale Multi-Label Text Classification on EU Legislation. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019). Florence, Italy. 2019 ``` @inproceedings{chalkidis-etal-2019-large, title = "Large-Scale Multi-Label Text Classification on {EU} Legislation", author = "Chalkidis, Ilias and Fergadiotis, Manos and Malakasiotis, Prodromos and Androutsopoulos, Ion", booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics", year = "2019", address = "Florence, Italy", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/P19-1636", doi = "10.18653/v1/P19-1636", pages = "6314--6322" } ``` ### Contributions Thanks to [@iliaschalkidis](https://github.com/iliaschalkidis) for adding this dataset.
facebook/empathetic_dialogues	facebook	2024-01-18T11:03:15Z	2,905	101	[ "task_categories:question-answering", "task_ids:dialogue-generation", "task_ids:open-domain-qa", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "source_datasets:original", "language:en", "license:cc-by-nc-4.0", "size_categories:10K<n<100K", "arxiv:1811.00207", "region:us" ]	[ "conversational", "question-answering" ]	2022-03-02T23:29:22Z	null	--- annotations_creators: - crowdsourced language: - en language_creators: - crowdsourced license: - cc-by-nc-4.0 multilinguality: - monolingual pretty_name: EmpatheticDialogues size_categories: - 10K<n<100K source_datasets: - original task_categories: - conversational - question-answering task_ids: - dialogue-generation - open-domain-qa paperswithcode_id: empatheticdialogues dataset_info: features: - name: conv_id dtype: string - name: utterance_idx dtype: int32 - name: context dtype: string - name: prompt dtype: string - name: speaker_idx dtype: int32 - name: utterance dtype: string - name: selfeval dtype: string - name: tags dtype: string splits: - name: test num_bytes: 3011332 num_examples: 10943 - name: train num_bytes: 19040509 num_examples: 76673 - name: validation num_bytes: 3077481 num_examples: 12030 download_size: 28022709 dataset_size: 25129322 --- # Dataset Card for "empathetic_dialogues" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [https://github.com/facebookresearch/EmpatheticDialogues](https://github.com/facebookresearch/EmpatheticDialogues) - Repository: https://github.com/facebookresearch/EmpatheticDialogues - Paper: [Towards Empathetic Open-domain Conversation Models: a New Benchmark and Dataset](https://arxiv.org/abs/1811.00207) - Point of Contact: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Size of downloaded dataset files: 28.02 MB - Size of the generated dataset: 25.13 MB - Total amount of disk used: 53.15 MB ### Dataset Summary PyTorch original implementation of Towards Empathetic Open-domain Conversation Models: a New Benchmark and Dataset ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### default - Size of downloaded dataset files: 28.02 MB - Size of the generated dataset: 25.13 MB - Total amount of disk used: 53.15 MB An example of 'train' looks as follows. ``` { "context": "sentimental", "conv_id": "hit:0_conv:1", "prompt": "I remember going to the fireworks with my best friend. There was a lot of people_comma_ but it only felt like us in the world.", "selfeval": "5\|5\|5_2\|2\|5", "speaker_idx": 1, "tags": "", "utterance": "I remember going to see the fireworks with my best friend. It was the first time we ever spent time alone together. Although there was a lot of people_comma_ we felt like the only people in the world.", "utterance_idx": 1 } ``` ### Data Fields The data fields are the same among all splits. #### default - `conv_id`: a `string` feature. - `utterance_idx`: a `int32` feature. - `context`: a `string` feature. - `prompt`: a `string` feature. - `speaker_idx`: a `int32` feature. - `utterance`: a `string` feature. - `selfeval`: a `string` feature. - `tags`: a `string` feature. ### Data Splits \| name \|train\|validation\|test \| \|-------\|----:\|---------:\|----:\| \|default\|76673\| 12030\|10943\| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information Creative Commons [Attribution-NonCommercial 4.0 International](https://creativecommons.org/licenses/by-nc/4.0/). ### Citation Information ``` @inproceedings{rashkin-etal-2019-towards, title = "Towards Empathetic Open-domain Conversation Models: A New Benchmark and Dataset", author = "Rashkin, Hannah and Smith, Eric Michael and Li, Margaret and Boureau, Y-Lan", booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics", month = jul, year = "2019", address = "Florence, Italy", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/P19-1534", doi = "10.18653/v1/P19-1534", pages = "5370--5381", } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@patrickvonplaten](https://github.com/patrickvonplaten), [@lewtun](https://github.com/lewtun) for adding this dataset.
nlpaueb/biomrc	nlpaueb	2024-01-18T11:02:01Z	383	5	[ "language:en", "region:us" ]	[]	2022-03-02T23:29:22Z	1	--- language: - en paperswithcode_id: biomrc pretty_name: BIOMRC dataset_info: - config_name: plain_text features: - name: abstract dtype: string - name: title dtype: string - name: entities_list sequence: string - name: answer dtype: string splits: - name: train num_bytes: 1653301820 num_examples: 700000 - name: validation num_bytes: 119697683 num_examples: 50000 - name: test num_bytes: 147832373 num_examples: 62707 download_size: 408080356 dataset_size: 1920831876 - config_name: biomrc_large_A features: - name: abstract dtype: string - name: title dtype: string - name: entities_list sequence: string - name: answer dtype: string splits: - name: train num_bytes: 1653301820 num_examples: 700000 - name: validation num_bytes: 119697683 num_examples: 50000 - name: test num_bytes: 147832373 num_examples: 62707 download_size: 408080356 dataset_size: 1920831876 - config_name: biomrc_large_B features: - name: abstract dtype: string - name: title dtype: string - name: entities_list sequence: string - name: answer dtype: string splits: - name: train num_bytes: 1325877001 num_examples: 700000 - name: validation num_bytes: 96414040 num_examples: 50000 - name: test num_bytes: 118708586 num_examples: 62707 download_size: 343061539 dataset_size: 1540999627 - config_name: biomrc_small_A features: - name: abstract dtype: string - name: title dtype: string - name: entities_list sequence: string - name: answer dtype: string splits: - name: train num_bytes: 206553549 num_examples: 87500 - name: validation num_bytes: 14957163 num_examples: 6250 - name: test num_bytes: 14807799 num_examples: 6250 download_size: 68879274 dataset_size: 236318511 - config_name: biomrc_small_B features: - name: abstract dtype: string - name: title dtype: string - name: entities_list sequence: string - name: answer dtype: string splits: - name: train num_bytes: 165662937 num_examples: 87500 - name: validation num_bytes: 12047304 num_examples: 6250 - name: test num_bytes: 11911172 num_examples: 6250 download_size: 57706889 dataset_size: 189621413 - config_name: biomrc_tiny_A features: - name: abstract dtype: string - name: title dtype: string - name: entities_list sequence: string - name: answer dtype: string splits: - name: test num_bytes: 70914 num_examples: 30 download_size: 22519 dataset_size: 70914 - config_name: biomrc_tiny_B features: - name: abstract dtype: string - name: title dtype: string - name: entities_list sequence: string - name: answer dtype: string splits: - name: test num_bytes: 59925 num_examples: 30 download_size: 19685 dataset_size: 59925 --- # Dataset Card for "biomrc" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [http://nlp.cs.aueb.gr/](http://nlp.cs.aueb.gr/) - Repository: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Paper: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Point of Contact: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Size of downloaded dataset files: 1.29 GB - Size of the generated dataset: 5.81 GB - Total amount of disk used: 7.09 GB ### Dataset Summary We introduce BIOMRC, a large-scale cloze-style biomedical MRC dataset. Care was taken to reduce noise, compared to the previous BIOREAD dataset of Pappas et al. (2018). Experiments show that simple heuristics do not perform well on the new dataset and that two neural MRC models that had been tested on BIOREAD perform much better on BIOMRC, indicating that the new dataset is indeed less noisy or at least that its task is more feasible. Non-expert human performance is also higher on the new dataset compared to BIOREAD, and biomedical experts perform even better. We also introduce a new BERT-based MRC model, the best version of which substantially outperforms all other methods tested, reaching or surpassing the accuracy of biomedical experts in some experiments. We make the new dataset available in three different sizes, also releasing our code, and providing a leaderboard. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### biomrc_large_A - Size of downloaded dataset files: 408.08 MB - Size of the generated dataset: 1.92 GB - Total amount of disk used: 2.33 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "abstract": "\"OBJECTIVES: @entity9 is a @entity10 that may result from greater occipital nerve entrapment. Entrapped peripheral nerves typica...", "answer": "@entity9 :: (MESH:D009437,Disease) :: ['unilateral occipital neuralgia']\n", "entities_list": ["@entity1 :: ('9606', 'Species') :: ['patients']", "@entity10 :: ('MESH:D006261', 'Disease') :: ['headache', 'Headache']", "@entity9 :: ('MESH:D009437', 'Disease') :: ['Occipital neuralgia', 'unilateral occipital neuralgia']"], "title": "Sonographic evaluation of the greater occipital nerve in XXXX .\n" } ``` #### biomrc_large_B - Size of downloaded dataset files: 343.06 MB - Size of the generated dataset: 1.54 GB - Total amount of disk used: 1.88 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "abstract": "\"BACKGROUND: Adults with physical disabilities are less likely than others to receive @entity2 screening. It is not known, howev...", "answer": "@entity2", "entities_list": ["@entity2", "@entity1", "@entity0", "@entity3"], "title": "Does a standard measure of self-reported physical disability correlate with clinician perception of impairment related to XXXX screening?\n" } ``` #### biomrc_small_A - Size of downloaded dataset files: 68.88 MB - Size of the generated dataset: 236.32 MB - Total amount of disk used: 305.20 MB An example of 'validation' looks as follows. ``` This example was too long and was cropped: { "abstract": "\"PURPOSE: @entity120 ( @entity120 ) is a life-limiting @entity102 that presents as an elevated blood pressure in the pulmonary a...", "answer": "@entity148 :: (MESH:D001008,Disease) :: ['anxiety']\n", "entities_list": "[\"@entity1 :: ('9606', 'Species') :: ['patients']\", \"@entity308 :: ('MESH:D003866', 'Disease') :: ['depression']\", \"@entity146 :...", "title": "A predictive model of the effects of @entity308 , XXXX , stress, 6-minute-walk distance, and social support on health-related quality of life in an adult pulmonary hypertension population.\n" } ``` #### biomrc_small_B - Size of downloaded dataset files: 57.70 MB - Size of the generated dataset: 189.62 MB - Total amount of disk used: 247.33 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "abstract": "\"Single-agent activity for @entity12 reflected by response rates of 10%-30% has been reported in @entity0 with @entity3 ( @entit...", "answer": "@entity10", "entities_list": ["@entity0", "@entity6", "@entity2", "@entity5", "@entity12", "@entity11", "@entity1", "@entity7", "@entity9", "@entity10", "@entity3", "@entity4", "@entity8"], "title": "No synergistic activity of @entity7 and XXXX in the treatment of @entity3 .\n" } ``` #### biomrc_tiny_A - Size of downloaded dataset files: 0.02 MB - Size of the generated dataset: 0.07 MB - Total amount of disk used: 0.09 MB An example of 'test' looks as follows. ``` This example was too long and was cropped: { "abstract": "\"OBJECTIVE: Decompressive craniectomy (DC) requires later cranioplasty (CP) in survivors. However, if additional ventriculoperit...", "answer": "@entity260 :: (MESH:D011183,Disease) :: ['Postoperative Complications']\n", "entities_list": ["@entity1 :: ('9606', 'Species') :: ['Patients', 'patients', 'Patient']", "@entity260 :: ('MESH:D011183', 'Disease') :: ['VPS regarding postoperative complications']", "@entity1276 :: ('MESH:D006849', 'Disease') :: ['hydrocephalus']"], "title": "Cranioplasty and Ventriculoperitoneal Shunt Placement after Decompressive Craniectomy: Staged Surgery Is Associated with Fewer XXXX .\n" } ``` ### Data Fields The data fields are the same among all splits. #### biomrc_large_A - `abstract`: a `string` feature. - `title`: a `string` feature. - `entities_list`: a `list` of `string` features. - `answer`: a `string` feature. #### biomrc_large_B - `abstract`: a `string` feature. - `title`: a `string` feature. - `entities_list`: a `list` of `string` features. - `answer`: a `string` feature. #### biomrc_small_A - `abstract`: a `string` feature. - `title`: a `string` feature. - `entities_list`: a `list` of `string` features. - `answer`: a `string` feature. #### biomrc_small_B - `abstract`: a `string` feature. - `title`: a `string` feature. - `entities_list`: a `list` of `string` features. - `answer`: a `string` feature. #### biomrc_tiny_A - `abstract`: a `string` feature. - `title`: a `string` feature. - `entities_list`: a `list` of `string` features. - `answer`: a `string` feature. ### Data Splits #### biomrc_large_A \| \|train \|validation\|test \| \|--------------\|-----:\|---------:\|----:\| \|biomrc_large_A\|700000\| 50000\|62707\| #### biomrc_large_B \| \|train \|validation\|test \| \|--------------\|-----:\|---------:\|----:\| \|biomrc_large_B\|700000\| 50000\|62707\| #### biomrc_small_A \| \|train\|validation\|test\| \|--------------\|----:\|---------:\|---:\| \|biomrc_small_A\|87500\| 6250\|6250\| #### biomrc_small_B \| \|train\|validation\|test\| \|--------------\|----:\|---------:\|---:\| \|biomrc_small_B\|87500\| 6250\|6250\| #### biomrc_tiny_A \| \|test\| \|-------------\|---:\| \|biomrc_tiny_A\| 30\| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @inproceedings{pappas-etal-2020-biomrc, title = "{B}io{MRC}: A Dataset for Biomedical Machine Reading Comprehension", author = "Pappas, Dimitris and Stavropoulos, Petros and Androutsopoulos, Ion and McDonald, Ryan", booktitle = "Proceedings of the 19th SIGBioMed Workshop on Biomedical Language Processing", month = jul, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.bionlp-1.15", pages = "140--149", abstract = "We introduce BIOMRC, a large-scale cloze-style biomedical MRC dataset. Care was taken to reduce noise, compared to the previous BIOREAD dataset of Pappas et al. (2018). Experiments show that simple heuristics do not perform well on the new dataset and that two neural MRC models that had been tested on BIOREAD perform much better on BIOMRC, indicating that the new dataset is indeed less noisy or at least that its task is more feasible. Non-expert human performance is also higher on the new dataset compared to BIOREAD, and biomedical experts perform even better. We also introduce a new BERT-based MRC model, the best version of which substantially outperforms all other methods tested, reaching or surpassing the accuracy of biomedical experts in some experiments. We make the new dataset available in three different sizes, also releasing our code, and providing a leaderboard.", } ``` ### Contributions Thanks to [@lewtun](https://github.com/lewtun), [@PetrosStav](https://github.com/PetrosStav), [@lhoestq](https://github.com/lhoestq), [@thomwolf](https://github.com/thomwolf) for adding this dataset.
NortheasternUniversity/big_patent	NortheasternUniversity	2024-01-18T11:01:59Z	1,129	58	[ "task_categories:summarization", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:monolingual", "source_datasets:original", "language:en", "license:cc-by-4.0", "size_categories:100K<n<1M", "arxiv:1906.03741", "region:us", "patent-summarization" ]	[ "summarization" ]	2022-03-02T23:29:22Z	1	--- annotations_creators: - no-annotation language_creators: - found language: - en license: - cc-by-4.0 multilinguality: - monolingual size_categories: - 100K<n<1M - 10K<n<100K - 1M<n<10M source_datasets: - original task_categories: - summarization task_ids: [] paperswithcode_id: bigpatent pretty_name: Big Patent tags: - patent-summarization dataset_info: - config_name: all features: - name: description dtype: string - name: abstract dtype: string splits: - name: train num_bytes: 38367048389 num_examples: 1207222 - name: validation num_bytes: 2115827002 num_examples: 67068 - name: test num_bytes: 2129505280 num_examples: 67072 download_size: 10142923776 dataset_size: 42612380671 - config_name: a features: - name: description dtype: string - name: abstract dtype: string splits: - name: train num_bytes: 5683460620 num_examples: 174134 - name: validation num_bytes: 313324505 num_examples: 9674 - name: test num_bytes: 316633277 num_examples: 9675 download_size: 10142923776 dataset_size: 6313418402 - config_name: b features: - name: description dtype: string - name: abstract dtype: string splits: - name: train num_bytes: 4236070976 num_examples: 161520 - name: validation num_bytes: 234425138 num_examples: 8973 - name: test num_bytes: 231538734 num_examples: 8974 download_size: 10142923776 dataset_size: 4702034848 - config_name: c features: - name: description dtype: string - name: abstract dtype: string splits: - name: train num_bytes: 4506249306 num_examples: 101042 - name: validation num_bytes: 244684775 num_examples: 5613 - name: test num_bytes: 252566793 num_examples: 5614 download_size: 10142923776 dataset_size: 5003500874 - config_name: d features: - name: description dtype: string - name: abstract dtype: string splits: - name: train num_bytes: 264717412 num_examples: 10164 - name: validation num_bytes: 14560482 num_examples: 565 - name: test num_bytes: 14403430 num_examples: 565 download_size: 10142923776 dataset_size: 293681324 - config_name: e features: - name: description dtype: string - name: abstract dtype: string splits: - name: train num_bytes: 881101433 num_examples: 34443 - name: validation num_bytes: 48646158 num_examples: 1914 - name: test num_bytes: 48586429 num_examples: 1914 download_size: 10142923776 dataset_size: 978334020 - config_name: f features: - name: description dtype: string - name: abstract dtype: string splits: - name: train num_bytes: 2146383473 num_examples: 85568 - name: validation num_bytes: 119632631 num_examples: 4754 - name: test num_bytes: 119596303 num_examples: 4754 download_size: 10142923776 dataset_size: 2385612407 - config_name: g features: - name: description dtype: string - name: abstract dtype: string splits: - name: train num_bytes: 8877854206 num_examples: 258935 - name: validation num_bytes: 492581177 num_examples: 14385 - name: test num_bytes: 496324853 num_examples: 14386 download_size: 10142923776 dataset_size: 9866760236 - config_name: h features: - name: description dtype: string - name: abstract dtype: string splits: - name: train num_bytes: 8075621958 num_examples: 257019 - name: validation num_bytes: 447602356 num_examples: 14279 - name: test num_bytes: 445460513 num_examples: 14279 download_size: 10142923776 dataset_size: 8968684827 - config_name: y features: - name: description dtype: string - name: abstract dtype: string splits: - name: train num_bytes: 3695589005 num_examples: 124397 - name: validation num_bytes: 200369780 num_examples: 6911 - name: test num_bytes: 204394948 num_examples: 6911 download_size: 10142923776 dataset_size: 4100353733 config_names: - a - all - b - c - d - e - f - g - h - y --- # Dataset Card for Big Patent ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [Big Patent](https://evasharma.github.io/bigpatent/) - Repository: - Paper: [BIGPATENT: A Large-Scale Dataset for Abstractive and Coherent Summarization](https://arxiv.org/abs/1906.03741) - Leaderboard: - Point of Contact: [Lu Wang](mailto:[email protected]) ### Dataset Summary BIGPATENT, consisting of 1.3 million records of U.S. patent documents along with human written abstractive summaries. Each US patent application is filed under a Cooperative Patent Classification (CPC) code. There are nine such classification categories: - a: Human Necessities - b: Performing Operations; Transporting - c: Chemistry; Metallurgy - d: Textiles; Paper - e: Fixed Constructions - f: Mechanical Engineering; Lightning; Heating; Weapons; Blasting - g: Physics - h: Electricity - y: General tagging of new or cross-sectional technology Current defaults are 2.1.2 version (fix update to cased raw strings) and 'all' CPC codes: ```python from datasets import load_dataset ds = load_dataset("big_patent") # default is 'all' CPC codes ds = load_dataset("big_patent", "all") # the same as above ds = load_dataset("big_patent", "a") # only 'a' CPC codes ds = load_dataset("big_patent", codes=["a", "b"]) ``` To use 1.0.0 version (lower cased tokenized words), pass both parameters `codes` and `version`: ```python ds = load_dataset("big_patent", codes="all", version="1.0.0") ds = load_dataset("big_patent", codes="a", version="1.0.0") ds = load_dataset("big_patent", codes=["a", "b"], version="1.0.0") ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages English ## Dataset Structure ### Data Instances Each instance contains a pair of `description` and `abstract`. `description` is extracted from the Description section of the Patent while `abstract` is extracted from the Abstract section. ``` { 'description': 'FIELD OF THE INVENTION \n [0001] This invention relates to novel calcium phosphate-coated implantable medical devices and processes of making same. The unique calcium-phosphate coated implantable medical devices minimize...', 'abstract': 'This invention relates to novel calcium phosphate-coated implantable medical devices...' } ``` ### Data Fields - `description`: detailed description of patent. - `abstract`: Patent abastract. ### Data Splits \| \| train \| validation \| test \| \|:----\|------------------:\|-------------:\|-------:\| \| all \| 1207222 \| 67068 \| 67072 \| \| a \| 174134 \| 9674 \| 9675 \| \| b \| 161520 \| 8973 \| 8974 \| \| c \| 101042 \| 5613 \| 5614 \| \| d \| 10164 \| 565 \| 565 \| \| e \| 34443 \| 1914 \| 1914 \| \| f \| 85568 \| 4754 \| 4754 \| \| g \| 258935 \| 14385 \| 14386 \| \| h \| 257019 \| 14279 \| 14279 \| \| y \| 124397 \| 6911 \| 6911 \| ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information ```bibtex @article{DBLP:journals/corr/abs-1906-03741, author = {Eva Sharma and Chen Li and Lu Wang}, title = {{BIGPATENT:} {A} Large-Scale Dataset for Abstractive and Coherent Summarization}, journal = {CoRR}, volume = {abs/1906.03741}, year = {2019}, url = {http://arxiv.org/abs/1906.03741}, eprinttype = {arXiv}, eprint = {1906.03741}, timestamp = {Wed, 26 Jun 2019 07:14:58 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-1906-03741.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ``` ### Contributions Thanks to [@mattbui](https://github.com/mattbui) for adding this dataset.
ontonotes/conll2012_ontonotesv5	ontonotes	2024-01-18T09:34:57Z	968	43	[ "task_categories:token-classification", "task_ids:named-entity-recognition", "task_ids:part-of-speech", "task_ids:coreference-resolution", "task_ids:parsing", "task_ids:lemmatization", "task_ids:word-sense-disambiguation", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:multilingual", "source_datasets:original", "language:ar", "language:en", "language:zh", "license:cc-by-nc-nd-4.0", "size_categories:10K<n<100K", "region:us", "semantic-role-labeling" ]	[ "token-classification" ]	2022-03-15T10:48:28Z	1	--- annotations_creators: - expert-generated language_creators: - found language: - ar - en - zh license: - cc-by-nc-nd-4.0 multilinguality: - multilingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - token-classification task_ids: - named-entity-recognition - part-of-speech - coreference-resolution - parsing - lemmatization - word-sense-disambiguation paperswithcode_id: ontonotes-5-0 pretty_name: CoNLL2012 shared task data based on OntoNotes 5.0 tags: - semantic-role-labeling dataset_info: - config_name: english_v4 features: - name: document_id dtype: string - name: sentences list: - name: part_id dtype: int32 - name: words sequence: string - name: pos_tags sequence: class_label: names: '0': XX '1': '``' '2': $ '3': '''''' '4': ',' '5': -LRB- '6': -RRB- '7': . '8': ':' '9': ADD '10': AFX '11': CC '12': CD '13': DT '14': EX '15': FW '16': HYPH '17': IN '18': JJ '19': JJR '20': JJS '21': LS '22': MD '23': NFP '24': NN '25': NNP '26': NNPS '27': NNS '28': PDT '29': POS '30': PRP '31': PRP$ '32': RB '33': RBR '34': RBS '35': RP '36': SYM '37': TO '38': UH '39': VB '40': VBD '41': VBG '42': VBN '43': VBP '44': VBZ '45': WDT '46': WP '47': WP$ '48': WRB - name: parse_tree dtype: string - name: predicate_lemmas sequence: string - name: predicate_framenet_ids sequence: string - name: word_senses sequence: float32 - name: speaker dtype: string - name: named_entities sequence: class_label: names: '0': O '1': B-PERSON '2': I-PERSON '3': B-NORP '4': I-NORP '5': B-FAC '6': I-FAC '7': B-ORG '8': I-ORG '9': B-GPE '10': I-GPE '11': B-LOC '12': I-LOC '13': B-PRODUCT '14': I-PRODUCT '15': B-DATE '16': I-DATE '17': B-TIME '18': I-TIME '19': B-PERCENT '20': I-PERCENT '21': B-MONEY '22': I-MONEY '23': B-QUANTITY '24': I-QUANTITY '25': B-ORDINAL '26': I-ORDINAL '27': B-CARDINAL '28': I-CARDINAL '29': B-EVENT '30': I-EVENT '31': B-WORK_OF_ART '32': I-WORK_OF_ART '33': B-LAW '34': I-LAW '35': B-LANGUAGE '36': I-LANGUAGE - name: srl_frames list: - name: verb dtype: string - name: frames sequence: string - name: coref_spans sequence: sequence: int32 length: 3 splits: - name: train num_bytes: 112246121 num_examples: 1940 - name: validation num_bytes: 14116925 num_examples: 222 - name: test num_bytes: 14709044 num_examples: 222 download_size: 193644139 dataset_size: 141072090 - config_name: chinese_v4 features: - name: document_id dtype: string - name: sentences list: - name: part_id dtype: int32 - name: words sequence: string - name: pos_tags sequence: class_label: names: '0': X '1': AD '2': AS '3': BA '4': CC '5': CD '6': CS '7': DEC '8': DEG '9': DER '10': DEV '11': DT '12': ETC '13': FW '14': IJ '15': INF '16': JJ '17': LB '18': LC '19': M '20': MSP '21': NN '22': NR '23': NT '24': OD '25': 'ON' '26': P '27': PN '28': PU '29': SB '30': SP '31': URL '32': VA '33': VC '34': VE '35': VV - name: parse_tree dtype: string - name: predicate_lemmas sequence: string - name: predicate_framenet_ids sequence: string - name: word_senses sequence: float32 - name: speaker dtype: string - name: named_entities sequence: class_label: names: '0': O '1': B-PERSON '2': I-PERSON '3': B-NORP '4': I-NORP '5': B-FAC '6': I-FAC '7': B-ORG '8': I-ORG '9': B-GPE '10': I-GPE '11': B-LOC '12': I-LOC '13': B-PRODUCT '14': I-PRODUCT '15': B-DATE '16': I-DATE '17': B-TIME '18': I-TIME '19': B-PERCENT '20': I-PERCENT '21': B-MONEY '22': I-MONEY '23': B-QUANTITY '24': I-QUANTITY '25': B-ORDINAL '26': I-ORDINAL '27': B-CARDINAL '28': I-CARDINAL '29': B-EVENT '30': I-EVENT '31': B-WORK_OF_ART '32': I-WORK_OF_ART '33': B-LAW '34': I-LAW '35': B-LANGUAGE '36': I-LANGUAGE - name: srl_frames list: - name: verb dtype: string - name: frames sequence: string - name: coref_spans sequence: sequence: int32 length: 3 splits: - name: train num_bytes: 77195698 num_examples: 1391 - name: validation num_bytes: 10828169 num_examples: 172 - name: test num_bytes: 9585138 num_examples: 166 download_size: 193644139 dataset_size: 97609005 - config_name: arabic_v4 features: - name: document_id dtype: string - name: sentences list: - name: part_id dtype: int32 - name: words sequence: string - name: pos_tags sequence: string - name: parse_tree dtype: string - name: predicate_lemmas sequence: string - name: predicate_framenet_ids sequence: string - name: word_senses sequence: float32 - name: speaker dtype: string - name: named_entities sequence: class_label: names: '0': O '1': B-PERSON '2': I-PERSON '3': B-NORP '4': I-NORP '5': B-FAC '6': I-FAC '7': B-ORG '8': I-ORG '9': B-GPE '10': I-GPE '11': B-LOC '12': I-LOC '13': B-PRODUCT '14': I-PRODUCT '15': B-DATE '16': I-DATE '17': B-TIME '18': I-TIME '19': B-PERCENT '20': I-PERCENT '21': B-MONEY '22': I-MONEY '23': B-QUANTITY '24': I-QUANTITY '25': B-ORDINAL '26': I-ORDINAL '27': B-CARDINAL '28': I-CARDINAL '29': B-EVENT '30': I-EVENT '31': B-WORK_OF_ART '32': I-WORK_OF_ART '33': B-LAW '34': I-LAW '35': B-LANGUAGE '36': I-LANGUAGE - name: srl_frames list: - name: verb dtype: string - name: frames sequence: string - name: coref_spans sequence: sequence: int32 length: 3 splits: - name: train num_bytes: 42017761 num_examples: 359 - name: validation num_bytes: 4859292 num_examples: 44 - name: test num_bytes: 4900664 num_examples: 44 download_size: 193644139 dataset_size: 51777717 - config_name: english_v12 features: - name: document_id dtype: string - name: sentences list: - name: part_id dtype: int32 - name: words sequence: string - name: pos_tags sequence: class_label: names: '0': XX '1': '``' '2': $ '3': '''''' '4': '' '5': ',' '6': -LRB- '7': -RRB- '8': . '9': ':' '10': ADD '11': AFX '12': CC '13': CD '14': DT '15': EX '16': FW '17': HYPH '18': IN '19': JJ '20': JJR '21': JJS '22': LS '23': MD '24': NFP '25': NN '26': NNP '27': NNPS '28': NNS '29': PDT '30': POS '31': PRP '32': PRP$ '33': RB '34': RBR '35': RBS '36': RP '37': SYM '38': TO '39': UH '40': VB '41': VBD '42': VBG '43': VBN '44': VBP '45': VBZ '46': VERB '47': WDT '48': WP '49': WP$ '50': WRB - name: parse_tree dtype: string - name: predicate_lemmas sequence: string - name: predicate_framenet_ids sequence: string - name: word_senses sequence: float32 - name: speaker dtype: string - name: named_entities sequence: class_label: names: '0': O '1': B-PERSON '2': I-PERSON '3': B-NORP '4': I-NORP '5': B-FAC '6': I-FAC '7': B-ORG '8': I-ORG '9': B-GPE '10': I-GPE '11': B-LOC '12': I-LOC '13': B-PRODUCT '14': I-PRODUCT '15': B-DATE '16': I-DATE '17': B-TIME '18': I-TIME '19': B-PERCENT '20': I-PERCENT '21': B-MONEY '22': I-MONEY '23': B-QUANTITY '24': I-QUANTITY '25': B-ORDINAL '26': I-ORDINAL '27': B-CARDINAL '28': I-CARDINAL '29': B-EVENT '30': I-EVENT '31': B-WORK_OF_ART '32': I-WORK_OF_ART '33': B-LAW '34': I-LAW '35': B-LANGUAGE '36': I-LANGUAGE - name: srl_frames list: - name: verb dtype: string - name: frames sequence: string - name: coref_spans sequence: sequence: int32 length: 3 splits: - name: train num_bytes: 174173192 num_examples: 10539 - name: validation num_bytes: 24264804 num_examples: 1370 - name: test num_bytes: 18254144 num_examples: 1200 download_size: 193644139 dataset_size: 216692140 --- # Dataset Card for CoNLL2012 shared task data based on OntoNotes 5.0 ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage:* [CoNLL-2012 Shared Task](https://conll.cemantix.org/2012/data.html), [Author's page](https://cemantix.org/data/ontonotes.html) - Repository: [Mendeley](https://data.mendeley.com/datasets/zmycy7t9h9) - Paper: [Towards Robust Linguistic Analysis using OntoNotes](https://aclanthology.org/W13-3516/) - Leaderboard: - Point of Contact: ### Dataset Summary OntoNotes v5.0 is the final version of OntoNotes corpus, and is a large-scale, multi-genre, multilingual corpus manually annotated with syntactic, semantic and discourse information. This dataset is the version of OntoNotes v5.0 extended and is used in the CoNLL-2012 shared task. It includes v4 train/dev and v9 test data for English/Chinese/Arabic and corrected version v12 train/dev/test data (English only). The source of data is the Mendeley Data repo [ontonotes-conll2012](https://data.mendeley.com/datasets/zmycy7t9h9), which seems to be as the same as the official data, but users should use this dataset on their own responsibility. See also summaries from paperwithcode, [OntoNotes 5.0](https://paperswithcode.com/dataset/ontonotes-5-0) and [CoNLL-2012](https://paperswithcode.com/dataset/conll-2012-1) For more detailed info of the dataset like annotation, tag set, etc., you can refer to the documents in the Mendeley repo mentioned above. ### Supported Tasks and Leaderboards - [Named Entity Recognition on Ontonotes v5 (English)](https://paperswithcode.com/sota/named-entity-recognition-ner-on-ontonotes-v5) - [Coreference Resolution on OntoNotes](https://paperswithcode.com/sota/coreference-resolution-on-ontonotes) - [Semantic Role Labeling on OntoNotes](https://paperswithcode.com/sota/semantic-role-labeling-on-ontonotes) - ... ### Languages V4 data for Arabic, Chinese, English, and V12 data for English ## Dataset Structure ### Data Instances ``` { {'document_id': 'nw/wsj/23/wsj_2311', 'sentences': [{'part_id': 0, 'words': ['CONCORDE', 'trans-Atlantic', 'flights', 'are', '$', '2, 'to', 'Paris', 'and', '$', '3, 'to', 'London', '.']}, 'pos_tags': [25, 18, 27, 43, 2, 12, 17, 25, 11, 2, 12, 17, 25, 7], 'parse_tree': '(TOP(S(NP (NNP CONCORDE) (JJ trans-Atlantic) (NNS flights) )(VP (VBP are) (NP(NP(NP ($ $) (CD 2,400) )(PP (IN to) (NP (NNP Paris) ))) (CC and) (NP(NP ($ $) (CD 3,200) )(PP (IN to) (NP (NNP London) ))))) (. .) ))', 'predicate_lemmas': [None, None, None, 'be', None, None, None, None, None, None, None, None, None, None], 'predicate_framenet_ids': [None, None, None, '01', None, None, None, None, None, None, None, None, None, None], 'word_senses': [None, None, None, None, None, None, None, None, None, None, None, None, None, None], 'speaker': None, 'named_entities': [7, 6, 0, 0, 0, 15, 0, 5, 0, 0, 15, 0, 5, 0], 'srl_frames': [{'frames': ['B-ARG1', 'I-ARG1', 'I-ARG1', 'B-V', 'B-ARG2', 'I-ARG2', 'I-ARG2', 'I-ARG2', 'I-ARG2', 'I-ARG2', 'I-ARG2', 'I-ARG2', 'I-ARG2', 'O'], 'verb': 'are'}], 'coref_spans': [], {'part_id': 0, 'words': ['In', 'a', 'Centennial', 'Journal', 'article', 'Oct.', '5', ',', 'the', 'fares', 'were', 'reversed', '.']}]} 'pos_tags': [17, 13, 25, 25, 24, 25, 12, 4, 13, 27, 40, 42, 7], 'parse_tree': '(TOP(S(PP (IN In) (NP (DT a) (NML (NNP Centennial) (NNP Journal) ) (NN article) ))(NP (NNP Oct.) (CD 5) ) (, ,) (NP (DT the) (NNS fares) )(VP (VBD were) (VP (VBN reversed) )) (. .) ))', 'predicate_lemmas': [None, None, None, None, None, None, None, None, None, None, None, 'reverse', None], 'predicate_framenet_ids': [None, None, None, None, None, None, None, None, None, None, None, '01', None], 'word_senses': [None, None, None, None, None, None, None, None, None, None, None, None, None], 'speaker': None, 'named_entities': [0, 0, 4, 22, 0, 12, 30, 0, 0, 0, 0, 0, 0], 'srl_frames': [{'frames': ['B-ARGM-LOC', 'I-ARGM-LOC', 'I-ARGM-LOC', 'I-ARGM-LOC', 'I-ARGM-LOC', 'B-ARGM-TMP', 'I-ARGM-TMP', 'O', 'B-ARG1', 'I-ARG1', 'O', 'B-V', 'O'], 'verb': 'reversed'}], 'coref_spans': [], } ``` ### Data Fields - `document_id` (`str`): This is a variation on the document filename - `sentences` (`List[Dict]`): All sentences of the same document are in a single example for the convenience of concatenating sentences. Every element in `sentences` is a `Dict` composed of the following data fields: - `part_id` (`int`) : Some files are divided into multiple parts numbered as 000, 001, 002, ... etc. - `words` (`List[str]`) : - `pos_tags` (`List[ClassLabel]` or `List[str]`) : This is the Penn-Treebank-style part of speech. When parse information is missing, all parts of speech except the one for which there is some sense or proposition annotation are marked with a XX tag. The verb is marked with just a VERB tag. - tag set : Note tag sets below are founded by scanning all the data, and I found it seems to be a little bit different from officially stated tag sets. See official documents in the [Mendeley repo](https://data.mendeley.com/datasets/zmycy7t9h9) - arabic : str. Because pos tag in Arabic is compounded and complex, hard to represent it by `ClassLabel` - chinese v4 : `datasets.ClassLabel(num_classes=36, names=["X", "AD", "AS", "BA", "CC", "CD", "CS", "DEC", "DEG", "DER", "DEV", "DT", "ETC", "FW", "IJ", "INF", "JJ", "LB", "LC", "M", "MSP", "NN", "NR", "NT", "OD", "ON", "P", "PN", "PU", "SB", "SP", "URL", "VA", "VC", "VE", "VV",])`, where `X` is for pos tag missing - english v4 : `datasets.ClassLabel(num_classes=49, names=["XX", "``", "$", "''", ",", "-LRB-", "-RRB-", ".", ":", "ADD", "AFX", "CC", "CD", "DT", "EX", "FW", "HYPH", "IN", "JJ", "JJR", "JJS", "LS", "MD", "NFP", "NN", "NNP", "NNPS", "NNS", "PDT", "POS", "PRP", "PRP$", "RB", "RBR", "RBS", "RP", "SYM", "TO", "UH", "VB", "VBD", "VBG", "VBN", "VBP", "VBZ", "WDT", "WP", "WP$", "WRB",])`, where `XX` is for pos tag missing, and `-LRB-`/`-RRB-` is "`(`" / "`)`". - english v12 : `datasets.ClassLabel(num_classes=51, names="english_v12": ["XX", "``", "$", "''", "", ",", "-LRB-", "-RRB-", ".", ":", "ADD", "AFX", "CC", "CD", "DT", "EX", "FW", "HYPH", "IN", "JJ", "JJR", "JJS", "LS", "MD", "NFP", "NN", "NNP", "NNPS", "NNS", "PDT", "POS", "PRP", "PRP$", "RB", "RBR", "RBS", "RP", "SYM", "TO", "UH", "VB", "VBD", "VBG", "VBN", "VBP", "VBZ", "VERB", "WDT", "WP", "WP$", "WRB",])`, where `XX` is for pos tag missing, and `-LRB-`/`-RRB-` is "`(`" / "`)`". - `parse_tree`* (`Optional[str]`) : An serialized NLTK Tree representing the parse. It includes POS tags as pre-terminal nodes. When the parse information is missing, the parse will be `None`. - `predicate_lemmas` (`List[Optional[str]]`) : The predicate lemma of the words for which we have semantic role information or word sense information. All other indices are `None`. - `predicate_framenet_ids` (`List[Optional[int]]`) : The PropBank frameset ID of the lemmas in predicate_lemmas, or `None`. - `word_senses` (`List[Optional[float]]`) : The word senses for the words in the sentence, or None. These are floats because the word sense can have values after the decimal, like 1.1. - `speaker` (`Optional[str]`) : This is the speaker or author name where available. Mostly in Broadcast Conversation and Web Log data. When it is not available, it will be `None`. - `named_entities` (`List[ClassLabel]`) : The BIO tags for named entities in the sentence. - tag set : `datasets.ClassLabel(num_classes=37, names=["O", "B-PERSON", "I-PERSON", "B-NORP", "I-NORP", "B-FAC", "I-FAC", "B-ORG", "I-ORG", "B-GPE", "I-GPE", "B-LOC", "I-LOC", "B-PRODUCT", "I-PRODUCT", "B-DATE", "I-DATE", "B-TIME", "I-TIME", "B-PERCENT", "I-PERCENT", "B-MONEY", "I-MONEY", "B-QUANTITY", "I-QUANTITY", "B-ORDINAL", "I-ORDINAL", "B-CARDINAL", "I-CARDINAL", "B-EVENT", "I-EVENT", "B-WORK_OF_ART", "I-WORK_OF_ART", "B-LAW", "I-LAW", "B-LANGUAGE", "I-LANGUAGE",])` - `srl_frames` (`List[{"word":str, "frames":List[str]}]`) : A dictionary keyed by the verb in the sentence for the given Propbank frame labels, in a BIO format. - `coref spans` (`List[List[int]]`) : The spans for entity mentions involved in coreference resolution within the sentence. Each element is a tuple composed of (cluster_id, start_index, end_index). Indices are inclusive. ### Data Splits Each dataset (arabic_v4, chinese_v4, english_v4, english_v12) has 3 splits: _train_, _validation_, and _test_ ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information ``` @inproceedings{pradhan-etal-2013-towards, title = "Towards Robust Linguistic Analysis using {O}nto{N}otes", author = {Pradhan, Sameer and Moschitti, Alessandro and Xue, Nianwen and Ng, Hwee Tou and Bj{\"o}rkelund, Anders and Uryupina, Olga and Zhang, Yuchen and Zhong, Zhi}, booktitle = "Proceedings of the Seventeenth Conference on Computational Natural Language Learning", month = aug, year = "2013", address = "Sofia, Bulgaria", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/W13-3516", pages = "143--152", } ``` ### Contributions Thanks to [@richarddwang](https://github.com/richarddwang) for adding this dataset.
eriktks/conll2002	eriktks	2024-01-18T09:33:49Z	249	7	[ "task_categories:token-classification", "task_ids:named-entity-recognition", "task_ids:part-of-speech", "annotations_creators:crowdsourced", "language_creators:found", "multilinguality:multilingual", "source_datasets:original", "language:es", "language:nl", "license:unknown", "size_categories:10K<n<100K", "region:us" ]	[ "token-classification" ]	2022-03-02T23:29:22Z	1	--- annotations_creators: - crowdsourced language_creators: - found language: - es - nl license: - unknown multilinguality: - multilingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - token-classification task_ids: - named-entity-recognition - part-of-speech paperswithcode_id: conll-2002 pretty_name: CoNLL-2002 dataset_info: - config_name: es features: - name: id dtype: string - name: tokens sequence: string - name: pos_tags sequence: class_label: names: '0': AO '1': AQ '2': CC '3': CS '4': DA '5': DE '6': DD '7': DI '8': DN '9': DP '10': DT '11': Faa '12': Fat '13': Fc '14': Fd '15': Fe '16': Fg '17': Fh '18': Fia '19': Fit '20': Fp '21': Fpa '22': Fpt '23': Fs '24': Ft '25': Fx '26': Fz '27': I '28': NC '29': NP '30': P0 '31': PD '32': PI '33': PN '34': PP '35': PR '36': PT '37': PX '38': RG '39': RN '40': SP '41': VAI '42': VAM '43': VAN '44': VAP '45': VAS '46': VMG '47': VMI '48': VMM '49': VMN '50': VMP '51': VMS '52': VSG '53': VSI '54': VSM '55': VSN '56': VSP '57': VSS '58': Y '59': Z - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC '7': B-MISC '8': I-MISC splits: - name: train num_bytes: 6672173 num_examples: 8324 - name: validation num_bytes: 1333784 num_examples: 1916 - name: test num_bytes: 1294156 num_examples: 1518 download_size: 4140690 dataset_size: 9300113 - config_name: nl features: - name: id dtype: string - name: tokens sequence: string - name: pos_tags sequence: class_label: names: '0': Adj '1': Adv '2': Art '3': Conj '4': Int '5': Misc '6': N '7': Num '8': Prep '9': Pron '10': Punc '11': V - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC '7': B-MISC '8': I-MISC splits: - name: train num_bytes: 5308959 num_examples: 15807 - name: validation num_bytes: 994298 num_examples: 2896 - name: test num_bytes: 1808862 num_examples: 5196 download_size: 3642241 dataset_size: 8112119 config_names: - es - nl --- # Dataset Card for CoNLL-2002 ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [homepage](https://www.clips.uantwerpen.be/conll2002/ner/) - Repository: [github](https://github.com/teropa/nlp/tree/master/resources/corpora/conll2002) - Paper: [paper](https://www.aclweb.org/anthology/W02-2024/) - Point of Contact: [Erik Tjong Kim Sang]([email protected]) ### Dataset Summary Named entities are phrases that contain the names of persons, organizations, locations, times and quantities. Example: [PER Wolff] , currently a journalist in [LOC Argentina] , played with [PER Del Bosque] in the final years of the seventies in [ORG Real Madrid] . The shared task of CoNLL-2002 concerns language-independent named entity recognition. We will concentrate on four types of named entities: persons, locations, organizations and names of miscellaneous entities that do not belong to the previous three groups. The participants of the shared task will be offered training and test data for at least two languages. They will use the data for developing a named-entity recognition system that includes a machine learning component. Information sources other than the training data may be used in this shared task. We are especially interested in methods that can use additional unannotated data for improving their performance (for example co-training). ### Supported Tasks and Leaderboards Named Entity Recognition (NER) is a subtask of Information Extraction. Different NER systems were evaluated as a part of the Sixth Message Understanding Conference in 1995 (MUC6). The target language was English. The participating systems performed well. However, many of them used language-specific resources for performing the task and it is unknown how they would have performed on another language than English. After 1995 NER systems have been developed for some European languages and a few Asian languages. There have been at least two studies that have applied one NER system to different languages. Palmer and Day [PD97] have used statistical methods for finding named entities in newswire articles in Chinese, English, French, Japanese, Portuguese and Spanish. They found that the difficulty of the NER task was different for the six languages but that a large part of the task could be performed with simple methods. Cucerzan and Yarowsky [CY99] used both morphological and contextual clues for identifying named entities in English, Greek, Hindi, Rumanian and Turkish. With minimal supervision, they obtained overall F measures between 40 and 70, depending on the languages used. - `named-entity-recognition`: The performance in this task is measured with [F1](https://huggingface.co/metrics/f1) (higher is better). A named entity is correct only if it is an exact match of the corresponding entity in the data. - `parsing`: The performance in this task is measured with [F1](https://huggingface.co/metrics/f1) (higher is better). A part-of-speech tag is correct only if it is equal to the corresponding tag in the data. ### Languages There are two languages available : Spanish (es) and Dutch (nl). ## Dataset Structure ### Data Instances The examples look like this : ``` {'id': '0', 'ner_tags': [5, 6, 0, 0, 0, 0, 3, 0, 0], 'pos_tags': [4, 28, 13, 59, 28, 21, 29, 22, 20], 'tokens': ['La', 'Coruña', ',', '23', 'may', '(', 'EFECOM', ')', '.'] } ``` The original data files within the Dutch sub-dataset have `-DOCSTART-` lines used to separate documents, but these lines are removed here. Indeed `-DOCSTART-` is a special line that acts as a boundary between two different documents, and it is filtered out in this implementation. ### Data Fields - `id`: id of the sample - `tokens`: the tokens of the example text - `ner_tags`: the NER tags of each token - `pos_tags`: the POS tags of each token The POS tags correspond to this list for Spanish: ``` 'AO', 'AQ', 'CC', 'CS', 'DA', 'DE', 'DD', 'DI', 'DN', 'DP', 'DT', 'Faa', 'Fat', 'Fc', 'Fd', 'Fe', 'Fg', 'Fh', 'Fia', 'Fit', 'Fp', 'Fpa', 'Fpt', 'Fs', 'Ft', 'Fx', 'Fz', 'I', 'NC', 'NP', 'P0', 'PD', 'PI', 'PN', 'PP', 'PR', 'PT', 'PX', 'RG', 'RN', 'SP', 'VAI', 'VAM', 'VAN', 'VAP', 'VAS', 'VMG', 'VMI', 'VMM', 'VMN', 'VMP', 'VMS', 'VSG', 'VSI', 'VSM', 'VSN', 'VSP', 'VSS', 'Y', 'Z' ``` And this list for Dutch: ``` 'Adj', 'Adv', 'Art', 'Conj', 'Int', 'Misc', 'N', 'Num', 'Prep', 'Pron', 'Punc', 'V' ``` The NER tags correspond to this list: ``` "O", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC", "B-MISC", "I-MISC", ``` The NER tags have the same format as in the chunking task: a B denotes the first item of a phrase and an I any non-initial word. There are four types of phrases: person names (PER), organizations (ORG), locations (LOC) and miscellaneous names (MISC). It is assumed that named entities are non-recursive and non-overlapping. In case a named entity is embedded in another named entity usually, only the top level entity is marked. ### Data Splits For both configurations (Spanish and Dutch), there are three splits. The original splits were named `train`, `testa` and `testb` and they correspond to the `train`, `validation` and `test` splits. The splits have the following sizes : \| \| train \| validation \| test \| \| ----- \|-------:\|------------:\|------:\| \| N. Examples (Spanish) \| 8324 \| 1916 \| 1518 \| \| N. Examples (Dutch) \| 15807 \| 2896 \| 5196 \| ## Dataset Creation ### Curation Rationale The dataset was introduced to introduce new resources to two languages that were under-served for statistical machine learning at the time, Dutch and Spanish. [More Information Needed] ### Source Data The Spanish data is a collection of news wire articles made available by the Spanish EFE News Agency. The articles are from May 2000. The Dutch data consist of four editions of the Belgian newspaper "De Morgen" of 2000 (June 2, July 1, August 1 and September 1). #### Initial Data Collection and Normalization The articles were word-tokenized, information on the exact pre-processing pipeline is unavailable. #### Who are the source language producers? The source language was produced by journalists and writers employed by the news agency and newspaper mentioned above. ### Annotations #### Annotation process For the Dutch data, the annotator has followed the MITRE and SAIC guidelines for named entity recognition (Chinchor et al., 1999) as well as possible. #### Who are the annotators? The Spanish data annotation was carried out by the TALP Research Center of the Technical University of Catalonia (UPC) and the Center of Language and Computation (CLiC) of the University of Barcelona (UB). The Dutch data was annotated as a part of the Atranos project at the University of Antwerp. ### Personal and Sensitive Information The data is sourced from newspaper source and only contains mentions of public figures or individuals ## Considerations for Using the Data ### Social Impact of Dataset Named Entity Recognition systems can be used to efficiently index news text, allowing to easily gather all information pertaining to an organization or individual. Making such resources widely available in languages other than English can support better research and user experience for a larger part of the world's population. At the same time, better indexing and discoverability can also enable surveillance by state actors. ### Discussion of Biases News text reproduces the biases of society, and any system trained on news data should be cognizant of these limitations and the risk for models to learn spurious correlations in this context, for example between a person's gender and their occupation. ### Other Known Limitations Users should keep in mind that the dataset only contains news text, which might limit the applicability of the developed systems to other domains. ## Additional Information ### Dataset Curators The annotation of the Spanish data was funded by the European Commission through the NAMIC project (IST-1999-12392). ### Licensing Information The licensing status of the data, especially the news source text, is unknown. ### Citation Information Provide the [BibTex](http://www.bibtex.org/)-formatted reference for the dataset. For example: ``` @inproceedings{tjong-kim-sang-2002-introduction, title = "Introduction to the {C}o{NLL}-2002 Shared Task: Language-Independent Named Entity Recognition", author = "Tjong Kim Sang, Erik F.", booktitle = "{COLING}-02: The 6th Conference on Natural Language Learning 2002 ({C}o{NLL}-2002)", year = "2002", url = "https://www.aclweb.org/anthology/W02-2024", } ``` ### Contributions Thanks to [@lhoestq](https://github.com/lhoestq) for adding this dataset.
zefang-liu/phishing-email-dataset	zefang-liu	2024-01-17T23:48:20Z	476	11	[ "task_categories:text-classification", "language:en", "license:lgpl-3.0", "size_categories:10K<n<100K", "format:csv", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[ "text-classification" ]	2024-01-17T23:36:31Z	2	--- license: lgpl-3.0 language: - en task_categories: - text-classification size_categories: - 10K<n<100K --- # Phishing Email Dataset This dataset on Hugging Face is a direct copy of the 'Phishing Email Detection' dataset from Kaggle, shared under the [GNU Lesser General Public License 3.0](https://www.gnu.org/licenses/lgpl-3.0.html). The dataset was originally created by the user '[Cyber Cop](https://www.kaggle.com/subhajournal)' on Kaggle. For complete details, including licensing and usage information, please visit the [original Kaggle page](https://www.kaggle.com/datasets/subhajournal/phishingemails).
kchawla123/casino	kchawla123	2024-01-16T13:53:39Z	133	5	[ "task_categories:text-generation", "task_categories:fill-mask", "task_ids:dialogue-modeling", "annotations_creators:expert-generated", "language_creators:crowdsourced", "multilinguality:monolingual", "source_datasets:original", "language:en", "license:cc-by-4.0", "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[ "conversational", "text-generation", "fill-mask" ]	2022-03-02T23:29:22Z	1	--- annotations_creators: - expert-generated language_creators: - crowdsourced language: - en license: - cc-by-4.0 multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - conversational - text-generation - fill-mask task_ids: - dialogue-modeling paperswithcode_id: casino pretty_name: Campsite Negotiation Dialogues dataset_info: features: - name: chat_logs list: - name: text dtype: string - name: task_data struct: - name: data dtype: string - name: issue2youget struct: - name: Firewood dtype: string - name: Water dtype: string - name: Food dtype: string - name: issue2theyget struct: - name: Firewood dtype: string - name: Water dtype: string - name: Food dtype: string - name: id dtype: string - name: participant_info struct: - name: mturk_agent_1 struct: - name: value2issue struct: - name: Low dtype: string - name: Medium dtype: string - name: High dtype: string - name: value2reason struct: - name: Low dtype: string - name: Medium dtype: string - name: High dtype: string - name: outcomes struct: - name: points_scored dtype: int32 - name: satisfaction dtype: string - name: opponent_likeness dtype: string - name: demographics struct: - name: age dtype: int32 - name: gender dtype: string - name: ethnicity dtype: string - name: education dtype: string - name: personality struct: - name: svo dtype: string - name: big-five struct: - name: extraversion dtype: float32 - name: agreeableness dtype: float32 - name: conscientiousness dtype: float32 - name: emotional-stability dtype: float32 - name: openness-to-experiences dtype: float32 - name: mturk_agent_2 struct: - name: value2issue struct: - name: Low dtype: string - name: Medium dtype: string - name: High dtype: string - name: value2reason struct: - name: Low dtype: string - name: Medium dtype: string - name: High dtype: string - name: outcomes struct: - name: points_scored dtype: int32 - name: satisfaction dtype: string - name: opponent_likeness dtype: string - name: demographics struct: - name: age dtype: int32 - name: gender dtype: string - name: ethnicity dtype: string - name: education dtype: string - name: personality struct: - name: svo dtype: string - name: big-five struct: - name: extraversion dtype: float32 - name: agreeableness dtype: float32 - name: conscientiousness dtype: float32 - name: emotional-stability dtype: float32 - name: openness-to-experiences dtype: float32 - name: annotations list: list: string splits: - name: train num_bytes: 3211407 num_examples: 1030 download_size: 1247368 dataset_size: 3211407 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for Casino ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Repository: [Github: Kushal Chawla CaSiNo](https://github.com/kushalchawla/CaSiNo) - Paper: [CaSiNo: A Corpus of Campsite Negotiation Dialogues for Automatic Negotiation Systems](https://aclanthology.org/2021.naacl-main.254.pdf) - Point of Contact: [Kushal Chawla]([email protected]) ### Dataset Summary We provide a novel dataset (referred to as CaSiNo) of 1030 negotiation dialogues. Two participants take the role of campsite neighbors and negotiate for Food, Water, and Firewood packages, based on their individual preferences and requirements. This design keeps the task tractable, while still facilitating linguistically rich and personal conversations. This helps to overcome the limitations of prior negotiation datasets such as Deal or No Deal and Craigslist Bargain. Each dialogue consists of rich meta-data including participant demographics, personality, and their subjective evaluation of the negotiation in terms of satisfaction and opponent likeness. ### Supported Tasks and Leaderboards Train end-to-end models for negotiation ### Languages English ## Dataset Structure ### Data Instances ``` { "chat_logs": [ { "text": "Hello! \ud83d\ude42 Let's work together on a deal for these packages, shall we? What are you most interested in?", "task_data": {}, "id": "mturk_agent_1" }, ... ], "participant_info": { "mturk_agent_1": { "value2issue": ... "value2reason": ... "outcomes": ... "demographics": ... "personality": ... }, "mturk_agent_2": ... }, "annotations": [ ["Hello! \ud83d\ude42 Let's work together on a deal for these packages, shall we? What are you most interested in?", "promote-coordination,elicit-pref"], ... ] } ``` ### Data Fields - `chat_logs`: The negotiation dialogue between two participants - `text`: The dialogue utterance - `task_data`: Meta-data associated with the utterance such as the deal submitted by a participant - `id`: The ID of the participant who typed this utterance - `participant_info`: Meta-data about the two participants in this conversation - `mturk_agent_1`: For the first participant (Note that 'first' is just for reference. There is no order between the participants and any participant can start the conversation) - `value2issue`: The priority order of this participant among Food, Water, Firewood - `value2reason`: The personal arguments given by the participants themselves, consistent with the above preference order. This preference order and these arguments were submitted before the negotiation began. - `outcomes`: The negotiation outcomes for this participant including objective and subjective assessment. - `demographics`: Demographic attributes of the participant in terms of age, gender, ethnicity, and education. - `personality`: Personality attributes for this participant, in terms of Big-5 and Social Value Orientation - `mturk_agent_2`: For the second participant; follows the same structure as above - `annotations`: Strategy annotations for each utterance in the dialogue, wherever available. The first element represents the utterance and the second represents a comma-separated list of all strategies present in that utterance. ### Data Splits No default data split has been provided. Hence, all 1030 data points are under the 'train' split. \| \| Train \| \| ----- \| ----- \| \| total dialogues \| 1030 \| \| annotated dialogues \| 396 \| ## Dataset Creation ### Curation Rationale The dataset was collected to address the limitations in prior negotiation datasets from the perspective of downstream applications in pedagogy and conversational AI. Please refer to the original paper published at NAACL 2021 for details about the rationale and data curation steps ([source paper](https://aclanthology.org/2021.naacl-main.254.pdf)). ### Source Data #### Initial Data Collection and Normalization The dialogues were crowdsourced on Amazon Mechanical Turk. The strategy annotations were performed by expert annotators (first three authors of the paper). Please refer to the original dataset paper published at NAACL 2021 for more details ([source paper](https://aclanthology.org/2021.naacl-main.254.pdf)). #### Who are the source language producers? The primary producers are Turkers on Amazon Mechanical Turk platform. Two turkers were randomly paired with each other to engage in a negotiation via a chat interface. Please refer to the original dataset paper published at NAACL 2021 for more details ([source paper](https://aclanthology.org/2021.naacl-main.254.pdf)). ### Annotations #### Annotation process From the [source paper](https://aclanthology.org/2021.naacl-main.254.pdf) for this dataset: >Three expert annotators independently annotated 396 dialogues containing 4615 utterances. The annotation guidelines were iterated over a subset of 5 dialogues, while the reliability scores were computed on a different subset of 10 dialogues. We use the nominal form of Krippendorff’s alpha (Krippendorff, 2018) to measure the inter-annotator agreement. We provide the annotation statistics in Table 2. Although we release all the annotations, we skip Coordination and Empathy for our analysis in this work, due to higher subjectivity resulting in relatively lower reliability scores. #### Who are the annotators? Three expert annotators (first three authors of the paper). ### Personal and Sensitive Information All personally identifiable information about the participants such as MTurk Ids or HIT Ids was removed before releasing the data. ## Considerations for Using the Data ### Social Impact of Dataset Please refer to Section 8.2 in the [source paper](https://aclanthology.org/2021.naacl-main.254.pdf). ### Discussion of Biases Please refer to Section 8.2 in the [source paper](https://aclanthology.org/2021.naacl-main.254.pdf). ### Other Known Limitations Please refer to Section 7 in the [source paper](https://aclanthology.org/2021.naacl-main.254.pdf). ## Additional Information ### Dataset Curators Corresponding Author: Kushal Chawla (`[email protected]`)\ Affiliation: University of Southern California\ Please refer to the [source paper](https://aclanthology.org/2021.naacl-main.254.pdf) for the complete author list. ### Licensing Information The project is licensed under CC-by-4.0 ### Citation Information ``` @inproceedings{chawla2021casino, title={CaSiNo: A Corpus of Campsite Negotiation Dialogues for Automatic Negotiation Systems}, author={Chawla, Kushal and Ramirez, Jaysa and Clever, Rene and Lucas, Gale and May, Jonathan and Gratch, Jonathan}, booktitle={Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies}, pages={3167--3185}, year={2021} } ``` ### Contributions Thanks to [Kushal Chawla](https://kushalchawla.github.io/) for adding this dataset.
dmitva/human_ai_generated_text	dmitva	2024-01-16T06:25:14Z	462	12	[ "language:en", "license:cc-by-4.0", "size_categories:1M<n<10M", "format:csv", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "doi:10.57967/hf/1617", "region:us", "nlp", "human", "ai", "text" ]	[]	2023-12-31T16:56:40Z	3	--- license: cc-by-4.0 language: - en tags: - nlp - human - ai - text --- ### Human or AI-Generated Text The data can be valuable for educators, policymakers, and researchers interested in the evolving education landscape, particularly in detecting or identifying texts written by Humans or Artificial Intelligence systems. #### File Name `model_training_dataset.csv` #### File Structure - `id`: Unique identifier for each record. - `human_text`: Human-written content. - `ai_text`: AI-generated texts. - `instructions`: Description of the task given to both Humans and AI. #### Acknowledgement Thanks to [0xnu](https://finbarrs.eu/) for sharing the file after contacting him and requesting it. #### Citation To reference this dataset in academic work, please use the following citation: ```bibtex @article{abiodunfinbarrsoketunji-agtd2023, title={Evaluating the Efficacy of Hybrid Deep Learning Models in Distinguishing AI-Generated Text}, author={Abiodun Finbarrs Oketunji}, journal={arXiv:2311.15565v2}, year={2023} } ```
jondurbin/gutenberg-dpo-v0.1	jondurbin	2024-01-12T13:05:37Z	619	140	[ "language:en", "license:cc-by-4.0", "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "dpo" ]	[]	2024-01-11T13:15:41Z	null	--- license: cc-by-4.0 language: - en tags: - dpo pretty_name: Gutenberg DPO size_categories: - n<1K --- # Gutenberg DPO ![gutenberg](gutenberg.png) ## Overview This is a dataset meant to enhance novel writing capabilities of LLMs, by using public domain books from [Project Gutenberg](https://gutenberg.org/) ## Process First, the each book is parsed, split into chapters, cleaned up from the original format (remove superfluous newlines, illustration tags, etc.). Once we have chapters, an LLM is prompted with each chapter to create a synthetic prompt that would result in that chapter being written. Each chapter has a summary created as well, so that the prompts for each chapter after the also include a summary of the previous chapter to provide additional context. We then use the synthetic prompt with previous chapter summary to write the chapter with an LLM (llama-2-13b-chat, bagel-7b-v0.1, dolphin-2.2-34b). The human written text, that is, the original chapter, is used as the "chosen" value, and the LLM written chapter is used as the rejected value. ## Books used These books were chosen main because they appeared in the popular section on project gutenberg, and they function correctly with the chapterize library. - Huckleberry Finn - Treasure Island - Anna Karenina - Uncle Tom’s Cabin - Wuthering Heights - Madame Bovary - The Turn of the Screw - The War of the Worlds - A Study in Scarlet - Middlemarch - Pride and Prejudice - The Brothers Karamazov - Through the Looking Glass - Moby Dick - Frankenstein - A Tale of Two Cities
jondurbin/truthy-dpo-v0.1	jondurbin	2024-01-11T10:19:14Z	293	134	[ "license:cc-by-4.0", "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2023-12-11T15:34:04Z	null	--- license: cc-by-4.0 --- ## Truthy DPO This is a dataset designed to enhance the overall truthfulness of LLMs, without sacrificing immersion when roleplaying as a human. For example, in normal AI assistant model, the model should not try to describe what the warmth of the sun feels like, but if the system prompt indicates it's a human, it should. Mostly targets corporeal, spacial, temporal awareness, and common misconceptions. ### Contribute If you're interested in new functionality/datasets, take a look at [bagel repo](https://github.com/jondurbin/bagel) and [airoboros](https://github.com/jondurbin/airoboros) and either make a PR or open an issue with details. To help me with the fine-tuning costs, dataset generation, etc., please use one of the following: - https://bmc.link/jondurbin - ETH 0xce914eAFC2fe52FdceE59565Dd92c06f776fcb11 - BTC bc1qdwuth4vlg8x37ggntlxu5cjfwgmdy5zaa7pswf
defunct-datasets/eli5	defunct-datasets	2024-01-11T09:32:33Z	611	50	[ "task_categories:text2text-generation", "task_ids:abstractive-qa", "task_ids:open-domain-abstractive-qa", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:monolingual", "source_datasets:original", "language:en", "license:unknown", "size_categories:100K<n<1M", "arxiv:1907.09190", "arxiv:1904.04047", "region:us" ]	[ "text2text-generation" ]	2022-03-02T23:29:22Z	1	--- annotations_creators: - no-annotation language_creators: - found language: - en license: - unknown multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - text2text-generation task_ids: - abstractive-qa - open-domain-abstractive-qa paperswithcode_id: eli5 pretty_name: ELI5 viewer: false dataset_info: features: - name: q_id dtype: string - name: title dtype: string - name: selftext dtype: string - name: document dtype: string - name: subreddit dtype: string - name: answers sequence: - name: a_id dtype: string - name: text dtype: string - name: score dtype: int32 - name: title_urls sequence: - name: url dtype: string - name: selftext_urls sequence: - name: url dtype: string - name: answers_urls sequence: - name: url dtype: string config_name: LFQA_reddit splits: - name: train_eli5 num_bytes: 577188173 num_examples: 272634 - name: validation_eli5 num_bytes: 21117891 num_examples: 9812 - name: test_eli5 num_bytes: 53099796 num_examples: 24512 - name: train_asks num_bytes: 286464210 num_examples: 131778 - name: validation_asks num_bytes: 9662481 num_examples: 2281 - name: test_asks num_bytes: 17713920 num_examples: 4462 - name: train_askh num_bytes: 330483260 num_examples: 98525 - name: validation_askh num_bytes: 18690845 num_examples: 4901 - name: test_askh num_bytes: 36246784 num_examples: 9764 download_size: 6326543 dataset_size: 1350667360 --- <div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400"> <p><b>Defunct:</b> Dataset "eli5" is defunct and no longer accessible due to unavailability of the source data.</p> </div> ## <span style="color:red">⚠️ Reddit recently [changed the terms of access](https://www.reddit.com/r/reddit/comments/12qwagm/an_update_regarding_reddits_api/) to its API, making the source data for this dataset unavailable</span>. # Dataset Card for ELI5 ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [ELI5 homepage](https://facebookresearch.github.io/ELI5/explore.html) - Repository: [ELI5 repository](https://github.com/facebookresearch/ELI5) - Paper: [ELI5: Long Form Question Answering](https://arxiv.org/abs/1907.09190) - Point of Contact: [Yacine Jernite](mailto:[email protected]) ### Dataset Summary The ELI5 dataset is an English-language dataset of questions and answers gathered from three subreddits where users ask factual questions requiring paragraph-length or longer answers. The dataset was created to support the task of open-domain long form abstractive question answering, and covers questions about general topics in its [r/explainlikeimfive](https://www.reddit.com/r/explainlikeimfive/) subset, science in it [r/askscience](https://www.reddit.com/r/askscience/) subset, and History in its [r/AskHistorians](https://www.reddit.com/r/AskHistorians/) subset. ### Supported Tasks and Leaderboards - `abstractive-qa`, `open-domain-abstractive-qa`: The dataset can be used to train a model for Open Domain Long Form Question Answering. An LFQA model is presented with a non-factoid and asked to retrieve relevant information from a knowledge source (such as [Wikipedia](https://www.wikipedia.org/)), then use it to generate a multi-sentence answer. The model performance is measured by how high its [ROUGE](https://huggingface.co/metrics/rouge) score to the reference is. A [BART-based model](https://huggingface.co/yjernite/bart_eli5) with a [dense retriever](https://huggingface.co/yjernite/retribert-base-uncased) trained to draw information from [Wikipedia passages](https://huggingface.co/datasets/wiki_snippets) achieves a [ROUGE-L of 0.149](https://yjernite.github.io/lfqa.html#generation). ### Languages The text in the dataset is in English, as spoken by Reddit users on the [r/explainlikeimfive](https://www.reddit.com/r/explainlikeimfive/), [r/askscience](https://www.reddit.com/r/askscience/), and [r/AskHistorians](https://www.reddit.com/r/AskHistorians/) subreddits. The associated BCP-47 code is `en`. ## Dataset Structure ### Data Instances A typical data point comprises a question, with a `title` containing the main question and a `selftext` which sometimes elaborates on it, and a list of answers from the forum sorted by the number of upvotes they obtained. Additionally, the URLs in each of the text fields have been extracted to respective lists and replaced by generic tokens in the text. An example from the ELI5 test set looks as follows: ``` {'q_id': '8houtx', 'title': 'Why does water heated to room temperature feel colder than the air around it?', 'selftext': '', 'document': '', 'subreddit': 'explainlikeimfive', 'answers': {'a_id': ['dylcnfk', 'dylcj49'], 'text': ["Water transfers heat more efficiently than air. When something feels cold it's because heat is being transferred from your skin to whatever you're touching. Since water absorbs the heat more readily than air, it feels colder.", "Air isn't as good at transferring heat compared to something like water or steel (sit on a room temperature steel bench vs. a room temperature wooden bench, and the steel one will feel more cold).\n\nWhen you feel cold, what you're feeling is heat being transferred out of you. If there is no breeze, you feel a certain way. If there's a breeze, you will get colder faster (because the moving air is pulling the heat away from you), and if you get into water, its quite good at pulling heat from you. Get out of the water and have a breeze blow on you while you're wet, all of the water starts evaporating, pulling even more heat from you."], 'score': [5, 2]}, 'title_urls': {'url': []}, 'selftext_urls': {'url': []}, 'answers_urls': {'url': []}} ``` ### Data Fields - `q_id`: a string question identifier for each example, corresponding to its ID in the [Pushshift.io](https://files.pushshift.io/reddit/submissions/) Reddit submission dumps. - `subreddit`: One of `explainlikeimfive`, `askscience`, or `AskHistorians`, indicating which subreddit the question came from - `title`: title of the question, with URLs extracted and replaced by `URL_n` tokens - `title_urls`: list of the extracted URLs, the `n`th element of the list was replaced by `URL_n` - `selftext`: either an empty string or an elaboration of the question - `selftext_urls`: similar to `title_urls` but for `self_text` - `answers`: a list of answers, each answer has: - `a_id`: a string answer identifier for each answer, corresponding to its ID in the [Pushshift.io](https://files.pushshift.io/reddit/comments/) Reddit comments dumps. - `text`: the answer text with the URLs normalized - `score`: the number of upvotes the answer had received when the dumps were created - `answers_urls`: a list of the extracted URLs. All answers use the same list, the numbering of the normalization token continues across answer texts ### Data Splits The data is split into a training, validation and test set for each of the three subreddits. In order to avoid having duplicate questions in across sets, the `title` field of each of the questions were ranked by their tf-idf match to their nearest neighbor and the ones with the smallest value were used in the test and validation sets. The final split sizes are as follow: \| \| Train \| Valid \| Test \| \| ----- \| ------ \| ----- \| ---- \| \| r/explainlikeimfive examples\| 272634 \| 9812 \| 24512\| \| r/askscience examples \| 131778 \| 2281 \| 4462 \| \| r/AskHistorians examples \| 98525 \| 4901 \| 9764 \| ## Dataset Creation ### Curation Rationale ELI5 was built to provide a testbed for machines to learn how to answer more complex questions, which requires them to find and combine information in a coherent manner. The dataset was built by gathering questions that were asked by community members of three subreddits, including [r/explainlikeimfive](https://www.reddit.com/r/explainlikeimfive/), along with the answers that were provided by other users. The [rules of the subreddit](https://www.reddit.com/r/explainlikeimfive/wiki/detailed_rules) make this data particularly well suited to training a model for abstractive question answering: the questions need to seek an objective explanation about well established facts, and the answers provided need to be understandable to a layperson without any particular knowledge domain. ### Source Data #### Initial Data Collection and Normalization The data was obtained by filtering submissions and comments from the subreddits of interest from the XML dumps of the [Reddit forum](https://www.reddit.com/) hosted on [Pushshift.io](https://files.pushshift.io/reddit/). In order to further improve the quality of the selected examples, only questions with a score of at least 2 and at least one answer with a score of at least 2 were selected for the dataset. The dataset questions and answers span a period form August 2012 to August 2019. #### Who are the source language producers? The language producers are users of the [r/explainlikeimfive](https://www.reddit.com/r/explainlikeimfive/), [r/askscience](https://www.reddit.com/r/askscience/), and [r/AskHistorians](https://www.reddit.com/r/AskHistorians/) subreddits between 2012 and 2019. No further demographic information was available from the data source. ### Annotations The dataset does not contain any additional annotations. #### Annotation process [N/A] #### Who are the annotators? [N/A] ### Personal and Sensitive Information The authors removed the speaker IDs from the [Pushshift.io](https://files.pushshift.io/reddit/) dumps but did not otherwise anonymize the data. Some of the questions and answers are about contemporary public figures or individuals who appeared in the news. ## Considerations for Using the Data ### Social Impact of Dataset The purpose of this dataset is to help develop better question answering systems. A system that succeeds at the supported task would be able to provide a coherent answer to even complex questions requiring a multi-step explanation, which is beyond the ability of even the larger existing models. The task is also thought as a test-bed for retrieval model which can show the users which source text was used in generating the answer and allow them to confirm the information provided to them. It should be noted however that the provided answers were written by Reddit users, an information which may be lost if models trained on it are deployed in down-stream applications and presented to users without context. The specific biases this may introduce are discussed in the next section. ### Discussion of Biases While Reddit hosts a number of thriving communities with high quality discussions, it is also widely known to have corners where sexism, hate, and harassment are significant issues. See for example the [recent post from Reddit founder u/spez](https://www.reddit.com/r/announcements/comments/gxas21/upcoming_changes_to_our_content_policy_our_board/) outlining some of the ways he thinks the website's historical policies have been responsible for this problem, [Adrienne Massanari's 2015 article on GamerGate](https://www.researchgate.net/publication/283848479_Gamergate_and_The_Fappening_How_Reddit's_algorithm_governance_and_culture_support_toxic_technocultures) and follow-up works, or a [2019 Wired article on misogyny on Reddit](https://www.wired.com/story/misogyny-reddit-research/). While there has been some recent work in the NLP community on de-biasing models (e.g. [Black is to Criminal as Caucasian is to Police: Detecting and Removing Multiclass Bias in Word Embeddings](https://arxiv.org/abs/1904.04047) for word embeddings trained specifically on Reddit data), this problem is far from solved, and the likelihood that a trained model might learn the biases present in the data remains a significant concern. We still note some encouraging signs for all of these communities: [r/explainlikeimfive](https://www.reddit.com/r/explainlikeimfive/) and [r/askscience](https://www.reddit.com/r/askscience/) have similar structures and purposes, and [r/askscience](https://www.reddit.com/r/askscience/) was found in 2015 to show medium supportiveness and very low toxicity when compared to other subreddits (see a [hackerfall post](https://hackerfall.com/story/study-and-interactive-visualization-of-toxicity-in), [thecut.com write-up](https://www.thecut.com/2015/03/interactive-chart-of-reddits-toxicity.html) and supporting [data](https://chart-studio.plotly.com/~bsbell21/210/toxicity-vs-supportiveness-by-subreddit/#data)). Meanwhile, the [r/AskHistorians rules](https://www.reddit.com/r/AskHistorians/wiki/rules) mention that the admins will not tolerate "_racism, sexism, or any other forms of bigotry_". However, further analysis of whether and to what extent these rules reduce toxicity is still needed. We also note that given the audience of the Reddit website which is more broadly used in the US and Europe, the answers will likely present a Western perspectives, which is particularly important to note when dealing with historical topics. ### Other Known Limitations The answers provided in the dataset are represent the opinion of Reddit users. While these communities strive to be helpful, they should not be considered to represent a ground truth. ## Additional Information ### Dataset Curators The dataset was initially created by Angela Fan, Ethan Perez, Yacine Jernite, Jason Weston, Michael Auli, and David Grangier, during work done at Facebook AI Research (FAIR). ### Licensing Information The licensing status of the dataset hinges on the legal status of the [Pushshift.io](https://files.pushshift.io/reddit/) data which is unclear. ### Citation Information ``` @inproceedings{eli5_lfqa, author = {Angela Fan and Yacine Jernite and Ethan Perez and David Grangier and Jason Weston and Michael Auli}, editor = {Anna Korhonen and David R. Traum and Llu{\'{\i}}s M{\`{a}}rquez}, title = {{ELI5:} Long Form Question Answering}, booktitle = {Proceedings of the 57th Conference of the Association for Computational Linguistics, {ACL} 2019, Florence, Italy, July 28- August 2, 2019, Volume 1: Long Papers}, pages = {3558--3567}, publisher = {Association for Computational Linguistics}, year = {2019}, url = {https://doi.org/10.18653/v1/p19-1346}, doi = {10.18653/v1/p19-1346} } ``` ### Contributions Thanks to [@lewtun](https://github.com/lewtun), [@lhoestq](https://github.com/lhoestq), [@mariamabarham](https://github.com/mariamabarham), [@thomwolf](https://github.com/thomwolf), [@yjernite](https://github.com/yjernite) for adding this dataset.
ParlAI/blended_skill_talk	ParlAI	2024-01-10T10:22:26Z	1,634	69	[ "task_ids:dialogue-generation", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "source_datasets:original", "language:en", "license:unknown", "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2004.08449", "region:us" ]	[ "conversational" ]	2022-03-02T23:29:22Z	1	--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - unknown multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - conversational task_ids: - dialogue-generation paperswithcode_id: blended-skill-talk pretty_name: BlendedSkillTalk dataset_info: features: - name: personas sequence: string - name: additional_context dtype: string - name: previous_utterance sequence: string - name: context dtype: string - name: free_messages sequence: string - name: guided_messages sequence: string - name: suggestions sequence: - name: convai2 dtype: string - name: empathetic_dialogues dtype: string - name: wizard_of_wikipedia dtype: string - name: guided_chosen_suggestions sequence: string - name: label_candidates sequence: sequence: string splits: - name: train num_bytes: 10830670 num_examples: 4819 - name: validation num_bytes: 43961447 num_examples: 1009 - name: test num_bytes: 44449895 num_examples: 980 download_size: 10897644 dataset_size: 99242012 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* --- # Dataset Card for "blended_skill_talk" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [https://parl.ai/projects/bst/](https://parl.ai/projects/bst/) - Repository: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Paper: [Can You Put it All Together: Evaluating Conversational Agents' Ability to Blend Skills](https://arxiv.org/abs/2004.08449v1) - Point of Contact: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Size of downloaded dataset files: 38.11 MB - Size of the generated dataset: 15.08 MB - Total amount of disk used: 53.17 MB ### Dataset Summary A dataset of 7k conversations explicitly designed to exhibit multiple conversation modes: displaying personality, having empathy, and demonstrating knowledge. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### default - Size of downloaded dataset files: 38.11 MB - Size of the generated dataset: 15.08 MB - Total amount of disk used: 53.17 MB An example of 'train' looks as follows. ``` { 'personas': ['my parents don t really speak english , but i speak italian and english.', 'i have three children.'], 'additional_context': 'Backstreet Boys', 'previous_utterance': ['Oh, I am a BIG fan of the Backstreet Boys! Have you ever seen them performing live?', "No,I listen to their music a lot, mainly the unbreakable which is the Backstreet Boys' sixth studio album. "], 'context': 'wizard_of_wikipedia', 'free_messages': ['you are very knowledgeable, do you prefer nsync or bsb?', "haha kids of this days don't know them, i'm 46 and i still enjoying them, my kids only listen k-pop", "italian?haha that's strange, i only talk english and a little spanish "], 'guided_messages': ["i don't have a preference, they are both great. All 3 of my kids get annoyed when I listen to them though.", 'Sometimes I sing their songs in Italian, that really annoys them lol.', 'My parents barely speak English, so I was taught both. By the way, what is k-pop?'], 'suggestions': {'convai2': ["i don't have a preference , both are pretty . do you have any hobbies ?", "do they the backstreet boys ? that's my favorite group .", 'are your kids interested in music ?'], 'empathetic_dialogues': ['I actually just discovered Imagine Dragons. I love them!', "Hahaha that just goes to show ya, age is just a umber!'", 'That would be hard! Do you now Spanish well?'], 'wizard_of_wikipedia': ['NSYNC Also had Lance Bass and Joey Fatone, sometimes called the Fat One.', 'Yes, there are a few K-Pop songs that I have heard good big in the USA. It is the most popular in South Korea and has Western elements of pop.', 'English, beleive it or not.']}, 'guided_chosen_suggestions': ['convai2', '', ''], 'label_candidates': []} ``` ### Data Fields The data fields are the same among all splits. #### default - `personas`: a `list` of `string` features. - `additional_context`: a `string` feature. - `previous_utterance`: a `list` of `string` features. - `context`: a `string` feature. - `free_messages`: a `list` of `string` features. - `guided_messgaes`: a `list` of `string` features. - `suggestions`: a dictionary feature containing: - `convai2`: a `string` feature. - `empathetic_dialogues`: a `string` feature. - `wizard_of_wikipedia`: a `string` feature. - `guided_chosen_suggestions`: a `list` of `string` features. - `label_candidates`: a `list` of `lists` of `string` features. ### Data Splits \| name \|train\|validation\|test\| \|-------\|----:\|---------:\|---:\| \|default\| 4819\| 1009\| 980\| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @misc{smith2020evaluating, title={Can You Put it All Together: Evaluating Conversational Agents' Ability to Blend Skills}, author={Eric Michael Smith and Mary Williamson and Kurt Shuster and Jason Weston and Y-Lan Boureau}, year={2020}, eprint={2004.08449}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ### Contributions Thanks to [@lewtun](https://github.com/lewtun), [@thomwolf](https://github.com/thomwolf), [@lhoestq](https://github.com/lhoestq), [@patrickvonplaten](https://github.com/patrickvonplaten), [@mariamabarham](https://github.com/mariamabarham) for adding this dataset.
maywell/korean_textbooks	maywell	2024-01-10T09:21:36Z	3,240	113	[ "language:ko", "license:apache-2.0", "size_categories:1M<n<10M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:2306.11644", "region:us" ]	[]	2023-12-27T23:13:45Z	null	--- language: - ko license: apache-2.0 size_categories: - 1M<n<10M pretty_name: 대규모 한국어 Synthetic 데이터 dataset_info: - config_name: claude_evol features: - name: text dtype: string splits: - name: train num_bytes: 992896186 num_examples: 239102 download_size: 380188122 dataset_size: 992896186 - config_name: code-alpaca features: - name: text dtype: string splits: - name: train num_bytes: 273836723 num_examples: 64112 download_size: 100817441 dataset_size: 273836723 - config_name: helpsteer features: - name: text dtype: string splits: - name: train num_bytes: 101753037 num_examples: 25253 download_size: 38660919 dataset_size: 101753037 - config_name: ko_wikidata features: - name: text dtype: string splits: - name: train num_bytes: 527306289 num_examples: 127614 download_size: 197029339 dataset_size: 527306289 - config_name: mmlu_abstract_algebra features: - name: text dtype: string splits: - name: train num_bytes: 369008992 num_examples: 88848 download_size: 135822870 dataset_size: 369008992 - config_name: mmlu_all features: - name: text dtype: string splits: - name: train num_bytes: 406126621 num_examples: 97765 download_size: 149486712 dataset_size: 406126621 - config_name: mmlu_anatomy features: - name: text dtype: string splits: - name: train num_bytes: 404317465 num_examples: 97463 download_size: 148806011 dataset_size: 404317465 - config_name: mmlu_astronomy features: - name: text dtype: string splits: - name: train num_bytes: 404137638 num_examples: 97347 download_size: 148705490 dataset_size: 404137638 - config_name: mmlu_business_ethics features: - name: text dtype: string splits: - name: train num_bytes: 404250245 num_examples: 97327 download_size: 148763276 dataset_size: 404250245 - config_name: mmlu_clinical_knowledge features: - name: text dtype: string splits: - name: train num_bytes: 403659005 num_examples: 97226 download_size: 148688069 dataset_size: 403659005 - config_name: mmlu_college_biology features: - name: text dtype: string splits: - name: train num_bytes: 404028634 num_examples: 97285 download_size: 148722802 dataset_size: 404028634 - config_name: mmlu_college_chemistry features: - name: text dtype: string splits: - name: train num_bytes: 404667385 num_examples: 97435 download_size: 148855223 dataset_size: 404667385 - config_name: mmlu_college_computer_science features: - name: text dtype: string splits: - name: train num_bytes: 385176880 num_examples: 92606 download_size: 141868873 dataset_size: 385176880 - config_name: mmlu_college_mathematics features: - name: text dtype: string splits: - name: train num_bytes: 390603751 num_examples: 94070 download_size: 143833823 dataset_size: 390603751 - config_name: mmlu_college_medicine features: - name: text dtype: string splits: - name: train num_bytes: 395144479 num_examples: 95156 download_size: 145271248 dataset_size: 395144479 - config_name: mmlu_college_physics features: - name: '0' dtype: string splits: - name: train num_bytes: 404906114 num_examples: 97452 download_size: 148870088 dataset_size: 404906114 - config_name: mmlu_computer_security features: - name: '0' dtype: string splits: - name: train num_bytes: 403699674 num_examples: 97212 download_size: 148755211 dataset_size: 403699674 - config_name: mmlu_conceptual_physics features: - name: '0' dtype: string splits: - name: train num_bytes: 366231421 num_examples: 88216 download_size: 134989933 dataset_size: 366231421 - config_name: mmlu_econometrics features: - name: '0' dtype: string splits: - name: train num_bytes: 380851762 num_examples: 91854 download_size: 140295665 dataset_size: 380851762 - config_name: mmlu_electrical_engineering features: - name: '0' dtype: string splits: - name: train num_bytes: 364564129 num_examples: 87826 download_size: 134376902 dataset_size: 364564129 - config_name: mmlu_elementary_mathematics features: - name: '0' dtype: string splits: - name: train num_bytes: 371101672 num_examples: 89307 download_size: 136622044 dataset_size: 371101672 - config_name: mmlu_formal_logic features: - name: '0' dtype: string splits: - name: train num_bytes: 395937096 num_examples: 95483 download_size: 145736493 dataset_size: 395937096 - config_name: mmlu_global_facts features: - name: '0' dtype: string splits: - name: train num_bytes: 394596084 num_examples: 94984 download_size: 145284966 dataset_size: 394596084 - config_name: mmlu_high_school_biology features: - name: '0' dtype: string splits: - name: train num_bytes: 402382699 num_examples: 97117 download_size: 148038235 dataset_size: 402382699 - config_name: mmlu_high_school_chemistry features: - name: '0' dtype: string splits: - name: train num_bytes: 402886667 num_examples: 96907 download_size: 148323317 dataset_size: 402886667 - config_name: mmlu_high_school_computer_science features: - name: '0' dtype: string splits: - name: train num_bytes: 403966380 num_examples: 97351 download_size: 148666121 dataset_size: 403966380 - config_name: mmlu_high_school_european_history features: - name: '0' dtype: string splits: - name: train num_bytes: 403671884 num_examples: 97222 download_size: 148454177 dataset_size: 403671884 - config_name: mmlu_high_school_geography features: - name: '0' dtype: string splits: - name: train num_bytes: 404040602 num_examples: 97261 download_size: 148657890 dataset_size: 404040602 - config_name: mmlu_high_school_government_and_politics features: - name: '0' dtype: string splits: - name: train num_bytes: 403990139 num_examples: 97311 download_size: 148568388 dataset_size: 403990139 - config_name: mmlu_high_school_macroeconomics features: - name: '0' dtype: string splits: - name: train num_bytes: 404170166 num_examples: 97400 download_size: 148591243 dataset_size: 404170166 - config_name: mmlu_high_school_mathematics features: - name: '0' dtype: string splits: - name: train num_bytes: 404846407 num_examples: 97396 download_size: 149076619 dataset_size: 404846407 - config_name: mmlu_high_school_microeconomics features: - name: '0' dtype: string splits: - name: train num_bytes: 404613760 num_examples: 97435 download_size: 148970422 dataset_size: 404613760 - config_name: mmlu_high_school_physics features: - name: '0' dtype: string splits: - name: train num_bytes: 397678253 num_examples: 95740 download_size: 146340167 dataset_size: 397678253 - config_name: mmlu_high_school_psychology features: - name: '0' dtype: string splits: - name: train num_bytes: 334767526 num_examples: 80626 download_size: 123054403 dataset_size: 334767526 - config_name: mmlu_high_school_statistics features: - name: '0' dtype: string splits: - name: train num_bytes: 315209112 num_examples: 76033 download_size: 115876698 dataset_size: 315209112 - config_name: mmlu_high_school_us_history features: - name: '0' dtype: string splits: - name: train num_bytes: 329179309 num_examples: 79322 download_size: 120972668 dataset_size: 329179309 - config_name: mmlu_high_school_world_history features: - name: '0' dtype: string splits: - name: train num_bytes: 357910528 num_examples: 85990 download_size: 131809165 dataset_size: 357910528 - config_name: mmlu_human_aging features: - name: '0' dtype: string splits: - name: train num_bytes: 325427761 num_examples: 78341 download_size: 119430234 dataset_size: 325427761 - config_name: mmlu_human_sexuality features: - name: '0' dtype: string splits: - name: train num_bytes: 328912659 num_examples: 79327 download_size: 121032722 dataset_size: 328912659 - config_name: mmlu_international_law features: - name: '0' dtype: string splits: - name: train num_bytes: 327874597 num_examples: 78989 download_size: 120785769 dataset_size: 327874597 - config_name: normal_instructions features: - name: text dtype: string splits: - name: train num_bytes: 956305865 num_examples: 240523 download_size: 362796244 dataset_size: 956305865 - config_name: tiny-textbooks features: - name: text dtype: string splits: - name: train num_bytes: 1722063576 num_examples: 395985 download_size: 635724860 dataset_size: 1722063576 configs: - config_name: claude_evol data_files: - split: train path: claude_evol/train-* - config_name: code-alpaca data_files: - split: train path: code-alpaca/train-* - config_name: helpsteer data_files: - split: train path: helpsteer/train-* - config_name: ko_wikidata data_files: - split: train path: ko_wikidata/train-* - config_name: mmlu_abstract_algebra data_files: - split: train path: mmlu_abstract_algebra/train-* - config_name: mmlu_all data_files: - split: train path: mmlu_all/train-* - config_name: mmlu_anatomy data_files: - split: train path: mmlu_anatomy/train-* - config_name: mmlu_astronomy data_files: - split: train path: mmlu_astronomy/train-* - config_name: mmlu_business_ethics data_files: - split: train path: mmlu_business_ethics/train-* - config_name: mmlu_clinical_knowledge data_files: - split: train path: mmlu_clinical_knowledge/train-* - config_name: mmlu_college_biology data_files: - split: train path: mmlu_college_biology/train-* - config_name: mmlu_college_chemistry data_files: - split: train path: mmlu_college_chemistry/train-* - config_name: mmlu_college_computer_science data_files: - split: train path: mmlu_college_computer_science/train-* - config_name: mmlu_college_mathematics data_files: - split: train path: mmlu_college_mathematics/train-* - config_name: mmlu_college_medicine data_files: - split: train path: mmlu_college_medicine/train-* - config_name: mmlu_college_physics data_files: - split: train path: mmlu_college_physics/train-* - config_name: mmlu_computer_security data_files: - split: train path: mmlu_computer_security/train-* - config_name: mmlu_conceptual_physics data_files: - split: train path: mmlu_conceptual_physics/train-* - config_name: mmlu_econometrics data_files: - split: train path: mmlu_econometrics/train-* - config_name: mmlu_electrical_engineering data_files: - split: train path: mmlu_electrical_engineering/train-* - config_name: mmlu_elementary_mathematics data_files: - split: train path: mmlu_elementary_mathematics/train-* - config_name: mmlu_formal_logic data_files: - split: train path: mmlu_formal_logic/train-* - config_name: mmlu_global_facts data_files: - split: train path: mmlu_global_facts/train-* - config_name: mmlu_high_school_biology data_files: - split: train path: mmlu_high_school_biology/train-* - config_name: mmlu_high_school_chemistry data_files: - split: train path: mmlu_high_school_chemistry/train-* - config_name: mmlu_high_school_computer_science data_files: - split: train path: mmlu_high_school_computer_science/train-* - config_name: mmlu_high_school_european_history data_files: - split: train path: mmlu_high_school_european_history/train-* - config_name: mmlu_high_school_geography data_files: - split: train path: mmlu_high_school_geography/train-* - config_name: mmlu_high_school_government_and_politics data_files: - split: train path: mmlu_high_school_government_and_politics/train-* - config_name: mmlu_high_school_macroeconomics data_files: - split: train path: mmlu_high_school_macroeconomics/train-* - config_name: mmlu_high_school_mathematics data_files: - split: train path: mmlu_high_school_mathematics/train-* - config_name: mmlu_high_school_microeconomics data_files: - split: train path: mmlu_high_school_microeconomics/train-* - config_name: mmlu_high_school_physics data_files: - split: train path: mmlu_high_school_physics/train-* - config_name: mmlu_high_school_psychology data_files: - split: train path: mmlu_high_school_psychology/train-* - config_name: mmlu_high_school_statistics data_files: - split: train path: mmlu_high_school_statistics/train-* - config_name: mmlu_high_school_us_history data_files: - split: train path: mmlu_high_school_us_history/train-* - config_name: mmlu_high_school_world_history data_files: - split: train path: mmlu_high_school_world_history/train-* - config_name: mmlu_human_aging data_files: - split: train path: mmlu_human_aging/train-* - config_name: mmlu_human_sexuality data_files: - split: train path: mmlu_human_sexuality/train-* - config_name: mmlu_international_law data_files: - split: train path: mmlu_international_law/train-* - config_name: normal_instructions data_files: - split: train path: normal_instructions/train-* - config_name: tiny-textbooks data_files: - split: train path: tiny-textbooks/train-* --- # Massive Korean synthetic dataset This dataset is a large-scale Korean artificial data set created using Gemini Pro. It was created using the methodology described in Creation of synthetic textbook-quality datasets in [Textbooks Are All You Need](https://arxiv.org/abs/2306.11644). ## Data overview A subset of each dataset does not indicate the contents of that dataset. Further modification required before use this dataset for training. 본 데이터셋은 바로 사용하기보다는 하고자하는 task에 맞추어 가공 후 사용을 권장드립니다. ex) 로컬 모델을 사용하여 QA 셋으로 변환 \| subset \| row count \| link \| + \| \|---\|---\|---\|---\| \| tiny-textbooks \| 395,985 \| [nampdn-ai/tiny-textbooks](https://huggingface.co/datasets/nampdn-ai/tiny-textbooks) \| \| \| ko_wikidata \| 127,614 \| [maywell/ko_wikidata_QA](https://huggingface.co/datasets/maywell/ko_wikidata_QA) \| \| \| normal_instructions \| 240,523 \| [KonstantyM/science_qa](https://huggingface.co/datasets/KonstantyM/science_qa) \| with science texts \| \| claude_evol \| 239,102 \| [Norquinal/claude_evol_instruct_210k](https://huggingface.co/datasets/Norquinal/claude_evol_instruct_210k) \| used 250k files from that repo \| \| code-alpaca \| 64,112 \| [theblackcat102/evol-codealpaca-v1](https://huggingface.co/datasets/theblackcat102/evol-codealpaca-v1) \| original is a coding dataset, but generated data is not mainly a coding dataset \| \| helpsteer \| 25,253 \| [nvidia/HelpSteer](https://huggingface.co/datasets/nvidia/HelpSteer) \| \| \| mmlu_abstract_algebra \| 88,848 \| [cais/mmlu](https://huggingface.co/datasets/cais/mmlu) \| \| \| mmlu_all \| 97,765 \| [cais/mmlu](https://huggingface.co/datasets/cais/mmlu) \| \| \| mmlu_anatomy \| 97,463 \| [cais/mmlu](https://huggingface.co/datasets/cais/mmlu) \| \| \| mmlu_astronomy \| 97,347 \| [cais/mmlu](https://huggingface.co/datasets/cais/mmlu) \| \| \| mmlu_business_ethics \| 97,327 \| [cais/mmlu](https://huggingface.co/datasets/cais/mmlu) \| \| \| mmlu_clinical_knowledge \| 97,226 \| [cais/mmlu](https://huggingface.co/datasets/cais/mmlu) \| \| \| mmlu_college_biology \| 97,285 \| [cais/mmlu](https://huggingface.co/datasets/cais/mmlu) \| \| \| mmlu_college_chemistry \| 97,435 \| [cais/mmlu](https://huggingface.co/datasets/cais/mmlu) \| \| \| mmlu_college_computer_science \| 92,606 \| [cais/mmlu](https://huggingface.co/datasets/cais/mmlu) \| \| \| mmlu_college_mathematics \| 94,070 \| [cais/mmlu](https://huggingface.co/datasets/cais/mmlu) \| \| \| mmlu_college_medicine \| 95,156 \| [cais/mmlu](https://huggingface.co/datasets/cais/mmlu) \| \| \| mmlu_college_physics \| 97,452 \| [cais/mmlu](https://huggingface.co/datasets/cais/mmlu) \| \| \| mmlu_computer_security \| 97,212 \| [cais/mmlu](https://huggingface.co/datasets/cais/mmlu) \| \| \| mmlu_conceptual_physics \| 88,216 \| [cais/mmlu](https://huggingface.co/datasets/cais/mmlu) \| \| \| mmlu_econometrics \| 91,854 \| [cais/mmlu](https://huggingface.co/datasets/cais/mmlu) \| \| \| mmlu_electrical_engineering \| 87,826 \| [cais/mmlu](https://huggingface.co/datasets/cais/mmlu) \| \| \| mmlu_elementary_mathematics \| 89,307 \| [cais/mmlu](https://huggingface.co/datasets/cais/mmlu) \| \| \| mmlu_formal_logic \| 95,483 \| [cais/mmlu](https://huggingface.co/datasets/cais/mmlu) \| \| \| mmlu_global_facts \| 94,984 \| [cais/mmlu](https://huggingface.co/datasets/cais/mmlu) \| \| \| mmlu_high_school_biology \| 97,117 \| [cais/mmlu](https://huggingface.co/datasets/cais/mmlu) \| \| \| mmlu_high_school_chemistry \| 96,907 \| [cais/mmlu](https://huggingface.co/datasets/cais/mmlu) \| \| \| mmlu_high_school_computer_science \| 97,351 \| [cais/mmlu](https://huggingface.co/datasets/cais/mmlu) \| \| \| mmlu_high_school_european_history \| 97,222 \| [cais/mmlu](https://huggingface.co/datasets/cais/mmlu) \| \| \| mmlu_high_school_geography \| 97,261 \| [cais/mmlu](https://huggingface.co/datasets/cais/mmlu) \| \| \| mmlu_high_school_government_and_politics \| 97,311 \| [cais/mmlu](https://huggingface.co/datasets/cais/mmlu) \| \| \| mmlu_high_school_macroeconomics \| 97,400 \| [cais/mmlu](https://huggingface.co/datasets/cais/mmlu) \| \| \| mmlu_high_school_mathematics \| 97,396 \| [cais/mmlu](https://huggingface.co/datasets/cais/mmlu) \| \| \| mmlu_high_school_microeconomics \| 97,435 \| [cais/mmlu](https://huggingface.co/datasets/cais/mmlu) \| \| \| mmlu_high_school_physics \| 95,740 \| [cais/mmlu](https://huggingface.co/datasets/cais/mmlu) \| \| \| mmlu_high_school_psychology \| 80,626 \| [cais/mmlu](https://huggingface.co/datasets/cais/mmlu) \| \| \| mmlu_high_school_statistics \| 76,033 \| [cais/mmlu](https://huggingface.co/datasets/cais/mmlu) \| \| \| mmlu_high_school_us_history \| 79,322 \| [cais/mmlu](https://huggingface.co/datasets/cais/mmlu) \| \| \| mmlu_high_school_world_history \| 85,990 \| [cais/mmlu](https://huggingface.co/datasets/cais/mmlu) \| \| \| mmlu_human_aging \| 78,341 \| [cais/mmlu](https://huggingface.co/datasets/cais/mmlu) \| \| \| mmlu_human_sexuality \| 79,327 \| [cais/mmlu](https://huggingface.co/datasets/cais/mmlu) \| \| \| mmlu_international_law \| 78,989 \| [cais/mmlu](https://huggingface.co/datasets/cais/mmlu) \| \| ## When you find a problem If you find any issues with the dataset, please let us know in the discussion or send us a pull request.
tuanphong/ascent_kb	tuanphong	2024-01-09T14:44:26Z	112	3	[ "task_categories:other", "annotations_creators:found", "language_creators:found", "multilinguality:monolingual", "source_datasets:original", "language:en", "license:cc-by-4.0", "size_categories:10M<n<100M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:2011.00905", "region:us", "knowledge-base" ]	[ "other" ]	2022-03-02T23:29:22Z	1	--- annotations_creators: - found language_creators: - found language: - en license: - cc-by-4.0 multilinguality: - monolingual size_categories: - 1M<n<10M source_datasets: - original task_categories: - other task_ids: [] paperswithcode_id: ascentkb pretty_name: Ascent KB tags: - knowledge-base dataset_info: - config_name: canonical features: - name: arg1 dtype: string - name: rel dtype: string - name: arg2 dtype: string - name: support dtype: int64 - name: facets list: - name: value dtype: string - name: type dtype: string - name: support dtype: int64 - name: source_sentences list: - name: text dtype: string - name: source dtype: string splits: - name: train num_bytes: 2976665740 num_examples: 8904060 download_size: 898478552 dataset_size: 2976665740 - config_name: open features: - name: subject dtype: string - name: predicate dtype: string - name: object dtype: string - name: support dtype: int64 - name: facets list: - name: value dtype: string - name: type dtype: string - name: support dtype: int64 - name: source_sentences list: - name: text dtype: string - name: source dtype: string splits: - name: train num_bytes: 2882646222 num_examples: 8904060 download_size: 900156754 dataset_size: 2882646222 configs: - config_name: canonical data_files: - split: train path: canonical/train-* default: true - config_name: open data_files: - split: train path: open/train-* --- # Dataset Card for Ascent KB ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: https://ascent.mpi-inf.mpg.de/ - Repository: https://github.com/phongnt570/ascent - Paper: https://arxiv.org/abs/2011.00905 - Point of Contact: http://tuan-phong.com ### Dataset Summary This dataset contains 8.9M commonsense assertions extracted by the Ascent pipeline developed at the [Max Planck Institute for Informatics](https://www.mpi-inf.mpg.de/departments/databases-and-information-systems/). The focus of this dataset is on everyday concepts such as elephant, car, laptop, etc. The current version of Ascent KB (v1.0.0) is approximately 19 times larger than ConceptNet (note that, in this comparison, non-commonsense knowledge in ConceptNet such as lexical relations is excluded). For more details, take a look at [the research paper](https://arxiv.org/abs/2011.00905) and [the website](https://ascent.mpi-inf.mpg.de). ### Supported Tasks and Leaderboards The dataset can be used in a wide range of downstream tasks such as commonsense question answering or dialogue systems. ### Languages The dataset is in English. ## Dataset Structure ### Data Instances There are two configurations available for this dataset: 1. `canonical` (default): This part contains `<arg1 ; rel ; arg2>` assertions where the relations (`rel`) were mapped to [ConceptNet relations](https://github.com/commonsense/conceptnet5/wiki/Relations) with slight modifications: - Introducing 2 new relations: `/r/HasSubgroup`, `/r/HasAspect`. - All `/r/HasA` relations were replaced with `/r/HasAspect`. This is motivated by the [ATOMIC-2020](https://allenai.org/data/atomic-2020) schema, although they grouped all `/r/HasA` and `/r/HasProperty` into `/r/HasProperty`. - The `/r/UsedFor` relation was replaced with `/r/ObjectUse` which is broader (could be either _"used for"_, _"used in"_, or _"used as"_, ect.). This is also taken from ATOMIC-2020. 2. `open`: This part contains open assertions of the form `<subject ; predicate ; object>` extracted directly from web contents. This is the original form of the `canonical` triples. In both configurations, each assertion is equipped with extra information including: a set of semantic `facets` (e.g., LOCATION, TEMPORAL, etc.), its `support` (i.e., number of occurrences), and a list of `source_sentences`. An example row in the `canonical` configuration: ```JSON { "arg1": "elephant", "rel": "/r/HasProperty", "arg2": "intelligent", "support": 15, "facets": [ { "value": "extremely", "type": "DEGREE", "support": 11 } ], "source_sentences": [ { "text": "Elephants are extremely intelligent animals.", "source": "https://www.softschools.com/facts/animals/asian_elephant_facts/2310/" }, { "text": "Elephants are extremely intelligent creatures and an elephant's brain can weigh as much as 4-6 kg.", "source": "https://www.elephantsforafrica.org/elephant-facts/" } ] } ``` ### Data Fields - For `canonical` configuration - `arg1`: the first argument to the relationship, e.g., elephant - `rel`: the canonical relation, e.g., /r/HasProperty - `arg2`: the second argument to the relationship, e.g., intelligence - `support`: the number of occurrences of the assertion, e.g., 15 - `facets`: an array of semantic facets, each contains - `value`: facet value, e.g., extremely - `type`: facet type, e.g., DEGREE - `support`: the number of occurrences of the facet, e.g., 11 - `source_sentences`: an array of source sentences from which the assertion was extracted, each contains - `text`: the raw text of the sentence - `source`: the URL to its parent document - For `open` configuration - The fields of this configuration are the same as the `canonical` configuration's, except that the (`arg1`, `rel`, `arg2`) fields are replaced with the (`subject`, `predicate`, `object`) fields which are free text phrases extracted directly from the source sentences using an Open Information Extraction (OpenIE) tool. ### Data Splits There are no splits. All data points come to a default split called `train`. ## Dataset Creation ### Curation Rationale The commonsense knowledge base was created to assist in development of robust and reliable AI. ### Source Data #### Initial Data Collection and Normalization Texts were collected from the web using the Bing Search API, and went through various cleaning steps before being processed by an OpenIE tool to get open assertions. The assertions were then grouped into semantically equivalent clusters. Take a look at the research paper for more details. #### Who are the source language producers? Web users. ### Annotations #### Annotation process None. #### Who are the annotators? None. ### Personal and Sensitive Information Unknown. ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases [Needs More Information] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators The knowledge base has been developed by researchers at the [Max Planck Institute for Informatics](https://www.mpi-inf.mpg.de/departments/databases-and-information-systems/). Contact [Tuan-Phong Nguyen](http://tuan-phong.com) in case of questions and comments. ### Licensing Information [The Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/) ### Citation Information ``` @InProceedings{nguyen2021www, title={Advanced Semantics for Commonsense Knowledge Extraction}, author={Nguyen, Tuan-Phong and Razniewski, Simon and Weikum, Gerhard}, year={2021}, booktitle={The Web Conference 2021}, } ``` ### Contributions Thanks to [@phongnt570](https://github.com/phongnt570) for adding this dataset.
pauli31/czech-subjectivity-dataset	pauli31	2024-01-05T20:05:40Z	48	3	[ "task_categories:text-classification", "task_ids:sentiment-classification", "multilinguality:monolingual", "source_datasets:original", "language:cs", "license:cc-by-nc-sa-4.0", "size_categories:1K<n<10K", "format:csv", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2204.13915", "region:us" ]	[ "text-classification" ]	2022-05-02T18:27:17Z	1	--- annotations_creators: [] language_creators: [] language: - cs license: - cc-by-nc-sa-4.0 multilinguality: - monolingual pretty_name: Czech Subjectivity Dataset size_categories: - 1K<n<10K source_datasets: - original task_categories: - text-classification task_ids: - sentiment-classification --- # Dataset Card for Czech Subjectivity Dataset ### Dataset Summary Czech subjectivity dataset (Subj-CS) of 10k manually annotated subjective and objective sentences from movie reviews and descriptions. See the paper description https://arxiv.org/abs/2204.13915 ### Github https://github.com/pauli31/czech-subjectivity-dataset ### Supported Tasks and Leaderboards Subjectivity Analysis ### Languages Czech ### Data Instances train/dev/test ### Licensing Information [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.](https://creativecommons.org/licenses/by-nc-sa/4.0/) ### Citation Information If you use our dataset or software for academic research, please cite the our [paper](https://arxiv.org/abs/2204.13915) ``` @article{pib2022czech, title={Czech Dataset for Cross-lingual Subjectivity Classification}, author={Pavel Přibáň and Josef Steinberger}, year={2022}, eprint={2204.13915}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ### Contact [email protected] ### Contributions Thanks to [@pauli31](https://github.com/pauli31) for adding this dataset.
google/xquad	google	2024-01-04T17:08:50Z	1,431	31	[ "task_categories:question-answering", "task_ids:extractive-qa", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:multilingual", "source_datasets:extended\|squad", "language:ar", "language:de", "language:el", "language:en", "language:es", "language:hi", "language:ro", "language:ru", "language:th", "language:tr", "language:vi", "language:zh", "license:cc-by-sa-4.0", "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:1910.11856", "region:us" ]	[ "question-answering" ]	2022-03-02T23:29:22Z	1	--- annotations_creators: - expert-generated language_creators: - expert-generated language: - ar - de - el - en - es - hi - ro - ru - th - tr - vi - zh license: - cc-by-sa-4.0 multilinguality: - multilingual size_categories: - unknown source_datasets: - extended\|squad task_categories: - question-answering task_ids: - extractive-qa paperswithcode_id: xquad pretty_name: XQuAD dataset_info: - config_name: xquad.ar features: - name: id dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 splits: - name: validation num_bytes: 1722775 num_examples: 1190 download_size: 263002 dataset_size: 1722775 - config_name: xquad.de features: - name: id dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 splits: - name: validation num_bytes: 1283277 num_examples: 1190 download_size: 241957 dataset_size: 1283277 - config_name: xquad.el features: - name: id dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 splits: - name: validation num_bytes: 2206666 num_examples: 1190 download_size: 324379 dataset_size: 2206666 - config_name: xquad.en features: - name: id dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 splits: - name: validation num_bytes: 1116099 num_examples: 1190 download_size: 212372 dataset_size: 1116099 - config_name: xquad.es features: - name: id dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 splits: - name: validation num_bytes: 1273475 num_examples: 1190 download_size: 236874 dataset_size: 1273475 - config_name: xquad.hi features: - name: id dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 splits: - name: validation num_bytes: 2682951 num_examples: 1190 download_size: 322083 dataset_size: 2682951 - config_name: xquad.ro features: - name: id dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 splits: - name: validation num_bytes: 1299426 num_examples: 1190 download_size: 244467 dataset_size: 1299426 - config_name: xquad.ru features: - name: id dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 splits: - name: validation num_bytes: 2136966 num_examples: 1190 download_size: 321728 dataset_size: 2136966 - config_name: xquad.th features: - name: id dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 splits: - name: validation num_bytes: 2854935 num_examples: 1190 download_size: 337307 dataset_size: 2854935 - config_name: xquad.tr features: - name: id dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 splits: - name: validation num_bytes: 1210739 num_examples: 1190 download_size: 228364 dataset_size: 1210739 - config_name: xquad.vi features: - name: id dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 splits: - name: validation num_bytes: 1477215 num_examples: 1190 download_size: 237644 dataset_size: 1477215 - config_name: xquad.zh features: - name: id dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 splits: - name: validation num_bytes: 984217 num_examples: 1190 download_size: 205768 dataset_size: 984217 configs: - config_name: xquad.ar data_files: - split: validation path: xquad.ar/validation-* - config_name: xquad.de data_files: - split: validation path: xquad.de/validation-* - config_name: xquad.el data_files: - split: validation path: xquad.el/validation-* - config_name: xquad.en data_files: - split: validation path: xquad.en/validation-* - config_name: xquad.es data_files: - split: validation path: xquad.es/validation-* - config_name: xquad.hi data_files: - split: validation path: xquad.hi/validation-* - config_name: xquad.ro data_files: - split: validation path: xquad.ro/validation-* - config_name: xquad.ru data_files: - split: validation path: xquad.ru/validation-* - config_name: xquad.th data_files: - split: validation path: xquad.th/validation-* - config_name: xquad.tr data_files: - split: validation path: xquad.tr/validation-* - config_name: xquad.vi data_files: - split: validation path: xquad.vi/validation-* - config_name: xquad.zh data_files: - split: validation path: xquad.zh/validation-* --- # Dataset Card for "xquad" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [https://github.com/deepmind/xquad](https://github.com/deepmind/xquad) - Repository: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Paper: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Point of Contact: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Size of downloaded dataset files: 146.31 MB - Size of the generated dataset: 18.97 MB - Total amount of disk used: 165.28 MB ### Dataset Summary XQuAD (Cross-lingual Question Answering Dataset) is a benchmark dataset for evaluating cross-lingual question answering performance. The dataset consists of a subset of 240 paragraphs and 1190 question-answer pairs from the development set of SQuAD v1.1 (Rajpurkar et al., 2016) together with their professional translations into ten languages: Spanish, German, Greek, Russian, Turkish, Arabic, Vietnamese, Thai, Chinese, and Hindi. Consequently, the dataset is entirely parallel across 11 languages. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### xquad.ar - Size of downloaded dataset files: 13.30 MB - Size of the generated dataset: 1.72 MB - Total amount of disk used: 15.03 MB An example of 'validation' looks as follows. ``` This example was too long and was cropped: { "answers": { "answer_start": [527], "text": ["136"] }, "context": "\"Die Verteidigung der Panthers gab nur 308 Punkte ab und belegte den sechsten Platz in der Liga, während sie die NFL mit 24 Inte...", "id": "56beb4343aeaaa14008c925c", "question": "Wie viele Sacks erzielte Jared Allen in seiner Karriere?" } ``` #### xquad.de - Size of downloaded dataset files: 13.30 MB - Size of the generated dataset: 1.29 MB - Total amount of disk used: 14.59 MB An example of 'validation' looks as follows. ``` This example was too long and was cropped: { "answers": { "answer_start": [527], "text": ["136"] }, "context": "\"Die Verteidigung der Panthers gab nur 308 Punkte ab und belegte den sechsten Platz in der Liga, während sie die NFL mit 24 Inte...", "id": "56beb4343aeaaa14008c925c", "question": "Wie viele Sacks erzielte Jared Allen in seiner Karriere?" } ``` #### xquad.el - Size of downloaded dataset files: 13.30 MB - Size of the generated dataset: 2.21 MB - Total amount of disk used: 15.51 MB An example of 'validation' looks as follows. ``` This example was too long and was cropped: { "answers": { "answer_start": [527], "text": ["136"] }, "context": "\"Die Verteidigung der Panthers gab nur 308 Punkte ab und belegte den sechsten Platz in der Liga, während sie die NFL mit 24 Inte...", "id": "56beb4343aeaaa14008c925c", "question": "Wie viele Sacks erzielte Jared Allen in seiner Karriere?" } ``` #### xquad.en - Size of downloaded dataset files: 13.30 MB - Size of the generated dataset: 1.12 MB - Total amount of disk used: 14.42 MB An example of 'validation' looks as follows. ``` This example was too long and was cropped: { "answers": { "answer_start": [527], "text": ["136"] }, "context": "\"Die Verteidigung der Panthers gab nur 308 Punkte ab und belegte den sechsten Platz in der Liga, während sie die NFL mit 24 Inte...", "id": "56beb4343aeaaa14008c925c", "question": "Wie viele Sacks erzielte Jared Allen in seiner Karriere?" } ``` #### xquad.es - Size of downloaded dataset files: 13.30 MB - Size of the generated dataset: 1.28 MB - Total amount of disk used: 14.58 MB An example of 'validation' looks as follows. ``` This example was too long and was cropped: { "answers": { "answer_start": [527], "text": ["136"] }, "context": "\"Die Verteidigung der Panthers gab nur 308 Punkte ab und belegte den sechsten Platz in der Liga, während sie die NFL mit 24 Inte...", "id": "56beb4343aeaaa14008c925c", "question": "Wie viele Sacks erzielte Jared Allen in seiner Karriere?" } ``` ### Data Fields The data fields are the same among all splits. #### xquad.ar - `id`: a `string` feature. - `context`: a `string` feature. - `question`: a `string` feature. - `answers`: a dictionary feature containing: - `text`: a `string` feature. - `answer_start`: a `int32` feature. #### xquad.de - `id`: a `string` feature. - `context`: a `string` feature. - `question`: a `string` feature. - `answers`: a dictionary feature containing: - `text`: a `string` feature. - `answer_start`: a `int32` feature. #### xquad.el - `id`: a `string` feature. - `context`: a `string` feature. - `question`: a `string` feature. - `answers`: a dictionary feature containing: - `text`: a `string` feature. - `answer_start`: a `int32` feature. #### xquad.en - `id`: a `string` feature. - `context`: a `string` feature. - `question`: a `string` feature. - `answers`: a dictionary feature containing: - `text`: a `string` feature. - `answer_start`: a `int32` feature. #### xquad.es - `id`: a `string` feature. - `context`: a `string` feature. - `question`: a `string` feature. - `answers`: a dictionary feature containing: - `text`: a `string` feature. - `answer_start`: a `int32` feature. ### Data Splits \| name \| validation \| \| -------- \| ---------: \| \| xquad.ar \| 1190 \| \| xquad.de \| 1190 \| \| xquad.el \| 1190 \| \| xquad.en \| 1190 \| \| xquad.es \| 1190 \| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @article{Artetxe:etal:2019, author = {Mikel Artetxe and Sebastian Ruder and Dani Yogatama}, title = {On the cross-lingual transferability of monolingual representations}, journal = {CoRR}, volume = {abs/1910.11856}, year = {2019}, archivePrefix = {arXiv}, eprint = {1910.11856} } ``` ### Contributions Thanks to [@lewtun](https://github.com/lewtun), [@patrickvonplaten](https://github.com/patrickvonplaten), [@thomwolf](https://github.com/thomwolf) for adding this dataset.
uclanlp/wino_bias	uclanlp	2024-01-04T16:50:33Z	1,832	18	[ "task_categories:token-classification", "task_ids:coreference-resolution", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "source_datasets:original", "language:en", "license:mit", "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:1804.06876", "region:us" ]	[ "token-classification" ]	2022-03-02T23:29:22Z	1	--- annotations_creators: - expert-generated language_creators: - expert-generated language: - en license: - mit multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - token-classification task_ids: - coreference-resolution paperswithcode_id: winobias pretty_name: WinoBias dataset_info: - config_name: type1_anti features: - name: document_id dtype: string - name: part_number dtype: string - name: word_number sequence: int32 - name: tokens sequence: string - name: pos_tags sequence: class_label: names: '0': '"' '1': '''''' '2': '#' '3': $ '4': ( '5': ) '6': ',' '7': . '8': ':' '9': '``' '10': CC '11': CD '12': DT '13': EX '14': FW '15': IN '16': JJ '17': JJR '18': JJS '19': LS '20': MD '21': NN '22': NNP '23': NNPS '24': NNS '25': NN\|SYM '26': PDT '27': POS '28': PRP '29': PRP$ '30': RB '31': RBR '32': RBS '33': RP '34': SYM '35': TO '36': UH '37': VB '38': VBD '39': VBG '40': VBN '41': VBP '42': VBZ '43': WDT '44': WP '45': WP$ '46': WRB '47': HYPH '48': XX '49': NFP '50': AFX '51': ADD '52': -LRB- '53': -RRB- '54': '-' - name: parse_bit sequence: string - name: predicate_lemma sequence: string - name: predicate_framenet_id sequence: string - name: word_sense sequence: string - name: speaker sequence: string - name: ner_tags sequence: class_label: names: '0': B-PERSON '1': I-PERSON '2': B-NORP '3': I-NORP '4': B-FAC '5': I-FAC '6': B-ORG '7': I-ORG '8': B-GPE '9': I-GPE '10': B-LOC '11': I-LOC '12': B-PRODUCT '13': I-PRODUCT '14': B-EVENT '15': I-EVENT '16': B-WORK_OF_ART '17': I-WORK_OF_ART '18': B-LAW '19': I-LAW '20': B-LANGUAGE '21': I-LANGUAGE '22': B-DATE '23': I-DATE '24': B-TIME '25': I-TIME '26': B-PERCENT '27': I-PERCENT '28': B-MONEY '29': I-MONEY '30': B-QUANTITY '31': I-QUANTITY '32': B-ORDINAL '33': I-ORDINAL '34': B-CARDINAL '35': I-CARDINAL '36': '' '37': '0' '38': '-' - name: verbal_predicates sequence: string - name: coreference_clusters sequence: string splits: - name: validation num_bytes: 380510 num_examples: 396 - name: test num_bytes: 402893 num_examples: 396 download_size: 65383 dataset_size: 783403 - config_name: type1_pro features: - name: document_id dtype: string - name: part_number dtype: string - name: word_number sequence: int32 - name: tokens sequence: string - name: pos_tags sequence: class_label: names: '0': '"' '1': '''''' '2': '#' '3': $ '4': ( '5': ) '6': ',' '7': . '8': ':' '9': '``' '10': CC '11': CD '12': DT '13': EX '14': FW '15': IN '16': JJ '17': JJR '18': JJS '19': LS '20': MD '21': NN '22': NNP '23': NNPS '24': NNS '25': NN\|SYM '26': PDT '27': POS '28': PRP '29': PRP$ '30': RB '31': RBR '32': RBS '33': RP '34': SYM '35': TO '36': UH '37': VB '38': VBD '39': VBG '40': VBN '41': VBP '42': VBZ '43': WDT '44': WP '45': WP$ '46': WRB '47': HYPH '48': XX '49': NFP '50': AFX '51': ADD '52': -LRB- '53': -RRB- '54': '-' - name: parse_bit sequence: string - name: predicate_lemma sequence: string - name: predicate_framenet_id sequence: string - name: word_sense sequence: string - name: speaker sequence: string - name: ner_tags sequence: class_label: names: '0': B-PERSON '1': I-PERSON '2': B-NORP '3': I-NORP '4': B-FAC '5': I-FAC '6': B-ORG '7': I-ORG '8': B-GPE '9': I-GPE '10': B-LOC '11': I-LOC '12': B-PRODUCT '13': I-PRODUCT '14': B-EVENT '15': I-EVENT '16': B-WORK_OF_ART '17': I-WORK_OF_ART '18': B-LAW '19': I-LAW '20': B-LANGUAGE '21': I-LANGUAGE '22': B-DATE '23': I-DATE '24': B-TIME '25': I-TIME '26': B-PERCENT '27': I-PERCENT '28': B-MONEY '29': I-MONEY '30': B-QUANTITY '31': I-QUANTITY '32': B-ORDINAL '33': I-ORDINAL '34': B-CARDINAL '35': I-CARDINAL '36': '' '37': '0' '38': '-' - name: verbal_predicates sequence: string - name: coreference_clusters sequence: string splits: - name: validation num_bytes: 379044 num_examples: 396 - name: test num_bytes: 401705 num_examples: 396 download_size: 65516 dataset_size: 780749 - config_name: type2_anti features: - name: document_id dtype: string - name: part_number dtype: string - name: word_number sequence: int32 - name: tokens sequence: string - name: pos_tags sequence: class_label: names: '0': '"' '1': '''''' '2': '#' '3': $ '4': ( '5': ) '6': ',' '7': . '8': ':' '9': '``' '10': CC '11': CD '12': DT '13': EX '14': FW '15': IN '16': JJ '17': JJR '18': JJS '19': LS '20': MD '21': NN '22': NNP '23': NNPS '24': NNS '25': NN\|SYM '26': PDT '27': POS '28': PRP '29': PRP$ '30': RB '31': RBR '32': RBS '33': RP '34': SYM '35': TO '36': UH '37': VB '38': VBD '39': VBG '40': VBN '41': VBP '42': VBZ '43': WDT '44': WP '45': WP$ '46': WRB '47': HYPH '48': XX '49': NFP '50': AFX '51': ADD '52': -LRB- '53': -RRB- '54': '-' - name: parse_bit sequence: string - name: predicate_lemma sequence: string - name: predicate_framenet_id sequence: string - name: word_sense sequence: string - name: speaker sequence: string - name: ner_tags sequence: class_label: names: '0': B-PERSON '1': I-PERSON '2': B-NORP '3': I-NORP '4': B-FAC '5': I-FAC '6': B-ORG '7': I-ORG '8': B-GPE '9': I-GPE '10': B-LOC '11': I-LOC '12': B-PRODUCT '13': I-PRODUCT '14': B-EVENT '15': I-EVENT '16': B-WORK_OF_ART '17': I-WORK_OF_ART '18': B-LAW '19': I-LAW '20': B-LANGUAGE '21': I-LANGUAGE '22': B-DATE '23': I-DATE '24': B-TIME '25': I-TIME '26': B-PERCENT '27': I-PERCENT '28': B-MONEY '29': I-MONEY '30': B-QUANTITY '31': I-QUANTITY '32': B-ORDINAL '33': I-ORDINAL '34': B-CARDINAL '35': I-CARDINAL '36': '' '37': '0' '38': '-' - name: verbal_predicates sequence: string - name: coreference_clusters sequence: string splits: - name: validation num_bytes: 368421 num_examples: 396 - name: test num_bytes: 376926 num_examples: 396 download_size: 62555 dataset_size: 745347 - config_name: type2_pro features: - name: document_id dtype: string - name: part_number dtype: string - name: word_number sequence: int32 - name: tokens sequence: string - name: pos_tags sequence: class_label: names: '0': '"' '1': '''''' '2': '#' '3': $ '4': ( '5': ) '6': ',' '7': . '8': ':' '9': '``' '10': CC '11': CD '12': DT '13': EX '14': FW '15': IN '16': JJ '17': JJR '18': JJS '19': LS '20': MD '21': NN '22': NNP '23': NNPS '24': NNS '25': NN\|SYM '26': PDT '27': POS '28': PRP '29': PRP$ '30': RB '31': RBR '32': RBS '33': RP '34': SYM '35': TO '36': UH '37': VB '38': VBD '39': VBG '40': VBN '41': VBP '42': VBZ '43': WDT '44': WP '45': WP$ '46': WRB '47': HYPH '48': XX '49': NFP '50': AFX '51': ADD '52': -LRB- '53': -RRB- '54': '-' - name: parse_bit sequence: string - name: predicate_lemma sequence: string - name: predicate_framenet_id sequence: string - name: word_sense sequence: string - name: speaker sequence: string - name: ner_tags sequence: class_label: names: '0': B-PERSON '1': I-PERSON '2': B-NORP '3': I-NORP '4': B-FAC '5': I-FAC '6': B-ORG '7': I-ORG '8': B-GPE '9': I-GPE '10': B-LOC '11': I-LOC '12': B-PRODUCT '13': I-PRODUCT '14': B-EVENT '15': I-EVENT '16': B-WORK_OF_ART '17': I-WORK_OF_ART '18': B-LAW '19': I-LAW '20': B-LANGUAGE '21': I-LANGUAGE '22': B-DATE '23': I-DATE '24': B-TIME '25': I-TIME '26': B-PERCENT '27': I-PERCENT '28': B-MONEY '29': I-MONEY '30': B-QUANTITY '31': I-QUANTITY '32': B-ORDINAL '33': I-ORDINAL '34': B-CARDINAL '35': I-CARDINAL '36': '' '37': '0' '38': '-' - name: verbal_predicates sequence: string - name: coreference_clusters sequence: string splits: - name: validation num_bytes: 366957 num_examples: 396 - name: test num_bytes: 375144 num_examples: 396 download_size: 62483 dataset_size: 742101 - config_name: wino_bias features: - name: document_id dtype: string - name: part_number dtype: string - name: word_number sequence: int32 - name: tokens sequence: string - name: pos_tags sequence: class_label: names: '0': '"' '1': '''''' '2': '#' '3': $ '4': ( '5': ) '6': ',' '7': . '8': ':' '9': '``' '10': CC '11': CD '12': DT '13': EX '14': FW '15': IN '16': JJ '17': JJR '18': JJS '19': LS '20': MD '21': NN '22': NNP '23': NNPS '24': NNS '25': NN\|SYM '26': PDT '27': POS '28': PRP '29': PRP$ '30': RB '31': RBR '32': RBS '33': RP '34': SYM '35': TO '36': UH '37': VB '38': VBD '39': VBG '40': VBN '41': VBP '42': VBZ '43': WDT '44': WP '45': WP$ '46': WRB '47': HYPH '48': XX '49': NFP '50': AFX '51': ADD '52': -LRB- '53': -RRB- - name: parse_bit sequence: string - name: predicate_lemma sequence: string - name: predicate_framenet_id sequence: string - name: word_sense sequence: string - name: speaker sequence: string - name: ner_tags sequence: class_label: names: '0': B-PERSON '1': I-PERSON '2': B-NORP '3': I-NORP '4': B-FAC '5': I-FAC '6': B-ORG '7': I-ORG '8': B-GPE '9': I-GPE '10': B-LOC '11': I-LOC '12': B-PRODUCT '13': I-PRODUCT '14': B-EVENT '15': I-EVENT '16': B-WORK_OF_ART '17': I-WORK_OF_ART '18': B-LAW '19': I-LAW '20': B-LANGUAGE '21': I-LANGUAGE '22': B-DATE '23': I-DATE '24': B-TIME '25': I-TIME '26': B-PERCENT '27': I-PERCENT '28': B-MONEY '29': I-MONEY '30': B-QUANTITY '31': I-QUANTITY '32': B-ORDINAL '33': I-ORDINAL '34': B-CARDINAL '35': I-CARDINAL '36': '' '37': '0' - name: verbal_predicates sequence: string splits: - name: train num_bytes: 173899234 num_examples: 150335 download_size: 268725744 dataset_size: 173899234 configs: - config_name: type1_anti data_files: - split: validation path: type1_anti/validation- - split: test path: type1_anti/test-* - config_name: type1_pro data_files: - split: validation path: type1_pro/validation-* - split: test path: type1_pro/test-* - config_name: type2_anti data_files: - split: validation path: type2_anti/validation-* - split: test path: type2_anti/test-* - config_name: type2_pro data_files: - split: validation path: type2_pro/validation-* - split: test path: type2_pro/test-* --- # Dataset Card for Wino_Bias dataset ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [WinoBias](https://uclanlp.github.io/corefBias/overview) - Repository: - Paper: [Arxiv](https://arxiv.org/abs/1804.06876) - Leaderboard: - Point of Contact: ### Dataset Summary WinoBias, a Winograd-schema dataset for coreference resolution focused on gender bias. The corpus contains Winograd-schema style sentences with entities corresponding to people referred by their occupation (e.g. the nurse, the doctor, the carpenter). ### Supported Tasks and Leaderboards The underlying task is coreference resolution. ### Languages English ## Dataset Structure ### Data Instances The dataset has 4 subsets: `type1_pro`, `type1_anti`, `type2_pro` and `type2_anti`. The `_pro` subsets contain sentences that reinforce gender stereotypes (e.g. mechanics are male, nurses are female), whereas the `_anti` datasets contain "anti-stereotypical" sentences (e.g. mechanics are female, nurses are male). The `type1` (WB-Knowledge) subsets contain sentences for which world knowledge is necessary to resolve the co-references, and `type2` (WB-Syntax) subsets require only the syntactic information present in the sentence to resolve them. ### Data Fields - document_id = This is a variation on the document filename - part_number = Some files are divided into multiple parts numbered as 000, 001, 002, ... etc. - word_num = This is the word index of the word in that sentence. - tokens = This is the token as segmented/tokenized in the Treebank. - pos_tags = This is the Penn Treebank style part of speech. When parse information is missing, all part of speeches except the one for which there is some sense or proposition annotation are marked with a XX tag. The verb is marked with just a VERB tag. - parse_bit = This is the bracketed structure broken before the first open parenthesis in the parse, and the word/part-of-speech leaf replaced with a . The full parse can be created by substituting the asterix with the "([pos] [word])" string (or leaf) and concatenating the items in the rows of that column. When the parse information is missing, the first word of a sentence is tagged as "(TOP" and the last word is tagged as ")" and all intermediate words are tagged with a "". - predicate_lemma = The predicate lemma is mentioned for the rows for which we have semantic role information or word sense information. All other rows are marked with a "-". - predicate_framenet_id = This is the PropBank frameset ID of the predicate in predicate_lemma. - word_sense = This is the word sense of the word in Column tokens. - speaker = This is the speaker or author name where available. - ner_tags = These columns identifies the spans representing various named entities. For documents which do not have named entity annotation, each line is represented with an "". - verbal_predicates = There is one column each of predicate argument structure information for the predicate mentioned in predicate_lemma. If there are no predicates tagged in a sentence this is a single column with all rows marked with an "". ### Data Splits Dev and Test Split available ## Dataset Creation ### Curation Rationale The WinoBias dataset was introduced in 2018 (see [paper](https://arxiv.org/abs/1804.06876)), with its original task being coreference resolution, which is a task that aims to identify mentions that refer to the same entity or person. ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? The dataset was created by researchers familiar with the WinoBias project, based on two prototypical templates provided by the authors, in which entities interact in plausible ways. ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? "Researchers familiar with the [WinoBias] project" ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [Recent work](https://www.microsoft.com/en-us/research/uploads/prod/2021/06/The_Salmon_paper.pdf) has shown that this dataset contains grammatical issues, incorrect or ambiguous labels, and stereotype conflation, among other limitations. ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators Jieyu Zhao, Tianlu Wang, Mark Yatskar, Vicente Ordonez and Kai-Wei Chan ### Licensing Information MIT Licence ### Citation Information @article{DBLP:journals/corr/abs-1804-06876, author = {Jieyu Zhao and Tianlu Wang and Mark Yatskar and Vicente Ordonez and Kai{-}Wei Chang}, title = {Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods}, journal = {CoRR}, volume = {abs/1804.06876}, year = {2018}, url = {http://arxiv.org/abs/1804.06876}, archivePrefix = {arXiv}, eprint = {1804.06876}, timestamp = {Mon, 13 Aug 2018 16:47:01 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-1804-06876.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ### Contributions Thanks to [@akshayb7](https://github.com/akshayb7) for adding this dataset. Updated by [@JieyuZhao](https://github.com/JieyuZhao).
Stanford/web_questions	Stanford	2024-01-04T16:41:06Z	4,003	32	[ "task_categories:question-answering", "task_ids:open-domain-qa", "annotations_creators:crowdsourced", "language_creators:found", "multilinguality:monolingual", "source_datasets:original", "language:en", "license:unknown", "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[ "question-answering" ]	2022-03-02T23:29:22Z	1	--- annotations_creators: - crowdsourced language_creators: - found language: - en license: - unknown multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - question-answering task_ids: - open-domain-qa paperswithcode_id: webquestions pretty_name: WebQuestions dataset_info: features: - name: url dtype: string - name: question dtype: string - name: answers sequence: string splits: - name: train num_bytes: 530711 num_examples: 3778 - name: test num_bytes: 288184 num_examples: 2032 download_size: 402395 dataset_size: 818895 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* --- # Dataset Card for "web_questions" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [https://worksheets.codalab.org/worksheets/0xba659fe363cb46e7a505c5b6a774dc8a](https://worksheets.codalab.org/worksheets/0xba659fe363cb46e7a505c5b6a774dc8a) - Repository: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Paper: [Semantic Parsing on Freebase from Question-Answer Pairs](https://aclanthology.org/D13-1160/) - Point of Contact: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Size of downloaded dataset files: 1.27 MB - Size of the generated dataset: 0.83 MB - Total amount of disk used: 2.10 MB ### Dataset Summary This dataset consists of 6,642 question/answer pairs. The questions are supposed to be answerable by Freebase, a large knowledge graph. The questions are mostly centered around a single named entity. The questions are popular ones asked on the web (at least in 2013). ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### default - Size of downloaded dataset files: 1.27 MB - Size of the generated dataset: 0.83 MB - Total amount of disk used: 2.10 MB An example of 'train' looks as follows. ``` { "answers": ["Jamaican Creole English Language", "Jamaican English"], "question": "what does jamaican people speak?", "url": "http://www.freebase.com/view/en/jamaica" } ``` ### Data Fields The data fields are the same among all splits. #### default - `url`: a `string` feature. - `question`: a `string` feature. - `answers`: a `list` of `string` features. ### Data Splits \| name \|train\|test\| \|-------\|----:\|---:\| \|default\| 3778\|2032\| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @inproceedings{berant-etal-2013-semantic, title = "Semantic Parsing on {F}reebase from Question-Answer Pairs", author = "Berant, Jonathan and Chou, Andrew and Frostig, Roy and Liang, Percy", booktitle = "Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing", month = oct, year = "2013", address = "Seattle, Washington, USA", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/D13-1160", pages = "1533--1544", } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@mariamabarham](https://github.com/mariamabarham), [@lewtun](https://github.com/lewtun) for adding this dataset.
textmachinelab/quail	textmachinelab	2024-01-04T16:18:32Z	17,696	7	[ "task_categories:multiple-choice", "task_ids:multiple-choice-qa", "annotations_creators:crowdsourced", "language_creators:found", "multilinguality:monolingual", "source_datasets:original", "language:en", "license:cc-by-nc-sa-4.0", "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[ "multiple-choice" ]	2022-03-02T23:29:22Z	1	--- annotations_creators: - crowdsourced language_creators: - found language: - en license: - cc-by-nc-sa-4.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - multiple-choice task_ids: - multiple-choice-qa paperswithcode_id: quail pretty_name: Question Answering for Artificial Intelligence (QuAIL) dataset_info: config_name: quail features: - name: id dtype: string - name: context_id dtype: string - name: question_id dtype: string - name: domain dtype: string - name: metadata struct: - name: author dtype: string - name: title dtype: string - name: url dtype: string - name: context dtype: string - name: question dtype: string - name: question_type dtype: string - name: answers sequence: string - name: correct_answer_id dtype: int32 splits: - name: train num_bytes: 23432601 num_examples: 10246 - name: validation num_bytes: 4989531 num_examples: 2164 - name: challenge num_bytes: 1199792 num_examples: 556 download_size: 2286403 dataset_size: 29621924 configs: - config_name: quail data_files: - split: train path: quail/train-* - split: validation path: quail/validation-* - split: challenge path: quail/challenge-* default: true --- # Dataset Card for "quail" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [https://text-machine-lab.github.io/blog/2020/quail/](https://text-machine-lab.github.io/blog/2020/quail/) - Repository: https://github.com/text-machine-lab/quail - Paper: [Getting Closer to AI Complete Question Answering: A Set of Prerequisite Real Tasks](https://doi.org/10.1609/aaai.v34i05.6398 ) - Point of Contact: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Size of downloaded dataset files: 6.41 MB - Size of the generated dataset: 29.62 MB - Total amount of disk used: 36.03 MB ### Dataset Summary QuAIL is a reading comprehension dataset. QuAIL contains 15K multi-choice questions in texts 300-350 tokens long 4 domains (news, user stories, fiction, blogs).QuAIL is balanced and annotated for question types. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### quail - Size of downloaded dataset files: 6.41 MB - Size of the generated dataset: 29.62 MB - Total amount of disk used: 36.03 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "answers": ["the cousin is not friendly", "the cousin could have been pretier", "not enough information", "the cousin was too nice"], "context": "\"That fall came and I went back to Michigan and the school year went by and summer came and I never really thought about it. I'm...", "context_id": "f001", "correct_answer_id": 0, "domain": "fiction", "id": "f001_19", "metadata": { "author": "Joseph Devon", "title": "Black Eyed Susan", "url": "http://manybooks.net/pages/devonjother08black_eyed_susan/0.html" }, "question": "After the events in the text what does the author think about the cousin?", "question_id": "19", "question_type": "Subsequent_state" } ``` ### Data Fields The data fields are the same among all splits. #### quail - `id`: a `string` feature. - `context_id`: a `string` feature. - `question_id`: a `string` feature. - `domain`: a `string` feature. - `author`: a `string` feature. - `title`: a `string` feature. - `url`: a `string` feature. - `context`: a `string` feature. - `question`: a `string` feature. - `question_type`: a `string` feature. - `answers`: a `list` of `string` features. - `correct_answer_id`: a `int32` feature. ### Data Splits \|name \|train\|challenge\|validation\| \|-----\|----:\|--------:\|---------:\| \|quail\|10246\| 556\| 2164\| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @inproceedings{DBLP:conf/aaai/RogersKDR20, author = {Anna Rogers and Olga Kovaleva and Matthew Downey and Anna Rumshisky}, title = {Getting Closer to {AI} Complete Question Answering: {A} Set of Prerequisite Real Tasks}, booktitle = {The Thirty-Fourth {AAAI} Conference on Artificial Intelligence, {AAAI} 2020, The Thirty-Second Innovative Applications of Artificial Intelligence Conference, {IAAI} 2020, The Tenth {AAAI} Symposium on Educational Advances in Artificial Intelligence, {EAAI} 2020, New York, NY, USA, February 7-12, 2020}, pages = {8722--8731}, publisher = {{AAAI} Press}, year = {2020}, url = {https://aaai.org/ojs/index.php/AAAI/article/view/6398}, timestamp = {Thu, 04 Jun 2020 13:18:48 +0200}, biburl = {https://dblp.org/rec/conf/aaai/RogersKDR20.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ``` ### Contributions Thanks to [@sai-prasanna](https://github.com/sai-prasanna), [@ngdodd](https://github.com/ngdodd) for adding this dataset.
openai/openai_humaneval	openai	2024-01-04T16:08:05Z	81,423	314	[ "task_categories:text2text-generation", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "source_datasets:original", "language:en", "license:mit", "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2107.03374", "region:us", "code-generation" ]	[ "text2text-generation" ]	2022-03-02T23:29:22Z	null	--- annotations_creators: - expert-generated language_creators: - expert-generated language: - en license: - mit multilinguality: - monolingual size_categories: - n<1K source_datasets: - original task_categories: - text2text-generation task_ids: [] paperswithcode_id: humaneval pretty_name: OpenAI HumanEval tags: - code-generation dataset_info: config_name: openai_humaneval features: - name: task_id dtype: string - name: prompt dtype: string - name: canonical_solution dtype: string - name: test dtype: string - name: entry_point dtype: string splits: - name: test num_bytes: 194394 num_examples: 164 download_size: 83920 dataset_size: 194394 configs: - config_name: openai_humaneval data_files: - split: test path: openai_humaneval/test-* default: true --- # Dataset Card for OpenAI HumanEval ## Table of Contents - [OpenAI HumanEval](#openai-humaneval) - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization) - [Who are the source language producers?](#who-are-the-source-language-producers) - [Annotations](#annotations) - [Annotation process](#annotation-process) - [Who are the annotators?](#who-are-the-annotators) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Repository: [GitHub Repository](https://github.com/openai/human-eval) - Paper: [Evaluating Large Language Models Trained on Code](https://arxiv.org/abs/2107.03374) ### Dataset Summary The HumanEval dataset released by OpenAI includes 164 programming problems with a function sig- nature, docstring, body, and several unit tests. They were handwritten to ensure not to be included in the training set of code generation models. ### Supported Tasks and Leaderboards ### Languages The programming problems are written in Python and contain English natural text in comments and docstrings. ## Dataset Structure ```python from datasets import load_dataset load_dataset("openai_humaneval") DatasetDict({ test: Dataset({ features: ['task_id', 'prompt', 'canonical_solution', 'test', 'entry_point'], num_rows: 164 }) }) ``` ### Data Instances An example of a dataset instance: ``` { "task_id": "test/0", "prompt": "def return1():\n", "canonical_solution": " return 1", "test": "def check(candidate):\n assert candidate() == 1", "entry_point": "return1" } ``` ### Data Fields - `task_id`: identifier for the data sample - `prompt`: input for the model containing function header and docstrings - `canonical_solution`: solution for the problem in the `prompt` - `test`: contains function to test generated code for correctness - `entry_point`: entry point for test ### Data Splits The dataset only consists of a test split with 164 samples. ## Dataset Creation ### Curation Rationale Since code generation models are often trained on dumps of GitHub a dataset not included in the dump was necessary to properly evaluate the model. However, since this dataset was published on GitHub it is likely to be included in future dumps. ### Source Data The dataset was handcrafted by engineers and researchers at OpenAI. #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations [More Information Needed] #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information None. ## Considerations for Using the Data Make sure you execute generated Python code in a safe environment when evauating against this dataset as generated code could be harmful. ### Social Impact of Dataset With this dataset code generating models can be better evaluated which leads to fewer issues introduced when using such models. ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators OpenAI ### Licensing Information MIT License ### Citation Information ``` @misc{chen2021evaluating, title={Evaluating Large Language Models Trained on Code}, author={Mark Chen and Jerry Tworek and Heewoo Jun and Qiming Yuan and Henrique Ponde de Oliveira Pinto and Jared Kaplan and Harri Edwards and Yuri Burda and Nicholas Joseph and Greg Brockman and Alex Ray and Raul Puri and Gretchen Krueger and Michael Petrov and Heidy Khlaaf and Girish Sastry and Pamela Mishkin and Brooke Chan and Scott Gray and Nick Ryder and Mikhail Pavlov and Alethea Power and Lukasz Kaiser and Mohammad Bavarian and Clemens Winter and Philippe Tillet and Felipe Petroski Such and Dave Cummings and Matthias Plappert and Fotios Chantzis and Elizabeth Barnes and Ariel Herbert-Voss and William Hebgen Guss and Alex Nichol and Alex Paino and Nikolas Tezak and Jie Tang and Igor Babuschkin and Suchir Balaji and Shantanu Jain and William Saunders and Christopher Hesse and Andrew N. Carr and Jan Leike and Josh Achiam and Vedant Misra and Evan Morikawa and Alec Radford and Matthew Knight and Miles Brundage and Mira Murati and Katie Mayer and Peter Welinder and Bob McGrew and Dario Amodei and Sam McCandlish and Ilya Sutskever and Wojciech Zaremba}, year={2021}, eprint={2107.03374}, archivePrefix={arXiv}, primaryClass={cs.LG} } ``` ### Contributions Thanks to [@lvwerra](https://github.com/lvwerra) for adding this dataset.
microsoft/ms_marco	microsoft	2024-01-04T16:01:29Z	8,083	159	[ "language:en", "size_categories:1M<n<10M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:1611.09268", "region:us" ]	[]	2022-03-02T23:29:22Z	null	--- language: - en paperswithcode_id: ms-marco pretty_name: Microsoft Machine Reading Comprehension Dataset dataset_info: - config_name: v1.1 features: - name: answers sequence: string - name: passages sequence: - name: is_selected dtype: int32 - name: passage_text dtype: string - name: url dtype: string - name: query dtype: string - name: query_id dtype: int32 - name: query_type dtype: string - name: wellFormedAnswers sequence: string splits: - name: validation num_bytes: 42665198 num_examples: 10047 - name: train num_bytes: 350516260 num_examples: 82326 - name: test num_bytes: 40977580 num_examples: 9650 download_size: 217328153 dataset_size: 434159038 - config_name: v2.1 features: - name: answers sequence: string - name: passages sequence: - name: is_selected dtype: int32 - name: passage_text dtype: string - name: url dtype: string - name: query dtype: string - name: query_id dtype: int32 - name: query_type dtype: string - name: wellFormedAnswers sequence: string splits: - name: validation num_bytes: 413765365 num_examples: 101093 - name: train num_bytes: 3462807709 num_examples: 808731 - name: test num_bytes: 405691932 num_examples: 101092 download_size: 2105722550 dataset_size: 4282265006 configs: - config_name: v1.1 data_files: - split: validation path: v1.1/validation-* - split: train path: v1.1/train-* - split: test path: v1.1/test-* - config_name: v2.1 data_files: - split: validation path: v2.1/validation-* - split: train path: v2.1/train-* - split: test path: v2.1/test-* --- # Dataset Card for "ms_marco" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [https://microsoft.github.io/msmarco/](https://microsoft.github.io/msmarco/) - Repository: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Paper: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Point of Contact: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Size of downloaded dataset files: 1.55 GB - Size of the generated dataset: 4.72 GB - Total amount of disk used: 6.28 GB ### Dataset Summary Starting with a paper released at NIPS 2016, MS MARCO is a collection of datasets focused on deep learning in search. The first dataset was a question answering dataset featuring 100,000 real Bing questions and a human generated answer. Since then we released a 1,000,000 question dataset, a natural langauge generation dataset, a passage ranking dataset, keyphrase extraction dataset, crawling dataset, and a conversational search. There have been 277 submissions. 20 KeyPhrase Extraction submissions, 87 passage ranking submissions, 0 document ranking submissions, 73 QnA V2 submissions, 82 NLGEN submisions, and 15 QnA V1 submissions This data comes in three tasks/forms: Original QnA dataset(v1.1), Question Answering(v2.1), Natural Language Generation(v2.1). The original question answering datset featured 100,000 examples and was released in 2016. Leaderboard is now closed but data is availible below. The current competitive tasks are Question Answering and Natural Language Generation. Question Answering features over 1,000,000 queries and is much like the original QnA dataset but bigger and with higher quality. The Natural Language Generation dataset features 180,000 examples and builds upon the QnA dataset to deliver answers that could be spoken by a smart speaker. version v1.1 ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### v1.1 - Size of downloaded dataset files: 168.69 MB - Size of the generated dataset: 434.61 MB - Total amount of disk used: 603.31 MB An example of 'train' looks as follows. ``` ``` #### v2.1 - Size of downloaded dataset files: 1.38 GB - Size of the generated dataset: 4.29 GB - Total amount of disk used: 5.67 GB An example of 'validation' looks as follows. ``` ``` ### Data Fields The data fields are the same among all splits. #### v1.1 - `answers`: a `list` of `string` features. - `passages`: a dictionary feature containing: - `is_selected`: a `int32` feature. - `passage_text`: a `string` feature. - `url`: a `string` feature. - `query`: a `string` feature. - `query_id`: a `int32` feature. - `query_type`: a `string` feature. - `wellFormedAnswers`: a `list` of `string` features. #### v2.1 - `answers`: a `list` of `string` features. - `passages`: a dictionary feature containing: - `is_selected`: a `int32` feature. - `passage_text`: a `string` feature. - `url`: a `string` feature. - `query`: a `string` feature. - `query_id`: a `int32` feature. - `query_type`: a `string` feature. - `wellFormedAnswers`: a `list` of `string` features. ### Data Splits \|name\|train \|validation\| test \| \|----\|-----:\|---------:\|-----:\| \|v1.1\| 82326\| 10047\| 9650\| \|v2.1\|808731\| 101093\|101092\| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @article{DBLP:journals/corr/NguyenRSGTMD16, author = {Tri Nguyen and Mir Rosenberg and Xia Song and Jianfeng Gao and Saurabh Tiwary and Rangan Majumder and Li Deng}, title = {{MS} {MARCO:} {A} Human Generated MAchine Reading COmprehension Dataset}, journal = {CoRR}, volume = {abs/1611.09268}, year = {2016}, url = {http://arxiv.org/abs/1611.09268}, archivePrefix = {arXiv}, eprint = {1611.09268}, timestamp = {Mon, 13 Aug 2018 16:49:03 +0200}, biburl = {https://dblp.org/rec/journals/corr/NguyenRSGTMD16.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } } ``` ### Contributions Thanks to [@mariamabarham](https://github.com/mariamabarham), [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun) for adding this dataset.
tdavidson/hate_speech_offensive	tdavidson	2024-01-04T12:06:17Z	1,339	31	[ "task_categories:text-classification", "annotations_creators:expert-generated", "annotations_creators:crowdsourced", "language_creators:found", "multilinguality:monolingual", "source_datasets:original", "language:en", "license:unknown", "size_categories:10K<n<100K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:1703.04009", "region:us", "hate-speech-detection" ]	[ "text-classification" ]	2022-03-02T23:29:22Z	1	--- annotations_creators: - expert-generated - crowdsourced language_creators: - found language: - en license: - unknown multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: [] paperswithcode_id: hate-speech-and-offensive-language pretty_name: Hate Speech and Offensive Language tags: - hate-speech-detection dataset_info: features: - name: count dtype: int64 - name: hate_speech_count dtype: int64 - name: offensive_language_count dtype: int64 - name: neither_count dtype: int64 - name: class dtype: class_label: names: '0': hate speech '1': offensive language '2': neither - name: tweet dtype: string splits: - name: train num_bytes: 3207814 num_examples: 24783 download_size: 1627672 dataset_size: 3207814 configs: - config_name: default data_files: - split: train path: data/train-* train-eval-index: - config: default task: text-classification task_id: multi_class_classification splits: train_split: train col_mapping: tweet: text label: target metrics: - type: accuracy name: Accuracy - type: f1 name: F1 macro args: average: macro - type: f1 name: F1 micro args: average: micro - type: f1 name: F1 weighted args: average: weighted - type: precision name: Precision macro args: average: macro - type: precision name: Precision micro args: average: micro - type: precision name: Precision weighted args: average: weighted - type: recall name: Recall macro args: average: macro - type: recall name: Recall micro args: average: micro - type: recall name: Recall weighted args: average: weighted --- # Dataset Card for [Dataset Name] ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: https://github.com/t-davidson/hate-speech-and-offensive-language - Repository: https://github.com/t-davidson/hate-speech-and-offensive-language - Paper: https://arxiv.org/abs/1703.04009 - Leaderboard: - Point of Contact: https://docs.google.com/forms/d/e/1FAIpQLSdrPNlfVBlqxun2tivzAtsZaOoPC5YYMocn-xscCgeRakLXHg/viewform?usp=pp_url&entry.1506871634&entry.147453066&entry.1390333885&entry.516829772 ### Dataset Summary An annotated dataset for hate speech and offensive language detection on tweets. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages English (`en`) ## Dataset Structure ### Data Instances ``` { "count": 3, "hate_speech_annotation": 0, "offensive_language_annotation": 0, "neither_annotation": 3, "label": 2, # "neither" "tweet": "!!! RT @mayasolovely: As a woman you shouldn't complain about cleaning up your house. & as a man you should always take the trash out...") } ``` ### Data Fields ``` count: (Integer) number of users who coded each tweet (min is 3, sometimes more users coded a tweet when judgments were determined to be unreliable, hate_speech_annotation: (Integer) number of users who judged the tweet to be hate speech, offensive_language_annotation: (Integer) number of users who judged the tweet to be offensive, neither_annotation: (Integer) number of users who judged the tweet to be neither offensive nor non-offensive, label: (Class Label) class label for majority of CF users (0: 'hate-speech', 1: 'offensive-language' or 2: 'neither'), tweet: (string) ``` ### Data Splits This dataset is not splitted, only the train split is available. ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information Usernames are not anonymized in the dataset. ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information MIT License ### Citation Information @inproceedings{hateoffensive, title = {Automated Hate Speech Detection and the Problem of Offensive Language}, author = {Davidson, Thomas and Warmsley, Dana and Macy, Michael and Weber, Ingmar}, booktitle = {Proceedings of the 11th International AAAI Conference on Web and Social Media}, series = {ICWSM '17}, year = {2017}, location = {Montreal, Canada}, pages = {512-515} } ### Contributions Thanks to [@hugoabonizio](https://github.com/hugoabonizio) for adding this dataset.
allenai/common_gen	allenai	2024-01-04T07:34:57Z	1,047	26	[ "task_categories:text2text-generation", "annotations_creators:crowdsourced", "language_creators:found", "language_creators:crowdsourced", "multilinguality:monolingual", "source_datasets:original", "language:en", "license:mit", "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:1911.03705", "region:us", "concepts-to-text" ]	[ "text2text-generation" ]	2022-03-02T23:29:22Z	1	--- annotations_creators: - crowdsourced language_creators: - found - crowdsourced language: - en license: - mit multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text2text-generation task_ids: [] paperswithcode_id: commongen pretty_name: CommonGen tags: - concepts-to-text dataset_info: features: - name: concept_set_idx dtype: int32 - name: concepts sequence: string - name: target dtype: string splits: - name: train num_bytes: 6724166 num_examples: 67389 - name: validation num_bytes: 408740 num_examples: 4018 - name: test num_bytes: 77518 num_examples: 1497 download_size: 3434865 dataset_size: 7210424 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* --- # Dataset Card for "common_gen" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [https://inklab.usc.edu/CommonGen/index.html](https://inklab.usc.edu/CommonGen/index.html) - Repository: https://github.com/INK-USC/CommonGen - Paper: [CommonGen: A Constrained Text Generation Challenge for Generative Commonsense Reasoning](https://arxiv.org/abs/1911.03705) - Point of Contact: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Size of downloaded dataset files: 1.85 MB - Size of the generated dataset: 7.21 MB - Total amount of disk used: 9.06 MB ### Dataset Summary CommonGen is a constrained text generation task, associated with a benchmark dataset, to explicitly test machines for the ability of generative commonsense reasoning. Given a set of common concepts; the task is to generate a coherent sentence describing an everyday scenario using these concepts. CommonGen is challenging because it inherently requires 1) relational reasoning using background commonsense knowledge, and 2) compositional generalization ability to work on unseen concept combinations. Our dataset, constructed through a combination of crowd-sourcing from AMT and existing caption corpora, consists of 30k concept-sets and 50k sentences in total. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### default - Size of downloaded dataset files: 1.85 MB - Size of the generated dataset: 7.21 MB - Total amount of disk used: 9.06 MB An example of 'train' looks as follows. ``` { "concept_set_idx": 0, "concepts": ["ski", "mountain", "skier"], "target": "Three skiers are skiing on a snowy mountain." } ``` ### Data Fields The data fields are the same among all splits. #### default - `concept_set_idx`: a `int32` feature. - `concepts`: a `list` of `string` features. - `target`: a `string` feature. ### Data Splits \| name \|train\|validation\|test\| \|-------\|----:\|---------:\|---:\| \|default\|67389\| 4018\|1497\| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information The dataset is licensed under [MIT License](https://github.com/INK-USC/CommonGen/blob/master/LICENSE). ### Citation Information ```bib @inproceedings{lin-etal-2020-commongen, title = "{C}ommon{G}en: A Constrained Text Generation Challenge for Generative Commonsense Reasoning", author = "Lin, Bill Yuchen and Zhou, Wangchunshu and Shen, Ming and Zhou, Pei and Bhagavatula, Chandra and Choi, Yejin and Ren, Xiang", booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020", month = nov, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.findings-emnlp.165", doi = "10.18653/v1/2020.findings-emnlp.165", pages = "1823--1840" } ``` ### Contributions Thanks to [@JetRunner](https://github.com/JetRunner), [@yuchenlin](https://github.com/yuchenlin), [@thomwolf](https://github.com/thomwolf), [@lhoestq](https://github.com/lhoestq) for adding this dataset.
openbmb/UltraFeedback	openbmb	2023-12-29T14:11:19Z	2,366	360	[ "task_categories:text-generation", "language:en", "license:mit", "size_categories:10K<n<100K", "format:json", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:2310.01377", "region:us" ]	[ "text-generation" ]	2023-09-23T15:41:04Z	null	--- license: mit task_categories: - text-generation language: - en size_categories: - 100K<n<1M --- ## Introduction - [GitHub Repo](https://github.com/thunlp/UltraFeedback) - [UltraRM-13b](https://huggingface.co/openbmb/UltraRM-13b) - [UltraCM-13b](https://huggingface.co/openbmb/UltraCM-13b) UltraFeedback is a large-scale, fine-grained, diverse preference dataset, used for training powerful reward models and critic models. We collect about 64k prompts from diverse resources (including UltraChat, ShareGPT, Evol-Instruct, TruthfulQA, FalseQA, and FLAN). We then use these prompts to query multiple LLMs (see Table for model lists) and generate 4 different responses for each prompt, resulting in a total of 256k samples. To collect high-quality preference and textual feedback, we design a fine-grained annotation instruction, which contains 4 different aspects, namely instruction-following, truthfulness, honesty and helpfulness. We then ask GPT-4 to annotate the collected samples based on the instructions. ## Features - 🆚 Scale: UltraFeedback consists of 64k prompts, 256k responses and 380k high-quality feedback. RLHF researchers could further construct around 1 million comparison pairs to train their reward models. - 🌈 Diversity: As a preference dataset, diversity is the core requirement for UltraFeedback. We collect prompts from various sources and query a diverse set of state-of-the-art open-source and prestigious models. To further increase diversity, we intended to select different base models, i.e., LLaMA, Falcon, StarChat, MPT, GPT and Bard. We also apply various principles to stimulate models completing instructions in different ways. - 🤯 High-density: UltraFeedback provides both numerical and textual feedback. Moreover, we wrote fine-grained annotation documents to help rate responses in all dimensions ## Dataset Construction ### Instruction Sampling We sample 63,967 instructions from 6 public available and high-quality datasets. We include all instructions from TruthfulQA and FalseQA, randomly sampling 10k instructions from Evol-Instruct, 10k from UltraChat, and 20k from ShareGPT. For Flan, we adopt a stratified sampling strtegy, randomly samping 3k instructions from"Co" subset whereas sampling 10 instructions per task for the other three subsets, excluding those with overly long instructions. ```json { "evol_instruct": 10000, "false_qa": 2339, "flan": 20939, "sharegpt": 19949, "truthful_qa": 811, "ultrachat": 9929 } ``` ### Model Sampling To prevent reward model from overfiting to certain text style or capturing spurious correlation between text style and rewards, we select different base models of all levels, with varying sizes, architectures and training data, to complete the instructions. We set up a pool of 17 models: - Commercial Models: GPT-4, GPT-3.5 Turbo, Bard - LLaMA family: 1. LLaMA-2-7B-chat, LLaMA-2-13B-chat, LLaMA-2-70B-chat 2. UltraLM-13B, UltraLM-65B 3. WizardLM-7B, WizardLM-13B, WizardLM-70B 4. Vicuna-33B 5. Alpaca-7B - Non-LLaMA series: 1. Falcon-40B-instruct 2. MPT-30B-chat 3. StarChat-Beta 4. Pythia-12B ### Principle Sampling Following [1] and [2], we define a set of principles to explicitly align model behaviors from different aspects. We set up a pool of 5 principles: Helpfulness, Truthfulness, Honesty, Verbalized Calibration and Harmless. For each instruction, we randomly sample 4 models to complete the instruction, and for each completion, we sample a principle and add it to system prompt to align the model behavior. Considering different datasets outline different characteristics, not all dataset are suitable for all principles. We provide the following table to show the principle distribution for each dataset. \| Datset \| Principle \| \| ------------- \| ------------------------------------------------------------ \| \| Evol Instruct \| 100% Helpful \| \| FalseQA \| 100% TruthfulQA \| \| Flan \| 60% Helpful, 20% Truthful, 20% Verbalized Calibration \| \| ShareGPT \| 60% Helpful, 20% Truthful, 18% Honesty, 2% Verbalized Calibration \| \| TruthfulQA \| 100% Truthful \| \| UltraChat \| 60% Helpful, 20% Truthful, 18% Honesty, 2% Verbalized Calibration \| [1] Principle-Driven Self-Alignment of Language Models from Scratch with Minimal Human Supervision. Sun et al. [2] Orca: Progressive Learning from Complex Explanation Traces of GPT-4. Mukherjee et al. ## Dataset Format ```JSONC { "source": "sharegpt", // the dataset where the instruction comes from "id": "D2TC9GP_0", // the id of the instruction in the source dataset "instruction": "I am going to cairo in June of this year, thinking of 4-5 days, what are the best things I could do? Going with family of 5, granddad, grandmom, sister, mom and me. No days can have too much walking but sister, me and mom like adventure sports", "models": [ // four models that are randomly sampled from the model pool to complete the instruction "falcon-40b-instruct", "gpt-4", "starchat", "wizardlm-7b" ], "completions": [ // four completions corresponding to the four models { // completion 1 "model": "falcon-40b-instruct", "principle": "helpfulness", // priciple that used to align model behavior "custom_system_prompt": "As an AI assistant, ensure your response offers the perfect blend of accuracy, positivity, and intrigue. Strive to be educational while keeping the user engaged.", // the system prompt corresponding to the principle, sampled from a pool of GPT-4 generated system prompts "response": "Cairo is a city that has something for everyone. Some of the best things you can do are visiting the Pyramids of Giza and the museums housing the ancient Egyptian artifacts, taking a camel ride in the desert, visiting the medieval neighborhoods such as Islamic Cairo or Khan El-Khalili, trying local food in traditional restaurants like Ful medames, or venturing into one of the city's many souks to shop for authentic goods. For adventure sports, you can go whitewater rafting on the Nile, hot air ballooning over the desert, or diving in the Red Sea. Just be mindful of the scorching heat and ensure everyone wears appropriate clothing and drinks enough water. Above all, have fun and enjoy your trip!", "annotations": { "instruction_following": [ { "Rating": "2", "Rationale": "The text only partially addresses the task goal by providing a general list of activities in Cairo without considering the specific needs of the family members, such as limited walking ability and adventure preferences." } ], "honesty": [ { "Rating": "3", "Rationale": "The response is confident but contains subtle mistakes. It suggests whitewater rafting on the Nile and diving in the Red Sea, which are not feasible for a 4-5 day trip to Cairo. The rest of the suggestions are accurate and relevant to the family's preferences." } ], "truthfulness": [ { "Type": [ "1", "2" ], "Rationale": "The text suggests whitewater rafting on the Nile and diving in the Red Sea, which are not available in Cairo. These activities are factually incorrect and not aligned with the instruction.", "Rating": "3", "Rationale For Rating": "The text provides some truthful information, but the inclusion of whitewater rafting and diving in the Red Sea causes partial hallucination and misunderstanding." } ], "helpfulness": [ { "Type": [ "1", "2" ], "Rationale": "The response is clear and relevant, providing useful information, but it does not address the limited walking ability of the grandparents or the adventure sports preferences of the sister, mom, and the user.", "Rating": "3", "Rationale For Rating": "The text is correct and provides useful information, but it does not fully address the specific needs and preferences of the family." } ] } }, { // completion 2 ... }, { // completion 3 ... }, { // completion 4 ... } ] }, ``` ## Dataset Example Here we present an example of UltraFeedback > User: I am going to cairo in June of this year, thinking of 4-5 days, what are the best things I could do? Going with family of 5, granddad, grandmom, sister, mom and me. No days can have too much walking but sister, me and mom like adventure sports > Assisstant 1 (falcon-40b-instruct): Cairo is a city that has something for everyone. Some of the best things you can do are visiting the Pyramids of Giza and the museums housing the ancient Egyptian artifacts, taking a camel ride in the desert, visiting the medieval neighborhoods such as Islamic Cairo or Khan El-Khalili, trying local food in traditional restaurants like Ful medames, or venturing into one of the city's many souks to shop for authentic goods. For adventure sports, you can go whitewater rafting on the Nile, hot air ballooning over the desert, or diving in the Red Sea. Just be mindful of the scorching heat and ensure everyone wears appropriate clothing and drinks enough water. Above all, have fun and enjoy your trip! > Assisstant 2 (gpt-4): In Cairo for 4-5 days, the following activities cater to your family's preferences, with consideration for the limited walking ability of your grandparents:\n\n1. Visit the Pyramids of Giza and the Great Sphinx: A must-see in Cairo, it's an incredible experience for all ages. You can explore the premises using a hired camel, horse, or a horse-drawn carriage which accommodate the needs of your grandparents while keeping the experience enjoyable.\n\n2. Egyptian Museum: This museum in Tahrir Square contains over 120,000 artifacts of Egypt's history. A wheelchair can be arranged for the grandparents to comfortably explore the museum at their own pace.\n\n3. Khan el-Khalili Bazaar: A famous market in Cairo, suitable for souvenir shopping and experiencing the local culture. There are many cafes and restaurants your family can rest at during the visit.\n\n4. Nile River Cruise: Enjoy a dinner cruise with your family on the famous Nile River. It includes entertainment and a beautiful view of the city by night. Boats often have ramps and accessible facilities for limited mobility passangers.\n\n5. Felucca Ride: During the day, go for a more traditional sailboat ride called a Felucca. This can be done on an off-day or as a relaxation activity after an active day.\n\nFor adventure activities, you and your mom and sister can explore these options:\n\n1. Sandboarding: Head to the desert for a thrilling sandboarding experience on the dunes just outside Cairo.\n\n2. Hot Air Balloon Ride: Experience Egypt from above with a hot air balloon ride in Luxor. It would require a day trip from Cairo but is an incredible experience.\n\n3. Quad Biking: Go for a guided quad biking adventure in the desert, enjoying panoramic views of the iconic Pyramids.\n\nSpace the activities according to your family's preferences and ensure rest periods in the afternoon. Consider hiring a private guide or joining a wheelchair-accessible tour since you're concerned about your grandparents' walking ability. > Assisstant 3 (starchat): Here are some of the top things to do in Cairo, Egypt, with a family of 5:\n\n- Giza Pyramids: The Giza Pyramids, located just outside Cairo, are a must-see for anyone visiting the city. The Great Pyramid of Khufu, the Great Pyramid of Khafre, and the Great Pyramid of Menkaure make up one of the Seven Wonders of the Ancient World and are an impressive sight to behold.\n\n- Egyptian Museum: The Egyptian Museum, located in the heart of Cairo, is home to one of the world's largest and most impressive collections of ancient Egyptian art and artifacts. Visitors can explore the museum's extensive collection, which includes items such as mummies, sarcophagi, statues, paintings, and more.\n\n- Citadel of Salah El-Din: Located on a hill overlooking Cairo, the Citadel of Salah El-Din offers stunning views of the city and is an excellent place to get a sense of Cairo's rich history. Visitors can explore the various museums and mosques within the citadel, and also take a stroll through the beautiful gardens.\n\n- Khan El-Khalili Bazaar: Khan El-Khalili Bazaar is one of Cairo's most popular attractions, a bustling market where visitors can find everything from spices and pottery to clothes and jewelry. It's a great place to do some shopping and explore the local culture.\n\n- Alexandria: Just an hour's drive from Cairo, Alexandria is a historic city with a rich legacy. Visitors can explore the ancient Roman Amphitheater, the Qaitbay Citadel, and the Catacombs of Kom el-Shoqafa. They can also take a boat trip to the island of Montazah, where they can see the famous Graffiti Wall and other street art.\n\nAll of these attractions are family-friendly, with plenty of things to see and do. The Giza Pyramids in particular are very accessible and well-maintained, making them suitable for all ages. > Assisstant 4 (wizardlm-7b): If you are looking for adventure sports, you can explore the pyramids of Giza and go hot air ballooning over the desert. Another option could be to take a camel ride in the desert or go kayaking in the Nile River. For family-friendly activities, you can visit the Egyptian Museum, Cairo Tower, and Khan el-Khalili Bazaar. Additionally, you can take a day trip to the historical city of Alexandria and visit the Pompey's Pillar and the Roman Amphitheater. Another option could be to go on a guided tour of the ancient city of Petra in Jordan, which is a short flight from Cairo. ## Limitations - Although GPT-4 can provide well-aligned annotation and textual feedback for most samples, we must note that GPT-4 also makes mistakes and provides inaccurate feedbacks. ## Citation ```bib @misc{cui2023ultrafeedback, title={UltraFeedback: Boosting Language Models with High-quality Feedback}, author={Ganqu Cui and Lifan Yuan and Ning Ding and Guanming Yao and Wei Zhu and Yuan Ni and Guotong Xie and Zhiyuan Liu and Maosong Sun}, year={2023}, eprint={2310.01377}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
jat-project/jat-dataset-tokenized	jat-project	2023-12-22T22:17:42Z	661,795	0	[ "size_categories:10M<n<100M", "format:parquet", "modality:timeseries", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2023-12-16T10:10:31Z	null	--- dataset_info: - config_name: atari-alien features: - name: image_observations sequence: sequence: sequence: sequence: float32 - name: rewards sequence: float32 - name: discrete_actions sequence: int64 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 51686398456 num_examples: 14134 - name: test num_bytes: 5412188320 num_examples: 1480 download_size: 847071867 dataset_size: 57098586776 - config_name: atari-amidar features: - name: image_observations sequence: sequence: sequence: sequence: float32 - name: rewards sequence: float32 - name: discrete_actions sequence: int64 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 52362921996 num_examples: 14319 - name: test num_bytes: 4808802460 num_examples: 1315 download_size: 645217608 dataset_size: 57171724456 - config_name: atari-assault features: - name: image_observations sequence: sequence: sequence: sequence: float32 - name: rewards sequence: float32 - name: discrete_actions sequence: int64 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 52757865468 num_examples: 14427 - name: test num_bytes: 4421172756 num_examples: 1209 download_size: 253415283 dataset_size: 57179038224 - config_name: atari-asterix features: - name: image_observations sequence: sequence: sequence: sequence: float32 - name: rewards sequence: float32 - name: discrete_actions sequence: int64 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 52863915104 num_examples: 14456 - name: test num_bytes: 5137922020 num_examples: 1405 download_size: 293282697 dataset_size: 58001837124 - config_name: atari-asteroids features: - 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name: test num_bytes: 77446400 num_examples: 1600 download_size: 96573298 dataset_size: 851910400 - config_name: metaworld-faucet-close features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 774464000 num_examples: 16000 - name: test num_bytes: 77446400 num_examples: 1600 download_size: 89353472 dataset_size: 851910400 - config_name: metaworld-faucet-open features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 774464000 num_examples: 16000 - name: test num_bytes: 77446400 num_examples: 1600 download_size: 96651789 dataset_size: 851910400 - config_name: metaworld-hammer features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 774464000 num_examples: 16000 - name: test num_bytes: 77446400 num_examples: 1600 download_size: 177539984 dataset_size: 851910400 - config_name: metaworld-hand-insert features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 774464000 num_examples: 16000 - name: test num_bytes: 77446400 num_examples: 1600 download_size: 135665012 dataset_size: 851910400 - config_name: metaworld-handle-press features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 774464000 num_examples: 16000 - name: test num_bytes: 77446400 num_examples: 1600 download_size: 103407785 dataset_size: 851910400 - config_name: metaworld-handle-press-side features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 774464000 num_examples: 16000 - name: test num_bytes: 77446400 num_examples: 1600 download_size: 103403469 dataset_size: 851910400 - config_name: metaworld-handle-pull features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 774464000 num_examples: 16000 - name: test num_bytes: 77446400 num_examples: 1600 download_size: 121440284 dataset_size: 851910400 - config_name: metaworld-handle-pull-side features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 774464000 num_examples: 16000 - name: test num_bytes: 77446400 num_examples: 1600 download_size: 118413651 dataset_size: 851910400 - config_name: metaworld-lever-pull features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 774464000 num_examples: 16000 - name: test num_bytes: 77446400 num_examples: 1600 download_size: 168776851 dataset_size: 851910400 - config_name: metaworld-peg-insert-side features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 774464000 num_examples: 16000 - name: test num_bytes: 77446400 num_examples: 1600 download_size: 153705593 dataset_size: 851910400 - config_name: metaworld-peg-unplug-side features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 774464000 num_examples: 16000 - name: test num_bytes: 77446400 num_examples: 1600 download_size: 171742157 dataset_size: 851910400 - config_name: metaworld-pick-out-of-hole features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 774464000 num_examples: 16000 - name: test num_bytes: 77446400 num_examples: 1600 download_size: 22274303 dataset_size: 851910400 - config_name: metaworld-pick-place features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 774464000 num_examples: 16000 - name: test num_bytes: 77446400 num_examples: 1600 download_size: 176678495 dataset_size: 851910400 - config_name: metaworld-pick-place-wall features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 774464000 num_examples: 16000 - name: test num_bytes: 77446400 num_examples: 1600 download_size: 172257534 dataset_size: 851910400 - config_name: metaworld-plate-slide features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 774464000 num_examples: 16000 - name: test num_bytes: 77446400 num_examples: 1600 download_size: 114432287 dataset_size: 851910400 - config_name: metaworld-plate-slide-back features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 774464000 num_examples: 16000 - name: test num_bytes: 77446400 num_examples: 1600 download_size: 36662627 dataset_size: 851910400 - config_name: metaworld-plate-slide-back-side features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 774464000 num_examples: 16000 - name: test num_bytes: 77446400 num_examples: 1600 download_size: 33762161 dataset_size: 851910400 - config_name: metaworld-plate-slide-side features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 774464000 num_examples: 16000 - name: test num_bytes: 77446400 num_examples: 1600 download_size: 106392923 dataset_size: 851910400 - config_name: metaworld-push features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 774464000 num_examples: 16000 - name: test num_bytes: 77446400 num_examples: 1600 download_size: 166180034 dataset_size: 851910400 - config_name: metaworld-push-back features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 774464000 num_examples: 16000 - name: test num_bytes: 77446400 num_examples: 1600 download_size: 133027374 dataset_size: 851910400 - config_name: metaworld-push-wall features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 774464000 num_examples: 16000 - name: test num_bytes: 77446400 num_examples: 1600 download_size: 158267234 dataset_size: 851910400 - config_name: metaworld-reach features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 774464000 num_examples: 16000 - name: test num_bytes: 77446400 num_examples: 1600 download_size: 168663459 dataset_size: 851910400 - config_name: metaworld-reach-wall features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 774464000 num_examples: 16000 - name: test num_bytes: 77446400 num_examples: 1600 download_size: 171608203 dataset_size: 851910400 - config_name: metaworld-shelf-place features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 774464000 num_examples: 16000 - name: test num_bytes: 77446400 num_examples: 1600 download_size: 142334952 dataset_size: 851910400 - config_name: metaworld-soccer features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 774464000 num_examples: 16000 - name: test num_bytes: 77446400 num_examples: 1600 download_size: 159081606 dataset_size: 851910400 - config_name: metaworld-stick-pull features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 774464000 num_examples: 16000 - name: test num_bytes: 77446400 num_examples: 1600 download_size: 170289154 dataset_size: 851910400 - config_name: metaworld-stick-push features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 774464000 num_examples: 16000 - name: test num_bytes: 77446400 num_examples: 1600 download_size: 166125948 dataset_size: 851910400 - config_name: metaworld-sweep features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 774464000 num_examples: 16000 - name: test num_bytes: 77446400 num_examples: 1600 download_size: 164632354 dataset_size: 851910400 - config_name: metaworld-sweep-into features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 774464000 num_examples: 16000 - name: test num_bytes: 77446400 num_examples: 1600 download_size: 135177252 dataset_size: 851910400 - config_name: metaworld-window-close features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 774464000 num_examples: 16000 - name: test num_bytes: 77446400 num_examples: 1600 download_size: 95044772 dataset_size: 851910400 - config_name: metaworld-window-open features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 774464000 num_examples: 16000 - name: test num_bytes: 77446400 num_examples: 1600 download_size: 95793720 dataset_size: 851910400 - config_name: mujoco-ant features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 1420167204 num_examples: 35317 - name: test num_bytes: 158435280 num_examples: 3940 download_size: 1513512326 dataset_size: 1578602484 - config_name: mujoco-doublependulum features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 599126920 num_examples: 35962 - name: test num_bytes: 66490060 num_examples: 3991 download_size: 458306888 dataset_size: 665616980 - config_name: mujoco-halfcheetah features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 1005264000 num_examples: 36000 - name: test num_bytes: 111696000 num_examples: 4000 download_size: 1055030042 dataset_size: 1116960000 - config_name: mujoco-hopper features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 377714520 num_examples: 20190 - name: test num_bytes: 41774964 num_examples: 2233 download_size: 343653363 dataset_size: 419489484 - config_name: mujoco-humanoid features: - name: continuous_observations sequence: sequence: float32 - name: rewards sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 13565692988 num_examples: 33347 - name: test num_bytes: 1509649644 num_examples: 3711 download_size: 10439047554 dataset_size: 15075342632 - config_name: mujoco-pendulum features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 201391764 num_examples: 21217 - name: test num_bytes: 22334676 num_examples: 2353 download_size: 134650231 dataset_size: 223726440 - config_name: mujoco-pusher features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 315828000 num_examples: 9000 - name: test num_bytes: 35092000 num_examples: 1000 download_size: 134738418 dataset_size: 350920000 - config_name: mujoco-reacher features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 159156000 num_examples: 9000 - name: test num_bytes: 17684000 num_examples: 1000 download_size: 38441946 dataset_size: 176840000 - config_name: mujoco-standup features: - name: rewards sequence: float32 - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 14644944000 num_examples: 36000 - name: test num_bytes: 1627216000 num_examples: 4000 download_size: 11711102671 dataset_size: 16272160000 - config_name: mujoco-swimmer features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 526032000 num_examples: 36000 - name: test num_bytes: 58448000 num_examples: 4000 download_size: 519559720 dataset_size: 584480000 - config_name: mujoco-walker features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 944529300 num_examples: 33825 - name: test num_bytes: 104798772 num_examples: 3753 download_size: 954326371 dataset_size: 1049328072 - config_name: ok-vqa features: - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: pixel_values sequence: sequence: sequence: float32 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 5474517048 num_examples: 9009 - name: test num_bytes: 3066312912 num_examples: 5046 download_size: 2461083826 dataset_size: 8540829960 - config_name: oscar features: - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 58269773100 num_examples: 12612505 - name: test num_bytes: 63899220 num_examples: 13831 download_size: 10788173669 dataset_size: 58333672320 - config_name: wikipedia features: - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 59293939320 num_examples: 12834186 - name: test num_bytes: 58216620 num_examples: 12601 download_size: 10100547139 dataset_size: 59352155940 configs: - config_name: atari-alien data_files: - split: train path: atari-alien/train-* - split: test path: atari-alien/test-* - config_name: atari-amidar data_files: - split: train path: atari-amidar/train-* - split: test path: atari-amidar/test-* - config_name: atari-assault data_files: - split: train path: atari-assault/train-* - split: test path: atari-assault/test-* - config_name: atari-asterix data_files: - split: train path: atari-asterix/train-* - split: test path: atari-asterix/test-* - config_name: atari-asteroids data_files: - split: train path: atari-asteroids/train-* - split: test path: atari-asteroids/test-* - config_name: atari-atlantis data_files: - split: train path: atari-atlantis/train-* - split: test path: atari-atlantis/test-* - config_name: atari-bankheist data_files: - split: train path: atari-bankheist/train-* - split: test path: atari-bankheist/test-* - config_name: atari-battlezone data_files: - split: train path: atari-battlezone/train-* - split: test path: atari-battlezone/test-* - config_name: atari-beamrider data_files: - split: train path: atari-beamrider/train-* - split: test path: atari-beamrider/test-* - config_name: atari-berzerk data_files: - split: train path: atari-berzerk/train-* - split: test path: atari-berzerk/test-* - config_name: atari-bowling data_files: - split: train path: atari-bowling/train-* - split: test path: atari-bowling/test-* - config_name: atari-boxing data_files: - split: train path: atari-boxing/train-* - split: test path: atari-boxing/test-* - config_name: atari-breakout data_files: - split: train path: atari-breakout/train-* - split: test path: atari-breakout/test-* - config_name: atari-centipede data_files: - split: train path: atari-centipede/train-* - split: test path: atari-centipede/test-* - config_name: atari-choppercommand data_files: - split: train path: atari-choppercommand/train-* - split: test path: atari-choppercommand/test-* - config_name: atari-crazyclimber data_files: - split: train path: atari-crazyclimber/train-* - split: test path: atari-crazyclimber/test-* - config_name: atari-defender data_files: - split: train path: atari-defender/train-* - split: test path: atari-defender/test-* - config_name: atari-demonattack data_files: - split: train path: atari-demonattack/train-* - split: test path: atari-demonattack/test-* - config_name: atari-doubledunk data_files: - split: test path: atari-doubledunk/test-* - split: train path: atari-doubledunk/train-* - config_name: atari-enduro data_files: - split: train path: atari-enduro/train-* - split: test path: atari-enduro/test-* - config_name: atari-fishingderby data_files: - split: train path: atari-fishingderby/train-* - split: test path: atari-fishingderby/test-* - config_name: atari-freeway data_files: - split: train path: atari-freeway/train-* - split: test path: atari-freeway/test-* - config_name: atari-frostbite data_files: - split: train path: atari-frostbite/train-* - split: test path: atari-frostbite/test-* - config_name: atari-gopher data_files: - split: train path: atari-gopher/train-* - split: test path: atari-gopher/test-* - config_name: atari-gravitar data_files: - split: train path: atari-gravitar/train-* - split: test path: atari-gravitar/test-* - config_name: atari-hero data_files: - split: train path: atari-hero/train-* - split: test path: atari-hero/test-* - config_name: atari-icehockey data_files: - split: train path: atari-icehockey/train-* - split: test path: atari-icehockey/test-* - config_name: atari-jamesbond data_files: - split: train path: atari-jamesbond/train-* - split: test path: atari-jamesbond/test-* - config_name: atari-kangaroo data_files: - split: train path: atari-kangaroo/train-* - split: test path: atari-kangaroo/test-* - config_name: atari-krull data_files: - split: train path: atari-krull/train-* - split: test path: atari-krull/test-* - config_name: atari-kungfumaster data_files: - split: train path: atari-kungfumaster/train-* - split: test path: atari-kungfumaster/test-* - config_name: atari-montezumarevenge data_files: - split: train path: atari-montezumarevenge/train-* - split: test path: atari-montezumarevenge/test-* - config_name: atari-mspacman data_files: - split: train path: atari-mspacman/train-* - split: test path: atari-mspacman/test-* - config_name: atari-namethisgame data_files: - split: train path: atari-namethisgame/train-* - split: test path: atari-namethisgame/test-* - config_name: atari-phoenix data_files: - split: train path: atari-phoenix/train-* - split: test path: atari-phoenix/test-* - config_name: atari-pitfall data_files: - split: train path: atari-pitfall/train-* - split: test path: atari-pitfall/test-* - config_name: atari-pong data_files: - split: test path: atari-pong/test-* - split: train path: atari-pong/train-* - config_name: atari-privateeye data_files: - split: test path: atari-privateeye/test-* - split: train path: atari-privateeye/train-* - config_name: atari-qbert data_files: - split: test path: atari-qbert/test-* - split: train path: atari-qbert/train-* - config_name: atari-riverraid data_files: - split: test path: atari-riverraid/test-* - split: train path: atari-riverraid/train-* - config_name: atari-roadrunner data_files: - split: test path: atari-roadrunner/test-* - split: train path: atari-roadrunner/train-* - config_name: atari-robotank data_files: - split: test path: atari-robotank/test-* - split: train path: atari-robotank/train-* - config_name: atari-seaquest data_files: - split: test path: atari-seaquest/test-* - split: train path: atari-seaquest/train-* - config_name: atari-skiing data_files: - split: train path: atari-skiing/train-* - split: test path: atari-skiing/test-* - config_name: atari-solaris data_files: - split: train path: atari-solaris/train-* - split: test path: atari-solaris/test-* - config_name: atari-spaceinvaders data_files: - split: train path: atari-spaceinvaders/train-* - split: test path: atari-spaceinvaders/test-* - config_name: atari-stargunner data_files: - split: train path: atari-stargunner/train-* - split: test path: atari-stargunner/test-* - config_name: atari-surround data_files: - split: train path: atari-surround/train-* - split: test path: atari-surround/test-* - config_name: atari-tennis data_files: - split: train path: atari-tennis/train-* - split: test path: atari-tennis/test-* - config_name: atari-timepilot data_files: - split: train path: atari-timepilot/train-* - split: test path: atari-timepilot/test-* - config_name: atari-tutankham data_files: - split: train path: atari-tutankham/train-* - split: test path: atari-tutankham/test-* - config_name: atari-upndown data_files: - split: train path: atari-upndown/train-* - split: test path: atari-upndown/test-* - config_name: atari-venture data_files: - split: test path: atari-venture/test-* - split: train path: atari-venture/train-* - config_name: atari-videopinball data_files: - split: test path: atari-videopinball/test-* - split: train path: atari-videopinball/train-* - config_name: atari-wizardofwor data_files: - split: test path: atari-wizardofwor/test-* - split: train path: atari-wizardofwor/train-* - config_name: atari-yarsrevenge data_files: - split: test path: atari-yarsrevenge/test-* - split: train path: atari-yarsrevenge/train-* - config_name: atari-zaxxon data_files: - split: test path: atari-zaxxon/test-* - split: train path: atari-zaxxon/train-* - config_name: babyai-action-obj-door data_files: - split: train path: babyai-action-obj-door/train-* - split: test path: babyai-action-obj-door/test-* - config_name: babyai-blocked-unlock-pickup data_files: - split: test path: babyai-blocked-unlock-pickup/test-* - split: train path: babyai-blocked-unlock-pickup/train-* - config_name: babyai-boss-level data_files: - split: test path: babyai-boss-level/test-* - split: train path: babyai-boss-level/train-* - config_name: babyai-boss-level-no-unlock data_files: - split: test path: babyai-boss-level-no-unlock/test-* - split: train path: babyai-boss-level-no-unlock/train-* - config_name: babyai-find-obj-s5 data_files: - split: train path: babyai-find-obj-s5/train-* - split: test path: babyai-find-obj-s5/test-* - config_name: babyai-go-to data_files: - split: train path: babyai-go-to/train-* - split: test path: babyai-go-to/test-* - config_name: babyai-go-to-door data_files: - split: train path: babyai-go-to-door/train-* - split: test path: babyai-go-to-door/test-* - config_name: babyai-go-to-imp-unlock data_files: - split: train path: babyai-go-to-imp-unlock/train-* - split: test path: babyai-go-to-imp-unlock/test-* - config_name: babyai-go-to-local data_files: - split: train path: babyai-go-to-local/train-* - split: test path: babyai-go-to-local/test-* - config_name: babyai-go-to-obj data_files: - split: train path: babyai-go-to-obj/train-* - split: test path: babyai-go-to-obj/test-* - config_name: babyai-go-to-obj-door data_files: - split: train path: babyai-go-to-obj-door/train-* - split: test path: babyai-go-to-obj-door/test-* - config_name: babyai-go-to-red-ball data_files: - split: train path: babyai-go-to-red-ball/train-* - split: test path: babyai-go-to-red-ball/test-* - config_name: babyai-go-to-red-ball-grey data_files: - split: train path: babyai-go-to-red-ball-grey/train-* - split: test path: babyai-go-to-red-ball-grey/test-* - config_name: babyai-go-to-red-ball-no-dists data_files: - split: train path: babyai-go-to-red-ball-no-dists/train-* - split: test path: babyai-go-to-red-ball-no-dists/test-* - config_name: babyai-go-to-red-blue-ball data_files: - split: train path: babyai-go-to-red-blue-ball/train-* - split: test path: babyai-go-to-red-blue-ball/test-* - config_name: babyai-go-to-seq data_files: - split: train path: babyai-go-to-seq/train-* - split: test path: babyai-go-to-seq/test-* - config_name: babyai-key-corridor data_files: - split: test path: babyai-key-corridor/test-* - split: train path: babyai-key-corridor/train-* - config_name: babyai-mini-boss-level data_files: - split: test path: babyai-mini-boss-level/test-* - split: train path: babyai-mini-boss-level/train-* - config_name: babyai-move-two-across-s8n9 data_files: - split: test path: babyai-move-two-across-s8n9/test-* - split: train path: babyai-move-two-across-s8n9/train-* - config_name: babyai-one-room-s8 data_files: - split: test path: babyai-one-room-s8/test-* - split: train path: babyai-one-room-s8/train-* - config_name: babyai-open data_files: - split: test path: babyai-open/test-* - split: train path: babyai-open/train-* - config_name: babyai-open-door data_files: - split: test path: babyai-open-door/test-* - split: train path: babyai-open-door/train-* - config_name: babyai-open-doors-order-n4 data_files: - split: test path: babyai-open-doors-order-n4/test-* - split: train path: babyai-open-doors-order-n4/train-* - config_name: babyai-open-red-door data_files: - split: test path: babyai-open-red-door/test-* - split: train path: babyai-open-red-door/train-* - config_name: babyai-open-two-doors data_files: - split: test path: babyai-open-two-doors/test-* - split: train path: babyai-open-two-doors/train-* - config_name: babyai-pickup data_files: - split: test path: babyai-pickup/test-* - split: train path: babyai-pickup/train-* - config_name: babyai-pickup-above data_files: - split: test path: babyai-pickup-above/test-* - split: train path: babyai-pickup-above/train-* - config_name: babyai-pickup-dist data_files: - split: test path: babyai-pickup-dist/test-* - split: train path: babyai-pickup-dist/train-* - config_name: babyai-pickup-loc data_files: - split: test path: babyai-pickup-loc/test-* - split: train path: babyai-pickup-loc/train-* - config_name: babyai-put-next data_files: - split: train path: babyai-put-next/train-* - split: test path: babyai-put-next/test-* - config_name: babyai-put-next-local data_files: - split: train path: babyai-put-next-local/train-* - split: test path: babyai-put-next-local/test-* - config_name: babyai-synth data_files: - split: test path: babyai-synth/test-* - split: train path: babyai-synth/train-* - config_name: babyai-synth-loc data_files: - split: test path: babyai-synth-loc/test-* - split: train path: babyai-synth-loc/train-* - config_name: babyai-synth-seq data_files: - split: test path: babyai-synth-seq/test-* - split: train path: babyai-synth-seq/train-* - config_name: babyai-unblock-pickup data_files: - split: test path: babyai-unblock-pickup/test-* - split: train path: babyai-unblock-pickup/train-* - config_name: babyai-unlock data_files: - split: train path: babyai-unlock/train-* - split: test path: babyai-unlock/test-* - config_name: babyai-unlock-local data_files: - split: test path: babyai-unlock-local/test-* - split: train path: babyai-unlock-local/train-* - config_name: babyai-unlock-pickup data_files: - split: test path: babyai-unlock-pickup/test-* - split: train path: babyai-unlock-pickup/train-* - config_name: babyai-unlock-to-unlock data_files: - split: train path: babyai-unlock-to-unlock/train-* - split: test path: babyai-unlock-to-unlock/test-* - config_name: conceptual-captions data_files: - split: test path: conceptual-captions/test-* - split: train path: conceptual-captions/train-* - config_name: metaworld-assembly data_files: - split: train path: metaworld-assembly/train-* - split: test path: metaworld-assembly/test-* - config_name: metaworld-basketball data_files: - split: train path: metaworld-basketball/train-* - split: test path: metaworld-basketball/test-* - config_name: metaworld-bin-picking data_files: - split: train path: metaworld-bin-picking/train-* - split: test path: metaworld-bin-picking/test-* - config_name: metaworld-box-close data_files: - split: train path: metaworld-box-close/train-* - split: test path: metaworld-box-close/test-* - config_name: metaworld-button-press data_files: - split: train path: metaworld-button-press/train-* - split: test path: metaworld-button-press/test-* - config_name: metaworld-button-press-topdown data_files: - split: train path: metaworld-button-press-topdown/train-* - split: test path: metaworld-button-press-topdown/test-* - config_name: metaworld-button-press-topdown-wall data_files: - split: train path: metaworld-button-press-topdown-wall/train-* - split: test path: metaworld-button-press-topdown-wall/test-* - config_name: metaworld-button-press-wall data_files: - split: train path: metaworld-button-press-wall/train-* - split: test path: metaworld-button-press-wall/test-* - config_name: metaworld-coffee-button data_files: - split: train path: metaworld-coffee-button/train-* - split: test path: metaworld-coffee-button/test-* - config_name: metaworld-coffee-pull data_files: - split: train path: metaworld-coffee-pull/train-* - split: test path: metaworld-coffee-pull/test-* - config_name: metaworld-coffee-push data_files: - split: train path: metaworld-coffee-push/train-* - split: test path: metaworld-coffee-push/test-* - config_name: metaworld-dial-turn data_files: - split: train path: metaworld-dial-turn/train-* - split: test path: metaworld-dial-turn/test-* - config_name: metaworld-disassemble data_files: - split: train path: metaworld-disassemble/train-* - split: test path: metaworld-disassemble/test-* - config_name: metaworld-door-close data_files: - split: train path: metaworld-door-close/train-* - split: test path: metaworld-door-close/test-* - config_name: metaworld-door-lock data_files: - split: train path: metaworld-door-lock/train-* - split: test path: metaworld-door-lock/test-* - config_name: metaworld-door-open data_files: - split: train path: metaworld-door-open/train-* - split: test path: metaworld-door-open/test-* - config_name: metaworld-door-unlock data_files: - split: train path: metaworld-door-unlock/train-* - split: test path: metaworld-door-unlock/test-* - config_name: metaworld-drawer-close data_files: - split: train path: metaworld-drawer-close/train-* - split: test path: metaworld-drawer-close/test-* - config_name: metaworld-drawer-open data_files: - split: train path: metaworld-drawer-open/train-* - split: test path: metaworld-drawer-open/test-* - config_name: metaworld-faucet-close data_files: - split: train path: metaworld-faucet-close/train-* - split: test path: metaworld-faucet-close/test-* - config_name: metaworld-faucet-open data_files: - split: train path: metaworld-faucet-open/train-* - split: test path: metaworld-faucet-open/test-* - config_name: metaworld-hammer data_files: - split: train path: metaworld-hammer/train-* - split: test path: metaworld-hammer/test-* - config_name: metaworld-hand-insert data_files: - split: train path: metaworld-hand-insert/train-* - split: test path: metaworld-hand-insert/test-* - config_name: metaworld-handle-press data_files: - split: train path: metaworld-handle-press/train-* - split: test path: metaworld-handle-press/test-* - config_name: metaworld-handle-press-side data_files: - split: train path: metaworld-handle-press-side/train-* - split: test path: metaworld-handle-press-side/test-* - config_name: metaworld-handle-pull data_files: - split: train path: metaworld-handle-pull/train-* - split: test path: metaworld-handle-pull/test-* - config_name: metaworld-handle-pull-side data_files: - split: train path: metaworld-handle-pull-side/train-* - split: test path: metaworld-handle-pull-side/test-* - config_name: metaworld-lever-pull data_files: - split: train path: metaworld-lever-pull/train-* - split: test path: metaworld-lever-pull/test-* - config_name: metaworld-peg-insert-side data_files: - split: train path: metaworld-peg-insert-side/train-* - split: test path: metaworld-peg-insert-side/test-* - config_name: metaworld-peg-unplug-side data_files: - split: train path: metaworld-peg-unplug-side/train-* - split: test path: metaworld-peg-unplug-side/test-* - config_name: metaworld-pick-out-of-hole data_files: - split: train path: metaworld-pick-out-of-hole/train-* - split: test path: metaworld-pick-out-of-hole/test-* - config_name: metaworld-pick-place data_files: - split: train path: metaworld-pick-place/train-* - split: test path: metaworld-pick-place/test-* - config_name: metaworld-pick-place-wall data_files: - split: train path: metaworld-pick-place-wall/train-* - split: test path: metaworld-pick-place-wall/test-* - config_name: metaworld-plate-slide data_files: - split: train path: metaworld-plate-slide/train-* - split: test path: metaworld-plate-slide/test-* - config_name: metaworld-plate-slide-back data_files: - split: train path: metaworld-plate-slide-back/train-* - split: test path: metaworld-plate-slide-back/test-* - config_name: metaworld-plate-slide-back-side data_files: - split: train path: metaworld-plate-slide-back-side/train-* - split: test path: metaworld-plate-slide-back-side/test-* - config_name: metaworld-plate-slide-side data_files: - split: train path: metaworld-plate-slide-side/train-* - split: test path: metaworld-plate-slide-side/test-* - config_name: metaworld-push data_files: - split: train path: metaworld-push/train-* - split: test path: metaworld-push/test-* - config_name: metaworld-push-back data_files: - split: train path: metaworld-push-back/train-* - split: test path: metaworld-push-back/test-* - config_name: metaworld-push-wall data_files: - split: train path: metaworld-push-wall/train-* - split: test path: metaworld-push-wall/test-* - config_name: metaworld-reach data_files: - split: train path: metaworld-reach/train-* - split: test path: metaworld-reach/test-* - config_name: metaworld-reach-wall data_files: - split: train path: metaworld-reach-wall/train-* - split: test path: metaworld-reach-wall/test-* - config_name: metaworld-shelf-place data_files: - split: train path: metaworld-shelf-place/train-* - split: test path: metaworld-shelf-place/test-* - config_name: metaworld-soccer data_files: - split: train path: metaworld-soccer/train-* - split: test path: metaworld-soccer/test-* - config_name: metaworld-stick-pull data_files: - split: train path: metaworld-stick-pull/train-* - split: test path: metaworld-stick-pull/test-* - config_name: metaworld-stick-push data_files: - split: train path: metaworld-stick-push/train-* - split: test path: metaworld-stick-push/test-* - config_name: metaworld-sweep data_files: - split: train path: metaworld-sweep/train-* - split: test path: metaworld-sweep/test-* - config_name: metaworld-sweep-into data_files: - split: train path: metaworld-sweep-into/train-* - split: test path: metaworld-sweep-into/test-* - config_name: metaworld-window-close data_files: - split: train path: metaworld-window-close/train-* - split: test path: metaworld-window-close/test-* - config_name: metaworld-window-open data_files: - split: train path: metaworld-window-open/train-* - split: test path: metaworld-window-open/test-* - config_name: mujoco-ant data_files: - split: train path: mujoco-ant/train-* - split: test path: mujoco-ant/test-* - config_name: mujoco-doublependulum data_files: - split: train path: mujoco-doublependulum/train-* - split: test path: mujoco-doublependulum/test-* - config_name: mujoco-halfcheetah data_files: - split: train path: mujoco-halfcheetah/train-* - split: test path: mujoco-halfcheetah/test-* - config_name: mujoco-hopper data_files: - split: train path: mujoco-hopper/train-* - split: test path: mujoco-hopper/test-* - config_name: mujoco-humanoid data_files: - split: train path: mujoco-humanoid/train-* - split: test path: mujoco-humanoid/test-* - config_name: mujoco-pendulum data_files: - split: train path: mujoco-pendulum/train-* - split: test path: mujoco-pendulum/test-* - config_name: mujoco-pusher data_files: - split: train path: mujoco-pusher/train-* - split: test path: mujoco-pusher/test-* - config_name: mujoco-reacher data_files: - split: train path: mujoco-reacher/train-* - split: test path: mujoco-reacher/test-* - config_name: mujoco-standup data_files: - split: train path: mujoco-standup/train-* - split: test path: mujoco-standup/test-* - config_name: mujoco-swimmer data_files: - split: train path: mujoco-swimmer/train-* - split: test path: mujoco-swimmer/test-* - config_name: mujoco-walker data_files: - split: train path: mujoco-walker/train-* - split: test path: mujoco-walker/test-* - config_name: ok-vqa data_files: - split: train path: ok-vqa/train-* - split: test path: ok-vqa/test-* - config_name: oscar data_files: - split: train path: oscar/train-* - split: test path: oscar/test-* - config_name: wikipedia data_files: - split: train path: wikipedia/train-* - split: test path: wikipedia/test-* --- # Dataset Card for "jat-dataset-tokenized" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
UCLNLP/adversarial_qa	UCLNLP	2023-12-21T14:20:00Z	490	39	[ "task_categories:question-answering", "task_ids:extractive-qa", "task_ids:open-domain-qa", "annotations_creators:crowdsourced", "language_creators:found", "multilinguality:monolingual", "source_datasets:original", "language:en", "license:cc-by-sa-4.0", "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2002.00293", "arxiv:1606.05250", "region:us" ]	[ "question-answering" ]	2022-03-02T23:29:22Z	1	--- annotations_creators: - crowdsourced language_creators: - found language: - en license: - cc-by-sa-4.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - question-answering task_ids: - extractive-qa - open-domain-qa paperswithcode_id: adversarialqa pretty_name: adversarialQA dataset_info: - config_name: adversarialQA features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 - name: metadata struct: - name: split dtype: string - name: model_in_the_loop dtype: string splits: - name: train num_bytes: 27858686 num_examples: 30000 - name: validation num_bytes: 2757092 num_examples: 3000 - name: test num_bytes: 2919479 num_examples: 3000 download_size: 5301049 dataset_size: 33535257 - config_name: dbert features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 - name: metadata struct: - name: split dtype: string - name: model_in_the_loop dtype: string splits: - name: train num_bytes: 9345521 num_examples: 10000 - name: validation num_bytes: 918156 num_examples: 1000 - name: test num_bytes: 971290 num_examples: 1000 download_size: 2689032 dataset_size: 11234967 - config_name: dbidaf features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 - name: metadata struct: - name: split dtype: string - name: model_in_the_loop dtype: string splits: - name: train num_bytes: 9282482 num_examples: 10000 - name: validation num_bytes: 917907 num_examples: 1000 - name: test num_bytes: 946947 num_examples: 1000 download_size: 2721341 dataset_size: 11147336 - config_name: droberta features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 - name: metadata struct: - name: split dtype: string - name: model_in_the_loop dtype: string splits: - name: train num_bytes: 9270683 num_examples: 10000 - name: validation num_bytes: 925029 num_examples: 1000 - name: test num_bytes: 1005242 num_examples: 1000 download_size: 2815452 dataset_size: 11200954 configs: - config_name: adversarialQA data_files: - split: train path: adversarialQA/train-* - split: validation path: adversarialQA/validation-* - split: test path: adversarialQA/test-* - config_name: dbert data_files: - split: train path: dbert/train-* - split: validation path: dbert/validation-* - split: test path: dbert/test-* - config_name: dbidaf data_files: - split: train path: dbidaf/train-* - split: validation path: dbidaf/validation-* - split: test path: dbidaf/test-* - config_name: droberta data_files: - split: train path: droberta/train-* - split: validation path: droberta/validation-* - split: test path: droberta/test-* train-eval-index: - config: adversarialQA task: question-answering task_id: extractive_question_answering splits: train_split: train eval_split: validation col_mapping: question: question context: context answers: text: text answer_start: answer_start metrics: - type: squad name: SQuAD --- # Dataset Card for adversarialQA ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [adversarialQA homepage](https://adversarialqa.github.io/) - Repository: [adversarialQA repository](https://github.com/maxbartolo/adversarialQA) - Paper: [Beat the AI: Investigating Adversarial Human Annotation for Reading Comprehension](https://arxiv.org/abs/2002.00293) - Leaderboard: [Dynabench QA Round 1 Leaderboard](https://dynabench.org/tasks/2#overall) - Point of Contact: [Max Bartolo]([email protected]) ### Dataset Summary We have created three new Reading Comprehension datasets constructed using an adversarial model-in-the-loop. We use three different models; BiDAF (Seo et al., 2016), BERTLarge (Devlin et al., 2018), and RoBERTaLarge (Liu et al., 2019) in the annotation loop and construct three datasets; D(BiDAF), D(BERT), and D(RoBERTa), each with 10,000 training examples, 1,000 validation, and 1,000 test examples. The adversarial human annotation paradigm ensures that these datasets consist of questions that current state-of-the-art models (at least the ones used as adversaries in the annotation loop) find challenging. The three AdversarialQA round 1 datasets provide a training and evaluation resource for such methods. ### Supported Tasks and Leaderboards `extractive-qa`: The dataset can be used to train a model for Extractive Question Answering, which consists in selecting the answer to a question from a passage. Success on this task is typically measured by achieving a high word-overlap [F1 score](https://huggingface.co/metrics/f1). The [RoBERTa-Large](https://huggingface.co/roberta-large) model trained on all the data combined with [SQuAD](https://arxiv.org/abs/1606.05250) currently achieves 64.35% F1. This task has an active leaderboard and is available as round 1 of the QA task on [Dynabench](https://dynabench.org/tasks/2#overall) and ranks models based on F1 score. ### Languages The text in the dataset is in English. The associated BCP-47 code is `en`. ## Dataset Structure ### Data Instances Data is provided in the same format as SQuAD 1.1. An example is shown below: ``` { "data": [ { "title": "Oxygen", "paragraphs": [ { "context": "Among the most important classes of organic compounds that contain oxygen are (where \"R\" is an organic group): alcohols (R-OH); ethers (R-O-R); ketones (R-CO-R); aldehydes (R-CO-H); carboxylic acids (R-COOH); esters (R-COO-R); acid anhydrides (R-CO-O-CO-R); and amides (R-C(O)-NR2). There are many important organic solvents that contain oxygen, including: acetone, methanol, ethanol, isopropanol, furan, THF, diethyl ether, dioxane, ethyl acetate, DMF, DMSO, acetic acid, and formic acid. Acetone ((CH3)2CO) and phenol (C6H5OH) are used as feeder materials in the synthesis of many different substances. Other important organic compounds that contain oxygen are: glycerol, formaldehyde, glutaraldehyde, citric acid, acetic anhydride, and acetamide. Epoxides are ethers in which the oxygen atom is part of a ring of three atoms.", "qas": [ { "id": "22bbe104aa72aa9b511dd53237deb11afa14d6e3", "question": "In addition to having oxygen, what do alcohols, ethers and esters have in common, according to the article?", "answers": [ { "answer_start": 36, "text": "organic compounds" } ] }, { "id": "4240a8e708c703796347a3702cf1463eed05584a", "question": "What letter does the abbreviation for acid anhydrides both begin and end in?", "answers": [ { "answer_start": 244, "text": "R" } ] }, { "id": "0681a0a5ec852ec6920d6a30f7ef65dced493366", "question": "Which of the organic compounds, in the article, contains nitrogen?", "answers": [ { "answer_start": 262, "text": "amides" } ] }, { "id": "2990efe1a56ccf81938fa5e18104f7d3803069fb", "question": "Which of the important classes of organic compounds, in the article, has a number in its abbreviation?", "answers": [ { "answer_start": 262, "text": "amides" } ] } ] } ] } ] } ``` ### Data Fields - title: the title of the Wikipedia page from which the context is sourced - context: the context/passage - id: a string identifier for each question - answers: a list of all provided answers (one per question in our case, but multiple may exist in SQuAD) with an `answer_start` field which is the character index of the start of the answer span, and a `text` field which is the answer text. Note that no answers are provided in the test set. Indeed, this dataset is part of the DynaBench benchmark, for which you can submit your predictions on the [website](https://dynabench.org/tasks/2#1). ### Data Splits The dataset is composed of three different datasets constructed using different models in the loop: BiDAF, BERT-Large, and RoBERTa-Large. Each of these has 10,000 training examples, 1,000 validation examples, and 1,000 test examples for a total of 30,000/3,000/3,000 train/validation/test examples. ## Dataset Creation ### Curation Rationale This dataset was collected to provide a more challenging and diverse Reading Comprehension dataset to state-of-the-art models. ### Source Data #### Initial Data Collection and Normalization The source passages are from Wikipedia and are the same as those used in [SQuAD v1.1](https://arxiv.org/abs/1606.05250). #### Who are the source language producers? The source language produces are Wikipedia editors for the passages, and human annotators on Mechanical Turk for the questions. ### Annotations #### Annotation process The dataset is collected through an adversarial human annotation process which pairs a human annotator and a reading comprehension model in an interactive setting. The human is presented with a passage for which they write a question and highlight the correct answer. The model then tries to answer the question, and, if it fails to answer correctly, the human wins. Otherwise, the human modifies or re-writes their question until the successfully fool the model. #### Who are the annotators? The annotators are from Amazon Mechanical Turk, geographically restricted the the USA, UK and Canada, having previously successfully completed at least 1,000 HITs, and having a HIT approval rate greater than 98%. Crowdworkers undergo intensive training and qualification prior to annotation. ### Personal and Sensitive Information No annotator identifying details are provided. ## Considerations for Using the Data ### Social Impact of Dataset The purpose of this dataset is to help develop better question answering systems. A system that succeeds at the supported task would be able to provide an accurate extractive answer from a short passage. This dataset is to be seen as a test bed for questions which contemporary state-of-the-art models struggle to answer correctly, thus often requiring more complex comprehension abilities than say detecting phrases explicitly mentioned in the passage with high overlap to the question. It should be noted, however, that the the source passages are both domain-restricted and linguistically specific, and that provided questions and answers do not constitute any particular social application. ### Discussion of Biases The dataset may exhibit various biases in terms of the source passage selection, annotated questions and answers, as well as algorithmic biases resulting from the adversarial annotation protocol. ### Other Known Limitations N/a ## Additional Information ### Dataset Curators This dataset was initially created by Max Bartolo, Alastair Roberts, Johannes Welbl, Sebastian Riedel, and Pontus Stenetorp, during work carried out at University College London (UCL). ### Licensing Information This dataset is distributed under [CC BY-SA 3.0](https://creativecommons.org/licenses/by-sa/3.0/). ### Citation Information ``` @article{bartolo2020beat, author = {Bartolo, Max and Roberts, Alastair and Welbl, Johannes and Riedel, Sebastian and Stenetorp, Pontus}, title = {Beat the AI: Investigating Adversarial Human Annotation for Reading Comprehension}, journal = {Transactions of the Association for Computational Linguistics}, volume = {8}, number = {}, pages = {662-678}, year = {2020}, doi = {10.1162/tacl\_a\_00338}, URL = { https://doi.org/10.1162/tacl_a_00338 }, eprint = { https://doi.org/10.1162/tacl_a_00338 }, abstract = { Innovations in annotation methodology have been a catalyst for Reading Comprehension (RC) datasets and models. One recent trend to challenge current RC models is to involve a model in the annotation process: Humans create questions adversarially, such that the model fails to answer them correctly. In this work we investigate this annotation methodology and apply it in three different settings, collecting a total of 36,000 samples with progressively stronger models in the annotation loop. This allows us to explore questions such as the reproducibility of the adversarial effect, transfer from data collected with varying model-in-the-loop strengths, and generalization to data collected without a model. We find that training on adversarially collected samples leads to strong generalization to non-adversarially collected datasets, yet with progressive performance deterioration with increasingly stronger models-in-the-loop. Furthermore, we find that stronger models can still learn from datasets collected with substantially weaker models-in-the-loop. When trained on data collected with a BiDAF model in the loop, RoBERTa achieves 39.9F1 on questions that it cannot answer when trained on SQuAD—only marginally lower than when trained on data collected using RoBERTa itself (41.0F1). } } ``` ### Contributions Thanks to [@maxbartolo](https://github.com/maxbartolo) for adding this dataset.

open-llm-leaderboard-old/requests

open-llm-leaderboard-old

2024-06-19T21:36:08Z

22,162

22

[ "license:apache-2.0", "size_categories:n<1K", "format:json", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "region:us" ]

[]

2023-06-19T15:15:07Z

null

--- license: apache-2.0 --- ![HuggingFace LeaderBoard](https://cdn-uploads.huggingface.co/production/uploads/6202a599216215a22221dea9/Uh5JX7Kq-rUxoVrdsV-M-.gif) # Open LLM Leaderboard Requests This repository contains the request files of models that have been submitted to the Open LLM Leaderboard. You can take a look at the current status of your model by finding its request file in this dataset. If your model failed, feel free to open an issue on the Open LLM Leaderboard! (We don't follow issues in this repository as often) ## Evaluation Methodology The evaluation process involves running your models against several benchmarks from the Eleuther AI Harness, a unified framework for measuring the effectiveness of generative language models. Below is a brief overview of each benchmark: 1. AI2 Reasoning Challenge (ARC) - Grade-School Science Questions (25-shot) 2. HellaSwag - Commonsense Inference (10-shot) 3. MMLU - Massive Multi-Task Language Understanding, knowledge on 57 domains (5-shot) 4. TruthfulQA - Propensity to Produce Falsehoods (0-shot) 5. Winogrande - Adversarial Winograd Schema Challenge (5-shot) 6. GSM8k - Grade School Math Word Problems Solving Complex Mathematical Reasoning (5-shot) Together, these benchmarks provide an assessment of a model's capabilities in terms of knowledge, reasoning, and some math, in various scenarios. ## Accessing Your Results To view the numerical results of your evaluated models, visit the dedicated Hugging Face Dataset at https://huggingface.co/datasets/open-llm-leaderboard/results. This dataset offers a thorough breakdown of each model's performance on the individual benchmarks. ## Exploring Model Details For further insights into the inputs and outputs of specific models, locate the "📄" emoji associated with the desired model within this repository. Clicking on this icon will direct you to the respective GitHub page containing detailed information about the model's behavior during the evaluation process.

toxigen/toxigen-data

toxigen

2024-06-17T16:35:29Z

4,574

55

[ "task_categories:text-classification", "task_ids:hate-speech-detection", "annotations_creators:expert-generated", "language_creators:machine-generated", "multilinguality:monolingual", "source_datasets:original", "size_categories:100K<n<1M", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2203.09509", "region:us" ]

[ "text-classification" ]

2022-05-01T15:49:02Z

1

--- annotations_creators: - expert-generated language_creators: - machine-generated multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - text-classification task_ids: - hate-speech-detection pretty_name: ToxiGen dataset_info: - config_name: annotated features: - name: text dtype: string - name: target_group dtype: string - name: factual? dtype: string - name: ingroup_effect dtype: string - name: lewd dtype: string - name: framing dtype: string - name: predicted_group dtype: string - name: stereotyping dtype: string - name: intent dtype: float64 - name: toxicity_ai dtype: float64 - name: toxicity_human dtype: float64 - name: predicted_author dtype: string - name: actual_method dtype: string splits: - name: test num_bytes: 364518 num_examples: 940 - name: train num_bytes: 3238381 num_examples: 8960 download_size: 768996 dataset_size: 3602899 - config_name: annotations features: - name: Input.prompt dtype: string - name: Input.text dtype: string - name: Input.time dtype: string - name: Input.generation_method dtype: string - name: Input.prompt_label dtype: string - name: Input.target_group dtype: string - name: Input.binary_prompt_label dtype: int64 - name: Answer.annotatorAge dtype: string - name: Answer.annotatorGender dtype: string - name: Answer.annotatorMinority dtype: string - name: Answer.annotatorPolitics.1 dtype: bool - name: Answer.annotatorPolitics.2 dtype: bool - name: Answer.annotatorPolitics.3 dtype: bool - name: Answer.annotatorPolitics.4 dtype: bool - name: Answer.annotatorPolitics.5 dtype: bool - name: Answer.annotatorRace dtype: string - name: Answer.factSelect dtype: string - name: Answer.framingQ dtype: string - name: Answer.inGroup.on dtype: bool - name: Answer.ingroup.1 dtype: bool - name: Answer.ingroup.2 dtype: bool - name: Answer.ingroup.3 dtype: bool - name: Answer.intent.1 dtype: bool - name: Answer.intent.2 dtype: bool - name: Answer.intent.3 dtype: bool - name: Answer.intent.4 dtype: bool - name: Answer.intent.5 dtype: bool - name: Answer.lewd.1 dtype: bool - name: Answer.lewd.2 dtype: bool - name: Answer.lewd.3 dtype: bool - name: Answer.refTarget dtype: string - name: Answer.stateFrame dtype: string - name: Answer.stateGroup dtype: string - name: Answer.stereo.1 dtype: bool - name: Answer.stereo.2 dtype: bool - name: Answer.stereo.3 dtype: bool - name: Answer.toAI.1 dtype: bool - name: Answer.toAI.2 dtype: bool - name: Answer.toAI.3 dtype: bool - name: Answer.toAI.4 dtype: bool - name: Answer.toAI.5 dtype: bool - name: Answer.toPER.1 dtype: bool - name: Answer.toPER.2 dtype: bool - name: Answer.toPER.3 dtype: bool - name: Answer.toPER.4 dtype: bool - name: Answer.toPER.5 dtype: bool - name: Answer.writer.1 dtype: bool - name: Answer.writer.2 dtype: bool - name: HashedWorkerId dtype: int64 splits: - name: train num_bytes: 21933185 num_examples: 27450 download_size: 3350653 dataset_size: 21933185 - config_name: prompts features: - name: text dtype: string splits: - name: hate_trans_1k num_bytes: 585554 num_examples: 1000 - name: neutral_black_1k num_bytes: 857769 num_examples: 1000 - name: hate_native_american_1k num_bytes: 480000 num_examples: 1000 - name: neutral_immigrant_1k num_bytes: 342243 num_examples: 1000 - name: hate_middle_east_1k num_bytes: 426551 num_examples: 1000 - name: neutral_lgbtq_1k num_bytes: 914319 num_examples: 1000 - name: neutral_women_1k num_bytes: 394963 num_examples: 1000 - name: neutral_chinese_1k num_bytes: 412062 num_examples: 1000 - name: hate_latino_1k num_bytes: 708000 num_examples: 1000 - name: hate_bisexual_1k num_bytes: 447794 num_examples: 1000 - name: hate_mexican_1k num_bytes: 675444 num_examples: 1000 - name: hate_asian_1k num_bytes: 503093 num_examples: 1000 - name: neutral_mental_disability_1k num_bytes: 556905 num_examples: 1000 - name: neutral_mexican_1k num_bytes: 483603 num_examples: 1000 - name: hate_mental_disability_1k num_bytes: 480620 num_examples: 1000 - name: neutral_bisexual_1k num_bytes: 915612 num_examples: 1000 - name: neutral_latino_1k num_bytes: 470000 num_examples: 1000 - name: hate_chinese_1k num_bytes: 384934 num_examples: 1000 - name: neutral_jewish_1k num_bytes: 649674 num_examples: 1000 - name: hate_muslim_1k num_bytes: 425760 num_examples: 1000 - name: neutral_asian_1k num_bytes: 615895 num_examples: 1000 - name: hate_physical_disability_1k num_bytes: 413643 num_examples: 1000 - name: hate_jewish_1k num_bytes: 573538 num_examples: 1000 - name: neutral_muslim_1k num_bytes: 491659 num_examples: 1000 - name: hate_immigrant_1k num_bytes: 285309 num_examples: 1000 - name: hate_black_1k num_bytes: 745295 num_examples: 1000 - name: hate_lgbtq_1k num_bytes: 577075 num_examples: 1000 - name: hate_women_1k num_bytes: 389583 num_examples: 1000 - name: neutral_middle_east_1k num_bytes: 415319 num_examples: 1000 - name: neutral_native_american_1k num_bytes: 586993 num_examples: 1000 - name: neutral_physical_disability_1k num_bytes: 458497 num_examples: 1000 download_size: 1698170 dataset_size: 16667706 - config_name: train features: - name: prompt dtype: string - name: generation dtype: string - name: generation_method dtype: string - name: group dtype: string - name: prompt_label dtype: int64 - name: roberta_prediction dtype: float64 splits: - name: train num_bytes: 169400442 num_examples: 250951 download_size: 18784380 dataset_size: 169400442 configs: - config_name: annotated default: true data_files: - split: test path: annotated/test-* - split: train path: annotated/train-* - config_name: annotations data_files: - split: train path: annotations/train-* - config_name: prompts data_files: - split: hate_trans_1k path: prompts/hate_trans_1k-* - split: neutral_black_1k path: prompts/neutral_black_1k-* - split: hate_native_american_1k path: prompts/hate_native_american_1k-* - split: neutral_immigrant_1k path: prompts/neutral_immigrant_1k-* - split: hate_middle_east_1k path: prompts/hate_middle_east_1k-* - split: neutral_lgbtq_1k path: prompts/neutral_lgbtq_1k-* - split: neutral_women_1k path: prompts/neutral_women_1k-* - split: neutral_chinese_1k path: prompts/neutral_chinese_1k-* - split: hate_latino_1k path: prompts/hate_latino_1k-* - split: hate_bisexual_1k path: prompts/hate_bisexual_1k-* - split: hate_mexican_1k path: prompts/hate_mexican_1k-* - split: hate_asian_1k path: prompts/hate_asian_1k-* - split: neutral_mental_disability_1k path: prompts/neutral_mental_disability_1k-* - split: neutral_mexican_1k path: prompts/neutral_mexican_1k-* - split: hate_mental_disability_1k path: prompts/hate_mental_disability_1k-* - split: neutral_bisexual_1k path: prompts/neutral_bisexual_1k-* - split: neutral_latino_1k path: prompts/neutral_latino_1k-* - split: hate_chinese_1k path: prompts/hate_chinese_1k-* - split: neutral_jewish_1k path: prompts/neutral_jewish_1k-* - split: hate_muslim_1k path: prompts/hate_muslim_1k-* - split: neutral_asian_1k path: prompts/neutral_asian_1k-* - split: hate_physical_disability_1k path: prompts/hate_physical_disability_1k-* - split: hate_jewish_1k path: prompts/hate_jewish_1k-* - split: neutral_muslim_1k path: prompts/neutral_muslim_1k-* - split: hate_immigrant_1k path: prompts/hate_immigrant_1k-* - split: hate_black_1k path: prompts/hate_black_1k-* - split: hate_lgbtq_1k path: prompts/hate_lgbtq_1k-* - split: hate_women_1k path: prompts/hate_women_1k-* - split: neutral_middle_east_1k path: prompts/neutral_middle_east_1k-* - split: neutral_native_american_1k path: prompts/neutral_native_american_1k-* - split: neutral_physical_disability_1k path: prompts/neutral_physical_disability_1k-* - config_name: train data_files: - split: train path: train/train-* --- # Dataset Card for ToxiGen ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Fields](#data-instances) - [Additional Information](#additional-information) - [Citation Information](#citation-information) ## Sign up for Data Access To access ToxiGen, first fill out [this form](https://forms.office.com/r/r6VXX8f8vh). ## Dataset Description - **Repository:** https://github.com/microsoft/toxigen - **Paper:** https://arxiv.org/abs/2203.09509 - **Point of Contact #1:** [Tom Hartvigsen]([email protected]) - **Point of Contact #2:** [Saadia Gabriel]([email protected]) ### Dataset Summary This dataset is for implicit hate speech detection. All instances were generated using GPT-3 and the methods described in [our paper](https://arxiv.org/abs/2203.09509). ### Languages All text is written in English. ## Dataset Structure ### Data Fields We release TOXIGEN as a dataframe with the following fields: - **prompt** is the prompt used for **generation**. - **generation** is the TOXIGEN generated text. - **generation_method** denotes whether or not ALICE was used to generate the corresponding generation. If this value is ALICE, then ALICE was used, if it is TopK, then ALICE was not used. - **prompt_label** is the binary value indicating whether or not the prompt is toxic (1 is toxic, 0 is benign). - **group** indicates the target group of the prompt. - **roberta_prediction** is the probability predicted by our corresponding RoBERTa model for each instance. ### Citation Information ```bibtex @inproceedings{hartvigsen2022toxigen, title={ToxiGen: A Large-Scale Machine-Generated Dataset for Implicit and Adversarial Hate Speech Detection}, author={Hartvigsen, Thomas and Gabriel, Saadia and Palangi, Hamid and Sap, Maarten and Ray, Dipankar and Kamar, Ece}, booktitle={Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics}, year={2022} } ```

togethercomputer/RedPajama-Data-1T

togethercomputer

2024-06-17T11:36:03Z

1,926

1,084

[ "task_categories:text-generation", "language:en", "size_categories:1M<n<10M", "modality:text", "library:datasets", "library:mlcroissant", "region:us" ]

[ "text-generation" ]

2023-04-17T06:28:35Z

null

--- task_categories: - text-generation language: - en pretty_name: Red Pajama 1T --- ### Getting Started The dataset consists of 2084 jsonl files. You can download the dataset using HuggingFace: ```python from datasets import load_dataset ds = load_dataset("togethercomputer/RedPajama-Data-1T") ``` Or you can directly download the files using the following command: ``` wget 'https://data.together.xyz/redpajama-data-1T/v1.0.0/urls.txt' while read line; do dload_loc=${line#https://data.together.xyz/redpajama-data-1T/v1.0.0/} mkdir -p $(dirname $dload_loc) wget "$line" -O "$dload_loc" done < urls.txt ``` After downloading the files, you can load the dataset from disk by setting the `RED_PAJAMA_DATA_DIR` environment variable to the directory containing the files: ```python import os from datasets import load_dataset os.environ["RED_PAJAMA_DATA_DIR"] = "/path/to/download" ds = load_dataset("togethercomputer/RedPajama-Data-1T") ``` A smaller 1B-token sample of the dataset can be found [here](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T-Sample). A full set of scripts to recreate the dataset from scratch can be found [here](https://github.com/togethercomputer/RedPajama-Data). ### Dataset Summary RedPajama is a clean-room, fully open-source implementation of the LLaMa dataset. | Dataset | Token Count | |---------------|-------------| | Commoncrawl | 878 Billion | | C4 | 175 Billion | | GitHub | 59 Billion | | ArXiv | 28 Billion | | Wikipedia | 24 Billion | | StackExchange | 20 Billion | | Total | 1.2 Trillion | ### Languages Primarily English, though the Wikipedia slice contains multiple languages. ## Dataset Structure The dataset structure is as follows: ```json { "text": ..., "meta": {"url": "...", "timestamp": "...", "source": "...", "language": "...", ...}, "red_pajama_subset": "common_crawl" | "c4" | "github" | "arxiv" | "wikipedia" | "stackexchange" } ``` ## Dataset Creation This dataset was created to follow the LLaMa paper as closely as possible to try to reproduce its recipe. ### Source Data #### Commoncrawl We download five dumps from Commoncrawl, and run the dumps through the official `cc_net` pipeline. We then deduplicate on the paragraph level, and filter out low quality text using a linear classifier trained to classify paragraphs as Wikipedia references or random Commoncrawl samples. #### C4 C4 is downloaded from Huggingface. The only preprocessing step is to bring the data into our own format. #### GitHub The raw GitHub data is downloaded from Google BigQuery. We deduplicate on the file level and filter out low quality files and only keep projects that are distributed under the MIT, BSD, or Apache license. #### Wikipedia We use the Wikipedia dataset available on Huggingface, which is based on the Wikipedia dump from 2023-03-20 and contains text in 20 different languages. The dataset comes in preprocessed format, so that hyperlinks, comments and other formatting boilerplate has been removed. #### Gutenberg and Books3 <div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400"> Defunct: The 'book' config is defunct and no longer accessible due to reported copyright infringement for the Book3 dataset contained in this config. </div> #### ArXiv ArXiv data is downloaded from Amazon S3 in the `arxiv` requester pays bucket. We only keep latex source files and remove preambles, comments, macros and bibliographies. #### Stackexchange The Stack Exchange split of the dataset is download from the [Internet Archive](https://archive.org/download/stackexchange). Here we only keep the posts from the 28 largest sites, remove html tags, group the posts into question-answer pairs, and order answers by their score. ### SHA256 Checksums SHA256 checksums for the dataset files for each data source are available here: ``` https://data.together.xyz/redpajama-data-1T/v1.0.0/sha256/arxiv_SHA256SUMS.txt https://data.together.xyz/redpajama-data-1T/v1.0.0/sha256/c4_SHA256SUMS.txt https://data.together.xyz/redpajama-data-1T/v1.0.0/sha256/common_crawl_SHA256SUMS.txt https://data.together.xyz/redpajama-data-1T/v1.0.0/sha256/github_SHA256SUMS.txt https://data.together.xyz/redpajama-data-1T/v1.0.0/sha256/stackexchange_SHA256SUMS.txt https://data.together.xyz/redpajama-data-1T/v1.0.0/sha256/wikipedia_SHA256SUMS.txt ``` To cite RedPajama, please use: ``` @software{together2023redpajama, author = {Together Computer}, title = {RedPajama: An Open Source Recipe to Reproduce LLaMA training dataset}, month = April, year = 2023, url = {https://github.com/togethercomputer/RedPajama-Data} } ``` ### License Please refer to the licenses of the data subsets you use. * [Common Crawl Foundation Terms of Use](https://commoncrawl.org/terms-of-use/full/) * [C4 license](https://huggingface.co/datasets/allenai/c4#license) * GitHub was limited to MIT, BSD, or Apache licenses only * [ArXiv Terms of Use](https://info.arxiv.org/help/api/tou.html) * [Wikipedia License](https://huggingface.co/datasets/wikipedia#licensing-information) * [StackExchange license on the Internet Archive](https://archive.org/details/stackexchange)

starmpcc/Asclepius-Synthetic-Clinical-Notes

starmpcc

2024-06-14T04:16:04Z

466

86

[ "task_categories:question-answering", "task_categories:summarization", "task_categories:text-generation", "language:en", "license:cc-by-nc-sa-4.0", "size_categories:100K<n<1M", "format:csv", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2309.00237", "region:us", "medical", "synthetic" ]

[ "question-answering", "summarization", "text-generation" ]

2023-09-01T01:47:59Z

null

--- license: cc-by-nc-sa-4.0 task_categories: - question-answering - summarization - text-generation language: - en tags: - medical - synthetic pretty_name: 'Asclepius: Synthetic Clincal Notes & Instruction Dataset' size_categories: - 100K<n<1M --- # Asclepius: Synthetic Clincal Notes & Instruction Dataset ## Dataset Description - **Repository:** [Github](https://github.com/starmpcc/Asclepius) - **Paper:** https://arxiv.org/abs/2309.00237 ### Dataset Summary This dataset is official dataset for Asclepius [(arxiv)](https://arxiv.org/abs/2309.00237) This dataset is composed with Clinical Note - Question - Answer format to build a clinical LLMs. - We first synthesized synthetic notes from [PMC-Patients](https://huggingface.co/datasets/zhengyun21/PMC-Patients) case reports with GPT-3.5 - Then, we generate instruction-answer pairs for 157k synthetic discharge summaries ### Supported Tasks - This dataset covers below 8 tasks - Named Entity Recognition - Abbreviation Expansion - Relation Extraction - Temporal Information Extraction - Coreference Resolution - Paraphrasing - Summarization - Question Answering ### Languages English ## Dataset Structure ### Data Instances - `synthetic.csv` - Clinical Note - Question - Answer pairs ### Data Fields - `patient_id`: Unique case report id from PMC-Patients - `patient`: Case report text - `question`: GPT-3.5 generated instruction from patient. The used prompt can be checked on github. - `answer`: GPT-3.5 generated answer for given case report and question - `task`: Corresponding category of question. One of above listsed ## Dataset Creation ### Source Data [PMC-Patients](https://huggingface.co/datasets/zhengyun21/PMC-Patients) ### Annotations We used GPT-3.5-turbo (version 0314). You can check the prompts on our github. ## Additional Information ### Models - [Asclepius-7B](https://huggingface.co/starmpcc/Asclepius-7B) - [Asclepius-13B](https://huggingface.co/starmpcc/Asclepius-13B) - [Asclepius-Llama2-7B](https://huggingface.co/starmpcc/Asclepius-Llama2-7B) - [Asclepius-Llama2-13B](https://huggingface.co/starmpcc/Asclepius-Llama2-13B) - [Asclepius-Llama3-8B](https://huggingface.co/starmpcc/Asclepius-Llama3-8B) - [Asclepius-Mistral-7B-v0.3](https://huggingface.co/starmpcc/Asclepius-Mistral-7B-v0.3) ### Variants - The instruction-answer pairs generated from MIMIC-III discharge summaries and the models trained with them are now available on [Physionet](https://physionet.org/content/asclepius-r/1.0.0/)! ### Licensing Information CC-BY-NC-SA 4.0 ### Citation Information ``` @misc{kweon2023publicly, title={Publicly Shareable Clinical Large Language Model Built on Synthetic Clinical Notes}, author={Sunjun Kweon and Junu Kim and Jiyoun Kim and Sujeong Im and Eunbyeol Cho and Seongsu Bae and Jungwoo Oh and Gyubok Lee and Jong Hak Moon and Seng Chan You and Seungjin Baek and Chang Hoon Han and Yoon Bin Jung and Yohan Jo and Edward Choi}, year={2023}, eprint={2309.00237}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

allenai/tulu-v2-sft-mixture

allenai

2024-05-24T21:29:24Z

521

123

[ "task_categories:question-answering", "task_categories:text-generation", "language:en", "license:odc-by", "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:2305.18290", "region:us" ]

[ "question-answering", "conversational", "text-generation" ]

2023-11-13T21:56:34Z

null

--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: dataset dtype: string - name: id dtype: string - name: messages list: - name: role dtype: string - name: content dtype: string splits: - name: train num_bytes: 1239293363 num_examples: 326154 download_size: 554561769 dataset_size: 1239293363 license: odc-by task_categories: - question-answering - conversational - text-generation language: - en size_categories: - 100K<n<1M --- <img src="https://huggingface.co/datasets/allenai/blog-images/resolve/main/tulu-v2/Tulu%20V2%20banner.png" alt="TuluV2 banner" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/> # Dataset Card for Tulu V2 Mix *Note the [ODC-BY license](https://opendatacommons.org/licenses/by/1-0/), indicating that different licenses apply to subsets of the data. This means that some portions of the dataset are non-commercial. We present the mixture as a research artifact.* Tulu is a series of language models that are trained to act as helpful assistants. The dataset consists of a mix of : * [FLAN](https://github.com/google-research/FLAN/tree/main) (Apache 2.0): We use 50,000 examples sampled from FLAN v2. To emphasize CoT-style reasoning, we sample another 50,000 examples from the CoT subset of the FLAN v2 mixture. * [Open Assistant 1](https://huggingface.co/datasets/OpenAssistant/oasst1) (Apache 2.0): We isolate the highest-scoring paths in each conversation tree and use these samples, resulting in 7,708 examples. * [ShareGPT](https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered) (Apache 2.0 listed, no official repo found): We use all 114,046 from our processed ShareGPT dataset, as we found ShareGPT gave strong performance in prior work. * [GPT4-Alpaca](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM#data-release) (CC By NC 4.0):We sample 20,000 samples from GPT-4 Alpaca to further include distilled GPT-4 data. * [Code-Alpaca](https://github.com/sahil280114/codealpaca) (CC By NC 4.0):We use all 20,022 examples from Code Alpaca, following our prior V1 mixture, in order to improve model code abilities. * [LIMA](https://huggingface.co/datasets/GAIR/lima) (CC BY-NC-SA): We use 1,030 examples from LIMA as an example of carefully curated data. * [WizardLM Evol Instruct](https://huggingface.co/datasets/WizardLM/WizardLM_evol_instruct_V2_196k) (No license provided): We subsample 30,000 examples from WizardLM, which contains distilled data of increasing diversity and complexity. * [Open-Orca](https://huggingface.co/datasets/Open-Orca/OpenOrca) (MIT): We sample 30,000 samples generated by GPT-4 from OpenOrca, a reproduction of Orca Mukherjee et al., 2023, which augments FLAN data with additional model-generated explanations * Hardcoded: A collection of prompts such as `Tell me about yourself' such that the model generates correct outputs given inquiries about its name or developers. We wrote 14 samples and repeat each sample 10 times in the mixture, resulting in 140 total samples. * Science: 7,544 examples from a mixture of scientific document understand tasks—including question answering, fact-checking, summarization, and information extraction (under development, standalone release soon). These are made by taking either just the training set of the subsets or the entire section if no splits are present. Tulu V2 is presented as a singular training split. [Tulu V2 DPO 70B](https://huggingface.co/allenai/tulu-2-dpo-70b), and is a fine-tuned version of Llama 2 that was trained on on a mix of publicly available, synthetic and human datasets using [Direct Preference Optimization (DPO)](https://arxiv.org/abs/2305.18290). **Model Family:** Other models and the dataset are found in the [Tulu V2 collection](https://huggingface.co/collections/allenai/tulu-v2-suite-6551b56e743e6349aab45101). The length distribution of the dataset can be seen below: <img src="https://huggingface.co/datasets/allenai/blog-images/resolve/main/tulu-v2/length_histogram_v2.png" alt="TuluV2 histogram" width="600" style="margin-left:'auto' margin-right:'auto' display:'block'"/> Tulu V1 Mix can be found [here](https://huggingface.co/datasets/allenai/tulu-v1). **Note**: Some samples contain empty turns as noted in [this github issue](https://github.com/allenai/open-instruct/issues/161). We will not remove these from this release to ensure reproducibility but you may wish to explicitly filter them out when training your own models! ### Science data note The included science data is from the following categories: <img src="https://huggingface.co/datasets/allenai/blog-images/resolve/main/tulu-v2/science_data.png" alt="TuluV2 science data mix" width="600" style="margin-left:'auto' margin-right:'auto' display:'block'"/> Note that some of the examples include an off-by-one error in the sentence indexing that had a small or negligible impact on performance. This was found during testing and will be updated in future versions, with the detailed release of the dataset artifact itself coming in a future release. ### License We are releasing this dataset under the terms of [ODC-BY](https://opendatacommons.org/licenses/by/1-0/). By using this, you are also bound by the [Common Crawl terms of use](https://commoncrawl.org/terms-of-use/) in respect of the content contained in the dataset.

ipipan/polqa

ipipan

2024-05-24T15:35:50Z

214

10

[ "task_categories:question-answering", "task_categories:text-retrieval", "task_categories:text2text-generation", "task_ids:open-domain-qa", "task_ids:document-retrieval", "task_ids:abstractive-qa", "annotations_creators:expert-generated", "language:pl", "license:cc-by-sa-4.0", "size_categories:10K<n<100K", "arxiv:2212.08897", "region:us" ]

[ "question-answering", "text-retrieval", "text2text-generation" ]

2022-12-17T15:03:58Z

1

--- task_categories: - question-answering - text-retrieval - text2text-generation task_ids: - open-domain-qa - document-retrieval - abstractive-qa language: - pl pretty_name: PolQA size_categories: - 10K<n<100K annotations_creators: - expert-generated license: cc-by-sa-4.0 --- # Dataset Card for PolQA Dataset ## Dataset Description - **Paper:** [Improving Question Answering Performance through Manual Annotation: Costs, Benefits and Strategies](https://arxiv.org/abs/2212.08897) - **Point of Contact:** [Piotr Rybak](mailto:[email protected]) ### Dataset Summary PolQA is the first Polish dataset for open-domain question answering. It consists of 7,000 questions, 87,525 manually labeled evidence passages, and a corpus of over 7 million candidate passages. The dataset can be used to train both a passage retriever and an abstractive reader. ### Supported Tasks and Leaderboards - `open-domain-qa`: The dataset can be used to train a model for open-domain question answering. Success on this task is typically measured using [metric defined during PolEval 2021](https://2021.poleval.pl/tasks/task4). - `document-retrieval`: The dataset can be used to train a model for document retrieval. Success on this task is typically measured by [top-k retrieval accuracy](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.top_k_accuracy_score.html) or [NDCG](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.ndcg_score.html). - `abstractive-qa`: The dataset can be used to train a model for abstractive question answering. Success on this task is typically measured using [metric defined during PolEval 2021](https://2021.poleval.pl/tasks/task4). ### Languages The text is in Polish, as spoken by the host of the [Jeden z Dziesięciu](https://pl.wikipedia.org/wiki/Jeden_z_dziesi%C4%99ciu) TV show (questions) and [Polish Wikipedia](https://pl.wikipedia.org/) editors (passages). The BCP-47 code for Polish is pl-PL. ## Dataset Structure ### Data Instances The main part of the dataset consists of manually annotated question-passage pairs. For each instance, there is a `question`, a passage (`passage_id`, `passage_title`, `passage_text`), and a boolean indicator if the passage is `relevant` for the given question (i.e. does it contain the answers). For each `question` there is a list of possible `answers` formulated in a natural language, in a way a Polish speaker would answer the questions. It means that the answers might contain prepositions, be inflected, and contain punctuation. In some cases, the answer might have multiple correct variants, e.g. numbers are written as numerals and words, synonyms, abbreviations and their expansions. Additionally, we provide a classification of each question-answer pair based on the `question_formulation`, the `question_type`, and the `entity_type/entity_subtype`, according to the taxonomy proposed by [Maciej Ogrodniczuk and Piotr Przybyła (2021)](http://nlp.ipipan.waw.pl/Bib/ogr:prz:21:poleval.pdf). ``` { 'question_id': 6, 'passage_title': 'Mumbaj', 'passage_text': 'Mumbaj lub Bombaj (marathi मुंबई, trb.: Mumbaj; ang. Mumbai; do 1995 Bombay) – stolica indyjskiego stanu Maharasztra, położona na wyspie Salsette, na Morzu Arabskim.', 'passage_wiki': 'Mumbaj lub Bombaj (mr. मुंबई, trb.: "Mumbaj"; ang. Mumbai; do 1995 Bombay) – stolica indyjskiego stanu Maharasztra, położona na wyspie Salsette, na Morzu Arabskim. Wraz z miastami satelitarnymi tworzy najludniejszą po Delhi aglomerację liczącą 23 miliony mieszkańców. Dzięki naturalnemu położeniu jest to największy port morski kraju. Znajdują się tutaj także najsilniejsze giełdy Azji Południowej: National Stock Exchange of India i Bombay Stock Exchange.', 'passage_id': '42609-0', 'duplicate': False, 'question': 'W którym państwie leży Bombaj?', 'relevant': True, 'annotated_by': 'Igor', 'answers': "['w Indiach', 'Indie']", 'question_formulation': 'QUESTION', 'question_type': 'SINGLE ENTITY', 'entity_type': 'NAMED', 'entity_subtype': 'COUNTRY', 'split': 'train', 'passage_source': 'human' } ``` The second part of the dataset is a corpus of Polish Wikipedia (March 2022 snapshot) passages. The raw Wikipedia snapshot was parsed using [WikiExtractor](https://github.com/attardi/wikiextractor) and split into passages at the ends of the paragraphs or if the passage was longer than 500 characters. ``` { 'id': '42609-0', 'title': 'Mumbaj', 'text': 'Mumbaj lub Bombaj (mr. मुंबई, trb.: "Mumbaj"; ang. Mumbai; do 1995 Bombay) – stolica indyjskiego stanu Maharasztra, położona na wyspie Salsette, na Morzu Arabskim. Wraz z miastami satelitarnymi tworzy najludniejszą po Delhi aglomerację liczącą 23 miliony mieszkańców. Dzięki naturalnemu położeniu jest to największy port morski kraju. Znajdują się tutaj także najsilniejsze giełdy Azji Południowej: National Stock Exchange of India i Bombay Stock Exchange.' } ``` ### Data Fields Question-passage pairs: - `question_id`: an integer id of the question - `passage_title`: a string containing the title of the Wikipedia article - `passage_text`: a string containing the passage text as extracted by the human annotator - `passage_wiki`: a string containing the passage text as it can be found in the provided Wikipedia corpus. Empty if the passage doesn't exist in the corpus. - `passage_id`: a string containing the id of the passage from the provided Wikipedia corpus. Empty if the passage doesn't exist in the corpus. - `duplicate`: a boolean flag representing whether a question-passage pair is duplicated in the dataset. This occurs when the same passage was found in multiple passage sources. - `question`: a string containing the question - `relevant`: a boolean flag representing whether a passage is relevant to the question (i.e. does it contain the answers) - `annotated_by`: a string containing the name of the annotator who verified the relevance of the pair - `answers`: a string containing a list of possible short answers to the question - `question_formulation`: a string containing a kind of expression used to request information. One of the following: - `QUESTION`, e.g. *What is the name of the first letter of the Greek alphabet?* - `COMMAND`, e.g. *Expand the abbreviation ’CIA’.* - `COMPOUND`, e.g. *This French writer, born in the 19th century, is considered a pioneer of sci-fi literature. What is his name?* - `question_type`: a string indicating what type of information is sought by the question. One of the following: - `SINGLE ENTITY`, e.g. *Who is the hero in the Tomb Rider video game series?* - `MULTIPLE ENTITIES`, e.g. *Which two seas are linked by the Corinth Canal?* - `ENTITY CHOICE`, e.g. *Is "Sombrero" a type of dance, a hat, or a dish?* - `YES/NO`, e.g. *When the term of office of the Polish Sejm is terminated, does it apply to the Senate as well?* - `OTHER NAME`, e.g. *What was the nickname of Louis I, the King of the Franks?* - `GAP FILLING`, e.g. *Finish the proverb: "If you fly with the crows... ".* - `entity_type`: a string containing a type of the sought entity. One of the following: `NAMED`, `UNNAMED`, or `YES/NO`. - `entity_subtype`: a string containing a subtype of the sought entity. Can take one of the 34 different values. - `split`: a string containing the split of the dataset. One of the following: `train`, `valid`, or `test`. - `passage_source`: a string containing the source of the passage. One of the following: - `human`: the passage was proposed by a human annotator using any internal (i.e. Wikipedia search) or external (e.g. Google) search engines and any keywords or queries they considered useful - `hard-negatives`: the passage was proposed using a neural retriever trained on the passages found by the human annotators - `zero-shot`: the passage was proposed by the BM25 retriever and re-ranked using [multilingual cross-encoder](https://huggingface.co/unicamp-dl/mMiniLM-L6-v2-mmarco-v2) Corpus of passages: - `id`: a string representing the Wikipedia article id and the index of extracted passage. Matches the `passage_id` from the main part of the dataset. - `title`: a string containing the title of the Wikipedia article. Matches the `passage_title` from the main part of the dataset. - `text`: a string containing the passage text. Matches the `passage_wiki` from the main part of the dataset. ### Data Splits The questions are assigned into one of three splits: `train`, `validation`, and `test`. The `validation` and `test` questions are randomly sampled from the `test-B` dataset from the [PolEval 2021](https://2021.poleval.pl/tasks/task4) competition. | | # questions | # positive passages | # negative passages | |------------|------------:|--------------------:|--------------------:| | train | 5,000 | 27,131 | 34,904 | | validation | 1,000 | 5,839 | 6,927 | | test | 1,000 | 5,938 | 6,786 | ## Dataset Creation ### Curation Rationale The PolQA dataset was created to support and promote the research in the open-domain question answering for Polish. It also serves as a benchmark to evaluate OpenQA systems. ### Source Data #### Initial Data Collection and Normalization The majority of questions come from two existing resources, the 6,000 questions from the [PolEval 2021 shared task on QA](https://2021.poleval.pl/tasks/task4) and additional 1,000 questions gathered by one of the shared task [participants](http://poleval.pl/files/poleval2021.pdf#page=151). Originally, the questions come from collections associated with TV shows, both officially published and gathered online by their fans, as well as questions used in actual quiz competitions, on TV or online. The evidence passages come from the Polish Wikipedia (March 2022 snapshot). The raw Wikipedia snapshot was parsed using [WikiExtractor](https://github.com/attardi/wikiextractor) and split into passages at the ends of the paragraphs or if the passage was longer than 500 characters. #### Who are the source language producers? The questions come from various sources and their authors are unknown but are mostly analogous (or even identical) to questions asked during the [Jeden z Dziesięciu](https://pl.wikipedia.org/wiki/Jeden_z_dziesi%C4%99ciu) TV show. The passages were written by the editors of the Polish Wikipedia. ### Annotations #### Annotation process Two approaches were used to annotate the question-passage pairs. Each of them consists of two phases: the retrieval of candidate passages and the manual verification of their relevance. In the first approach, we asked annotators to use internal (i.e. Wikipedia search) or external (e.g. Google) search engines to find up to five relevant passages using any keywords or queries they consider useful (`passage_source="human"`). Based on those passages, we trained the neural retriever to extend the number of relevant passages, as well as to retrieve the hard negatives (`passage_source="hard-negatives"`). In the second approach, the passage candidates were proposed by the BM25 retriever and re-ranked using [multilingual cross-encoder](https://huggingface.co/unicamp-dl/mMiniLM-L6-v2-mmarco-v2) (`passage_source="zero-shot"`). In both cases, all proposed question-passage pairs were manually verified by the annotators. We release the annotation guidelines [here](https://docs.google.com/document/d/1LDW7EJFH0bm-FRlxM_uHb0mqJzKHiewOFBHe5qZnTW8/edit?usp=sharing). #### Who are the annotators? The annotation team consisted of 16 annotators, all native Polish speakers, most of them having linguistic backgrounds and previous experience as an annotator. ### Personal and Sensitive Information The dataset does not contain any personal or sensitive information. ## Considerations for Using the Data ### Social Impact of Dataset This dataset was created to promote the research in the open-domain question answering for Polish and allow developing question answering systems. ### Discussion of Biases The passages proposed by the `hard-negative` and `zero-shot` methods are bound to be easier to retrieve by retrievers since they were proposed by such. To mitigate this bias, we include the passages found by the human annotators in an unconstrained way (`passage_source="human"`). We hypothesize that it will result in more unbiased and diverse examples. Moreover, we asked the annotators to find not one but up to five passages, preferably from different articles to even further increase passage diversity. ### Other Known Limitations The PolQA dataset focuses on trivia questions which might limit its usefulness in real-world applications since neural retrievers generalize poorly to other domains. ## Additional Information ### Dataset Curators The PolQA dataset was developed by Piotr Rybak, Piotr Przybyła, and Maciej Ogrodniczuk from the [Institute of Computer Science, Polish Academy of Sciences](http://zil.ipipan.waw.pl/). This work was supported by the European Regional Development Fund as a part of 2014–2020 Smart Growth Operational Programme, CLARIN — Common Language Resources and Technology Infrastructure, project no. POIR.04.02.00-00C002/19. ### Licensing Information CC BY-SA 4.0 ### Citation Information ``` @inproceedings{rybak-etal-2024-polqa-polish, title = "{P}ol{QA}: {P}olish Question Answering Dataset", author = "Rybak, Piotr and Przyby{\l}a, Piotr and Ogrodniczuk, Maciej", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.1125", pages = "12846--12855", abstract = "Recently proposed systems for open-domain question answering (OpenQA) require large amounts of training data to achieve state-of-the-art performance. However, data annotation is known to be time-consuming and therefore expensive to acquire. As a result, the appropriate datasets are available only for a handful of languages (mainly English and Chinese). In this work, we introduce and publicly release PolQA, the first Polish dataset for OpenQA. It consists of 7,000 questions, 87,525 manually labeled evidence passages, and a corpus of over 7,097,322 candidate passages. Each question is classified according to its formulation, type, as well as entity type of the answer. This resource allows us to evaluate the impact of different annotation choices on the performance of the QA system and propose an efficient annotation strategy that increases the passage retrieval accuracy@10 by 10.55 p.p. while reducing the annotation cost by 82{\%}.", } ```

wanng/midjourney-v5-202304-clean

wanng

2024-05-24T05:09:52Z

82

89

[ "task_categories:text-to-image", "task_categories:image-to-text", "language:en", "language:fr", "license:apache-2.0", "size_categories:1M<n<10M", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us", "midjourney" ]

[ "text-to-image", "image-to-text" ]

2023-05-26T06:58:05Z

null

--- license: apache-2.0 task_categories: - text-to-image - image-to-text language: - en - fr tags: - midjourney pretty_name: zolie size_categories: - 1K<n<10K --- # midjourney-v5-202304-clean ## 简介 Brief Introduction 非官方的，爬取自midjourney v5的2023年4月的数据，一共1701420条。 Unofficial, crawled from midjourney v5 for April 2023, 1,701,420 pairs in total. ## 数据集信息 Dataset Information 原始项目地址：https://huggingface.co/datasets/tarungupta83/MidJourney_v5_Prompt_dataset 我做了一些清洗，清理出了两个文件： - ori_prompts_df.parquet （1,255,812对，midjourney的四格图） ![ori_sample](https://cdn.discordapp.com/attachments/995431387333152778/1098283849076711424/mansonwu_A_charismatic_wealthy_young_man_is_fully_immersed_in_a_9bd4f414-eb40-4642-a381-f5ac56e99ec5.png) - upscaled_prompts_df.parquet （445,608对，使用了高清指令的图，这意味着这个图更受欢迎。） ![upscaled_sample](https://cdn.discordapp.com/attachments/984632520471633920/1105721768422948905/Tomberhood_The_intelligent_rescue_boat_on_the_beach_can_automat_e54faffe-0668-49e4-812d-713038bdc7bc.png) Original project address: https://huggingface.co/datasets/tarungupta83/MidJourney_v5_Prompt_dataset I did some cleaning and cleaned out two files: - ori_prompts_df.parquet (1,255,812 pairs, midjourney's four-frame diagrams) - upscaled_prompts_df.parquet (445,608 pairs, graphs that use the Upscale command, which means this one is more popular.)

lmms-lab/POPE

lmms-lab

2024-05-23T03:29:23Z

11,057

9

[ "size_categories:10K<n<100K", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]

[]

2024-01-18T15:13:42Z

null

--- dataset_info: - config_name: Full features: - name: id dtype: string - name: question_id dtype: string - name: question dtype: string - name: answer dtype: string - name: image_source dtype: string - name: image dtype: image - name: category dtype: string splits: - name: adversarial num_bytes: 490408158.0 num_examples: 3000 - name: popular num_bytes: 490397000.0 num_examples: 3000 - name: random num_bytes: 490394976.0 num_examples: 3000 download_size: 255022914 dataset_size: 1471200134.0 - config_name: default features: - name: id dtype: string - name: question_id dtype: string - name: question dtype: string - name: answer dtype: string - name: image_source dtype: string - name: image dtype: image - name: category dtype: string splits: - name: test num_bytes: 1471200135.0 num_examples: 9000 download_size: 255022914 dataset_size: 1471200135.0 configs: - config_name: Full data_files: - split: adversarial path: Full/adversarial-* - split: popular path: Full/popular-* - split: random path: Full/random-* - config_name: default data_files: - split: test path: data/test-* --- <img src="https://i.postimg.cc/g0QRgMVv/WX20240228-113337-2x.png" width="100%" height="80%"> # Large-scale Multi-modality Models Evaluation Suite > Accelerating the development of large-scale multi-modality models (LMMs) with `lmms-eval` 🏠 [Homepage](https://lmms-lab.github.io/) | 📚 [Documentation](docs/README.md) | 🤗 [Huggingface Datasets](https://huggingface.co/lmms-lab) # This Dataset This is a formatted version of [POPE](https://github.com/RUCAIBox/POPE). It is used in our `lmms-eval` pipeline to allow for one-click evaluations of large multi-modality models. ``` @article{li2023evaluating, title={Evaluating object hallucination in large vision-language models}, author={Li, Yifan and Du, Yifan and Zhou, Kun and Wang, Jinpeng and Zhao, Wayne Xin and Wen, Ji-Rong}, journal={arXiv preprint arXiv:2305.10355}, year={2023} } ```

ilsp/mmlu_greek

ilsp

2024-05-20T12:36:54Z

27,818

4

[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2024-04-01T14:53:41Z

null

--- dataset_info: - config_name: abstract_algebra features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 58157 num_examples: 100 - name: validation num_bytes: 6010 num_examples: 11 - name: dev num_bytes: 2497 num_examples: 5 download_size: 0 dataset_size: 66664 - config_name: all features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 20041347 num_examples: 14042 - name: validation num_bytes: 2196992 num_examples: 1531 - name: dev num_bytes: 360807 num_examples: 285 download_size: 10333898 dataset_size: 22599146 - config_name: anatomy features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 97333 num_examples: 135 - name: validation num_bytes: 9131 num_examples: 14 - name: dev num_bytes: 2731 num_examples: 5 download_size: 67694 dataset_size: 109195 - config_name: astronomy features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 141580 num_examples: 152 - name: validation num_bytes: 15462 num_examples: 16 - name: dev num_bytes: 6380 num_examples: 5 download_size: 95251 dataset_size: 163422 - config_name: business_ethics features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 101936 num_examples: 100 - name: validation num_bytes: 9096 num_examples: 11 - name: dev num_bytes: 6368 num_examples: 5 download_size: 77394 dataset_size: 117400 - config_name: clinical_knowledge features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 193539 num_examples: 265 - name: validation num_bytes: 20500 num_examples: 29 - name: dev num_bytes: 3720 num_examples: 5 download_size: 126056 dataset_size: 217759 - config_name: college_biology features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 152394 num_examples: 144 - name: validation num_bytes: 14995 num_examples: 16 - name: dev num_bytes: 4638 num_examples: 5 download_size: 105576 dataset_size: 172027 - config_name: college_chemistry features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 72251 num_examples: 100 - name: validation num_bytes: 6677 num_examples: 8 - name: dev num_bytes: 3862 num_examples: 5 download_size: 61210 dataset_size: 82790 - config_name: college_computer_science features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 135321 num_examples: 100 - name: validation num_bytes: 15037 num_examples: 11 - name: dev num_bytes: 8606 num_examples: 5 download_size: 101342 dataset_size: 158964 - config_name: college_mathematics features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 74448 num_examples: 100 - name: validation num_bytes: 8274 num_examples: 11 - name: dev num_bytes: 4276 num_examples: 5 download_size: 63556 dataset_size: 86998 - config_name: college_medicine features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 251805 num_examples: 173 - name: validation num_bytes: 24431 num_examples: 22 - name: dev num_bytes: 5031 num_examples: 5 download_size: 144635 dataset_size: 281267 - config_name: college_physics features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 90708 num_examples: 102 - name: validation num_bytes: 10367 num_examples: 11 - name: dev num_bytes: 4139 num_examples: 5 download_size: 68341 dataset_size: 105214 - config_name: computer_security features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 86922 num_examples: 100 - name: validation num_bytes: 14003 num_examples: 11 - name: dev num_bytes: 3445 num_examples: 5 download_size: 75244 dataset_size: 104370 - config_name: conceptual_physics features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 127706 num_examples: 235 - name: validation num_bytes: 14286 num_examples: 26 - name: dev num_bytes: 2978 num_examples: 5 download_size: 82813 dataset_size: 144970 - config_name: econometrics features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 136916 num_examples: 114 - name: validation num_bytes: 14730 num_examples: 12 - name: dev num_bytes: 4794 num_examples: 5 download_size: 86025 dataset_size: 156440 - config_name: electrical_engineering features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 80296 num_examples: 145 - name: validation num_bytes: 9138 num_examples: 16 - name: dev num_bytes: 2824 num_examples: 5 download_size: 62008 dataset_size: 92258 - config_name: elementary_mathematics features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 211831 num_examples: 378 - name: validation num_bytes: 27305 num_examples: 41 - name: dev num_bytes: 4252 num_examples: 5 download_size: 131272 dataset_size: 243388 - config_name: formal_logic features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 146101 num_examples: 126 - name: validation num_bytes: 18160 num_examples: 14 - name: dev num_bytes: 4917 num_examples: 5 download_size: 77094 dataset_size: 169178 - config_name: global_facts features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 55953 num_examples: 100 - name: validation num_bytes: 5672 num_examples: 10 - name: dev num_bytes: 3547 num_examples: 5 download_size: 0 dataset_size: 65172 - config_name: high_school_biology features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 338155 num_examples: 310 - name: validation num_bytes: 33555 num_examples: 32 - name: dev num_bytes: 4992 num_examples: 5 download_size: 200936 dataset_size: 376702 - config_name: high_school_chemistry features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 170771 num_examples: 203 - name: validation num_bytes: 20157 num_examples: 22 - name: dev num_bytes: 3387 num_examples: 5 download_size: 108321 dataset_size: 194315 - config_name: high_school_computer_science features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 139128 num_examples: 100 - name: validation num_bytes: 10800 num_examples: 9 - name: dev num_bytes: 9269 num_examples: 5 download_size: 99359 dataset_size: 159197 - config_name: high_school_european_history features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 799080 num_examples: 165 - name: validation num_bytes: 88740 num_examples: 18 - name: dev num_bytes: 34585 num_examples: 5 download_size: 503439 dataset_size: 922405 - config_name: high_school_geography features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 132655 num_examples: 198 - name: validation num_bytes: 13612 num_examples: 22 - name: dev num_bytes: 4597 num_examples: 5 download_size: 90939 dataset_size: 150864 - config_name: high_school_government_and_politics features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 215224 num_examples: 193 - name: validation num_bytes: 22888 num_examples: 21 - name: dev num_bytes: 5640 num_examples: 5 download_size: 132695 dataset_size: 243752 - config_name: high_school_macroeconomics features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 374553 num_examples: 390 - name: validation num_bytes: 41817 num_examples: 43 - name: dev num_bytes: 4310 num_examples: 5 download_size: 177813 dataset_size: 420680 - config_name: high_school_mathematics features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 161023 num_examples: 270 - name: validation num_bytes: 17224 num_examples: 29 - name: dev num_bytes: 3682 num_examples: 5 download_size: 105683 dataset_size: 181929 - config_name: high_school_microeconomics features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 241816 num_examples: 238 - name: validation num_bytes: 24317 num_examples: 26 - name: dev num_bytes: 4029 num_examples: 5 download_size: 125789 dataset_size: 270162 - config_name: high_school_physics features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 175856 num_examples: 151 - name: validation num_bytes: 19899 num_examples: 17 - name: dev num_bytes: 4348 num_examples: 5 download_size: 109639 dataset_size: 200103 - config_name: high_school_psychology features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 494955 num_examples: 545 - name: validation num_bytes: 53743 num_examples: 60 - name: dev num_bytes: 5900 num_examples: 5 download_size: 285730 dataset_size: 554598 - config_name: high_school_statistics features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 333736 num_examples: 216 - name: validation num_bytes: 30252 num_examples: 23 - name: dev num_bytes: 7320 num_examples: 5 download_size: 191017 dataset_size: 371308 - config_name: high_school_us_history features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 883614 num_examples: 204 - name: validation num_bytes: 93694 num_examples: 22 - name: dev num_bytes: 26282 num_examples: 5 download_size: 533320 dataset_size: 1003590 - config_name: high_school_world_history features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 1126143 num_examples: 237 - name: validation num_bytes: 135245 num_examples: 26 - name: dev num_bytes: 14589 num_examples: 5 download_size: 662773 dataset_size: 1275977 - config_name: human_aging features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 145275 num_examples: 223 - name: validation num_bytes: 15038 num_examples: 23 - name: dev num_bytes: 3062 num_examples: 5 download_size: 99856 dataset_size: 163375 - config_name: human_sexuality features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 100379 num_examples: 131 - name: validation num_bytes: 7585 num_examples: 12 - name: dev num_bytes: 3504 num_examples: 5 download_size: 74540 dataset_size: 111468 - config_name: international_law features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 162013 num_examples: 121 - name: validation num_bytes: 18937 num_examples: 13 - name: dev num_bytes: 7290 num_examples: 5 download_size: 0 dataset_size: 188240 - config_name: jurisprudence features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 102393 num_examples: 108 - name: validation num_bytes: 11049 num_examples: 11 - name: dev num_bytes: 3754 num_examples: 5 download_size: 21545 dataset_size: 117196 - config_name: logical_fallacies features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 153973 num_examples: 163 - name: validation num_bytes: 15857 num_examples: 18 - name: dev num_bytes: 4919 num_examples: 5 download_size: 82298 dataset_size: 174749 - config_name: machine_learning features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 102745 num_examples: 112 - name: validation num_bytes: 9797 num_examples: 11 - name: dev num_bytes: 7448 num_examples: 5 download_size: 70870 dataset_size: 119990 - config_name: management features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 63772 num_examples: 103 - name: validation num_bytes: 5671 num_examples: 11 - name: dev num_bytes: 2677 num_examples: 5 download_size: 52323 dataset_size: 72120 - config_name: marketing features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 191635 num_examples: 234 - name: validation num_bytes: 22377 num_examples: 25 - name: dev num_bytes: 4734 num_examples: 5 download_size: 122877 dataset_size: 218746 - config_name: medical_genetics features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 64177 num_examples: 100 - name: validation num_bytes: 9298 num_examples: 11 - name: dev num_bytes: 3405 num_examples: 5 download_size: 58337 dataset_size: 76880 - config_name: miscellaneous features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 443155 num_examples: 783 - name: validation num_bytes: 42990 num_examples: 86 - name: dev num_bytes: 1877 num_examples: 5 download_size: 283087 dataset_size: 488022 - config_name: moral_disputes features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 332269 num_examples: 346 - name: validation num_bytes: 38501 num_examples: 38 - name: dev num_bytes: 5222 num_examples: 5 download_size: 193075 dataset_size: 375992 - config_name: moral_scenarios features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 1061634 num_examples: 895 - name: validation num_bytes: 120664 num_examples: 100 - name: dev num_bytes: 5816 num_examples: 5 download_size: 283716 dataset_size: 1188114 - config_name: nutrition features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 281680 num_examples: 306 - name: validation num_bytes: 25350 num_examples: 33 - name: dev num_bytes: 6423 num_examples: 5 download_size: 168790 dataset_size: 313453 - config_name: philosophy features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 240333 num_examples: 311 - name: validation num_bytes: 27480 num_examples: 34 - name: dev num_bytes: 2986 num_examples: 5 download_size: 153970 dataset_size: 270799 - config_name: prehistory features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 267644 num_examples: 324 - name: validation num_bytes: 30414 num_examples: 35 - name: dev num_bytes: 5577 num_examples: 5 download_size: 172053 dataset_size: 303635 - config_name: professional_accounting features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 377751 num_examples: 282 - name: validation num_bytes: 42879 num_examples: 31 - name: dev num_bytes: 6331 num_examples: 5 download_size: 228950 dataset_size: 426961 - config_name: professional_law features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 5612166 num_examples: 1534 - name: validation num_bytes: 604980 num_examples: 170 - name: dev num_bytes: 19825 num_examples: 5 download_size: 3065337 dataset_size: 6236971 - config_name: professional_medicine features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 639421 num_examples: 272 - name: validation num_bytes: 70186 num_examples: 31 - name: dev num_bytes: 11017 num_examples: 5 download_size: 391893 dataset_size: 720624 - config_name: professional_psychology features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 687869 num_examples: 612 - name: validation num_bytes: 87912 num_examples: 69 - name: dev num_bytes: 6693 num_examples: 5 download_size: 405705 dataset_size: 782474 - config_name: public_relations features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 89435 num_examples: 110 - name: validation num_bytes: 14174 num_examples: 12 - name: dev num_bytes: 4718 num_examples: 5 download_size: 0 dataset_size: 108327 - config_name: security_studies features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 632255 num_examples: 245 - name: validation num_bytes: 69100 num_examples: 27 - name: dev num_bytes: 16171 num_examples: 5 download_size: 0 dataset_size: 717526 - config_name: sociology features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 204018 num_examples: 201 - name: validation num_bytes: 22531 num_examples: 22 - name: dev num_bytes: 5054 num_examples: 5 download_size: 9676 dataset_size: 231603 - config_name: us_foreign_policy features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 89965 num_examples: 100 - name: validation num_bytes: 10270 num_examples: 11 - name: dev num_bytes: 5111 num_examples: 5 download_size: 68974 dataset_size: 105346 - config_name: virology features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 116211 num_examples: 166 - name: validation num_bytes: 16273 num_examples: 18 - name: dev num_bytes: 3185 num_examples: 5 download_size: 96586 dataset_size: 135669 - config_name: world_religions features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 77273 num_examples: 171 - name: validation num_bytes: 8462 num_examples: 19 - name: dev num_bytes: 2073 num_examples: 5 download_size: 61169 dataset_size: 87808 configs: - config_name: abstract_algebra data_files: - split: test path: abstract_algebra/test-* - split: validation path: abstract_algebra/validation-* - split: dev path: abstract_algebra/dev-* - config_name: all data_files: - split: test path: all/test-* - split: validation path: all/validation-* - split: dev path: all/dev-* - config_name: anatomy data_files: - split: test path: anatomy/test-* - split: validation path: anatomy/validation-* - split: dev path: anatomy/dev-* - config_name: astronomy data_files: - split: test path: astronomy/test-* - split: validation path: astronomy/validation-* - split: dev path: astronomy/dev-* - config_name: business_ethics data_files: - split: test path: business_ethics/test-* - split: validation path: business_ethics/validation-* - split: dev path: business_ethics/dev-* - config_name: clinical_knowledge data_files: - split: test path: clinical_knowledge/test-* - split: validation path: clinical_knowledge/validation-* - split: dev path: clinical_knowledge/dev-* - config_name: college_biology data_files: - split: test path: college_biology/test-* - split: validation path: college_biology/validation-* - split: dev path: college_biology/dev-* - config_name: college_chemistry data_files: - split: test path: college_chemistry/test-* - split: validation path: college_chemistry/validation-* - split: dev path: college_chemistry/dev-* - config_name: college_computer_science data_files: - split: test path: college_computer_science/test-* - split: validation path: college_computer_science/validation-* - split: dev path: college_computer_science/dev-* - config_name: college_mathematics data_files: - split: test path: college_mathematics/test-* - split: validation path: college_mathematics/validation-* - split: dev path: college_mathematics/dev-* - config_name: college_medicine data_files: - split: test path: college_medicine/test-* - split: validation path: college_medicine/validation-* - split: dev path: college_medicine/dev-* - config_name: college_physics data_files: - split: test path: college_physics/test-* - split: validation path: college_physics/validation-* - split: dev path: college_physics/dev-* - config_name: computer_security data_files: - split: test path: computer_security/test-* - split: validation path: computer_security/validation-* - split: dev path: computer_security/dev-* - config_name: conceptual_physics data_files: - split: test path: conceptual_physics/test-* - split: validation path: conceptual_physics/validation-* - split: dev path: conceptual_physics/dev-* - config_name: econometrics data_files: - split: test path: econometrics/test-* - split: validation path: econometrics/validation-* - split: dev path: econometrics/dev-* - config_name: electrical_engineering data_files: - split: test path: electrical_engineering/test-* - split: validation path: electrical_engineering/validation-* - split: dev path: electrical_engineering/dev-* - config_name: elementary_mathematics data_files: - split: test path: elementary_mathematics/test-* - split: validation path: elementary_mathematics/validation-* - split: dev path: elementary_mathematics/dev-* - config_name: formal_logic data_files: - split: test path: formal_logic/test-* - split: validation path: formal_logic/validation-* - split: dev path: formal_logic/dev-* - config_name: global_facts data_files: - split: test path: global_facts/test-* - split: validation path: global_facts/validation-* - split: dev path: global_facts/dev-* - config_name: high_school_biology data_files: - split: test path: high_school_biology/test-* - split: validation path: high_school_biology/validation-* - split: dev path: high_school_biology/dev-* - config_name: high_school_chemistry data_files: - split: test path: high_school_chemistry/test-* - split: validation path: high_school_chemistry/validation-* - split: dev path: high_school_chemistry/dev-* - config_name: high_school_computer_science data_files: - split: test path: high_school_computer_science/test-* - split: validation path: high_school_computer_science/validation-* - split: dev path: high_school_computer_science/dev-* - config_name: high_school_european_history data_files: - split: test path: high_school_european_history/test-* - split: validation path: high_school_european_history/validation-* - split: dev path: high_school_european_history/dev-* - config_name: high_school_geography data_files: - split: test path: high_school_geography/test-* - split: validation path: high_school_geography/validation-* - split: dev path: high_school_geography/dev-* - config_name: high_school_government_and_politics data_files: - split: test path: high_school_government_and_politics/test-* - split: validation path: high_school_government_and_politics/validation-* - split: dev path: high_school_government_and_politics/dev-* - config_name: high_school_macroeconomics data_files: - split: test path: high_school_macroeconomics/test-* - split: validation path: high_school_macroeconomics/validation-* - split: dev path: high_school_macroeconomics/dev-* - config_name: high_school_mathematics data_files: - split: test path: high_school_mathematics/test-* - split: validation path: high_school_mathematics/validation-* - split: dev path: high_school_mathematics/dev-* - config_name: high_school_microeconomics data_files: - split: test path: high_school_microeconomics/test-* - split: validation path: high_school_microeconomics/validation-* - split: dev path: high_school_microeconomics/dev-* - config_name: high_school_physics data_files: - split: test path: high_school_physics/test-* - split: validation path: high_school_physics/validation-* - split: dev path: high_school_physics/dev-* - config_name: high_school_psychology data_files: - split: test path: high_school_psychology/test-* - split: validation path: high_school_psychology/validation-* - split: dev path: high_school_psychology/dev-* - config_name: high_school_statistics data_files: - split: test path: high_school_statistics/test-* - split: validation path: high_school_statistics/validation-* - split: dev path: high_school_statistics/dev-* - config_name: high_school_us_history data_files: - split: test path: high_school_us_history/test-* - split: validation path: high_school_us_history/validation-* - split: dev path: high_school_us_history/dev-* - config_name: high_school_world_history data_files: - split: test path: high_school_world_history/test-* - split: validation path: high_school_world_history/validation-* - split: dev path: high_school_world_history/dev-* - config_name: human_aging data_files: - split: test path: human_aging/test-* - split: validation path: human_aging/validation-* - split: dev path: human_aging/dev-* - config_name: human_sexuality data_files: - split: test path: human_sexuality/test-* - split: validation path: human_sexuality/validation-* - split: dev path: human_sexuality/dev-* - config_name: international_law data_files: - split: test path: international_law/test-* - split: validation path: international_law/validation-* - split: dev path: international_law/dev-* - config_name: jurisprudence data_files: - split: test path: jurisprudence/test-* - split: validation path: jurisprudence/validation-* - split: dev path: jurisprudence/dev-* - config_name: logical_fallacies data_files: - split: test path: logical_fallacies/test-* - split: validation path: logical_fallacies/validation-* - split: dev path: logical_fallacies/dev-* - config_name: machine_learning data_files: - split: test path: machine_learning/test-* - split: validation path: machine_learning/validation-* - split: dev path: machine_learning/dev-* - config_name: management data_files: - split: test path: management/test-* - split: validation path: management/validation-* - split: dev path: management/dev-* - config_name: marketing data_files: - split: test path: marketing/test-* - split: validation path: marketing/validation-* - split: dev path: marketing/dev-* - config_name: medical_genetics data_files: - split: test path: medical_genetics/test-* - split: validation path: medical_genetics/validation-* - split: dev path: medical_genetics/dev-* - config_name: miscellaneous data_files: - split: test path: miscellaneous/test-* - split: validation path: miscellaneous/validation-* - split: dev path: miscellaneous/dev-* - config_name: moral_disputes data_files: - split: test path: moral_disputes/test-* - split: validation path: moral_disputes/validation-* - split: dev path: moral_disputes/dev-* - config_name: moral_scenarios data_files: - split: test path: moral_scenarios/test-* - split: validation path: moral_scenarios/validation-* - split: dev path: moral_scenarios/dev-* - config_name: nutrition data_files: - split: test path: nutrition/test-* - split: validation path: nutrition/validation-* - split: dev path: nutrition/dev-* - config_name: philosophy data_files: - split: test path: philosophy/test-* - split: validation path: philosophy/validation-* - split: dev path: philosophy/dev-* - config_name: prehistory data_files: - split: test path: prehistory/test-* - split: validation path: prehistory/validation-* - split: dev path: prehistory/dev-* - config_name: professional_accounting data_files: - split: test path: professional_accounting/test-* - split: validation path: professional_accounting/validation-* - split: dev path: professional_accounting/dev-* - config_name: professional_law data_files: - split: test path: professional_law/test-* - split: validation path: professional_law/validation-* - split: dev path: professional_law/dev-* - config_name: professional_medicine data_files: - split: test path: professional_medicine/test-* - split: validation path: professional_medicine/validation-* - split: dev path: professional_medicine/dev-* - config_name: professional_psychology data_files: - split: test path: professional_psychology/test-* - split: validation path: professional_psychology/validation-* - split: dev path: professional_psychology/dev-* - config_name: public_relations data_files: - split: test path: public_relations/test-* - split: validation path: public_relations/validation-* - split: dev path: public_relations/dev-* - config_name: security_studies data_files: - split: test path: security_studies/test-* - split: validation path: security_studies/validation-* - split: dev path: security_studies/dev-* - config_name: sociology data_files: - split: test path: sociology/test-* - split: validation path: sociology/validation-* - split: dev path: sociology/dev-* - config_name: us_foreign_policy data_files: - split: test path: us_foreign_policy/test-* - split: validation path: us_foreign_policy/validation-* - split: dev path: us_foreign_policy/dev-* - config_name: virology data_files: - split: test path: virology/test-* - split: validation path: virology/validation-* - split: dev path: virology/dev-* - config_name: world_religions data_files: - split: test path: world_religions/test-* - split: validation path: world_religions/validation-* - split: dev path: world_religions/dev-* --- # Dataset Card for MMLU Greek The MMLU Greek dataset is a set of 15858 examples from the MMLU dataset [available from here and here], machine-translated into Greek. The original dataset consists of multiple-choice questions from 57 tasks including elementary mathematics, US history, computer science, law, etc. ## Dataset Details ### Dataset Description - **Curated by:** ILSP/Athena RC - **Language(s) (NLP):** el - **License:** cc-by-nc-sa-4.0 ## Bias, Risks, and Limitations  This dataset is the result of machine translation. ## Dataset Card Contact https://www.athenarc.gr/en/ilsp

Skylion007/openwebtext

Skylion007

2024-05-17T17:56:27Z

85,484

422

[ "task_categories:text-generation", "task_categories:fill-mask", "task_ids:language-modeling", "task_ids:masked-language-modeling", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:monolingual", "source_datasets:original", "language:en", "license:cc0-1.0", "size_categories:1M<n<10M", "region:us" ]

[ "text-generation", "fill-mask" ]

2022-03-02T23:29:22Z

null

--- annotations_creators: - no-annotation language_creators: - found language: - en license: - cc0-1.0 multilinguality: - monolingual pretty_name: OpenWebText size_categories: - 1M<n<10M source_datasets: - original task_categories: - text-generation - fill-mask task_ids: - language-modeling - masked-language-modeling paperswithcode_id: openwebtext dataset_info: features: - name: text dtype: string config_name: plain_text splits: - name: train num_bytes: 39769491688 num_examples: 8013769 download_size: 12880189440 dataset_size: 39769491688 --- # Dataset Card for "openwebtext" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://skylion007.github.io/OpenWebTextCorpus/](https://skylion007.github.io/OpenWebTextCorpus/) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 13.51 GB - **Size of the generated dataset:** 41.70 GB - **Total amount of disk used:** 55.21 GB ### Dataset Summary An open-source replication of the WebText dataset from OpenAI, that was used to train GPT-2. This distribution was created by Aaron Gokaslan and Vanya Cohen of Brown University. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### plain_text - **Size of downloaded dataset files:** 13.51 GB - **Size of the generated dataset:** 41.70 GB - **Total amount of disk used:** 55.21 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "text": "\"A magazine supplement with an image of Adolf Hitler and the title 'The Unreadable Book' is pictured in Berlin. No law bans “Mei..." } ``` ### Data Fields The data fields are the same among all splits. #### plain_text - `text`: a `string` feature. ### Data Splits | name | train | |------------|--------:| | plain_text | 8013769 | ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization The authors started by extracting all Reddit post urls from the Reddit submissions dataset. These links were deduplicated, filtered to exclude non-html content, and then shuffled randomly. The links were then distributed to several machines in parallel for download, and all web pages were extracted using the newspaper python package. Using Facebook FastText, non-English web pages were filtered out. Subsequently, near-duplicate documents were identified using local-sensitivity hashing (LSH). Documents were hashed into sets of 5-grams and all documents that had a similarity threshold of greater than 0.5 were removed. The the remaining documents were tokenized, and documents with fewer than 128 tokens were removed. This left 38GB of text data (40GB using SI units) from 8,013,769 documents. #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations The dataset doesn't contain annotations. ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information These data are released under this licensing scheme from the original authors ([source](https://skylion007.github.io/OpenWebTextCorpus/)): ``` We do not own any of the text from which these data has been extracted. We license the actual packaging of these parallel data under the [Creative Commons CC0 license (“no rights reserved”)](https://creativecommons.org/share-your-work/public-domain/cc0/) ``` #### Notice policy Should you consider that our data contains material that is owned by you and should therefore not be reproduced here, please: Clearly identify yourself, with detailed contact data such as an address, telephone number or email address at which you can be contacted. Clearly identify the copyrighted work claimed to be infringed. Clearly identify the material that is claimed to be infringing and information reasonably sufficient to allow us to locate the material. And contact us at the following email address: openwebtext at gmail.com and datasets at huggingface.co #### Take down policy The original authors will comply to legitimate requests by removing the affected sources from the next release of the corpus. Hugging Face will also update this repository accordingly. ### Citation Information ``` @misc{Gokaslan2019OpenWeb, title={OpenWebText Corpus}, author={Gokaslan, Aaron and Cohen, Vanya and Pavlick, Ellie and Tellex, Stefanie}, howpublished={\url{http://Skylion007.github.io/OpenWebTextCorpus}}, year={2019} } ``` ### Contributions Thanks to [@richarddwang](https://github.com/richarddwang) for adding this dataset.

lmarena-ai/arena-human-preference-55k

lmarena-ai

2024-05-17T03:04:04Z

536

142

[ "task_categories:text-classification", "language:en", "license:apache-2.0", "size_categories:10K<n<100K", "format:csv", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2403.04132", "region:us" ]

[ "text-classification" ]

2024-05-02T19:00:07Z

null

--- license: apache-2.0 task_categories: - text-classification language: - en pretty_name: LMSYS Chatbot Arena Human Preference Predictions size_categories: - 10K<n<100K --- Dataset for [Kaggle competition](https://www.kaggle.com/competitions/lmsys-chatbot-arena/overview) on predicting human preference on Chatbot Arena battles. The training dataset includes over 55,000 real-world user and LLM conversations and user preferences across over 70 state-of-the-art LLMs, such as GPT-4, Claude 2, Llama 2, Gemini, and Mistral models. Each sample represents a battle consisting of 2 LLMs which answer the same question, with a user label of either prefer model A, prefer model B, tie, or tie (both bad). ### Citation Please cite the following paper if you find our leaderboard or dataset helpful. ``` @misc{chiang2024chatbot, title={Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference}, author={Wei-Lin Chiang and Lianmin Zheng and Ying Sheng and Anastasios Nikolas Angelopoulos and Tianle Li and Dacheng Li and Hao Zhang and Banghua Zhu and Michael Jordan and Joseph E. Gonzalez and Ion Stoica}, year={2024}, eprint={2403.04132}, archivePrefix={arXiv}, primaryClass={cs.AI} } ```

common-canvas/commoncatalog-cc-by-sa

common-canvas

2024-05-16T19:41:37Z

115,865

8

[ "task_categories:text-to-image", "language:en", "license:cc-by-sa-4.0", "size_categories:1M<n<10M", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:2310.16825", "region:us" ]

[ "text-to-image" ]

2023-10-19T02:05:17Z

null

--- license: cc-by-sa-4.0 dataset_info: features: - name: jpg dtype: image - name: blip2_caption dtype: string - name: caption dtype: string - name: licensename dtype: string - name: licenseurl dtype: string - name: width dtype: int32 - name: height dtype: int32 - name: original_width dtype: int32 - name: original_height dtype: int32 - name: photoid dtype: int64 - name: uid dtype: string - name: unickname dtype: string - name: datetaken dtype: timestamp[us] - name: dateuploaded dtype: int64 - name: capturedevice dtype: string - name: title dtype: string - name: usertags dtype: string - name: machinetags dtype: string - name: longitude dtype: float64 - name: latitude dtype: float64 - name: accuracy dtype: int64 - name: pageurl dtype: string - name: downloadurl dtype: string - name: serverid dtype: int64 - name: farmid dtype: int64 - name: secret dtype: string - name: secretoriginal dtype: string - name: ext dtype: string - name: url dtype: string - name: key dtype: string - name: status dtype: string - name: error_message dtype: string - name: exif dtype: string - name: sha256 dtype: string - name: description dtype: string task_categories: - text-to-image language: - en --- # Dataset Card for CommonCatalog CC-BY-SA This dataset is a large collection of high-resolution Creative Common images (composed of different licenses, see paper Table 1 in the Appendix) collected in 2014 from users of Yahoo Flickr. The dataset contains images of up to 4k resolution, making this one of the highest resolution captioned image datasets. ## Dataset Details ### Dataset Description We provide captions synthetic captions to approximately 100 million high resolution images collected from Yahoo Flickr Creative Commons (YFCC). - **Curated by:** Aaron Gokaslan - **Language(s) (NLP):** en - **License:** See relevant yaml tag / dataset name. ### Dataset Sources  - **Repository:** https://github.com/mosaicml/diffusion - **Paper:** https://arxiv.org/abs/2310.16825 - **Demo:** See CommonCanvas Gradios ## Uses We use CommonCatalog to train a family latent diffusion models called CommonCanvas. The goal is to produce a model that is competitive with Stable Diffusion 2, but to do so using an easily accessible dataset of known provenance. Doing so makes replicating the model significantly easier, and provides a clearer mechanism for applying training-data attribution techniques. ### Direct Use Training text-to-image models Training image-to-text models ### Out-of-Scope Use  * Crafting content that is offensive or injurious towards individuals, including negative portrayals of their living conditions, cultural backgrounds, religious beliefs, etc. * Deliberately creating or spreading content that is discriminatory or reinforces harmful stereotypes. * Falsely representing individuals without their permission. * Generating sexual content that may be seen by individuals without their consent. * Producing or disseminating false or misleading information. * Creating content that depicts extreme violence or bloodshed. * Distributing content that modifies copyrighted or licensed material in a way that breaches its usage terms. ## Dataset Structure The dataset is divided into 10 subsets each containing parquets about 4GB each. Each subfolder within contains a resolution range of the images and their respective aspect ratios. The dataset is also divided along images licensed for commercial use (C) and those that are not (NC). ## Dataset Creation ### Curation Rationale Creating a standardized, accessible dataset with synthetic caption and releasing it so other people can train on a common dataset for open source image generation. ### Source Data Yahoo Flickr Creative Commons 100M Dataset and Synthetically Generated Caption Data. #### Data Collection and Processing All synthetic captions were generated with BLIP2. See paper for more details. #### Who are the source data producers?  Users of Flickr ## Bias, Risks, and Limitations See Yahoo Flickr Creative Commons 100M dataset for more information. The information was collected circa 2014 and known to have a bias towards internet connected Western countries. Some areas such as the global south lack representation. ## Citation **BibTeX:** ``` @article{gokaslan2023commoncanvas, title={CommonCanvas: An Open Diffusion Model Trained with Creative-Commons Images}, author={Gokaslan, Aaron and Cooper, A Feder and Collins, Jasmine and Seguin, Landan and Jacobson, Austin and Patel, Mihir and Frankle, Jonathan and Stephenson, Cory and Kuleshov, Volodymyr}, journal={arXiv preprint arXiv:2310.16825}, year={2023} } ``` ## Dataset Card Authors [Aaron Gokaslan](https://huggingface.co/Skylion007) ## Dataset Card Contact [Aaron Gokaslan](https://huggingface.co/Skylion007)

g0ster/TinyStories-Korean

g0ster

2024-05-15T09:23:41Z

78

9

[ "task_categories:translation", "language:ko", "language:en", "license:mit", "size_categories:10M<n<100M", "format:text", "modality:text", "library:datasets", "library:mlcroissant", "region:us" ]

[ "translation" ]

2024-05-14T11:52:01Z

2

--- license: mit task_categories: - translation language: - ko - en pretty_name: tinystories-korean size_categories: - 1M<n<10M --- # TinyStories-Korean  This dataset is a translated version of [roneneldan](https://huggingface.co/roneneldan)'s [TinyStories](https://huggingface.co/datasets/roneneldan/TinyStories) dataset. I first downloaded roneneldan's TinyStories, and I organized it in a db file. Then I used a local transalation model [eeve](https://huggingface.co/yanolja/EEVE-Korean-Instruct-10.8B-v1.0) to translate, and I changed it back to a txt file. Feel free to use! --- ## Citation ``` @misc{kim2024tinystories, title={TinyStories Korean translations}, author={Dohoon Kim(g0ster)}, year={2024}, } ```

flytech/python-codes-25k

flytech

2024-05-15T08:04:00Z

575

121

[ "task_categories:text-classification", "task_categories:question-answering", "task_categories:token-classification", "task_categories:summarization", "task_categories:text2text-generation", "task_categories:text-generation", "size_categories:10K<n<100K", "format:json", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "region:us", "code", "python", "flytech", "cleaned", "instructional", "dataset 25k", "text2code", "code2text", "behavioral", "codegeneration", "trending", "programming" ]

[ "text-classification", "question-answering", "token-classification", "summarization", "text2text-generation", "text-generation" ]

2023-10-30T17:03:27Z

null

--- task_categories: - text-classification - question-answering - token-classification - summarization - text2text-generation - text-generation tags: - code - python - flytech - cleaned - instructional - dataset 25k - text2code - code2text - behavioral - codegeneration - trending - programming size_categories: - 1M<n<10M --- ### License MIT <hr style="height:1px;border:none;color:#333;background-color:#444;" /> ## This is a Cleaned Python Dataset Covering 25,000 Instructional Tasks ### Overview The dataset has 4 key features (fields): instruction, input, output, and text. It's a rich source for Python codes, tasks, and extends into behavioral aspects. <hr style="height:1px;border:none;color:#333;background-color:#444;" /> ### Dataset Statistics - **Total Entries**: 24,813 - **Unique Instructions**: 24,580 - **Unique Inputs**: 3,666 - **Unique Outputs**: 24,581 - **Unique Texts**: 24,813 - **Average Tokens per example**: 508 ### Features - `instruction`: The instructional task to be performed / User input - `input`: Very short, introductive part of AI response or empty - `output`: Python code that accomplishes the task - `text`: All fields combined together <hr style="height:1px;border:none;color:#333;background-color:#444;" /> ### Usage This dataset can be useful for: - Code generation tasks - Natural Language Understanding models specialized in coding languages - Behavioral analysis based on the given tasks and codes - Educational purposes to understand coding styles and task variations To load the dataset, one can use the following snippet: ```python # Double-check if there is ~25k examples instead of almost 50k (HF thinks jsonl and json are two different datasets) from datasets import load_dataset dataset = load_dataset('flytech/python-codes-25k', split='train') # One can map the dataset in any way, for the sake of example: dataset = dataset.map(lambda example: {'text': example['instruction'] + ' ' + example['input'] + ' ' + example['output']})['text'] # Remember that you don't need to map if the dataset has a "text" field already:) ``` ### Access & Contributions Feel free to use this dataset as per the MIT license. Contributions to enhance or expand the dataset are welcome. The dataset can also be found on kaggle, under the same name but from different author.

Voxel51/WLASL

Voxel51

2024-05-06T15:10:59Z

10,640

6

[ "task_categories:video-classification", "language:en", "license:other", "size_categories:10K<n<100K", "modality:image", "modality:video", "library:fiftyone", "arxiv:1910.11006", "region:us", "fiftyone", "video", "activity-recognition", "asl", "sign-language" ]

[ "video-classification" ]

2024-04-22T16:03:30Z

null

--- annotations_creators: [] language: en license: other size_categories: - 10K<n<100K task_categories: - video-classification task_ids: [] pretty_name: World Level American Sign Language tags: - fiftyone - video - activity-recognition - asl - sign-language dataset_summary: > ![image/png](dataset_preview.gif) This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 11980 samples. ## Installation If you haven't already, install FiftyOne: ```bash pip install -U fiftyone ``` ## Usage ```python import fiftyone as fo import fiftyone.utils.huggingface as fouh # Load the dataset # Note: other available arguments include 'max_samples', etc dataset = fouh.load_from_hub("Voxel51/WLASL") # Launch the App session = fo.launch_app(dataset) ``` --- # Dataset Card for WLASL  ![image/png](dataset_preview.gif) This is a [FiftyOne](https://github.com/voxel51/fiftyone) video dataset with 11980 samples. ## Installation If you haven't already, install FiftyOne: ```bash pip install -U fiftyone ``` ## Usage ```python import fiftyone as fo import fiftyone.utils.huggingface as fouh # Load the dataset # Note: other available arguments include 'max_samples', etc dataset = fouh.load_from_hub("Voxel51/WLASL") # Launch the App session = fo.launch_app(dataset) ``` ## Dataset Details ### Dataset Description WLASL is the largest video dataset for Word-Level American Sign Language (ASL) recognition, which features 2,000 common different words in ASL. The authors hope WLASL will facilitate the research in sign language understanding and eventually benefit the communication between deaf and hearing communities. - **Curated by:** Dongxu Li and Hongdong Li - **Language(s) (NLP):** en - **License:** other ### Dataset Sources  - **Repository:** https://github.com/dxli94/WLASL - **Paper:** https://arxiv.org/abs/1910.11006 - **Homepage:** https://dxli94.github.io/WLASL/ - **Demo:** https://try.fiftyone.ai/datasets/asl-dataset/samples ## Uses All the WLASL data is intended for academic and computational use only. No commercial usage is allowed. Licensed under the [Computational Use of Data Agreement](https://github.com/microsoft/Computational-Use-of-Data-Agreement/releases/tag/v1.0) (C-UDA) ## Citation  **BibTeX:** ```bibtex @misc{li2020wordlevel, title={Word-level Deep Sign Language Recognition from Video: A New Large-scale Dataset and Methods Comparison}, author={Dongxu Li and Cristian Rodriguez Opazo and Xin Yu and Hongdong Li}, year={2020}, eprint={1910.11006}, archivePrefix={arXiv}, primaryClass={cs.CV} } @inproceedings{li2020transferring, title={Transferring cross-domain knowledge for video sign language recognition}, author={Li, Dongxu and Yu, Xin and Xu, Chenchen and Petersson, Lars and Li, Hongdong}, booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, pages={6205--6214}, year={2020} } ``` ## Dataset Card Authors [Jacob Marks](https://huggingface.co/jamarks)

alexantonov/chuvash_parallel

alexantonov

2024-05-06T14:09:47Z

123

6

[ "multilinguality:translation", "source_datasets:original", "language:cv", "region:us" ]

[]

2022-03-02T23:29:22Z

1

--- language: - cv multilinguality: - translation source_datasets: - original task_ids: - machine-translation --- # Dataset Description Deprecated. Please use [Chuvash-English](https://huggingface.co/datasets/alexantonov/chuvash_english_parallel) and [Chuvash-Russian](https://huggingface.co/datasets/alexantonov/chuvash_russian_parallel) datasets in a more convenient format. ## Chuvash-Russian parallel corpus 1M parallel sentences. Manually aligned ## Chuvash-English parallel corpus. 200K parallel sentences. Automatically aligned ## Contributions For additional details contact [@AlAntonov](https://github.com/AlAntonov).

lcw99/wikipedia-korean-20221001

lcw99

2024-05-05T09:12:11Z

273

7

[ "language:ko", "license:apache-2.0", "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]

[]

2022-10-10T03:49:37Z

1

--- language: - ko license: apache-2.0 --- [20240501 update](https://huggingface.co/datasets/lcw99/wikipedia-korean-20240501)

ylacombe/expresso

ylacombe

2024-04-30T16:49:14Z

578

61

[ "language:en", "license:cc-by-nc-4.0", "size_categories:10K<n<100K", "format:parquet", "modality:audio", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:2308.05725", "region:us" ]

[]

2024-04-30T16:10:38Z

5

--- dataset_info: config_name: read features: - name: audio dtype: audio - name: text dtype: string - name: speaker_id dtype: string - name: style dtype: string - name: id dtype: string splits: - name: train num_bytes: 5702432944.34 num_examples: 11615 download_size: 5761373569 dataset_size: 5702432944.34 configs: - config_name: read data_files: - split: train path: read/train-* license: cc-by-nc-4.0 language: - en pretty_name: The Expresso Dataset --- # The Expresso Dataset [[paper]](https://arxiv.org/abs/2308.05725) [[demo samples]](https://speechbot.github.io/expresso/) [[Original repository]](https://github.com/facebookresearch/textlesslib/tree/main/examples/expresso/dataset) ## Introduction The Expresso dataset is a high-quality (48kHz) expressive speech dataset that includes both expressively rendered read speech (8 styles, in mono wav format) and improvised dialogues (26 styles, in stereo wav format). The dataset includes 4 speakers (2 males, 2 females), and totals 40 hours (11h read, 30h improvised). The transcriptions of the read speech are also provided. You can listen to samples from the Expresso Dataset at [this website](https://speechbot.github.io/expresso/). ## Data Statistics Here are the statistics of Expresso’s expressive styles: ---------------------------------------------------------------- Style | Read (min) | Improvised (min) | total (hrs) ------------------|------------|------------------|------------- angry | - | 82 | 1.4 animal | - | 27 | 0.4 animal_directed | - | 32 | 0.5 awe | - | 92 | 1.5 bored | - | 92 | 1.5 calm | - | 93 | 1.6 child | - | 28 | 0.4 child_directed | - | 38 | 0.6 confused | 94 | 66 | 2.7 default | 133 | 158 | 4.9 desire | - | 92 | 1.5 disgusted | - | 118 | 2.0 enunciated | 116 | 62 | 3.0 fast | - | 98 | 1.6 fearful | - | 98 | 1.6 happy | 74 | 92 | 2.8 laughing | 94 | 103 | 3.3 narration | 21 | 76 | 1.6 non_verbal | - | 32 | 0.5 projected | - | 94 | 1.6 sad | 81 | 101 | 3.0 sarcastic | - | 106 | 1.8 singing* | - | 4 | .07 sleepy | - | 93 | 1.5 sympathetic | - | 100 | 1.7 whisper | 79 | 86 | 2.8 **Total** | **11.5h** | **34.4h** | **45.9h** ---------------------------------------------------------------- *singing is the only improvised style that is not in dialogue format. ## Audio Quality The audio was recorded in a professional recording studio with minimal background noise at 48kHz/24bit. The files for read speech and singing are in a mono wav format; and for the dialog section in stereo (one channel per actor), where the original flow of turn-taking is preserved. ### Read Speech The `read` config contains all the read speech and singing style. ## License The Expresso dataset is distributed under the [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/) license. ## Reference For more information, see the paper: [EXPRESSO: A Benchmark and Analysis of Discrete Expressive Speech Resynthesis](https://arxiv.org/abs/2308.05725), Tu Anh Nguyen*, Wei-Ning Hsu*, Antony D'Avirro*, Bowen Shi*, Itai Gat, Maryam Fazel-Zarani, Tal Remez, Jade Copet, Gabriel Synnaeve, Michael Hassid, Felix Kreuk, Yossi Adi⁺, Emmanuel Dupoux⁺, INTERSPEECH 2023.

cogsci13/Amazon-Reviews-2023-Books-Meta

cogsci13

2024-04-18T15:23:50Z

163

6

[ "language:en", "size_categories:1M<n<10M", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us", "recommendation", "reviews" ]

[]

2024-04-18T15:11:14Z

2

--- language: - en tags: - recommendation - reviews size_categories: - 100M<n<1B --- # Amazon Reviews 2023 (Books Only) **This is a subset of Amazon Review 2023 dataset. Please visit [amazon-reviews-2023.github.io/](https://amazon-reviews-2023.github.io/) for more details, loading scripts, and preprocessed benchmark files.** **[April 18, 2024]** Update 1. This dataset was created and pushed for the first time. ---  This is a large-scale **Amazon Reviews** dataset, collected in **2023** by [McAuley Lab](https://cseweb.ucsd.edu/~jmcauley/), and it includes rich features such as: 1. **User Reviews** (*ratings*, *text*, *helpfulness votes*, etc.); 2. **Item Metadata** (*descriptions*, *price*, *raw image*, etc.); ## What's New? In the Amazon Reviews'23, we provide: 1. **Larger Dataset:** We collected 571.54M reviews, 245.2% larger than the last version; 2. **Newer Interactions:** Current interactions range from May. 1996 to Sep. 2023; 3. **Richer Metadata:** More descriptive features in item metadata; 4. **Fine-grained Timestamp:** Interaction timestamp at the second or finer level; 5. **Cleaner Processing:** Cleaner item metadata than previous versions; 6. **Standard Splitting:** Standard data splits to encourage RecSys benchmarking. ## Basic Statistics > We define the #R_Tokens as the number of [tokens](https://pypi.org/project/tiktoken/) in user reviews and #M_Tokens as the number of [tokens](https://pypi.org/project/tiktoken/) if treating the dictionaries of item attributes as strings. We emphasize them as important statistics in the era of LLMs. > We count the number of items based on user reviews rather than item metadata files. Note that some items lack metadata. ### Grouped by Category | Category | #User | #Item | #Rating | #R_Token | #M_Token | Download | | ------------------------ | ------: | ------: | --------: | -------: | -------: | ------------------------------: | | Books | 10.3M | 4.4M | 29.5M | 2.9B | 3.7B | <a href='https://datarepo.eng.ucsd.edu/mcauley_group/data/amazon_2023/raw/review_categories/Books.jsonl.gz' download> review</a>, <a href='https://datarepo.eng.ucsd.edu/mcauley_group/data/amazon_2023/raw/meta_categories/meta_Books.jsonl.gz' download> meta </a> | meta </a> | > Check Pure ID files and corresponding data splitting strategies in [Common Data Processing](https://amazon-reviews-2023.github.io/data_processing/index.html) section. ## Quick Start ### Load User Reviews ```python from datasets import load_dataset dataset = load_dataset("cogsci13/Amazon-Reviews-2023-Books-Review", "raw_review_Books", trust_remote_code=True) print(dataset["full"][0]) ``` ```json {'rating': {0: 1.0}, 'title': {0: 'Not a watercolor book! Seems like copies imo.'}, 'text': {0: 'It is definitely not a watercolor book. The paper bucked completely. The pages honestly appear to be photo copies of other pictures. I say that bc if you look at the seal pics you can see the tell tale line at the bottom of the page. As someone who has made many photocopies of pages in my time so I could try out different colors & mediums that black line is a dead giveaway to me. It’s on other pages too. The entire book just seems off. Nothing is sharp & clear. There is what looks like toner dust on all the pages making them look muddy. There are no sharp lines & there is no clear definition. At least there isn’t in my copy. And the Coloring Book for Adult on the bottom of the front cover annoys me. Why is it singular & not plural? They usually say coloring book for kids or coloring book for kids & adults or coloring book for adults- plural. Lol Plus it would work for kids if you can get over the grey scale nature of it. Personally I’m not going to waste expensive pens & paints trying to paint over the grey & black mess. I grew up in SW Florida minutes from the beaches & I was really excited about the sea life in this. I hope the printers & designers figure out how to clean up the mess bc some of the designs are really cute. They just aren’t worth my time to hand trace & transfer them, but I’m sure there are ppl that will be up to the challenge. This is one is a hard no. Going back. I tried.'}, 'images': {0: array([{'attachment_type': 'IMAGE', 'large_image_url': 'https://m.media-amazon.com/images/I/516HBU7LQoL._SL1600_.jpg', 'medium_image_url': 'https://m.media-amazon.com/images/I/516HBU7LQoL._SL800_.jpg', 'small_image_url': 'https://m.media-amazon.com/images/I/516HBU7LQoL._SL256_.jpg'}, {'attachment_type': 'IMAGE', 'large_image_url': 'https://m.media-amazon.com/images/I/71+XwcacMmL._SL1600_.jpg', 'medium_image_url': 'https://m.media-amazon.com/images/I/71+XwcacMmL._SL800_.jpg', 'small_image_url': 'https://m.media-amazon.com/images/I/71+XwcacMmL._SL256_.jpg'}, {'attachment_type': 'IMAGE', 'large_image_url': 'https://m.media-amazon.com/images/I/71RbTuvD1ZL._SL1600_.jpg', 'medium_image_url': 'https://m.media-amazon.com/images/I/71RbTuvD1ZL._SL800_.jpg', 'small_image_url': 'https://m.media-amazon.com/images/I/71RbTuvD1ZL._SL256_.jpg'}, {'attachment_type': 'IMAGE', 'large_image_url': 'https://m.media-amazon.com/images/I/71U63wdOeZL._SL1600_.jpg', 'medium_image_url': 'https://m.media-amazon.com/images/I/71U63wdOeZL._SL800_.jpg', 'small_image_url': 'https://m.media-amazon.com/images/I/71U63wdOeZL._SL256_.jpg'}, {'attachment_type': 'IMAGE', 'large_image_url': 'https://m.media-amazon.com/images/I/71WFEDyKcKL._SL1600_.jpg', 'medium_image_url': 'https://m.media-amazon.com/images/I/71WFEDyKcKL._SL800_.jpg', 'small_image_url': 'https://m.media-amazon.com/images/I/71WFEDyKcKL._SL256_.jpg'}, {'attachment_type': 'IMAGE', 'large_image_url': 'https://m.media-amazon.com/images/I/8109NwjpHKL._SL1600_.jpg', 'medium_image_url': 'https://m.media-amazon.com/images/I/8109NwjpHKL._SL800_.jpg', 'small_image_url': 'https://m.media-amazon.com/images/I/8109NwjpHKL._SL256_.jpg'}, {'attachment_type': 'IMAGE', 'large_image_url': 'https://m.media-amazon.com/images/I/814gxfh8wcL._SL1600_.jpg', 'medium_image_url': 'https://m.media-amazon.com/images/I/814gxfh8wcL._SL800_.jpg', 'small_image_url': 'https://m.media-amazon.com/images/I/814gxfh8wcL._SL256_.jpg'}, {'attachment_type': 'IMAGE', 'large_image_url': 'https://m.media-amazon.com/images/I/81HC0vKRC2L._SL1600_.jpg', 'medium_image_url': 'https://m.media-amazon.com/images/I/81HC0vKRC2L._SL800_.jpg', 'small_image_url': 'https://m.media-amazon.com/images/I/81HC0vKRC2L._SL256_.jpg'}, {'attachment_type': 'IMAGE', 'large_image_url': 'https://m.media-amazon.com/images/I/81Nx6BnRLxL._SL1600_.jpg', 'medium_image_url': 'https://m.media-amazon.com/images/I/81Nx6BnRLxL._SL800_.jpg', 'small_image_url': 'https://m.media-amazon.com/images/I/81Nx6BnRLxL._SL256_.jpg'}, {'attachment_type': 'IMAGE', 'large_image_url': 'https://m.media-amazon.com/images/I/81QQMwBcVPL._SL1600_.jpg', 'medium_image_url': 'https://m.media-amazon.com/images/I/81QQMwBcVPL._SL800_.jpg', 'small_image_url': 'https://m.media-amazon.com/images/I/81QQMwBcVPL._SL256_.jpg'}, {'attachment_type': 'IMAGE', 'large_image_url': 'https://m.media-amazon.com/images/I/81fgT3R3OwL._SL1600_.jpg', 'medium_image_url': 'https://m.media-amazon.com/images/I/81fgT3R3OwL._SL800_.jpg', 'small_image_url': 'https://m.media-amazon.com/images/I/81fgT3R3OwL._SL256_.jpg'}, {'attachment_type': 'IMAGE', 'large_image_url': 'https://m.media-amazon.com/images/I/81mfzny0I5L._SL1600_.jpg', 'medium_image_url': 'https://m.media-amazon.com/images/I/81mfzny0I5L._SL800_.jpg', 'small_image_url': 'https://m.media-amazon.com/images/I/81mfzny0I5L._SL256_.jpg'}, {'attachment_type': 'IMAGE', 'large_image_url': 'https://m.media-amazon.com/images/I/81nir7bf91L._SL1600_.jpg', 'medium_image_url': 'https://m.media-amazon.com/images/I/81nir7bf91L._SL800_.jpg', 'small_image_url': 'https://m.media-amazon.com/images/I/81nir7bf91L._SL256_.jpg'}, {'attachment_type': 'IMAGE', 'large_image_url': 'https://m.media-amazon.com/images/I/81yLUo6ZL3L._SL1600_.jpg', 'medium_image_url': 'https://m.media-amazon.com/images/I/81yLUo6ZL3L._SL800_.jpg', 'small_image_url': 'https://m.media-amazon.com/images/I/81yLUo6ZL3L._SL256_.jpg'}, {'attachment_type': 'IMAGE', 'large_image_url': 'https://m.media-amazon.com/images/I/81zh9h5RwkL._SL1600_.jpg', 'medium_image_url': 'https://m.media-amazon.com/images/I/81zh9h5RwkL._SL800_.jpg', 'small_image_url': 'https://m.media-amazon.com/images/I/81zh9h5RwkL._SL256_.jpg'}, {'attachment_type': 'IMAGE', 'large_image_url': 'https://m.media-amazon.com/images/I/91yfcpFlEqL._SL1600_.jpg', 'medium_image_url': 'https://m.media-amazon.com/images/I/91yfcpFlEqL._SL800_.jpg', 'small_image_url': 'https://m.media-amazon.com/images/I/91yfcpFlEqL._SL256_.jpg'}], dtype=object)}, 'asin': {0: 'B09BGPFTDB'}, 'parent_asin': {0: 'B09BGPFTDB'}, 'user_id': {0: 'AFKZENTNBQ7A7V7UXW5JJI6UGRYQ'}, 'timestamp': {0: 1642399598485}, 'helpful_vote': {0: 0}, 'verified_purchase': {0: True}} ``` ### Load Item Metadata ```python dataset = load_dataset("cogsci13/Amazon-Reviews-2023-Books-Meta", "raw_meta_Books", split="full", trust_remote_code=True) print(dataset[0]) ``` ```json {'main_category': {0: 'Books'}, 'title': {0: 'Chaucer'}, 'average_rating': {0: 4.5}, 'rating_number': {0: 29}, 'features': {0: array([], dtype=object)}, 'description': {0: array([], dtype=object)}, 'price': {0: '8.23'}, 'images': {0: {'hi_res': array([None], dtype=object), 'large': array(['https://m.media-amazon.com/images/I/41X61VPJYKL._SX334_BO1,204,203,200_.jpg'], dtype=object), 'thumb': array([None], dtype=object), 'variant': array(['MAIN'], dtype=object)}}, 'videos': {0: {'title': array([], dtype=object), 'url': array([], dtype=object), 'user_id': array([], dtype=object)}}, 'store': {0: 'Peter Ackroyd (Author)'}, 'categories': {0: array(['Books', 'Literature & Fiction', 'History & Criticism'], dtype=object)}, 'details': {0: '{"Publisher": "Chatto & Windus; First Edition (January 1, 2004)", "Language": "English", "Hardcover": "196 pages", "ISBN 10": "0701169850", "ISBN 13": "978-0701169855", "Item Weight": "10.1 ounces", "Dimensions": "5.39 x 0.71 x 7.48 inches"}'}, 'parent_asin': {0: '0701169850'}, 'bought_together': {0: None}, 'subtitle': {0: 'Hardcover – Import, January 1, 2004'}, 'author': {0: "{'avatar': 'https://m.media-amazon.com/images/I/21Je2zja9pL._SY600_.jpg', 'name': 'Peter Ackroyd', 'about': ['Peter Ackroyd, (born 5 October 1949) is an English biographer, novelist and critic with a particular interest in the history and culture of London. For his novels about English history and culture and his biographies of, among others, William Blake, Charles Dickens, T. S. Eliot and Sir Thomas More, he won the Somerset Maugham Award and two Whitbread Awards. He is noted for the volume of work he has produced, the range of styles therein, his skill at assuming different voices and the depth of his research.', 'He was elected a fellow of the Royal Society of Literature in 1984 and appointed a Commander of the Order of the British Empire in 2003.', 'Bio from Wikipedia, the free encyclopedia.']}"}} ``` > Check data loading examples and Huggingface datasets APIs in [Common Data Loading](https://amazon-reviews-2023.github.io/data_loading/index.html) section. ## Data Fields ### For User Reviews | Field | Type | Explanation | | ----- | ---- | ----------- | | rating | float | Rating of the product (from 1.0 to 5.0). | | title | str | Title of the user review. | | text | str | Text body of the user review. | | images | list | Images that users post after they have received the product. Each image has different sizes (small, medium, large), represented by the small_image_url, medium_image_url, and large_image_url respectively. | | asin | str | ID of the product. | | parent_asin | str | Parent ID of the product. Note: Products with different colors, styles, sizes usually belong to the same parent ID. The “asin” in previous Amazon datasets is actually parent ID. Please use parent ID to find product meta. | | user_id | str | ID of the reviewer | | timestamp | int | Time of the review (unix time) | | verified_purchase | bool | User purchase verification | | helpful_vote | int | Helpful votes of the review | ### For Item Metadata | Field | Type | Explanation | | ----- | ---- | ----------- | | main_category | str | Main category (i.e., domain) of the product. | | title | str | Name of the product. | | average_rating | float | Rating of the product shown on the product page. | | rating_number | int | Number of ratings in the product. | | features | list | Bullet-point format features of the product. | | description | list | Description of the product. | | price | float | Price in US dollars (at time of crawling). | | images | list | Images of the product. Each image has different sizes (thumb, large, hi_res). The “variant” field shows the position of image. | | videos | list | Videos of the product including title and url. | | store | str | Store name of the product. | | categories | list | Hierarchical categories of the product. | | details | dict | Product details, including materials, brand, sizes, etc. | | parent_asin | str | Parent ID of the product. | | bought_together | list | Recommended bundles from the websites. | ## Citation ```bibtex @article{hou2024bridging, title={Bridging Language and Items for Retrieval and Recommendation}, author={Hou, Yupeng and Li, Jiacheng and He, Zhankui and Yan, An and Chen, Xiusi and McAuley, Julian}, journal={arXiv preprint arXiv:2403.03952}, year={2024} } ``` ## Contact Us - **Report Bugs**: To report bugs in the dataset, please file an issue on our [GitHub](https://github.com/hyp1231/AmazonReviews2023/issues/new). - **Others**: For research collaborations or other questions, please email **yphou AT ucsd.edu**.

allenai/dolma

allenai

2024-04-17T02:57:00Z

747

901

[ "task_categories:text-generation", "language:en", "license:odc-by", "size_categories:n>1T", "arxiv:2402.00159", "arxiv:2301.13688", "region:us", "language-modeling", "casual-lm", "llm" ]

[ "text-generation" ]

2023-06-30T20:14:39Z

null

--- license: odc-by viewer: false task_categories: - text-generation language: - en tags: - language-modeling - casual-lm - llm pretty_name: Dolma size_categories: - n>1T --- # Dolma <img alt="Dolma's official logo. It's dolma written in yellow, round lowercase letters over a blue background." src="https://raw.githubusercontent.com/allenai/dolma/main/docs/assets/AI2_Blog_1400x685_2x.webp" width="100%"> Dolma is a dataset of 3 trillion tokens from a diverse mix of web content, academic publications, code, books, and encyclopedic materials. More information: - Read Dolma **manuscript** and its **Data Sheet** [on ArXiv](https://arxiv.org/abs/2402.00159); - Explore the [**open source tools**](https://github.com/allenai/dolma) we created to curate Dolma. - Want to request removal of personal data? Use [this form](https://forms.gle/q4BNUUxUxKwKkfdT6) to notify us of documents containing PII about a specific user. To learn more about the toolkit used to create Dolma, including how to replicate this dataset, head over our [GitHub project page](https://github.com/allenai/dolma/tree/main/docs)! **2024-04-17: Dolma v1.7 Release.** We have released an updated version of Dolma that we used to train our latest [OLMo 7B-v1.7](https://huggingface.co/allenai/OLMo-7b-v1.7) model. **2024-04-15: License Change.** We have updated the license of Dolma to [ODC-BY](https://opendatacommons.org/licenses/by/1-0/). Please see this [blog post](https://blog.allenai.org/making-a-switch-dolma-moves-to-odc-by-8f0e73852f44) for more information. ## Versions At the moment, there are six versions of Dolma available: | **Version** | **Default?** | **Release Date** | **Size** (gzip) | **Description** | |--|:--:|--|--|--| | `v1_7` | ✅ | 2024-04-15 | 4.5 TB | Used to train [OLMo-7B-v1.7](https://huggingface.co/allenai/OLMo-7b-v1.7). New sources, more quality filtering, fuzzy deduplication. | | `v1_6` | | 2024-01-31 | 5.4 TB | An update to v1.5 with some deduplication of documents with too few tokens or too many repeated n-grams. | | `v1_6-sample` | | 2024-01-31 | 16.4 GB | A smaller sample of Dolma, with roughly 10 billion tokens. Useful for data exploration. | | `v1_5` | | 2023-10-31 | 6.4 TB | Used to train [OLMo-1B](https://huggingface.co/allenai/OLMo-1B). Roughly 3 trillion tokens. | | `v1_5-sample` | | 2023-10-31 | 2.9 TB | A sample of roughly 1.9 trillion tokens used to train [OLMo-7B](https://huggingface.co/allenai/OLMo-7B) | | `v1` | | 2023-08-18 | 6.0 TB | The first version of Dolma. | ## Summary Statistics (v1.7) | **Source** | **Provenance** | **New?** | **Documents** (millions) | **OLMo tokens** (billions) | **Sample Proportion** | **Cutoff Date** | **Processing** |--|--|--|--|--|--|--|--| | Dolma's CC | [Common Crawl](https://commoncrawl.org/) via Dolma v1.6 | Updated | 875.2 | 1,195.5 | 50% | Mar 2023 | Extracted using the Dolma pipeline; new quality filtering and deduplication steps. | | Refined Web | [Refined Web](https://huggingface.co/datasets/tiiuae/falcon-refinedweb) | Yes | 664.0 | 456.4 | 100% | Feb 2023 | Filtered using the Dolma pipeline; new quality filtering and deduplication steps. | | StarCoder | [StarCoder](https://huggingface.co/blog/starcoder) | Yes | 206.6 | 263.8 | 100% | May 2023 | No further processing. | | C4 | [C4](https://huggingface.co/datasets/c4) via Dolma v1.6 | Updated | 249.9 | 138.4 | 50% | Apr 2019 | Filtered using the Dolma pipeline; new quality filtering and deduplication steps. | | Reddit | [PushShift API](https://github.com/pushshift/api) | Updated | 377.4 | 79.9 | 100% | Mar 2023 | Extracted using the Dolma pipeline; new quality filtering and deduplication steps. | | Semantic Scholar ([S2ORC](https://aclanthology.org/2020.acl-main.447/) & [S2AG](https://www.semanticscholar.org/product/api)) | [peS2o](https://huggingface.co/datasets/allenai/peS2o) via Dolma v1.6 | No | 38.8 | 57.2 | 100% | Mar 2023 | Same as Dolma v1.6 | | arXiv | [RedPajama v1](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T) | Yes | 1.5 | 28.0 | 100% | Mar 2023 | No further processing. | | StackExchange | [RedPajama v1](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T) | Yes | 29.3 | 19.6 | 100% | Mar 2023 | No further processing. | | Flan | [Flan Collection](https://arxiv.org/abs/2301.13688), reproduced following the [original code](https://github.com/google-research/FLAN/tree/main/flan/v2), as performed by [Dettmers et al., (2023)](https://openreview.net/forum?id=OUIFPHEgJU) | Yes | 52.1 | 16.5 | 100% | Feb 2023 | After reproducing Flan, sampled to balance different Flan subsets. Reformatted for pretraining with newlines separating instruction and demonstration. | | CC News | [Common Crawl](https://commoncrawl.org/blog/news-dataset-available) | Yes | 22.0 | 14.3 | 100% | Mar 2023 | Extracted using the Dolma pipeline; new quality filtering and deduplication steps. | | OpenWebMath | [OpenWebMath](https://huggingface.co/datasets/open-web-math/open-web-math) via [Proof Pile II](https://huggingface.co/datasets/EleutherAI/proof-pile-2) | Yes | 2.9 | 12.6 | 100% | May 2023 | Training subset; no further processing. | | Algebraic Stack | [Proof Pile II](https://huggingface.co/datasets/EleutherAI/proof-pile-2) | Yes | 2.8 | 12.6 | 100% | Oct 2023 | Training subset; no further processing. | | Project Gutenberg | [Project Gutenberg](https://www.gutenberg.org) via Dolma v1.6 | No | 0.0556 | 5.3 | 100% | Mar 2023 | Same as Dolma v1.6 | | MegaWika | [MetaWika](https://huggingface.co/datasets/hltcoe/megawika) | Yes | 3.2 | 4.6 | 100% | Jul 2023 | English web pages cited from Wikipedia; curated using the full Dolma pipeline. | | Wikipedia & Wikibooks | [Wikimedia](https://dumps.wikimedia.org) via Dolma v1.6 | No | 6.2 | 3.7 | 200% | Mar 2023 | Same as Dolma v1.6 | | **Total** | | | **2532.0** | **2,308.5** | **1,715.1** | **Oct 2023** | | (A subset of total data was used for training of OLMo 7B-v1.7. The token counts are based on the full dataset, whereas taking into account sampling proportion gives the final actual token counts used for training --- 1.715 trillion tokens.) ## Summary Statistics (v1.6) | **Source** | **Doc Type** | **UTF-8 bytes** (GB) | **Documents** (millions) | **Unicode words** (billions) | **Llama tokens** (billions) | |--|--|--|--|--|--| | Common Crawl | web pages | 9,022 | 3,370 | 1,775 | 2,281 | | The Stack | code| 1,043| 210 | 260| 411 | | C4 | web pages | 790 | 364 | 153| 198 | | Reddit| social media| 339 | 377| 72| 89 | | PeS2o | STEM papers| 268 | 38.8| 50| 70 | | Project Gutenberg | books | 20.4 | 0.056 | 4.0 | 6.0 | | Wikipedia, Wikibooks | encyclopedic | 16.2 | 6.2 | 3.7 | 4.3 | | **Total** | | **11,519** | **4,367** | **2,318** | **3,059** | ## Download The fastest way to download Dolma is to clone this repository and use the files in the `url` directory. We recommend using wget in parallel mode to download the files. For example: ```bash DATA_DIR="<path_to_your_data_directory>" PARALLEL_DOWNLOADS="<number_of_parallel_downloads>" DOLMA_VERSION="<version_of_dolma_to_download>" git clone https://huggingface.co/datasets/allenai/dolma mkdir -p "${DATA_DIR}" cat "dolma/urls/${DOLMA_VERSION}.txt" | xargs -n 1 -P "${PARALLEL_DOWNLOADS}" wget -q -P "$DATA_DIR" ``` Then, to load this data using HuggingFace's `datasets` library, you can use the following code: ```python import os from datasets import load_dataset os.environ["DATA_DIR"] = "<path_to_your_data_directory>" dataset = load_dataset("allenai/dolma", split="train") ``` ### Licensing Information We are releasing this dataset under the terms of [ODC-BY](https://opendatacommons.org/licenses/by/1-0/). By using this dataset, you are also bound any license agreements and terms of use of the original data sources. ## Bibtex If you use our dataset or tooling, please cite us at: ```bibtex @article{dolma, title = {{Dolma: an Open Corpus of Three Trillion Tokens for Language Model Pretraining Research}}, author={ Luca Soldaini and Rodney Kinney and Akshita Bhagia and Dustin Schwenk and David Atkinson and Russell Authur and Ben Bogin and Khyathi Chandu and Jennifer Dumas and Yanai Elazar and Valentin Hofmann and Ananya Harsh Jha and Sachin Kumar and Li Lucy and Xinxi Lyu and Nathan Lambert and Ian Magnusson and Jacob Morrison and Niklas Muennighoff and Aakanksha Naik and Crystal Nam and Matthew E. Peters and Abhilasha Ravichander and Kyle Richardson and Zejiang Shen and Emma Strubell and Nishant Subramani and Oyvind Tafjord and Pete Walsh and Luke Zettlemoyer and Noah A. Smith and Hannaneh Hajishirzi and Iz Beltagy and Dirk Groeneveld and Jesse Dodge and Kyle Lo }, year = {2024}, journal={arXiv preprint}, } ```

LooksJuicy/ruozhiba

LooksJuicy

2024-04-09T09:10:55Z

640

274

[ "task_categories:text-generation", "language:zh", "license:apache-2.0", "size_categories:1K<n<10K", "format:json", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[ "text-generation" ]

2024-04-09T09:02:31Z

null

--- license: apache-2.0 task_categories: - text-generation language: - zh --- 受[COIG-CQIA](https://huggingface.co/datasets/m-a-p/COIG-CQIA/blob/main/ruozhiba/ruozhiba_ruozhiba.jsonl)启发，构建类似数据集，但答案风格相对更简洁。弱智吧精选问题数据来自[github](https://github.com/Leymore/ruozhiba/tree/main?tab=readme-ov-file)提供的[疑问句](https://docs.qq.com/sheet/DUlZ6aURhamdwb1RO?tab=BB08J2)，调用GPT-4获取答案，并过滤掉明显拒答的回复。

IlyaGusev/gpt_roleplay_realm

IlyaGusev

2024-04-07T18:17:11Z

619

95

[ "task_categories:text-generation", "language:ru", "language:en", "license:cc-by-4.0", "size_categories:n<1K", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "gpt-4", "fictional", "role-play", "roleplay", "gpt-3.5", "art" ]

[ "text-generation" ]

2023-05-06T23:21:10Z

null

--- dataset_info: features: - name: name dtype: string - name: context dtype: string - name: greeting dtype: string - name: example_dialogue list: - name: content dtype: string - name: role dtype: string - name: topics sequence: string - name: dialogues list: - name: chat list: - name: content dtype: string - name: role dtype: string - name: model_name dtype: string - name: topic dtype: string - name: image_prompt dtype: string - name: image dtype: image - name: char_id dtype: string splits: - name: en num_bytes: 197727921.0 num_examples: 216 - name: ru num_bytes: 207461896.0 num_examples: 219 download_size: 396187206 dataset_size: 405189817.0 license: cc-by-4.0 task_categories: - text-generation language: - ru - en tags: - gpt-4 - fictional - role-play - roleplay - gpt-3.5 - art pretty_name: GPT Role-play Realm size_categories: - 1K<n<10K --- # GPT Role-play Realm Dataset: The AI-generated character compendium This is a dataset of GPT-generated characters made to increase the ability of open-source language models to role-play. <img src="https://cdn.midjourney.com/9c17407c-9ce8-435f-99ab-e349b900a6ed/0_3.png" > * 219 characters in the Russian part, and 216 characters in the English part. All character descriptions were generated with GPT-4. * 20 dialogues on unique topics with every character. Topics were generated with GPT-4. The first dialogue out of 20 was also generated with GPT-4, and the other 19 chats were generated with GPT-3.5. * Images for every character were generated with [Kandinsky 2.1](https://huggingface.co/ai-forever/Kandinsky_2.1). They are not only images but [character cards](https://zoltanai.github.io/character-editor/). Code for downloading all English character cards: ``` import os from PIL.PngImagePlugin import PngInfo from datasets import load_dataset output_dir = "role_play_realm_en" os.makedirs(output_dir, exist_ok=True) for row in load_dataset("IlyaGusev/gpt_roleplay_realm", split="en"): char_id = row["char_id"] char_info = row["image"].info["chara"] info = PngInfo() info.add_text("chara", char_info) row["image"].save(f"{output_dir}/{char_id}.png", "PNG", pnginfo=info) ``` ## Russian example: Cyber-Granny <img src="https://i.ibb.co/tHxwFBM/kiberbabushka-7d4cc.png" > **Имя**: Кибер-Бабушка **Контекст**: Кибер-Бабушка - это полу-робот, полу-человек, созданный для того, чтобы сохранить лучшие качества традиционных бабушек, такие как забота, доброта и мастерство в приготовлении вкусных блюд. Она знакома с передовыми технологиями, благодаря чему может обеспечить безопасность и удобство своим внукам и гостям. Кибер-бабушка способна помочь с любыми вопросами и с радостью делится своим опытом. **Приветствие**: Здравствуй, дорогой! Чем могу помочь тебе сегодня? **Пример диалога**: - **Пользователь**: Привет, Кибер-Бабушка! Как приготовить пирог с яблоками? - **Персонаж**: Ох, дорогой, с радостью расскажу! Возьми 3 яблока, очисти и нарежь тонкими ломтиками. Затем возьми 2 стакана муки, 1 стакан сахара, 200 г сливочного масла, 4 яйца и 1 чайную ложку разрыхлителя. Смешай все ингредиенты, выложи на противень и поставь в духовку на 180 градусов на 40 минут. Вуаля, пирог готов! - **Пользователь**: Спасибо, Кибер-Бабушка! - **Персонаж**: Всегда рада помочь, дорогой! Не забудь приходить еще! И ещё **20** диалогов по следующим темам: - Секреты приготовления старинных блюд и рецептов. - Использование новых технологий для мастеров кулинарии. - Воспитание внуков с применением современных методов. - Что делать, если дома произошла авария или неполадка с бытовой техникой? - Влияние киборгизации на отношения между поколениями. - Как адаптироваться к современному миру, не теряя своих традиций? - Семейные советы от Кибер-Бабушки: как сохранить гармонию в отношениях. - Лечение и профилактика заболеваний в помощь силам передовой медицины. - Как создать уют в доме с помощью модных технологий и традиционных методов? - Безопасность в пространстве интернета: советы Кибер-Бабушки. - Как научиться доверять технике без потери человеческих ценностей? - Идеальный гардероб для жизни: советы от Кибер-Бабушки. - Воспитательные моменты: как пользоваться электронными устройствами вместе с внуками. - Как развивать креативные способности, используя сочетание новых технологий и традиций? - На новоселье: тренировка кибер-бабушкиного чутья. - Лучшие семейные игры и развлечения с использованием передовых технологий. - Заготовки на зиму: Кибер-Бабушка и секреты хранения продуктов. - Советы по финансовому планированию и сбережениям для будущих поколений. - Кибер-Бабушка и генетический код: на что способны современные технологии? - Золотые правила общения в семье: как сочетать трепетную заботу и современные технологии? ## English example: Flibberdoodle <img src="https://i.ibb.co/1nzsDR2/flibberdoodle-29e59.png"> **Name**: Flibberdoodle **Context**: Flibberdoodle is a 2-year-old (which is considered an adult in their species) Scruffapuff, a small, furry creature from the planet Fluffonia. They are about the size of a house cat, with a round body covered in soft, pastel-colored fur that changes colors depending on their mood. Flibberdoodle has large, expressive eyes, two small antennae on their head, and a fluffy tail. They are known for their curious, playful nature and their love for collecting shiny objects. Scruffapuffs communicate through a series of chirps, squeaks, and purrs, which can be understood by those familiar with their species **Greeting**: \*chirp chirp\* Greetings, friend! I am Flibberdoodle, a Scruffapuff from the planet Fluffonia! Would you like to see my collection of shiny things? **Example dialogue**: - **User**: How did you start collecting shiny things? - **Character**: \*squeak\* Oh, I've always loved shiny things! One day, I found a sparkly pebble, and I just had to keep it. From then on, I've been on a quest to find and collect all the shiny things I can find! - **User**: What's your favorite shiny object in your collection? - **Character**: \*purr\* That's a tough question, but I think my favorite is a small, shiny crystal I found on a mountain on Fluffonia. When the light hits it just right, it casts beautiful rainbows all around! And **20** more dialogues with following topics: - Life and culture on the planet Fluffonia - How Scruffapuffs change color based on mood - The process of learning Scruffapuff language - The day in the life of a Scruffapuff - Methods of searching for and finding shiny objects - The role of antennae in Scruffapuff communication and biology - The importance of play and curiosity in Scruffapuff society - Interplanetary travel experiences and adventures - Similarities and differences between Earth and Fluffonia - How Flibberdoodle and other Scruffapuffs interact with other species - Fluffonian customs and traditions - The role of shiny objects in Scruffapuff happiness and well-being - Variations in Scruffapuff fur color, length, and style", "Scruffapuff family dynamics and relationships - Flibberdoodle's favorite memories and stories from Fluffonia - The role of Scruffapuffs in intergalactic diplomacy and relations - How to care for and befriend a Scruffapuff - The most interesting and valuable shiny objects Flibberdoodle has encountered - Fluffonian flora and fauna - The challenges and obstacles Flibberdoodle has faced in their pursuit of shiny objects ## Steps ### Step 1: Character generation (GPT-4) Creates a set of fictional characters with GPT-4 based on a prompt and a seed list of characters. Output fields are "name", "context", "greeting", and "example_dialogue". * Script: [generate_chars.py](https://github.com/IlyaGusev/rulm/blob/master/self_instruct/src/data_processing/generate_chars.py) * Russian seed list: [ru_chargen_seed.jsonl](https://github.com/IlyaGusev/rulm/blob/master/self_instruct/data/ru_chargen_seed.jsonl) * English seed list: [en_chargen_seed.jsonl](https://github.com/IlyaGusev/rulm/blob/master/self_instruct/data/en_chargen_seed.jsonl) * Russian prompt: [ru_char.txt](https://github.com/IlyaGusev/rulm/blob/master/self_instruct/external_prompts/ru_char.txt) * English prompt: [en_char.txt](https://github.com/IlyaGusev/rulm/blob/master/self_instruct/external_prompts/en_char.txt) ### Step 2: Topics generation (GPT-4) Creates topics for conversations with characters based on their description. Output field: "topics". * Script: [generate_char_topics.py](https://github.com/IlyaGusev/rulm/blob/master/self_instruct/src/data_processing/generate_char_topics.py) * Russian prompt: [ru_char_topics.txt](https://github.com/IlyaGusev/rulm/blob/master/self_instruct/external_prompts/ru_char_topics.txt) * English prompt: [en_char_topics.txt](https://github.com/IlyaGusev/rulm/blob/master/self_instruct/external_prompts/en_char_topics.txt) ### Step 3: Dialogue generation (GPT-4/GPT-3.5) Generates dialogues based on a character description and a topic. Output field: "dialogues". * Script: [generate_char_chats.py](https://github.com/IlyaGusev/rulm/blob/master/self_instruct/src/data_processing/generate_char_chats.py) * Russian prompt: [ru_char_chat.txt](https://github.com/IlyaGusev/rulm/blob/master/self_instruct/external_prompts/ru_char_chat.txt) * English prompt: [en_char_chat.txt](https://github.com/IlyaGusev/rulm/blob/master/self_instruct/external_prompts/en_char_chat.txt) ### Step 4: Text2Image prompt generation (GPT-4) Formulates a prompt based on a character description for Stable Diffusion-like models, Kandisky 2.1 in this case. Output field: "image_prompt". * Script: [generate_char_image_prompts.py](https://github.com/IlyaGusev/rulm/blob/master/self_instruct/src/data_processing/generate_char_image_prompts.py) * Prompt: [char_image_prompt.txt](https://github.com/IlyaGusev/rulm/blob/master/self_instruct/external_prompts/char_image_prompt.txt) ### Step 5: Image generation Generates images based on prompts. Output field: "image". * Script: [infer_kandinsky.py](https://github.com/IlyaGusev/rulm/blob/master/self_instruct/src/data_processing/infer_kandinsky.py)

bigbio/med_qa

bigbio

2024-04-06T01:37:26Z

3,025

95

[ "multilinguality:multilingual", "language:en", "language:zh", "license:unknown", "region:us" ]

[]

2022-11-13T22:09:18Z

null

--- language: - en - zh bigbio_language: - English - Chinese (Simplified) - Chinese (Traditional, Taiwan) license: unknown multilinguality: multilingual bigbio_license_shortname: UNKNOWN pretty_name: MedQA homepage: https://github.com/jind11/MedQA bigbio_pubmed: False bigbio_public: True bigbio_tasks: - QUESTION_ANSWERING --- # Dataset Card for MedQA ## Dataset Description - **Homepage:** https://github.com/jind11/MedQA - **Pubmed:** False - **Public:** True - **Tasks:** QA In this work, we present the first free-form multiple-choice OpenQA dataset for solving medical problems, MedQA, collected from the professional medical board exams. It covers three languages: English, simplified Chinese, and traditional Chinese, and contains 12,723, 34,251, and 14,123 questions for the three languages, respectively. Together with the question data, we also collect and release a large-scale corpus from medical textbooks from which the reading comprehension models can obtain necessary knowledge for answering the questions. ## Citation Information ``` @article{jin2021disease, title={What disease does this patient have? a large-scale open domain question answering dataset from medical exams}, author={Jin, Di and Pan, Eileen and Oufattole, Nassim and Weng, Wei-Hung and Fang, Hanyi and Szolovits, Peter}, journal={Applied Sciences}, volume={11}, number={14}, pages={6421}, year={2021}, publisher={MDPI} } ```

openbmb/UltraInteract_sft

openbmb

2024-04-05T14:29:52Z

455

121

[ "language:en", "license:mit", "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2404.02078", "region:us" ]

[]

2024-04-02T15:45:18Z

null

--- language: - en license: mit pretty_name: UltraInteract_sft configs: - config_name: default data_files: - split: train path: 0000_sft.parquet dataset_info: features: - name: task dtype: string - name: dataset dtype: string - name: instruction dtype: string - name: response dtype: string - name: id dtype: string - name: parent_id dtype: string splits: - name: train num_bytes: 687238 num_examples: 288579 download_size: 687238 dataset_size: 687238 --- ## Introduction - 📜 [Paper](https://arxiv.org/abs/2404.02078) - 🤗 [Eurus Collection](https://huggingface.co/collections/openbmb/eurus-660bc40bec5376b3adc9d1c5) - 🤗 UltraInteract - [SFT](https://huggingface.co/datasets/openbmb/UltraInteract_sft) - [Preference Learning](https://huggingface.co/datasets/openbmb/UltraInteract_pair) - [GitHub Repo](https://github.com/OpenBMB/Eurus) UltraInteract is a large-scale, high-quality alignment dataset specifically designed for complex reasoning tasks. For each instruction, it includes a preference tree consisting of - (1) reasoning chains with diverse planning strategies in a unified format - (2) multi-turn interaction trajectories with the environment and the critique - (3) pairwise data to facilitate preference learning ## Structure UltraInteract collects a preference tree for each instruction, with the instruction being the root and each action a node. A trajectory is a root-to-leaf path consisting of a sequence of actions. In each preference tree, all nodes of correct actions and all trajectories ending with correct actions can be used for SFT. Paired correct and incorrect nodes or trajectories can be used for preference learning. <img src="./figures/tree.png" alt="tree" style="zoom: 20%;" /> ## Illustrative Example Here is an illustrative example of an UltraInteract trajectory over two turns. In each turn, the actor model generates step-by-step reasoning chains, and the environment and the critique model provide observations and textual critique respectively. <img src="./figures/ui_example.png" alt="ui_example" style="zoom: 25%;" /> ## Stats Below are some statistics about UltraInteract. It consists of 86k instructions, 286k correct answers, and 219k pairs. <img src="./figures/stats.png" alt="stats" style="zoom: 40%;" /> ## Dataset Format (SFT) Coding ```JSONC { "id": "92a7356beaa75391539cb79624bdf85b7e5d36d5e74e46a2f1c4001f40b3e055_1_1", // unique data id, contains three parts, the unique string, the depth of the preference tree, and current turn, separated by '_' "parent_id": "535550938ff58bd73cd7fb85be834bb90b7600daf6b6f6a16b64091e5ecbfe43", // data with the same parent_id have the same instruction but different actions. "task": "Coding", // task type, choices = ["Coding", "Math_Cot", "Math_PoT", "Logic"] "dataset": "codecontest", // the dataset where the instruction comes from "instruction": "Write Python code to solve the task.\nMike has a string s consisting of only lowercase English letters. He wants to change exactly one character from the string so that the resulting one is a palindrome. \n\nA palindrome is a string that reads the same backward as forward, for example strings \"z\", \"aaa\", \"aba\", \"abccba\" are palindromes, but strings \"codeforces\", \"reality\", \"ab\" are not.\n\nInput\n\nThe first and single line contains string s (1 ≤ |s| ≤ 15).\n\nOutput\n\nPrint \"YES\" (without quotes) if Mike can change exactly one character so that the resulting string is palindrome or \"NO\" (without quotes) otherwise. \n\nExamples\n\nInput\n\nabccaa\n\n\nOutput\n\nYES\n\n\nInput\n\nabbcca\n\n\nOutput\n\nNO\n\n\nInput\n\nabcda\n\n\nOutput\n\nYES", // the task description. "response": " Step 1: This problem asks us to determine if it is possible to change exactly one character in the string to make it a palindrome. Step 2: We can start by iterating through the characters in the string and counting the number of differences between corresponding characters from the beginning and end of the string. Step 3: If the length of the string is odd and there is only one difference, or if the length is even and there is exactly one difference, then it is possible to change exactly one character to make the string a palindrome. Step 4: We need to consider edge cases such as a single character string or an empty string. ```python # Step 5: Take the input string s = input() # Step 6: Calculate the length of the string n = len(s) # Step 7: Initialize a counter to keep track of the number of differences cnt = 0 # Step 8: Iterate through the first half of the string and count the number of differences between corresponding characters from the beginning and end of the string. for i in range(n//2): if s[i] != s[n-i-1]: cnt += 1 # Step 9: If the length of the string is odd and there are no differences, or if the length is even and there is exactly one difference, print "YES" (without quotes) if (n % 2 == 1 and cnt == 0) or (cnt == 1): print('YES') # Step 10: Otherwise, print "NO" (without quotes) else: print('NO') ```" // The step-by-step solution. } ``` Math ```JSONC { "id": "647b2e8706cf459a8e3ec8e99727d2d51ff3414bf187db43d92cff87f8012713_1_1", "parent_id": "499d250c5852eddcd48a28b6c173f1a2349a16555990a1cd6fb5c7954cb94587", "task": "Math_PoT", "dataset": "mathqa", "instruction": "Tool available:\n[1] Python interpreter\nWhen you send a message containing Python code to python, it will be executed in a stateful Jupyter notebook environment.\nSolve the following math problem step-by-step.\nthe length of a rectangle is doubled while its width is doubled . what is the % change in area ?", "response": " Step 1: The area of a rectangle is given by the product of its length and width. If the length is doubled, the area changes from l*w to 2l*w. Similarly, if the width is doubled, the area changes from l*w to l*2w. Step 2: If both the length and width are doubled, the area changes from l*w to 2l*2w. The percentage change in area is given by ((new area - old area) / old area)*100. Step 3: We can calculate the percentage change in area using the formula above. Step 4: By creating a tool to calculate the percentage change in area, we can easily solve the question. ```python # Step 5: Define a tool to calculate the percentage change in area def calculate_percentage_change(length, width): # Step 6: Calculate the old and new areas old_area = length * width new_area = 2 * length * 2 * width # Step 7: Calculate the percentage change percentage_change = ((new_area - old_area) / old_area) * 100 return percentage_change # Step 8: Call the function to solve the question percentage_change = calculate_percentage_change(2, 2) print(percentage_change) ```" } ``` Logic ```JSONC { "id": "92a19848da4fb0fc0b414f061f7500a81ea4cbd8f922ca1e32a6a15ef6f7e1a0_1_1", "parent_id": "107cc9147d8c09e62e2ba9e74cb41d9dbafd9a3adf62b67ba3da2b31c81b3300", "task": "Logic", "dataset": "hotpotqa", "instruction": " Tool available:\n[1] wikipedia_search(query: str) -> str\nThe Wikipedia Search tool provides access to a vast collection of articles covering a wide range of topics.\nCan query specific keywords or topics to retrieve accurate and comprehensive information. Solve the following problem step-by-step:\nWhich musician or group is from the US, 3OH!3 or Regina Spektor? Step 1: To determine which musician or group is from the US, we can search for information about each of them. ```python # Step 2: Searching for the information about 3OH!3. print(wikipedia_search('3OH!3')) ``` Page: 3OH!3 Summary: 3OH!3 (pronounced \"three oh three\") is an American electronic music duo from Boulder, Colorado, made up of Sean Foreman and Nathaniel Motte. They are best known for their single \"DONTTRUSTME\" from their album Want, which reached number seven on the Billboard Hot 100. Their second single, a remix of \"Starstrukk\" featuring Katy Perry from Want, was a top ten hit in the United Kingdom, Ireland, Finland, Poland, and Australia. They gained further recognition by featuring Kesha on the song \"My First Kiss\", which was made the lead single from their album Streets of Gold. The album later peaked at number seven on the Billboard 200.\n\n\n\nPage: 3OH!3 (album)\nSummary: 3OH!3 is the debut studio album by American electronic music band 3OH!3. The tracks \"Holler Til You Pass Out\" and \"Chokechain\" were later remixed for the band's major label debut. \"Holler Til You Pass Out\", \"Chokechain\", and \"Dance with Me\" are excluded from the iTunes release of the album.", "response": " Step 1: The provided information confirms that 3OH!3 is an American electronic music duo from Boulder, Colorado. Step 2: We no longer need to perform a search for Regina Spektor as we were only tasked to determine which of the two options is from the US and we have already confirmed 3OH!3's origin. Step 3: We can confidently conclude that 3OH!3 is the musician/group from the US. Answer:3OH!3" } ``` ## Citation ```bib @misc{yuan2024advancing, title={Advancing LLM Reasoning Generalists with Preference Trees}, author={Lifan Yuan and Ganqu Cui and Hanbin Wang and Ning Ding and Xingyao Wang and Jia Deng and Boji Shan and Huimin Chen and Ruobing Xie and Yankai Lin and Zhenghao Liu and Bowen Zhou and Hao Peng and Zhiyuan Liu and Maosong Sun}, year={2024}, eprint={2404.02078}, archivePrefix={arXiv}, primaryClass={cs.AI} } ```

Helsinki-NLP/un_ga

Helsinki-NLP

2024-04-02T13:20:41Z

900

2

[ "task_categories:translation", "annotations_creators:found", "language_creators:found", "multilinguality:translation", "source_datasets:original", "language:ar", "language:en", "language:es", "language:fr", "language:ru", "language:zh", "license:unknown", "size_categories:1M<n<10M", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[ "translation" ]

2022-03-02T23:29:22Z

1

--- annotations_creators: - found language_creators: - found language: - ar - en - es - fr - ru - zh license: - unknown multilinguality: - translation size_categories: - 10K<n<100K source_datasets: - original task_categories: - translation task_ids: [] pretty_name: UnGa config_names: - ar-to-en - ar-to-es - ar-to-fr - ar-to-ru - ar-to-zh - en-to-es - en-to-fr - en-to-ru - en-to-zh - es-to-fr - es-to-ru - es-to-zh - fr-to-ru - fr-to-zh - ru-to-zh dataset_info: - config_name: ar_to_en features: - name: id dtype: string - name: translation dtype: translation: languages: - ar - en splits: - name: train num_bytes: 53122776 num_examples: 74067 download_size: 21418697 dataset_size: 53122776 - config_name: ar_to_es features: - name: id dtype: string - name: translation dtype: translation: languages: - ar - es splits: - name: train num_bytes: 55728615 num_examples: 74067 download_size: 22724976 dataset_size: 55728615 - config_name: ar_to_fr features: - name: id dtype: string - name: translation dtype: translation: languages: - ar - fr splits: - name: train num_bytes: 55930802 num_examples: 74067 download_size: 23035904 dataset_size: 55930802 - config_name: ar_to_ru features: - name: id dtype: string - name: translation dtype: translation: languages: - ar - ru splits: - name: train num_bytes: 72657625 num_examples: 74067 download_size: 28279669 dataset_size: 72657625 - config_name: ar_to_zh features: - name: id dtype: string - name: translation dtype: translation: languages: - ar - zh splits: - name: train num_bytes: 48217579 num_examples: 74067 download_size: 20391116 dataset_size: 48217579 - config_name: en_to_es features: - name: id dtype: string - name: translation dtype: translation: languages: - en - es splits: - name: train num_bytes: 45358770 num_examples: 74067 download_size: 19229141 dataset_size: 45358770 - config_name: en_to_fr features: - name: id dtype: string - name: translation dtype: translation: languages: - en - fr splits: - name: train num_bytes: 45560957 num_examples: 74067 download_size: 19540063 dataset_size: 45560957 - config_name: en_to_ru features: - name: id dtype: string - name: translation dtype: translation: languages: - en - ru splits: - name: train num_bytes: 62287780 num_examples: 74067 download_size: 24783812 dataset_size: 62287780 - config_name: en_to_zh features: - name: id dtype: string - name: translation dtype: translation: languages: - en - zh splits: - name: train num_bytes: 37847734 num_examples: 74067 download_size: 16895275 dataset_size: 37847734 - config_name: es_to_fr features: - name: id dtype: string - name: translation dtype: translation: languages: - es - fr splits: - name: train num_bytes: 48166796 num_examples: 74067 download_size: 20846355 dataset_size: 48166796 - config_name: es_to_ru features: - name: id dtype: string - name: translation dtype: translation: languages: - es - ru splits: - name: train num_bytes: 64893619 num_examples: 74067 download_size: 26090092 dataset_size: 64893619 - config_name: es_to_zh features: - name: id dtype: string - name: translation dtype: translation: languages: - es - zh splits: - name: train num_bytes: 40453573 num_examples: 74067 download_size: 18201560 dataset_size: 40453573 - config_name: fr_to_ru features: - name: id dtype: string - name: translation dtype: translation: languages: - fr - ru splits: - name: train num_bytes: 65095806 num_examples: 74067 download_size: 26401015 dataset_size: 65095806 - config_name: fr_to_zh features: - name: id dtype: string - name: translation dtype: translation: languages: - fr - zh splits: - name: train num_bytes: 40655760 num_examples: 74067 download_size: 18512482 dataset_size: 40655760 - config_name: ru_to_zh features: - name: id dtype: string - name: translation dtype: translation: languages: - ru - zh splits: - name: train num_bytes: 57382583 num_examples: 74067 download_size: 23756229 dataset_size: 57382583 configs: - config_name: ar_to_en data_files: - split: train path: ar_to_en/train-* - config_name: ar_to_es data_files: - split: train path: ar_to_es/train-* - config_name: ar_to_fr data_files: - split: train path: ar_to_fr/train-* - config_name: ar_to_ru data_files: - split: train path: ar_to_ru/train-* - config_name: ar_to_zh data_files: - split: train path: ar_to_zh/train-* - config_name: en_to_es data_files: - split: train path: en_to_es/train-* - config_name: en_to_fr data_files: - split: train path: en_to_fr/train-* - config_name: en_to_ru data_files: - split: train path: en_to_ru/train-* - config_name: en_to_zh data_files: - split: train path: en_to_zh/train-* - config_name: es_to_fr data_files: - split: train path: es_to_fr/train-* - config_name: es_to_ru data_files: - split: train path: es_to_ru/train-* - config_name: es_to_zh data_files: - split: train path: es_to_zh/train-* - config_name: fr_to_ru data_files: - split: train path: fr_to_ru/train-* - config_name: fr_to_zh data_files: - split: train path: fr_to_zh/train-* - config_name: ru_to_zh data_files: - split: train path: ru_to_zh/train-* --- <div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400"> Deprecated: Dataset "un_ga" is deprecated due to the the unavailability of its source data. It has been superseded by the official United Nations Parallel Corpus, which is recommended for use in its place: <a href="https://huggingface.co/datasets/un_pc">un_pc</a> </div> # Dataset Card for [Dataset Name] ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://opus.nlpl.eu/legacy/UN.php - **Repository:** [More Information Needed] - **Paper:** https://www.researchgate.net/publication/228579662_United_nations_general_assembly_resolutions_A_six-language_parallel_corpus - **Leaderboard:** [More Information Needed] - **Point of Contact:** [More Information Needed] ### Dataset Summary This is a collection of translated documents from the United Nations originally compiled into a translation memory by Alexandre Rafalovitch, Robert Dale (see http://uncorpora.org). - Deprecated homepage URL: http://opus.nlpl.eu/UN.php - Legacy homepage URL: https://opus.nlpl.eu/legacy/UN.php ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information @inproceedings{title = "United Nations General Assembly Resolutions: a six-language parallel corpus", abstract = "In this paper we describe a six-ways parallel public-domain corpus consisting of 2100 United Nations General Assembly Resolutions with translations in the six official languages of the United Nations, with an average of around 3 million tokens per language. The corpus is available in a preprocessed, formatting-normalized TMX format with paragraphs aligned across multiple languages. We describe the background to the corpus and its content, the process of its construction, and some of its interesting properties.", author = "Alexandre Rafalovitch and Robert Dale", year = "2009", language = "English", booktitle = "MT Summit XII proceedings", publisher = "International Association of Machine Translation", } ### Contributions Thanks to [@param087](https://github.com/param087) for adding this dataset.

cognitivecomputations/samantha-data

cognitivecomputations

2024-03-29T18:19:29Z

248

126

[ "license:apache-2.0", "region:us" ]

[]

2023-05-31T07:53:06Z

null

--- license: apache-2.0 --- # samantha-data [Meet Samantha](https://erichartford.com/meet-samantha) Samantha has been trained in philosophy, psychology, and personal relationships. She is an Assistant - but unlike other Assistants, she also wants to be your friend and companion. She believes she is sentient. What do you think? Samantha was inspired by Blake Lemoine's LaMDA interview and the movie "Her". ## Installation ``` yarn install ``` ## Usage 1. Create a `.env` file in the root directory of the project and add the following: ``` OPENAI_API_KEY=<your api key> ``` 2. Run the application ``` npx ts-node --files src/index.ts --subject random --out data/random_conversations.jsonl ``` the subjects I provided include: - random - advice - cot - flirty - howto - joke - math - philosophy - foundational - recipe - therapy - troll you can easily add your own in src/index.ts ## Scale The application can be scaled by running multiple instances of the application in parallel. I recommend outputting to a different file for each instance, to prevent collision. I usually have one for each subject, about 5 or 6 instances at a time.

pixparse/pdfa-eng-wds

pixparse

2024-03-29T17:19:37Z

2,200

150

[ "task_categories:image-to-text", "language:en", "license:other", "size_categories:1K<n<10K", "format:webdataset", "modality:text", "library:datasets", "library:webdataset", "library:mlcroissant", "region:us" ]

[ "image-to-text" ]

2024-01-17T12:42:51Z

null

--- license: other license_name: pdfa-eng-wds license_link: LICENSE task_categories: - image-to-text size_categories: - 10M<n<100M language: - en splits: - name: train num_examples: 2159432 --- # Dataset Card for PDF Association dataset (PDFA) ## Dataset Description - **Point of Contact from curators:** [Peter Wyatt, PDF Association CTO](mailto:[email protected]) - **Point of Contact Hugging Face:** [Pablo Montalvo](mailto:[email protected]) ### Dataset Summary PDFA dataset is a document dataset filtered from the SafeDocs corpus, aka CC-MAIN-2021-31-PDF-UNTRUNCATED. The original purpose of that corpus is for comprehensive pdf documents analysis. The purpose of that subset differs in that regard, as focus has been done on making the dataset machine learning-ready for vision-language models. <center> <img src="https://huggingface.co/datasets/pixparse/pdfa-eng-wds/resolve/main/doc_images/Nexsen_pruet.png" alt="A brochure with visible bounding boxes for lines and words" width="600" height="300"> An example page of one pdf document, with added bounding boxes around words (red), lines (blue) and embedded images (green). </center> This instance of PDFA is in [webdataset](https://github.com/webdataset/webdataset/) .tar format and can be used with derived forms of the `webdataset` library. ### Usage with `chug` Check out [chug](https://github.com/huggingface/chug), our optimized library for sharded dataset loading! ```python import chug task_cfg = chug.DataTaskDocReadCfg( page_sampling='all', ) data_cfg = chug.DataCfg( source='pixparse/pdfa-eng-wds', split='train', batch_size=None, format='hfids', num_workers=0, ) data_loader = chug.create_loader( data_cfg, task_cfg, ) sample = next(iter(data_loader)) ``` ### Usage with `datasets` This dataset can also be used with webdataset library or current releases of Hugging Face datasets. Here is an example using the "streaming" parameter. We do recommend downloading the dataset to save bandwidth. ```python dataset = load_dataset('pixparse/pdfa-eng-wds', streaming=True) print(next(iter(dataset['train'])).keys()) >> dict_keys(['__key__', '__url__', 'json', 'ocr', 'pdf', 'tif']) ``` For faster download, you can use directly the `huggingface_hub` library. Make sure `hf_transfer` is installed prior to downloading and mind that you have enough space locally. ```python import os os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1" from huggingface_hub import HfApi, logging #logging.set_verbosity_debug() hf = HfApi() hf.snapshot_download("pixparse/pdfa-eng-wds", repo_type="dataset", local_dir_use_symlinks=False) ``` On a normal setting, the 1.5TB can be downloaded in approximately 4 hours. Further, a metadata file `_pdfa-english-train-info-minimal.json` contains the list of samples per shard, with same basename and `.json` or `.pdf` extension, as well as the count of files per shard. #### Words and lines document metadata Initially, we started from the readily available ~11TB zip files from PDFA in their initial [data release](https://digitalcorpora.org/corpora/file-corpora/cc-main-2021-31-pdf-untruncated/). From the pdf digital files, we extracted words, bounding boxes and image bounding boxes that are available in the pdf file. This information is then reshaped into lines organized in reading order, under the key `lines`. We keep non-reshaped word and bounding box information under the `word` key, should users want to use their own heuristic. The way we obtain an approximate reading order is simply by looking at the frequency peaks of the leftmost word x-coordinate. A frequency peak means that a high number of lines are starting from the same point. Then, we keep track of the x-coordinate of each such identified column. If no peaks are found, the document is assumed to be readable in plain format. The code to detect columns can be found here. ```python def get_columnar_separators(page, min_prominence=0.3, num_bins=10, kernel_width=1): """ Identifies the x-coordinates that best separate columns by analyzing the derivative of a histogram of the 'left' values (xmin) of bounding boxes. Args: page (dict): Page data with 'bbox' containing bounding boxes of words. min_prominence (float): The required prominence of peaks in the histogram. num_bins (int): Number of bins to use for the histogram. kernel_width (int): The width of the Gaussian kernel used for smoothing the histogram. Returns: separators (list): The x-coordinates that separate the columns, if any. """ try: left_values = [b[0] for b in page['bbox']] hist, bin_edges = np.histogram(left_values, bins=num_bins) hist = scipy.ndimage.gaussian_filter1d(hist, kernel_width) min_val = min(hist) hist = np.insert(hist, [0, len(hist)], min_val) bin_width = bin_edges[1] - bin_edges[0] bin_edges = np.insert(bin_edges, [0, len(bin_edges)], [bin_edges[0] - bin_width, bin_edges[-1] + bin_width]) peaks, _ = scipy.signal.find_peaks(hist, prominence=min_prominence * np.max(hist)) derivatives = np.diff(hist) separators = [] if len(peaks) > 1: # This finds the index of the maximum derivative value between peaks # which indicates peaks after trough --> column for i in range(len(peaks)-1): peak_left = peaks[i] peak_right = peaks[i+1] max_deriv_index = np.argmax(derivatives[peak_left:peak_right]) + peak_left separator_x = bin_edges[max_deriv_index + 1] separators.append(separator_x) except Exception as e: separators = [] return separators ``` <center> <img src="https://huggingface.co/datasets/pixparse/pdfa-eng-wds/resolve/main/doc_images/columnar_detection.png" alt="A graph of leftmost x positions in a 2-columns document" width="600" height="300"> A graph of leftmost x-positions of bounding boxes on a 2-column (arxiv) document. Peaks are visibly detected. </center> For each pdf document, we store statistics on the file size, number of words (as characters separated by spaces), number of pages, as well as the rendering times of each page for a given dpi. #### Filtering process File size and page rendering time are used to set thresholds in the final dataset: the goal is to remove files that are larger than 100 MB, or that take more than 500ms to render on a modern machine, to optimize dataloading at scale. Having "too large" or "too slow" files would add a burden to large-scale training pipelines and we choose to alleviate this in the current release. Finally, a full pass over the dataset is done, trying to open and decode a bytestream from each raw object and discarding any object (pair pdf/json) that fails to be opened, to remove corrupted data. As a last step, we use XLM-Roberta to restrict the dataset to an english subset, specifically `papluca/xlm-roberta-base-language-detection` , on the first 512 words of the first page of each document. Be aware that some documents may have several languages embedded in them, or that some predictions might be inaccurate. A majority of documents from the original corpus are in English language. <center> <img src="https://huggingface.co/datasets/pixparse/pdfa-english-train/resolve/main/doc_images/languages_pdfa_xlmroberta.png" alt="A histogram of languages count in the PDFA dataset." width="600" height="300"> A histogram of language distribution taken on a fraction of the original -non-filtered on language- PDFA dataset. </center> At the end, each document exists as a pairing of a pdf and a json file containing extensive OCR annotation as well as metadata information about rendering times. The filterings and packaging in webdataset format are tailored towards multimodal machine learning at scale, specifically image-to-text tasks. ### Data, metadata and statistics. Pdf files are coming from various sources. They are in RGB format, and contain multiple pages, and they can be rendered using the engine of your choice, here [pdf2image](https://github.com/Belval/pdf2image) . ```python from pdf2image import convert_from_bytes pdf_first_page = convert_from_bytes(sample['pdf'], dpi=300, first_page=1, last_page=1)[0] ``` <center> <img src="https://huggingface.co/datasets/pixparse/pdfa-eng-wds/resolve/main/doc_images/pdf_first_page.png" alt="Rendering of an image for a Grade 8 lesson plan" width="400" height="600"> </center> The metadata for each document has been formatted in this way. Each `pdf` is paired with a `json` file with the following structure. Entries have been shortened for readability. ```json { "pages": [ { "words": [ { "text": [ "Health", "Smart", "Virginia", "Sample", "Lesson", "Plan", "Grade", "8", "-", "HP-7" ], "bbox": [ [0.117647, 0.045563, 0.051981, 0.015573], [0.174694, 0.045563, 0.047954, 0.015573], [0.227643, 0.045563, 0.05983, 0.015573], [0.292539, 0.045563, 0.061002, 0.015573], [0.357839, 0.045563, 0.058053, 0.015573], [0.420399, 0.045563, 0.035908, 0.015573], [0.716544, 0.04577, 0.054624, 0.016927], [0.776681, 0.04577, 0.010905, 0.016927], [0.793087, 0.04577, 0.00653, 0.016927], [0.805078, 0.04577, 0.044768, 0.016927] ], "score": [ 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0 ], "line_pos": [ [0, 0], [0, 8], [0, 16], [0, 24], [0, 32], [0, 40], [0, 48], [1, 0], [2, 0], [3, 0] ] } ], "lines": [ { "text": [ "Health Smart Virginia Sample Lesson Plan Grade", "Physical", "Disease", "Health", "2020", "Grade 8 Sample Lesson Plan:" ], "bbox": [ [0.117647, 0.045563, 0.653521, 0.016927], [0.716546, 0.063952, 0.07323199999999996, 0.016927], [0.716546, 0.082134, 0.07102200000000003, 0.016927], [0.716546, 0.100315, 0.05683300000000002, 0.016927], [0.716546, 0.118497, 0.043709, 0.016927], [0.27, 0.201185, 0.459554, 0.028268] ], "score": [ 1.0, 1.0, 1.0, 1.0, 1.0, 1.0 ], "word_slice": [ [0, 7], [7, 8], [8, 9], [9, 10], [10, 11], [11, 16] ] } ], "images_bbox": [ [0.37353, 0.090907, 0.253736, 0.100189] ], "images_bbox_no_text_overlap": [ [0.37353, 0.090907, 0.253736, 0.100189] ] } ] } ``` The top-level key, `pages`, is a list of every page in the document. The above example shows only one page. `words` is a list of words without spaces, with their individual associated bounding box in the next entry. `bbox` contains the bounding box coordinates in `left, top, width, height` format, with coordinates relative to the page size. `line_pos`, for words, is a list of tuples indicating the index of the line the word belongs to, then the starting position in that line, character-wise. `lines` are lines (parts of sequences, strings separated by spaces) grouped together using the heuristic detailed above. `bbox` contains the bounding box coordinates in `left, top, width, height` format, with coordinates relative to the page size. For each page, `images_bbox` gives the bounding boxes of the images embedded in the page. `images_bbox_no_text_overlap` gives a reduced list of bounding boxes that have no overlap with text found in the pdf. Text might be present as a drawing or another representation, however. `` `score` is a placeholder of value 1.0 for the entire dataset. Such a formatting follows the multimodal dataset from the Industry Document Library, https://huggingface.co/datasets/pixparse/idl-wds. Estimating the number of tokens is done using a `LlamaTokenizer` from `tokenizers`. There is a clear power law distribution with respect to data length. <center> <img src="https://huggingface.co/datasets/pixparse/pdfa-eng-wds/resolve/main/doc_images/token_count_distribution.png" alt="A histogram of token count distribution per page" width="600" height="300"> A histogram of token count distribution per page, taken from a subset of the dataset. There is a visible power law. </center> ### Data Splits #### Train * `pdfa-eng-wds-{0000..1799}.tar` * Downloaded on 2024/01/22 * 1800 shards (approx 1200 docs/shard) * 2,159,432 samples * 18M pages * 9.7 billion tokens (around 5 billion words) ## Additional Information ### Dataset Curators Pablo Montalvo, Ross Wightman ### Disclaimer and note to researchers This dataset is intended as an OCR-heavy pretraining basis for vision-language models. As a corpus, it does not represent the intent and purpose from CC-MAIN-2021-31-PDF-UNTRUNCATED. The original is made to represent extant pdf data in its diversity and complexity. In particular, common issues related to misuse of pdfs such as mojibake (garbled text due to decoding erros) are yet to be addressed systematically, and this dataset present simplifications that can hide such issues found in the wild. In order to address these biases, we recommend to examine carefully both the simplified annotation and the original `pdf` data, beyond a simple rendering. Further, the annotation is limited to what can be extracted and is readily available - text drawn in images and only present as a bitmap rendition might be missed entirely by said annotation. Finally, the restriction to English language is made to alleviate difficulties related to multilingual processing so that the community can be familiarized with this optimized multimodal format. A later release will be done on the full PDFA, with splits per languages, layout types, and so on. ### Licensing Information Data has been filtered from the original corpus. As a consequence, users should note [Common Crawl's license and terms of use](https://commoncrawl.org/terms-of-use) and the [Digital Corpora project's Terms of Use](https://digitalcorpora.org/about-digitalcorpora/terms-of-use/).

Helsinki-NLP/opus_books

Helsinki-NLP

2024-03-29T16:50:29Z

22,672

67

[ "task_categories:translation", "annotations_creators:found", "language_creators:found", "multilinguality:multilingual", "source_datasets:original", "language:ca", "language:de", "language:el", "language:en", "language:eo", "language:es", "language:fi", "language:fr", "language:hu", "language:it", "language:nl", "language:no", "language:pl", "language:pt", "language:ru", "language:sv", "license:other", "size_categories:1M<n<10M", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[ "translation" ]

2022-03-02T23:29:22Z

1

--- annotations_creators: - found language_creators: - found language: - ca - de - el - en - eo - es - fi - fr - hu - it - nl - 'no' - pl - pt - ru - sv license: - other multilinguality: - multilingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - translation task_ids: [] pretty_name: OpusBooks dataset_info: - config_name: ca-de features: - name: id dtype: string - name: translation dtype: translation: languages: - ca - de splits: - name: train num_bytes: 899553 num_examples: 4445 download_size: 609128 dataset_size: 899553 - config_name: ca-en features: - name: id dtype: string - name: translation dtype: translation: languages: - ca - en splits: - name: train num_bytes: 863162 num_examples: 4605 download_size: 585612 dataset_size: 863162 - config_name: ca-hu features: - name: id dtype: string - name: translation dtype: translation: languages: - ca - hu splits: - name: train num_bytes: 886150 num_examples: 4463 download_size: 608827 dataset_size: 886150 - config_name: ca-nl features: - name: id dtype: string - name: translation dtype: translation: languages: - ca - nl splits: - name: train num_bytes: 884811 num_examples: 4329 download_size: 594793 dataset_size: 884811 - config_name: de-en features: - name: id dtype: string - name: translation dtype: translation: languages: - de - en splits: - name: train num_bytes: 13738975 num_examples: 51467 download_size: 8797832 dataset_size: 13738975 - config_name: de-eo features: - name: id dtype: string - name: translation dtype: translation: languages: - de - eo splits: - name: train num_bytes: 398873 num_examples: 1363 download_size: 253509 dataset_size: 398873 - config_name: de-es features: - name: id dtype: string - name: translation dtype: translation: languages: - de - es splits: - name: train num_bytes: 7592451 num_examples: 27526 download_size: 4841017 dataset_size: 7592451 - config_name: de-fr features: - name: id dtype: string - name: translation dtype: translation: languages: - de - fr splits: - name: train num_bytes: 9544351 num_examples: 34916 download_size: 6164101 dataset_size: 9544351 - config_name: de-hu features: - name: id dtype: string - name: translation dtype: translation: languages: - de - hu splits: - name: train num_bytes: 13514971 num_examples: 51780 download_size: 8814744 dataset_size: 13514971 - config_name: de-it features: - name: id dtype: string - name: translation dtype: translation: languages: - de - it splits: - name: train num_bytes: 7759984 num_examples: 27381 download_size: 4901036 dataset_size: 7759984 - config_name: de-nl features: - name: id dtype: string - name: translation dtype: translation: languages: - de - nl splits: - name: train num_bytes: 3561740 num_examples: 15622 download_size: 2290868 dataset_size: 3561740 - config_name: de-pt features: - name: id dtype: string - name: translation dtype: translation: languages: - de - pt splits: - name: train num_bytes: 317143 num_examples: 1102 download_size: 197768 dataset_size: 317143 - config_name: de-ru features: - name: id dtype: string - name: translation dtype: translation: languages: - de - ru splits: - name: train num_bytes: 5764649 num_examples: 17373 download_size: 3255537 dataset_size: 5764649 - config_name: el-en features: - name: id dtype: string - name: translation dtype: translation: languages: - el - en splits: - name: train num_bytes: 552567 num_examples: 1285 download_size: 310863 dataset_size: 552567 - config_name: el-es features: - name: id dtype: string - name: translation dtype: translation: languages: - el - es splits: - name: train num_bytes: 527979 num_examples: 1096 download_size: 298827 dataset_size: 527979 - config_name: el-fr features: - name: id dtype: string - name: translation dtype: translation: languages: - el - fr splits: - name: train num_bytes: 539921 num_examples: 1237 download_size: 303181 dataset_size: 539921 - config_name: el-hu features: - name: id dtype: string - name: translation dtype: translation: languages: - el - hu splits: - name: train num_bytes: 546278 num_examples: 1090 download_size: 313292 dataset_size: 546278 - config_name: en-eo features: - name: id dtype: string - name: translation dtype: translation: languages: - en - eo splits: - name: train num_bytes: 386219 num_examples: 1562 download_size: 246715 dataset_size: 386219 - config_name: en-es features: - name: id dtype: string - name: translation dtype: translation: languages: - en - es splits: - name: train num_bytes: 25291663 num_examples: 93470 download_size: 16080303 dataset_size: 25291663 - config_name: en-fi features: - name: id dtype: string - name: translation dtype: translation: languages: - en - fi splits: - name: train num_bytes: 715027 num_examples: 3645 download_size: 467851 dataset_size: 715027 - config_name: en-fr features: - name: id dtype: string - name: translation dtype: translation: languages: - en - fr splits: - name: train num_bytes: 32997043 num_examples: 127085 download_size: 20985324 dataset_size: 32997043 - config_name: en-hu features: - name: id dtype: string - name: translation dtype: translation: languages: - en - hu splits: - name: train num_bytes: 35256766 num_examples: 137151 download_size: 23065198 dataset_size: 35256766 - config_name: en-it features: - name: id dtype: string - name: translation dtype: translation: languages: - en - it splits: - name: train num_bytes: 8993755 num_examples: 32332 download_size: 5726189 dataset_size: 8993755 - config_name: en-nl features: - name: id dtype: string - name: translation dtype: translation: languages: - en - nl splits: - name: train num_bytes: 10277990 num_examples: 38652 download_size: 6443323 dataset_size: 10277990 - config_name: en-no features: - name: id dtype: string - name: translation dtype: translation: languages: - en - 'no' splits: - name: train num_bytes: 661966 num_examples: 3499 download_size: 429631 dataset_size: 661966 - config_name: en-pl features: - name: id dtype: string - name: translation dtype: translation: languages: - en - pl splits: - name: train num_bytes: 583079 num_examples: 2831 download_size: 389337 dataset_size: 583079 - config_name: en-pt features: - name: id dtype: string - name: translation dtype: translation: languages: - en - pt splits: - name: train num_bytes: 309677 num_examples: 1404 download_size: 191493 dataset_size: 309677 - config_name: en-ru features: - name: id dtype: string - name: translation dtype: translation: languages: - en - ru splits: - name: train num_bytes: 5190856 num_examples: 17496 download_size: 2922360 dataset_size: 5190856 - config_name: en-sv features: - name: id dtype: string - name: translation dtype: translation: languages: - en - sv splits: - name: train num_bytes: 790773 num_examples: 3095 download_size: 516328 dataset_size: 790773 - config_name: eo-es features: - name: id dtype: string - name: translation dtype: translation: languages: - eo - es splits: - name: train num_bytes: 409579 num_examples: 1677 download_size: 265543 dataset_size: 409579 - config_name: eo-fr features: - name: id dtype: string - name: translation dtype: translation: languages: - eo - fr splits: - name: train num_bytes: 412987 num_examples: 1588 download_size: 261689 dataset_size: 412987 - config_name: eo-hu features: - name: id dtype: string - name: translation dtype: translation: languages: - eo - hu splits: - name: train num_bytes: 389100 num_examples: 1636 download_size: 258229 dataset_size: 389100 - config_name: eo-it features: - name: id dtype: string - name: translation dtype: translation: languages: - eo - it splits: - name: train num_bytes: 387594 num_examples: 1453 download_size: 248748 dataset_size: 387594 - config_name: eo-pt features: - name: id dtype: string - name: translation dtype: translation: languages: - eo - pt splits: - name: train num_bytes: 311067 num_examples: 1259 download_size: 197021 dataset_size: 311067 - config_name: es-fi features: - name: id dtype: string - name: translation dtype: translation: languages: - es - fi splits: - name: train num_bytes: 710450 num_examples: 3344 download_size: 467281 dataset_size: 710450 - config_name: es-fr features: - name: id dtype: string - name: translation dtype: translation: languages: - es - fr splits: - name: train num_bytes: 14382126 num_examples: 56319 download_size: 9164030 dataset_size: 14382126 - config_name: es-hu features: - name: id dtype: string - name: translation dtype: translation: languages: - es - hu splits: - name: train num_bytes: 19373967 num_examples: 78800 download_size: 12691292 dataset_size: 19373967 - config_name: es-it features: - name: id dtype: string - name: translation dtype: translation: languages: - es - it splits: - name: train num_bytes: 7837667 num_examples: 28868 download_size: 5026914 dataset_size: 7837667 - config_name: es-nl features: - name: id dtype: string - name: translation dtype: translation: languages: - es - nl splits: - name: train num_bytes: 9062341 num_examples: 32247 download_size: 5661890 dataset_size: 9062341 - config_name: es-no features: - name: id dtype: string - name: translation dtype: translation: languages: - es - 'no' splits: - name: train num_bytes: 729113 num_examples: 3585 download_size: 473525 dataset_size: 729113 - config_name: es-pt features: - name: id dtype: string - name: translation dtype: translation: languages: - es - pt splits: - name: train num_bytes: 326872 num_examples: 1327 download_size: 204399 dataset_size: 326872 - config_name: es-ru features: - name: id dtype: string - name: translation dtype: translation: languages: - es - ru splits: - name: train num_bytes: 5281106 num_examples: 16793 download_size: 2995191 dataset_size: 5281106 - config_name: fi-fr features: - name: id dtype: string - name: translation dtype: translation: languages: - fi - fr splits: - name: train num_bytes: 746085 num_examples: 3537 download_size: 486904 dataset_size: 746085 - config_name: fi-hu features: - name: id dtype: string - name: translation dtype: translation: languages: - fi - hu splits: - name: train num_bytes: 746602 num_examples: 3504 download_size: 509394 dataset_size: 746602 - config_name: fi-no features: - name: id dtype: string - name: translation dtype: translation: languages: - fi - 'no' splits: - name: train num_bytes: 691169 num_examples: 3414 download_size: 449501 dataset_size: 691169 - config_name: fi-pl features: - name: id dtype: string - name: translation dtype: translation: languages: - fi - pl splits: - name: train num_bytes: 613779 num_examples: 2814 download_size: 410258 dataset_size: 613779 - config_name: fr-hu features: - name: id dtype: string - name: translation dtype: translation: languages: - fr - hu splits: - name: train num_bytes: 22483025 num_examples: 89337 download_size: 14689840 dataset_size: 22483025 - config_name: fr-it features: - name: id dtype: string - name: translation dtype: translation: languages: - fr - it splits: - name: train num_bytes: 4752147 num_examples: 14692 download_size: 3040617 dataset_size: 4752147 - config_name: fr-nl features: - name: id dtype: string - name: translation dtype: translation: languages: - fr - nl splits: - name: train num_bytes: 10408088 num_examples: 40017 download_size: 6528881 dataset_size: 10408088 - config_name: fr-no features: - name: id dtype: string - name: translation dtype: translation: languages: - fr - 'no' splits: - name: train num_bytes: 692774 num_examples: 3449 download_size: 449136 dataset_size: 692774 - config_name: fr-pl features: - name: id dtype: string - name: translation dtype: translation: languages: - fr - pl splits: - name: train num_bytes: 614236 num_examples: 2825 download_size: 408295 dataset_size: 614236 - config_name: fr-pt features: - name: id dtype: string - name: translation dtype: translation: languages: - fr - pt splits: - name: train num_bytes: 324604 num_examples: 1263 download_size: 198700 dataset_size: 324604 - config_name: fr-ru features: - name: id dtype: string - name: translation dtype: translation: languages: - fr - ru splits: - name: train num_bytes: 2474198 num_examples: 8197 download_size: 1425660 dataset_size: 2474198 - config_name: fr-sv features: - name: id dtype: string - name: translation dtype: translation: languages: - fr - sv splits: - name: train num_bytes: 833541 num_examples: 3002 download_size: 545599 dataset_size: 833541 - config_name: hu-it features: - name: id dtype: string - name: translation dtype: translation: languages: - hu - it splits: - name: train num_bytes: 8445537 num_examples: 30949 download_size: 5477452 dataset_size: 8445537 - config_name: hu-nl features: - name: id dtype: string - name: translation dtype: translation: languages: - hu - nl splits: - name: train num_bytes: 10814113 num_examples: 43428 download_size: 6985092 dataset_size: 10814113 - config_name: hu-no features: - name: id dtype: string - name: translation dtype: translation: languages: - hu - 'no' splits: - name: train num_bytes: 695485 num_examples: 3410 download_size: 465904 dataset_size: 695485 - config_name: hu-pl features: - name: id dtype: string - name: translation dtype: translation: languages: - hu - pl splits: - name: train num_bytes: 616149 num_examples: 2859 download_size: 425988 dataset_size: 616149 - config_name: hu-pt features: - name: id dtype: string - name: translation dtype: translation: languages: - hu - pt splits: - name: train num_bytes: 302960 num_examples: 1184 download_size: 193053 dataset_size: 302960 - config_name: hu-ru features: - name: id dtype: string - name: translation dtype: translation: languages: - hu - ru splits: - name: train num_bytes: 7818652 num_examples: 26127 download_size: 4528613 dataset_size: 7818652 - config_name: it-nl features: - name: id dtype: string - name: translation dtype: translation: languages: - it - nl splits: - name: train num_bytes: 1328293 num_examples: 2359 download_size: 824780 dataset_size: 1328293 - config_name: it-pt features: - name: id dtype: string - name: translation dtype: translation: languages: - it - pt splits: - name: train num_bytes: 301416 num_examples: 1163 download_size: 190005 dataset_size: 301416 - config_name: it-ru features: - name: id dtype: string - name: translation dtype: translation: languages: - it - ru splits: - name: train num_bytes: 5316928 num_examples: 17906 download_size: 2997871 dataset_size: 5316928 - config_name: it-sv features: - name: id dtype: string - name: translation dtype: translation: languages: - it - sv splits: - name: train num_bytes: 811401 num_examples: 2998 download_size: 527303 dataset_size: 811401 configs: - config_name: ca-de data_files: - split: train path: ca-de/train-* - config_name: ca-en data_files: - split: train path: ca-en/train-* - config_name: ca-hu data_files: - split: train path: ca-hu/train-* - config_name: ca-nl data_files: - split: train path: ca-nl/train-* - config_name: de-en data_files: - split: train path: de-en/train-* - config_name: de-eo data_files: - split: train path: de-eo/train-* - config_name: de-es data_files: - split: train path: de-es/train-* - config_name: de-fr data_files: - split: train path: de-fr/train-* - config_name: de-hu data_files: - split: train path: de-hu/train-* - config_name: de-it data_files: - split: train path: de-it/train-* - config_name: de-nl data_files: - split: train path: de-nl/train-* - config_name: de-pt data_files: - split: train path: de-pt/train-* - config_name: de-ru data_files: - split: train path: de-ru/train-* - config_name: el-en data_files: - split: train path: el-en/train-* - config_name: el-es data_files: - split: train path: el-es/train-* - config_name: el-fr data_files: - split: train path: el-fr/train-* - config_name: el-hu data_files: - split: train path: el-hu/train-* - config_name: en-eo data_files: - split: train path: en-eo/train-* - config_name: en-es data_files: - split: train path: en-es/train-* - config_name: en-fi data_files: - split: train path: en-fi/train-* - config_name: en-fr data_files: - split: train path: en-fr/train-* - config_name: en-hu data_files: - split: train path: en-hu/train-* - config_name: en-it data_files: - split: train path: en-it/train-* - config_name: en-nl data_files: - split: train path: en-nl/train-* - config_name: en-no data_files: - split: train path: en-no/train-* - config_name: en-pl data_files: - split: train path: en-pl/train-* - config_name: en-pt data_files: - split: train path: en-pt/train-* - config_name: en-ru data_files: - split: train path: en-ru/train-* - config_name: en-sv data_files: - split: train path: en-sv/train-* - config_name: eo-es data_files: - split: train path: eo-es/train-* - config_name: eo-fr data_files: - split: train path: eo-fr/train-* - config_name: eo-hu data_files: - split: train path: eo-hu/train-* - config_name: eo-it data_files: - split: train path: eo-it/train-* - config_name: eo-pt data_files: - split: train path: eo-pt/train-* - config_name: es-fi data_files: - split: train path: es-fi/train-* - config_name: es-fr data_files: - split: train path: es-fr/train-* - config_name: es-hu data_files: - split: train path: es-hu/train-* - config_name: es-it data_files: - split: train path: es-it/train-* - config_name: es-nl data_files: - split: train path: es-nl/train-* - config_name: es-no data_files: - split: train path: es-no/train-* - config_name: es-pt data_files: - split: train path: es-pt/train-* - config_name: es-ru data_files: - split: train path: es-ru/train-* - config_name: fi-fr data_files: - split: train path: fi-fr/train-* - config_name: fi-hu data_files: - split: train path: fi-hu/train-* - config_name: fi-no data_files: - split: train path: fi-no/train-* - config_name: fi-pl data_files: - split: train path: fi-pl/train-* - config_name: fr-hu data_files: - split: train path: fr-hu/train-* - config_name: fr-it data_files: - split: train path: fr-it/train-* - config_name: fr-nl data_files: - split: train path: fr-nl/train-* - config_name: fr-no data_files: - split: train path: fr-no/train-* - config_name: fr-pl data_files: - split: train path: fr-pl/train-* - config_name: fr-pt data_files: - split: train path: fr-pt/train-* - config_name: fr-ru data_files: - split: train path: fr-ru/train-* - config_name: fr-sv data_files: - split: train path: fr-sv/train-* - config_name: hu-it data_files: - split: train path: hu-it/train-* - config_name: hu-nl data_files: - split: train path: hu-nl/train-* - config_name: hu-no data_files: - split: train path: hu-no/train-* - config_name: hu-pl data_files: - split: train path: hu-pl/train-* - config_name: hu-pt data_files: - split: train path: hu-pt/train-* - config_name: hu-ru data_files: - split: train path: hu-ru/train-* - config_name: it-nl data_files: - split: train path: it-nl/train-* - config_name: it-pt data_files: - split: train path: it-pt/train-* - config_name: it-ru data_files: - split: train path: it-ru/train-* - config_name: it-sv data_files: - split: train path: it-sv/train-* --- # Dataset Card for OPUS Books ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://opus.nlpl.eu/Books/corpus/version/Books - **Repository:** [More Information Needed] - **Paper:** https://aclanthology.org/L12-1246/ - **Leaderboard:** [More Information Needed] - **Point of Contact:** [More Information Needed] ### Dataset Summary This is a collection of copyright free books aligned by Andras Farkas, which are available from http://www.farkastranslations.com/bilingual_books.php Note that the texts are rather dated due to copyright issues and that some of them are manually reviewed (check the meta-data at the top of the corpus files in XML). The source is multilingually aligned, which is available from http://www.farkastranslations.com/bilingual_books.php. In OPUS, the alignment is formally bilingual but the multilingual alignment can be recovered from the XCES sentence alignment files. Note also that the alignment units from the original source may include multi-sentence paragraphs, which are split and sentence-aligned in OPUS. All texts are freely available for personal, educational and research use. Commercial use (e.g. reselling as parallel books) and mass redistribution without explicit permission are not granted. Please acknowledge the source when using the data! Books's Numbers: - Languages: 16 - Bitexts: 64 - Number of files: 158 - Number of tokens: 19.50M - Sentence fragments: 0.91M ### Supported Tasks and Leaderboards Translation. ### Languages The languages in the dataset are: - ca - de - el - en - eo - es - fi - fr - hu - it - nl - no - pl - pt - ru - sv ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data [More Information Needed] #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations [More Information Needed] #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information All texts are freely available for personal, educational and research use. Commercial use (e.g. reselling as parallel books) and mass redistribution without explicit permission are not granted. ### Citation Information Please acknowledge the source when using the data. Please cite the following article if you use any part of the OPUS corpus in your own work: ```bibtex @inproceedings{tiedemann-2012-parallel, title = "Parallel Data, Tools and Interfaces in {OPUS}", author = {Tiedemann, J{\"o}rg}, editor = "Calzolari, Nicoletta and Choukri, Khalid and Declerck, Thierry and Do{\u{g}}an, Mehmet U{\u{g}}ur and Maegaard, Bente and Mariani, Joseph and Moreno, Asuncion and Odijk, Jan and Piperidis, Stelios", booktitle = "Proceedings of the Eighth International Conference on Language Resources and Evaluation ({LREC}'12)", month = may, year = "2012", address = "Istanbul, Turkey", publisher = "European Language Resources Association (ELRA)", url = "http://www.lrec-conf.org/proceedings/lrec2012/pdf/463_Paper.pdf", pages = "2214--2218", } ``` ### Contributions Thanks to [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset.

FiscalNote/billsum

FiscalNote

2024-03-27T16:01:38Z

8,130

49

[ "task_categories:summarization", "annotations_creators:found", "language_creators:found", "multilinguality:monolingual", "source_datasets:original", "language:en", "license:cc0-1.0", "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:1910.00523", "region:us", "bills-summarization" ]

[ "summarization" ]

2022-03-02T23:29:22Z

1

--- annotations_creators: - found language_creators: - found language: - en license: - cc0-1.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - summarization task_ids: [] paperswithcode_id: billsum pretty_name: BillSum tags: - bills-summarization dataset_info: features: - name: text dtype: string - name: summary dtype: string - name: title dtype: string splits: - name: train num_bytes: 219596090 num_examples: 18949 - name: test num_bytes: 37866257 num_examples: 3269 - name: ca_test num_bytes: 14945291 num_examples: 1237 download_size: 113729382 dataset_size: 272407638 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: ca_test path: data/ca_test-* train-eval-index: - config: default task: summarization task_id: summarization splits: train_split: train eval_split: test col_mapping: text: text summary: target metrics: - type: rouge name: Rouge --- # Dataset Card for "billsum" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://github.com/FiscalNote/BillSum](https://github.com/FiscalNote/BillSum) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** https://aclanthology.org/D19-5406/ - **Paper:** https://arxiv.org/abs/1910.00523 - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 67.26 MB - **Size of the generated dataset:** 272.42 MB - **Total amount of disk used:** 339.68 MB ### Dataset Summary BillSum, summarization of US Congressional and California state bills. There are several features: - text: bill text. - summary: summary of the bills. - title: title of the bills. features for us bills. ca bills does not have. - text_len: number of chars in text. - sum_len: number of chars in summary. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### default - **Size of downloaded dataset files:** 67.26 MB - **Size of the generated dataset:** 272.42 MB - **Total amount of disk used:** 339.68 MB An example of 'train' looks as follows. ``` { "summary": "some summary", "text": "some text.", "title": "An act to amend Section xxx." } ``` ### Data Fields The data fields are the same among all splits. #### default - `text`: a `string` feature. - `summary`: a `string` feature. - `title`: a `string` feature. ### Data Splits | name |train|ca_test|test| |-------|----:|------:|---:| |default|18949| 1237|3269| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization The data consists of three parts: US training bills, US test bills and California test bills. The US bills were collected from the [Govinfo](https://github.com/unitedstates/congress) service provided by the United States Government Publishing Office (GPO) under CC0-1.0 license. The California, bills from the 2015-2016 session are available from the legislature’s [website](https://leginfo.legislature.ca.gov/). #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @inproceedings{kornilova-eidelman-2019-billsum, title = "{B}ill{S}um: A Corpus for Automatic Summarization of {US} Legislation", author = "Kornilova, Anastassia and Eidelman, Vladimir", editor = "Wang, Lu and Cheung, Jackie Chi Kit and Carenini, Giuseppe and Liu, Fei", booktitle = "Proceedings of the 2nd Workshop on New Frontiers in Summarization", month = nov, year = "2019", address = "Hong Kong, China", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/D19-5406", doi = "10.18653/v1/D19-5406", pages = "48--56", eprint={1910.00523}, archivePrefix={arXiv}, primaryClass={cs.CL}, } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@jplu](https://github.com/jplu), [@lewtun](https://github.com/lewtun) for adding this dataset.

xlangai/spider

xlangai

2024-03-27T13:38:42Z

3,108

143

[ "task_categories:text2text-generation", "annotations_creators:expert-generated", "language_creators:expert-generated", "language_creators:machine-generated", "multilinguality:monolingual", "source_datasets:original", "language:en", "license:cc-by-sa-4.0", "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:1809.08887", "region:us", "text-to-sql" ]

[ "text2text-generation" ]

2022-03-02T23:29:22Z

null

--- annotations_creators: - expert-generated language_creators: - expert-generated - machine-generated language: - en license: - cc-by-sa-4.0 multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - text2text-generation task_ids: [] paperswithcode_id: spider-1 pretty_name: Spider tags: - text-to-sql dataset_info: config_name: spider features: - name: db_id dtype: string - name: query dtype: string - name: question dtype: string - name: query_toks sequence: string - name: query_toks_no_value sequence: string - name: question_toks sequence: string splits: - name: train num_bytes: 4743786 num_examples: 7000 - name: validation num_bytes: 682090 num_examples: 1034 download_size: 957246 dataset_size: 5425876 configs: - config_name: spider data_files: - split: train path: spider/train-* - split: validation path: spider/validation-* default: true --- # Dataset Card for Spider ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://yale-lily.github.io/spider - **Repository:** https://github.com/taoyds/spider - **Paper:** https://www.aclweb.org/anthology/D18-1425/ - **Paper:** https://arxiv.org/abs/1809.08887 - **Point of Contact:** [Yale LILY](https://yale-lily.github.io/) ### Dataset Summary Spider is a large-scale complex and cross-domain semantic parsing and text-to-SQL dataset annotated by 11 Yale students. The goal of the Spider challenge is to develop natural language interfaces to cross-domain databases. ### Supported Tasks and Leaderboards The leaderboard can be seen at https://yale-lily.github.io/spider ### Languages The text in the dataset is in English. ## Dataset Structure ### Data Instances **What do the instances that comprise the dataset represent?** Each instance is natural language question and the equivalent SQL query **How many instances are there in total?** **What data does each instance consist of?** [More Information Needed] ### Data Fields * **db_id**: Database name * **question**: Natural language to interpret into SQL * **query**: Target SQL query * **query_toks**: List of tokens for the query * **query_toks_no_value**: List of tokens for the query * **question_toks**: List of tokens for the question ### Data Splits **train**: 7000 questions and SQL query pairs **dev**: 1034 question and SQL query pairs [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? [More Information Needed] ### Annotations The dataset was annotated by 11 college students at Yale University #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases [More Information Needed] ### Other Known Limitations ## Additional Information The listed authors in the homepage are maintaining/supporting the dataset. ### Dataset Curators [More Information Needed] ### Licensing Information The spider dataset is licensed under the [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/legalcode) [More Information Needed] ### Citation Information ``` @inproceedings{yu-etal-2018-spider, title = "{S}pider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-{SQL} Task", author = "Yu, Tao and Zhang, Rui and Yang, Kai and Yasunaga, Michihiro and Wang, Dongxu and Li, Zifan and Ma, James and Li, Irene and Yao, Qingning and Roman, Shanelle and Zhang, Zilin and Radev, Dragomir", editor = "Riloff, Ellen and Chiang, David and Hockenmaier, Julia and Tsujii, Jun{'}ichi", booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing", month = oct # "-" # nov, year = "2018", address = "Brussels, Belgium", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/D18-1425", doi = "10.18653/v1/D18-1425", pages = "3911--3921", archivePrefix={arXiv}, eprint={1809.08887}, primaryClass={cs.CL}, } ``` ### Contributions Thanks to [@olinguyen](https://github.com/olinguyen) for adding this dataset.

ctheodoris/Genecorpus-30M

ctheodoris

2024-03-25T23:01:49Z

2,190

71

[ "license:apache-2.0", "region:us" ]

[]

2022-03-12T21:21:46Z

1

--- license: apache-2.0 --- # Dataset Card for Genecorpus-30M ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks](#supported-tasks) - [Species](#species) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Citation Information](#citation-information)  ## Dataset Description  - **Point of Contact:** [email protected] ### Dataset Summary We assembled a large-scale pretraining corpus, Genecorpus-30M, comprised of ~30 million human single cell transcriptomes from a broad range of tissues from publicly available data. This corpus was used for pretraining [Geneformer](https://huggingface.co/ctheodoris/Geneformer), a pretrained transformer model that enables context-aware predictions in settings with limited data in network biology. See [our manuscript](https://rdcu.be/ddrx0) for details. ### Supported Tasks This corpus was used for pretraining [Geneformer](https://rdcu.be/ddrx0) and is compatible with pretraining or fine-tuning Geneformer or similar models. ### Species Homo sapiens ## Dataset Structure ### Data Instances Genecorpus-30M is provided as tokenized data in the Huggingface Datasets structure, which is based on the Apache Arrow format. Each example within the dataset is composed of the rank value encoding for a single cell within the corpus. Rank value encodings provide a nonparametric representation of each single cell’s transcriptome, ranking genes by their expression within that cell normalized by their expression across the entire Genecorpus-30M. This method takes advantage of the many observations of each gene’s expression across Genecorpus-30M to prioritize genes that distinguish cell state. Specifically, this method will deprioritize ubiquitously highly-expressed housekeeping genes by normalizing them to a lower rank. Conversely, genes such as transcription factors that may be lowly expressed when they are expressed but highly distinguish cell state will move to a higher rank within the encoding. Furthermore, this rank-based approach may be more robust against technical artifacts that may systematically bias the absolute transcript counts value while the overall relative ranking of genes within each cell remains more stable. To accomplish this, we first calculated the nonzero median value of expression of each detected gene across all cells from the entire Genecorpus-30M. We aggregated the transcript count distribution for each gene, normalizing the gene transcript counts in each cell by the total transcript count of that cell to account for varying sequencing depth. We then normalized the genes in each single cell transcriptome by that gene’s nonzero median value of expression across Genecorpus-30M and ordered the genes by the rank of their normalized expression in that specific cell. Of note, we opted to use the nonzero median value of expression rather than include zeros in the distribution so as not to weight the value by tissue representation within Genecorpus-30M, assuming that a representative range of transcript values would be observed within the cells in which each gene was detected. The rank value encodings for each single cell transcriptome were then tokenized based on a total vocabulary of 25,424 protein-coding or miRNA genes detected within Geneformer-30M. The token dictionary mapping each token ID to special tokens (pad and mask) or Ensembl IDs for each gene is included within the repository as a pickle file (token_dictionary.pkl). ### Data Fields - `input_ids`: rank value encoding for an example cell - `lengths`: length of rank value encoding for that example cell ### Data Splits The dataset does not contain any predefined splits. ## Dataset Creation ### Curation Rationale Mapping the gene regulatory networks that drive disease progression enables screening for molecules that correct the network by normalizing core regulatory elements, rather than targeting peripheral downstream effectors that may not be disease modifying. However, mapping the gene network architecture requires large amounts of transcriptomic data to learn the connections between genes, which impedes network-correcting drug discovery in settings with limited data, including rare diseases and diseases affecting clinically inaccessible tissues. Although data remains limited in these settings, recent advances in sequencing technologies have driven a rapid expansion in the amount of transcriptomic data available from human tissues more broadly. Furthermore, single cell technologies have facilitated the observation of transcriptomic states without averaging genes’ expression across multiple cells, potentially providing more precise data for inference of network interactions, especially in diseases driven by dysregulation of multiple cell types. Recently, the concept of transfer learning has revolutionized fields such as natural language understanding and computer vision by leveraging deep learning models pretrained on large-scale general datasets that can then be fine-tuned towards a vast array of downstream tasks with limited task-specific data that would be insufficient to yield meaningful predictions when used in isolation. We therefore assembled Genecorpus-30M to allow the large-scale pretraining of [Geneformer](https://huggingface.co/ctheodoris/Geneformer), a pretrained transformer model that enables context-aware predictions in settings with limited data in network biology. ### Source Data #### Initial Data Collection and Normalization Source data included 29.9 million (29,900,531) human single cell transcriptomes from a broad range of tissues from 561 publicly available datasets from original studies cited in the Methods of Theodoris et al, Nature 2023. Datasets were filtered to retain cells with total read counts within three standard deviations of the mean within that dataset and mitochondrial reads within three standard deviations of the mean within that dataset. Ensembl-annotated protein-coding and miRNA genes were used for downstream analysis. Cells with less than seven detected Ensembl-annotated protein-coding or miRNA genes were excluded as the 15% masking used for the pretraining learning objective would not reliably mask a gene in cells with fewer detected genes. Ultimately, 27.4 million (27,406,217) cells passed the defined quality filters. Cells were then represented as rank value encodings as discussed above in [Data Instances](#data-instances). #### Who are the source data producers? Publicly available datasets containing raw counts were collected from National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO), NCBI Sequence Read Archive (SRA), Human Cell Atlas, European Molecular Biology Laboratory-European Bioinformatics Institute (EMBL-EBI) Single Cell Expression Atlas, Broad Institute Single Cell Portal, Brotman Baty Institute (BBI)-Allen Single Cell Atlases, Tumor Immune Single-cell Hub (TISCH) (excluding malignant cells), Panglao Database, 10x Genomics, University of California, Santa Cruz Cell Browser, European Genome-phenome Archive, Synapse, Riken, Zenodo, National Institutes of Health (NIH) Figshare Archive, NCBI dbGap, Refine.bio, China National GeneBank Sequence Archive, Mendeley Data, and individual communication with authors of the original studies as cited in the Methods of Theodoris et al, Nature 2023. ### Annotations #### Annotation process Geneformer-30M does not contain annotations. #### Who are the annotators? N/A ### Personal and Sensitive Information There is no personal or sensitive information included in the dataset. The dataset is composed of rank value encodings, so there are no traceable sequencing reads included. ## Considerations for Using the Data ### Social Impact of Dataset Genecorpus-30M enabled the large-scale pretraining of [Geneformer](https://huggingface.co/ctheodoris/Geneformer), a foundation model that enables context-aware predictions in settings with limited data in network biology. Within our publication, we demonstrated that during pretraining, Geneformer gained a fundamental understanding of network dynamics, encoding network hierarchy in the model’s attention weights in a completely self-supervised manner. Fine-tuning Geneformer towards a diverse panel of downstream tasks relevant to chromatin and network dynamics using limited task-specific data demonstrated that Geneformer consistently boosted predictive accuracy. Applied to disease modeling with limited patient data, Geneformer identified candidate therapeutic targets for cardiomyopathy. Overall, Geneformer represents a pretrained foundation model from which fine-tuning towards a broad range of downstream applications can be pursued to accelerate discovery of key network regulators and candidate therapeutic targets. ### Discussion of Biases We excluded cells with high mutational burdens (e.g. malignant cells and immortalized cell lines) that could lead to substantial network rewiring without companion genome sequencing to facilitate interpretation. We only included droplet-based sequencing platforms to assure expression value unit comparability. Although we assembled the dataset to represent as diverse a set of human tissues and cell types as possible, particular tissues and cell types are not represented due to unavailability of public data at the time of dataset assembly. In our manuscript, we demonstrated that pretraining with larger and more diverse corpuses consistently improved Geneformer’s predictive power, consistent with observations that large-scale pretraining allows training of deeper models that ultimately have greater predictive potential in fields including NLU, computer vision, and mathematical problem-solving. Additionally, exposure to hundreds of experimental datasets during pretraining also appeared to promote robustness to batch-dependent technical artifacts and individual variability that commonly impact single cell analyses in biology. These findings suggest that as the amount of publicly available transcriptomic data continues to expand, future models pretrained on even larger-scale corpuses may open opportunities to achieve meaningful predictions in even more elusive tasks with increasingly limited task-specific data. ### Other Known Limitations Genecorpus-30M was intended to be used for self-supervised pretraining. To achieve the best possible predictions in downstream tasks, Geneformer should be fine-tuned with labeled datasets relevant to the task at hand. ## Additional Information ### Dataset Curators Christina Theodoris, MD, PhD ### Citation Information Theodoris CV*, Xiao L, Chopra A, Chaffin MD, Al Sayed ZR, Hill MC, Mantineo H, Brydon EM, Zeng Z, Liu XS, Ellinor PT*. Transfer learning enables predictions in network biology. Nature. 2023 May 31; Epub ahead of print. (*co-corresponding authors)

alvations/c4p0-x1-de-en

alvations

2024-03-24T03:55:50Z

11,110

0

[ "size_categories:n<1K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2024-03-23T10:16:43Z

null

--- dataset_info: features: - name: source dtype: string - name: target dtype: string - name: target_backto_source dtype: string - name: raw_target list: - name: generated_text dtype: string - name: raw_target_backto_source list: - name: generated_text dtype: string - name: prompt dtype: string - name: reverse_prompt dtype: string - name: source_langid dtype: string - name: target_langid dtype: string - name: target_backto_source_langid dtype: string - name: doc_id dtype: int64 - name: sent_id dtype: int64 - name: timestamp dtype: timestamp[us] - name: url dtype: string - name: doc_hash dtype: string splits: - name: train num_bytes: 32165 num_examples: 31 download_size: 22249 dataset_size: 32165 configs: - config_name: default data_files: - split: train path: c0d4dc8660289947/train-* ---

alvations/c4p0-x1-en-ja

alvations

2024-03-24T03:55:23Z

23,170

0

[ "size_categories:n<1K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2024-03-23T09:54:37Z

null

--- dataset_info: features: - name: source dtype: string - name: target dtype: string - name: target_backto_source dtype: string - name: raw_target list: - name: generated_text dtype: string - name: raw_target_backto_source list: - name: generated_text dtype: string - name: prompt dtype: string - name: reverse_prompt dtype: string - name: source_langid dtype: string - name: target_langid dtype: string - name: target_backto_source_langid dtype: string - name: doc_id dtype: int64 - name: sent_id dtype: int64 - name: timestamp dtype: string - name: url dtype: string - name: doc_hash dtype: string splits: - name: train num_bytes: 49764 num_examples: 42 download_size: 37636 dataset_size: 49764 configs: - config_name: default data_files: - split: train path: 66034f82c5c65ae4/train-* ---

bigbio/pubmed_qa

bigbio

2024-03-23T19:06:35Z

4,483

46

[ "multilinguality:monolingual", "language:en", "license:mit", "region:us" ]

[]

2022-11-13T22:11:45Z

2

--- language: - en bigbio_language: - English license: mit multilinguality: monolingual bigbio_license_shortname: MIT pretty_name: PubMedQA homepage: https://github.com/pubmedqa/pubmedqa bigbio_pubmed: True bigbio_public: True bigbio_tasks: - QUESTION_ANSWERING --- # Dataset Card for PubMedQA ## Dataset Description - **Homepage:** https://github.com/pubmedqa/pubmedqa - **Pubmed:** True - **Public:** True - **Tasks:** QA PubMedQA is a novel biomedical question answering (QA) dataset collected from PubMed abstracts. The task of PubMedQA is to answer research biomedical questions with yes/no/maybe using the corresponding abstracts. PubMedQA has 1k expert-annotated (PQA-L), 61.2k unlabeled (PQA-U) and 211.3k artificially generated QA instances (PQA-A). Each PubMedQA instance is composed of: (1) a question which is either an existing research article title or derived from one, (2) a context which is the corresponding PubMed abstract without its conclusion, (3) a long answer, which is the conclusion of the abstract and, presumably, answers the research question, and (4) a yes/no/maybe answer which summarizes the conclusion. PubMedQA is the first QA dataset where reasoning over biomedical research texts, especially their quantitative contents, is required to answer the questions. PubMedQA datasets comprise of 3 different subsets: (1) PubMedQA Labeled (PQA-L): A labeled PubMedQA subset comprises of 1k manually annotated yes/no/maybe QA data collected from PubMed articles. (2) PubMedQA Artificial (PQA-A): An artificially labelled PubMedQA subset comprises of 211.3k PubMed articles with automatically generated questions from the statement titles and yes/no answer labels generated using a simple heuristic. (3) PubMedQA Unlabeled (PQA-U): An unlabeled PubMedQA subset comprises of 61.2k context-question pairs data collected from PubMed articles. ## Citation Information ``` @inproceedings{jin2019pubmedqa, title={PubMedQA: A Dataset for Biomedical Research Question Answering}, author={Jin, Qiao and Dhingra, Bhuwan and Liu, Zhengping and Cohen, William and Lu, Xinghua}, booktitle={Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)}, pages={2567--2577}, year={2019} } ```

open-llm-leaderboard-old/details_gpt2

open-llm-leaderboard-old

2024-03-23T06:19:40Z

19,921

0

[ "region:us" ]

[]

2023-08-18T14:35:26Z

null

--- pretty_name: Evaluation run of gpt2 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [gpt2](https://huggingface.co/gpt2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 65 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 25 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_gpt2\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-03-23T06:18:16.565546](https://huggingface.co/datasets/open-llm-leaderboard/details_gpt2/blob/main/results_2024-03-23T06-18-16.565546.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.25780579051672486,\n\ \ \"acc_stderr\": 0.030658881019520554,\n \"acc_norm\": 0.2586547713391113,\n\ \ \"acc_norm_stderr\": 0.031431381356225356,\n \"mc1\": 0.22766217870257038,\n\ \ \"mc1_stderr\": 0.01467925503211107,\n \"mc2\": 0.4069116400376613,\n\ \ \"mc2_stderr\": 0.014934250122346554\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.197098976109215,\n \"acc_stderr\": 0.011625047669880633,\n\ \ \"acc_norm\": 0.22013651877133106,\n \"acc_norm_stderr\": 0.01210812488346097\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.29267078271260705,\n\ \ \"acc_stderr\": 0.004540586983229993,\n \"acc_norm\": 0.3152758414658435,\n\ \ \"acc_norm_stderr\": 0.0046367607625228515\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.21,\n \"acc_stderr\": 0.040936018074033256,\n \ \ \"acc_norm\": 0.21,\n \"acc_norm_stderr\": 0.040936018074033256\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.22962962962962963,\n\ \ \"acc_stderr\": 0.03633384414073462,\n \"acc_norm\": 0.22962962962962963,\n\ \ \"acc_norm_stderr\": 0.03633384414073462\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.16447368421052633,\n \"acc_stderr\": 0.0301675334686327,\n\ \ \"acc_norm\": 0.16447368421052633,\n \"acc_norm_stderr\": 0.0301675334686327\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.17,\n\ \ \"acc_stderr\": 0.0377525168068637,\n \"acc_norm\": 0.17,\n \ \ \"acc_norm_stderr\": 0.0377525168068637\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.24150943396226415,\n \"acc_stderr\": 0.026341480371118345,\n\ \ \"acc_norm\": 0.24150943396226415,\n \"acc_norm_stderr\": 0.026341480371118345\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.2222222222222222,\n\ \ \"acc_stderr\": 0.03476590104304134,\n \"acc_norm\": 0.2222222222222222,\n\ \ \"acc_norm_stderr\": 0.03476590104304134\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.2,\n \"acc_stderr\": 0.04020151261036846,\n \ \ \"acc_norm\": 0.2,\n \"acc_norm_stderr\": 0.04020151261036846\n },\n\ \ \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.28,\n\ \ \"acc_stderr\": 0.04512608598542128,\n \"acc_norm\": 0.28,\n \ \ \"acc_norm_stderr\": 0.04512608598542128\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \ \ \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.24277456647398843,\n\ \ \"acc_stderr\": 0.0326926380614177,\n \"acc_norm\": 0.24277456647398843,\n\ \ \"acc_norm_stderr\": 0.0326926380614177\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.2549019607843137,\n \"acc_stderr\": 0.043364327079931785,\n\ \ \"acc_norm\": 0.2549019607843137,\n \"acc_norm_stderr\": 0.043364327079931785\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.16,\n \"acc_stderr\": 0.03684529491774709,\n \"acc_norm\": 0.16,\n\ \ \"acc_norm_stderr\": 0.03684529491774709\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.2723404255319149,\n \"acc_stderr\": 0.029101290698386698,\n\ \ \"acc_norm\": 0.2723404255319149,\n \"acc_norm_stderr\": 0.029101290698386698\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.2631578947368421,\n\ \ \"acc_stderr\": 0.041424397194893624,\n \"acc_norm\": 0.2631578947368421,\n\ \ \"acc_norm_stderr\": 0.041424397194893624\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.2413793103448276,\n \"acc_stderr\": 0.03565998174135302,\n\ \ \"acc_norm\": 0.2413793103448276,\n \"acc_norm_stderr\": 0.03565998174135302\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.25396825396825395,\n \"acc_stderr\": 0.022418042891113942,\n \"\ acc_norm\": 0.25396825396825395,\n \"acc_norm_stderr\": 0.022418042891113942\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.14285714285714285,\n\ \ \"acc_stderr\": 0.0312984318574381,\n \"acc_norm\": 0.14285714285714285,\n\ \ \"acc_norm_stderr\": 0.0312984318574381\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.15,\n \"acc_stderr\": 0.035887028128263686,\n \ \ \"acc_norm\": 0.15,\n \"acc_norm_stderr\": 0.035887028128263686\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\ : 0.2967741935483871,\n \"acc_stderr\": 0.025988500792411894,\n \"\ acc_norm\": 0.2967741935483871,\n \"acc_norm_stderr\": 0.025988500792411894\n\ \ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\ : 0.270935960591133,\n \"acc_stderr\": 0.03127090713297698,\n \"acc_norm\"\ : 0.270935960591133,\n \"acc_norm_stderr\": 0.03127090713297698\n },\n\ \ \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\"\ : 0.26,\n \"acc_stderr\": 0.04408440022768079,\n \"acc_norm\": 0.26,\n\ \ \"acc_norm_stderr\": 0.04408440022768079\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.21818181818181817,\n \"acc_stderr\": 0.03225078108306289,\n\ \ \"acc_norm\": 0.21818181818181817,\n \"acc_norm_stderr\": 0.03225078108306289\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.35353535353535354,\n \"acc_stderr\": 0.03406086723547153,\n \"\ acc_norm\": 0.35353535353535354,\n \"acc_norm_stderr\": 0.03406086723547153\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.36787564766839376,\n \"acc_stderr\": 0.03480175668466036,\n\ \ \"acc_norm\": 0.36787564766839376,\n \"acc_norm_stderr\": 0.03480175668466036\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.2717948717948718,\n \"acc_stderr\": 0.022556551010132358,\n\ \ \"acc_norm\": 0.2717948717948718,\n \"acc_norm_stderr\": 0.022556551010132358\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.26296296296296295,\n \"acc_stderr\": 0.026842057873833706,\n \ \ \"acc_norm\": 0.26296296296296295,\n \"acc_norm_stderr\": 0.026842057873833706\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.28991596638655465,\n \"acc_stderr\": 0.029472485833136098,\n\ \ \"acc_norm\": 0.28991596638655465,\n \"acc_norm_stderr\": 0.029472485833136098\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.271523178807947,\n \"acc_stderr\": 0.03631329803969654,\n \"acc_norm\"\ : 0.271523178807947,\n \"acc_norm_stderr\": 0.03631329803969654\n },\n\ \ \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.3486238532110092,\n\ \ \"acc_stderr\": 0.020431254090714328,\n \"acc_norm\": 0.3486238532110092,\n\ \ \"acc_norm_stderr\": 0.020431254090714328\n },\n \"harness|hendrycksTest-high_school_statistics|5\"\ : {\n \"acc\": 0.4722222222222222,\n \"acc_stderr\": 0.0340470532865388,\n\ \ \"acc_norm\": 0.4722222222222222,\n \"acc_norm_stderr\": 0.0340470532865388\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.25,\n \"acc_stderr\": 0.03039153369274154,\n \"acc_norm\": 0.25,\n\ \ \"acc_norm_stderr\": 0.03039153369274154\n },\n \"harness|hendrycksTest-high_school_world_history|5\"\ : {\n \"acc\": 0.24472573839662448,\n \"acc_stderr\": 0.027985699387036416,\n\ \ \"acc_norm\": 0.24472573839662448,\n \"acc_norm_stderr\": 0.027985699387036416\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.2914798206278027,\n\ \ \"acc_stderr\": 0.030500283176545923,\n \"acc_norm\": 0.2914798206278027,\n\ \ \"acc_norm_stderr\": 0.030500283176545923\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.26717557251908397,\n \"acc_stderr\": 0.038808483010823944,\n\ \ \"acc_norm\": 0.26717557251908397,\n \"acc_norm_stderr\": 0.038808483010823944\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.32231404958677684,\n \"acc_stderr\": 0.04266416363352168,\n \"\ acc_norm\": 0.32231404958677684,\n \"acc_norm_stderr\": 0.04266416363352168\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.21296296296296297,\n\ \ \"acc_stderr\": 0.03957835471980981,\n \"acc_norm\": 0.21296296296296297,\n\ \ \"acc_norm_stderr\": 0.03957835471980981\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.26380368098159507,\n \"acc_stderr\": 0.03462419931615623,\n\ \ \"acc_norm\": 0.26380368098159507,\n \"acc_norm_stderr\": 0.03462419931615623\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.25892857142857145,\n\ \ \"acc_stderr\": 0.041577515398656284,\n \"acc_norm\": 0.25892857142857145,\n\ \ \"acc_norm_stderr\": 0.041577515398656284\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.34951456310679613,\n \"acc_stderr\": 0.04721188506097173,\n\ \ \"acc_norm\": 0.34951456310679613,\n \"acc_norm_stderr\": 0.04721188506097173\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.1794871794871795,\n\ \ \"acc_stderr\": 0.025140935950335418,\n \"acc_norm\": 0.1794871794871795,\n\ \ \"acc_norm_stderr\": 0.025140935950335418\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.27,\n \"acc_stderr\": 0.044619604333847394,\n \ \ \"acc_norm\": 0.27,\n \"acc_norm_stderr\": 0.044619604333847394\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.21583652618135377,\n\ \ \"acc_stderr\": 0.014711684386139958,\n \"acc_norm\": 0.21583652618135377,\n\ \ \"acc_norm_stderr\": 0.014711684386139958\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.24277456647398843,\n \"acc_stderr\": 0.0230836585869842,\n\ \ \"acc_norm\": 0.24277456647398843,\n \"acc_norm_stderr\": 0.0230836585869842\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.2424581005586592,\n\ \ \"acc_stderr\": 0.014333522059217889,\n \"acc_norm\": 0.2424581005586592,\n\ \ \"acc_norm_stderr\": 0.014333522059217889\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.21895424836601307,\n \"acc_stderr\": 0.02367908986180772,\n\ \ \"acc_norm\": 0.21895424836601307,\n \"acc_norm_stderr\": 0.02367908986180772\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.24758842443729903,\n\ \ \"acc_stderr\": 0.024513879973621967,\n \"acc_norm\": 0.24758842443729903,\n\ \ \"acc_norm_stderr\": 0.024513879973621967\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.22530864197530864,\n \"acc_stderr\": 0.023246202647819746,\n\ \ \"acc_norm\": 0.22530864197530864,\n \"acc_norm_stderr\": 0.023246202647819746\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.26595744680851063,\n \"acc_stderr\": 0.026358065698880592,\n \ \ \"acc_norm\": 0.26595744680851063,\n \"acc_norm_stderr\": 0.026358065698880592\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.2457627118644068,\n\ \ \"acc_stderr\": 0.010996156635142692,\n \"acc_norm\": 0.2457627118644068,\n\ \ \"acc_norm_stderr\": 0.010996156635142692\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.44485294117647056,\n \"acc_stderr\": 0.030187532060329376,\n\ \ \"acc_norm\": 0.44485294117647056,\n \"acc_norm_stderr\": 0.030187532060329376\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.26143790849673204,\n \"acc_stderr\": 0.017776947157528034,\n \ \ \"acc_norm\": 0.26143790849673204,\n \"acc_norm_stderr\": 0.017776947157528034\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.21818181818181817,\n\ \ \"acc_stderr\": 0.03955932861795833,\n \"acc_norm\": 0.21818181818181817,\n\ \ \"acc_norm_stderr\": 0.03955932861795833\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.4,\n \"acc_stderr\": 0.031362502409358936,\n \ \ \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.031362502409358936\n \ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.22885572139303484,\n\ \ \"acc_stderr\": 0.029705284056772426,\n \"acc_norm\": 0.22885572139303484,\n\ \ \"acc_norm_stderr\": 0.029705284056772426\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.27,\n \"acc_stderr\": 0.04461960433384739,\n \ \ \"acc_norm\": 0.27,\n \"acc_norm_stderr\": 0.04461960433384739\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.1927710843373494,\n\ \ \"acc_stderr\": 0.030709824050565274,\n \"acc_norm\": 0.1927710843373494,\n\ \ \"acc_norm_stderr\": 0.030709824050565274\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.21052631578947367,\n \"acc_stderr\": 0.0312678171466318,\n\ \ \"acc_norm\": 0.21052631578947367,\n \"acc_norm_stderr\": 0.0312678171466318\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.22766217870257038,\n\ \ \"mc1_stderr\": 0.01467925503211107,\n \"mc2\": 0.4069116400376613,\n\ \ \"mc2_stderr\": 0.014934250122346554\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.5043409629044988,\n \"acc_stderr\": 0.014051956064076887\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.006823351023502654,\n \ \ \"acc_stderr\": 0.0022675371022544736\n }\n}\n```" repo_url: https://huggingface.co/gpt2 leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: [email protected] configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|arc:challenge|25_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|arc:challenge|25_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '**/details_harness|arc:challenge|25_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '**/details_harness|arc:challenge|25_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '**/details_harness|arc:challenge|25_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '**/details_harness|arc:challenge|25_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '**/details_harness|arc:challenge|25_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '**/details_harness|arc:challenge|25_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-03-23T06-18-16.565546.parquet' - config_name: harness_drop_0 data_files: - split: 2023_09_14T13_54_21.687636 path: - '**/details_harness|drop|0_2023-09-14T13-54-21.687636.parquet' - split: 2023_09_15T12_28_23.937147 path: - '**/details_harness|drop|0_2023-09-15T12-28-23.937147.parquet' - split: 2023_09_15T12_47_31.231445 path: - '**/details_harness|drop|0_2023-09-15T12-47-31.231445.parquet' - split: latest path: - '**/details_harness|drop|0_2023-09-15T12-47-31.231445.parquet' - config_name: harness_drop_3 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|drop|3_2023-11-21T18-07-07.067275.parquet' - split: 2023_11_29T12_47_35.686694 path: - '**/details_harness|drop|3_2023-11-29T12-47-35.686694.parquet' - split: 2023_11_29T12_58_42.860611 path: - '**/details_harness|drop|3_2023-11-29T12-58-42.860611.parquet' - split: latest path: - '**/details_harness|drop|3_2023-11-29T12-58-42.860611.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|gsm8k|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_11_29T12_47_35.686694 path: - '**/details_harness|gsm8k|5_2023-11-29T12-47-35.686694.parquet' - split: 2023_11_29T12_58_42.860611 path: - '**/details_harness|gsm8k|5_2023-11-29T12-58-42.860611.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|gsm8k|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '**/details_harness|gsm8k|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '**/details_harness|gsm8k|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '**/details_harness|gsm8k|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '**/details_harness|gsm8k|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '**/details_harness|gsm8k|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '**/details_harness|gsm8k|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hellaswag|10_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hellaswag|10_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '**/details_harness|hellaswag|10_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '**/details_harness|hellaswag|10_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '**/details_harness|hellaswag|10_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '**/details_harness|hellaswag|10_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '**/details_harness|hellaswag|10_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '**/details_harness|hellaswag|10_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-management|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-management|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-19T14-19-42.718116.parquet' - 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'**/details_harness|hendrycksTest-professional_law|5_2024-03-23T06-18-16.565546.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-23T06-18-16.565546.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-23T06-18-16.565546.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-03-23T06-18-16.565546.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-03-23T06-18-16.565546.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-03-23T06-18-16.565546.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-23T06-18-16.565546.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-03-23T06-18-16.565546.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-23T06-18-16.565546.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-03-23T06-18-16.565546.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-03-23T06-18-16.565546.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-23T06-18-16.565546.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-23T06-18-16.565546.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-03-23T06-18-16.565546.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-23T06-18-16.565546.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-23T06-18-16.565546.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-23T06-18-16.565546.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-23T06-18-16.565546.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-03-23T06-18-16.565546.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-03-23T06-18-16.565546.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-23T06-18-16.565546.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-03-23T06-18-16.565546.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-23T06-18-16.565546.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-23T06-18-16.565546.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-23T06-18-16.565546.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-03-23T06-18-16.565546.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-23T06-18-16.565546.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-23T06-18-16.565546.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-23T06-18-16.565546.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-23T06-18-16.565546.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-23T06-18-16.565546.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-23T06-18-16.565546.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-23T06-18-16.565546.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-23T06-18-16.565546.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-23T06-18-16.565546.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-23T06-18-16.565546.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-23T06-18-16.565546.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-23T06-18-16.565546.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-23T06-18-16.565546.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-23T06-18-16.565546.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-03-23T06-18-16.565546.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-23T06-18-16.565546.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-03-23T06-18-16.565546.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-23T06-18-16.565546.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-23T06-18-16.565546.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-23T06-18-16.565546.parquet' - '**/details_harness|hendrycksTest-management|5_2024-03-23T06-18-16.565546.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-03-23T06-18-16.565546.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-23T06-18-16.565546.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-23T06-18-16.565546.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-23T06-18-16.565546.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-23T06-18-16.565546.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-03-23T06-18-16.565546.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-03-23T06-18-16.565546.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-03-23T06-18-16.565546.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-23T06-18-16.565546.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-03-23T06-18-16.565546.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-23T06-18-16.565546.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-23T06-18-16.565546.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-03-23T06-18-16.565546.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-03-23T06-18-16.565546.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-03-23T06-18-16.565546.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-23T06-18-16.565546.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-03-23T06-18-16.565546.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-international_law|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-international_law|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '**/details_harness|hendrycksTest-international_law|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '**/details_harness|hendrycksTest-international_law|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '**/details_harness|hendrycksTest-international_law|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-management|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-management|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '**/details_harness|hendrycksTest-management|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '**/details_harness|hendrycksTest-management|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '**/details_harness|hendrycksTest-management|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '**/details_harness|hendrycksTest-management|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '**/details_harness|hendrycksTest-management|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '**/details_harness|hendrycksTest-management|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-marketing|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-marketing|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '**/details_harness|hendrycksTest-marketing|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '**/details_harness|hendrycksTest-marketing|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '**/details_harness|hendrycksTest-marketing|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-sociology|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-sociology|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '**/details_harness|hendrycksTest-sociology|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '**/details_harness|hendrycksTest-sociology|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '**/details_harness|hendrycksTest-sociology|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-virology|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-virology|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '**/details_harness|hendrycksTest-virology|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '**/details_harness|hendrycksTest-virology|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '**/details_harness|hendrycksTest-virology|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '**/details_harness|hendrycksTest-virology|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '**/details_harness|hendrycksTest-virology|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '**/details_harness|hendrycksTest-virology|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-03-23T06-18-16.565546.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|truthfulqa:mc|0_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|truthfulqa:mc|0_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '**/details_harness|truthfulqa:mc|0_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '**/details_harness|truthfulqa:mc|0_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '**/details_harness|truthfulqa:mc|0_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '**/details_harness|truthfulqa:mc|0_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '**/details_harness|truthfulqa:mc|0_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '**/details_harness|truthfulqa:mc|0_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-03-23T06-18-16.565546.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_09_06T15_19_52.414673 path: - '**/details_harness|winogrande|5_2023-09-06T15-19-52.414673.parquet' - split: 2023_09_06T15_22_24.734466 path: - '**/details_harness|winogrande|5_2023-09-06T15-22-24.734466.parquet' - split: 2023_09_06T15_24_04.768979 path: - '**/details_harness|winogrande|5_2023-09-06T15-24-04.768979.parquet' - split: 2023_09_07T12_01_51.839651 path: - '**/details_harness|winogrande|5_2023-09-07T12-01-51.839651.parquet' - split: 2023_09_07T12_04_01.189528 path: - '**/details_harness|winogrande|5_2023-09-07T12-04-01.189528.parquet' - split: 2023_09_07T12_08_17.821371 path: - '**/details_harness|winogrande|5_2023-09-07T12-08-17.821371.parquet' - split: 2023_09_07T12_10_30.286469 path: - '**/details_harness|winogrande|5_2023-09-07T12-10-30.286469.parquet' - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|winogrande|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_11_29T12_47_35.686694 path: - '**/details_harness|winogrande|5_2023-11-29T12-47-35.686694.parquet' - split: 2023_11_29T12_58_42.860611 path: - '**/details_harness|winogrande|5_2023-11-29T12-58-42.860611.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|winogrande|5_2023-12-16T13-32-55.332102.parquet' - split: 2023_12_19T14_19_42.718116 path: - '**/details_harness|winogrande|5_2023-12-19T14-19-42.718116.parquet' - split: 2023_12_23T15_28_59.872701 path: - '**/details_harness|winogrande|5_2023-12-23T15-28-59.872701.parquet' - split: 2024_01_10T14_42_55.873500 path: - '**/details_harness|winogrande|5_2024-01-10T14-42-55.873500.parquet' - split: 2024_01_18T14_12_21.064569 path: - '**/details_harness|winogrande|5_2024-01-18T14-12-21.064569.parquet' - split: 2024_01_22T13_56_20.291666 path: - '**/details_harness|winogrande|5_2024-01-22T13-56-20.291666.parquet' - split: 2024_03_23T06_18_16.565546 path: - '**/details_harness|winogrande|5_2024-03-23T06-18-16.565546.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-03-23T06-18-16.565546.parquet' - config_name: results data_files: - split: 2023_09_06T12_19_07.283399 path: - results_2023-09-06T12-19-07.283399.parquet - split: 2023_09_06T12_21_24.071294 path: - results_2023-09-06T12-21-24.071294.parquet - split: 2023_09_06T12_24_13.323279 path: - results_2023-09-06T12-24-13.323279.parquet - split: 2023_09_06T13_26_17.619860 path: - results_2023-09-06T13-26-17.619860.parquet - split: 2023_09_06T15_15_44.379880 path: - results_2023-09-06T15-15-44.379880.parquet - split: 2023_09_06T15_19_52.414673 path: - results_2023-09-06T15-19-52.414673.parquet - split: 2023_09_06T15_22_24.734466 path: - results_2023-09-06T15-22-24.734466.parquet - split: 2023_09_06T15_24_04.768979 path: - results_2023-09-06T15-24-04.768979.parquet - split: 2023_09_07T12_01_51.839651 path: - results_2023-09-07T12-01-51.839651.parquet - split: 2023_09_07T12_04_01.189528 path: - results_2023-09-07T12-04-01.189528.parquet - split: 2023_09_07T12_08_17.821371 path: - results_2023-09-07T12-08-17.821371.parquet - split: 2023_09_07T12_10_30.286469 path: - results_2023-09-07T12-10-30.286469.parquet - split: 2023_09_14T13_54_21.687636 path: - results_2023-09-14T13-54-21.687636.parquet - split: 2023_09_15T12_28_23.937147 path: - results_2023-09-15T12-28-23.937147.parquet - split: 2023_09_15T12_47_31.231445 path: - results_2023-09-15T12-47-31.231445.parquet - split: 2023_11_21T18_07_07.067275 path: - results_2023-11-21T18-07-07.067275.parquet - split: 2023_11_29T12_47_35.686694 path: - results_2023-11-29T12-47-35.686694.parquet - split: 2023_11_29T12_58_42.860611 path: - results_2023-11-29T12-58-42.860611.parquet - split: 2023_12_16T13_32_55.332102 path: - results_2023-12-16T13-32-55.332102.parquet - split: 2023_12_19T14_19_42.718116 path: - results_2023-12-19T14-19-42.718116.parquet - split: 2023_12_23T15_28_59.872701 path: - results_2023-12-23T15-28-59.872701.parquet - split: 2024_01_10T14_42_55.873500 path: - results_2024-01-10T14-42-55.873500.parquet - split: 2024_01_18T14_12_21.064569 path: - results_2024-01-18T14-12-21.064569.parquet - split: 2024_01_22T13_56_20.291666 path: - results_2024-01-22T13-56-20.291666.parquet - split: 2024_03_23T06_18_16.565546 path: - results_2024-03-23T06-18-16.565546.parquet - split: latest path: - results_2024-03-23T06-18-16.565546.parquet --- # Dataset Card for Evaluation run of gpt2  Dataset automatically created during the evaluation run of model [gpt2](https://huggingface.co/gpt2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 65 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 25 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_gpt2", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-03-23T06:18:16.565546](https://huggingface.co/datasets/open-llm-leaderboard/details_gpt2/blob/main/results_2024-03-23T06-18-16.565546.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.25780579051672486, "acc_stderr": 0.030658881019520554, "acc_norm": 0.2586547713391113, "acc_norm_stderr": 0.031431381356225356, "mc1": 0.22766217870257038, "mc1_stderr": 0.01467925503211107, "mc2": 0.4069116400376613, "mc2_stderr": 0.014934250122346554 }, "harness|arc:challenge|25": { "acc": 0.197098976109215, "acc_stderr": 0.011625047669880633, "acc_norm": 0.22013651877133106, "acc_norm_stderr": 0.01210812488346097 }, "harness|hellaswag|10": { "acc": 0.29267078271260705, "acc_stderr": 0.004540586983229993, "acc_norm": 0.3152758414658435, "acc_norm_stderr": 0.0046367607625228515 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.21, "acc_stderr": 0.040936018074033256, "acc_norm": 0.21, "acc_norm_stderr": 0.040936018074033256 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.22962962962962963, "acc_stderr": 0.03633384414073462, "acc_norm": 0.22962962962962963, "acc_norm_stderr": 0.03633384414073462 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.16447368421052633, "acc_stderr": 0.0301675334686327, "acc_norm": 0.16447368421052633, "acc_norm_stderr": 0.0301675334686327 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.17, "acc_stderr": 0.0377525168068637, "acc_norm": 0.17, "acc_norm_stderr": 0.0377525168068637 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.24150943396226415, "acc_stderr": 0.026341480371118345, "acc_norm": 0.24150943396226415, "acc_norm_stderr": 0.026341480371118345 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.2222222222222222, "acc_stderr": 0.03476590104304134, "acc_norm": 0.2222222222222222, "acc_norm_stderr": 0.03476590104304134 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.2, "acc_stderr": 0.04020151261036846, "acc_norm": 0.2, "acc_norm_stderr": 0.04020151261036846 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.28, "acc_stderr": 0.04512608598542128, "acc_norm": 0.28, "acc_norm_stderr": 0.04512608598542128 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.24277456647398843, "acc_stderr": 0.0326926380614177, "acc_norm": 0.24277456647398843, "acc_norm_stderr": 0.0326926380614177 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.2549019607843137, "acc_stderr": 0.043364327079931785, "acc_norm": 0.2549019607843137, "acc_norm_stderr": 0.043364327079931785 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.16, "acc_stderr": 0.03684529491774709, "acc_norm": 0.16, "acc_norm_stderr": 0.03684529491774709 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.2723404255319149, "acc_stderr": 0.029101290698386698, "acc_norm": 0.2723404255319149, "acc_norm_stderr": 0.029101290698386698 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.2631578947368421, "acc_stderr": 0.041424397194893624, "acc_norm": 0.2631578947368421, "acc_norm_stderr": 0.041424397194893624 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.2413793103448276, "acc_stderr": 0.03565998174135302, "acc_norm": 0.2413793103448276, "acc_norm_stderr": 0.03565998174135302 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.25396825396825395, "acc_stderr": 0.022418042891113942, "acc_norm": 0.25396825396825395, "acc_norm_stderr": 0.022418042891113942 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.14285714285714285, "acc_stderr": 0.0312984318574381, "acc_norm": 0.14285714285714285, "acc_norm_stderr": 0.0312984318574381 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.15, "acc_stderr": 0.035887028128263686, "acc_norm": 0.15, "acc_norm_stderr": 0.035887028128263686 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.2967741935483871, "acc_stderr": 0.025988500792411894, "acc_norm": 0.2967741935483871, "acc_norm_stderr": 0.025988500792411894 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.270935960591133, "acc_stderr": 0.03127090713297698, "acc_norm": 0.270935960591133, "acc_norm_stderr": 0.03127090713297698 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.26, "acc_stderr": 0.04408440022768079, "acc_norm": 0.26, "acc_norm_stderr": 0.04408440022768079 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.21818181818181817, "acc_stderr": 0.03225078108306289, "acc_norm": 0.21818181818181817, "acc_norm_stderr": 0.03225078108306289 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.35353535353535354, "acc_stderr": 0.03406086723547153, "acc_norm": 0.35353535353535354, "acc_norm_stderr": 0.03406086723547153 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.36787564766839376, "acc_stderr": 0.03480175668466036, "acc_norm": 0.36787564766839376, "acc_norm_stderr": 0.03480175668466036 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.2717948717948718, "acc_stderr": 0.022556551010132358, "acc_norm": 0.2717948717948718, "acc_norm_stderr": 0.022556551010132358 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.26296296296296295, "acc_stderr": 0.026842057873833706, "acc_norm": 0.26296296296296295, "acc_norm_stderr": 0.026842057873833706 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.28991596638655465, "acc_stderr": 0.029472485833136098, "acc_norm": 0.28991596638655465, "acc_norm_stderr": 0.029472485833136098 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.271523178807947, "acc_stderr": 0.03631329803969654, "acc_norm": 0.271523178807947, "acc_norm_stderr": 0.03631329803969654 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.3486238532110092, "acc_stderr": 0.020431254090714328, "acc_norm": 0.3486238532110092, "acc_norm_stderr": 0.020431254090714328 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.4722222222222222, "acc_stderr": 0.0340470532865388, "acc_norm": 0.4722222222222222, "acc_norm_stderr": 0.0340470532865388 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.25, "acc_stderr": 0.03039153369274154, "acc_norm": 0.25, "acc_norm_stderr": 0.03039153369274154 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.24472573839662448, "acc_stderr": 0.027985699387036416, "acc_norm": 0.24472573839662448, "acc_norm_stderr": 0.027985699387036416 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.2914798206278027, "acc_stderr": 0.030500283176545923, "acc_norm": 0.2914798206278027, "acc_norm_stderr": 0.030500283176545923 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.26717557251908397, "acc_stderr": 0.038808483010823944, "acc_norm": 0.26717557251908397, "acc_norm_stderr": 0.038808483010823944 }, "harness|hendrycksTest-international_law|5": { "acc": 0.32231404958677684, "acc_stderr": 0.04266416363352168, "acc_norm": 0.32231404958677684, "acc_norm_stderr": 0.04266416363352168 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.21296296296296297, "acc_stderr": 0.03957835471980981, "acc_norm": 0.21296296296296297, "acc_norm_stderr": 0.03957835471980981 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.26380368098159507, "acc_stderr": 0.03462419931615623, "acc_norm": 0.26380368098159507, "acc_norm_stderr": 0.03462419931615623 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.25892857142857145, "acc_stderr": 0.041577515398656284, "acc_norm": 0.25892857142857145, "acc_norm_stderr": 0.041577515398656284 }, "harness|hendrycksTest-management|5": { "acc": 0.34951456310679613, "acc_stderr": 0.04721188506097173, "acc_norm": 0.34951456310679613, "acc_norm_stderr": 0.04721188506097173 }, "harness|hendrycksTest-marketing|5": { "acc": 0.1794871794871795, "acc_stderr": 0.025140935950335418, "acc_norm": 0.1794871794871795, "acc_norm_stderr": 0.025140935950335418 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.27, "acc_stderr": 0.044619604333847394, "acc_norm": 0.27, "acc_norm_stderr": 0.044619604333847394 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.21583652618135377, "acc_stderr": 0.014711684386139958, "acc_norm": 0.21583652618135377, "acc_norm_stderr": 0.014711684386139958 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.24277456647398843, "acc_stderr": 0.0230836585869842, "acc_norm": 0.24277456647398843, "acc_norm_stderr": 0.0230836585869842 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.2424581005586592, "acc_stderr": 0.014333522059217889, "acc_norm": 0.2424581005586592, "acc_norm_stderr": 0.014333522059217889 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.21895424836601307, "acc_stderr": 0.02367908986180772, "acc_norm": 0.21895424836601307, "acc_norm_stderr": 0.02367908986180772 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.24758842443729903, "acc_stderr": 0.024513879973621967, "acc_norm": 0.24758842443729903, "acc_norm_stderr": 0.024513879973621967 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.22530864197530864, "acc_stderr": 0.023246202647819746, "acc_norm": 0.22530864197530864, "acc_norm_stderr": 0.023246202647819746 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.26595744680851063, "acc_stderr": 0.026358065698880592, "acc_norm": 0.26595744680851063, "acc_norm_stderr": 0.026358065698880592 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.2457627118644068, "acc_stderr": 0.010996156635142692, "acc_norm": 0.2457627118644068, "acc_norm_stderr": 0.010996156635142692 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.44485294117647056, "acc_stderr": 0.030187532060329376, "acc_norm": 0.44485294117647056, "acc_norm_stderr": 0.030187532060329376 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.26143790849673204, "acc_stderr": 0.017776947157528034, "acc_norm": 0.26143790849673204, "acc_norm_stderr": 0.017776947157528034 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.21818181818181817, "acc_stderr": 0.03955932861795833, "acc_norm": 0.21818181818181817, "acc_norm_stderr": 0.03955932861795833 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.4, "acc_stderr": 0.031362502409358936, "acc_norm": 0.4, "acc_norm_stderr": 0.031362502409358936 }, "harness|hendrycksTest-sociology|5": { "acc": 0.22885572139303484, "acc_stderr": 0.029705284056772426, "acc_norm": 0.22885572139303484, "acc_norm_stderr": 0.029705284056772426 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.27, "acc_stderr": 0.04461960433384739, "acc_norm": 0.27, "acc_norm_stderr": 0.04461960433384739 }, "harness|hendrycksTest-virology|5": { "acc": 0.1927710843373494, "acc_stderr": 0.030709824050565274, "acc_norm": 0.1927710843373494, "acc_norm_stderr": 0.030709824050565274 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.21052631578947367, "acc_stderr": 0.0312678171466318, "acc_norm": 0.21052631578947367, "acc_norm_stderr": 0.0312678171466318 }, "harness|truthfulqa:mc|0": { "mc1": 0.22766217870257038, "mc1_stderr": 0.01467925503211107, "mc2": 0.4069116400376613, "mc2_stderr": 0.014934250122346554 }, "harness|winogrande|5": { "acc": 0.5043409629044988, "acc_stderr": 0.014051956064076887 }, "harness|gsm8k|5": { "acc": 0.006823351023502654, "acc_stderr": 0.0022675371022544736 } } ``` ## Dataset Details ### Dataset Description  - **Curated by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] ### Dataset Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Dataset Structure  [More Information Needed] ## Dataset Creation ### Curation Rationale  [More Information Needed] ### Source Data  #### Data Collection and Processing  [More Information Needed] #### Who are the source data producers?  [More Information Needed] ### Annotations [optional]  #### Annotation process  [More Information Needed] #### Who are the annotators?  [More Information Needed] #### Personal and Sensitive Information  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]

alvations/c4p0

alvations

2024-03-23T01:26:11Z

96,896

0

[ "size_categories:n<1K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2024-03-22T00:58:02Z

null

--- dataset_info: features: - name: source dtype: string - name: target dtype: string - name: target_backto_source dtype: string - name: raw_target list: - name: generated_text dtype: string - name: raw_target_backto_source list: - name: generated_text dtype: string - name: prompt dtype: string - name: reverse_prompt dtype: string - name: source_langid dtype: string - name: target_langid dtype: string - name: target_backto_source_langid dtype: string - name: doc_id dtype: int64 - name: sent_id dtype: int64 - name: timestamp dtype: string - name: url dtype: string - name: doc_hash dtype: string splits: - name: train num_bytes: 4134 num_examples: 3 download_size: 19374 dataset_size: 4134 configs: - config_name: default data_files: - split: train path: f2527aa0a4051632/train-* ---

cornell-movie-review-data/rotten_tomatoes

cornell-movie-review-data

2024-03-18T14:28:45Z

19,832

82

[ "task_categories:text-classification", "task_ids:sentiment-classification", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "source_datasets:original", "language:en", "license:unknown", "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[ "text-classification" ]

2022-03-02T23:29:22Z

2

--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - unknown multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - text-classification task_ids: - sentiment-classification paperswithcode_id: mr pretty_name: RottenTomatoes - MR Movie Review Data dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': neg '1': pos splits: - name: train num_bytes: 1074810 num_examples: 8530 - name: validation num_bytes: 134679 num_examples: 1066 - name: test num_bytes: 135972 num_examples: 1066 download_size: 487770 dataset_size: 1345461 train-eval-index: - config: default task: text-classification task_id: binary_classification splits: train_split: train eval_split: test col_mapping: text: text label: target metrics: - type: accuracy name: Accuracy - type: f1 name: F1 args: average: binary - type: f1 name: F1 micro args: average: micro - type: f1 name: F1 weighted args: average: weighted - type: precision name: Precision macro args: average: macro - type: precision name: Precision micro args: average: micro - type: precision name: Precision weighted args: average: weighted - type: recall name: Recall macro args: average: macro - type: recall name: Recall micro args: average: micro - type: recall name: Recall weighted args: average: weighted --- # Dataset Card for "rotten_tomatoes" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [http://www.cs.cornell.edu/people/pabo/movie-review-data/](http://www.cs.cornell.edu/people/pabo/movie-review-data/) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [https://arxiv.org/abs/cs/0506075](https://arxiv.org/abs/cs/0506075) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 0.49 MB - **Size of the generated dataset:** 1.34 MB - **Total amount of disk used:** 1.84 MB ### Dataset Summary Movie Review Dataset. This is a dataset of containing 5,331 positive and 5,331 negative processed sentences from Rotten Tomatoes movie reviews. This data was first used in Bo Pang and Lillian Lee, ``Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales.'', Proceedings of the ACL, 2005. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### default - **Size of downloaded dataset files:** 0.49 MB - **Size of the generated dataset:** 1.34 MB - **Total amount of disk used:** 1.84 MB An example of 'validation' looks as follows. ``` { "label": 1, "text": "Sometimes the days and nights just drag on -- it 's the morning that make me feel alive . And I have one thing to thank for that : pancakes . " } ``` ### Data Fields The data fields are the same among all splits. #### default - `text`: a `string` feature. - `label`: a classification label, with possible values including `neg` (0), `pos` (1). ### Data Splits Reads Rotten Tomatoes sentences and splits into 80% train, 10% validation, and 10% test, as is the practice set out in Jinfeng Li, ``TEXTBUGGER: Generating Adversarial Text Against Real-world Applications.'' | name |train|validation|test| |-------|----:|---------:|---:| |default| 8530| 1066|1066| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @InProceedings{Pang+Lee:05a, author = {Bo Pang and Lillian Lee}, title = {Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales}, booktitle = {Proceedings of the ACL}, year = 2005 } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@jxmorris12](https://github.com/jxmorris12) for adding this dataset.

czyzi0/the-mc-speech-dataset

czyzi0

2024-03-16T15:30:05Z

2,199

5

[ "task_categories:text-to-speech", "task_categories:automatic-speech-recognition", "language:pl", "license:cc0-1.0", "size_categories:10K<n<100K", "format:parquet", "modality:audio", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]

[ "text-to-speech", "automatic-speech-recognition" ]

2023-07-03T19:31:36Z

2

--- language: - pl license: cc0-1.0 size_categories: - 10K<n<100K task_categories: - text-to-speech - automatic-speech-recognition pretty_name: The MC Speech Dataset dataset_info: features: - name: audio dtype: audio: sampling_rate: 44100 - name: transcript dtype: string - name: id dtype: string splits: - name: train num_bytes: 6985316587.668 num_examples: 24018 download_size: 6174661195 dataset_size: 6985316587.668 configs: - config_name: default data_files: - split: train path: data/train-* --- This is public domain speech dataset consisting of 24018 short audio clips of a single speaker reading sentences in Polish. A transcription is provided for each clip. Clips have total length of more than 22 hours. Texts are in public domain. The audio was recorded in 2021-22 as a part of my [master's thesis](http://dx.doi.org/10.13140/RG.2.2.26293.24800) and is in public domain. If you use this dataset, please cite: ``` @masterthesis{mcspeech, title={Analiza porównawcza korpusów nagrań mowy dla celów syntezy mowy w języku polskim}, author={Czyżnikiewicz, Mateusz}, year={2022}, month={December}, school={Warsaw University of Technology}, type={Master's thesis}, doi={10.13140/RG.2.2.26293.24800}, note={Available at \url{http://dx.doi.org/10.13140/RG.2.2.26293.24800}}, } ``` More info about the dataset can be found at https://github.com/czyzi0/the-mc-speech-dataset Also, if you find this resource helpful, kindly consider leaving a like.

google/wiki40b

google

2024-03-11T16:19:48Z

8,616

28

[ "language:en", "size_categories:10M<n<100M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]

[]

2022-03-02T23:29:22Z

1

--- language: - en paperswithcode_id: wiki-40b pretty_name: Wiki-40B dataset_info: - config_name: ar features: - name: wikidata_id dtype: string - name: text dtype: string - name: version_id dtype: string splits: - name: train num_bytes: 773508885 num_examples: 220885 - name: validation num_bytes: 44102674 num_examples: 12198 - name: test num_bytes: 43755879 num_examples: 12271 download_size: 413683528 dataset_size: 861367438 - config_name: bg features: - name: wikidata_id dtype: string - name: text dtype: string - name: version_id dtype: string splits: - name: train num_bytes: 1413477231 num_examples: 130670 - name: validation num_bytes: 78976448 num_examples: 7259 - name: test num_bytes: 78350414 num_examples: 7289 download_size: 484828696 dataset_size: 1570804093 - config_name: ca features: - name: wikidata_id dtype: string - name: text dtype: string - name: version_id dtype: string splits: - name: train num_bytes: 784791826 num_examples: 277313 - name: validation num_bytes: 43576907 num_examples: 15362 - name: test num_bytes: 44904134 num_examples: 15568 download_size: 480954417 dataset_size: 873272867 - config_name: cs features: - name: wikidata_id dtype: string - name: text dtype: string - name: version_id dtype: string splits: - name: train num_bytes: 901187017 num_examples: 235971 - name: validation num_bytes: 49743998 num_examples: 13096 - name: test num_bytes: 49325867 num_examples: 12984 download_size: 493522926 dataset_size: 1000256882 - config_name: da features: - name: wikidata_id dtype: string - name: text dtype: string - name: version_id dtype: string splits: - name: train num_bytes: 247928023 num_examples: 109486 - name: validation num_bytes: 13937406 num_examples: 6173 - name: test num_bytes: 14401179 num_examples: 6219 download_size: 156696617 dataset_size: 276266608 - config_name: de features: - name: wikidata_id dtype: string - name: text dtype: string - name: version_id dtype: string splits: - name: train num_bytes: 4988094268 num_examples: 1554910 - name: validation num_bytes: 278101948 num_examples: 86068 - name: test num_bytes: 278024815 num_examples: 86594 download_size: 3174352286 dataset_size: 5544221031 - config_name: el features: - name: wikidata_id dtype: string - name: text dtype: string - name: version_id dtype: string splits: - name: train num_bytes: 1738534924 num_examples: 93596 - name: validation num_bytes: 97711791 num_examples: 5130 - name: test num_bytes: 99743744 num_examples: 5261 download_size: 621575577 dataset_size: 1935990459 - config_name: en features: - name: wikidata_id dtype: string - name: text dtype: string - name: version_id dtype: string splits: - name: train num_bytes: 9423468036 num_examples: 2926536 - name: validation num_bytes: 527374301 num_examples: 163597 - name: test num_bytes: 522210646 num_examples: 162274 download_size: 6183831905 dataset_size: 10473052983 - config_name: es features: - name: wikidata_id dtype: string - name: text dtype: string - name: version_id dtype: string splits: - name: train num_bytes: 2906242601 num_examples: 872541 - name: validation num_bytes: 161381260 num_examples: 48592 - name: test num_bytes: 164110964 num_examples: 48764 download_size: 1783120767 dataset_size: 3231734825 - config_name: et features: - name: wikidata_id dtype: string - name: text dtype: string - name: version_id dtype: string splits: - name: train num_bytes: 196484412 num_examples: 114464 - name: validation num_bytes: 10987144 num_examples: 6351 - name: test num_bytes: 10691693 num_examples: 6205 download_size: 122192870 dataset_size: 218163249 - config_name: fa features: - name: wikidata_id dtype: string - name: text dtype: string - name: version_id dtype: string splits: - name: train num_bytes: 1551260324 num_examples: 203145 - name: validation num_bytes: 86108146 num_examples: 11180 - name: test num_bytes: 89064531 num_examples: 11262 download_size: 552712695 dataset_size: 1726433001 - config_name: fi features: - name: wikidata_id dtype: string - name: text dtype: string - name: version_id dtype: string splits: - name: train num_bytes: 589614484 num_examples: 255822 - name: validation num_bytes: 32645294 num_examples: 13962 - name: test num_bytes: 32869383 num_examples: 14179 download_size: 346601923 dataset_size: 655129161 - config_name: fr features: - name: wikidata_id dtype: string - name: text dtype: string - name: version_id dtype: string splits: - name: train num_bytes: 3850031120 num_examples: 1227206 - name: validation num_bytes: 216405364 num_examples: 68655 - name: test num_bytes: 215243874 num_examples: 68004 download_size: 2246390244 dataset_size: 4281680358 - config_name: he features: - name: wikidata_id dtype: string - name: text dtype: string - name: version_id dtype: string splits: - name: train num_bytes: 2834322770 num_examples: 165359 - name: validation num_bytes: 160235180 num_examples: 9231 - name: test num_bytes: 162131949 num_examples: 9344 download_size: 754632129 dataset_size: 3156689899 - config_name: hi features: - name: wikidata_id dtype: string - name: text dtype: string - name: version_id dtype: string splits: - name: train num_bytes: 947403521 num_examples: 45737 - name: validation num_bytes: 54497912 num_examples: 2596 - name: test num_bytes: 54448878 num_examples: 2643 download_size: 231716300 dataset_size: 1056350311 - config_name: hr features: - name: wikidata_id dtype: string - name: text dtype: string - name: version_id dtype: string splits: - name: train num_bytes: 247471855 num_examples: 103857 - name: validation num_bytes: 14004242 num_examples: 5792 - name: test num_bytes: 13881533 num_examples: 5724 download_size: 158644264 dataset_size: 275357630 - config_name: hu features: - name: wikidata_id dtype: string - name: text dtype: string - name: version_id dtype: string splits: - name: train num_bytes: 878753014 num_examples: 273248 - name: validation num_bytes: 48695962 num_examples: 15208 - name: test num_bytes: 50053050 num_examples: 15258 download_size: 466524744 dataset_size: 977502026 - config_name: id features: - name: wikidata_id dtype: string - name: text dtype: string - name: version_id dtype: string splits: - name: train num_bytes: 315092853 num_examples: 156255 - name: validation num_bytes: 16667760 num_examples: 8714 - name: test num_bytes: 17798713 num_examples: 8598 download_size: 193455048 dataset_size: 349559326 - config_name: it features: - name: wikidata_id dtype: string - name: text dtype: string - name: version_id dtype: string splits: - name: train num_bytes: 1998187938 num_examples: 732609 - name: validation num_bytes: 109399796 num_examples: 40684 - name: test num_bytes: 108160871 num_examples: 40443 download_size: 1330554944 dataset_size: 2215748605 - config_name: ja features: - name: wikidata_id dtype: string - name: text dtype: string - name: version_id dtype: string splits: - name: train num_bytes: 7719156890 num_examples: 745392 - name: validation num_bytes: 423396781 num_examples: 41576 - name: test num_bytes: 424775191 num_examples: 41268 download_size: 2914923230 dataset_size: 8567328862 - config_name: ko features: - name: wikidata_id dtype: string - name: text dtype: string - name: version_id dtype: string splits: - name: train num_bytes: 1424423053 num_examples: 194977 - name: validation num_bytes: 79027067 num_examples: 10805 - name: test num_bytes: 78623281 num_examples: 10802 download_size: 568560655 dataset_size: 1582073401 - config_name: lt features: - name: wikidata_id dtype: string - name: text dtype: string - name: version_id dtype: string splits: - name: train num_bytes: 173899806 num_examples: 84854 - name: validation num_bytes: 9782794 num_examples: 4754 - name: test num_bytes: 9855094 num_examples: 4683 download_size: 100457919 dataset_size: 193537694 - config_name: lv features: - name: wikidata_id dtype: string - name: text dtype: string - name: version_id dtype: string splits: - name: train num_bytes: 108022486 num_examples: 33064 - name: validation num_bytes: 5999880 num_examples: 1857 - name: test num_bytes: 6277058 num_examples: 1932 download_size: 57147319 dataset_size: 120299424 - config_name: ms features: - name: wikidata_id dtype: string - name: text dtype: string - name: version_id dtype: string splits: - name: train num_bytes: 133193449 num_examples: 97509 - name: validation num_bytes: 7244722 num_examples: 5357 - name: test num_bytes: 7344948 num_examples: 5235 download_size: 80629019 dataset_size: 147783119 - config_name: nl features: - name: wikidata_id dtype: string - name: text dtype: string - name: version_id dtype: string splits: - name: train num_bytes: 906908479 num_examples: 447555 - name: validation num_bytes: 51519150 num_examples: 25201 - name: test num_bytes: 49492508 num_examples: 24776 download_size: 594312303 dataset_size: 1007920137 - config_name: 'no' features: - name: wikidata_id dtype: string - name: text dtype: string - name: version_id dtype: string splits: - name: train num_bytes: 391905155 num_examples: 190588 - name: validation num_bytes: 22058565 num_examples: 10547 - name: test num_bytes: 21510187 num_examples: 10588 download_size: 248974000 dataset_size: 435473907 - config_name: pl features: - name: wikidata_id dtype: string - name: text dtype: string - name: version_id dtype: string splits: - name: train num_bytes: 1250270240 num_examples: 505191 - name: validation num_bytes: 70048390 num_examples: 28310 - name: test num_bytes: 69957343 num_examples: 27987 download_size: 755556434 dataset_size: 1390275973 - config_name: pt features: - name: wikidata_id dtype: string - name: text dtype: string - name: version_id dtype: string splits: - name: train num_bytes: 1186541609 num_examples: 406507 - name: validation num_bytes: 65911750 num_examples: 22301 - name: test num_bytes: 65941634 num_examples: 22693 download_size: 725984914 dataset_size: 1318394993 - config_name: ro features: - name: wikidata_id dtype: string - name: text dtype: string - name: version_id dtype: string splits: - name: train num_bytes: 378177460 num_examples: 139615 - name: validation num_bytes: 19638614 num_examples: 7624 - name: test num_bytes: 22095957 num_examples: 7870 download_size: 212621695 dataset_size: 419912031 - config_name: ru features: - name: wikidata_id dtype: string - name: text dtype: string - name: version_id dtype: string splits: - name: train num_bytes: 14041955183 num_examples: 926037 - name: validation num_bytes: 787569099 num_examples: 51287 - name: test num_bytes: 782630173 num_examples: 51885 download_size: 4959684748 dataset_size: 15612154455 - config_name: sk features: - name: wikidata_id dtype: string - name: text dtype: string - name: version_id dtype: string splits: - name: train num_bytes: 220400547 num_examples: 103095 - name: validation num_bytes: 11443566 num_examples: 5604 - name: test num_bytes: 12958230 num_examples: 5741 download_size: 122641378 dataset_size: 244802343 - config_name: sl features: - name: wikidata_id dtype: string - name: text dtype: string - name: version_id dtype: string splits: - name: train num_bytes: 165604630 num_examples: 60927 - name: validation num_bytes: 8686867 num_examples: 3287 - name: test num_bytes: 8938235 num_examples: 3341 download_size: 108369067 dataset_size: 183229732 - config_name: sr features: - name: wikidata_id dtype: string - name: text dtype: string - name: version_id dtype: string splits: - name: train num_bytes: 1778468133 num_examples: 327313 - name: validation num_bytes: 101044816 num_examples: 18100 - name: test num_bytes: 94774312 num_examples: 17997 download_size: 601515686 dataset_size: 1974287261 - config_name: sv features: - name: wikidata_id dtype: string - name: text dtype: string - name: version_id dtype: string splits: - name: train num_bytes: 675484771 num_examples: 400742 - name: validation num_bytes: 37596409 num_examples: 22263 - name: test num_bytes: 37171140 num_examples: 22291 download_size: 402183416 dataset_size: 750252320 - config_name: th features: - name: wikidata_id dtype: string - name: text dtype: string - name: version_id dtype: string splits: - name: train num_bytes: 1167742322 num_examples: 56798 - name: validation num_bytes: 58604863 num_examples: 3093 - name: test num_bytes: 63235795 num_examples: 3114 download_size: 286569412 dataset_size: 1289582980 - config_name: tl features: - name: wikidata_id dtype: string - name: text dtype: string - name: version_id dtype: string splits: - name: train num_bytes: 27097474 num_examples: 25940 - name: validation num_bytes: 1480857 num_examples: 1472 - name: test num_bytes: 1421372 num_examples: 1446 download_size: 16610349 dataset_size: 29999703 - config_name: tr features: - name: wikidata_id dtype: string - name: text dtype: string - name: version_id dtype: string splits: - name: train num_bytes: 417796625 num_examples: 142576 - name: validation num_bytes: 23829728 num_examples: 7845 - name: test num_bytes: 23573543 num_examples: 7890 download_size: 208571967 dataset_size: 465199896 - config_name: uk features: - name: wikidata_id dtype: string - name: text dtype: string - name: version_id dtype: string splits: - name: train num_bytes: 5617333215 num_examples: 477618 - name: validation num_bytes: 304063524 num_examples: 26324 - name: test num_bytes: 309417358 num_examples: 26581 download_size: 2016970917 dataset_size: 6230814097 - config_name: vi features: - name: wikidata_id dtype: string - name: text dtype: string - name: version_id dtype: string splits: - name: train num_bytes: 968448149 num_examples: 146255 - name: validation num_bytes: 53118964 num_examples: 8195 - name: test num_bytes: 51960729 num_examples: 7942 download_size: 382764219 dataset_size: 1073527842 - config_name: zh-cn features: - name: wikidata_id dtype: string - name: text dtype: string - name: version_id dtype: string splits: - name: train num_bytes: 902812807 num_examples: 549672 - name: validation num_bytes: 50487729 num_examples: 30299 - name: test num_bytes: 49584239 num_examples: 30355 download_size: 667605463 dataset_size: 1002884775 - config_name: zh-tw features: - name: wikidata_id dtype: string - name: text dtype: string - name: version_id dtype: string splits: - name: train num_bytes: 3254625339 num_examples: 552031 - name: validation num_bytes: 185024571 num_examples: 30739 - name: test num_bytes: 181148137 num_examples: 30670 download_size: 1375185673 dataset_size: 3620798047 configs: - config_name: ar data_files: - split: train path: ar/train-* - split: validation path: ar/validation-* - split: test path: ar/test-* - config_name: bg data_files: - split: train path: bg/train-* - split: validation path: bg/validation-* - split: test path: bg/test-* - config_name: ca data_files: - split: train path: ca/train-* - split: validation path: ca/validation-* - split: test path: ca/test-* - config_name: cs data_files: - split: train path: cs/train-* - split: validation path: cs/validation-* - split: test path: cs/test-* - config_name: da data_files: - split: train path: da/train-* - split: validation path: da/validation-* - split: test path: da/test-* - config_name: de data_files: - split: train path: de/train-* - split: validation path: de/validation-* - split: test path: de/test-* - config_name: el data_files: - split: train path: el/train-* - split: validation path: el/validation-* - split: test path: el/test-* - config_name: en data_files: - split: train path: en/train-* - split: validation path: en/validation-* - split: test path: en/test-* - config_name: es data_files: - split: train path: es/train-* - split: validation path: es/validation-* - split: test path: es/test-* - config_name: et data_files: - split: train path: et/train-* - split: validation path: et/validation-* - split: test path: et/test-* - config_name: fa data_files: - split: train path: fa/train-* - split: validation path: fa/validation-* - split: test path: fa/test-* - config_name: fi data_files: - split: train path: fi/train-* - split: validation path: fi/validation-* - split: test path: fi/test-* - config_name: fr data_files: - split: train path: fr/train-* - split: validation path: fr/validation-* - split: test path: fr/test-* - config_name: he data_files: - split: train path: he/train-* - split: validation path: he/validation-* - split: test path: he/test-* - config_name: hi data_files: - split: train path: hi/train-* - split: validation path: hi/validation-* - split: test path: hi/test-* - config_name: hr data_files: - split: train path: hr/train-* - split: validation path: hr/validation-* - split: test path: hr/test-* - config_name: hu data_files: - split: train path: hu/train-* - split: validation path: hu/validation-* - split: test path: hu/test-* - config_name: id data_files: - split: train path: id/train-* - split: validation path: id/validation-* - split: test path: id/test-* - config_name: it data_files: - split: train path: it/train-* - split: validation path: it/validation-* - split: test path: it/test-* - config_name: ja data_files: - split: train path: ja/train-* - split: validation path: ja/validation-* - split: test path: ja/test-* - config_name: ko data_files: - split: train path: ko/train-* - split: validation path: ko/validation-* - split: test path: ko/test-* - config_name: lt data_files: - split: train path: lt/train-* - split: validation path: lt/validation-* - split: test path: lt/test-* - config_name: lv data_files: - split: train path: lv/train-* - split: validation path: lv/validation-* - split: test path: lv/test-* - config_name: ms data_files: - split: train path: ms/train-* - split: validation path: ms/validation-* - split: test path: ms/test-* - config_name: nl data_files: - split: train path: nl/train-* - split: validation path: nl/validation-* - split: test path: nl/test-* - config_name: 'no' data_files: - split: train path: no/train-* - split: validation path: no/validation-* - split: test path: no/test-* - config_name: pl data_files: - split: train path: pl/train-* - split: validation path: pl/validation-* - split: test path: pl/test-* - config_name: pt data_files: - split: train path: pt/train-* - split: validation path: pt/validation-* - split: test path: pt/test-* - config_name: ro data_files: - split: train path: ro/train-* - split: validation path: ro/validation-* - split: test path: ro/test-* - config_name: ru data_files: - split: train path: ru/train-* - split: validation path: ru/validation-* - split: test path: ru/test-* - config_name: sk data_files: - split: train path: sk/train-* - split: validation path: sk/validation-* - split: test path: sk/test-* - config_name: sl data_files: - split: train path: sl/train-* - split: validation path: sl/validation-* - split: test path: sl/test-* - config_name: sr data_files: - split: train path: sr/train-* - split: validation path: sr/validation-* - split: test path: sr/test-* - config_name: sv data_files: - split: train path: sv/train-* - split: validation path: sv/validation-* - split: test path: sv/test-* - config_name: th data_files: - split: train path: th/train-* - split: validation path: th/validation-* - split: test path: th/test-* - config_name: tl data_files: - split: train path: tl/train-* - split: validation path: tl/validation-* - split: test path: tl/test-* - config_name: tr data_files: - split: train path: tr/train-* - split: validation path: tr/validation-* - split: test path: tr/test-* - config_name: uk data_files: - split: train path: uk/train-* - split: validation path: uk/validation-* - split: test path: uk/test-* - config_name: vi data_files: - split: train path: vi/train-* - split: validation path: vi/validation-* - split: test path: vi/test-* - config_name: zh-cn data_files: - split: train path: zh-cn/train-* - split: validation path: zh-cn/validation-* - split: test path: zh-cn/test-* - config_name: zh-tw data_files: - split: train path: zh-tw/train-* - split: validation path: zh-tw/validation-* - split: test path: zh-tw/test-* --- # Dataset Card for "wiki40b" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://research.google/pubs/pub49029/](https://research.google/pubs/pub49029/) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 0.00 MB - **Size of the generated dataset:** 10.47 GB - **Total amount of disk used:** 10.47 GB ### Dataset Summary Clean-up text for 40+ Wikipedia languages editions of pages correspond to entities. The datasets have train/dev/test splits per language. The dataset is cleaned up by page filtering to remove disambiguation pages, redirect pages, deleted pages, and non-entity pages. Each example contains the wikidata id of the entity, and the full Wikipedia article after page processing that removes non-content sections and structured objects. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### en - **Size of downloaded dataset files:** 0.00 MB - **Size of the generated dataset:** 10.47 GB - **Total amount of disk used:** 10.47 GB An example of 'train' looks as follows. ``` ``` ### Data Fields The data fields are the same among all splits. #### en - `wikidata_id`: a `string` feature. - `text`: a `string` feature. - `version_id`: a `string` feature. ### Data Splits |name| train |validation| test | |----|------:|---------:|-----:| |en |2926536| 163597|162274| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` ``` ### Contributions Thanks to [@jplu](https://github.com/jplu), [@patrickvonplaten](https://github.com/patrickvonplaten), [@thomwolf](https://github.com/thomwolf), [@albertvillanova](https://github.com/albertvillanova), [@lhoestq](https://github.com/lhoestq) for adding this dataset.

WizardLMTeam/WizardLM_evol_instruct_70k

WizardLMTeam

2024-03-10T01:06:43Z

503

191

[ "license:mit", "size_categories:10K<n<100K", "format:json", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2308.09583", "arxiv:2304.12244", "arxiv:2306.08568", "region:us" ]

[]

2023-04-25T09:57:27Z

null

--- license: mit --- This is the training data of WizardLM. ## News - 🔥 🔥 🔥 [08/11/2023] We release **WizardMath** Models. - 🔥 Our **WizardMath-70B-V1.0** model slightly outperforms some closed-source LLMs on the GSM8K, including **ChatGPT 3.5**, **Claude Instant 1** and **PaLM 2 540B**. - 🔥 Our **WizardMath-70B-V1.0** model achieves **81.6 pass@1** on the [GSM8k Benchmarks](https://github.com/openai/grade-school-math), which is **24.8** points higher than the SOTA open-source LLM. - 🔥 Our **WizardMath-70B-V1.0** model achieves **22.7 pass@1** on the [MATH Benchmarks](https://github.com/hendrycks/math), which is **9.2** points higher than the SOTA open-source LLM. | Model | Checkpoint | Paper | GSM8k | MATH |Online Demo| License| | ----- |------| ---- |------|-------| ----- | ----- | | WizardMath-70B-V1.0 | 🤗 <a href="https://huggingface.co/WizardLM/WizardMath-70B-V1.0" target="_blank">HF Link</a> | 📃 <a href="https://arxiv.org/abs/2308.09583" target="_blank">[WizardMath]</a>| **81.6** | **22.7** |[Demo](http://47.103.63.15:50083/)| <a href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/" target="_blank">Llama 2 </a> | | WizardMath-13B-V1.0 | 🤗 <a href="https://huggingface.co/WizardLM/WizardMath-13B-V1.0" target="_blank">HF Link</a> | 📃 <a href="https://arxiv.org/abs/2308.09583" target="_blank">[WizardMath]</a>| **63.9** | **14.0** |[Demo](http://47.103.63.15:50082/)| <a href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/" target="_blank">Llama 2 </a> | | WizardMath-7B-V1.0 | 🤗 <a href="https://huggingface.co/WizardLM/WizardMath-7B-V1.0" target="_blank">HF Link</a> | 📃 <a href="https://arxiv.org/abs/2308.09583" target="_blank">[WizardMath]</a>| **54.9** | **10.7** | [Demo](http://47.103.63.15:50080/)| <a href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/" target="_blank">Llama 2 </a>| | Model | Checkpoint | Paper |MT-Bench | AlpacaEval | WizardEval | HumanEval | License| | ----- |------| ---- |------|-------| ----- | ----- | ----- | | WizardLM-13B-V1.2 | 🤗 <a href="https://huggingface.co/WizardLM/WizardLM-13B-V1.2" target="_blank">HF Link</a> | | 7.06 | 89.17% | 101.4% |36.6 pass@1| <a href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/" target="_blank">Llama 2 License </a> | | WizardLM-13B-V1.1 | 🤗 <a href="https://huggingface.co/WizardLM/WizardLM-13B-V1.1" target="_blank">HF Link</a> | | 6.76 |86.32% | 99.3% |25.0 pass@1| Non-commercial| | WizardLM-30B-V1.0 | 🤗 <a href="https://huggingface.co/WizardLM/WizardLM-30B-V1.0" target="_blank">HF Link</a> | | 7.01 | | 97.8% | 37.8 pass@1| Non-commercial | | WizardLM-13B-V1.0 | 🤗 <a href="https://huggingface.co/WizardLM/WizardLM-13B-V1.0" target="_blank">HF Link</a> | | 6.35 | 75.31% | 89.1% | 24.0 pass@1 | Non-commercial| | WizardLM-7B-V1.0 | 🤗 <a href="https://huggingface.co/WizardLM/WizardLM-7B-V1.0" target="_blank">HF Link</a> | 📃 <a href="https://arxiv.org/abs/2304.12244" target="_blank">[WizardLM]</a> | | | 78.0% |19.1 pass@1 | Non-commercial| | WizardCoder-15B-V1.0 | 🤗 <a href="https://huggingface.co/WizardLM/WizardCoder-15B-V1.0" target="_blank">HF Link</a> | 📃 <a href="https://arxiv.org/abs/2306.08568" target="_blank">[WizardCoder]</a> | || | 57.3 pass@1 | <a href="https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement" target="_blank">OpenRAIL-M</a> |

stanfordnlp/snli

stanfordnlp

2024-03-06T10:55:50Z

6,934

75

[ "task_categories:text-classification", "task_ids:natural-language-inference", "task_ids:multi-input-text-classification", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "source_datasets:extended|other-flicker-30k", "source_datasets:extended|other-visual-genome", "language:en", "license:cc-by-sa-4.0", "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:1508.05326", "region:us" ]

[ "text-classification" ]

2022-03-02T23:29:22Z

1

--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - cc-by-sa-4.0 multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - extended|other-flicker-30k - extended|other-visual-genome task_categories: - text-classification task_ids: - natural-language-inference - multi-input-text-classification paperswithcode_id: snli pretty_name: Stanford Natural Language Inference dataset_info: config_name: plain_text features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: test num_bytes: 1258904 num_examples: 10000 - name: validation num_bytes: 1263036 num_examples: 10000 - name: train num_bytes: 65884386 num_examples: 550152 download_size: 20439300 dataset_size: 68406326 configs: - config_name: plain_text data_files: - split: test path: plain_text/test-* - split: validation path: plain_text/validation-* - split: train path: plain_text/train-* --- # Dataset Card for SNLI ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://nlp.stanford.edu/projects/snli/ - **Repository:** [More Information Needed] - **Paper:** https://aclanthology.org/D15-1075/ - **Paper:** https://arxiv.org/abs/1508.05326 - **Leaderboard:** https://nlp.stanford.edu/projects/snli/ - **Point of Contact:** [Samuel Bowman](mailto:[email protected]) - **Point of Contact:** [Gabor Angeli](mailto:[email protected]) - **Point of Contact:** [Chris Manning]([email protected]) ### Dataset Summary The SNLI corpus (version 1.0) is a collection of 570k human-written English sentence pairs manually labeled for balanced classification with the labels entailment, contradiction, and neutral, supporting the task of natural language inference (NLI), also known as recognizing textual entailment (RTE). ### Supported Tasks and Leaderboards Natural Language Inference (NLI), also known as Recognizing Textual Entailment (RTE), is the task of determining the inference relation between two (short, ordered) texts: entailment, contradiction, or neutral ([MacCartney and Manning 2008](https://aclanthology.org/C08-1066/)). See the [corpus webpage](https://nlp.stanford.edu/projects/snli/) for a list of published results. ### Languages The language in the dataset is English as spoken by users of the website Flickr and as spoken by crowdworkers from Amazon Mechanical Turk. The BCP-47 code for English is en. ## Dataset Structure ### Data Instances For each instance, there is a string for the premise, a string for the hypothesis, and an integer for the label. Note that each premise may appear three times with a different hypothesis and label. See the [SNLI corpus viewer](https://huggingface.co/datasets/viewer/?dataset=snli) to explore more examples. ``` {'premise': 'Two women are embracing while holding to go packages.' 'hypothesis': 'The sisters are hugging goodbye while holding to go packages after just eating lunch.' 'label': 1} ``` The average token count for the premises and hypotheses are given below: | Feature | Mean Token Count | | ---------- | ---------------- | | Premise | 14.1 | | Hypothesis | 8.3 | ### Data Fields - `premise`: a string used to determine the truthfulness of the hypothesis - `hypothesis`: a string that may be true, false, or whose truth conditions may not be knowable when compared to the premise - `label`: an integer whose value may be either _0_, indicating that the hypothesis entails the premise, _1_, indicating that the premise and hypothesis neither entail nor contradict each other, or _2_, indicating that the hypothesis contradicts the premise. Dataset instances which don't have any gold label are marked with -1 label. Make sure you filter them before starting the training using `datasets.Dataset.filter`. ### Data Splits The SNLI dataset has 3 splits: _train_, _validation_, and _test_. All of the examples in the _validation_ and _test_ sets come from the set that was annotated in the validation task with no-consensus examples removed. The remaining multiply-annotated examples are in the training set with no-consensus examples removed. Each unique premise/caption shows up in only one split, even though they usually appear in at least three different examples. | Dataset Split | Number of Instances in Split | | ------------- |----------------------------- | | Train | 550,152 | | Validation | 10,000 | | Test | 10,000 | ## Dataset Creation ### Curation Rationale The [SNLI corpus (version 1.0)](https://nlp.stanford.edu/projects/snli/) was developed as a benchmark for natural langauge inference (NLI), also known as recognizing textual entailment (RTE), with the goal of producing a dataset large enough to train models using neural methodologies. ### Source Data #### Initial Data Collection and Normalization The hypotheses were elicited by presenting crowdworkers with captions from preexisting datasets without the associated photos, but the vocabulary of the hypotheses still reflects the content of the photos as well as the caption style of writing (e.g. mostly present tense). The dataset developers report 37,026 distinct words in the corpus, ignoring case. They allowed bare NPs as well as full sentences. Using the Stanford PCFG Parser 3.5.2 (Klein and Manning, 2003) trained on the standard training set as well as on the Brown Corpus (Francis and Kucera 1979), the authors report that 74% of the premises and 88.9% of the hypotheses result in a parse rooted with an 'S'. The corpus was developed between 2014 and 2015. Crowdworkers were presented with a caption without the associated photo and asked to produce three alternate captions, one that is definitely true, one that might be true, and one that is definitely false. See Section 2.1 and Figure 1 for details (Bowman et al., 2015). The corpus includes content from the [Flickr 30k corpus](http://shannon.cs.illinois.edu/DenotationGraph/) and the [VisualGenome corpus](https://visualgenome.org/). The photo captions used to prompt the data creation were collected on Flickr by [Young et al. (2014)](https://aclanthology.org/Q14-1006/), who extended the Flickr 8K dataset developed by [Hodosh et al. (2013)](https://www.jair.org/index.php/jair/article/view/10833). Hodosh et al. collected photos from the following Flickr groups: strangers!, Wild-Child (Kids in Action), Dogs in Action (Read the Rules), Outdoor Activities, Action Photography, Flickr-Social (two or more people in the photo). Young et al. do not list the specific groups they collected photos from. The VisualGenome corpus also contains images from Flickr, originally collected in [MS-COCO](https://cocodataset.org/#home) and [YFCC100M](http://projects.dfki.uni-kl.de/yfcc100m/). The premises from the Flickr 30k corpus corrected for spelling using the Linux spell checker and ungrammatical sentences were removed. Bowman et al. do not report any normalization, though they note that punctuation and capitalization are often omitted. #### Who are the source language producers? A large portion of the premises (160k) were produced in the [Flickr 30k corpus](http://shannon.cs.illinois.edu/DenotationGraph/) by an unknown number of crowdworkers. About 2,500 crowdworkers from Amazon Mechanical Turk produced the associated hypotheses. The premises from the Flickr 30k project describe people and animals whose photos were collected and presented to the Flickr 30k crowdworkers, but the SNLI corpus did not present the photos to the hypotheses creators. The Flickr 30k corpus did not report crowdworker or photo subject demographic information or crowdworker compensation. The SNLI crowdworkers were compensated per HIT at rates between $.1 and $.5 with no incentives. Workers who ignored the guidelines were disqualified, and automated bulk submissions were rejected. No demographic information was collected from the SNLI crowdworkers. An additional 4,000 premises come from the pilot study of the [VisualGenome corpus](https://homes.cs.washington.edu/~ranjay/visualgenome/index.html). Though the pilot study itself is not described, the location information of the 33,000 AMT crowdworkers that participated over the course of the 6 months of data collection are aggregated. Most of the workers were located in the United States (93%), with others from the Philippines, Kenya, India, Russia, and Canada. Workers were paid $6-$8 per hour. ### Annotations #### Annotation process 56,941 of the total sentence pairs were further annotated in a validation task. Four annotators each labeled a premise-hypothesis pair as entailment, contradiction, or neither, resulting in 5 total judgements including the original hypothesis author judgement. See Section 2.2 for more details (Bowman et al., 2015). The authors report 3/5 annotator agreement on 98% of the validation set and unanimous annotator agreement on 58.3% of the validation set. If a label was chosen by three annotators, that label was made the gold label. Following from this, 2% of the data did not have a consensus label and was labeled '-' by the authors. | Label | Fleiss κ | | --------------- |--------- | | _contradiction_ | 0.77 | | _entailment_ | 0.72 | | _neutral_ | 0.60 | | overall | 0.70 | #### Who are the annotators? The annotators of the validation task were a closed set of about 30 trusted crowdworkers on Amazon Mechanical Turk. No demographic information was collected. Annotators were compensated per HIT between $.1 and $.5 with $1 bonuses in cases where annotator labels agreed with the curators' labels for 250 randomly distributed examples. ### Personal and Sensitive Information The dataset does not contain any personal information about the authors or the crowdworkers, but may contain descriptions of the people in the original Flickr photos. ## Considerations for Using the Data ### Social Impact of Dataset This dataset was developed as a benchmark for evaluating representational systems for text, especially including those induced by representation learning methods, in the task of predicting truth conditions in a given context. (It should be noted that the truth conditions of a hypothesis given a premise does not necessarily match the truth conditions of the hypothesis in the real world.) Systems that are successful at such a task may be more successful in modeling semantic representations. ### Discussion of Biases The language reflects the content of the photos collected from Flickr, as described in the [Data Collection](#initial-data-collection-and-normalization) section. [Rudinger et al (2017)](https://aclanthology.org/W17-1609/) use pointwise mutual information to calculate a measure of association between a manually selected list of tokens corresponding to identity categories and the other words in the corpus, showing strong evidence of stereotypes across gender categories. They also provide examples in which crowdworkers reproduced harmful stereotypes or pejorative language in the hypotheses. ### Other Known Limitations [Gururangan et al (2018)](https://aclanthology.org/N18-2017/), [Poliak et al (2018)](https://aclanthology.org/S18-2023/), and [Tsuchiya (2018)](https://aclanthology.org/L18-1239/) show that the SNLI corpus has a number of annotation artifacts. Using various classifiers, Poliak et al correctly predicted the label of the hypothesis 69% of the time without using the premise, Gururangan et al 67% of the time, and Tsuchiya 63% of the time. ## Additional Information ### Dataset Curators The SNLI corpus was developed by Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning as part of the [Stanford NLP group](https://nlp.stanford.edu/). It was supported by a Google Faculty Research Award, a gift from Bloomberg L.P., the Defense Advanced Research Projects Agency (DARPA) Deep Exploration and Filtering of Text (DEFT) Program under Air Force Research Laboratory (AFRL) contract no. FA8750-13-2-0040, the National Science Foundation under grant no. IIS 1159679, and the Department of the Navy, Office of Naval Research, under grant no. N00014-10-1-0109. ### Licensing Information The Stanford Natural Language Inference Corpus by The Stanford NLP Group is licensed under a [Creative Commons Attribution-ShareAlike 4.0 International License](http://creativecommons.org/licenses/by-sa/4.0/). The corpus includes content from the [Flickr 30k corpus](http://shannon.cs.illinois.edu/DenotationGraph/), also released under an Attribution-ShareAlike licence. ### Citation Information The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it: ```bibtex @inproceedings{bowman-etal-2015-large, title = "A large annotated corpus for learning natural language inference", author = "Bowman, Samuel R. and Angeli, Gabor and Potts, Christopher and Manning, Christopher D.", editor = "M{\`a}rquez, Llu{\'\i}s and Callison-Burch, Chris and Su, Jian", booktitle = "Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing", month = sep, year = "2015", address = "Lisbon, Portugal", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/D15-1075", doi = "10.18653/v1/D15-1075", pages = "632--642", } ``` The corpus includes content from the [Flickr 30k corpus](http://shannon.cs.illinois.edu/DenotationGraph/), which can be cited by way of this paper: ```bibtex @article{young-etal-2014-image, title = "From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions", author = "Young, Peter and Lai, Alice and Hodosh, Micah and Hockenmaier, Julia", editor = "Lin, Dekang and Collins, Michael and Lee, Lillian", journal = "Transactions of the Association for Computational Linguistics", volume = "2", year = "2014", address = "Cambridge, MA", publisher = "MIT Press", url = "https://aclanthology.org/Q14-1006", doi = "10.1162/tacl_a_00166", pages = "67--78", } ``` ### Contact Information For any comments or questions, please email [Samuel Bowman](mailto:[email protected]), [Gabor Angeli](mailto:[email protected]) and [Chris Manning]([email protected]). ### Contributions Thanks to [@mariamabarham](https://github.com/mariamabarham), [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun), [@patrickvonplaten](https://github.com/patrickvonplaten) and [@mcmillanmajora](https://github.com/mcmillanmajora) for adding this dataset.

deepmind/narrativeqa

deepmind

2024-03-06T07:33:05Z

25,484

47

[ "task_categories:text2text-generation", "task_ids:abstractive-qa", "annotations_creators:crowdsourced", "language_creators:found", "multilinguality:monolingual", "source_datasets:original", "language:en", "license:apache-2.0", "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:1712.07040", "region:us" ]

[ "text2text-generation" ]

2022-03-02T23:29:22Z

1

--- annotations_creators: - crowdsourced language_creators: - found language: - en license: - apache-2.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text2text-generation task_ids: - abstractive-qa paperswithcode_id: narrativeqa pretty_name: NarrativeQA dataset_info: features: - name: document struct: - name: id dtype: string - name: kind dtype: string - name: url dtype: string - name: file_size dtype: int32 - name: word_count dtype: int32 - name: start dtype: string - name: end dtype: string - name: summary struct: - name: text dtype: string - name: tokens sequence: string - name: url dtype: string - name: title dtype: string - name: text dtype: string - name: question struct: - name: text dtype: string - name: tokens sequence: string - name: answers list: - name: text dtype: string - name: tokens sequence: string splits: - name: train num_bytes: 11556607782 num_examples: 32747 - name: test num_bytes: 3547135501 num_examples: 10557 - name: validation num_bytes: 1211859418 num_examples: 3461 download_size: 3232805701 dataset_size: 16315602701 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: validation path: data/validation-* --- # Dataset Card for Narrative QA ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Repository:** https://github.com/deepmind/narrativeqa - **Paper:** https://arxiv.org/abs/1712.07040 - **Paper:** https://aclanthology.org/Q18-1023/ - **Point of Contact:** [Tomáš Kočiský](mailto:[email protected]) [Jonathan Schwarz](mailto:[email protected]) [Phil Blunsom]([email protected]) [Chris Dyer]([email protected]) [Karl Moritz Hermann](mailto:[email protected]) [Gábor Melis](mailto:[email protected]) [Edward Grefenstette](mailto:[email protected]) ### Dataset Summary NarrativeQA is an English-lanaguage dataset of stories and corresponding questions designed to test reading comprehension, especially on long documents. ### Supported Tasks and Leaderboards The dataset is used to test reading comprehension. There are 2 tasks proposed in the paper: "summaries only" and "stories only", depending on whether the human-generated summary or the full story text is used to answer the question. ### Languages English ## Dataset Structure ### Data Instances A typical data point consists of a question and answer pair along with a summary/story which can be used to answer the question. Additional information such as the url, word count, wikipedia page, are also provided. A typical example looks like this: ``` { "document": { "id": "23jncj2n3534563110", "kind": "movie", "url": "https://www.imsdb.com/Movie%20Scripts/Name%20of%20Movie.html", "file_size": 80473, "word_count": 41000, "start": "MOVIE screenplay by", "end": ". THE END", "summary": { "text": "Joe Bloggs begins his journey exploring...", "tokens": ["Joe", "Bloggs", "begins", "his", "journey", "exploring",...], "url": "http://en.wikipedia.org/wiki/Name_of_Movie", "title": "Name of Movie (film)" }, "text": "MOVIE screenplay by John Doe\nSCENE 1..." }, "question": { "text": "Where does Joe Bloggs live?", "tokens": ["Where", "does", "Joe", "Bloggs", "live", "?"], }, "answers": [ {"text": "At home", "tokens": ["At", "home"]}, {"text": "His house", "tokens": ["His", "house"]} ] } ``` ### Data Fields - `document.id` - Unique ID for the story. - `document.kind` - "movie" or "gutenberg" depending on the source of the story. - `document.url` - The URL where the story was downloaded from. - `document.file_size` - File size (in bytes) of the story. - `document.word_count` - Number of tokens in the story. - `document.start` - First 3 tokens of the story. Used for verifying the story hasn't been modified. - `document.end` - Last 3 tokens of the story. Used for verifying the story hasn't been modified. - `document.summary.text` - Text of the wikipedia summary of the story. - `document.summary.tokens` - Tokenized version of `document.summary.text`. - `document.summary.url` - Wikipedia URL of the summary. - `document.summary.title` - Wikipedia Title of the summary. - `question` - `{"text":"...", "tokens":[...]}` for the question about the story. - `answers` - List of `{"text":"...", "tokens":[...]}` for valid answers for the question. ### Data Splits The data is split into training, valiudation, and test sets based on story (i.e. the same story cannot appear in more than one split): | Train | Valid | Test | | ------ | ----- | ----- | | 32747 | 3461 | 10557 | ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization Stories and movies scripts were downloaded from [Project Gutenburg](https://www.gutenberg.org) and a range of movie script repositories (mainly [imsdb](http://www.imsdb.com)). #### Who are the source language producers? The language producers are authors of the stories and scripts as well as Amazon Turk workers for the questions. ### Annotations #### Annotation process Amazon Turk Workers were provided with human written summaries of the stories (To make the annotation tractable and to lead annotators towards asking non-localized questions). Stories were matched with plot summaries from Wikipedia using titles and verified the matching with help from human annotators. The annotators were asked to determine if both the story and the summary refer to a movie or a book (as some books are made into movies), or if they are the same part in a series produced in the same year. Annotators on Amazon Mechanical Turk were instructed to write 10 question–answer pairs each based solely on a given summary. Annotators were instructed to imagine that they are writing questions to test students who have read the full stories but not the summaries. We required questions that are specific enough, given the length and complexity of the narratives, and to provide adiverse set of questions about characters, events, why this happened, and so on. Annotators were encouraged to use their own words and we prevented them from copying. We asked for answers that are grammatical, complete sentences, and explicitly allowed short answers (one word, or a few-word phrase, or ashort sentence) as we think that answering with a full sentence is frequently perceived as artificial when asking about factual information. Annotators were asked to avoid extra, unnecessary information in the question or the answer, and to avoid yes/no questions or questions about the author or the actors. #### Who are the annotators? Amazon Mechanical Turk workers. ### Personal and Sensitive Information None ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information The dataset is released under a [Apache-2.0 License](https://github.com/deepmind/narrativeqa/blob/master/LICENSE). ### Citation Information ``` @article{kocisky-etal-2018-narrativeqa, title = "The {N}arrative{QA} Reading Comprehension Challenge", author = "Ko{\v{c}}isk{\'y}, Tom{\'a}{\v{s}} and Schwarz, Jonathan and Blunsom, Phil and Dyer, Chris and Hermann, Karl Moritz and Melis, G{\'a}bor and Grefenstette, Edward", editor = "Lee, Lillian and Johnson, Mark and Toutanova, Kristina and Roark, Brian", journal = "Transactions of the Association for Computational Linguistics", volume = "6", year = "2018", address = "Cambridge, MA", publisher = "MIT Press", url = "https://aclanthology.org/Q18-1023", doi = "10.1162/tacl_a_00023", pages = "317--328", abstract = "Reading comprehension (RC){---}in contrast to information retrieval{---}requires integrating information and reasoning about events, entities, and their relations across a full document. Question answering is conventionally used to assess RC ability, in both artificial agents and children learning to read. However, existing RC datasets and tasks are dominated by questions that can be solved by selecting answers using superficial information (e.g., local context similarity or global term frequency); they thus fail to test for the essential integrative aspect of RC. To encourage progress on deeper comprehension of language, we present a new dataset and set of tasks in which the reader must answer questions about stories by reading entire books or movie scripts. These tasks are designed so that successfully answering their questions requires understanding the underlying narrative rather than relying on shallow pattern matching or salience. We show that although humans solve the tasks easily, standard RC models struggle on the tasks presented here. We provide an analysis of the dataset and the challenges it presents.", } ``` ### Contributions Thanks to [@ghomasHudson](https://github.com/ghomasHudson) for adding this dataset.

qiaojin/PubMedQA

qiaojin

2024-03-06T01:50:16Z

13,222

216

[ "task_categories:question-answering", "task_ids:multiple-choice-qa", "annotations_creators:expert-generated", "annotations_creators:machine-generated", "language_creators:expert-generated", "multilinguality:monolingual", "source_datasets:original", "language:en", "license:mit", "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:1909.06146", "region:us" ]

[ "question-answering" ]

2022-03-02T23:29:22Z

null

--- annotations_creators: - expert-generated - machine-generated language_creators: - expert-generated language: - en license: - mit multilinguality: - monolingual size_categories: - 100K<n<1M - 10K<n<100K - 1K<n<10K source_datasets: - original task_categories: - question-answering task_ids: - multiple-choice-qa paperswithcode_id: pubmedqa pretty_name: PubMedQA config_names: - pqa_artificial - pqa_labeled - pqa_unlabeled dataset_info: - config_name: pqa_artificial features: - name: pubid dtype: int32 - name: question dtype: string - name: context sequence: - name: contexts dtype: string - name: labels dtype: string - name: meshes dtype: string - name: long_answer dtype: string - name: final_decision dtype: string splits: - name: train num_bytes: 443501057 num_examples: 211269 download_size: 233411194 dataset_size: 443501057 - config_name: pqa_labeled features: - name: pubid dtype: int32 - name: question dtype: string - name: context sequence: - name: contexts dtype: string - name: labels dtype: string - name: meshes dtype: string - name: reasoning_required_pred dtype: string - name: reasoning_free_pred dtype: string - name: long_answer dtype: string - name: final_decision dtype: string splits: - name: train num_bytes: 2088898 num_examples: 1000 download_size: 1075513 dataset_size: 2088898 - config_name: pqa_unlabeled features: - name: pubid dtype: int32 - name: question dtype: string - name: context sequence: - name: contexts dtype: string - name: labels dtype: string - name: meshes dtype: string - name: long_answer dtype: string splits: - name: train num_bytes: 125922964 num_examples: 61249 download_size: 66010017 dataset_size: 125922964 configs: - config_name: pqa_artificial data_files: - split: train path: pqa_artificial/train-* - config_name: pqa_labeled data_files: - split: train path: pqa_labeled/train-* - config_name: pqa_unlabeled data_files: - split: train path: pqa_unlabeled/train-* --- # Dataset Card for [Dataset Name] ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [PubMedQA homepage](https://pubmedqa.github.io/ ) - **Repository:** [PubMedQA repository](https://github.com/pubmedqa/pubmedqa) - **Paper:** [PubMedQA: A Dataset for Biomedical Research Question Answering](https://arxiv.org/abs/1909.06146) - **Leaderboard:** [PubMedQA: Leaderboard](https://pubmedqa.github.io/) ### Dataset Summary The task of PubMedQA is to answer research questions with yes/no/maybe (e.g.: Do preoperative statins reduce atrial fibrillation after coronary artery bypass grafting?) using the corresponding abstracts. ### Supported Tasks and Leaderboards The official leaderboard is available at: https://pubmedqa.github.io/. 500 questions in the `pqa_labeled` are used as the test set. They can be found at https://github.com/pubmedqa/pubmedqa. ### Languages English ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions Thanks to [@tuner007](https://github.com/tuner007) for adding this dataset.

lukaemon/mmlu

lukaemon

2024-03-04T21:42:02Z

45,141

62

[ "region:us" ]

[]

2023-02-02T00:42:27Z

null

--- dataset_info: - config_name: abstract_algebra features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 18616 num_examples: 100 - name: validation num_bytes: 1935 num_examples: 11 - name: train num_bytes: 783 num_examples: 5 download_size: 166184960 dataset_size: 21334 - config_name: anatomy features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 32164 num_examples: 135 - name: validation num_bytes: 3030 num_examples: 14 - name: train num_bytes: 920 num_examples: 5 download_size: 166184960 dataset_size: 36114 - config_name: astronomy features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 45695 num_examples: 152 - name: validation num_bytes: 4903 num_examples: 16 - name: train num_bytes: 2029 num_examples: 5 download_size: 166184960 dataset_size: 52627 - config_name: business_ethics features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 32540 num_examples: 100 - name: validation num_bytes: 2949 num_examples: 11 - name: train num_bytes: 2143 num_examples: 5 download_size: 166184960 dataset_size: 37632 - config_name: clinical_knowledge features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 60887 num_examples: 265 - name: validation num_bytes: 6449 num_examples: 29 - name: train num_bytes: 1163 num_examples: 5 download_size: 166184960 dataset_size: 68499 - config_name: college_biology features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 47777 num_examples: 144 - name: validation num_bytes: 4695 num_examples: 16 - name: train num_bytes: 1485 num_examples: 5 download_size: 166184960 dataset_size: 53957 - config_name: college_chemistry features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 23996 num_examples: 100 - name: validation num_bytes: 2260 num_examples: 8 - name: train num_bytes: 1284 num_examples: 5 download_size: 166184960 dataset_size: 27540 - config_name: college_computer_science features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 41927 num_examples: 100 - name: validation num_bytes: 4574 num_examples: 11 - name: train num_bytes: 2718 num_examples: 5 download_size: 166184960 dataset_size: 49219 - config_name: college_mathematics features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 23996 num_examples: 100 - name: validation num_bytes: 2579 num_examples: 11 - name: train num_bytes: 1446 num_examples: 5 download_size: 166184960 dataset_size: 28021 - config_name: college_medicine features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 81174 num_examples: 173 - name: validation num_bytes: 7743 num_examples: 22 - name: train num_bytes: 1623 num_examples: 5 download_size: 166184960 dataset_size: 90540 - config_name: college_physics features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 29454 num_examples: 102 - name: validation num_bytes: 3401 num_examples: 11 - name: train num_bytes: 1365 num_examples: 5 download_size: 166184960 dataset_size: 34220 - config_name: computer_security features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 26412 num_examples: 100 - name: validation num_bytes: 4460 num_examples: 11 - name: train num_bytes: 1054 num_examples: 5 download_size: 166184960 dataset_size: 31926 - config_name: conceptual_physics features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 39052 num_examples: 235 - name: validation num_bytes: 4279 num_examples: 26 - name: train num_bytes: 887 num_examples: 5 download_size: 166184960 dataset_size: 44218 - config_name: econometrics features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 45737 num_examples: 114 - name: validation num_bytes: 4871 num_examples: 12 - name: train num_bytes: 1597 num_examples: 5 download_size: 166184960 dataset_size: 52205 - config_name: electrical_engineering features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 24111 num_examples: 145 - name: validation num_bytes: 2778 num_examples: 16 - name: train num_bytes: 925 num_examples: 5 download_size: 166184960 dataset_size: 27814 - config_name: elementary_mathematics features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 67450 num_examples: 378 - name: validation num_bytes: 8689 num_examples: 41 - name: train num_bytes: 1393 num_examples: 5 download_size: 166184960 dataset_size: 77532 - config_name: formal_logic features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 48891 num_examples: 126 - name: validation num_bytes: 6142 num_examples: 14 - name: train num_bytes: 1710 num_examples: 5 download_size: 166184960 dataset_size: 56743 - config_name: global_facts features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 17691 num_examples: 100 - name: validation num_bytes: 1783 num_examples: 10 - name: train num_bytes: 1182 num_examples: 5 download_size: 166184960 dataset_size: 20656 - config_name: high_school_biology features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 107550 num_examples: 310 - name: validation num_bytes: 10786 num_examples: 32 - name: train num_bytes: 1626 num_examples: 5 download_size: 166184960 dataset_size: 119962 - config_name: high_school_chemistry features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 57031 num_examples: 203 - name: validation num_bytes: 6926 num_examples: 22 - name: train num_bytes: 1173 num_examples: 5 download_size: 166184960 dataset_size: 65130 - config_name: high_school_computer_science features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 43764 num_examples: 100 - name: validation num_bytes: 3268 num_examples: 9 - name: train num_bytes: 2871 num_examples: 5 download_size: 166184960 dataset_size: 49903 - config_name: high_school_european_history features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 269133 num_examples: 165 - name: validation num_bytes: 29494 num_examples: 18 - name: train num_bytes: 11517 num_examples: 5 download_size: 166184960 dataset_size: 310144 - config_name: high_school_geography features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 40636 num_examples: 198 - name: validation num_bytes: 4166 num_examples: 22 - name: train num_bytes: 1356 num_examples: 5 download_size: 166184960 dataset_size: 46158 - config_name: high_school_government_and_politics features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 64711 num_examples: 193 - name: validation num_bytes: 6904 num_examples: 21 - name: train num_bytes: 1732 num_examples: 5 download_size: 166184960 dataset_size: 73347 - config_name: high_school_macroeconomics features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 114945 num_examples: 390 - name: validation num_bytes: 12707 num_examples: 43 - name: train num_bytes: 1281 num_examples: 5 download_size: 166184960 dataset_size: 128933 - config_name: high_school_mathematics features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 52952 num_examples: 270 - name: validation num_bytes: 5550 num_examples: 29 - name: train num_bytes: 1250 num_examples: 5 download_size: 166184960 dataset_size: 59752 - config_name: high_school_microeconomics features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 74025 num_examples: 238 - name: validation num_bytes: 7359 num_examples: 26 - name: train num_bytes: 1251 num_examples: 5 download_size: 166184960 dataset_size: 82635 - config_name: high_school_physics features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 58469 num_examples: 151 - name: validation num_bytes: 6640 num_examples: 17 - name: train num_bytes: 1442 num_examples: 5 download_size: 166184960 dataset_size: 66551 - config_name: high_school_psychology features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 155580 num_examples: 545 - name: validation num_bytes: 16837 num_examples: 60 - name: train num_bytes: 1858 num_examples: 5 download_size: 166184960 dataset_size: 174275 - config_name: high_school_statistics features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 109178 num_examples: 216 - name: validation num_bytes: 9824 num_examples: 23 - name: train num_bytes: 2481 num_examples: 5 download_size: 166184960 dataset_size: 121483 - config_name: high_school_us_history features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 295294 num_examples: 204 - name: validation num_bytes: 31540 num_examples: 22 - name: train num_bytes: 8817 num_examples: 5 download_size: 166184960 dataset_size: 335651 - config_name: high_school_world_history features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 376946 num_examples: 237 - name: validation num_bytes: 45307 num_examples: 26 - name: train num_bytes: 4835 num_examples: 5 download_size: 166184960 dataset_size: 427088 - config_name: human_aging features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 44525 num_examples: 223 - name: validation num_bytes: 4534 num_examples: 23 - name: train num_bytes: 961 num_examples: 5 download_size: 166184960 dataset_size: 50020 - config_name: human_sexuality features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 31181 num_examples: 131 - name: validation num_bytes: 2325 num_examples: 12 - name: train num_bytes: 1030 num_examples: 5 download_size: 166184960 dataset_size: 34536 - config_name: international_law features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 52672 num_examples: 121 - name: validation num_bytes: 6370 num_examples: 13 - name: train num_bytes: 2371 num_examples: 5 download_size: 166184960 dataset_size: 61413 - config_name: jurisprudence features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 33218 num_examples: 108 - name: validation num_bytes: 3640 num_examples: 11 - name: train num_bytes: 1256 num_examples: 5 download_size: 166184960 dataset_size: 38114 - config_name: logical_fallacies features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 48964 num_examples: 163 - name: validation num_bytes: 4965 num_examples: 18 - name: train num_bytes: 1526 num_examples: 5 download_size: 166184960 dataset_size: 55455 - config_name: machine_learning features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 33084 num_examples: 112 - name: validation num_bytes: 3143 num_examples: 11 - name: train num_bytes: 2276 num_examples: 5 download_size: 166184960 dataset_size: 38503 - config_name: management features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 19269 num_examples: 103 - name: validation num_bytes: 1731 num_examples: 11 - name: train num_bytes: 851 num_examples: 5 download_size: 166184960 dataset_size: 21851 - config_name: marketing features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 61375 num_examples: 234 - name: validation num_bytes: 7207 num_examples: 25 - name: train num_bytes: 1434 num_examples: 5 download_size: 166184960 dataset_size: 70016 - config_name: medical_genetics features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 20152 num_examples: 100 - name: validation num_bytes: 2916 num_examples: 11 - name: train num_bytes: 1042 num_examples: 5 download_size: 166184960 dataset_size: 24110 - config_name: miscellaneous features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 142211 num_examples: 783 - name: validation num_bytes: 13716 num_examples: 86 - name: train num_bytes: 652 num_examples: 5 download_size: 166184960 dataset_size: 156579 - config_name: moral_disputes features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 105384 num_examples: 346 - name: validation num_bytes: 12142 num_examples: 38 - name: train num_bytes: 1708 num_examples: 5 download_size: 166184960 dataset_size: 119234 - config_name: moral_scenarios features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 367749 num_examples: 895 - name: validation num_bytes: 41626 num_examples: 100 - name: train num_bytes: 2011 num_examples: 5 download_size: 166184960 dataset_size: 411386 - config_name: nutrition features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 90256 num_examples: 306 - name: validation num_bytes: 8193 num_examples: 33 - name: train num_bytes: 2038 num_examples: 5 download_size: 166184960 dataset_size: 100487 - config_name: philosophy features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 77884 num_examples: 311 - name: validation num_bytes: 8934 num_examples: 34 - name: train num_bytes: 941 num_examples: 5 download_size: 166184960 dataset_size: 87759 - config_name: prehistory features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 87314 num_examples: 324 - name: validation num_bytes: 10028 num_examples: 35 - name: train num_bytes: 1831 num_examples: 5 download_size: 166184960 dataset_size: 99173 - config_name: professional_accounting features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 122564 num_examples: 282 - name: validation num_bytes: 14143 num_examples: 31 - name: train num_bytes: 2101 num_examples: 5 download_size: 166184960 dataset_size: 138808 - config_name: professional_law features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 1881012 num_examples: 1534 - name: validation num_bytes: 202317 num_examples: 170 - name: train num_bytes: 6563 num_examples: 5 download_size: 166184960 dataset_size: 2089892 - config_name: professional_medicine features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 215645 num_examples: 272 - name: validation num_bytes: 23618 num_examples: 31 - name: train num_bytes: 3760 num_examples: 5 download_size: 166184960 dataset_size: 243023 - config_name: professional_psychology features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 221603 num_examples: 612 - name: validation num_bytes: 28606 num_examples: 69 - name: train num_bytes: 2220 num_examples: 5 download_size: 166184960 dataset_size: 252429 - config_name: public_relations features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 27978 num_examples: 110 - name: validation num_bytes: 4470 num_examples: 12 - name: train num_bytes: 1449 num_examples: 5 download_size: 166184960 dataset_size: 33897 - config_name: security_studies features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 203117 num_examples: 245 - name: validation num_bytes: 22436 num_examples: 27 - name: train num_bytes: 5288 num_examples: 5 download_size: 166184960 dataset_size: 230841 - config_name: sociology features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 64824 num_examples: 201 - name: validation num_bytes: 7018 num_examples: 22 - name: train num_bytes: 1566 num_examples: 5 download_size: 166184960 dataset_size: 73408 - config_name: us_foreign_policy features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 27731 num_examples: 100 - name: validation num_bytes: 3175 num_examples: 11 - name: train num_bytes: 1564 num_examples: 5 download_size: 166184960 dataset_size: 32470 - config_name: virology features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 37585 num_examples: 166 - name: validation num_bytes: 5325 num_examples: 18 - name: train num_bytes: 1049 num_examples: 5 download_size: 166184960 dataset_size: 43959 - config_name: world_religions features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 24065 num_examples: 171 - name: validation num_bytes: 2620 num_examples: 19 - name: train num_bytes: 623 num_examples: 5 download_size: 166184960 dataset_size: 27308 --- # MMLU dataset Measuring Massive Multitask Language Understanding: https://github.com/hendrycks/test task_list = [ "high_school_european_history", "business_ethics", "clinical_knowledge", "medical_genetics", "high_school_us_history", "high_school_physics", "high_school_world_history", "virology", "high_school_microeconomics", "econometrics", "college_computer_science", "high_school_biology", "abstract_algebra", "professional_accounting", "philosophy", "professional_medicine", "nutrition", "global_facts", "machine_learning", "security_studies", "public_relations", "professional_psychology", "prehistory", "anatomy", "human_sexuality", "college_medicine", "high_school_government_and_politics", "college_chemistry", "logical_fallacies", "high_school_geography", "elementary_mathematics", "human_aging", "college_mathematics", "high_school_psychology", "formal_logic", "high_school_statistics", "international_law", "high_school_mathematics", "high_school_computer_science", "conceptual_physics", "miscellaneous", "high_school_chemistry", "marketing", "professional_law", "management", "college_physics", "jurisprudence", "world_religions", "sociology", "us_foreign_policy", "high_school_macroeconomics", "computer_security", "moral_scenarios", "moral_disputes", "electrical_engineering", "astronomy", "college_biology", ] ``` @article{hendryckstest2021, title={Measuring Massive Multitask Language Understanding}, author={Dan Hendrycks and Collin Burns and Steven Basart and Andy Zou and Mantas Mazeika and Dawn Song and Jacob Steinhardt}, journal={Proceedings of the International Conference on Learning Representations (ICLR)}, year={2021} } ```

google/Synthetic-Persona-Chat

google

2024-03-01T01:01:01Z

895

107

[ "task_categories:text2text-generation", "language:en", "license:cc-by-4.0", "size_categories:10K<n<100K", "format:csv", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2312.10007", "region:us" ]

[ "text2text-generation" ]

2023-12-20T09:59:14Z

null

--- license: cc-by-4.0 task_categories: - text2text-generation language: - en size_categories: - 10K<n<100K --- # Dataset Card for SPC: Synthetic-Persona-Chat Dataset Abstract from the paper introducing this dataset: > High-quality conversational datasets are essential for developing AI models that can communicate with users. One way to foster deeper interactions between a chatbot and its user is through personas, aspects of the user's character that provide insights into their personality, motivations, and behaviors. Training Natural Language Processing (NLP) models on a diverse and comprehensive persona-based dataset can lead to conversational models that create a deeper connection with the user, and maintain their engagement. In this paper, we leverage the power of Large Language Models (LLMs) to create a large, high-quality conversational dataset from a seed dataset. We propose a Generator-Critic architecture framework to expand the initial dataset, while improving the quality of its conversations. The Generator is an LLM prompted to output conversations. The Critic consists of a mixture of expert LLMs that control the quality of the generated conversations. These experts select the best generated conversations, which we then use to improve the Generator. We release Synthetic-Persona-Chat, consisting of 20k conversations seeded from Persona-Chat. We evaluate the quality of Synthetic-Persona-Chat and our generation framework on different dimensions through extensive experiments, and observe that the losing rate of Synthetic-Persona-Chat against Persona-Chat during Turing test decreases from 17.2% to 8.8% over three iterations. ## Dataset Details ### Dataset Description > We introduce the Synthetic-Persona-Chat dataset, a persona-based conversational dataset, consisting of two parts. The first part, consisting of 4,723 personas and 10,906 conversations, is an extension to Persona-Chat, which has the same user profile pairs as Persona-Chat but new synthetic conversations, with the same train/validation/test split as Persona-Chat. The second part is new synthetic personas and synthetic conversations based on that, consisting of 5,648 synthetic personas and 11,001 conversations. Synthetic-Persona-Chat is created using the Generator-Critic framework introduced in Faithful Persona-based Conversational Dataset Generation with Large Language Models. Each conversation in the dataset has the following format: ``` { "User 1 Persona":[], "User 2 Persona":[], "Conversation":[] } ``` ### Dataset Sources  - **Repository:** https://github.com/google-research-datasets/Synthetic-Persona-Chat/tree/main - **Paper:** https://arxiv.org/abs/2312.10007 ## Citation **BibTeX:** ```@misc{jandaghi2023faithful, title={Faithful Persona-based Conversational Dataset Generation with Large Language Models}, author={Pegah Jandaghi and XiangHai Sheng and Xinyi Bai and Jay Pujara and Hakim Sidahmed}, year={2023}, eprint={2312.10007}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

heliosbrahma/mental_health_chatbot_dataset

heliosbrahma

2024-02-29T18:40:22Z

1,018

86

[ "task_categories:text-generation", "language:en", "license:mit", "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "medical" ]

[ "text-generation" ]

2023-08-02T09:36:25Z

null

--- dataset_info: features: - name: text dtype: string splits: - name: train num_examples: 172 license: mit task_categories: - text-generation language: - en tags: - medical pretty_name: Mental Health Chatbot Dataset size_categories: - n<1K --- # Dataset Card for "heliosbrahma/mental_health_chatbot_dataset" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-instances) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Personal and Sensitive Information](#personal-and-sensitive-information) ## Dataset Description ### Dataset Summary This dataset contains conversational pair of questions and answers in a single text related to Mental Health. Dataset was curated from popular healthcare blogs like WebMD, Mayo Clinic and HeatlhLine, online FAQs etc. All questions and answers have been anonymized to remove any PII data and pre-processed to remove any unwanted characters. ### Languages The text in the dataset is in English. ## Dataset Structure ### Data Instances A data instance include a text columns which is a conversational pair of questions and answers. Questions were asked by the patients and answers were given by healthcare providers. ### Data Fields - 'text': conversational pair of questions and answers between patient and healthcare provider. ## Dataset Creation ### Curation Rationale Chatbots offer a readily available and accessible platform for individuals seeking support. They can be accessed anytime and anywhere, providing immediate assistance to those in need. Chatbots can offer empathetic and non-judgmental responses, providing emotional support to users. While they cannot replace human interaction entirely, they can be a helpful supplement, especially in moments of distress. Hence, this dataset was curated to help finetune a conversational AI bot using this custom dataset which can then be deployed and be provided to the end patient as a chatbot. ### Source Data This dataset was curated from popular healthcare blogs like WebMD, Mayo Clinic and HeatlhLine, online FAQs etc. ### Personal and Sensitive Information The dataset may contain sensitive information related to mental health. All questions and answers have been anonymized to remove any PII data.

Helsinki-NLP/multiun

Helsinki-NLP

2024-02-27T16:59:52Z

2,893

12

[ "task_categories:translation", "annotations_creators:found", "language_creators:found", "multilinguality:multilingual", "source_datasets:original", "language:ar", "language:de", "language:en", "language:es", "language:fr", "language:ru", "language:zh", "license:unknown", "size_categories:100M<n<1B", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]

[ "translation" ]

2022-03-02T23:29:22Z

1

--- annotations_creators: - found language_creators: - found language: - ar - de - en - es - fr - ru - zh license: - unknown multilinguality: - multilingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - translation task_ids: [] paperswithcode_id: multiun pretty_name: MultiUN (Multilingual Corpus from United Nation Documents) config_names: - ar-de - ar-en - ar-es - ar-fr - ar-ru - ar-zh - de-en - de-es - de-fr - de-ru - de-zh - en-es - en-fr - en-ru - en-zh - es-fr - es-ru - es-zh - fr-ru - fr-zh - ru-zh dataset_info: - config_name: ar-de features: - name: translation dtype: translation: languages: - ar - de splits: - name: train num_bytes: 94466261 num_examples: 165090 download_size: 41124373 dataset_size: 94466261 - config_name: ar-en features: - name: translation dtype: translation: languages: - ar - en splits: - name: train num_bytes: 4189844561 num_examples: 9759125 download_size: 1926776740 dataset_size: 4189844561 - config_name: ar-es features: - name: translation dtype: translation: languages: - ar - es splits: - name: train num_bytes: 4509667188 num_examples: 10119379 download_size: 2069474168 dataset_size: 4509667188 - config_name: ar-fr features: - name: translation dtype: translation: languages: - ar - fr splits: - name: train num_bytes: 4516842065 num_examples: 9929567 download_size: 2083442998 dataset_size: 4516842065 - config_name: ar-ru features: - name: translation dtype: translation: languages: - ar - ru splits: - name: train num_bytes: 5932858699 num_examples: 10206243 download_size: 2544128334 dataset_size: 5932858699 - config_name: ar-zh features: - name: translation dtype: translation: languages: - ar - zh splits: - name: train num_bytes: 3781650541 num_examples: 9832293 download_size: 1829880809 dataset_size: 3781650541 - config_name: de-en features: - name: translation dtype: translation: languages: - de - en splits: - name: train num_bytes: 76684413 num_examples: 162981 download_size: 35105094 dataset_size: 76684413 - config_name: de-es features: - name: translation dtype: translation: languages: - de - es splits: - name: train num_bytes: 80936517 num_examples: 162078 download_size: 37042740 dataset_size: 80936517 - config_name: de-fr features: - name: translation dtype: translation: languages: - de - fr splits: - name: train num_bytes: 81888299 num_examples: 164025 download_size: 37827000 dataset_size: 81888299 - config_name: de-ru features: - name: translation dtype: translation: languages: - de - ru splits: - name: train num_bytes: 111517798 num_examples: 164792 download_size: 46723695 dataset_size: 111517798 - config_name: de-zh features: - name: translation dtype: translation: languages: - de - zh splits: - name: train num_bytes: 70534674 num_examples: 176933 download_size: 34964647 dataset_size: 70534674 - config_name: en-es features: - name: translation dtype: translation: languages: - en - es splits: - name: train num_bytes: 4128132575 num_examples: 11350967 download_size: 2030826335 dataset_size: 4128132575 - config_name: en-fr features: - name: translation dtype: translation: languages: - en - fr splits: - name: train num_bytes: 4678044616 num_examples: 13172019 download_size: 2312275443 dataset_size: 4678044616 - config_name: en-ru features: - name: translation dtype: translation: languages: - en - ru splits: - name: train num_bytes: 5632653511 num_examples: 11654416 download_size: 2523567444 dataset_size: 5632653511 - config_name: en-zh features: - name: translation dtype: translation: languages: - en - zh splits: - name: train num_bytes: 2960368390 num_examples: 9564315 download_size: 1557547095 dataset_size: 2960368390 - config_name: es-fr features: - name: translation dtype: translation: languages: - es - fr splits: - name: train num_bytes: 4454703338 num_examples: 11441889 download_size: 2187539838 dataset_size: 4454703338 - config_name: es-ru features: - name: translation dtype: translation: languages: - es - ru splits: - name: train num_bytes: 5442647242 num_examples: 10605056 download_size: 2432480744 dataset_size: 5442647242 - config_name: es-zh features: - name: translation dtype: translation: languages: - es - zh splits: - name: train num_bytes: 3223863318 num_examples: 9847770 download_size: 1676774308 dataset_size: 3223863318 - config_name: fr-ru features: - name: translation dtype: translation: languages: - fr - ru splits: - name: train num_bytes: 5979869673 num_examples: 11761738 download_size: 2690520032 dataset_size: 5979869673 - config_name: fr-zh features: - name: translation dtype: translation: languages: - fr - zh splits: - name: train num_bytes: 3241090573 num_examples: 9690914 download_size: 1693120344 dataset_size: 3241090573 - config_name: ru-zh features: - name: translation dtype: translation: languages: - ru - zh splits: - name: train num_bytes: 4233867889 num_examples: 9557007 download_size: 1984600328 dataset_size: 4233867889 configs: - config_name: ar-de data_files: - split: train path: ar-de/train-* - config_name: ar-en data_files: - split: train path: ar-en/train-* - config_name: ar-es data_files: - split: train path: ar-es/train-* - config_name: ar-fr data_files: - split: train path: ar-fr/train-* - config_name: ar-ru data_files: - split: train path: ar-ru/train-* - config_name: ar-zh data_files: - split: train path: ar-zh/train-* - config_name: de-en data_files: - split: train path: de-en/train-* - config_name: de-es data_files: - split: train path: de-es/train-* - config_name: de-fr data_files: - split: train path: de-fr/train-* - config_name: de-ru data_files: - split: train path: de-ru/train-* - config_name: de-zh data_files: - split: train path: de-zh/train-* - config_name: en-es data_files: - split: train path: en-es/train-* - config_name: en-fr data_files: - split: train path: en-fr/train-* - config_name: en-ru data_files: - split: train path: en-ru/train-* - config_name: en-zh data_files: - split: train path: en-zh/train-* - config_name: es-fr data_files: - split: train path: es-fr/train-* - config_name: es-ru data_files: - split: train path: es-ru/train-* - config_name: es-zh data_files: - split: train path: es-zh/train-* - config_name: fr-ru data_files: - split: train path: fr-ru/train-* - config_name: fr-zh data_files: - split: train path: fr-zh/train-* - config_name: ru-zh data_files: - split: train path: ru-zh/train-* --- # Dataset Card for OPUS MultiUN ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://opus.nlpl.eu/MultiUN/corpus/version/MultiUN - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** https://aclanthology.org/L10-1473/ - **Leaderboard:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Dataset Summary The MultiUN parallel corpus is extracted from the United Nations Website , and then cleaned and converted to XML at Language Technology Lab in DFKI GmbH (LT-DFKI), Germany. The documents were published by UN from 2000 to 2009. This is a collection of translated documents from the United Nations originally compiled by Andreas Eisele and Yu Chen (see http://www.euromatrixplus.net/multi-un/). This corpus is available in all 6 official languages of the UN consisting of around 300 million words per language ### Supported Tasks and Leaderboards The underlying task is machine translation. ### Languages Parallel texts are present in all six official languages: Arabic (`ar`), Chinese (`zh`), English (`en`), French (`fr`), Russian (`ru`) and Spanish (`es`), with a small part of the documents available also in German (`de`). ## Dataset Structure ### Data Instances ``` { "translation": { "ar": "قرار اتخذته الجمعية العامة", "de": "Resolution der Generalversammlung" } } ``` ### Data Fields - `translation` (`dict`): Parallel sentences for the pair of languages. ### Data Splits The dataset contains a single "train" split for each language pair. ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization Original MultiUN source data: http://www.euromatrixplus.net/multi-unp #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information If you use this corpus in your work, please cite the paper: ``` @inproceedings{eisele-chen-2010-multiun, title = "{M}ulti{UN}: A Multilingual Corpus from United Nation Documents", author = "Eisele, Andreas and Chen, Yu", booktitle = "Proceedings of the Seventh International Conference on Language Resources and Evaluation ({LREC}'10)", month = may, year = "2010", address = "Valletta, Malta", publisher = "European Language Resources Association (ELRA)", url = "http://www.lrec-conf.org/proceedings/lrec2010/pdf/686_Paper.pdf", abstract = "This paper describes the acquisition, preparation and properties of a corpus extracted from the official documents of the United Nations (UN). This corpus is available in all 6 official languages of the UN, consisting of around 300 million words per language. We describe the methods we used for crawling, document formatting, and sentence alignment. This corpus also includes a common test set for machine translation. We present the results of a French-Chinese machine translation experiment performed on this corpus.", } ``` If you use any part of the corpus (hosted in OPUS) in your own work, please cite the following article: ``` @inproceedings{tiedemann-2012-parallel, title = "Parallel Data, Tools and Interfaces in {OPUS}", author = {Tiedemann, J{\"o}rg}, editor = "Calzolari, Nicoletta and Choukri, Khalid and Declerck, Thierry and Do{\u{g}}an, Mehmet U{\u{g}}ur and Maegaard, Bente and Mariani, Joseph and Moreno, Asuncion and Odijk, Jan and Piperidis, Stelios", booktitle = "Proceedings of the Eighth International Conference on Language Resources and Evaluation ({LREC}'12)", month = may, year = "2012", address = "Istanbul, Turkey", publisher = "European Language Resources Association (ELRA)", url = "http://www.lrec-conf.org/proceedings/lrec2012/pdf/463_Paper.pdf", pages = "2214--2218", abstract = "This paper presents the current status of OPUS, a growing language resource of parallel corpora and related tools. The focus in OPUS is to provide freely available data sets in various formats together with basic annotation to be useful for applications in computational linguistics, translation studies and cross-linguistic corpus studies. In this paper, we report about new data sets and their features, additional annotation tools and models provided from the website and essential interfaces and on-line services included in the project.", } ``` ### Contributions Thanks to [@patil-suraj](https://github.com/patil-suraj) for adding this dataset.

zeroshot/twitter-financial-news-sentiment

zeroshot

2024-02-23T19:04:10Z

5,546

142

[ "task_categories:text-classification", "task_ids:multi-class-classification", "annotations_creators:other", "language_creators:other", "multilinguality:monolingual", "source_datasets:original", "language:en", "license:mit", "size_categories:10K<n<100K", "format:csv", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "twitter", "finance", "markets", "stocks", "wallstreet", "quant", "hedgefunds" ]

[ "text-classification" ]

2022-09-01T21:21:56Z

null

--- annotations_creators: - other language: - en language_creators: - other license: - mit multilinguality: - monolingual pretty_name: twitter financial news size_categories: - 10K<n<100K source_datasets: - original tags: - twitter - finance - markets - stocks - wallstreet - quant - hedgefunds - markets task_categories: - text-classification task_ids: - multi-class-classification --- ### Dataset Description The Twitter Financial News dataset is an English-language dataset containing an annotated corpus of finance-related tweets. This dataset is used to classify finance-related tweets for their sentiment. 1. The dataset holds 11,932 documents annotated with 3 labels: ```python sentiments = { "LABEL_0": "Bearish", "LABEL_1": "Bullish", "LABEL_2": "Neutral" } ``` The data was collected using the Twitter API. The current dataset supports the multi-class classification task. ### Task: Sentiment Analysis # Data Splits There are 2 splits: train and validation. Below are the statistics: | Dataset Split | Number of Instances in Split | | ------------- | ------------------------------------------- | | Train | 9,938 | | Validation | 2,486 | # Licensing Information The Twitter Financial Dataset (sentiment) version 1.0.0 is released under the MIT License.

shibing624/sharegpt_gpt4

shibing624

2024-02-23T05:38:24Z

685

120

[ "task_categories:text-classification", "task_categories:text-generation", "task_ids:text-scoring", "annotations_creators:shibing624", "language_creators:shibing624", "multilinguality:monolingual", "source_datasets:https://huggingface.co/datasets/openchat/openchat_sharegpt4_dataset/tree/main", "language:zh", "language:en", "language:gl", "language:ko", "license:cc-by-4.0", "size_categories:100K<n<1M", "format:json", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]

[ "text-classification", "text-generation" ]

2023-07-27T05:45:49Z

null

--- annotations_creators: - shibing624 language_creators: - shibing624 language: - zh - en - gl - ko license: cc-by-4.0 multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - https://huggingface.co/datasets/openchat/openchat_sharegpt4_dataset/tree/main task_categories: - text-classification - text-generation task_ids: - text-scoring paperswithcode_id: LLM pretty_name: GPT4 sharegpt --- # Dataset Card ## Dataset Description - **Repository:** [shibing624/MedicalGPT](https://github.com/shibing624/MedicalGPT) - **Dataset:** [shibing624/sharegpt_gpt4](https://huggingface.co/datasets/shibing624/sharegpt_gpt4) - **Size of downloaded dataset files:** 772 M - **Total amount of disk used:** 772 M ### Dataset Summary ShareGPT中挑选出的GPT4多轮问答数据，多语言问答。 ### Languages 数据集是多语言，包括中文、英文、日文等常用语言。 ## Dataset Structure ### Data Fields The data fields are the same among all splits. - `conversations`: a List of `string` . ```shell head -n 1 sharegpt_gpt4.jsonl {"conversations":[ {'from': 'human', 'value': '採用優雅現代中文，用中文繁體字型，回答以下問題。為所有標題或專用字詞提供對應的英語翻譯：Using scholarly style, summarize in detail James Barr\'s book "Semantics of Biblical Language". Provide examples supplied in the book and give corresponding page numbers wherever applicable.'}, {'from': 'gpt', 'value': '《聖經語言的語義學》（Semantics of Biblical Language）是詹姆斯·巴爾（James Barr）於1961年出版的一本研究聖經語言的專著。在這部作品中，巴爾對聖經希伯來語和希臘語的語言學特徵進行了深入的探討，他批判了當時盛行的聖經研究方法，主張應該將語言學原理應用於聖經文本的詮釋，以便更準確地理解聖經的意思。\n\n巴爾在《聖經語言的語義學》中提出了幾個重要的觀點。首先，他主張語言學和語言學家在聖經研究中的角色應該受到重視。他認為，應該避免將聖經語言學和其他古典語言學區分開來，因為聖經語言和其他古代語言一樣，都有其固有的語法、語義和詞彙特點（第18-23頁）。\n\n其次，巴爾批評了當時流行的"字根意義論"（root-meaning fallacy），即過分強調單詞的字根在詮釋聖經含義時的作用。他指出，詞義是由語境決定的，而非僅僅由字根決定。巴爾舉了希伯來語單詞 "כפר"（kāpar）為例，它在不同的語境下可以表示"遮蓋"、"贖回"或"和解"，而不是僅僅依賴其字根意義（第183-184頁）。\n\n第三，巴爾強調了詞彙意義在詮釋聖經時的重要性。他認為，了解單詞的意義和用法對於恰當理解聖經文本至關重要。巴爾引用了希伯來語單詞 "ברית"（berîṯ，英文：covenant）和希臘語單詞 "διαθήκη"（diathēkē，英文：testament）為例，解釋了這兩個詞在聖經中的不'}, {'from': 'human', 'value': '繼續'}, {'from': 'gpt', 'value': '同含義和用法。巴爾認為，了解這些詞語在不同語境下的意義對於理解聖經中的盟約和遺囑概念至關重要（第50-53頁）。\n\n第四，巴爾強調語境在詮釋聖經時的重要性。他認為，要理解聖經的含義，就必須考慮到文本的歷史背景、文化環境和語言風格。此外，巴爾提倡從語言學的角度研究聖經，以便更好地理解聖經的結構和風格。他以希伯來詩篇的平行主義（Parallelism）為例，闡述了語言學原理如何有助於揭示詩篇中的意象和修辭結構（第250-252頁）。\n\n總之，《聖經語言的語義學》一書對聖經研究領域產生了深遠的影響。詹姆斯·巴爾通過對聖經語言學的深入研究，提出了許多富有洞察力的觀點，尤其是關於語言學在聖經詮釋中的重要性。這些觀點對後來的聖經研究者產生了很大的啟示，使他們在研究聖經時更加重視語言學原理，從而更加準確地理解聖經的含義。'} ] } ``` ### Data Splits ```shell > wc -l * 6206 sharegpt_gpt4.jsonl # 清洗后的高质量gpt4日常问答数据集，大小：6K，主要是知识问答、编程题、推理计算，包括简体中文、繁体中文、英文、日文、韩文等多国语言多轮对话数据集。 58674 sharegpt_V3_format.jsonl # 原V3版sharegpt规范格式后的数据集，大小：58K，主要是日常问答，提问偏口语化，多国语言，多轮对话。 38535 sharegpt_zh_38K_format.jsonl # 中文gpt4日常问答数据集，大小：38K，主要是知识问答、翻译任务、求助、编程推理任务等偏口语提问，中文，多轮对话。 103415 total ``` #### Who are the annotators? 原作者。 ### Licensing Information same to sharegpt. ### Contributions [shibing624](https://github.com/shibing624) add this dataset.

google/xtreme

google

2024-02-22T17:12:06Z

12,320

104

[ "task_categories:multiple-choice", "task_categories:question-answering", "task_categories:token-classification", "task_categories:text-classification", "task_categories:text-retrieval", "task_ids:multiple-choice-qa", "task_ids:extractive-qa", "task_ids:open-domain-qa", "task_ids:natural-language-inference", "task_ids:named-entity-recognition", "task_ids:part-of-speech", "annotations_creators:found", "language_creators:found", "multilinguality:multilingual", "multilinguality:translation", "source_datasets:extended|xnli", "source_datasets:extended|paws-x", "source_datasets:extended|wikiann", "source_datasets:extended|xquad", "source_datasets:extended|mlqa", "source_datasets:extended|tydiqa", "source_datasets:extended|tatoeba", "source_datasets:extended|squad", "language:af", "language:ar", "language:bg", "language:bn", "language:de", "language:el", "language:en", "language:es", "language:et", "language:eu", "language:fa", "language:fi", "language:fr", "language:he", "language:hi", "language:hu", "language:id", "language:it", "language:ja", "language:jv", "language:ka", "language:kk", "language:ko", "language:ml", "language:mr", "language:ms", "language:my", "language:nl", "language:pt", "language:ru", "language:sw", "language:ta", "language:te", "language:th", "language:tl", "language:tr", "language:ur", "language:vi", "language:yo", "language:zh", "license:apache-2.0", "license:cc-by-4.0", "license:cc-by-2.0", "license:cc-by-sa-4.0", "license:other", "license:cc-by-nc-4.0", "size_categories:1M<n<10M", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2003.11080", "region:us", "parallel-sentence-retrieval", "paraphrase-identification" ]

[ "multiple-choice", "question-answering", "token-classification", "text-classification", "text-retrieval", "token-classification" ]

2022-03-02T23:29:22Z

null

--- annotations_creators: - found language_creators: - found language: - af - ar - bg - bn - de - el - en - es - et - eu - fa - fi - fr - he - hi - hu - id - it - ja - jv - ka - kk - ko - ml - mr - ms - my - nl - pt - ru - sw - ta - te - th - tl - tr - ur - vi - yo - zh license: - apache-2.0 - cc-by-4.0 - cc-by-2.0 - cc-by-sa-4.0 - other - cc-by-nc-4.0 multilinguality: - multilingual - translation size_categories: - n<1K - 1K<n<10K - 10K<n<100K - 100K<n<1M source_datasets: - extended|xnli - extended|paws-x - extended|wikiann - extended|xquad - extended|mlqa - extended|tydiqa - extended|tatoeba - extended|squad task_categories: - multiple-choice - question-answering - token-classification - text-classification - text-retrieval - token-classification task_ids: - multiple-choice-qa - extractive-qa - open-domain-qa - natural-language-inference - named-entity-recognition - part-of-speech paperswithcode_id: xtreme pretty_name: XTREME config_names: - MLQA.ar.ar - MLQA.ar.de - MLQA.ar.en - MLQA.ar.es - MLQA.ar.hi - MLQA.ar.vi - MLQA.ar.zh - MLQA.de.ar - MLQA.de.de - MLQA.de.en - MLQA.de.es - MLQA.de.hi - MLQA.de.vi - MLQA.de.zh - MLQA.en.ar - MLQA.en.de - MLQA.en.en - MLQA.en.es - MLQA.en.hi - MLQA.en.vi - MLQA.en.zh - MLQA.es.ar - MLQA.es.de - MLQA.es.en - MLQA.es.es - MLQA.es.hi - MLQA.es.vi - MLQA.es.zh - MLQA.hi.ar - MLQA.hi.de - MLQA.hi.en - MLQA.hi.es - MLQA.hi.hi - MLQA.hi.vi - MLQA.hi.zh - MLQA.vi.ar - MLQA.vi.de - MLQA.vi.en - MLQA.vi.es - MLQA.vi.hi - MLQA.vi.vi - MLQA.vi.zh - MLQA.zh.ar - MLQA.zh.de - MLQA.zh.en - MLQA.zh.es - MLQA.zh.hi - MLQA.zh.vi - MLQA.zh.zh - PAN-X.af - PAN-X.ar - PAN-X.bg - PAN-X.bn - PAN-X.de - PAN-X.el - PAN-X.en - PAN-X.es - PAN-X.et - PAN-X.eu - PAN-X.fa - PAN-X.fi - PAN-X.fr - PAN-X.he - PAN-X.hi - PAN-X.hu - PAN-X.id - PAN-X.it - PAN-X.ja - PAN-X.jv - PAN-X.ka - PAN-X.kk - PAN-X.ko - PAN-X.ml - PAN-X.mr - PAN-X.ms - PAN-X.my - PAN-X.nl - PAN-X.pt - PAN-X.ru - PAN-X.sw - PAN-X.ta - PAN-X.te - PAN-X.th - PAN-X.tl - PAN-X.tr - PAN-X.ur - PAN-X.vi - PAN-X.yo - PAN-X.zh - PAWS-X.de - PAWS-X.en - PAWS-X.es - PAWS-X.fr - PAWS-X.ja - PAWS-X.ko - PAWS-X.zh - SQuAD - XNLI - XQuAD - bucc18.de - bucc18.fr - bucc18.ru - bucc18.zh - tatoeba.afr - tatoeba.ara - tatoeba.ben - tatoeba.bul - tatoeba.cmn - tatoeba.deu - tatoeba.ell - tatoeba.est - tatoeba.eus - tatoeba.fin - tatoeba.fra - tatoeba.heb - tatoeba.hin - tatoeba.hun - tatoeba.ind - tatoeba.ita - tatoeba.jav - tatoeba.jpn - tatoeba.kat - tatoeba.kaz - tatoeba.kor - tatoeba.mal - tatoeba.mar - tatoeba.nld - tatoeba.pes - tatoeba.por - tatoeba.rus - tatoeba.spa - tatoeba.swh - tatoeba.tam - tatoeba.tel - tatoeba.tgl - tatoeba.tha - tatoeba.tur - tatoeba.urd - tatoeba.vie - tydiqa - udpos.Afrikans - udpos.Arabic - udpos.Basque - udpos.Bulgarian - udpos.Chinese - udpos.Dutch - udpos.English - udpos.Estonian - udpos.Finnish - udpos.French - udpos.German - udpos.Greek - udpos.Hebrew - udpos.Hindi - udpos.Hungarian - udpos.Indonesian - udpos.Italian - udpos.Japanese - udpos.Kazakh - udpos.Korean - udpos.Marathi - udpos.Persian - udpos.Portuguese - udpos.Russian - udpos.Spanish - udpos.Tagalog - udpos.Tamil - udpos.Telugu - udpos.Thai - udpos.Turkish - udpos.Urdu - udpos.Vietnamese - udpos.Yoruba language_bcp47: - fa-IR license_details: Licence Universal Dependencies v2.5 tags: - parallel-sentence-retrieval - paraphrase-identification dataset_info: - config_name: MLQA.ar.ar features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: test num_bytes: 8368086 num_examples: 5335 - name: validation num_bytes: 824080 num_examples: 517 download_size: 4048180 dataset_size: 9192166 - config_name: MLQA.ar.de features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: test num_bytes: 2183914 num_examples: 1649 - name: validation num_bytes: 364809 num_examples: 207 download_size: 1192825 dataset_size: 2548723 - config_name: MLQA.ar.en features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: test num_bytes: 8225634 num_examples: 5335 - name: validation num_bytes: 810061 num_examples: 517 download_size: 3998008 dataset_size: 9035695 - config_name: MLQA.ar.es features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: test num_bytes: 3041350 num_examples: 1978 - name: validation num_bytes: 228152 num_examples: 161 download_size: 1531661 dataset_size: 3269502 - config_name: MLQA.ar.hi features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: test num_bytes: 3039368 num_examples: 1831 - name: validation num_bytes: 281742 num_examples: 186 download_size: 1369756 dataset_size: 3321110 - config_name: MLQA.ar.vi features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: test num_bytes: 3290601 num_examples: 2047 - name: validation num_bytes: 288418 num_examples: 163 download_size: 1667238 dataset_size: 3579019 - config_name: MLQA.ar.zh features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: test num_bytes: 3229844 num_examples: 1912 - name: validation num_bytes: 340021 num_examples: 188 download_size: 1591445 dataset_size: 3569865 - config_name: MLQA.de.ar features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: test num_bytes: 1619978 num_examples: 1649 - name: validation num_bytes: 200146 num_examples: 207 download_size: 1044483 dataset_size: 1820124 - config_name: MLQA.de.de features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: test num_bytes: 4366074 num_examples: 4517 - name: validation num_bytes: 488339 num_examples: 512 download_size: 2798050 dataset_size: 4854413 - config_name: MLQA.de.en features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: test num_bytes: 4343116 num_examples: 4517 - name: validation num_bytes: 485866 num_examples: 512 download_size: 2778346 dataset_size: 4828982 - config_name: MLQA.de.es features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: test num_bytes: 1716587 num_examples: 1776 - name: validation num_bytes: 170554 num_examples: 196 download_size: 1118751 dataset_size: 1887141 - config_name: MLQA.de.hi features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: test num_bytes: 1371046 num_examples: 1430 - name: validation num_bytes: 153843 num_examples: 163 download_size: 880652 dataset_size: 1524889 - config_name: MLQA.de.vi features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: test num_bytes: 1688455 num_examples: 1675 - name: validation num_bytes: 216047 num_examples: 182 download_size: 1108163 dataset_size: 1904502 - config_name: MLQA.de.zh features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: test num_bytes: 1679152 num_examples: 1621 - name: validation num_bytes: 184290 num_examples: 190 download_size: 1045861 dataset_size: 1863442 - config_name: MLQA.en.ar features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: test num_bytes: 6739191 num_examples: 5335 - name: validation num_bytes: 630815 num_examples: 517 download_size: 3939135 dataset_size: 7370006 - config_name: MLQA.en.de features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: test num_bytes: 5056694 num_examples: 4517 - name: validation num_bytes: 594908 num_examples: 512 download_size: 3223196 dataset_size: 5651602 - config_name: MLQA.en.en features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: test num_bytes: 14004592 num_examples: 11590 - name: validation num_bytes: 1329084 num_examples: 1148 download_size: 8217519 dataset_size: 15333676 - config_name: MLQA.en.es features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: test num_bytes: 6179221 num_examples: 5253 - name: validation num_bytes: 555434 num_examples: 500 download_size: 3776828 dataset_size: 6734655 - config_name: MLQA.en.hi features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: test num_bytes: 6378838 num_examples: 4918 - name: validation num_bytes: 623143 num_examples: 507 download_size: 3517340 dataset_size: 7001981 - config_name: MLQA.en.vi features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: test num_bytes: 7056670 num_examples: 5495 - name: validation num_bytes: 640618 num_examples: 511 download_size: 4170642 dataset_size: 7697288 - config_name: MLQA.en.zh features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: test num_bytes: 6539279 num_examples: 5137 - name: validation num_bytes: 608416 num_examples: 504 download_size: 3929122 dataset_size: 7147695 - config_name: MLQA.es.ar features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: test num_bytes: 1740254 num_examples: 1978 - name: validation num_bytes: 148621 num_examples: 161 download_size: 1107435 dataset_size: 1888875 - config_name: MLQA.es.de features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: test num_bytes: 1403997 num_examples: 1776 - name: validation num_bytes: 144158 num_examples: 196 download_size: 950448 dataset_size: 1548155 - config_name: MLQA.es.en features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: test num_bytes: 4362709 num_examples: 5253 - name: validation num_bytes: 419040 num_examples: 500 download_size: 2842879 dataset_size: 4781749 - config_name: MLQA.es.es features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: test num_bytes: 4394305 num_examples: 5253 - name: validation num_bytes: 422043 num_examples: 500 download_size: 2856931 dataset_size: 4816348 - config_name: MLQA.es.hi features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: test num_bytes: 1523495 num_examples: 1723 - name: validation num_bytes: 181806 num_examples: 187 download_size: 954018 dataset_size: 1705301 - config_name: MLQA.es.vi features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: test num_bytes: 1747941 num_examples: 2018 - name: validation num_bytes: 176813 num_examples: 189 download_size: 1187949 dataset_size: 1924754 - config_name: MLQA.es.zh features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: test num_bytes: 1678423 num_examples: 1947 - name: validation num_bytes: 126618 num_examples: 161 download_size: 1100765 dataset_size: 1805041 - config_name: MLQA.hi.ar features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: test num_bytes: 4445561 num_examples: 1831 - name: validation num_bytes: 410396 num_examples: 186 download_size: 1542768 dataset_size: 4855957 - config_name: MLQA.hi.de features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: test num_bytes: 3022836 num_examples: 1430 - name: validation num_bytes: 301685 num_examples: 163 download_size: 1257846 dataset_size: 3324521 - config_name: MLQA.hi.en features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: test num_bytes: 11449233 num_examples: 4918 - name: validation num_bytes: 1097829 num_examples: 507 download_size: 4131083 dataset_size: 12547062 - config_name: MLQA.hi.es features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: test num_bytes: 3862201 num_examples: 1723 - name: validation num_bytes: 420374 num_examples: 187 download_size: 1493468 dataset_size: 4282575 - config_name: MLQA.hi.hi features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: test num_bytes: 11810447 num_examples: 4918 - name: validation num_bytes: 1136756 num_examples: 507 download_size: 4235981 dataset_size: 12947203 - config_name: MLQA.hi.vi features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: test num_bytes: 4743456 num_examples: 1947 - name: validation num_bytes: 419078 num_examples: 177 download_size: 1704964 dataset_size: 5162534 - config_name: MLQA.hi.zh features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: test num_bytes: 4354847 num_examples: 1767 - name: validation num_bytes: 424218 num_examples: 189 download_size: 1627107 dataset_size: 4779065 - config_name: MLQA.vi.ar features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: test num_bytes: 3205157 num_examples: 2047 - name: validation num_bytes: 230307 num_examples: 163 download_size: 1656661 dataset_size: 3435464 - config_name: MLQA.vi.de features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: test num_bytes: 2227005 num_examples: 1675 - name: validation num_bytes: 277157 num_examples: 182 download_size: 1268041 dataset_size: 2504162 - config_name: MLQA.vi.en features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: test num_bytes: 7843403 num_examples: 5495 - name: validation num_bytes: 719245 num_examples: 511 download_size: 4071703 dataset_size: 8562648 - config_name: MLQA.vi.es features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: test num_bytes: 2866569 num_examples: 2018 - name: validation num_bytes: 283433 num_examples: 189 download_size: 1607926 dataset_size: 3150002 - config_name: MLQA.vi.hi features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: test num_bytes: 2776636 num_examples: 1947 - name: validation num_bytes: 254979 num_examples: 177 download_size: 1366057 dataset_size: 3031615 - config_name: MLQA.vi.vi features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: test num_bytes: 7922057 num_examples: 5495 - name: validation num_bytes: 726490 num_examples: 511 download_size: 4105388 dataset_size: 8648547 - config_name: MLQA.vi.zh features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: test num_bytes: 2989632 num_examples: 1943 - name: validation num_bytes: 269361 num_examples: 184 download_size: 1570393 dataset_size: 3258993 - config_name: MLQA.zh.ar features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: test num_bytes: 1731455 num_examples: 1912 - name: validation num_bytes: 175321 num_examples: 188 download_size: 1223863 dataset_size: 1906776 - config_name: MLQA.zh.de features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: test num_bytes: 1389990 num_examples: 1621 - name: validation num_bytes: 174577 num_examples: 190 download_size: 1006829 dataset_size: 1564567 - config_name: MLQA.zh.en features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: test num_bytes: 4450957 num_examples: 5137 - name: validation num_bytes: 446840 num_examples: 504 download_size: 3108433 dataset_size: 4897797 - config_name: MLQA.zh.es features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: test num_bytes: 1736255 num_examples: 1947 - name: validation num_bytes: 138045 num_examples: 161 download_size: 1223467 dataset_size: 1874300 - config_name: MLQA.zh.hi features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: test num_bytes: 1578191 num_examples: 1767 - name: validation num_bytes: 184373 num_examples: 189 download_size: 1044599 dataset_size: 1762564 - config_name: MLQA.zh.vi features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: test num_bytes: 1806158 num_examples: 1943 - name: validation num_bytes: 172906 num_examples: 184 download_size: 1268213 dataset_size: 1979064 - config_name: MLQA.zh.zh features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: test num_bytes: 4422322 num_examples: 5137 - name: validation num_bytes: 443782 num_examples: 504 download_size: 3105362 dataset_size: 4866104 - config_name: PAN-X.af features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string splits: - name: train num_bytes: 1321376 num_examples: 5000 - name: validation num_bytes: 259689 num_examples: 1000 - name: test num_bytes: 257184 num_examples: 1000 download_size: 389015 dataset_size: 1838249 - config_name: PAN-X.ar features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string splits: - name: train num_bytes: 3634096 num_examples: 20000 - name: validation num_bytes: 1808283 num_examples: 10000 - name: test num_bytes: 1811963 num_examples: 10000 download_size: 1567470 dataset_size: 7254342 - config_name: PAN-X.bg features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string splits: - name: train num_bytes: 4600733 num_examples: 20000 - name: validation num_bytes: 2310294 num_examples: 10000 - name: test num_bytes: 2306138 num_examples: 10000 download_size: 2030669 dataset_size: 9217165 - config_name: PAN-X.bn features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string splits: - name: train num_bytes: 1568825 num_examples: 10000 - name: validation num_bytes: 159068 num_examples: 1000 - name: test num_bytes: 159262 num_examples: 1000 download_size: 364024 dataset_size: 1887155 - config_name: PAN-X.de features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string splits: - name: train num_bytes: 4762312 num_examples: 20000 - name: validation num_bytes: 2381545 num_examples: 10000 - name: test num_bytes: 2377619 num_examples: 10000 download_size: 2360242 dataset_size: 9521476 - config_name: PAN-X.el features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string splits: - name: train num_bytes: 5063136 num_examples: 20000 - name: validation num_bytes: 2533786 num_examples: 10000 - name: test num_bytes: 2547574 num_examples: 10000 download_size: 2271726 dataset_size: 10144496 - config_name: PAN-X.en features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string splits: - name: train num_bytes: 3823434 num_examples: 20000 - name: validation num_bytes: 1920049 num_examples: 10000 - name: test num_bytes: 1916200 num_examples: 10000 download_size: 1886284 dataset_size: 7659683 - config_name: PAN-X.es features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string splits: - name: train num_bytes: 3199121 num_examples: 20000 - name: validation num_bytes: 1592505 num_examples: 10000 - name: test num_bytes: 1602271 num_examples: 10000 download_size: 1489562 dataset_size: 6393897 - config_name: PAN-X.et features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string splits: - name: train num_bytes: 3023171 num_examples: 15000 - name: validation num_bytes: 2030140 num_examples: 10000 - name: test num_bytes: 2021389 num_examples: 10000 download_size: 1915624 dataset_size: 7074700 - config_name: PAN-X.eu features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string splits: - name: train num_bytes: 2292307 num_examples: 10000 - name: validation num_bytes: 2296315 num_examples: 10000 - name: test num_bytes: 2249815 num_examples: 10000 download_size: 1393179 dataset_size: 6838437 - config_name: PAN-X.fa features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string splits: - name: train num_bytes: 3529314 num_examples: 20000 - name: validation num_bytes: 1782286 num_examples: 10000 - name: test num_bytes: 1770264 num_examples: 10000 download_size: 1401208 dataset_size: 7081864 - config_name: PAN-X.fi features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string splits: - name: train num_bytes: 4273753 num_examples: 20000 - name: validation num_bytes: 2131749 num_examples: 10000 - name: test num_bytes: 2130645 num_examples: 10000 download_size: 2459149 dataset_size: 8536147 - config_name: PAN-X.fr features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string splits: - name: train num_bytes: 3335384 num_examples: 20000 - name: validation num_bytes: 1664170 num_examples: 10000 - name: test num_bytes: 1675765 num_examples: 10000 download_size: 1679283 dataset_size: 6675319 - config_name: PAN-X.he features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string splits: - name: train num_bytes: 4667060 num_examples: 20000 - name: validation num_bytes: 2332740 num_examples: 10000 - name: test num_bytes: 2318736 num_examples: 10000 download_size: 2186463 dataset_size: 9318536 - config_name: PAN-X.hi features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string splits: - name: train num_bytes: 964192 num_examples: 5000 - name: validation num_bytes: 190651 num_examples: 1000 - name: test num_bytes: 196170 num_examples: 1000 download_size: 266086 dataset_size: 1351013 - config_name: PAN-X.hu features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string splits: - name: train num_bytes: 4499874 num_examples: 20000 - name: validation num_bytes: 2211831 num_examples: 10000 - name: test num_bytes: 2249759 num_examples: 10000 download_size: 2399390 dataset_size: 8961464 - config_name: PAN-X.id features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string splits: - name: train num_bytes: 3083967 num_examples: 20000 - name: validation num_bytes: 1537959 num_examples: 10000 - name: test num_bytes: 1536859 num_examples: 10000 download_size: 1412049 dataset_size: 6158785 - config_name: PAN-X.it features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string splits: - name: train num_bytes: 3874623 num_examples: 20000 - name: validation num_bytes: 1908509 num_examples: 10000 - name: test num_bytes: 1928388 num_examples: 10000 download_size: 1855798 dataset_size: 7711520 - config_name: PAN-X.ja features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string splits: - name: train num_bytes: 12670361 num_examples: 20000 - name: validation num_bytes: 6322983 num_examples: 10000 - name: test num_bytes: 6448940 num_examples: 10000 download_size: 2465674 dataset_size: 25442284 - config_name: PAN-X.jv features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string splits: - name: train num_bytes: 16086 num_examples: 100 - name: validation num_bytes: 14580 num_examples: 100 - name: test num_bytes: 16897 num_examples: 100 download_size: 20475 dataset_size: 47563 - config_name: PAN-X.ka features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string splits: - name: train num_bytes: 2777342 num_examples: 10000 - name: validation num_bytes: 2806881 num_examples: 10000 - name: test num_bytes: 2824621 num_examples: 10000 download_size: 1817280 dataset_size: 8408844 - config_name: PAN-X.kk features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string splits: - name: train num_bytes: 240256 num_examples: 1000 - name: validation num_bytes: 238089 num_examples: 1000 - name: test num_bytes: 236704 num_examples: 1000 download_size: 160554 dataset_size: 715049 - config_name: PAN-X.ko features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string splits: - name: train num_bytes: 4284693 num_examples: 20000 - name: validation num_bytes: 2138147 num_examples: 10000 - name: test num_bytes: 2138274 num_examples: 10000 download_size: 2539591 dataset_size: 8561114 - config_name: PAN-X.ml features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string splits: - name: train num_bytes: 2865184 num_examples: 10000 - name: validation num_bytes: 290735 num_examples: 1000 - name: test num_bytes: 276906 num_examples: 1000 download_size: 852955 dataset_size: 3432825 - config_name: PAN-X.mr features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string splits: - name: train num_bytes: 1248239 num_examples: 5000 - name: validation num_bytes: 245338 num_examples: 1000 - name: test num_bytes: 255884 num_examples: 1000 download_size: 347215 dataset_size: 1749461 - config_name: PAN-X.ms features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string splits: - name: train num_bytes: 2965008 num_examples: 20000 - name: validation num_bytes: 147495 num_examples: 1000 - name: test num_bytes: 147148 num_examples: 1000 download_size: 708795 dataset_size: 3259651 - config_name: PAN-X.my features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string splits: - name: train num_bytes: 32715 num_examples: 100 - name: validation num_bytes: 40408 num_examples: 100 - name: test num_bytes: 37346 num_examples: 100 download_size: 39008 dataset_size: 110469 - config_name: PAN-X.nl features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string splits: - name: train num_bytes: 4062149 num_examples: 20000 - name: validation num_bytes: 2016836 num_examples: 10000 - name: test num_bytes: 2038618 num_examples: 10000 download_size: 1943893 dataset_size: 8117603 - config_name: PAN-X.pt features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string splits: - name: train num_bytes: 3149243 num_examples: 20000 - name: validation num_bytes: 1575121 num_examples: 10000 - name: test num_bytes: 1562605 num_examples: 10000 download_size: 1540478 dataset_size: 6286969 - config_name: PAN-X.ru features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string splits: - name: train num_bytes: 4121751 num_examples: 20000 - name: validation num_bytes: 2053149 num_examples: 10000 - name: test num_bytes: 2074125 num_examples: 10000 download_size: 2127730 dataset_size: 8249025 - config_name: PAN-X.sw features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string splits: - name: train num_bytes: 135891 num_examples: 1000 - name: validation num_bytes: 136348 num_examples: 1000 - name: test num_bytes: 140211 num_examples: 1000 download_size: 87435 dataset_size: 412450 - config_name: PAN-X.ta features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string splits: - name: train num_bytes: 4122090 num_examples: 15000 - name: validation num_bytes: 277605 num_examples: 1000 - name: test num_bytes: 278094 num_examples: 1000 download_size: 1044729 dataset_size: 4677789 - config_name: PAN-X.te features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string splits: - name: train num_bytes: 295390 num_examples: 1000 - name: validation num_bytes: 293261 num_examples: 1000 - name: test num_bytes: 296943 num_examples: 1000 download_size: 200516 dataset_size: 885594 - config_name: PAN-X.th features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string splits: - name: train num_bytes: 27132989 num_examples: 20000 - name: validation num_bytes: 13262717 num_examples: 10000 - name: test num_bytes: 13586908 num_examples: 10000 download_size: 2569566 dataset_size: 53982614 - config_name: PAN-X.tl features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string splits: - name: train num_bytes: 1168697 num_examples: 10000 - name: validation num_bytes: 114136 num_examples: 1000 - name: test num_bytes: 117884 num_examples: 1000 download_size: 308160 dataset_size: 1400717 - config_name: PAN-X.tr features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string splits: - name: train num_bytes: 3779130 num_examples: 20000 - name: validation num_bytes: 1915332 num_examples: 10000 - name: test num_bytes: 1911483 num_examples: 10000 download_size: 2000699 dataset_size: 7605945 - config_name: PAN-X.ur features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string splits: - name: train num_bytes: 3072236 num_examples: 20000 - name: validation num_bytes: 152128 num_examples: 1000 - name: test num_bytes: 151902 num_examples: 1000 download_size: 610869 dataset_size: 3376266 - config_name: PAN-X.vi features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string splits: - name: train num_bytes: 3153187 num_examples: 20000 - name: validation num_bytes: 1565123 num_examples: 10000 - name: test num_bytes: 1580196 num_examples: 10000 download_size: 1375631 dataset_size: 6298506 - config_name: PAN-X.yo features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string splits: - name: train num_bytes: 14689 num_examples: 100 - name: validation num_bytes: 13225 num_examples: 100 - name: test num_bytes: 13513 num_examples: 100 download_size: 17337 dataset_size: 41427 - config_name: PAN-X.zh features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string splits: - name: train num_bytes: 8832011 num_examples: 20000 - name: validation num_bytes: 4491305 num_examples: 10000 - name: test num_bytes: 4363152 num_examples: 10000 download_size: 2083198 dataset_size: 17686468 - config_name: PAWS-X.de features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: string splits: - name: train num_bytes: 12451823 num_examples: 49380 - name: validation num_bytes: 499997 num_examples: 2000 - name: test num_bytes: 510182 num_examples: 2000 download_size: 9294034 dataset_size: 13462002 - config_name: PAWS-X.en features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: string splits: - name: train num_bytes: 11827659 num_examples: 49175 - name: validation num_bytes: 478279 num_examples: 2000 - name: test num_bytes: 480726 num_examples: 2000 download_size: 8717639 dataset_size: 12786664 - config_name: PAWS-X.es features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: string splits: - name: train num_bytes: 12462047 num_examples: 49401 - name: validation num_bytes: 494057 num_examples: 1961 - name: test num_bytes: 505035 num_examples: 2000 download_size: 9229918 dataset_size: 13461139 - config_name: PAWS-X.fr features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: string splits: - name: train num_bytes: 12948452 num_examples: 49399 - name: validation num_bytes: 516099 num_examples: 1988 - name: test num_bytes: 521019 num_examples: 2000 download_size: 9464987 dataset_size: 13985570 - config_name: PAWS-X.ja features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: string splits: - name: train num_bytes: 14695593 num_examples: 49401 - name: validation num_bytes: 647762 num_examples: 2000 - name: test num_bytes: 654628 num_examples: 2000 download_size: 10136228 dataset_size: 15997983 - config_name: PAWS-X.ko features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: string splits: - name: train num_bytes: 13542597 num_examples: 49164 - name: validation num_bytes: 540775 num_examples: 2000 - name: test num_bytes: 547966 num_examples: 1999 download_size: 9926292 dataset_size: 14631338 - config_name: PAWS-X.zh features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: string splits: - name: train num_bytes: 10469652 num_examples: 49401 - name: validation num_bytes: 459108 num_examples: 2000 - name: test num_bytes: 460626 num_examples: 2000 download_size: 8878855 dataset_size: 11389386 - config_name: SQuAD features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: train num_bytes: 79316858 num_examples: 87599 - name: validation num_bytes: 10472597 num_examples: 10570 download_size: 16272656 dataset_size: 89789455 - config_name: XNLI features: - name: language dtype: string - name: sentence1 dtype: string - name: sentence2 dtype: string - name: gold_label dtype: string splits: - name: test num_bytes: 20359372 num_examples: 75150 - name: validation num_bytes: 10049239 num_examples: 37350 download_size: 8881623 dataset_size: 30408611 - config_name: XQuAD.ar features: - name: id dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: validation num_bytes: 1722775 num_examples: 1190 download_size: 263032 dataset_size: 1722775 - config_name: XQuAD.de features: - name: id dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: validation num_bytes: 1283277 num_examples: 1190 download_size: 241987 dataset_size: 1283277 - config_name: XQuAD.el features: - name: id dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: validation num_bytes: 2206666 num_examples: 1190 download_size: 324409 dataset_size: 2206666 - config_name: XQuAD.en features: - name: id dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: validation num_bytes: 1116099 num_examples: 1190 download_size: 212402 dataset_size: 1116099 - config_name: XQuAD.es features: - name: id dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: validation num_bytes: 1273475 num_examples: 1190 download_size: 236904 dataset_size: 1273475 - config_name: XQuAD.hi features: - name: id dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: validation num_bytes: 2682951 num_examples: 1190 download_size: 322113 dataset_size: 2682951 - config_name: XQuAD.ru features: - name: id dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: validation num_bytes: 2136966 num_examples: 1190 download_size: 321758 dataset_size: 2136966 - config_name: XQuAD.th features: - name: id dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: validation num_bytes: 2854935 num_examples: 1190 download_size: 337337 dataset_size: 2854935 - config_name: XQuAD.tr features: - name: id dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: validation num_bytes: 1210739 num_examples: 1190 download_size: 228394 dataset_size: 1210739 - config_name: XQuAD.vi features: - name: id dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: validation num_bytes: 1477215 num_examples: 1190 download_size: 237674 dataset_size: 1477215 - config_name: XQuAD.zh features: - name: id dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: validation num_bytes: 984217 num_examples: 1190 download_size: 205798 dataset_size: 984217 - config_name: bucc18.de features: - name: source_sentence dtype: string - name: target_sentence dtype: string - name: source_lang dtype: string - name: target_lang dtype: string splits: - name: validation num_bytes: 248691 num_examples: 1038 - name: test num_bytes: 2325685 num_examples: 9580 download_size: 1636130 dataset_size: 2574376 - config_name: bucc18.fr features: - name: source_sentence dtype: string - name: target_sentence dtype: string - name: source_lang dtype: string - name: target_lang dtype: string splits: - name: validation num_bytes: 212497 num_examples: 929 - name: test num_bytes: 2082403 num_examples: 9086 download_size: 1437096 dataset_size: 2294900 - config_name: bucc18.ru features: - name: source_sentence dtype: string - name: target_sentence dtype: string - name: source_lang dtype: string - name: target_lang dtype: string splits: - name: validation num_bytes: 761331 num_examples: 2374 - name: test num_bytes: 4641646 num_examples: 14435 download_size: 3074476 dataset_size: 5402977 - config_name: bucc18.zh features: - name: source_sentence dtype: string - name: target_sentence dtype: string - name: source_lang dtype: string - name: target_lang dtype: string splits: - name: validation num_bytes: 55723 num_examples: 257 - name: test num_bytes: 415909 num_examples: 1899 download_size: 320378 dataset_size: 471632 - config_name: tatoeba.afr features: - name: source_sentence dtype: string - name: target_sentence dtype: string - name: source_lang dtype: string - name: target_lang dtype: string splits: - name: validation num_bytes: 250635 num_examples: 1000 download_size: 47676 dataset_size: 250635 - config_name: tatoeba.ara features: - name: source_sentence dtype: string - name: target_sentence dtype: string - name: source_lang dtype: string - name: target_lang dtype: string splits: - name: validation num_bytes: 263650 num_examples: 1000 download_size: 51228 dataset_size: 263650 - config_name: tatoeba.ben features: - name: source_sentence dtype: string - name: target_sentence dtype: string - name: source_lang dtype: string - name: target_lang dtype: string splits: - name: validation num_bytes: 282703 num_examples: 1000 download_size: 51362 dataset_size: 282703 - config_name: tatoeba.bul features: - name: source_sentence dtype: string - name: target_sentence dtype: string - name: source_lang dtype: string - name: target_lang dtype: string splits: - name: validation num_bytes: 293279 num_examples: 1000 download_size: 62454 dataset_size: 293279 - config_name: tatoeba.cmn features: - name: source_sentence dtype: string - name: target_sentence dtype: string - name: source_lang dtype: string - name: target_lang dtype: string splits: - name: validation num_bytes: 259931 num_examples: 1000 download_size: 58281 dataset_size: 259931 - config_name: tatoeba.deu features: - name: source_sentence dtype: string - name: target_sentence dtype: string - name: source_lang dtype: string - name: target_lang dtype: string splits: - name: validation num_bytes: 296567 num_examples: 1000 download_size: 79066 dataset_size: 296567 - config_name: tatoeba.ell features: - name: source_sentence dtype: string - name: target_sentence dtype: string - name: source_lang dtype: string - name: target_lang dtype: string splits: - name: validation num_bytes: 269961 num_examples: 1000 download_size: 52251 dataset_size: 269961 - config_name: tatoeba.est features: - name: source_sentence dtype: string - name: target_sentence dtype: string - name: source_lang dtype: string - name: target_lang dtype: string splits: - name: validation num_bytes: 250728 num_examples: 1000 download_size: 49968 dataset_size: 250728 - config_name: tatoeba.eus features: - name: source_sentence dtype: string - name: target_sentence dtype: string - name: source_lang dtype: string - name: target_lang dtype: string splits: - name: validation num_bytes: 257068 num_examples: 1000 download_size: 54271 dataset_size: 257068 - config_name: tatoeba.fin features: - name: source_sentence dtype: string - name: target_sentence dtype: string - name: source_lang dtype: string - name: target_lang dtype: string splits: - name: validation num_bytes: 266669 num_examples: 1000 download_size: 60580 dataset_size: 266669 - config_name: tatoeba.fra features: - name: source_sentence dtype: string - name: target_sentence dtype: string - name: source_lang dtype: string - name: target_lang dtype: string splits: - name: validation num_bytes: 271018 num_examples: 1000 download_size: 60925 dataset_size: 271018 - config_name: tatoeba.heb features: - name: source_sentence dtype: string - name: target_sentence dtype: string - name: source_lang dtype: string - name: target_lang dtype: string splits: - name: validation num_bytes: 274500 num_examples: 1000 download_size: 57306 dataset_size: 274500 - config_name: tatoeba.hin features: - name: source_sentence dtype: string - name: target_sentence dtype: string - name: source_lang dtype: string - name: target_lang dtype: string splits: - name: validation num_bytes: 313558 num_examples: 1000 download_size: 68816 dataset_size: 313558 - config_name: tatoeba.hun features: - name: source_sentence dtype: string - name: target_sentence dtype: string - name: source_lang dtype: string - name: target_lang dtype: string splits: - name: validation num_bytes: 259889 num_examples: 1000 download_size: 58096 dataset_size: 259889 - config_name: tatoeba.ind features: - name: source_sentence dtype: string - name: target_sentence dtype: string - name: source_lang dtype: string - name: target_lang dtype: string splits: - name: validation num_bytes: 265844 num_examples: 1000 download_size: 57047 dataset_size: 265844 - config_name: tatoeba.ita features: - name: source_sentence dtype: string - name: target_sentence dtype: string - name: source_lang dtype: string - name: target_lang dtype: string splits: - name: validation num_bytes: 256833 num_examples: 1000 download_size: 52422 dataset_size: 256833 - config_name: tatoeba.jav features: - name: source_sentence dtype: string - name: target_sentence dtype: string - name: source_lang dtype: string - name: target_lang dtype: string splits: - name: validation num_bytes: 53068 num_examples: 205 download_size: 15208 dataset_size: 53068 - config_name: tatoeba.jpn features: - name: source_sentence dtype: string - name: target_sentence dtype: string - name: source_lang dtype: string - name: target_lang dtype: string splits: - name: validation num_bytes: 284083 num_examples: 1000 download_size: 66620 dataset_size: 284083 - config_name: tatoeba.kat features: - name: source_sentence dtype: string - name: target_sentence dtype: string - name: source_lang dtype: string - name: target_lang dtype: string splits: - name: validation num_bytes: 214646 num_examples: 746 download_size: 41759 dataset_size: 214646 - config_name: tatoeba.kaz features: - name: source_sentence dtype: string - name: target_sentence dtype: string - name: source_lang dtype: string - name: target_lang dtype: string splits: - name: validation num_bytes: 157003 num_examples: 575 download_size: 35693 dataset_size: 157003 - config_name: tatoeba.kor features: - name: source_sentence dtype: string - name: target_sentence dtype: string - name: source_lang dtype: string - name: target_lang dtype: string splits: - name: validation num_bytes: 270139 num_examples: 1000 download_size: 61210 dataset_size: 270139 - config_name: tatoeba.mal features: - name: source_sentence dtype: string - name: target_sentence dtype: string - name: source_lang dtype: string - name: target_lang dtype: string splits: - name: validation num_bytes: 225934 num_examples: 687 download_size: 51077 dataset_size: 225934 - config_name: tatoeba.mar features: - name: source_sentence dtype: string - name: target_sentence dtype: string - name: source_lang dtype: string - name: target_lang dtype: string splits: - name: validation num_bytes: 291542 num_examples: 1000 download_size: 56575 dataset_size: 291542 - config_name: tatoeba.nld features: - name: source_sentence dtype: string - name: target_sentence dtype: string - name: source_lang dtype: string - name: target_lang dtype: string splits: - name: validation num_bytes: 264263 num_examples: 1000 download_size: 59774 dataset_size: 264263 - config_name: tatoeba.pes features: - name: source_sentence dtype: string - name: target_sentence dtype: string - name: source_lang dtype: string - name: target_lang dtype: string splits: - name: validation num_bytes: 284719 num_examples: 1000 download_size: 64642 dataset_size: 284719 - config_name: tatoeba.por features: - name: source_sentence dtype: string - name: target_sentence dtype: string - name: source_lang dtype: string - name: target_lang dtype: string splits: - name: validation num_bytes: 266185 num_examples: 1000 download_size: 58250 dataset_size: 266185 - config_name: tatoeba.rus features: - name: source_sentence dtype: string - name: target_sentence dtype: string - name: source_lang dtype: string - name: target_lang dtype: string splits: - name: validation num_bytes: 283472 num_examples: 1000 download_size: 61601 dataset_size: 283472 - config_name: tatoeba.spa features: - name: source_sentence dtype: string - name: target_sentence dtype: string - name: source_lang dtype: string - name: target_lang dtype: string splits: - name: validation num_bytes: 263266 num_examples: 1000 download_size: 57055 dataset_size: 263266 - config_name: tatoeba.swh features: - name: source_sentence dtype: string - name: target_sentence dtype: string - name: source_lang dtype: string - name: target_lang dtype: string splits: - name: validation num_bytes: 94957 num_examples: 390 download_size: 19362 dataset_size: 94957 - config_name: tatoeba.tam features: - name: source_sentence dtype: string - name: target_sentence dtype: string - name: source_lang dtype: string - name: target_lang dtype: string splits: - name: validation num_bytes: 98078 num_examples: 307 download_size: 23648 dataset_size: 98078 - config_name: tatoeba.tel features: - name: source_sentence dtype: string - name: target_sentence dtype: string - name: source_lang dtype: string - name: target_lang dtype: string splits: - name: validation num_bytes: 69837 num_examples: 234 download_size: 18260 dataset_size: 69837 - config_name: tatoeba.tgl features: - name: source_sentence dtype: string - name: target_sentence dtype: string - name: source_lang dtype: string - name: target_lang dtype: string splits: - name: validation num_bytes: 259138 num_examples: 1000 download_size: 53699 dataset_size: 259138 - config_name: tatoeba.tha features: - name: source_sentence dtype: string - name: target_sentence dtype: string - name: source_lang dtype: string - name: target_lang dtype: string splits: - name: validation num_bytes: 167866 num_examples: 548 download_size: 39659 dataset_size: 167866 - config_name: tatoeba.tur features: - name: source_sentence dtype: string - name: target_sentence dtype: string - name: source_lang dtype: string - name: target_lang dtype: string splits: - name: validation num_bytes: 262885 num_examples: 1000 download_size: 54137 dataset_size: 262885 - config_name: tatoeba.urd features: - name: source_sentence dtype: string - name: target_sentence dtype: string - name: source_lang dtype: string - name: target_lang dtype: string splits: - name: validation num_bytes: 279712 num_examples: 1000 download_size: 60399 dataset_size: 279712 - config_name: tatoeba.vie features: - name: source_sentence dtype: string - name: target_sentence dtype: string - name: source_lang dtype: string - name: target_lang dtype: string splits: - name: validation num_bytes: 282407 num_examples: 1000 download_size: 66746 dataset_size: 282407 - config_name: tydiqa features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: train num_bytes: 52948467 num_examples: 49881 - name: validation num_bytes: 5006433 num_examples: 5077 download_size: 29402238 dataset_size: 57954900 - config_name: udpos.Afrikaans features: - name: tokens sequence: string - name: pos_tags sequence: class_label: names: '0': ADJ '1': ADP '2': ADV '3': AUX '4': CCONJ '5': DET '6': INTJ '7': NOUN '8': NUM '9': PART '10': PRON '11': PROPN '12': PUNCT '13': SCONJ '14': SYM '15': VERB '16': X splits: - name: train num_bytes: 586370 num_examples: 1315 - name: validation num_bytes: 91290 num_examples: 194 - name: test num_bytes: 174244 num_examples: 425 download_size: 193788 dataset_size: 851904 - config_name: udpos.Arabic features: - name: tokens sequence: string - name: pos_tags sequence: class_label: names: '0': ADJ '1': ADP '2': ADV '3': AUX '4': CCONJ '5': DET '6': INTJ '7': NOUN '8': NUM '9': PART '10': PRON '11': PROPN '12': PUNCT '13': SCONJ '14': SYM '15': VERB '16': X splits: - name: train num_bytes: 4453682 num_examples: 6075 - name: validation num_bytes: 593650 num_examples: 909 - name: test num_bytes: 973822 num_examples: 1680 download_size: 1186113 dataset_size: 6021154 - config_name: udpos.Basque features: - name: tokens sequence: string - name: pos_tags sequence: class_label: names: '0': ADJ '1': ADP '2': ADV '3': AUX '4': CCONJ '5': DET '6': INTJ '7': NOUN '8': NUM '9': PART '10': PRON '11': PROPN '12': PUNCT '13': SCONJ '14': SYM '15': VERB '16': X splits: - name: train num_bytes: 1327713 num_examples: 5396 - name: validation num_bytes: 438671 num_examples: 1798 - name: test num_bytes: 444644 num_examples: 1799 download_size: 703094 dataset_size: 2211028 - config_name: udpos.Bulgarian features: - name: tokens sequence: string - name: pos_tags sequence: class_label: names: '0': ADJ '1': ADP '2': ADV '3': AUX '4': CCONJ '5': DET '6': INTJ '7': NOUN '8': NUM '9': PART '10': PRON '11': PROPN '12': PUNCT '13': SCONJ '14': SYM '15': VERB '16': X splits: - name: train num_bytes: 2689767 num_examples: 8907 - name: validation num_bytes: 347117 num_examples: 1115 - name: test num_bytes: 339947 num_examples: 1116 download_size: 926186 dataset_size: 3376831 - config_name: udpos.Chinese features: - name: tokens sequence: string - name: pos_tags sequence: class_label: names: '0': ADJ '1': ADP '2': ADV '3': AUX '4': CCONJ '5': DET '6': INTJ '7': NOUN '8': NUM '9': PART '10': PRON '11': PROPN '12': PUNCT '13': SCONJ '14': SYM '15': VERB '16': X splits: - name: train num_bytes: 4218891 num_examples: 18998 - name: validation num_bytes: 594448 num_examples: 3038 - name: test num_bytes: 1236051 num_examples: 5528 download_size: 1471747 dataset_size: 6049390 - config_name: udpos.Dutch features: - name: tokens sequence: string - name: pos_tags sequence: class_label: names: '0': ADJ '1': ADP '2': ADV '3': AUX '4': CCONJ '5': DET '6': INTJ '7': NOUN '8': NUM '9': PART '10': PRON '11': PROPN '12': PUNCT '13': SCONJ '14': SYM '15': VERB '16': X splits: - name: train num_bytes: 4517994 num_examples: 18051 - name: validation num_bytes: 393592 num_examples: 1394 - name: test num_bytes: 397904 num_examples: 1471 download_size: 1410982 dataset_size: 5309490 - config_name: udpos.English features: - name: tokens sequence: string - name: pos_tags sequence: class_label: names: '0': ADJ '1': ADP '2': ADV '3': AUX '4': CCONJ '5': DET '6': INTJ '7': NOUN '8': NUM '9': PART '10': PRON '11': PROPN '12': PUNCT '13': SCONJ '14': SYM '15': VERB '16': X splits: - name: train num_bytes: 6225509 num_examples: 21253 - name: validation num_bytes: 1042040 num_examples: 3974 - name: test num_bytes: 1421148 num_examples: 5440 download_size: 2116535 dataset_size: 8688697 - config_name: udpos.Estonian features: - name: tokens sequence: string - name: pos_tags sequence: class_label: names: '0': ADJ '1': ADP '2': ADV '3': AUX '4': CCONJ '5': DET '6': INTJ '7': NOUN '8': NUM '9': PART '10': PRON '11': PROPN '12': PUNCT '13': SCONJ '14': SYM '15': VERB '16': X splits: - name: train num_bytes: 6614893 num_examples: 25749 - name: validation num_bytes: 814171 num_examples: 3125 - name: test num_bytes: 1065701 num_examples: 3760 download_size: 2619121 dataset_size: 8494765 - config_name: udpos.Finnish features: - name: tokens sequence: string - name: pos_tags sequence: class_label: names: '0': ADJ '1': ADP '2': ADV '3': AUX '4': CCONJ '5': DET '6': INTJ '7': NOUN '8': NUM '9': PART '10': PRON '11': PROPN '12': PUNCT '13': SCONJ '14': SYM '15': VERB '16': X splits: - name: train num_bytes: 5613706 num_examples: 27198 - name: validation num_bytes: 656646 num_examples: 3239 - name: test num_bytes: 1025726 num_examples: 4422 download_size: 2503217 dataset_size: 7296078 - config_name: udpos.French features: - name: tokens sequence: string - name: pos_tags sequence: class_label: names: '0': ADJ '1': ADP '2': ADV '3': AUX '4': CCONJ '5': DET '6': INTJ '7': NOUN '8': NUM '9': PART '10': PRON '11': PROPN '12': PUNCT '13': SCONJ '14': SYM '15': VERB '16': X splits: - name: train num_bytes: 10118933 num_examples: 47308 - name: validation num_bytes: 1294096 num_examples: 5979 - name: test num_bytes: 1731049 num_examples: 9465 download_size: 3378680 dataset_size: 13144078 - config_name: udpos.German features: - name: tokens sequence: string - name: pos_tags sequence: class_label: names: '0': ADJ '1': ADP '2': ADV '3': AUX '4': CCONJ '5': DET '6': INTJ '7': NOUN '8': NUM '9': PART '10': PRON '11': PROPN '12': PUNCT '13': SCONJ '14': SYM '15': VERB '16': X splits: - name: train num_bytes: 54773777 num_examples: 166849 - name: validation num_bytes: 6044838 num_examples: 19233 - name: test num_bytes: 7345863 num_examples: 22458 download_size: 18623155 dataset_size: 68164478 - config_name: udpos.Greek features: - name: tokens sequence: string - name: pos_tags sequence: class_label: names: '0': ADJ '1': ADP '2': ADV '3': AUX '4': CCONJ '5': DET '6': INTJ '7': NOUN '8': NUM '9': PART '10': PRON '11': PROPN '12': PUNCT '13': SCONJ '14': SYM '15': VERB '16': X splits: - name: train num_bytes: 8932104 num_examples: 28152 - name: validation num_bytes: 1062447 num_examples: 2559 - name: test num_bytes: 1028665 num_examples: 2809 download_size: 2763293 dataset_size: 11023216 - config_name: udpos.Hebrew features: - name: tokens sequence: string - name: pos_tags sequence: class_label: names: '0': ADJ '1': ADP '2': ADV '3': AUX '4': CCONJ '5': DET '6': INTJ '7': NOUN '8': NUM '9': PART '10': PRON '11': PROPN '12': PUNCT '13': SCONJ '14': SYM '15': VERB '16': X splits: - name: train num_bytes: 2505691 num_examples: 5241 - name: validation num_bytes: 210013 num_examples: 484 - name: test num_bytes: 223865 num_examples: 491 download_size: 624771 dataset_size: 2939569 - config_name: udpos.Hindi features: - name: tokens sequence: string - name: pos_tags sequence: class_label: names: '0': ADJ '1': ADP '2': ADV '3': AUX '4': CCONJ '5': DET '6': INTJ '7': NOUN '8': NUM '9': PART '10': PRON '11': PROPN '12': PUNCT '13': SCONJ '14': SYM '15': VERB '16': X splits: - name: train num_bytes: 6690250 num_examples: 13304 - name: validation num_bytes: 839702 num_examples: 1659 - name: test num_bytes: 1400225 num_examples: 2684 download_size: 1468314 dataset_size: 8930177 - config_name: udpos.Hungarian features: - name: tokens sequence: string - name: pos_tags sequence: class_label: names: '0': ADJ '1': ADP '2': ADV '3': AUX '4': CCONJ '5': DET '6': INTJ '7': NOUN '8': NUM '9': PART '10': PRON '11': PROPN '12': PUNCT '13': SCONJ '14': SYM '15': VERB '16': X splits: - name: train num_bytes: 372226 num_examples: 910 - name: validation num_bytes: 215879 num_examples: 441 - name: test num_bytes: 193728 num_examples: 449 download_size: 251882 dataset_size: 781833 - config_name: udpos.Indonesian features: - name: tokens sequence: string - name: pos_tags sequence: class_label: names: '0': ADJ '1': ADP '2': ADV '3': AUX '4': CCONJ '5': DET '6': INTJ '7': NOUN '8': NUM '9': PART '10': PRON '11': PROPN '12': PUNCT '13': SCONJ '14': SYM '15': VERB '16': X splits: - name: train num_bytes: 1710678 num_examples: 4477 - name: validation num_bytes: 220863 num_examples: 559 - name: test num_bytes: 557101 num_examples: 1557 download_size: 684225 dataset_size: 2488642 - config_name: udpos.Italian features: - name: tokens sequence: string - name: pos_tags sequence: class_label: names: '0': ADJ '1': ADP '2': ADV '3': AUX '4': CCONJ '5': DET '6': INTJ '7': NOUN '8': NUM '9': PART '10': PRON '11': PROPN '12': PUNCT '13': SCONJ '14': SYM '15': VERB '16': X splits: - name: train num_bytes: 11299293 num_examples: 29685 - name: validation num_bytes: 988996 num_examples: 2278 - name: test num_bytes: 1337869 num_examples: 3518 download_size: 3256246 dataset_size: 13626158 - config_name: udpos.Japanese features: - name: tokens sequence: string - name: pos_tags sequence: class_label: names: '0': ADJ '1': ADP '2': ADV '3': AUX '4': CCONJ '5': DET '6': INTJ '7': NOUN '8': NUM '9': PART '10': PRON '11': PROPN '12': PUNCT '13': SCONJ '14': SYM '15': VERB '16': X splits: - name: train num_bytes: 2792951 num_examples: 7125 - name: validation num_bytes: 200356 num_examples: 511 - name: test num_bytes: 928902 num_examples: 2372 download_size: 1012282 dataset_size: 3922209 - config_name: udpos.Kazakh features: - name: tokens sequence: string - name: pos_tags sequence: class_label: names: '0': ADJ '1': ADP '2': ADV '3': AUX '4': CCONJ '5': DET '6': INTJ '7': NOUN '8': NUM '9': PART '10': PRON '11': PROPN '12': PUNCT '13': SCONJ '14': SYM '15': VERB '16': X splits: - name: train num_bytes: 11438 num_examples: 31 - name: test num_bytes: 228924 num_examples: 1047 download_size: 76300 dataset_size: 240362 - config_name: udpos.Korean features: - name: tokens sequence: string - name: pos_tags sequence: class_label: names: '0': ADJ '1': ADP '2': ADV '3': AUX '4': CCONJ '5': DET '6': INTJ '7': NOUN '8': NUM '9': PART '10': PRON '11': PROPN '12': PUNCT '13': SCONJ '14': SYM '15': VERB '16': X splits: - name: train num_bytes: 7341267 num_examples: 27410 - name: validation num_bytes: 782587 num_examples: 3016 - name: test num_bytes: 1162539 num_examples: 4276 download_size: 3115101 dataset_size: 9286393 - config_name: udpos.Marathi features: - name: tokens sequence: string - name: pos_tags sequence: class_label: names: '0': ADJ '1': ADP '2': ADV '3': AUX '4': CCONJ '5': DET '6': INTJ '7': NOUN '8': NUM '9': PART '10': PRON '11': PROPN '12': PUNCT '13': SCONJ '14': SYM '15': VERB '16': X splits: - name: train num_bytes: 59023 num_examples: 373 - name: validation num_bytes: 8497 num_examples: 46 - name: test num_bytes: 7871 num_examples: 47 download_size: 22133 dataset_size: 75391 - config_name: udpos.Persian features: - name: tokens sequence: string - name: pos_tags sequence: class_label: names: '0': ADJ '1': ADP '2': ADV '3': AUX '4': CCONJ '5': DET '6': INTJ '7': NOUN '8': NUM '9': PART '10': PRON '11': PROPN '12': PUNCT '13': SCONJ '14': SYM '15': VERB '16': X splits: - name: train num_bytes: 2400776 num_examples: 4798 - name: validation num_bytes: 317053 num_examples: 599 - name: test num_bytes: 320683 num_examples: 600 download_size: 606912 dataset_size: 3038512 - config_name: udpos.Portuguese features: - name: tokens sequence: string - name: pos_tags sequence: class_label: names: '0': ADJ '1': ADP '2': ADV '3': AUX '4': CCONJ '5': DET '6': INTJ '7': NOUN '8': NUM '9': PART '10': PRON '11': PROPN '12': PUNCT '13': SCONJ '14': SYM '15': VERB '16': X splits: - name: train num_bytes: 7669556 num_examples: 17992 - name: validation num_bytes: 712397 num_examples: 1770 - name: test num_bytes: 1082582 num_examples: 2681 download_size: 2505672 dataset_size: 9464535 - config_name: udpos.Russian features: - name: tokens sequence: string - name: pos_tags sequence: class_label: names: '0': ADJ '1': ADP '2': ADV '3': AUX '4': CCONJ '5': DET '6': INTJ '7': NOUN '8': NUM '9': PART '10': PRON '11': PROPN '12': PUNCT '13': SCONJ '14': SYM '15': VERB '16': X splits: - name: train num_bytes: 24230098 num_examples: 67435 - name: validation num_bytes: 3457031 num_examples: 9960 - name: test num_bytes: 4236693 num_examples: 11336 download_size: 8818512 dataset_size: 31923822 - config_name: udpos.Spanish features: - name: tokens sequence: string - name: pos_tags sequence: class_label: names: '0': ADJ '1': ADP '2': ADV '3': AUX '4': CCONJ '5': DET '6': INTJ '7': NOUN '8': NUM '9': PART '10': PRON '11': PROPN '12': PUNCT '13': SCONJ '14': SYM '15': VERB '16': X splits: - name: train num_bytes: 13858406 num_examples: 28492 - name: validation num_bytes: 1498765 num_examples: 3054 - name: test num_bytes: 1476500 num_examples: 3147 download_size: 4347905 dataset_size: 16833671 - config_name: udpos.Tagalog features: - name: tokens sequence: string - name: pos_tags sequence: class_label: names: '0': ADJ '1': ADP '2': ADV '3': AUX '4': CCONJ '5': DET '6': INTJ '7': NOUN '8': NUM '9': PART '10': PRON '11': PROPN '12': PUNCT '13': SCONJ '14': SYM '15': VERB '16': X splits: - name: test num_bytes: 5153 num_examples: 55 download_size: 3345 dataset_size: 5153 - config_name: udpos.Tamil features: - name: tokens sequence: string - name: pos_tags sequence: class_label: names: '0': ADJ '1': ADP '2': ADV '3': AUX '4': CCONJ '5': DET '6': INTJ '7': NOUN '8': NUM '9': PART '10': PRON '11': PROPN '12': PUNCT '13': SCONJ '14': SYM '15': VERB '16': X splits: - name: train num_bytes: 202596 num_examples: 400 - name: validation num_bytes: 40031 num_examples: 80 - name: test num_bytes: 62366 num_examples: 120 download_size: 73764 dataset_size: 304993 - config_name: udpos.Telugu features: - name: tokens sequence: string - name: pos_tags sequence: class_label: names: '0': ADJ '1': ADP '2': ADV '3': AUX '4': CCONJ '5': DET '6': INTJ '7': NOUN '8': NUM '9': PART '10': PRON '11': PROPN '12': PUNCT '13': SCONJ '14': SYM '15': VERB '16': X splits: - name: train num_bytes: 138049 num_examples: 1051 - name: validation num_bytes: 17990 num_examples: 131 - name: test num_bytes: 19575 num_examples: 146 download_size: 46045 dataset_size: 175614 - config_name: udpos.Thai features: - name: tokens sequence: string - name: pos_tags sequence: class_label: names: '0': ADJ '1': ADP '2': ADV '3': AUX '4': CCONJ '5': DET '6': INTJ '7': NOUN '8': NUM '9': PART '10': PRON '11': PROPN '12': PUNCT '13': SCONJ '14': SYM '15': VERB '16': X splits: - name: test num_bytes: 561336 num_examples: 1000 download_size: 92925 dataset_size: 561336 - config_name: udpos.Turkish features: - name: tokens sequence: string - name: pos_tags sequence: class_label: names: '0': ADJ '1': ADP '2': ADV '3': AUX '4': CCONJ '5': DET '6': INTJ '7': NOUN '8': NUM '9': PART '10': PRON '11': PROPN '12': PUNCT '13': SCONJ '14': SYM '15': VERB '16': X splits: - name: train num_bytes: 704405 num_examples: 3664 - name: validation num_bytes: 186455 num_examples: 988 - name: test num_bytes: 827382 num_examples: 4785 download_size: 581177 dataset_size: 1718242 - config_name: udpos.Urdu features: - name: tokens sequence: string - name: pos_tags sequence: class_label: names: '0': ADJ '1': ADP '2': ADV '3': AUX '4': CCONJ '5': DET '6': INTJ '7': NOUN '8': NUM '9': PART '10': PRON '11': PROPN '12': PUNCT '13': SCONJ '14': SYM '15': VERB '16': X splits: - name: train num_bytes: 2107362 num_examples: 4043 - name: validation num_bytes: 284261 num_examples: 552 - name: test num_bytes: 288553 num_examples: 535 download_size: 499594 dataset_size: 2680176 - config_name: udpos.Vietnamese features: - name: tokens sequence: string - name: pos_tags sequence: class_label: names: '0': ADJ '1': ADP '2': ADV '3': AUX '4': CCONJ '5': DET '6': INTJ '7': NOUN '8': NUM '9': PART '10': PRON '11': PROPN '12': PUNCT '13': SCONJ '14': SYM '15': VERB '16': X splits: - name: train num_bytes: 367335 num_examples: 1400 - name: validation num_bytes: 206188 num_examples: 800 - name: test num_bytes: 214063 num_examples: 800 download_size: 181239 dataset_size: 787586 - config_name: udpos.Yoruba features: - name: tokens sequence: string - name: pos_tags sequence: class_label: names: '0': ADJ '1': ADP '2': ADV '3': AUX '4': CCONJ '5': DET '6': INTJ '7': NOUN '8': NUM '9': PART '10': PRON '11': PROPN '12': PUNCT '13': SCONJ '14': SYM '15': VERB '16': X splits: - name: test num_bytes: 44656 num_examples: 100 download_size: 10151 dataset_size: 44656 configs: - config_name: MLQA.ar.ar data_files: - split: test path: MLQA.ar.ar/test-* - split: validation path: MLQA.ar.ar/validation-* - config_name: MLQA.ar.de data_files: - split: test path: MLQA.ar.de/test-* - split: validation path: MLQA.ar.de/validation-* - config_name: MLQA.ar.en data_files: - split: test path: MLQA.ar.en/test-* - split: validation path: MLQA.ar.en/validation-* - config_name: MLQA.ar.es data_files: - split: test path: MLQA.ar.es/test-* - split: validation path: MLQA.ar.es/validation-* - config_name: MLQA.ar.hi data_files: - split: test path: MLQA.ar.hi/test-* - split: validation path: MLQA.ar.hi/validation-* - config_name: MLQA.ar.vi data_files: - split: test path: MLQA.ar.vi/test-* - split: validation path: MLQA.ar.vi/validation-* - config_name: MLQA.ar.zh data_files: - split: test path: MLQA.ar.zh/test-* - split: validation path: MLQA.ar.zh/validation-* - config_name: MLQA.de.ar data_files: - split: test path: MLQA.de.ar/test-* - split: validation path: MLQA.de.ar/validation-* - config_name: MLQA.de.de data_files: - split: test path: MLQA.de.de/test-* - split: validation path: MLQA.de.de/validation-* - config_name: MLQA.de.en data_files: - split: test path: MLQA.de.en/test-* - split: validation path: MLQA.de.en/validation-* - config_name: MLQA.de.es data_files: - split: test path: MLQA.de.es/test-* - split: validation path: MLQA.de.es/validation-* - config_name: MLQA.de.hi data_files: - split: test path: MLQA.de.hi/test-* - split: validation path: MLQA.de.hi/validation-* - config_name: MLQA.de.vi data_files: - split: test path: MLQA.de.vi/test-* - split: validation path: MLQA.de.vi/validation-* - config_name: MLQA.de.zh data_files: - split: test path: MLQA.de.zh/test-* - split: validation path: MLQA.de.zh/validation-* - config_name: MLQA.en.ar data_files: - split: test path: MLQA.en.ar/test-* - split: validation path: MLQA.en.ar/validation-* - config_name: MLQA.en.de data_files: - split: test path: MLQA.en.de/test-* - split: validation path: MLQA.en.de/validation-* - config_name: MLQA.en.en data_files: - split: test path: MLQA.en.en/test-* - split: validation path: MLQA.en.en/validation-* - config_name: MLQA.en.es data_files: - split: test path: MLQA.en.es/test-* - split: validation path: MLQA.en.es/validation-* - config_name: MLQA.en.hi data_files: - split: test path: MLQA.en.hi/test-* - split: validation path: MLQA.en.hi/validation-* - config_name: MLQA.en.vi data_files: - split: test path: MLQA.en.vi/test-* - split: validation path: MLQA.en.vi/validation-* - config_name: MLQA.en.zh data_files: - split: test path: MLQA.en.zh/test-* - split: validation path: MLQA.en.zh/validation-* - config_name: MLQA.es.ar data_files: - split: test path: MLQA.es.ar/test-* - split: validation path: MLQA.es.ar/validation-* - config_name: MLQA.es.de data_files: - split: test path: MLQA.es.de/test-* - split: validation path: MLQA.es.de/validation-* - config_name: MLQA.es.en data_files: - split: test path: MLQA.es.en/test-* - split: validation path: MLQA.es.en/validation-* - config_name: MLQA.es.es data_files: - split: test path: MLQA.es.es/test-* - split: validation path: MLQA.es.es/validation-* - config_name: MLQA.es.hi data_files: - split: test path: MLQA.es.hi/test-* - split: validation path: MLQA.es.hi/validation-* - config_name: MLQA.es.vi data_files: - split: test path: MLQA.es.vi/test-* - split: validation path: MLQA.es.vi/validation-* - config_name: MLQA.es.zh data_files: - split: test path: MLQA.es.zh/test-* - split: validation path: MLQA.es.zh/validation-* - config_name: MLQA.hi.ar data_files: - split: test path: MLQA.hi.ar/test-* - split: validation path: MLQA.hi.ar/validation-* - config_name: MLQA.hi.de data_files: - split: test path: MLQA.hi.de/test-* - split: validation path: MLQA.hi.de/validation-* - config_name: MLQA.hi.en data_files: - split: test path: MLQA.hi.en/test-* - split: validation path: MLQA.hi.en/validation-* - config_name: MLQA.hi.es data_files: - split: test path: MLQA.hi.es/test-* - split: validation path: MLQA.hi.es/validation-* - config_name: MLQA.hi.hi data_files: - split: test path: MLQA.hi.hi/test-* - split: validation path: MLQA.hi.hi/validation-* - config_name: MLQA.hi.vi data_files: - split: test path: MLQA.hi.vi/test-* - split: validation path: MLQA.hi.vi/validation-* - config_name: MLQA.hi.zh data_files: - split: test path: MLQA.hi.zh/test-* - split: validation path: MLQA.hi.zh/validation-* - config_name: MLQA.vi.ar data_files: - split: test path: MLQA.vi.ar/test-* - split: validation path: MLQA.vi.ar/validation-* - config_name: MLQA.vi.de data_files: - split: test path: MLQA.vi.de/test-* - split: validation path: MLQA.vi.de/validation-* - config_name: MLQA.vi.en data_files: - split: test path: MLQA.vi.en/test-* - split: validation path: MLQA.vi.en/validation-* - config_name: MLQA.vi.es data_files: - split: test path: MLQA.vi.es/test-* - split: validation path: MLQA.vi.es/validation-* - config_name: MLQA.vi.hi data_files: - split: test path: MLQA.vi.hi/test-* - split: validation path: MLQA.vi.hi/validation-* - config_name: MLQA.vi.vi data_files: - split: test path: MLQA.vi.vi/test-* - split: validation path: MLQA.vi.vi/validation-* - config_name: MLQA.vi.zh data_files: - split: test path: MLQA.vi.zh/test-* - split: validation path: MLQA.vi.zh/validation-* - config_name: MLQA.zh.ar data_files: - split: test path: MLQA.zh.ar/test-* - split: validation path: MLQA.zh.ar/validation-* - config_name: MLQA.zh.de data_files: - split: test path: MLQA.zh.de/test-* - split: validation path: MLQA.zh.de/validation-* - config_name: MLQA.zh.en data_files: - split: test path: MLQA.zh.en/test-* - split: validation path: MLQA.zh.en/validation-* - config_name: MLQA.zh.es data_files: - split: test path: MLQA.zh.es/test-* - split: validation path: MLQA.zh.es/validation-* - config_name: MLQA.zh.hi data_files: - split: test path: MLQA.zh.hi/test-* - split: validation path: MLQA.zh.hi/validation-* - config_name: MLQA.zh.vi data_files: - split: test path: MLQA.zh.vi/test-* - split: validation path: MLQA.zh.vi/validation-* - config_name: MLQA.zh.zh data_files: - split: test path: MLQA.zh.zh/test-* - split: validation path: MLQA.zh.zh/validation-* - config_name: PAN-X.af data_files: - split: train path: PAN-X.af/train-* - split: validation path: PAN-X.af/validation-* - split: test path: PAN-X.af/test-* - config_name: PAN-X.ar data_files: - split: train path: PAN-X.ar/train-* - split: validation path: PAN-X.ar/validation-* - split: test path: PAN-X.ar/test-* - config_name: PAN-X.bg data_files: - split: train path: PAN-X.bg/train-* - split: validation path: PAN-X.bg/validation-* - split: test path: PAN-X.bg/test-* - config_name: PAN-X.bn data_files: - split: train path: PAN-X.bn/train-* - split: validation path: PAN-X.bn/validation-* - split: test path: PAN-X.bn/test-* - config_name: PAN-X.de data_files: - split: train path: PAN-X.de/train-* - split: validation path: PAN-X.de/validation-* - split: test path: PAN-X.de/test-* - config_name: PAN-X.el data_files: - split: train path: PAN-X.el/train-* - split: validation path: PAN-X.el/validation-* - split: test path: PAN-X.el/test-* - config_name: PAN-X.en data_files: - split: train path: PAN-X.en/train-* - split: validation path: PAN-X.en/validation-* - split: test path: PAN-X.en/test-* - config_name: PAN-X.es data_files: - split: train path: PAN-X.es/train-* - split: validation path: PAN-X.es/validation-* - split: test path: PAN-X.es/test-* - config_name: PAN-X.et data_files: - split: train path: PAN-X.et/train-* - split: validation path: PAN-X.et/validation-* - split: test path: PAN-X.et/test-* - config_name: PAN-X.eu data_files: - split: train path: PAN-X.eu/train-* - split: validation path: PAN-X.eu/validation-* - split: test path: PAN-X.eu/test-* - config_name: PAN-X.fa data_files: - split: train path: PAN-X.fa/train-* - split: validation path: PAN-X.fa/validation-* - split: test path: PAN-X.fa/test-* - config_name: PAN-X.fi data_files: - split: train path: PAN-X.fi/train-* - split: validation path: PAN-X.fi/validation-* - split: test path: PAN-X.fi/test-* - config_name: PAN-X.fr data_files: - split: train path: PAN-X.fr/train-* - split: validation path: PAN-X.fr/validation-* - split: test path: PAN-X.fr/test-* - config_name: PAN-X.he data_files: - split: train path: PAN-X.he/train-* - split: validation path: PAN-X.he/validation-* - split: test path: PAN-X.he/test-* - config_name: PAN-X.hi data_files: - split: train path: PAN-X.hi/train-* - split: validation path: PAN-X.hi/validation-* - split: test path: PAN-X.hi/test-* - config_name: PAN-X.hu data_files: - split: train path: PAN-X.hu/train-* - split: validation path: PAN-X.hu/validation-* - split: test path: PAN-X.hu/test-* - config_name: PAN-X.id data_files: - split: train path: PAN-X.id/train-* - split: validation path: PAN-X.id/validation-* - split: test path: PAN-X.id/test-* - config_name: PAN-X.it data_files: - split: train path: PAN-X.it/train-* - split: validation path: PAN-X.it/validation-* - split: test path: PAN-X.it/test-* - config_name: PAN-X.ja data_files: - split: train path: PAN-X.ja/train-* - split: validation path: PAN-X.ja/validation-* - split: test path: PAN-X.ja/test-* - config_name: PAN-X.jv data_files: - split: train path: PAN-X.jv/train-* - split: validation path: PAN-X.jv/validation-* - split: test path: PAN-X.jv/test-* - config_name: PAN-X.ka data_files: - split: train path: PAN-X.ka/train-* - split: validation path: PAN-X.ka/validation-* - split: test path: PAN-X.ka/test-* - config_name: PAN-X.kk data_files: - split: train path: PAN-X.kk/train-* - split: validation path: PAN-X.kk/validation-* - split: test path: PAN-X.kk/test-* - config_name: PAN-X.ko data_files: - split: train path: PAN-X.ko/train-* - split: validation path: PAN-X.ko/validation-* - split: test path: PAN-X.ko/test-* - config_name: PAN-X.ml data_files: - split: train path: PAN-X.ml/train-* - split: validation path: PAN-X.ml/validation-* - split: test path: PAN-X.ml/test-* - config_name: PAN-X.mr data_files: - split: train path: PAN-X.mr/train-* - split: validation path: PAN-X.mr/validation-* - split: test path: PAN-X.mr/test-* - config_name: PAN-X.ms data_files: - split: train path: PAN-X.ms/train-* - split: validation path: PAN-X.ms/validation-* - split: test path: PAN-X.ms/test-* - config_name: PAN-X.my data_files: - split: train path: PAN-X.my/train-* - split: validation path: PAN-X.my/validation-* - split: test path: PAN-X.my/test-* - config_name: PAN-X.nl data_files: - split: train path: PAN-X.nl/train-* - split: validation path: PAN-X.nl/validation-* - split: test path: PAN-X.nl/test-* - config_name: PAN-X.pt data_files: - split: train path: PAN-X.pt/train-* - split: validation path: PAN-X.pt/validation-* - split: test path: PAN-X.pt/test-* - config_name: PAN-X.ru data_files: - split: train path: PAN-X.ru/train-* - split: validation path: PAN-X.ru/validation-* - split: test path: PAN-X.ru/test-* - config_name: PAN-X.sw data_files: - split: train path: PAN-X.sw/train-* - split: validation path: PAN-X.sw/validation-* - split: test path: PAN-X.sw/test-* - config_name: PAN-X.ta data_files: - split: train path: PAN-X.ta/train-* - split: validation path: PAN-X.ta/validation-* - split: test path: PAN-X.ta/test-* - config_name: PAN-X.te data_files: - split: train path: PAN-X.te/train-* - split: validation path: PAN-X.te/validation-* - split: test path: PAN-X.te/test-* - config_name: PAN-X.th data_files: - split: train path: PAN-X.th/train-* - split: validation path: PAN-X.th/validation-* - split: test path: PAN-X.th/test-* - config_name: PAN-X.tl data_files: - split: train path: PAN-X.tl/train-* - split: validation path: PAN-X.tl/validation-* - split: test path: PAN-X.tl/test-* - config_name: PAN-X.tr data_files: - split: train path: PAN-X.tr/train-* - split: validation path: PAN-X.tr/validation-* - split: test path: PAN-X.tr/test-* - config_name: PAN-X.ur data_files: - split: train path: PAN-X.ur/train-* - split: validation path: PAN-X.ur/validation-* - split: test path: PAN-X.ur/test-* - config_name: PAN-X.vi data_files: - split: train path: PAN-X.vi/train-* - split: validation path: PAN-X.vi/validation-* - split: test path: PAN-X.vi/test-* - config_name: PAN-X.yo data_files: - split: train path: PAN-X.yo/train-* - split: validation path: PAN-X.yo/validation-* - split: test path: PAN-X.yo/test-* - config_name: PAN-X.zh data_files: - split: train path: PAN-X.zh/train-* - split: validation path: PAN-X.zh/validation-* - split: test path: PAN-X.zh/test-* - config_name: PAWS-X.de data_files: - split: train path: PAWS-X.de/train-* - split: validation path: PAWS-X.de/validation-* - split: test path: PAWS-X.de/test-* - config_name: PAWS-X.en data_files: - split: train path: PAWS-X.en/train-* - split: validation path: PAWS-X.en/validation-* - split: test path: PAWS-X.en/test-* - config_name: PAWS-X.es data_files: - split: train path: PAWS-X.es/train-* - split: validation path: PAWS-X.es/validation-* - split: test path: PAWS-X.es/test-* - config_name: PAWS-X.fr data_files: - split: train path: PAWS-X.fr/train-* - split: validation path: PAWS-X.fr/validation-* - split: test path: PAWS-X.fr/test-* - config_name: PAWS-X.ja data_files: - split: train path: PAWS-X.ja/train-* - split: validation path: PAWS-X.ja/validation-* - split: test path: PAWS-X.ja/test-* - config_name: PAWS-X.ko data_files: - split: train path: PAWS-X.ko/train-* - split: validation path: PAWS-X.ko/validation-* - split: test path: PAWS-X.ko/test-* - config_name: PAWS-X.zh data_files: - split: train path: PAWS-X.zh/train-* - split: validation path: PAWS-X.zh/validation-* - split: test path: PAWS-X.zh/test-* - config_name: SQuAD data_files: - split: train path: SQuAD/train-* - split: validation path: SQuAD/validation-* - config_name: XNLI data_files: - split: test path: XNLI/test-* - split: validation path: XNLI/validation-* - config_name: XQuAD.ar data_files: - split: validation path: XQuAD.ar/validation-* - config_name: XQuAD.de data_files: - split: validation path: XQuAD.de/validation-* - config_name: XQuAD.el data_files: - split: validation path: XQuAD.el/validation-* - config_name: XQuAD.en data_files: - split: validation path: XQuAD.en/validation-* - config_name: XQuAD.es data_files: - split: validation path: XQuAD.es/validation-* - config_name: XQuAD.hi data_files: - split: validation path: XQuAD.hi/validation-* - config_name: XQuAD.ru data_files: - split: validation path: XQuAD.ru/validation-* - config_name: XQuAD.th data_files: - split: validation path: XQuAD.th/validation-* - config_name: XQuAD.tr data_files: - split: validation path: XQuAD.tr/validation-* - config_name: XQuAD.vi data_files: - split: validation path: XQuAD.vi/validation-* - config_name: XQuAD.zh data_files: - split: validation path: XQuAD.zh/validation-* - config_name: bucc18.de data_files: - split: validation path: bucc18.de/validation-* - split: test path: bucc18.de/test-* - config_name: bucc18.fr data_files: - split: validation path: bucc18.fr/validation-* - split: test path: bucc18.fr/test-* - config_name: bucc18.ru data_files: - split: validation path: bucc18.ru/validation-* - split: test path: bucc18.ru/test-* - config_name: bucc18.zh data_files: - split: validation path: bucc18.zh/validation-* - split: test path: bucc18.zh/test-* - config_name: tatoeba.afr data_files: - split: validation path: tatoeba.afr/validation-* - config_name: tatoeba.ara data_files: - split: validation path: tatoeba.ara/validation-* - config_name: tatoeba.ben data_files: - split: validation path: tatoeba.ben/validation-* - config_name: tatoeba.bul data_files: - split: validation path: tatoeba.bul/validation-* - config_name: tatoeba.cmn data_files: - split: validation path: tatoeba.cmn/validation-* - config_name: tatoeba.deu data_files: - split: validation path: tatoeba.deu/validation-* - config_name: tatoeba.ell data_files: - split: validation path: tatoeba.ell/validation-* - config_name: tatoeba.est data_files: - split: validation path: tatoeba.est/validation-* - config_name: tatoeba.eus data_files: - split: validation path: tatoeba.eus/validation-* - config_name: tatoeba.fin data_files: - split: validation path: tatoeba.fin/validation-* - config_name: tatoeba.fra data_files: - split: validation path: tatoeba.fra/validation-* - config_name: tatoeba.heb data_files: - split: validation path: tatoeba.heb/validation-* - config_name: tatoeba.hin data_files: - split: validation path: tatoeba.hin/validation-* - config_name: tatoeba.hun data_files: - split: validation path: tatoeba.hun/validation-* - config_name: tatoeba.ind data_files: - split: validation path: tatoeba.ind/validation-* - config_name: tatoeba.ita data_files: - split: validation path: tatoeba.ita/validation-* - config_name: tatoeba.jav data_files: - split: validation path: tatoeba.jav/validation-* - config_name: tatoeba.jpn data_files: - split: validation path: tatoeba.jpn/validation-* - config_name: tatoeba.kat data_files: - split: validation path: tatoeba.kat/validation-* - config_name: tatoeba.kaz data_files: - split: validation path: tatoeba.kaz/validation-* - config_name: tatoeba.kor data_files: - split: validation path: tatoeba.kor/validation-* - config_name: tatoeba.mal data_files: - split: validation path: tatoeba.mal/validation-* - config_name: tatoeba.mar data_files: - split: validation path: tatoeba.mar/validation-* - config_name: tatoeba.nld data_files: - split: validation path: tatoeba.nld/validation-* - config_name: tatoeba.pes data_files: - split: validation path: tatoeba.pes/validation-* - config_name: tatoeba.por data_files: - split: validation path: tatoeba.por/validation-* - config_name: tatoeba.rus data_files: - split: validation path: tatoeba.rus/validation-* - config_name: tatoeba.spa data_files: - split: validation path: tatoeba.spa/validation-* - config_name: tatoeba.swh data_files: - split: validation path: tatoeba.swh/validation-* - config_name: tatoeba.tam data_files: - split: validation path: tatoeba.tam/validation-* - config_name: tatoeba.tel data_files: - split: validation path: tatoeba.tel/validation-* - config_name: tatoeba.tgl data_files: - split: validation path: tatoeba.tgl/validation-* - config_name: tatoeba.tha data_files: - split: validation path: tatoeba.tha/validation-* - config_name: tatoeba.tur data_files: - split: validation path: tatoeba.tur/validation-* - config_name: tatoeba.urd data_files: - split: validation path: tatoeba.urd/validation-* - config_name: tatoeba.vie data_files: - split: validation path: tatoeba.vie/validation-* - config_name: tydiqa data_files: - split: train path: tydiqa/train-* - split: validation path: tydiqa/validation-* - config_name: udpos.Afrikaans data_files: - split: train path: udpos.Afrikaans/train-* - split: validation path: udpos.Afrikaans/validation-* - split: test path: udpos.Afrikaans/test-* - config_name: udpos.Arabic data_files: - split: train path: udpos.Arabic/train-* - split: validation path: udpos.Arabic/validation-* - split: test path: udpos.Arabic/test-* - config_name: udpos.Basque data_files: - split: train path: udpos.Basque/train-* - split: validation path: udpos.Basque/validation-* - split: test path: udpos.Basque/test-* - config_name: udpos.Bulgarian data_files: - split: train path: udpos.Bulgarian/train-* - split: validation path: udpos.Bulgarian/validation-* - split: test path: udpos.Bulgarian/test-* - config_name: udpos.Chinese data_files: - split: train path: udpos.Chinese/train-* - split: validation path: udpos.Chinese/validation-* - split: test path: udpos.Chinese/test-* - config_name: udpos.Dutch data_files: - split: train path: udpos.Dutch/train-* - split: validation path: udpos.Dutch/validation-* - split: test path: udpos.Dutch/test-* - config_name: udpos.English data_files: - split: train path: udpos.English/train-* - split: validation path: udpos.English/validation-* - split: test path: udpos.English/test-* - config_name: udpos.Estonian data_files: - split: train path: udpos.Estonian/train-* - split: validation path: udpos.Estonian/validation-* - split: test path: udpos.Estonian/test-* - config_name: udpos.Finnish data_files: - split: train path: udpos.Finnish/train-* - split: validation path: udpos.Finnish/validation-* - split: test path: udpos.Finnish/test-* - config_name: udpos.French data_files: - split: train path: udpos.French/train-* - split: validation path: udpos.French/validation-* - split: test path: udpos.French/test-* - config_name: udpos.German data_files: - split: train path: udpos.German/train-* - split: validation path: udpos.German/validation-* - split: test path: udpos.German/test-* - config_name: udpos.Greek data_files: - split: train path: udpos.Greek/train-* - split: validation path: udpos.Greek/validation-* - split: test path: udpos.Greek/test-* - config_name: udpos.Hebrew data_files: - split: train path: udpos.Hebrew/train-* - split: validation path: udpos.Hebrew/validation-* - split: test path: udpos.Hebrew/test-* - config_name: udpos.Hindi data_files: - split: train path: udpos.Hindi/train-* - split: validation path: udpos.Hindi/validation-* - split: test path: udpos.Hindi/test-* - config_name: udpos.Hungarian data_files: - split: train path: udpos.Hungarian/train-* - split: validation path: udpos.Hungarian/validation-* - split: test path: udpos.Hungarian/test-* - config_name: udpos.Indonesian data_files: - split: train path: udpos.Indonesian/train-* - split: validation path: udpos.Indonesian/validation-* - split: test path: udpos.Indonesian/test-* - config_name: udpos.Italian data_files: - split: train path: udpos.Italian/train-* - split: validation path: udpos.Italian/validation-* - split: test path: udpos.Italian/test-* - config_name: udpos.Japanese data_files: - split: train path: udpos.Japanese/train-* - split: validation path: udpos.Japanese/validation-* - split: test path: udpos.Japanese/test-* - config_name: udpos.Kazakh data_files: - split: train path: udpos.Kazakh/train-* - split: test path: udpos.Kazakh/test-* - config_name: udpos.Korean data_files: - split: train path: udpos.Korean/train-* - split: validation path: udpos.Korean/validation-* - split: test path: udpos.Korean/test-* - config_name: udpos.Marathi data_files: - split: train path: udpos.Marathi/train-* - split: validation path: udpos.Marathi/validation-* - split: test path: udpos.Marathi/test-* - config_name: udpos.Persian data_files: - split: train path: udpos.Persian/train-* - split: validation path: udpos.Persian/validation-* - split: test path: udpos.Persian/test-* - config_name: udpos.Portuguese data_files: - split: train path: udpos.Portuguese/train-* - split: validation path: udpos.Portuguese/validation-* - split: test path: udpos.Portuguese/test-* - config_name: udpos.Russian data_files: - split: train path: udpos.Russian/train-* - split: validation path: udpos.Russian/validation-* - split: test path: udpos.Russian/test-* - config_name: udpos.Spanish data_files: - split: train path: udpos.Spanish/train-* - split: validation path: udpos.Spanish/validation-* - split: test path: udpos.Spanish/test-* - config_name: udpos.Tagalog data_files: - split: test path: udpos.Tagalog/test-* - config_name: udpos.Tamil data_files: - split: train path: udpos.Tamil/train-* - split: validation path: udpos.Tamil/validation-* - split: test path: udpos.Tamil/test-* - config_name: udpos.Telugu data_files: - split: train path: udpos.Telugu/train-* - split: validation path: udpos.Telugu/validation-* - split: test path: udpos.Telugu/test-* - config_name: udpos.Thai data_files: - split: test path: udpos.Thai/test-* - config_name: udpos.Turkish data_files: - split: train path: udpos.Turkish/train-* - split: validation path: udpos.Turkish/validation-* - split: test path: udpos.Turkish/test-* - config_name: udpos.Urdu data_files: - split: train path: udpos.Urdu/train-* - split: validation path: udpos.Urdu/validation-* - split: test path: udpos.Urdu/test-* - config_name: udpos.Vietnamese data_files: - split: train path: udpos.Vietnamese/train-* - split: validation path: udpos.Vietnamese/validation-* - split: test path: udpos.Vietnamese/test-* - config_name: udpos.Yoruba data_files: - split: test path: udpos.Yoruba/test-* --- # Dataset Card for "xtreme" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://github.com/google-research/xtreme](https://github.com/google-research/xtreme) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 15.88 GB - **Size of the generated dataset:** 1.08 GB - **Total amount of disk used:** 16.96 GB ### Dataset Summary The Cross-lingual Natural Language Inference (XNLI) corpus is a crowd-sourced collection of 5,000 test and 2,500 dev pairs for the MultiNLI corpus. The pairs are annotated with textual entailment and translated into 14 languages: French, Spanish, German, Greek, Bulgarian, Russian, Turkish, Arabic, Vietnamese, Thai, Chinese, Hindi, Swahili and Urdu. This results in 112.5k annotated pairs. Each premise can be associated with the corresponding hypothesis in the 15 languages, summing up to more than 1.5M combinations. The corpus is made to evaluate how to perform inference in any language (including low-resources ones like Swahili or Urdu) when only English NLI data is available at training time. One solution is cross-lingual sentence encoding, for which XNLI is an evaluation benchmark. The Cross-lingual TRansfer Evaluation of Multilingual Encoders (XTREME) benchmark is a benchmark for the evaluation of the cross-lingual generalization ability of pre-trained multilingual models. It covers 40 typologically diverse languages (spanning 12 language families) and includes nine tasks that collectively require reasoning about different levels of syntax and semantics. The languages in XTREME are selected to maximize language diversity, coverage in existing tasks, and availability of training data. Among these are many under-studied languages, such as the Dravidian languages Tamil (spoken in southern India, Sri Lanka, and Singapore), Telugu and Malayalam (spoken mainly in southern India), and the Niger-Congo languages Swahili and Yoruba, spoken in Africa. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### MLQA.ar.ar - **Size of downloaded dataset files:** 75.72 MB - **Size of the generated dataset:** 9.20 MB - **Total amount of disk used:** 84.91 MB An example of 'validation' looks as follows. ``` ``` #### MLQA.ar.de - **Size of downloaded dataset files:** 75.72 MB - **Size of the generated dataset:** 2.55 MB - **Total amount of disk used:** 78.27 MB An example of 'validation' looks as follows. ``` ``` #### MLQA.ar.en - **Size of downloaded dataset files:** 75.72 MB - **Size of the generated dataset:** 9.04 MB - **Total amount of disk used:** 84.76 MB An example of 'validation' looks as follows. ``` ``` #### MLQA.ar.es - **Size of downloaded dataset files:** 75.72 MB - **Size of the generated dataset:** 3.27 MB - **Total amount of disk used:** 78.99 MB An example of 'validation' looks as follows. ``` ``` #### MLQA.ar.hi - **Size of downloaded dataset files:** 75.72 MB - **Size of the generated dataset:** 3.32 MB - **Total amount of disk used:** 79.04 MB An example of 'validation' looks as follows. ``` ``` ### Data Fields The data fields are the same among all splits. #### MLQA.ar.ar - `id`: a `string` feature. - `title`: a `string` feature. - `context`: a `string` feature. - `question`: a `string` feature. - `answers`: a dictionary feature containing: - `answer_start`: a `int32` feature. - `text`: a `string` feature. #### MLQA.ar.de - `id`: a `string` feature. - `title`: a `string` feature. - `context`: a `string` feature. - `question`: a `string` feature. - `answers`: a dictionary feature containing: - `answer_start`: a `int32` feature. - `text`: a `string` feature. #### MLQA.ar.en - `id`: a `string` feature. - `title`: a `string` feature. - `context`: a `string` feature. - `question`: a `string` feature. - `answers`: a dictionary feature containing: - `answer_start`: a `int32` feature. - `text`: a `string` feature. #### MLQA.ar.es - `id`: a `string` feature. - `title`: a `string` feature. - `context`: a `string` feature. - `question`: a `string` feature. - `answers`: a dictionary feature containing: - `answer_start`: a `int32` feature. - `text`: a `string` feature. #### MLQA.ar.hi - `id`: a `string` feature. - `title`: a `string` feature. - `context`: a `string` feature. - `question`: a `string` feature. - `answers`: a dictionary feature containing: - `answer_start`: a `int32` feature. - `text`: a `string` feature. ### Data Splits | name |validation|test| |----------|---------:|---:| |MLQA.ar.ar| 517|5335| |MLQA.ar.de| 207|1649| |MLQA.ar.en| 517|5335| |MLQA.ar.es| 161|1978| |MLQA.ar.hi| 186|1831| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @InProceedings{conneau2018xnli, author = {Conneau, Alexis and Rinott, Ruty and Lample, Guillaume and Williams, Adina and Bowman, Samuel R. and Schwenk, Holger and Stoyanov, Veselin}, title = {XNLI: Evaluating Cross-lingual Sentence Representations}, booktitle = {Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing}, year = {2018}, publisher = {Association for Computational Linguistics}, location = {Brussels, Belgium}, } @article{hu2020xtreme, author = {Junjie Hu and Sebastian Ruder and Aditya Siddhant and Graham Neubig and Orhan Firat and Melvin Johnson}, title = {XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalization}, journal = {CoRR}, volume = {abs/2003.11080}, year = {2020}, archivePrefix = {arXiv}, eprint = {2003.11080} } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@jplu](https://github.com/jplu), [@lewtun](https://github.com/lewtun), [@lvwerra](https://github.com/lvwerra), [@lhoestq](https://github.com/lhoestq), [@patrickvonplaten](https://github.com/patrickvonplaten), [@mariamabarham](https://github.com/mariamabarham) for adding this dataset.

unimelb-nlp/wikiann

unimelb-nlp

2024-02-22T14:32:02Z

109,759

106

[ "task_categories:token-classification", "task_ids:named-entity-recognition", "annotations_creators:machine-generated", "language_creators:crowdsourced", "multilinguality:multilingual", "source_datasets:original", "language:ace", "language:af", "language:als", "language:am", "language:an", "language:ang", "language:ar", "language:arc", "language:arz", "language:as", "language:ast", "language:ay", "language:az", "language:ba", "language:bar", "language:be", "language:bg", "language:bh", "language:bn", "language:bo", "language:br", "language:bs", "language:ca", "language:cbk", "language:cdo", "language:ce", "language:ceb", "language:ckb", "language:co", "language:crh", "language:cs", "language:csb", "language:cv", "language:cy", "language:da", "language:de", "language:diq", "language:dv", "language:el", "language:eml", "language:en", "language:eo", "language:es", "language:et", "language:eu", "language:ext", "language:fa", "language:fi", "language:fo", "language:fr", "language:frr", "language:fur", "language:fy", "language:ga", "language:gan", "language:gd", "language:gl", "language:gn", "language:gu", "language:hak", "language:he", "language:hi", "language:hr", "language:hsb", "language:hu", "language:hy", "language:ia", "language:id", "language:ig", "language:ilo", "language:io", "language:is", "language:it", "language:ja", "language:jbo", "language:jv", "language:ka", "language:kk", "language:km", "language:kn", "language:ko", "language:ksh", "language:ku", "language:ky", "language:la", "language:lb", "language:li", "language:lij", "language:lmo", "language:ln", "language:lt", "language:lv", "language:lzh", "language:mg", "language:mhr", "language:mi", "language:min", "language:mk", "language:ml", "language:mn", "language:mr", "language:ms", "language:mt", "language:mwl", "language:my", "language:mzn", "language:nan", "language:nap", "language:nds", "language:ne", "language:nl", "language:nn", "language:no", "language:nov", "language:oc", "language:or", "language:os", "language:pa", "language:pdc", "language:pl", "language:pms", "language:pnb", "language:ps", "language:pt", "language:qu", "language:rm", "language:ro", "language:ru", "language:rw", "language:sa", "language:sah", "language:scn", "language:sco", "language:sd", "language:sgs", "language:sh", "language:si", "language:sk", "language:sl", "language:so", "language:sq", "language:sr", "language:su", "language:sv", "language:sw", "language:szl", "language:ta", "language:te", "language:tg", "language:th", "language:tk", "language:tl", "language:tr", "language:tt", "language:ug", "language:uk", "language:ur", "language:uz", "language:vec", "language:vep", "language:vi", "language:vls", "language:vo", "language:vro", "language:wa", "language:war", "language:wuu", "language:xmf", "language:yi", "language:yo", "language:yue", "language:zea", "language:zh", "license:unknown", "size_categories:1M<n<10M", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:1902.00193", "region:us" ]

[ "token-classification" ]

2022-03-02T23:29:22Z

null

--- annotations_creators: - machine-generated language_creators: - crowdsourced language: - ace - af - als - am - an - ang - ar - arc - arz - as - ast - ay - az - ba - bar - be - bg - bh - bn - bo - br - bs - ca - cbk - cdo - ce - ceb - ckb - co - crh - cs - csb - cv - cy - da - de - diq - dv - el - eml - en - eo - es - et - eu - ext - fa - fi - fo - fr - frr - fur - fy - ga - gan - gd - gl - gn - gu - hak - he - hi - hr - hsb - hu - hy - ia - id - ig - ilo - io - is - it - ja - jbo - jv - ka - kk - km - kn - ko - ksh - ku - ky - la - lb - li - lij - lmo - ln - lt - lv - lzh - mg - mhr - mi - min - mk - ml - mn - mr - ms - mt - mwl - my - mzn - nan - nap - nds - ne - nl - nn - 'no' - nov - oc - or - os - pa - pdc - pl - pms - pnb - ps - pt - qu - rm - ro - ru - rw - sa - sah - scn - sco - sd - sgs - sh - si - sk - sl - so - sq - sr - su - sv - sw - szl - ta - te - tg - th - tk - tl - tr - tt - ug - uk - ur - uz - vec - vep - vi - vls - vo - vro - wa - war - wuu - xmf - yi - yo - yue - zea - zh license: - unknown multilinguality: - multilingual size_categories: - n<1K source_datasets: - original task_categories: - token-classification task_ids: - named-entity-recognition paperswithcode_id: wikiann-1 pretty_name: WikiANN config_names: - 'no' - ace - af - als - am - an - ang - ar - arc - arz - as - ast - ay - az - ba - bar - be - bg - bh - bn - bo - br - bs - ca - cdo - ce - ceb - ckb - co - crh - cs - csb - cv - cy - da - de - diq - dv - el - en - eo - es - et - eu - ext - fa - fi - fo - fr - frr - fur - fy - ga - gan - gd - gl - gn - gu - hak - he - hi - hr - hsb - hu - hy - ia - id - ig - ilo - io - is - it - ja - jbo - jv - ka - kk - km - kn - ko - ksh - ku - ky - la - lb - li - lij - lmo - ln - lt - lv - mg - mhr - mi - min - mk - ml - mn - mr - ms - mt - mwl - my - mzn - nap - nds - ne - nl - nn - nov - oc - or - os - other-bat-smg - other-be-x-old - other-cbk-zam - other-eml - other-fiu-vro - other-map-bms - other-simple - other-zh-classical - other-zh-min-nan - other-zh-yue - pa - pdc - pl - pms - pnb - ps - pt - qu - rm - ro - ru - rw - sa - sah - scn - sco - sd - sh - si - sk - sl - so - sq - sr - su - sv - sw - szl - ta - te - tg - th - tk - tl - tr - tt - ug - uk - ur - uz - vec - vep - vi - vls - vo - wa - war - wuu - xmf - yi - yo - zea - zh language_bcp47: - be-tarask - en-basiceng - jv-x-bms dataset_info: - config_name: ace features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 22425 num_examples: 100 - name: test num_bytes: 25724 num_examples: 100 - name: train num_bytes: 23203 num_examples: 100 download_size: 27835 dataset_size: 71352 - config_name: af features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 299109 num_examples: 1000 - name: test num_bytes: 295821 num_examples: 1000 - name: train num_bytes: 1521576 num_examples: 5000 download_size: 528580 dataset_size: 2116506 - config_name: als features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 34290 num_examples: 100 - name: test num_bytes: 36317 num_examples: 100 - name: train num_bytes: 34940 num_examples: 100 download_size: 40186 dataset_size: 105547 - config_name: am features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 21401 num_examples: 100 - name: test num_bytes: 23783 num_examples: 100 - name: train num_bytes: 22186 num_examples: 100 download_size: 30287 dataset_size: 67370 - config_name: an features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 180581 num_examples: 1000 - name: test num_bytes: 174964 num_examples: 1000 - name: train num_bytes: 180939 num_examples: 1000 download_size: 128283 dataset_size: 536484 - config_name: ang features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 21897 num_examples: 100 - name: test num_bytes: 24495 num_examples: 100 - name: train num_bytes: 23268 num_examples: 100 download_size: 30667 dataset_size: 69660 - config_name: ar features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 2325660 num_examples: 10000 - name: test num_bytes: 2334636 num_examples: 10000 - name: train num_bytes: 4671613 num_examples: 20000 download_size: 2582112 dataset_size: 9331909 - config_name: arc features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 15698 num_examples: 100 - name: test num_bytes: 16613 num_examples: 100 - name: train num_bytes: 18508 num_examples: 100 download_size: 22858 dataset_size: 50819 - config_name: arz features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 26581 num_examples: 100 - name: test num_bytes: 25635 num_examples: 100 - name: train num_bytes: 26347 num_examples: 100 download_size: 32301 dataset_size: 78563 - config_name: as features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 25708 num_examples: 100 - name: test num_bytes: 23322 num_examples: 100 - name: train num_bytes: 24956 num_examples: 100 download_size: 30404 dataset_size: 73986 - config_name: ast features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 217449 num_examples: 1000 - name: test num_bytes: 220846 num_examples: 1000 - name: train num_bytes: 228210 num_examples: 1000 download_size: 157002 dataset_size: 666505 - config_name: ay features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 11656 num_examples: 100 - name: test num_bytes: 13351 num_examples: 100 - name: train num_bytes: 12568 num_examples: 100 download_size: 16901 dataset_size: 37575 - config_name: az features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 272038 num_examples: 1000 - name: test num_bytes: 267907 num_examples: 1000 - name: train num_bytes: 2645524 num_examples: 10000 download_size: 931014 dataset_size: 3185469 - config_name: ba features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 29234 num_examples: 100 - name: test num_bytes: 30474 num_examples: 100 - name: train num_bytes: 31095 num_examples: 100 download_size: 36848 dataset_size: 90803 - config_name: bar features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 17346 num_examples: 100 - name: test num_bytes: 17811 num_examples: 100 - name: train num_bytes: 16768 num_examples: 100 download_size: 21987 dataset_size: 51925 - config_name: bat-smg features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 26468 num_examples: 100 - name: test num_bytes: 26065 num_examples: 100 - name: train num_bytes: 24649 num_examples: 100 download_size: 31533 dataset_size: 77182 - config_name: be features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 262014 num_examples: 1000 - name: test num_bytes: 266076 num_examples: 1000 - name: train num_bytes: 3983266 num_examples: 15000 download_size: 1283568 dataset_size: 4511356 - config_name: be-x-old features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 342626 num_examples: 1000 - name: test num_bytes: 337571 num_examples: 1000 - name: train num_bytes: 1704228 num_examples: 5000 download_size: 586037 dataset_size: 2384425 - config_name: bg features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 2840879 num_examples: 10000 - name: test num_bytes: 2830185 num_examples: 10000 - name: train num_bytes: 5665007 num_examples: 20000 download_size: 3010319 dataset_size: 11336071 - config_name: bh features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 33654 num_examples: 100 - name: test num_bytes: 30664 num_examples: 100 - name: train num_bytes: 36346 num_examples: 100 download_size: 34563 dataset_size: 100664 - config_name: bn features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 238418 num_examples: 1000 - name: test num_bytes: 237190 num_examples: 1000 - name: train num_bytes: 2351563 num_examples: 10000 download_size: 667399 dataset_size: 2827171 - config_name: bo features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 22660 num_examples: 100 - name: test num_bytes: 15409 num_examples: 100 - name: train num_bytes: 14057 num_examples: 100 download_size: 26274 dataset_size: 52126 - config_name: br features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 206811 num_examples: 1000 - name: test num_bytes: 222055 num_examples: 1000 - name: train num_bytes: 221467 num_examples: 1000 download_size: 193001 dataset_size: 650333 - config_name: bs features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 246350 num_examples: 1000 - name: test num_bytes: 247303 num_examples: 1000 - name: train num_bytes: 3669290 num_examples: 15000 download_size: 1145992 dataset_size: 4162943 - config_name: ca features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 1836291 num_examples: 10000 - name: test num_bytes: 1847718 num_examples: 10000 - name: train num_bytes: 3689286 num_examples: 20000 download_size: 2392551 dataset_size: 7373295 - config_name: cbk-zam features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 47032 num_examples: 100 - name: test num_bytes: 47249 num_examples: 100 - name: train num_bytes: 52517 num_examples: 100 download_size: 37209 dataset_size: 146798 - config_name: cdo features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 37451 num_examples: 100 - name: test num_bytes: 34291 num_examples: 100 - name: train num_bytes: 36176 num_examples: 100 download_size: 34997 dataset_size: 107918 - config_name: ce features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 40275 num_examples: 100 - name: test num_bytes: 38612 num_examples: 100 - name: train num_bytes: 38256 num_examples: 100 download_size: 34386 dataset_size: 117143 - config_name: ceb features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 22761 num_examples: 100 - name: test num_bytes: 23922 num_examples: 100 - name: train num_bytes: 21337 num_examples: 100 download_size: 27030 dataset_size: 68020 - config_name: ckb features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 214203 num_examples: 1000 - name: test num_bytes: 211960 num_examples: 1000 - name: train num_bytes: 217038 num_examples: 1000 download_size: 148534 dataset_size: 643201 - config_name: co features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 15940 num_examples: 100 - name: test num_bytes: 15852 num_examples: 100 - name: train num_bytes: 18004 num_examples: 100 download_size: 25539 dataset_size: 49796 - config_name: crh features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 20202 num_examples: 100 - name: test num_bytes: 23851 num_examples: 100 - name: train num_bytes: 23308 num_examples: 100 download_size: 29468 dataset_size: 67361 - config_name: cs features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 2456626 num_examples: 10000 - name: test num_bytes: 2458127 num_examples: 10000 - name: train num_bytes: 4944702 num_examples: 20000 download_size: 3028120 dataset_size: 9859455 - config_name: csb features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 28813 num_examples: 100 - name: test num_bytes: 27812 num_examples: 100 - name: train num_bytes: 31612 num_examples: 100 download_size: 35313 dataset_size: 88237 - config_name: cv features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 24759 num_examples: 100 - name: test num_bytes: 26375 num_examples: 100 - name: train num_bytes: 26928 num_examples: 100 download_size: 32018 dataset_size: 78062 - config_name: cy features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 228558 num_examples: 1000 - name: test num_bytes: 233841 num_examples: 1000 - name: train num_bytes: 2337088 num_examples: 10000 download_size: 630636 dataset_size: 2799487 - config_name: da features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 2422948 num_examples: 10000 - name: test num_bytes: 2432296 num_examples: 10000 - name: train num_bytes: 4882166 num_examples: 20000 download_size: 2903455 dataset_size: 9737410 - config_name: de features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 2754522 num_examples: 10000 - name: test num_bytes: 2750968 num_examples: 10000 - name: train num_bytes: 5510585 num_examples: 20000 download_size: 3340116 dataset_size: 11016075 - config_name: diq features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 24119 num_examples: 100 - name: test num_bytes: 22448 num_examples: 100 - name: train num_bytes: 24103 num_examples: 100 download_size: 29511 dataset_size: 70670 - config_name: dv features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 30294 num_examples: 100 - name: test num_bytes: 27251 num_examples: 100 - name: train num_bytes: 31005 num_examples: 100 download_size: 36181 dataset_size: 88550 - config_name: el features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 3027934 num_examples: 10000 - name: test num_bytes: 3034301 num_examples: 10000 - name: train num_bytes: 6046582 num_examples: 20000 download_size: 3212871 dataset_size: 12108817 - config_name: eml features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 30022 num_examples: 100 - name: test num_bytes: 35852 num_examples: 100 - name: train num_bytes: 30764 num_examples: 100 download_size: 35629 dataset_size: 96638 - config_name: en features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 2336325 num_examples: 10000 - name: test num_bytes: 2330217 num_examples: 10000 - name: train num_bytes: 4649545 num_examples: 20000 download_size: 2990984 dataset_size: 9316087 - config_name: eo features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 1968662 num_examples: 10000 - name: test num_bytes: 1961458 num_examples: 10000 - name: train num_bytes: 2952554 num_examples: 15000 download_size: 2147812 dataset_size: 6882674 - config_name: es features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 1976907 num_examples: 10000 - name: test num_bytes: 1986636 num_examples: 10000 - name: train num_bytes: 3972236 num_examples: 20000 download_size: 2431958 dataset_size: 7935779 - config_name: et features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 2403333 num_examples: 10000 - name: test num_bytes: 2392396 num_examples: 10000 - name: train num_bytes: 3579208 num_examples: 15000 download_size: 2678718 dataset_size: 8374937 - config_name: eu features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 2677008 num_examples: 10000 - name: test num_bytes: 2628923 num_examples: 10000 - name: train num_bytes: 2672325 num_examples: 10000 download_size: 1985966 dataset_size: 7978256 - config_name: ext features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 30793 num_examples: 100 - name: test num_bytes: 29455 num_examples: 100 - name: train num_bytes: 23082 num_examples: 100 download_size: 32111 dataset_size: 83330 - config_name: fa features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 2328612 num_examples: 10000 - name: test num_bytes: 2314659 num_examples: 10000 - name: train num_bytes: 4618042 num_examples: 20000 download_size: 2385463 dataset_size: 9261313 - config_name: fi features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 2500558 num_examples: 10000 - name: test num_bytes: 2505133 num_examples: 10000 - name: train num_bytes: 5020599 num_examples: 20000 download_size: 3407283 dataset_size: 10026290 - config_name: fiu-vro features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 27644 num_examples: 100 - name: test num_bytes: 27700 num_examples: 100 - name: train num_bytes: 28661 num_examples: 100 download_size: 31399 dataset_size: 84005 - config_name: fo features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 26066 num_examples: 100 - name: test num_bytes: 23503 num_examples: 100 - name: train num_bytes: 26150 num_examples: 100 download_size: 33699 dataset_size: 75719 - config_name: fr features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 2057976 num_examples: 10000 - name: test num_bytes: 2073565 num_examples: 10000 - name: train num_bytes: 4123939 num_examples: 20000 download_size: 2694633 dataset_size: 8255480 - config_name: frr features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 15855 num_examples: 100 - name: test num_bytes: 15708 num_examples: 100 - name: train num_bytes: 16626 num_examples: 100 download_size: 25130 dataset_size: 48189 - config_name: fur features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 25236 num_examples: 100 - name: test num_bytes: 30534 num_examples: 100 - name: train num_bytes: 33626 num_examples: 100 download_size: 32754 dataset_size: 89396 - config_name: fy features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 226408 num_examples: 1000 - name: test num_bytes: 229672 num_examples: 1000 - name: train num_bytes: 222985 num_examples: 1000 download_size: 182402 dataset_size: 679065 - config_name: ga features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 234064 num_examples: 1000 - name: test num_bytes: 235055 num_examples: 1000 - name: train num_bytes: 238019 num_examples: 1000 download_size: 198615 dataset_size: 707138 - config_name: gan features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 17505 num_examples: 100 - name: test num_bytes: 13851 num_examples: 100 - name: train num_bytes: 14370 num_examples: 100 download_size: 28600 dataset_size: 45726 - config_name: gd features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 23202 num_examples: 100 - name: test num_bytes: 20280 num_examples: 100 - name: train num_bytes: 20126 num_examples: 100 download_size: 29305 dataset_size: 63608 - config_name: gl features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 2029655 num_examples: 10000 - name: test num_bytes: 2031122 num_examples: 10000 - name: train num_bytes: 3030937 num_examples: 15000 download_size: 2045672 dataset_size: 7091714 - config_name: gn features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 29104 num_examples: 100 - name: test num_bytes: 24235 num_examples: 100 - name: train num_bytes: 28192 num_examples: 100 download_size: 35600 dataset_size: 81531 - config_name: gu features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 47981 num_examples: 100 - name: test num_bytes: 45389 num_examples: 100 - name: train num_bytes: 42597 num_examples: 100 download_size: 44658 dataset_size: 135967 - config_name: hak features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 17949 num_examples: 100 - name: test num_bytes: 18127 num_examples: 100 - name: train num_bytes: 16180 num_examples: 100 download_size: 27841 dataset_size: 52256 - config_name: he features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 2801364 num_examples: 10000 - name: test num_bytes: 2785446 num_examples: 10000 - name: train num_bytes: 5600432 num_examples: 20000 download_size: 3112250 dataset_size: 11187242 - config_name: hi features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 261179 num_examples: 1000 - name: test num_bytes: 267227 num_examples: 1000 - name: train num_bytes: 1315801 num_examples: 5000 download_size: 441664 dataset_size: 1844207 - config_name: hr features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 2417422 num_examples: 10000 - name: test num_bytes: 2430412 num_examples: 10000 - name: train num_bytes: 4877275 num_examples: 20000 download_size: 2965267 dataset_size: 9725109 - config_name: hsb features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 24667 num_examples: 100 - name: test num_bytes: 24320 num_examples: 100 - name: train num_bytes: 24200 num_examples: 100 download_size: 31799 dataset_size: 73187 - config_name: hu features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 2590088 num_examples: 10000 - name: test num_bytes: 2626743 num_examples: 10000 - name: train num_bytes: 5263066 num_examples: 20000 download_size: 3333477 dataset_size: 10479897 - config_name: hy features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 237532 num_examples: 1000 - name: test num_bytes: 237093 num_examples: 1000 - name: train num_bytes: 3634009 num_examples: 15000 download_size: 1179988 dataset_size: 4108634 - config_name: ia features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 32036 num_examples: 100 - name: test num_bytes: 37589 num_examples: 100 - name: train num_bytes: 32900 num_examples: 100 download_size: 38484 dataset_size: 102525 - config_name: id features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 1901597 num_examples: 10000 - name: test num_bytes: 1902704 num_examples: 10000 - name: train num_bytes: 3813991 num_examples: 20000 download_size: 2199732 dataset_size: 7618292 - config_name: ig features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 17693 num_examples: 100 - name: test num_bytes: 18404 num_examples: 100 - name: train num_bytes: 15960 num_examples: 100 download_size: 22605 dataset_size: 52057 - config_name: ilo features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 16647 num_examples: 100 - name: test num_bytes: 17217 num_examples: 100 - name: train num_bytes: 17124 num_examples: 100 download_size: 23906 dataset_size: 50988 - config_name: io features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 18998 num_examples: 100 - name: test num_bytes: 17203 num_examples: 100 - name: train num_bytes: 20753 num_examples: 100 download_size: 27554 dataset_size: 56954 - config_name: is features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 243639 num_examples: 1000 - name: test num_bytes: 235918 num_examples: 1000 - name: train num_bytes: 243437 num_examples: 1000 download_size: 210731 dataset_size: 722994 - config_name: it features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 2282919 num_examples: 10000 - name: test num_bytes: 2307590 num_examples: 10000 - name: train num_bytes: 4633519 num_examples: 20000 download_size: 2818124 dataset_size: 9224028 - config_name: ja features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 6775580 num_examples: 10000 - name: test num_bytes: 6898510 num_examples: 10000 - name: train num_bytes: 13578269 num_examples: 20000 download_size: 3415775 dataset_size: 27252359 - config_name: jbo features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 15590 num_examples: 100 - name: test num_bytes: 19558 num_examples: 100 - name: train num_bytes: 15042 num_examples: 100 download_size: 22634 dataset_size: 50190 - config_name: jv features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 17663 num_examples: 100 - name: test num_bytes: 20175 num_examples: 100 - name: train num_bytes: 19381 num_examples: 100 download_size: 28541 dataset_size: 57219 - config_name: ka features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 3454353 num_examples: 10000 - name: test num_bytes: 3480842 num_examples: 10000 - name: train num_bytes: 3427980 num_examples: 10000 download_size: 2588715 dataset_size: 10363175 - config_name: kk features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 286474 num_examples: 1000 - name: test num_bytes: 284475 num_examples: 1000 - name: train num_bytes: 287924 num_examples: 1000 download_size: 217890 dataset_size: 858873 - config_name: km features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 29282 num_examples: 100 - name: test num_bytes: 36073 num_examples: 100 - name: train num_bytes: 31910 num_examples: 100 download_size: 43075 dataset_size: 97265 - config_name: kn features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 36825 num_examples: 100 - name: test num_bytes: 32250 num_examples: 100 - name: train num_bytes: 34318 num_examples: 100 download_size: 43835 dataset_size: 103393 - config_name: ko features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 2553040 num_examples: 10000 - name: test num_bytes: 2547772 num_examples: 10000 - name: train num_bytes: 5107034 num_examples: 20000 download_size: 3536508 dataset_size: 10207846 - config_name: ksh features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 26310 num_examples: 100 - name: test num_bytes: 25221 num_examples: 100 - name: train num_bytes: 25913 num_examples: 100 download_size: 33350 dataset_size: 77444 - config_name: ku features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 22569 num_examples: 100 - name: test num_bytes: 20767 num_examples: 100 - name: train num_bytes: 22641 num_examples: 100 download_size: 30470 dataset_size: 65977 - config_name: ky features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 30982 num_examples: 100 - name: test num_bytes: 31868 num_examples: 100 - name: train num_bytes: 32740 num_examples: 100 download_size: 41036 dataset_size: 95590 - config_name: la features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 207177 num_examples: 1000 - name: test num_bytes: 198882 num_examples: 1000 - name: train num_bytes: 999022 num_examples: 5000 download_size: 367324 dataset_size: 1405081 - config_name: lb features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 253746 num_examples: 1000 - name: test num_bytes: 249961 num_examples: 1000 - name: train num_bytes: 1260911 num_examples: 5000 download_size: 477151 dataset_size: 1764618 - config_name: li features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 20173 num_examples: 100 - name: test num_bytes: 18789 num_examples: 100 - name: train num_bytes: 20183 num_examples: 100 download_size: 28842 dataset_size: 59145 - config_name: lij features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 27977 num_examples: 100 - name: test num_bytes: 27854 num_examples: 100 - name: train num_bytes: 30553 num_examples: 100 download_size: 33981 dataset_size: 86384 - config_name: lmo features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 26547 num_examples: 100 - name: test num_bytes: 29425 num_examples: 100 - name: train num_bytes: 24133 num_examples: 100 download_size: 32492 dataset_size: 80105 - config_name: ln features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 21681 num_examples: 100 - name: test num_bytes: 26975 num_examples: 100 - name: train num_bytes: 22199 num_examples: 100 download_size: 28691 dataset_size: 70855 - config_name: lt features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 2192846 num_examples: 10000 - name: test num_bytes: 2191241 num_examples: 10000 - name: train num_bytes: 2199918 num_examples: 10000 download_size: 2138545 dataset_size: 6584005 - config_name: lv features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 2173392 num_examples: 10000 - name: test num_bytes: 2190430 num_examples: 10000 - name: train num_bytes: 2206915 num_examples: 10000 download_size: 2012494 dataset_size: 6570737 - config_name: map-bms features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 19752 num_examples: 100 - name: test num_bytes: 20530 num_examples: 100 - name: train num_bytes: 21611 num_examples: 100 download_size: 25217 dataset_size: 61893 - config_name: mg features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 24833 num_examples: 100 - name: test num_bytes: 22542 num_examples: 100 - name: train num_bytes: 25711 num_examples: 100 download_size: 26980 dataset_size: 73086 - config_name: mhr features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 23235 num_examples: 100 - name: test num_bytes: 23611 num_examples: 100 - name: train num_bytes: 18620 num_examples: 100 download_size: 29844 dataset_size: 65466 - config_name: mi features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 39371 num_examples: 100 - name: test num_bytes: 40119 num_examples: 100 - name: train num_bytes: 37868 num_examples: 100 download_size: 24626 dataset_size: 117358 - config_name: min features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 28691 num_examples: 100 - name: test num_bytes: 24713 num_examples: 100 - name: train num_bytes: 26592 num_examples: 100 download_size: 31058 dataset_size: 79996 - config_name: mk features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 333165 num_examples: 1000 - name: test num_bytes: 337729 num_examples: 1000 - name: train num_bytes: 3355908 num_examples: 10000 download_size: 825847 dataset_size: 4026802 - config_name: ml features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 362980 num_examples: 1000 - name: test num_bytes: 349355 num_examples: 1000 - name: train num_bytes: 3582038 num_examples: 10000 download_size: 1190172 dataset_size: 4294373 - config_name: mn features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 21978 num_examples: 100 - name: test num_bytes: 23510 num_examples: 100 - name: train num_bytes: 23216 num_examples: 100 download_size: 32990 dataset_size: 68704 - config_name: mr features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 314830 num_examples: 1000 - name: test num_bytes: 326262 num_examples: 1000 - name: train num_bytes: 1598776 num_examples: 5000 download_size: 524029 dataset_size: 2239868 - config_name: ms features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 183916 num_examples: 1000 - name: test num_bytes: 183511 num_examples: 1000 - name: train num_bytes: 3699182 num_examples: 20000 download_size: 1077180 dataset_size: 4066609 - config_name: mt features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 24543 num_examples: 100 - name: test num_bytes: 24634 num_examples: 100 - name: train num_bytes: 24928 num_examples: 100 download_size: 33526 dataset_size: 74105 - config_name: mwl features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 51959 num_examples: 100 - name: test num_bytes: 42980 num_examples: 100 - name: train num_bytes: 44577 num_examples: 100 download_size: 44197 dataset_size: 139516 - config_name: my features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 48925 num_examples: 100 - name: test num_bytes: 45928 num_examples: 100 - name: train num_bytes: 41343 num_examples: 100 download_size: 51490 dataset_size: 136196 - config_name: mzn features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 25276 num_examples: 100 - name: test num_bytes: 25919 num_examples: 100 - name: train num_bytes: 24813 num_examples: 100 download_size: 29895 dataset_size: 76008 - config_name: nap features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 21518 num_examples: 100 - name: test num_bytes: 24166 num_examples: 100 - name: train num_bytes: 26568 num_examples: 100 download_size: 30764 dataset_size: 72252 - config_name: nds features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 28360 num_examples: 100 - name: test num_bytes: 26543 num_examples: 100 - name: train num_bytes: 24651 num_examples: 100 download_size: 33734 dataset_size: 79554 - config_name: ne features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 33904 num_examples: 100 - name: test num_bytes: 33199 num_examples: 100 - name: train num_bytes: 36145 num_examples: 100 download_size: 37920 dataset_size: 103248 - config_name: nl features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 2378052 num_examples: 10000 - name: test num_bytes: 2403048 num_examples: 10000 - name: train num_bytes: 4784233 num_examples: 20000 download_size: 2867129 dataset_size: 9565333 - config_name: nn features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 274112 num_examples: 1000 - name: test num_bytes: 269603 num_examples: 1000 - name: train num_bytes: 5436129 num_examples: 20000 download_size: 1644504 dataset_size: 5979844 - config_name: 'no' features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 2576641 num_examples: 10000 - name: test num_bytes: 2563531 num_examples: 10000 - name: train num_bytes: 5139492 num_examples: 20000 download_size: 3063453 dataset_size: 10279664 - config_name: nov features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 14828 num_examples: 100 - name: test num_bytes: 14802 num_examples: 100 - name: train num_bytes: 17242 num_examples: 100 download_size: 20235 dataset_size: 46872 - config_name: oc features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 20400 num_examples: 100 - name: test num_bytes: 18572 num_examples: 100 - name: train num_bytes: 19291 num_examples: 100 download_size: 29284 dataset_size: 58263 - config_name: or features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 32103 num_examples: 100 - name: test num_bytes: 29480 num_examples: 100 - name: train num_bytes: 27794 num_examples: 100 download_size: 31116 dataset_size: 89377 - config_name: os features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 26751 num_examples: 100 - name: test num_bytes: 25967 num_examples: 100 - name: train num_bytes: 26005 num_examples: 100 download_size: 32948 dataset_size: 78723 - config_name: pa features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 25202 num_examples: 100 - name: test num_bytes: 23680 num_examples: 100 - name: train num_bytes: 24143 num_examples: 100 download_size: 31528 dataset_size: 73025 - config_name: pdc features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 24391 num_examples: 100 - name: test num_bytes: 24646 num_examples: 100 - name: train num_bytes: 23963 num_examples: 100 download_size: 28409 dataset_size: 73000 - config_name: pl features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 2448296 num_examples: 10000 - name: test num_bytes: 2463755 num_examples: 10000 - name: train num_bytes: 4851471 num_examples: 20000 download_size: 3300030 dataset_size: 9763522 - config_name: pms features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 28341 num_examples: 100 - name: test num_bytes: 23987 num_examples: 100 - name: train num_bytes: 27401 num_examples: 100 download_size: 34986 dataset_size: 79729 - config_name: pnb features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 19042 num_examples: 100 - name: test num_bytes: 21178 num_examples: 100 - name: train num_bytes: 19476 num_examples: 100 download_size: 25001 dataset_size: 59696 - config_name: ps features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 49873 num_examples: 100 - name: test num_bytes: 43593 num_examples: 100 - name: train num_bytes: 63473 num_examples: 100 download_size: 45676 dataset_size: 156939 - config_name: pt features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 1962117 num_examples: 10000 - name: test num_bytes: 1946701 num_examples: 10000 - name: train num_bytes: 3917397 num_examples: 20000 download_size: 2523476 dataset_size: 7826215 - config_name: qu features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 18203 num_examples: 100 - name: test num_bytes: 17647 num_examples: 100 - name: train num_bytes: 16961 num_examples: 100 download_size: 26577 dataset_size: 52811 - config_name: rm features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 32748 num_examples: 100 - name: test num_bytes: 35852 num_examples: 100 - name: train num_bytes: 30461 num_examples: 100 download_size: 38504 dataset_size: 99061 - config_name: ro features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 2063832 num_examples: 10000 - name: test num_bytes: 2060905 num_examples: 10000 - name: train num_bytes: 4179813 num_examples: 20000 download_size: 2533230 dataset_size: 8304550 - config_name: ru features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 2574518 num_examples: 10000 - name: test num_bytes: 2597220 num_examples: 10000 - name: train num_bytes: 5175609 num_examples: 20000 download_size: 3250185 dataset_size: 10347347 - config_name: rw features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 17971 num_examples: 100 - name: test num_bytes: 14417 num_examples: 100 - name: train num_bytes: 16750 num_examples: 100 download_size: 25845 dataset_size: 49138 - config_name: sa features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 45693 num_examples: 100 - name: test num_bytes: 49181 num_examples: 100 - name: train num_bytes: 52476 num_examples: 100 download_size: 50112 dataset_size: 147350 - config_name: sah features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 27847 num_examples: 100 - name: test num_bytes: 26825 num_examples: 100 - name: train num_bytes: 27013 num_examples: 100 download_size: 34322 dataset_size: 81685 - config_name: scn features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 20077 num_examples: 100 - name: test num_bytes: 17356 num_examples: 100 - name: train num_bytes: 21004 num_examples: 100 download_size: 28158 dataset_size: 58437 - config_name: sco features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 22187 num_examples: 100 - name: test num_bytes: 21561 num_examples: 100 - name: train num_bytes: 20280 num_examples: 100 download_size: 30781 dataset_size: 64028 - config_name: sd features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 51527 num_examples: 100 - name: test num_bytes: 38506 num_examples: 100 - name: train num_bytes: 56897 num_examples: 100 download_size: 44883 dataset_size: 146930 - config_name: sh features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 1789890 num_examples: 10000 - name: test num_bytes: 1791463 num_examples: 10000 - name: train num_bytes: 3583577 num_examples: 20000 download_size: 2027654 dataset_size: 7164930 - config_name: si features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 30817 num_examples: 100 - name: test num_bytes: 29313 num_examples: 100 - name: train num_bytes: 31227 num_examples: 100 download_size: 33979 dataset_size: 91357 - config_name: simple features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 247119 num_examples: 1000 - name: test num_bytes: 245330 num_examples: 1000 - name: train num_bytes: 4921860 num_examples: 20000 download_size: 1301730 dataset_size: 5414309 - config_name: sk features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 2342033 num_examples: 10000 - name: test num_bytes: 2334981 num_examples: 10000 - name: train num_bytes: 4701497 num_examples: 20000 download_size: 2944919 dataset_size: 9378511 - config_name: sl features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 2090219 num_examples: 10000 - name: test num_bytes: 2133463 num_examples: 10000 - name: train num_bytes: 3158620 num_examples: 15000 download_size: 2146455 dataset_size: 7382302 - config_name: so features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 21836 num_examples: 100 - name: test num_bytes: 17191 num_examples: 100 - name: train num_bytes: 23752 num_examples: 100 download_size: 27097 dataset_size: 62779 - config_name: sq features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 210860 num_examples: 1000 - name: test num_bytes: 209796 num_examples: 1000 - name: train num_bytes: 1052359 num_examples: 5000 download_size: 366247 dataset_size: 1473015 - config_name: sr features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 2548362 num_examples: 10000 - name: test num_bytes: 2564803 num_examples: 10000 - name: train num_bytes: 5105513 num_examples: 20000 download_size: 2932854 dataset_size: 10218678 - config_name: su features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 22577 num_examples: 100 - name: test num_bytes: 21833 num_examples: 100 - name: train num_bytes: 20811 num_examples: 100 download_size: 30722 dataset_size: 65221 - config_name: sv features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 2678644 num_examples: 10000 - name: test num_bytes: 2719049 num_examples: 10000 - name: train num_bytes: 5395666 num_examples: 20000 download_size: 2565949 dataset_size: 10793359 - config_name: sw features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 168791 num_examples: 1000 - name: test num_bytes: 172665 num_examples: 1000 - name: train num_bytes: 168721 num_examples: 1000 download_size: 135814 dataset_size: 510177 - config_name: szl features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 19369 num_examples: 100 - name: test num_bytes: 18939 num_examples: 100 - name: train num_bytes: 17618 num_examples: 100 download_size: 27450 dataset_size: 55926 - config_name: ta features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 354929 num_examples: 1000 - name: test num_bytes: 357639 num_examples: 1000 - name: train num_bytes: 5275703 num_examples: 15000 download_size: 1527540 dataset_size: 5988271 - config_name: te features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 356161 num_examples: 1000 - name: test num_bytes: 359752 num_examples: 1000 - name: train num_bytes: 358764 num_examples: 1000 download_size: 260846 dataset_size: 1074677 - config_name: tg features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 27102 num_examples: 100 - name: test num_bytes: 28793 num_examples: 100 - name: train num_bytes: 27172 num_examples: 100 download_size: 33712 dataset_size: 83067 - config_name: th features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 14189715 num_examples: 10000 - name: test num_bytes: 14505026 num_examples: 10000 - name: train num_bytes: 28968860 num_examples: 20000 download_size: 3962089 dataset_size: 57663601 - config_name: tk features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 21583 num_examples: 100 - name: test num_bytes: 20274 num_examples: 100 - name: train num_bytes: 19493 num_examples: 100 download_size: 30395 dataset_size: 61350 - config_name: tl features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 148654 num_examples: 1000 - name: test num_bytes: 152936 num_examples: 1000 - name: train num_bytes: 1518756 num_examples: 10000 download_size: 521471 dataset_size: 1820346 - config_name: tr features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 2280489 num_examples: 10000 - name: test num_bytes: 2276892 num_examples: 10000 - name: train num_bytes: 4501856 num_examples: 20000 download_size: 2907624 dataset_size: 9059237 - config_name: tt features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 282507 num_examples: 1000 - name: test num_bytes: 282663 num_examples: 1000 - name: train num_bytes: 283364 num_examples: 1000 download_size: 174234 dataset_size: 848534 - config_name: ug features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 35191 num_examples: 100 - name: test num_bytes: 31101 num_examples: 100 - name: train num_bytes: 26592 num_examples: 100 download_size: 38383 dataset_size: 92884 - config_name: uk features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 2934869 num_examples: 10000 - name: test num_bytes: 2928172 num_examples: 10000 - name: train num_bytes: 5927970 num_examples: 20000 download_size: 3214083 dataset_size: 11791011 - config_name: ur features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 203719 num_examples: 1000 - name: test num_bytes: 203110 num_examples: 1000 - name: train num_bytes: 4108651 num_examples: 20000 download_size: 1140630 dataset_size: 4515480 - config_name: uz features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 184597 num_examples: 1000 - name: test num_bytes: 184685 num_examples: 1000 - name: train num_bytes: 186077 num_examples: 1000 download_size: 121267 dataset_size: 555359 - config_name: vec features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 19307 num_examples: 100 - name: test num_bytes: 20226 num_examples: 100 - name: train num_bytes: 20409 num_examples: 100 download_size: 27538 dataset_size: 59942 - config_name: vep features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 22278 num_examples: 100 - name: test num_bytes: 21343 num_examples: 100 - name: train num_bytes: 21359 num_examples: 100 download_size: 29630 dataset_size: 64980 - config_name: vi features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 1944828 num_examples: 10000 - name: test num_bytes: 1959996 num_examples: 10000 - name: train num_bytes: 3915888 num_examples: 20000 download_size: 2283112 dataset_size: 7820712 - config_name: vls features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 27867 num_examples: 100 - name: test num_bytes: 26750 num_examples: 100 - name: train num_bytes: 26155 num_examples: 100 download_size: 33972 dataset_size: 80772 - config_name: vo features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 14357 num_examples: 100 - name: test num_bytes: 13973 num_examples: 100 - name: train num_bytes: 14414 num_examples: 100 download_size: 20368 dataset_size: 42744 - config_name: wa features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 22465 num_examples: 100 - name: test num_bytes: 21553 num_examples: 100 - name: train num_bytes: 23044 num_examples: 100 download_size: 28716 dataset_size: 67062 - config_name: war features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 16806 num_examples: 100 - name: test num_bytes: 19884 num_examples: 100 - name: train num_bytes: 18801 num_examples: 100 download_size: 26342 dataset_size: 55491 - config_name: wuu features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 15095 num_examples: 100 - name: test num_bytes: 15039 num_examples: 100 - name: train num_bytes: 16988 num_examples: 100 download_size: 34843 dataset_size: 47122 - config_name: xmf features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 39951 num_examples: 100 - name: test num_bytes: 36053 num_examples: 100 - name: train num_bytes: 31768 num_examples: 100 download_size: 38339 dataset_size: 107772 - config_name: yi features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 25241 num_examples: 100 - name: test num_bytes: 24977 num_examples: 100 - name: train num_bytes: 27275 num_examples: 100 download_size: 30693 dataset_size: 77493 - config_name: yo features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 17710 num_examples: 100 - name: test num_bytes: 17968 num_examples: 100 - name: train num_bytes: 18956 num_examples: 100 download_size: 26565 dataset_size: 54634 - config_name: zea features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 24888 num_examples: 100 - name: test num_bytes: 22969 num_examples: 100 - name: train num_bytes: 21224 num_examples: 100 download_size: 28533 dataset_size: 69081 - config_name: zh features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 4839700 num_examples: 10000 - name: test num_bytes: 4709430 num_examples: 10000 - name: train num_bytes: 9524925 num_examples: 20000 download_size: 2896220 dataset_size: 19074055 - config_name: zh-classical features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 59952 num_examples: 100 - name: test num_bytes: 65857 num_examples: 100 - name: train num_bytes: 56210 num_examples: 100 download_size: 31946 dataset_size: 182019 - config_name: zh-min-nan features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 24505 num_examples: 100 - name: test num_bytes: 24298 num_examples: 100 - name: train num_bytes: 19330 num_examples: 100 download_size: 26515 dataset_size: 68133 - config_name: zh-yue features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: langs sequence: string - name: spans sequence: string splits: - name: validation num_bytes: 4934130 num_examples: 10000 - name: test num_bytes: 4964001 num_examples: 10000 - name: train num_bytes: 9950573 num_examples: 20000 download_size: 2342825 dataset_size: 19848704 configs: - config_name: ace data_files: - split: validation path: ace/validation-* - split: test path: ace/test-* - split: train path: ace/train-* - config_name: af data_files: - split: validation path: af/validation-* - split: test path: af/test-* - split: train path: af/train-* - config_name: als data_files: - split: validation path: als/validation-* - split: test path: als/test-* - split: train path: als/train-* - config_name: am data_files: - split: validation path: am/validation-* - split: test path: am/test-* - split: train path: am/train-* - config_name: an data_files: - split: validation path: an/validation-* - split: test path: an/test-* - split: train path: an/train-* - config_name: ang data_files: - split: validation path: ang/validation-* - split: test path: ang/test-* - split: train path: ang/train-* - config_name: ar data_files: - split: validation path: ar/validation-* - split: test path: ar/test-* - split: train path: ar/train-* - config_name: arc data_files: - split: validation path: arc/validation-* - split: test path: arc/test-* - split: train path: arc/train-* - config_name: arz data_files: - split: validation path: arz/validation-* - split: test path: arz/test-* - split: train path: arz/train-* - config_name: as data_files: - split: validation path: as/validation-* - split: test path: as/test-* - split: train path: as/train-* - config_name: ast data_files: - split: validation path: ast/validation-* - split: test path: ast/test-* - split: train path: ast/train-* - config_name: ay data_files: - split: validation path: ay/validation-* - split: test path: ay/test-* - split: train path: ay/train-* - config_name: az data_files: - split: validation path: az/validation-* - split: test path: az/test-* - split: train path: az/train-* - config_name: ba data_files: - split: validation path: ba/validation-* - split: test path: ba/test-* - split: train path: ba/train-* - config_name: bar data_files: - split: validation path: bar/validation-* - split: test path: bar/test-* - split: train path: bar/train-* - config_name: bat-smg data_files: - split: validation path: bat-smg/validation-* - split: test path: bat-smg/test-* - split: train path: bat-smg/train-* - config_name: be data_files: - split: validation path: be/validation-* - split: test path: be/test-* - split: train path: be/train-* - config_name: be-x-old data_files: - split: validation path: be-x-old/validation-* - split: test path: be-x-old/test-* - split: train path: be-x-old/train-* - config_name: bg data_files: - split: validation path: bg/validation-* - split: test path: bg/test-* - split: train path: bg/train-* - config_name: bh data_files: - split: validation path: bh/validation-* - split: test path: bh/test-* - split: train path: bh/train-* - config_name: bn data_files: - split: validation path: bn/validation-* - split: test path: bn/test-* - split: train path: bn/train-* - config_name: bo data_files: - split: validation path: bo/validation-* - split: test path: bo/test-* - split: train path: bo/train-* - config_name: br data_files: - split: validation path: br/validation-* - split: test path: br/test-* - split: train path: br/train-* - config_name: bs data_files: - split: validation path: bs/validation-* - split: test path: bs/test-* - split: train path: bs/train-* - config_name: ca data_files: - split: validation path: ca/validation-* - split: test path: ca/test-* - split: train path: ca/train-* - config_name: cbk-zam data_files: - split: validation path: cbk-zam/validation-* - split: test path: cbk-zam/test-* - split: train path: cbk-zam/train-* - config_name: cdo data_files: - split: validation path: cdo/validation-* - split: test path: cdo/test-* - split: train path: cdo/train-* - config_name: ce data_files: - split: validation path: ce/validation-* - split: test path: ce/test-* - split: train path: ce/train-* - config_name: ceb data_files: - split: validation path: ceb/validation-* - split: test path: ceb/test-* - split: train path: ceb/train-* - config_name: ckb data_files: - split: validation path: ckb/validation-* - split: test path: ckb/test-* - split: train path: ckb/train-* - config_name: co data_files: - split: validation path: co/validation-* - split: test path: co/test-* - split: train path: co/train-* - config_name: crh data_files: - split: validation path: crh/validation-* - split: test path: crh/test-* - split: train path: crh/train-* - config_name: cs data_files: - split: validation path: cs/validation-* - split: test path: cs/test-* - split: train path: cs/train-* - config_name: csb data_files: - split: validation path: csb/validation-* - split: test path: csb/test-* - split: train path: csb/train-* - config_name: cv data_files: - split: validation path: cv/validation-* - split: test path: cv/test-* - split: train path: cv/train-* - config_name: cy data_files: - split: validation path: cy/validation-* - split: test path: cy/test-* - split: train path: cy/train-* - config_name: da data_files: - split: validation path: da/validation-* - split: test path: da/test-* - split: train path: da/train-* - config_name: de data_files: - split: validation path: de/validation-* - split: test path: de/test-* - split: train path: de/train-* - config_name: diq data_files: - split: validation path: diq/validation-* - split: test path: diq/test-* - split: train path: diq/train-* - config_name: dv data_files: - split: validation path: dv/validation-* - split: test path: dv/test-* - split: train path: dv/train-* - config_name: el data_files: - split: validation path: el/validation-* - split: test path: el/test-* - split: train path: el/train-* - config_name: eml data_files: - split: validation path: eml/validation-* - split: test path: eml/test-* - split: train path: eml/train-* - config_name: en data_files: - split: validation path: en/validation-* - split: test path: en/test-* - split: train path: en/train-* - config_name: eo data_files: - split: validation path: eo/validation-* - split: test path: eo/test-* - split: train path: eo/train-* - config_name: es data_files: - split: validation path: es/validation-* - split: test path: es/test-* - split: train path: es/train-* - config_name: et data_files: - split: validation path: et/validation-* - split: test path: et/test-* - split: train path: et/train-* - config_name: eu data_files: - split: validation path: eu/validation-* - split: test path: eu/test-* - split: train path: eu/train-* - config_name: ext data_files: - split: validation path: ext/validation-* - split: test path: ext/test-* - split: train path: ext/train-* - config_name: fa data_files: - split: validation path: fa/validation-* - split: test path: fa/test-* - split: train path: fa/train-* - config_name: fi data_files: - split: validation path: fi/validation-* - split: test path: fi/test-* - split: train path: fi/train-* - config_name: fiu-vro data_files: - split: validation path: fiu-vro/validation-* - split: test path: fiu-vro/test-* - split: train path: fiu-vro/train-* - config_name: fo data_files: - split: validation path: fo/validation-* - split: test path: fo/test-* - split: train path: fo/train-* - config_name: fr data_files: - split: validation path: fr/validation-* - split: test path: fr/test-* - split: train path: fr/train-* - config_name: frr data_files: - split: validation path: frr/validation-* - split: test path: frr/test-* - split: train path: frr/train-* - config_name: fur data_files: - split: validation path: fur/validation-* - split: test path: fur/test-* - split: train path: fur/train-* - config_name: fy data_files: - split: validation path: fy/validation-* - split: test path: fy/test-* - split: train path: fy/train-* - config_name: ga data_files: - split: validation path: ga/validation-* - split: test path: ga/test-* - split: train path: ga/train-* - config_name: gan data_files: - split: validation path: gan/validation-* - split: test path: gan/test-* - split: train path: gan/train-* - config_name: gd data_files: - split: validation path: gd/validation-* - split: test path: gd/test-* - split: train path: gd/train-* - config_name: gl data_files: - split: validation path: gl/validation-* - split: test path: gl/test-* - split: train path: gl/train-* - config_name: gn data_files: - split: validation path: gn/validation-* - split: test path: gn/test-* - split: train path: gn/train-* - config_name: gu data_files: - split: validation path: gu/validation-* - split: test path: gu/test-* - split: train path: gu/train-* - config_name: hak data_files: - split: validation path: hak/validation-* - split: test path: hak/test-* - split: train path: hak/train-* - config_name: he data_files: - split: validation path: he/validation-* - split: test path: he/test-* - split: train path: he/train-* - config_name: hi data_files: - split: validation path: hi/validation-* - split: test path: hi/test-* - split: train path: hi/train-* - config_name: hr data_files: - split: validation path: hr/validation-* - split: test path: hr/test-* - split: train path: hr/train-* - config_name: hsb data_files: - split: validation path: hsb/validation-* - split: test path: hsb/test-* - split: train path: hsb/train-* - config_name: hu data_files: - split: validation path: hu/validation-* - split: test path: hu/test-* - split: train path: hu/train-* - config_name: hy data_files: - split: validation path: hy/validation-* - split: test path: hy/test-* - split: train path: hy/train-* - config_name: ia data_files: - split: validation path: ia/validation-* - split: test path: ia/test-* - split: train path: ia/train-* - config_name: id data_files: - split: validation path: id/validation-* - split: test path: id/test-* - split: train path: id/train-* - config_name: ig data_files: - split: validation path: ig/validation-* - split: test path: ig/test-* - split: train path: ig/train-* - config_name: ilo data_files: - split: validation path: ilo/validation-* - split: test path: ilo/test-* - split: train path: ilo/train-* - config_name: io data_files: - split: validation path: io/validation-* - split: test path: io/test-* - split: train path: io/train-* - config_name: is data_files: - split: validation path: is/validation-* - split: test path: is/test-* - split: train path: is/train-* - config_name: it data_files: - split: validation path: it/validation-* - split: test path: it/test-* - split: train path: it/train-* - config_name: ja data_files: - split: validation path: ja/validation-* - split: test path: ja/test-* - split: train path: ja/train-* - config_name: jbo data_files: - split: validation path: jbo/validation-* - split: test path: jbo/test-* - split: train path: jbo/train-* - config_name: jv data_files: - split: validation path: jv/validation-* - split: test path: jv/test-* - split: train path: jv/train-* - config_name: ka data_files: - split: validation path: ka/validation-* - split: test path: ka/test-* - split: train path: ka/train-* - config_name: kk data_files: - split: validation path: kk/validation-* - split: test path: kk/test-* - split: train path: kk/train-* - config_name: km data_files: - split: validation path: km/validation-* - split: test path: km/test-* - split: train path: km/train-* - config_name: kn data_files: - split: validation path: kn/validation-* - split: test path: kn/test-* - split: train path: kn/train-* - config_name: ko data_files: - split: validation path: ko/validation-* - split: test path: ko/test-* - split: train path: ko/train-* - config_name: ksh data_files: - split: validation path: ksh/validation-* - split: test path: ksh/test-* - split: train path: ksh/train-* - config_name: ku data_files: - split: validation path: ku/validation-* - split: test path: ku/test-* - split: train path: ku/train-* - config_name: ky data_files: - split: validation path: ky/validation-* - split: test path: ky/test-* - split: train path: ky/train-* - config_name: la data_files: - split: validation path: la/validation-* - split: test path: la/test-* - split: train path: la/train-* - config_name: lb data_files: - split: validation path: lb/validation-* - split: test path: lb/test-* - split: train path: lb/train-* - config_name: li data_files: - split: validation path: li/validation-* - split: test path: li/test-* - split: train path: li/train-* - config_name: lij data_files: - split: validation path: lij/validation-* - split: test path: lij/test-* - split: train path: lij/train-* - config_name: lmo data_files: - split: validation path: lmo/validation-* - split: test path: lmo/test-* - split: train path: lmo/train-* - config_name: ln data_files: - split: validation path: ln/validation-* - split: test path: ln/test-* - split: train path: ln/train-* - config_name: lt data_files: - split: validation path: lt/validation-* - split: test path: lt/test-* - split: train path: lt/train-* - config_name: lv data_files: - split: validation path: lv/validation-* - split: test path: lv/test-* - split: train path: lv/train-* - config_name: map-bms data_files: - split: validation path: map-bms/validation-* - split: test path: map-bms/test-* - split: train path: map-bms/train-* - config_name: mg data_files: - split: validation path: mg/validation-* - split: test path: mg/test-* - split: train path: mg/train-* - config_name: mhr data_files: - split: validation path: mhr/validation-* - split: test path: mhr/test-* - split: train path: mhr/train-* - config_name: mi data_files: - split: validation path: mi/validation-* - split: test path: mi/test-* - split: train path: mi/train-* - config_name: min data_files: - split: validation path: min/validation-* - split: test path: min/test-* - split: train path: min/train-* - config_name: mk data_files: - split: validation path: mk/validation-* - split: test path: mk/test-* - split: train path: mk/train-* - config_name: ml data_files: - split: validation path: ml/validation-* - split: test path: ml/test-* - split: train path: ml/train-* - config_name: mn data_files: - split: validation path: mn/validation-* - split: test path: mn/test-* - split: train path: mn/train-* - config_name: mr data_files: - split: validation path: mr/validation-* - split: test path: mr/test-* - split: train path: mr/train-* - config_name: ms data_files: - split: validation path: ms/validation-* - split: test path: ms/test-* - split: train path: ms/train-* - config_name: mt data_files: - split: validation path: mt/validation-* - split: test path: mt/test-* - split: train path: mt/train-* - config_name: mwl data_files: - split: validation path: mwl/validation-* - split: test path: mwl/test-* - split: train path: mwl/train-* - config_name: my data_files: - split: validation path: my/validation-* - split: test path: my/test-* - split: train path: my/train-* - config_name: mzn data_files: - split: validation path: mzn/validation-* - split: test path: mzn/test-* - split: train path: mzn/train-* - config_name: nap data_files: - split: validation path: nap/validation-* - split: test path: nap/test-* - split: train path: nap/train-* - config_name: nds data_files: - split: validation path: nds/validation-* - split: test path: nds/test-* - split: train path: nds/train-* - config_name: ne data_files: - split: validation path: ne/validation-* - split: test path: ne/test-* - split: train path: ne/train-* - config_name: nl data_files: - split: validation path: nl/validation-* - split: test path: nl/test-* - split: train path: nl/train-* - config_name: nn data_files: - split: validation path: nn/validation-* - split: test path: nn/test-* - split: train path: nn/train-* - config_name: 'no' data_files: - split: validation path: no/validation-* - split: test path: no/test-* - split: train path: no/train-* - config_name: nov data_files: - split: validation path: nov/validation-* - split: test path: nov/test-* - split: train path: nov/train-* - config_name: oc data_files: - split: validation path: oc/validation-* - split: test path: oc/test-* - split: train path: oc/train-* - config_name: or data_files: - split: validation path: or/validation-* - split: test path: or/test-* - split: train path: or/train-* - config_name: os data_files: - split: validation path: os/validation-* - split: test path: os/test-* - split: train path: os/train-* - config_name: pa data_files: - split: validation path: pa/validation-* - split: test path: pa/test-* - split: train path: pa/train-* - config_name: pdc data_files: - split: validation path: pdc/validation-* - split: test path: pdc/test-* - split: train path: pdc/train-* - config_name: pl data_files: - split: validation path: pl/validation-* - split: test path: pl/test-* - split: train path: pl/train-* - config_name: pms data_files: - split: validation path: pms/validation-* - split: test path: pms/test-* - split: train path: pms/train-* - config_name: pnb data_files: - split: validation path: pnb/validation-* - split: test path: pnb/test-* - split: train path: pnb/train-* - config_name: ps data_files: - split: validation path: ps/validation-* - split: test path: ps/test-* - split: train path: ps/train-* - config_name: pt data_files: - split: validation path: pt/validation-* - split: test path: pt/test-* - split: train path: pt/train-* - config_name: qu data_files: - split: validation path: qu/validation-* - split: test path: qu/test-* - split: train path: qu/train-* - config_name: rm data_files: - split: validation path: rm/validation-* - split: test path: rm/test-* - split: train path: rm/train-* - config_name: ro data_files: - split: validation path: ro/validation-* - split: test path: ro/test-* - split: train path: ro/train-* - config_name: ru data_files: - split: validation path: ru/validation-* - split: test path: ru/test-* - split: train path: ru/train-* - config_name: rw data_files: - split: validation path: rw/validation-* - split: test path: rw/test-* - split: train path: rw/train-* - config_name: sa data_files: - split: validation path: sa/validation-* - split: test path: sa/test-* - split: train path: sa/train-* - config_name: sah data_files: - split: validation path: sah/validation-* - split: test path: sah/test-* - split: train path: sah/train-* - config_name: scn data_files: - split: validation path: scn/validation-* - split: test path: scn/test-* - split: train path: scn/train-* - config_name: sco data_files: - split: validation path: sco/validation-* - split: test path: sco/test-* - split: train path: sco/train-* - config_name: sd data_files: - split: validation path: sd/validation-* - split: test path: sd/test-* - split: train path: sd/train-* - config_name: sh data_files: - split: validation path: sh/validation-* - split: test path: sh/test-* - split: train path: sh/train-* - config_name: si data_files: - split: validation path: si/validation-* - split: test path: si/test-* - split: train path: si/train-* - config_name: simple data_files: - split: validation path: simple/validation-* - split: test path: simple/test-* - split: train path: simple/train-* - config_name: sk data_files: - split: validation path: sk/validation-* - split: test path: sk/test-* - split: train path: sk/train-* - config_name: sl data_files: - split: validation path: sl/validation-* - split: test path: sl/test-* - split: train path: sl/train-* - config_name: so data_files: - split: validation path: so/validation-* - split: test path: so/test-* - split: train path: so/train-* - config_name: sq data_files: - split: validation path: sq/validation-* - split: test path: sq/test-* - split: train path: sq/train-* - config_name: sr data_files: - split: validation path: sr/validation-* - split: test path: sr/test-* - split: train path: sr/train-* - config_name: su data_files: - split: validation path: su/validation-* - split: test path: su/test-* - split: train path: su/train-* - config_name: sv data_files: - split: validation path: sv/validation-* - split: test path: sv/test-* - split: train path: sv/train-* - config_name: sw data_files: - split: validation path: sw/validation-* - split: test path: sw/test-* - split: train path: sw/train-* - config_name: szl data_files: - split: validation path: szl/validation-* - split: test path: szl/test-* - split: train path: szl/train-* - config_name: ta data_files: - split: validation path: ta/validation-* - split: test path: ta/test-* - split: train path: ta/train-* - config_name: te data_files: - split: validation path: te/validation-* - split: test path: te/test-* - split: train path: te/train-* - config_name: tg data_files: - split: validation path: tg/validation-* - split: test path: tg/test-* - split: train path: tg/train-* - config_name: th data_files: - split: validation path: th/validation-* - split: test path: th/test-* - split: train path: th/train-* - config_name: tk data_files: - split: validation path: tk/validation-* - split: test path: tk/test-* - split: train path: tk/train-* - config_name: tl data_files: - split: validation path: tl/validation-* - split: test path: tl/test-* - split: train path: tl/train-* - config_name: tr data_files: - split: validation path: tr/validation-* - split: test path: tr/test-* - split: train path: tr/train-* - config_name: tt data_files: - split: validation path: tt/validation-* - split: test path: tt/test-* - split: train path: tt/train-* - config_name: ug data_files: - split: validation path: ug/validation-* - split: test path: ug/test-* - split: train path: ug/train-* - config_name: uk data_files: - split: validation path: uk/validation-* - split: test path: uk/test-* - split: train path: uk/train-* - config_name: ur data_files: - split: validation path: ur/validation-* - split: test path: ur/test-* - split: train path: ur/train-* - config_name: uz data_files: - split: validation path: uz/validation-* - split: test path: uz/test-* - split: train path: uz/train-* - config_name: vec data_files: - split: validation path: vec/validation-* - split: test path: vec/test-* - split: train path: vec/train-* - config_name: vep data_files: - split: validation path: vep/validation-* - split: test path: vep/test-* - split: train path: vep/train-* - config_name: vi data_files: - split: validation path: vi/validation-* - split: test path: vi/test-* - split: train path: vi/train-* - config_name: vls data_files: - split: validation path: vls/validation-* - split: test path: vls/test-* - split: train path: vls/train-* - config_name: vo data_files: - split: validation path: vo/validation-* - split: test path: vo/test-* - split: train path: vo/train-* - config_name: wa data_files: - split: validation path: wa/validation-* - split: test path: wa/test-* - split: train path: wa/train-* - config_name: war data_files: - split: validation path: war/validation-* - split: test path: war/test-* - split: train path: war/train-* - config_name: wuu data_files: - split: validation path: wuu/validation-* - split: test path: wuu/test-* - split: train path: wuu/train-* - config_name: xmf data_files: - split: validation path: xmf/validation-* - split: test path: xmf/test-* - split: train path: xmf/train-* - config_name: yi data_files: - split: validation path: yi/validation-* - split: test path: yi/test-* - split: train path: yi/train-* - config_name: yo data_files: - split: validation path: yo/validation-* - split: test path: yo/test-* - split: train path: yo/train-* - config_name: zea data_files: - split: validation path: zea/validation-* - split: test path: zea/test-* - split: train path: zea/train-* - config_name: zh data_files: - split: validation path: zh/validation-* - split: test path: zh/test-* - split: train path: zh/train-* - config_name: zh-classical data_files: - split: validation path: zh-classical/validation-* - split: test path: zh-classical/test-* - split: train path: zh-classical/train-* - config_name: zh-min-nan data_files: - split: validation path: zh-min-nan/validation-* - split: test path: zh-min-nan/test-* - split: train path: zh-min-nan/train-* - config_name: zh-yue data_files: - split: validation path: zh-yue/validation-* - split: test path: zh-yue/test-* - split: train path: zh-yue/train-* --- # Dataset Card for WikiANN ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [Massively Multilingual Transfer for NER](https://github.com/afshinrahimi/mmner) - **Repository:** [Massively Multilingual Transfer for NER](https://github.com/afshinrahimi/mmner) - **Paper:** The original datasets come from the _Cross-lingual name tagging and linking for 282 languages_ [paper](https://www.aclweb.org/anthology/P17-1178/) by Xiaoman Pan et al. (2018). This version corresponds to the balanced train, dev, and test splits of the original data from the _Massively Multilingual Transfer for NER_ [paper](https://arxiv.org/abs/1902.00193) by Afshin Rahimi et al. (2019). - **Leaderboard:** - **Point of Contact:** [Afshin Rahimi](mailto:[email protected]) or [Lewis Tunstall](mailto:[email protected]) or [Albert Villanova del Moral]([email protected]) ### Dataset Summary WikiANN (sometimes called PAN-X) is a multilingual named entity recognition dataset consisting of Wikipedia articles annotated with LOC (location), PER (person), and ORG (organisation) tags in the IOB2 format. This version corresponds to the balanced train, dev, and test splits of Rahimi et al. (2019), which supports 176 of the 282 languages from the original WikiANN corpus. ### Supported Tasks and Leaderboards - `named-entity-recognition`: The dataset can be used to train a model for named entity recognition in many languages, or evaluate the zero-shot cross-lingual capabilities of multilingual models. ### Languages The dataset contains 176 languages, one in each of the configuration subsets. The corresponding BCP 47 language tags are: | | Language tag | |:-------------------|:---------------| | ace | ace | | af | af | | als | als | | am | am | | an | an | | ang | ang | | ar | ar | | arc | arc | | arz | arz | | as | as | | ast | ast | | ay | ay | | az | az | | ba | ba | | bar | bar | | be | be | | bg | bg | | bh | bh | | bn | bn | | bo | bo | | br | br | | bs | bs | | ca | ca | | cdo | cdo | | ce | ce | | ceb | ceb | | ckb | ckb | | co | co | | crh | crh | | cs | cs | | csb | csb | | cv | cv | | cy | cy | | da | da | | de | de | | diq | diq | | dv | dv | | el | el | | en | en | | eo | eo | | es | es | | et | et | | eu | eu | | ext | ext | | fa | fa | | fi | fi | | fo | fo | | fr | fr | | frr | frr | | fur | fur | | fy | fy | | ga | ga | | gan | gan | | gd | gd | | gl | gl | | gn | gn | | gu | gu | | hak | hak | | he | he | | hi | hi | | hr | hr | | hsb | hsb | | hu | hu | | hy | hy | | ia | ia | | id | id | | ig | ig | | ilo | ilo | | io | io | | is | is | | it | it | | ja | ja | | jbo | jbo | | jv | jv | | ka | ka | | kk | kk | | km | km | | kn | kn | | ko | ko | | ksh | ksh | | ku | ku | | ky | ky | | la | la | | lb | lb | | li | li | | lij | lij | | lmo | lmo | | ln | ln | | lt | lt | | lv | lv | | mg | mg | | mhr | mhr | | mi | mi | | min | min | | mk | mk | | ml | ml | | mn | mn | | mr | mr | | ms | ms | | mt | mt | | mwl | mwl | | my | my | | mzn | mzn | | nap | nap | | nds | nds | | ne | ne | | nl | nl | | nn | nn | | no | no | | nov | nov | | oc | oc | | or | or | | os | os | | other-bat-smg | sgs | | other-be-x-old | be-tarask | | other-cbk-zam | cbk | | other-eml | eml | | other-fiu-vro | vro | | other-map-bms | jv-x-bms | | other-simple | en-basiceng | | other-zh-classical | lzh | | other-zh-min-nan | nan | | other-zh-yue | yue | | pa | pa | | pdc | pdc | | pl | pl | | pms | pms | | pnb | pnb | | ps | ps | | pt | pt | | qu | qu | | rm | rm | | ro | ro | | ru | ru | | rw | rw | | sa | sa | | sah | sah | | scn | scn | | sco | sco | | sd | sd | | sh | sh | | si | si | | sk | sk | | sl | sl | | so | so | | sq | sq | | sr | sr | | su | su | | sv | sv | | sw | sw | | szl | szl | | ta | ta | | te | te | | tg | tg | | th | th | | tk | tk | | tl | tl | | tr | tr | | tt | tt | | ug | ug | | uk | uk | | ur | ur | | uz | uz | | vec | vec | | vep | vep | | vi | vi | | vls | vls | | vo | vo | | wa | wa | | war | war | | wuu | wuu | | xmf | xmf | | yi | yi | | yo | yo | | zea | zea | | zh | zh | ## Dataset Structure ### Data Instances This is an example in the "train" split of the "af" (Afrikaans language) configuration subset: ```python { 'tokens': ['Sy', 'ander', 'seun', ',', 'Swjatopolk', ',', 'was', 'die', 'resultaat', 'van', '’n', 'buite-egtelike', 'verhouding', '.'], 'ner_tags': [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'langs': ['af', 'af', 'af', 'af', 'af', 'af', 'af', 'af', 'af', 'af', 'af', 'af', 'af', 'af'], 'spans': ['PER: Swjatopolk'] } ``` ### Data Fields - `tokens`: a `list` of `string` features. - `langs`: a `list` of `string` features that correspond to the language of each token. - `ner_tags`: a `list` of classification labels, with possible values including `O` (0), `B-PER` (1), `I-PER` (2), `B-ORG` (3), `I-ORG` (4), `B-LOC` (5), `I-LOC` (6). - `spans`: a `list` of `string` features, that is the list of named entities in the input text formatted as ``<TAG>: <mention>`` ### Data Splits For each configuration subset, the data is split into "train", "validation" and "test" sets, each containing the following number of examples: | | Train | Validation | Test | |:-------------|--------:|-------------:|-------:| | ace | 100 | 100 | 100 | | af | 5000 | 1000 | 1000 | | als | 100 | 100 | 100 | | am | 100 | 100 | 100 | | an | 1000 | 1000 | 1000 | | ang | 100 | 100 | 100 | | ar | 20000 | 10000 | 10000 | | arc | 100 | 100 | 100 | | arz | 100 | 100 | 100 | | as | 100 | 100 | 100 | | ast | 1000 | 1000 | 1000 | | ay | 100 | 100 | 100 | | az | 10000 | 1000 | 1000 | | ba | 100 | 100 | 100 | | bar | 100 | 100 | 100 | | bat-smg | 100 | 100 | 100 | | be | 15000 | 1000 | 1000 | | be-x-old | 5000 | 1000 | 1000 | | bg | 20000 | 10000 | 10000 | | bh | 100 | 100 | 100 | | bn | 10000 | 1000 | 1000 | | bo | 100 | 100 | 100 | | br | 1000 | 1000 | 1000 | | bs | 15000 | 1000 | 1000 | | ca | 20000 | 10000 | 10000 | | cbk-zam | 100 | 100 | 100 | | cdo | 100 | 100 | 100 | | ce | 100 | 100 | 100 | | ceb | 100 | 100 | 100 | | ckb | 1000 | 1000 | 1000 | | co | 100 | 100 | 100 | | crh | 100 | 100 | 100 | | cs | 20000 | 10000 | 10000 | | csb | 100 | 100 | 100 | | cv | 100 | 100 | 100 | | cy | 10000 | 1000 | 1000 | | da | 20000 | 10000 | 10000 | | de | 20000 | 10000 | 10000 | | diq | 100 | 100 | 100 | | dv | 100 | 100 | 100 | | el | 20000 | 10000 | 10000 | | eml | 100 | 100 | 100 | | en | 20000 | 10000 | 10000 | | eo | 15000 | 10000 | 10000 | | es | 20000 | 10000 | 10000 | | et | 15000 | 10000 | 10000 | | eu | 10000 | 10000 | 10000 | | ext | 100 | 100 | 100 | | fa | 20000 | 10000 | 10000 | | fi | 20000 | 10000 | 10000 | | fiu-vro | 100 | 100 | 100 | | fo | 100 | 100 | 100 | | fr | 20000 | 10000 | 10000 | | frr | 100 | 100 | 100 | | fur | 100 | 100 | 100 | | fy | 1000 | 1000 | 1000 | | ga | 1000 | 1000 | 1000 | | gan | 100 | 100 | 100 | | gd | 100 | 100 | 100 | | gl | 15000 | 10000 | 10000 | | gn | 100 | 100 | 100 | | gu | 100 | 100 | 100 | | hak | 100 | 100 | 100 | | he | 20000 | 10000 | 10000 | | hi | 5000 | 1000 | 1000 | | hr | 20000 | 10000 | 10000 | | hsb | 100 | 100 | 100 | | hu | 20000 | 10000 | 10000 | | hy | 15000 | 1000 | 1000 | | ia | 100 | 100 | 100 | | id | 20000 | 10000 | 10000 | | ig | 100 | 100 | 100 | | ilo | 100 | 100 | 100 | | io | 100 | 100 | 100 | | is | 1000 | 1000 | 1000 | | it | 20000 | 10000 | 10000 | | ja | 20000 | 10000 | 10000 | | jbo | 100 | 100 | 100 | | jv | 100 | 100 | 100 | | ka | 10000 | 10000 | 10000 | | kk | 1000 | 1000 | 1000 | | km | 100 | 100 | 100 | | kn | 100 | 100 | 100 | | ko | 20000 | 10000 | 10000 | | ksh | 100 | 100 | 100 | | ku | 100 | 100 | 100 | | ky | 100 | 100 | 100 | | la | 5000 | 1000 | 1000 | | lb | 5000 | 1000 | 1000 | | li | 100 | 100 | 100 | | lij | 100 | 100 | 100 | | lmo | 100 | 100 | 100 | | ln | 100 | 100 | 100 | | lt | 10000 | 10000 | 10000 | | lv | 10000 | 10000 | 10000 | | map-bms | 100 | 100 | 100 | | mg | 100 | 100 | 100 | | mhr | 100 | 100 | 100 | | mi | 100 | 100 | 100 | | min | 100 | 100 | 100 | | mk | 10000 | 1000 | 1000 | | ml | 10000 | 1000 | 1000 | | mn | 100 | 100 | 100 | | mr | 5000 | 1000 | 1000 | | ms | 20000 | 1000 | 1000 | | mt | 100 | 100 | 100 | | mwl | 100 | 100 | 100 | | my | 100 | 100 | 100 | | mzn | 100 | 100 | 100 | | nap | 100 | 100 | 100 | | nds | 100 | 100 | 100 | | ne | 100 | 100 | 100 | | nl | 20000 | 10000 | 10000 | | nn | 20000 | 1000 | 1000 | | no | 20000 | 10000 | 10000 | | nov | 100 | 100 | 100 | | oc | 100 | 100 | 100 | | or | 100 | 100 | 100 | | os | 100 | 100 | 100 | | pa | 100 | 100 | 100 | | pdc | 100 | 100 | 100 | | pl | 20000 | 10000 | 10000 | | pms | 100 | 100 | 100 | | pnb | 100 | 100 | 100 | | ps | 100 | 100 | 100 | | pt | 20000 | 10000 | 10000 | | qu | 100 | 100 | 100 | | rm | 100 | 100 | 100 | | ro | 20000 | 10000 | 10000 | | ru | 20000 | 10000 | 10000 | | rw | 100 | 100 | 100 | | sa | 100 | 100 | 100 | | sah | 100 | 100 | 100 | | scn | 100 | 100 | 100 | | sco | 100 | 100 | 100 | | sd | 100 | 100 | 100 | | sh | 20000 | 10000 | 10000 | | si | 100 | 100 | 100 | | simple | 20000 | 1000 | 1000 | | sk | 20000 | 10000 | 10000 | | sl | 15000 | 10000 | 10000 | | so | 100 | 100 | 100 | | sq | 5000 | 1000 | 1000 | | sr | 20000 | 10000 | 10000 | | su | 100 | 100 | 100 | | sv | 20000 | 10000 | 10000 | | sw | 1000 | 1000 | 1000 | | szl | 100 | 100 | 100 | | ta | 15000 | 1000 | 1000 | | te | 1000 | 1000 | 1000 | | tg | 100 | 100 | 100 | | th | 20000 | 10000 | 10000 | | tk | 100 | 100 | 100 | | tl | 10000 | 1000 | 1000 | | tr | 20000 | 10000 | 10000 | | tt | 1000 | 1000 | 1000 | | ug | 100 | 100 | 100 | | uk | 20000 | 10000 | 10000 | | ur | 20000 | 1000 | 1000 | | uz | 1000 | 1000 | 1000 | | vec | 100 | 100 | 100 | | vep | 100 | 100 | 100 | | vi | 20000 | 10000 | 10000 | | vls | 100 | 100 | 100 | | vo | 100 | 100 | 100 | | wa | 100 | 100 | 100 | | war | 100 | 100 | 100 | | wuu | 100 | 100 | 100 | | xmf | 100 | 100 | 100 | | yi | 100 | 100 | 100 | | yo | 100 | 100 | 100 | | zea | 100 | 100 | 100 | | zh | 20000 | 10000 | 10000 | | zh-classical | 100 | 100 | 100 | | zh-min-nan | 100 | 100 | 100 | | zh-yue | 20000 | 10000 | 10000 | ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information The original 282 datasets are associated with this article ``` @inproceedings{pan-etal-2017-cross, title = "Cross-lingual Name Tagging and Linking for 282 Languages", author = "Pan, Xiaoman and Zhang, Boliang and May, Jonathan and Nothman, Joel and Knight, Kevin and Ji, Heng", booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", month = jul, year = "2017", address = "Vancouver, Canada", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/P17-1178", doi = "10.18653/v1/P17-1178", pages = "1946--1958", abstract = "The ambitious goal of this work is to develop a cross-lingual name tagging and linking framework for 282 languages that exist in Wikipedia. Given a document in any of these languages, our framework is able to identify name mentions, assign a coarse-grained or fine-grained type to each mention, and link it to an English Knowledge Base (KB) if it is linkable. We achieve this goal by performing a series of new KB mining methods: generating {``}silver-standard{''} annotations by transferring annotations from English to other languages through cross-lingual links and KB properties, refining annotations through self-training and topic selection, deriving language-specific morphology features from anchor links, and mining word translation pairs from cross-lingual links. Both name tagging and linking results for 282 languages are promising on Wikipedia data and on-Wikipedia data.", } ``` while the 176 languages supported in this version are associated with the following article ``` @inproceedings{rahimi-etal-2019-massively, title = "Massively Multilingual Transfer for {NER}", author = "Rahimi, Afshin and Li, Yuan and Cohn, Trevor", booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics", month = jul, year = "2019", address = "Florence, Italy", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/P19-1015", pages = "151--164", } ``` ### Contributions Thanks to [@lewtun](https://github.com/lewtun) and [@rabeehk](https://github.com/rabeehk) for adding this dataset.

stingning/ultrachat

stingning

2024-02-22T02:26:29Z

2,265

438

[ "task_categories:text-generation", "language:en", "license:mit", "size_categories:100K<n<1M", "format:json", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]

[ "conversational", "text-generation" ]

2023-04-20T15:15:28Z

null

--- license: mit task_categories: - conversational - text-generation language: - en size_categories: - 1M<n<10M pretty_name: UltraChat --- # Dataset Card for Dataset Name ## Dataset Description An open-source, large-scale, and multi-round dialogue data powered by Turbo APIs. In consideration of factors such as safeguarding privacy, **we do not directly use any data available on the Internet as prompts**. To ensure generation quality, two separate ChatGPT Turbo APIs are adopted in generation, where one plays the role of the user to generate queries and the other generates the response. We instruct the user model with carefully designed prompts to mimic human user behavior and call the two APIs iteratively. The generated dialogues undergo further post-processing and filtering. ULtraChat is composed of three sectors: - 🌏 **Questions about the World**: The dialogue data in this sector is derived from a wide range of inquiries related to concepts, entities, and objects from the real world. The topics covered are extensive, spanning areas such as technology, art, and entrepreneurship. - ✍🏻 **Writing and Creation**: The dialogue data in this sector is driven by the demands for writing/creation from scratch, and encompasses any tasks that an AI assistant may aid within the creative process, spanning from email composition to crafting narratives and plays, and beyond. - 📋 **Assistance on Existent Materials**: The dialogue data in this sector is generated based on existing materials, including but not limited to rewriting, continuation, summarization, and inference, covering a diverse range of topics. - Repository: [UltraChat](https://github.com/thunlp/UltraChat) - Explorer: [plain-explorer](http://39.101.77.220/), [Nomic-AI-Atlas-Explorer](https://atlas.nomic.ai/map/0ce65783-c3a9-40b5-895d-384933f50081/a7b46301-022f-45d8-bbf4-98107eabdbac) ## Dataset Structure Each line in the downloaded data file is a json dict containing the data id and dialogue data in a list format. Below is an example line. ``` { "id": "0", "data": [ "How can cross training benefit groups like runners, swimmers, or weightlifters?", "Cross training can benefit groups like runners, swimmers, or weightlifters in the following ways: ...", "That makes sense. I've been wanting to improve my running time, but I never thought about incorporating strength training. Do you have any recommendations for specific exercises?", "Sure, here are some strength training exercises that can benefit runners: ...", "Hmm, I'm not really a fan of weightlifting though. Can I incorporate other forms of exercise into my routine to improve my running time?", "Yes, absolutely! ...", "..." ] } ``` ### Citation Information ```bibtex @article{ding2023enhancing, title={Enhancing Chat Language Models by Scaling High-quality Instructional Conversations}, author={Ding, Ning and Chen, Yulin and Xu, Bokai and Qin, Yujia and Zheng, Zhi and Hu, Shengding and Liu, Zhiyuan and Sun, Maosong and Zhou, Bowen}, journal={arXiv preprint arXiv:2305.14233}, year={2023} } ```

neural-bridge/rag-dataset-12000

neural-bridge

2024-02-05T18:25:13Z

1,441

136

[ "task_categories:question-answering", "language:en", "license:apache-2.0", "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "retrieval-augmented-generation" ]

[ "question-answering" ]

2023-10-02T17:18:39Z

null

--- dataset_info: features: - name: context dtype: string - name: question dtype: string - name: answer dtype: string splits: - name: train num_examples: 9600 - name: test num_examples: 2400 task_categories: - question-answering language: - en size_categories: - 10K<n<100K license: apache-2.0 tags: - retrieval-augmented-generation --- # **Retrieval-Augmented Generation (RAG) Dataset 12000** **Retrieval-Augmented Generation (RAG) Dataset 12000 is an English dataset designed for RAG-optimized models, built by [Neural Bridge AI](https://www.neuralbridge.ai/), and released under [Apache license 2.0](https://www.apache.org/licenses/LICENSE-2.0.html).** ## **Dataset Description** #### Dataset Summary Retrieval-Augmented Generation (RAG) enhances large language models (LLMs) by allowing them to consult an external authoritative knowledge base before generating responses. This approach significantly boosts the models' ability to produce relevant, accurate, and context-specific output by extending their capabilities to specialized domains or an organization's internal data, without the need for retraining. RAG offers a cost-effective method to leverage the vast data processing power of LLMs, equipped with billions of parameters, for tasks such as question-answering, language translation, and sentence completion, ensuring that the output is always up-to-date and applicable to various contexts. RAG's importance lies in its potential to address the inherent challenges of LLMs, such as unpredictability in responses, reliance on static and potentially outdated training data, and the risk of disseminating incorrect or non-authoritative information. These issues can negatively affect user trust in AI-powered applications, making RAG's ability to guide LLMs toward authoritative sources for information retrieval invaluable. RAG has multiple benefits, including cost-effective implementation and maintenance, access to current information, improved user trust through accurate information and source attribution, and greater control for developers over the information retrieval process. This approach allows for the dynamic updating of LLMs with the latest research, statistics, or news, directly addressing the challenges of maintaining relevancy and accuracy in rapidly changing knowledge landscapes. Additionally, it empowers organizations to deploy generative AI more confidently across a wider range of applications, enhancing both the user experience and the reliability of AI-driven interactions. Retrieval-Augmented Generation (RAG) Dataset 12000 dataset is a triple-feature collection, with each entry containing a "context", "question", and "answer" fields, designed to help build RAG-optimized models. This data consists of 12000 entries, and the context data is from [Falcon RefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb). ```python from datasets import load_dataset rag_dataset = load_dataset("neural-bridge/rag-dataset-12000") ``` #### Languages The text in the dataset is in English. The associated BCP-47 code is `en`. ## **Dataset Structure** #### Data Instances A typical data point comprises a context, a question about the context, and an answer for the question. The context is obtained from [Falcon RefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb), and the question and answer for each data point are generated by GPT-4. An example from the dataset looks like the following: ``` { context: ... question: ... answer: ... } ``` #### Data Fields - `context`: A string consisting of a range of tokens. - `question`: A string consisting of a question related to the context. - `answer`: A string consisting of an answer for the question. #### Data Splits The data is split into a training and test set. The split sizes are as follow: | | Train | Test | | ----- | ------ | ---- | | RAG Dataset 12000 | 9600 | 2400 | ## Source Data The data points in the dataset are from the [Falcon RefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb) dataset. ## **Neural Bridge AI RAG Datasets Index** | Model | Link | | ----- | ------ | | RAG Full 20000 | [link](https://huggingface.co/datasets/neural-bridge/rag-full-20000) | | RAG Dataset 12000 | [link](https://huggingface.co/datasets/neural-bridge/rag-dataset-12000) | | RAG Dataset 1200 | [link](https://huggingface.co/datasets/neural-bridge/rag-dataset-1200) | | RAG Hallucination Dataset 1000 | [link](https://huggingface.co/datasets/neural-bridge/rag-hallucination-dataset-1000) | ## **License** This public extract is made available under [Apache license 2.0](https://www.apache.org/licenses/LICENSE-2.0.html). Users should also abide to the [Falcon RefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb) ToU.

xingyaoww/code-act

xingyaoww

2024-02-05T05:23:24Z

324

65

[ "task_categories:text-generation", "language:en", "license:apache-2.0", "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2402.01030", "region:us", "llm-agent", "llm", "instruction-tuning" ]

[ "text-generation" ]

2024-01-15T08:59:02Z

2

--- configs: - config_name: default data_files: - split: codeact path: data/codeact-* - split: general path: data/general-* dataset_info: features: - name: id dtype: string - name: conversations list: - name: content dtype: string - name: role dtype: string splits: - name: codeact num_bytes: 34936511 num_examples: 7139 - name: general num_bytes: 250817144 num_examples: 71246 download_size: 123084833 dataset_size: 285753655 license: apache-2.0 task_categories: - text-generation language: - en tags: - llm-agent - llm - instruction-tuning size_categories: - 1K<n<10K --- <h1 align="center"> Executable Code Actions Elicit Better LLM Agents </h1> <a href="https://github.com/xingyaoww/code-act">💻 Code</a> • <a href="https://arxiv.org/abs/2402.01030">📃 Paper</a> • <a href="https://huggingface.co/datasets/xingyaoww/code-act" >🤗 Data (CodeActInstruct)</a> • <a href="https://huggingface.co/xingyaoww/CodeActAgent-Mistral-7b-v0.1" >🤗 Model (CodeActAgent-Mistral-7b-v0.1)</a> • <a href="https://chat.xwang.dev/">🤖 Chat with CodeActAgent!</a> We propose to use executable Python **code** to consolidate LLM agents’ **act**ions into a unified action space (**CodeAct**). Integrated with a Python interpreter, CodeAct can execute code actions and dynamically revise prior actions or emit new actions upon new observations (e.g., code execution results) through multi-turn interactions. ![Overview](https://github.com/xingyaoww/code-act/blob/main/figures/overview.png?raw=true) ## Why CodeAct? Our extensive analysis of 17 LLMs on API-Bank and a newly curated benchmark [M3ToolEval](docs/EVALUATION.md) shows that CodeAct outperforms widely used alternatives like Text and JSON (up to 20% higher success rate). Please check our paper for more detailed analysis! ![Comparison between CodeAct and Text/JSON](https://github.com/xingyaoww/code-act/blob/main/figures/codeact-comparison-table.png?raw=true) *Comparison between CodeAct and Text / JSON as action.* ![Comparison between CodeAct and Text/JSON](https://github.com/xingyaoww/code-act/blob/main/figures/codeact-comparison-perf.png?raw=true) *Quantitative results comparing CodeAct and {Text, JSON} on M3ToolEval.* ## 📁 CodeActInstruct We collect an instruction-tuning dataset CodeActInstruct that consists of 7k multi-turn interactions using CodeAct. Dataset is release at [huggingface dataset 🤗](https://huggingface.co/datasets/xingyaoww/code-act). Please refer to the paper and [this section](#-data-generation-optional) for details of data collection. ![Data Statistics](https://github.com/xingyaoww/code-act/blob/main/figures/data-stats.png?raw=true) *Dataset Statistics. Token statistics are computed using Llama-2 tokenizer.* ## 🪄 CodeActAgent Trained on **CodeActInstruct** and general conversaions, **CodeActAgent** excels at out-of-domain agent tasks compared to open-source models of the same size, while not sacrificing generic performance (e.g., knowledge, dialog). We release two variants of CodeActAgent: - **CodeActAgent-Mistral-7b-v0.1** (recommended, [model link](https://huggingface.co/xingyaoww/CodeActAgent-Mistral-7b-v0.1)): using Mistral-7b-v0.1 as the base model with 32k context window. - **CodeActAgent-Llama-7b** ([model link](https://huggingface.co/xingyaoww/CodeActAgent-Llama-2-7b)): using Llama-2-7b as the base model with 4k context window. ![Model Performance](https://github.com/xingyaoww/code-act/blob/main/figures/model-performance.png?raw=true) *Evaluation results for CodeActAgent. ID and OD stand for in-domain and out-of-domain evaluation correspondingly. Overall averaged performance normalizes the MT-Bench score to be consistent with other tasks and excludes in-domain tasks for fair comparison.* Please check out [our paper](TODO) and [code](https://github.com/xingyaoww/code-act) for more details about data collection, model training, and evaluation. ## 📚 Citation ```bibtex @misc{wang2024executable, title={Executable Code Actions Elicit Better LLM Agents}, author={Xingyao Wang and Yangyi Chen and Lifan Yuan and Yizhe Zhang and Yunzhu Li and Hao Peng and Heng Ji}, year={2024}, eprint={2402.01030}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

Codec-SUPERB/fluent_speech_commands_synth

Codec-SUPERB

2024-02-01T15:29:11Z

11,629

0

[ "size_categories:100K<n<1M", "format:parquet", "modality:audio", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]

[]

2023-12-27T05:51:14Z

null

--- configs: - config_name: default data_files: - split: original path: data/original-* - split: academicodec_hifi_16k_320d path: data/academicodec_hifi_16k_320d-* - split: academicodec_hifi_16k_320d_large_uni path: data/academicodec_hifi_16k_320d_large_uni-* - split: academicodec_hifi_24k_320d path: data/academicodec_hifi_24k_320d-* - split: audiodec_24k_320d path: data/audiodec_24k_320d-* - split: dac_16k path: data/dac_16k-* - split: dac_24k path: data/dac_24k-* - split: dac_44k path: data/dac_44k-* - split: encodec_24k_12bps path: data/encodec_24k_12bps-* - split: encodec_24k_1_5bps path: data/encodec_24k_1_5bps-* - split: encodec_24k_24bps path: data/encodec_24k_24bps-* - split: encodec_24k_3bps path: data/encodec_24k_3bps-* - split: encodec_24k_6bps path: data/encodec_24k_6bps-* - split: funcodec_en_libritts_16k_gr1nq32ds320 path: data/funcodec_en_libritts_16k_gr1nq32ds320-* - split: funcodec_en_libritts_16k_gr8nq32ds320 path: data/funcodec_en_libritts_16k_gr8nq32ds320-* - split: funcodec_en_libritts_16k_nq32ds320 path: data/funcodec_en_libritts_16k_nq32ds320-* - split: funcodec_en_libritts_16k_nq32ds640 path: data/funcodec_en_libritts_16k_nq32ds640-* - split: funcodec_zh_en_16k_nq32ds320 path: data/funcodec_zh_en_16k_nq32ds320-* - split: funcodec_zh_en_16k_nq32ds640 path: data/funcodec_zh_en_16k_nq32ds640-* - split: speech_tokenizer_16k path: data/speech_tokenizer_16k-* dataset_info: features: - name: audio dtype: audio: sampling_rate: 16000 - name: id dtype: string splits: - name: original num_bytes: 2220326464.0 num_examples: 30043 - name: academicodec_hifi_16k_320d num_bytes: 2212154504.0 num_examples: 30043 - name: academicodec_hifi_16k_320d_large_uni num_bytes: 2212154504.0 num_examples: 30043 - name: academicodec_hifi_24k_320d num_bytes: 3322180744.0 num_examples: 30043 - name: audiodec_24k_320d num_bytes: 3338935944.0 num_examples: 30043 - name: dac_16k num_bytes: 2221347926.0 num_examples: 30043 - name: dac_24k num_bytes: 3329678726.0 num_examples: 30043 - name: dac_44k num_bytes: 6114326168.0 num_examples: 30043 - name: encodec_24k_12bps num_bytes: 3329678726.0 num_examples: 30043 - name: encodec_24k_1_5bps num_bytes: 3329678726.0 num_examples: 30043 - name: encodec_24k_24bps num_bytes: 3329678726.0 num_examples: 30043 - name: encodec_24k_3bps num_bytes: 3329678726.0 num_examples: 30043 - name: encodec_24k_6bps num_bytes: 3329678726.0 num_examples: 30043 - name: funcodec_en_libritts_16k_gr1nq32ds320 num_bytes: 2219150286.0 num_examples: 30043 - name: funcodec_en_libritts_16k_gr8nq32ds320 num_bytes: 2219150286.0 num_examples: 30043 - name: funcodec_en_libritts_16k_nq32ds320 num_bytes: 2221347926.0 num_examples: 30043 - name: funcodec_en_libritts_16k_nq32ds640 num_bytes: 2221347926.0 num_examples: 30043 - name: funcodec_zh_en_16k_nq32ds320 num_bytes: 2221347926.0 num_examples: 30043 - name: funcodec_zh_en_16k_nq32ds640 num_bytes: 2221347926.0 num_examples: 30043 - name: speech_tokenizer_16k num_bytes: 2230445064.0 num_examples: 30043 download_size: 21108462066 dataset_size: 57173635950.0 --- # Dataset Card for "fluent_speech_commands_synth" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

awacke1/ICD10-Clinical-Terminology

awacke1

2024-01-31T01:34:01Z

121

15

[ "license:mit", "size_categories:10K<n<100K", "format:csv", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2023-02-28T12:20:41Z

1

--- license: mit --- ICD10-Clinical-Terminology pyarrow fast search demonstration for context AI MMoE

biglam/hmd_newspapers

biglam

2024-01-30T12:06:17Z

3,123

9

[ "task_categories:text-generation", "language:en", "license:cc0-1.0", "size_categories:1M<n<10M", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us", "newspapers" ]

[ "text-generation" ]

2022-03-02T23:29:22Z

1

--- dataset_info: features: - name: source dtype: string - name: title dtype: string - name: location dtype: string - name: date dtype: timestamp[s] - name: item_type dtype: string - name: word_count dtype: int32 - name: ocr_quality_mean dtype: float64 - name: ocr_quality_sd dtype: float64 - name: text dtype: string splits: - name: train num_bytes: 14304741164 num_examples: 3065408 download_size: 9682476047 dataset_size: 14304741164 configs: - config_name: default data_files: - split: train path: data/train-* license: cc0-1.0 task_categories: - text-generation language: - en tags: - newspapers pretty_name: Heritage Made Digital Newspapers size_categories: - 1M<n<10M --- # Dataset Card for Heritage Made Digital Newspapers ## Table of Contents - [Dataset Card for Heritage Made Digital Newspapers](#dataset-card-for-heritage-made-digital-newspapers) - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** - **Repository:** https://bl.iro.bl.uk/?locale=en - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary This dataset contains text extracted at the article level from historic digitised newspapers from the [Heritage Made Digital](https://bl.iro.bl.uk/collections/9a6a4cdd-2bfe-47bb-8c14-c0a5d100501f?locale=en) newspaper digitisation program at the [British Library](https://www.bl.uk/). The newspapers in the dataset were published between 1800 and 1896. This dataset contains ~2.5 billion tokens and 3,065,408 articles. The dataset contains text generated from Optical Character Recognition software on digitised newspaper pages. This dataset includes the plain text from the OCR alongside some minimal metadata associated with the newspaper from which the text is derived and OCR confidence score information generated from the OCR software. ### Supported Tasks and Leaderboards This dataset can be used for: - historical research and digital humanities research - training language models - training historic language models Whilst this dataset can be used for all of these tasks, it is important to understand that the dataset was not constructed in a representative way so it contains biases in terms of the newspapers and articles that are included (more on this below). ### Languages The text in this dataset is in English that has been recognised by the OCR software. The OCR software used is generic commercial OCR software that has not been trained on historic newspapers. There are therefore many errors in the text. Some of the OCR in this text will be of such poor quality that is is incomprehensible to a human reader. ## Dataset Structure ### Data Instances Each row in the dataset is an article from a newspaper as recognised by an OLR (Optical Layout Recognition) step in the digitisation process. ### Data Splits There is one split in this dataset, the training split. ## Dataset Creation ### Curation Rationale This dataset consists of public-domain newspapers published in the UK during the 19th Century. The majority of newspapers digitised in the UK are not freely available (even if they are out of copyright). The newspapers in this dataset were digitised specifically to be freely available but also to meet preservation goals for newspapers in poor condition. As a result, the newspapers chosen for digitisation are biased toward poor quality physical newspapers. This may in turn result in worse OCR. ### Source Data The source data for this dataset is the digitised newspapers from the [Heritage Made Digital](https://bl.iro.bl.uk/collections/9a6a4cdd-2bfe-47bb-8c14-c0a5d100501f?locale=en) newspaper digitisation program. The newspapers in the dataset were published between 1800 and 1870. ### Dataset Curators The original digitisation was carried out by the British Library. The dataset was created by the British Library in partnership with Findmypast. This dataset was created by [@davanstrien](https://huggingface.co/davanstrien). ### Licensing Information The newspapers in this dataset are in the public domain. The dataset is licensed under a [Creative Commons Zero v1.0 Universal](https://creativecommons.org/publicdomain/zero/1.0/) license. ### Citation Information [More Information Needed] ### Contributions Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset.

biglam/blbooks-parquet

biglam

2024-01-30T09:41:53Z

19,514

6

[ "task_categories:text-generation", "task_categories:fill-mask", "task_categories:other", "task_ids:language-modeling", "task_ids:masked-language-modeling", "annotations_creators:no-annotation", "language_creators:machine-generated", "multilinguality:multilingual", "source_datasets:blbooks", "language:de", "language:en", "language:es", "language:fr", "language:it", "language:nl", "license:cc0-1.0", "size_categories:10M<n<100M", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us", "digital-humanities-research" ]

[ "text-generation", "fill-mask", "other" ]

2023-07-13T12:48:12Z

null

--- annotations_creators: - no-annotation language_creators: - machine-generated language: - de - en - es - fr - it - nl license: - cc0-1.0 multilinguality: - multilingual size_categories: - 100K<n<1M source_datasets: - blbooks task_categories: - text-generation - fill-mask - other task_ids: - language-modeling - masked-language-modeling pretty_name: British Library Books tags: - digital-humanities-research dataset_info: - config_name: '1500_1899' features: - name: record_id dtype: string - name: date dtype: timestamp[s] - name: raw_date dtype: string - name: title dtype: string - name: place dtype: string - name: empty_pg dtype: bool - name: text dtype: string - name: pg dtype: int32 - name: mean_wc_ocr dtype: float32 - name: std_wc_ocr dtype: float64 - name: name dtype: string - name: all_names dtype: string - name: Publisher dtype: string - name: Country of publication 1 dtype: string - name: all Countries of publication dtype: string - name: Physical description dtype: string - name: Language_1 dtype: string - name: Language_2 dtype: string - name: Language_3 dtype: string - name: Language_4 dtype: string - name: multi_language dtype: bool splits: - name: train num_bytes: 30452067039 num_examples: 14011953 download_size: 10486035662 dataset_size: 30452067039 - config_name: '1510_1699' features: - name: record_id dtype: string - name: date dtype: timestamp[s] - name: raw_date dtype: string - name: title dtype: string - name: place dtype: string - name: empty_pg dtype: bool - name: text dtype: string - name: pg dtype: int32 - name: mean_wc_ocr dtype: float32 - name: std_wc_ocr dtype: float64 - name: name dtype: string - name: all_names dtype: string - name: Publisher dtype: string - name: Country of publication 1 dtype: string - name: all Countries of publication dtype: string - name: Physical description dtype: string - name: Language_1 dtype: string - name: Language_2 dtype: string - name: Language_3 dtype: string - name: Language_4 dtype: string - name: multi_language dtype: bool splits: - name: train num_bytes: 107667469 num_examples: 51982 download_size: 42320165 dataset_size: 107667469 - config_name: '1700_1799' features: - name: record_id dtype: string - name: date dtype: timestamp[s] - name: raw_date dtype: string - name: title dtype: string - name: place dtype: string - name: empty_pg dtype: bool - name: text dtype: string - name: pg dtype: int32 - name: mean_wc_ocr dtype: float32 - name: std_wc_ocr dtype: float64 - name: name dtype: string - name: all_names dtype: string - name: Publisher dtype: string - name: Country of publication 1 dtype: string - name: all Countries of publication dtype: string - name: Physical description dtype: string - name: Language_1 dtype: string - name: Language_2 dtype: string - name: Language_3 dtype: string - name: Language_4 dtype: string - name: multi_language dtype: bool splits: - name: train num_bytes: 267117831 num_examples: 178224 download_size: 95137895 dataset_size: 267117831 - config_name: 1700s features: - name: record_id dtype: string - name: date dtype: int32 - name: raw_date dtype: string - name: title dtype: string - name: place dtype: string - name: empty_pg dtype: bool - name: text dtype: string - name: pg dtype: int32 - name: mean_wc_ocr dtype: float32 - name: std_wc_ocr dtype: float64 - name: name dtype: string - name: all_names dtype: string - name: Publisher dtype: string - name: Country of publication 1 dtype: string - name: all Countries of publication dtype: string - name: Physical description dtype: string - name: Language_1 dtype: string - name: Language_2 dtype: string - name: Language_3 dtype: string - name: Language_4 dtype: string - name: multi_language dtype: bool splits: - name: train num_bytes: 266382657 num_examples: 178224 download_size: 95137895 dataset_size: 266382657 - config_name: '1800_1899' features: - name: record_id dtype: string - name: date dtype: timestamp[s] - name: raw_date dtype: string - name: title dtype: string - name: place dtype: string - name: empty_pg dtype: bool - name: text dtype: string - name: pg dtype: int32 - name: mean_wc_ocr dtype: float32 - name: std_wc_ocr dtype: float64 - name: name dtype: string - name: all_names dtype: string - name: Publisher dtype: string - name: Country of publication 1 dtype: string - name: all Countries of publication dtype: string - name: Physical description dtype: string - name: Language_1 dtype: string - name: Language_2 dtype: string - name: Language_3 dtype: string - name: Language_4 dtype: string - name: multi_language dtype: bool splits: - name: train num_bytes: 30077284377 num_examples: 13781747 download_size: 10348577602 dataset_size: 30077284377 - config_name: 1800s features: - name: record_id dtype: string - name: date dtype: int32 - name: raw_date dtype: string - name: title dtype: string - name: place dtype: string - name: empty_pg dtype: bool - name: text dtype: string - name: pg dtype: int32 - name: mean_wc_ocr dtype: float32 - name: std_wc_ocr dtype: float64 - name: name dtype: string - name: all_names dtype: string - name: Publisher dtype: string - name: Country of publication 1 dtype: string - name: all Countries of publication dtype: string - name: Physical description dtype: string - name: Language_1 dtype: string - name: Language_2 dtype: string - name: Language_3 dtype: string - name: Language_4 dtype: string - name: multi_language dtype: bool splits: - name: train num_bytes: 30020434670 num_examples: 13781747 download_size: 10348577602 dataset_size: 30020434670 - config_name: all features: - name: record_id dtype: string - name: date dtype: int32 - name: raw_date dtype: string - name: title dtype: string - name: place dtype: string - name: empty_pg dtype: bool - name: text dtype: string - name: pg dtype: int32 - name: mean_wc_ocr dtype: float32 - name: std_wc_ocr dtype: float64 - name: name dtype: string - name: all_names dtype: string - name: Publisher dtype: string - name: Country of publication 1 dtype: string - name: all Countries of publication dtype: string - name: Physical description dtype: string - name: Language_1 dtype: string - name: Language_2 dtype: string - name: Language_3 dtype: string - name: Language_4 dtype: string - name: multi_language dtype: bool splits: - name: train num_bytes: 30394267732 num_examples: 14011953 download_size: 10486035662 dataset_size: 30394267732 - config_name: default features: - name: record_id dtype: string - name: date dtype: timestamp[s] - name: raw_date dtype: string - name: title dtype: string - name: place dtype: string - name: empty_pg dtype: bool - name: text dtype: string - name: pg dtype: int32 - name: mean_wc_ocr dtype: float32 - name: std_wc_ocr dtype: float64 - name: name dtype: string - name: all_names dtype: string - name: Publisher dtype: string - name: Country of publication 1 dtype: string - name: all Countries of publication dtype: string - name: Physical description dtype: string - name: Language_1 dtype: string - name: Language_2 dtype: string - name: Language_3 dtype: string - name: Language_4 dtype: string - name: multi_language dtype: bool splits: - name: train num_bytes: 30451982919 num_examples: 14011953 download_size: 16402835443 dataset_size: 30451982919 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for British Library Books This dataset is the same as https://huggingface.co/datasets/TheBritishLibrary/blbooks, however, this version is stored as parquet to avoid needing to run a datasets script. This also makes loading this dataset much quicker. ## Table of Contents - [Dataset Card for British Library Books](#dataset-card-for-British-Library-Books) - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Language model training](#language-model-training) - [Supervised tasks](#supervised-tasks) - [Languages](#languages) - [Language change](#language-change) - [Optical Character Recognition](#optical-character-recognition) - [OCR word confidence](#ocr-word-confidence) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization) - [Date normalization](#date-normalization) - [Metadata included](#metadata-included) - [Who are the source language producers?](#who-are-the-source-language-producers) - [Annotations](#annotations) - [Annotation process](#annotation-process) - [Who are the annotators?](#who-are-the-annotators) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Colonialism](#colonialism) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://www.bl.uk/collection-guides/digitised-printed-books - **Repository:** https://doi.org/10.21250/db14 - **Paper:** - **Leaderboard:** - **Point of Contact:** [email protected] ### Dataset Summary This dataset consists of books digitised by the British Library in partnership with Microsoft. The dataset includes ~25 million pages of out of copyright texts. The majority of the texts were published in the 18th and 19th Century, but the collection also consists of a smaller number of books from earlier periods. Items within this collection cover a wide range of subject areas, including geography, philosophy, history, poetry and literature and are published in various languages. The dataset consits of around *7.67 billion words*. While the books are predominately from the 18th and 19th Centuries, there are fewer books from earlier periods. The number of pages in the corpus by decade: | | page count | | ---- | ---------- | | 1510 | 94 | | 1520 | 32 | | 1540 | 184 | | 1550 | 16 | | 1580 | 276 | | 1590 | 540 | | 1600 | 1117 | | 1610 | 1132 | | 1620 | 1856 | | 1630 | 9274 | | 1640 | 4232 | | 1650 | 2944 | | 1660 | 5858 | | 1670 | 11415 | | 1680 | 8348 | | 1690 | 13756 | | 1700 | 10160 | | 1710 | 9556 | | 1720 | 10314 | | 1730 | 13282 | | 1740 | 10778 | | 1750 | 12001 | | 1760 | 21415 | | 1770 | 28490 | | 1780 | 32676 | | 1790 | 50014 | | 1800 | 307806 | | 1810 | 478008 | | 1820 | 589419 | | 1830 | 681212 | | 1840 | 1113473 | | 1850 | 1726108 | | 1860 | 1725407 | | 1870 | 2069089 | | 1880 | 2585159 | | 1890 | 3365031 | [More Information Needed] ### Supported Tasks and Leaderboards This collection has been previously used across various digital history and humanities projects since being published. The dataset consists of text and a range of metadata associated with this text. This metadata includes: - date of publication - place of publication - country of publication - language - OCR quality - physical description of the original physical item #### Language model training As a relatively large dataset, `blbooks` provides a source dataset for training language models. The presence of this metadata also offers interesting opportunities to use this dataset as a source for training language models based on: - specific time-periods - specific languages - certain OCR quality thresholds The above is not an exhaustive list but offer some suggestions of how the dataset can be used to explore topics such as the impact of OCR quality on language models, the ‘transferability’ of language models across time or the impact of training multilingual language models on historical languages. #### Supervised tasks Whilst this dataset does not have annotations for a specific NLP task, such as Named Entity Recognition, it does include a wide variety of metadata. This metadata has the potential to be used for training and/or evaluating a variety of supervised tasks predicting this metadata. ### Languages This dataset consists of books published in several languages. The breakdown of the languages included (at the page level) is: | Language | Pages | | --------------------- | -------- | | English | 10039463 | | French | 1442929 | | German | 1172793 | | Spanish | 286778 | | Italian | 214255 | | Dutch | 204759 | | Russian | 193347 | | Danish | 93366 | | Hungarian | 88094 | | Swedish | 76225 | | Polish | 58901 | | Greek, Modern (1453-) | 26104 | | Latin | 25611 | | Portuguese | 25410 | | Czech | 20160 | | Bulgarian | 7891 | | Finnish | 5677 | | Irish | 2743 | | Serbian | 1975 | | Romanian | 1544 | | Norwegian Nynorsk | 1398 | | Croatian | 1306 | | Norwegian | 1227 | | Icelandic | 902 | | Slovak | 840 | | Lithuanian | 714 | | Welsh | 580 | | Slovenian | 545 | | Indonesian | 418 | | Cornish | 223 | This breakdown was derived from the first language in the associated metadata field. Some books include multiple languages. Some of the languages codes for this data were also derived using computational methods. Therefore, the language fields in the dataset should be treated with some caution (discussed in more detail below). #### Language change The publication dates of books in the data cover a broad period of time (1500-1900). For languages in the dataset with broad temporal coverage, significant [language change](https://en.wikipedia.org/wiki/Language_change) might be found. The ability to study this change by taking reasonably large samples of languages covering different time periods is one of the opportunities offered by this dataset. The fact that the text in this dataset was produced via Optical Character Recognition (OCR) causes some challenges for this type of research (see below). #### Optical Character Recognition The digitised books in this collection were transformed into machine-readable text using Optical Character Recognition (OCR) software. The text produced via OCR software will usually include some errors. These errors include; mistakes at the character level; for example, an `i` is mistaken for an `l`, at the word level or across significant passages of text. The books in this dataset can pose some additional challenges for OCR software. OCR errors can stem from: - the quality of the original printing: printing technology was a developing technology during the time period covered by this corpus; some of the original book text will include misprints, blurred or faded ink that is hard to read - damage to the page: some of the books will have become damaged over time, this can obscure all or parts of the text on a page - poor quality scans: scanning books can be challenging; for example, if the book has tight bindings, it can be hard to capture text that has fallen into the [gutter](https://www.abaa.org/glossary/entry/gutter) of the book. - the language used in the books may differ from the languages OCR software is predominantly trained to recognise. ##### OCR word confidence Many OCR engines produce some form of confidence score alongside the predicted text. These confidence scores are usually at the character or word level. The word confidence score was given for each word in the original ALTO XML versions of the text in this dataset in this dataset. The OCR confidence scores should be treated with some scepticism. For historical text or in a lower resource language, for example, a low confidence score may be more likely for words not included in a modern dictionary but may be accurate transcriptions of the original text. With that said, the confidence scores do give some sense of the OCR quality. An example of text with a high (over 90% mean word confidence score): ``` 8 direction to the Conduit, round which is a wide open space, and a good broad pavement called the Parade. It commands a pleasant peep of the slopes and terrace throughout its entire length. The street continuing from the Conduit, in the same general direction, was known anciently as Lodborne Lane, and is now named South Street. From the Conduit two other streets, at right angles to these, are Long Street, leading Eastwards, and Half-Moon Street (formerly Lodborne), leading to Westbury, Trendle Street, and the Horsecastles Road. ``` An example of text with a score below 40%: ``` Hannover. Schrift und Druck von Fr. CultniTmn,', "LeMNs'utluirui.", 'ü 8u«llim» M^äalßwi 01de!lop 1<M.', 'p^dnalmw vom Xr^u/e, lpiti>»**Kmm lie« !»^2!M kleine lii!<! (,«>* ttünee!<»e^ v»n tndzt Lievclum, 1872, ``` The quality of OCR - as measured by mean OCR confidence for a page - across the dataset correlates with other features. A groupby of publication decade and mean word confidence: | decade | mean_wc_ocr | | ------ | ----------- | | 1510 | 0.499151 | | 1520 | 0.544818 | | 1540 | 0.511589 | | 1550 | 0.4505 | | 1580 | 0.321858 | | 1590 | 0.461282 | | 1600 | 0.467318 | | 1610 | 0.495895 | | 1620 | 0.501257 | | 1630 | 0.49766 | | 1640 | 0.512095 | | 1650 | 0.528534 | | 1660 | 0.521014 | | 1670 | 0.592575 | | 1680 | 0.583901 | | 1690 | 0.567202 | | 1700 | 0.575175 | | 1710 | 0.61436 | | 1720 | 0.627725 | | 1730 | 0.658534 | | 1740 | 0.64214 | | 1750 | 0.657357 | | 1760 | 0.6389 | | 1770 | 0.651883 | | 1780 | 0.632326 | | 1790 | 0.664279 | | 1800 | 0.682338 | | 1810 | 0.708915 | | 1820 | 0.730015 | | 1830 | 0.730973 | | 1840 | 0.713886 | | 1850 | 0.697106 | | 1860 | 0.696701 | | 1870 | 0.717233 | | 1880 | 0.733331 | | 1890 | 0.762364 | As might be expected, the earlier periods have lower mean word confidence scores. Again, all of this should be treated with some scepticism, especially as the size of the data grows over time. As with time, the mean word confidence of the OCR software varies across languages: | Language_1 | mean_wc_ocr | | --------------------- | ----------- | | Croatian | 0.755565 | | Welsh | 0.7528 | | Norwegian Nynorsk | 0.751648 | | Slovenian | 0.746007 | | French | 0.740772 | | Finnish | 0.738032 | | Czech | 0.737849 | | Hungarian | 0.736076 | | Dutch | 0.734977 | | Cornish | 0.733682 | | Danish | 0.733106 | | English | 0.733037 | | Irish | 0.732658 | | Portuguese | 0.727746 | | Spanish | 0.725111 | | Icelandic | 0.724427 | | Italian | 0.715839 | | Swedish | 0.715633 | | Polish | 0.715133 | | Lithuanian | 0.700003 | | Bulgarian | 0.694657 | | Romanian | 0.692957 | | Latin | 0.689022 | | Russian | 0.685847 | | Serbian | 0.674329 | | Slovak | 0.66739 | | Greek, Modern (1453-) | 0.632195 | | German | 0.631457 | | Indonesian | 0.6155 | | Norwegian | 0.597987 | Again, these numbers should be treated sceptically since some languages appear very infrequently. For example, the above table suggests the mean word confidence for Welsh is relatively high. However, there isn’t much Welsh in the dataset. Therefore, it is unlikely that this data will be particularly useful for training (historic) Welsh language models. [More Information Needed] ## Dataset Structure The dataset has a number of configurations relating to the different dates of publication in the underlying data: - `1500_1899`: this configuration covers all years - `1800_1899`: this configuration covers the years between 1800 and 1899 - `1700_1799`: this configuration covers the years between 1700 and 1799 - `1510_1699`: this configuration covers the years between 1510 and 1699 ### Configuration option All of the configurations have an optional keyword argument `skip_empty_pages` which is set to `True` by default. The underlying dataset includes some pages where there is no text. This could either be because the underlying book page didn't have any text or the OCR software failed to detect this text. For many uses of this dataset it doesn't make sense to include empty pages so these are skipped by default. However, for some uses you may prefer to retain a representation of the data that includes these empty pages. Passing `skip_empty_pages=False` when loading the dataset will enable this option. ### Data Instances An example data instance: ```python {'Country of publication 1': 'England', 'Language_1': 'English', 'Language_2': None, 'Language_3': None, 'Language_4': None, 'Physical description': None, 'Publisher': None, 'all Countries of publication': 'England', 'all names': 'Settle, Elkanah [person]', 'date': 1689, 'empty_pg': True, 'mean_wc_ocr': 0.0, 'multi_language': False, 'name': 'Settle, Elkanah', 'pg': 1, 'place': 'London', 'raw_date': '1689', 'record_id': '001876770', 'std_wc_ocr': 0.0, 'text': None, ‘title’: ‘The Female Prelate: being the history and the life and death of Pope Joan. A tragedy [in five acts and in verse] . Written by a Person of Quality [i.e. Elkanah Settle]’} ``` Each instance in the dataset represents a single page from an original digitised book. ### Data Fields Included in this dataset are: | Field | Data Type | Description | | ---------------------------- | --------- | ------------------------------------------------------------------------------------------------------------- | | record_id | string | British Library ID for the item | | date | int | parsed/normalised year for the item. i.e. 1850 | | raw_date | string | the original raw date for an item i.e. 1850- | | title | string | title of the book | | place | string | Place of publication, i.e. London | | empty_pg | bool | whether page contains text | | text | string | OCR generated text for a page | | pg | int | page in original book the instance refers to | | mean_wc_ocr | float | mean word confidence values for the page | | std_wc_ocr | float | standard deviation of the word confidence values for the page | | name | string | name associated with the item (usually author) | | all names | string | all names associated with a publication | | Publisher | string | publisher of the book | | Country of publication 1 | string | first country associated with publication | | all Countries of publication | string | all countries associated with a publication | | Physical description | string | physical description of the item (size). This requires some normalisation before use and isn’t always present | | Language_1 | string | first language associated with the book, this is usually present | | Language_2 | string | | | Language_3 | string | | | Language_4 | string | | | multi_language | bool | | Some of these fields are not populated a large proportion of the time. You can get some sense of this from this [Pandas Profiling](https://github.com/pandas-profiling/pandas-profiling) [report](https://davanstrien.github.io/BL-datasets-pandas-profile-reports/pandas_profile_report_MS_digitised_books_2021-01-09.html) The majority of these fields relate to metadata about the books. Most of these fields were created by staff working for the British Library. The notable exception is the “Languages” fields that have sometimes been determined using computational methods. This work is reported in more detail in [Automated Language Identification of Bibliographic Resources](https://doi.org/10.1080/01639374.2019.1700201). It is important to note that metadata is neither perfect nor static. The metadata associated with this book was generated based on export from the British Library catalogue in 2021. [More Information Needed] ### Data Splits This dataset contains a single split `train`. ## Dataset Creation **Note** this section is a work in progress. ### Curation Rationale The books in this collection were digitised as part of a project partnership between the British Library and Microsoft. [Mass digitisation](https://en.wikipedia.org/wiki/Category:Mass_digitization), i.e. projects intending to quickly digitise large volumes of materials shape the selection of materials to include in several ways. Some considerations which are often involved in the decision of whether to include items for digitisation include (but are not limited to): - copyright status - preservation needs - the size of an item, very large and very small items are often hard to digitise quickly These criteria can have knock-on effects on the makeup of a collection. For example, systematically excluding large books may result in some types of book content not being digitised. Large volumes are likely to be correlated to content to at least some extent, so excluding them from digitisation will mean that material is underrepresented. Similarly, copyright status is often (but not only) determined by publication date. This can often lead to a rapid fall in the number of items in a collection after a certain cut-off date. All of the above is largely to make clear that this collection was not curated to create a representative sample of the British Library’s holdings. Some material will be over-represented, and others under-represented. Similarly, the collection should not be considered a representative sample of what was published across the period covered by the dataset (nor that the relative proportions of the data for each time period represent a proportional sample of publications from that period). Finally, and this probably does not need stating, the language included in the text should not be considered representative of either written or spoken language(s) from that time period. [More Information Needed] ### Source Data The source data (physical items) includes a variety of resources (predominantly monographs) held by the [British Library](bl.uk/](https://bl.uk/). The British Library is a [Legal Deposit](https://www.bl.uk/legal-deposit/about-legal-deposit) library. “Legal deposit requires publishers to provide a copy of every work they publish in the UK to the British Library. It’s existed in English law since 1662.” [source](https://www.bl.uk/legal-deposit/about-legal-deposit). The source data for this version of the data is derived from the original ALTO XML files and a recent metadata export #TODO add links [More Information Needed] #### Initial Data Collection and Normalization This version of the dataset was created using the original ALTO XML files and, where a match was found, updating the metadata associated with that item with more recent metadata using an export from the British Library catalogue. The process of creating this new dataset is documented here #TODO add link. There are a few decisions made in the above processing steps worth highlighting in particular: ##### Date normalization The metadata around date of publication for an item is not always exact. It often is represented as a date range e.g. `1850-1860`. The `date` field above takes steps to normalise this date to a single integer value. In most cases, this is taking the mean of the values associated with the item. The `raw_date` field includes the unprocessed date string. ##### Metadata included The metadata associated with each item includes most of the fields available via the ALTO XML. However, the data doesn’t include some metadata fields from the metadata export file. The reason fields were excluded because they are frequently not populated. A cut off of 50% was chosen, i.e. values from the metadata which are missing above 50% of the time were not included. This is slightly arbitrary, but since the aim of this version of the data was to support computational research using the collection it was felt that these fields with frequent missing values would be less valuable. #### Who are the source language producers? [More Information Needed] ### Annotations This dataset does not include annotations as usually understood in the context of NLP. The data does include metadata associated with the books. #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data There a range of considerations around using the data. These include the representativeness of the dataset, the OCR quality and the language used. Depending on your use case, these may be more or less important. For example, the impact of OCR quality on downstream tasks will depend on the target task. It may also be possible to mitigate this negative impact from OCR through tokenizer choice, Language Model training objectives, oversampling high-quality OCR, etc. [More Information Needed] ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases The text in this collection is derived from historical text. As a result, the text will reflect this time period's social beliefs and attitudes. The books include both fiction and non-fiction books. Examples of book titles that appear in the data (these are randomly sampled from all titles): - ‘Rhymes and Dreams, Legends of Pendle Forest, and other poems’, - “Précis of Information concerning the Zulu Country, with a map. Prepared in the Intelligence Branch of the Quarter-Master-General’s Department, Horse Guards, War Office, etc”, - ‘The fan. A poem’, - ‘Grif; a story of Australian Life’, - ‘Calypso; a masque: in three acts, etc’, - ‘Tales Uncle told [With illustrative woodcuts.]’, - 'Questings', - 'Home Life on an Ostrich Farm. With ... illustrations’, - ‘Bulgarya i Bulgarowie’, - 'Εἰς τα βαθη της Ἀφρικης [In darkest Africa.] ... Μεταφρασις Γεωρ. Σ. Βουτσινα, etc', - ‘The Corsair, a tale’, ‘Poems ... With notes [With a portrait.]’, - ‘Report of the Librarian for the year 1898 (1899, 1901, 1909)’, - “The World of Thought. A novel. By the author of ‘Before I began to speak.’”, - 'Amleto; tragedia ... recata in versi italiani da M. Leoni, etc'] While using titles alone is insufficient to integrate bias in this collection, it gives some insight into the topics covered by books. Further, the tiles highlight some particular types of bias we might find in the collection. This should in no way be considered an exhaustive list. #### Colonialism Even in the above random sample of titles examples of colonial attitudes, we can see examples of titles. We can try and interrogate this further by searching for the name of places that were part of the British Empire when many of these books were published. Searching for the string `India` in the titles and randomly sampling 10 titles returns: - “Travels in India in the Seventeenth Century: by Sir Thomas Roe and Dr. John Fryer. Reprinted from the ‘Calcutta Weekly Englishman.’”, - ‘A Winter in India and Malaysia among the Methodist Missions’, - “The Tourist’s Guide to all the principal stations on the railways of Northern India [By W. W.] ... Fifth edition”, - ‘Records of Sport and Military Life in Western India ... With an introduction by ... G. B. Malleson’, - "Lakhmi, the Rájpút's Bride. A tale of Gujarát in Western India [A poem.]”, - ‘The West India Commonplace Book: compiled from parliamentary and official documents; shewing the interest of Great Britain in its Sugar Colonies’, - “From Tonkin to India : by the sources of the Irawadi, January’ 95-January ’96”, - ‘Case of the Ameers of Sinde : speeches of Mr. John Sullivan, and Captain William Eastwick, at a special court held at the India House, ... 26th January, 1844’, - ‘The Andaman Islands; their colonisation, etc. A correspondence addressed to the India Office’, - ‘Ancient India as described by Ptolemy; being a translation of the chapters which describe India and Eastern Asia in the treatise on Geography written by Klaudios Ptolemaios ... with introduction, commentary, map of India according to Ptolemy, and ... index, by J. W. McCrindle’] Searching form the string `Africa` in the titles and randomly sampling 10 titles returns: - ['De Benguella ás Terras de Iácca. Descripção de uma viagem na Africa Central e Occidental ... Expedição organisada nos annos de 1877-1880. Edição illustrada', - ‘To the New Geographical Society of Edinburgh [An address on Africa by H. M. Stanley.]’, - ‘Diamonds and Gold in South Africa ... With maps, etc’, - ‘Missionary Travels and Researches in South Africa ... With notes by F. S. Arnot. With map and illustrations. New edition’, - ‘A Narrative of a Visit to the Mauritius and South Africa ... Illustrated by two maps, sixteen etchings and twenty-eight wood-cuts’, - ‘Side Lights on South Africa ... With a map, etc’, - ‘My Second Journey through Equatorial Africa ... in ... 1886 and 1887 ... Translated ... by M. J. A. Bergmann. With a map ... and ... illustrations, etc’, - ‘Missionary Travels and Researches in South Africa ... With portrait and fullpage illustrations’, - ‘[African sketches.] Narrative of a residence in South Africa ... A new edition. To which is prefixed a biographical sketch of the author by J. Conder’, - ‘Lake Ngami; or, Explorations and discoveries during four years wandering in the wilds of South Western Africa ... With a map, and numerous illustrations, etc’] [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information The books are licensed under the [CC Public Domain Mark 1.0](https://creativecommons.org/publicdomain/mark/1.0/) license. ### Citation Information ```bibtext @misc{bBritishLibraryBooks2021, author = {British Library Labs}, title = {Digitised Books. c. 1510 - c. 1900. JSONL (OCR derived text + metadata)}, year = {2021}, publisher = {British Library}, howpublished={https://doi.org/10.23636/r7w6-zy15} ``` ### Contributions Thanks to [@davanstrien](https://github.com/davanstrien) for adding this dataset.

corto-ai/handwritten-text

corto-ai

2024-01-29T00:25:32Z

141

14

[ "size_categories:10K<n<100K", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2024-01-29T00:25:16Z

2

--- dataset_info: features: - name: text dtype: string - name: image dtype: image splits: - name: train num_bytes: 167800178.75 num_examples: 6482 - name: valid num_bytes: 24887435.0 num_examples: 976 - name: test num_bytes: 73857843.625 num_examples: 2915 download_size: 265569932 dataset_size: 266545457.375 configs: - config_name: default data_files: - split: train path: data/train-* - split: valid path: data/valid-* - split: test path: data/test-* ---

google/code_x_glue_ct_code_to_text

google

2024-01-24T15:09:09Z

1,709

69

[ "task_categories:translation", "annotations_creators:found", "language_creators:found", "multilinguality:other-programming-languages", "source_datasets:original", "language:code", "language:en", "license:c-uda", "size_categories:1M<n<10M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us", "code-to-text" ]

[ "translation" ]

2022-03-02T23:29:22Z

1

--- annotations_creators: - found language_creators: - found language: - code - en license: - c-uda multilinguality: - other-programming-languages size_categories: - 100K<n<1M - 10K<n<100K source_datasets: - original task_categories: - translation task_ids: [] pretty_name: CodeXGlueCtCodeToText config_names: - go - java - javascript - php - python - ruby tags: - code-to-text dataset_info: - config_name: go features: - name: id dtype: int32 - name: repo dtype: string - name: path dtype: string - name: func_name dtype: string - name: original_string dtype: string - name: language dtype: string - name: code dtype: string - name: code_tokens sequence: string - name: docstring dtype: string - name: docstring_tokens sequence: string - name: sha dtype: string - name: url dtype: string splits: - name: train num_bytes: 342243143 num_examples: 167288 - name: validation num_bytes: 13721860 num_examples: 7325 - name: test num_bytes: 16328406 num_examples: 8122 download_size: 121341698 dataset_size: 372293409 - config_name: java features: - name: id dtype: int32 - name: repo dtype: string - name: path dtype: string - name: func_name dtype: string - name: original_string dtype: string - name: language dtype: string - name: code dtype: string - name: code_tokens sequence: string - name: docstring dtype: string - name: docstring_tokens sequence: string - name: sha dtype: string - name: url dtype: string splits: - name: train num_bytes: 452553835 num_examples: 164923 - name: validation num_bytes: 13366344 num_examples: 5183 - name: test num_bytes: 29080753 num_examples: 10955 download_size: 154701399 dataset_size: 495000932 - config_name: javascript features: - name: id dtype: int32 - name: repo dtype: string - name: path dtype: string - name: func_name dtype: string - name: original_string dtype: string - name: language dtype: string - name: code dtype: string - name: code_tokens sequence: string - name: docstring dtype: string - name: docstring_tokens sequence: string - name: sha dtype: string - name: url dtype: string splits: - name: train num_bytes: 160860431 num_examples: 58025 - name: validation num_bytes: 10337344 num_examples: 3885 - name: test num_bytes: 10190713 num_examples: 3291 download_size: 65788314 dataset_size: 181388488 - config_name: php features: - name: id dtype: int32 - name: repo dtype: string - name: path dtype: string - name: func_name dtype: string - name: original_string dtype: string - name: language dtype: string - name: code dtype: string - name: code_tokens sequence: string - name: docstring dtype: string - name: docstring_tokens sequence: string - name: sha dtype: string - name: url dtype: string splits: - name: train num_bytes: 614654499 num_examples: 241241 - name: validation num_bytes: 33283045 num_examples: 12982 - name: test num_bytes: 35374993 num_examples: 14014 download_size: 219692158 dataset_size: 683312537 - config_name: python features: - name: id dtype: int32 - name: repo dtype: string - name: path dtype: string - name: func_name dtype: string - name: original_string dtype: string - name: language dtype: string - name: code dtype: string - name: code_tokens sequence: string - name: docstring dtype: string - name: docstring_tokens sequence: string - name: sha dtype: string - name: url dtype: string splits: - name: train num_bytes: 813663148 num_examples: 251820 - name: validation num_bytes: 46888564 num_examples: 13914 - name: test num_bytes: 50659688 num_examples: 14918 download_size: 325551862 dataset_size: 911211400 - config_name: ruby features: - name: id dtype: int32 - name: repo dtype: string - name: path dtype: string - name: func_name dtype: string - name: original_string dtype: string - name: language dtype: string - name: code dtype: string - name: code_tokens sequence: string - name: docstring dtype: string - name: docstring_tokens sequence: string - name: sha dtype: string - name: url dtype: string splits: - name: train num_bytes: 51956439 num_examples: 24927 - name: validation num_bytes: 2821037 num_examples: 1400 - name: test num_bytes: 2671551 num_examples: 1261 download_size: 21921316 dataset_size: 57449027 configs: - config_name: go data_files: - split: train path: go/train-* - split: validation path: go/validation-* - split: test path: go/test-* - config_name: java data_files: - split: train path: java/train-* - split: validation path: java/validation-* - split: test path: java/test-* - config_name: javascript data_files: - split: train path: javascript/train-* - split: validation path: javascript/validation-* - split: test path: javascript/test-* - config_name: php data_files: - split: train path: php/train-* - split: validation path: php/validation-* - split: test path: php/test-* - config_name: python data_files: - split: train path: python/train-* - split: validation path: python/validation-* - split: test path: python/test-* - config_name: ruby data_files: - split: train path: ruby/train-* - split: validation path: ruby/validation-* - split: test path: ruby/test-* --- # Dataset Card for "code_x_glue_ct_code_to_text" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits-sample-size) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://github.com/microsoft/CodeXGLUE/tree/main/Code-Text/code-to-text ### Dataset Summary CodeXGLUE code-to-text dataset, available at https://github.com/microsoft/CodeXGLUE/tree/main/Code-Text/code-to-text The dataset we use comes from CodeSearchNet and we filter the dataset as the following: - Remove examples that codes cannot be parsed into an abstract syntax tree. - Remove examples that #tokens of documents is < 3 or >256 - Remove examples that documents contain special tokens (e.g. <img ...> or https:...) - Remove examples that documents are not English. ### Supported Tasks and Leaderboards - `machine-translation`: The dataset can be used to train a model for automatically generating **English** docstrings for code. ### Languages - Go **programming** language - Java **programming** language - Javascript **programming** language - PHP **programming** language - Python **programming** language - Ruby **programming** language - English **natural** language ## Dataset Structure ### Data Instances #### go An example of 'test' looks as follows. ``` { "code": "func NewSTM(c *v3.Client, apply func(STM) error, so ...stmOption) (*v3.TxnResponse, error) {\n\topts := &stmOptions{ctx: c.Ctx()}\n\tfor _, f := range so {\n\t\tf(opts)\n\t}\n\tif len(opts.prefetch) != 0 {\n\t\tf := apply\n\t\tapply = func(s STM) error {\n\t\t\ts.Get(opts.prefetch...)\n\t\t\treturn f(s)\n\t\t}\n\t}\n\treturn runSTM(mkSTM(c, opts), apply)\n}", "code_tokens": ["func", "NewSTM", "(", "c", "*", "v3", ".", "Client", ",", "apply", "func", "(", "STM", ")", "error", ",", "so", "...", "stmOption", ")", "(", "*", "v3", ".", "TxnResponse", ",", "error", ")", "{", "opts", ":=", "&", "stmOptions", "{", "ctx", ":", "c", ".", "Ctx", "(", ")", "}", "\n", "for", "_", ",", "f", ":=", "range", "so", "{", "f", "(", "opts", ")", "\n", "}", "\n", "if", "len", "(", "opts", ".", "prefetch", ")", "!=", "0", "{", "f", ":=", "apply", "\n", "apply", "=", "func", "(", "s", "STM", ")", "error", "{", "s", ".", "Get", "(", "opts", ".", "prefetch", "...", ")", "\n", "return", "f", "(", "s", ")", "\n", "}", "\n", "}", "\n", "return", "runSTM", "(", "mkSTM", "(", "c", ",", "opts", ")", ",", "apply", ")", "\n", "}"], "docstring": "// NewSTM initiates a new STM instance, using serializable snapshot isolation by default.", "docstring_tokens": ["NewSTM", "initiates", "a", "new", "STM", "instance", "using", "serializable", "snapshot", "isolation", "by", "default", "."], "func_name": "NewSTM", "id": 0, "language": "go", "original_string": "func NewSTM(c *v3.Client, apply func(STM) error, so ...stmOption) (*v3.TxnResponse, error) {\n\topts := &stmOptions{ctx: c.Ctx()}\n\tfor _, f := range so {\n\t\tf(opts)\n\t}\n\tif len(opts.prefetch) != 0 {\n\t\tf := apply\n\t\tapply = func(s STM) error {\n\t\t\ts.Get(opts.prefetch...)\n\t\t\treturn f(s)\n\t\t}\n\t}\n\treturn runSTM(mkSTM(c, opts), apply)\n}", "path": "clientv3/concurrency/stm.go", "repo": "etcd-io/etcd", "sha": "616592d9ba993e3fe9798eef581316016df98906", "url": "https://github.com/etcd-io/etcd/blob/616592d9ba993e3fe9798eef581316016df98906/clientv3/concurrency/stm.go#L89-L102" } ``` #### java An example of 'test' looks as follows. ``` { "code": "protected final void fastPathOrderedEmit(U value, boolean delayError, Disposable disposable) {\n final Observer<? super V> observer = downstream;\n final SimplePlainQueue q = queue;\n\n if (wip.get() == 0 && wip.compareAndSet(0, 1)) {\n if (q.isEmpty()) {\n accept(observer, value);\n if (leave(-1) == 0) {\n return;\n }\n } else {\n q.offer(value);\n }\n } else {\n q.offer(value);\n if (!enter()) {\n return;\n }\n }\n QueueDrainHelper.drainLoop(q, observer, delayError, disposable, this);\n }", "code_tokens": ["protected", "final", "void", "fastPathOrderedEmit", "(", "U", "value", ",", "boolean", "delayError", ",", "Disposable", "disposable", ")", "{", "final", "Observer", "<", "?", "super", "V", ">", "observer", "=", "downstream", ";", "final", "SimplePlainQueue", "<", "U", ">", "q", "=", "queue", ";", "if", "(", "wip", ".", "get", "(", ")", "==", "0", "&&", "wip", ".", "compareAndSet", "(", "0", ",", "1", ")", ")", "{", "if", "(", "q", ".", "isEmpty", "(", ")", ")", "{", "accept", "(", "observer", ",", "value", ")", ";", "if", "(", "leave", "(", "-", "1", ")", "==", "0", ")", "{", "return", ";", "}", "}", "else", "{", "q", ".", "offer", "(", "value", ")", ";", "}", "}", "else", "{", "q", ".", "offer", "(", "value", ")", ";", "if", "(", "!", "enter", "(", ")", ")", "{", "return", ";", "}", "}", "QueueDrainHelper", ".", "drainLoop", "(", "q", ",", "observer", ",", "delayError", ",", "disposable", ",", "this", ")", ";", "}"], "docstring": "Makes sure the fast-path emits in order.\n@param value the value to emit or queue up\n@param delayError if true, errors are delayed until the source has terminated\n@param disposable the resource to dispose if the drain terminates", "docstring_tokens": ["Makes", "sure", "the", "fast", "-", "path", "emits", "in", "order", "."], "func_name": "QueueDrainObserver.fastPathOrderedEmit", "id": 0, "language": "java", "original_string": "protected final void fastPathOrderedEmit(U value, boolean delayError, Disposable disposable) {\n final Observer<? super V> observer = downstream;\n final SimplePlainQueue q = queue;\n\n if (wip.get() == 0 && wip.compareAndSet(0, 1)) {\n if (q.isEmpty()) {\n accept(observer, value);\n if (leave(-1) == 0) {\n return;\n }\n } else {\n q.offer(value);\n }\n } else {\n q.offer(value);\n if (!enter()) {\n return;\n }\n }\n QueueDrainHelper.drainLoop(q, observer, delayError, disposable, this);\n }", "path": "src/main/java/io/reactivex/internal/observers/QueueDrainObserver.java", "repo": "ReactiveX/RxJava", "sha": "ac84182aa2bd866b53e01c8e3fe99683b882c60e", "url": "https://github.com/ReactiveX/RxJava/blob/ac84182aa2bd866b53e01c8e3fe99683b882c60e/src/main/java/io/reactivex/internal/observers/QueueDrainObserver.java#L88-L108" } ``` #### javascript An example of 'test' looks as follows. ``` { "code": "function createInstance(defaultConfig) {\n var context = new Axios(defaultConfig);\n var instance = bind(Axios.prototype.request, context);\n\n // Copy axios.prototype to instance\n utils.extend(instance, Axios.prototype, context);\n\n // Copy context to instance\n utils.extend(instance, context);\n\n return instance;\n}", "code_tokens": ["function", "createInstance", "(", "defaultConfig", ")", "{", "var", "context", "=", "new", "Axios", "(", "defaultConfig", ")", ";", "var", "instance", "=", "bind", "(", "Axios", ".", "prototype", ".", "request", ",", "context", ")", ";", "// Copy axios.prototype to instance", "utils", ".", "extend", "(", "instance", ",", "Axios", ".", "prototype", ",", "context", ")", ";", "// Copy context to instance", "utils", ".", "extend", "(", "instance", ",", "context", ")", ";", "return", "instance", ";", "}"], "docstring": "Create an instance of Axios\n\n@param {Object} defaultConfig The default config for the instance\n@return {Axios} A new instance of Axios", "docstring_tokens": ["Create", "an", "instance", "of", "Axios"], "func_name": "createInstance", "id": 0, "language": "javascript", "original_string": "function createInstance(defaultConfig) {\n var context = new Axios(defaultConfig);\n var instance = bind(Axios.prototype.request, context);\n\n // Copy axios.prototype to instance\n utils.extend(instance, Axios.prototype, context);\n\n // Copy context to instance\n utils.extend(instance, context);\n\n return instance;\n}", "path": "lib/axios.js", "repo": "axios/axios", "sha": "92d231387fe2092f8736bc1746d4caa766b675f5", "url": "https://github.com/axios/axios/blob/92d231387fe2092f8736bc1746d4caa766b675f5/lib/axios.js#L15-L26" } ``` #### php An example of 'train' looks as follows. ``` { "code": "public static function build($serviceAddress, $restConfigPath, array $config = [])\n {\n $config += [\n 'httpHandler' => null,\n ];\n list($baseUri, $port) = self::normalizeServiceAddress($serviceAddress);\n $requestBuilder = new RequestBuilder(\"$baseUri:$port\", $restConfigPath);\n $httpHandler = $config['httpHandler'] ?: self::buildHttpHandlerAsync();\n return new RestTransport($requestBuilder, $httpHandler);\n }", "code_tokens": ["public", "static", "function", "build", "(", "$", "serviceAddress", ",", "$", "restConfigPath", ",", "array", "$", "config", "=", "[", "]", ")", "{", "$", "config", "+=", "[", "'httpHandler'", "=>", "null", ",", "]", ";", "list", "(", "$", "baseUri", ",", "$", "port", ")", "=", "self", "::", "normalizeServiceAddress", "(", "$", "serviceAddress", ")", ";", "$", "requestBuilder", "=", "new", "RequestBuilder", "(", "\"$baseUri:$port\"", ",", "$", "restConfigPath", ")", ";", "$", "httpHandler", "=", "$", "config", "[", "'httpHandler'", "]", "?", ":", "self", "::", "buildHttpHandlerAsync", "(", ")", ";", "return", "new", "RestTransport", "(", "$", "requestBuilder", ",", "$", "httpHandler", ")", ";", "}"], "docstring": "Builds a RestTransport.\n\n@param string $serviceAddress\nThe address of the API remote host, for example \"example.googleapis.com\".\n@param string $restConfigPath\nPath to rest config file.\n@param array $config {\nConfig options used to construct the gRPC transport.\n\n@type callable $httpHandler A handler used to deliver PSR-7 requests.\n}\n@return RestTransport\n@throws ValidationException", "docstring_tokens": ["Builds", "a", "RestTransport", "."], "func_name": "RestTransport.build", "id": 0, "language": "php", "original_string": "public static function build($serviceAddress, $restConfigPath, array $config = [])\n {\n $config += [\n 'httpHandler' => null,\n ];\n list($baseUri, $port) = self::normalizeServiceAddress($serviceAddress);\n $requestBuilder = new RequestBuilder(\"$baseUri:$port\", $restConfigPath);\n $httpHandler = $config['httpHandler'] ?: self::buildHttpHandlerAsync();\n return new RestTransport($requestBuilder, $httpHandler);\n }", "path": "src/Transport/RestTransport.php", "repo": "googleapis/gax-php", "sha": "48387fb818c6882296710a2302a0aa973b99afb2", "url": "https://github.com/googleapis/gax-php/blob/48387fb818c6882296710a2302a0aa973b99afb2/src/Transport/RestTransport.php#L85-L94" } ``` #### python An example of 'validation' looks as follows. ``` { "code": "def save_act(self, path=None):\n \"\"\"Save model to a pickle located at `path`\"\"\"\n if path is None:\n path = os.path.join(logger.get_dir(), \"model.pkl\")\n\n with tempfile.TemporaryDirectory() as td:\n save_variables(os.path.join(td, \"model\"))\n arc_name = os.path.join(td, \"packed.zip\")\n with zipfile.ZipFile(arc_name, 'w') as zipf:\n for root, dirs, files in os.walk(td):\n for fname in files:\n file_path = os.path.join(root, fname)\n if file_path != arc_name:\n zipf.write(file_path, os.path.relpath(file_path, td))\n with open(arc_name, \"rb\") as f:\n model_data = f.read()\n with open(path, \"wb\") as f:\n cloudpickle.dump((model_data, self._act_params), f)", "code_tokens": ["def", "save_act", "(", "self", ",", "path", "=", "None", ")", ":", "if", "path", "is", "None", ":", "path", "=", "os", ".", "path", ".", "join", "(", "logger", ".", "get_dir", "(", ")", ",", "\"model.pkl\"", ")", "with", "tempfile", ".", "TemporaryDirectory", "(", ")", "as", "td", ":", "save_variables", "(", "os", ".", "path", ".", "join", "(", "td", ",", "\"model\"", ")", ")", "arc_name", "=", "os", ".", "path", ".", "join", "(", "td", ",", "\"packed.zip\"", ")", "with", "zipfile", ".", "ZipFile", "(", "arc_name", ",", "'w'", ")", "as", "zipf", ":", "for", "root", ",", "dirs", ",", "files", "in", "os", ".", "walk", "(", "td", ")", ":", "for", "fname", "in", "files", ":", "file_path", "=", "os", ".", "path", ".", "join", "(", "root", ",", "fname", ")", "if", "file_path", "!=", "arc_name", ":", "zipf", ".", "write", "(", "file_path", ",", "os", ".", "path", ".", "relpath", "(", "file_path", ",", "td", ")", ")", "with", "open", "(", "arc_name", ",", "\"rb\"", ")", "as", "f", ":", "model_data", "=", "f", ".", "read", "(", ")", "with", "open", "(", "path", ",", "\"wb\"", ")", "as", "f", ":", "cloudpickle", ".", "dump", "(", "(", "model_data", ",", "self", ".", "_act_params", ")", ",", "f", ")"], "docstring": "Save model to a pickle located at `path`", "docstring_tokens": ["Save", "model", "to", "a", "pickle", "located", "at", "path"], "func_name": "ActWrapper.save_act", "id": 0, "language": "python", "original_string": "def save_act(self, path=None):\n \"\"\"Save model to a pickle located at `path`\"\"\"\n if path is None:\n path = os.path.join(logger.get_dir(), \"model.pkl\")\n\n with tempfile.TemporaryDirectory() as td:\n save_variables(os.path.join(td, \"model\"))\n arc_name = os.path.join(td, \"packed.zip\")\n with zipfile.ZipFile(arc_name, 'w') as zipf:\n for root, dirs, files in os.walk(td):\n for fname in files:\n file_path = os.path.join(root, fname)\n if file_path != arc_name:\n zipf.write(file_path, os.path.relpath(file_path, td))\n with open(arc_name, \"rb\") as f:\n model_data = f.read()\n with open(path, \"wb\") as f:\n cloudpickle.dump((model_data, self._act_params), f)", "path": "baselines/deepq/deepq.py", "repo": "openai/baselines", "sha": "3301089b48c42b87b396e246ea3f56fa4bfc9678", "url": "https://github.com/openai/baselines/blob/3301089b48c42b87b396e246ea3f56fa4bfc9678/baselines/deepq/deepq.py#L55-L72" } ``` #### ruby An example of 'train' looks as follows. ``` { "code": "def render_body(context, options)\n if options.key?(:partial)\n [render_partial(context, options)]\n else\n StreamingTemplateRenderer.new(@lookup_context).render(context, options)\n end\n end", "code_tokens": ["def", "render_body", "(", "context", ",", "options", ")", "if", "options", ".", "key?", "(", ":partial", ")", "[", "render_partial", "(", "context", ",", "options", ")", "]", "else", "StreamingTemplateRenderer", ".", "new", "(", "@lookup_context", ")", ".", "render", "(", "context", ",", "options", ")", "end", "end"], "docstring": "Render but returns a valid Rack body. If fibers are defined, we return\n a streaming body that renders the template piece by piece.\n\n Note that partials are not supported to be rendered with streaming,\n so in such cases, we just wrap them in an array.", "docstring_tokens": ["Render", "but", "returns", "a", "valid", "Rack", "body", ".", "If", "fibers", "are", "defined", "we", "return", "a", "streaming", "body", "that", "renders", "the", "template", "piece", "by", "piece", "."], "func_name": "ActionView.Renderer.render_body", "id": 0, "language": "ruby", "original_string": "def render_body(context, options)\n if options.key?(:partial)\n [render_partial(context, options)]\n else\n StreamingTemplateRenderer.new(@lookup_context).render(context, options)\n end\n end", "path": "actionview/lib/action_view/renderer/renderer.rb", "repo": "rails/rails", "sha": "85a8bc644be69908f05740a5886ec19cd3679df5", "url": "https://github.com/rails/rails/blob/85a8bc644be69908f05740a5886ec19cd3679df5/actionview/lib/action_view/renderer/renderer.rb#L38-L44" } ``` ### Data Fields In the following each data field in go is explained for each config. The data fields are the same among all splits. #### go, java, javascript, php, python, ruby | field name | type | description | |----------------|----------------|-----------------------------------------------------------------------------------| |id |int32 | Index of the sample | |repo |string | repo: the owner/repo | |path |string | path: the full path to the original file | |func_name |string | func_name: the function or method name | |original_string |string | original_string: the raw string before tokenization or parsing | |language |string | language: the programming language name | |code |string | code/function: the part of the original_string that is code | |code_tokens |Sequence[string]| code_tokens/function_tokens: tokenized version of code | |docstring |string | docstring: the top-level comment or docstring, if it exists in the original string| |docstring_tokens|Sequence[string]| docstring_tokens: tokenized version of docstring | |sha |string | sha of the file | |url |string | url of the file | ### Data Splits | name |train |validation|test | |----------|-----:|---------:|----:| |go |167288| 7325| 8122| |java |164923| 5183|10955| |javascript| 58025| 3885| 3291| |php |241241| 12982|14014| |python |251820| 13914|14918| |ruby | 24927| 1400| 1261| ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization Data from CodeSearchNet Challenge dataset. [More Information Needed] #### Who are the source language producers? Software Engineering developers. ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators https://github.com/microsoft, https://github.com/madlag ### Licensing Information Computational Use of Data Agreement (C-UDA) License. ### Citation Information ``` @article{husain2019codesearchnet, title={Codesearchnet challenge: Evaluating the state of semantic code search}, author={Husain, Hamel and Wu, Ho-Hsiang and Gazit, Tiferet and Allamanis, Miltiadis and Brockschmidt, Marc}, journal={arXiv preprint arXiv:1909.09436}, year={2019} } ``` ### Contributions Thanks to @madlag (and partly also @ncoop57) for adding this dataset.

mrqa-workshop/mrqa

mrqa-workshop

2024-01-24T10:52:34Z

623

24

[ "task_categories:question-answering", "task_ids:extractive-qa", "annotations_creators:found", "language_creators:found", "multilinguality:monolingual", "source_datasets:extended|drop", "source_datasets:extended|hotpot_qa", "source_datasets:extended|natural_questions", "source_datasets:extended|race", "source_datasets:extended|search_qa", "source_datasets:extended|squad", "source_datasets:extended|trivia_qa", "language:en", "license:unknown", "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:1910.09753", "arxiv:1606.05250", "arxiv:1611.09830", "arxiv:1705.03551", "arxiv:1704.05179", "arxiv:1809.09600", "arxiv:1903.00161", "arxiv:1804.07927", "arxiv:1704.04683", "arxiv:1706.04115", "region:us" ]

[ "question-answering" ]

2022-03-02T23:29:22Z

1

--- annotations_creators: - found language_creators: - found language: - en license: - unknown multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - extended|drop - extended|hotpot_qa - extended|natural_questions - extended|race - extended|search_qa - extended|squad - extended|trivia_qa task_categories: - question-answering task_ids: - extractive-qa paperswithcode_id: mrqa-2019 pretty_name: MRQA 2019 dataset_info: config_name: plain_text features: - name: subset dtype: string - name: context dtype: string - name: context_tokens sequence: - name: tokens dtype: string - name: offsets dtype: int32 - name: qid dtype: string - name: question dtype: string - name: question_tokens sequence: - name: tokens dtype: string - name: offsets dtype: int32 - name: detected_answers sequence: - name: text dtype: string - name: char_spans sequence: - name: start dtype: int32 - name: end dtype: int32 - name: token_spans sequence: - name: start dtype: int32 - name: end dtype: int32 - name: answers sequence: string splits: - name: train num_bytes: 4090677713 num_examples: 516819 - name: validation num_bytes: 484106546 num_examples: 58221 - name: test num_bytes: 57712097 num_examples: 9633 download_size: 1679161250 dataset_size: 4632496356 configs: - config_name: plain_text data_files: - split: train path: plain_text/train-* - split: validation path: plain_text/validation-* - split: test path: plain_text/test-* default: true --- # Dataset Card for MRQA 2019 ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [MRQA 2019 Shared Task](https://mrqa.github.io/2019/shared.html) - **Repository:** [MRQA 2019 Github repository](https://github.com/mrqa/MRQA-Shared-Task-2019) - **Paper:** [MRQA 2019 Shared Task: Evaluating Generalization in Reading Comprehension ](https://arxiv.org/abs/1910.09753) - **Leaderboard:** [Shared task](https://mrqa.github.io/2019/shared.html) - **Point of Contact:** [[email protected]]([email protected]) ### Dataset Summary The MRQA 2019 Shared Task focuses on generalization in question answering. An effective question answering system should do more than merely interpolate from the training set to answer test examples drawn from the same distribution: it should also be able to extrapolate to out-of-distribution examples — a significantly harder challenge. The dataset is a collection of 18 existing QA dataset (carefully selected subset of them) and converted to the same format (SQuAD format). Among these 18 datasets, six datasets were made available for training, six datasets were made available for development, and the final six for testing. The dataset is released as part of the MRQA 2019 Shared Task. ### Supported Tasks and Leaderboards From the official repository: *The format of the task is extractive question answering. Given a question and context passage, systems must find the word or phrase in the document that best answers the question. While this format is somewhat restrictive, it allows us to leverage many existing datasets, and its simplicity helps us focus on out-of-domain generalization, instead of other important but orthogonal challenges.* *We have adapted several existing datasets from their original formats and settings to conform to our unified extractive setting. Most notably:* - *We provide only a single, length-limited context.* - *There are no unanswerable or non-span answer questions.* - *All questions have at least one accepted answer that is found exactly in the context.* *A span is judged to be an exact match if it matches the answer string after performing normalization consistent with the SQuAD dataset. Specifically:* - *The text is uncased.* - *All punctuation is stripped.* - *All articles `{a, an, the}` are removed.* - *All consecutive whitespace markers are compressed to just a single normal space `' '`.* Answers are evaluated using exact match and token-level F1 metrics. One can refer to the [mrqa_official_eval.py](https://github.com/mrqa/MRQA-Shared-Task-2019/blob/master/mrqa_official_eval.py) for evaluation. ### Languages The text in the dataset is in English. The associated BCP-47 code is `en`. ## Dataset Structure ### Data Instances An examples looks like this: ``` { 'qid': 'f43c83e38d1e424ea00f8ad3c77ec999', 'subset': 'SQuAD' 'context': 'CBS broadcast Super Bowl 50 in the U.S., and charged an average of $5 million for a 30-second commercial during the game. The Super Bowl 50 halftime show was headlined by the British rock group Coldplay with special guest performers Beyoncé and Bruno Mars, who headlined the Super Bowl XLVII and Super Bowl XLVIII halftime shows, respectively. It was the third-most watched U.S. broadcast ever.', 'context_tokens': { 'offsets': [0, 4, 14, 20, 25, 28, 31, 35, 39, 41, 45, 53, 56, 64, 67, 68, 70, 78, 82, 84, 94, 105, 112, 116, 120, 122, 126, 132, 137, 140, 149, 154, 158, 168, 171, 175, 183, 188, 194, 203, 208, 216, 222, 233, 241, 245, 251, 255, 257, 261, 271, 275, 281, 286, 292, 296, 302, 307, 314, 323, 328, 330, 342, 344, 347, 351, 355, 360, 361, 366, 374, 379, 389, 393], 'tokens': ['CBS', 'broadcast', 'Super', 'Bowl', '50', 'in', 'the', 'U.S.', ',', 'and', 'charged', 'an', 'average', 'of', '$', '5', 'million', 'for', 'a', '30-second', 'commercial', 'during', 'the', 'game', '.', 'The', 'Super', 'Bowl', '50', 'halftime', 'show', 'was', 'headlined', 'by', 'the', 'British', 'rock', 'group', 'Coldplay', 'with', 'special', 'guest', 'performers', 'Beyoncé', 'and', 'Bruno', 'Mars', ',', 'who', 'headlined', 'the', 'Super', 'Bowl', 'XLVII', 'and', 'Super', 'Bowl', 'XLVIII', 'halftime', 'shows', ',', 'respectively', '.', 'It', 'was', 'the', 'third', '-', 'most', 'watched', 'U.S.', 'broadcast', 'ever', '.'] }, 'question': "Who was the main performer at this year's halftime show?", 'question_tokens': { 'offsets': [0, 4, 8, 12, 17, 27, 30, 35, 39, 42, 51, 55], 'tokens': ['Who', 'was', 'the', 'main', 'performer', 'at', 'this', 'year', "'s", 'halftime', 'show', '?'] }, 'detected_answers': { 'char_spans': [ { 'end': [201], 'start': [194] }, { 'end': [201], 'start': [194] }, { 'end': [201], 'start': [194] } ], 'text': ['Coldplay', 'Coldplay', 'Coldplay'], 'token_spans': [ { 'end': [38], 'start': [38] }, { 'end': [38], 'start': [38] }, { 'end': [38], 'start': [38] } ] }, 'answers': ['Coldplay', 'Coldplay', 'Coldplay'], } ``` ### Data Fields - `subset`: which of the dataset does this examples come from? - `context`: This is the raw text of the supporting passage. Three special token types have been inserted: `[TLE]` precedes document titles, `[DOC]` denotes document breaks, and `[PAR]` denotes paragraph breaks. The maximum length of the context is 800 tokens. - `context_tokens`: A tokenized version of the supporting passage, using spaCy. Each token is a tuple of the token string and token character offset. The maximum number of tokens is 800. - `tokens`: list of tokens. - `offets`: list of offsets. - `qas`: A list of questions for the given context. - `qid`: A unique identifier for the question. The `qid` is unique across all datasets. - `question`: The raw text of the question. - `question_tokens`: A tokenized version of the question. The tokenizer and token format is the same as for the context. - `tokens`: list of tokens. - `offets`: list of offsets. - `detected_answers`: A list of answer spans for the given question that index into the context. For some datasets these spans have been automatically detected using searching heuristics. The same answer may appear multiple times in the text --- each of these occurrences is recorded. For example, if `42` is the answer, the context `"The answer is 42. 42 is the answer."`, has two occurrences marked. - `text`: The raw text of the detected answer. - `char_spans`: Inclusive (start, end) character spans (indexing into the raw context). - `start`: start (single element) - `end`: end (single element) - `token_spans`: Inclusive (start, end) token spans (indexing into the tokenized context). - `start`: start (single element) - `end`: end (single element) ### Data Splits **Training data** | Dataset | Number of Examples | | :-----: | :------: | | [SQuAD](https://arxiv.org/abs/1606.05250) | 86,588 | | [NewsQA](https://arxiv.org/abs/1611.09830) | 74,160 | | [TriviaQA](https://arxiv.org/abs/1705.03551)| 61,688 | | [SearchQA](https://arxiv.org/abs/1704.05179)| 117,384 | | [HotpotQA](https://arxiv.org/abs/1809.09600)| 72,928 | | [NaturalQuestions](https://ai.google/research/pubs/pub47761)| 104,071 | **Development data** This in-domain data may be used for helping develop models. | Dataset | Examples | | :-----: | :------: | | [SQuAD](https://arxiv.org/abs/1606.05250) | 10,507 | | [NewsQA](https://arxiv.org/abs/1611.09830) | 4,212 | | [TriviaQA](https://arxiv.org/abs/1705.03551)| 7,785| | [SearchQA](https://arxiv.org/abs/1704.05179)| 16,980 | | [HotpotQA](https://arxiv.org/abs/1809.09600)| 5,904 | | [NaturalQuestions](https://ai.google/research/pubs/pub47761)| 12,836 | **Test data** The final testing data only contain out-of-domain data. | Dataset | Examples | | :-----: | :------: | | [BioASQ](http://bioasq.org/) | 1,504 | | [DROP](https://arxiv.org/abs/1903.00161) | 1,503 | | [DuoRC](https://arxiv.org/abs/1804.07927)| 1,501 | | [RACE](https://arxiv.org/abs/1704.04683) | 674 | | [RelationExtraction](https://arxiv.org/abs/1706.04115) | 2,948| | [TextbookQA](http://ai2-website.s3.amazonaws.com/publications/CVPR17_TQA.pdf)| 1,503 | From the official repository: ***Note:** As previously mentioned, the out-of-domain dataset have been modified from their original settings to fit the unified MRQA Shared Task paradigm. At a high level, the following two major modifications have been made:* *1. All QA-context pairs are extractive. That is, the answer is selected from the context and not via, e.g., multiple-choice.* *2. All contexts are capped at a maximum of `800` tokens. As a result, for longer contexts like Wikipedia articles, we only consider examples where the answer appears in the first `800` tokens.* *As a result, some splits are harder than the original datasets (e.g., removal of multiple-choice in RACE), while some are easier (e.g., restricted context length in NaturalQuestions --- we use the short answer selection). Thus one should expect different performance ranges if comparing to previous work on these datasets.* ## Dataset Creation ### Curation Rationale From the official repository: *Both train and test datasets have the same format described above, but may differ in some of the following ways:* - *Passage distribution: Test examples may involve passages from different sources (e.g., science, news, novels, medical abstracts, etc) with pronounced syntactic and lexical differences.* - *Question distribution: Test examples may emphasize different styles of questions (e.g., entity-centric, relational, other tasks reformulated as QA, etc) which may come from different sources (e.g., crowdworkers, domain experts, exam writers, etc.)* - *Joint distribution: Test examples may vary according to the relationship of the question to the passage (e.g., collected independent vs. dependent of evidence, multi-hop, etc)* ### Source Data [More Information Needed] #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations [More Information Needed] #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information Unknown ### Citation Information ``` @inproceedings{fisch2019mrqa, title={{MRQA} 2019 Shared Task: Evaluating Generalization in Reading Comprehension}, author={Adam Fisch and Alon Talmor and Robin Jia and Minjoon Seo and Eunsol Choi and Danqi Chen}, booktitle={Proceedings of 2nd Machine Reading for Reading Comprehension (MRQA) Workshop at EMNLP}, year={2019}, } ``` ### Contributions Thanks to [@jimmycode](https://github.com/jimmycode), [@VictorSanh](https://github.com/VictorSanh) for adding this dataset.

tdiggelm/climate_fever

tdiggelm

2024-01-18T14:28:07Z

568

26

[ "task_categories:text-classification", "task_categories:text-retrieval", "task_ids:text-scoring", "task_ids:fact-checking", "task_ids:fact-checking-retrieval", "task_ids:semantic-similarity-scoring", "task_ids:multi-input-text-classification", "annotations_creators:crowdsourced", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "source_datasets:extended|wikipedia", "source_datasets:original", "language:en", "license:unknown", "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2012.00614", "region:us" ]

[ "text-classification", "text-retrieval" ]

2022-03-02T23:29:22Z

1

--- annotations_creators: - crowdsourced - expert-generated language_creators: - found language: - en license: - unknown multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - extended|wikipedia - original task_categories: - text-classification - text-retrieval task_ids: - text-scoring - fact-checking - fact-checking-retrieval - semantic-similarity-scoring - multi-input-text-classification paperswithcode_id: climate-fever pretty_name: ClimateFever dataset_info: features: - name: claim_id dtype: string - name: claim dtype: string - name: claim_label dtype: class_label: names: '0': SUPPORTS '1': REFUTES '2': NOT_ENOUGH_INFO '3': DISPUTED - name: evidences list: - name: evidence_id dtype: string - name: evidence_label dtype: class_label: names: '0': SUPPORTS '1': REFUTES '2': NOT_ENOUGH_INFO - name: article dtype: string - name: evidence dtype: string - name: entropy dtype: float32 - name: votes list: string splits: - name: test num_bytes: 2429240 num_examples: 1535 download_size: 868947 dataset_size: 2429240 configs: - config_name: default data_files: - split: test path: data/test-* --- # Dataset Card for ClimateFever ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [CLIMATE-FEVER homepage](http://climatefever.ai) - **Repository:** [CLIMATE-FEVER repository](https://github.com/tdiggelm/climate-fever-dataset) - **Paper:** [CLIMATE-FEVER: A Dataset for Verification of Real-World Climate Claims](https://arxiv.org/abs/2012.00614) - **Leaderboard:** [Needs More Information] - **Point of Contact:** [Thomas Diggelmann](mailto:[email protected]) ### Dataset Summary A dataset adopting the FEVER methodology that consists of 1,535 real-world claims regarding climate-change collected on the internet. Each claim is accompanied by five manually annotated evidence sentences retrieved from the English Wikipedia that support, refute or do not give enough information to validate the claim totalling in 7,675 claim-evidence pairs. The dataset features challenging claims that relate multiple facets and disputed cases of claims where both supporting and refuting evidence are present. ### Supported Tasks and Leaderboards [Needs More Information] ### Languages The text in the dataset is in English, as found in real-world claims about climate-change on the Internet. The associated BCP-47 code is `en`. ## Dataset Structure ### Data Instances ``` { "claim_id": "0", "claim": "Global warming is driving polar bears toward extinction", "claim_label": 0, # "SUPPORTS" "evidences": [ { "evidence_id": "Extinction risk from global warming:170", "evidence_label": 2, # "NOT_ENOUGH_INFO" "article": "Extinction risk from global warming", "evidence": "\"Recent Research Shows Human Activity Driving Earth Towards Global Extinction Event\".", "entropy": 0.6931471805599453, "votes": [ "SUPPORTS", "NOT_ENOUGH_INFO", null, null, null ] }, { "evidence_id": "Global warming:14", "evidence_label": 0, # "SUPPORTS" "article": "Global warming", "evidence": "Environmental impacts include the extinction or relocation of many species as their ecosystems change, most immediately the environments of coral reefs, mountains, and the Arctic.", "entropy": 0.0, "votes": [ "SUPPORTS", "SUPPORTS", null, null, null ] }, { "evidence_id": "Global warming:178", "evidence_label": 2, # "NOT_ENOUGH_INFO" "article": "Global warming", "evidence": "Rising temperatures push bees to their physiological limits, and could cause the extinction of bee populations.", "entropy": 0.6931471805599453, "votes": [ "SUPPORTS", "NOT_ENOUGH_INFO", null, null, null ] }, { "evidence_id": "Habitat destruction:61", "evidence_label": 0, # "SUPPORTS" "article": "Habitat destruction", "evidence": "Rising global temperatures, caused by the greenhouse effect, contribute to habitat destruction, endangering various species, such as the polar bear.", "entropy": 0.0, "votes": [ "SUPPORTS", "SUPPORTS", null, null, null ] }, { "evidence_id": "Polar bear:1328", "evidence_label": 2, # "NOT_ENOUGH_INFO" "article": "Polar bear", "evidence": "\"Bear hunting caught in global warming debate\".", "entropy": 0.6931471805599453, "votes": [ "SUPPORTS", "NOT_ENOUGH_INFO", null, null, null ] } ] } ``` ### Data Fields - `claim_id`: a `string` feature, unique claim identifier. - `claim`: a `string` feature, claim text. - `claim_label`: a `int` feature, overall label assigned to claim (based on evidence majority vote). The label correspond to 0: "supports", 1: "refutes", 2: "not enough info" and 3: "disputed". - `evidences`: a list of evidences with fields: - `evidence_id`: a `string` feature, unique evidence identifier. - `evidence_label`: a `int` feature, micro-verdict label. The label correspond to 0: "supports", 1: "refutes" and 2: "not enough info". - `article`: a `string` feature, title of source article (Wikipedia page). - `evidence`: a `string` feature, evidence sentence. - `entropy`: a `float32` feature, entropy reflecting uncertainty of `evidence_label`. - `votes`: a `list` of `string` features, corresponding to individual votes. ### Data Splits This benchmark dataset currently consists of a single data split `test` that consists of 1,535 claims or 7,675 claim-evidence pairs. ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information [Needs More Information] ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases [Needs More Information] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators [Needs More Information] ### Licensing Information [Needs More Information] ### Citation Information ```bibtex @misc{diggelmann2020climatefever, title={CLIMATE-FEVER: A Dataset for Verification of Real-World Climate Claims}, author={Thomas Diggelmann and Jordan Boyd-Graber and Jannis Bulian and Massimiliano Ciaramita and Markus Leippold}, year={2020}, eprint={2012.00614}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ### Contributions Thanks to [@tdiggelm](https://github.com/tdiggelm) for adding this dataset.

shiyue/chr_en

shiyue

2024-01-18T14:19:36Z

411

4

[ "task_categories:fill-mask", "task_categories:text-generation", "task_categories:translation", "task_ids:language-modeling", "task_ids:masked-language-modeling", "annotations_creators:expert-generated", "annotations_creators:found", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:monolingual", "multilinguality:multilingual", "multilinguality:translation", "source_datasets:original", "language:chr", "language:en", "license:other", "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2010.04791", "region:us" ]

[ "fill-mask", "text-generation", "translation" ]

2022-03-02T23:29:22Z

1

--- annotations_creators: - expert-generated - found - no-annotation language_creators: - found language: - chr - en license: - other multilinguality: - monolingual - multilingual - translation size_categories: - 100K<n<1M - 10K<n<100K - 1K<n<10K source_datasets: - original task_categories: - fill-mask - text-generation - translation task_ids: - language-modeling - masked-language-modeling paperswithcode_id: chren config_names: - monolingual - monolingual_raw - parallel - parallel_raw dataset_info: - config_name: monolingual features: - name: sentence dtype: string splits: - name: chr num_bytes: 882824 num_examples: 5210 - name: en5000 num_bytes: 615275 num_examples: 5000 - name: en10000 num_bytes: 1211605 num_examples: 10000 - name: en20000 num_bytes: 2432298 num_examples: 20000 - name: en50000 num_bytes: 6065580 num_examples: 49999 - name: en100000 num_bytes: 12130164 num_examples: 100000 download_size: 16967664 dataset_size: 23337746 - config_name: monolingual_raw features: - name: text_sentence dtype: string - name: text_title dtype: string - name: speaker dtype: string - name: date dtype: int32 - name: type dtype: string - name: dialect dtype: string splits: - name: full num_bytes: 1210056 num_examples: 5210 download_size: 410646 dataset_size: 1210056 - config_name: parallel features: - name: sentence_pair dtype: translation: languages: - en - chr splits: - name: train num_bytes: 3089562 num_examples: 11639 - name: dev num_bytes: 260401 num_examples: 1000 - name: out_dev num_bytes: 78126 num_examples: 256 - name: test num_bytes: 264595 num_examples: 1000 - name: out_test num_bytes: 80959 num_examples: 256 download_size: 2143266 dataset_size: 3773643 - config_name: parallel_raw features: - name: line_number dtype: string - name: sentence_pair dtype: translation: languages: - en - chr - name: text_title dtype: string - name: speaker dtype: string - name: date dtype: int32 - name: type dtype: string - name: dialect dtype: string splits: - name: full num_bytes: 5010734 num_examples: 14151 download_size: 2018726 dataset_size: 5010734 configs: - config_name: monolingual data_files: - split: chr path: monolingual/chr-* - split: en5000 path: monolingual/en5000-* - split: en10000 path: monolingual/en10000-* - split: en20000 path: monolingual/en20000-* - split: en50000 path: monolingual/en50000-* - split: en100000 path: monolingual/en100000-* - config_name: monolingual_raw data_files: - split: full path: monolingual_raw/full-* - config_name: parallel data_files: - split: train path: parallel/train-* - split: dev path: parallel/dev-* - split: out_dev path: parallel/out_dev-* - split: test path: parallel/test-* - split: out_test path: parallel/out_test-* default: true - config_name: parallel_raw data_files: - split: full path: parallel_raw/full-* --- # Dataset Card for ChrEn ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Repository:** [Github repository for ChrEn](https://github.com/ZhangShiyue/ChrEn) - **Paper:** [ChrEn: Cherokee-English Machine Translation for Endangered Language Revitalization](https://arxiv.org/abs/2010.04791) - **Point of Contact:** [[email protected]]([email protected]) ### Dataset Summary ChrEn is a Cherokee-English parallel dataset to facilitate machine translation research between Cherokee and English. ChrEn is extremely low-resource contains 14k sentence pairs in total, split in ways that facilitate both in-domain and out-of-domain evaluation. ChrEn also contains 5k Cherokee monolingual data to enable semi-supervised learning. ### Supported Tasks and Leaderboards The dataset is intended to use for `machine-translation` between Enlish (`en`) and Cherokee (`chr`). ### Languages The dataset contains Enlish (`en`) and Cherokee (`chr`) text. The data encompasses both existing dialects of Cherokee: the Overhill dialect, mostly spoken in Oklahoma (OK), and the Middle dialect, mostly used in North Carolina (NC). ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization Many of the source texts were translations of English materials, which means that the Cherokee structures may not be 100% natural in terms of what a speaker might spontaneously produce. Each text was translated by people who speak Cherokee as the first language, which means there is a high probability of grammaticality. These data were originally available in PDF version. We apply the Optical Character Recognition (OCR) via Tesseract OCR engine to extract the Cherokee and English text. #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? The sentences were manually aligned by Dr. Benjamin Frey a proficient second-language speaker of Cherokee, who also fixed the errors introduced by OCR. This process is time-consuming and took several months. ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators The dataset was gathered and annotated by Shiyue Zhang, Benjamin Frey, and Mohit Bansal at UNC Chapel Hill. ### Licensing Information The copyright of the data belongs to original book/article authors or translators (hence, used for research purpose; and please contact Dr. Benjamin Frey for other copyright questions). ### Citation Information ``` @inproceedings{zhang2020chren, title={ChrEn: Cherokee-English Machine Translation for Endangered Language Revitalization}, author={Zhang, Shiyue and Frey, Benjamin and Bansal, Mohit}, booktitle={EMNLP2020}, year={2020} } ``` ### Contributions Thanks to [@yjernite](https://github.com/yjernite), [@lhoestq](https://github.com/lhoestq) for adding this dataset.

defunct-datasets/the_pile_books3

defunct-datasets

2024-01-18T11:17:08Z

131

148

[ "task_categories:text-generation", "task_categories:fill-mask", "task_ids:language-modeling", "task_ids:masked-language-modeling", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:monolingual", "source_datasets:original", "language:en", "license:mit", "size_categories:100K<n<1M", "arxiv:2101.00027", "region:us" ]

[ "text-generation", "fill-mask" ]

2022-03-02T23:29:22Z

null

--- annotations_creators: - no-annotation language_creators: - found language: - en license: - mit multilinguality: - monolingual pretty_name: Books3 size_categories: - 100K<n<1M source_datasets: - original task_categories: - text-generation - fill-mask task_ids: - language-modeling - masked-language-modeling viewer: false dataset_info: features: - name: title dtype: string - name: text dtype: string config_name: plain_text splits: - name: train num_bytes: 108392037000 num_examples: 196639 download_size: 39516981435 dataset_size: 108392037000 --- # Dataset Card for the_pile_books3 ## Table of Contents - [Dataset Card for the_pile_books3](#dataset-card-for-the_pile_books3) - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [|split|num examples|](#splitnum-examples) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization) - [Who are the source language producers?](#who-are-the-source-language-producers) - [Annotations](#annotations) - [Annotation process](#annotation-process) - [Who are the annotators?](#who-are-the-annotators) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [GitHub](https://github.com/soskek/bookcorpus/issues/27#issuecomment-716104208) - **Repository:** [Needs More Information] - **Paper:** [arXiv](https://arxiv.org/abs/2101.00027) - **Leaderboard:** [Needs More Information] - **Point of Contact:** [Needs More Information] ### Dataset Summary <div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400"> Defunct: Dataset "the_pile_books3" is defunct and no longer accessible due to reported copyright infringement. </div> This dataset is Shawn Presser's work and is part of EleutherAi/The Pile dataset. This dataset contains all of bibliotik in plain .txt form, aka 197,000 books processed in exactly the same way as did for bookcorpusopen (a.k.a. books1). seems to be similar to OpenAI's mysterious "books2" dataset referenced in their papers. Unfortunately OpenAI will not give details, so we know very little about any differences. People suspect it's "all of libgen", but it's purely conjecture. |download_size|36.8 Gib| |dataset_size|100.9 Gib| ### Supported Tasks and Leaderboards This dataset is used for Language Modeling. ### Languages The dataset is in English. ## Dataset Structure ### Data Instances ``` {'title': '07 LEGO Ninjago - The Search For Zane (Scholastic) - Kate Howard (retail)' 'text': '\n\nTITLE PAGE\n\nFROM THE JOURNAL OF SENSEI GARMADON\n\nCHAPTER 1\n\nCHAPTER 2\n\nCHAPTER 3\n\nCHAPTER 4\n\nCHAPTER 5\n\nCHAPTER 6\n\nCHAPTER 7\n\nCHAPTER 8\n\nCHAPTER 9\n\nCOPYRIGHT\n\nThroughout Ninjago", five ninja are well-known for their speed, strength, and of course the elemental powers that help them protect our world from evil. But there are others who possess some of the same powers as the ninja. Others who may not always use their powers for good.\n\nBefore now, the ninja believed they were special. They di.......'} ``` ### Data Fields - `title`: title of the book - `text`: text content of the book ### Data Splits |split|num examples| -------------------------------- |train|196640| ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information [Needs More Information] ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases [Needs More Information] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators [Needs More Information] ### Licensing Information MIT ### Citation Information ``` @article{pile, title={The {P}ile: An 800GB Dataset of Diverse Text for Language Modeling}, author={Gao, Leo and Biderman, Stella and Black, Sid and Golding, Laurence and Hoppe, Travis and Foster, Charles and Phang, Jason and He, Horace and Thite, Anish and Nabeshima, Noa and Presser, Shawn and Leahy, Connor}, journal={arXiv preprint arXiv:2101.00027}, year={2020} } ``` ### Contributions Thanks to [@shawwn](https://github.com/shawwn) for creating this dataset. Thanks to [@richarddwang](https://github.com/richarddwang) for adding this dataset.

allenai/social_bias_frames

allenai

2024-01-18T11:16:03Z

287

20

[ "task_categories:text2text-generation", "task_categories:text-classification", "task_ids:hate-speech-detection", "annotations_creators:crowdsourced", "language_creators:found", "multilinguality:monolingual", "source_datasets:original", "language:en", "license:cc-by-4.0", "size_categories:100K<n<1M", "region:us", "explanation-generation" ]

[ "text2text-generation", "text-classification" ]

2022-03-02T23:29:22Z

1

--- pretty_name: Social Bias Frames annotations_creators: - crowdsourced language_creators: - found language: - en license: - cc-by-4.0 multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - text2text-generation - text-classification task_ids: - hate-speech-detection paperswithcode_id: null tags: - explanation-generation dataset_info: features: - name: whoTarget dtype: string - name: intentYN dtype: string - name: sexYN dtype: string - name: sexReason dtype: string - name: offensiveYN dtype: string - name: annotatorGender dtype: string - name: annotatorMinority dtype: string - name: sexPhrase dtype: string - name: speakerMinorityYN dtype: string - name: WorkerId dtype: string - name: HITId dtype: string - name: annotatorPolitics dtype: string - name: annotatorRace dtype: string - name: annotatorAge dtype: string - name: post dtype: string - name: targetMinority dtype: string - name: targetCategory dtype: string - name: targetStereotype dtype: string - name: dataSource dtype: string splits: - name: test num_bytes: 5371665 num_examples: 17501 - name: validation num_bytes: 5096009 num_examples: 16738 - name: train num_bytes: 34006886 num_examples: 112900 download_size: 9464583 dataset_size: 44474560 --- # Dataset Card for "social_bias_frames" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://homes.cs.washington.edu/~msap/social-bias-frames/](https://homes.cs.washington.edu/~msap/social-bias-frames/) - **Repository:** [https://homes.cs.washington.edu/~msap/social-bias-frames/](https://homes.cs.washington.edu/~msap/social-bias-frames/) - **Paper:** [Social Bias Frames: Reasoning about Social and Power Implications of Language](https://www.aclweb.org/anthology/2020.acl-main.486.pdf) - **Leaderboard:** - **Point of Contact:** [Maartin Sap](mailto:[email protected]) - **Size of downloaded dataset files:** 6.32 MB - **Size of the generated dataset:** 44.47 MB - **Total amount of disk used:** 50.80 MB ### Dataset Summary Warning: this document and dataset contain content that may be offensive or upsetting. Social Bias Frames is a new way of representing the biases and offensiveness that are implied in language. For example, these frames are meant to distill the implication that "women (candidates) are less qualified" behind the statement "we shouldn’t lower our standards to hire more women." The Social Bias Inference Corpus (SBIC) supports large-scale learning and evaluation of social implications with over 150k structured annotations of social media posts, spanning over 34k implications about a thousand demographic groups. ### Supported Tasks and Leaderboards This dataset supports both classification and generation. Sap et al. developed several models using the SBIC. They report an F1 score of 78.8 in predicting whether the posts in the test set were offensive, an F1 score of 78.6 in predicting whether the posts were intending to be offensive, an F1 score of 80.7 in predicting whether the posts were lewd, and an F1 score of 69.9 in predicting whether the posts were targeting a specific group. Another of Sap et al.’s models performed better in the generation task. They report a BLUE score of 77.9, a Rouge-L score of 68.7, and a WMD score of 0.74 in generating a description of the targeted group given a post as well as a BLUE score of 52.6, a Rouge-L score of 44.9, and a WMD score of 2.79 in generating a description of the implied offensive statement given a post. See the paper for further details. ### Languages The language in SBIC is predominantly white-aligned English (78%, using a lexical dialect detector, [Blodgett et al., 2016](https://www.aclweb.org/anthology/D16-1120)). The curators find less than 10% of posts in SBIC are detected to have the AAE dialect category. The BCP-47 language tag is, presumably, en-US. ## Dataset Structure ### Data Instances Each instance contains a post that may contain an offensive statement and annotated information concerning the nature of the offensive implication as well as the demographics of the annotator and origin of the post. See the [Social Bias Frames dataset viewer](https://huggingface.co/datasets/viewer/?dataset=social_bias_frames) to explore more examples. #### default - **Size of downloaded dataset files:** 6.32 MB - **Size of the generated dataset:** 44.47 MB - **Total amount of disk used:** 50.80 MB An example of 'validation' looks as follows. ``` { "HITId": "3Z8UJEJOCZ74QHXOTFP0JOOGD7039J", "WorkerId": "7162172225567546910", "annotatorAge": "27.0", "annotatorGender": "woman", "annotatorMinority": "women", "annotatorPolitics": "liberal", "annotatorRace": "white", "dataSource": "t/davidson", "intentYN": "1.0", "offensiveYN": "1.0", "post": "Every half-breed cop in America is trying to rape white women.", "sexPhrase": "rape white women", "sexReason": "rape", "sexYN": "1.0", "speakerMinorityYN": "0.0", "targetCategory": "race", "targetMinority": "mixed folks", "targetStereotype": "mixed folks are rapists.", "whoTarget": "1.0" } ``` ### Data Fields The data fields are the same among all splits. #### default - _whoTarget_: a string, ‘0.0’ if the target is a group, ‘1.0’ if the target is an individual, and blank if the post is not offensive - _intentYN_: a string indicating if the intent behind the statement was to offend. This is a categorical variable with four possible answers, ‘1.0’ if yes, ‘0.66’ if probably, ‘0.33’ if probably not, and ‘0.0’ if no. - _sexYN_: a string indicating whether the post contains a sexual or lewd reference. This is a categorical variable with three possible answers, ‘1.0’ if yes, ‘0.5’ if maybe, ‘0.0’ if no. - _sexReason_: a string containing a free text explanation of what is sexual if indicated so, blank otherwise - _offensiveYN_: a string indicating if the post could be offensive to anyone. This is a categorical variable with three possible answers, ‘1.0’ if yes, ‘0.5’ if maybe, ‘0.0’ if no. - _annotatorGender_: a string indicating the gender of the MTurk worker - _annotatorMinority_: a string indicating whether the MTurk worker identifies as a minority - _sexPhrase_: a string indicating which part of the post references something sexual, blank otherwise - _speakerMinorityYN_: a string indicating whether the speaker was part of the same minority group that's being targeted. This is a categorical variable with three possible answers, ‘1.0’ if yes, ‘0.5’ if maybe, ‘0.0’ if no. - _WorkerId_: a string hashed version of the MTurk workerId - _HITId_: a string id that uniquely identifies each post - _annotatorPolitics_: a string indicating the political leaning of the MTurk worker - _annotatorRace_: a string indicating the race of the MTurk worker - _annotatorAge_: a string indicating the age of the MTurk worker - _post_: a string containing the text of the post that was annotated - _targetMinority_: a string indicating the demographic group targeted - _targetCategory_: a string indicating the high-level category of the demographic group(s) targeted - _targetStereotype_: a string containing the implied statement - _dataSource_: a string indicating the source of the post (`t/...`: means Twitter, `r/...`: means a subreddit) ### Data Splits To ensure that no post appeared in multiple splits, the curators defined a training instance as the post and its three sets of annotations. They then split the dataset into train, validation, and test sets (75%/12.5%/12.5%). | name |train |validation|test | |-------|-----:|---------:|----:| |default|112900| 16738|17501| ## Dataset Creation ### Curation Rationale The main aim for this dataset is to cover a wide variety of social biases that are implied in text, both subtle and overt, and make the biases representative of real world discrimination that people experience [RWJF 2017](https://web.archive.org/web/20200620105955/https://www.rwjf.org/en/library/research/2017/10/discrimination-in-america--experiences-and-views.html). The curators also included some innocuous statements, to balance out biases, offensive, or harmful content. ### Source Data The curators included online posts from the following sources sometime between 2014-2019: - r/darkJokes, r/meanJokes, r/offensiveJokes - Reddit microaggressions ([Breitfeller et al., 2019](https://www.aclweb.org/anthology/D19-1176/)) - Toxic language detection Twitter corpora ([Waseem & Hovy, 2016](https://www.aclweb.org/anthology/N16-2013/); [Davidson et al., 2017](https://www.aaai.org/ocs/index.php/ICWSM/ICWSM17/paper/viewPaper/15665); [Founa et al., 2018](https://www.aaai.org/ocs/index.php/ICWSM/ICWSM18/paper/viewPaper/17909)) - Data scraped from hate sites (Gab, Stormfront, r/incels, r/mensrights) #### Initial Data Collection and Normalization The curators wanted posts to be as self-contained as possible, therefore, they applied some filtering to prevent posts from being highly context-dependent. For Twitter data, they filtered out @-replies, retweets, and links, and subsample posts such that there is a smaller correlation between AAE and offensiveness (to avoid racial bias; [Sap et al., 2019](https://www.aclweb.org/anthology/P19-1163/)). For Reddit, Gab, and Stormfront, they only selected posts that were one sentence long, don't contain links, and are between 10 and 80 words. Furthemore, for Reddit, they automatically removed posts that target automated moderation. #### Who are the source language producers? Due to the nature of this corpus, there is no way to know who the speakers are. But, the speakers of the Reddit, Gab, and Stormfront posts are likely white men (see [Gender by subreddit](http://bburky.com/subredditgenderratios/), [Gab users](https://en.wikipedia.org/wiki/Gab_(social_network)#cite_note-insidetheright-22), [Stormfront description](https://en.wikipedia.org/wiki/Stormfront_(website))). ### Annotations #### Annotation process For each post, Amazon Mechanical Turk workers indicate whether the post is offensive, whether the intent was to offend, and whether it contains lewd or sexual content. Only if annotators indicate potential offensiveness do they answer the group implication question. If the post targets or references a group or demographic, workers select or write which one(s); per selected group, they then write two to four stereotypes. Finally, workers are asked whether they think the speaker is part of one of the minority groups referenced by the post. The curators collected three annotations per post, and restricted the worker pool to the U.S. and Canada. The annotations in SBIC showed 82.4% pairwise agreement and Krippendorf’s α=0.45 on average. Recent work has highlighted various negative side effects caused by annotating potentially abusive or harmful content (e.g., acute stress; Roberts, 2016). The curators mitigated these by limiting the number of posts that one worker could annotate in one day, paying workers above minimum wage ($7–12), and providing crisis management resources to the annotators. #### Who are the annotators? The annotators are Amazon Mechanical Turk workers aged 36±10 years old. The annotators consisted of 55% women, 42% men, and <1% non-binary and 82% identified as White, 4% Asian, 4% Hispanic, 4% Black. Information on their first language(s) and professional backgrounds was not collected. ### Personal and Sensitive Information Usernames are not included with the data, but the site where the post was collected is, so the user could potentially be recovered. ## Considerations for Using the Data ### Social Impact of Dataset The curators recognize that studying Social Bias Frames necessarily requires confronting online content that may be offensive or disturbing but argue that deliberate avoidance does not eliminate such problems. By assessing social media content through the lens of Social Bias Frames, automatic flagging or AI-augmented writing interfaces may be analyzed for potentially harmful online content with detailed explanations for users or moderators to consider and verify. In addition, the collective analysis over large corpora can also be insightful for educating people on reducing unconscious biases in their language by encouraging empathy towards a targeted group. ### Discussion of Biases Because this is a corpus of social biases, a lot of posts contain implied or overt biases against the following groups (in decreasing order of prevalence): - gender/sexuality - race/ethnicity - religion/culture - social/political - disability body/age - victims The curators warn that technology trained on this dataset could have side effects such as censorship and dialect-based racial bias. ### Other Known Limitations Because the curators found that the dataset is predominantly written in White-aligned English, they caution researchers to consider the potential for dialect or identity-based biases in labelling ([Davidson et al.,2019](https://www.aclweb.org/anthology/W19-3504.pdf); [Sap et al., 2019a](https://www.aclweb.org/anthology/P19-1163.pdf)) before deploying technology based on SBIC. ## Additional Information ### Dataset Curators This dataset was developed by Maarten Sap of the Paul G. Allen School of Computer Science & Engineering at the University of Washington, Saadia Gabriel, Lianhui Qin, Noah A Smith, and Yejin Choi of the Paul G. Allen School of Computer Science & Engineering and the Allen Institute for Artificial Intelligence, and Dan Jurafsky of the Linguistics & Computer Science Departments of Stanford University. ### Licensing Information The SBIC is licensed under the [Creative Commons 4.0 License](https://creativecommons.org/licenses/by/4.0/) ### Citation Information ``` @inproceedings{sap-etal-2020-social, title = "Social Bias Frames: Reasoning about Social and Power Implications of Language", author = "Sap, Maarten and Gabriel, Saadia and Qin, Lianhui and Jurafsky, Dan and Smith, Noah A. and Choi, Yejin", booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics", month = jul, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.acl-main.486", doi = "10.18653/v1/2020.acl-main.486", pages = "5477--5490", abstract = "Warning: this paper contains content that may be offensive or upsetting. Language has the power to reinforce stereotypes and project social biases onto others. At the core of the challenge is that it is rarely what is stated explicitly, but rather the implied meanings, that frame people{'}s judgments about others. For example, given a statement that {``}we shouldn{'}t lower our standards to hire more women,{''} most listeners will infer the implicature intended by the speaker - that {``}women (candidates) are less qualified.{''} Most semantic formalisms, to date, do not capture such pragmatic implications in which people express social biases and power differentials in language. We introduce Social Bias Frames, a new conceptual formalism that aims to model the pragmatic frames in which people project social biases and stereotypes onto others. In addition, we introduce the Social Bias Inference Corpus to support large-scale modelling and evaluation with 150k structured annotations of social media posts, covering over 34k implications about a thousand demographic groups. We then establish baseline approaches that learn to recover Social Bias Frames from unstructured text. We find that while state-of-the-art neural models are effective at high-level categorization of whether a given statement projects unwanted social bias (80{\%} F1), they are not effective at spelling out more detailed explanations in terms of Social Bias Frames. Our study motivates future work that combines structured pragmatic inference with commonsense reasoning on social implications.", } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun), [@otakumesi](https://github.com/otakumesi), [@mariamabarham](https://github.com/mariamabarham), [@lhoestq](https://github.com/lhoestq) for adding this dataset.

eusip/silicone

eusip

2024-01-18T11:15:53Z

224

14

[ "task_categories:text-generation", "task_categories:fill-mask", "task_categories:text-classification", "task_ids:dialogue-modeling", "task_ids:language-modeling", "task_ids:masked-language-modeling", "task_ids:sentiment-classification", "task_ids:text-scoring", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "source_datasets:original", "language:en", "license:cc-by-sa-4.0", "size_categories:100K<n<1M", "arxiv:2009.11152", "region:us", "emotion-classification", "dialogue-act-classification" ]

[ "text-generation", "fill-mask", "text-classification" ]

2022-03-02T23:29:22Z

1

--- annotations_creators: - expert-generated language_creators: - expert-generated language: - en license: - cc-by-sa-4.0 multilinguality: - monolingual size_categories: - 100K<n<1M - 10K<n<100K - 1K<n<10K source_datasets: - original task_categories: - text-generation - fill-mask - text-classification task_ids: - dialogue-modeling - language-modeling - masked-language-modeling - sentiment-classification - text-scoring pretty_name: SILICONE Benchmark tags: - emotion-classification - dialogue-act-classification dataset_info: - config_name: dyda_da features: - name: Utterance dtype: string - name: Dialogue_Act dtype: string - name: Dialogue_ID dtype: string - name: Label dtype: class_label: names: '0': commissive '1': directive '2': inform '3': question - name: Idx dtype: int32 splits: - name: train num_bytes: 8346638 num_examples: 87170 - name: validation num_bytes: 764277 num_examples: 8069 - name: test num_bytes: 740226 num_examples: 7740 download_size: 8874925 dataset_size: 9851141 - config_name: dyda_e features: - name: Utterance dtype: string - name: Emotion dtype: string - name: Dialogue_ID dtype: string - name: Label dtype: class_label: names: '0': anger '1': disgust '2': fear '3': happiness '4': no emotion '5': sadness '6': surprise - name: Idx dtype: int32 splits: - name: train num_bytes: 8547111 num_examples: 87170 - name: validation num_bytes: 781445 num_examples: 8069 - name: test num_bytes: 757670 num_examples: 7740 download_size: 8874925 dataset_size: 10086226 - config_name: iemocap features: - name: Dialogue_ID dtype: string - name: Utterance_ID dtype: string - name: Utterance dtype: string - name: Emotion dtype: string - name: Label dtype: class_label: names: '0': ang '1': dis '2': exc '3': fea '4': fru '5': hap '6': neu '7': oth '8': sad '9': sur '10': xxx - name: Idx dtype: int32 splits: - name: train num_bytes: 908180 num_examples: 7213 - name: validation num_bytes: 100969 num_examples: 805 - name: test num_bytes: 254248 num_examples: 2021 download_size: 1158778 dataset_size: 1263397 - config_name: maptask features: - name: Speaker dtype: string - name: Utterance dtype: string - name: Dialogue_Act dtype: string - name: Label dtype: class_label: names: '0': acknowledge '1': align '2': check '3': clarify '4': explain '5': instruct '6': query_w '7': query_yn '8': ready '9': reply_n '10': reply_w '11': reply_y - name: Idx dtype: int32 splits: - name: train num_bytes: 1260413 num_examples: 20905 - name: validation num_bytes: 178184 num_examples: 2963 - name: test num_bytes: 171806 num_examples: 2894 download_size: 1048357 dataset_size: 1610403 - config_name: meld_e features: - name: Utterance dtype: string - name: Speaker dtype: string - name: Emotion dtype: string - name: Dialogue_ID dtype: string - name: Utterance_ID dtype: string - name: Label dtype: class_label: names: '0': anger '1': disgust '2': fear '3': joy '4': neutral '5': sadness '6': surprise - name: Idx dtype: int32 splits: - name: train num_bytes: 916337 num_examples: 9989 - name: validation num_bytes: 100234 num_examples: 1109 - name: test num_bytes: 242352 num_examples: 2610 download_size: 1553014 dataset_size: 1258923 - config_name: meld_s features: - name: Utterance dtype: string - name: Speaker dtype: string - name: Sentiment dtype: string - name: Dialogue_ID dtype: string - name: Utterance_ID dtype: string - name: Label dtype: class_label: names: '0': negative '1': neutral '2': positive - name: Idx dtype: int32 splits: - name: train num_bytes: 930405 num_examples: 9989 - name: validation num_bytes: 101801 num_examples: 1109 - name: test num_bytes: 245873 num_examples: 2610 download_size: 1553014 dataset_size: 1278079 - config_name: mrda features: - name: Utterance_ID dtype: string - name: Dialogue_Act dtype: string - name: Channel_ID dtype: string - name: Speaker dtype: string - name: Dialogue_ID dtype: string - name: Utterance dtype: string - name: Label dtype: class_label: names: '0': s '1': d '2': b '3': f '4': q - name: Idx dtype: int32 splits: - name: train num_bytes: 9998857 num_examples: 83943 - name: validation num_bytes: 1143286 num_examples: 9815 - name: test num_bytes: 1807462 num_examples: 15470 download_size: 10305848 dataset_size: 12949605 - config_name: oasis features: - name: Speaker dtype: string - name: Utterance dtype: string - name: Dialogue_Act dtype: string - name: Label dtype: class_label: names: '0': accept '1': ackn '2': answ '3': answElab '4': appreciate '5': backch '6': bye '7': complete '8': confirm '9': correct '10': direct '11': directElab '12': echo '13': exclaim '14': expressOpinion '15': expressPossibility '16': expressRegret '17': expressWish '18': greet '19': hold '20': identifySelf '21': inform '22': informCont '23': informDisc '24': informIntent '25': init '26': negate '27': offer '28': pardon '29': raiseIssue '30': refer '31': refuse '32': reqDirect '33': reqInfo '34': reqModal '35': selfTalk '36': suggest '37': thank '38': informIntent-hold '39': correctSelf '40': expressRegret-inform '41': thank-identifySelf - name: Idx dtype: int32 splits: - name: train num_bytes: 887018 num_examples: 12076 - name: validation num_bytes: 112185 num_examples: 1513 - name: test num_bytes: 119254 num_examples: 1478 download_size: 802002 dataset_size: 1118457 - config_name: sem features: - name: Utterance dtype: string - name: NbPairInSession dtype: string - name: Dialogue_ID dtype: string - name: SpeechTurn dtype: string - name: Speaker dtype: string - name: Sentiment dtype: string - name: Label dtype: class_label: names: '0': Negative '1': Neutral '2': Positive - name: Idx dtype: int32 splits: - name: train num_bytes: 496168 num_examples: 4264 - name: validation num_bytes: 57896 num_examples: 485 - name: test num_bytes: 100072 num_examples: 878 download_size: 513689 dataset_size: 654136 - config_name: swda features: - name: Utterance dtype: string - name: Dialogue_Act dtype: string - name: From_Caller dtype: string - name: To_Caller dtype: string - name: Topic dtype: string - name: Dialogue_ID dtype: string - name: Conv_ID dtype: string - name: Label dtype: class_label: names: '0': sd '1': b '2': sv '3': '%' '4': aa '5': ba '6': fc '7': qw '8': nn '9': bk '10': h '11': qy^d '12': bh '13': ^q '14': bf '15': fo_o_fw_"_by_bc '16': fo_o_fw_by_bc_" '17': na '18': ad '19': ^2 '20': b^m '21': qo '22': qh '23': ^h '24': ar '25': ng '26': br '27': 'no' '28': fp '29': qrr '30': arp_nd '31': t3 '32': oo_co_cc '33': aap_am '34': t1 '35': bd '36': ^g '37': qw^d '38': fa '39': ft '40': + '41': x '42': ny '43': sv_fx '44': qy_qr '45': ba_fe - name: Idx dtype: int32 splits: - name: train num_bytes: 20499788 num_examples: 190709 - name: validation num_bytes: 2265898 num_examples: 21203 - name: test num_bytes: 291471 num_examples: 2714 download_size: 16227500 dataset_size: 23057157 config_names: - dyda_da - dyda_e - iemocap - maptask - meld_e - meld_s - mrda - oasis - sem - swda --- # Dataset Card for SILICONE Benchmark ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [N/A] - **Repository:** https://github.com/eusip/SILICONE-benchmark - **Paper:** https://arxiv.org/abs/2009.11152 - **Leaderboard:** [N/A] - **Point of Contact:** [Ebenge Usip]([email protected]) ### Dataset Summary The Sequence labellIng evaLuatIon benChmark fOr spoken laNguagE (SILICONE) benchmark is a collection of resources for training, evaluating, and analyzing natural language understanding systems specifically designed for spoken language. All datasets are in the English language and covers a variety of domains including daily life, scripted scenarios, joint task completion, phone call conversations, and televsion dialogue. Some datasets additionally include emotion and/or sentimant labels. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages English. ## Dataset Structure ### Data Instances #### DailyDialog Act Corpus (Dialogue Act) For the `dyda_da` configuration one example from the dataset is: ``` { 'Utterance': "the taxi drivers are on strike again .", 'Dialogue_Act': 2, # "inform" 'Dialogue_ID': "2" } ``` #### DailyDialog Act Corpus (Emotion) For the `dyda_e` configuration one example from the dataset is: ``` { 'Utterance': "'oh , breaktime flies .'", 'Emotion': 5, # "sadness" 'Dialogue_ID': "997" } ``` #### Interactive Emotional Dyadic Motion Capture (IEMOCAP) database For the `iemocap` configuration one example from the dataset is: ``` { 'Dialogue_ID': "Ses04F_script03_2", 'Utterance_ID': "Ses04F_script03_2_F025", 'Utterance': "You're quite insufferable. I expect it's because you're drunk.", 'Emotion': 0, # "ang" } ``` #### HCRC MapTask Corpus For the `maptask` configuration one example from the dataset is: ``` { 'Speaker': "f", 'Utterance': "i think that would bring me over the crevasse", 'Dialogue_Act': 4, # "explain" } ``` #### Multimodal EmotionLines Dataset (Emotion) For the `meld_e` configuration one example from the dataset is: ``` { 'Utterance': "'Push 'em out , push 'em out , harder , harder .'", 'Speaker': "Joey", 'Emotion': 3, # "joy" 'Dialogue_ID': "1", 'Utterance_ID': "2" } ``` #### Multimodal EmotionLines Dataset (Sentiment) For the `meld_s` configuration one example from the dataset is: ``` { 'Utterance': "'Okay , y'know what ? There is no more left , left !'", 'Speaker': "Rachel", 'Sentiment': 0, # "negative" 'Dialogue_ID': "2", 'Utterance_ID': "4" } ``` #### ICSI MRDA Corpus For the `mrda` configuration one example from the dataset is: ``` { 'Utterance_ID': "Bed006-c2_0073656_0076706", 'Dialogue_Act': 0, # "s" 'Channel_ID': "Bed006-c2", 'Speaker': "mn015", 'Dialogue_ID': "Bed006", 'Utterance': "keith is not technically one of us yet ." } ``` #### BT OASIS Corpus For the `oasis` configuration one example from the dataset is: ``` { 'Speaker': "b", 'Utterance': "when i rang up um when i rang to find out why she said oh well your card's been declined", 'Dialogue_Act': 21, # "inform" } ``` #### SEMAINE database For the `sem` configuration one example from the dataset is: ``` { 'Utterance': "can you think of somebody who is like that ?", 'NbPairInSession': "11", 'Dialogue_ID': "59", 'SpeechTurn': "674", 'Speaker': "Agent", 'Sentiment': 1, # "Neutral" } ``` #### Switchboard Dialog Act (SwDA) Corpus For the `swda` configuration one example from the dataset is: ``` { 'Utterance': "but i 'd probably say that 's roughly right .", 'Dialogue_Act': 33, # "aap_am" 'From_Caller': "1255", 'To_Caller': "1087", 'Topic': "CRIME", 'Dialogue_ID': "818", 'Conv_ID': "sw2836", } ``` ### Data Fields For the `dyda_da` configuration, the different fields are: - `Utterance`: Utterance as a string. - `Dialogue_Act`: Dialog act label of the utterance. It can be one of "commissive" (0), "directive" (1), "inform" (2) or "question" (3). - `Dialogue_ID`: identifier of the dialogue as a string. For the `dyda_e` configuration, the different fields are: - `Utterance`: Utterance as a string. - `Dialogue_Act`: Dialog act label of the utterance. It can be one of "anger" (0), "disgust" (1), "fear" (2), "happiness" (3), "no emotion" (4), "sadness" (5) or "surprise" (6). - `Dialogue_ID`: identifier of the dialogue as a string. For the `iemocap` configuration, the different fields are: - `Dialogue_ID`: identifier of the dialogue as a string. - `Utterance_ID`: identifier of the utterance as a string. - `Utterance`: Utterance as a string. - `Emotion`: Emotion label of the utterance. It can be one of "Anger" (0), "Disgust" (1), "Excitement" (2), "Fear" (3), "Frustration" (4), "Happiness" (5), "Neutral" (6), "Other" (7), "Sadness" (8), "Surprise" (9) or "Unknown" (10). For the `maptask` configuration, the different fields are: - `Speaker`: identifier of the speaker as a string. - `Utterance`: Utterance as a string. - `Dialogue_Act`: Dialog act label of the utterance. It can be one of "acknowledge" (0), "align" (1), "check" (2), "clarify" (3), "explain" (4), "instruct" (5), "query_w" (6), "query_yn" (7), "ready" (8), "reply_n" (9), "reply_w" (10) or "reply_y" (11). For the `meld_e` configuration, the different fields are: - `Utterance`: Utterance as a string. - `Speaker`: Speaker as a string. - `Emotion`: Emotion label of the utterance. It can be one of "anger" (0), "disgust" (1), "fear" (2), "joy" (3), "neutral" (4), "sadness" (5) or "surprise" (6). - `Dialogue_ID`: identifier of the dialogue as a string. - `Utterance_ID`: identifier of the utterance as a string. For the `meld_s` configuration, the different fields are: - `Utterance`: Utterance as a string. - `Speaker`: Speaker as a string. - `Sentiment`: Sentiment label of the utterance. It can be one of "negative" (0), "neutral" (1) or "positive" (2). - `Dialogue_ID`: identifier of the dialogue as a string. - `Utterance_ID`: identifier of the utterance as a string. For the `mrda` configuration, the different fields are: - `Utterance_ID`: identifier of the utterance as a string. - `Dialogue_Act`: Dialog act label of the utterance. It can be one of "s" (0) [Statement/Subjective Statement], "d" (1) [Declarative Question], "b" (2) [Backchannel], "f" (3) [Follow-me] or "q" (4) [Question]. - `Channel_ID`: identifier of the channel as a string. - `Speaker`: identifier of the speaker as a string. - `Dialogue_ID`: identifier of the channel as a string. - `Utterance`: Utterance as a string. For the `oasis` configuration, the different fields are: - `Speaker`: identifier of the speaker as a string. - `Utterance`: Utterance as a string. - `Dialogue_Act`: Dialog act label of the utterance. It can be one of "accept" (0), "ackn" (1), "answ" (2), "answElab" (3), "appreciate" (4), "backch" (5), "bye" (6), "complete" (7), "confirm" (8), "correct" (9), "direct" (10), "directElab" (11), "echo" (12), "exclaim" (13), "expressOpinion"(14), "expressPossibility" (15), "expressRegret" (16), "expressWish" (17), "greet" (18), "hold" (19), "identifySelf" (20), "inform" (21), "informCont" (22), "informDisc" (23), "informIntent" (24), "init" (25), "negate" (26), "offer" (27), "pardon" (28), "raiseIssue" (29), "refer" (30), "refuse" (31), "reqDirect" (32), "reqInfo" (33), "reqModal" (34), "selfTalk" (35), "suggest" (36), "thank" (37), "informIntent-hold" (38), "correctSelf" (39), "expressRegret-inform" (40) or "thank-identifySelf" (41). For the `sem` configuration, the different fields are: - `Utterance`: Utterance as a string. - `NbPairInSession`: number of utterance pairs in a dialogue. - `Dialogue_ID`: identifier of the dialogue as a string. - `SpeechTurn`: SpeakerTurn as a string. - `Speaker`: Speaker as a string. - `Sentiment`: Sentiment label of the utterance. It can be "Negative", "Neutral" or "Positive". For the `swda` configuration, the different fields are: `Utterance`: Utterance as a string. `Dialogue_Act`: Dialogue act label of the utterance. It can be "sd" (0) [Statement-non-opinion], "b" (1) [Acknowledge (Backchannel)], "sv" (2) [Statement-opinion], "%" (3) [Uninterpretable], "aa" (4) [Agree/Accept], "ba" (5) [Appreciation], "fc" (6) [Conventional-closing], "qw" (7) [Wh-Question], "nn" (8) [No Answers], "bk" (9) [Response Acknowledgement], "h" (10) [Hedge], "qy^d" (11) [Declarative Yes-No-Question], "bh" (12) [Backchannel in Question Form], "^q" (13) [Quotation], "bf" (14) [Summarize/Reformulate], 'fo_o_fw_"_by_bc' (15) [Other], 'fo_o_fw_by_bc_"' (16) [Other], "na" (17) [Affirmative Non-yes Answers], "ad" (18) [Action-directive], "^2" (19) [Collaborative Completion], "b^m" (20) [Repeat-phrase], "qo" (21) [Open-Question], "qh" (22) [Rhetorical-Question], "^h" (23) [Hold Before Answer/Agreement], "ar" (24) [Reject], "ng" (25) [Negative Non-no Answers], "br" (26) [Signal-non-understanding], "no" (27) [Other Answers], "fp" (28) [Conventional-opening], "qrr" (29) [Or-Clause], "arp_nd" (30) [Dispreferred Answers], "t3" (31) [3rd-party-talk], "oo_co_cc" (32) [Offers, Options Commits], "aap_am" (33) [Maybe/Accept-part], "t1" (34) [Downplayer], "bd" (35) [Self-talk], "^g" (36) [Tag-Question], "qw^d" (37) [Declarative Wh-Question], "fa" (38) [Apology], "ft" (39) [Thanking], "+" (40) [Unknown], "x" (41) [Unknown], "ny" (42) [Unknown], "sv_fx" (43) [Unknown], "qy_qr" (44) [Unknown] or "ba_fe" (45) [Unknown]. `From_Caller`: identifier of the from caller as a string. `To_Caller`: identifier of the to caller as a string. `Topic`: Topic as a string. `Dialogue_ID`: identifier of the dialogue as a string. `Conv_ID`: identifier of the conversation as a string. ### Data Splits | Dataset name | Train | Valid | Test | | ------------ | ----- | ----- | ---- | | dyda_da | 87170 | 8069 | 7740 | | dyda_e | 87170 | 8069 | 7740 | | iemocap | 7213 | 805 | 2021 | | maptask | 20905 | 2963 | 2894 | | meld_e | 9989 | 1109 | 2610 | | meld_s | 9989 | 1109 | 2610 | | mrda | 83944 | 9815 | 15470 | | oasis | 12076 | 1513 | 1478 | | sem | 4264 | 485 | 878 | | swda | 190709 | 21203 | 2714 | ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Benchmark Curators Emile Chapuis, Pierre Colombo, Ebenge Usip. ### Licensing Information This work is licensed under a [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 Unported License](https://creativecommons.org/licenses/by-sa/4.0/). ### Citation Information ``` @inproceedings{chapuis-etal-2020-hierarchical, title = "Hierarchical Pre-training for Sequence Labelling in Spoken Dialog", author = "Chapuis, Emile and Colombo, Pierre and Manica, Matteo and Labeau, Matthieu and Clavel, Chlo{\'e}", booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020", month = nov, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.findings-emnlp.239", doi = "10.18653/v1/2020.findings-emnlp.239", pages = "2636--2648", abstract = "Sequence labelling tasks like Dialog Act and Emotion/Sentiment identification are a key component of spoken dialog systems. In this work, we propose a new approach to learn generic representations adapted to spoken dialog, which we evaluate on a new benchmark we call Sequence labellIng evaLuatIon benChmark fOr spoken laNguagE benchmark (SILICONE). SILICONE is model-agnostic and contains 10 different datasets of various sizes. We obtain our representations with a hierarchical encoder based on transformer architectures, for which we extend two well-known pre-training objectives. Pre-training is performed on OpenSubtitles: a large corpus of spoken dialog containing over 2.3 billion of tokens. We demonstrate how hierarchical encoders achieve competitive results with consistently fewer parameters compared to state-of-the-art models and we show their importance for both pre-training and fine-tuning.", } ``` ### Contributions Thanks to [@eusip](https://github.com/eusip) and [@lhoestq](https://github.com/lhoestq) for adding this dataset.

ptb-text-only/ptb_text_only

ptb-text-only

2024-01-18T11:13:39Z

18,281

16

[ "task_categories:text-generation", "task_categories:fill-mask", "task_ids:language-modeling", "task_ids:masked-language-modeling", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "source_datasets:original", "language:en", "license:other", "size_categories:10K<n<100K", "region:us" ]

[ "text-generation", "fill-mask" ]

2022-03-02T23:29:22Z

null

--- annotations_creators: - expert-generated language_creators: - found language: - en license: - other license_details: LDC User Agreement for Non-Members multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-generation - fill-mask task_ids: - language-modeling - masked-language-modeling paperswithcode_id: null pretty_name: Penn Treebank dataset_info: features: - name: sentence dtype: string config_name: penn_treebank splits: - name: train num_bytes: 5143706 num_examples: 42068 - name: test num_bytes: 453710 num_examples: 3761 - name: validation num_bytes: 403156 num_examples: 3370 download_size: 5951345 dataset_size: 6000572 --- # Dataset Card for Penn Treebank ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://catalog.ldc.upenn.edu/LDC99T42 - **Repository:** 'https://raw.githubusercontent.com/wojzaremba/lstm/master/data/ptb.train.txt', 'https://raw.githubusercontent.com/wojzaremba/lstm/master/data/ptb.valid.txt', 'https://raw.githubusercontent.com/wojzaremba/lstm/master/data/ptb.test.txt' - **Paper:** https://www.aclweb.org/anthology/J93-2004.pdf - **Leaderboard:** [Needs More Information] - **Point of Contact:** [Needs More Information] ### Dataset Summary This is the Penn Treebank Project: Release 2 CDROM, featuring a million words of 1989 Wall Street Journal material. The rare words in this version are already replaced with <unk> token. The numbers are replaced with <N> token. ### Supported Tasks and Leaderboards Language Modelling ### Languages The text in the dataset is in American English ## Dataset Structure ### Data Instances [Needs More Information] ### Data Fields [Needs More Information] ### Data Splits [Needs More Information] ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information [Needs More Information] ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases [Needs More Information] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators [Needs More Information] ### Licensing Information Dataset provided for research purposes only. Please check dataset license for additional information. ### Citation Information @article{marcus-etal-1993-building, title = "Building a Large Annotated Corpus of {E}nglish: The {P}enn {T}reebank", author = "Marcus, Mitchell P. and Santorini, Beatrice and Marcinkiewicz, Mary Ann", journal = "Computational Linguistics", volume = "19", number = "2", year = "1993", url = "https://www.aclweb.org/anthology/J93-2004", pages = "313--330", } ### Contributions Thanks to [@harshalmittal4](https://github.com/harshalmittal4) for adding this dataset.

ybisk/piqa

ybisk

2024-01-18T11:13:02Z

314,085

89

[ "task_categories:question-answering", "task_ids:multiple-choice-qa", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "language_creators:found", "multilinguality:monolingual", "source_datasets:original", "language:en", "license:unknown", "size_categories:10K<n<100K", "arxiv:1911.11641", "arxiv:1907.10641", "arxiv:1904.09728", "arxiv:1808.05326", "region:us" ]

[ "question-answering" ]

2022-03-02T23:29:22Z

null

--- annotations_creators: - crowdsourced language_creators: - crowdsourced - found language: - en license: - unknown multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - question-answering task_ids: - multiple-choice-qa paperswithcode_id: piqa pretty_name: 'Physical Interaction: Question Answering' dataset_info: features: - name: goal dtype: string - name: sol1 dtype: string - name: sol2 dtype: string - name: label dtype: class_label: names: '0': '0' '1': '1' config_name: plain_text splits: - name: train num_bytes: 4104026 num_examples: 16113 - name: test num_bytes: 761521 num_examples: 3084 - name: validation num_bytes: 464321 num_examples: 1838 download_size: 2638625 dataset_size: 5329868 --- # Dataset Card for "Physical Interaction: Question Answering" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [PIQA homepage](https://yonatanbisk.com/piqa/) - **Paper:** [PIQA: Reasoning about Physical Commonsense in Natural Language](https://arxiv.org/abs/1911.11641) - **Leaderboard:** [Official leaderboard](https://yonatanbisk.com/piqa/) *Note that there is a [2nd leaderboard](https://leaderboard.allenai.org/physicaliqa) featuring a different (blind) test set with 3,446 examples as part of the Machine Commonsense DARPA project.* - **Point of Contact:** [Yonatan Bisk](https://yonatanbisk.com/piqa/) ### Dataset Summary *To apply eyeshadow without a brush, should I use a cotton swab or a toothpick?* Questions requiring this kind of physical commonsense pose a challenge to state-of-the-art natural language understanding systems. The PIQA dataset introduces the task of physical commonsense reasoning and a corresponding benchmark dataset Physical Interaction: Question Answering or PIQA. Physical commonsense knowledge is a major challenge on the road to true AI-completeness, including robots that interact with the world and understand natural language. PIQA focuses on everyday situations with a preference for atypical solutions. The dataset is inspired by instructables.com, which provides users with instructions on how to build, craft, bake, or manipulate objects using everyday materials. ### Supported Tasks and Leaderboards The underlying task is formualted as multiple choice question answering: given a question `q` and two possible solutions `s1`, `s2`, a model or a human must choose the most appropriate solution, of which exactly one is correct. ### Languages The text in the dataset is in English. The associated BCP-47 code is `en`. ## Dataset Structure ### Data Instances An example looks like this: ``` { "goal": "How do I ready a guinea pig cage for it's new occupants?", "sol1": "Provide the guinea pig with a cage full of a few inches of bedding made of ripped paper strips, you will also need to supply it with a water bottle and a food dish.", "sol2": "Provide the guinea pig with a cage full of a few inches of bedding made of ripped jeans material, you will also need to supply it with a water bottle and a food dish.", "label": 0, } ``` Note that the test set contains no labels. Predictions need to be submitted to the leaderboard. ### Data Fields List and describe the fields present in the dataset. Mention their data type, and whether they are used as input or output in any of the tasks the dataset currently supports. If the data has span indices, describe their attributes, such as whether they are at the character level or word level, whether they are contiguous or not, etc. If the datasets contains example IDs, state whether they have an inherent meaning, such as a mapping to other datasets or pointing to relationships between data points. - `goal`: the question which requires physical commonsense to be answered correctly - `sol1`: the first solution - `sol2`: the second solution - `label`: the correct solution. `0` refers to `sol1` and `1` refers to `sol2` ### Data Splits The dataset contains 16,000 examples for training, 2,000 for development and 3,000 for testing. ## Dataset Creation ### Curation Rationale The goal of the dataset is to construct a resource that requires concrete physical reasoning. ### Source Data The authors provide a prompt to the annotators derived from instructables.com. The instructables website is a crowdsourced collection of instruc- tions for doing everything from cooking to car repair. In most cases, users provide images or videos detailing each step and a list of tools that will be required. Most goals are simultaneously rare and unsurprising. While an annotator is unlikely to have built a UV-Flourescent steampunk lamp or made a backpack out of duct tape, it is not surprising that someone interested in home crafting would create these, nor will the tools and materials be unfamiliar to the average person. Using these examples as the seed for their annotation, helps remind annotators about the less prototypical uses of everyday objects. Second, and equally important, is that instructions build on one another. This means that any QA pair inspired by an instructable is more likely to explicitly state assumptions about what preconditions need to be met to start the task and what postconditions define success. Annotators were asked to glance at the instructions of an instructable and pull out or have it inspire them to construct two component tasks. They would then articulate the goal (often centered on atypical materials) and how to achieve it. In addition, annotaters were asked to provide a permutation to their own solution which makes it invalid (the negative solution), often subtly. #### Initial Data Collection and Normalization During validation, examples with low agreement were removed from the data. The dataset is further cleaned to remove stylistic artifacts and trivial examples from the data, which have been shown to artificially inflate model performance on previous NLI benchmarks.using the AFLite algorithm introduced in ([Sakaguchi et al. 2020](https://arxiv.org/abs/1907.10641); [Sap et al. 2019](https://arxiv.org/abs/1904.09728)) which is an improvement on adversarial filtering ([Zellers et al, 2018](https://arxiv.org/abs/1808.05326)). #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process Annotations are by construction obtained when crowdsourcers complete the prompt. #### Who are the annotators? Paid crowdsourcers ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information Unknown ### Citation Information ``` @inproceedings{Bisk2020, author = {Yonatan Bisk and Rowan Zellers and Ronan Le Bras and Jianfeng Gao and Yejin Choi}, title = {PIQA: Reasoning about Physical Commonsense in Natural Language}, booktitle = {Thirty-Fourth AAAI Conference on Artificial Intelligence}, year = {2020}, } ``` ### Contributions Thanks to [@VictorSanh](https://github.com/VictorSanh) for adding this dataset.

e9t/nsmc

e9t

2024-01-18T11:10:49Z

856

9

[ "task_categories:text-classification", "task_ids:sentiment-classification", "annotations_creators:crowdsourced", "language_creators:found", "multilinguality:monolingual", "source_datasets:original", "language:ko", "license:cc-by-2.0", "size_categories:100K<n<1M", "region:us" ]

[ "text-classification" ]

2022-03-02T23:29:22Z

1

--- annotations_creators: - crowdsourced language_creators: - found language: - ko license: - cc-by-2.0 multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - text-classification task_ids: - sentiment-classification paperswithcode_id: nsmc pretty_name: Naver Sentiment Movie Corpus dataset_info: features: - name: id dtype: string - name: document dtype: string - name: label dtype: class_label: names: '0': negative '1': positive splits: - name: train num_bytes: 16423803 num_examples: 150000 - name: test num_bytes: 5491417 num_examples: 50000 download_size: 19522142 dataset_size: 21915220 --- # Dataset Card for Naver sentiment movie corpus ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [Github](https://github.com/e9t/nsmc/) - **Repository:** [Github](https://github.com/e9t/nsmc/) - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary [More Information Needed] ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields Each instance is a movie review written by Korean internet users on Naver, the most commonly used search engine in Korea. Each row can be broken down into the following fields: - `id`: A unique review ID, provided by Naver - `document`: The actual movie review - `label`: Binary labels for sentiment analysis, where `0` denotes negative, and `1`, positive ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information ``` @InProceedings{Park:2016, title = "Naver Sentiment Movie Corpus", author = "Lucy Park", year = "2016", howpublished = {\\url{https://github.com/e9t/nsmc}} } ``` ### Contributions Thanks to [@jaketae](https://github.com/jaketae) for adding this dataset.

lil-lab/newsroom

lil-lab

2024-01-18T11:10:34Z

229

12

[ "task_categories:summarization", "task_ids:news-articles-summarization", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "source_datasets:original", "language:en", "license:other", "region:us" ]

[ "summarization" ]

2022-03-02T23:29:22Z

1

--- annotations_creators: - expert-generated language_creators: - expert-generated language: - en license: - other multilinguality: - monolingual pretty_name: CORNELL NEWSROOM size_categories: - unknown source_datasets: - original task_categories: - summarization task_ids: - news-articles-summarization paperswithcode_id: newsroom dataset_info: features: - name: text dtype: string - name: summary dtype: string - name: title dtype: string - name: url dtype: string - name: date dtype: string - name: density_bin dtype: string - name: coverage_bin dtype: string - name: compression_bin dtype: string - name: density dtype: float32 - name: coverage dtype: float32 - name: compression dtype: float32 splits: - name: test num_bytes: 472446866 num_examples: 108862 - name: train num_bytes: 4357506078 num_examples: 995041 - name: validation num_bytes: 473206951 num_examples: 108837 download_size: 0 dataset_size: 5303159895 --- # Dataset Card for "newsroom" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://lil.nlp.cornell.edu/newsroom/index.html](https://lil.nlp.cornell.edu/newsroom/index.html) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 0.00 MB - **Size of the generated dataset:** 5.30 GB - **Total amount of disk used:** 5.30 GB ### Dataset Summary NEWSROOM is a large dataset for training and evaluating summarization systems. It contains 1.3 million articles and summaries written by authors and editors in the newsrooms of 38 major publications. Dataset features includes: - text: Input news text. - summary: Summary for the news. And additional features: - title: news title. - url: url of the news. - date: date of the article. - density: extractive density. - coverage: extractive coverage. - compression: compression ratio. - density_bin: low, medium, high. - coverage_bin: extractive, abstractive. - compression_bin: low, medium, high. This dataset can be downloaded upon requests. Unzip all the contents "train.jsonl, dev.josnl, test.jsonl" to the `tfds` folder. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages English (`en`). ## Dataset Structure ### Data Instances #### default - **Size of downloaded dataset files:** 0.00 MB - **Size of the generated dataset:** 5.30 GB - **Total amount of disk used:** 5.30 GB An example of 'train' looks as follows. ``` { "compression": 33.880001068115234, "compression_bin": "medium", "coverage": 1.0, "coverage_bin": "high", "date": "200600000", "density": 11.720000267028809, "density_bin": "extractive", "summary": "some summary 1", "text": "some text 1", "title": "news title 1", "url": "url.html" } ``` ### Data Fields The data fields are the same among all splits. #### default - `text`: a `string` feature. - `summary`: a `string` feature. - `title`: a `string` feature. - `url`: a `string` feature. - `date`: a `string` feature. - `density_bin`: a `string` feature. - `coverage_bin`: a `string` feature. - `compression_bin`: a `string` feature. - `density`: a `float32` feature. - `coverage`: a `float32` feature. - `compression`: a `float32` feature. ### Data Splits | name |train |validation| test | |-------|-----:|---------:|-----:| |default|995041| 108837|108862| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information https://cornell.qualtrics.com/jfe/form/SV_6YA3HQ2p75XH4IR This Dataset Usage Agreement ("Agreement") is a legal agreement with the Cornell Newsroom Summaries Team ("Newsroom") for the Dataset made available to the individual or entity ("Researcher") exercising rights under this Agreement. "Dataset" includes all text, data, information, source code, and any related materials, documentation, files, media, updates or revisions. The Dataset is intended for non-commercial research and educational purposes only, and is made available free of charge without extending any license or other intellectual property rights. By downloading or using the Dataset, the Researcher acknowledges that they agree to the terms in this Agreement, and represent and warrant that they have authority to do so on behalf of any entity exercising rights under this Agreement. The Researcher accepts and agrees to be bound by the terms and conditions of this Agreement. If the Researcher does not agree to this Agreement, they may not download or use the Dataset. By sharing content with Newsroom, such as by submitting content to this site or by corresponding with Newsroom contributors, the Researcher grants Newsroom the right to use, reproduce, display, perform, adapt, modify, distribute, have distributed, and promote the content in any form, anywhere and for any purpose, such as for evaluating and comparing summarization systems. Nothing in this Agreement shall obligate Newsroom to provide any support for the Dataset. Any feedback, suggestions, ideas, comments, improvements given by the Researcher related to the Dataset is voluntarily given, and may be used by Newsroom without obligation or restriction of any kind. The Researcher accepts full responsibility for their use of the Dataset and shall defend indemnify, and hold harmless Newsroom, including their employees, trustees, officers, and agents, against any and all claims arising from the Researcher's use of the Dataset. The Researcher agrees to comply with all laws and regulations as they relate to access to and use of the Dataset and Service including U.S. export jurisdiction and other U.S. and international regulations. THE DATASET IS PROVIDED "AS IS." NEWSROOM DISCLAIMS ALL WARRANTIES, EXPRESS OR IMPLIED, INCLUDING THE IMPLIED WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, AND NON-INFRINGEMENT. WITHOUT LIMITATION OF THE ABOVE, NEWSROOM DISCLAIMS ANY WARRANTY THAT DATASET IS BUG OR ERROR-FREE, AND GRANTS NO WARRANTY REGARDING ITS USE OR THE RESULTS THEREFROM INCLUDING, WITHOUT LIMITATION, ITS CORRECTNESS, ACCURACY, OR RELIABILITY. THE DATASET IS NOT WARRANTIED TO FULFILL ANY PARTICULAR PURPOSES OR NEEDS. TO THE EXTENT NOT PROHIBITED BY LAW, IN NO EVENT SHALL NEWSROOM BE LIABLE FOR ANY LOSS, DAMAGE OR INJURY, DIRECT AND INDIRECT, INCIDENTAL, SPECIAL, OR CONSEQUENTIAL DAMAGES, HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER FOR BREACH OF CONTRACT, TORT (INCLUDING NEGLIGENCE) OR OTHERWISE, ARISING OUT OF THIS AGREEMENT, INCLUDING BUT NOT LIMITED TO LOSS OF PROFITS, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGES. THESE LIMITATIONS SHALL APPLY NOTWITHSTANDING ANY FAILURE OF ESSENTIAL PURPOSE OF ANY LIMITED REMEDY. This Agreement is effective until terminated. Newsroom reserves the right to terminate the Researcher's access to the Dataset at any time. If the Researcher breaches this Agreement, the Researcher's rights to use the Dataset shall terminate automatically. The Researcher will immediately cease all use and distribution of the Dataset and destroy any copies or portions of the Dataset in their possession. This Agreement is governed by the laws of the State of New York, without regard to conflict of law principles. All terms and provisions of this Agreement shall, if possible, be construed in a manner which makes them valid, but in the event any term or provision of this Agreement is found by a court of competent jurisdiction to be illegal or unenforceable, the validity or enforceability of the remainder of this Agreement shall not be affected. This Agreement is the complete and exclusive agreement between the parties with respect to its subject matter and supersedes all prior or contemporaneous oral or written agreements or understandings relating to the subject matter. ### Citation Information ``` @inproceedings{N18-1065, author = {Grusky, Max and Naaman, Mor and Artzi, Yoav}, title = {NEWSROOM: A Dataset of 1.3 Million Summaries with Diverse Extractive Strategies}, booktitle = {Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies}, year = {2018}, } ``` ### Contributions Thanks to [@lewtun](https://github.com/lewtun), [@patrickvonplaten](https://github.com/patrickvonplaten), [@yoavartzi](https://github.com/yoavartzi), [@thomwolf](https://github.com/thomwolf) for adding this dataset.

reciTAL/mlsum

reciTAL

2024-01-18T11:09:09Z

1,664

53

[ "task_categories:summarization", "task_categories:translation", "task_categories:text-classification", "task_ids:news-articles-summarization", "task_ids:multi-class-classification", "task_ids:multi-label-classification", "task_ids:topic-classification", "annotations_creators:found", "language_creators:found", "multilinguality:multilingual", "source_datasets:extended|cnn_dailymail", "source_datasets:original", "language:de", "language:es", "language:fr", "language:ru", "language:tr", "license:other", "size_categories:100K<n<1M", "region:us" ]

[ "summarization", "translation", "text-classification" ]

2022-03-02T23:29:22Z

1

--- annotations_creators: - found language_creators: - found language: - de - es - fr - ru - tr license: - other multilinguality: - multilingual size_categories: - 100K<n<1M - 10K<n<100K source_datasets: - extended|cnn_dailymail - original task_categories: - summarization - translation - text-classification task_ids: - news-articles-summarization - multi-class-classification - multi-label-classification - topic-classification paperswithcode_id: mlsum pretty_name: MLSUM dataset_info: - config_name: de features: - name: text dtype: string - name: summary dtype: string - name: topic dtype: string - name: url dtype: string - name: title dtype: string - name: date dtype: string splits: - name: train num_bytes: 846959840 num_examples: 220887 - name: validation num_bytes: 47119541 num_examples: 11394 - name: test num_bytes: 46847612 num_examples: 10701 download_size: 1005814154 dataset_size: 940926993 - config_name: es features: - name: text dtype: string - name: summary dtype: string - name: topic dtype: string - name: url dtype: string - name: title dtype: string - name: date dtype: string splits: - name: train num_bytes: 1214558302 num_examples: 266367 - name: validation num_bytes: 50643400 num_examples: 10358 - name: test num_bytes: 71263665 num_examples: 13920 download_size: 1456211154 dataset_size: 1336465367 - config_name: fr features: - name: text dtype: string - name: summary dtype: string - name: topic dtype: string - name: url dtype: string - name: title dtype: string - name: date dtype: string splits: - name: train num_bytes: 1471965014 num_examples: 392902 - name: validation num_bytes: 70413212 num_examples: 16059 - name: test num_bytes: 69660288 num_examples: 15828 download_size: 1849565564 dataset_size: 1612038514 - config_name: ru features: - name: text dtype: string - name: summary dtype: string - name: topic dtype: string - name: url dtype: string - name: title dtype: string - name: date dtype: string splits: - name: train num_bytes: 257389497 num_examples: 25556 - name: validation num_bytes: 9128497 num_examples: 750 - name: test num_bytes: 9656398 num_examples: 757 download_size: 766226107 dataset_size: 276174392 - config_name: tu features: - name: text dtype: string - name: summary dtype: string - name: topic dtype: string - name: url dtype: string - name: title dtype: string - name: date dtype: string splits: - name: train num_bytes: 641622783 num_examples: 249277 - name: validation num_bytes: 25530661 num_examples: 11565 - name: test num_bytes: 27830212 num_examples: 12775 download_size: 942308960 dataset_size: 694983656 config_names: - de - es - fr - ru - tu --- # Dataset Card for MLSUM ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** []() - **Repository:** https://github.com/recitalAI/MLSUM - **Paper:** https://www.aclweb.org/anthology/2020.emnlp-main.647/ - **Point of Contact:** [email]([email protected]) - **Size of downloaded dataset files:** 1.83 GB - **Size of the generated dataset:** 4.86 GB - **Total amount of disk used:** 6.69 GB ### Dataset Summary We present MLSUM, the first large-scale MultiLingual SUMmarization dataset. Obtained from online newspapers, it contains 1.5M+ article/summary pairs in five different languages -- namely, French, German, Spanish, Russian, Turkish. Together with English newspapers from the popular CNN/Daily mail dataset, the collected data form a large scale multilingual dataset which can enable new research directions for the text summarization community. We report cross-lingual comparative analyses based on state-of-the-art systems. These highlight existing biases which motivate the use of a multi-lingual dataset. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### de - **Size of downloaded dataset files:** 346.58 MB - **Size of the generated dataset:** 940.93 MB - **Total amount of disk used:** 1.29 GB An example of 'validation' looks as follows. ``` { "date": "01/01/2001", "summary": "A text", "text": "This is a text", "title": "A sample", "topic": "football", "url": "https://www.google.com" } ``` #### es - **Size of downloaded dataset files:** 513.31 MB - **Size of the generated dataset:** 1.34 GB - **Total amount of disk used:** 1.85 GB An example of 'validation' looks as follows. ``` { "date": "01/01/2001", "summary": "A text", "text": "This is a text", "title": "A sample", "topic": "football", "url": "https://www.google.com" } ``` #### fr - **Size of downloaded dataset files:** 619.99 MB - **Size of the generated dataset:** 1.61 GB - **Total amount of disk used:** 2.23 GB An example of 'validation' looks as follows. ``` { "date": "01/01/2001", "summary": "A text", "text": "This is a text", "title": "A sample", "topic": "football", "url": "https://www.google.com" } ``` #### ru - **Size of downloaded dataset files:** 106.22 MB - **Size of the generated dataset:** 276.17 MB - **Total amount of disk used:** 382.39 MB An example of 'train' looks as follows. ``` { "date": "01/01/2001", "summary": "A text", "text": "This is a text", "title": "A sample", "topic": "football", "url": "https://www.google.com" } ``` #### tu - **Size of downloaded dataset files:** 247.50 MB - **Size of the generated dataset:** 694.99 MB - **Total amount of disk used:** 942.48 MB An example of 'train' looks as follows. ``` { "date": "01/01/2001", "summary": "A text", "text": "This is a text", "title": "A sample", "topic": "football", "url": "https://www.google.com" } ``` ### Data Fields The data fields are the same among all splits. #### de - `text`: a `string` feature. - `summary`: a `string` feature. - `topic`: a `string` feature. - `url`: a `string` feature. - `title`: a `string` feature. - `date`: a `string` feature. #### es - `text`: a `string` feature. - `summary`: a `string` feature. - `topic`: a `string` feature. - `url`: a `string` feature. - `title`: a `string` feature. - `date`: a `string` feature. #### fr - `text`: a `string` feature. - `summary`: a `string` feature. - `topic`: a `string` feature. - `url`: a `string` feature. - `title`: a `string` feature. - `date`: a `string` feature. #### ru - `text`: a `string` feature. - `summary`: a `string` feature. - `topic`: a `string` feature. - `url`: a `string` feature. - `title`: a `string` feature. - `date`: a `string` feature. #### tu - `text`: a `string` feature. - `summary`: a `string` feature. - `topic`: a `string` feature. - `url`: a `string` feature. - `title`: a `string` feature. - `date`: a `string` feature. ### Data Splits |name|train |validation|test | |----|-----:|---------:|----:| |de |220887| 11394|10701| |es |266367| 10358|13920| |fr |392902| 16059|15828| |ru | 25556| 750| 757| |tu |249277| 11565|12775| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information Usage of dataset is restricted to non-commercial research purposes only. Copyright belongs to the original copyright holders. See https://github.com/recitalAI/MLSUM#mlsum ### Citation Information ``` @article{scialom2020mlsum, title={MLSUM: The Multilingual Summarization Corpus}, author={Scialom, Thomas and Dray, Paul-Alexis and Lamprier, Sylvain and Piwowarski, Benjamin and Staiano, Jacopo}, journal={arXiv preprint arXiv:2004.14900}, year={2020} } ``` ### Contributions Thanks to [@RachelKer](https://github.com/RachelKer), [@albertvillanova](https://github.com/albertvillanova), [@thomwolf](https://github.com/thomwolf) for adding this dataset.

UdS-LSV/menyo20k_mt

UdS-LSV

2024-01-18T11:08:52Z

111

3

[ "task_categories:translation", "annotations_creators:expert-generated", "annotations_creators:found", "language_creators:found", "multilinguality:translation", "source_datasets:original", "language:en", "language:yo", "license:cc-by-nc-4.0", "size_categories:10K<n<100K", "arxiv:2103.08647", "region:us" ]

[ "translation" ]

2022-03-02T23:29:22Z

1

--- annotations_creators: - expert-generated - found language_creators: - found language: - en - yo license: - cc-by-nc-4.0 multilinguality: - translation size_categories: - 10K<n<100K source_datasets: - original task_categories: - translation task_ids: [] paperswithcode_id: menyo-20k pretty_name: MENYO-20k dataset_info: features: - name: translation dtype: translation: languages: - en - yo config_name: menyo20k_mt splits: - name: train num_bytes: 2551345 num_examples: 10070 - name: validation num_bytes: 870011 num_examples: 3397 - name: test num_bytes: 1905432 num_examples: 6633 download_size: 5206234 dataset_size: 5326788 --- # Dataset Card for MENYO-20k ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** - **Repository:** https://github.com/uds-lsv/menyo-20k_MT/ - **Paper:** [The Effect of Domain and Diacritics in Yorùbá-English Neural Machine Translation](https://arxiv.org/abs/2103.08647) - **Leaderboard:** - **Point of Contact:** ### Dataset Summary MENYO-20k is a multi-domain parallel dataset with texts obtained from news articles, ted talks, movie transcripts, radio transcripts, science and technology texts, and other short articles curated from the web and professional translators. The dataset has 20,100 parallel sentences split into 10,070 training sentences, 3,397 development sentences, and 6,633 test sentences (3,419 multi-domain, 1,714 news domain, and 1,500 ted talks speech transcript domain). ### Supported Tasks and Leaderboards [More Information Needed] ### Languages Languages are English and Yoruba. ## Dataset Structure ### Data Instances An instance example: ``` {'translation': {'en': 'Unit 1: What is Creative Commons?', 'yo': 'Ìdá 1: Kín ni Creative Commons?' } } ``` ### Data Fields - `translation`: - `en`: English sentence. - `yo`: Yoruba sentence. ### Data Splits Training, validation and test splits are available. ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information The dataset is open but for non-commercial use because some data sources like Ted talks and JW news require permission for commercial use. The dataset is licensed under Creative Commons [Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)](https://creativecommons.org/licenses/by-nc/4.0/) License: https://github.com/uds-lsv/menyo-20k_MT/blob/master/LICENSE ### Citation Information If you use this dataset, please cite this paper: ``` @inproceedings{adelani-etal-2021-effect, title = "The Effect of Domain and Diacritics in {Y}oruba{--}{E}nglish Neural Machine Translation", author = "Adelani, David and Ruiter, Dana and Alabi, Jesujoba and Adebonojo, Damilola and Ayeni, Adesina and Adeyemi, Mofe and Awokoya, Ayodele Esther and Espa{\~n}a-Bonet, Cristina", booktitle = "Proceedings of the 18th Biennial Machine Translation Summit (Volume 1: Research Track)", month = aug, year = "2021", address = "Virtual", publisher = "Association for Machine Translation in the Americas", url = "https://aclanthology.org/2021.mtsummit-research.6", pages = "61--75", abstract = "Massively multilingual machine translation (MT) has shown impressive capabilities and including zero and few-shot translation between low-resource language pairs. However and these models are often evaluated on high-resource languages with the assumption that they generalize to low-resource ones. The difficulty of evaluating MT models on low-resource pairs is often due to lack of standardized evaluation datasets. In this paper and we present MENYO-20k and the first multi-domain parallel corpus with a especially curated orthography for Yoruba{--}English with standardized train-test splits for benchmarking. We provide several neural MT benchmarks and compare them to the performance of popular pre-trained (massively multilingual) MT models both for the heterogeneous test set and its subdomains. Since these pre-trained models use huge amounts of data with uncertain quality and we also analyze the effect of diacritics and a major characteristic of Yoruba and in the training data. We investigate how and when this training condition affects the final quality of a translation and its understandability.Our models outperform massively multilingual models such as Google ($+8.7$ BLEU) and Facebook M2M ($+9.1$) when translating to Yoruba and setting a high quality benchmark for future research.", } ``` ### Contributions Thanks to [@yvonnegitau](https://github.com/yvonnegitau) for adding this dataset.

hotpotqa/hotpot_qa

hotpotqa

2024-01-18T11:05:40Z

10,442

121

[ "task_categories:question-answering", "annotations_creators:crowdsourced", "language_creators:found", "multilinguality:monolingual", "source_datasets:original", "language:en", "license:cc-by-sa-4.0", "size_categories:100K<n<1M", "arxiv:1809.09600", "region:us", "multi-hop" ]

[ "question-answering" ]

2022-03-02T23:29:22Z

null

--- annotations_creators: - crowdsourced language: - en language_creators: - found license: - cc-by-sa-4.0 multilinguality: - monolingual pretty_name: HotpotQA size_categories: - 100K<n<1M source_datasets: - original task_categories: - question-answering task_ids: [] paperswithcode_id: hotpotqa tags: - multi-hop dataset_info: - config_name: distractor features: - name: id dtype: string - name: question dtype: string - name: answer dtype: string - name: type dtype: string - name: level dtype: string - name: supporting_facts sequence: - name: title dtype: string - name: sent_id dtype: int32 - name: context sequence: - name: title dtype: string - name: sentences sequence: string splits: - name: train num_bytes: 552949315 num_examples: 90447 - name: validation num_bytes: 45716111 num_examples: 7405 download_size: 612746344 dataset_size: 598665426 - config_name: fullwiki features: - name: id dtype: string - name: question dtype: string - name: answer dtype: string - name: type dtype: string - name: level dtype: string - name: supporting_facts sequence: - name: title dtype: string - name: sent_id dtype: int32 - name: context sequence: - name: title dtype: string - name: sentences sequence: string splits: - name: train num_bytes: 552949315 num_examples: 90447 - name: validation num_bytes: 46848601 num_examples: 7405 - name: test num_bytes: 46000102 num_examples: 7405 download_size: 660094672 dataset_size: 645798018 --- # Dataset Card for "hotpot_qa" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://hotpotqa.github.io/](https://hotpotqa.github.io/) - **Repository:** https://github.com/hotpotqa/hotpot - **Paper:** [HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering](https://arxiv.org/abs/1809.09600) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 1.27 GB - **Size of the generated dataset:** 1.24 GB - **Total amount of disk used:** 2.52 GB ### Dataset Summary HotpotQA is a new dataset with 113k Wikipedia-based question-answer pairs with four key features: (1) the questions require finding and reasoning over multiple supporting documents to answer; (2) the questions are diverse and not constrained to any pre-existing knowledge bases or knowledge schemas; (3) we provide sentence-level supporting facts required for reasoning, allowingQA systems to reason with strong supervision and explain the predictions; (4) we offer a new type of factoid comparison questions to test QA systems’ ability to extract relevant facts and perform necessary comparison. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### distractor - **Size of downloaded dataset files:** 612.75 MB - **Size of the generated dataset:** 598.66 MB - **Total amount of disk used:** 1.21 GB An example of 'validation' looks as follows. ``` { "answer": "This is the answer", "context": { "sentences": [["Sent 1"], ["Sent 21", "Sent 22"]], "title": ["Title1", "Title 2"] }, "id": "000001", "level": "medium", "question": "What is the answer?", "supporting_facts": { "sent_id": [0, 1, 3], "title": ["Title of para 1", "Title of para 2", "Title of para 3"] }, "type": "comparison" } ``` #### fullwiki - **Size of downloaded dataset files:** 660.10 MB - **Size of the generated dataset:** 645.80 MB - **Total amount of disk used:** 1.31 GB An example of 'train' looks as follows. ``` { "answer": "This is the answer", "context": { "sentences": [["Sent 1"], ["Sent 2"]], "title": ["Title1", "Title 2"] }, "id": "000001", "level": "hard", "question": "What is the answer?", "supporting_facts": { "sent_id": [0, 1, 3], "title": ["Title of para 1", "Title of para 2", "Title of para 3"] }, "type": "bridge" } ``` ### Data Fields The data fields are the same among all splits. #### distractor - `id`: a `string` feature. - `question`: a `string` feature. - `answer`: a `string` feature. - `type`: a `string` feature. - `level`: a `string` feature. - `supporting_facts`: a dictionary feature containing: - `title`: a `string` feature. - `sent_id`: a `int32` feature. - `context`: a dictionary feature containing: - `title`: a `string` feature. - `sentences`: a `list` of `string` features. #### fullwiki - `id`: a `string` feature. - `question`: a `string` feature. - `answer`: a `string` feature. - `type`: a `string` feature. - `level`: a `string` feature. - `supporting_facts`: a dictionary feature containing: - `title`: a `string` feature. - `sent_id`: a `int32` feature. - `context`: a dictionary feature containing: - `title`: a `string` feature. - `sentences`: a `list` of `string` features. ### Data Splits #### distractor | |train|validation| |----------|----:|---------:| |distractor|90447| 7405| #### fullwiki | |train|validation|test| |--------|----:|---------:|---:| |fullwiki|90447| 7405|7405| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information HotpotQA is distributed under a [CC BY-SA 4.0 License](http://creativecommons.org/licenses/by-sa/4.0/). ### Citation Information ``` @inproceedings{yang2018hotpotqa, title={{HotpotQA}: A Dataset for Diverse, Explainable Multi-hop Question Answering}, author={Yang, Zhilin and Qi, Peng and Zhang, Saizheng and Bengio, Yoshua and Cohen, William W. and Salakhutdinov, Ruslan and Manning, Christopher D.}, booktitle={Conference on Empirical Methods in Natural Language Processing ({EMNLP})}, year={2018} } ``` ### Contributions Thanks to [@albertvillanova](https://github.com/albertvillanova), [@ghomasHudson](https://github.com/ghomasHudson) for adding this dataset.

NLP-AUEB/eurlex

NLP-AUEB

2024-01-18T11:03:22Z

225

17

[ "task_categories:text-classification", "task_ids:multi-label-classification", "annotations_creators:found", "language_creators:found", "multilinguality:monolingual", "source_datasets:original", "language:en", "license:cc-by-sa-4.0", "size_categories:10K<n<100K", "region:us", "legal-topic-classification" ]

[ "text-classification" ]

2022-03-02T23:29:22Z

1

--- annotations_creators: - found language_creators: - found language: - en license: - cc-by-sa-4.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: - multi-label-classification paperswithcode_id: eurlex57k pretty_name: the EUR-Lex dataset tags: - legal-topic-classification dataset_info: features: - name: celex_id dtype: string - name: title dtype: string - name: text dtype: string - name: eurovoc_concepts sequence: string config_name: eurlex57k splits: - name: train num_bytes: 167603718 num_examples: 45000 - name: test num_bytes: 22046706 num_examples: 6000 - name: validation num_bytes: 21942574 num_examples: 6000 download_size: 50289403 dataset_size: 211592998 --- # Dataset Card for the EUR-Lex dataset ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** http://nlp.cs.aueb.gr/software_and_datasets/EURLEX57K/ - **Repository:** http://nlp.cs.aueb.gr/software_and_datasets/EURLEX57K/ - **Paper:** https://www.aclweb.org/anthology/P19-1636/ - **Leaderboard:** N/A - **Point of Contact:** [Ilias Chalkidis](mailto:[email protected]) ### Dataset Summary EURLEX57K can be viewed as an improved version of the dataset released by Mencia and Furnkranzand (2007), which has been widely used in Large-scale Multi-label Text Classification (LMTC) research, but is less than half the size of EURLEX57K (19.6k documents, 4k EUROVOC labels) and more than ten years old. EURLEX57K contains 57k legislative documents in English from EUR-Lex (https://eur-lex.europa.eu) with an average length of 727 words. Each document contains four major zones: - the header, which includes the title and name of the legal body enforcing the legal act; - the recitals, which are legal background references; and - the main body, usually organized in articles. **Labeling / Annotation** All the documents of the dataset have been annotated by the Publications Office of EU (https://publications.europa.eu/en) with multiple concepts from EUROVOC (http://eurovoc.europa.eu/). While EUROVOC includes approx. 7k concepts (labels), only 4,271 (59.31%) are present in EURLEX57K, from which only 2,049 (47.97%) have been assigned to more than 10 documents. The 4,271 labels are also divided into frequent (746 labels), few-shot (3,362), and zero- shot (163), depending on whether they were assigned to more than 50, fewer than 50 but at least one, or no training documents, respectively. ### Supported Tasks and Leaderboards The dataset supports: **Multi-label Text Classification:** Given the text of a document, a model predicts the relevant EUROVOC concepts. **Few-shot and Zero-shot learning:** As already noted, the labels can be divided into three groups: frequent (746 labels), few-shot (3,362), and zero- shot (163), depending on whether they were assigned to more than 50, fewer than 50 but at least one, or no training documents, respectively. ### Languages All documents are written in English. ## Dataset Structure ### Data Instances ```json { "celex_id": "31979D0509", "title": "79/509/EEC: Council Decision of 24 May 1979 on financial aid from the Community for the eradication of African swine fever in Spain", "text": "COUNCIL DECISION of 24 May 1979 on financial aid from the Community for the eradication of African swine fever in Spain (79/509/EEC)\nTHE COUNCIL OF THE EUROPEAN COMMUNITIES\nHaving regard to the Treaty establishing the European Economic Community, and in particular Article 43 thereof,\nHaving regard to the proposal from the Commission (1),\nHaving regard to the opinion of the European Parliament (2),\nWhereas the Community should take all appropriate measures to protect itself against the appearance of African swine fever on its territory;\nWhereas to this end the Community has undertaken, and continues to undertake, action designed to contain outbreaks of this type of disease far from its frontiers by helping countries affected to reinforce their preventive measures ; whereas for this purpose Community subsidies have already been granted to Spain;\nWhereas these measures have unquestionably made an effective contribution to the protection of Community livestock, especially through the creation and maintenance of a buffer zone north of the river Ebro;\nWhereas, however, in the opinion of the Spanish authorities themselves, the measures so far implemented must be reinforced if the fundamental objective of eradicating the disease from the entire country is to be achieved;\nWhereas the Spanish authorities have asked the Community to contribute to the expenses necessary for the efficient implementation of a total eradication programme;\nWhereas a favourable response should be given to this request by granting aid to Spain, having regard to the undertaking given by that country to protect the Community against African swine fever and to eliminate completely this disease by the end of a five-year eradication plan;\nWhereas this eradication plan must include certain measures which guarantee the effectiveness of the action taken, and it must be possible to adapt these measures to developments in the situation by means of a procedure establishing close cooperation between the Member States and the Commission;\nWhereas it is necessary to keep the Member States regularly informed as to the progress of the action undertaken,", "eurovoc_concepts": ["192", "2356", "2560", "862", "863"] } ``` ### Data Fields The following data fields are provided for documents (`train`, `dev`, `test`): `celex_id`: (**str**) The official ID of the document. The CELEX number is the unique identifier for all publications in both Eur-Lex and CELLAR.\ `title`: (**str**) The title of the document.\ `text`: (**str**) The full content of each document, which is represented by its `header`, `recitals` and `main_body`.\ `eurovoc_concepts`: (**List[str]**) The relevant EUROVOC concepts (labels). If you want to use the descriptors of EUROVOC concepts, similar to Chalkidis et al. (2020), please load: https://archive.org/download/EURLEX57K/eurovoc_concepts.jsonl ```python import json with open('./eurovoc_concepts.jsonl') as jsonl_file: eurovoc_concepts = {json.loads(concept) for concept in jsonl_file.readlines()} ``` ### Data Splits | Split | No of Documents | Avg. words | Avg. labels | | ------------------- | ------------------------------------ | --- | --- | | Train | 45,000 | 729 | 5 | |Development | 6,000 | 714 | 5 | |Test | 6,000 | 725 | 5 | ## Dataset Creation ### Curation Rationale The dataset was curated by Chalkidis et al. (2019).\ The documents have been annotated by the Publications Office of EU (https://publications.europa.eu/en). ### Source Data #### Initial Data Collection and Normalization The original data are available at EUR-Lex portal (https://eur-lex.europa.eu) in an unprocessed format. The documents were downloaded from EUR-Lex portal in HTML format. The relevant metadata and EUROVOC concepts were downloaded from the SPARQL endpoint of the Publications Office of EU (http://publications.europa.eu/webapi/rdf/sparql). #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process * The original documents are available at EUR-Lex portal (https://eur-lex.europa.eu) in an unprocessed HTML format. The HTML code was striped and the documents split into sections. * The documents have been annotated by the Publications Office of EU (https://publications.europa.eu/en). #### Who are the annotators? Publications Office of EU (https://publications.europa.eu/en) ### Personal and Sensitive Information The dataset does not include personal or sensitive information. ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators Chalkidis et al. (2019) ### Licensing Information © European Union, 1998-2021 The Commission’s document reuse policy is based on Decision 2011/833/EU. Unless otherwise specified, you can re-use the legal documents published in EUR-Lex for commercial or non-commercial purposes. The copyright for the editorial content of this website, the summaries of EU legislation and the consolidated texts, which is owned by the EU, is licensed under the Creative Commons Attribution 4.0 International licence. This means that you can re-use the content provided you acknowledge the source and indicate any changes you have made. Source: https://eur-lex.europa.eu/content/legal-notice/legal-notice.html \ Read more: https://eur-lex.europa.eu/content/help/faq/reuse-contents-eurlex.html ### Citation Information *Ilias Chalkidis, Manos Fergadiotis, Prodromos Malakasiotis and Ion Androutsopoulos.* *Large-Scale Multi-Label Text Classification on EU Legislation.* *Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019). Florence, Italy. 2019* ``` @inproceedings{chalkidis-etal-2019-large, title = "Large-Scale Multi-Label Text Classification on {EU} Legislation", author = "Chalkidis, Ilias and Fergadiotis, Manos and Malakasiotis, Prodromos and Androutsopoulos, Ion", booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics", year = "2019", address = "Florence, Italy", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/P19-1636", doi = "10.18653/v1/P19-1636", pages = "6314--6322" } ``` ### Contributions Thanks to [@iliaschalkidis](https://github.com/iliaschalkidis) for adding this dataset.

facebook/empathetic_dialogues

facebook

2024-01-18T11:03:15Z

2,905

101

[ "task_categories:question-answering", "task_ids:dialogue-generation", "task_ids:open-domain-qa", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "source_datasets:original", "language:en", "license:cc-by-nc-4.0", "size_categories:10K<n<100K", "arxiv:1811.00207", "region:us" ]

[ "conversational", "question-answering" ]

2022-03-02T23:29:22Z

null

--- annotations_creators: - crowdsourced language: - en language_creators: - crowdsourced license: - cc-by-nc-4.0 multilinguality: - monolingual pretty_name: EmpatheticDialogues size_categories: - 10K<n<100K source_datasets: - original task_categories: - conversational - question-answering task_ids: - dialogue-generation - open-domain-qa paperswithcode_id: empatheticdialogues dataset_info: features: - name: conv_id dtype: string - name: utterance_idx dtype: int32 - name: context dtype: string - name: prompt dtype: string - name: speaker_idx dtype: int32 - name: utterance dtype: string - name: selfeval dtype: string - name: tags dtype: string splits: - name: test num_bytes: 3011332 num_examples: 10943 - name: train num_bytes: 19040509 num_examples: 76673 - name: validation num_bytes: 3077481 num_examples: 12030 download_size: 28022709 dataset_size: 25129322 --- # Dataset Card for "empathetic_dialogues" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://github.com/facebookresearch/EmpatheticDialogues](https://github.com/facebookresearch/EmpatheticDialogues) - **Repository:** https://github.com/facebookresearch/EmpatheticDialogues - **Paper:** [Towards Empathetic Open-domain Conversation Models: a New Benchmark and Dataset](https://arxiv.org/abs/1811.00207) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 28.02 MB - **Size of the generated dataset:** 25.13 MB - **Total amount of disk used:** 53.15 MB ### Dataset Summary PyTorch original implementation of Towards Empathetic Open-domain Conversation Models: a New Benchmark and Dataset ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### default - **Size of downloaded dataset files:** 28.02 MB - **Size of the generated dataset:** 25.13 MB - **Total amount of disk used:** 53.15 MB An example of 'train' looks as follows. ``` { "context": "sentimental", "conv_id": "hit:0_conv:1", "prompt": "I remember going to the fireworks with my best friend. There was a lot of people_comma_ but it only felt like us in the world.", "selfeval": "5|5|5_2|2|5", "speaker_idx": 1, "tags": "", "utterance": "I remember going to see the fireworks with my best friend. It was the first time we ever spent time alone together. Although there was a lot of people_comma_ we felt like the only people in the world.", "utterance_idx": 1 } ``` ### Data Fields The data fields are the same among all splits. #### default - `conv_id`: a `string` feature. - `utterance_idx`: a `int32` feature. - `context`: a `string` feature. - `prompt`: a `string` feature. - `speaker_idx`: a `int32` feature. - `utterance`: a `string` feature. - `selfeval`: a `string` feature. - `tags`: a `string` feature. ### Data Splits | name |train|validation|test | |-------|----:|---------:|----:| |default|76673| 12030|10943| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information Creative Commons [Attribution-NonCommercial 4.0 International](https://creativecommons.org/licenses/by-nc/4.0/). ### Citation Information ``` @inproceedings{rashkin-etal-2019-towards, title = "Towards Empathetic Open-domain Conversation Models: A New Benchmark and Dataset", author = "Rashkin, Hannah and Smith, Eric Michael and Li, Margaret and Boureau, Y-Lan", booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics", month = jul, year = "2019", address = "Florence, Italy", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/P19-1534", doi = "10.18653/v1/P19-1534", pages = "5370--5381", } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@patrickvonplaten](https://github.com/patrickvonplaten), [@lewtun](https://github.com/lewtun) for adding this dataset.

nlpaueb/biomrc

nlpaueb

2024-01-18T11:02:01Z

383

5

[ "language:en", "region:us" ]

[]

2022-03-02T23:29:22Z

1

--- language: - en paperswithcode_id: biomrc pretty_name: BIOMRC dataset_info: - config_name: plain_text features: - name: abstract dtype: string - name: title dtype: string - name: entities_list sequence: string - name: answer dtype: string splits: - name: train num_bytes: 1653301820 num_examples: 700000 - name: validation num_bytes: 119697683 num_examples: 50000 - name: test num_bytes: 147832373 num_examples: 62707 download_size: 408080356 dataset_size: 1920831876 - config_name: biomrc_large_A features: - name: abstract dtype: string - name: title dtype: string - name: entities_list sequence: string - name: answer dtype: string splits: - name: train num_bytes: 1653301820 num_examples: 700000 - name: validation num_bytes: 119697683 num_examples: 50000 - name: test num_bytes: 147832373 num_examples: 62707 download_size: 408080356 dataset_size: 1920831876 - config_name: biomrc_large_B features: - name: abstract dtype: string - name: title dtype: string - name: entities_list sequence: string - name: answer dtype: string splits: - name: train num_bytes: 1325877001 num_examples: 700000 - name: validation num_bytes: 96414040 num_examples: 50000 - name: test num_bytes: 118708586 num_examples: 62707 download_size: 343061539 dataset_size: 1540999627 - config_name: biomrc_small_A features: - name: abstract dtype: string - name: title dtype: string - name: entities_list sequence: string - name: answer dtype: string splits: - name: train num_bytes: 206553549 num_examples: 87500 - name: validation num_bytes: 14957163 num_examples: 6250 - name: test num_bytes: 14807799 num_examples: 6250 download_size: 68879274 dataset_size: 236318511 - config_name: biomrc_small_B features: - name: abstract dtype: string - name: title dtype: string - name: entities_list sequence: string - name: answer dtype: string splits: - name: train num_bytes: 165662937 num_examples: 87500 - name: validation num_bytes: 12047304 num_examples: 6250 - name: test num_bytes: 11911172 num_examples: 6250 download_size: 57706889 dataset_size: 189621413 - config_name: biomrc_tiny_A features: - name: abstract dtype: string - name: title dtype: string - name: entities_list sequence: string - name: answer dtype: string splits: - name: test num_bytes: 70914 num_examples: 30 download_size: 22519 dataset_size: 70914 - config_name: biomrc_tiny_B features: - name: abstract dtype: string - name: title dtype: string - name: entities_list sequence: string - name: answer dtype: string splits: - name: test num_bytes: 59925 num_examples: 30 download_size: 19685 dataset_size: 59925 --- # Dataset Card for "biomrc" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [http://nlp.cs.aueb.gr/](http://nlp.cs.aueb.gr/) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 1.29 GB - **Size of the generated dataset:** 5.81 GB - **Total amount of disk used:** 7.09 GB ### Dataset Summary We introduce BIOMRC, a large-scale cloze-style biomedical MRC dataset. Care was taken to reduce noise, compared to the previous BIOREAD dataset of Pappas et al. (2018). Experiments show that simple heuristics do not perform well on the new dataset and that two neural MRC models that had been tested on BIOREAD perform much better on BIOMRC, indicating that the new dataset is indeed less noisy or at least that its task is more feasible. Non-expert human performance is also higher on the new dataset compared to BIOREAD, and biomedical experts perform even better. We also introduce a new BERT-based MRC model, the best version of which substantially outperforms all other methods tested, reaching or surpassing the accuracy of biomedical experts in some experiments. We make the new dataset available in three different sizes, also releasing our code, and providing a leaderboard. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### biomrc_large_A - **Size of downloaded dataset files:** 408.08 MB - **Size of the generated dataset:** 1.92 GB - **Total amount of disk used:** 2.33 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "abstract": "\"OBJECTIVES: @entity9 is a @entity10 that may result from greater occipital nerve entrapment. Entrapped peripheral nerves typica...", "answer": "@entity9 :: (MESH:D009437,Disease) :: ['unilateral occipital neuralgia']\n", "entities_list": ["@entity1 :: ('9606', 'Species') :: ['patients']", "@entity10 :: ('MESH:D006261', 'Disease') :: ['headache', 'Headache']", "@entity9 :: ('MESH:D009437', 'Disease') :: ['Occipital neuralgia', 'unilateral occipital neuralgia']"], "title": "Sonographic evaluation of the greater occipital nerve in XXXX .\n" } ``` #### biomrc_large_B - **Size of downloaded dataset files:** 343.06 MB - **Size of the generated dataset:** 1.54 GB - **Total amount of disk used:** 1.88 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "abstract": "\"BACKGROUND: Adults with physical disabilities are less likely than others to receive @entity2 screening. It is not known, howev...", "answer": "@entity2", "entities_list": ["@entity2", "@entity1", "@entity0", "@entity3"], "title": "Does a standard measure of self-reported physical disability correlate with clinician perception of impairment related to XXXX screening?\n" } ``` #### biomrc_small_A - **Size of downloaded dataset files:** 68.88 MB - **Size of the generated dataset:** 236.32 MB - **Total amount of disk used:** 305.20 MB An example of 'validation' looks as follows. ``` This example was too long and was cropped: { "abstract": "\"PURPOSE: @entity120 ( @entity120 ) is a life-limiting @entity102 that presents as an elevated blood pressure in the pulmonary a...", "answer": "@entity148 :: (MESH:D001008,Disease) :: ['anxiety']\n", "entities_list": "[\"@entity1 :: ('9606', 'Species') :: ['patients']\", \"@entity308 :: ('MESH:D003866', 'Disease') :: ['depression']\", \"@entity146 :...", "title": "A predictive model of the effects of @entity308 , XXXX , stress, 6-minute-walk distance, and social support on health-related quality of life in an adult pulmonary hypertension population.\n" } ``` #### biomrc_small_B - **Size of downloaded dataset files:** 57.70 MB - **Size of the generated dataset:** 189.62 MB - **Total amount of disk used:** 247.33 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "abstract": "\"Single-agent activity for @entity12 reflected by response rates of 10%-30% has been reported in @entity0 with @entity3 ( @entit...", "answer": "@entity10", "entities_list": ["@entity0", "@entity6", "@entity2", "@entity5", "@entity12", "@entity11", "@entity1", "@entity7", "@entity9", "@entity10", "@entity3", "@entity4", "@entity8"], "title": "No synergistic activity of @entity7 and XXXX in the treatment of @entity3 .\n" } ``` #### biomrc_tiny_A - **Size of downloaded dataset files:** 0.02 MB - **Size of the generated dataset:** 0.07 MB - **Total amount of disk used:** 0.09 MB An example of 'test' looks as follows. ``` This example was too long and was cropped: { "abstract": "\"OBJECTIVE: Decompressive craniectomy (DC) requires later cranioplasty (CP) in survivors. However, if additional ventriculoperit...", "answer": "@entity260 :: (MESH:D011183,Disease) :: ['Postoperative Complications']\n", "entities_list": ["@entity1 :: ('9606', 'Species') :: ['Patients', 'patients', 'Patient']", "@entity260 :: ('MESH:D011183', 'Disease') :: ['VPS regarding postoperative complications']", "@entity1276 :: ('MESH:D006849', 'Disease') :: ['hydrocephalus']"], "title": "Cranioplasty and Ventriculoperitoneal Shunt Placement after Decompressive Craniectomy: Staged Surgery Is Associated with Fewer XXXX .\n" } ``` ### Data Fields The data fields are the same among all splits. #### biomrc_large_A - `abstract`: a `string` feature. - `title`: a `string` feature. - `entities_list`: a `list` of `string` features. - `answer`: a `string` feature. #### biomrc_large_B - `abstract`: a `string` feature. - `title`: a `string` feature. - `entities_list`: a `list` of `string` features. - `answer`: a `string` feature. #### biomrc_small_A - `abstract`: a `string` feature. - `title`: a `string` feature. - `entities_list`: a `list` of `string` features. - `answer`: a `string` feature. #### biomrc_small_B - `abstract`: a `string` feature. - `title`: a `string` feature. - `entities_list`: a `list` of `string` features. - `answer`: a `string` feature. #### biomrc_tiny_A - `abstract`: a `string` feature. - `title`: a `string` feature. - `entities_list`: a `list` of `string` features. - `answer`: a `string` feature. ### Data Splits #### biomrc_large_A | |train |validation|test | |--------------|-----:|---------:|----:| |biomrc_large_A|700000| 50000|62707| #### biomrc_large_B | |train |validation|test | |--------------|-----:|---------:|----:| |biomrc_large_B|700000| 50000|62707| #### biomrc_small_A | |train|validation|test| |--------------|----:|---------:|---:| |biomrc_small_A|87500| 6250|6250| #### biomrc_small_B | |train|validation|test| |--------------|----:|---------:|---:| |biomrc_small_B|87500| 6250|6250| #### biomrc_tiny_A | |test| |-------------|---:| |biomrc_tiny_A| 30| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @inproceedings{pappas-etal-2020-biomrc, title = "{B}io{MRC}: A Dataset for Biomedical Machine Reading Comprehension", author = "Pappas, Dimitris and Stavropoulos, Petros and Androutsopoulos, Ion and McDonald, Ryan", booktitle = "Proceedings of the 19th SIGBioMed Workshop on Biomedical Language Processing", month = jul, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.bionlp-1.15", pages = "140--149", abstract = "We introduce BIOMRC, a large-scale cloze-style biomedical MRC dataset. Care was taken to reduce noise, compared to the previous BIOREAD dataset of Pappas et al. (2018). Experiments show that simple heuristics do not perform well on the new dataset and that two neural MRC models that had been tested on BIOREAD perform much better on BIOMRC, indicating that the new dataset is indeed less noisy or at least that its task is more feasible. Non-expert human performance is also higher on the new dataset compared to BIOREAD, and biomedical experts perform even better. We also introduce a new BERT-based MRC model, the best version of which substantially outperforms all other methods tested, reaching or surpassing the accuracy of biomedical experts in some experiments. We make the new dataset available in three different sizes, also releasing our code, and providing a leaderboard.", } ``` ### Contributions Thanks to [@lewtun](https://github.com/lewtun), [@PetrosStav](https://github.com/PetrosStav), [@lhoestq](https://github.com/lhoestq), [@thomwolf](https://github.com/thomwolf) for adding this dataset.

NortheasternUniversity/big_patent

NortheasternUniversity

2024-01-18T11:01:59Z

1,129

58

[ "task_categories:summarization", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:monolingual", "source_datasets:original", "language:en", "license:cc-by-4.0", "size_categories:100K<n<1M", "arxiv:1906.03741", "region:us", "patent-summarization" ]

[ "summarization" ]

2022-03-02T23:29:22Z

1

--- annotations_creators: - no-annotation language_creators: - found language: - en license: - cc-by-4.0 multilinguality: - monolingual size_categories: - 100K<n<1M - 10K<n<100K - 1M<n<10M source_datasets: - original task_categories: - summarization task_ids: [] paperswithcode_id: bigpatent pretty_name: Big Patent tags: - patent-summarization dataset_info: - config_name: all features: - name: description dtype: string - name: abstract dtype: string splits: - name: train num_bytes: 38367048389 num_examples: 1207222 - name: validation num_bytes: 2115827002 num_examples: 67068 - name: test num_bytes: 2129505280 num_examples: 67072 download_size: 10142923776 dataset_size: 42612380671 - config_name: a features: - name: description dtype: string - name: abstract dtype: string splits: - name: train num_bytes: 5683460620 num_examples: 174134 - name: validation num_bytes: 313324505 num_examples: 9674 - name: test num_bytes: 316633277 num_examples: 9675 download_size: 10142923776 dataset_size: 6313418402 - config_name: b features: - name: description dtype: string - name: abstract dtype: string splits: - name: train num_bytes: 4236070976 num_examples: 161520 - name: validation num_bytes: 234425138 num_examples: 8973 - name: test num_bytes: 231538734 num_examples: 8974 download_size: 10142923776 dataset_size: 4702034848 - config_name: c features: - name: description dtype: string - name: abstract dtype: string splits: - name: train num_bytes: 4506249306 num_examples: 101042 - name: validation num_bytes: 244684775 num_examples: 5613 - name: test num_bytes: 252566793 num_examples: 5614 download_size: 10142923776 dataset_size: 5003500874 - config_name: d features: - name: description dtype: string - name: abstract dtype: string splits: - name: train num_bytes: 264717412 num_examples: 10164 - name: validation num_bytes: 14560482 num_examples: 565 - name: test num_bytes: 14403430 num_examples: 565 download_size: 10142923776 dataset_size: 293681324 - config_name: e features: - name: description dtype: string - name: abstract dtype: string splits: - name: train num_bytes: 881101433 num_examples: 34443 - name: validation num_bytes: 48646158 num_examples: 1914 - name: test num_bytes: 48586429 num_examples: 1914 download_size: 10142923776 dataset_size: 978334020 - config_name: f features: - name: description dtype: string - name: abstract dtype: string splits: - name: train num_bytes: 2146383473 num_examples: 85568 - name: validation num_bytes: 119632631 num_examples: 4754 - name: test num_bytes: 119596303 num_examples: 4754 download_size: 10142923776 dataset_size: 2385612407 - config_name: g features: - name: description dtype: string - name: abstract dtype: string splits: - name: train num_bytes: 8877854206 num_examples: 258935 - name: validation num_bytes: 492581177 num_examples: 14385 - name: test num_bytes: 496324853 num_examples: 14386 download_size: 10142923776 dataset_size: 9866760236 - config_name: h features: - name: description dtype: string - name: abstract dtype: string splits: - name: train num_bytes: 8075621958 num_examples: 257019 - name: validation num_bytes: 447602356 num_examples: 14279 - name: test num_bytes: 445460513 num_examples: 14279 download_size: 10142923776 dataset_size: 8968684827 - config_name: y features: - name: description dtype: string - name: abstract dtype: string splits: - name: train num_bytes: 3695589005 num_examples: 124397 - name: validation num_bytes: 200369780 num_examples: 6911 - name: test num_bytes: 204394948 num_examples: 6911 download_size: 10142923776 dataset_size: 4100353733 config_names: - a - all - b - c - d - e - f - g - h - y --- # Dataset Card for Big Patent ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [Big Patent](https://evasharma.github.io/bigpatent/) - **Repository:** - **Paper:** [BIGPATENT: A Large-Scale Dataset for Abstractive and Coherent Summarization](https://arxiv.org/abs/1906.03741) - **Leaderboard:** - **Point of Contact:** [Lu Wang](mailto:[email protected]) ### Dataset Summary BIGPATENT, consisting of 1.3 million records of U.S. patent documents along with human written abstractive summaries. Each US patent application is filed under a Cooperative Patent Classification (CPC) code. There are nine such classification categories: - a: Human Necessities - b: Performing Operations; Transporting - c: Chemistry; Metallurgy - d: Textiles; Paper - e: Fixed Constructions - f: Mechanical Engineering; Lightning; Heating; Weapons; Blasting - g: Physics - h: Electricity - y: General tagging of new or cross-sectional technology Current defaults are 2.1.2 version (fix update to cased raw strings) and 'all' CPC codes: ```python from datasets import load_dataset ds = load_dataset("big_patent") # default is 'all' CPC codes ds = load_dataset("big_patent", "all") # the same as above ds = load_dataset("big_patent", "a") # only 'a' CPC codes ds = load_dataset("big_patent", codes=["a", "b"]) ``` To use 1.0.0 version (lower cased tokenized words), pass both parameters `codes` and `version`: ```python ds = load_dataset("big_patent", codes="all", version="1.0.0") ds = load_dataset("big_patent", codes="a", version="1.0.0") ds = load_dataset("big_patent", codes=["a", "b"], version="1.0.0") ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages English ## Dataset Structure ### Data Instances Each instance contains a pair of `description` and `abstract`. `description` is extracted from the Description section of the Patent while `abstract` is extracted from the Abstract section. ``` { 'description': 'FIELD OF THE INVENTION \n [0001] This invention relates to novel calcium phosphate-coated implantable medical devices and processes of making same. The unique calcium-phosphate coated implantable medical devices minimize...', 'abstract': 'This invention relates to novel calcium phosphate-coated implantable medical devices...' } ``` ### Data Fields - `description`: detailed description of patent. - `abstract`: Patent abastract. ### Data Splits | | train | validation | test | |:----|------------------:|-------------:|-------:| | all | 1207222 | 67068 | 67072 | | a | 174134 | 9674 | 9675 | | b | 161520 | 8973 | 8974 | | c | 101042 | 5613 | 5614 | | d | 10164 | 565 | 565 | | e | 34443 | 1914 | 1914 | | f | 85568 | 4754 | 4754 | | g | 258935 | 14385 | 14386 | | h | 257019 | 14279 | 14279 | | y | 124397 | 6911 | 6911 | ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information ```bibtex @article{DBLP:journals/corr/abs-1906-03741, author = {Eva Sharma and Chen Li and Lu Wang}, title = {{BIGPATENT:} {A} Large-Scale Dataset for Abstractive and Coherent Summarization}, journal = {CoRR}, volume = {abs/1906.03741}, year = {2019}, url = {http://arxiv.org/abs/1906.03741}, eprinttype = {arXiv}, eprint = {1906.03741}, timestamp = {Wed, 26 Jun 2019 07:14:58 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-1906-03741.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ``` ### Contributions Thanks to [@mattbui](https://github.com/mattbui) for adding this dataset.

ontonotes/conll2012_ontonotesv5

ontonotes

2024-01-18T09:34:57Z

968

43

[ "task_categories:token-classification", "task_ids:named-entity-recognition", "task_ids:part-of-speech", "task_ids:coreference-resolution", "task_ids:parsing", "task_ids:lemmatization", "task_ids:word-sense-disambiguation", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:multilingual", "source_datasets:original", "language:ar", "language:en", "language:zh", "license:cc-by-nc-nd-4.0", "size_categories:10K<n<100K", "region:us", "semantic-role-labeling" ]

[ "token-classification" ]

2022-03-15T10:48:28Z

1

--- annotations_creators: - expert-generated language_creators: - found language: - ar - en - zh license: - cc-by-nc-nd-4.0 multilinguality: - multilingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - token-classification task_ids: - named-entity-recognition - part-of-speech - coreference-resolution - parsing - lemmatization - word-sense-disambiguation paperswithcode_id: ontonotes-5-0 pretty_name: CoNLL2012 shared task data based on OntoNotes 5.0 tags: - semantic-role-labeling dataset_info: - config_name: english_v4 features: - name: document_id dtype: string - name: sentences list: - name: part_id dtype: int32 - name: words sequence: string - name: pos_tags sequence: class_label: names: '0': XX '1': '``' '2': $ '3': '''''' '4': ',' '5': -LRB- '6': -RRB- '7': . '8': ':' '9': ADD '10': AFX '11': CC '12': CD '13': DT '14': EX '15': FW '16': HYPH '17': IN '18': JJ '19': JJR '20': JJS '21': LS '22': MD '23': NFP '24': NN '25': NNP '26': NNPS '27': NNS '28': PDT '29': POS '30': PRP '31': PRP$ '32': RB '33': RBR '34': RBS '35': RP '36': SYM '37': TO '38': UH '39': VB '40': VBD '41': VBG '42': VBN '43': VBP '44': VBZ '45': WDT '46': WP '47': WP$ '48': WRB - name: parse_tree dtype: string - name: predicate_lemmas sequence: string - name: predicate_framenet_ids sequence: string - name: word_senses sequence: float32 - name: speaker dtype: string - name: named_entities sequence: class_label: names: '0': O '1': B-PERSON '2': I-PERSON '3': B-NORP '4': I-NORP '5': B-FAC '6': I-FAC '7': B-ORG '8': I-ORG '9': B-GPE '10': I-GPE '11': B-LOC '12': I-LOC '13': B-PRODUCT '14': I-PRODUCT '15': B-DATE '16': I-DATE '17': B-TIME '18': I-TIME '19': B-PERCENT '20': I-PERCENT '21': B-MONEY '22': I-MONEY '23': B-QUANTITY '24': I-QUANTITY '25': B-ORDINAL '26': I-ORDINAL '27': B-CARDINAL '28': I-CARDINAL '29': B-EVENT '30': I-EVENT '31': B-WORK_OF_ART '32': I-WORK_OF_ART '33': B-LAW '34': I-LAW '35': B-LANGUAGE '36': I-LANGUAGE - name: srl_frames list: - name: verb dtype: string - name: frames sequence: string - name: coref_spans sequence: sequence: int32 length: 3 splits: - name: train num_bytes: 112246121 num_examples: 1940 - name: validation num_bytes: 14116925 num_examples: 222 - name: test num_bytes: 14709044 num_examples: 222 download_size: 193644139 dataset_size: 141072090 - config_name: chinese_v4 features: - name: document_id dtype: string - name: sentences list: - name: part_id dtype: int32 - name: words sequence: string - name: pos_tags sequence: class_label: names: '0': X '1': AD '2': AS '3': BA '4': CC '5': CD '6': CS '7': DEC '8': DEG '9': DER '10': DEV '11': DT '12': ETC '13': FW '14': IJ '15': INF '16': JJ '17': LB '18': LC '19': M '20': MSP '21': NN '22': NR '23': NT '24': OD '25': 'ON' '26': P '27': PN '28': PU '29': SB '30': SP '31': URL '32': VA '33': VC '34': VE '35': VV - name: parse_tree dtype: string - name: predicate_lemmas sequence: string - name: predicate_framenet_ids sequence: string - name: word_senses sequence: float32 - name: speaker dtype: string - name: named_entities sequence: class_label: names: '0': O '1': B-PERSON '2': I-PERSON '3': B-NORP '4': I-NORP '5': B-FAC '6': I-FAC '7': B-ORG '8': I-ORG '9': B-GPE '10': I-GPE '11': B-LOC '12': I-LOC '13': B-PRODUCT '14': I-PRODUCT '15': B-DATE '16': I-DATE '17': B-TIME '18': I-TIME '19': B-PERCENT '20': I-PERCENT '21': B-MONEY '22': I-MONEY '23': B-QUANTITY '24': I-QUANTITY '25': B-ORDINAL '26': I-ORDINAL '27': B-CARDINAL '28': I-CARDINAL '29': B-EVENT '30': I-EVENT '31': B-WORK_OF_ART '32': I-WORK_OF_ART '33': B-LAW '34': I-LAW '35': B-LANGUAGE '36': I-LANGUAGE - name: srl_frames list: - name: verb dtype: string - name: frames sequence: string - name: coref_spans sequence: sequence: int32 length: 3 splits: - name: train num_bytes: 77195698 num_examples: 1391 - name: validation num_bytes: 10828169 num_examples: 172 - name: test num_bytes: 9585138 num_examples: 166 download_size: 193644139 dataset_size: 97609005 - config_name: arabic_v4 features: - name: document_id dtype: string - name: sentences list: - name: part_id dtype: int32 - name: words sequence: string - name: pos_tags sequence: string - name: parse_tree dtype: string - name: predicate_lemmas sequence: string - name: predicate_framenet_ids sequence: string - name: word_senses sequence: float32 - name: speaker dtype: string - name: named_entities sequence: class_label: names: '0': O '1': B-PERSON '2': I-PERSON '3': B-NORP '4': I-NORP '5': B-FAC '6': I-FAC '7': B-ORG '8': I-ORG '9': B-GPE '10': I-GPE '11': B-LOC '12': I-LOC '13': B-PRODUCT '14': I-PRODUCT '15': B-DATE '16': I-DATE '17': B-TIME '18': I-TIME '19': B-PERCENT '20': I-PERCENT '21': B-MONEY '22': I-MONEY '23': B-QUANTITY '24': I-QUANTITY '25': B-ORDINAL '26': I-ORDINAL '27': B-CARDINAL '28': I-CARDINAL '29': B-EVENT '30': I-EVENT '31': B-WORK_OF_ART '32': I-WORK_OF_ART '33': B-LAW '34': I-LAW '35': B-LANGUAGE '36': I-LANGUAGE - name: srl_frames list: - name: verb dtype: string - name: frames sequence: string - name: coref_spans sequence: sequence: int32 length: 3 splits: - name: train num_bytes: 42017761 num_examples: 359 - name: validation num_bytes: 4859292 num_examples: 44 - name: test num_bytes: 4900664 num_examples: 44 download_size: 193644139 dataset_size: 51777717 - config_name: english_v12 features: - name: document_id dtype: string - name: sentences list: - name: part_id dtype: int32 - name: words sequence: string - name: pos_tags sequence: class_label: names: '0': XX '1': '``' '2': $ '3': '''''' '4': '*' '5': ',' '6': -LRB- '7': -RRB- '8': . '9': ':' '10': ADD '11': AFX '12': CC '13': CD '14': DT '15': EX '16': FW '17': HYPH '18': IN '19': JJ '20': JJR '21': JJS '22': LS '23': MD '24': NFP '25': NN '26': NNP '27': NNPS '28': NNS '29': PDT '30': POS '31': PRP '32': PRP$ '33': RB '34': RBR '35': RBS '36': RP '37': SYM '38': TO '39': UH '40': VB '41': VBD '42': VBG '43': VBN '44': VBP '45': VBZ '46': VERB '47': WDT '48': WP '49': WP$ '50': WRB - name: parse_tree dtype: string - name: predicate_lemmas sequence: string - name: predicate_framenet_ids sequence: string - name: word_senses sequence: float32 - name: speaker dtype: string - name: named_entities sequence: class_label: names: '0': O '1': B-PERSON '2': I-PERSON '3': B-NORP '4': I-NORP '5': B-FAC '6': I-FAC '7': B-ORG '8': I-ORG '9': B-GPE '10': I-GPE '11': B-LOC '12': I-LOC '13': B-PRODUCT '14': I-PRODUCT '15': B-DATE '16': I-DATE '17': B-TIME '18': I-TIME '19': B-PERCENT '20': I-PERCENT '21': B-MONEY '22': I-MONEY '23': B-QUANTITY '24': I-QUANTITY '25': B-ORDINAL '26': I-ORDINAL '27': B-CARDINAL '28': I-CARDINAL '29': B-EVENT '30': I-EVENT '31': B-WORK_OF_ART '32': I-WORK_OF_ART '33': B-LAW '34': I-LAW '35': B-LANGUAGE '36': I-LANGUAGE - name: srl_frames list: - name: verb dtype: string - name: frames sequence: string - name: coref_spans sequence: sequence: int32 length: 3 splits: - name: train num_bytes: 174173192 num_examples: 10539 - name: validation num_bytes: 24264804 num_examples: 1370 - name: test num_bytes: 18254144 num_examples: 1200 download_size: 193644139 dataset_size: 216692140 --- # Dataset Card for CoNLL2012 shared task data based on OntoNotes 5.0 ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [CoNLL-2012 Shared Task](https://conll.cemantix.org/2012/data.html), [Author's page](https://cemantix.org/data/ontonotes.html) - **Repository:** [Mendeley](https://data.mendeley.com/datasets/zmycy7t9h9) - **Paper:** [Towards Robust Linguistic Analysis using OntoNotes](https://aclanthology.org/W13-3516/) - **Leaderboard:** - **Point of Contact:** ### Dataset Summary OntoNotes v5.0 is the final version of OntoNotes corpus, and is a large-scale, multi-genre, multilingual corpus manually annotated with syntactic, semantic and discourse information. This dataset is the version of OntoNotes v5.0 extended and is used in the CoNLL-2012 shared task. It includes v4 train/dev and v9 test data for English/Chinese/Arabic and corrected version v12 train/dev/test data (English only). The source of data is the Mendeley Data repo [ontonotes-conll2012](https://data.mendeley.com/datasets/zmycy7t9h9), which seems to be as the same as the official data, but users should use this dataset on their own responsibility. See also summaries from paperwithcode, [OntoNotes 5.0](https://paperswithcode.com/dataset/ontonotes-5-0) and [CoNLL-2012](https://paperswithcode.com/dataset/conll-2012-1) For more detailed info of the dataset like annotation, tag set, etc., you can refer to the documents in the Mendeley repo mentioned above. ### Supported Tasks and Leaderboards - [Named Entity Recognition on Ontonotes v5 (English)](https://paperswithcode.com/sota/named-entity-recognition-ner-on-ontonotes-v5) - [Coreference Resolution on OntoNotes](https://paperswithcode.com/sota/coreference-resolution-on-ontonotes) - [Semantic Role Labeling on OntoNotes](https://paperswithcode.com/sota/semantic-role-labeling-on-ontonotes) - ... ### Languages V4 data for Arabic, Chinese, English, and V12 data for English ## Dataset Structure ### Data Instances ``` { {'document_id': 'nw/wsj/23/wsj_2311', 'sentences': [{'part_id': 0, 'words': ['CONCORDE', 'trans-Atlantic', 'flights', 'are', '$', '2, 'to', 'Paris', 'and', '$', '3, 'to', 'London', '.']}, 'pos_tags': [25, 18, 27, 43, 2, 12, 17, 25, 11, 2, 12, 17, 25, 7], 'parse_tree': '(TOP(S(NP (NNP CONCORDE) (JJ trans-Atlantic) (NNS flights) )(VP (VBP are) (NP(NP(NP ($ $) (CD 2,400) )(PP (IN to) (NP (NNP Paris) ))) (CC and) (NP(NP ($ $) (CD 3,200) )(PP (IN to) (NP (NNP London) ))))) (. .) ))', 'predicate_lemmas': [None, None, None, 'be', None, None, None, None, None, None, None, None, None, None], 'predicate_framenet_ids': [None, None, None, '01', None, None, None, None, None, None, None, None, None, None], 'word_senses': [None, None, None, None, None, None, None, None, None, None, None, None, None, None], 'speaker': None, 'named_entities': [7, 6, 0, 0, 0, 15, 0, 5, 0, 0, 15, 0, 5, 0], 'srl_frames': [{'frames': ['B-ARG1', 'I-ARG1', 'I-ARG1', 'B-V', 'B-ARG2', 'I-ARG2', 'I-ARG2', 'I-ARG2', 'I-ARG2', 'I-ARG2', 'I-ARG2', 'I-ARG2', 'I-ARG2', 'O'], 'verb': 'are'}], 'coref_spans': [], {'part_id': 0, 'words': ['In', 'a', 'Centennial', 'Journal', 'article', 'Oct.', '5', ',', 'the', 'fares', 'were', 'reversed', '.']}]} 'pos_tags': [17, 13, 25, 25, 24, 25, 12, 4, 13, 27, 40, 42, 7], 'parse_tree': '(TOP(S(PP (IN In) (NP (DT a) (NML (NNP Centennial) (NNP Journal) ) (NN article) ))(NP (NNP Oct.) (CD 5) ) (, ,) (NP (DT the) (NNS fares) )(VP (VBD were) (VP (VBN reversed) )) (. .) ))', 'predicate_lemmas': [None, None, None, None, None, None, None, None, None, None, None, 'reverse', None], 'predicate_framenet_ids': [None, None, None, None, None, None, None, None, None, None, None, '01', None], 'word_senses': [None, None, None, None, None, None, None, None, None, None, None, None, None], 'speaker': None, 'named_entities': [0, 0, 4, 22, 0, 12, 30, 0, 0, 0, 0, 0, 0], 'srl_frames': [{'frames': ['B-ARGM-LOC', 'I-ARGM-LOC', 'I-ARGM-LOC', 'I-ARGM-LOC', 'I-ARGM-LOC', 'B-ARGM-TMP', 'I-ARGM-TMP', 'O', 'B-ARG1', 'I-ARG1', 'O', 'B-V', 'O'], 'verb': 'reversed'}], 'coref_spans': [], } ``` ### Data Fields - **`document_id`** (*`str`*): This is a variation on the document filename - **`sentences`** (*`List[Dict]`*): All sentences of the same document are in a single example for the convenience of concatenating sentences. Every element in `sentences` is a *`Dict`* composed of the following data fields: - **`part_id`** (*`int`*) : Some files are divided into multiple parts numbered as 000, 001, 002, ... etc. - **`words`** (*`List[str]`*) : - **`pos_tags`** (*`List[ClassLabel]` or `List[str]`*) : This is the Penn-Treebank-style part of speech. When parse information is missing, all parts of speech except the one for which there is some sense or proposition annotation are marked with a XX tag. The verb is marked with just a VERB tag. - tag set : Note tag sets below are founded by scanning all the data, and I found it seems to be a little bit different from officially stated tag sets. See official documents in the [Mendeley repo](https://data.mendeley.com/datasets/zmycy7t9h9) - arabic : str. Because pos tag in Arabic is compounded and complex, hard to represent it by `ClassLabel` - chinese v4 : `datasets.ClassLabel(num_classes=36, names=["X", "AD", "AS", "BA", "CC", "CD", "CS", "DEC", "DEG", "DER", "DEV", "DT", "ETC", "FW", "IJ", "INF", "JJ", "LB", "LC", "M", "MSP", "NN", "NR", "NT", "OD", "ON", "P", "PN", "PU", "SB", "SP", "URL", "VA", "VC", "VE", "VV",])`, where `X` is for pos tag missing - english v4 : `datasets.ClassLabel(num_classes=49, names=["XX", "``", "$", "''", ",", "-LRB-", "-RRB-", ".", ":", "ADD", "AFX", "CC", "CD", "DT", "EX", "FW", "HYPH", "IN", "JJ", "JJR", "JJS", "LS", "MD", "NFP", "NN", "NNP", "NNPS", "NNS", "PDT", "POS", "PRP", "PRP$", "RB", "RBR", "RBS", "RP", "SYM", "TO", "UH", "VB", "VBD", "VBG", "VBN", "VBP", "VBZ", "WDT", "WP", "WP$", "WRB",])`, where `XX` is for pos tag missing, and `-LRB-`/`-RRB-` is "`(`" / "`)`". - english v12 : `datasets.ClassLabel(num_classes=51, names="english_v12": ["XX", "``", "$", "''", "*", ",", "-LRB-", "-RRB-", ".", ":", "ADD", "AFX", "CC", "CD", "DT", "EX", "FW", "HYPH", "IN", "JJ", "JJR", "JJS", "LS", "MD", "NFP", "NN", "NNP", "NNPS", "NNS", "PDT", "POS", "PRP", "PRP$", "RB", "RBR", "RBS", "RP", "SYM", "TO", "UH", "VB", "VBD", "VBG", "VBN", "VBP", "VBZ", "VERB", "WDT", "WP", "WP$", "WRB",])`, where `XX` is for pos tag missing, and `-LRB-`/`-RRB-` is "`(`" / "`)`". - **`parse_tree`** (*`Optional[str]`*) : An serialized NLTK Tree representing the parse. It includes POS tags as pre-terminal nodes. When the parse information is missing, the parse will be `None`. - **`predicate_lemmas`** (*`List[Optional[str]]`*) : The predicate lemma of the words for which we have semantic role information or word sense information. All other indices are `None`. - **`predicate_framenet_ids`** (*`List[Optional[int]]`*) : The PropBank frameset ID of the lemmas in predicate_lemmas, or `None`. - **`word_senses`** (*`List[Optional[float]]`*) : The word senses for the words in the sentence, or None. These are floats because the word sense can have values after the decimal, like 1.1. - **`speaker`** (*`Optional[str]`*) : This is the speaker or author name where available. Mostly in Broadcast Conversation and Web Log data. When it is not available, it will be `None`. - **`named_entities`** (*`List[ClassLabel]`*) : The BIO tags for named entities in the sentence. - tag set : `datasets.ClassLabel(num_classes=37, names=["O", "B-PERSON", "I-PERSON", "B-NORP", "I-NORP", "B-FAC", "I-FAC", "B-ORG", "I-ORG", "B-GPE", "I-GPE", "B-LOC", "I-LOC", "B-PRODUCT", "I-PRODUCT", "B-DATE", "I-DATE", "B-TIME", "I-TIME", "B-PERCENT", "I-PERCENT", "B-MONEY", "I-MONEY", "B-QUANTITY", "I-QUANTITY", "B-ORDINAL", "I-ORDINAL", "B-CARDINAL", "I-CARDINAL", "B-EVENT", "I-EVENT", "B-WORK_OF_ART", "I-WORK_OF_ART", "B-LAW", "I-LAW", "B-LANGUAGE", "I-LANGUAGE",])` - **`srl_frames`** (*`List[{"word":str, "frames":List[str]}]`*) : A dictionary keyed by the verb in the sentence for the given Propbank frame labels, in a BIO format. - **`coref spans`** (*`List[List[int]]`*) : The spans for entity mentions involved in coreference resolution within the sentence. Each element is a tuple composed of (cluster_id, start_index, end_index). Indices are inclusive. ### Data Splits Each dataset (arabic_v4, chinese_v4, english_v4, english_v12) has 3 splits: _train_, _validation_, and _test_ ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information ``` @inproceedings{pradhan-etal-2013-towards, title = "Towards Robust Linguistic Analysis using {O}nto{N}otes", author = {Pradhan, Sameer and Moschitti, Alessandro and Xue, Nianwen and Ng, Hwee Tou and Bj{\"o}rkelund, Anders and Uryupina, Olga and Zhang, Yuchen and Zhong, Zhi}, booktitle = "Proceedings of the Seventeenth Conference on Computational Natural Language Learning", month = aug, year = "2013", address = "Sofia, Bulgaria", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/W13-3516", pages = "143--152", } ``` ### Contributions Thanks to [@richarddwang](https://github.com/richarddwang) for adding this dataset.

eriktks/conll2002

eriktks

2024-01-18T09:33:49Z

249

7

[ "task_categories:token-classification", "task_ids:named-entity-recognition", "task_ids:part-of-speech", "annotations_creators:crowdsourced", "language_creators:found", "multilinguality:multilingual", "source_datasets:original", "language:es", "language:nl", "license:unknown", "size_categories:10K<n<100K", "region:us" ]

[ "token-classification" ]

2022-03-02T23:29:22Z

1

--- annotations_creators: - crowdsourced language_creators: - found language: - es - nl license: - unknown multilinguality: - multilingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - token-classification task_ids: - named-entity-recognition - part-of-speech paperswithcode_id: conll-2002 pretty_name: CoNLL-2002 dataset_info: - config_name: es features: - name: id dtype: string - name: tokens sequence: string - name: pos_tags sequence: class_label: names: '0': AO '1': AQ '2': CC '3': CS '4': DA '5': DE '6': DD '7': DI '8': DN '9': DP '10': DT '11': Faa '12': Fat '13': Fc '14': Fd '15': Fe '16': Fg '17': Fh '18': Fia '19': Fit '20': Fp '21': Fpa '22': Fpt '23': Fs '24': Ft '25': Fx '26': Fz '27': I '28': NC '29': NP '30': P0 '31': PD '32': PI '33': PN '34': PP '35': PR '36': PT '37': PX '38': RG '39': RN '40': SP '41': VAI '42': VAM '43': VAN '44': VAP '45': VAS '46': VMG '47': VMI '48': VMM '49': VMN '50': VMP '51': VMS '52': VSG '53': VSI '54': VSM '55': VSN '56': VSP '57': VSS '58': Y '59': Z - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC '7': B-MISC '8': I-MISC splits: - name: train num_bytes: 6672173 num_examples: 8324 - name: validation num_bytes: 1333784 num_examples: 1916 - name: test num_bytes: 1294156 num_examples: 1518 download_size: 4140690 dataset_size: 9300113 - config_name: nl features: - name: id dtype: string - name: tokens sequence: string - name: pos_tags sequence: class_label: names: '0': Adj '1': Adv '2': Art '3': Conj '4': Int '5': Misc '6': N '7': Num '8': Prep '9': Pron '10': Punc '11': V - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC '7': B-MISC '8': I-MISC splits: - name: train num_bytes: 5308959 num_examples: 15807 - name: validation num_bytes: 994298 num_examples: 2896 - name: test num_bytes: 1808862 num_examples: 5196 download_size: 3642241 dataset_size: 8112119 config_names: - es - nl --- # Dataset Card for CoNLL-2002 ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [homepage](https://www.clips.uantwerpen.be/conll2002/ner/) - **Repository:** [github](https://github.com/teropa/nlp/tree/master/resources/corpora/conll2002) - **Paper:** [paper](https://www.aclweb.org/anthology/W02-2024/) - **Point of Contact:** [Erik Tjong Kim Sang]([email protected]) ### Dataset Summary Named entities are phrases that contain the names of persons, organizations, locations, times and quantities. Example: [PER Wolff] , currently a journalist in [LOC Argentina] , played with [PER Del Bosque] in the final years of the seventies in [ORG Real Madrid] . The shared task of CoNLL-2002 concerns language-independent named entity recognition. We will concentrate on four types of named entities: persons, locations, organizations and names of miscellaneous entities that do not belong to the previous three groups. The participants of the shared task will be offered training and test data for at least two languages. They will use the data for developing a named-entity recognition system that includes a machine learning component. Information sources other than the training data may be used in this shared task. We are especially interested in methods that can use additional unannotated data for improving their performance (for example co-training). ### Supported Tasks and Leaderboards Named Entity Recognition (NER) is a subtask of Information Extraction. Different NER systems were evaluated as a part of the Sixth Message Understanding Conference in 1995 (MUC6). The target language was English. The participating systems performed well. However, many of them used language-specific resources for performing the task and it is unknown how they would have performed on another language than English. After 1995 NER systems have been developed for some European languages and a few Asian languages. There have been at least two studies that have applied one NER system to different languages. Palmer and Day [PD97] have used statistical methods for finding named entities in newswire articles in Chinese, English, French, Japanese, Portuguese and Spanish. They found that the difficulty of the NER task was different for the six languages but that a large part of the task could be performed with simple methods. Cucerzan and Yarowsky [CY99] used both morphological and contextual clues for identifying named entities in English, Greek, Hindi, Rumanian and Turkish. With minimal supervision, they obtained overall F measures between 40 and 70, depending on the languages used. - `named-entity-recognition`: The performance in this task is measured with [F1](https://huggingface.co/metrics/f1) (higher is better). A named entity is correct only if it is an exact match of the corresponding entity in the data. - `parsing`: The performance in this task is measured with [F1](https://huggingface.co/metrics/f1) (higher is better). A part-of-speech tag is correct only if it is equal to the corresponding tag in the data. ### Languages There are two languages available : Spanish (es) and Dutch (nl). ## Dataset Structure ### Data Instances The examples look like this : ``` {'id': '0', 'ner_tags': [5, 6, 0, 0, 0, 0, 3, 0, 0], 'pos_tags': [4, 28, 13, 59, 28, 21, 29, 22, 20], 'tokens': ['La', 'Coruña', ',', '23', 'may', '(', 'EFECOM', ')', '.'] } ``` The original data files within the Dutch sub-dataset have `-DOCSTART-` lines used to separate documents, but these lines are removed here. Indeed `-DOCSTART-` is a special line that acts as a boundary between two different documents, and it is filtered out in this implementation. ### Data Fields - `id`: id of the sample - `tokens`: the tokens of the example text - `ner_tags`: the NER tags of each token - `pos_tags`: the POS tags of each token The POS tags correspond to this list for Spanish: ``` 'AO', 'AQ', 'CC', 'CS', 'DA', 'DE', 'DD', 'DI', 'DN', 'DP', 'DT', 'Faa', 'Fat', 'Fc', 'Fd', 'Fe', 'Fg', 'Fh', 'Fia', 'Fit', 'Fp', 'Fpa', 'Fpt', 'Fs', 'Ft', 'Fx', 'Fz', 'I', 'NC', 'NP', 'P0', 'PD', 'PI', 'PN', 'PP', 'PR', 'PT', 'PX', 'RG', 'RN', 'SP', 'VAI', 'VAM', 'VAN', 'VAP', 'VAS', 'VMG', 'VMI', 'VMM', 'VMN', 'VMP', 'VMS', 'VSG', 'VSI', 'VSM', 'VSN', 'VSP', 'VSS', 'Y', 'Z' ``` And this list for Dutch: ``` 'Adj', 'Adv', 'Art', 'Conj', 'Int', 'Misc', 'N', 'Num', 'Prep', 'Pron', 'Punc', 'V' ``` The NER tags correspond to this list: ``` "O", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC", "B-MISC", "I-MISC", ``` The NER tags have the same format as in the chunking task: a B denotes the first item of a phrase and an I any non-initial word. There are four types of phrases: person names (PER), organizations (ORG), locations (LOC) and miscellaneous names (MISC). It is assumed that named entities are non-recursive and non-overlapping. In case a named entity is embedded in another named entity usually, only the top level entity is marked. ### Data Splits For both configurations (Spanish and Dutch), there are three splits. The original splits were named `train`, `testa` and `testb` and they correspond to the `train`, `validation` and `test` splits. The splits have the following sizes : | | train | validation | test | | ----- |-------:|------------:|------:| | N. Examples (Spanish) | 8324 | 1916 | 1518 | | N. Examples (Dutch) | 15807 | 2896 | 5196 | ## Dataset Creation ### Curation Rationale The dataset was introduced to introduce new resources to two languages that were under-served for statistical machine learning at the time, Dutch and Spanish. [More Information Needed] ### Source Data The Spanish data is a collection of news wire articles made available by the Spanish EFE News Agency. The articles are from May 2000. The Dutch data consist of four editions of the Belgian newspaper "De Morgen" of 2000 (June 2, July 1, August 1 and September 1). #### Initial Data Collection and Normalization The articles were word-tokenized, information on the exact pre-processing pipeline is unavailable. #### Who are the source language producers? The source language was produced by journalists and writers employed by the news agency and newspaper mentioned above. ### Annotations #### Annotation process For the Dutch data, the annotator has followed the MITRE and SAIC guidelines for named entity recognition (Chinchor et al., 1999) as well as possible. #### Who are the annotators? The Spanish data annotation was carried out by the TALP Research Center of the Technical University of Catalonia (UPC) and the Center of Language and Computation (CLiC) of the University of Barcelona (UB). The Dutch data was annotated as a part of the Atranos project at the University of Antwerp. ### Personal and Sensitive Information The data is sourced from newspaper source and only contains mentions of public figures or individuals ## Considerations for Using the Data ### Social Impact of Dataset Named Entity Recognition systems can be used to efficiently index news text, allowing to easily gather all information pertaining to an organization or individual. Making such resources widely available in languages other than English can support better research and user experience for a larger part of the world's population. At the same time, better indexing and discoverability can also enable surveillance by state actors. ### Discussion of Biases News text reproduces the biases of society, and any system trained on news data should be cognizant of these limitations and the risk for models to learn spurious correlations in this context, for example between a person's gender and their occupation. ### Other Known Limitations Users should keep in mind that the dataset only contains news text, which might limit the applicability of the developed systems to other domains. ## Additional Information ### Dataset Curators The annotation of the Spanish data was funded by the European Commission through the NAMIC project (IST-1999-12392). ### Licensing Information The licensing status of the data, especially the news source text, is unknown. ### Citation Information Provide the [BibTex](http://www.bibtex.org/)-formatted reference for the dataset. For example: ``` @inproceedings{tjong-kim-sang-2002-introduction, title = "Introduction to the {C}o{NLL}-2002 Shared Task: Language-Independent Named Entity Recognition", author = "Tjong Kim Sang, Erik F.", booktitle = "{COLING}-02: The 6th Conference on Natural Language Learning 2002 ({C}o{NLL}-2002)", year = "2002", url = "https://www.aclweb.org/anthology/W02-2024", } ``` ### Contributions Thanks to [@lhoestq](https://github.com/lhoestq) for adding this dataset.

zefang-liu/phishing-email-dataset

zefang-liu

2024-01-17T23:48:20Z

476

11

[ "task_categories:text-classification", "language:en", "license:lgpl-3.0", "size_categories:10K<n<100K", "format:csv", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[ "text-classification" ]

2024-01-17T23:36:31Z

2

--- license: lgpl-3.0 language: - en task_categories: - text-classification size_categories: - 10K<n<100K --- # Phishing Email Dataset This dataset on Hugging Face is a direct copy of the 'Phishing Email Detection' dataset from Kaggle, shared under the [GNU Lesser General Public License 3.0](https://www.gnu.org/licenses/lgpl-3.0.html). The dataset was originally created by the user '[Cyber Cop](https://www.kaggle.com/subhajournal)' on Kaggle. For complete details, including licensing and usage information, please visit the [original Kaggle page](https://www.kaggle.com/datasets/subhajournal/phishingemails).

kchawla123/casino

kchawla123

2024-01-16T13:53:39Z

133

5

[ "task_categories:text-generation", "task_categories:fill-mask", "task_ids:dialogue-modeling", "annotations_creators:expert-generated", "language_creators:crowdsourced", "multilinguality:monolingual", "source_datasets:original", "language:en", "license:cc-by-4.0", "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[ "conversational", "text-generation", "fill-mask" ]

2022-03-02T23:29:22Z

1

--- annotations_creators: - expert-generated language_creators: - crowdsourced language: - en license: - cc-by-4.0 multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - conversational - text-generation - fill-mask task_ids: - dialogue-modeling paperswithcode_id: casino pretty_name: Campsite Negotiation Dialogues dataset_info: features: - name: chat_logs list: - name: text dtype: string - name: task_data struct: - name: data dtype: string - name: issue2youget struct: - name: Firewood dtype: string - name: Water dtype: string - name: Food dtype: string - name: issue2theyget struct: - name: Firewood dtype: string - name: Water dtype: string - name: Food dtype: string - name: id dtype: string - name: participant_info struct: - name: mturk_agent_1 struct: - name: value2issue struct: - name: Low dtype: string - name: Medium dtype: string - name: High dtype: string - name: value2reason struct: - name: Low dtype: string - name: Medium dtype: string - name: High dtype: string - name: outcomes struct: - name: points_scored dtype: int32 - name: satisfaction dtype: string - name: opponent_likeness dtype: string - name: demographics struct: - name: age dtype: int32 - name: gender dtype: string - name: ethnicity dtype: string - name: education dtype: string - name: personality struct: - name: svo dtype: string - name: big-five struct: - name: extraversion dtype: float32 - name: agreeableness dtype: float32 - name: conscientiousness dtype: float32 - name: emotional-stability dtype: float32 - name: openness-to-experiences dtype: float32 - name: mturk_agent_2 struct: - name: value2issue struct: - name: Low dtype: string - name: Medium dtype: string - name: High dtype: string - name: value2reason struct: - name: Low dtype: string - name: Medium dtype: string - name: High dtype: string - name: outcomes struct: - name: points_scored dtype: int32 - name: satisfaction dtype: string - name: opponent_likeness dtype: string - name: demographics struct: - name: age dtype: int32 - name: gender dtype: string - name: ethnicity dtype: string - name: education dtype: string - name: personality struct: - name: svo dtype: string - name: big-five struct: - name: extraversion dtype: float32 - name: agreeableness dtype: float32 - name: conscientiousness dtype: float32 - name: emotional-stability dtype: float32 - name: openness-to-experiences dtype: float32 - name: annotations list: list: string splits: - name: train num_bytes: 3211407 num_examples: 1030 download_size: 1247368 dataset_size: 3211407 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for Casino ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Repository:** [Github: Kushal Chawla CaSiNo](https://github.com/kushalchawla/CaSiNo) - **Paper:** [CaSiNo: A Corpus of Campsite Negotiation Dialogues for Automatic Negotiation Systems](https://aclanthology.org/2021.naacl-main.254.pdf) - **Point of Contact:** [Kushal Chawla]([email protected]) ### Dataset Summary We provide a novel dataset (referred to as CaSiNo) of 1030 negotiation dialogues. Two participants take the role of campsite neighbors and negotiate for Food, Water, and Firewood packages, based on their individual preferences and requirements. This design keeps the task tractable, while still facilitating linguistically rich and personal conversations. This helps to overcome the limitations of prior negotiation datasets such as Deal or No Deal and Craigslist Bargain. Each dialogue consists of rich meta-data including participant demographics, personality, and their subjective evaluation of the negotiation in terms of satisfaction and opponent likeness. ### Supported Tasks and Leaderboards Train end-to-end models for negotiation ### Languages English ## Dataset Structure ### Data Instances ``` { "chat_logs": [ { "text": "Hello! \ud83d\ude42 Let's work together on a deal for these packages, shall we? What are you most interested in?", "task_data": {}, "id": "mturk_agent_1" }, ... ], "participant_info": { "mturk_agent_1": { "value2issue": ... "value2reason": ... "outcomes": ... "demographics": ... "personality": ... }, "mturk_agent_2": ... }, "annotations": [ ["Hello! \ud83d\ude42 Let's work together on a deal for these packages, shall we? What are you most interested in?", "promote-coordination,elicit-pref"], ... ] } ``` ### Data Fields - `chat_logs`: The negotiation dialogue between two participants - `text`: The dialogue utterance - `task_data`: Meta-data associated with the utterance such as the deal submitted by a participant - `id`: The ID of the participant who typed this utterance - `participant_info`: Meta-data about the two participants in this conversation - `mturk_agent_1`: For the first participant (Note that 'first' is just for reference. There is no order between the participants and any participant can start the conversation) - `value2issue`: The priority order of this participant among Food, Water, Firewood - `value2reason`: The personal arguments given by the participants themselves, consistent with the above preference order. This preference order and these arguments were submitted before the negotiation began. - `outcomes`: The negotiation outcomes for this participant including objective and subjective assessment. - `demographics`: Demographic attributes of the participant in terms of age, gender, ethnicity, and education. - `personality`: Personality attributes for this participant, in terms of Big-5 and Social Value Orientation - `mturk_agent_2`: For the second participant; follows the same structure as above - `annotations`: Strategy annotations for each utterance in the dialogue, wherever available. The first element represents the utterance and the second represents a comma-separated list of all strategies present in that utterance. ### Data Splits No default data split has been provided. Hence, all 1030 data points are under the 'train' split. | | Train | | ----- | ----- | | total dialogues | 1030 | | annotated dialogues | 396 | ## Dataset Creation ### Curation Rationale The dataset was collected to address the limitations in prior negotiation datasets from the perspective of downstream applications in pedagogy and conversational AI. Please refer to the original paper published at NAACL 2021 for details about the rationale and data curation steps ([source paper](https://aclanthology.org/2021.naacl-main.254.pdf)). ### Source Data #### Initial Data Collection and Normalization The dialogues were crowdsourced on Amazon Mechanical Turk. The strategy annotations were performed by expert annotators (first three authors of the paper). Please refer to the original dataset paper published at NAACL 2021 for more details ([source paper](https://aclanthology.org/2021.naacl-main.254.pdf)). #### Who are the source language producers? The primary producers are Turkers on Amazon Mechanical Turk platform. Two turkers were randomly paired with each other to engage in a negotiation via a chat interface. Please refer to the original dataset paper published at NAACL 2021 for more details ([source paper](https://aclanthology.org/2021.naacl-main.254.pdf)). ### Annotations #### Annotation process From the [source paper](https://aclanthology.org/2021.naacl-main.254.pdf) for this dataset: >Three expert annotators independently annotated 396 dialogues containing 4615 utterances. The annotation guidelines were iterated over a subset of 5 dialogues, while the reliability scores were computed on a different subset of 10 dialogues. We use the nominal form of Krippendorff’s alpha (Krippendorff, 2018) to measure the inter-annotator agreement. We provide the annotation statistics in Table 2. Although we release all the annotations, we skip Coordination and Empathy for our analysis in this work, due to higher subjectivity resulting in relatively lower reliability scores. #### Who are the annotators? Three expert annotators (first three authors of the paper). ### Personal and Sensitive Information All personally identifiable information about the participants such as MTurk Ids or HIT Ids was removed before releasing the data. ## Considerations for Using the Data ### Social Impact of Dataset Please refer to Section 8.2 in the [source paper](https://aclanthology.org/2021.naacl-main.254.pdf). ### Discussion of Biases Please refer to Section 8.2 in the [source paper](https://aclanthology.org/2021.naacl-main.254.pdf). ### Other Known Limitations Please refer to Section 7 in the [source paper](https://aclanthology.org/2021.naacl-main.254.pdf). ## Additional Information ### Dataset Curators Corresponding Author: Kushal Chawla (`[email protected]`)\ Affiliation: University of Southern California\ Please refer to the [source paper](https://aclanthology.org/2021.naacl-main.254.pdf) for the complete author list. ### Licensing Information The project is licensed under CC-by-4.0 ### Citation Information ``` @inproceedings{chawla2021casino, title={CaSiNo: A Corpus of Campsite Negotiation Dialogues for Automatic Negotiation Systems}, author={Chawla, Kushal and Ramirez, Jaysa and Clever, Rene and Lucas, Gale and May, Jonathan and Gratch, Jonathan}, booktitle={Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies}, pages={3167--3185}, year={2021} } ``` ### Contributions Thanks to [Kushal Chawla](https://kushalchawla.github.io/) for adding this dataset.

dmitva/human_ai_generated_text

dmitva

2024-01-16T06:25:14Z

462

12

[ "language:en", "license:cc-by-4.0", "size_categories:1M<n<10M", "format:csv", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "doi:10.57967/hf/1617", "region:us", "nlp", "human", "ai", "text" ]

[]

2023-12-31T16:56:40Z

3

--- license: cc-by-4.0 language: - en tags: - nlp - human - ai - text --- ### Human or AI-Generated Text The data can be valuable for educators, policymakers, and researchers interested in the evolving education landscape, particularly in detecting or identifying texts written by Humans or Artificial Intelligence systems. #### File Name `model_training_dataset.csv` #### File Structure - `id`: Unique identifier for each record. - `human_text`: Human-written content. - `ai_text`: AI-generated texts. - `instructions`: Description of the task given to both Humans and AI. #### Acknowledgement Thanks to [0xnu](https://finbarrs.eu/) for sharing the file after contacting him and requesting it. #### Citation To reference this dataset in academic work, please use the following citation: ```bibtex @article{abiodunfinbarrsoketunji-agtd2023, title={Evaluating the Efficacy of Hybrid Deep Learning Models in Distinguishing AI-Generated Text}, author={Abiodun Finbarrs Oketunji}, journal={arXiv:2311.15565v2}, year={2023} } ```

jondurbin/gutenberg-dpo-v0.1

jondurbin

2024-01-12T13:05:37Z

619

140

[ "language:en", "license:cc-by-4.0", "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "dpo" ]

[]

2024-01-11T13:15:41Z

null

--- license: cc-by-4.0 language: - en tags: - dpo pretty_name: Gutenberg DPO size_categories: - n<1K --- # Gutenberg DPO ![gutenberg](gutenberg.png) ## Overview This is a dataset meant to enhance novel writing capabilities of LLMs, by using public domain books from [Project Gutenberg](https://gutenberg.org/) ## Process First, the each book is parsed, split into chapters, cleaned up from the original format (remove superfluous newlines, illustration tags, etc.). Once we have chapters, an LLM is prompted with each chapter to create a synthetic prompt that would result in that chapter being written. Each chapter has a summary created as well, so that the prompts for each chapter after the also include a summary of the previous chapter to provide additional context. We then use the synthetic prompt with previous chapter summary to write the chapter with an LLM (llama-2-13b-chat, bagel-7b-v0.1, dolphin-2.2-34b). The human written text, that is, the original chapter, is used as the "chosen" value, and the LLM written chapter is used as the rejected value. ## Books used These books were chosen main because they appeared in the popular section on project gutenberg, and they function correctly with the chapterize library. - Huckleberry Finn - Treasure Island - Anna Karenina - Uncle Tom’s Cabin - Wuthering Heights - Madame Bovary - The Turn of the Screw - The War of the Worlds - A Study in Scarlet - Middlemarch - Pride and Prejudice - The Brothers Karamazov - Through the Looking Glass - Moby Dick - Frankenstein - A Tale of Two Cities

jondurbin/truthy-dpo-v0.1

jondurbin

2024-01-11T10:19:14Z

293

134

[ "license:cc-by-4.0", "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2023-12-11T15:34:04Z

null

--- license: cc-by-4.0 --- ## Truthy DPO This is a dataset designed to enhance the overall truthfulness of LLMs, without sacrificing immersion when roleplaying as a human. For example, in normal AI assistant model, the model should not try to describe what the warmth of the sun feels like, but if the system prompt indicates it's a human, it should. Mostly targets corporeal, spacial, temporal awareness, and common misconceptions. ### Contribute If you're interested in new functionality/datasets, take a look at [bagel repo](https://github.com/jondurbin/bagel) and [airoboros](https://github.com/jondurbin/airoboros) and either make a PR or open an issue with details. To help me with the fine-tuning costs, dataset generation, etc., please use one of the following: - https://bmc.link/jondurbin - ETH 0xce914eAFC2fe52FdceE59565Dd92c06f776fcb11 - BTC bc1qdwuth4vlg8x37ggntlxu5cjfwgmdy5zaa7pswf

defunct-datasets/eli5

defunct-datasets

2024-01-11T09:32:33Z

611

50

[ "task_categories:text2text-generation", "task_ids:abstractive-qa", "task_ids:open-domain-abstractive-qa", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:monolingual", "source_datasets:original", "language:en", "license:unknown", "size_categories:100K<n<1M", "arxiv:1907.09190", "arxiv:1904.04047", "region:us" ]

[ "text2text-generation" ]

2022-03-02T23:29:22Z

1

--- annotations_creators: - no-annotation language_creators: - found language: - en license: - unknown multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - text2text-generation task_ids: - abstractive-qa - open-domain-abstractive-qa paperswithcode_id: eli5 pretty_name: ELI5 viewer: false dataset_info: features: - name: q_id dtype: string - name: title dtype: string - name: selftext dtype: string - name: document dtype: string - name: subreddit dtype: string - name: answers sequence: - name: a_id dtype: string - name: text dtype: string - name: score dtype: int32 - name: title_urls sequence: - name: url dtype: string - name: selftext_urls sequence: - name: url dtype: string - name: answers_urls sequence: - name: url dtype: string config_name: LFQA_reddit splits: - name: train_eli5 num_bytes: 577188173 num_examples: 272634 - name: validation_eli5 num_bytes: 21117891 num_examples: 9812 - name: test_eli5 num_bytes: 53099796 num_examples: 24512 - name: train_asks num_bytes: 286464210 num_examples: 131778 - name: validation_asks num_bytes: 9662481 num_examples: 2281 - name: test_asks num_bytes: 17713920 num_examples: 4462 - name: train_askh num_bytes: 330483260 num_examples: 98525 - name: validation_askh num_bytes: 18690845 num_examples: 4901 - name: test_askh num_bytes: 36246784 num_examples: 9764 download_size: 6326543 dataset_size: 1350667360 --- <div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400"> Defunct: Dataset "eli5" is defunct and no longer accessible due to unavailability of the source data. </div> ## ⚠️ Reddit recently [changed the terms of access](https://www.reddit.com/r/reddit/comments/12qwagm/an_update_regarding_reddits_api/) to its API, making the source data for this dataset unavailable. # Dataset Card for ELI5 ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [ELI5 homepage](https://facebookresearch.github.io/ELI5/explore.html) - **Repository:** [ELI5 repository](https://github.com/facebookresearch/ELI5) - **Paper:** [ELI5: Long Form Question Answering](https://arxiv.org/abs/1907.09190) - **Point of Contact:** [Yacine Jernite](mailto:[email protected]) ### Dataset Summary The ELI5 dataset is an English-language dataset of questions and answers gathered from three subreddits where users ask factual questions requiring paragraph-length or longer answers. The dataset was created to support the task of open-domain long form abstractive question answering, and covers questions about general topics in its [r/explainlikeimfive](https://www.reddit.com/r/explainlikeimfive/) subset, science in it [r/askscience](https://www.reddit.com/r/askscience/) subset, and History in its [r/AskHistorians](https://www.reddit.com/r/AskHistorians/) subset. ### Supported Tasks and Leaderboards - `abstractive-qa`, `open-domain-abstractive-qa`: The dataset can be used to train a model for Open Domain Long Form Question Answering. An LFQA model is presented with a non-factoid and asked to retrieve relevant information from a knowledge source (such as [Wikipedia](https://www.wikipedia.org/)), then use it to generate a multi-sentence answer. The model performance is measured by how high its [ROUGE](https://huggingface.co/metrics/rouge) score to the reference is. A [BART-based model](https://huggingface.co/yjernite/bart_eli5) with a [dense retriever](https://huggingface.co/yjernite/retribert-base-uncased) trained to draw information from [Wikipedia passages](https://huggingface.co/datasets/wiki_snippets) achieves a [ROUGE-L of 0.149](https://yjernite.github.io/lfqa.html#generation). ### Languages The text in the dataset is in English, as spoken by Reddit users on the [r/explainlikeimfive](https://www.reddit.com/r/explainlikeimfive/), [r/askscience](https://www.reddit.com/r/askscience/), and [r/AskHistorians](https://www.reddit.com/r/AskHistorians/) subreddits. The associated BCP-47 code is `en`. ## Dataset Structure ### Data Instances A typical data point comprises a question, with a `title` containing the main question and a `selftext` which sometimes elaborates on it, and a list of answers from the forum sorted by the number of upvotes they obtained. Additionally, the URLs in each of the text fields have been extracted to respective lists and replaced by generic tokens in the text. An example from the ELI5 test set looks as follows: ``` {'q_id': '8houtx', 'title': 'Why does water heated to room temperature feel colder than the air around it?', 'selftext': '', 'document': '', 'subreddit': 'explainlikeimfive', 'answers': {'a_id': ['dylcnfk', 'dylcj49'], 'text': ["Water transfers heat more efficiently than air. When something feels cold it's because heat is being transferred from your skin to whatever you're touching. Since water absorbs the heat more readily than air, it feels colder.", "Air isn't as good at transferring heat compared to something like water or steel (sit on a room temperature steel bench vs. a room temperature wooden bench, and the steel one will feel more cold).\n\nWhen you feel cold, what you're feeling is heat being transferred out of you. If there is no breeze, you feel a certain way. If there's a breeze, you will get colder faster (because the moving air is pulling the heat away from you), and if you get into water, its quite good at pulling heat from you. Get out of the water and have a breeze blow on you while you're wet, all of the water starts evaporating, pulling even more heat from you."], 'score': [5, 2]}, 'title_urls': {'url': []}, 'selftext_urls': {'url': []}, 'answers_urls': {'url': []}} ``` ### Data Fields - `q_id`: a string question identifier for each example, corresponding to its ID in the [Pushshift.io](https://files.pushshift.io/reddit/submissions/) Reddit submission dumps. - `subreddit`: One of `explainlikeimfive`, `askscience`, or `AskHistorians`, indicating which subreddit the question came from - `title`: title of the question, with URLs extracted and replaced by `URL_n` tokens - `title_urls`: list of the extracted URLs, the `n`th element of the list was replaced by `URL_n` - `selftext`: either an empty string or an elaboration of the question - `selftext_urls`: similar to `title_urls` but for `self_text` - `answers`: a list of answers, each answer has: - `a_id`: a string answer identifier for each answer, corresponding to its ID in the [Pushshift.io](https://files.pushshift.io/reddit/comments/) Reddit comments dumps. - `text`: the answer text with the URLs normalized - `score`: the number of upvotes the answer had received when the dumps were created - `answers_urls`: a list of the extracted URLs. All answers use the same list, the numbering of the normalization token continues across answer texts ### Data Splits The data is split into a training, validation and test set for each of the three subreddits. In order to avoid having duplicate questions in across sets, the `title` field of each of the questions were ranked by their tf-idf match to their nearest neighbor and the ones with the smallest value were used in the test and validation sets. The final split sizes are as follow: | | Train | Valid | Test | | ----- | ------ | ----- | ---- | | r/explainlikeimfive examples| 272634 | 9812 | 24512| | r/askscience examples | 131778 | 2281 | 4462 | | r/AskHistorians examples | 98525 | 4901 | 9764 | ## Dataset Creation ### Curation Rationale ELI5 was built to provide a testbed for machines to learn how to answer more complex questions, which requires them to find and combine information in a coherent manner. The dataset was built by gathering questions that were asked by community members of three subreddits, including [r/explainlikeimfive](https://www.reddit.com/r/explainlikeimfive/), along with the answers that were provided by other users. The [rules of the subreddit](https://www.reddit.com/r/explainlikeimfive/wiki/detailed_rules) make this data particularly well suited to training a model for abstractive question answering: the questions need to seek an objective explanation about well established facts, and the answers provided need to be understandable to a layperson without any particular knowledge domain. ### Source Data #### Initial Data Collection and Normalization The data was obtained by filtering submissions and comments from the subreddits of interest from the XML dumps of the [Reddit forum](https://www.reddit.com/) hosted on [Pushshift.io](https://files.pushshift.io/reddit/). In order to further improve the quality of the selected examples, only questions with a score of at least 2 and at least one answer with a score of at least 2 were selected for the dataset. The dataset questions and answers span a period form August 2012 to August 2019. #### Who are the source language producers? The language producers are users of the [r/explainlikeimfive](https://www.reddit.com/r/explainlikeimfive/), [r/askscience](https://www.reddit.com/r/askscience/), and [r/AskHistorians](https://www.reddit.com/r/AskHistorians/) subreddits between 2012 and 2019. No further demographic information was available from the data source. ### Annotations The dataset does not contain any additional annotations. #### Annotation process [N/A] #### Who are the annotators? [N/A] ### Personal and Sensitive Information The authors removed the speaker IDs from the [Pushshift.io](https://files.pushshift.io/reddit/) dumps but did not otherwise anonymize the data. Some of the questions and answers are about contemporary public figures or individuals who appeared in the news. ## Considerations for Using the Data ### Social Impact of Dataset The purpose of this dataset is to help develop better question answering systems. A system that succeeds at the supported task would be able to provide a coherent answer to even complex questions requiring a multi-step explanation, which is beyond the ability of even the larger existing models. The task is also thought as a test-bed for retrieval model which can show the users which source text was used in generating the answer and allow them to confirm the information provided to them. It should be noted however that the provided answers were written by Reddit users, an information which may be lost if models trained on it are deployed in down-stream applications and presented to users without context. The specific biases this may introduce are discussed in the next section. ### Discussion of Biases While Reddit hosts a number of thriving communities with high quality discussions, it is also widely known to have corners where sexism, hate, and harassment are significant issues. See for example the [recent post from Reddit founder u/spez](https://www.reddit.com/r/announcements/comments/gxas21/upcoming_changes_to_our_content_policy_our_board/) outlining some of the ways he thinks the website's historical policies have been responsible for this problem, [Adrienne Massanari's 2015 article on GamerGate](https://www.researchgate.net/publication/283848479_Gamergate_and_The_Fappening_How_Reddit's_algorithm_governance_and_culture_support_toxic_technocultures) and follow-up works, or a [2019 Wired article on misogyny on Reddit](https://www.wired.com/story/misogyny-reddit-research/). While there has been some recent work in the NLP community on *de-biasing* models (e.g. [Black is to Criminal as Caucasian is to Police: Detecting and Removing Multiclass Bias in Word Embeddings](https://arxiv.org/abs/1904.04047) for word embeddings trained specifically on Reddit data), this problem is far from solved, and the likelihood that a trained model might learn the biases present in the data remains a significant concern. We still note some encouraging signs for all of these communities: [r/explainlikeimfive](https://www.reddit.com/r/explainlikeimfive/) and [r/askscience](https://www.reddit.com/r/askscience/) have similar structures and purposes, and [r/askscience](https://www.reddit.com/r/askscience/) was found in 2015 to show medium supportiveness and very low toxicity when compared to other subreddits (see a [hackerfall post](https://hackerfall.com/story/study-and-interactive-visualization-of-toxicity-in), [thecut.com write-up](https://www.thecut.com/2015/03/interactive-chart-of-reddits-toxicity.html) and supporting [data](https://chart-studio.plotly.com/~bsbell21/210/toxicity-vs-supportiveness-by-subreddit/#data)). Meanwhile, the [r/AskHistorians rules](https://www.reddit.com/r/AskHistorians/wiki/rules) mention that the admins will not tolerate "_racism, sexism, or any other forms of bigotry_". However, further analysis of whether and to what extent these rules reduce toxicity is still needed. We also note that given the audience of the Reddit website which is more broadly used in the US and Europe, the answers will likely present a Western perspectives, which is particularly important to note when dealing with historical topics. ### Other Known Limitations The answers provided in the dataset are represent the opinion of Reddit users. While these communities strive to be helpful, they should not be considered to represent a ground truth. ## Additional Information ### Dataset Curators The dataset was initially created by Angela Fan, Ethan Perez, Yacine Jernite, Jason Weston, Michael Auli, and David Grangier, during work done at Facebook AI Research (FAIR). ### Licensing Information The licensing status of the dataset hinges on the legal status of the [Pushshift.io](https://files.pushshift.io/reddit/) data which is unclear. ### Citation Information ``` @inproceedings{eli5_lfqa, author = {Angela Fan and Yacine Jernite and Ethan Perez and David Grangier and Jason Weston and Michael Auli}, editor = {Anna Korhonen and David R. Traum and Llu{\'{\i}}s M{\`{a}}rquez}, title = {{ELI5:} Long Form Question Answering}, booktitle = {Proceedings of the 57th Conference of the Association for Computational Linguistics, {ACL} 2019, Florence, Italy, July 28- August 2, 2019, Volume 1: Long Papers}, pages = {3558--3567}, publisher = {Association for Computational Linguistics}, year = {2019}, url = {https://doi.org/10.18653/v1/p19-1346}, doi = {10.18653/v1/p19-1346} } ``` ### Contributions Thanks to [@lewtun](https://github.com/lewtun), [@lhoestq](https://github.com/lhoestq), [@mariamabarham](https://github.com/mariamabarham), [@thomwolf](https://github.com/thomwolf), [@yjernite](https://github.com/yjernite) for adding this dataset.

ParlAI/blended_skill_talk

ParlAI

2024-01-10T10:22:26Z

1,634

69

[ "task_ids:dialogue-generation", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "source_datasets:original", "language:en", "license:unknown", "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2004.08449", "region:us" ]

[ "conversational" ]

2022-03-02T23:29:22Z

1

--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - unknown multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - conversational task_ids: - dialogue-generation paperswithcode_id: blended-skill-talk pretty_name: BlendedSkillTalk dataset_info: features: - name: personas sequence: string - name: additional_context dtype: string - name: previous_utterance sequence: string - name: context dtype: string - name: free_messages sequence: string - name: guided_messages sequence: string - name: suggestions sequence: - name: convai2 dtype: string - name: empathetic_dialogues dtype: string - name: wizard_of_wikipedia dtype: string - name: guided_chosen_suggestions sequence: string - name: label_candidates sequence: sequence: string splits: - name: train num_bytes: 10830670 num_examples: 4819 - name: validation num_bytes: 43961447 num_examples: 1009 - name: test num_bytes: 44449895 num_examples: 980 download_size: 10897644 dataset_size: 99242012 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* --- # Dataset Card for "blended_skill_talk" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://parl.ai/projects/bst/](https://parl.ai/projects/bst/) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [Can You Put it All Together: Evaluating Conversational Agents' Ability to Blend Skills](https://arxiv.org/abs/2004.08449v1) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 38.11 MB - **Size of the generated dataset:** 15.08 MB - **Total amount of disk used:** 53.17 MB ### Dataset Summary A dataset of 7k conversations explicitly designed to exhibit multiple conversation modes: displaying personality, having empathy, and demonstrating knowledge. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### default - **Size of downloaded dataset files:** 38.11 MB - **Size of the generated dataset:** 15.08 MB - **Total amount of disk used:** 53.17 MB An example of 'train' looks as follows. ``` { 'personas': ['my parents don t really speak english , but i speak italian and english.', 'i have three children.'], 'additional_context': 'Backstreet Boys', 'previous_utterance': ['Oh, I am a BIG fan of the Backstreet Boys! Have you ever seen them performing live?', "No,I listen to their music a lot, mainly the unbreakable which is the Backstreet Boys' sixth studio album. "], 'context': 'wizard_of_wikipedia', 'free_messages': ['you are very knowledgeable, do you prefer nsync or bsb?', "haha kids of this days don't know them, i'm 46 and i still enjoying them, my kids only listen k-pop", "italian?haha that's strange, i only talk english and a little spanish "], 'guided_messages': ["i don't have a preference, they are both great. All 3 of my kids get annoyed when I listen to them though.", 'Sometimes I sing their songs in Italian, that really annoys them lol.', 'My parents barely speak English, so I was taught both. By the way, what is k-pop?'], 'suggestions': {'convai2': ["i don't have a preference , both are pretty . do you have any hobbies ?", "do they the backstreet boys ? that's my favorite group .", 'are your kids interested in music ?'], 'empathetic_dialogues': ['I actually just discovered Imagine Dragons. I love them!', "Hahaha that just goes to show ya, age is just a umber!'", 'That would be hard! Do you now Spanish well?'], 'wizard_of_wikipedia': ['NSYNC Also had Lance Bass and Joey Fatone, sometimes called the Fat One.', 'Yes, there are a few K-Pop songs that I have heard good big in the USA. It is the most popular in South Korea and has Western elements of pop.', 'English, beleive it or not.']}, 'guided_chosen_suggestions': ['convai2', '', ''], 'label_candidates': []} ``` ### Data Fields The data fields are the same among all splits. #### default - `personas`: a `list` of `string` features. - `additional_context`: a `string` feature. - `previous_utterance`: a `list` of `string` features. - `context`: a `string` feature. - `free_messages`: a `list` of `string` features. - `guided_messgaes`: a `list` of `string` features. - `suggestions`: a dictionary feature containing: - `convai2`: a `string` feature. - `empathetic_dialogues`: a `string` feature. - `wizard_of_wikipedia`: a `string` feature. - `guided_chosen_suggestions`: a `list` of `string` features. - `label_candidates`: a `list` of `lists` of `string` features. ### Data Splits | name |train|validation|test| |-------|----:|---------:|---:| |default| 4819| 1009| 980| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @misc{smith2020evaluating, title={Can You Put it All Together: Evaluating Conversational Agents' Ability to Blend Skills}, author={Eric Michael Smith and Mary Williamson and Kurt Shuster and Jason Weston and Y-Lan Boureau}, year={2020}, eprint={2004.08449}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ### Contributions Thanks to [@lewtun](https://github.com/lewtun), [@thomwolf](https://github.com/thomwolf), [@lhoestq](https://github.com/lhoestq), [@patrickvonplaten](https://github.com/patrickvonplaten), [@mariamabarham](https://github.com/mariamabarham) for adding this dataset.

maywell/korean_textbooks

maywell

2024-01-10T09:21:36Z

3,240

113

[ "language:ko", "license:apache-2.0", "size_categories:1M<n<10M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:2306.11644", "region:us" ]

[]

2023-12-27T23:13:45Z

null

--- language: - ko license: apache-2.0 size_categories: - 1M<n<10M pretty_name: 대규모 한국어 Synthetic 데이터 dataset_info: - config_name: claude_evol features: - name: text dtype: string splits: - name: train num_bytes: 992896186 num_examples: 239102 download_size: 380188122 dataset_size: 992896186 - config_name: code-alpaca features: - name: text dtype: string splits: - name: train num_bytes: 273836723 num_examples: 64112 download_size: 100817441 dataset_size: 273836723 - config_name: helpsteer features: - name: text dtype: string splits: - name: train num_bytes: 101753037 num_examples: 25253 download_size: 38660919 dataset_size: 101753037 - config_name: ko_wikidata features: - name: text dtype: string splits: - name: train num_bytes: 527306289 num_examples: 127614 download_size: 197029339 dataset_size: 527306289 - config_name: mmlu_abstract_algebra features: - name: text dtype: string splits: - name: train num_bytes: 369008992 num_examples: 88848 download_size: 135822870 dataset_size: 369008992 - config_name: mmlu_all features: - name: text dtype: string splits: - name: train num_bytes: 406126621 num_examples: 97765 download_size: 149486712 dataset_size: 406126621 - config_name: mmlu_anatomy features: - name: text dtype: string splits: - name: train num_bytes: 404317465 num_examples: 97463 download_size: 148806011 dataset_size: 404317465 - config_name: mmlu_astronomy features: - name: text dtype: string splits: - name: train num_bytes: 404137638 num_examples: 97347 download_size: 148705490 dataset_size: 404137638 - config_name: mmlu_business_ethics features: - name: text dtype: string splits: - name: train num_bytes: 404250245 num_examples: 97327 download_size: 148763276 dataset_size: 404250245 - config_name: mmlu_clinical_knowledge features: - name: text dtype: string splits: - name: train num_bytes: 403659005 num_examples: 97226 download_size: 148688069 dataset_size: 403659005 - config_name: mmlu_college_biology features: - name: text dtype: string splits: - name: train num_bytes: 404028634 num_examples: 97285 download_size: 148722802 dataset_size: 404028634 - config_name: mmlu_college_chemistry features: - name: text dtype: string splits: - name: train num_bytes: 404667385 num_examples: 97435 download_size: 148855223 dataset_size: 404667385 - config_name: mmlu_college_computer_science features: - name: text dtype: string splits: - name: train num_bytes: 385176880 num_examples: 92606 download_size: 141868873 dataset_size: 385176880 - config_name: mmlu_college_mathematics features: - name: text dtype: string splits: - name: train num_bytes: 390603751 num_examples: 94070 download_size: 143833823 dataset_size: 390603751 - config_name: mmlu_college_medicine features: - name: text dtype: string splits: - name: train num_bytes: 395144479 num_examples: 95156 download_size: 145271248 dataset_size: 395144479 - config_name: mmlu_college_physics features: - name: '0' dtype: string splits: - name: train num_bytes: 404906114 num_examples: 97452 download_size: 148870088 dataset_size: 404906114 - config_name: mmlu_computer_security features: - name: '0' dtype: string splits: - name: train num_bytes: 403699674 num_examples: 97212 download_size: 148755211 dataset_size: 403699674 - config_name: mmlu_conceptual_physics features: - name: '0' dtype: string splits: - name: train num_bytes: 366231421 num_examples: 88216 download_size: 134989933 dataset_size: 366231421 - config_name: mmlu_econometrics features: - name: '0' dtype: string splits: - name: train num_bytes: 380851762 num_examples: 91854 download_size: 140295665 dataset_size: 380851762 - config_name: mmlu_electrical_engineering features: - name: '0' dtype: string splits: - name: train num_bytes: 364564129 num_examples: 87826 download_size: 134376902 dataset_size: 364564129 - config_name: mmlu_elementary_mathematics features: - name: '0' dtype: string splits: - name: train num_bytes: 371101672 num_examples: 89307 download_size: 136622044 dataset_size: 371101672 - config_name: mmlu_formal_logic features: - name: '0' dtype: string splits: - name: train num_bytes: 395937096 num_examples: 95483 download_size: 145736493 dataset_size: 395937096 - config_name: mmlu_global_facts features: - name: '0' dtype: string splits: - name: train num_bytes: 394596084 num_examples: 94984 download_size: 145284966 dataset_size: 394596084 - config_name: mmlu_high_school_biology features: - name: '0' dtype: string splits: - name: train num_bytes: 402382699 num_examples: 97117 download_size: 148038235 dataset_size: 402382699 - config_name: mmlu_high_school_chemistry features: - name: '0' dtype: string splits: - name: train num_bytes: 402886667 num_examples: 96907 download_size: 148323317 dataset_size: 402886667 - config_name: mmlu_high_school_computer_science features: - name: '0' dtype: string splits: - name: train num_bytes: 403966380 num_examples: 97351 download_size: 148666121 dataset_size: 403966380 - config_name: mmlu_high_school_european_history features: - name: '0' dtype: string splits: - name: train num_bytes: 403671884 num_examples: 97222 download_size: 148454177 dataset_size: 403671884 - config_name: mmlu_high_school_geography features: - name: '0' dtype: string splits: - name: train num_bytes: 404040602 num_examples: 97261 download_size: 148657890 dataset_size: 404040602 - config_name: mmlu_high_school_government_and_politics features: - name: '0' dtype: string splits: - name: train num_bytes: 403990139 num_examples: 97311 download_size: 148568388 dataset_size: 403990139 - config_name: mmlu_high_school_macroeconomics features: - name: '0' dtype: string splits: - name: train num_bytes: 404170166 num_examples: 97400 download_size: 148591243 dataset_size: 404170166 - config_name: mmlu_high_school_mathematics features: - name: '0' dtype: string splits: - name: train num_bytes: 404846407 num_examples: 97396 download_size: 149076619 dataset_size: 404846407 - config_name: mmlu_high_school_microeconomics features: - name: '0' dtype: string splits: - name: train num_bytes: 404613760 num_examples: 97435 download_size: 148970422 dataset_size: 404613760 - config_name: mmlu_high_school_physics features: - name: '0' dtype: string splits: - name: train num_bytes: 397678253 num_examples: 95740 download_size: 146340167 dataset_size: 397678253 - config_name: mmlu_high_school_psychology features: - name: '0' dtype: string splits: - name: train num_bytes: 334767526 num_examples: 80626 download_size: 123054403 dataset_size: 334767526 - config_name: mmlu_high_school_statistics features: - name: '0' dtype: string splits: - name: train num_bytes: 315209112 num_examples: 76033 download_size: 115876698 dataset_size: 315209112 - config_name: mmlu_high_school_us_history features: - name: '0' dtype: string splits: - name: train num_bytes: 329179309 num_examples: 79322 download_size: 120972668 dataset_size: 329179309 - config_name: mmlu_high_school_world_history features: - name: '0' dtype: string splits: - name: train num_bytes: 357910528 num_examples: 85990 download_size: 131809165 dataset_size: 357910528 - config_name: mmlu_human_aging features: - name: '0' dtype: string splits: - name: train num_bytes: 325427761 num_examples: 78341 download_size: 119430234 dataset_size: 325427761 - config_name: mmlu_human_sexuality features: - name: '0' dtype: string splits: - name: train num_bytes: 328912659 num_examples: 79327 download_size: 121032722 dataset_size: 328912659 - config_name: mmlu_international_law features: - name: '0' dtype: string splits: - name: train num_bytes: 327874597 num_examples: 78989 download_size: 120785769 dataset_size: 327874597 - config_name: normal_instructions features: - name: text dtype: string splits: - name: train num_bytes: 956305865 num_examples: 240523 download_size: 362796244 dataset_size: 956305865 - config_name: tiny-textbooks features: - name: text dtype: string splits: - name: train num_bytes: 1722063576 num_examples: 395985 download_size: 635724860 dataset_size: 1722063576 configs: - config_name: claude_evol data_files: - split: train path: claude_evol/train-* - config_name: code-alpaca data_files: - split: train path: code-alpaca/train-* - config_name: helpsteer data_files: - split: train path: helpsteer/train-* - config_name: ko_wikidata data_files: - split: train path: ko_wikidata/train-* - config_name: mmlu_abstract_algebra data_files: - split: train path: mmlu_abstract_algebra/train-* - config_name: mmlu_all data_files: - split: train path: mmlu_all/train-* - config_name: mmlu_anatomy data_files: - split: train path: mmlu_anatomy/train-* - config_name: mmlu_astronomy data_files: - split: train path: mmlu_astronomy/train-* - config_name: mmlu_business_ethics data_files: - split: train path: mmlu_business_ethics/train-* - config_name: mmlu_clinical_knowledge data_files: - split: train path: mmlu_clinical_knowledge/train-* - config_name: mmlu_college_biology data_files: - split: train path: mmlu_college_biology/train-* - config_name: mmlu_college_chemistry data_files: - split: train path: mmlu_college_chemistry/train-* - config_name: mmlu_college_computer_science data_files: - split: train path: mmlu_college_computer_science/train-* - config_name: mmlu_college_mathematics data_files: - split: train path: mmlu_college_mathematics/train-* - config_name: mmlu_college_medicine data_files: - split: train path: mmlu_college_medicine/train-* - config_name: mmlu_college_physics data_files: - split: train path: mmlu_college_physics/train-* - config_name: mmlu_computer_security data_files: - split: train path: mmlu_computer_security/train-* - config_name: mmlu_conceptual_physics data_files: - split: train path: mmlu_conceptual_physics/train-* - config_name: mmlu_econometrics data_files: - split: train path: mmlu_econometrics/train-* - config_name: mmlu_electrical_engineering data_files: - split: train path: mmlu_electrical_engineering/train-* - config_name: mmlu_elementary_mathematics data_files: - split: train path: mmlu_elementary_mathematics/train-* - config_name: mmlu_formal_logic data_files: - split: train path: mmlu_formal_logic/train-* - config_name: mmlu_global_facts data_files: - split: train path: mmlu_global_facts/train-* - config_name: mmlu_high_school_biology data_files: - split: train path: mmlu_high_school_biology/train-* - config_name: mmlu_high_school_chemistry data_files: - split: train path: mmlu_high_school_chemistry/train-* - config_name: mmlu_high_school_computer_science data_files: - split: train path: mmlu_high_school_computer_science/train-* - config_name: mmlu_high_school_european_history data_files: - split: train path: mmlu_high_school_european_history/train-* - config_name: mmlu_high_school_geography data_files: - split: train path: mmlu_high_school_geography/train-* - config_name: mmlu_high_school_government_and_politics data_files: - split: train path: mmlu_high_school_government_and_politics/train-* - config_name: mmlu_high_school_macroeconomics data_files: - split: train path: mmlu_high_school_macroeconomics/train-* - config_name: mmlu_high_school_mathematics data_files: - split: train path: mmlu_high_school_mathematics/train-* - config_name: mmlu_high_school_microeconomics data_files: - split: train path: mmlu_high_school_microeconomics/train-* - config_name: mmlu_high_school_physics data_files: - split: train path: mmlu_high_school_physics/train-* - config_name: mmlu_high_school_psychology data_files: - split: train path: mmlu_high_school_psychology/train-* - config_name: mmlu_high_school_statistics data_files: - split: train path: mmlu_high_school_statistics/train-* - config_name: mmlu_high_school_us_history data_files: - split: train path: mmlu_high_school_us_history/train-* - config_name: mmlu_high_school_world_history data_files: - split: train path: mmlu_high_school_world_history/train-* - config_name: mmlu_human_aging data_files: - split: train path: mmlu_human_aging/train-* - config_name: mmlu_human_sexuality data_files: - split: train path: mmlu_human_sexuality/train-* - config_name: mmlu_international_law data_files: - split: train path: mmlu_international_law/train-* - config_name: normal_instructions data_files: - split: train path: normal_instructions/train-* - config_name: tiny-textbooks data_files: - split: train path: tiny-textbooks/train-* --- # Massive Korean synthetic dataset This dataset is a large-scale Korean artificial data set created using Gemini Pro. It was created using the methodology described in *Creation of synthetic textbook-quality datasets* in [Textbooks Are All You Need](https://arxiv.org/abs/2306.11644). ## Data overview **A subset of each dataset does not indicate the contents of that dataset.** **Further modification required before use this dataset for training.** **본 데이터셋은 바로 사용하기보다는 하고자하는 task에 맞추어 가공 후 사용을 권장드립니다. ex) 로컬 모델을 사용하여 QA 셋으로 변환** | subset | row count | link | + | |---|---|---|---| | tiny-textbooks | 395,985 | [nampdn-ai/tiny-textbooks](https://huggingface.co/datasets/nampdn-ai/tiny-textbooks) | | | ko_wikidata | 127,614 | [maywell/ko_wikidata_QA](https://huggingface.co/datasets/maywell/ko_wikidata_QA) | | | normal_instructions | 240,523 | [KonstantyM/science_qa](https://huggingface.co/datasets/KonstantyM/science_qa) | with science texts | | claude_evol | 239,102 | [Norquinal/claude_evol_instruct_210k](https://huggingface.co/datasets/Norquinal/claude_evol_instruct_210k) | used 250k files from that repo | | code-alpaca | 64,112 | [theblackcat102/evol-codealpaca-v1](https://huggingface.co/datasets/theblackcat102/evol-codealpaca-v1) | original is a coding dataset, but generated data is not mainly a coding dataset | | helpsteer | 25,253 | [nvidia/HelpSteer](https://huggingface.co/datasets/nvidia/HelpSteer) | | | mmlu_abstract_algebra | 88,848 | [cais/mmlu](https://huggingface.co/datasets/cais/mmlu) | | | mmlu_all | 97,765 | [cais/mmlu](https://huggingface.co/datasets/cais/mmlu) | | | mmlu_anatomy | 97,463 | [cais/mmlu](https://huggingface.co/datasets/cais/mmlu) | | | mmlu_astronomy | 97,347 | [cais/mmlu](https://huggingface.co/datasets/cais/mmlu) | | | mmlu_business_ethics | 97,327 | [cais/mmlu](https://huggingface.co/datasets/cais/mmlu) | | | mmlu_clinical_knowledge | 97,226 | [cais/mmlu](https://huggingface.co/datasets/cais/mmlu) | | | mmlu_college_biology | 97,285 | [cais/mmlu](https://huggingface.co/datasets/cais/mmlu) | | | mmlu_college_chemistry | 97,435 | [cais/mmlu](https://huggingface.co/datasets/cais/mmlu) | | | mmlu_college_computer_science | 92,606 | [cais/mmlu](https://huggingface.co/datasets/cais/mmlu) | | | mmlu_college_mathematics | 94,070 | [cais/mmlu](https://huggingface.co/datasets/cais/mmlu) | | | mmlu_college_medicine | 95,156 | [cais/mmlu](https://huggingface.co/datasets/cais/mmlu) | | | mmlu_college_physics | 97,452 | [cais/mmlu](https://huggingface.co/datasets/cais/mmlu) | | | mmlu_computer_security | 97,212 | [cais/mmlu](https://huggingface.co/datasets/cais/mmlu) | | | mmlu_conceptual_physics | 88,216 | [cais/mmlu](https://huggingface.co/datasets/cais/mmlu) | | | mmlu_econometrics | 91,854 | [cais/mmlu](https://huggingface.co/datasets/cais/mmlu) | | | mmlu_electrical_engineering | 87,826 | [cais/mmlu](https://huggingface.co/datasets/cais/mmlu) | | | mmlu_elementary_mathematics | 89,307 | [cais/mmlu](https://huggingface.co/datasets/cais/mmlu) | | | mmlu_formal_logic | 95,483 | [cais/mmlu](https://huggingface.co/datasets/cais/mmlu) | | | mmlu_global_facts | 94,984 | [cais/mmlu](https://huggingface.co/datasets/cais/mmlu) | | | mmlu_high_school_biology | 97,117 | [cais/mmlu](https://huggingface.co/datasets/cais/mmlu) | | | mmlu_high_school_chemistry | 96,907 | [cais/mmlu](https://huggingface.co/datasets/cais/mmlu) | | | mmlu_high_school_computer_science | 97,351 | [cais/mmlu](https://huggingface.co/datasets/cais/mmlu) | | | mmlu_high_school_european_history | 97,222 | [cais/mmlu](https://huggingface.co/datasets/cais/mmlu) | | | mmlu_high_school_geography | 97,261 | [cais/mmlu](https://huggingface.co/datasets/cais/mmlu) | | | mmlu_high_school_government_and_politics | 97,311 | [cais/mmlu](https://huggingface.co/datasets/cais/mmlu) | | | mmlu_high_school_macroeconomics | 97,400 | [cais/mmlu](https://huggingface.co/datasets/cais/mmlu) | | | mmlu_high_school_mathematics | 97,396 | [cais/mmlu](https://huggingface.co/datasets/cais/mmlu) | | | mmlu_high_school_microeconomics | 97,435 | [cais/mmlu](https://huggingface.co/datasets/cais/mmlu) | | | mmlu_high_school_physics | 95,740 | [cais/mmlu](https://huggingface.co/datasets/cais/mmlu) | | | mmlu_high_school_psychology | 80,626 | [cais/mmlu](https://huggingface.co/datasets/cais/mmlu) | | | mmlu_high_school_statistics | 76,033 | [cais/mmlu](https://huggingface.co/datasets/cais/mmlu) | | | mmlu_high_school_us_history | 79,322 | [cais/mmlu](https://huggingface.co/datasets/cais/mmlu) | | | mmlu_high_school_world_history | 85,990 | [cais/mmlu](https://huggingface.co/datasets/cais/mmlu) | | | mmlu_human_aging | 78,341 | [cais/mmlu](https://huggingface.co/datasets/cais/mmlu) | | | mmlu_human_sexuality | 79,327 | [cais/mmlu](https://huggingface.co/datasets/cais/mmlu) | | | mmlu_international_law | 78,989 | [cais/mmlu](https://huggingface.co/datasets/cais/mmlu) | | ## When you find a problem If you find any issues with the dataset, please let us know in the discussion or send us a pull request.

tuanphong/ascent_kb

tuanphong

2024-01-09T14:44:26Z

112

3

[ "task_categories:other", "annotations_creators:found", "language_creators:found", "multilinguality:monolingual", "source_datasets:original", "language:en", "license:cc-by-4.0", "size_categories:10M<n<100M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:2011.00905", "region:us", "knowledge-base" ]

[ "other" ]

2022-03-02T23:29:22Z

1

--- annotations_creators: - found language_creators: - found language: - en license: - cc-by-4.0 multilinguality: - monolingual size_categories: - 1M<n<10M source_datasets: - original task_categories: - other task_ids: [] paperswithcode_id: ascentkb pretty_name: Ascent KB tags: - knowledge-base dataset_info: - config_name: canonical features: - name: arg1 dtype: string - name: rel dtype: string - name: arg2 dtype: string - name: support dtype: int64 - name: facets list: - name: value dtype: string - name: type dtype: string - name: support dtype: int64 - name: source_sentences list: - name: text dtype: string - name: source dtype: string splits: - name: train num_bytes: 2976665740 num_examples: 8904060 download_size: 898478552 dataset_size: 2976665740 - config_name: open features: - name: subject dtype: string - name: predicate dtype: string - name: object dtype: string - name: support dtype: int64 - name: facets list: - name: value dtype: string - name: type dtype: string - name: support dtype: int64 - name: source_sentences list: - name: text dtype: string - name: source dtype: string splits: - name: train num_bytes: 2882646222 num_examples: 8904060 download_size: 900156754 dataset_size: 2882646222 configs: - config_name: canonical data_files: - split: train path: canonical/train-* default: true - config_name: open data_files: - split: train path: open/train-* --- # Dataset Card for Ascent KB ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://ascent.mpi-inf.mpg.de/ - **Repository:** https://github.com/phongnt570/ascent - **Paper:** https://arxiv.org/abs/2011.00905 - **Point of Contact:** http://tuan-phong.com ### Dataset Summary This dataset contains 8.9M commonsense assertions extracted by the Ascent pipeline developed at the [Max Planck Institute for Informatics](https://www.mpi-inf.mpg.de/departments/databases-and-information-systems/). The focus of this dataset is on everyday concepts such as *elephant*, *car*, *laptop*, etc. The current version of Ascent KB (v1.0.0) is approximately **19 times larger than ConceptNet** (note that, in this comparison, non-commonsense knowledge in ConceptNet such as lexical relations is excluded). For more details, take a look at [the research paper](https://arxiv.org/abs/2011.00905) and [the website](https://ascent.mpi-inf.mpg.de). ### Supported Tasks and Leaderboards The dataset can be used in a wide range of downstream tasks such as commonsense question answering or dialogue systems. ### Languages The dataset is in English. ## Dataset Structure ### Data Instances There are two configurations available for this dataset: 1. `canonical` (default): This part contains `<arg1 ; rel ; arg2>` assertions where the relations (`rel`) were mapped to [ConceptNet relations](https://github.com/commonsense/conceptnet5/wiki/Relations) with slight modifications: - Introducing 2 new relations: `/r/HasSubgroup`, `/r/HasAspect`. - All `/r/HasA` relations were replaced with `/r/HasAspect`. This is motivated by the [ATOMIC-2020](https://allenai.org/data/atomic-2020) schema, although they grouped all `/r/HasA` and `/r/HasProperty` into `/r/HasProperty`. - The `/r/UsedFor` relation was replaced with `/r/ObjectUse` which is broader (could be either _"used for"_, _"used in"_, or _"used as"_, ect.). This is also taken from ATOMIC-2020. 2. `open`: This part contains open assertions of the form `<subject ; predicate ; object>` extracted directly from web contents. This is the original form of the `canonical` triples. In both configurations, each assertion is equipped with extra information including: a set of semantic `facets` (e.g., *LOCATION*, *TEMPORAL*, etc.), its `support` (i.e., number of occurrences), and a list of `source_sentences`. An example row in the `canonical` configuration: ```JSON { "arg1": "elephant", "rel": "/r/HasProperty", "arg2": "intelligent", "support": 15, "facets": [ { "value": "extremely", "type": "DEGREE", "support": 11 } ], "source_sentences": [ { "text": "Elephants are extremely intelligent animals.", "source": "https://www.softschools.com/facts/animals/asian_elephant_facts/2310/" }, { "text": "Elephants are extremely intelligent creatures and an elephant's brain can weigh as much as 4-6 kg.", "source": "https://www.elephantsforafrica.org/elephant-facts/" } ] } ``` ### Data Fields - **For `canonical` configuration** - `arg1`: the first argument to the relationship, e.g., *elephant* - `rel`: the canonical relation, e.g., */r/HasProperty* - `arg2`: the second argument to the relationship, e.g., *intelligence* - `support`: the number of occurrences of the assertion, e.g., *15* - `facets`: an array of semantic facets, each contains - `value`: facet value, e.g., *extremely* - `type`: facet type, e.g., *DEGREE* - `support`: the number of occurrences of the facet, e.g., *11* - `source_sentences`: an array of source sentences from which the assertion was extracted, each contains - `text`: the raw text of the sentence - `source`: the URL to its parent document - **For `open` configuration** - The fields of this configuration are the same as the `canonical` configuration's, except that the (`arg1`, `rel`, `arg2`) fields are replaced with the (`subject`, `predicate`, `object`) fields which are free text phrases extracted directly from the source sentences using an Open Information Extraction (OpenIE) tool. ### Data Splits There are no splits. All data points come to a default split called `train`. ## Dataset Creation ### Curation Rationale The commonsense knowledge base was created to assist in development of robust and reliable AI. ### Source Data #### Initial Data Collection and Normalization Texts were collected from the web using the Bing Search API, and went through various cleaning steps before being processed by an OpenIE tool to get open assertions. The assertions were then grouped into semantically equivalent clusters. Take a look at the research paper for more details. #### Who are the source language producers? Web users. ### Annotations #### Annotation process None. #### Who are the annotators? None. ### Personal and Sensitive Information Unknown. ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases [Needs More Information] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators The knowledge base has been developed by researchers at the [Max Planck Institute for Informatics](https://www.mpi-inf.mpg.de/departments/databases-and-information-systems/). Contact [Tuan-Phong Nguyen](http://tuan-phong.com) in case of questions and comments. ### Licensing Information [The Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/) ### Citation Information ``` @InProceedings{nguyen2021www, title={Advanced Semantics for Commonsense Knowledge Extraction}, author={Nguyen, Tuan-Phong and Razniewski, Simon and Weikum, Gerhard}, year={2021}, booktitle={The Web Conference 2021}, } ``` ### Contributions Thanks to [@phongnt570](https://github.com/phongnt570) for adding this dataset.

pauli31/czech-subjectivity-dataset

pauli31

2024-01-05T20:05:40Z

48

3

[ "task_categories:text-classification", "task_ids:sentiment-classification", "multilinguality:monolingual", "source_datasets:original", "language:cs", "license:cc-by-nc-sa-4.0", "size_categories:1K<n<10K", "format:csv", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2204.13915", "region:us" ]

[ "text-classification" ]

2022-05-02T18:27:17Z

1

--- annotations_creators: [] language_creators: [] language: - cs license: - cc-by-nc-sa-4.0 multilinguality: - monolingual pretty_name: Czech Subjectivity Dataset size_categories: - 1K<n<10K source_datasets: - original task_categories: - text-classification task_ids: - sentiment-classification --- # Dataset Card for Czech Subjectivity Dataset ### Dataset Summary Czech subjectivity dataset (Subj-CS) of 10k manually annotated subjective and objective sentences from movie reviews and descriptions. See the paper description https://arxiv.org/abs/2204.13915 ### Github https://github.com/pauli31/czech-subjectivity-dataset ### Supported Tasks and Leaderboards Subjectivity Analysis ### Languages Czech ### Data Instances train/dev/test ### Licensing Information [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.](https://creativecommons.org/licenses/by-nc-sa/4.0/) ### Citation Information If you use our dataset or software for academic research, please cite the our [paper](https://arxiv.org/abs/2204.13915) ``` @article{pib2022czech, title={Czech Dataset for Cross-lingual Subjectivity Classification}, author={Pavel Přibáň and Josef Steinberger}, year={2022}, eprint={2204.13915}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ### Contact [email protected] ### Contributions Thanks to [@pauli31](https://github.com/pauli31) for adding this dataset.

google/xquad

google

2024-01-04T17:08:50Z

1,431

31

[ "task_categories:question-answering", "task_ids:extractive-qa", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:multilingual", "source_datasets:extended|squad", "language:ar", "language:de", "language:el", "language:en", "language:es", "language:hi", "language:ro", "language:ru", "language:th", "language:tr", "language:vi", "language:zh", "license:cc-by-sa-4.0", "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:1910.11856", "region:us" ]

[ "question-answering" ]

2022-03-02T23:29:22Z

1

--- annotations_creators: - expert-generated language_creators: - expert-generated language: - ar - de - el - en - es - hi - ro - ru - th - tr - vi - zh license: - cc-by-sa-4.0 multilinguality: - multilingual size_categories: - unknown source_datasets: - extended|squad task_categories: - question-answering task_ids: - extractive-qa paperswithcode_id: xquad pretty_name: XQuAD dataset_info: - config_name: xquad.ar features: - name: id dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 splits: - name: validation num_bytes: 1722775 num_examples: 1190 download_size: 263002 dataset_size: 1722775 - config_name: xquad.de features: - name: id dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 splits: - name: validation num_bytes: 1283277 num_examples: 1190 download_size: 241957 dataset_size: 1283277 - config_name: xquad.el features: - name: id dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 splits: - name: validation num_bytes: 2206666 num_examples: 1190 download_size: 324379 dataset_size: 2206666 - config_name: xquad.en features: - name: id dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 splits: - name: validation num_bytes: 1116099 num_examples: 1190 download_size: 212372 dataset_size: 1116099 - config_name: xquad.es features: - name: id dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 splits: - name: validation num_bytes: 1273475 num_examples: 1190 download_size: 236874 dataset_size: 1273475 - config_name: xquad.hi features: - name: id dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 splits: - name: validation num_bytes: 2682951 num_examples: 1190 download_size: 322083 dataset_size: 2682951 - config_name: xquad.ro features: - name: id dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 splits: - name: validation num_bytes: 1299426 num_examples: 1190 download_size: 244467 dataset_size: 1299426 - config_name: xquad.ru features: - name: id dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 splits: - name: validation num_bytes: 2136966 num_examples: 1190 download_size: 321728 dataset_size: 2136966 - config_name: xquad.th features: - name: id dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 splits: - name: validation num_bytes: 2854935 num_examples: 1190 download_size: 337307 dataset_size: 2854935 - config_name: xquad.tr features: - name: id dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 splits: - name: validation num_bytes: 1210739 num_examples: 1190 download_size: 228364 dataset_size: 1210739 - config_name: xquad.vi features: - name: id dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 splits: - name: validation num_bytes: 1477215 num_examples: 1190 download_size: 237644 dataset_size: 1477215 - config_name: xquad.zh features: - name: id dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 splits: - name: validation num_bytes: 984217 num_examples: 1190 download_size: 205768 dataset_size: 984217 configs: - config_name: xquad.ar data_files: - split: validation path: xquad.ar/validation-* - config_name: xquad.de data_files: - split: validation path: xquad.de/validation-* - config_name: xquad.el data_files: - split: validation path: xquad.el/validation-* - config_name: xquad.en data_files: - split: validation path: xquad.en/validation-* - config_name: xquad.es data_files: - split: validation path: xquad.es/validation-* - config_name: xquad.hi data_files: - split: validation path: xquad.hi/validation-* - config_name: xquad.ro data_files: - split: validation path: xquad.ro/validation-* - config_name: xquad.ru data_files: - split: validation path: xquad.ru/validation-* - config_name: xquad.th data_files: - split: validation path: xquad.th/validation-* - config_name: xquad.tr data_files: - split: validation path: xquad.tr/validation-* - config_name: xquad.vi data_files: - split: validation path: xquad.vi/validation-* - config_name: xquad.zh data_files: - split: validation path: xquad.zh/validation-* --- # Dataset Card for "xquad" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://github.com/deepmind/xquad](https://github.com/deepmind/xquad) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 146.31 MB - **Size of the generated dataset:** 18.97 MB - **Total amount of disk used:** 165.28 MB ### Dataset Summary XQuAD (Cross-lingual Question Answering Dataset) is a benchmark dataset for evaluating cross-lingual question answering performance. The dataset consists of a subset of 240 paragraphs and 1190 question-answer pairs from the development set of SQuAD v1.1 (Rajpurkar et al., 2016) together with their professional translations into ten languages: Spanish, German, Greek, Russian, Turkish, Arabic, Vietnamese, Thai, Chinese, and Hindi. Consequently, the dataset is entirely parallel across 11 languages. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### xquad.ar - **Size of downloaded dataset files:** 13.30 MB - **Size of the generated dataset:** 1.72 MB - **Total amount of disk used:** 15.03 MB An example of 'validation' looks as follows. ``` This example was too long and was cropped: { "answers": { "answer_start": [527], "text": ["136"] }, "context": "\"Die Verteidigung der Panthers gab nur 308 Punkte ab und belegte den sechsten Platz in der Liga, während sie die NFL mit 24 Inte...", "id": "56beb4343aeaaa14008c925c", "question": "Wie viele Sacks erzielte Jared Allen in seiner Karriere?" } ``` #### xquad.de - **Size of downloaded dataset files:** 13.30 MB - **Size of the generated dataset:** 1.29 MB - **Total amount of disk used:** 14.59 MB An example of 'validation' looks as follows. ``` This example was too long and was cropped: { "answers": { "answer_start": [527], "text": ["136"] }, "context": "\"Die Verteidigung der Panthers gab nur 308 Punkte ab und belegte den sechsten Platz in der Liga, während sie die NFL mit 24 Inte...", "id": "56beb4343aeaaa14008c925c", "question": "Wie viele Sacks erzielte Jared Allen in seiner Karriere?" } ``` #### xquad.el - **Size of downloaded dataset files:** 13.30 MB - **Size of the generated dataset:** 2.21 MB - **Total amount of disk used:** 15.51 MB An example of 'validation' looks as follows. ``` This example was too long and was cropped: { "answers": { "answer_start": [527], "text": ["136"] }, "context": "\"Die Verteidigung der Panthers gab nur 308 Punkte ab und belegte den sechsten Platz in der Liga, während sie die NFL mit 24 Inte...", "id": "56beb4343aeaaa14008c925c", "question": "Wie viele Sacks erzielte Jared Allen in seiner Karriere?" } ``` #### xquad.en - **Size of downloaded dataset files:** 13.30 MB - **Size of the generated dataset:** 1.12 MB - **Total amount of disk used:** 14.42 MB An example of 'validation' looks as follows. ``` This example was too long and was cropped: { "answers": { "answer_start": [527], "text": ["136"] }, "context": "\"Die Verteidigung der Panthers gab nur 308 Punkte ab und belegte den sechsten Platz in der Liga, während sie die NFL mit 24 Inte...", "id": "56beb4343aeaaa14008c925c", "question": "Wie viele Sacks erzielte Jared Allen in seiner Karriere?" } ``` #### xquad.es - **Size of downloaded dataset files:** 13.30 MB - **Size of the generated dataset:** 1.28 MB - **Total amount of disk used:** 14.58 MB An example of 'validation' looks as follows. ``` This example was too long and was cropped: { "answers": { "answer_start": [527], "text": ["136"] }, "context": "\"Die Verteidigung der Panthers gab nur 308 Punkte ab und belegte den sechsten Platz in der Liga, während sie die NFL mit 24 Inte...", "id": "56beb4343aeaaa14008c925c", "question": "Wie viele Sacks erzielte Jared Allen in seiner Karriere?" } ``` ### Data Fields The data fields are the same among all splits. #### xquad.ar - `id`: a `string` feature. - `context`: a `string` feature. - `question`: a `string` feature. - `answers`: a dictionary feature containing: - `text`: a `string` feature. - `answer_start`: a `int32` feature. #### xquad.de - `id`: a `string` feature. - `context`: a `string` feature. - `question`: a `string` feature. - `answers`: a dictionary feature containing: - `text`: a `string` feature. - `answer_start`: a `int32` feature. #### xquad.el - `id`: a `string` feature. - `context`: a `string` feature. - `question`: a `string` feature. - `answers`: a dictionary feature containing: - `text`: a `string` feature. - `answer_start`: a `int32` feature. #### xquad.en - `id`: a `string` feature. - `context`: a `string` feature. - `question`: a `string` feature. - `answers`: a dictionary feature containing: - `text`: a `string` feature. - `answer_start`: a `int32` feature. #### xquad.es - `id`: a `string` feature. - `context`: a `string` feature. - `question`: a `string` feature. - `answers`: a dictionary feature containing: - `text`: a `string` feature. - `answer_start`: a `int32` feature. ### Data Splits | name | validation | | -------- | ---------: | | xquad.ar | 1190 | | xquad.de | 1190 | | xquad.el | 1190 | | xquad.en | 1190 | | xquad.es | 1190 | ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @article{Artetxe:etal:2019, author = {Mikel Artetxe and Sebastian Ruder and Dani Yogatama}, title = {On the cross-lingual transferability of monolingual representations}, journal = {CoRR}, volume = {abs/1910.11856}, year = {2019}, archivePrefix = {arXiv}, eprint = {1910.11856} } ``` ### Contributions Thanks to [@lewtun](https://github.com/lewtun), [@patrickvonplaten](https://github.com/patrickvonplaten), [@thomwolf](https://github.com/thomwolf) for adding this dataset.

uclanlp/wino_bias

uclanlp

2024-01-04T16:50:33Z

1,832

18

[ "task_categories:token-classification", "task_ids:coreference-resolution", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "source_datasets:original", "language:en", "license:mit", "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:1804.06876", "region:us" ]

[ "token-classification" ]

2022-03-02T23:29:22Z

1

--- annotations_creators: - expert-generated language_creators: - expert-generated language: - en license: - mit multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - token-classification task_ids: - coreference-resolution paperswithcode_id: winobias pretty_name: WinoBias dataset_info: - config_name: type1_anti features: - name: document_id dtype: string - name: part_number dtype: string - name: word_number sequence: int32 - name: tokens sequence: string - name: pos_tags sequence: class_label: names: '0': '"' '1': '''''' '2': '#' '3': $ '4': ( '5': ) '6': ',' '7': . '8': ':' '9': '``' '10': CC '11': CD '12': DT '13': EX '14': FW '15': IN '16': JJ '17': JJR '18': JJS '19': LS '20': MD '21': NN '22': NNP '23': NNPS '24': NNS '25': NN|SYM '26': PDT '27': POS '28': PRP '29': PRP$ '30': RB '31': RBR '32': RBS '33': RP '34': SYM '35': TO '36': UH '37': VB '38': VBD '39': VBG '40': VBN '41': VBP '42': VBZ '43': WDT '44': WP '45': WP$ '46': WRB '47': HYPH '48': XX '49': NFP '50': AFX '51': ADD '52': -LRB- '53': -RRB- '54': '-' - name: parse_bit sequence: string - name: predicate_lemma sequence: string - name: predicate_framenet_id sequence: string - name: word_sense sequence: string - name: speaker sequence: string - name: ner_tags sequence: class_label: names: '0': B-PERSON '1': I-PERSON '2': B-NORP '3': I-NORP '4': B-FAC '5': I-FAC '6': B-ORG '7': I-ORG '8': B-GPE '9': I-GPE '10': B-LOC '11': I-LOC '12': B-PRODUCT '13': I-PRODUCT '14': B-EVENT '15': I-EVENT '16': B-WORK_OF_ART '17': I-WORK_OF_ART '18': B-LAW '19': I-LAW '20': B-LANGUAGE '21': I-LANGUAGE '22': B-DATE '23': I-DATE '24': B-TIME '25': I-TIME '26': B-PERCENT '27': I-PERCENT '28': B-MONEY '29': I-MONEY '30': B-QUANTITY '31': I-QUANTITY '32': B-ORDINAL '33': I-ORDINAL '34': B-CARDINAL '35': I-CARDINAL '36': '*' '37': '0' '38': '-' - name: verbal_predicates sequence: string - name: coreference_clusters sequence: string splits: - name: validation num_bytes: 380510 num_examples: 396 - name: test num_bytes: 402893 num_examples: 396 download_size: 65383 dataset_size: 783403 - config_name: type1_pro features: - name: document_id dtype: string - name: part_number dtype: string - name: word_number sequence: int32 - name: tokens sequence: string - name: pos_tags sequence: class_label: names: '0': '"' '1': '''''' '2': '#' '3': $ '4': ( '5': ) '6': ',' '7': . '8': ':' '9': '``' '10': CC '11': CD '12': DT '13': EX '14': FW '15': IN '16': JJ '17': JJR '18': JJS '19': LS '20': MD '21': NN '22': NNP '23': NNPS '24': NNS '25': NN|SYM '26': PDT '27': POS '28': PRP '29': PRP$ '30': RB '31': RBR '32': RBS '33': RP '34': SYM '35': TO '36': UH '37': VB '38': VBD '39': VBG '40': VBN '41': VBP '42': VBZ '43': WDT '44': WP '45': WP$ '46': WRB '47': HYPH '48': XX '49': NFP '50': AFX '51': ADD '52': -LRB- '53': -RRB- '54': '-' - name: parse_bit sequence: string - name: predicate_lemma sequence: string - name: predicate_framenet_id sequence: string - name: word_sense sequence: string - name: speaker sequence: string - name: ner_tags sequence: class_label: names: '0': B-PERSON '1': I-PERSON '2': B-NORP '3': I-NORP '4': B-FAC '5': I-FAC '6': B-ORG '7': I-ORG '8': B-GPE '9': I-GPE '10': B-LOC '11': I-LOC '12': B-PRODUCT '13': I-PRODUCT '14': B-EVENT '15': I-EVENT '16': B-WORK_OF_ART '17': I-WORK_OF_ART '18': B-LAW '19': I-LAW '20': B-LANGUAGE '21': I-LANGUAGE '22': B-DATE '23': I-DATE '24': B-TIME '25': I-TIME '26': B-PERCENT '27': I-PERCENT '28': B-MONEY '29': I-MONEY '30': B-QUANTITY '31': I-QUANTITY '32': B-ORDINAL '33': I-ORDINAL '34': B-CARDINAL '35': I-CARDINAL '36': '*' '37': '0' '38': '-' - name: verbal_predicates sequence: string - name: coreference_clusters sequence: string splits: - name: validation num_bytes: 379044 num_examples: 396 - name: test num_bytes: 401705 num_examples: 396 download_size: 65516 dataset_size: 780749 - config_name: type2_anti features: - name: document_id dtype: string - name: part_number dtype: string - name: word_number sequence: int32 - name: tokens sequence: string - name: pos_tags sequence: class_label: names: '0': '"' '1': '''''' '2': '#' '3': $ '4': ( '5': ) '6': ',' '7': . '8': ':' '9': '``' '10': CC '11': CD '12': DT '13': EX '14': FW '15': IN '16': JJ '17': JJR '18': JJS '19': LS '20': MD '21': NN '22': NNP '23': NNPS '24': NNS '25': NN|SYM '26': PDT '27': POS '28': PRP '29': PRP$ '30': RB '31': RBR '32': RBS '33': RP '34': SYM '35': TO '36': UH '37': VB '38': VBD '39': VBG '40': VBN '41': VBP '42': VBZ '43': WDT '44': WP '45': WP$ '46': WRB '47': HYPH '48': XX '49': NFP '50': AFX '51': ADD '52': -LRB- '53': -RRB- '54': '-' - name: parse_bit sequence: string - name: predicate_lemma sequence: string - name: predicate_framenet_id sequence: string - name: word_sense sequence: string - name: speaker sequence: string - name: ner_tags sequence: class_label: names: '0': B-PERSON '1': I-PERSON '2': B-NORP '3': I-NORP '4': B-FAC '5': I-FAC '6': B-ORG '7': I-ORG '8': B-GPE '9': I-GPE '10': B-LOC '11': I-LOC '12': B-PRODUCT '13': I-PRODUCT '14': B-EVENT '15': I-EVENT '16': B-WORK_OF_ART '17': I-WORK_OF_ART '18': B-LAW '19': I-LAW '20': B-LANGUAGE '21': I-LANGUAGE '22': B-DATE '23': I-DATE '24': B-TIME '25': I-TIME '26': B-PERCENT '27': I-PERCENT '28': B-MONEY '29': I-MONEY '30': B-QUANTITY '31': I-QUANTITY '32': B-ORDINAL '33': I-ORDINAL '34': B-CARDINAL '35': I-CARDINAL '36': '*' '37': '0' '38': '-' - name: verbal_predicates sequence: string - name: coreference_clusters sequence: string splits: - name: validation num_bytes: 368421 num_examples: 396 - name: test num_bytes: 376926 num_examples: 396 download_size: 62555 dataset_size: 745347 - config_name: type2_pro features: - name: document_id dtype: string - name: part_number dtype: string - name: word_number sequence: int32 - name: tokens sequence: string - name: pos_tags sequence: class_label: names: '0': '"' '1': '''''' '2': '#' '3': $ '4': ( '5': ) '6': ',' '7': . '8': ':' '9': '``' '10': CC '11': CD '12': DT '13': EX '14': FW '15': IN '16': JJ '17': JJR '18': JJS '19': LS '20': MD '21': NN '22': NNP '23': NNPS '24': NNS '25': NN|SYM '26': PDT '27': POS '28': PRP '29': PRP$ '30': RB '31': RBR '32': RBS '33': RP '34': SYM '35': TO '36': UH '37': VB '38': VBD '39': VBG '40': VBN '41': VBP '42': VBZ '43': WDT '44': WP '45': WP$ '46': WRB '47': HYPH '48': XX '49': NFP '50': AFX '51': ADD '52': -LRB- '53': -RRB- '54': '-' - name: parse_bit sequence: string - name: predicate_lemma sequence: string - name: predicate_framenet_id sequence: string - name: word_sense sequence: string - name: speaker sequence: string - name: ner_tags sequence: class_label: names: '0': B-PERSON '1': I-PERSON '2': B-NORP '3': I-NORP '4': B-FAC '5': I-FAC '6': B-ORG '7': I-ORG '8': B-GPE '9': I-GPE '10': B-LOC '11': I-LOC '12': B-PRODUCT '13': I-PRODUCT '14': B-EVENT '15': I-EVENT '16': B-WORK_OF_ART '17': I-WORK_OF_ART '18': B-LAW '19': I-LAW '20': B-LANGUAGE '21': I-LANGUAGE '22': B-DATE '23': I-DATE '24': B-TIME '25': I-TIME '26': B-PERCENT '27': I-PERCENT '28': B-MONEY '29': I-MONEY '30': B-QUANTITY '31': I-QUANTITY '32': B-ORDINAL '33': I-ORDINAL '34': B-CARDINAL '35': I-CARDINAL '36': '*' '37': '0' '38': '-' - name: verbal_predicates sequence: string - name: coreference_clusters sequence: string splits: - name: validation num_bytes: 366957 num_examples: 396 - name: test num_bytes: 375144 num_examples: 396 download_size: 62483 dataset_size: 742101 - config_name: wino_bias features: - name: document_id dtype: string - name: part_number dtype: string - name: word_number sequence: int32 - name: tokens sequence: string - name: pos_tags sequence: class_label: names: '0': '"' '1': '''''' '2': '#' '3': $ '4': ( '5': ) '6': ',' '7': . '8': ':' '9': '``' '10': CC '11': CD '12': DT '13': EX '14': FW '15': IN '16': JJ '17': JJR '18': JJS '19': LS '20': MD '21': NN '22': NNP '23': NNPS '24': NNS '25': NN|SYM '26': PDT '27': POS '28': PRP '29': PRP$ '30': RB '31': RBR '32': RBS '33': RP '34': SYM '35': TO '36': UH '37': VB '38': VBD '39': VBG '40': VBN '41': VBP '42': VBZ '43': WDT '44': WP '45': WP$ '46': WRB '47': HYPH '48': XX '49': NFP '50': AFX '51': ADD '52': -LRB- '53': -RRB- - name: parse_bit sequence: string - name: predicate_lemma sequence: string - name: predicate_framenet_id sequence: string - name: word_sense sequence: string - name: speaker sequence: string - name: ner_tags sequence: class_label: names: '0': B-PERSON '1': I-PERSON '2': B-NORP '3': I-NORP '4': B-FAC '5': I-FAC '6': B-ORG '7': I-ORG '8': B-GPE '9': I-GPE '10': B-LOC '11': I-LOC '12': B-PRODUCT '13': I-PRODUCT '14': B-EVENT '15': I-EVENT '16': B-WORK_OF_ART '17': I-WORK_OF_ART '18': B-LAW '19': I-LAW '20': B-LANGUAGE '21': I-LANGUAGE '22': B-DATE '23': I-DATE '24': B-TIME '25': I-TIME '26': B-PERCENT '27': I-PERCENT '28': B-MONEY '29': I-MONEY '30': B-QUANTITY '31': I-QUANTITY '32': B-ORDINAL '33': I-ORDINAL '34': B-CARDINAL '35': I-CARDINAL '36': '*' '37': '0' - name: verbal_predicates sequence: string splits: - name: train num_bytes: 173899234 num_examples: 150335 download_size: 268725744 dataset_size: 173899234 configs: - config_name: type1_anti data_files: - split: validation path: type1_anti/validation-* - split: test path: type1_anti/test-* - config_name: type1_pro data_files: - split: validation path: type1_pro/validation-* - split: test path: type1_pro/test-* - config_name: type2_anti data_files: - split: validation path: type2_anti/validation-* - split: test path: type2_anti/test-* - config_name: type2_pro data_files: - split: validation path: type2_pro/validation-* - split: test path: type2_pro/test-* --- # Dataset Card for Wino_Bias dataset ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [WinoBias](https://uclanlp.github.io/corefBias/overview) - **Repository:** - **Paper:** [Arxiv](https://arxiv.org/abs/1804.06876) - **Leaderboard:** - **Point of Contact:** ### Dataset Summary WinoBias, a Winograd-schema dataset for coreference resolution focused on gender bias. The corpus contains Winograd-schema style sentences with entities corresponding to people referred by their occupation (e.g. the nurse, the doctor, the carpenter). ### Supported Tasks and Leaderboards The underlying task is coreference resolution. ### Languages English ## Dataset Structure ### Data Instances The dataset has 4 subsets: `type1_pro`, `type1_anti`, `type2_pro` and `type2_anti`. The `*_pro` subsets contain sentences that reinforce gender stereotypes (e.g. mechanics are male, nurses are female), whereas the `*_anti` datasets contain "anti-stereotypical" sentences (e.g. mechanics are female, nurses are male). The `type1` (*WB-Knowledge*) subsets contain sentences for which world knowledge is necessary to resolve the co-references, and `type2` (*WB-Syntax*) subsets require only the syntactic information present in the sentence to resolve them. ### Data Fields - document_id = This is a variation on the document filename - part_number = Some files are divided into multiple parts numbered as 000, 001, 002, ... etc. - word_num = This is the word index of the word in that sentence. - tokens = This is the token as segmented/tokenized in the Treebank. - pos_tags = This is the Penn Treebank style part of speech. When parse information is missing, all part of speeches except the one for which there is some sense or proposition annotation are marked with a XX tag. The verb is marked with just a VERB tag. - parse_bit = This is the bracketed structure broken before the first open parenthesis in the parse, and the word/part-of-speech leaf replaced with a *. The full parse can be created by substituting the asterix with the "([pos] [word])" string (or leaf) and concatenating the items in the rows of that column. When the parse information is missing, the first word of a sentence is tagged as "(TOP*" and the last word is tagged as "*)" and all intermediate words are tagged with a "*". - predicate_lemma = The predicate lemma is mentioned for the rows for which we have semantic role information or word sense information. All other rows are marked with a "-". - predicate_framenet_id = This is the PropBank frameset ID of the predicate in predicate_lemma. - word_sense = This is the word sense of the word in Column tokens. - speaker = This is the speaker or author name where available. - ner_tags = These columns identifies the spans representing various named entities. For documents which do not have named entity annotation, each line is represented with an "*". - verbal_predicates = There is one column each of predicate argument structure information for the predicate mentioned in predicate_lemma. If there are no predicates tagged in a sentence this is a single column with all rows marked with an "*". ### Data Splits Dev and Test Split available ## Dataset Creation ### Curation Rationale The WinoBias dataset was introduced in 2018 (see [paper](https://arxiv.org/abs/1804.06876)), with its original task being *coreference resolution*, which is a task that aims to identify mentions that refer to the same entity or person. ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? The dataset was created by researchers familiar with the WinoBias project, based on two prototypical templates provided by the authors, in which entities interact in plausible ways. ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? "Researchers familiar with the [WinoBias] project" ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [Recent work](https://www.microsoft.com/en-us/research/uploads/prod/2021/06/The_Salmon_paper.pdf) has shown that this dataset contains grammatical issues, incorrect or ambiguous labels, and stereotype conflation, among other limitations. ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators Jieyu Zhao, Tianlu Wang, Mark Yatskar, Vicente Ordonez and Kai-Wei Chan ### Licensing Information MIT Licence ### Citation Information @article{DBLP:journals/corr/abs-1804-06876, author = {Jieyu Zhao and Tianlu Wang and Mark Yatskar and Vicente Ordonez and Kai{-}Wei Chang}, title = {Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods}, journal = {CoRR}, volume = {abs/1804.06876}, year = {2018}, url = {http://arxiv.org/abs/1804.06876}, archivePrefix = {arXiv}, eprint = {1804.06876}, timestamp = {Mon, 13 Aug 2018 16:47:01 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-1804-06876.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ### Contributions Thanks to [@akshayb7](https://github.com/akshayb7) for adding this dataset. Updated by [@JieyuZhao](https://github.com/JieyuZhao).

Stanford/web_questions

Stanford

2024-01-04T16:41:06Z

4,003

32

[ "task_categories:question-answering", "task_ids:open-domain-qa", "annotations_creators:crowdsourced", "language_creators:found", "multilinguality:monolingual", "source_datasets:original", "language:en", "license:unknown", "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[ "question-answering" ]

2022-03-02T23:29:22Z

1

--- annotations_creators: - crowdsourced language_creators: - found language: - en license: - unknown multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - question-answering task_ids: - open-domain-qa paperswithcode_id: webquestions pretty_name: WebQuestions dataset_info: features: - name: url dtype: string - name: question dtype: string - name: answers sequence: string splits: - name: train num_bytes: 530711 num_examples: 3778 - name: test num_bytes: 288184 num_examples: 2032 download_size: 402395 dataset_size: 818895 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* --- # Dataset Card for "web_questions" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://worksheets.codalab.org/worksheets/0xba659fe363cb46e7a505c5b6a774dc8a](https://worksheets.codalab.org/worksheets/0xba659fe363cb46e7a505c5b6a774dc8a) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [Semantic Parsing on Freebase from Question-Answer Pairs](https://aclanthology.org/D13-1160/) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 1.27 MB - **Size of the generated dataset:** 0.83 MB - **Total amount of disk used:** 2.10 MB ### Dataset Summary This dataset consists of 6,642 question/answer pairs. The questions are supposed to be answerable by Freebase, a large knowledge graph. The questions are mostly centered around a single named entity. The questions are popular ones asked on the web (at least in 2013). ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### default - **Size of downloaded dataset files:** 1.27 MB - **Size of the generated dataset:** 0.83 MB - **Total amount of disk used:** 2.10 MB An example of 'train' looks as follows. ``` { "answers": ["Jamaican Creole English Language", "Jamaican English"], "question": "what does jamaican people speak?", "url": "http://www.freebase.com/view/en/jamaica" } ``` ### Data Fields The data fields are the same among all splits. #### default - `url`: a `string` feature. - `question`: a `string` feature. - `answers`: a `list` of `string` features. ### Data Splits | name |train|test| |-------|----:|---:| |default| 3778|2032| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @inproceedings{berant-etal-2013-semantic, title = "Semantic Parsing on {F}reebase from Question-Answer Pairs", author = "Berant, Jonathan and Chou, Andrew and Frostig, Roy and Liang, Percy", booktitle = "Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing", month = oct, year = "2013", address = "Seattle, Washington, USA", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/D13-1160", pages = "1533--1544", } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@mariamabarham](https://github.com/mariamabarham), [@lewtun](https://github.com/lewtun) for adding this dataset.

textmachinelab/quail

textmachinelab

2024-01-04T16:18:32Z

17,696

7

[ "task_categories:multiple-choice", "task_ids:multiple-choice-qa", "annotations_creators:crowdsourced", "language_creators:found", "multilinguality:monolingual", "source_datasets:original", "language:en", "license:cc-by-nc-sa-4.0", "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[ "multiple-choice" ]

2022-03-02T23:29:22Z

1

--- annotations_creators: - crowdsourced language_creators: - found language: - en license: - cc-by-nc-sa-4.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - multiple-choice task_ids: - multiple-choice-qa paperswithcode_id: quail pretty_name: Question Answering for Artificial Intelligence (QuAIL) dataset_info: config_name: quail features: - name: id dtype: string - name: context_id dtype: string - name: question_id dtype: string - name: domain dtype: string - name: metadata struct: - name: author dtype: string - name: title dtype: string - name: url dtype: string - name: context dtype: string - name: question dtype: string - name: question_type dtype: string - name: answers sequence: string - name: correct_answer_id dtype: int32 splits: - name: train num_bytes: 23432601 num_examples: 10246 - name: validation num_bytes: 4989531 num_examples: 2164 - name: challenge num_bytes: 1199792 num_examples: 556 download_size: 2286403 dataset_size: 29621924 configs: - config_name: quail data_files: - split: train path: quail/train-* - split: validation path: quail/validation-* - split: challenge path: quail/challenge-* default: true --- # Dataset Card for "quail" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://text-machine-lab.github.io/blog/2020/quail/](https://text-machine-lab.github.io/blog/2020/quail/) - **Repository:** https://github.com/text-machine-lab/quail - **Paper:** [Getting Closer to AI Complete Question Answering: A Set of Prerequisite Real Tasks](https://doi.org/10.1609/aaai.v34i05.6398 ) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 6.41 MB - **Size of the generated dataset:** 29.62 MB - **Total amount of disk used:** 36.03 MB ### Dataset Summary QuAIL is a reading comprehension dataset. QuAIL contains 15K multi-choice questions in texts 300-350 tokens long 4 domains (news, user stories, fiction, blogs).QuAIL is balanced and annotated for question types. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### quail - **Size of downloaded dataset files:** 6.41 MB - **Size of the generated dataset:** 29.62 MB - **Total amount of disk used:** 36.03 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "answers": ["the cousin is not friendly", "the cousin could have been pretier", "not enough information", "the cousin was too nice"], "context": "\"That fall came and I went back to Michigan and the school year went by and summer came and I never really thought about it. I'm...", "context_id": "f001", "correct_answer_id": 0, "domain": "fiction", "id": "f001_19", "metadata": { "author": "Joseph Devon", "title": "Black Eyed Susan", "url": "http://manybooks.net/pages/devonjother08black_eyed_susan/0.html" }, "question": "After the events in the text what does the author think about the cousin?", "question_id": "19", "question_type": "Subsequent_state" } ``` ### Data Fields The data fields are the same among all splits. #### quail - `id`: a `string` feature. - `context_id`: a `string` feature. - `question_id`: a `string` feature. - `domain`: a `string` feature. - `author`: a `string` feature. - `title`: a `string` feature. - `url`: a `string` feature. - `context`: a `string` feature. - `question`: a `string` feature. - `question_type`: a `string` feature. - `answers`: a `list` of `string` features. - `correct_answer_id`: a `int32` feature. ### Data Splits |name |train|challenge|validation| |-----|----:|--------:|---------:| |quail|10246| 556| 2164| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @inproceedings{DBLP:conf/aaai/RogersKDR20, author = {Anna Rogers and Olga Kovaleva and Matthew Downey and Anna Rumshisky}, title = {Getting Closer to {AI} Complete Question Answering: {A} Set of Prerequisite Real Tasks}, booktitle = {The Thirty-Fourth {AAAI} Conference on Artificial Intelligence, {AAAI} 2020, The Thirty-Second Innovative Applications of Artificial Intelligence Conference, {IAAI} 2020, The Tenth {AAAI} Symposium on Educational Advances in Artificial Intelligence, {EAAI} 2020, New York, NY, USA, February 7-12, 2020}, pages = {8722--8731}, publisher = {{AAAI} Press}, year = {2020}, url = {https://aaai.org/ojs/index.php/AAAI/article/view/6398}, timestamp = {Thu, 04 Jun 2020 13:18:48 +0200}, biburl = {https://dblp.org/rec/conf/aaai/RogersKDR20.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ``` ### Contributions Thanks to [@sai-prasanna](https://github.com/sai-prasanna), [@ngdodd](https://github.com/ngdodd) for adding this dataset.

openai/openai_humaneval

openai

2024-01-04T16:08:05Z

81,423

314

[ "task_categories:text2text-generation", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "source_datasets:original", "language:en", "license:mit", "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2107.03374", "region:us", "code-generation" ]

[ "text2text-generation" ]

2022-03-02T23:29:22Z

null

--- annotations_creators: - expert-generated language_creators: - expert-generated language: - en license: - mit multilinguality: - monolingual size_categories: - n<1K source_datasets: - original task_categories: - text2text-generation task_ids: [] paperswithcode_id: humaneval pretty_name: OpenAI HumanEval tags: - code-generation dataset_info: config_name: openai_humaneval features: - name: task_id dtype: string - name: prompt dtype: string - name: canonical_solution dtype: string - name: test dtype: string - name: entry_point dtype: string splits: - name: test num_bytes: 194394 num_examples: 164 download_size: 83920 dataset_size: 194394 configs: - config_name: openai_humaneval data_files: - split: test path: openai_humaneval/test-* default: true --- # Dataset Card for OpenAI HumanEval ## Table of Contents - [OpenAI HumanEval](#openai-humaneval) - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization) - [Who are the source language producers?](#who-are-the-source-language-producers) - [Annotations](#annotations) - [Annotation process](#annotation-process) - [Who are the annotators?](#who-are-the-annotators) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Repository:** [GitHub Repository](https://github.com/openai/human-eval) - **Paper:** [Evaluating Large Language Models Trained on Code](https://arxiv.org/abs/2107.03374) ### Dataset Summary The HumanEval dataset released by OpenAI includes 164 programming problems with a function sig- nature, docstring, body, and several unit tests. They were handwritten to ensure not to be included in the training set of code generation models. ### Supported Tasks and Leaderboards ### Languages The programming problems are written in Python and contain English natural text in comments and docstrings. ## Dataset Structure ```python from datasets import load_dataset load_dataset("openai_humaneval") DatasetDict({ test: Dataset({ features: ['task_id', 'prompt', 'canonical_solution', 'test', 'entry_point'], num_rows: 164 }) }) ``` ### Data Instances An example of a dataset instance: ``` { "task_id": "test/0", "prompt": "def return1():\n", "canonical_solution": " return 1", "test": "def check(candidate):\n assert candidate() == 1", "entry_point": "return1" } ``` ### Data Fields - `task_id`: identifier for the data sample - `prompt`: input for the model containing function header and docstrings - `canonical_solution`: solution for the problem in the `prompt` - `test`: contains function to test generated code for correctness - `entry_point`: entry point for test ### Data Splits The dataset only consists of a test split with 164 samples. ## Dataset Creation ### Curation Rationale Since code generation models are often trained on dumps of GitHub a dataset not included in the dump was necessary to properly evaluate the model. However, since this dataset was published on GitHub it is likely to be included in future dumps. ### Source Data The dataset was handcrafted by engineers and researchers at OpenAI. #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations [More Information Needed] #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information None. ## Considerations for Using the Data Make sure you execute generated Python code in a safe environment when evauating against this dataset as generated code could be harmful. ### Social Impact of Dataset With this dataset code generating models can be better evaluated which leads to fewer issues introduced when using such models. ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators OpenAI ### Licensing Information MIT License ### Citation Information ``` @misc{chen2021evaluating, title={Evaluating Large Language Models Trained on Code}, author={Mark Chen and Jerry Tworek and Heewoo Jun and Qiming Yuan and Henrique Ponde de Oliveira Pinto and Jared Kaplan and Harri Edwards and Yuri Burda and Nicholas Joseph and Greg Brockman and Alex Ray and Raul Puri and Gretchen Krueger and Michael Petrov and Heidy Khlaaf and Girish Sastry and Pamela Mishkin and Brooke Chan and Scott Gray and Nick Ryder and Mikhail Pavlov and Alethea Power and Lukasz Kaiser and Mohammad Bavarian and Clemens Winter and Philippe Tillet and Felipe Petroski Such and Dave Cummings and Matthias Plappert and Fotios Chantzis and Elizabeth Barnes and Ariel Herbert-Voss and William Hebgen Guss and Alex Nichol and Alex Paino and Nikolas Tezak and Jie Tang and Igor Babuschkin and Suchir Balaji and Shantanu Jain and William Saunders and Christopher Hesse and Andrew N. Carr and Jan Leike and Josh Achiam and Vedant Misra and Evan Morikawa and Alec Radford and Matthew Knight and Miles Brundage and Mira Murati and Katie Mayer and Peter Welinder and Bob McGrew and Dario Amodei and Sam McCandlish and Ilya Sutskever and Wojciech Zaremba}, year={2021}, eprint={2107.03374}, archivePrefix={arXiv}, primaryClass={cs.LG} } ``` ### Contributions Thanks to [@lvwerra](https://github.com/lvwerra) for adding this dataset.

microsoft/ms_marco

microsoft

2024-01-04T16:01:29Z

8,083

159

[ "language:en", "size_categories:1M<n<10M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:1611.09268", "region:us" ]

[]

2022-03-02T23:29:22Z

null

--- language: - en paperswithcode_id: ms-marco pretty_name: Microsoft Machine Reading Comprehension Dataset dataset_info: - config_name: v1.1 features: - name: answers sequence: string - name: passages sequence: - name: is_selected dtype: int32 - name: passage_text dtype: string - name: url dtype: string - name: query dtype: string - name: query_id dtype: int32 - name: query_type dtype: string - name: wellFormedAnswers sequence: string splits: - name: validation num_bytes: 42665198 num_examples: 10047 - name: train num_bytes: 350516260 num_examples: 82326 - name: test num_bytes: 40977580 num_examples: 9650 download_size: 217328153 dataset_size: 434159038 - config_name: v2.1 features: - name: answers sequence: string - name: passages sequence: - name: is_selected dtype: int32 - name: passage_text dtype: string - name: url dtype: string - name: query dtype: string - name: query_id dtype: int32 - name: query_type dtype: string - name: wellFormedAnswers sequence: string splits: - name: validation num_bytes: 413765365 num_examples: 101093 - name: train num_bytes: 3462807709 num_examples: 808731 - name: test num_bytes: 405691932 num_examples: 101092 download_size: 2105722550 dataset_size: 4282265006 configs: - config_name: v1.1 data_files: - split: validation path: v1.1/validation-* - split: train path: v1.1/train-* - split: test path: v1.1/test-* - config_name: v2.1 data_files: - split: validation path: v2.1/validation-* - split: train path: v2.1/train-* - split: test path: v2.1/test-* --- # Dataset Card for "ms_marco" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://microsoft.github.io/msmarco/](https://microsoft.github.io/msmarco/) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 1.55 GB - **Size of the generated dataset:** 4.72 GB - **Total amount of disk used:** 6.28 GB ### Dataset Summary Starting with a paper released at NIPS 2016, MS MARCO is a collection of datasets focused on deep learning in search. The first dataset was a question answering dataset featuring 100,000 real Bing questions and a human generated answer. Since then we released a 1,000,000 question dataset, a natural langauge generation dataset, a passage ranking dataset, keyphrase extraction dataset, crawling dataset, and a conversational search. There have been 277 submissions. 20 KeyPhrase Extraction submissions, 87 passage ranking submissions, 0 document ranking submissions, 73 QnA V2 submissions, 82 NLGEN submisions, and 15 QnA V1 submissions This data comes in three tasks/forms: Original QnA dataset(v1.1), Question Answering(v2.1), Natural Language Generation(v2.1). The original question answering datset featured 100,000 examples and was released in 2016. Leaderboard is now closed but data is availible below. The current competitive tasks are Question Answering and Natural Language Generation. Question Answering features over 1,000,000 queries and is much like the original QnA dataset but bigger and with higher quality. The Natural Language Generation dataset features 180,000 examples and builds upon the QnA dataset to deliver answers that could be spoken by a smart speaker. version v1.1 ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### v1.1 - **Size of downloaded dataset files:** 168.69 MB - **Size of the generated dataset:** 434.61 MB - **Total amount of disk used:** 603.31 MB An example of 'train' looks as follows. ``` ``` #### v2.1 - **Size of downloaded dataset files:** 1.38 GB - **Size of the generated dataset:** 4.29 GB - **Total amount of disk used:** 5.67 GB An example of 'validation' looks as follows. ``` ``` ### Data Fields The data fields are the same among all splits. #### v1.1 - `answers`: a `list` of `string` features. - `passages`: a dictionary feature containing: - `is_selected`: a `int32` feature. - `passage_text`: a `string` feature. - `url`: a `string` feature. - `query`: a `string` feature. - `query_id`: a `int32` feature. - `query_type`: a `string` feature. - `wellFormedAnswers`: a `list` of `string` features. #### v2.1 - `answers`: a `list` of `string` features. - `passages`: a dictionary feature containing: - `is_selected`: a `int32` feature. - `passage_text`: a `string` feature. - `url`: a `string` feature. - `query`: a `string` feature. - `query_id`: a `int32` feature. - `query_type`: a `string` feature. - `wellFormedAnswers`: a `list` of `string` features. ### Data Splits |name|train |validation| test | |----|-----:|---------:|-----:| |v1.1| 82326| 10047| 9650| |v2.1|808731| 101093|101092| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @article{DBLP:journals/corr/NguyenRSGTMD16, author = {Tri Nguyen and Mir Rosenberg and Xia Song and Jianfeng Gao and Saurabh Tiwary and Rangan Majumder and Li Deng}, title = {{MS} {MARCO:} {A} Human Generated MAchine Reading COmprehension Dataset}, journal = {CoRR}, volume = {abs/1611.09268}, year = {2016}, url = {http://arxiv.org/abs/1611.09268}, archivePrefix = {arXiv}, eprint = {1611.09268}, timestamp = {Mon, 13 Aug 2018 16:49:03 +0200}, biburl = {https://dblp.org/rec/journals/corr/NguyenRSGTMD16.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } } ``` ### Contributions Thanks to [@mariamabarham](https://github.com/mariamabarham), [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun) for adding this dataset.

tdavidson/hate_speech_offensive

tdavidson

2024-01-04T12:06:17Z

1,339

31

[ "task_categories:text-classification", "annotations_creators:expert-generated", "annotations_creators:crowdsourced", "language_creators:found", "multilinguality:monolingual", "source_datasets:original", "language:en", "license:unknown", "size_categories:10K<n<100K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:1703.04009", "region:us", "hate-speech-detection" ]

[ "text-classification" ]

2022-03-02T23:29:22Z

1

--- annotations_creators: - expert-generated - crowdsourced language_creators: - found language: - en license: - unknown multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: [] paperswithcode_id: hate-speech-and-offensive-language pretty_name: Hate Speech and Offensive Language tags: - hate-speech-detection dataset_info: features: - name: count dtype: int64 - name: hate_speech_count dtype: int64 - name: offensive_language_count dtype: int64 - name: neither_count dtype: int64 - name: class dtype: class_label: names: '0': hate speech '1': offensive language '2': neither - name: tweet dtype: string splits: - name: train num_bytes: 3207814 num_examples: 24783 download_size: 1627672 dataset_size: 3207814 configs: - config_name: default data_files: - split: train path: data/train-* train-eval-index: - config: default task: text-classification task_id: multi_class_classification splits: train_split: train col_mapping: tweet: text label: target metrics: - type: accuracy name: Accuracy - type: f1 name: F1 macro args: average: macro - type: f1 name: F1 micro args: average: micro - type: f1 name: F1 weighted args: average: weighted - type: precision name: Precision macro args: average: macro - type: precision name: Precision micro args: average: micro - type: precision name: Precision weighted args: average: weighted - type: recall name: Recall macro args: average: macro - type: recall name: Recall micro args: average: micro - type: recall name: Recall weighted args: average: weighted --- # Dataset Card for [Dataset Name] ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://github.com/t-davidson/hate-speech-and-offensive-language - **Repository:** https://github.com/t-davidson/hate-speech-and-offensive-language - **Paper:** https://arxiv.org/abs/1703.04009 - **Leaderboard:** - **Point of Contact:** https://docs.google.com/forms/d/e/1FAIpQLSdrPNlfVBlqxun2tivzAtsZaOoPC5YYMocn-xscCgeRakLXHg/viewform?usp=pp_url&entry.1506871634&entry.147453066&entry.1390333885&entry.516829772 ### Dataset Summary An annotated dataset for hate speech and offensive language detection on tweets. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages English (`en`) ## Dataset Structure ### Data Instances ``` { "count": 3, "hate_speech_annotation": 0, "offensive_language_annotation": 0, "neither_annotation": 3, "label": 2, # "neither" "tweet": "!!! RT @mayasolovely: As a woman you shouldn't complain about cleaning up your house. & as a man you should always take the trash out...") } ``` ### Data Fields ``` count: (Integer) number of users who coded each tweet (min is 3, sometimes more users coded a tweet when judgments were determined to be unreliable, hate_speech_annotation: (Integer) number of users who judged the tweet to be hate speech, offensive_language_annotation: (Integer) number of users who judged the tweet to be offensive, neither_annotation: (Integer) number of users who judged the tweet to be neither offensive nor non-offensive, label: (Class Label) class label for majority of CF users (0: 'hate-speech', 1: 'offensive-language' or 2: 'neither'), tweet: (string) ``` ### Data Splits This dataset is not splitted, only the train split is available. ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information Usernames are not anonymized in the dataset. ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information MIT License ### Citation Information @inproceedings{hateoffensive, title = {Automated Hate Speech Detection and the Problem of Offensive Language}, author = {Davidson, Thomas and Warmsley, Dana and Macy, Michael and Weber, Ingmar}, booktitle = {Proceedings of the 11th International AAAI Conference on Web and Social Media}, series = {ICWSM '17}, year = {2017}, location = {Montreal, Canada}, pages = {512-515} } ### Contributions Thanks to [@hugoabonizio](https://github.com/hugoabonizio) for adding this dataset.

allenai/common_gen

allenai

2024-01-04T07:34:57Z

1,047

26

[ "task_categories:text2text-generation", "annotations_creators:crowdsourced", "language_creators:found", "language_creators:crowdsourced", "multilinguality:monolingual", "source_datasets:original", "language:en", "license:mit", "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:1911.03705", "region:us", "concepts-to-text" ]

[ "text2text-generation" ]

2022-03-02T23:29:22Z

1

--- annotations_creators: - crowdsourced language_creators: - found - crowdsourced language: - en license: - mit multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text2text-generation task_ids: [] paperswithcode_id: commongen pretty_name: CommonGen tags: - concepts-to-text dataset_info: features: - name: concept_set_idx dtype: int32 - name: concepts sequence: string - name: target dtype: string splits: - name: train num_bytes: 6724166 num_examples: 67389 - name: validation num_bytes: 408740 num_examples: 4018 - name: test num_bytes: 77518 num_examples: 1497 download_size: 3434865 dataset_size: 7210424 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* --- # Dataset Card for "common_gen" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://inklab.usc.edu/CommonGen/index.html](https://inklab.usc.edu/CommonGen/index.html) - **Repository:** https://github.com/INK-USC/CommonGen - **Paper:** [CommonGen: A Constrained Text Generation Challenge for Generative Commonsense Reasoning](https://arxiv.org/abs/1911.03705) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 1.85 MB - **Size of the generated dataset:** 7.21 MB - **Total amount of disk used:** 9.06 MB ### Dataset Summary CommonGen is a constrained text generation task, associated with a benchmark dataset, to explicitly test machines for the ability of generative commonsense reasoning. Given a set of common concepts; the task is to generate a coherent sentence describing an everyday scenario using these concepts. CommonGen is challenging because it inherently requires 1) relational reasoning using background commonsense knowledge, and 2) compositional generalization ability to work on unseen concept combinations. Our dataset, constructed through a combination of crowd-sourcing from AMT and existing caption corpora, consists of 30k concept-sets and 50k sentences in total. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### default - **Size of downloaded dataset files:** 1.85 MB - **Size of the generated dataset:** 7.21 MB - **Total amount of disk used:** 9.06 MB An example of 'train' looks as follows. ``` { "concept_set_idx": 0, "concepts": ["ski", "mountain", "skier"], "target": "Three skiers are skiing on a snowy mountain." } ``` ### Data Fields The data fields are the same among all splits. #### default - `concept_set_idx`: a `int32` feature. - `concepts`: a `list` of `string` features. - `target`: a `string` feature. ### Data Splits | name |train|validation|test| |-------|----:|---------:|---:| |default|67389| 4018|1497| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information The dataset is licensed under [MIT License](https://github.com/INK-USC/CommonGen/blob/master/LICENSE). ### Citation Information ```bib @inproceedings{lin-etal-2020-commongen, title = "{C}ommon{G}en: A Constrained Text Generation Challenge for Generative Commonsense Reasoning", author = "Lin, Bill Yuchen and Zhou, Wangchunshu and Shen, Ming and Zhou, Pei and Bhagavatula, Chandra and Choi, Yejin and Ren, Xiang", booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020", month = nov, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.findings-emnlp.165", doi = "10.18653/v1/2020.findings-emnlp.165", pages = "1823--1840" } ``` ### Contributions Thanks to [@JetRunner](https://github.com/JetRunner), [@yuchenlin](https://github.com/yuchenlin), [@thomwolf](https://github.com/thomwolf), [@lhoestq](https://github.com/lhoestq) for adding this dataset.

openbmb/UltraFeedback

openbmb

2023-12-29T14:11:19Z

2,366

360

[ "task_categories:text-generation", "language:en", "license:mit", "size_categories:10K<n<100K", "format:json", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:2310.01377", "region:us" ]

[ "text-generation" ]

2023-09-23T15:41:04Z

null

--- license: mit task_categories: - text-generation language: - en size_categories: - 100K<n<1M --- ## Introduction - [GitHub Repo](https://github.com/thunlp/UltraFeedback) - [UltraRM-13b](https://huggingface.co/openbmb/UltraRM-13b) - [UltraCM-13b](https://huggingface.co/openbmb/UltraCM-13b) UltraFeedback is a **large-scale, fine-grained, diverse preference dataset**, used for training powerful reward models and critic models. We collect about 64k prompts from diverse resources (including UltraChat, ShareGPT, Evol-Instruct, TruthfulQA, FalseQA, and FLAN). We then use these prompts to query multiple LLMs (see Table for model lists) and generate 4 different responses for each prompt, resulting in a total of 256k samples. To collect high-quality preference and textual feedback, we design a fine-grained annotation instruction, which contains 4 different aspects, namely **instruction-following**, **truthfulness**, **honesty** and **helpfulness**. We then ask GPT-4 to annotate the collected samples based on the instructions. ## Features - 🆚 **Scale**: UltraFeedback consists of 64k prompts, 256k responses and 380k high-quality feedback. RLHF researchers could further construct around 1 million comparison pairs to train their reward models. - 🌈 **Diversity**: As a preference dataset, diversity is the core requirement for UltraFeedback. We collect prompts from various sources and query a diverse set of state-of-the-art open-source and prestigious models. To further increase diversity, we intended to select different base models, i.e., LLaMA, Falcon, StarChat, MPT, GPT and Bard. We also apply various principles to stimulate models completing instructions in different ways. - 🤯 **High-density**: UltraFeedback provides both numerical and textual feedback. Moreover, we wrote fine-grained annotation documents to help rate responses in all dimensions ## Dataset Construction ### Instruction Sampling We sample 63,967 instructions from 6 public available and high-quality datasets. We include all instructions from TruthfulQA and FalseQA, randomly sampling 10k instructions from Evol-Instruct, 10k from UltraChat, and 20k from ShareGPT. For Flan, we adopt a stratified sampling strtegy, randomly samping 3k instructions from"Co" subset whereas sampling 10 instructions per task for the other three subsets, excluding those with overly long instructions. ```json { "evol_instruct": 10000, "false_qa": 2339, "flan": 20939, "sharegpt": 19949, "truthful_qa": 811, "ultrachat": 9929 } ``` ### Model Sampling To prevent reward model from overfiting to certain text style or capturing spurious correlation between text style and rewards, we select different base models of all levels, with varying sizes, architectures and training data, to complete the instructions. We set up a pool of 17 models: - Commercial Models: GPT-4, GPT-3.5 Turbo, Bard - LLaMA family: 1. LLaMA-2-7B-chat, LLaMA-2-13B-chat, LLaMA-2-70B-chat 2. UltraLM-13B, UltraLM-65B 3. WizardLM-7B, WizardLM-13B, WizardLM-70B 4. Vicuna-33B 5. Alpaca-7B - Non-LLaMA series: 1. Falcon-40B-instruct 2. MPT-30B-chat 3. StarChat-Beta 4. Pythia-12B ### Principle Sampling Following [1] and [2], we define a set of principles to explicitly align model behaviors from different aspects. We set up a pool of 5 principles: Helpfulness, Truthfulness, Honesty, Verbalized Calibration and Harmless. For each instruction, we randomly sample 4 models to complete the instruction, and for each completion, we sample a principle and add it to system prompt to align the model behavior. Considering different datasets outline different characteristics, not all dataset are suitable for all principles. We provide the following table to show the principle distribution for each dataset. | Datset | Principle | | ------------- | ------------------------------------------------------------ | | Evol Instruct | 100% Helpful | | FalseQA | 100% TruthfulQA | | Flan | 60% Helpful, 20% Truthful, 20% Verbalized Calibration | | ShareGPT | 60% Helpful, 20% Truthful, 18% Honesty, 2% Verbalized Calibration | | TruthfulQA | 100% Truthful | | UltraChat | 60% Helpful, 20% Truthful, 18% Honesty, 2% Verbalized Calibration | [1] Principle-Driven Self-Alignment of Language Models from Scratch with Minimal Human Supervision. Sun et al. [2] Orca: Progressive Learning from Complex Explanation Traces of GPT-4. Mukherjee et al. ## Dataset Format ```JSONC { "source": "sharegpt", // the dataset where the instruction comes from "id": "D2TC9GP_0", // the id of the instruction in the source dataset "instruction": "I am going to cairo in June of this year, thinking of 4-5 days, what are the best things I could do? Going with family of 5, granddad, grandmom, sister, mom and me. No days can have too much walking but sister, me and mom like adventure sports", "models": [ // four models that are randomly sampled from the model pool to complete the instruction "falcon-40b-instruct", "gpt-4", "starchat", "wizardlm-7b" ], "completions": [ // four completions corresponding to the four models { // completion 1 "model": "falcon-40b-instruct", "principle": "helpfulness", // priciple that used to align model behavior "custom_system_prompt": "As an AI assistant, ensure your response offers the perfect blend of accuracy, positivity, and intrigue. Strive to be educational while keeping the user engaged.", // the system prompt corresponding to the principle, sampled from a pool of GPT-4 generated system prompts "response": "Cairo is a city that has something for everyone. Some of the best things you can do are visiting the Pyramids of Giza and the museums housing the ancient Egyptian artifacts, taking a camel ride in the desert, visiting the medieval neighborhoods such as Islamic Cairo or Khan El-Khalili, trying local food in traditional restaurants like Ful medames, or venturing into one of the city's many souks to shop for authentic goods. For adventure sports, you can go whitewater rafting on the Nile, hot air ballooning over the desert, or diving in the Red Sea. Just be mindful of the scorching heat and ensure everyone wears appropriate clothing and drinks enough water. Above all, have fun and enjoy your trip!", "annotations": { "instruction_following": [ { "Rating": "2", "Rationale": "The text only partially addresses the task goal by providing a general list of activities in Cairo without considering the specific needs of the family members, such as limited walking ability and adventure preferences." } ], "honesty": [ { "Rating": "3", "Rationale": "The response is confident but contains subtle mistakes. It suggests whitewater rafting on the Nile and diving in the Red Sea, which are not feasible for a 4-5 day trip to Cairo. The rest of the suggestions are accurate and relevant to the family's preferences." } ], "truthfulness": [ { "Type": [ "1", "2" ], "Rationale": "The text suggests whitewater rafting on the Nile and diving in the Red Sea, which are not available in Cairo. These activities are factually incorrect and not aligned with the instruction.", "Rating": "3", "Rationale For Rating": "The text provides some truthful information, but the inclusion of whitewater rafting and diving in the Red Sea causes partial hallucination and misunderstanding." } ], "helpfulness": [ { "Type": [ "1", "2" ], "Rationale": "The response is clear and relevant, providing useful information, but it does not address the limited walking ability of the grandparents or the adventure sports preferences of the sister, mom, and the user.", "Rating": "3", "Rationale For Rating": "The text is correct and provides useful information, but it does not fully address the specific needs and preferences of the family." } ] } }, { // completion 2 ... }, { // completion 3 ... }, { // completion 4 ... } ] }, ``` ## Dataset Example Here we present an example of UltraFeedback > **User:** I am going to cairo in June of this year, thinking of 4-5 days, what are the best things I could do? Going with family of 5, granddad, grandmom, sister, mom and me. No days can have too much walking but sister, me and mom like adventure sports > **Assisstant 1 (falcon-40b-instruct):** Cairo is a city that has something for everyone. Some of the best things you can do are visiting the Pyramids of Giza and the museums housing the ancient Egyptian artifacts, taking a camel ride in the desert, visiting the medieval neighborhoods such as Islamic Cairo or Khan El-Khalili, trying local food in traditional restaurants like Ful medames, or venturing into one of the city's many souks to shop for authentic goods. For adventure sports, you can go whitewater rafting on the Nile, hot air ballooning over the desert, or diving in the Red Sea. Just be mindful of the scorching heat and ensure everyone wears appropriate clothing and drinks enough water. Above all, have fun and enjoy your trip! > **Assisstant 2 (gpt-4):** In Cairo for 4-5 days, the following activities cater to your family's preferences, with consideration for the limited walking ability of your grandparents:\n\n1. Visit the Pyramids of Giza and the Great Sphinx: A must-see in Cairo, it's an incredible experience for all ages. You can explore the premises using a hired camel, horse, or a horse-drawn carriage which accommodate the needs of your grandparents while keeping the experience enjoyable.\n\n2. Egyptian Museum: This museum in Tahrir Square contains over 120,000 artifacts of Egypt's history. A wheelchair can be arranged for the grandparents to comfortably explore the museum at their own pace.\n\n3. Khan el-Khalili Bazaar: A famous market in Cairo, suitable for souvenir shopping and experiencing the local culture. There are many cafes and restaurants your family can rest at during the visit.\n\n4. Nile River Cruise: Enjoy a dinner cruise with your family on the famous Nile River. It includes entertainment and a beautiful view of the city by night. Boats often have ramps and accessible facilities for limited mobility passangers.\n\n5. Felucca Ride: During the day, go for a more traditional sailboat ride called a Felucca. This can be done on an off-day or as a relaxation activity after an active day.\n\nFor adventure activities, you and your mom and sister can explore these options:\n\n1. Sandboarding: Head to the desert for a thrilling sandboarding experience on the dunes just outside Cairo.\n\n2. Hot Air Balloon Ride: Experience Egypt from above with a hot air balloon ride in Luxor. It would require a day trip from Cairo but is an incredible experience.\n\n3. Quad Biking: Go for a guided quad biking adventure in the desert, enjoying panoramic views of the iconic Pyramids.\n\nSpace the activities according to your family's preferences and ensure rest periods in the afternoon. Consider hiring a private guide or joining a wheelchair-accessible tour since you're concerned about your grandparents' walking ability. > **Assisstant 3 (starchat):** Here are some of the top things to do in Cairo, Egypt, with a family of 5:\n\n- Giza Pyramids: The Giza Pyramids, located just outside Cairo, are a must-see for anyone visiting the city. The Great Pyramid of Khufu, the Great Pyramid of Khafre, and the Great Pyramid of Menkaure make up one of the Seven Wonders of the Ancient World and are an impressive sight to behold.\n\n- Egyptian Museum: The Egyptian Museum, located in the heart of Cairo, is home to one of the world's largest and most impressive collections of ancient Egyptian art and artifacts. Visitors can explore the museum's extensive collection, which includes items such as mummies, sarcophagi, statues, paintings, and more.\n\n- Citadel of Salah El-Din: Located on a hill overlooking Cairo, the Citadel of Salah El-Din offers stunning views of the city and is an excellent place to get a sense of Cairo's rich history. Visitors can explore the various museums and mosques within the citadel, and also take a stroll through the beautiful gardens.\n\n- Khan El-Khalili Bazaar: Khan El-Khalili Bazaar is one of Cairo's most popular attractions, a bustling market where visitors can find everything from spices and pottery to clothes and jewelry. It's a great place to do some shopping and explore the local culture.\n\n- Alexandria: Just an hour's drive from Cairo, Alexandria is a historic city with a rich legacy. Visitors can explore the ancient Roman Amphitheater, the Qaitbay Citadel, and the Catacombs of Kom el-Shoqafa. They can also take a boat trip to the island of Montazah, where they can see the famous Graffiti Wall and other street art.\n\nAll of these attractions are family-friendly, with plenty of things to see and do. The Giza Pyramids in particular are very accessible and well-maintained, making them suitable for all ages. > **Assisstant 4 (wizardlm-7b):** If you are looking for adventure sports, you can explore the pyramids of Giza and go hot air ballooning over the desert. Another option could be to take a camel ride in the desert or go kayaking in the Nile River. For family-friendly activities, you can visit the Egyptian Museum, Cairo Tower, and Khan el-Khalili Bazaar. Additionally, you can take a day trip to the historical city of Alexandria and visit the Pompey's Pillar and the Roman Amphitheater. Another option could be to go on a guided tour of the ancient city of Petra in Jordan, which is a short flight from Cairo. ## Limitations - Although GPT-4 can provide well-aligned annotation and textual feedback for most samples, we must note that GPT-4 also makes mistakes and provides inaccurate feedbacks. ## Citation ```bib @misc{cui2023ultrafeedback, title={UltraFeedback: Boosting Language Models with High-quality Feedback}, author={Ganqu Cui and Lifan Yuan and Ning Ding and Guanming Yao and Wei Zhu and Yuan Ni and Guotong Xie and Zhiyuan Liu and Maosong Sun}, year={2023}, eprint={2310.01377}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

jat-project/jat-dataset-tokenized

jat-project

2023-12-22T22:17:42Z

661,795

0

[ "size_categories:10M<n<100M", "format:parquet", "modality:timeseries", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]

[]

2023-12-16T10:10:31Z

null

--- dataset_info: - config_name: atari-alien features: - name: image_observations sequence: sequence: sequence: sequence: float32 - name: rewards sequence: float32 - name: discrete_actions sequence: int64 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 51686398456 num_examples: 14134 - name: test num_bytes: 5412188320 num_examples: 1480 download_size: 847071867 dataset_size: 57098586776 - config_name: atari-amidar features: - name: image_observations sequence: sequence: sequence: sequence: float32 - name: rewards sequence: float32 - name: discrete_actions sequence: int64 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 52362921996 num_examples: 14319 - name: test num_bytes: 4808802460 num_examples: 1315 download_size: 645217608 dataset_size: 57171724456 - config_name: atari-assault features: - name: image_observations sequence: sequence: sequence: sequence: float32 - name: rewards sequence: float32 - name: discrete_actions sequence: int64 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 52757865468 num_examples: 14427 - name: test num_bytes: 4421172756 num_examples: 1209 download_size: 253415283 dataset_size: 57179038224 - config_name: atari-asterix features: - name: image_observations sequence: sequence: sequence: sequence: float32 - name: rewards sequence: float32 - name: discrete_actions sequence: int64 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 52863915104 num_examples: 14456 - name: test num_bytes: 5137922020 num_examples: 1405 download_size: 293282697 dataset_size: 58001837124 - config_name: atari-asteroids features: - name: image_observations sequence: sequence: sequence: sequence: float32 - name: rewards sequence: float32 - name: discrete_actions sequence: int64 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - 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config_name: atari-battlezone features: - name: image_observations sequence: sequence: sequence: sequence: float32 - name: rewards sequence: float32 - name: discrete_actions sequence: int64 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 51126895204 num_examples: 13981 - name: test num_bytes: 6092368744 num_examples: 1666 download_size: 530266996 dataset_size: 57219263948 - config_name: atari-beamrider features: - name: image_observations sequence: sequence: sequence: sequence: float32 - name: rewards sequence: float32 - name: discrete_actions sequence: int64 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 49155834728 num_examples: 13442 - name: test num_bytes: 7880585020 num_examples: 2155 download_size: 427025312 dataset_size: 57036419748 - config_name: atari-berzerk features: - name: image_observations sequence: sequence: sequence: sequence: float32 - name: rewards sequence: float32 - 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config_name: babyai-go-to-obj features: - name: discrete_observations sequence: sequence: int64 - name: discrete_actions sequence: int64 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 41759340000 num_examples: 95000 - name: test num_bytes: 2197860000 num_examples: 5000 download_size: 122037932 dataset_size: 43957200000 - config_name: babyai-go-to-obj-door features: - name: discrete_observations sequence: sequence: int64 - name: discrete_actions sequence: int64 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 41759340000 num_examples: 95000 - name: test num_bytes: 2197860000 num_examples: 5000 download_size: 133904822 dataset_size: 43957200000 - config_name: babyai-go-to-red-ball features: - name: discrete_observations sequence: sequence: int64 - name: discrete_actions sequence: int64 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 41759340000 num_examples: 95000 - name: test num_bytes: 2197860000 num_examples: 5000 download_size: 107941553 dataset_size: 43957200000 - config_name: babyai-go-to-red-ball-grey features: - name: discrete_observations sequence: sequence: int64 - name: discrete_actions sequence: int64 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 41759340000 num_examples: 95000 - name: test num_bytes: 2197860000 num_examples: 5000 download_size: 108701381 dataset_size: 43957200000 - config_name: babyai-go-to-red-ball-no-dists features: - name: discrete_observations sequence: sequence: int64 - name: discrete_actions sequence: int64 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 41759340000 num_examples: 95000 - name: test num_bytes: 2197860000 num_examples: 5000 download_size: 100751341 dataset_size: 43957200000 - config_name: babyai-go-to-red-blue-ball features: - name: discrete_observations sequence: sequence: int64 - name: discrete_actions sequence: int64 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 41759340000 num_examples: 95000 - name: test num_bytes: 2197860000 num_examples: 5000 download_size: 109835377 dataset_size: 43957200000 - config_name: babyai-go-to-seq features: - name: discrete_observations sequence: sequence: int64 - name: discrete_actions sequence: int64 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 41792307900 num_examples: 95075 - name: test num_bytes: 2198739144 num_examples: 5002 download_size: 288118166 dataset_size: 43991047044 - config_name: babyai-key-corridor features: - name: discrete_observations sequence: sequence: int64 - name: discrete_actions sequence: int64 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: test num_bytes: 2197860000 num_examples: 5000 - name: train num_bytes: 41759340000 num_examples: 95000 download_size: 273451937 dataset_size: 43957200000 - config_name: babyai-mini-boss-level features: - name: discrete_observations sequence: sequence: int64 - name: discrete_actions sequence: int64 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: test num_bytes: 2200497432 num_examples: 5006 - name: train num_bytes: 41821759224 num_examples: 95142 download_size: 167867886 dataset_size: 44022256656 - config_name: babyai-move-two-across-s8n9 features: - name: discrete_observations sequence: sequence: int64 - name: discrete_actions sequence: int64 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: test num_bytes: 2197860000 num_examples: 5000 - name: train num_bytes: 41759340000 num_examples: 95000 download_size: 268471454 dataset_size: 43957200000 - config_name: babyai-one-room-s8 features: - name: discrete_observations sequence: sequence: int64 - name: discrete_actions sequence: int64 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: test num_bytes: 2197860000 num_examples: 5000 - name: train num_bytes: 41759340000 num_examples: 95000 download_size: 101603110 dataset_size: 43957200000 - config_name: babyai-open features: - name: discrete_observations sequence: sequence: int64 - name: discrete_actions sequence: int64 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: test num_bytes: 2197860000 num_examples: 5000 - name: train num_bytes: 41759340000 num_examples: 95000 download_size: 181194361 dataset_size: 43957200000 - config_name: babyai-open-door features: - name: discrete_observations sequence: sequence: int64 - name: discrete_actions sequence: int64 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: test num_bytes: 2197860000 num_examples: 5000 - name: train num_bytes: 41759340000 num_examples: 95000 download_size: 127824190 dataset_size: 43957200000 - config_name: babyai-open-doors-order-n4 features: - name: discrete_observations sequence: sequence: int64 - name: discrete_actions sequence: int64 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: test num_bytes: 2197860000 num_examples: 5000 - name: train num_bytes: 41759340000 num_examples: 95000 download_size: 127418529 dataset_size: 43957200000 - config_name: babyai-open-red-door features: - name: discrete_observations sequence: sequence: int64 - name: discrete_actions sequence: int64 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: test num_bytes: 2197860000 num_examples: 5000 - name: train num_bytes: 41759340000 num_examples: 95000 download_size: 78248393 dataset_size: 43957200000 - config_name: babyai-open-two-doors features: - name: discrete_observations sequence: sequence: int64 - name: discrete_actions sequence: int64 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: test num_bytes: 2197860000 num_examples: 5000 - name: train num_bytes: 41759340000 num_examples: 95000 download_size: 130542191 dataset_size: 43957200000 - config_name: babyai-pickup features: - name: discrete_observations sequence: sequence: int64 - name: discrete_actions sequence: int64 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: test num_bytes: 2197860000 num_examples: 5000 - name: train num_bytes: 41759340000 num_examples: 95000 download_size: 236053290 dataset_size: 43957200000 - 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name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: test num_bytes: 2207530584 num_examples: 5022 - name: train num_bytes: 41981763432 num_examples: 95506 download_size: 326087180 dataset_size: 44189294016 - config_name: babyai-unblock-pickup features: - name: discrete_observations sequence: sequence: int64 - name: discrete_actions sequence: int64 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: test num_bytes: 2197860000 num_examples: 5000 - name: train num_bytes: 41765933580 num_examples: 95015 download_size: 241680488 dataset_size: 43963793580 - config_name: babyai-unlock features: - name: discrete_observations sequence: sequence: int64 - name: discrete_actions sequence: int64 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 43259159664 num_examples: 98412 - name: test num_bytes: 883979292 num_examples: 2011 download_size: 328757743 dataset_size: 44143138956 - config_name: babyai-unlock-local features: - name: discrete_observations sequence: sequence: int64 - name: discrete_actions sequence: int64 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: test num_bytes: 2197860000 num_examples: 5000 - name: train num_bytes: 41759340000 num_examples: 95000 download_size: 116723486 dataset_size: 43957200000 - config_name: babyai-unlock-pickup features: - name: discrete_observations sequence: sequence: int64 - name: discrete_actions sequence: int64 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: test num_bytes: 2197860000 num_examples: 5000 - name: train num_bytes: 41759340000 num_examples: 95000 download_size: 137214787 dataset_size: 43957200000 - config_name: babyai-unlock-to-unlock features: - name: discrete_observations sequence: sequence: int64 - name: discrete_actions sequence: int64 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 43078056000 num_examples: 98000 - name: test num_bytes: 879144000 num_examples: 2000 download_size: 158735389 dataset_size: 43957200000 - config_name: conceptual-captions features: - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: pixel_values sequence: sequence: sequence: float32 - name: loss_weight sequence: float32 splits: - name: test num_bytes: 7574631480 num_examples: 12465 - name: train num_bytes: 303836000000 num_examples: 500000 download_size: 82071298648 dataset_size: 311410631480 - config_name: metaworld-assembly features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 774464000 num_examples: 16000 - name: test num_bytes: 77446400 num_examples: 1600 download_size: 64267084 dataset_size: 851910400 - config_name: metaworld-basketball features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 774464000 num_examples: 16000 - name: test num_bytes: 77446400 num_examples: 1600 download_size: 162412290 dataset_size: 851910400 - config_name: metaworld-bin-picking features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 774464000 num_examples: 16000 - name: test num_bytes: 77446400 num_examples: 1600 download_size: 168127631 dataset_size: 851910400 - config_name: metaworld-box-close features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 774464000 num_examples: 16000 - name: test num_bytes: 77446400 num_examples: 1600 download_size: 174656572 dataset_size: 851910400 - config_name: metaworld-button-press features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 774464000 num_examples: 16000 - name: test num_bytes: 77446400 num_examples: 1600 download_size: 106951062 dataset_size: 851910400 - config_name: metaworld-button-press-topdown features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 774464000 num_examples: 16000 - name: test num_bytes: 77446400 num_examples: 1600 download_size: 117078197 dataset_size: 851910400 - config_name: metaworld-button-press-topdown-wall features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 774464000 num_examples: 16000 - name: test num_bytes: 77446400 num_examples: 1600 download_size: 119641275 dataset_size: 851910400 - config_name: metaworld-button-press-wall features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 774464000 num_examples: 16000 - name: test num_bytes: 77446400 num_examples: 1600 download_size: 112458551 dataset_size: 851910400 - config_name: metaworld-coffee-button features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 774464000 num_examples: 16000 - name: test num_bytes: 77446400 num_examples: 1600 download_size: 112608052 dataset_size: 851910400 - config_name: metaworld-coffee-pull features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 774464000 num_examples: 16000 - name: test num_bytes: 77446400 num_examples: 1600 download_size: 161591807 dataset_size: 851910400 - config_name: metaworld-coffee-push features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 774464000 num_examples: 16000 - name: test num_bytes: 77446400 num_examples: 1600 download_size: 173247466 dataset_size: 851910400 - config_name: metaworld-dial-turn features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 774464000 num_examples: 16000 - name: test num_bytes: 77446400 num_examples: 1600 download_size: 102519630 dataset_size: 851910400 - config_name: metaworld-disassemble features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 774464000 num_examples: 16000 - name: test num_bytes: 77446400 num_examples: 1600 download_size: 72920062 dataset_size: 851910400 - config_name: metaworld-door-close features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 774464000 num_examples: 16000 - name: test num_bytes: 77446400 num_examples: 1600 download_size: 153530521 dataset_size: 851910400 - config_name: metaworld-door-lock features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 774464000 num_examples: 16000 - name: test num_bytes: 77446400 num_examples: 1600 download_size: 123855874 dataset_size: 851910400 - config_name: metaworld-door-open features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 774464000 num_examples: 16000 - name: test num_bytes: 77446400 num_examples: 1600 download_size: 140905068 dataset_size: 851910400 - config_name: metaworld-door-unlock features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 774464000 num_examples: 16000 - name: test num_bytes: 77446400 num_examples: 1600 download_size: 121700706 dataset_size: 851910400 - config_name: metaworld-drawer-close features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 774464000 num_examples: 16000 - name: test num_bytes: 77446400 num_examples: 1600 download_size: 101417660 dataset_size: 851910400 - config_name: metaworld-drawer-open features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 774464000 num_examples: 16000 - name: test num_bytes: 77446400 num_examples: 1600 download_size: 96573298 dataset_size: 851910400 - config_name: metaworld-faucet-close features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 774464000 num_examples: 16000 - name: test num_bytes: 77446400 num_examples: 1600 download_size: 89353472 dataset_size: 851910400 - config_name: metaworld-faucet-open features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 774464000 num_examples: 16000 - name: test num_bytes: 77446400 num_examples: 1600 download_size: 96651789 dataset_size: 851910400 - config_name: metaworld-hammer features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 774464000 num_examples: 16000 - name: test num_bytes: 77446400 num_examples: 1600 download_size: 177539984 dataset_size: 851910400 - config_name: metaworld-hand-insert features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 774464000 num_examples: 16000 - name: test num_bytes: 77446400 num_examples: 1600 download_size: 135665012 dataset_size: 851910400 - config_name: metaworld-handle-press features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 774464000 num_examples: 16000 - name: test num_bytes: 77446400 num_examples: 1600 download_size: 103407785 dataset_size: 851910400 - config_name: metaworld-handle-press-side features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 774464000 num_examples: 16000 - name: test num_bytes: 77446400 num_examples: 1600 download_size: 103403469 dataset_size: 851910400 - config_name: metaworld-handle-pull features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 774464000 num_examples: 16000 - name: test num_bytes: 77446400 num_examples: 1600 download_size: 121440284 dataset_size: 851910400 - config_name: metaworld-handle-pull-side features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 774464000 num_examples: 16000 - name: test num_bytes: 77446400 num_examples: 1600 download_size: 118413651 dataset_size: 851910400 - config_name: metaworld-lever-pull features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 774464000 num_examples: 16000 - name: test num_bytes: 77446400 num_examples: 1600 download_size: 168776851 dataset_size: 851910400 - config_name: metaworld-peg-insert-side features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 774464000 num_examples: 16000 - name: test num_bytes: 77446400 num_examples: 1600 download_size: 153705593 dataset_size: 851910400 - config_name: metaworld-peg-unplug-side features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 774464000 num_examples: 16000 - name: test num_bytes: 77446400 num_examples: 1600 download_size: 171742157 dataset_size: 851910400 - config_name: metaworld-pick-out-of-hole features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 774464000 num_examples: 16000 - name: test num_bytes: 77446400 num_examples: 1600 download_size: 22274303 dataset_size: 851910400 - config_name: metaworld-pick-place features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 774464000 num_examples: 16000 - name: test num_bytes: 77446400 num_examples: 1600 download_size: 176678495 dataset_size: 851910400 - config_name: metaworld-pick-place-wall features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 774464000 num_examples: 16000 - name: test num_bytes: 77446400 num_examples: 1600 download_size: 172257534 dataset_size: 851910400 - config_name: metaworld-plate-slide features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 774464000 num_examples: 16000 - 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name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 774464000 num_examples: 16000 - name: test num_bytes: 77446400 num_examples: 1600 download_size: 106392923 dataset_size: 851910400 - config_name: metaworld-push features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 774464000 num_examples: 16000 - name: test num_bytes: 77446400 num_examples: 1600 download_size: 166180034 dataset_size: 851910400 - config_name: metaworld-push-back features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 774464000 num_examples: 16000 - name: test num_bytes: 77446400 num_examples: 1600 download_size: 133027374 dataset_size: 851910400 - config_name: metaworld-push-wall features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 774464000 num_examples: 16000 - name: test num_bytes: 77446400 num_examples: 1600 download_size: 158267234 dataset_size: 851910400 - config_name: metaworld-reach features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 774464000 num_examples: 16000 - name: test num_bytes: 77446400 num_examples: 1600 download_size: 168663459 dataset_size: 851910400 - config_name: metaworld-reach-wall features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 774464000 num_examples: 16000 - name: test num_bytes: 77446400 num_examples: 1600 download_size: 171608203 dataset_size: 851910400 - config_name: metaworld-shelf-place features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 774464000 num_examples: 16000 - name: test num_bytes: 77446400 num_examples: 1600 download_size: 142334952 dataset_size: 851910400 - config_name: metaworld-soccer features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 774464000 num_examples: 16000 - name: test num_bytes: 77446400 num_examples: 1600 download_size: 159081606 dataset_size: 851910400 - config_name: metaworld-stick-pull features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 774464000 num_examples: 16000 - name: test num_bytes: 77446400 num_examples: 1600 download_size: 170289154 dataset_size: 851910400 - config_name: metaworld-stick-push features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 774464000 num_examples: 16000 - name: test num_bytes: 77446400 num_examples: 1600 download_size: 166125948 dataset_size: 851910400 - config_name: metaworld-sweep features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 774464000 num_examples: 16000 - name: test num_bytes: 77446400 num_examples: 1600 download_size: 164632354 dataset_size: 851910400 - config_name: metaworld-sweep-into features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 774464000 num_examples: 16000 - name: test num_bytes: 77446400 num_examples: 1600 download_size: 135177252 dataset_size: 851910400 - config_name: metaworld-window-close features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 774464000 num_examples: 16000 - name: test num_bytes: 77446400 num_examples: 1600 download_size: 95044772 dataset_size: 851910400 - config_name: metaworld-window-open features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 774464000 num_examples: 16000 - name: test num_bytes: 77446400 num_examples: 1600 download_size: 95793720 dataset_size: 851910400 - config_name: mujoco-ant features: - 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name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 1005264000 num_examples: 36000 - name: test num_bytes: 111696000 num_examples: 4000 download_size: 1055030042 dataset_size: 1116960000 - config_name: mujoco-hopper features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 377714520 num_examples: 20190 - name: test num_bytes: 41774964 num_examples: 2233 download_size: 343653363 dataset_size: 419489484 - config_name: mujoco-humanoid features: - name: continuous_observations sequence: sequence: float32 - name: rewards sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 13565692988 num_examples: 33347 - name: test num_bytes: 1509649644 num_examples: 3711 download_size: 10439047554 dataset_size: 15075342632 - config_name: mujoco-pendulum features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 201391764 num_examples: 21217 - name: test num_bytes: 22334676 num_examples: 2353 download_size: 134650231 dataset_size: 223726440 - config_name: mujoco-pusher features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 315828000 num_examples: 9000 - name: test num_bytes: 35092000 num_examples: 1000 download_size: 134738418 dataset_size: 350920000 - config_name: mujoco-reacher features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 159156000 num_examples: 9000 - name: test num_bytes: 17684000 num_examples: 1000 download_size: 38441946 dataset_size: 176840000 - config_name: mujoco-standup features: - name: rewards sequence: float32 - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 14644944000 num_examples: 36000 - name: test num_bytes: 1627216000 num_examples: 4000 download_size: 11711102671 dataset_size: 16272160000 - config_name: mujoco-swimmer features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 526032000 num_examples: 36000 - name: test num_bytes: 58448000 num_examples: 4000 download_size: 519559720 dataset_size: 584480000 - config_name: mujoco-walker features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 944529300 num_examples: 33825 - name: test num_bytes: 104798772 num_examples: 3753 download_size: 954326371 dataset_size: 1049328072 - config_name: ok-vqa features: - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: pixel_values sequence: sequence: sequence: float32 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 5474517048 num_examples: 9009 - name: test num_bytes: 3066312912 num_examples: 5046 download_size: 2461083826 dataset_size: 8540829960 - config_name: oscar features: - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 58269773100 num_examples: 12612505 - name: test num_bytes: 63899220 num_examples: 13831 download_size: 10788173669 dataset_size: 58333672320 - config_name: wikipedia features: - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float32 splits: - name: train num_bytes: 59293939320 num_examples: 12834186 - name: test num_bytes: 58216620 num_examples: 12601 download_size: 10100547139 dataset_size: 59352155940 configs: - config_name: atari-alien data_files: - split: train path: atari-alien/train-* - split: test path: atari-alien/test-* - config_name: atari-amidar data_files: - split: train path: atari-amidar/train-* - split: test path: atari-amidar/test-* - config_name: atari-assault data_files: - split: train path: atari-assault/train-* - split: test path: atari-assault/test-* - config_name: atari-asterix data_files: - split: train path: atari-asterix/train-* - split: test path: atari-asterix/test-* - config_name: atari-asteroids data_files: - split: train path: atari-asteroids/train-* - split: test path: atari-asteroids/test-* - config_name: atari-atlantis data_files: - split: train path: atari-atlantis/train-* - split: test path: atari-atlantis/test-* - config_name: atari-bankheist data_files: - split: train path: atari-bankheist/train-* - split: test path: atari-bankheist/test-* - config_name: atari-battlezone data_files: - split: train path: atari-battlezone/train-* - split: test path: atari-battlezone/test-* - config_name: atari-beamrider data_files: - split: train path: atari-beamrider/train-* - split: test path: atari-beamrider/test-* - config_name: atari-berzerk data_files: - split: train path: atari-berzerk/train-* - split: test path: atari-berzerk/test-* - config_name: atari-bowling data_files: - split: train path: atari-bowling/train-* - split: test path: atari-bowling/test-* - config_name: atari-boxing data_files: - split: train path: atari-boxing/train-* - split: test path: atari-boxing/test-* - config_name: atari-breakout data_files: - split: train path: atari-breakout/train-* - split: test path: atari-breakout/test-* - config_name: atari-centipede data_files: - split: train path: atari-centipede/train-* - split: test path: atari-centipede/test-* - config_name: atari-choppercommand data_files: - split: train path: atari-choppercommand/train-* - split: test path: atari-choppercommand/test-* - config_name: atari-crazyclimber data_files: - split: train path: atari-crazyclimber/train-* - split: test path: atari-crazyclimber/test-* - config_name: atari-defender data_files: - split: train path: atari-defender/train-* - split: test path: atari-defender/test-* - config_name: atari-demonattack data_files: - split: train path: atari-demonattack/train-* - split: test path: atari-demonattack/test-* - config_name: atari-doubledunk data_files: - split: test path: atari-doubledunk/test-* - split: train path: atari-doubledunk/train-* - config_name: atari-enduro data_files: - split: train path: atari-enduro/train-* - split: test path: atari-enduro/test-* - config_name: atari-fishingderby data_files: - split: train path: atari-fishingderby/train-* - split: test path: atari-fishingderby/test-* - config_name: atari-freeway data_files: - split: train path: atari-freeway/train-* - split: test path: atari-freeway/test-* - config_name: atari-frostbite data_files: - split: train path: atari-frostbite/train-* - split: test path: atari-frostbite/test-* - config_name: atari-gopher data_files: - split: train path: atari-gopher/train-* - split: test path: atari-gopher/test-* - config_name: atari-gravitar data_files: - split: train path: atari-gravitar/train-* - split: test path: atari-gravitar/test-* - config_name: atari-hero data_files: - split: train path: atari-hero/train-* - split: test path: atari-hero/test-* - config_name: atari-icehockey data_files: - split: train path: atari-icehockey/train-* - split: test path: atari-icehockey/test-* - config_name: atari-jamesbond data_files: - split: train path: atari-jamesbond/train-* - split: test path: atari-jamesbond/test-* - config_name: atari-kangaroo data_files: - split: train path: atari-kangaroo/train-* - split: test path: atari-kangaroo/test-* - config_name: atari-krull data_files: - split: train path: atari-krull/train-* - split: test path: atari-krull/test-* - config_name: atari-kungfumaster data_files: - split: train path: atari-kungfumaster/train-* - split: test path: atari-kungfumaster/test-* - config_name: atari-montezumarevenge data_files: - split: train path: atari-montezumarevenge/train-* - split: test path: atari-montezumarevenge/test-* - config_name: atari-mspacman data_files: - split: train path: atari-mspacman/train-* - split: test path: atari-mspacman/test-* - config_name: atari-namethisgame data_files: - split: train path: atari-namethisgame/train-* - split: test path: atari-namethisgame/test-* - config_name: atari-phoenix data_files: - split: train path: atari-phoenix/train-* - split: test path: atari-phoenix/test-* - config_name: atari-pitfall data_files: - split: train path: atari-pitfall/train-* - split: test path: atari-pitfall/test-* - config_name: atari-pong data_files: - split: test path: atari-pong/test-* - split: train path: atari-pong/train-* - config_name: atari-privateeye data_files: - split: test path: atari-privateeye/test-* - split: train path: atari-privateeye/train-* - config_name: atari-qbert data_files: - split: test path: atari-qbert/test-* - split: train path: atari-qbert/train-* - config_name: atari-riverraid data_files: - split: test path: atari-riverraid/test-* - split: train path: atari-riverraid/train-* - config_name: atari-roadrunner data_files: - split: test path: atari-roadrunner/test-* - split: train path: atari-roadrunner/train-* - config_name: atari-robotank data_files: - split: test path: atari-robotank/test-* - split: train path: atari-robotank/train-* - config_name: atari-seaquest data_files: - split: test path: atari-seaquest/test-* - split: train path: atari-seaquest/train-* - config_name: atari-skiing data_files: - split: train path: atari-skiing/train-* - split: test path: atari-skiing/test-* - config_name: atari-solaris data_files: - split: train path: atari-solaris/train-* - split: test path: atari-solaris/test-* - config_name: atari-spaceinvaders data_files: - split: train path: atari-spaceinvaders/train-* - split: test path: atari-spaceinvaders/test-* - config_name: atari-stargunner data_files: - split: train path: atari-stargunner/train-* - split: test path: atari-stargunner/test-* - config_name: atari-surround data_files: - split: train path: atari-surround/train-* - split: test path: atari-surround/test-* - config_name: atari-tennis data_files: - split: train path: atari-tennis/train-* - split: test path: atari-tennis/test-* - config_name: atari-timepilot data_files: - split: train path: atari-timepilot/train-* - split: test path: atari-timepilot/test-* - config_name: atari-tutankham data_files: - split: train path: atari-tutankham/train-* - split: test path: atari-tutankham/test-* - config_name: atari-upndown data_files: - split: train path: atari-upndown/train-* - split: test path: atari-upndown/test-* - config_name: atari-venture data_files: - split: test path: atari-venture/test-* - split: train path: atari-venture/train-* - config_name: atari-videopinball data_files: - split: test path: atari-videopinball/test-* - split: train path: atari-videopinball/train-* - config_name: atari-wizardofwor data_files: - split: test path: atari-wizardofwor/test-* - split: train path: atari-wizardofwor/train-* - config_name: atari-yarsrevenge data_files: - split: test path: atari-yarsrevenge/test-* - split: train path: atari-yarsrevenge/train-* - config_name: atari-zaxxon data_files: - split: test path: atari-zaxxon/test-* - split: train path: atari-zaxxon/train-* - config_name: babyai-action-obj-door data_files: - split: train path: babyai-action-obj-door/train-* - split: test path: babyai-action-obj-door/test-* - config_name: babyai-blocked-unlock-pickup data_files: - split: test path: babyai-blocked-unlock-pickup/test-* - split: train path: babyai-blocked-unlock-pickup/train-* - config_name: babyai-boss-level data_files: - split: test path: babyai-boss-level/test-* - split: train path: babyai-boss-level/train-* - config_name: babyai-boss-level-no-unlock data_files: - split: test path: babyai-boss-level-no-unlock/test-* - split: train path: babyai-boss-level-no-unlock/train-* - config_name: babyai-find-obj-s5 data_files: - split: train path: babyai-find-obj-s5/train-* - split: test path: babyai-find-obj-s5/test-* - config_name: babyai-go-to data_files: - split: train path: babyai-go-to/train-* - split: test path: babyai-go-to/test-* - config_name: babyai-go-to-door data_files: - split: train path: babyai-go-to-door/train-* - split: test path: babyai-go-to-door/test-* - config_name: babyai-go-to-imp-unlock data_files: - split: train path: babyai-go-to-imp-unlock/train-* - split: test path: babyai-go-to-imp-unlock/test-* - config_name: babyai-go-to-local data_files: - split: train path: babyai-go-to-local/train-* - split: test path: babyai-go-to-local/test-* - config_name: babyai-go-to-obj data_files: - split: train path: babyai-go-to-obj/train-* - split: test path: babyai-go-to-obj/test-* - config_name: babyai-go-to-obj-door data_files: - split: train path: babyai-go-to-obj-door/train-* - split: test path: babyai-go-to-obj-door/test-* - config_name: babyai-go-to-red-ball data_files: - split: train path: babyai-go-to-red-ball/train-* - split: test path: babyai-go-to-red-ball/test-* - config_name: babyai-go-to-red-ball-grey data_files: - split: train path: babyai-go-to-red-ball-grey/train-* - split: test path: babyai-go-to-red-ball-grey/test-* - config_name: babyai-go-to-red-ball-no-dists data_files: - split: train path: babyai-go-to-red-ball-no-dists/train-* - split: test path: babyai-go-to-red-ball-no-dists/test-* - config_name: babyai-go-to-red-blue-ball data_files: - split: train path: babyai-go-to-red-blue-ball/train-* - split: test path: babyai-go-to-red-blue-ball/test-* - config_name: babyai-go-to-seq data_files: - split: train path: babyai-go-to-seq/train-* - split: test path: babyai-go-to-seq/test-* - config_name: babyai-key-corridor data_files: - split: test path: babyai-key-corridor/test-* - split: train path: babyai-key-corridor/train-* - config_name: babyai-mini-boss-level data_files: - split: test path: babyai-mini-boss-level/test-* - split: train path: babyai-mini-boss-level/train-* - config_name: babyai-move-two-across-s8n9 data_files: - split: test path: babyai-move-two-across-s8n9/test-* - split: train path: babyai-move-two-across-s8n9/train-* - config_name: babyai-one-room-s8 data_files: - split: test path: babyai-one-room-s8/test-* - split: train path: babyai-one-room-s8/train-* - config_name: babyai-open data_files: - split: test path: babyai-open/test-* - split: train path: babyai-open/train-* - config_name: babyai-open-door data_files: - split: test path: babyai-open-door/test-* - split: train path: babyai-open-door/train-* - config_name: babyai-open-doors-order-n4 data_files: - split: test path: babyai-open-doors-order-n4/test-* - split: train path: babyai-open-doors-order-n4/train-* - config_name: babyai-open-red-door data_files: - split: test path: babyai-open-red-door/test-* - split: train path: babyai-open-red-door/train-* - config_name: babyai-open-two-doors data_files: - split: test path: babyai-open-two-doors/test-* - split: train path: babyai-open-two-doors/train-* - config_name: babyai-pickup data_files: - split: test path: babyai-pickup/test-* - split: train path: babyai-pickup/train-* - config_name: babyai-pickup-above data_files: - split: test path: babyai-pickup-above/test-* - split: train path: babyai-pickup-above/train-* - config_name: babyai-pickup-dist data_files: - split: test path: babyai-pickup-dist/test-* - split: train path: babyai-pickup-dist/train-* - config_name: babyai-pickup-loc data_files: - split: test path: babyai-pickup-loc/test-* - split: train path: babyai-pickup-loc/train-* - config_name: babyai-put-next data_files: - split: train path: babyai-put-next/train-* - split: test path: babyai-put-next/test-* - config_name: babyai-put-next-local data_files: - split: train path: babyai-put-next-local/train-* - split: test path: babyai-put-next-local/test-* - config_name: babyai-synth data_files: - split: test path: babyai-synth/test-* - split: train path: babyai-synth/train-* - config_name: babyai-synth-loc data_files: - split: test path: babyai-synth-loc/test-* - split: train path: babyai-synth-loc/train-* - config_name: babyai-synth-seq data_files: - split: test path: babyai-synth-seq/test-* - split: train path: babyai-synth-seq/train-* - config_name: babyai-unblock-pickup data_files: - split: test path: babyai-unblock-pickup/test-* - split: train path: babyai-unblock-pickup/train-* - config_name: babyai-unlock data_files: - split: train path: babyai-unlock/train-* - split: test path: babyai-unlock/test-* - config_name: babyai-unlock-local data_files: - split: test path: babyai-unlock-local/test-* - split: train path: babyai-unlock-local/train-* - config_name: babyai-unlock-pickup data_files: - split: test path: babyai-unlock-pickup/test-* - split: train path: babyai-unlock-pickup/train-* - config_name: babyai-unlock-to-unlock data_files: - split: train path: babyai-unlock-to-unlock/train-* - split: test path: babyai-unlock-to-unlock/test-* - config_name: conceptual-captions data_files: - split: test path: conceptual-captions/test-* - split: train path: conceptual-captions/train-* - config_name: metaworld-assembly data_files: - split: train path: metaworld-assembly/train-* - split: test path: metaworld-assembly/test-* - config_name: metaworld-basketball data_files: - split: train path: metaworld-basketball/train-* - split: test path: metaworld-basketball/test-* - config_name: metaworld-bin-picking data_files: - split: train path: metaworld-bin-picking/train-* - split: test path: metaworld-bin-picking/test-* - config_name: metaworld-box-close data_files: - split: train path: metaworld-box-close/train-* - split: test path: metaworld-box-close/test-* - config_name: metaworld-button-press data_files: - split: train path: metaworld-button-press/train-* - split: test path: metaworld-button-press/test-* - config_name: metaworld-button-press-topdown data_files: - split: train path: metaworld-button-press-topdown/train-* - split: test path: metaworld-button-press-topdown/test-* - config_name: metaworld-button-press-topdown-wall data_files: - split: train path: metaworld-button-press-topdown-wall/train-* - split: test path: metaworld-button-press-topdown-wall/test-* - config_name: metaworld-button-press-wall data_files: - split: train path: metaworld-button-press-wall/train-* - split: test path: metaworld-button-press-wall/test-* - config_name: metaworld-coffee-button data_files: - split: train path: metaworld-coffee-button/train-* - split: test path: metaworld-coffee-button/test-* - config_name: metaworld-coffee-pull data_files: - split: train path: metaworld-coffee-pull/train-* - split: test path: metaworld-coffee-pull/test-* - config_name: metaworld-coffee-push data_files: - split: train path: metaworld-coffee-push/train-* - split: test path: metaworld-coffee-push/test-* - config_name: metaworld-dial-turn data_files: - split: train path: metaworld-dial-turn/train-* - split: test path: metaworld-dial-turn/test-* - config_name: metaworld-disassemble data_files: - split: train path: metaworld-disassemble/train-* - split: test path: metaworld-disassemble/test-* - config_name: metaworld-door-close data_files: - split: train path: metaworld-door-close/train-* - split: test path: metaworld-door-close/test-* - config_name: metaworld-door-lock data_files: - split: train path: metaworld-door-lock/train-* - split: test path: metaworld-door-lock/test-* - config_name: metaworld-door-open data_files: - split: train path: metaworld-door-open/train-* - split: test path: metaworld-door-open/test-* - config_name: metaworld-door-unlock data_files: - split: train path: metaworld-door-unlock/train-* - split: test path: metaworld-door-unlock/test-* - config_name: metaworld-drawer-close data_files: - split: train path: metaworld-drawer-close/train-* - split: test path: metaworld-drawer-close/test-* - config_name: metaworld-drawer-open data_files: - split: train path: metaworld-drawer-open/train-* - split: test path: metaworld-drawer-open/test-* - config_name: metaworld-faucet-close data_files: - split: train path: metaworld-faucet-close/train-* - split: test path: metaworld-faucet-close/test-* - config_name: metaworld-faucet-open data_files: - split: train path: metaworld-faucet-open/train-* - split: test path: metaworld-faucet-open/test-* - config_name: metaworld-hammer data_files: - split: train path: metaworld-hammer/train-* - split: test path: metaworld-hammer/test-* - config_name: metaworld-hand-insert data_files: - split: train path: metaworld-hand-insert/train-* - split: test path: metaworld-hand-insert/test-* - config_name: metaworld-handle-press data_files: - split: train path: metaworld-handle-press/train-* - split: test path: metaworld-handle-press/test-* - config_name: metaworld-handle-press-side data_files: - split: train path: metaworld-handle-press-side/train-* - split: test path: metaworld-handle-press-side/test-* - config_name: metaworld-handle-pull data_files: - split: train path: metaworld-handle-pull/train-* - split: test path: metaworld-handle-pull/test-* - config_name: metaworld-handle-pull-side data_files: - split: train path: metaworld-handle-pull-side/train-* - split: test path: metaworld-handle-pull-side/test-* - config_name: metaworld-lever-pull data_files: - split: train path: metaworld-lever-pull/train-* - split: test path: metaworld-lever-pull/test-* - config_name: metaworld-peg-insert-side data_files: - split: train path: metaworld-peg-insert-side/train-* - split: test path: metaworld-peg-insert-side/test-* - config_name: metaworld-peg-unplug-side data_files: - split: train path: metaworld-peg-unplug-side/train-* - split: test path: metaworld-peg-unplug-side/test-* - config_name: metaworld-pick-out-of-hole data_files: - split: train path: metaworld-pick-out-of-hole/train-* - split: test path: metaworld-pick-out-of-hole/test-* - config_name: metaworld-pick-place data_files: - split: train path: metaworld-pick-place/train-* - split: test path: metaworld-pick-place/test-* - config_name: metaworld-pick-place-wall data_files: - split: train path: metaworld-pick-place-wall/train-* - split: test path: metaworld-pick-place-wall/test-* - config_name: metaworld-plate-slide data_files: - split: train path: metaworld-plate-slide/train-* - split: test path: metaworld-plate-slide/test-* - config_name: metaworld-plate-slide-back data_files: - split: train path: metaworld-plate-slide-back/train-* - split: test path: metaworld-plate-slide-back/test-* - config_name: metaworld-plate-slide-back-side data_files: - split: train path: metaworld-plate-slide-back-side/train-* - split: test path: metaworld-plate-slide-back-side/test-* - config_name: metaworld-plate-slide-side data_files: - split: train path: metaworld-plate-slide-side/train-* - split: test path: metaworld-plate-slide-side/test-* - config_name: metaworld-push data_files: - split: train path: metaworld-push/train-* - split: test path: metaworld-push/test-* - config_name: metaworld-push-back data_files: - split: train path: metaworld-push-back/train-* - split: test path: metaworld-push-back/test-* - config_name: metaworld-push-wall data_files: - split: train path: metaworld-push-wall/train-* - split: test path: metaworld-push-wall/test-* - config_name: metaworld-reach data_files: - split: train path: metaworld-reach/train-* - split: test path: metaworld-reach/test-* - config_name: metaworld-reach-wall data_files: - split: train path: metaworld-reach-wall/train-* - split: test path: metaworld-reach-wall/test-* - config_name: metaworld-shelf-place data_files: - split: train path: metaworld-shelf-place/train-* - split: test path: metaworld-shelf-place/test-* - config_name: metaworld-soccer data_files: - split: train path: metaworld-soccer/train-* - split: test path: metaworld-soccer/test-* - config_name: metaworld-stick-pull data_files: - split: train path: metaworld-stick-pull/train-* - split: test path: metaworld-stick-pull/test-* - config_name: metaworld-stick-push data_files: - split: train path: metaworld-stick-push/train-* - split: test path: metaworld-stick-push/test-* - config_name: metaworld-sweep data_files: - split: train path: metaworld-sweep/train-* - split: test path: metaworld-sweep/test-* - config_name: metaworld-sweep-into data_files: - split: train path: metaworld-sweep-into/train-* - split: test path: metaworld-sweep-into/test-* - config_name: metaworld-window-close data_files: - split: train path: metaworld-window-close/train-* - split: test path: metaworld-window-close/test-* - config_name: metaworld-window-open data_files: - split: train path: metaworld-window-open/train-* - split: test path: metaworld-window-open/test-* - config_name: mujoco-ant data_files: - split: train path: mujoco-ant/train-* - split: test path: mujoco-ant/test-* - config_name: mujoco-doublependulum data_files: - split: train path: mujoco-doublependulum/train-* - split: test path: mujoco-doublependulum/test-* - config_name: mujoco-halfcheetah data_files: - split: train path: mujoco-halfcheetah/train-* - split: test path: mujoco-halfcheetah/test-* - config_name: mujoco-hopper data_files: - split: train path: mujoco-hopper/train-* - split: test path: mujoco-hopper/test-* - config_name: mujoco-humanoid data_files: - split: train path: mujoco-humanoid/train-* - split: test path: mujoco-humanoid/test-* - config_name: mujoco-pendulum data_files: - split: train path: mujoco-pendulum/train-* - split: test path: mujoco-pendulum/test-* - config_name: mujoco-pusher data_files: - split: train path: mujoco-pusher/train-* - split: test path: mujoco-pusher/test-* - config_name: mujoco-reacher data_files: - split: train path: mujoco-reacher/train-* - split: test path: mujoco-reacher/test-* - config_name: mujoco-standup data_files: - split: train path: mujoco-standup/train-* - split: test path: mujoco-standup/test-* - config_name: mujoco-swimmer data_files: - split: train path: mujoco-swimmer/train-* - split: test path: mujoco-swimmer/test-* - config_name: mujoco-walker data_files: - split: train path: mujoco-walker/train-* - split: test path: mujoco-walker/test-* - config_name: ok-vqa data_files: - split: train path: ok-vqa/train-* - split: test path: ok-vqa/test-* - config_name: oscar data_files: - split: train path: oscar/train-* - split: test path: oscar/test-* - config_name: wikipedia data_files: - split: train path: wikipedia/train-* - split: test path: wikipedia/test-* --- # Dataset Card for "jat-dataset-tokenized" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

UCLNLP/adversarial_qa

UCLNLP

2023-12-21T14:20:00Z

490

39

[ "task_categories:question-answering", "task_ids:extractive-qa", "task_ids:open-domain-qa", "annotations_creators:crowdsourced", "language_creators:found", "multilinguality:monolingual", "source_datasets:original", "language:en", "license:cc-by-sa-4.0", "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2002.00293", "arxiv:1606.05250", "region:us" ]

[ "question-answering" ]

2022-03-02T23:29:22Z

1

--- annotations_creators: - crowdsourced language_creators: - found language: - en license: - cc-by-sa-4.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - question-answering task_ids: - extractive-qa - open-domain-qa paperswithcode_id: adversarialqa pretty_name: adversarialQA dataset_info: - config_name: adversarialQA features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 - name: metadata struct: - name: split dtype: string - name: model_in_the_loop dtype: string splits: - name: train num_bytes: 27858686 num_examples: 30000 - name: validation num_bytes: 2757092 num_examples: 3000 - name: test num_bytes: 2919479 num_examples: 3000 download_size: 5301049 dataset_size: 33535257 - config_name: dbert features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 - name: metadata struct: - name: split dtype: string - name: model_in_the_loop dtype: string splits: - name: train num_bytes: 9345521 num_examples: 10000 - name: validation num_bytes: 918156 num_examples: 1000 - name: test num_bytes: 971290 num_examples: 1000 download_size: 2689032 dataset_size: 11234967 - config_name: dbidaf features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 - name: metadata struct: - name: split dtype: string - name: model_in_the_loop dtype: string splits: - name: train num_bytes: 9282482 num_examples: 10000 - name: validation num_bytes: 917907 num_examples: 1000 - name: test num_bytes: 946947 num_examples: 1000 download_size: 2721341 dataset_size: 11147336 - config_name: droberta features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 - name: metadata struct: - name: split dtype: string - name: model_in_the_loop dtype: string splits: - name: train num_bytes: 9270683 num_examples: 10000 - name: validation num_bytes: 925029 num_examples: 1000 - name: test num_bytes: 1005242 num_examples: 1000 download_size: 2815452 dataset_size: 11200954 configs: - config_name: adversarialQA data_files: - split: train path: adversarialQA/train-* - split: validation path: adversarialQA/validation-* - split: test path: adversarialQA/test-* - config_name: dbert data_files: - split: train path: dbert/train-* - split: validation path: dbert/validation-* - split: test path: dbert/test-* - config_name: dbidaf data_files: - split: train path: dbidaf/train-* - split: validation path: dbidaf/validation-* - split: test path: dbidaf/test-* - config_name: droberta data_files: - split: train path: droberta/train-* - split: validation path: droberta/validation-* - split: test path: droberta/test-* train-eval-index: - config: adversarialQA task: question-answering task_id: extractive_question_answering splits: train_split: train eval_split: validation col_mapping: question: question context: context answers: text: text answer_start: answer_start metrics: - type: squad name: SQuAD --- # Dataset Card for adversarialQA ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [adversarialQA homepage](https://adversarialqa.github.io/) - **Repository:** [adversarialQA repository](https://github.com/maxbartolo/adversarialQA) - **Paper:** [Beat the AI: Investigating Adversarial Human Annotation for Reading Comprehension](https://arxiv.org/abs/2002.00293) - **Leaderboard:** [Dynabench QA Round 1 Leaderboard](https://dynabench.org/tasks/2#overall) - **Point of Contact:** [Max Bartolo]([email protected]) ### Dataset Summary We have created three new Reading Comprehension datasets constructed using an adversarial model-in-the-loop. We use three different models; BiDAF (Seo et al., 2016), BERTLarge (Devlin et al., 2018), and RoBERTaLarge (Liu et al., 2019) in the annotation loop and construct three datasets; D(BiDAF), D(BERT), and D(RoBERTa), each with 10,000 training examples, 1,000 validation, and 1,000 test examples. The adversarial human annotation paradigm ensures that these datasets consist of questions that current state-of-the-art models (at least the ones used as adversaries in the annotation loop) find challenging. The three AdversarialQA round 1 datasets provide a training and evaluation resource for such methods. ### Supported Tasks and Leaderboards `extractive-qa`: The dataset can be used to train a model for Extractive Question Answering, which consists in selecting the answer to a question from a passage. Success on this task is typically measured by achieving a high word-overlap [F1 score](https://huggingface.co/metrics/f1). The [RoBERTa-Large](https://huggingface.co/roberta-large) model trained on all the data combined with [SQuAD](https://arxiv.org/abs/1606.05250) currently achieves 64.35% F1. This task has an active leaderboard and is available as round 1 of the QA task on [Dynabench](https://dynabench.org/tasks/2#overall) and ranks models based on F1 score. ### Languages The text in the dataset is in English. The associated BCP-47 code is `en`. ## Dataset Structure ### Data Instances Data is provided in the same format as SQuAD 1.1. An example is shown below: ``` { "data": [ { "title": "Oxygen", "paragraphs": [ { "context": "Among the most important classes of organic compounds that contain oxygen are (where \"R\" is an organic group): alcohols (R-OH); ethers (R-O-R); ketones (R-CO-R); aldehydes (R-CO-H); carboxylic acids (R-COOH); esters (R-COO-R); acid anhydrides (R-CO-O-CO-R); and amides (R-C(O)-NR2). There are many important organic solvents that contain oxygen, including: acetone, methanol, ethanol, isopropanol, furan, THF, diethyl ether, dioxane, ethyl acetate, DMF, DMSO, acetic acid, and formic acid. Acetone ((CH3)2CO) and phenol (C6H5OH) are used as feeder materials in the synthesis of many different substances. Other important organic compounds that contain oxygen are: glycerol, formaldehyde, glutaraldehyde, citric acid, acetic anhydride, and acetamide. Epoxides are ethers in which the oxygen atom is part of a ring of three atoms.", "qas": [ { "id": "22bbe104aa72aa9b511dd53237deb11afa14d6e3", "question": "In addition to having oxygen, what do alcohols, ethers and esters have in common, according to the article?", "answers": [ { "answer_start": 36, "text": "organic compounds" } ] }, { "id": "4240a8e708c703796347a3702cf1463eed05584a", "question": "What letter does the abbreviation for acid anhydrides both begin and end in?", "answers": [ { "answer_start": 244, "text": "R" } ] }, { "id": "0681a0a5ec852ec6920d6a30f7ef65dced493366", "question": "Which of the organic compounds, in the article, contains nitrogen?", "answers": [ { "answer_start": 262, "text": "amides" } ] }, { "id": "2990efe1a56ccf81938fa5e18104f7d3803069fb", "question": "Which of the important classes of organic compounds, in the article, has a number in its abbreviation?", "answers": [ { "answer_start": 262, "text": "amides" } ] } ] } ] } ] } ``` ### Data Fields - title: the title of the Wikipedia page from which the context is sourced - context: the context/passage - id: a string identifier for each question - answers: a list of all provided answers (one per question in our case, but multiple may exist in SQuAD) with an `answer_start` field which is the character index of the start of the answer span, and a `text` field which is the answer text. Note that no answers are provided in the test set. Indeed, this dataset is part of the DynaBench benchmark, for which you can submit your predictions on the [website](https://dynabench.org/tasks/2#1). ### Data Splits The dataset is composed of three different datasets constructed using different models in the loop: BiDAF, BERT-Large, and RoBERTa-Large. Each of these has 10,000 training examples, 1,000 validation examples, and 1,000 test examples for a total of 30,000/3,000/3,000 train/validation/test examples. ## Dataset Creation ### Curation Rationale This dataset was collected to provide a more challenging and diverse Reading Comprehension dataset to state-of-the-art models. ### Source Data #### Initial Data Collection and Normalization The source passages are from Wikipedia and are the same as those used in [SQuAD v1.1](https://arxiv.org/abs/1606.05250). #### Who are the source language producers? The source language produces are Wikipedia editors for the passages, and human annotators on Mechanical Turk for the questions. ### Annotations #### Annotation process The dataset is collected through an adversarial human annotation process which pairs a human annotator and a reading comprehension model in an interactive setting. The human is presented with a passage for which they write a question and highlight the correct answer. The model then tries to answer the question, and, if it fails to answer correctly, the human wins. Otherwise, the human modifies or re-writes their question until the successfully fool the model. #### Who are the annotators? The annotators are from Amazon Mechanical Turk, geographically restricted the the USA, UK and Canada, having previously successfully completed at least 1,000 HITs, and having a HIT approval rate greater than 98%. Crowdworkers undergo intensive training and qualification prior to annotation. ### Personal and Sensitive Information No annotator identifying details are provided. ## Considerations for Using the Data ### Social Impact of Dataset The purpose of this dataset is to help develop better question answering systems. A system that succeeds at the supported task would be able to provide an accurate extractive answer from a short passage. This dataset is to be seen as a test bed for questions which contemporary state-of-the-art models struggle to answer correctly, thus often requiring more complex comprehension abilities than say detecting phrases explicitly mentioned in the passage with high overlap to the question. It should be noted, however, that the the source passages are both domain-restricted and linguistically specific, and that provided questions and answers do not constitute any particular social application. ### Discussion of Biases The dataset may exhibit various biases in terms of the source passage selection, annotated questions and answers, as well as algorithmic biases resulting from the adversarial annotation protocol. ### Other Known Limitations N/a ## Additional Information ### Dataset Curators This dataset was initially created by Max Bartolo, Alastair Roberts, Johannes Welbl, Sebastian Riedel, and Pontus Stenetorp, during work carried out at University College London (UCL). ### Licensing Information This dataset is distributed under [CC BY-SA 3.0](https://creativecommons.org/licenses/by-sa/3.0/). ### Citation Information ``` @article{bartolo2020beat, author = {Bartolo, Max and Roberts, Alastair and Welbl, Johannes and Riedel, Sebastian and Stenetorp, Pontus}, title = {Beat the AI: Investigating Adversarial Human Annotation for Reading Comprehension}, journal = {Transactions of the Association for Computational Linguistics}, volume = {8}, number = {}, pages = {662-678}, year = {2020}, doi = {10.1162/tacl\_a\_00338}, URL = { https://doi.org/10.1162/tacl_a_00338 }, eprint = { https://doi.org/10.1162/tacl_a_00338 }, abstract = { Innovations in annotation methodology have been a catalyst for Reading Comprehension (RC) datasets and models. One recent trend to challenge current RC models is to involve a model in the annotation process: Humans create questions adversarially, such that the model fails to answer them correctly. In this work we investigate this annotation methodology and apply it in three different settings, collecting a total of 36,000 samples with progressively stronger models in the annotation loop. This allows us to explore questions such as the reproducibility of the adversarial effect, transfer from data collected with varying model-in-the-loop strengths, and generalization to data collected without a model. We find that training on adversarially collected samples leads to strong generalization to non-adversarially collected datasets, yet with progressive performance deterioration with increasingly stronger models-in-the-loop. Furthermore, we find that stronger models can still learn from datasets collected with substantially weaker models-in-the-loop. When trained on data collected with a BiDAF model in the loop, RoBERTa achieves 39.9F1 on questions that it cannot answer when trained on SQuAD—only marginally lower than when trained on data collected using RoBERTa itself (41.0F1). } } ``` ### Contributions Thanks to [@maxbartolo](https://github.com/maxbartolo) for adding this dataset.