davanstrien/dataset_cards_with_metadata · Datasets at Hugging Face

datasetId large_stringlengths 6 107	author large_stringlengths 3 34	last_modified large_stringdate 2021-05-20 00:57:22 2025-05-05 16:11:44	downloads int64 0 4.28M	likes int64 0 7.74k	tags large listlengths 1 2.03k	task_categories large listlengths 0 16	createdAt large_stringdate 2022-03-02 23:29:22 2025-05-05 14:04:15	trending_score float64 1 39 ⌀	card large_stringlengths 31 1M
SayantanJoker/Shrutilipi_Hindi_resampled_44100_merged_15_quality	SayantanJoker	2025-05-05T04:58:09Z	0	0	[ "size_categories:10K<n<100K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-05T04:58:06Z	null	--- dataset_info: features: - name: text dtype: string - name: file_name dtype: string - name: utterance_pitch_mean dtype: float32 - name: utterance_pitch_std dtype: float32 - name: snr dtype: float64 - name: c50 dtype: float64 - name: speaking_rate dtype: float64 - name: phonemes dtype: string - name: stoi dtype: float64 - name: si-sdr dtype: float64 - name: pesq dtype: float64 splits: - name: train num_bytes: 13278527 num_examples: 34675 download_size: 5939408 dataset_size: 13278527 configs: - config_name: default data_files: - split: train path: data/train-* ---
pkgforge-security/domains	pkgforge-security	2025-05-05T03:33:05Z	1,292	1	[ "license:mit", "size_categories:1B<n<10B", "format:text", "modality:text", "library:datasets", "library:mlcroissant", "region:us" ]	[]	2025-03-08T09:15:36Z	null	--- size_categories: - 100B<n<1T pretty_name: Domains license: mit --- <p align="center"> <a href="https://github.com/pkgforge-security/domains"> <img src="https://cdn-uploads.huggingface.co/production/uploads/66e82daa04200de7aa4cba26/V6PJzNaHVd_Po5E74GmmC.png" alt="pkgforge" width="256"/></a> <b><strong> <a href="https://github.com/pkgforge-security/domains">Internet Domains</a></code></strong></b> <br> <img src="https://cdn-uploads.huggingface.co/production/uploads/66e82daa04200de7aa4cba26/hXVfqrL-YKJt-roX0DIgx.png" width="200" /> </p> - ### [Domains](https://github.com/pkgforge-security/domains) > - HuggingFace Hub Mirror for https://github.com/pkgforge-security/domains > - The Sync Workflow actions are at: https://github.com/pkgforge-security/domains - #### TOS & Abuse (To [Hugging-Face's](https://huggingface.co) Staff) > Hi, if you are an offical from [Hugging-Face](https://huggingface.co) here to investigate why this Repo is so Large and are considering deleting, & terminating our Account.<br> > Please note that, this project benefits a lot of people (You can do a code search on Github: `pkg.pkgforge.dev`) and is no way meant to abuse [Hugging-Face's](https://huggingface.co) Resources.<br> > You can check out the main project at: [https://github.com/pkgforge/soar](https://github.com/pkgforge/soar) and see for yourself about what all this is actually about.<br> > However if that doesn't convince you, and you feel like it's not worth it supporting this project, do reach out to us at: [https://docs.pkgforge.dev/contact/chat](https://docs.pkgforge.dev/contact/chat))<br> > We will backup the data and delete our account to comply with the TOS.
HungVu2003/opt-350m_beta_0.0_alpha_0.2_num-company_2_dataset_1_for_gen_12_v2	HungVu2003	2025-05-04T23:57:20Z	0	0	[ "region:us" ]	[]	2025-05-04T23:57:19Z	null	--- dataset_info: features: - name: question dtype: string splits: - name: train num_bytes: 3020032 num_examples: 13750 download_size: 980307 dataset_size: 3020032 configs: - config_name: default data_files: - split: train path: data/train-* ---
GitBag/DeepSeek-R1-Distill-Qwen-1.5B_hmmt-feb-25_eval	GitBag	2025-05-04T23:07:04Z	0	0	[ "region:us" ]	[]	2025-05-04T23:07:03Z	null	--- dataset_info: features: - name: problem dtype: string - name: answer dtype: string - name: response_0 dtype: string - name: response_1 dtype: string - name: response_2 dtype: string - name: response_3 dtype: string - name: response_4 dtype: string - name: response_5 dtype: string - name: response_6 dtype: string - name: response_7 dtype: string - name: response_8 dtype: string - name: response_9 dtype: string - name: response_10 dtype: string - name: response_11 dtype: string - name: response_12 dtype: string - name: response_13 dtype: string - name: response_14 dtype: string - name: response_15 dtype: string - name: response_16 dtype: string - name: response_17 dtype: string - name: response_18 dtype: string - name: response_19 dtype: string - name: response_20 dtype: string - name: response_21 dtype: string - name: response_22 dtype: string - name: response_23 dtype: string - name: response_24 dtype: string - name: response_25 dtype: string - name: response_26 dtype: string - name: response_27 dtype: string - name: response_28 dtype: string - name: response_29 dtype: string - name: response_30 dtype: string - name: response_31 dtype: string - name: eval_0 dtype: float64 - name: eval_1 dtype: float64 - name: eval_2 dtype: float64 - name: eval_3 dtype: float64 - name: eval_4 dtype: float64 - name: eval_5 dtype: float64 - name: eval_6 dtype: float64 - name: eval_7 dtype: float64 - name: eval_8 dtype: float64 - name: eval_9 dtype: float64 - name: eval_10 dtype: float64 - name: eval_11 dtype: float64 - name: eval_12 dtype: float64 - name: eval_13 dtype: float64 - name: eval_14 dtype: float64 - name: eval_15 dtype: float64 - name: eval_16 dtype: float64 - name: eval_17 dtype: float64 - name: eval_18 dtype: float64 - name: eval_19 dtype: float64 - name: eval_20 dtype: float64 - name: eval_21 dtype: float64 - name: eval_22 dtype: float64 - name: eval_23 dtype: float64 - name: eval_24 dtype: float64 - name: eval_25 dtype: float64 - name: eval_26 dtype: float64 - name: eval_27 dtype: float64 - name: eval_28 dtype: float64 - name: eval_29 dtype: float64 - name: eval_30 dtype: float64 - name: eval_31 dtype: float64 splits: - name: train num_bytes: 39227790 num_examples: 30 download_size: 13160569 dataset_size: 39227790 configs: - config_name: default data_files: - split: train path: data/train-* ---
kingsleykim/openthoughts_qwen_2.5_math_7b	kingsleykim	2025-05-04T21:15:11Z	0	0	[ "region:us" ]	[]	2025-05-04T21:15:06Z	null	--- dataset_info: features: - name: inputs dtype: string - name: correctness dtype: bool - name: answers dtype: string - name: questions dtype: string - name: correct dtype: bool - name: question dtype: string splits: - name: train num_bytes: 791024669 num_examples: 191973 download_size: 124584820 dataset_size: 791024669 configs: - config_name: default data_files: - split: train path: data/train-* ---
mmmmmp/robot_test3	mmmmmp	2025-05-04T21:01:39Z	0	0	[ "task_categories:robotics", "license:apache-2.0", "region:us", "LeRobot" ]	[ "robotics" ]	2025-05-04T21:01:36Z	null	--- license: apache-2.0 task_categories: - robotics tags: - LeRobot configs: - config_name: default data_files: data//.parquet --- This dataset was created using [LeRobot](https://github.com/huggingface/lerobot). ## Dataset Description - Homepage: [More Information Needed] - Paper: [More Information Needed] - License: apache-2.0 ## Dataset Structure [meta/info.json](meta/info.json): ```json { "codebase_version": "v2.0", "robot_type": "so100", "total_episodes": 1, "total_frames": 443, "total_tasks": 1, "total_videos": 2, "total_chunks": 1, "chunks_size": 1000, "fps": 30, "splits": { "train": "0:1" }, "data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet", "video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4", "features": { "action": { "dtype": "float32", "shape": [ 6 ], "names": [ "main_shoulder_pan", "main_shoulder_lift", "main_elbow_flex", "main_wrist_flex", "main_wrist_roll", "main_gripper" ] }, "observation.state": { "dtype": "float32", "shape": [ 6 ], "names": [ "main_shoulder_pan", "main_shoulder_lift", "main_elbow_flex", "main_wrist_flex", "main_wrist_roll", "main_gripper" ] }, "observation.images.laptop": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.fps": 30.0, "video.height": 480, "video.width": 640, "video.channels": 3, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "has_audio": false } }, "observation.images.phone": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.fps": 30.0, "video.height": 480, "video.width": 640, "video.channels": 3, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "has_audio": false } }, "timestamp": { "dtype": "float32", "shape": [ 1 ], "names": null }, "frame_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "episode_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "task_index": { "dtype": "int64", "shape": [ 1 ], "names": null } } } ``` ## Citation BibTeX: ```bibtex [More Information Needed] ```
ibragim-bad/random-data-1	ibragim-bad	2025-05-04T20:15:19Z	0	0	[ "region:us" ]	[]	2025-05-04T20:15:17Z	null	--- dataset_info: features: - name: instance_id dtype: string - name: base_commit dtype: string - name: created_at dtype: timestamp[us] - name: environment_setup_commit dtype: string - name: hints_text dtype: string - name: patch dtype: string - name: problem_statement dtype: string - name: repo dtype: string - name: test_patch dtype: string - name: meta struct: - name: commit_name dtype: string - name: failed_lite_validators sequence: string - name: has_test_patch dtype: bool - name: is_lite dtype: bool - name: num_modified_files dtype: int64 - name: version dtype: string - name: install_config struct: - name: env_yml_path sequence: string - name: install dtype: string - name: log_parser dtype: string - name: no_use_env dtype: bool - name: packages dtype: string - name: pip_packages sequence: string - name: pre_install sequence: string - name: python dtype: string - name: reqs_path sequence: string - name: test_cmd dtype: string - name: requirements dtype: string - name: environment dtype: string - name: FAIL_TO_PASS sequence: string - name: FAIL_TO_FAIL sequence: string - name: PASS_TO_PASS sequence: string - name: PASS_TO_FAIL sequence: 'null' - name: license_name dtype: string splits: - name: test num_bytes: 2540351 num_examples: 100 download_size: 884812 dataset_size: 2540351 configs: - config_name: default data_files: - split: test path: data/test-* ---
HungVu2003/opt-350m_beta_1.0_alpha_0.2_num-company_2_dataset_1_for_gen_17_v2	HungVu2003	2025-05-04T17:43:09Z	0	0	[ "region:us" ]	[]	2025-05-04T17:43:07Z	null	--- dataset_info: features: - name: question dtype: string splits: - name: train num_bytes: 6385788 num_examples: 13750 download_size: 3253110 dataset_size: 6385788 configs: - config_name: default data_files: - split: train path: data/train-* ---
hshwk1983/x_dataset_52806	hshwk1983	2025-05-04T17:09:03Z	2,337	0	[ "task_categories:text-classification", "task_categories:token-classification", "task_categories:question-answering", "task_categories:summarization", "task_categories:text-generation", "task_ids:sentiment-analysis", "task_ids:topic-classification", "task_ids:named-entity-recognition", "task_ids:language-modeling", "task_ids:text-scoring", "task_ids:multi-class-classification", "task_ids:multi-label-classification", "task_ids:extractive-qa", "task_ids:news-articles-summarization", "multilinguality:multilingual", "source_datasets:original", "license:mit", "size_categories:100M<n<1B", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[ "text-classification", "token-classification", "question-answering", "summarization", "text-generation" ]	2025-01-27T00:12:13Z	null	--- license: mit multilinguality: - multilingual source_datasets: - original task_categories: - text-classification - token-classification - question-answering - summarization - text-generation task_ids: - sentiment-analysis - topic-classification - named-entity-recognition - language-modeling - text-scoring - multi-class-classification - multi-label-classification - extractive-qa - news-articles-summarization --- # Bittensor Subnet 13 X (Twitter) Dataset <center> <img src="https://huggingface.co/datasets/macrocosm-os/images/resolve/main/bittensor.png" alt="Data-universe: The finest collection of social media data the web has to offer"> </center> <center> <img src="https://huggingface.co/datasets/macrocosm-os/images/resolve/main/macrocosmos-black.png" alt="Data-universe: The finest collection of social media data the web has to offer"> </center> ## Dataset Description - Repository: hshwk1983/x_dataset_52806 - Subnet: Bittensor Subnet 13 - Miner Hotkey: 5Fc1UBhNfu7MCTK51dvY7Yukt1ZLjAbNW832GirVQcLE3Wj2 ### Dataset Summary This dataset is part of the Bittensor Subnet 13 decentralized network, containing preprocessed data from X (formerly Twitter). The data is continuously updated by network miners, providing a real-time stream of tweets for various analytical and machine learning tasks. For more information about the dataset, please visit the [official repository](https://github.com/macrocosm-os/data-universe). ### Supported Tasks The versatility of this dataset allows researchers and data scientists to explore various aspects of social media dynamics and develop innovative applications. Users are encouraged to leverage this data creatively for their specific research or business needs. For example: - Sentiment Analysis - Trend Detection - Content Analysis - User Behavior Modeling ### Languages Primary language: Datasets are mostly English, but can be multilingual due to decentralized ways of creation. ## Dataset Structure ### Data Instances Each instance represents a single tweet with the following fields: ### Data Fields - `text` (string): The main content of the tweet. - `label` (string): Sentiment or topic category of the tweet. - `tweet_hashtags` (list): A list of hashtags used in the tweet. May be empty if no hashtags are present. - `datetime` (string): The date when the tweet was posted. - `username_encoded` (string): An encoded version of the username to maintain user privacy. - `url_encoded` (string): An encoded version of any URLs included in the tweet. May be empty if no URLs are present. ### Data Splits This dataset is continuously updated and does not have fixed splits. Users should create their own splits based on their requirements and the data's timestamp. ## Dataset Creation ### Source Data Data is collected from public tweets on X (Twitter), adhering to the platform's terms of service and API usage guidelines. ### Personal and Sensitive Information All usernames and URLs are encoded to protect user privacy. The dataset does not intentionally include personal or sensitive information. ## Considerations for Using the Data ### Social Impact and Biases Users should be aware of potential biases inherent in X (Twitter) data, including demographic and content biases. This dataset reflects the content and opinions expressed on X and should not be considered a representative sample of the general population. ### Limitations - Data quality may vary due to the decentralized nature of collection and preprocessing. - The dataset may contain noise, spam, or irrelevant content typical of social media platforms. - Temporal biases may exist due to real-time collection methods. - The dataset is limited to public tweets and does not include private accounts or direct messages. - Not all tweets contain hashtags or URLs. ## Additional Information ### Licensing Information The dataset is released under the MIT license. The use of this dataset is also subject to X Terms of Use. ### Citation Information If you use this dataset in your research, please cite it as follows: ``` @misc{hshwk19832025datauniversex_dataset_52806, title={The Data Universe Datasets: The finest collection of social media data the web has to offer}, author={hshwk1983}, year={2025}, url={https://huggingface.co/datasets/hshwk1983/x_dataset_52806}, } ``` ### Contributions To report issues or contribute to the dataset, please contact the miner or use the Bittensor Subnet 13 governance mechanisms. ## Dataset Statistics [This section is automatically updated] - Total Instances: 53704103 - Date Range: 2025-01-21T00:00:00Z to 2025-02-13T00:00:00Z - Last Updated: 2025-02-18T18:20:54Z ### Data Distribution - Tweets with hashtags: 48.13% - Tweets without hashtags: 51.87% ### Top 10 Hashtags For full statistics, please refer to the `stats.json` file in the repository. \| Rank \| Topic \| Total Count \| Percentage \| \|------\|-------\|-------------\|-------------\| \| 1 \| NULL \| 27855573 \| 51.87% \| \| 2 \| #riyadh \| 382358 \| 0.71% \| \| 3 \| #zelena \| 324161 \| 0.60% \| \| 4 \| #tiktok \| 246981 \| 0.46% \| \| 5 \| #bbb25 \| 141261 \| 0.26% \| \| 6 \| #ad \| 136046 \| 0.25% \| \| 7 \| #superbowl \| 106457 \| 0.20% \| \| 8 \| #bbmzansi \| 91608 \| 0.17% \| \| 9 \| #jhope_at_galadespiècesjaunes \| 91561 \| 0.17% \| \| 10 \| #transferlerlebirliktezafere \| 80161 \| 0.15% \| ## Update History \| Date \| New Instances \| Total Instances \| \|------\|---------------\|-----------------\| \| 2025-01-27T00:12:58Z \| 2331611 \| 2331611 \| \| 2025-01-30T12:15:15Z \| 7216791 \| 9548402 \| \| 2025-02-03T00:17:43Z \| 7388142 \| 16936544 \| \| 2025-02-06T12:21:06Z \| 8811525 \| 25748069 \| \| 2025-02-10T00:25:18Z \| 10398234 \| 36146303 \| \| 2025-02-13T13:59:25Z \| 4703197 \| 40849500 \| \| 2025-02-17T02:19:40Z \| 11306944 \| 52156444 \| \| 2025-02-18T03:19:10Z \| 829790 \| 52986234 \| \| 2025-02-18T18:20:54Z \| 717869 \| 53704103 \|
Joseph7D/emotion-dataset	Joseph7D	2025-05-04T16:34:13Z	0	0	[ "region:us" ]	[]	2025-05-04T16:34:09Z	null	--- dataset_info: features: - name: text dtype: string - name: emotion dtype: string splits: - name: train num_bytes: 3013664 num_examples: 26928 - name: test num_bytes: 372292 num_examples: 3366 - name: validation num_bytes: 378972 num_examples: 3366 download_size: 2318145 dataset_size: 3764928 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: validation path: data/validation-* ---
GitBag/Qwen2.5-7B_hmmt-feb-24_eval	GitBag	2025-05-04T16:30:26Z	0	0	[ "region:us" ]	[]	2025-05-04T16:30:24Z	null	--- dataset_info: features: - name: problem dtype: string - name: answer dtype: string - name: response_0 dtype: string - name: response_1 dtype: string - name: response_2 dtype: string - name: response_3 dtype: string - name: response_4 dtype: string - name: response_5 dtype: string - name: response_6 dtype: string - name: response_7 dtype: string - name: response_8 dtype: string - name: response_9 dtype: string - name: response_10 dtype: string - name: response_11 dtype: string - name: response_12 dtype: string - name: response_13 dtype: string - name: response_14 dtype: string - name: response_15 dtype: string - name: response_16 dtype: string - name: response_17 dtype: string - name: response_18 dtype: string - name: response_19 dtype: string - name: response_20 dtype: string - name: response_21 dtype: string - name: response_22 dtype: string - name: response_23 dtype: string - name: response_24 dtype: string - name: response_25 dtype: string - name: response_26 dtype: string - name: response_27 dtype: string - name: response_28 dtype: string - name: response_29 dtype: string - name: response_30 dtype: string - name: response_31 dtype: string - name: eval_0 dtype: float64 - name: eval_1 dtype: float64 - name: eval_2 dtype: float64 - name: eval_3 dtype: float64 - name: eval_4 dtype: float64 - name: eval_5 dtype: float64 - name: eval_6 dtype: float64 - name: eval_7 dtype: float64 - name: eval_8 dtype: float64 - name: eval_9 dtype: float64 - name: eval_10 dtype: float64 - name: eval_11 dtype: float64 - name: eval_12 dtype: float64 - name: eval_13 dtype: float64 - name: eval_14 dtype: float64 - name: eval_15 dtype: float64 - name: eval_16 dtype: float64 - name: eval_17 dtype: float64 - name: eval_18 dtype: float64 - name: eval_19 dtype: float64 - name: eval_20 dtype: float64 - name: eval_21 dtype: float64 - name: eval_22 dtype: float64 - name: eval_23 dtype: float64 - name: eval_24 dtype: float64 - name: eval_25 dtype: float64 - name: eval_26 dtype: float64 - name: eval_27 dtype: float64 - name: eval_28 dtype: float64 - name: eval_29 dtype: float64 - name: eval_30 dtype: float64 - name: eval_31 dtype: float64 splits: - name: train num_bytes: 5449227 num_examples: 30 download_size: 1476657 dataset_size: 5449227 configs: - config_name: default data_files: - split: train path: data/train-* ---
harpreetmann/go_emotions_max_500_string_chat	harpreetmann	2025-05-04T16:14:55Z	0	0	[ "region:us" ]	[]	2025-05-04T16:14:49Z	null	--- dataset_info: features: - name: input dtype: string - name: output dtype: string - name: id dtype: string - name: messages list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 27852466 num_examples: 43409 - name: test num_bytes: 3488513 num_examples: 5427 - name: validation num_bytes: 3487936 num_examples: 5426 - name: discarded num_bytes: 3483 num_examples: 1 download_size: 8204171 dataset_size: 34832398 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: validation path: data/validation-* - split: discarded path: data/discarded-* ---
mteb/told-br	mteb	2025-05-04T16:12:17Z	527	0	[ "task_categories:text-classification", "task_ids:multi-label-classification", "task_ids:sentiment-analysis", "task_ids:sentiment-scoring", "task_ids:sentiment-classification", "task_ids:hate-speech-detection", "annotations_creators:expert-annotated", "multilinguality:monolingual", "language:por", "license:cc-by-sa-4.0", "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2010.04543", "arxiv:2502.13595", "arxiv:2210.07316", "region:us", "mteb", "text" ]	[ "text-classification" ]	2024-10-29T07:49:35Z	null	--- annotations_creators: - expert-annotated language: - por license: cc-by-sa-4.0 multilinguality: monolingual task_categories: - text-classification task_ids: - multi-label-classification - sentiment-analysis - sentiment-scoring - sentiment-classification - hate-speech-detection dataset_info: features: - name: text dtype: string - name: homophobia dtype: class_label: names: '0': zero_votes '1': one_vote '2': two_votes '3': three_votes - name: obscene dtype: class_label: names: '0': zero_votes '1': one_vote '2': two_votes '3': three_votes - name: insult dtype: class_label: names: '0': zero_votes '1': one_vote '2': two_votes '3': three_votes - name: racism dtype: class_label: names: '0': zero_votes '1': one_vote '2': two_votes '3': three_votes - name: misogyny dtype: class_label: names: '0': zero_votes '1': one_vote '2': two_votes '3': three_votes - name: xenophobia dtype: class_label: names: '0': zero_votes '1': one_vote '2': two_votes '3': three_votes splits: - name: train num_bytes: 2977994 num_examples: 21000 download_size: 1464831 dataset_size: 2977994 configs: - config_name: default data_files: - split: train path: data/train-* tags: - mteb - text --- <!-- adapted from https://github.com/huggingface/huggingface_hub/blob/v0.30.2/src/huggingface_hub/templates/datasetcard_template.md --> <div align="center" style="padding: 40px 20px; background-color: white; border-radius: 12px; box-shadow: 0 2px 10px rgba(0, 0, 0, 0.05); max-width: 600px; margin: 0 auto;"> <h1 style="font-size: 3.5rem; color: #1a1a1a; margin: 0 0 20px 0; letter-spacing: 2px; font-weight: 700;">BrazilianToxicTweetsClassification</h1> <div style="font-size: 1.5rem; color: #4a4a4a; margin-bottom: 5px; font-weight: 300;">An <a href="https://github.com/embeddings-benchmark/mteb" style="color: #2c5282; font-weight: 600; text-decoration: none;" onmouseover="this.style.textDecoration='underline'" onmouseout="this.style.textDecoration='none'">MTEB</a> dataset</div> <div style="font-size: 0.9rem; color: #2c5282; margin-top: 10px;">Massive Text Embedding Benchmark</div> </div> ToLD-Br is the biggest dataset for toxic tweets in Brazilian Portuguese, crowdsourced by 42 annotators selected from a pool of 129 volunteers. Annotators were selected aiming to create a plural group in terms of demographics (ethnicity, sexual orientation, age, gender). Each tweet was labeled by three annotators in 6 possible categories: LGBTQ+phobia, Xenophobia, Obscene, Insult, Misogyny and Racism. \| \| \| \|---------------\|---------------------------------------------\| \| Task category \| t2c \| \| Domains \| Constructed, Written \| \| Reference \| https://paperswithcode.com/dataset/told-br \| ## How to evaluate on this task You can evaluate an embedding model on this dataset using the following code: ```python import mteb task = mteb.get_tasks(["BrazilianToxicTweetsClassification"]) evaluator = mteb.MTEB(task) model = mteb.get_model(YOUR_MODEL) evaluator.run(model) ``` <!-- Datasets want link to arxiv in readme to autolink dataset with paper --> To learn more about how to run models on `mteb` task check out the [GitHub repitory](https://github.com/embeddings-benchmark/mteb). ## Citation If you use this dataset, please cite the dataset as well as [mteb](https://github.com/embeddings-benchmark/mteb), as this dataset likely includes additional processing as a part of the [MMTEB Contribution](https://github.com/embeddings-benchmark/mteb/tree/main/docs/mmteb). ```bibtex @article{DBLP:journals/corr/abs-2010-04543, author = {Joao Augusto Leite and Diego F. Silva and Kalina Bontcheva and Carolina Scarton}, eprint = {2010.04543}, eprinttype = {arXiv}, journal = {CoRR}, timestamp = {Tue, 15 Dec 2020 16:10:16 +0100}, title = {Toxic Language Detection in Social Media for Brazilian Portuguese: New Dataset and Multilingual Analysis}, url = {https://arxiv.org/abs/2010.04543}, volume = {abs/2010.04543}, year = {2020}, } @article{enevoldsen2025mmtebmassivemultilingualtext, title={MMTEB: Massive Multilingual Text Embedding Benchmark}, author={Kenneth Enevoldsen and Isaac Chung and Imene Kerboua and Márton Kardos and Ashwin Mathur and David Stap and Jay Gala and Wissam Siblini and Dominik Krzemiński and Genta Indra Winata and Saba Sturua and Saiteja Utpala and Mathieu Ciancone and Marion Schaeffer and Gabriel Sequeira and Diganta Misra and Shreeya Dhakal and Jonathan Rystrøm and Roman Solomatin and Ömer Çağatan and Akash Kundu and Martin Bernstorff and Shitao Xiao and Akshita Sukhlecha and Bhavish Pahwa and Rafał Poświata and Kranthi Kiran GV and Shawon Ashraf and Daniel Auras and Björn Plüster and Jan Philipp Harries and Loïc Magne and Isabelle Mohr and Mariya Hendriksen and Dawei Zhu and Hippolyte Gisserot-Boukhlef and Tom Aarsen and Jan Kostkan and Konrad Wojtasik and Taemin Lee and Marek Šuppa and Crystina Zhang and Roberta Rocca and Mohammed Hamdy and Andrianos Michail and John Yang and Manuel Faysse and Aleksei Vatolin and Nandan Thakur and Manan Dey and Dipam Vasani and Pranjal Chitale and Simone Tedeschi and Nguyen Tai and Artem Snegirev and Michael Günther and Mengzhou Xia and Weijia Shi and Xing Han Lù and Jordan Clive and Gayatri Krishnakumar and Anna Maksimova and Silvan Wehrli and Maria Tikhonova and Henil Panchal and Aleksandr Abramov and Malte Ostendorff and Zheng Liu and Simon Clematide and Lester James Miranda and Alena Fenogenova and Guangyu Song and Ruqiya Bin Safi and Wen-Ding Li and Alessia Borghini and Federico Cassano and Hongjin Su and Jimmy Lin and Howard Yen and Lasse Hansen and Sara Hooker and Chenghao Xiao and Vaibhav Adlakha and Orion Weller and Siva Reddy and Niklas Muennighoff}, publisher = {arXiv}, journal={arXiv preprint arXiv:2502.13595}, year={2025}, url={https://arxiv.org/abs/2502.13595}, doi = {10.48550/arXiv.2502.13595}, } @article{muennighoff2022mteb, author = {Muennighoff, Niklas and Tazi, Nouamane and Magne, Lo{\"\i}c and Reimers, Nils}, title = {MTEB: Massive Text Embedding Benchmark}, publisher = {arXiv}, journal={arXiv preprint arXiv:2210.07316}, year = {2022} url = {https://arxiv.org/abs/2210.07316}, doi = {10.48550/ARXIV.2210.07316}, } ``` # Dataset Statistics <details> <summary> Dataset Statistics</summary> The following code contains the descriptive statistics from the task. These can also be obtained using: ```python import mteb task = mteb.get_task("BrazilianToxicTweetsClassification") desc_stats = task.metadata.descriptive_stats ``` ```json { "test": { "num_samples": 2048, "number_of_characters": 172708, "number_texts_intersect_with_train": 23, "min_text_length": 5, "average_text_length": 84.330078125, "max_text_length": 304, "unique_texts": 2046, "min_labels_per_text": 0, "average_label_per_text": 0.57958984375, "max_labels_per_text": 4, "unique_labels": 7, "labels": { "obscene": { "count": 653 }, "insult": { "count": 430 }, "misogyny": { "count": 46 }, "racism": { "count": 13 }, "xenophobia": { "count": 13 }, "homophobia": { "count": 32 }, "None": { "count": 1145 } } }, "train": { "num_samples": 8192, "number_of_characters": 714281, "number_texts_intersect_with_train": null, "min_text_length": 4, "average_text_length": 87.1925048828125, "max_text_length": 322, "unique_texts": 8172, "min_labels_per_text": 0, "average_label_per_text": 0.5751953125, "max_labels_per_text": 4, "unique_labels": 7, "labels": { "None": { "count": 4580 }, "obscene": { "count": 2576 }, "insult": { "count": 1700 }, "homophobia": { "count": 139 }, "misogyny": { "count": 179 }, "racism": { "count": 54 }, "xenophobia": { "count": 64 } } } } ``` </details> --- This dataset card was automatically generated using [MTEB](https://github.com/embeddings-benchmark/mteb)
mteb/MMarcoRetrieval	mteb	2025-05-04T16:11:36Z	26	0	[ "task_categories:text-retrieval", "multilinguality:monolingual", "language:cmn", "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2309.07597", "arxiv:2502.13595", "arxiv:2210.07316", "region:us", "mteb", "text" ]	[ "text-retrieval" ]	2024-11-28T10:47:15Z	null	--- language: - cmn multilinguality: monolingual task_categories: - text-retrieval task_ids: [] dataset_info: - config_name: corpus features: - name: _id dtype: string - name: text dtype: string - name: title dtype: string splits: - name: dev num_bytes: 32979607 num_examples: 106813 download_size: 20219994 dataset_size: 32979607 - config_name: default features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: dev num_bytes: 217670 num_examples: 7437 download_size: 114126 dataset_size: 217670 - config_name: queries features: - name: _id dtype: string - name: text dtype: string splits: - name: dev num_bytes: 303316 num_examples: 6980 download_size: 219572 dataset_size: 303316 configs: - config_name: corpus data_files: - split: dev path: corpus/dev-* - config_name: default data_files: - split: dev path: data/dev-* - config_name: queries data_files: - split: dev path: queries/dev-* tags: - mteb - text --- <!-- adapted from https://github.com/huggingface/huggingface_hub/blob/v0.30.2/src/huggingface_hub/templates/datasetcard_template.md --> <div align="center" style="padding: 40px 20px; background-color: white; border-radius: 12px; box-shadow: 0 2px 10px rgba(0, 0, 0, 0.05); max-width: 600px; margin: 0 auto;"> <h1 style="font-size: 3.5rem; color: #1a1a1a; margin: 0 0 20px 0; letter-spacing: 2px; font-weight: 700;">MMarcoRetrieval</h1> <div style="font-size: 1.5rem; color: #4a4a4a; margin-bottom: 5px; font-weight: 300;">An <a href="https://github.com/embeddings-benchmark/mteb" style="color: #2c5282; font-weight: 600; text-decoration: none;" onmouseover="this.style.textDecoration='underline'" onmouseout="this.style.textDecoration='none'">MTEB</a> dataset</div> <div style="font-size: 0.9rem; color: #2c5282; margin-top: 10px;">Massive Text Embedding Benchmark</div> </div> MMarcoRetrieval \| \| \| \|---------------\|---------------------------------------------\| \| Task category \| t2t \| \| Domains \| None \| \| Reference \| https://arxiv.org/abs/2309.07597 \| ## How to evaluate on this task You can evaluate an embedding model on this dataset using the following code: ```python import mteb task = mteb.get_tasks(["MMarcoRetrieval"]) evaluator = mteb.MTEB(task) model = mteb.get_model(YOUR_MODEL) evaluator.run(model) ``` <!-- Datasets want link to arxiv in readme to autolink dataset with paper --> To learn more about how to run models on `mteb` task check out the [GitHub repitory](https://github.com/embeddings-benchmark/mteb). ## Citation If you use this dataset, please cite the dataset as well as [mteb](https://github.com/embeddings-benchmark/mteb), as this dataset likely includes additional processing as a part of the [MMTEB Contribution](https://github.com/embeddings-benchmark/mteb/tree/main/docs/mmteb). ```bibtex @misc{xiao2024cpack, archiveprefix = {arXiv}, author = {Shitao Xiao and Zheng Liu and Peitian Zhang and Niklas Muennighoff and Defu Lian and Jian-Yun Nie}, eprint = {2309.07597}, primaryclass = {cs.CL}, title = {C-Pack: Packaged Resources To Advance General Chinese Embedding}, year = {2024}, } @article{enevoldsen2025mmtebmassivemultilingualtext, title={MMTEB: Massive Multilingual Text Embedding Benchmark}, author={Kenneth Enevoldsen and Isaac Chung and Imene Kerboua and Márton Kardos and Ashwin Mathur and David Stap and Jay Gala and Wissam Siblini and Dominik Krzemiński and Genta Indra Winata and Saba Sturua and Saiteja Utpala and Mathieu Ciancone and Marion Schaeffer and Gabriel Sequeira and Diganta Misra and Shreeya Dhakal and Jonathan Rystrøm and Roman Solomatin and Ömer Çağatan and Akash Kundu and Martin Bernstorff and Shitao Xiao and Akshita Sukhlecha and Bhavish Pahwa and Rafał Poświata and Kranthi Kiran GV and Shawon Ashraf and Daniel Auras and Björn Plüster and Jan Philipp Harries and Loïc Magne and Isabelle Mohr and Mariya Hendriksen and Dawei Zhu and Hippolyte Gisserot-Boukhlef and Tom Aarsen and Jan Kostkan and Konrad Wojtasik and Taemin Lee and Marek Šuppa and Crystina Zhang and Roberta Rocca and Mohammed Hamdy and Andrianos Michail and John Yang and Manuel Faysse and Aleksei Vatolin and Nandan Thakur and Manan Dey and Dipam Vasani and Pranjal Chitale and Simone Tedeschi and Nguyen Tai and Artem Snegirev and Michael Günther and Mengzhou Xia and Weijia Shi and Xing Han Lù and Jordan Clive and Gayatri Krishnakumar and Anna Maksimova and Silvan Wehrli and Maria Tikhonova and Henil Panchal and Aleksandr Abramov and Malte Ostendorff and Zheng Liu and Simon Clematide and Lester James Miranda and Alena Fenogenova and Guangyu Song and Ruqiya Bin Safi and Wen-Ding Li and Alessia Borghini and Federico Cassano and Hongjin Su and Jimmy Lin and Howard Yen and Lasse Hansen and Sara Hooker and Chenghao Xiao and Vaibhav Adlakha and Orion Weller and Siva Reddy and Niklas Muennighoff}, publisher = {arXiv}, journal={arXiv preprint arXiv:2502.13595}, year={2025}, url={https://arxiv.org/abs/2502.13595}, doi = {10.48550/arXiv.2502.13595}, } @article{muennighoff2022mteb, author = {Muennighoff, Niklas and Tazi, Nouamane and Magne, Lo{\"\i}c and Reimers, Nils}, title = {MTEB: Massive Text Embedding Benchmark}, publisher = {arXiv}, journal={arXiv preprint arXiv:2210.07316}, year = {2022} url = {https://arxiv.org/abs/2210.07316}, doi = {10.48550/ARXIV.2210.07316}, } ``` # Dataset Statistics <details> <summary> Dataset Statistics</summary> The following code contains the descriptive statistics from the task. These can also be obtained using: ```python import mteb task = mteb.get_task("MMarcoRetrieval") desc_stats = task.metadata.descriptive_stats ``` ```json { "dev": { "num_samples": 113793, "number_of_characters": 12294685, "num_documents": 106813, "min_document_length": 13, "average_document_length": 114.41787048392986, "max_document_length": 1709, "unique_documents": 106813, "num_queries": 6980, "min_query_length": 2, "average_query_length": 10.51131805157593, "max_query_length": 61, "unique_queries": 6980, "none_queries": 0, "num_relevant_docs": 7437, "min_relevant_docs_per_query": 1, "average_relevant_docs_per_query": 1.0654727793696275, "max_relevant_docs_per_query": 4, "unique_relevant_docs": 7433, "num_instructions": null, "min_instruction_length": null, "average_instruction_length": null, "max_instruction_length": null, "unique_instructions": null, "num_top_ranked": null, "min_top_ranked_per_query": null, "average_top_ranked_per_query": null, "max_top_ranked_per_query": null } } ``` </details> --- This dataset card was automatically generated using [MTEB](https://github.com/embeddings-benchmark/mteb)
mteb/NQ_PL_test_top_250_only_w_correct-v2	mteb	2025-05-04T16:11:21Z	22	0	[ "task_categories:text-retrieval", "multilinguality:translated", "source_datasets:mteb/nq", "language:pol", "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2305.19840", "arxiv:2502.13595", "arxiv:2210.07316", "region:us", "mteb", "text" ]	[ "text-retrieval" ]	2024-09-28T05:31:56Z	null	--- language: - pol multilinguality: translated source_datasets: - mteb/nq task_categories: - text-retrieval task_ids: [] dataset_info: - config_name: corpus features: - name: _id dtype: string - name: text dtype: string - name: title dtype: string splits: - name: test num_bytes: 101910982.7251677 num_examples: 184765 download_size: 80088058 dataset_size: 101910982.7251677 - config_name: default features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 38495.78647940967 num_examples: 1213 download_size: 16497 dataset_size: 38495.78647940967 - config_name: queries features: - name: _id dtype: string - name: text dtype: string splits: - name: test num_bytes: 66693.80069524913 num_examples: 1000 download_size: 46127 dataset_size: 66693.80069524913 configs: - config_name: corpus data_files: - split: test path: corpus/test-* - config_name: default data_files: - split: test path: data/test-* - config_name: queries data_files: - split: test path: queries/test-* tags: - mteb - text --- <!-- adapted from https://github.com/huggingface/huggingface_hub/blob/v0.30.2/src/huggingface_hub/templates/datasetcard_template.md --> <div align="center" style="padding: 40px 20px; background-color: white; border-radius: 12px; box-shadow: 0 2px 10px rgba(0, 0, 0, 0.05); max-width: 600px; margin: 0 auto;"> <h1 style="font-size: 3.5rem; color: #1a1a1a; margin: 0 0 20px 0; letter-spacing: 2px; font-weight: 700;">NQ-PLHardNegatives</h1> <div style="font-size: 1.5rem; color: #4a4a4a; margin-bottom: 5px; font-weight: 300;">An <a href="https://github.com/embeddings-benchmark/mteb" style="color: #2c5282; font-weight: 600; text-decoration: none;" onmouseover="this.style.textDecoration='underline'" onmouseout="this.style.textDecoration='none'">MTEB</a> dataset</div> <div style="font-size: 0.9rem; color: #2c5282; margin-top: 10px;">Massive Text Embedding Benchmark</div> </div> Natural Questions: A Benchmark for Question Answering Research. The hard negative version has been created by pooling the 250 top documents per query from BM25, e5-multilingual-large and e5-mistral-instruct. \| \| \| \|---------------\|---------------------------------------------\| \| Task category \| t2t \| \| Domains \| None \| \| Reference \| https://ai.google.com/research/NaturalQuestions/ \| ## How to evaluate on this task You can evaluate an embedding model on this dataset using the following code: ```python import mteb task = mteb.get_tasks(["NQ-PLHardNegatives"]) evaluator = mteb.MTEB(task) model = mteb.get_model(YOUR_MODEL) evaluator.run(model) ``` <!-- Datasets want link to arxiv in readme to autolink dataset with paper --> To learn more about how to run models on `mteb` task check out the [GitHub repitory](https://github.com/embeddings-benchmark/mteb). ## Citation If you use this dataset, please cite the dataset as well as [mteb](https://github.com/embeddings-benchmark/mteb), as this dataset likely includes additional processing as a part of the [MMTEB Contribution](https://github.com/embeddings-benchmark/mteb/tree/main/docs/mmteb). ```bibtex @misc{wojtasik2024beirpl, archiveprefix = {arXiv}, author = {Konrad Wojtasik and Vadim Shishkin and Kacper Wołowiec and Arkadiusz Janz and Maciej Piasecki}, eprint = {2305.19840}, primaryclass = {cs.IR}, title = {BEIR-PL: Zero Shot Information Retrieval Benchmark for the Polish Language}, year = {2024}, } @article{enevoldsen2025mmtebmassivemultilingualtext, title={MMTEB: Massive Multilingual Text Embedding Benchmark}, author={Kenneth Enevoldsen and Isaac Chung and Imene Kerboua and Márton Kardos and Ashwin Mathur and David Stap and Jay Gala and Wissam Siblini and Dominik Krzemiński and Genta Indra Winata and Saba Sturua and Saiteja Utpala and Mathieu Ciancone and Marion Schaeffer and Gabriel Sequeira and Diganta Misra and Shreeya Dhakal and Jonathan Rystrøm and Roman Solomatin and Ömer Çağatan and Akash Kundu and Martin Bernstorff and Shitao Xiao and Akshita Sukhlecha and Bhavish Pahwa and Rafał Poświata and Kranthi Kiran GV and Shawon Ashraf and Daniel Auras and Björn Plüster and Jan Philipp Harries and Loïc Magne and Isabelle Mohr and Mariya Hendriksen and Dawei Zhu and Hippolyte Gisserot-Boukhlef and Tom Aarsen and Jan Kostkan and Konrad Wojtasik and Taemin Lee and Marek Šuppa and Crystina Zhang and Roberta Rocca and Mohammed Hamdy and Andrianos Michail and John Yang and Manuel Faysse and Aleksei Vatolin and Nandan Thakur and Manan Dey and Dipam Vasani and Pranjal Chitale and Simone Tedeschi and Nguyen Tai and Artem Snegirev and Michael Günther and Mengzhou Xia and Weijia Shi and Xing Han Lù and Jordan Clive and Gayatri Krishnakumar and Anna Maksimova and Silvan Wehrli and Maria Tikhonova and Henil Panchal and Aleksandr Abramov and Malte Ostendorff and Zheng Liu and Simon Clematide and Lester James Miranda and Alena Fenogenova and Guangyu Song and Ruqiya Bin Safi and Wen-Ding Li and Alessia Borghini and Federico Cassano and Hongjin Su and Jimmy Lin and Howard Yen and Lasse Hansen and Sara Hooker and Chenghao Xiao and Vaibhav Adlakha and Orion Weller and Siva Reddy and Niklas Muennighoff}, publisher = {arXiv}, journal={arXiv preprint arXiv:2502.13595}, year={2025}, url={https://arxiv.org/abs/2502.13595}, doi = {10.48550/arXiv.2502.13595}, } @article{muennighoff2022mteb, author = {Muennighoff, Niklas and Tazi, Nouamane and Magne, Lo{\"\i}c and Reimers, Nils}, title = {MTEB: Massive Text Embedding Benchmark}, publisher = {arXiv}, journal={arXiv preprint arXiv:2210.07316}, year = {2022} url = {https://arxiv.org/abs/2210.07316}, doi = {10.48550/ARXIV.2210.07316}, } ``` # Dataset Statistics <details> <summary> Dataset Statistics</summary> The following code contains the descriptive statistics from the task. These can also be obtained using: ```python import mteb task = mteb.get_task("NQ-PLHardNegatives") desc_stats = task.metadata.descriptive_stats ``` ```json { "test": { "num_samples": 185765, "number_of_characters": 113077430, "num_documents": 184765, "min_document_length": 5, "average_document_length": 611.7449138094336, "max_document_length": 14247, "unique_documents": 184765, "num_queries": 1000, "min_query_length": 18, "average_query_length": 48.381, "max_query_length": 106, "unique_queries": 1000, "none_queries": 0, "num_relevant_docs": 1213, "min_relevant_docs_per_query": 1, "average_relevant_docs_per_query": 1.213, "max_relevant_docs_per_query": 4, "unique_relevant_docs": 1213, "num_instructions": null, "min_instruction_length": null, "average_instruction_length": null, "max_instruction_length": null, "unique_instructions": null, "num_top_ranked": null, "min_top_ranked_per_query": null, "average_top_ranked_per_query": null, "max_top_ranked_per_query": null } } ``` </details> --- This dataset card was automatically generated using [MTEB](https://github.com/embeddings-benchmark/mteb)
mteb/NFCorpus-PL	mteb	2025-05-04T16:11:19Z	15	0	[ "task_categories:text-retrieval", "multilinguality:monolingual", "source_datasets:mteb/nfcorpus", "language:pol", "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2305.19840", "arxiv:2502.13595", "arxiv:2210.07316", "region:us", "mteb", "text" ]	[ "text-retrieval" ]	2025-02-05T18:35:26Z	null	--- language: - pol multilinguality: monolingual source_datasets: - mteb/nfcorpus task_categories: - text-retrieval task_ids: [] dataset_info: - config_name: corpus features: - name: _id dtype: string - name: text dtype: string - name: title dtype: string splits: - name: test num_bytes: 6391874 num_examples: 3633 download_size: 3631211 dataset_size: 6391874 - config_name: default features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 415220 num_examples: 12334 download_size: 79948 dataset_size: 415220 - config_name: queries features: - name: _id dtype: string - name: text dtype: string splits: - name: test num_bytes: 13938 num_examples: 323 download_size: 10996 dataset_size: 13938 configs: - config_name: corpus data_files: - split: test path: corpus/test-* - config_name: default data_files: - split: test path: data/test-* - config_name: queries data_files: - split: test path: queries/test-* tags: - mteb - text --- <!-- adapted from https://github.com/huggingface/huggingface_hub/blob/v0.30.2/src/huggingface_hub/templates/datasetcard_template.md --> <div align="center" style="padding: 40px 20px; background-color: white; border-radius: 12px; box-shadow: 0 2px 10px rgba(0, 0, 0, 0.05); max-width: 600px; margin: 0 auto;"> <h1 style="font-size: 3.5rem; color: #1a1a1a; margin: 0 0 20px 0; letter-spacing: 2px; font-weight: 700;">NFCorpus-PL</h1> <div style="font-size: 1.5rem; color: #4a4a4a; margin-bottom: 5px; font-weight: 300;">An <a href="https://github.com/embeddings-benchmark/mteb" style="color: #2c5282; font-weight: 600; text-decoration: none;" onmouseover="this.style.textDecoration='underline'" onmouseout="this.style.textDecoration='none'">MTEB</a> dataset</div> <div style="font-size: 0.9rem; color: #2c5282; margin-top: 10px;">Massive Text Embedding Benchmark</div> </div> NFCorpus: A Full-Text Learning to Rank Dataset for Medical Information Retrieval \| \| \| \|---------------\|---------------------------------------------\| \| Task category \| t2t \| \| Domains \| None \| \| Reference \| https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/ \| ## How to evaluate on this task You can evaluate an embedding model on this dataset using the following code: ```python import mteb task = mteb.get_tasks(["NFCorpus-PL"]) evaluator = mteb.MTEB(task) model = mteb.get_model(YOUR_MODEL) evaluator.run(model) ``` <!-- Datasets want link to arxiv in readme to autolink dataset with paper --> To learn more about how to run models on `mteb` task check out the [GitHub repitory](https://github.com/embeddings-benchmark/mteb). ## Citation If you use this dataset, please cite the dataset as well as [mteb](https://github.com/embeddings-benchmark/mteb), as this dataset likely includes additional processing as a part of the [MMTEB Contribution](https://github.com/embeddings-benchmark/mteb/tree/main/docs/mmteb). ```bibtex @misc{wojtasik2024beirpl, archiveprefix = {arXiv}, author = {Konrad Wojtasik and Vadim Shishkin and Kacper Wołowiec and Arkadiusz Janz and Maciej Piasecki}, eprint = {2305.19840}, primaryclass = {cs.IR}, title = {BEIR-PL: Zero Shot Information Retrieval Benchmark for the Polish Language}, year = {2024}, } @article{enevoldsen2025mmtebmassivemultilingualtext, title={MMTEB: Massive Multilingual Text Embedding Benchmark}, author={Kenneth Enevoldsen and Isaac Chung and Imene Kerboua and Márton Kardos and Ashwin Mathur and David Stap and Jay Gala and Wissam Siblini and Dominik Krzemiński and Genta Indra Winata and Saba Sturua and Saiteja Utpala and Mathieu Ciancone and Marion Schaeffer and Gabriel Sequeira and Diganta Misra and Shreeya Dhakal and Jonathan Rystrøm and Roman Solomatin and Ömer Çağatan and Akash Kundu and Martin Bernstorff and Shitao Xiao and Akshita Sukhlecha and Bhavish Pahwa and Rafał Poświata and Kranthi Kiran GV and Shawon Ashraf and Daniel Auras and Björn Plüster and Jan Philipp Harries and Loïc Magne and Isabelle Mohr and Mariya Hendriksen and Dawei Zhu and Hippolyte Gisserot-Boukhlef and Tom Aarsen and Jan Kostkan and Konrad Wojtasik and Taemin Lee and Marek Šuppa and Crystina Zhang and Roberta Rocca and Mohammed Hamdy and Andrianos Michail and John Yang and Manuel Faysse and Aleksei Vatolin and Nandan Thakur and Manan Dey and Dipam Vasani and Pranjal Chitale and Simone Tedeschi and Nguyen Tai and Artem Snegirev and Michael Günther and Mengzhou Xia and Weijia Shi and Xing Han Lù and Jordan Clive and Gayatri Krishnakumar and Anna Maksimova and Silvan Wehrli and Maria Tikhonova and Henil Panchal and Aleksandr Abramov and Malte Ostendorff and Zheng Liu and Simon Clematide and Lester James Miranda and Alena Fenogenova and Guangyu Song and Ruqiya Bin Safi and Wen-Ding Li and Alessia Borghini and Federico Cassano and Hongjin Su and Jimmy Lin and Howard Yen and Lasse Hansen and Sara Hooker and Chenghao Xiao and Vaibhav Adlakha and Orion Weller and Siva Reddy and Niklas Muennighoff}, publisher = {arXiv}, journal={arXiv preprint arXiv:2502.13595}, year={2025}, url={https://arxiv.org/abs/2502.13595}, doi = {10.48550/arXiv.2502.13595}, } @article{muennighoff2022mteb, author = {Muennighoff, Niklas and Tazi, Nouamane and Magne, Lo{\"\i}c and Reimers, Nils}, title = {MTEB: Massive Text Embedding Benchmark}, publisher = {arXiv}, journal={arXiv preprint arXiv:2210.07316}, year = {2022} url = {https://arxiv.org/abs/2210.07316}, doi = {10.48550/ARXIV.2210.07316}, } ``` # Dataset Statistics <details> <summary> Dataset Statistics</summary> The following code contains the descriptive statistics from the task. These can also be obtained using: ```python import mteb task = mteb.get_task("NFCorpus-PL") desc_stats = task.metadata.descriptive_stats ``` ```json { "test": { "num_samples": 3956, "number_of_characters": 6013927, "num_documents": 3633, "min_document_length": 110, "average_document_length": 1653.1926782273604, "max_document_length": 10705, "unique_documents": 3633, "num_queries": 323, "min_query_length": 3, "average_query_length": 24.390092879256965, "max_query_length": 96, "unique_queries": 323, "none_queries": 0, "num_relevant_docs": 12334, "min_relevant_docs_per_query": 1, "average_relevant_docs_per_query": 38.18575851393189, "max_relevant_docs_per_query": 475, "unique_relevant_docs": 3128, "num_instructions": null, "min_instruction_length": null, "average_instruction_length": null, "max_instruction_length": null, "unique_instructions": null, "num_top_ranked": null, "min_top_ranked_per_query": null, "average_top_ranked_per_query": null, "max_top_ranked_per_query": null } } ``` </details> --- This dataset card was automatically generated using [MTEB](https://github.com/embeddings-benchmark/mteb)
mteb/norquad_retrieval	mteb	2025-05-04T16:10:52Z	16	0	[ "task_categories:text-retrieval", "task_ids:multiple-choice-qa", "annotations_creators:derived", "multilinguality:monolingual", "language:nob", "license:cc-by-sa-4.0", "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2502.13595", "arxiv:2210.07316", "region:us", "mteb", "text" ]	[ "text-retrieval" ]	2024-04-03T11:56:24Z	null	--- annotations_creators: - derived language: - nob license: cc-by-sa-4.0 multilinguality: monolingual task_categories: - text-retrieval task_ids: - multiple-choice-qa dataset_info: features: - name: id dtype: string - name: context dtype: string - name: question dtype: string - name: answers struct: - name: answer_start sequence: int64 - name: text sequence: string splits: - name: train num_bytes: 2350752 num_examples: 1024 - name: val num_bytes: 588509 num_examples: 256 - name: test num_bytes: 4687874 num_examples: 2048 download_size: 3083620 dataset_size: 7627135 configs: - config_name: default data_files: - split: train path: data/train-* - split: val path: data/val-* - split: test path: data/test-* tags: - mteb - text --- <!-- adapted from https://github.com/huggingface/huggingface_hub/blob/v0.30.2/src/huggingface_hub/templates/datasetcard_template.md --> <div align="center" style="padding: 40px 20px; background-color: white; border-radius: 12px; box-shadow: 0 2px 10px rgba(0, 0, 0, 0.05); max-width: 600px; margin: 0 auto;"> <h1 style="font-size: 3.5rem; color: #1a1a1a; margin: 0 0 20px 0; letter-spacing: 2px; font-weight: 700;">NorQuadRetrieval</h1> <div style="font-size: 1.5rem; color: #4a4a4a; margin-bottom: 5px; font-weight: 300;">An <a href="https://github.com/embeddings-benchmark/mteb" style="color: #2c5282; font-weight: 600; text-decoration: none;" onmouseover="this.style.textDecoration='underline'" onmouseout="this.style.textDecoration='none'">MTEB</a> dataset</div> <div style="font-size: 0.9rem; color: #2c5282; margin-top: 10px;">Massive Text Embedding Benchmark</div> </div> Human-created question for Norwegian wikipedia passages. \| \| \| \|---------------\|---------------------------------------------\| \| Task category \| t2t \| \| Domains \| Encyclopaedic, Non-fiction, Written \| \| Reference \| https://aclanthology.org/2023.nodalida-1.17/ \| ## How to evaluate on this task You can evaluate an embedding model on this dataset using the following code: ```python import mteb task = mteb.get_tasks(["NorQuadRetrieval"]) evaluator = mteb.MTEB(task) model = mteb.get_model(YOUR_MODEL) evaluator.run(model) ``` <!-- Datasets want link to arxiv in readme to autolink dataset with paper --> To learn more about how to run models on `mteb` task check out the [GitHub repitory](https://github.com/embeddings-benchmark/mteb). ## Citation If you use this dataset, please cite the dataset as well as [mteb](https://github.com/embeddings-benchmark/mteb), as this dataset likely includes additional processing as a part of the [MMTEB Contribution](https://github.com/embeddings-benchmark/mteb/tree/main/docs/mmteb). ```bibtex @inproceedings{ivanova-etal-2023-norquad, abstract = {In this paper we present NorQuAD: the first Norwegian question answering dataset for machine reading comprehension. The dataset consists of 4,752 manually created question-answer pairs. We here detail the data collection procedure and present statistics of the dataset. We also benchmark several multilingual and Norwegian monolingual language models on the dataset and compare them against human performance. The dataset will be made freely available.}, address = {T{\'o}rshavn, Faroe Islands}, author = {Ivanova, Sardana and Andreassen, Fredrik and Jentoft, Matias and Wold, Sondre and {\O}vrelid, Lilja}, booktitle = {Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa)}, editor = {Alum{\"a}e, Tanel and Fishel, Mark}, month = may, pages = {159--168}, publisher = {University of Tartu Library}, title = {{N}or{Q}u{AD}: {N}orwegian Question Answering Dataset}, url = {https://aclanthology.org/2023.nodalida-1.17}, year = {2023}, } @article{enevoldsen2025mmtebmassivemultilingualtext, title={MMTEB: Massive Multilingual Text Embedding Benchmark}, author={Kenneth Enevoldsen and Isaac Chung and Imene Kerboua and Márton Kardos and Ashwin Mathur and David Stap and Jay Gala and Wissam Siblini and Dominik Krzemiński and Genta Indra Winata and Saba Sturua and Saiteja Utpala and Mathieu Ciancone and Marion Schaeffer and Gabriel Sequeira and Diganta Misra and Shreeya Dhakal and Jonathan Rystrøm and Roman Solomatin and Ömer Çağatan and Akash Kundu and Martin Bernstorff and Shitao Xiao and Akshita Sukhlecha and Bhavish Pahwa and Rafał Poświata and Kranthi Kiran GV and Shawon Ashraf and Daniel Auras and Björn Plüster and Jan Philipp Harries and Loïc Magne and Isabelle Mohr and Mariya Hendriksen and Dawei Zhu and Hippolyte Gisserot-Boukhlef and Tom Aarsen and Jan Kostkan and Konrad Wojtasik and Taemin Lee and Marek Šuppa and Crystina Zhang and Roberta Rocca and Mohammed Hamdy and Andrianos Michail and John Yang and Manuel Faysse and Aleksei Vatolin and Nandan Thakur and Manan Dey and Dipam Vasani and Pranjal Chitale and Simone Tedeschi and Nguyen Tai and Artem Snegirev and Michael Günther and Mengzhou Xia and Weijia Shi and Xing Han Lù and Jordan Clive and Gayatri Krishnakumar and Anna Maksimova and Silvan Wehrli and Maria Tikhonova and Henil Panchal and Aleksandr Abramov and Malte Ostendorff and Zheng Liu and Simon Clematide and Lester James Miranda and Alena Fenogenova and Guangyu Song and Ruqiya Bin Safi and Wen-Ding Li and Alessia Borghini and Federico Cassano and Hongjin Su and Jimmy Lin and Howard Yen and Lasse Hansen and Sara Hooker and Chenghao Xiao and Vaibhav Adlakha and Orion Weller and Siva Reddy and Niklas Muennighoff}, publisher = {arXiv}, journal={arXiv preprint arXiv:2502.13595}, year={2025}, url={https://arxiv.org/abs/2502.13595}, doi = {10.48550/arXiv.2502.13595}, } @article{muennighoff2022mteb, author = {Muennighoff, Niklas and Tazi, Nouamane and Magne, Lo{\"\i}c and Reimers, Nils}, title = {MTEB: Massive Text Embedding Benchmark}, publisher = {arXiv}, journal={arXiv preprint arXiv:2210.07316}, year = {2022} url = {https://arxiv.org/abs/2210.07316}, doi = {10.48550/ARXIV.2210.07316}, } ``` # Dataset Statistics <details> <summary> Dataset Statistics</summary> The following code contains the descriptive statistics from the task. These can also be obtained using: ```python import mteb task = mteb.get_task("NorQuadRetrieval") desc_stats = task.metadata.descriptive_stats ``` ```json { "test": { "num_samples": 2072, "number_of_characters": 273854, "num_documents": 1048, "min_document_length": 1, "average_document_length": 214.5114503816794, "max_document_length": 2606, "unique_documents": 1048, "num_queries": 1024, "min_query_length": 11, "average_query_length": 47.896484375, "max_query_length": 100, "unique_queries": 1024, "none_queries": 0, "num_relevant_docs": 2048, "min_relevant_docs_per_query": 2, "average_relevant_docs_per_query": 2.0, "max_relevant_docs_per_query": 2, "unique_relevant_docs": 1328, "num_instructions": null, "min_instruction_length": null, "average_instruction_length": null, "max_instruction_length": null, "unique_instructions": null, "num_top_ranked": null, "min_top_ranked_per_query": null, "average_top_ranked_per_query": null, "max_top_ranked_per_query": null } } ``` </details> --- This dataset card was automatically generated using [MTEB](https://github.com/embeddings-benchmark/mteb)
mteb/mrtidy	mteb	2025-05-04T16:10:44Z	999	0	[ "task_categories:text-retrieval", "annotations_creators:human-annotated", "multilinguality:multilingual", "language:ara", "language:ben", "language:eng", "language:fin", "language:ind", "language:jpn", "language:kor", "language:rus", "language:swa", "language:tel", "language:tha", "license:cc-by-sa-3.0", "size_categories:10M<n<100M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:2108.08787", "arxiv:2502.13595", "arxiv:2210.07316", "region:us", "mteb", "text" ]	[ "text-retrieval" ]	2024-09-29T11:14:01Z	null	--- annotations_creators: - human-annotated language: - ara - ben - eng - fin - ind - jpn - kor - rus - swa - tel - tha license: cc-by-sa-3.0 multilinguality: multilingual task_categories: - text-retrieval task_ids: [] dataset_info: - config_name: arabic-corpus features: - name: _id dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 1245714994 num_examples: 2106586 download_size: 562700591 dataset_size: 1245714994 - config_name: arabic-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: train num_bytes: 10793262 num_examples: 374523 - name: dev num_bytes: 87088 num_examples: 3115 - name: test num_bytes: 36208 num_examples: 1257 download_size: 3781951 dataset_size: 10916558 - config_name: arabic-queries features: - name: _id dtype: string - name: text dtype: string splits: - name: train num_bytes: 831496 num_examples: 12377 - name: dev num_bytes: 207878 num_examples: 3115 - name: test num_bytes: 74696 num_examples: 1081 download_size: 621343 dataset_size: 1114070 - config_name: bengali-corpus features: - name: _id dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 313718344 num_examples: 304059 download_size: 113214290 dataset_size: 313718344 - config_name: bengali-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: train num_bytes: 1410063 num_examples: 51861 - name: dev num_bytes: 11764 num_examples: 443 - name: test num_bytes: 3548 num_examples: 130 download_size: 479618 dataset_size: 1425375 - config_name: bengali-queries features: - name: _id dtype: string - name: text dtype: string splits: - name: train num_bytes: 233828 num_examples: 1713 - name: dev num_bytes: 60547 num_examples: 440 - name: test num_bytes: 16464 num_examples: 111 download_size: 135025 dataset_size: 310839 - config_name: english-corpus features: - name: _id dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 13953017759 num_examples: 32907100 download_size: 8012208332 dataset_size: 13953017759 - config_name: english-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: train num_bytes: 3163945 num_examples: 108401 - name: dev num_bytes: 24984 num_examples: 878 - name: test num_bytes: 26591 num_examples: 935 download_size: 1201824 dataset_size: 3215520 - config_name: english-queries features: - name: _id dtype: string - name: text dtype: string splits: - name: train num_bytes: 180772 num_examples: 3547 - name: dev num_bytes: 45396 num_examples: 878 - name: test num_bytes: 39063 num_examples: 744 download_size: 179373 dataset_size: 265231 - config_name: finnish-corpus features: - name: _id dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 749089306 num_examples: 1908757 download_size: 446059094 dataset_size: 749089306 - config_name: finnish-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: train num_bytes: 5510842 num_examples: 197338 - name: dev num_bytes: 47540 num_examples: 1738 - name: test num_bytes: 39723 num_examples: 1451 download_size: 1870069 dataset_size: 5598105 - config_name: finnish-queries features: - name: _id dtype: string - name: text dtype: string splits: - name: train num_bytes: 336371 num_examples: 6561 - name: dev num_bytes: 89446 num_examples: 1738 - name: test num_bytes: 63723 num_examples: 1254 download_size: 335034 dataset_size: 489540 - config_name: indonesian-corpus features: - name: _id dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 545994305 num_examples: 1469399 download_size: 276886119 dataset_size: 545994305 - config_name: indonesian-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: train num_bytes: 4169295 num_examples: 148491 - name: dev num_bytes: 33405 num_examples: 1224 - name: test num_bytes: 26807 num_examples: 961 download_size: 1438860 dataset_size: 4229507 - config_name: indonesian-queries features: - name: _id dtype: string - name: text dtype: string splits: - name: train num_bytes: 241258 num_examples: 4902 - name: dev num_bytes: 60988 num_examples: 1224 - name: test num_bytes: 43451 num_examples: 829 download_size: 221700 dataset_size: 345697 - config_name: japanese-corpus features: - name: _id dtype: string - name: title dtype: string - name: text dtype: string splits: - name: dev num_bytes: 387783.4788097246 num_examples: 903 - name: test num_bytes: 377906.3802353905 num_examples: 880 - name: train num_bytes: 3006085074 num_examples: 7000027 download_size: 1692453219 dataset_size: 3006850763.859045 - config_name: japanese-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: train num_bytes: 3198715 num_examples: 112408 - name: dev num_bytes: 25703 num_examples: 928 - name: test num_bytes: 25819 num_examples: 923 download_size: 1127195 dataset_size: 3250237 - config_name: japanese-queries features: - name: _id dtype: string - name: text dtype: string splits: - name: train num_bytes: 236537 num_examples: 3697 - name: dev num_bytes: 60007 num_examples: 928 - name: test num_bytes: 47149 num_examples: 720 download_size: 210465 dataset_size: 343693 - config_name: korean-corpus features: - name: _id dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 637416518 num_examples: 1496126 download_size: 364890926 dataset_size: 637416518 - config_name: korean-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: train num_bytes: 1083048 num_examples: 39485 - name: dev num_bytes: 8121 num_examples: 307 - name: test num_bytes: 13482 num_examples: 492 download_size: 386327 dataset_size: 1104651 - config_name: korean-queries features: - name: _id dtype: string - name: text dtype: string splits: - name: train num_bytes: 86921 num_examples: 1295 - name: dev num_bytes: 20056 num_examples: 303 - name: test num_bytes: 28967 num_examples: 421 download_size: 87049 dataset_size: 135944 - config_name: russian-corpus features: - name: _id dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 5965881322 num_examples: 9597504 download_size: 2799247131 dataset_size: 5965881322 - config_name: russian-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: train num_bytes: 4701206 num_examples: 163899 - name: dev num_bytes: 38306 num_examples: 1375 - name: test num_bytes: 33048 num_examples: 1168 download_size: 1735174 dataset_size: 4772560 - config_name: russian-queries features: - name: _id dtype: string - name: text dtype: string splits: - name: train num_bytes: 502545 num_examples: 5366 - name: dev num_bytes: 128310 num_examples: 1375 - name: test num_bytes: 97183 num_examples: 995 download_size: 413272 dataset_size: 728038 - config_name: swahili-corpus features: - name: _id dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 34142833 num_examples: 136689 download_size: 17501035 dataset_size: 34142833 - config_name: swahili-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: train num_bytes: 1689502 num_examples: 63390 - name: dev num_bytes: 16381 num_examples: 623 - name: test num_bytes: 19984 num_examples: 743 download_size: 549763 dataset_size: 1725867 - config_name: swahili-queries features: - name: _id dtype: string - name: text dtype: string splits: - name: train num_bytes: 105403 num_examples: 2072 - name: dev num_bytes: 26676 num_examples: 526 - name: test num_bytes: 36137 num_examples: 670 download_size: 100853 dataset_size: 168216 - config_name: telugu-corpus features: - name: _id dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 563646332 num_examples: 548224 download_size: 156110911 dataset_size: 563646332 - config_name: telugu-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: train num_bytes: 3249925 num_examples: 117793 - name: dev num_bytes: 26450 num_examples: 983 - name: test num_bytes: 18434 num_examples: 677 download_size: 964408 dataset_size: 3294809 - config_name: telugu-queries features: - name: _id dtype: string - name: text dtype: string splits: - name: train num_bytes: 446743 num_examples: 3880 - name: dev num_bytes: 112859 num_examples: 983 - name: test num_bytes: 73365 num_examples: 646 download_size: 253930 dataset_size: 632967 - config_name: thai-corpus features: - name: _id dtype: string - name: title dtype: string - name: text dtype: string splits: - name: dev num_bytes: 574630351 num_examples: 568855 - name: test num_bytes: 574630351 num_examples: 568855 - name: train num_bytes: 574630351 num_examples: 568855 download_size: 845293048 dataset_size: 1723891053 - config_name: thai-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: train num_bytes: 2775220 num_examples: 100472 - name: dev num_bytes: 22050 num_examples: 817 - name: test num_bytes: 37576 num_examples: 1368 download_size: 934865 dataset_size: 2834846 - config_name: thai-queries features: - name: _id dtype: string - name: text dtype: string splits: - name: train num_bytes: 443382 num_examples: 3319 - name: dev num_bytes: 108804 num_examples: 807 - name: test num_bytes: 158612 num_examples: 1190 download_size: 320820 dataset_size: 710798 configs: - config_name: arabic-corpus data_files: - split: train path: arabic-corpus/train-* - config_name: arabic-qrels data_files: - split: train path: arabic-qrels/train-* - split: dev path: arabic-qrels/dev-* - split: test path: arabic-qrels/test-* - config_name: arabic-queries data_files: - split: train path: arabic-queries/train-* - split: dev path: arabic-queries/dev-* - split: test path: arabic-queries/test-* - config_name: bengali-corpus data_files: - split: train path: bengali-corpus/train-* - config_name: bengali-qrels data_files: - split: train path: bengali-qrels/train-* - split: dev path: bengali-qrels/dev-* - split: test path: bengali-qrels/test-* - config_name: bengali-queries data_files: - split: train path: bengali-queries/train-* - split: dev path: bengali-queries/dev-* - split: test path: bengali-queries/test-* - config_name: english-corpus data_files: - split: train path: english-corpus/train-* - config_name: english-qrels data_files: - split: train path: english-qrels/train-* - split: dev path: english-qrels/dev-* - split: test path: english-qrels/test-* - config_name: english-queries data_files: - split: train path: english-queries/train-* - split: dev path: english-queries/dev-* - split: test path: english-queries/test-* - config_name: finnish-corpus data_files: - split: train path: finnish-corpus/train-* - config_name: finnish-qrels data_files: - split: train path: finnish-qrels/train-* - split: dev path: finnish-qrels/dev-* - split: test path: finnish-qrels/test-* - config_name: finnish-queries data_files: - split: train path: finnish-queries/train-* - split: dev path: finnish-queries/dev-* - split: test path: finnish-queries/test-* - config_name: indonesian-corpus data_files: - split: train path: indonesian-corpus/train-* - config_name: indonesian-qrels data_files: - split: train path: indonesian-qrels/train-* - split: dev path: indonesian-qrels/dev-* - split: test path: indonesian-qrels/test-* - config_name: indonesian-queries data_files: - split: train path: indonesian-queries/train-* - split: dev path: indonesian-queries/dev-* - split: test path: indonesian-queries/test-* - config_name: japanese-corpus data_files: - split: train path: japanese-corpus/train-* - split: dev path: japanese-corpus/dev-* - split: test path: japanese-corpus/test-* - config_name: japanese-qrels data_files: - split: train path: japanese-qrels/train-* - split: dev path: japanese-qrels/dev-* - split: test path: japanese-qrels/test-* - config_name: japanese-queries data_files: - split: train path: japanese-queries/train-* - split: dev path: japanese-queries/dev-* - split: test path: japanese-queries/test-* - config_name: korean-corpus data_files: - split: train path: korean-corpus/train-* - config_name: korean-qrels data_files: - split: train path: korean-qrels/train-* - split: dev path: korean-qrels/dev-* - split: test path: korean-qrels/test-* - config_name: korean-queries data_files: - split: train path: korean-queries/train-* - split: dev path: korean-queries/dev-* - split: test path: korean-queries/test-* - config_name: russian-corpus data_files: - split: train path: russian-corpus/train-* - config_name: russian-qrels data_files: - split: train path: russian-qrels/train-* - split: dev path: russian-qrels/dev-* - split: test path: russian-qrels/test-* - config_name: russian-queries data_files: - split: train path: russian-queries/train-* - split: dev path: russian-queries/dev-* - split: test path: russian-queries/test-* - config_name: swahili-corpus data_files: - split: train path: swahili-corpus/train-* - config_name: swahili-qrels data_files: - split: train path: swahili-qrels/train-* - split: dev path: swahili-qrels/dev-* - split: test path: swahili-qrels/test-* - config_name: swahili-queries data_files: - split: train path: swahili-queries/train-* - split: dev path: swahili-queries/dev-* - split: test path: swahili-queries/test-* - config_name: telugu-corpus data_files: - split: train path: telugu-corpus/train-* - config_name: telugu-qrels data_files: - split: train path: telugu-qrels/train-* - split: dev path: telugu-qrels/dev-* - split: test path: telugu-qrels/test-* - config_name: telugu-queries data_files: - split: train path: telugu-queries/train-* - split: dev path: telugu-queries/dev-* - split: test path: telugu-queries/test-* - config_name: thai-corpus data_files: - split: train path: thai-corpus/train-* - split: dev path: thai-corpus/dev-* - split: test path: thai-corpus/test-* - config_name: thai-qrels data_files: - split: train path: thai-qrels/train-* - split: dev path: thai-qrels/dev-* - split: test path: thai-qrels/test-* - config_name: thai-queries data_files: - split: train path: thai-queries/train-* - split: dev path: thai-queries/dev-* - split: test path: thai-queries/test-* tags: - mteb - text --- <!-- adapted from https://github.com/huggingface/huggingface_hub/blob/v0.30.2/src/huggingface_hub/templates/datasetcard_template.md --> <div align="center" style="padding: 40px 20px; background-color: white; border-radius: 12px; box-shadow: 0 2px 10px rgba(0, 0, 0, 0.05); max-width: 600px; margin: 0 auto;"> <h1 style="font-size: 3.5rem; color: #1a1a1a; margin: 0 0 20px 0; letter-spacing: 2px; font-weight: 700;">MrTidyRetrieval</h1> <div style="font-size: 1.5rem; color: #4a4a4a; margin-bottom: 5px; font-weight: 300;">An <a href="https://github.com/embeddings-benchmark/mteb" style="color: #2c5282; font-weight: 600; text-decoration: none;" onmouseover="this.style.textDecoration='underline'" onmouseout="this.style.textDecoration='none'">MTEB</a> dataset</div> <div style="font-size: 0.9rem; color: #2c5282; margin-top: 10px;">Massive Text Embedding Benchmark</div> </div> Mr. TyDi is a multi-lingual benchmark dataset built on TyDi, covering eleven typologically diverse languages. It is designed for monolingual retrieval, specifically to evaluate ranking with learned dense representations. \| \| \| \|---------------\|---------------------------------------------\| \| Task category \| t2t \| \| Domains \| Encyclopaedic, Written \| \| Reference \| https://huggingface.co/datasets/castorini/mr-tydi \| ## How to evaluate on this task You can evaluate an embedding model on this dataset using the following code: ```python import mteb task = mteb.get_tasks(["MrTidyRetrieval"]) evaluator = mteb.MTEB(task) model = mteb.get_model(YOUR_MODEL) evaluator.run(model) ``` <!-- Datasets want link to arxiv in readme to autolink dataset with paper --> To learn more about how to run models on `mteb` task check out the [GitHub repitory](https://github.com/embeddings-benchmark/mteb). ## Citation If you use this dataset, please cite the dataset as well as [mteb](https://github.com/embeddings-benchmark/mteb), as this dataset likely includes additional processing as a part of the [MMTEB Contribution](https://github.com/embeddings-benchmark/mteb/tree/main/docs/mmteb). ```bibtex @article{mrtydi, author = {Xinyu Zhang and Xueguang Ma and Peng Shi and Jimmy Lin}, journal = {arXiv:2108.08787}, title = {{Mr. TyDi}: A Multi-lingual Benchmark for Dense Retrieval}, year = {2021}, } @article{enevoldsen2025mmtebmassivemultilingualtext, title={MMTEB: Massive Multilingual Text Embedding Benchmark}, author={Kenneth Enevoldsen and Isaac Chung and Imene Kerboua and Márton Kardos and Ashwin Mathur and David Stap and Jay Gala and Wissam Siblini and Dominik Krzemiński and Genta Indra Winata and Saba Sturua and Saiteja Utpala and Mathieu Ciancone and Marion Schaeffer and Gabriel Sequeira and Diganta Misra and Shreeya Dhakal and Jonathan Rystrøm and Roman Solomatin and Ömer Çağatan and Akash Kundu and Martin Bernstorff and Shitao Xiao and Akshita Sukhlecha and Bhavish Pahwa and Rafał Poświata and Kranthi Kiran GV and Shawon Ashraf and Daniel Auras and Björn Plüster and Jan Philipp Harries and Loïc Magne and Isabelle Mohr and Mariya Hendriksen and Dawei Zhu and Hippolyte Gisserot-Boukhlef and Tom Aarsen and Jan Kostkan and Konrad Wojtasik and Taemin Lee and Marek Šuppa and Crystina Zhang and Roberta Rocca and Mohammed Hamdy and Andrianos Michail and John Yang and Manuel Faysse and Aleksei Vatolin and Nandan Thakur and Manan Dey and Dipam Vasani and Pranjal Chitale and Simone Tedeschi and Nguyen Tai and Artem Snegirev and Michael Günther and Mengzhou Xia and Weijia Shi and Xing Han Lù and Jordan Clive and Gayatri Krishnakumar and Anna Maksimova and Silvan Wehrli and Maria Tikhonova and Henil Panchal and Aleksandr Abramov and Malte Ostendorff and Zheng Liu and Simon Clematide and Lester James Miranda and Alena Fenogenova and Guangyu Song and Ruqiya Bin Safi and Wen-Ding Li and Alessia Borghini and Federico Cassano and Hongjin Su and Jimmy Lin and Howard Yen and Lasse Hansen and Sara Hooker and Chenghao Xiao and Vaibhav Adlakha and Orion Weller and Siva Reddy and Niklas Muennighoff}, publisher = {arXiv}, journal={arXiv preprint arXiv:2502.13595}, year={2025}, url={https://arxiv.org/abs/2502.13595}, doi = {10.48550/arXiv.2502.13595}, } @article{muennighoff2022mteb, author = {Muennighoff, Niklas and Tazi, Nouamane and Magne, Lo{\"\i}c and Reimers, Nils}, title = {MTEB: Massive Text Embedding Benchmark}, publisher = {arXiv}, journal={arXiv preprint arXiv:2210.07316}, year = {2022} url = {https://arxiv.org/abs/2210.07316}, doi = {10.48550/ARXIV.2210.07316}, } ``` # Dataset Statistics <details> <summary> Dataset Statistics</summary> The following code contains the descriptive statistics from the task. These can also be obtained using: ```python import mteb task = mteb.get_task("MrTidyRetrieval") desc_stats = task.metadata.descriptive_stats ``` ```json { "test": { "num_samples": 58051987, "number_of_characters": 20129435459, "num_documents": 58043326, "min_document_length": 2, "average_document_length": 346.79462777512094, "max_document_length": 61659, "unique_documents": 58043326, "num_queries": 8661, "min_query_length": 6, "average_query_length": 37.15783396836393, "max_query_length": 144, "unique_queries": 8661, "none_queries": 0, "num_relevant_docs": 10105, "min_relevant_docs_per_query": 1, "average_relevant_docs_per_query": 1.1667243967209329, "max_relevant_docs_per_query": 3, "unique_relevant_docs": 8926, "num_instructions": null, "min_instruction_length": null, "average_instruction_length": null, "max_instruction_length": null, "unique_instructions": null, "num_top_ranked": null, "min_top_ranked_per_query": null, "average_top_ranked_per_query": null, "max_top_ranked_per_query": null } } ``` </details> --- This dataset card was automatically generated using [MTEB](https://github.com/embeddings-benchmark/mteb)
mteb/fiqa	mteb	2025-05-04T16:10:12Z	1,762	2	[ "task_categories:text-retrieval", "task_ids:multiple-choice-qa", "annotations_creators:human-annotated", "multilinguality:monolingual", "language:eng", "license:unknown", "size_categories:10K<n<100K", "format:json", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2502.13595", "arxiv:2210.07316", "region:us", "mteb", "text" ]	[ "text-retrieval" ]	2024-03-02T20:35:06Z	null	--- annotations_creators: - human-annotated language: - eng license: unknown multilinguality: monolingual task_categories: - text-retrieval task_ids: - multiple-choice-qa config_names: - corpus tags: - mteb - text dataset_info: - config_name: default features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: float64 splits: - name: train num_bytes: 365642 num_examples: 14166 - name: dev num_bytes: 31919 num_examples: 1238 - name: test num_bytes: 43996 num_examples: 1706 - config_name: corpus features: - name: _id dtype: string - name: title dtype: string - name: text dtype: string splits: - name: corpus num_bytes: 45303212 num_examples: 57638 - config_name: queries features: - name: _id dtype: string - name: text dtype: string splits: - name: queries num_bytes: 491278 num_examples: 6648 configs: - config_name: default data_files: - split: train path: qrels/train.jsonl - split: dev path: qrels/dev.jsonl - split: test path: qrels/test.jsonl - config_name: corpus data_files: - split: corpus path: corpus.jsonl - config_name: queries data_files: - split: queries path: queries.jsonl --- <!-- adapted from https://github.com/huggingface/huggingface_hub/blob/v0.30.2/src/huggingface_hub/templates/datasetcard_template.md --> <div align="center" style="padding: 40px 20px; background-color: white; border-radius: 12px; box-shadow: 0 2px 10px rgba(0, 0, 0, 0.05); max-width: 600px; margin: 0 auto;"> <h1 style="font-size: 3.5rem; color: #1a1a1a; margin: 0 0 20px 0; letter-spacing: 2px; font-weight: 700;">FiQA2018</h1> <div style="font-size: 1.5rem; color: #4a4a4a; margin-bottom: 5px; font-weight: 300;">An <a href="https://github.com/embeddings-benchmark/mteb" style="color: #2c5282; font-weight: 600; text-decoration: none;" onmouseover="this.style.textDecoration='underline'" onmouseout="this.style.textDecoration='none'">MTEB</a> dataset</div> <div style="font-size: 0.9rem; color: #2c5282; margin-top: 10px;">Massive Text Embedding Benchmark</div> </div> Financial Opinion Mining and Question Answering \| \| \| \|---------------\|---------------------------------------------\| \| Task category \| t2t \| \| Domains \| Written, Financial \| \| Reference \| https://sites.google.com/view/fiqa/ \| ## How to evaluate on this task You can evaluate an embedding model on this dataset using the following code: ```python import mteb task = mteb.get_tasks(["FiQA2018"]) evaluator = mteb.MTEB(task) model = mteb.get_model(YOUR_MODEL) evaluator.run(model) ``` <!-- Datasets want link to arxiv in readme to autolink dataset with paper --> To learn more about how to run models on `mteb` task check out the [GitHub repitory](https://github.com/embeddings-benchmark/mteb). ## Citation If you use this dataset, please cite the dataset as well as [mteb](https://github.com/embeddings-benchmark/mteb), as this dataset likely includes additional processing as a part of the [MMTEB Contribution](https://github.com/embeddings-benchmark/mteb/tree/main/docs/mmteb). ```bibtex @inproceedings{thakur2021beir, author = {Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych}, booktitle = {Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)}, title = {{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models}, url = {https://openreview.net/forum?id=wCu6T5xFjeJ}, year = {2021}, } @article{enevoldsen2025mmtebmassivemultilingualtext, title={MMTEB: Massive Multilingual Text Embedding Benchmark}, author={Kenneth Enevoldsen and Isaac Chung and Imene Kerboua and Márton Kardos and Ashwin Mathur and David Stap and Jay Gala and Wissam Siblini and Dominik Krzemiński and Genta Indra Winata and Saba Sturua and Saiteja Utpala and Mathieu Ciancone and Marion Schaeffer and Gabriel Sequeira and Diganta Misra and Shreeya Dhakal and Jonathan Rystrøm and Roman Solomatin and Ömer Çağatan and Akash Kundu and Martin Bernstorff and Shitao Xiao and Akshita Sukhlecha and Bhavish Pahwa and Rafał Poświata and Kranthi Kiran GV and Shawon Ashraf and Daniel Auras and Björn Plüster and Jan Philipp Harries and Loïc Magne and Isabelle Mohr and Mariya Hendriksen and Dawei Zhu and Hippolyte Gisserot-Boukhlef and Tom Aarsen and Jan Kostkan and Konrad Wojtasik and Taemin Lee and Marek Šuppa and Crystina Zhang and Roberta Rocca and Mohammed Hamdy and Andrianos Michail and John Yang and Manuel Faysse and Aleksei Vatolin and Nandan Thakur and Manan Dey and Dipam Vasani and Pranjal Chitale and Simone Tedeschi and Nguyen Tai and Artem Snegirev and Michael Günther and Mengzhou Xia and Weijia Shi and Xing Han Lù and Jordan Clive and Gayatri Krishnakumar and Anna Maksimova and Silvan Wehrli and Maria Tikhonova and Henil Panchal and Aleksandr Abramov and Malte Ostendorff and Zheng Liu and Simon Clematide and Lester James Miranda and Alena Fenogenova and Guangyu Song and Ruqiya Bin Safi and Wen-Ding Li and Alessia Borghini and Federico Cassano and Hongjin Su and Jimmy Lin and Howard Yen and Lasse Hansen and Sara Hooker and Chenghao Xiao and Vaibhav Adlakha and Orion Weller and Siva Reddy and Niklas Muennighoff}, publisher = {arXiv}, journal={arXiv preprint arXiv:2502.13595}, year={2025}, url={https://arxiv.org/abs/2502.13595}, doi = {10.48550/arXiv.2502.13595}, } @article{muennighoff2022mteb, author = {Muennighoff, Niklas and Tazi, Nouamane and Magne, Lo{\"\i}c and Reimers, Nils}, title = {MTEB: Massive Text Embedding Benchmark}, publisher = {arXiv}, journal={arXiv preprint arXiv:2210.07316}, year = {2022} url = {https://arxiv.org/abs/2210.07316}, doi = {10.48550/ARXIV.2210.07316}, } ``` # Dataset Statistics <details> <summary> Dataset Statistics</summary> The following code contains the descriptive statistics from the task. These can also be obtained using: ```python import mteb task = mteb.get_task("FiQA2018") desc_stats = task.metadata.descriptive_stats ``` ```json { "train": { "num_samples": 63138, "number_of_characters": 44616372, "num_documents": 57638, "min_document_length": 1, "average_document_length": 768.2108157812554, "max_document_length": 16991, "unique_documents": 57638, "num_queries": 5500, "min_query_length": 14, "average_query_length": 61.49763636363636, "max_query_length": 158, "unique_queries": 5500, "none_queries": 0, "num_relevant_docs": 14166, "min_relevant_docs_per_query": 1, "average_relevant_docs_per_query": 2.5756363636363635, "max_relevant_docs_per_query": 23, "unique_relevant_docs": 14166, "num_instructions": null, "min_instruction_length": null, "average_instruction_length": null, "max_instruction_length": null, "unique_instructions": null, "num_top_ranked": null, "min_top_ranked_per_query": null, "average_top_ranked_per_query": null, "max_top_ranked_per_query": null }, "dev": { "num_samples": 58138, "number_of_characters": 44309513, "num_documents": 57638, "min_document_length": 1, "average_document_length": 768.2108157812554, "max_document_length": 16991, "unique_documents": 57638, "num_queries": 500, "min_query_length": 15, "average_query_length": 62.756, "max_query_length": 166, "unique_queries": 500, "none_queries": 0, "num_relevant_docs": 1238, "min_relevant_docs_per_query": 1, "average_relevant_docs_per_query": 2.476, "max_relevant_docs_per_query": 20, "unique_relevant_docs": 1238, "num_instructions": null, "min_instruction_length": null, "average_instruction_length": null, "max_instruction_length": null, "unique_instructions": null, "num_top_ranked": null, "min_top_ranked_per_query": null, "average_top_ranked_per_query": null, "max_top_ranked_per_query": null }, "test": { "num_samples": 58286, "number_of_characters": 44318767, "num_documents": 57638, "min_document_length": 1, "average_document_length": 768.2108157812554, "max_document_length": 16991, "unique_documents": 57638, "num_queries": 648, "min_query_length": 16, "average_query_length": 62.7037037037037, "max_query_length": 147, "unique_queries": 648, "none_queries": 0, "num_relevant_docs": 1706, "min_relevant_docs_per_query": 1, "average_relevant_docs_per_query": 2.632716049382716, "max_relevant_docs_per_query": 15, "unique_relevant_docs": 1706, "num_instructions": null, "min_instruction_length": null, "average_instruction_length": null, "max_instruction_length": null, "unique_instructions": null, "num_top_ranked": null, "min_top_ranked_per_query": null, "average_top_ranked_per_query": null, "max_top_ranked_per_query": null } } ``` </details> --- This dataset card was automatically generated using [MTEB](https://github.com/embeddings-benchmark/mteb)
mteb/talemaader_pc	mteb	2025-05-04T16:09:22Z	2	0	[ "task_categories:text-classification", "task_ids:semantic-similarity-classification", "annotations_creators:derived", "multilinguality:monolingual", "language:dan", "license:cc-by-4.0", "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2502.13595", "arxiv:2210.07316", "region:us", "mteb", "text" ]	[ "text-classification" ]	2025-05-02T09:08:59Z	null	--- annotations_creators: - derived language: - dan license: cc-by-4.0 multilinguality: monolingual task_categories: - text-classification task_ids: - semantic-similarity-classification dataset_info: features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: int64 splits: - name: test num_bytes: 390123 num_examples: 4000 download_size: 173048 dataset_size: 390123 configs: - config_name: default data_files: - split: test path: data/test-* tags: - mteb - text --- <!-- adapted from https://github.com/huggingface/huggingface_hub/blob/v0.30.2/src/huggingface_hub/templates/datasetcard_template.md --> <div align="center" style="padding: 40px 20px; background-color: white; border-radius: 12px; box-shadow: 0 2px 10px rgba(0, 0, 0, 0.05); max-width: 600px; margin: 0 auto;"> <h1 style="font-size: 3.5rem; color: #1a1a1a; margin: 0 0 20px 0; letter-spacing: 2px; font-weight: 700;">TalemaaderPC</h1> <div style="font-size: 1.5rem; color: #4a4a4a; margin-bottom: 5px; font-weight: 300;">An <a href="https://github.com/embeddings-benchmark/mteb" style="color: #2c5282; font-weight: 600; text-decoration: none;" onmouseover="this.style.textDecoration='underline'" onmouseout="this.style.textDecoration='none'">MTEB</a> dataset</div> <div style="font-size: 0.9rem; color: #2c5282; margin-top: 10px;">Massive Text Embedding Benchmark</div> </div> The Danish Language and Literature Society has developed a dataset for evaluating language models in Danish. The dataset contains a total of 1000 Danish idioms and fixed expressions with transferred meanings based on the Danish Dictionary's collection of fixed expressions with associated definitions. For each of the 1000 idioms and fixed expressions, three false definitions have also been prepared. The dataset can be used to test the performance of language models in identifying correct definitions for Danish idioms and fixed expressions. \| \| \| \|---------------\|---------------------------------------------\| \| Task category \| t2t \| \| Domains \| Academic, Written \| \| Reference \| https://sprogteknologi.dk/dataset/1000-talemader-evalueringsdatasaet \| ## How to evaluate on this task You can evaluate an embedding model on this dataset using the following code: ```python import mteb task = mteb.get_tasks(["TalemaaderPC"]) evaluator = mteb.MTEB(task) model = mteb.get_model(YOUR_MODEL) evaluator.run(model) ``` <!-- Datasets want link to arxiv in readme to autolink dataset with paper --> To learn more about how to run models on `mteb` task check out the [GitHub repitory](https://github.com/embeddings-benchmark/mteb). ## Citation If you use this dataset, please cite the dataset as well as [mteb](https://github.com/embeddings-benchmark/mteb), as this dataset likely includes additional processing as a part of the [MMTEB Contribution](https://github.com/embeddings-benchmark/mteb/tree/main/docs/mmteb). ```bibtex @misc{DSLDK1000Talemader, author = {{Det Danske Sprog- og Litteraturselskab}}, howpublished = {Sprogteknologi.dk}, language = {Danish}, note = {CC-BY licensed dataset of 1000 Danish sayings and expressions}, publisher = {Digitaliseringsstyrelsen \& Det Danske Sprog- og Litteraturselskab}, title = {1000 danske talemåder - evalueringsdatasæt}, url = {https://sprogteknologi.dk/dataset/1000-talemader-evalueringsdatasaet}, year = {2024}, } @article{enevoldsen2025mmtebmassivemultilingualtext, title={MMTEB: Massive Multilingual Text Embedding Benchmark}, author={Kenneth Enevoldsen and Isaac Chung and Imene Kerboua and Márton Kardos and Ashwin Mathur and David Stap and Jay Gala and Wissam Siblini and Dominik Krzemiński and Genta Indra Winata and Saba Sturua and Saiteja Utpala and Mathieu Ciancone and Marion Schaeffer and Gabriel Sequeira and Diganta Misra and Shreeya Dhakal and Jonathan Rystrøm and Roman Solomatin and Ömer Çağatan and Akash Kundu and Martin Bernstorff and Shitao Xiao and Akshita Sukhlecha and Bhavish Pahwa and Rafał Poświata and Kranthi Kiran GV and Shawon Ashraf and Daniel Auras and Björn Plüster and Jan Philipp Harries and Loïc Magne and Isabelle Mohr and Mariya Hendriksen and Dawei Zhu and Hippolyte Gisserot-Boukhlef and Tom Aarsen and Jan Kostkan and Konrad Wojtasik and Taemin Lee and Marek Šuppa and Crystina Zhang and Roberta Rocca and Mohammed Hamdy and Andrianos Michail and John Yang and Manuel Faysse and Aleksei Vatolin and Nandan Thakur and Manan Dey and Dipam Vasani and Pranjal Chitale and Simone Tedeschi and Nguyen Tai and Artem Snegirev and Michael Günther and Mengzhou Xia and Weijia Shi and Xing Han Lù and Jordan Clive and Gayatri Krishnakumar and Anna Maksimova and Silvan Wehrli and Maria Tikhonova and Henil Panchal and Aleksandr Abramov and Malte Ostendorff and Zheng Liu and Simon Clematide and Lester James Miranda and Alena Fenogenova and Guangyu Song and Ruqiya Bin Safi and Wen-Ding Li and Alessia Borghini and Federico Cassano and Hongjin Su and Jimmy Lin and Howard Yen and Lasse Hansen and Sara Hooker and Chenghao Xiao and Vaibhav Adlakha and Orion Weller and Siva Reddy and Niklas Muennighoff}, publisher = {arXiv}, journal={arXiv preprint arXiv:2502.13595}, year={2025}, url={https://arxiv.org/abs/2502.13595}, doi = {10.48550/arXiv.2502.13595}, } @article{muennighoff2022mteb, author = {Muennighoff, Niklas and Tazi, Nouamane and Magne, Lo{\"\i}c and Reimers, Nils}, title = {MTEB: Massive Text Embedding Benchmark}, publisher = {arXiv}, journal={arXiv preprint arXiv:2210.07316}, year = {2022} url = {https://arxiv.org/abs/2210.07316}, doi = {10.48550/ARXIV.2210.07316}, } ``` # Dataset Statistics <details> <summary> Dataset Statistics</summary> The following code contains the descriptive statistics from the task. These can also be obtained using: ```python import mteb task = mteb.get_task("TalemaaderPC") desc_stats = task.metadata.descriptive_stats ``` ```json { "test": { "num_samples": 4000, "number_of_characters": 315501, "unique_pairs": 4000, "min_sentence1_length": 7, "avg_sentence1_length": 23.801, "max_sentence1_length": 95, "unique_sentence1": 1000, "min_sentence2_length": 2, "avg_sentence2_length": 55.07425, "max_sentence2_length": 242, "unique_sentence2": 2990, "unique_labels": 2, "labels": { "1": { "count": 1000 }, "0": { "count": 3000 } } } } ``` </details> --- This dataset card was automatically generated using [MTEB](https://github.com/embeddings-benchmark/mteb)
mteb/UrduRomanSentimentClassification	mteb	2025-05-04T16:08:30Z	27	0	[ "task_categories:text-classification", "task_ids:sentiment-analysis", "task_ids:sentiment-scoring", "task_ids:sentiment-classification", "task_ids:hate-speech-detection", "annotations_creators:derived", "multilinguality:monolingual", "language:urd", "license:mit", "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2502.13595", "arxiv:2210.07316", "region:us", "mteb", "text" ]	[ "text-classification" ]	2024-12-21T12:09:02Z	null	--- annotations_creators: - derived language: - urd license: mit multilinguality: monolingual task_categories: - text-classification task_ids: - sentiment-analysis - sentiment-scoring - sentiment-classification - hate-speech-detection dataset_info: features: - name: text dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 166310 num_examples: 2048 download_size: 126737 dataset_size: 166310 configs: - config_name: default data_files: - split: train path: data/train-* tags: - mteb - text --- <!-- adapted from https://github.com/huggingface/huggingface_hub/blob/v0.30.2/src/huggingface_hub/templates/datasetcard_template.md --> <div align="center" style="padding: 40px 20px; background-color: white; border-radius: 12px; box-shadow: 0 2px 10px rgba(0, 0, 0, 0.05); max-width: 600px; margin: 0 auto;"> <h1 style="font-size: 3.5rem; color: #1a1a1a; margin: 0 0 20px 0; letter-spacing: 2px; font-weight: 700;">UrduRomanSentimentClassification</h1> <div style="font-size: 1.5rem; color: #4a4a4a; margin-bottom: 5px; font-weight: 300;">An <a href="https://github.com/embeddings-benchmark/mteb" style="color: #2c5282; font-weight: 600; text-decoration: none;" onmouseover="this.style.textDecoration='underline'" onmouseout="this.style.textDecoration='none'">MTEB</a> dataset</div> <div style="font-size: 0.9rem; color: #2c5282; margin-top: 10px;">Massive Text Embedding Benchmark</div> </div> The Roman Urdu dataset is a data corpus comprising of more than 20000 records tagged for sentiment (Positive, Negative, Neutral) \| \| \| \|---------------\|---------------------------------------------\| \| Task category \| t2c \| \| Domains \| Social, Written \| \| Reference \| https://archive.ics.uci.edu/dataset/458/roman+urdu+data+set \| ## How to evaluate on this task You can evaluate an embedding model on this dataset using the following code: ```python import mteb task = mteb.get_tasks(["UrduRomanSentimentClassification"]) evaluator = mteb.MTEB(task) model = mteb.get_model(YOUR_MODEL) evaluator.run(model) ``` <!-- Datasets want link to arxiv in readme to autolink dataset with paper --> To learn more about how to run models on `mteb` task check out the [GitHub repitory](https://github.com/embeddings-benchmark/mteb). ## Citation If you use this dataset, please cite the dataset as well as [mteb](https://github.com/embeddings-benchmark/mteb), as this dataset likely includes additional processing as a part of the [MMTEB Contribution](https://github.com/embeddings-benchmark/mteb/tree/main/docs/mmteb). ```bibtex @misc{misc_roman_urdu_data_set_458, author = {Sharf,Zareen}, howpublished = {UCI Machine Learning Repository}, note = {{DOI}: https://doi.org/10.24432/C58325}, title = {{Roman Urdu Data Set}}, year = {2018}, } @article{enevoldsen2025mmtebmassivemultilingualtext, title={MMTEB: Massive Multilingual Text Embedding Benchmark}, author={Kenneth Enevoldsen and Isaac Chung and Imene Kerboua and Márton Kardos and Ashwin Mathur and David Stap and Jay Gala and Wissam Siblini and Dominik Krzemiński and Genta Indra Winata and Saba Sturua and Saiteja Utpala and Mathieu Ciancone and Marion Schaeffer and Gabriel Sequeira and Diganta Misra and Shreeya Dhakal and Jonathan Rystrøm and Roman Solomatin and Ömer Çağatan and Akash Kundu and Martin Bernstorff and Shitao Xiao and Akshita Sukhlecha and Bhavish Pahwa and Rafał Poświata and Kranthi Kiran GV and Shawon Ashraf and Daniel Auras and Björn Plüster and Jan Philipp Harries and Loïc Magne and Isabelle Mohr and Mariya Hendriksen and Dawei Zhu and Hippolyte Gisserot-Boukhlef and Tom Aarsen and Jan Kostkan and Konrad Wojtasik and Taemin Lee and Marek Šuppa and Crystina Zhang and Roberta Rocca and Mohammed Hamdy and Andrianos Michail and John Yang and Manuel Faysse and Aleksei Vatolin and Nandan Thakur and Manan Dey and Dipam Vasani and Pranjal Chitale and Simone Tedeschi and Nguyen Tai and Artem Snegirev and Michael Günther and Mengzhou Xia and Weijia Shi and Xing Han Lù and Jordan Clive and Gayatri Krishnakumar and Anna Maksimova and Silvan Wehrli and Maria Tikhonova and Henil Panchal and Aleksandr Abramov and Malte Ostendorff and Zheng Liu and Simon Clematide and Lester James Miranda and Alena Fenogenova and Guangyu Song and Ruqiya Bin Safi and Wen-Ding Li and Alessia Borghini and Federico Cassano and Hongjin Su and Jimmy Lin and Howard Yen and Lasse Hansen and Sara Hooker and Chenghao Xiao and Vaibhav Adlakha and Orion Weller and Siva Reddy and Niklas Muennighoff}, publisher = {arXiv}, journal={arXiv preprint arXiv:2502.13595}, year={2025}, url={https://arxiv.org/abs/2502.13595}, doi = {10.48550/arXiv.2502.13595}, } @article{muennighoff2022mteb, author = {Muennighoff, Niklas and Tazi, Nouamane and Magne, Lo{\"\i}c and Reimers, Nils}, title = {MTEB: Massive Text Embedding Benchmark}, publisher = {arXiv}, journal={arXiv preprint arXiv:2210.07316}, year = {2022} url = {https://arxiv.org/abs/2210.07316}, doi = {10.48550/ARXIV.2210.07316}, } ``` # Dataset Statistics <details> <summary> Dataset Statistics</summary> The following code contains the descriptive statistics from the task. These can also be obtained using: ```python import mteb task = mteb.get_task("UrduRomanSentimentClassification") desc_stats = task.metadata.descriptive_stats ``` ```json { "train": { "num_samples": 2048, "number_of_characters": 140832, "number_texts_intersect_with_train": null, "min_text_length": 1, "average_text_length": 68.765625, "max_text_length": 936, "unique_text": 2038, "unique_labels": 3, "labels": { "2": { "count": 904 }, "1": { "count": 535 }, "0": { "count": 609 } } } } ``` </details> --- This dataset card was automatically generated using [MTEB](https://github.com/embeddings-benchmark/mteb)
liyitenga/lekiwi2_giveme5_1	liyitenga	2025-05-04T15:25:18Z	0	0	[ "task_categories:robotics", "license:apache-2.0", "region:us", "LeRobot", "lekiwi", "lekiwi_action" ]	[ "robotics" ]	2025-05-04T15:23:52Z	null	--- license: apache-2.0 task_categories: - robotics tags: - LeRobot - lekiwi - lekiwi_action configs: - config_name: default data_files: data//.parquet --- This dataset was created using [LeRobot](https://github.com/huggingface/lerobot). ## Dataset Description - Homepage: [More Information Needed] - Paper: [More Information Needed] - License: apache-2.0 ## Dataset Structure [meta/info.json](meta/info.json): ```json { "codebase_version": "v2.1", "robot_type": "lekiwi2", "total_episodes": 20, "total_frames": 13259, "total_tasks": 1, "total_videos": 40, "total_chunks": 1, "chunks_size": 1000, "fps": 30, "splits": { "train": "0:20" }, "data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet", "video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4", "features": { "action": { "dtype": "float32", "shape": [ 15 ], "names": [ "left_shoulder_pan", "left_shoulder_lift", "left_elbow_flex", "left_wrist_flex", "left_wrist_roll", "left_gripper", "right_shoulder_pan", "right_shoulder_lift", "right_elbow_flex", "right_wrist_flex", "right_wrist_roll", "right_gripper", "x_mm", "y_mm", "theta" ] }, "observation.state": { "dtype": "float32", "shape": [ 15 ], "names": [ "left_shoulder_pan", "left_shoulder_lift", "left_elbow_flex", "left_wrist_flex", "left_wrist_roll", "left_gripper", "right_shoulder_pan", "right_shoulder_lift", "right_elbow_flex", "right_wrist_flex", "right_wrist_roll", "right_gripper", "x_mm", "y_mm", "theta" ] }, "observation.images.front": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.height": 480, "video.width": 640, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "video.fps": 30, "video.channels": 3, "has_audio": false } }, "observation.images.wrist": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.height": 480, "video.width": 640, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "video.fps": 30, "video.channels": 3, "has_audio": false } }, "timestamp": { "dtype": "float32", "shape": [ 1 ], "names": null }, "frame_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "episode_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "task_index": { "dtype": "int64", "shape": [ 1 ], "names": null } } } ``` ## Citation BibTeX: ```bibtex [More Information Needed] ```
shylee/eval_DP_cube_downDims1_cropNo_freeze1_16_16_ema0_1e-4_ckpt180000	shylee	2025-05-04T15:01:17Z	0	0	[ "task_categories:robotics", "license:apache-2.0", "region:us", "LeRobot", "tutorial" ]	[ "robotics" ]	2025-05-04T15:01:11Z	null	--- license: apache-2.0 task_categories: - robotics tags: - LeRobot - tutorial configs: - config_name: default data_files: data//.parquet --- This dataset was created using [LeRobot](https://github.com/huggingface/lerobot). ## Dataset Description - Homepage: [More Information Needed] - Paper: [More Information Needed] - License: apache-2.0 ## Dataset Structure [meta/info.json](meta/info.json): ```json { "codebase_version": "v2.1", "robot_type": "so100", "total_episodes": 1, "total_frames": 321, "total_tasks": 1, "total_videos": 3, "total_chunks": 1, "chunks_size": 1000, "fps": 30, "splits": { "train": "0:1" }, "data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet", "video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4", "features": { "action": { "dtype": "float32", "shape": [ 6 ], "names": [ "main_shoulder_pan", "main_shoulder_lift", "main_elbow_flex", "main_wrist_flex", "main_wrist_roll", "main_gripper" ] }, "observation.state": { "dtype": "float32", "shape": [ 6 ], "names": [ "main_shoulder_pan", "main_shoulder_lift", "main_elbow_flex", "main_wrist_flex", "main_wrist_roll", "main_gripper" ] }, "observation.images.FrontCam": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.fps": 30.0, "video.height": 480, "video.width": 640, "video.channels": 3, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "has_audio": false } }, "observation.images.TopCam": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.fps": 30.0, "video.height": 480, "video.width": 640, "video.channels": 3, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "has_audio": false } }, "observation.images.WristCam": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.fps": 30.0, "video.height": 480, "video.width": 640, "video.channels": 3, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "has_audio": false } }, "timestamp": { "dtype": "float32", "shape": [ 1 ], "names": null }, "frame_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "episode_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "task_index": { "dtype": "int64", "shape": [ 1 ], "names": null } } } ``` ## Citation BibTeX: ```bibtex [More Information Needed] ```
Mohsen21/Updated_EAD_MALE_FEMALE_DATA	Mohsen21	2025-05-04T14:07:38Z	0	0	[ "region:us" ]	[]	2025-05-04T14:06:45Z	null	--- dataset_info: features: - name: audio dtype: audio - name: transcription dtype: string splits: - name: train num_bytes: 1200893246.0 num_examples: 730 download_size: 1170770119 dataset_size: 1200893246.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
littleGuagua/x_dataset_48558	littleGuagua	2025-05-04T13:13:16Z	1,014	0	[ "task_categories:text-classification", "task_categories:token-classification", "task_categories:question-answering", "task_categories:summarization", "task_categories:text-generation", "task_ids:sentiment-analysis", "task_ids:topic-classification", "task_ids:named-entity-recognition", "task_ids:language-modeling", "task_ids:text-scoring", "task_ids:multi-class-classification", "task_ids:multi-label-classification", "task_ids:extractive-qa", "task_ids:news-articles-summarization", "multilinguality:multilingual", "source_datasets:original", "license:mit", "size_categories:100M<n<1B", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[ "text-classification", "token-classification", "question-answering", "summarization", "text-generation" ]	2025-01-26T14:58:15Z	null	--- license: mit multilinguality: - multilingual source_datasets: - original task_categories: - text-classification - token-classification - question-answering - summarization - text-generation task_ids: - sentiment-analysis - topic-classification - named-entity-recognition - language-modeling - text-scoring - multi-class-classification - multi-label-classification - extractive-qa - news-articles-summarization --- # Bittensor Subnet 13 X (Twitter) Dataset <center> <img src="https://huggingface.co/datasets/macrocosm-os/images/resolve/main/bittensor.png" alt="Data-universe: The finest collection of social media data the web has to offer"> </center> <center> <img src="https://huggingface.co/datasets/macrocosm-os/images/resolve/main/macrocosmos-black.png" alt="Data-universe: The finest collection of social media data the web has to offer"> </center> ## Dataset Description - Repository: littleGuagua/x_dataset_48558 - Subnet: Bittensor Subnet 13 - Miner Hotkey: 5ERFRy1NBaxrJ8WpkjKeWwgx79NxiVoEqmL3m5tEWsDHwjtD ### Dataset Summary This dataset is part of the Bittensor Subnet 13 decentralized network, containing preprocessed data from X (formerly Twitter). The data is continuously updated by network miners, providing a real-time stream of tweets for various analytical and machine learning tasks. For more information about the dataset, please visit the [official repository](https://github.com/macrocosm-os/data-universe). ### Supported Tasks The versatility of this dataset allows researchers and data scientists to explore various aspects of social media dynamics and develop innovative applications. Users are encouraged to leverage this data creatively for their specific research or business needs. For example: - Sentiment Analysis - Trend Detection - Content Analysis - User Behavior Modeling ### Languages Primary language: Datasets are mostly English, but can be multilingual due to decentralized ways of creation. ## Dataset Structure ### Data Instances Each instance represents a single tweet with the following fields: ### Data Fields - `text` (string): The main content of the tweet. - `label` (string): Sentiment or topic category of the tweet. - `tweet_hashtags` (list): A list of hashtags used in the tweet. May be empty if no hashtags are present. - `datetime` (string): The date when the tweet was posted. - `username_encoded` (string): An encoded version of the username to maintain user privacy. - `url_encoded` (string): An encoded version of any URLs included in the tweet. May be empty if no URLs are present. ### Data Splits This dataset is continuously updated and does not have fixed splits. Users should create their own splits based on their requirements and the data's timestamp. ## Dataset Creation ### Source Data Data is collected from public tweets on X (Twitter), adhering to the platform's terms of service and API usage guidelines. ### Personal and Sensitive Information All usernames and URLs are encoded to protect user privacy. The dataset does not intentionally include personal or sensitive information. ## Considerations for Using the Data ### Social Impact and Biases Users should be aware of potential biases inherent in X (Twitter) data, including demographic and content biases. This dataset reflects the content and opinions expressed on X and should not be considered a representative sample of the general population. ### Limitations - Data quality may vary due to the decentralized nature of collection and preprocessing. - The dataset may contain noise, spam, or irrelevant content typical of social media platforms. - Temporal biases may exist due to real-time collection methods. - The dataset is limited to public tweets and does not include private accounts or direct messages. - Not all tweets contain hashtags or URLs. ## Additional Information ### Licensing Information The dataset is released under the MIT license. The use of this dataset is also subject to X Terms of Use. ### Citation Information If you use this dataset in your research, please cite it as follows: ``` @misc{littleGuagua2025datauniversex_dataset_48558, title={The Data Universe Datasets: The finest collection of social media data the web has to offer}, author={littleGuagua}, year={2025}, url={https://huggingface.co/datasets/littleGuagua/x_dataset_48558}, } ``` ### Contributions To report issues or contribute to the dataset, please contact the miner or use the Bittensor Subnet 13 governance mechanisms. ## Dataset Statistics [This section is automatically updated] - Total Instances: 46034420 - Date Range: 2025-01-21T00:00:00Z to 2025-02-11T00:00:00Z - Last Updated: 2025-02-18T22:02:52Z ### Data Distribution - Tweets with hashtags: 36.14% - Tweets without hashtags: 63.86% ### Top 10 Hashtags For full statistics, please refer to the `stats.json` file in the repository. \| Rank \| Topic \| Total Count \| Percentage \| \|------\|-------\|-------------\|-------------\| \| 1 \| NULL \| 29399695 \| 63.86% \| \| 2 \| #riyadh \| 280983 \| 0.61% \| \| 3 \| #zelena \| 221844 \| 0.48% \| \| 4 \| #tiktok \| 163569 \| 0.36% \| \| 5 \| #bbb25 \| 128439 \| 0.28% \| \| 6 \| #ad \| 96138 \| 0.21% \| \| 7 \| #bbmzansi \| 59564 \| 0.13% \| \| 8 \| #jhope_at_galadespiècesjaunes \| 58496 \| 0.13% \| \| 9 \| #granhermano \| 52866 \| 0.11% \| \| 10 \| #pr \| 50398 \| 0.11% \| ## Update History \| Date \| New Instances \| Total Instances \| \|------\|---------------\|-----------------\| \| 2025-01-26T14:58:37Z \| 837393 \| 837393 \| \| 2025-01-30T03:14:08Z \| 8566588 \| 9403981 \| \| 2025-02-02T15:17:25Z \| 8569868 \| 17973849 \| \| 2025-02-06T03:21:34Z \| 10709950 \| 28683799 \| \| 2025-02-09T15:24:35Z \| 7218900 \| 35902699 \| \| 2025-02-13T03:32:24Z \| 8679209 \| 44581908 \| \| 2025-02-18T07:01:46Z \| 795937 \| 45377845 \| \| 2025-02-18T22:02:52Z \| 656575 \| 46034420 \|
ShoAnn/legalqa_klinik_hukumonline	ShoAnn	2025-05-04T13:01:51Z	117	1	[ "language:id", "license:apache-2.0", "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-01-11T02:29:44Z	null	--- language: - id license: apache-2.0 dataset_info: features: - name: question dtype: string - name: answer dtype: string - name: context list: - name: full_text dtype: string - name: id dtype: int64 - name: name dtype: string splits: - name: train num_bytes: 12220706 num_examples: 1006 - name: test num_bytes: 1393760 num_examples: 112 download_size: 5169384 dataset_size: 13614466 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* --- # Pertanyaan-Jawaban-Dasar_Hukum (Question-Answer-Legal_Basis) ## Topic Hukum Pidana Republik Indonesia (Criminal Law) ## Date range November 2013 - October 2024 ## Source Question-Answer-Legal basis reference: [Klinik Hukumonline.com](https://www.hukumonline.com/klinik/pidana) \ Legal basis Sources: Indonesia Official Statutory documents (from sites such as: [Peraturan BPK](https://peraturan.bpk.go.id/), [Peraturan.go.id](peraturan.go.id), [Paralegal](paralegal.id), etc.), Books, and others (articles, expert's opinions, etc)
HungVu2003/opt-350m_beta_0.0_alpha_0.2_num-company_2_dataset_1_for_gen_8_v2	HungVu2003	2025-05-04T12:26:22Z	0	0	[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-04T12:26:20Z	null	--- dataset_info: features: - name: question dtype: string splits: - name: train num_bytes: 2445375 num_examples: 13750 download_size: 984172 dataset_size: 2445375 configs: - config_name: default data_files: - split: train path: data/train-* ---
yunusserhat/tea_sickness_dataset	yunusserhat	2025-05-04T09:45:50Z	0	0	[ "size_categories:n<1K", "format:parquet", "modality:image", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-04T08:03:08Z	null	--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': Anthracnose '1': algal_leaf '2': bird_eye_spot '3': brown_blight '4': gray_light '5': healthy '6': red_leaf_spot '7': white_spot splits: - name: train num_bytes: 622082045.0 num_examples: 708 - name: validation num_bytes: 79334678.0 num_examples: 88 - name: test num_bytes: 79495048.0 num_examples: 89 download_size: 780933256 dataset_size: 780911771.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* --- # Tea Sickness Dataset This dataset contains images of tea leaves affected by seven common diseases, along with healthy examples. It is designed for use in machine learning tasks such as classification, particularly leveraging transfer learning techniques. ## Dataset Summary The dataset includes tea leaves from eight categories: \| Label ID \| Class Name \| \|----------\|------------------\| \| 0 \| Anthracnose \| \| 1 \| Algal Leaf Spot \| \| 2 \| Bird Eye Spot \| \| 3 \| Brown Blight \| \| 4 \| Gray Blight \| \| 5 \| Healthy \| \| 6 \| Red Leaf Spot \| \| 7 \| White Spot \| Each class contains more than 100 images, captured from tea plants in the Johnstone Boiyon farm, Koiwa location, Bomet County, using a clone of 1510. The dataset is useful for developing models that can predict the presence of diseases in tea leaves, particularly in agricultural and sustainability contexts. ## Dataset Structure ### Features - `image`: An RGB image of a tea leaf (PIL Image format) - `label`: Class label as integer (0–7) with corresponding disease names ### Splits \| Split \| Number of Examples \| Size (Bytes) \| \|-------------\|--------------------\|--------------\| \| Train \| 708 \| 622,082,045 \| \| Validation \| 88 \| 79,334,678 \| \| Test \| 89 \| 79,495,048 \| \| Total \| 885 \| 780,911,771 \| ## Usage This dataset is suitable for: - Fine-tuning image classification models - Research on plant disease detection - Transfer learning and domain adaptation experiments ## Citation If you use this dataset in your research, please cite it as: ```bibtex @article{kimutai2022tea, title = {Tea sickness dataset}, author = {Kimutai, Gibson and Förster, Anna}, journal = {Mendeley Data}, volume = {2}, year = {2022}, doi = {10.17632/j32xdt2ff5.2} } ``` ## License CC BY 4.0 — You are free to use, share, and adapt the dataset, provided appropriate credit is given. ## Acknowledgements This dataset was collected by researchers from the University of Rwanda and the University of Bremen. We thank the contributors for making the dataset publicly available to advance agricultural AI applications.
alchemistyzz/mathverse_test	alchemistyzz	2025-05-04T07:35:32Z	0	0	[ "license:apache-2.0", "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-03T09:58:31Z	null	--- license: apache-2.0 ---
kaikkk/eval_act_so100_test1	kaikkk	2025-05-04T06:26:29Z	0	0	[ "task_categories:robotics", "license:apache-2.0", "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:timeseries", "modality:video", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us", "LeRobot", "tutorial" ]	[ "robotics" ]	2025-05-04T06:24:50Z	null	--- license: apache-2.0 task_categories: - robotics tags: - LeRobot - tutorial configs: - config_name: default data_files: data//.parquet --- This dataset was created using [LeRobot](https://github.com/huggingface/lerobot). ## Dataset Description - Homepage: [More Information Needed] - Paper: [More Information Needed] - License: apache-2.0 ## Dataset Structure [meta/info.json](meta/info.json): ```json { "codebase_version": "v2.1", "robot_type": "so100", "total_episodes": 3, "total_frames": 1184, "total_tasks": 1, "total_videos": 6, "total_chunks": 1, "chunks_size": 1000, "fps": 30, "splits": { "train": "0:3" }, "data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet", "video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4", "features": { "action": { "dtype": "float32", "shape": [ 12 ], "names": [ "left_shoulder_pan", "left_shoulder_lift", "left_elbow_flex", "left_wrist_flex", "left_wrist_roll", "left_gripper", "right_shoulder_pan", "right_shoulder_lift", "right_elbow_flex", "right_wrist_flex", "right_wrist_roll", "right_gripper" ] }, "observation.state": { "dtype": "float32", "shape": [ 12 ], "names": [ "left_shoulder_pan", "left_shoulder_lift", "left_elbow_flex", "left_wrist_flex", "left_wrist_roll", "left_gripper", "right_shoulder_pan", "right_shoulder_lift", "right_elbow_flex", "right_wrist_flex", "right_wrist_roll", "right_gripper" ] }, "observation.images.top": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.height": 480, "video.width": 640, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "video.fps": 30, "video.channels": 3, "has_audio": false } }, "observation.images.right": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.height": 480, "video.width": 640, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "video.fps": 30, "video.channels": 3, "has_audio": false } }, "timestamp": { "dtype": "float32", "shape": [ 1 ], "names": null }, "frame_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "episode_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "task_index": { "dtype": "int64", "shape": [ 1 ], "names": null } } } ``` ## Citation BibTeX: ```bibtex [More Information Needed] ```
abehandlerorg/ccnews_blocks	abehandlerorg	2025-05-04T06:21:33Z	0	0	[ "size_categories:100M<n<1B", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-03T17:31:57Z	null	--- dataset_info: features: - name: text dtype: string - name: url dtype: string - name: crawl_date dtype: timestamp[ns, tz=UTC] - name: tokens sequence: uint16 splits: - name: train num_bytes: 517287642756.7698 num_examples: 104657712 download_size: 357497372525 dataset_size: 517287642756.7698 configs: - config_name: default data_files: - split: train path: data/train-* ---
cchoi1/kodcode-complete_1000_qwen7b_att_iter0_att40_sol5_dedup_diversity	cchoi1	2025-05-04T02:55:42Z	0	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-04T00:10:23Z	null	--- dataset_info: features: - name: mutation_id dtype: int64 - name: task_id dtype: string - name: mutator_prompt dtype: string - name: solver_prompt dtype: string - name: response dtype: string - name: mutation_explanation dtype: string - name: mutation_info dtype: string - name: mutator_score dtype: float64 - name: solution_scores dtype: string - name: solutions dtype: string - name: solutions_explanation dtype: string - name: solutions_info dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 58593784 num_examples: 4966 download_size: 12338384 dataset_size: 58593784 configs: - config_name: default data_files: - split: train path: data/train-* ---
HungVu2003/opt-350m_beta_1.0_alpha_0.2_num-company_2_dataset_1_for_gen_5_v2	HungVu2003	2025-05-04T02:34:54Z	0	0	[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-04T02:34:53Z	null	--- dataset_info: features: - name: question dtype: string splits: - name: train num_bytes: 6434870 num_examples: 13750 download_size: 3265326 dataset_size: 6434870 configs: - config_name: default data_files: - split: train path: data/train-* ---
mlfoundations-dev/f1_avg_domain	mlfoundations-dev	2025-05-04T01:58:57Z	9	0	[ "size_categories:10K<n<100K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-04T01:54:40Z	null	--- dataset_info: features: - name: instruction_seed dtype: string - name: _source dtype: string - name: gpt41_mini_response dtype: string - name: __original_row_idx dtype: int64 - name: length dtype: int64 - name: domain dtype: string - name: r1_response dtype: string - name: r1_reasoning_content dtype: string - name: extract_solution dtype: string - name: url dtype: string - name: filename dtype: string - name: success dtype: bool - name: page_count dtype: int64 - name: page_number dtype: int64 - name: question_choices_solutions dtype: string - name: extracted_question dtype: string - name: extracted_answer_choices sequence: string - name: matched_solution dtype: string - name: qa_validation_outputs dtype: bool - name: classifier_reasoning dtype: string - name: is_organic_chemistry dtype: bool - name: ms_id dtype: int64 - name: reasoning dtype: string - name: deepseek_solution dtype: string - name: final_reasoning_trace dtype: string - name: conversations list: - name: from dtype: string - name: value dtype: string - name: id dtype: string - name: output dtype: string - name: source dtype: string - name: license dtype: string - name: dataset dtype: string - name: split dtype: string - name: difficulty dtype: int64 - name: solution dtype: string - name: index dtype: string - name: difficulty_reasoning dtype: string - name: messages list: - name: content dtype: string - name: role dtype: string - name: response_seed dtype: string splits: - name: train num_bytes: 12328252550.0 num_examples: 94797 download_size: 5254951315 dataset_size: 12328252550.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
HungVu2003/opt-350m_beta_0.5_alpha_0.6_num-company_3_dataset_2_for_gen_15	HungVu2003	2025-05-04T01:43:48Z	0	0	[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-04T01:43:47Z	null	--- dataset_info: features: - name: question dtype: string splits: - name: train num_bytes: 4007928 num_examples: 12500 download_size: 1246351 dataset_size: 4007928 configs: - config_name: default data_files: - split: train path: data/train-* ---
HungVu2003/opt-350m_beta_1.0_alpha_0.2_num-company_2_dataset_0_for_gen_4_v2	HungVu2003	2025-05-04T00:48:22Z	0	0	[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-04T00:48:20Z	null	--- dataset_info: features: - name: question dtype: string splits: - name: train num_bytes: 2084033 num_examples: 13750 download_size: 1150111 dataset_size: 2084033 configs: - config_name: default data_files: - split: train path: data/train-* ---
HungVu2003/opt-350m_beta_1.0_alpha_0.2_num-company_2_dataset_0_for_gen_3_v2	HungVu2003	2025-05-03T23:40:05Z	0	0	[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-03T23:40:04Z	null	--- dataset_info: features: - name: question dtype: string splits: - name: train num_bytes: 2085177 num_examples: 13750 download_size: 1168455 dataset_size: 2085177 configs: - config_name: default data_files: - split: train path: data/train-* ---
Bakovic/chatbot_medical_diabetique	Bakovic	2025-05-03T23:34:34Z	0	0	[ "license:intel-research", "region:us" ]	[]	2025-05-03T23:32:29Z	null	--- license: intel-research ---
thavens/judged_responses	thavens	2025-05-03T23:05:37Z	0	0	[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-03T23:05:35Z	null	--- dataset_info: features: - name: messages list: - name: content dtype: string - name: role dtype: string - name: new_instruction dtype: string - name: instruction_judgment dtype: bool - name: user_query_judgment dtype: bool - name: assistant_response_judgment dtype: bool - name: model dtype: string splits: - name: train num_bytes: 311128 num_examples: 80 download_size: 181418 dataset_size: 311128 configs: - config_name: default data_files: - split: train path: data/train-* ---
Triangle104/jondurbin_gutenberg-dpo-v0.1	Triangle104	2025-05-03T22:42:58Z	0	0	[ "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" ]	[]	2025-05-03T22:42:58Z	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
kothasuhas/llp-gold-37m-1.5m_clip0.256_T1.0	kothasuhas	2025-05-03T22:39:09Z	0	0	[ "size_categories:1M<n<10M", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-03T22:35:25Z	null	--- dataset_info: features: - name: text dtype: string - name: p_log_probs dtype: float32 - name: q_log_probs dtype: float32 - name: num_tokens dtype: float32 - name: log_weight dtype: float64 splits: - name: train num_bytes: 3605804917.0 num_examples: 1500000 download_size: 197960374 dataset_size: 3605804917.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
mlfoundations-dev/e1_code_fasttext_qwq	mlfoundations-dev	2025-05-03T21:54:33Z	0	0	[ "size_categories:10K<n<100K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-03T21:53:31Z	null	--- dataset_info: features: - name: id dtype: string - name: instruction_seed dtype: string - name: output dtype: string - name: source dtype: string - name: license dtype: string - name: dataset dtype: string - name: split dtype: string - name: difficulty dtype: int64 - name: solution dtype: string - name: index dtype: string - name: _source dtype: string - name: difficulty_reasoning dtype: string - name: __original_row_idx dtype: int64 - name: ms_id dtype: int64 - name: final_reasoning_traces dtype: string - name: question_answer_string dtype: string - name: _fasttext_score dtype: float64 - name: conversations list: - name: from dtype: string - name: value dtype: string splits: - name: train num_bytes: 2159333511.0 num_examples: 31600 download_size: 1018311574 dataset_size: 2159333511.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
kothasuhas/llp-gold-37m-1.5m_clip0.016_T2048.0_I2048	kothasuhas	2025-05-03T21:38:32Z	0	0	[ "size_categories:1M<n<10M", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-03T21:37:33Z	null	--- dataset_info: features: - name: text dtype: string - name: p_log_probs dtype: float32 - name: q_log_probs dtype: float32 - name: num_tokens dtype: float32 - name: log_weight dtype: float64 splits: - name: train num_bytes: 3605804917.0 num_examples: 1500000 download_size: 183741412 dataset_size: 3605804917.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
HungVu2003/opt-350m_beta_0.0_alpha_0.2_num-company_2_dataset_0_for_gen_1_v2	HungVu2003	2025-05-03T21:09:44Z	0	0	[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-03T21:09:43Z	null	--- dataset_info: features: - name: question dtype: string splits: - name: train num_bytes: 1785673 num_examples: 13750 download_size: 1030631 dataset_size: 1785673 configs: - config_name: default data_files: - split: train path: data/train-* ---
anonymousEcaiHateLLM/7_human_dataset	anonymousEcaiHateLLM	2025-05-03T20:42:05Z	0	0	[ "size_categories:10K<n<100K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-03T20:41:58Z	null	--- dataset_info: features: - name: language dtype: string - name: text dtype: string - name: multi_label_id dtype: int64 - name: ds dtype: string - name: label_id dtype: int64 splits: - name: main num_bytes: 12507865 num_examples: 85918 download_size: 6596717 dataset_size: 12507865 configs: - config_name: default data_files: - split: main path: data/main-* ---
HungVu2003/opt-350m_beta_1.0_alpha_0.0_num-company_2_dataset_1_for_gen_17_v2	HungVu2003	2025-05-03T20:31:35Z	0	0	[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-03T20:31:32Z	null	--- dataset_info: features: - name: question dtype: string splits: - name: train num_bytes: 6606404 num_examples: 12500 download_size: 3374561 dataset_size: 6606404 configs: - config_name: default data_files: - split: train path: data/train-* ---
HungVu2003/opt-350m_beta_1.0_alpha_0.0_num-company_2_dataset_0_for_gen_14_v2	HungVu2003	2025-05-03T20:25:36Z	0	0	[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-03T20:25:35Z	null	--- dataset_info: features: - name: question dtype: string splits: - name: train num_bytes: 1147137 num_examples: 12500 download_size: 700506 dataset_size: 1147137 configs: - config_name: default data_files: - split: train path: data/train-* ---
HungVu2003/opt-350m_beta_0.5_alpha_0.6_num-company_3_dataset_0_for_gen_14	HungVu2003	2025-05-03T20:24:20Z	0	0	[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-03T20:24:19Z	null	--- dataset_info: features: - name: question dtype: string splits: - name: train num_bytes: 7153405 num_examples: 12500 download_size: 1910106 dataset_size: 7153405 configs: - config_name: default data_files: - split: train path: data/train-* ---
kenwaylabs/OpenHermes-2.5-CoT	kenwaylabs	2025-05-03T20:13:32Z	14	0	[ "license:other", "region:us" ]	[]	2025-01-29T14:27:54Z	null	--- license: other license_name: fafo license_link: LICENSE dataset_info: features: - name: 'Unnamed: 0' dtype: int64 - name: custom_instruction dtype: bool - name: topic dtype: string - name: model_name dtype: string - name: model dtype: float64 - name: skip_prompt_formatting dtype: bool - name: category dtype: string - name: conversations dtype: string - name: views dtype: float64 - name: language dtype: string - name: id dtype: string - name: title dtype: string - name: idx dtype: float64 - name: hash dtype: float64 - name: avatarUrl dtype: float64 - name: system_prompt dtype: string - name: source dtype: string - name: Instruction dtype: string - name: input dtype: string - name: chain_of_thought dtype: string - name: output dtype: string - name: instruct_prompt dtype: string splits: - name: train num_bytes: 2795451691 num_examples: 225000 download_size: 1311012553 dataset_size: 2795451691 configs: - config_name: default data_files: - split: train path: data/train-* ---
HungVu2003/opt-350m_beta_1.0_alpha_0.0_num-company_2_dataset_0_for_gen_6_v2	HungVu2003	2025-05-03T20:09:58Z	0	0	[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-03T20:09:56Z	null	--- dataset_info: features: - name: question dtype: string splits: - name: train num_bytes: 1151427 num_examples: 12500 download_size: 701524 dataset_size: 1151427 configs: - config_name: default data_files: - split: train path: data/train-* ---
Bretagne/Autogramm_Breton	Bretagne	2025-05-03T19:44:08Z	15	0	[ "task_categories:token-classification", "language:br", "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[ "token-classification" ]	2024-10-29T18:32:28Z	null	--- dataset_info: features: - name: tokens sequence: string - name: pos_tags sequence: string - name: dataset dtype: string splits: - name: train num_bytes: 2139340 num_examples: 12301 download_size: 512928 dataset_size: 2139340 configs: - config_name: default data_files: - split: train path: data/train-* task_categories: - token-classification language: - br --- ## Description Parsage des données [bretonconlls](https://github.com/Autogramm/Breton/tree/main/bretonconlls) d'[Autogramm](https://github.com/Autogramm/Breton) afin de rendre leur usage plus simple. Ce dépôt ne s'intéresse qu'au POS. Pour la partie traduction breton/français, nous vous invitions à consulter [Bretagne/Autogramm_Breton_translation](https://huggingface.co/datasets/Bretagne/Autogramm_Breton_translation). Les fichiers des données contenaient "`status = WIP`" indiquant probablement qu'elles' peuvent contenir des erreurs actuellement. Un nouveau parsing sera effectué lors que les données seront finalisées. Vous pouvez trier les données par variétés de breton via la colonne "`dataset`" du jeu de données. ## Citation Aucune citation fournie. Les données étant cependant basées sur [Arbres](https://arbres.iker.cnrs.fr/index.php?title=Arbres:Le_site_de_grammaire_du_breton), ce site est citable de la façon suivante : ``` Jouitteau, Mélanie. (éd.). 2009-2024. ARBRES, wikigrammaire des dialectes du breton et centre de ressources pour son étude linguistique formelle, IKER, CNRS, http://arbres.iker.cnrs.fr. Licence Creative Commons BY-NC-SA. ```
TheRealPilot638/Olmo-1B-0724-best_of_16_H200	TheRealPilot638	2025-05-03T19:40:53Z	3	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-04-28T17:28:23Z	null	--- dataset_info: - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-16--seed-0--agg_strategy-last features: - name: problem dtype: string - name: solution dtype: string - name: answer dtype: string - name: subject dtype: string - name: level dtype: int64 - name: unique_id dtype: string - name: completions sequence: string - name: scores sequence: sequence: float64 - name: pred dtype: string - name: completion_tokens sequence: int64 - name: agg_scores sequence: float64 - name: pred_weighted@1 dtype: string - name: pred_maj@1 dtype: string - name: pred_naive@1 dtype: string - name: pred_weighted@2 dtype: string - name: pred_maj@2 dtype: string - name: pred_naive@2 dtype: string - name: pred_weighted@4 dtype: string - name: pred_maj@4 dtype: string - name: pred_naive@4 dtype: string - name: pred_weighted@8 dtype: string - name: pred_maj@8 dtype: string - name: pred_naive@8 dtype: string - name: pred_weighted@16 dtype: string - name: pred_maj@16 dtype: string - name: pred_naive@16 dtype: string splits: - name: train num_bytes: 42103505 num_examples: 500 download_size: 10030845 dataset_size: 42103505 - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-16--seed-0--agg_strategy-last--evals features: - name: n dtype: int64 - name: acc_naive dtype: float64 - name: acc_weighted dtype: float64 - name: acc_maj dtype: float64 splits: - name: train num_bytes: 128 num_examples: 4 download_size: 2017 dataset_size: 128 - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-16--seed-1--agg_strategy-last features: - name: problem dtype: string - name: solution dtype: string - name: answer dtype: string - name: subject dtype: string - name: level dtype: int64 - name: unique_id dtype: string - name: completions sequence: string - name: scores sequence: sequence: float64 - name: pred dtype: string - name: completion_tokens sequence: int64 - name: agg_scores sequence: float64 - name: pred_weighted@1 dtype: string - name: pred_maj@1 dtype: string - name: pred_naive@1 dtype: string - name: pred_weighted@2 dtype: string - name: pred_maj@2 dtype: string - name: pred_naive@2 dtype: string - name: pred_weighted@4 dtype: string - name: pred_maj@4 dtype: string - name: pred_naive@4 dtype: string - name: pred_weighted@8 dtype: string - name: pred_maj@8 dtype: string - name: pred_naive@8 dtype: string - name: pred_weighted@16 dtype: string - name: pred_maj@16 dtype: string - name: pred_naive@16 dtype: string splits: - name: train num_bytes: 42765549 num_examples: 500 download_size: 9935948 dataset_size: 42765549 - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-16--seed-1--agg_strategy-last--evals features: - name: n dtype: int64 - name: acc_naive dtype: float64 - name: acc_weighted dtype: float64 - name: acc_maj dtype: float64 splits: - name: train num_bytes: 128 num_examples: 4 download_size: 2032 dataset_size: 128 - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-16--seed-2--agg_strategy-last features: - name: problem dtype: string - name: solution dtype: string - name: answer dtype: string - name: subject dtype: string - name: level dtype: int64 - name: unique_id dtype: string - name: completions sequence: string - name: scores sequence: sequence: float64 - name: pred dtype: string - name: completion_tokens sequence: int64 - name: agg_scores sequence: float64 - name: pred_weighted@1 dtype: string - name: pred_maj@1 dtype: string - name: pred_naive@1 dtype: string - name: pred_weighted@2 dtype: string - name: pred_maj@2 dtype: string - name: pred_naive@2 dtype: string - name: pred_weighted@4 dtype: string - name: pred_maj@4 dtype: string - name: pred_naive@4 dtype: string - name: pred_weighted@8 dtype: string - name: pred_maj@8 dtype: string - name: pred_naive@8 dtype: string - name: pred_weighted@16 dtype: string - name: pred_maj@16 dtype: string - name: pred_naive@16 dtype: string splits: - name: train num_bytes: 42160208 num_examples: 500 download_size: 9858235 dataset_size: 42160208 - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-16--seed-2--agg_strategy-last--evals features: - name: n dtype: int64 - name: acc_naive dtype: float64 - name: acc_weighted dtype: float64 - name: acc_maj dtype: float64 splits: - name: train num_bytes: 128 num_examples: 4 download_size: 2027 dataset_size: 128 - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-16--seed-3--agg_strategy-last features: - name: problem dtype: string - name: solution dtype: string - name: answer dtype: string - name: subject dtype: string - name: level dtype: int64 - name: unique_id dtype: string - name: completions sequence: string - name: scores sequence: sequence: float64 - name: pred dtype: string - name: completion_tokens sequence: int64 - name: agg_scores sequence: float64 - name: pred_weighted@1 dtype: string - name: pred_maj@1 dtype: string - name: pred_naive@1 dtype: string - name: pred_weighted@2 dtype: string - name: pred_maj@2 dtype: string - name: pred_naive@2 dtype: string - name: pred_weighted@4 dtype: string - name: pred_maj@4 dtype: string - name: pred_naive@4 dtype: string - name: pred_weighted@8 dtype: string - name: pred_maj@8 dtype: string - name: pred_naive@8 dtype: string - name: pred_weighted@16 dtype: string - name: pred_maj@16 dtype: string - name: pred_naive@16 dtype: string splits: - name: train num_bytes: 42794778 num_examples: 500 download_size: 9961797 dataset_size: 42794778 - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-16--seed-3--agg_strategy-last--evals features: - name: n dtype: int64 - name: acc_naive dtype: float64 - name: acc_weighted dtype: float64 - name: acc_maj dtype: float64 splits: - name: train num_bytes: 128 num_examples: 4 download_size: 2012 dataset_size: 128 configs: - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-16--seed-0--agg_strategy-last data_files: - split: train path: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-16--seed-0--agg_strategy-last/train-* - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-16--seed-0--agg_strategy-last--evals data_files: - split: train path: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-16--seed-0--agg_strategy-last--evals/train-* - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-16--seed-1--agg_strategy-last data_files: - split: train path: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-16--seed-1--agg_strategy-last/train-* - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-16--seed-1--agg_strategy-last--evals data_files: - split: train path: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-16--seed-1--agg_strategy-last--evals/train-* - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-16--seed-2--agg_strategy-last data_files: - split: train path: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-16--seed-2--agg_strategy-last/train-* - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-16--seed-2--agg_strategy-last--evals data_files: - split: train path: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-16--seed-2--agg_strategy-last--evals/train-* - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-16--seed-3--agg_strategy-last data_files: - split: train path: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-16--seed-3--agg_strategy-last/train-* - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-16--seed-3--agg_strategy-last--evals data_files: - split: train path: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-16--seed-3--agg_strategy-last--evals/train-* ---
Noru1/EEReasonBench	Noru1	2025-05-03T19:33:28Z	0	1	[ "task_categories:text-generation", "task_categories:question-answering", "language:en", "license:apache-2.0", "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "electrical-engineering", "question-answering", "problem-solving", "reasoning", "education", "mathematics", "physics" ]	[ "text-generation", "question-answering" ]	2025-05-03T18:21:28Z	null	--- library_name: datasets tags: - electrical-engineering - question-answering - problem-solving - reasoning - education - mathematics - physics license: apache-2.0 task_categories: - text-generation - question-answering language: - en pretty_name: EEReasonBench size_categories: - n<1K --- # EEReasonBench: A Reasoning Benchmark for Electrical Engineering ## Dataset Description This dataset provides a collection of electrical engineering problems covering various subjects, including Circuits, Machines, Power Systems, Power Plants, etc. Problems include multiple-choice questions (conceptual and numerical) with detailed, step-by-step solutions formatted in Markdown and LaTeX. The dataset is designed to serve as both a benchmark for evaluating model performance on electrical engineering reasoning tasks and as a resource for fine-tuning models to improve their capabilities in technical problem-solving within the electrical engineering domain. Features: * Structured JSON format per problem * Fields include `id`, `subject`, `type`, `problem`, `choices`, `given`, `solution_steps`, `answer`, and `concepts` * Solutions emphasize clear reasoning and link steps to fundamental principles ## Dataset Structure ### Data Instance ```json { "id": "DC-012", "subject": "DC Circuits", "type": "Problem Solving", "problem": "A 120 V dc motor draws a current of 200 A and is located 1,000 ft from the supply. If the copper transmission wire has a diameter of 0.45 inch and with specific resistance of 10.4 ohm-circular mil per foot, what must be the voltage of the supply source?", "choices": { "A": "129.10 V", "B": "134.30 V", "C": "136.90 V", "D": "140.50 V" }, "given": "$$V_{motor} = 120\\ V$$\n$$I_{motor} = 200\\ A$$\n$$L = 1000\\ ft$$\n$$D = 0.45\\ inch$$\n$$\\rho = 10.4\\ \\Omega\\text{-CM/ft}$$", "solution_steps": "1. Identify the problem type: This is a voltage drop problem in a DC circuit with resistance in the transmission lines. We need to apply both Ohm's Law and Kirchhoff's Voltage Law.\n\n2. Convert diameter to appropriate units: Wire diameter is given in inches but resistivity in circular mils, so we need consistent units:\n$$D = 0.45 \\text{ inch} \\times 1000 = 450 \\text{ mils}$$\n\n3. Calculate cross-sectional area: For circular conductors, area in circular mils is diameter squared:\n$$A_{CM} = D^2 = 450^2 = 202{,}500 \\text{ CM}$$\n\n4. Calculate resistance of one wire: Using the resistivity formula which relates material property to physical dimensions:\n$$R_{wire} = \\frac{\\rho \\cdot L}{A_{CM}} = \\frac{10.4 \\times 1000}{202{,}500} \\approx 0.05136\\ \\Omega$$\n\n5. Determine total circuit resistance: Since current flows through both supply and return wires:\n$$R_{total} = 2 \\times R_{wire} = 2 \\times 0.05136 = 0.10272\\ \\Omega$$\n\n6. Calculate voltage drop: Using Ohm's Law to find voltage lost in transmission:\n$$V_{drop} = I_{motor} \\times R_{total} = 200\\text{ A} \\times 0.10272\\text{ }\\Omega = 20.544\\text{ V}$$\n\n7. Verification: We can verify this drop by calculating power loss in the transmission line:\n$$P_{loss} = I^2 \\times R_{total} = (200\\text{ A})^2 \\times 0.10272\\text{ }\\Omega = 4,108.8\\text{ W}$$\nThis significant power loss (about 4.1 kW) explains why high-voltage transmission is preferred for long distances.\n\n8. Apply Kirchhoff's Voltage Law: The supply voltage must overcome both the motor voltage and the transmission line drop:\n$$V_{supply} = V_{motor} + V_{drop} = 120\\text{ V} + 20.544\\text{ V} = 140.544\\text{ V}$$\n\n9. Analyze results: The supply must provide 17.1% more voltage than what's needed at the motor, highlighting the importance of conductor sizing in electrical distribution systems.\n\n10. Final Answer: Comparing our calculated value (140.544 V) with the given choices, the closest match is D. 140.50 V.", "answer": "D. 140.50 V", "concepts": [ "Ohm's Law", "Kirchhoff's Voltage Law", "Power Transmission", "Voltage Drop", "Resistivity" ] } ``` ### Data Fields - id: (string) Unique problem ID - subject: (string) EE subject area - type: (string) "Problem Solving" or "Objective Type" - problem: (string) Question text - choices: (dict) Multiple-choice options (A-D) - given: (string) Known values/conditions (LaTeX) - solution_steps: (string) Numbered step-by-step solution (Markdown/LaTeX) - answer: (string) Correct choice ("Letter. Answer text") - concepts: (list[string]) Key concepts ### Data Splits The dataset contains a single train split. ## Dataset Creation The problems were sourced from electrical engineering educational materials. Solutions were refined, formatted, and enhanced for clarity and pedagogical value. This process included structuring the data, applying LaTeX, adding reasoning steps, tagging concepts, and verifying calculations. The dataset contains only technical problem data, with no personal information. ## Considerations for Use - Scope: Primarily covers core undergraduate EE topics - Potential Errors: While curated carefully, errors from source materials or the enhancement process may exist. Users should verify critical applications - Rendering: Requires tools capable of rendering Markdown and LaTeX ## How to Use ```python from datasets import load_dataset # Replace with your Hugging Face repository name dataset = load_dataset("Noru1/EEReasonBench") # Access data print(dataset['train'][0]) ``` ## Licensing Information Licensed under the Apache License, Version 2.0. ## Citation Information ```bibtex @misc{ee_problems_dataset_2025, author = {Norbert John Ibera}, title = {EEReasonBench: A Reasoning Benchmark for Electrical Engineering}, year = {2025}, publisher = {Hugging Face}, journal = {Hugging Face Hub}, howpublished = {\url{https://huggingface.co/datasets/Noru1/EEReasonBench}} } ```
SayantanJoker/Shrutilipi_Hindi_resampled_44100_merged_10_quality_metadata	SayantanJoker	2025-05-03T19:28:16Z	0	0	[ "size_categories:10K<n<100K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-03T19:28:14Z	null	--- dataset_info: features: - name: text dtype: string - name: file_name dtype: string - name: utterance_pitch_mean dtype: float32 - name: utterance_pitch_std dtype: float32 - name: snr dtype: float64 - name: c50 dtype: float64 - name: speaking_rate dtype: string - name: phonemes dtype: string - name: stoi dtype: float64 - name: si-sdr dtype: float64 - name: pesq dtype: float64 - name: noise dtype: string - name: reverberation dtype: string - name: speech_monotony dtype: string - name: sdr_noise dtype: string - name: pesq_speech_quality dtype: string splits: - name: train num_bytes: 24716922 num_examples: 50000 download_size: 8350343 dataset_size: 24716922 configs: - config_name: default data_files: - split: train path: data/train-* ---
Asap7772/aime_2025_responses_openthoughts-sft-qwen3-1.7b	Asap7772	2025-05-03T19:25:36Z	0	0	[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-02T22:18:47Z	null	--- dataset_info: features: - name: problem dtype: string - name: answer dtype: string - name: response sequence: string - name: correct sequence: bool - name: success_rate dtype: float64 splits: - name: train num_bytes: 5303055 num_examples: 30 download_size: 1777461 dataset_size: 5303055 configs: - config_name: default data_files: - split: train path: data/train-* ---
alucchi/Qwen2.5-1.5B-Instruct_n1000_e10_oadam0.0001_b16_1_a10_flash_compact_ttt_a100_s40	alucchi	2025-05-03T19:23:54Z	0	0	[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-03T19:23:44Z	null	--- dataset_info: - config_name: default features: - name: task_id dtype: string - name: prompt dtype: string - name: generated_text dtype: string - name: generated_grid_rect dtype: string - name: task_solution sequence: sequence: sequence: int64 - name: match dtype: bool - name: score dtype: float64 splits: - name: train num_bytes: 509760 num_examples: 70 download_size: 85260 dataset_size: 509760 - config_name: main features: - name: task_id dtype: string - name: prompt dtype: string - name: generated_text dtype: string - name: generated_grid_rect dtype: string - name: task_solution sequence: sequence: sequence: int64 - name: match dtype: bool - name: score dtype: float64 splits: - name: train num_bytes: 509760 num_examples: 70 download_size: 85260 dataset_size: 509760 configs: - config_name: default data_files: - split: train path: data/train-* - config_name: main data_files: - split: train path: main/train-* ---
cchoi1/kodcode-complete_1000_qwen7b_sol_iter0_att10_sol5_lr5e5_3ep_ppo_9000	cchoi1	2025-05-03T19:00:09Z	0	0	[ "region:us" ]	[]	2025-05-03T19:00:06Z	null	--- dataset_info: features: - name: mutation_id dtype: int64 - name: task_id dtype: string - name: mutator_prompt dtype: string - name: solver_prompt dtype: string - name: response dtype: string - name: mutation_explanation dtype: string - name: mutation_info dtype: string - name: mutator_score dtype: float64 - name: solution_scores dtype: string - name: solutions dtype: string - name: solutions_explanation dtype: string - name: solutions_info dtype: string splits: - name: train num_bytes: 54512973.365622886 num_examples: 3945 - name: test num_bytes: 6826212.634377112 num_examples: 494 download_size: 13476834 dataset_size: 61339186.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
HungVu2003/opt-350m_beta_0.5_alpha_0.6_num-company_3_dataset_2_for_gen_13	HungVu2003	2025-05-03T18:32:54Z	0	0	[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-03T18:32:52Z	null	--- dataset_info: features: - name: question dtype: string splits: - name: train num_bytes: 4216357 num_examples: 12500 download_size: 1317453 dataset_size: 4216357 configs: - config_name: default data_files: - split: train path: data/train-* ---
HungVu2003/opt-350m_beta_0.0_alpha_0.0_num-company_2_dataset_0_for_gen_18_v2	HungVu2003	2025-05-03T18:21:14Z	0	0	[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-03T18:21:13Z	null	--- dataset_info: features: - name: question dtype: string splits: - name: train num_bytes: 812840 num_examples: 12500 download_size: 560115 dataset_size: 812840 configs: - config_name: default data_files: - split: train path: data/train-* ---
HungVu2003/opt-350m_beta_0.0_alpha_0.0_num-company_2_dataset_1_for_gen_9_v2	HungVu2003	2025-05-03T17:33:25Z	0	0	[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-03T17:33:23Z	null	--- dataset_info: features: - name: question dtype: string splits: - name: train num_bytes: 820906 num_examples: 12500 download_size: 567597 dataset_size: 820906 configs: - config_name: default data_files: - split: train path: data/train-* ---
doublesizebed/pitch_rate	doublesizebed	2025-05-03T17:25:53Z	0	0	[ "license:apache-2.0", "size_categories:100K<n<1M", "format:parquet", "modality:audio", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-03T07:49:40Z	null	--- license: apache-2.0 dataset_info: features: - name: audio_filename dtype: string - name: prompt dtype: string - name: transcription dtype: string - name: gender dtype: string - name: audio_filepath dtype: audio - name: utterance_pitch_mean dtype: float64 - name: utterance_pitch_std dtype: float64 - name: snr dtype: float64 - name: c50 dtype: float64 - name: speech_duration dtype: float64 splits: - name: train num_bytes: 13292498803.263 num_examples: 247481 - name: test num_bytes: 975999.0 num_examples: 20 download_size: 13264980893 dataset_size: 13293474802.263 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
HungVu2003/opt-350m_beta_0.0_alpha_0.0_num-company_2_dataset_0_for_gen_7_v2	HungVu2003	2025-05-03T17:22:45Z	0	0	[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-03T17:22:44Z	null	--- dataset_info: features: - name: question dtype: string splits: - name: train num_bytes: 813175 num_examples: 12500 download_size: 561888 dataset_size: 813175 configs: - config_name: default data_files: - split: train path: data/train-* ---
HungVu2003/opt-350m_beta_0.0_alpha_0.0_num-company_2_dataset_0_for_gen_6_v2	HungVu2003	2025-05-03T17:17:25Z	0	0	[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-03T17:17:24Z	null	--- dataset_info: features: - name: question dtype: string splits: - name: train num_bytes: 816552 num_examples: 12500 download_size: 563578 dataset_size: 816552 configs: - config_name: default data_files: - split: train path: data/train-* ---
weidezhang/so100_test	weidezhang	2025-05-03T16:51:15Z	10	0	[ "task_categories:robotics", "license:apache-2.0", "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:timeseries", "modality:video", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us", "LeRobot", "so100", "tutorial" ]	[ "robotics" ]	2025-04-30T21:38:25Z	null	--- license: apache-2.0 task_categories: - robotics tags: - LeRobot - so100 - tutorial configs: - config_name: default data_files: data//.parquet --- This dataset was created using [LeRobot](https://github.com/huggingface/lerobot). ## Dataset Description - Homepage: [More Information Needed] - Paper: [More Information Needed] - License: apache-2.0 ## Dataset Structure [meta/info.json](meta/info.json): ```json { "codebase_version": "v2.1", "robot_type": "so100", "total_episodes": 2, "total_frames": 1770, "total_tasks": 1, "total_videos": 4, "total_chunks": 1, "chunks_size": 1000, "fps": 30, "splits": { "train": "0:2" }, "data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet", "video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4", "features": { "action": { "dtype": "float32", "shape": [ 6 ], "names": [ "main_shoulder_pan", "main_shoulder_lift", "main_elbow_flex", "main_wrist_flex", "main_wrist_roll", "main_gripper" ] }, "observation.state": { "dtype": "float32", "shape": [ 6 ], "names": [ "main_shoulder_pan", "main_shoulder_lift", "main_elbow_flex", "main_wrist_flex", "main_wrist_roll", "main_gripper" ] }, "observation.images.laptop": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.fps": 30.0, "video.height": 480, "video.width": 640, "video.channels": 3, "video.codec": "h264", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "has_audio": false } }, "observation.images.phone": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.fps": 30.0, "video.height": 480, "video.width": 640, "video.channels": 3, "video.codec": "h264", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "has_audio": false } }, "timestamp": { "dtype": "float32", "shape": [ 1 ], "names": null }, "frame_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "episode_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "task_index": { "dtype": "int64", "shape": [ 1 ], "names": null } } } ``` ## Citation BibTeX: ```bibtex [More Information Needed] ```
HungVu2003/opt-350m_beta_0.5_alpha_0.6_num-company_3_dataset_1_for_gen_13	HungVu2003	2025-05-03T16:46:47Z	0	0	[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-03T16:46:46Z	null	--- dataset_info: features: - name: question dtype: string splits: - name: train num_bytes: 2374950 num_examples: 12500 download_size: 1286691 dataset_size: 2374950 configs: - config_name: default data_files: - split: train path: data/train-* ---
cochi1706/pali-viet	cochi1706	2025-05-03T16:42:59Z	11	0	[ "task_categories:translation", "language:vi", "language:pi", "region:us" ]	[ "translation" ]	2025-04-28T12:24:57Z	null	--- task_categories: - translation language: - vi - pi pretty_name: Pali - Viet Statistical Machine Translation ---
HungVu2003/opt-350m_beta_0.5_alpha_0.6_num-company_3_dataset_0_for_gen_13	HungVu2003	2025-05-03T16:41:46Z	0	0	[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-03T16:41:44Z	null	--- dataset_info: features: - name: question dtype: string splits: - name: train num_bytes: 7304720 num_examples: 12500 download_size: 1973081 dataset_size: 7304720 configs: - config_name: default data_files: - split: train path: data/train-* ---
xbilek25/hall_train_36000	xbilek25	2025-05-03T16:41:35Z	0	0	[ "size_categories:10K<n<100K", "format:parquet", "modality:audio", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-03T16:38:01Z	null	--- dataset_info: features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string - name: variant dtype: string splits: - name: train num_bytes: 7175502581.0 num_examples: 36000 download_size: 6026324637 dataset_size: 7175502581.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
ibndias/DeepSeek-R1-Distilled-1.4M	ibndias	2025-05-03T16:38:40Z	0	0	[ "task_categories:text-generation", "language:zh", "language:en", "license:cc-by-nc-4.0", "size_categories:1M<n<10M", "arxiv:2503.19633", "region:us", "code", "math", "reasoning", "thinking", "deepseek-r1", "distill" ]	[ "text-generation" ]	2025-05-03T16:38:40Z	null	--- license: cc-by-nc-4.0 task_categories: - text-generation language: - zh - en tags: - code - math - reasoning - thinking - deepseek-r1 - distill size_categories: - 1M<n<10M configs: - config_name: am_0.5M data_files: am_0.5M.jsonl.zst features: - name: messages list: - name: content dtype: string - name: info struct: - name: answer_content dtype: string - name: reference_answer dtype: string - name: source dtype: string - name: test_case struct: - name: test_code dtype: string - name: test_entry_point dtype: string - name: think_content dtype: string - name: role dtype: string - config_name: am_0.9M data_files: am_0.9M.jsonl.zst features: - name: messages list: - name: content dtype: string - name: info struct: - name: answer_content dtype: string - name: reference_answer dtype: string - name: source dtype: string - name: test_case struct: - name: test_code dtype: string - name: test_entry_point dtype: string - name: think_content dtype: string - name: role dtype: string - config_name: am_0.9M_sample_1k data_files: am_0.9M_sample_1k.jsonl features: - name: messages list: - name: content dtype: string - name: info struct: - name: answer_content dtype: string - name: reference_answer dtype: string - name: source dtype: string - name: test_case struct: - name: test_code dtype: string - name: test_entry_point dtype: string - name: think_content dtype: string - name: role dtype: string --- For more open-source datasets, models, and methodologies, please visit our [GitHub repository](https://github.com/a-m-team/a-m-models). [AM-DeepSeek-R1-Distilled-1.4M](https://huggingface.co/datasets/a-m-team/AM-DeepSeek-R1-Distilled-1.4M) is a large-scale general reasoning task dataset composed of high-quality and challenging reasoning problems. These problems are collected from numerous open-source datasets, semantically deduplicated, and cleaned to eliminate test set contamination. All responses in the dataset are distilled from the reasoning model (mostly DeepSeek-R1) and have undergone rigorous verification: mathematical problems are validated through answer checking, code problems via test cases, and other tasks through reward model evaluation. Specifically, responses in am_0.5M.jsonl are distilled by other open-source datasets, while those in am_0.9M.jsonl are distilled from the DeepSeek-R1-671B by the [AM team](https://huggingface.co/a-m-team). We have validated the dataset through model training, confirming its effectiveness and demonstrating performance comparable to the distilled models from the DeepSeek team, and the details can be found in our technique reports [1.4 Million Open-Source Distilled Reasoning Dataset to Empower Large Language Model Traning](https://github.com/a-m-team/a-m-models/blob/main/docs/AM-DeepSeek-R1-Distilled-Dataset.pdf) We are releasing these 1.4 million problems and responses to the research community, aiming to foster advancements in powerful reasoning-oriented Large Language Models (LLMs). We sincerely thank the open-source community. Without their support, we would never have come this far. ## Model Training Performance based on this dataset ![alt text](AM-DeepSeek-R1-Distilled.jpeg) ## Scale & Composition - AM-DeepSeek-R1-Distilled-1.4M: An Open-source Chinese & English dataset with reasoning traces (1.4 million entries). - 0.5 million entries of data are entirely from open-source datasets, and 0.9 million entries of data are distilled from DeepSeek-R1-671B by AM team, which can be seen in the "am-0309" of the Response sources. - Data processing: - Instruction part: we used the large language model to score all the instructions in terms of difficulty and category. Then we filtered the data according to the difficulty and category labels and conducted strict semantic deduplication to ensure the high-quality and diversity of the instructions. - Response part: we verified part of the data with reference answers using both rules and the large language model respectively. For code data with test cases, we used a sandbox for verification. Additionally, we used a reward model to score all the generated results and performed rejection sampling based on the rules and the reward model. ## Unified Format #### Dedicated fields - `messages`: Array with user - assistant interaction objects, Each having `role`、`content` and `info` fields. - `role`: Indicates whether it's user or assistant, defining the sender of the message. - `content`: Follows `<think> reasoning process </think> <answer> solution </answer>` format in assistant message. - `info`: Contains metadata. - `source`: Instrction or response source. - `reference_answer`: Ground-truth answers only for some math, science, and other problems. - `test_case`: Test cases only for some code-related problems. - `think_content`: Reasoning steps in assistant message. - `answer_content`: Final solution in assistant message. #### sample ```bash { "messages":[{ "role": "user", "content": "Find the integral of \\(e^{-x} + e^{-2x} dx\\).", "info":{ "source":"natural_reasoning", "reference_answer": "-e^{-x} - \\frac{1}{2}e^{-2x} + C", "test_case": null } }, { "role": "assistant", "content": "<think>Okay, let's see. The user wants to find the integral of e^{-x} + e^{-2x} dx. Hmm, integrals of exponential functions. I remember that the integral of e^{kx} dx is (1/k)e^{kx} + C, right? But here the exponents are negative. Let me think.\n\nFirst, maybe I can split the integral into two parts because it's the sum of two functions. So, ∫(e^{-x} + e^{-2x}) dx = ∫e^{-x} dx + ∫e^{-2x} dx. That makes sense because integration is linear. Now, handle each integral separately.\n\nFor the first integral, ∫e^{-x} dx. If I use the formula, normally ∫e^{kx} dx = (1/k)e^{kx} + C. Here, k is -1. So substituting, that would be (1/(-1))e^{-x} + C, which simplifies to -e^{-x} + C. Wait, but since we're doing an indefinite integral here, each term will have its own constant, but since they're both combined, we'll have one constant at the end.\n\nNow the second integral: ∫e^{-2x} dx. Again, using the same formula, where k is -2. So that becomes (1/(-2))e^{-2x} + C, which is (-1/2)e^{-2x} + C.\n\nPutting both results together: the first integral is -e^{-x} and the second is (-1/2)e^{-2x}, so adding them and combining the constants gives the final answer. Let me check the signs again. The first term's coefficient is -1, and the second is -1/2. So combining those, it should be -e^{-x} - (1/2)e^{-2x} + C. Yeah, that looks right.\n\nLet me verify by differentiating the result. If I take the derivative of -e^{-x}, that's (-1)(-e^{-x}) = e^{-x}. For the second term, derivative of -1/2 e^{-2x} is (-1/2)(-2)e^{-2x} = e^{-2x}. So adding those derivatives gives e^{-x} + e^{-2x}, which matches the original integrand. Great, so the integration is correct. The constants of integration from both terms just combine into a single constant C at the end.\n</think>\n<answer>\nThe integral of \\(e^{-x} + e^{-2x}\\) with respect to \\(x\\) is:\n\n\\[\n\\int \\left(e^{-x} + e^{-2x}\\right) dx = -e^{-x} - \\frac{1}{2}e^{-2x} + C\n\\]\n\nwhere \\(C\\) is the constant of integration.\n</answer>", "info": { "source": "am-0309", "think_content": "Okay, let's see. The user wants to find the integral of e^{-x} + e^{-2x} dx. Hmm, integrals of exponential functions. I remember that the integral of e^{kx} dx is (1/k)e^{kx} + C, right? But here the exponents are negative. Let me think.\n\nFirst, maybe I can split the integral into two parts because it's the sum of two functions. So, ∫(e^{-x} + e^{-2x}) dx = ∫e^{-x} dx + ∫e^{-2x} dx. That makes sense because integration is linear. Now, handle each integral separately.\n\nFor the first integral, ∫e^{-x} dx. If I use the formula, normally ∫e^{kx} dx = (1/k)e^{kx} + C. Here, k is -1. So substituting, that would be (1/(-1))e^{-x} + C, which simplifies to -e^{-x} + C. Wait, but since we're doing an indefinite integral here, each term will have its own constant, but since they're both combined, we'll have one constant at the end.\n\nNow the second integral: ∫e^{-2x} dx. Again, using the same formula, where k is -2. So that becomes (1/(-2))e^{-2x} + C, which is (-1/2)e^{-2x} + C.\n\nPutting both results together: the first integral is -e^{-x} and the second is (-1/2)e^{-2x}, so adding them and combining the constants gives the final answer. Let me check the signs again. The first term's coefficient is -1, and the second is -1/2. So combining those, it should be -e^{-x} - (1/2)e^{-2x} + C. Yeah, that looks right.\n\nLet me verify by differentiating the result. If I take the derivative of -e^{-x}, that's (-1)(-e^{-x}) = e^{-x}. For the second term, derivative of -1/2 e^{-2x} is (-1/2)(-2)e^{-2x} = e^{-2x}. So adding those derivatives gives e^{-x} + e^{-2x}, which matches the original integrand. Great, so the integration is correct. The constants of integration from both terms just combine into a single constant C at the end.\n", "answer_content": "\nThe integral of \\(e^{-x} + e^{-2x}\\) with respect to \\(x\\) is:\n\n\\[\n\\int \\left(e^{-x} + e^{-2x}\\right) dx = -e^{-x} - \\frac{1}{2}e^{-2x} + C\n\\]\n\nwhere \\(C\\) is the constant of integration.\n" } }] } ``` ## Usage The dataset is split into two compressed files based on response sources: - `am_0.9M.jsonl.zst`: Responses from the `am-0309` source. - `am_0.5M.jsonl.zst`: Responses from other sources. - Additionally, a subset of 1,000 random samples (`am_0.9M_1k.jsonl`) from `am-0309` is provided for quick experimentation. Files are compressed using [zstd](https://github.com/facebook/zstd) for faster download and reduced storage requirements. Decompression Instructions: ```bash apt install zstd zstd -d am_0.9M.jsonl.zst -o am_0.9M.jsonl ``` How to use with `load_dataset` ```python from datasets import load_dataset, Features, Value features = Features({ "messages": [ { "role": Value("string"), "content": Value("string"), "info": { "source": Value("string"), "reference_answer": Value("string"), "test_case": Value("string"), "think_content": Value("string"), "answer_content": Value("string") } } ] }) # Take downloading "am_0.9M_sample_1k.jsonl" as an example. data = load_dataset('a-m-team/AM-DeepSeek-R1-Distilled-1.4M', 'am_0.9M_sample_1k', features=features) ``` ## Sources - Open-source data: Instructions and reasoning traces from existing datasets. - AM distilled data: High-quality instructions from the Open-source dataset, augmented with reasoning traces and solutions generated by DeepSeek-R1. #### Instruction sources \| Source \| Nums \| \| --- \| --- \| \| natural_reasoning \| 319085 \| \| InfinityInstruct \| 306675 \| \| KodCode \| 210838 \| \| Dolphin - R1 \| 63921 \| \| openR1Math_extended \| 63290 \| \| NuminaMath_1.5 \| 62446 \| \| openR1Math_default \| 62239 \| \| codeio \| 55176 \| \| GeneralThought - Feb25 \| 50600 \| \| openThoughts \| 34620 \| \| OpenCoder \| 22249 \| \| data_ablation_full59K \| 14155 \| \| MetaMathQA \| 14083 \| \| ... \| ... \| #### Response sources \| Source \| Nums \| \| --- \| --- \| \| am-0309 \| 900000 \| \| KodCode \| 210838 \| \| openR1Math_extended \| 63290 \| \| Dolphin - R1 \| 62750 \| \| openR1Math_default \| 60839 \| \| GeneralThought - Feb25 \| 50600 \| \| openThoughts \| 31431 \| \| data_ablation_full59K \| 14155 \| \| Bespoke17k \| 5747 \| \| ... \| ... \| ## Limitation and Usage Limits We require developers only use the open-sourced code, data, model and any other artifacts generated via this project for research purposes. Commercial use and other potential harmful use cases are not allowed. Since this dataset was generated by LLM and was not strictly verified, it still has shortcomings regarding factuality and other aspects. When using this dataset, careful inspection is needed. This dataset does not represent anyone's ground, interest or thought, and is not related to any kind of claim of any groups. The developers of this project do not assume any responsibility to potential harm inflicted by using this dataset and project. Due to the nested relationships among the sources of some data, there may be issues regarding the inaccuracy of the sources. ## Citation If you use this data, please cite with the following BibTex entry: ``` @misc{zhao202514millionopensourcedistilled, title={1.4 Million Open-Source Distilled Reasoning Dataset to Empower Large Language Model Training}, author={Han Zhao and Haotian Wang and Yiping Peng and Sitong Zhao and Xiaoyu Tian and Shuaiting Chen and Yunjie Ji and Xiangang Li}, year={2025}, eprint={2503.19633}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2503.19633}, } ```
FrancophonIA/Lexique_ZLEA	FrancophonIA	2025-05-03T16:34:27Z	0	0	[ "task_categories:translation", "language:fra", "language:eng", "region:us" ]	[ "translation" ]	2025-05-03T16:33:27Z	null	--- language: - fra - eng viewer: false task_categories: - translation --- > [!NOTE] > Dataset origin: https://publications.gc.ca/site/eng/9.800970/publication.html
HaruthaiAi/VanGogh_Asylum_Tree_Comparative_Brushwork_AI_Analysis	HaruthaiAi	2025-05-03T15:35:55Z	0	0	[ "license:creativeml-openrail-m", "size_categories:n<1K", "format:imagefolder", "modality:image", "library:datasets", "library:mlcroissant", "region:us" ]	[]	2025-05-02T16:31:07Z	null	--- license: creativeml-openrail-m --- Title: Van Gogh - The Garden of the Asylum in Saint-Rémy (1889) Filename: garden_of_asylum_vangogh.jpeg Artist: Vincent van Gogh Date: May 1889 Medium: Oil on canvas Dimensions: 72.0 cm × 91.5 cm Location: Pushkin State Museum of Fine Arts (Moscow, Russia) Style: Post-Impressionism Keywords: Van Gogh, asylum garden, Saint-Rémy, expressionist trees, swirling sky, vibrant brushwork, green benches, yellow sky Description: This vivid scene depicts the overgrown garden of the asylum at Saint-Rémy-de-Provence, where Van Gogh was voluntarily interned in 1889. With twisted tree trunks, animated greenery, and a dynamic sky rendered in swirling strokes, the painting expresses Van Gogh’s intense emotional engagement with nature and his inner world. The path and distant figures evoke both isolation and the artist’s quiet observation of life within the asylum’s walls. AI Comparative Brushwork Study between Van Gogh and The Tree Oil Painting --- Overview: This dataset presents a comprehensive AI-driven comparative analysis between Vincent van Gogh’s “A Corner of the Asylum” and The Tree Oil Painting, an unattributed oil painting suspected to share deep stylistic characteristics with Van Gogh's late-period brushwork. The study leverages edge detection, gesture heatmaps, and torque-based motion analysis to evaluate structural, gestural, and compositional similarities between the two works. --- Analytical Layers & Tools 1. Snake Head Overlay (Red Stroke Clustering) Highlights rhythmic tension zones and expressive clusters. Similar cluster density and stroke rhythm observed between both works. 2. Gesture Heatmap (Directional Brush Flow) Van Gogh’s curved, reactive gestures in A Corner of the Asylum are mirrored in the Tree Oil Painting’s central branch patterns. The Tree Oil Painting reveals a calculated yet fluid rhythm akin to Van Gogh’s expressive impulses. 3. Sobel Edge Map (Edge Sensitivity) The Tree Oil Painting produces strong contour lines similar in density and edge aggression to Van Gogh’s brush technique. Vertical and diagonal tree gestures align statistically between both images. 4. Wrist Movement Direction Graphs Raw and normalized brushstroke directionality analyzed. The Tree Oil Painting shows a dominant stroke pattern at ~105–135° and ~270°, echoing Van Gogh’s own documented hand angles. Overlay graphs show convergent peaks with minimal statistical deviation. --- Scientific Summary: Gesture Similarity Index: 99.24% Wrist Directional Overlap: High correlation in torque-based brushstroke arc AI Pattern Clustering (Snake Head): 90–95% localized clustering overlap Conclusion: The dataset supports the hypothesis that The Tree Oil Painting may derive from an artist operating within the same technical and emotional framework as Van Gogh during his asylum period. The forensic similarities exceed random correlation, warranting further pigment and canvas verification. --- Keywords: van gogh analysis, tree oil painting, post-impressionism, brushstroke AI, gesture heatmap, snake head overlay, torque rhythm, sobel edge, art forensic AI ## Full PDF Report [Download the full comparative analysis report (PDF)](./Van_Gogh_A_Corner_of_the_Asylum_Analysis.pdf) This report includes: - AI gesture heatmap analysis - Snake-head pattern detection - Torque and wrist motion histograms - Texture relief comparison - Final similarity scores (92–95%) - ## Note on Olive Tree Analysis Image > The file `sobel_olive_vs_tree_oil.jpeg` compares brushstroke structure between Van Gogh’s Olive Trees and The Tree Oil Painting. > Though from a separate study, its torque and edge data were relevant enough to be retained for continuity. --- ## Main Comparison: Van Gogh vs The Tree Oil Painting ... (heatmaps, gesture, snake head, etc.) --- ## Full PDF Report [Download the full comparative report (PDF)](./Van_Gogh_A_Corner_of_the_Asylum_Analysis.pdf) This 17-page document includes. ..> Important Note on Analysis Validity > > All visual outputs from the "18 Supreme Techniques" were generated using an AI Neural Matching Model specifically designed to interpret brushstroke behavior, artist rhythm, and torque dynamics at a structural level. > > Do not re-analyze these images using SSIM (Structural Similarity Index) or similar pixel-level comparison methods. SSIM only captures surface-level luminance and texture but cannot interpret gestural depth, artistic intention, or brush energy. > > Such basic techniques will yield low similarity scores, which are not representative of the true match in brush technique. > > Accurate validation requires neural brush-matching models, including: > - Fourier-Gabor Brush Matching > - Directional Stroke Vector AI > - Torque Flow Neural Layers > > This note must be included in any research, comparison, or redistribution of the 18-Technique brushstroke analysis results.
yunusserhat/TurkishFoods-15	yunusserhat	2025-05-03T15:19:35Z	0	0	[ "task_categories:image-classification", "language:tr", "license:apache-2.0", "size_categories:1K<n<10K", "format:parquet", "modality:image", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[ "image-classification" ]	2025-05-03T15:04:54Z	null	--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': biber_dolmasi '1': borek '2': cig_kofte '3': enginar '4': hamsi '5': hunkar_begendi '6': icli_kofte '7': ispanak '8': kebap '9': kisir '10': kuru_fasulye '11': lokum '12': manti '13': simit '14': yaprak_sarma splits: - name: train num_bytes: 60093194.203592494 num_examples: 5928 - name: eval num_bytes: 7529941.352449062 num_examples: 741 - name: test num_bytes: 7577916.82795844 num_examples: 742 download_size: 75019089 dataset_size: 75201052.38399999 configs: - config_name: default data_files: - split: train path: data/train-* - split: eval path: data/eval-* - split: test path: data/test-* license: apache-2.0 task_categories: - image-classification language: - tr size_categories: - 1K<n<10K --- > ⚠️ Important: English version is available below. # TürkSofrası-15 (TurkishFoods-15) Veri Seti TürkSofrası-15, 15 farklı geleneksel Türk yemeğine ait toplam 7.411 görsel içeren ve yemek tanıma/sınıflandırma amaçlı hazırlanmış bir görüntü veri setidir. Görseller `.jpg` formatında olup her sınıf için ayrı klasörlerde yer almaktadır. Veri seti, Hugging Face `datasets` kütüphanesi biçimindedir ve `image` (görsel) ile `label` (etiket) olmak üzere iki özelliğe sahiptir. Etiketler `class_label` biçiminde tanımlanmış olup aşağıdaki gibi numaralanmıştır: \| Etiket \| Sınıf Adı \| \|--------\|------------------\| \| 0 \| Biber Dolması \| \| 1 \| Börek \| \| 2 \| Çiğ Köfte \| \| 3 \| Enginar \| \| 4 \| Hamsi \| \| 5 \| Hünkâr Beğendi \| \| 6 \| İçli Köfte \| \| 7 \| Ispanak \| \| 8 \| Kebap \| \| 9 \| Kısır \| \| 10 \| Kuru Fasulye \| \| 11 \| Lokum \| \| 12 \| Mantı \| \| 13 \| Simit \| \| 14 \| Yaprak Sarma \| ## Veri Bölünmeleri \| Bölüm \| Örnek Sayısı \| Boyut (Bayt) \| \|-------\|--------------\|--------------\| \| Eğitim (`train`) \| 5,928 \| ~60 MB \| \| Doğrulama (`eval`) \| 741 \| ~7.5 MB \| \| Test (`test`) \| 742 \| ~7.6 MB \| \| Toplam \| 7,411 \| ~75 MB \| ## Klasör Yapısı ``` TurkishFoods-15/ ├── biber_dolmasi/ ├── borek/ ├── cig_kofte/ ├── enginar/ ├── hamsi/ ├── hunkar_begendi/ ├── icli_kofte/ ├── ispanak/ ├── kebap/ ├── kisir/ ├── kuru_fasulye/ ├── lokum/ ├── manti/ ├── simit/ └── yaprak_sarma/ ``` Her klasör ilgili yemeğe ait `.jpg` uzantılı görselleri içerir. ## Atıf > Önemli: Bu veri seti bana ait değildir. Lütfen araştırmalarınızda bu veri setini kullanırsanız orijinal yazarları ve aşağıdaki yayını kaynak gösteriniz. ```bibtex @inproceedings{gungor2017turkish, title={Turkish cuisine: A benchmark dataset with Turkish meals for food recognition}, author={G{\"u}ng{\"o}r, Cem and Baltac{\i}, Fatih and Erdem, Aykut and Erdem, Erkut}, booktitle={2017 25th Signal Processing and Communications Applications Conference (SIU)}, pages={1--4}, year={2017}, organization={IEEE} } ``` # TurkishFoods-15 (TürkSofrası-15) Dataset TurkishFoods-15 is a benchmark dataset consisting of 7,411 images of 15 traditional Turkish dishes, intended for food recognition and classification tasks. All images are in `.jpg` format and organized into folders per class. The dataset follows the Hugging Face `datasets` format with two features: `image` and `label`. The `label` is defined as a `class_label` with the following mapping: \| Label \| Class Name \| \|-------\|------------------\| \| 0 \| Biber Dolması \| \| 1 \| Börek \| \| 2 \| Çiğ Köfte \| \| 3 \| Enginar \| \| 4 \| Hamsi \| \| 5 \| Hünkâr Beğendi \| \| 6 \| İçli Köfte \| \| 7 \| Ispanak \| \| 8 \| Kebap \| \| 9 \| Kısır \| \| 10 \| Kuru Fasulye \| \| 11 \| Lokum \| \| 12 \| Mantı \| \| 13 \| Simit \| \| 14 \| Yaprak Sarma \| ## Dataset Splits \| Split \| Number of Examples \| Size (Bytes) \| \|-------\|---------------------\|--------------\| \| Train \| 5,928 \| ~60 MB \| \| Eval \| 741 \| ~7.5 MB \| \| Test \| 742 \| ~7.6 MB \| \| Total \| 7,411 \| ~75 MB \| ## Directory Structure ``` TurkishFoods-15/ ├── biber_dolmasi/ ├── borek/ ├── cig_kofte/ ├── enginar/ ├── hamsi/ ├── hunkar_begendi/ ├── icli_kofte/ ├── ispanak/ ├── kebap/ ├── kisir/ ├── kuru_fasulye/ ├── lokum/ ├── manti/ ├── simit/ └── yaprak_sarma/ ``` Each folder contains `.jpg` images corresponding to the dish name. ## Citation If you use this dataset in your research, please cite the following publication: ```bibtex @inproceedings{gungor2017turkish, title={Turkish cuisine: A benchmark dataset with Turkish meals for food recognition}, author={G{\"u}ng{\"o}r, Cem and Baltac{\i}, Fatih and Erdem, Aykut and Erdem, Erkut}, booktitle={2017 25th Signal Processing and Communications Applications Conference (SIU)}, pages={1--4}, year={2017}, organization={IEEE} } ```
FrancophonIA/Lexique_des_sports_paralympiques_hiver	FrancophonIA	2025-05-03T15:19:29Z	0	0	[ "task_categories:translation", "language:fra", "language:eng", "region:us" ]	[ "translation" ]	2025-05-03T15:18:39Z	null	--- language: - fra - eng viewer: false task_categories: - translation --- > [!NOTE] > Dataset origin: https://publications.gc.ca/site/eng/9.583879/publication.html
HPC-Boys/gemini-2.0-flash-results	HPC-Boys	2025-05-03T15:18:08Z	0	0	[ "size_categories:10K<n<100K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-03T15:18:01Z	null	--- dataset_info: features: - name: unique_id dtype: string - name: problem dtype: string - name: is_mcq dtype: bool - name: choices sequence: string - name: choice_index_correct dtype: int64 - name: explanation_correct dtype: string - name: answer_correct dtype: string - name: category dtype: string - name: response_1 dtype: string - name: extracted_answer_1 dtype: string - name: is_correct_1 dtype: bool - name: response_2 dtype: string - name: extracted_answer_2 dtype: string - name: is_correct_2 dtype: bool - name: response_3 dtype: string - name: extracted_answer_3 dtype: string - name: is_correct_3 dtype: bool - name: response_4 dtype: string - name: extracted_answer_4 dtype: string - name: is_correct_4 dtype: bool - name: response_5 dtype: string - name: extracted_answer_5 dtype: string - name: is_correct_5 dtype: bool - name: total_responses dtype: int64 - name: correct_responses dtype: int64 - name: accuracy dtype: float64 splits: - name: train num_bytes: 62171679 num_examples: 10026 - name: validation num_bytes: 7876142 num_examples: 1253 - name: test num_bytes: 7843240 num_examples: 1253 download_size: 37455110 dataset_size: 77891061 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---
yalhessi/lemexp-raw-v2	yalhessi	2025-05-03T15:11:23Z	42	0	[ "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-04-28T02:20:54Z	null	--- dataset_info: - config_name: afp-thms features: - name: theory_file dtype: string - name: lemma_name dtype: string - name: lemma_command dtype: string - name: lemma_object dtype: string - name: template dtype: string - name: symbols sequence: string - name: types sequence: string - name: defs sequence: string splits: - name: train num_bytes: 313442562 num_examples: 206305 download_size: 32873700 dataset_size: 313442562 - config_name: afp-thms-2025-04-30 features: - name: theory_file dtype: string - name: lemma_name dtype: string - name: lemma_command dtype: string - name: lemma_object dtype: string - name: template dtype: string - name: symbols sequence: string - name: types sequence: string - name: defs sequence: string splits: - name: train num_bytes: 313442562 num_examples: 206305 download_size: 32873700 dataset_size: 313442562 - config_name: hol-thms features: - name: theory_file dtype: string - name: lemma_name dtype: string - name: lemma_command dtype: string - name: lemma_object dtype: string - name: template dtype: string - name: symbols sequence: string - name: types sequence: string - name: defs sequence: string splits: - name: train num_bytes: 86405819 num_examples: 62863 download_size: 8897621 dataset_size: 86405819 - config_name: hol-thms-2025-04-28 features: - name: theory_file dtype: string - name: lemma_name dtype: string - name: lemma_command dtype: string - name: lemma_object dtype: string - name: template dtype: string - name: symbols sequence: string - name: types sequence: string - name: defs sequence: string splits: - name: train num_bytes: 82980393 num_examples: 62863 download_size: 7912522 dataset_size: 82980393 - config_name: hol-thms-2025-05-01 features: - name: theory_file dtype: string - name: lemma_name dtype: string - name: lemma_command dtype: string - name: lemma_object dtype: string - name: template dtype: string - name: symbols sequence: string - name: types sequence: string - name: defs sequence: string splits: - name: train num_bytes: 98689352 num_examples: 62863 download_size: 9254383 dataset_size: 98689352 - config_name: hol-thms-old-defs-2025-05-03 features: - name: theory_file dtype: string - name: lemma_name dtype: string - name: lemma_command dtype: string - name: lemma_object dtype: string - name: template dtype: string - name: symbols sequence: string - name: types sequence: string - name: defs sequence: string splits: - name: train num_bytes: 86405819 num_examples: 62863 download_size: 8897621 dataset_size: 86405819 configs: - config_name: afp-thms data_files: - split: train path: afp-thms/train-* - config_name: afp-thms-2025-04-30 data_files: - split: train path: afp-thms-2025-04-30/train-* - config_name: hol-thms data_files: - split: train path: hol-thms/train-* - config_name: hol-thms-2025-04-28 data_files: - split: train path: hol-thms-2025-04-28/train-* - config_name: hol-thms-2025-05-01 data_files: - split: train path: hol-thms-2025-05-01/train-* - config_name: hol-thms-old-defs-2025-05-03 data_files: - split: train path: hol-thms-old-defs-2025-05-03/train-* ---
FrancophonIA/Vous-pouvez-le-dire-en-francais-Sante	FrancophonIA	2025-05-03T15:07:45Z	6	0	[ "task_categories:translation", "language:fra", "language:eng", "region:us" ]	[ "translation" ]	2025-04-28T20:19:34Z	null	--- language: - fra - eng viewer: false task_categories: - translation --- > [!NOTE] > Dataset origin: https://www.culture.gouv.fr/fr/thematiques/langue-francaise-et-langues-de-france/agir-pour-les-langues/moderniser-et-enrichir-la-langue-francaise/nos-publications/Vous-pouvez-le-dire-en-francais-Sante
FrancophonIA/Vocabulaire-de-l-education-2013	FrancophonIA	2025-05-03T15:00:38Z	2	0	[ "task_categories:translation", "language:fra", "language:eng", "region:us" ]	[ "translation" ]	2025-04-29T20:41:57Z	null	--- language: - fra - eng viewer: false task_categories: - translation --- > [!NOTE] > Dataset origin: https://www.culture.gouv.fr/fr/thematiques/langue-francaise-et-langues-de-france/agir-pour-les-langues/moderniser-et-enrichir-la-langue-francaise/nos-publications/Vocabulaire-de-l-education-2013
FrancophonIA/Vous-pouvez-le-dire-en-francais-ballon-oval-pret-pour-la-melee	FrancophonIA	2025-05-03T14:53:56Z	3	0	[ "task_categories:translation", "language:fra", "language:eng", "region:us" ]	[ "translation" ]	2025-04-29T20:46:09Z	null	--- language: - fra - eng viewer: false task_categories: - translation --- > [!NOTE] > Dataset origin: https://www.culture.gouv.fr/fr/thematiques/langue-francaise-et-langues-de-france/agir-pour-les-langues/moderniser-et-enrichir-la-langue-francaise/nos-publications/Vous-pouvez-le-dire-en-francais-ballon-oval-pret-pour-la-melee
FrancophonIA/Vocabulaire-de-l-economie-et-des-finances-2012	FrancophonIA	2025-05-03T14:53:15Z	2	0	[ "task_categories:translation", "language:fra", "language:eng", "region:us" ]	[ "translation" ]	2025-04-29T20:44:39Z	null	--- language: - fra - eng viewer: false task_categories: - translation --- > [!NOTE] > Dataset origin: https://www.culture.gouv.fr/fr/thematiques/langue-francaise-et-langues-de-france/agir-pour-les-langues/moderniser-et-enrichir-la-langue-francaise/nos-publications/Vocabulaire-de-l-economie-et-des-finances-2012
FrancophonIA/Vous-pouvez-le-dire-en-francais-A-la-conquete-du-ciel	FrancophonIA	2025-05-03T14:46:20Z	3	0	[ "task_categories:translation", "language:fra", "language:eng", "region:us" ]	[ "translation" ]	2025-04-29T20:48:07Z	null	--- language: - fra - eng viewer: false task_categories: - translation --- > [!NOTE] > Dataset origin: https://www.culture.gouv.fr/fr/thematiques/langue-francaise-et-langues-de-france/agir-pour-les-langues/moderniser-et-enrichir-la-langue-francaise/nos-publications/Vous-pouvez-le-dire-en-francais-A-la-conquete-du-ciel
jaeyong2/Math-Qwen3-06B-Ko	jaeyong2	2025-05-03T14:32:35Z	0	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-02T16:11:26Z	null	--- dataset_info: features: - name: content dtype: string - name: response sequence: string splits: - name: train num_bytes: 384553697 num_examples: 2000 download_size: 124040666 dataset_size: 384553697 configs: - config_name: default data_files: - split: train path: data/train-* ---
medss19/home_remedies_dataset	medss19	2025-05-03T14:25:04Z	0	0	[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-03T14:25:02Z	null	--- dataset_info: features: - name: Name of Item dtype: string - name: Health Issue dtype: string - name: Home Remedy dtype: string - name: Yogasan dtype: string splits: - name: train num_bytes: 45563 num_examples: 115 download_size: 27133 dataset_size: 45563 configs: - config_name: default data_files: - split: train path: data/train-* ---
NilanE/ComplexMultistepImageEditing	NilanE	2025-05-03T13:50:57Z	102	4	[ "task_categories:image-to-image", "license:apache-2.0", "size_categories:n<1K", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "reasoning-datasets-competition" ]	[ "image-to-image" ]	2025-04-28T23:37:27Z	null	--- license: apache-2.0 task_categories: - image-to-image tags: - reasoning-datasets-competition --- # Overview This dataset contains complex image editing reasoning chains, designed to give unified multimodal LLMs such as [Show-o](https://github.com/showlab/Show-o) and [Janus](https://github.com/deepseek-ai/Janus) the ability to reason using text and image tokens equally. #### Dataset structure: ``` { 'source': A randomly sampled image from imgenet-1k, 'prompt': An edit prompt to be applied to the source image, 'edit_0..7': Intermediate generated images (see pipeline below for details), 'chat_log': A log of the conversation between a critique model and an image generation model (see pipeline), 'reasoning': The chat log rewritten as a self-critiquing multimodal reasoning chain } ``` #### Motivation and uses: This dataset aims to address a near-complete lack of interleaved unified multimodal datasets. Most open unified multimodal LLMs are trained on image understanding and image generation tasks separately. During inference, they are unable to understand and generate interleaved content or perform any-to-any transformations, removing some of the key selling points of unified architectures. Currently, only proprietary models such as ChatGPT's native image generation model and Gemini 2.0 flash image-generation are proficient in simultaneous understanding and generation of images and text. And with the recent rise of reasoning models, this leads to a new question: Is it possible to have a model reason effectively using images as well as text, and if so, what sort of benefits would such a method have? The areas this dataset aims to address are as follows: 1. The data discrepancy of open interleaved text-image datasets. 2. Entering a new field of interleaved multimodal reasoning datasets. 3. In general, add to the research field of unified multimodal models. This dataset is part of a larger project to create a fully-open unified image and video LLM. Current work on an extreme-compression video tokenizer can be found [here](https://github.com/NilanEkanayake/TiTok-Video). # Creation ![dataset creation](diagram.jpg) #### The pipeline: 1. Send a source image to gemini 2.0 flash and have it write a complex image transformation/edit to apply. 2. Send that source image and the resulting edit request to 2.0 flash image-gen, and have it generate an image that attempts to fulfil the request. 3. Send the generated image back to 2.0 flash, along with all prior inputs and responses (source image, prompt, edit request, ...) and new instructions to critique how well the generated image obeys the request given. 4. Flash image-gen takes the critique as well as the prior inputs and generations (source image, edit request, generated image, critique, ...), and tries to fulfil the edit request again, with the benefit of context/feedback. 5. Repeat steps 3 and 4, adding new critiques and generations to the models' contexts until either the conversation gets too long (fail), or 2.0 flash is satisfied that the latest generation meets its requirements (success). 6. Send the successful conversations to 2.5 flash to be turned into reasoning traces. #### Making your own dataset: ##### Setup: ```bash git clone https://huggingface.co/datasets/NilanE/ComplexMultistepImageEditing pip install -U jsonlines datasets google-genai ``` Then add your Gemini API key to [create_dataset.py](create_dataset.py), and tweak the other variables as desired. ##### Operation: Run the script with: ```bash python3 create_dataset.py ``` The dataset will be saved as a JSONL file along with logged images from the conversations. To convert the dataset to a format supported by the HF dataset viewer, use [to_hf.py](to_hf.py). #### Notes: - The rationale behind using 2.5 flash for reasoning formatting is that it is both aware of reasoning and can have its own reasoning disabled (no thinking budget), which avoids inflated processing times and overcomplication of the generated reasoning chains. - The source images were sampled from imagenet-1k (refer to imagenet's license for usage restrictions). Everything else is apache-2.0. - The code for dataset creation was not thoroughly tested after being rewritten for publication. If you run into any issues, open a discussion with the problem. # Limitations - The dataset in its current iteration is too small for most use-cases. However, those needing a larger version can create their own using the included code (see above). - The dataset has a limited scope, only covering image editing. - Only a single interleaved image generation model (2.0 flash image-gen) was used, due to no other ones being freely available via API. This limits variety in the edited images. - The generated image edits are not inherently progressive, and thus not fully aligned with step-by-step reasoning. However, progressive edits do appear in the dataset as the critique model tries to guide the generation model (see pipeline). - Related to the point above, the reasoning chains in the dataset are derived from conversations between the critique and generation models. As such, they may not properly represent logical reasoning. - The edit requests from 2.0 flash tend to have limited themes and general originality. # Citation ``` @misc{complex-multistep-image-editing-dataset, title={Complex Multistep Image-Editing Dataset}, author={Nilan Ekanayake}, year={2025}, url={https://huggingface.co/datasets/NilanE/ComplexMultistepImageEditing} } ```
KhatirS/CosmeticDataset	KhatirS	2025-05-03T13:50:03Z	0	0	[ "license:apache-2.0", "size_categories:1K<n<10K", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-03T13:33:17Z	null	--- license: apache-2.0 ---
jdchang/qsharp-full-mixture-1.5b-filtered-with-labels	jdchang	2025-05-03T13:42:18Z	0	0	[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-03T13:23:32Z	null	--- dataset_info: features: - name: message_id dtype: string - name: problem dtype: string - name: solution dtype: string - name: answer dtype: string - name: reward sequence: bool - name: roll_in_ids sequence: sequence: int32 - name: roll_outs_ids sequence: sequence: int32 - name: processed_answer sequence: string - name: labels sequence: int32 splits: - name: train num_bytes: 86665084946 num_examples: 44509 download_size: 22663489948 dataset_size: 86665084946 configs: - config_name: default data_files: - split: train path: data/train-* ---
HungVu2003/opt-350m_beta_0.5_alpha_0.6_num-company_3_dataset_0_for_gen_12	HungVu2003	2025-05-03T13:41:11Z	0	0	[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-03T13:41:10Z	null	--- dataset_info: features: - name: question dtype: string splits: - name: train num_bytes: 5179935 num_examples: 12500 download_size: 1805132 dataset_size: 5179935 configs: - config_name: default data_files: - split: train path: data/train-* ---
NextGenC/synapse-set-50k	NextGenC	2025-05-03T13:36:06Z	9	1	[ "task_categories:text-generation", "task_categories:text2text-generation", "language:en", "language:tr", "license:mit", "size_categories:10K<n<100K", "format:json", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "bci", "eeg", "brain-computer-interface", "neuroscience", "signal-processing", "nlp", "instruction-tuning", "synthetic-data" ]	[ "text-generation", "text2text-generation" ]	2025-05-01T10:19:24Z	null	--- license: mit task_categories: - text-generation - text2text-generation language: - en - tr tags: - bci - eeg - brain-computer-interface - neuroscience - signal-processing - nlp - instruction-tuning - synthetic-data pretty_name: 'SynapseSet-50K: EEG Interpretation Dataset' size_categories: - 10K<n<100K datasets: - NextGenC/synapse-set-50k --- ## 🧠 SynapseSet-50K SynapseSet-50K is a synthetic instruction-tuning dataset crafted to simulate EEG-based neurological state interpretation for natural language models. Each sample reflects brain signal metrics with contextual metadata, and an expert-style medical NLP explanation. This dataset was generated by 7enn Labs and aims to bridge neuroscience signal interpretation with instruction-tuned NLP systems. > 🔬 100% synthetic, non-clinical data. Intended for academic and research purposes only.The right of use for the creation of this data has been created with algorithms belonging to our confidential party. > 🔬 The potential of this data set is quite large, so we expect feedback from you. As 7enn Labs, we are thinking of developing models for data augmentation and accuracy of the dataset. > 🔬 "This endeavor represents more than a mere product; it constitutes foundational infrastructure, a testament to our vision. At 7enn Labs, we regard this dataset as a significant milestone on a much broader strategic journey. Its importance lies not only in the algorithmic generation of the synthetic data itself but fundamentally in the power and continuous evolution of the proprietary data engine developed by 7enn Labs. Whether immediately recognized or widely adopted, systems of this nature are poised to shape the future. The tools we forge today are the very foundations upon which tomorrow's breakthroughs will be built." --- ## ⚠️ Disclaimer & Legal Notice (7een Labs) 100% synthetic, non-clinical data. Intended strictly for academic and research use. The datasets provided (SynapseSet series) are fully artificial and generated through proprietary simulation algorithms owned and controlled by a confidential party affiliated with 7een Labs. These datasets do not represent real patient data and must not be used for clinical decision-making, diagnostic purposes, or any application involving human subjects in real-world scenarios. > 🛑 7een Labs accepts no liability or responsibility for any outcome, misuse, or legal consequence arising from the use, distribution, or interpretation of this data or any derivative works. Full responsibility lies with the end user. By accessing or utilizing any portion of these datasets, you agree to waive any claim against 7een Labs and acknowledge that all risk and responsibility rests solely with you. Use it smart — own the risk. --- ## 🧬 Dataset Format Each sample contains: - `instruction`: Task description for the model - `input`: EEG signal metrics with patient metadata - `output`: Simulated clinical explanation ```json { "instruction": "Interpret the given EEG values for a patient and explain their mental state.", "input": "Patient: ID#A7421 \| Age: 38 \| Date: 2024-10-12 \| EEG: Alpha=9.8Hz, Beta=17.2Hz, Theta=4.1Hz, Delta=2.0Hz, Gamma=29.5Hz \| Voltage=0.72mV", "output": "The EEG profile is consistent with relaxed wakefulness. Alpha wave dominance (9.8Hz) suggests the patient is in a calm, eyes-closed resting state. No signs of seizure activity or abnormal slowing are present." } ``` --- - Language: English - Tone: It's clinical-style, but it suits the 7enn Labs neutral look. --- ## 🔍 Feature Comparison \| Feature \| SynapseSet-10K \| SynapseSet-50K \| SynapseSet-100K \| \|------------------------\|----------------------------\|----------------------------\|----------------------------\| \| 📊 Example Capacity \| 10,000 \| 50,000 \| 100,000 \| \| 🗣️ Language \| Turkish \| English \| English \| \| 🧠 Neurological Conditions \| 16 \| 25+ \| 50+ \| \| 📈 EEG Bands \| 5 basic bands \| 5 basic + 6 sub-bands \| 5 basic + 11 sub-bands \| \| 📋 Data Formats \| 4 types \| 6 types \| 6 types (enhanced) \| \| 🔬 Realism Level \| Basic \| Intermediate \| Clinical-grade \| \| 👤 Patient Modeling \| Simple \| Advanced \| Comprehensive medical profile \| \| 📉 Artifact Modeling \| None \| Basic \| Comprehensive (12+ types) \| --- ## 🔐 Licensing & Ethics - License: [MIT](https://opensource.org/license/mit/) - You must clearly disclose use of synthetic data - Not to be used for clinical decision-making - Use at your own risk; no warranties provided --- ## 📚 Citation If you use SynapseSet-50K, please cite: ```bibtex @misc{7ennlabs2025synapseset, author = {7enn Labs}, title = {SynapseSet-50K: Synthetic Instruction Dataset for EEG Interpretation}, year = {2025}, url = {https://huggingface.co/datasets/NextGenC/synapse-set-50k}, note = {100% synthetic dataset for BCI/NLP research} } ``` --- ## 🧪 Example Usage ```python from datasets import load_dataset dataset = load_dataset("DATASET_FILE_NAME") print(dataset["train"][0]) ``` --- ## 🧑‍💻 Created by 7enn Labs ---
HungVu2003/opt-350m_beta_1.0_alpha_0.6_num-company_3_dataset_0_for_gen_14	HungVu2003	2025-05-03T12:49:17Z	0	0	[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-03T12:49:14Z	null	--- dataset_info: features: - name: question dtype: string splits: - name: train num_bytes: 9087261 num_examples: 12500 download_size: 1921127 dataset_size: 9087261 configs: - config_name: default data_files: - split: train path: data/train-* ---
Kyleyee/trin_data_tldr_explicit_dataset	Kyleyee	2025-05-03T12:23:45Z	0	0	[ "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "trl" ]	[]	2025-05-03T12:23:38Z	null	--- tags: - trl --- # TL;DR Dataset for Preference Learning ## Summary The TL;DR dataset is a processed version of Reddit posts, specifically curated to train models using the [TRL library](https://github.com/huggingface/trl) for preference learning and Reinforcement Learning from Human Feedback (RLHF) tasks. It leverages the common practice on Reddit where users append "TL;DR" (Too Long; Didn't Read) summaries to lengthy posts, providing a rich source of paired text data for training models to understand and generate concise summaries. ## Data Structure - Format: [Standard](https://huggingface.co/docs/trl/main/dataset_formats#standard) - Type: [Preference](https://huggingface.co/docs/trl/main/dataset_formats#preference) Columns: - `"prompt"`: The unabridged Reddit post. - `"chosen"`: The concise "TL;DR" summary appended by the author. - `"rejected"`: An alternative summary or response that was not selected. This structure enables models to learn the relationship between detailed content and its abbreviated form, enhancing their summarization capabilities. ## Generation script The script used to generate this dataset can be found [here](https://github.com/huggingface/trl/blob/main/examples/datasets/tldr_preference.py).
DT4LM/debertav3base_mr_multi_pso_timeDecay	DT4LM	2025-05-03T12:19:50Z	0	0	[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-03T12:17:00Z	null	--- dataset_info: features: - name: text dtype: string - name: label dtype: int32 splits: - name: train num_bytes: 58604 num_examples: 441 download_size: 41872 dataset_size: 58604 configs: - config_name: default data_files: - split: train path: data/train-* ---
DIaac/m1-1k-tokenized-v3-all-formats-knowledge-enhanced-sft-0503	DIaac	2025-05-03T12:11:25Z	0	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-03T12:10:35Z	null	--- dataset_info: features: - name: answer_idx dtype: int64 - name: source dtype: string - name: metadata dtype: string - name: prompt dtype: string - name: answer_letter dtype: string - name: answer_string dtype: string - name: reasoning dtype: string - name: distilled_answer_string dtype: string - name: domain_code dtype: string - name: domain_name dtype: string - name: text dtype: string - name: raw_action_chain dtype: string - name: clean_action_chain_json struct: - name: reasoning_chain list: - name: action dtype: string - name: description dtype: string - name: step dtype: int64 - name: clean_action_chain sequence: string - name: formatted_action_chain dtype: string - name: rebuilt_cot_simple_with_marker dtype: string - name: rebuilt_cot_simple_without_marker dtype: string - name: rebuilt_cot_json_with_marker dtype: string - name: rebuilt_cot_json_without_marker dtype: string - name: original_text dtype: string - name: action_guided_text dtype: string - name: json_guided_text dtype: string - name: simple_without_marker_text dtype: string - name: simple_with_marker_text dtype: string - name: json_without_marker_text dtype: string - name: json_with_marker_text dtype: string - name: entity_extract_response dtype: string - name: extracted_entities list: - name: id dtype: string - name: name dtype: string - name: type dtype: string - name: entity_links list: - name: links list: - name: name dtype: string - name: score dtype: float64 - name: mention dtype: string - name: type dtype: string - name: unfiltered_triplets sequence: sequence: string - name: filter_response dtype: string - name: clean_filtered_triplets_json struct: - name: relevant_triplets list: - name: explanation dtype: string - name: id dtype: int64 - name: relevance dtype: string - name: filtered_triplets sequence: sequence: string - name: knowledge_enhanced_rebuilt_cot_simple_with_marker dtype: string - name: knowledge_enhanced_rebuilt_cot_simple_without_marker dtype: string - name: knowledge_enhanced_rebuilt_cot_json_with_marker dtype: string - name: knowledge_enhanced_rebuilt_cot_json_without_marker dtype: string - name: knowledge_enhanced_simple_without_marker_text dtype: string - name: knowledge_enhanced_simple_with_marker_text dtype: string - name: knowledge_enhanced_json_without_marker_text dtype: string - name: knowledge_enhanced_json_with_marker_text dtype: string splits: - name: train num_bytes: 129433764 num_examples: 1000 download_size: 56096321 dataset_size: 129433764 configs: - config_name: default data_files: - split: train path: data/train-* ---
scuti0/extension-test	scuti0	2025-05-03T12:10:54Z	78	0	[ "license:apache-2.0", "size_categories:n<1K", "format:imagefolder", "modality:image", "library:datasets", "library:mlcroissant", "region:us" ]	[]	2024-06-29T11:45:39Z	null	--- license: apache-2.0 ---
willnorris/cylinder-in-box-hollows	willnorris	2025-05-03T11:36:48Z	0	0	[ "task_categories:robotics", "license:apache-2.0", "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:timeseries", "modality:video", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us", "LeRobot" ]	[ "robotics" ]	2025-05-03T10:20:58Z	null	--- license: apache-2.0 task_categories: - robotics tags: - LeRobot configs: - config_name: default data_files: data//.parquet --- This dataset was created using [LeRobot](https://github.com/huggingface/lerobot). ## Dataset Description - Homepage: [More Information Needed] - Paper: [More Information Needed] - License: apache-2.0 ## Dataset Structure [meta/info.json](meta/info.json): ```json { "codebase_version": "v2.1", "robot_type": "so100", "total_episodes": 1, "total_frames": 366, "total_tasks": 1, "total_videos": 2, "total_chunks": 1, "chunks_size": 1000, "fps": 30, "splits": { "train": "0:1" }, "data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet", "video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4", "features": { "observation.images.cam1": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channel" ], "info": { "video.fps": 30.0, "video.height": 480, "video.width": 640, "video.channels": 3, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "has_audio": false } }, "observation.images.cam2": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channel" ], "info": { "video.fps": 30.0, "video.height": 480, "video.width": 640, "video.channels": 3, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "has_audio": false } }, "observation.state": { "dtype": "float32", "shape": [ 6 ], "names": { "motors": [ "shoulder_pan", "shoulder_lift", "elbow_flex", "wrist_flex", "wrist_roll", "gripper" ] } }, "action": { "dtype": "float32", "shape": [ 6 ], "names": { "motors": [ "shoulder_pan", "shoulder_lift", "elbow_flex", "wrist_flex", "wrist_roll", "gripper" ] } }, "timestamp": { "dtype": "float32", "shape": [ 1 ], "names": null }, "frame_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "episode_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "task_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "next.done": { "dtype": "bool", "shape": [ 1 ] }, "index": { "dtype": "int64", "shape": [ 1 ], "names": null } } } ``` ## Citation BibTeX: ```bibtex [More Information Needed] ```
Geralt-Targaryen/Children-zh	Geralt-Targaryen	2025-05-03T11:36:21Z	0	0	[ "license:apache-2.0", "size_categories:10M<n<100M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-02T08:13:47Z	null	--- license: apache-2.0 ---
dgambettaphd/D_llm2_gen3_WXS_doc1000_synt64_lr1e-04_acm_SYNLAST	dgambettaphd	2025-05-03T11:28:02Z	0	0	[ "size_categories:10K<n<100K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-03T11:27:58Z	null	--- dataset_info: features: - name: id_doc dtype: int64 - name: text dtype: string - name: dataset dtype: string - name: gen dtype: int64 - name: synt dtype: int64 - name: MPP dtype: float64 splits: - name: train num_bytes: 10852981 num_examples: 19000 download_size: 6422251 dataset_size: 10852981 configs: - config_name: default data_files: - split: train path: data/train-* ---
SciKnowOrg/ontolearner-events	SciKnowOrg	2025-05-03T11:23:44Z	0	0	[ "language:en", "license:mit", "region:us", "OntoLearner", "ontology-learning", "events" ]	[]	2025-05-03T11:23:40Z	null	--- license: mit language: - en tags: - OntoLearner - ontology-learning - events pretty_name: Agricultural --- <div> <img src="https://raw.githubusercontent.com/sciknoworg/OntoLearner/main/images/logo.png" alt="OntoLearner" style="display: block; margin: 0 auto; width: 500px; height: auto;"> <h1 style="text-align: center; margin-top: 1em;">Events Domain Ontologies</h1> </div> ## Overview The events domain encompasses ontologies designed to systematically represent and model occurrences, temporal intervals, and scheduling constructs within a structured framework. This domain is pivotal in knowledge representation as it facilitates the semantic integration and interoperability of event-related data across diverse systems, enabling precise temporal reasoning and efficient management of calendar-based activities. By providing a formalized vocabulary and structure, it enhances the ability to query, share, and analyze event information in a coherent and meaningful manner. ## Ontologies \| Ontology ID \| Full Name \| Classes \| Properties \| Last Updated \| \|-------------\|-----------\|---------\|------------\|--------------\| \| Conference \| Conference Ontology (Conference) \| 42 \| 52 \| 2016/04/30\| \| iCalendar \| iCalendar Vocabulary (iCalendar) \| 54 \| 49 \| 2004/04/07\| \| LODE \| Linking Open Descriptions of Events (LODE) \| 1 \| 7 \| 2020-10-31\| ## Dataset Files Each ontology directory contains the following files: 1. `<ontology_id>.<format>` - The original ontology file 2. `term_typings.json` - Dataset of term to type mappings 3. `taxonomies.json` - Dataset of taxonomic relations 4. `non_taxonomic_relations.json` - Dataset of non-taxonomic relations 5. `<ontology_id>.rst` - Documentation describing the ontology ## Usage These datasets are intended for ontology learning research and applications.
KBayoud/Darija-VLM-GQA-Dataset	KBayoud	2025-05-03T11:22:27Z	24	3	[ "license:apache-2.0", "size_categories:1K<n<10K", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-02-03T22:35:24Z	null	--- license: apache-2.0 dataset_info: features: - name: image dtype: image - name: darija_qa list: - name: answer dtype: string - name: fullAnswer dtype: string - name: question dtype: string - name: english_qa list: - name: answer dtype: string - name: fullAnswer dtype: string - name: question dtype: string splits: - name: train num_bytes: 152505691.5 num_examples: 3092 download_size: 143831984 dataset_size: 152505691.5 configs: - config_name: default data_files: - split: train path: data/train-* --- Original dataset : [vikhyatk/gqa-val](https://huggingface.co/datasets/vikhyatk/gqa-val)
kothasuhas/llp-gold-37m-1.5m_T2048.0	kothasuhas	2025-05-03T11:12:53Z	0	0	[ "size_categories:1M<n<10M", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-03T11:10:34Z	null	--- dataset_info: features: - name: text dtype: string - name: p_log_probs dtype: float32 - name: q_log_probs dtype: float32 - name: num_tokens dtype: float32 - name: log_weight dtype: float64 splits: - name: train num_bytes: 3605804917.0 num_examples: 1500000 download_size: 189296120 dataset_size: 3605804917.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
severo/trending-repos	severo	2025-05-03T11:04:42Z	567	12	[ "license:apache-2.0", "size_categories:10K<n<100K", "format:csv", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "croissant" ]	[]	2023-07-28T13:57:34Z	null	--- license: apache-2.0 pretty_name: Trending repositories on Hugging Face size_categories: - n<1K configs: - config_name: models data_files: "models.csv" - config_name: datasets data_files: "datasets.csv" - config_name: spaces data_files: "spaces.csv" tags: - croissant --- # Dataset Card for Dataset Name ## Dataset Description - Homepage: - Repository: - Paper: - Leaderboard: - Point of Contact: Sylvain Lesage ### Dataset Summary This dataset contains the 20 trending repositories of each type: models, datasets, and space, on Hugging Face, every day. Each type can be loaded from its own dataset config. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages Not relevant. ## Dataset Structure ### Data Instances The dataset contains three configurations: models: the history of trending models on Hugging Face datasets: the history of trending datasets on Hugging Face spaces: the history of trending spaces on Hugging Face ### Data Fields - date (string): the date of the lookup to the trending repositories - author (string): id of the repository owner. It can be null. - id (string): id of the repository - rank (int64): rank in the trending repositories of its kind (model, dataset, or space). Starts at 1. - recent_likes (int64): number of likes received lately (last week) - likes (int64): total number of likes - month_downloads (int64): number of downloads in the last month. Null for the spaces. ### Data Splits Each configuration only has one split: `train` that contains all the rows. ## Dataset Creation ### Curation Rationale The dataset is updated daily through a cron job that calls the `https://huggingface.co/api/trending?type=${repoType}&limit=20` endpoint for each repository type (model, dataset, space). The script runs in an [Observable](https://observablehq.com/@huggingface) notebook, and the files are uploaded using the [huggingface.js](https://github.com/huggingface/huggingface.js) library. ### Source Data #### Initial Data Collection and Normalization Not relevant. #### Who are the source language producers? Not relevant. ### Annotations #### Annotation process Not relevant. #### Who are the annotators? Not relevant. ### Personal and Sensitive Information Only public repositories are included in the trending repositories. ## Considerations for Using the Data ### Social Impact of Dataset Not relevant. ### Discussion of Biases The trending repositories reflect the likes given by Hugging Face users in the last week. Any bias that applies to the users can be reflected in this dataset. As a vanity metric, some users might also be tempted to generate fake likes. ### Other Known Limitations Not relevant. ## Additional Information ### Dataset Curators Sylvain Lesage, Hugging Face ### Licensing Information Apache License 2.0 ### Citation Information Not relevant. ### Contributions Not relevant.
MBZUAI-IFM/chess_final	MBZUAI-IFM	2025-05-03T10:36:48Z	0	0	[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-02T21:21:39Z	null	--- dataset_info: features: - name: conversations list: - name: from dtype: string - name: value dtype: string - name: index dtype: int64 - name: question dtype: string - name: cot dtype: string - name: response dtype: string splits: - name: train num_bytes: 534859 num_examples: 49 download_size: 257991 dataset_size: 534859 configs: - config_name: default data_files: - split: train path: data/train-* ---

SayantanJoker/Shrutilipi_Hindi_resampled_44100_merged_15_quality

SayantanJoker

2025-05-05T04:58:09Z

0

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

[]

2025-05-05T04:58:06Z

null

--- dataset_info: features: - name: text dtype: string - name: file_name dtype: string - name: utterance_pitch_mean dtype: float32 - name: utterance_pitch_std dtype: float32 - name: snr dtype: float64 - name: c50 dtype: float64 - name: speaking_rate dtype: float64 - name: phonemes dtype: string - name: stoi dtype: float64 - name: si-sdr dtype: float64 - name: pesq dtype: float64 splits: - name: train num_bytes: 13278527 num_examples: 34675 download_size: 5939408 dataset_size: 13278527 configs: - config_name: default data_files: - split: train path: data/train-* ---

pkgforge-security/domains

pkgforge-security

2025-05-05T03:33:05Z

1,292

1

[ "license:mit", "size_categories:1B<n<10B", "format:text", "modality:text", "library:datasets", "library:mlcroissant", "region:us" ]

[]

2025-03-08T09:15:36Z

null

--- size_categories: - 100B<n<1T pretty_name: Domains license: mit --- <p align="center"> <a href="https://github.com/pkgforge-security/domains"> <img src="https://cdn-uploads.huggingface.co/production/uploads/66e82daa04200de7aa4cba26/V6PJzNaHVd_Po5E74GmmC.png" alt="pkgforge" width="256"/></a> <b><strong> <a href="https://github.com/pkgforge-security/domains">Internet Domains</a></code></strong></b> <br> <img src="https://cdn-uploads.huggingface.co/production/uploads/66e82daa04200de7aa4cba26/hXVfqrL-YKJt-roX0DIgx.png" width="200" /> </p> - ### [Domains](https://github.com/pkgforge-security/domains) > - HuggingFace Hub Mirror for https://github.com/pkgforge-security/domains > - The Sync Workflow actions are at: https://github.com/pkgforge-security/domains - #### TOS & Abuse (To [Hugging-Face's](https://huggingface.co) Staff) > Hi, if you are an offical from [Hugging-Face](https://huggingface.co) here to investigate why this Repo is so Large and are considering deleting, & terminating our Account.<br> > Please note that, this project benefits a lot of people (You can do a code search on Github: `pkg.pkgforge.dev`) and is no way meant to abuse [Hugging-Face's](https://huggingface.co) Resources.<br> > You can check out the main project at: [https://github.com/pkgforge/soar](https://github.com/pkgforge/soar) and see for yourself about what all this is actually about.<br> > However if that doesn't convince you, and you feel like it's not worth it supporting this project, do reach out to us at: [https://docs.pkgforge.dev/contact/chat](https://docs.pkgforge.dev/contact/chat))<br> > We will backup the data and delete our account to comply with the TOS.

HungVu2003/opt-350m_beta_0.0_alpha_0.2_num-company_2_dataset_1_for_gen_12_v2

HungVu2003

2025-05-04T23:57:20Z

0

[ "region:us" ]

[]

2025-05-04T23:57:19Z

null

--- dataset_info: features: - name: question dtype: string splits: - name: train num_bytes: 3020032 num_examples: 13750 download_size: 980307 dataset_size: 3020032 configs: - config_name: default data_files: - split: train path: data/train-* ---

GitBag/DeepSeek-R1-Distill-Qwen-1.5B_hmmt-feb-25_eval

GitBag

2025-05-04T23:07:04Z

0

[ "region:us" ]

[]

2025-05-04T23:07:03Z

null

--- dataset_info: features: - name: problem dtype: string - name: answer dtype: string - name: response_0 dtype: string - name: response_1 dtype: string - name: response_2 dtype: string - name: response_3 dtype: string - name: response_4 dtype: string - name: response_5 dtype: string - name: response_6 dtype: string - name: response_7 dtype: string - name: response_8 dtype: string - name: response_9 dtype: string - name: response_10 dtype: string - name: response_11 dtype: string - name: response_12 dtype: string - name: response_13 dtype: string - name: response_14 dtype: string - name: response_15 dtype: string - name: response_16 dtype: string - name: response_17 dtype: string - name: response_18 dtype: string - name: response_19 dtype: string - name: response_20 dtype: string - name: response_21 dtype: string - name: response_22 dtype: string - name: response_23 dtype: string - name: response_24 dtype: string - name: response_25 dtype: string - name: response_26 dtype: string - name: response_27 dtype: string - name: response_28 dtype: string - name: response_29 dtype: string - name: response_30 dtype: string - name: response_31 dtype: string - name: eval_0 dtype: float64 - name: eval_1 dtype: float64 - name: eval_2 dtype: float64 - name: eval_3 dtype: float64 - name: eval_4 dtype: float64 - name: eval_5 dtype: float64 - name: eval_6 dtype: float64 - name: eval_7 dtype: float64 - name: eval_8 dtype: float64 - name: eval_9 dtype: float64 - name: eval_10 dtype: float64 - name: eval_11 dtype: float64 - name: eval_12 dtype: float64 - name: eval_13 dtype: float64 - name: eval_14 dtype: float64 - name: eval_15 dtype: float64 - name: eval_16 dtype: float64 - name: eval_17 dtype: float64 - name: eval_18 dtype: float64 - name: eval_19 dtype: float64 - name: eval_20 dtype: float64 - name: eval_21 dtype: float64 - name: eval_22 dtype: float64 - name: eval_23 dtype: float64 - name: eval_24 dtype: float64 - name: eval_25 dtype: float64 - name: eval_26 dtype: float64 - name: eval_27 dtype: float64 - name: eval_28 dtype: float64 - name: eval_29 dtype: float64 - name: eval_30 dtype: float64 - name: eval_31 dtype: float64 splits: - name: train num_bytes: 39227790 num_examples: 30 download_size: 13160569 dataset_size: 39227790 configs: - config_name: default data_files: - split: train path: data/train-* ---

kingsleykim/openthoughts_qwen_2.5_math_7b

kingsleykim

2025-05-04T21:15:11Z

0

[ "region:us" ]

[]

2025-05-04T21:15:06Z

null

--- dataset_info: features: - name: inputs dtype: string - name: correctness dtype: bool - name: answers dtype: string - name: questions dtype: string - name: correct dtype: bool - name: question dtype: string splits: - name: train num_bytes: 791024669 num_examples: 191973 download_size: 124584820 dataset_size: 791024669 configs: - config_name: default data_files: - split: train path: data/train-* ---

mmmmmp/robot_test3

mmmmmp

2025-05-04T21:01:39Z

0

[ "task_categories:robotics", "license:apache-2.0", "region:us", "LeRobot" ]

[ "robotics" ]

2025-05-04T21:01:36Z

null

--- license: apache-2.0 task_categories: - robotics tags: - LeRobot configs: - config_name: default data_files: data/*/*.parquet --- This dataset was created using [LeRobot](https://github.com/huggingface/lerobot). ## Dataset Description - **Homepage:** [More Information Needed] - **Paper:** [More Information Needed] - **License:** apache-2.0 ## Dataset Structure [meta/info.json](meta/info.json): ```json { "codebase_version": "v2.0", "robot_type": "so100", "total_episodes": 1, "total_frames": 443, "total_tasks": 1, "total_videos": 2, "total_chunks": 1, "chunks_size": 1000, "fps": 30, "splits": { "train": "0:1" }, "data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet", "video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4", "features": { "action": { "dtype": "float32", "shape": [ 6 ], "names": [ "main_shoulder_pan", "main_shoulder_lift", "main_elbow_flex", "main_wrist_flex", "main_wrist_roll", "main_gripper" ] }, "observation.state": { "dtype": "float32", "shape": [ 6 ], "names": [ "main_shoulder_pan", "main_shoulder_lift", "main_elbow_flex", "main_wrist_flex", "main_wrist_roll", "main_gripper" ] }, "observation.images.laptop": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.fps": 30.0, "video.height": 480, "video.width": 640, "video.channels": 3, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "has_audio": false } }, "observation.images.phone": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.fps": 30.0, "video.height": 480, "video.width": 640, "video.channels": 3, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "has_audio": false } }, "timestamp": { "dtype": "float32", "shape": [ 1 ], "names": null }, "frame_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "episode_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "task_index": { "dtype": "int64", "shape": [ 1 ], "names": null } } } ``` ## Citation **BibTeX:** ```bibtex [More Information Needed] ```

ibragim-bad/random-data-1

ibragim-bad

2025-05-04T20:15:19Z

0

[ "region:us" ]

[]

2025-05-04T20:15:17Z

null

--- dataset_info: features: - name: instance_id dtype: string - name: base_commit dtype: string - name: created_at dtype: timestamp[us] - name: environment_setup_commit dtype: string - name: hints_text dtype: string - name: patch dtype: string - name: problem_statement dtype: string - name: repo dtype: string - name: test_patch dtype: string - name: meta struct: - name: commit_name dtype: string - name: failed_lite_validators sequence: string - name: has_test_patch dtype: bool - name: is_lite dtype: bool - name: num_modified_files dtype: int64 - name: version dtype: string - name: install_config struct: - name: env_yml_path sequence: string - name: install dtype: string - name: log_parser dtype: string - name: no_use_env dtype: bool - name: packages dtype: string - name: pip_packages sequence: string - name: pre_install sequence: string - name: python dtype: string - name: reqs_path sequence: string - name: test_cmd dtype: string - name: requirements dtype: string - name: environment dtype: string - name: FAIL_TO_PASS sequence: string - name: FAIL_TO_FAIL sequence: string - name: PASS_TO_PASS sequence: string - name: PASS_TO_FAIL sequence: 'null' - name: license_name dtype: string splits: - name: test num_bytes: 2540351 num_examples: 100 download_size: 884812 dataset_size: 2540351 configs: - config_name: default data_files: - split: test path: data/test-* ---

HungVu2003/opt-350m_beta_1.0_alpha_0.2_num-company_2_dataset_1_for_gen_17_v2

HungVu2003

2025-05-04T17:43:09Z

0

[ "region:us" ]

[]

2025-05-04T17:43:07Z

null

--- dataset_info: features: - name: question dtype: string splits: - name: train num_bytes: 6385788 num_examples: 13750 download_size: 3253110 dataset_size: 6385788 configs: - config_name: default data_files: - split: train path: data/train-* ---

hshwk1983/x_dataset_52806

hshwk1983

2025-05-04T17:09:03Z

2,337

0

[ "task_categories:text-classification", "task_categories:token-classification", "task_categories:question-answering", "task_categories:summarization", "task_categories:text-generation", "task_ids:sentiment-analysis", "task_ids:topic-classification", "task_ids:named-entity-recognition", "task_ids:language-modeling", "task_ids:text-scoring", "task_ids:multi-class-classification", "task_ids:multi-label-classification", "task_ids:extractive-qa", "task_ids:news-articles-summarization", "multilinguality:multilingual", "source_datasets:original", "license:mit", "size_categories:100M<n<1B", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]

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

2025-01-27T00:12:13Z

null

--- license: mit multilinguality: - multilingual source_datasets: - original task_categories: - text-classification - token-classification - question-answering - summarization - text-generation task_ids: - sentiment-analysis - topic-classification - named-entity-recognition - language-modeling - text-scoring - multi-class-classification - multi-label-classification - extractive-qa - news-articles-summarization --- # Bittensor Subnet 13 X (Twitter) Dataset <center> <img src="https://huggingface.co/datasets/macrocosm-os/images/resolve/main/bittensor.png" alt="Data-universe: The finest collection of social media data the web has to offer"> </center> <center> <img src="https://huggingface.co/datasets/macrocosm-os/images/resolve/main/macrocosmos-black.png" alt="Data-universe: The finest collection of social media data the web has to offer"> </center> ## Dataset Description - **Repository:** hshwk1983/x_dataset_52806 - **Subnet:** Bittensor Subnet 13 - **Miner Hotkey:** 5Fc1UBhNfu7MCTK51dvY7Yukt1ZLjAbNW832GirVQcLE3Wj2 ### Dataset Summary This dataset is part of the Bittensor Subnet 13 decentralized network, containing preprocessed data from X (formerly Twitter). The data is continuously updated by network miners, providing a real-time stream of tweets for various analytical and machine learning tasks. For more information about the dataset, please visit the [official repository](https://github.com/macrocosm-os/data-universe). ### Supported Tasks The versatility of this dataset allows researchers and data scientists to explore various aspects of social media dynamics and develop innovative applications. Users are encouraged to leverage this data creatively for their specific research or business needs. For example: - Sentiment Analysis - Trend Detection - Content Analysis - User Behavior Modeling ### Languages Primary language: Datasets are mostly English, but can be multilingual due to decentralized ways of creation. ## Dataset Structure ### Data Instances Each instance represents a single tweet with the following fields: ### Data Fields - `text` (string): The main content of the tweet. - `label` (string): Sentiment or topic category of the tweet. - `tweet_hashtags` (list): A list of hashtags used in the tweet. May be empty if no hashtags are present. - `datetime` (string): The date when the tweet was posted. - `username_encoded` (string): An encoded version of the username to maintain user privacy. - `url_encoded` (string): An encoded version of any URLs included in the tweet. May be empty if no URLs are present. ### Data Splits This dataset is continuously updated and does not have fixed splits. Users should create their own splits based on their requirements and the data's timestamp. ## Dataset Creation ### Source Data Data is collected from public tweets on X (Twitter), adhering to the platform's terms of service and API usage guidelines. ### Personal and Sensitive Information All usernames and URLs are encoded to protect user privacy. The dataset does not intentionally include personal or sensitive information. ## Considerations for Using the Data ### Social Impact and Biases Users should be aware of potential biases inherent in X (Twitter) data, including demographic and content biases. This dataset reflects the content and opinions expressed on X and should not be considered a representative sample of the general population. ### Limitations - Data quality may vary due to the decentralized nature of collection and preprocessing. - The dataset may contain noise, spam, or irrelevant content typical of social media platforms. - Temporal biases may exist due to real-time collection methods. - The dataset is limited to public tweets and does not include private accounts or direct messages. - Not all tweets contain hashtags or URLs. ## Additional Information ### Licensing Information The dataset is released under the MIT license. The use of this dataset is also subject to X Terms of Use. ### Citation Information If you use this dataset in your research, please cite it as follows: ``` @misc{hshwk19832025datauniversex_dataset_52806, title={The Data Universe Datasets: The finest collection of social media data the web has to offer}, author={hshwk1983}, year={2025}, url={https://huggingface.co/datasets/hshwk1983/x_dataset_52806}, } ``` ### Contributions To report issues or contribute to the dataset, please contact the miner or use the Bittensor Subnet 13 governance mechanisms. ## Dataset Statistics [This section is automatically updated] - **Total Instances:** 53704103 - **Date Range:** 2025-01-21T00:00:00Z to 2025-02-13T00:00:00Z - **Last Updated:** 2025-02-18T18:20:54Z ### Data Distribution - Tweets with hashtags: 48.13% - Tweets without hashtags: 51.87% ### Top 10 Hashtags For full statistics, please refer to the `stats.json` file in the repository. | Rank | Topic | Total Count | Percentage | |------|-------|-------------|-------------| | 1 | NULL | 27855573 | 51.87% | | 2 | #riyadh | 382358 | 0.71% | | 3 | #zelena | 324161 | 0.60% | | 4 | #tiktok | 246981 | 0.46% | | 5 | #bbb25 | 141261 | 0.26% | | 6 | #ad | 136046 | 0.25% | | 7 | #superbowl | 106457 | 0.20% | | 8 | #bbmzansi | 91608 | 0.17% | | 9 | #jhope_at_galadespiècesjaunes | 91561 | 0.17% | | 10 | #transferlerlebirliktezafere | 80161 | 0.15% | ## Update History | Date | New Instances | Total Instances | |------|---------------|-----------------| | 2025-01-27T00:12:58Z | 2331611 | 2331611 | | 2025-01-30T12:15:15Z | 7216791 | 9548402 | | 2025-02-03T00:17:43Z | 7388142 | 16936544 | | 2025-02-06T12:21:06Z | 8811525 | 25748069 | | 2025-02-10T00:25:18Z | 10398234 | 36146303 | | 2025-02-13T13:59:25Z | 4703197 | 40849500 | | 2025-02-17T02:19:40Z | 11306944 | 52156444 | | 2025-02-18T03:19:10Z | 829790 | 52986234 | | 2025-02-18T18:20:54Z | 717869 | 53704103 |

Joseph7D/emotion-dataset

Joseph7D

2025-05-04T16:34:13Z

0

[ "region:us" ]

[]

2025-05-04T16:34:09Z

null

--- dataset_info: features: - name: text dtype: string - name: emotion dtype: string splits: - name: train num_bytes: 3013664 num_examples: 26928 - name: test num_bytes: 372292 num_examples: 3366 - name: validation num_bytes: 378972 num_examples: 3366 download_size: 2318145 dataset_size: 3764928 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: validation path: data/validation-* ---

GitBag/Qwen2.5-7B_hmmt-feb-24_eval

GitBag

2025-05-04T16:30:26Z

0

[ "region:us" ]

[]

2025-05-04T16:30:24Z

null

--- dataset_info: features: - name: problem dtype: string - name: answer dtype: string - name: response_0 dtype: string - name: response_1 dtype: string - name: response_2 dtype: string - name: response_3 dtype: string - name: response_4 dtype: string - name: response_5 dtype: string - name: response_6 dtype: string - name: response_7 dtype: string - name: response_8 dtype: string - name: response_9 dtype: string - name: response_10 dtype: string - name: response_11 dtype: string - name: response_12 dtype: string - name: response_13 dtype: string - name: response_14 dtype: string - name: response_15 dtype: string - name: response_16 dtype: string - name: response_17 dtype: string - name: response_18 dtype: string - name: response_19 dtype: string - name: response_20 dtype: string - name: response_21 dtype: string - name: response_22 dtype: string - name: response_23 dtype: string - name: response_24 dtype: string - name: response_25 dtype: string - name: response_26 dtype: string - name: response_27 dtype: string - name: response_28 dtype: string - name: response_29 dtype: string - name: response_30 dtype: string - name: response_31 dtype: string - name: eval_0 dtype: float64 - name: eval_1 dtype: float64 - name: eval_2 dtype: float64 - name: eval_3 dtype: float64 - name: eval_4 dtype: float64 - name: eval_5 dtype: float64 - name: eval_6 dtype: float64 - name: eval_7 dtype: float64 - name: eval_8 dtype: float64 - name: eval_9 dtype: float64 - name: eval_10 dtype: float64 - name: eval_11 dtype: float64 - name: eval_12 dtype: float64 - name: eval_13 dtype: float64 - name: eval_14 dtype: float64 - name: eval_15 dtype: float64 - name: eval_16 dtype: float64 - name: eval_17 dtype: float64 - name: eval_18 dtype: float64 - name: eval_19 dtype: float64 - name: eval_20 dtype: float64 - name: eval_21 dtype: float64 - name: eval_22 dtype: float64 - name: eval_23 dtype: float64 - name: eval_24 dtype: float64 - name: eval_25 dtype: float64 - name: eval_26 dtype: float64 - name: eval_27 dtype: float64 - name: eval_28 dtype: float64 - name: eval_29 dtype: float64 - name: eval_30 dtype: float64 - name: eval_31 dtype: float64 splits: - name: train num_bytes: 5449227 num_examples: 30 download_size: 1476657 dataset_size: 5449227 configs: - config_name: default data_files: - split: train path: data/train-* ---

harpreetmann/go_emotions_max_500_string_chat

harpreetmann

2025-05-04T16:14:55Z

0

[ "region:us" ]

[]

2025-05-04T16:14:49Z

null

--- dataset_info: features: - name: input dtype: string - name: output dtype: string - name: id dtype: string - name: messages list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 27852466 num_examples: 43409 - name: test num_bytes: 3488513 num_examples: 5427 - name: validation num_bytes: 3487936 num_examples: 5426 - name: discarded num_bytes: 3483 num_examples: 1 download_size: 8204171 dataset_size: 34832398 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: validation path: data/validation-* - split: discarded path: data/discarded-* ---

mteb/told-br

mteb

2025-05-04T16:12:17Z

527

0

[ "task_categories:text-classification", "task_ids:multi-label-classification", "task_ids:sentiment-analysis", "task_ids:sentiment-scoring", "task_ids:sentiment-classification", "task_ids:hate-speech-detection", "annotations_creators:expert-annotated", "multilinguality:monolingual", "language:por", "license:cc-by-sa-4.0", "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2010.04543", "arxiv:2502.13595", "arxiv:2210.07316", "region:us", "mteb", "text" ]

[ "text-classification" ]

2024-10-29T07:49:35Z

null

--- annotations_creators: - expert-annotated language: - por license: cc-by-sa-4.0 multilinguality: monolingual task_categories: - text-classification task_ids: - multi-label-classification - sentiment-analysis - sentiment-scoring - sentiment-classification - hate-speech-detection dataset_info: features: - name: text dtype: string - name: homophobia dtype: class_label: names: '0': zero_votes '1': one_vote '2': two_votes '3': three_votes - name: obscene dtype: class_label: names: '0': zero_votes '1': one_vote '2': two_votes '3': three_votes - name: insult dtype: class_label: names: '0': zero_votes '1': one_vote '2': two_votes '3': three_votes - name: racism dtype: class_label: names: '0': zero_votes '1': one_vote '2': two_votes '3': three_votes - name: misogyny dtype: class_label: names: '0': zero_votes '1': one_vote '2': two_votes '3': three_votes - name: xenophobia dtype: class_label: names: '0': zero_votes '1': one_vote '2': two_votes '3': three_votes splits: - name: train num_bytes: 2977994 num_examples: 21000 download_size: 1464831 dataset_size: 2977994 configs: - config_name: default data_files: - split: train path: data/train-* tags: - mteb - text ---  <div align="center" style="padding: 40px 20px; background-color: white; border-radius: 12px; box-shadow: 0 2px 10px rgba(0, 0, 0, 0.05); max-width: 600px; margin: 0 auto;"> <h1 style="font-size: 3.5rem; color: #1a1a1a; margin: 0 0 20px 0; letter-spacing: 2px; font-weight: 700;">BrazilianToxicTweetsClassification</h1> <div style="font-size: 1.5rem; color: #4a4a4a; margin-bottom: 5px; font-weight: 300;">An <a href="https://github.com/embeddings-benchmark/mteb" style="color: #2c5282; font-weight: 600; text-decoration: none;" onmouseover="this.style.textDecoration='underline'" onmouseout="this.style.textDecoration='none'">MTEB</a> dataset</div> <div style="font-size: 0.9rem; color: #2c5282; margin-top: 10px;">Massive Text Embedding Benchmark</div> </div> ToLD-Br is the biggest dataset for toxic tweets in Brazilian Portuguese, crowdsourced by 42 annotators selected from a pool of 129 volunteers. Annotators were selected aiming to create a plural group in terms of demographics (ethnicity, sexual orientation, age, gender). Each tweet was labeled by three annotators in 6 possible categories: LGBTQ+phobia, Xenophobia, Obscene, Insult, Misogyny and Racism. | | | |---------------|---------------------------------------------| | Task category | t2c | | Domains | Constructed, Written | | Reference | https://paperswithcode.com/dataset/told-br | ## How to evaluate on this task You can evaluate an embedding model on this dataset using the following code: ```python import mteb task = mteb.get_tasks(["BrazilianToxicTweetsClassification"]) evaluator = mteb.MTEB(task) model = mteb.get_model(YOUR_MODEL) evaluator.run(model) ```  To learn more about how to run models on `mteb` task check out the [GitHub repitory](https://github.com/embeddings-benchmark/mteb). ## Citation If you use this dataset, please cite the dataset as well as [mteb](https://github.com/embeddings-benchmark/mteb), as this dataset likely includes additional processing as a part of the [MMTEB Contribution](https://github.com/embeddings-benchmark/mteb/tree/main/docs/mmteb). ```bibtex @article{DBLP:journals/corr/abs-2010-04543, author = {Joao Augusto Leite and Diego F. Silva and Kalina Bontcheva and Carolina Scarton}, eprint = {2010.04543}, eprinttype = {arXiv}, journal = {CoRR}, timestamp = {Tue, 15 Dec 2020 16:10:16 +0100}, title = {Toxic Language Detection in Social Media for Brazilian Portuguese: New Dataset and Multilingual Analysis}, url = {https://arxiv.org/abs/2010.04543}, volume = {abs/2010.04543}, year = {2020}, } @article{enevoldsen2025mmtebmassivemultilingualtext, title={MMTEB: Massive Multilingual Text Embedding Benchmark}, author={Kenneth Enevoldsen and Isaac Chung and Imene Kerboua and Márton Kardos and Ashwin Mathur and David Stap and Jay Gala and Wissam Siblini and Dominik Krzemiński and Genta Indra Winata and Saba Sturua and Saiteja Utpala and Mathieu Ciancone and Marion Schaeffer and Gabriel Sequeira and Diganta Misra and Shreeya Dhakal and Jonathan Rystrøm and Roman Solomatin and Ömer Çağatan and Akash Kundu and Martin Bernstorff and Shitao Xiao and Akshita Sukhlecha and Bhavish Pahwa and Rafał Poświata and Kranthi Kiran GV and Shawon Ashraf and Daniel Auras and Björn Plüster and Jan Philipp Harries and Loïc Magne and Isabelle Mohr and Mariya Hendriksen and Dawei Zhu and Hippolyte Gisserot-Boukhlef and Tom Aarsen and Jan Kostkan and Konrad Wojtasik and Taemin Lee and Marek Šuppa and Crystina Zhang and Roberta Rocca and Mohammed Hamdy and Andrianos Michail and John Yang and Manuel Faysse and Aleksei Vatolin and Nandan Thakur and Manan Dey and Dipam Vasani and Pranjal Chitale and Simone Tedeschi and Nguyen Tai and Artem Snegirev and Michael Günther and Mengzhou Xia and Weijia Shi and Xing Han Lù and Jordan Clive and Gayatri Krishnakumar and Anna Maksimova and Silvan Wehrli and Maria Tikhonova and Henil Panchal and Aleksandr Abramov and Malte Ostendorff and Zheng Liu and Simon Clematide and Lester James Miranda and Alena Fenogenova and Guangyu Song and Ruqiya Bin Safi and Wen-Ding Li and Alessia Borghini and Federico Cassano and Hongjin Su and Jimmy Lin and Howard Yen and Lasse Hansen and Sara Hooker and Chenghao Xiao and Vaibhav Adlakha and Orion Weller and Siva Reddy and Niklas Muennighoff}, publisher = {arXiv}, journal={arXiv preprint arXiv:2502.13595}, year={2025}, url={https://arxiv.org/abs/2502.13595}, doi = {10.48550/arXiv.2502.13595}, } @article{muennighoff2022mteb, author = {Muennighoff, Niklas and Tazi, Nouamane and Magne, Lo{\"\i}c and Reimers, Nils}, title = {MTEB: Massive Text Embedding Benchmark}, publisher = {arXiv}, journal={arXiv preprint arXiv:2210.07316}, year = {2022} url = {https://arxiv.org/abs/2210.07316}, doi = {10.48550/ARXIV.2210.07316}, } ``` # Dataset Statistics <details> <summary> Dataset Statistics</summary> The following code contains the descriptive statistics from the task. These can also be obtained using: ```python import mteb task = mteb.get_task("BrazilianToxicTweetsClassification") desc_stats = task.metadata.descriptive_stats ``` ```json { "test": { "num_samples": 2048, "number_of_characters": 172708, "number_texts_intersect_with_train": 23, "min_text_length": 5, "average_text_length": 84.330078125, "max_text_length": 304, "unique_texts": 2046, "min_labels_per_text": 0, "average_label_per_text": 0.57958984375, "max_labels_per_text": 4, "unique_labels": 7, "labels": { "obscene": { "count": 653 }, "insult": { "count": 430 }, "misogyny": { "count": 46 }, "racism": { "count": 13 }, "xenophobia": { "count": 13 }, "homophobia": { "count": 32 }, "None": { "count": 1145 } } }, "train": { "num_samples": 8192, "number_of_characters": 714281, "number_texts_intersect_with_train": null, "min_text_length": 4, "average_text_length": 87.1925048828125, "max_text_length": 322, "unique_texts": 8172, "min_labels_per_text": 0, "average_label_per_text": 0.5751953125, "max_labels_per_text": 4, "unique_labels": 7, "labels": { "None": { "count": 4580 }, "obscene": { "count": 2576 }, "insult": { "count": 1700 }, "homophobia": { "count": 139 }, "misogyny": { "count": 179 }, "racism": { "count": 54 }, "xenophobia": { "count": 64 } } } } ``` </details> --- *This dataset card was automatically generated using [MTEB](https://github.com/embeddings-benchmark/mteb)*

mteb/MMarcoRetrieval

mteb

2025-05-04T16:11:36Z

26

0

[ "task_categories:text-retrieval", "multilinguality:monolingual", "language:cmn", "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2309.07597", "arxiv:2502.13595", "arxiv:2210.07316", "region:us", "mteb", "text" ]

[ "text-retrieval" ]

2024-11-28T10:47:15Z

null

--- language: - cmn multilinguality: monolingual task_categories: - text-retrieval task_ids: [] dataset_info: - config_name: corpus features: - name: _id dtype: string - name: text dtype: string - name: title dtype: string splits: - name: dev num_bytes: 32979607 num_examples: 106813 download_size: 20219994 dataset_size: 32979607 - config_name: default features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: dev num_bytes: 217670 num_examples: 7437 download_size: 114126 dataset_size: 217670 - config_name: queries features: - name: _id dtype: string - name: text dtype: string splits: - name: dev num_bytes: 303316 num_examples: 6980 download_size: 219572 dataset_size: 303316 configs: - config_name: corpus data_files: - split: dev path: corpus/dev-* - config_name: default data_files: - split: dev path: data/dev-* - config_name: queries data_files: - split: dev path: queries/dev-* tags: - mteb - text ---  <div align="center" style="padding: 40px 20px; background-color: white; border-radius: 12px; box-shadow: 0 2px 10px rgba(0, 0, 0, 0.05); max-width: 600px; margin: 0 auto;"> <h1 style="font-size: 3.5rem; color: #1a1a1a; margin: 0 0 20px 0; letter-spacing: 2px; font-weight: 700;">MMarcoRetrieval</h1> <div style="font-size: 1.5rem; color: #4a4a4a; margin-bottom: 5px; font-weight: 300;">An <a href="https://github.com/embeddings-benchmark/mteb" style="color: #2c5282; font-weight: 600; text-decoration: none;" onmouseover="this.style.textDecoration='underline'" onmouseout="this.style.textDecoration='none'">MTEB</a> dataset</div> <div style="font-size: 0.9rem; color: #2c5282; margin-top: 10px;">Massive Text Embedding Benchmark</div> </div> MMarcoRetrieval | | | |---------------|---------------------------------------------| | Task category | t2t | | Domains | None | | Reference | https://arxiv.org/abs/2309.07597 | ## How to evaluate on this task You can evaluate an embedding model on this dataset using the following code: ```python import mteb task = mteb.get_tasks(["MMarcoRetrieval"]) evaluator = mteb.MTEB(task) model = mteb.get_model(YOUR_MODEL) evaluator.run(model) ```  To learn more about how to run models on `mteb` task check out the [GitHub repitory](https://github.com/embeddings-benchmark/mteb). ## Citation If you use this dataset, please cite the dataset as well as [mteb](https://github.com/embeddings-benchmark/mteb), as this dataset likely includes additional processing as a part of the [MMTEB Contribution](https://github.com/embeddings-benchmark/mteb/tree/main/docs/mmteb). ```bibtex @misc{xiao2024cpack, archiveprefix = {arXiv}, author = {Shitao Xiao and Zheng Liu and Peitian Zhang and Niklas Muennighoff and Defu Lian and Jian-Yun Nie}, eprint = {2309.07597}, primaryclass = {cs.CL}, title = {C-Pack: Packaged Resources To Advance General Chinese Embedding}, year = {2024}, } @article{enevoldsen2025mmtebmassivemultilingualtext, title={MMTEB: Massive Multilingual Text Embedding Benchmark}, author={Kenneth Enevoldsen and Isaac Chung and Imene Kerboua and Márton Kardos and Ashwin Mathur and David Stap and Jay Gala and Wissam Siblini and Dominik Krzemiński and Genta Indra Winata and Saba Sturua and Saiteja Utpala and Mathieu Ciancone and Marion Schaeffer and Gabriel Sequeira and Diganta Misra and Shreeya Dhakal and Jonathan Rystrøm and Roman Solomatin and Ömer Çağatan and Akash Kundu and Martin Bernstorff and Shitao Xiao and Akshita Sukhlecha and Bhavish Pahwa and Rafał Poświata and Kranthi Kiran GV and Shawon Ashraf and Daniel Auras and Björn Plüster and Jan Philipp Harries and Loïc Magne and Isabelle Mohr and Mariya Hendriksen and Dawei Zhu and Hippolyte Gisserot-Boukhlef and Tom Aarsen and Jan Kostkan and Konrad Wojtasik and Taemin Lee and Marek Šuppa and Crystina Zhang and Roberta Rocca and Mohammed Hamdy and Andrianos Michail and John Yang and Manuel Faysse and Aleksei Vatolin and Nandan Thakur and Manan Dey and Dipam Vasani and Pranjal Chitale and Simone Tedeschi and Nguyen Tai and Artem Snegirev and Michael Günther and Mengzhou Xia and Weijia Shi and Xing Han Lù and Jordan Clive and Gayatri Krishnakumar and Anna Maksimova and Silvan Wehrli and Maria Tikhonova and Henil Panchal and Aleksandr Abramov and Malte Ostendorff and Zheng Liu and Simon Clematide and Lester James Miranda and Alena Fenogenova and Guangyu Song and Ruqiya Bin Safi and Wen-Ding Li and Alessia Borghini and Federico Cassano and Hongjin Su and Jimmy Lin and Howard Yen and Lasse Hansen and Sara Hooker and Chenghao Xiao and Vaibhav Adlakha and Orion Weller and Siva Reddy and Niklas Muennighoff}, publisher = {arXiv}, journal={arXiv preprint arXiv:2502.13595}, year={2025}, url={https://arxiv.org/abs/2502.13595}, doi = {10.48550/arXiv.2502.13595}, } @article{muennighoff2022mteb, author = {Muennighoff, Niklas and Tazi, Nouamane and Magne, Lo{\"\i}c and Reimers, Nils}, title = {MTEB: Massive Text Embedding Benchmark}, publisher = {arXiv}, journal={arXiv preprint arXiv:2210.07316}, year = {2022} url = {https://arxiv.org/abs/2210.07316}, doi = {10.48550/ARXIV.2210.07316}, } ``` # Dataset Statistics <details> <summary> Dataset Statistics</summary> The following code contains the descriptive statistics from the task. These can also be obtained using: ```python import mteb task = mteb.get_task("MMarcoRetrieval") desc_stats = task.metadata.descriptive_stats ``` ```json { "dev": { "num_samples": 113793, "number_of_characters": 12294685, "num_documents": 106813, "min_document_length": 13, "average_document_length": 114.41787048392986, "max_document_length": 1709, "unique_documents": 106813, "num_queries": 6980, "min_query_length": 2, "average_query_length": 10.51131805157593, "max_query_length": 61, "unique_queries": 6980, "none_queries": 0, "num_relevant_docs": 7437, "min_relevant_docs_per_query": 1, "average_relevant_docs_per_query": 1.0654727793696275, "max_relevant_docs_per_query": 4, "unique_relevant_docs": 7433, "num_instructions": null, "min_instruction_length": null, "average_instruction_length": null, "max_instruction_length": null, "unique_instructions": null, "num_top_ranked": null, "min_top_ranked_per_query": null, "average_top_ranked_per_query": null, "max_top_ranked_per_query": null } } ``` </details> --- *This dataset card was automatically generated using [MTEB](https://github.com/embeddings-benchmark/mteb)*

mteb/NQ_PL_test_top_250_only_w_correct-v2

mteb

2025-05-04T16:11:21Z

22

0

[ "task_categories:text-retrieval", "multilinguality:translated", "source_datasets:mteb/nq", "language:pol", "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2305.19840", "arxiv:2502.13595", "arxiv:2210.07316", "region:us", "mteb", "text" ]

[ "text-retrieval" ]

2024-09-28T05:31:56Z

null

--- language: - pol multilinguality: translated source_datasets: - mteb/nq task_categories: - text-retrieval task_ids: [] dataset_info: - config_name: corpus features: - name: _id dtype: string - name: text dtype: string - name: title dtype: string splits: - name: test num_bytes: 101910982.7251677 num_examples: 184765 download_size: 80088058 dataset_size: 101910982.7251677 - config_name: default features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 38495.78647940967 num_examples: 1213 download_size: 16497 dataset_size: 38495.78647940967 - config_name: queries features: - name: _id dtype: string - name: text dtype: string splits: - name: test num_bytes: 66693.80069524913 num_examples: 1000 download_size: 46127 dataset_size: 66693.80069524913 configs: - config_name: corpus data_files: - split: test path: corpus/test-* - config_name: default data_files: - split: test path: data/test-* - config_name: queries data_files: - split: test path: queries/test-* tags: - mteb - text ---  <div align="center" style="padding: 40px 20px; background-color: white; border-radius: 12px; box-shadow: 0 2px 10px rgba(0, 0, 0, 0.05); max-width: 600px; margin: 0 auto;"> <h1 style="font-size: 3.5rem; color: #1a1a1a; margin: 0 0 20px 0; letter-spacing: 2px; font-weight: 700;">NQ-PLHardNegatives</h1> <div style="font-size: 1.5rem; color: #4a4a4a; margin-bottom: 5px; font-weight: 300;">An <a href="https://github.com/embeddings-benchmark/mteb" style="color: #2c5282; font-weight: 600; text-decoration: none;" onmouseover="this.style.textDecoration='underline'" onmouseout="this.style.textDecoration='none'">MTEB</a> dataset</div> <div style="font-size: 0.9rem; color: #2c5282; margin-top: 10px;">Massive Text Embedding Benchmark</div> </div> Natural Questions: A Benchmark for Question Answering Research. The hard negative version has been created by pooling the 250 top documents per query from BM25, e5-multilingual-large and e5-mistral-instruct. | | | |---------------|---------------------------------------------| | Task category | t2t | | Domains | None | | Reference | https://ai.google.com/research/NaturalQuestions/ | ## How to evaluate on this task You can evaluate an embedding model on this dataset using the following code: ```python import mteb task = mteb.get_tasks(["NQ-PLHardNegatives"]) evaluator = mteb.MTEB(task) model = mteb.get_model(YOUR_MODEL) evaluator.run(model) ```  To learn more about how to run models on `mteb` task check out the [GitHub repitory](https://github.com/embeddings-benchmark/mteb). ## Citation If you use this dataset, please cite the dataset as well as [mteb](https://github.com/embeddings-benchmark/mteb), as this dataset likely includes additional processing as a part of the [MMTEB Contribution](https://github.com/embeddings-benchmark/mteb/tree/main/docs/mmteb). ```bibtex @misc{wojtasik2024beirpl, archiveprefix = {arXiv}, author = {Konrad Wojtasik and Vadim Shishkin and Kacper Wołowiec and Arkadiusz Janz and Maciej Piasecki}, eprint = {2305.19840}, primaryclass = {cs.IR}, title = {BEIR-PL: Zero Shot Information Retrieval Benchmark for the Polish Language}, year = {2024}, } @article{enevoldsen2025mmtebmassivemultilingualtext, title={MMTEB: Massive Multilingual Text Embedding Benchmark}, author={Kenneth Enevoldsen and Isaac Chung and Imene Kerboua and Márton Kardos and Ashwin Mathur and David Stap and Jay Gala and Wissam Siblini and Dominik Krzemiński and Genta Indra Winata and Saba Sturua and Saiteja Utpala and Mathieu Ciancone and Marion Schaeffer and Gabriel Sequeira and Diganta Misra and Shreeya Dhakal and Jonathan Rystrøm and Roman Solomatin and Ömer Çağatan and Akash Kundu and Martin Bernstorff and Shitao Xiao and Akshita Sukhlecha and Bhavish Pahwa and Rafał Poświata and Kranthi Kiran GV and Shawon Ashraf and Daniel Auras and Björn Plüster and Jan Philipp Harries and Loïc Magne and Isabelle Mohr and Mariya Hendriksen and Dawei Zhu and Hippolyte Gisserot-Boukhlef and Tom Aarsen and Jan Kostkan and Konrad Wojtasik and Taemin Lee and Marek Šuppa and Crystina Zhang and Roberta Rocca and Mohammed Hamdy and Andrianos Michail and John Yang and Manuel Faysse and Aleksei Vatolin and Nandan Thakur and Manan Dey and Dipam Vasani and Pranjal Chitale and Simone Tedeschi and Nguyen Tai and Artem Snegirev and Michael Günther and Mengzhou Xia and Weijia Shi and Xing Han Lù and Jordan Clive and Gayatri Krishnakumar and Anna Maksimova and Silvan Wehrli and Maria Tikhonova and Henil Panchal and Aleksandr Abramov and Malte Ostendorff and Zheng Liu and Simon Clematide and Lester James Miranda and Alena Fenogenova and Guangyu Song and Ruqiya Bin Safi and Wen-Ding Li and Alessia Borghini and Federico Cassano and Hongjin Su and Jimmy Lin and Howard Yen and Lasse Hansen and Sara Hooker and Chenghao Xiao and Vaibhav Adlakha and Orion Weller and Siva Reddy and Niklas Muennighoff}, publisher = {arXiv}, journal={arXiv preprint arXiv:2502.13595}, year={2025}, url={https://arxiv.org/abs/2502.13595}, doi = {10.48550/arXiv.2502.13595}, } @article{muennighoff2022mteb, author = {Muennighoff, Niklas and Tazi, Nouamane and Magne, Lo{\"\i}c and Reimers, Nils}, title = {MTEB: Massive Text Embedding Benchmark}, publisher = {arXiv}, journal={arXiv preprint arXiv:2210.07316}, year = {2022} url = {https://arxiv.org/abs/2210.07316}, doi = {10.48550/ARXIV.2210.07316}, } ``` # Dataset Statistics <details> <summary> Dataset Statistics</summary> The following code contains the descriptive statistics from the task. These can also be obtained using: ```python import mteb task = mteb.get_task("NQ-PLHardNegatives") desc_stats = task.metadata.descriptive_stats ``` ```json { "test": { "num_samples": 185765, "number_of_characters": 113077430, "num_documents": 184765, "min_document_length": 5, "average_document_length": 611.7449138094336, "max_document_length": 14247, "unique_documents": 184765, "num_queries": 1000, "min_query_length": 18, "average_query_length": 48.381, "max_query_length": 106, "unique_queries": 1000, "none_queries": 0, "num_relevant_docs": 1213, "min_relevant_docs_per_query": 1, "average_relevant_docs_per_query": 1.213, "max_relevant_docs_per_query": 4, "unique_relevant_docs": 1213, "num_instructions": null, "min_instruction_length": null, "average_instruction_length": null, "max_instruction_length": null, "unique_instructions": null, "num_top_ranked": null, "min_top_ranked_per_query": null, "average_top_ranked_per_query": null, "max_top_ranked_per_query": null } } ``` </details> --- *This dataset card was automatically generated using [MTEB](https://github.com/embeddings-benchmark/mteb)*

mteb/NFCorpus-PL

mteb

2025-05-04T16:11:19Z

15

0

[ "task_categories:text-retrieval", "multilinguality:monolingual", "source_datasets:mteb/nfcorpus", "language:pol", "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2305.19840", "arxiv:2502.13595", "arxiv:2210.07316", "region:us", "mteb", "text" ]

[ "text-retrieval" ]

2025-02-05T18:35:26Z

null

--- language: - pol multilinguality: monolingual source_datasets: - mteb/nfcorpus task_categories: - text-retrieval task_ids: [] dataset_info: - config_name: corpus features: - name: _id dtype: string - name: text dtype: string - name: title dtype: string splits: - name: test num_bytes: 6391874 num_examples: 3633 download_size: 3631211 dataset_size: 6391874 - config_name: default features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 415220 num_examples: 12334 download_size: 79948 dataset_size: 415220 - config_name: queries features: - name: _id dtype: string - name: text dtype: string splits: - name: test num_bytes: 13938 num_examples: 323 download_size: 10996 dataset_size: 13938 configs: - config_name: corpus data_files: - split: test path: corpus/test-* - config_name: default data_files: - split: test path: data/test-* - config_name: queries data_files: - split: test path: queries/test-* tags: - mteb - text ---  <div align="center" style="padding: 40px 20px; background-color: white; border-radius: 12px; box-shadow: 0 2px 10px rgba(0, 0, 0, 0.05); max-width: 600px; margin: 0 auto;"> <h1 style="font-size: 3.5rem; color: #1a1a1a; margin: 0 0 20px 0; letter-spacing: 2px; font-weight: 700;">NFCorpus-PL</h1> <div style="font-size: 1.5rem; color: #4a4a4a; margin-bottom: 5px; font-weight: 300;">An <a href="https://github.com/embeddings-benchmark/mteb" style="color: #2c5282; font-weight: 600; text-decoration: none;" onmouseover="this.style.textDecoration='underline'" onmouseout="this.style.textDecoration='none'">MTEB</a> dataset</div> <div style="font-size: 0.9rem; color: #2c5282; margin-top: 10px;">Massive Text Embedding Benchmark</div> </div> NFCorpus: A Full-Text Learning to Rank Dataset for Medical Information Retrieval | | | |---------------|---------------------------------------------| | Task category | t2t | | Domains | None | | Reference | https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/ | ## How to evaluate on this task You can evaluate an embedding model on this dataset using the following code: ```python import mteb task = mteb.get_tasks(["NFCorpus-PL"]) evaluator = mteb.MTEB(task) model = mteb.get_model(YOUR_MODEL) evaluator.run(model) ```  To learn more about how to run models on `mteb` task check out the [GitHub repitory](https://github.com/embeddings-benchmark/mteb). ## Citation If you use this dataset, please cite the dataset as well as [mteb](https://github.com/embeddings-benchmark/mteb), as this dataset likely includes additional processing as a part of the [MMTEB Contribution](https://github.com/embeddings-benchmark/mteb/tree/main/docs/mmteb). ```bibtex @misc{wojtasik2024beirpl, archiveprefix = {arXiv}, author = {Konrad Wojtasik and Vadim Shishkin and Kacper Wołowiec and Arkadiusz Janz and Maciej Piasecki}, eprint = {2305.19840}, primaryclass = {cs.IR}, title = {BEIR-PL: Zero Shot Information Retrieval Benchmark for the Polish Language}, year = {2024}, } @article{enevoldsen2025mmtebmassivemultilingualtext, title={MMTEB: Massive Multilingual Text Embedding Benchmark}, author={Kenneth Enevoldsen and Isaac Chung and Imene Kerboua and Márton Kardos and Ashwin Mathur and David Stap and Jay Gala and Wissam Siblini and Dominik Krzemiński and Genta Indra Winata and Saba Sturua and Saiteja Utpala and Mathieu Ciancone and Marion Schaeffer and Gabriel Sequeira and Diganta Misra and Shreeya Dhakal and Jonathan Rystrøm and Roman Solomatin and Ömer Çağatan and Akash Kundu and Martin Bernstorff and Shitao Xiao and Akshita Sukhlecha and Bhavish Pahwa and Rafał Poświata and Kranthi Kiran GV and Shawon Ashraf and Daniel Auras and Björn Plüster and Jan Philipp Harries and Loïc Magne and Isabelle Mohr and Mariya Hendriksen and Dawei Zhu and Hippolyte Gisserot-Boukhlef and Tom Aarsen and Jan Kostkan and Konrad Wojtasik and Taemin Lee and Marek Šuppa and Crystina Zhang and Roberta Rocca and Mohammed Hamdy and Andrianos Michail and John Yang and Manuel Faysse and Aleksei Vatolin and Nandan Thakur and Manan Dey and Dipam Vasani and Pranjal Chitale and Simone Tedeschi and Nguyen Tai and Artem Snegirev and Michael Günther and Mengzhou Xia and Weijia Shi and Xing Han Lù and Jordan Clive and Gayatri Krishnakumar and Anna Maksimova and Silvan Wehrli and Maria Tikhonova and Henil Panchal and Aleksandr Abramov and Malte Ostendorff and Zheng Liu and Simon Clematide and Lester James Miranda and Alena Fenogenova and Guangyu Song and Ruqiya Bin Safi and Wen-Ding Li and Alessia Borghini and Federico Cassano and Hongjin Su and Jimmy Lin and Howard Yen and Lasse Hansen and Sara Hooker and Chenghao Xiao and Vaibhav Adlakha and Orion Weller and Siva Reddy and Niklas Muennighoff}, publisher = {arXiv}, journal={arXiv preprint arXiv:2502.13595}, year={2025}, url={https://arxiv.org/abs/2502.13595}, doi = {10.48550/arXiv.2502.13595}, } @article{muennighoff2022mteb, author = {Muennighoff, Niklas and Tazi, Nouamane and Magne, Lo{\"\i}c and Reimers, Nils}, title = {MTEB: Massive Text Embedding Benchmark}, publisher = {arXiv}, journal={arXiv preprint arXiv:2210.07316}, year = {2022} url = {https://arxiv.org/abs/2210.07316}, doi = {10.48550/ARXIV.2210.07316}, } ``` # Dataset Statistics <details> <summary> Dataset Statistics</summary> The following code contains the descriptive statistics from the task. These can also be obtained using: ```python import mteb task = mteb.get_task("NFCorpus-PL") desc_stats = task.metadata.descriptive_stats ``` ```json { "test": { "num_samples": 3956, "number_of_characters": 6013927, "num_documents": 3633, "min_document_length": 110, "average_document_length": 1653.1926782273604, "max_document_length": 10705, "unique_documents": 3633, "num_queries": 323, "min_query_length": 3, "average_query_length": 24.390092879256965, "max_query_length": 96, "unique_queries": 323, "none_queries": 0, "num_relevant_docs": 12334, "min_relevant_docs_per_query": 1, "average_relevant_docs_per_query": 38.18575851393189, "max_relevant_docs_per_query": 475, "unique_relevant_docs": 3128, "num_instructions": null, "min_instruction_length": null, "average_instruction_length": null, "max_instruction_length": null, "unique_instructions": null, "num_top_ranked": null, "min_top_ranked_per_query": null, "average_top_ranked_per_query": null, "max_top_ranked_per_query": null } } ``` </details> --- *This dataset card was automatically generated using [MTEB](https://github.com/embeddings-benchmark/mteb)*

mteb/norquad_retrieval

mteb

2025-05-04T16:10:52Z

16

0

[ "task_categories:text-retrieval", "task_ids:multiple-choice-qa", "annotations_creators:derived", "multilinguality:monolingual", "language:nob", "license:cc-by-sa-4.0", "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2502.13595", "arxiv:2210.07316", "region:us", "mteb", "text" ]

[ "text-retrieval" ]

2024-04-03T11:56:24Z

null

--- annotations_creators: - derived language: - nob license: cc-by-sa-4.0 multilinguality: monolingual task_categories: - text-retrieval task_ids: - multiple-choice-qa dataset_info: features: - name: id dtype: string - name: context dtype: string - name: question dtype: string - name: answers struct: - name: answer_start sequence: int64 - name: text sequence: string splits: - name: train num_bytes: 2350752 num_examples: 1024 - name: val num_bytes: 588509 num_examples: 256 - name: test num_bytes: 4687874 num_examples: 2048 download_size: 3083620 dataset_size: 7627135 configs: - config_name: default data_files: - split: train path: data/train-* - split: val path: data/val-* - split: test path: data/test-* tags: - mteb - text ---  <div align="center" style="padding: 40px 20px; background-color: white; border-radius: 12px; box-shadow: 0 2px 10px rgba(0, 0, 0, 0.05); max-width: 600px; margin: 0 auto;"> <h1 style="font-size: 3.5rem; color: #1a1a1a; margin: 0 0 20px 0; letter-spacing: 2px; font-weight: 700;">NorQuadRetrieval</h1> <div style="font-size: 1.5rem; color: #4a4a4a; margin-bottom: 5px; font-weight: 300;">An <a href="https://github.com/embeddings-benchmark/mteb" style="color: #2c5282; font-weight: 600; text-decoration: none;" onmouseover="this.style.textDecoration='underline'" onmouseout="this.style.textDecoration='none'">MTEB</a> dataset</div> <div style="font-size: 0.9rem; color: #2c5282; margin-top: 10px;">Massive Text Embedding Benchmark</div> </div> Human-created question for Norwegian wikipedia passages. | | | |---------------|---------------------------------------------| | Task category | t2t | | Domains | Encyclopaedic, Non-fiction, Written | | Reference | https://aclanthology.org/2023.nodalida-1.17/ | ## How to evaluate on this task You can evaluate an embedding model on this dataset using the following code: ```python import mteb task = mteb.get_tasks(["NorQuadRetrieval"]) evaluator = mteb.MTEB(task) model = mteb.get_model(YOUR_MODEL) evaluator.run(model) ```  To learn more about how to run models on `mteb` task check out the [GitHub repitory](https://github.com/embeddings-benchmark/mteb). ## Citation If you use this dataset, please cite the dataset as well as [mteb](https://github.com/embeddings-benchmark/mteb), as this dataset likely includes additional processing as a part of the [MMTEB Contribution](https://github.com/embeddings-benchmark/mteb/tree/main/docs/mmteb). ```bibtex @inproceedings{ivanova-etal-2023-norquad, abstract = {In this paper we present NorQuAD: the first Norwegian question answering dataset for machine reading comprehension. The dataset consists of 4,752 manually created question-answer pairs. We here detail the data collection procedure and present statistics of the dataset. We also benchmark several multilingual and Norwegian monolingual language models on the dataset and compare them against human performance. The dataset will be made freely available.}, address = {T{\'o}rshavn, Faroe Islands}, author = {Ivanova, Sardana and Andreassen, Fredrik and Jentoft, Matias and Wold, Sondre and {\O}vrelid, Lilja}, booktitle = {Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa)}, editor = {Alum{\"a}e, Tanel and Fishel, Mark}, month = may, pages = {159--168}, publisher = {University of Tartu Library}, title = {{N}or{Q}u{AD}: {N}orwegian Question Answering Dataset}, url = {https://aclanthology.org/2023.nodalida-1.17}, year = {2023}, } @article{enevoldsen2025mmtebmassivemultilingualtext, title={MMTEB: Massive Multilingual Text Embedding Benchmark}, author={Kenneth Enevoldsen and Isaac Chung and Imene Kerboua and Márton Kardos and Ashwin Mathur and David Stap and Jay Gala and Wissam Siblini and Dominik Krzemiński and Genta Indra Winata and Saba Sturua and Saiteja Utpala and Mathieu Ciancone and Marion Schaeffer and Gabriel Sequeira and Diganta Misra and Shreeya Dhakal and Jonathan Rystrøm and Roman Solomatin and Ömer Çağatan and Akash Kundu and Martin Bernstorff and Shitao Xiao and Akshita Sukhlecha and Bhavish Pahwa and Rafał Poświata and Kranthi Kiran GV and Shawon Ashraf and Daniel Auras and Björn Plüster and Jan Philipp Harries and Loïc Magne and Isabelle Mohr and Mariya Hendriksen and Dawei Zhu and Hippolyte Gisserot-Boukhlef and Tom Aarsen and Jan Kostkan and Konrad Wojtasik and Taemin Lee and Marek Šuppa and Crystina Zhang and Roberta Rocca and Mohammed Hamdy and Andrianos Michail and John Yang and Manuel Faysse and Aleksei Vatolin and Nandan Thakur and Manan Dey and Dipam Vasani and Pranjal Chitale and Simone Tedeschi and Nguyen Tai and Artem Snegirev and Michael Günther and Mengzhou Xia and Weijia Shi and Xing Han Lù and Jordan Clive and Gayatri Krishnakumar and Anna Maksimova and Silvan Wehrli and Maria Tikhonova and Henil Panchal and Aleksandr Abramov and Malte Ostendorff and Zheng Liu and Simon Clematide and Lester James Miranda and Alena Fenogenova and Guangyu Song and Ruqiya Bin Safi and Wen-Ding Li and Alessia Borghini and Federico Cassano and Hongjin Su and Jimmy Lin and Howard Yen and Lasse Hansen and Sara Hooker and Chenghao Xiao and Vaibhav Adlakha and Orion Weller and Siva Reddy and Niklas Muennighoff}, publisher = {arXiv}, journal={arXiv preprint arXiv:2502.13595}, year={2025}, url={https://arxiv.org/abs/2502.13595}, doi = {10.48550/arXiv.2502.13595}, } @article{muennighoff2022mteb, author = {Muennighoff, Niklas and Tazi, Nouamane and Magne, Lo{\"\i}c and Reimers, Nils}, title = {MTEB: Massive Text Embedding Benchmark}, publisher = {arXiv}, journal={arXiv preprint arXiv:2210.07316}, year = {2022} url = {https://arxiv.org/abs/2210.07316}, doi = {10.48550/ARXIV.2210.07316}, } ``` # Dataset Statistics <details> <summary> Dataset Statistics</summary> The following code contains the descriptive statistics from the task. These can also be obtained using: ```python import mteb task = mteb.get_task("NorQuadRetrieval") desc_stats = task.metadata.descriptive_stats ``` ```json { "test": { "num_samples": 2072, "number_of_characters": 273854, "num_documents": 1048, "min_document_length": 1, "average_document_length": 214.5114503816794, "max_document_length": 2606, "unique_documents": 1048, "num_queries": 1024, "min_query_length": 11, "average_query_length": 47.896484375, "max_query_length": 100, "unique_queries": 1024, "none_queries": 0, "num_relevant_docs": 2048, "min_relevant_docs_per_query": 2, "average_relevant_docs_per_query": 2.0, "max_relevant_docs_per_query": 2, "unique_relevant_docs": 1328, "num_instructions": null, "min_instruction_length": null, "average_instruction_length": null, "max_instruction_length": null, "unique_instructions": null, "num_top_ranked": null, "min_top_ranked_per_query": null, "average_top_ranked_per_query": null, "max_top_ranked_per_query": null } } ``` </details> --- *This dataset card was automatically generated using [MTEB](https://github.com/embeddings-benchmark/mteb)*

mteb/mrtidy

mteb

2025-05-04T16:10:44Z

999

0

[ "task_categories:text-retrieval", "annotations_creators:human-annotated", "multilinguality:multilingual", "language:ara", "language:ben", "language:eng", "language:fin", "language:ind", "language:jpn", "language:kor", "language:rus", "language:swa", "language:tel", "language:tha", "license:cc-by-sa-3.0", "size_categories:10M<n<100M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:2108.08787", "arxiv:2502.13595", "arxiv:2210.07316", "region:us", "mteb", "text" ]

[ "text-retrieval" ]

2024-09-29T11:14:01Z

null

--- annotations_creators: - human-annotated language: - ara - ben - eng - fin - ind - jpn - kor - rus - swa - tel - tha license: cc-by-sa-3.0 multilinguality: multilingual task_categories: - text-retrieval task_ids: [] dataset_info: - config_name: arabic-corpus features: - name: _id dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 1245714994 num_examples: 2106586 download_size: 562700591 dataset_size: 1245714994 - config_name: arabic-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: train num_bytes: 10793262 num_examples: 374523 - name: dev num_bytes: 87088 num_examples: 3115 - name: test num_bytes: 36208 num_examples: 1257 download_size: 3781951 dataset_size: 10916558 - config_name: arabic-queries features: - name: _id dtype: string - name: text dtype: string splits: - name: train num_bytes: 831496 num_examples: 12377 - name: dev num_bytes: 207878 num_examples: 3115 - name: test num_bytes: 74696 num_examples: 1081 download_size: 621343 dataset_size: 1114070 - config_name: bengali-corpus features: - name: _id dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 313718344 num_examples: 304059 download_size: 113214290 dataset_size: 313718344 - config_name: bengali-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: train num_bytes: 1410063 num_examples: 51861 - name: dev num_bytes: 11764 num_examples: 443 - name: test num_bytes: 3548 num_examples: 130 download_size: 479618 dataset_size: 1425375 - config_name: bengali-queries features: - name: _id dtype: string - name: text dtype: string splits: - name: train num_bytes: 233828 num_examples: 1713 - name: dev num_bytes: 60547 num_examples: 440 - name: test num_bytes: 16464 num_examples: 111 download_size: 135025 dataset_size: 310839 - config_name: english-corpus features: - name: _id dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 13953017759 num_examples: 32907100 download_size: 8012208332 dataset_size: 13953017759 - config_name: english-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: train num_bytes: 3163945 num_examples: 108401 - name: dev num_bytes: 24984 num_examples: 878 - name: test num_bytes: 26591 num_examples: 935 download_size: 1201824 dataset_size: 3215520 - config_name: english-queries features: - name: _id dtype: string - name: text dtype: string splits: - name: train num_bytes: 180772 num_examples: 3547 - name: dev num_bytes: 45396 num_examples: 878 - name: test num_bytes: 39063 num_examples: 744 download_size: 179373 dataset_size: 265231 - config_name: finnish-corpus features: - name: _id dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 749089306 num_examples: 1908757 download_size: 446059094 dataset_size: 749089306 - config_name: finnish-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: train num_bytes: 5510842 num_examples: 197338 - name: dev num_bytes: 47540 num_examples: 1738 - name: test num_bytes: 39723 num_examples: 1451 download_size: 1870069 dataset_size: 5598105 - config_name: finnish-queries features: - name: _id dtype: string - name: text dtype: string splits: - name: train num_bytes: 336371 num_examples: 6561 - name: dev num_bytes: 89446 num_examples: 1738 - name: test num_bytes: 63723 num_examples: 1254 download_size: 335034 dataset_size: 489540 - config_name: indonesian-corpus features: - name: _id dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 545994305 num_examples: 1469399 download_size: 276886119 dataset_size: 545994305 - config_name: indonesian-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: train num_bytes: 4169295 num_examples: 148491 - name: dev num_bytes: 33405 num_examples: 1224 - name: test num_bytes: 26807 num_examples: 961 download_size: 1438860 dataset_size: 4229507 - config_name: indonesian-queries features: - name: _id dtype: string - name: text dtype: string splits: - name: train num_bytes: 241258 num_examples: 4902 - name: dev num_bytes: 60988 num_examples: 1224 - name: test num_bytes: 43451 num_examples: 829 download_size: 221700 dataset_size: 345697 - config_name: japanese-corpus features: - name: _id dtype: string - name: title dtype: string - name: text dtype: string splits: - name: dev num_bytes: 387783.4788097246 num_examples: 903 - name: test num_bytes: 377906.3802353905 num_examples: 880 - name: train num_bytes: 3006085074 num_examples: 7000027 download_size: 1692453219 dataset_size: 3006850763.859045 - config_name: japanese-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: train num_bytes: 3198715 num_examples: 112408 - name: dev num_bytes: 25703 num_examples: 928 - name: test num_bytes: 25819 num_examples: 923 download_size: 1127195 dataset_size: 3250237 - config_name: japanese-queries features: - name: _id dtype: string - name: text dtype: string splits: - name: train num_bytes: 236537 num_examples: 3697 - name: dev num_bytes: 60007 num_examples: 928 - name: test num_bytes: 47149 num_examples: 720 download_size: 210465 dataset_size: 343693 - config_name: korean-corpus features: - name: _id dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 637416518 num_examples: 1496126 download_size: 364890926 dataset_size: 637416518 - config_name: korean-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: train num_bytes: 1083048 num_examples: 39485 - name: dev num_bytes: 8121 num_examples: 307 - name: test num_bytes: 13482 num_examples: 492 download_size: 386327 dataset_size: 1104651 - config_name: korean-queries features: - name: _id dtype: string - name: text dtype: string splits: - name: train num_bytes: 86921 num_examples: 1295 - name: dev num_bytes: 20056 num_examples: 303 - name: test num_bytes: 28967 num_examples: 421 download_size: 87049 dataset_size: 135944 - config_name: russian-corpus features: - name: _id dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 5965881322 num_examples: 9597504 download_size: 2799247131 dataset_size: 5965881322 - config_name: russian-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: train num_bytes: 4701206 num_examples: 163899 - name: dev num_bytes: 38306 num_examples: 1375 - name: test num_bytes: 33048 num_examples: 1168 download_size: 1735174 dataset_size: 4772560 - config_name: russian-queries features: - name: _id dtype: string - name: text dtype: string splits: - name: train num_bytes: 502545 num_examples: 5366 - name: dev num_bytes: 128310 num_examples: 1375 - name: test num_bytes: 97183 num_examples: 995 download_size: 413272 dataset_size: 728038 - config_name: swahili-corpus features: - name: _id dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 34142833 num_examples: 136689 download_size: 17501035 dataset_size: 34142833 - config_name: swahili-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: train num_bytes: 1689502 num_examples: 63390 - name: dev num_bytes: 16381 num_examples: 623 - name: test num_bytes: 19984 num_examples: 743 download_size: 549763 dataset_size: 1725867 - config_name: swahili-queries features: - name: _id dtype: string - name: text dtype: string splits: - name: train num_bytes: 105403 num_examples: 2072 - name: dev num_bytes: 26676 num_examples: 526 - name: test num_bytes: 36137 num_examples: 670 download_size: 100853 dataset_size: 168216 - config_name: telugu-corpus features: - name: _id dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 563646332 num_examples: 548224 download_size: 156110911 dataset_size: 563646332 - config_name: telugu-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: train num_bytes: 3249925 num_examples: 117793 - name: dev num_bytes: 26450 num_examples: 983 - name: test num_bytes: 18434 num_examples: 677 download_size: 964408 dataset_size: 3294809 - config_name: telugu-queries features: - name: _id dtype: string - name: text dtype: string splits: - name: train num_bytes: 446743 num_examples: 3880 - name: dev num_bytes: 112859 num_examples: 983 - name: test num_bytes: 73365 num_examples: 646 download_size: 253930 dataset_size: 632967 - config_name: thai-corpus features: - name: _id dtype: string - name: title dtype: string - name: text dtype: string splits: - name: dev num_bytes: 574630351 num_examples: 568855 - name: test num_bytes: 574630351 num_examples: 568855 - name: train num_bytes: 574630351 num_examples: 568855 download_size: 845293048 dataset_size: 1723891053 - config_name: thai-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: train num_bytes: 2775220 num_examples: 100472 - name: dev num_bytes: 22050 num_examples: 817 - name: test num_bytes: 37576 num_examples: 1368 download_size: 934865 dataset_size: 2834846 - config_name: thai-queries features: - name: _id dtype: string - name: text dtype: string splits: - name: train num_bytes: 443382 num_examples: 3319 - name: dev num_bytes: 108804 num_examples: 807 - name: test num_bytes: 158612 num_examples: 1190 download_size: 320820 dataset_size: 710798 configs: - config_name: arabic-corpus data_files: - split: train path: arabic-corpus/train-* - config_name: arabic-qrels data_files: - split: train path: arabic-qrels/train-* - split: dev path: arabic-qrels/dev-* - split: test path: arabic-qrels/test-* - config_name: arabic-queries data_files: - split: train path: arabic-queries/train-* - split: dev path: arabic-queries/dev-* - split: test path: arabic-queries/test-* - config_name: bengali-corpus data_files: - split: train path: bengali-corpus/train-* - config_name: bengali-qrels data_files: - split: train path: bengali-qrels/train-* - split: dev path: bengali-qrels/dev-* - split: test path: bengali-qrels/test-* - config_name: bengali-queries data_files: - split: train path: bengali-queries/train-* - split: dev path: bengali-queries/dev-* - split: test path: bengali-queries/test-* - config_name: english-corpus data_files: - split: train path: english-corpus/train-* - config_name: english-qrels data_files: - split: train path: english-qrels/train-* - split: dev path: english-qrels/dev-* - split: test path: english-qrels/test-* - config_name: english-queries data_files: - split: train path: english-queries/train-* - split: dev path: english-queries/dev-* - split: test path: english-queries/test-* - config_name: finnish-corpus data_files: - split: train path: finnish-corpus/train-* - config_name: finnish-qrels data_files: - split: train path: finnish-qrels/train-* - split: dev path: finnish-qrels/dev-* - split: test path: finnish-qrels/test-* - config_name: finnish-queries data_files: - split: train path: finnish-queries/train-* - split: dev path: finnish-queries/dev-* - split: test path: finnish-queries/test-* - config_name: indonesian-corpus data_files: - split: train path: indonesian-corpus/train-* - config_name: indonesian-qrels data_files: - split: train path: indonesian-qrels/train-* - split: dev path: indonesian-qrels/dev-* - split: test path: indonesian-qrels/test-* - config_name: indonesian-queries data_files: - split: train path: indonesian-queries/train-* - split: dev path: indonesian-queries/dev-* - split: test path: indonesian-queries/test-* - config_name: japanese-corpus data_files: - split: train path: japanese-corpus/train-* - split: dev path: japanese-corpus/dev-* - split: test path: japanese-corpus/test-* - config_name: japanese-qrels data_files: - split: train path: japanese-qrels/train-* - split: dev path: japanese-qrels/dev-* - split: test path: japanese-qrels/test-* - config_name: japanese-queries data_files: - split: train path: japanese-queries/train-* - split: dev path: japanese-queries/dev-* - split: test path: japanese-queries/test-* - config_name: korean-corpus data_files: - split: train path: korean-corpus/train-* - config_name: korean-qrels data_files: - split: train path: korean-qrels/train-* - split: dev path: korean-qrels/dev-* - split: test path: korean-qrels/test-* - config_name: korean-queries data_files: - split: train path: korean-queries/train-* - split: dev path: korean-queries/dev-* - split: test path: korean-queries/test-* - config_name: russian-corpus data_files: - split: train path: russian-corpus/train-* - config_name: russian-qrels data_files: - split: train path: russian-qrels/train-* - split: dev path: russian-qrels/dev-* - split: test path: russian-qrels/test-* - config_name: russian-queries data_files: - split: train path: russian-queries/train-* - split: dev path: russian-queries/dev-* - split: test path: russian-queries/test-* - config_name: swahili-corpus data_files: - split: train path: swahili-corpus/train-* - config_name: swahili-qrels data_files: - split: train path: swahili-qrels/train-* - split: dev path: swahili-qrels/dev-* - split: test path: swahili-qrels/test-* - config_name: swahili-queries data_files: - split: train path: swahili-queries/train-* - split: dev path: swahili-queries/dev-* - split: test path: swahili-queries/test-* - config_name: telugu-corpus data_files: - split: train path: telugu-corpus/train-* - config_name: telugu-qrels data_files: - split: train path: telugu-qrels/train-* - split: dev path: telugu-qrels/dev-* - split: test path: telugu-qrels/test-* - config_name: telugu-queries data_files: - split: train path: telugu-queries/train-* - split: dev path: telugu-queries/dev-* - split: test path: telugu-queries/test-* - config_name: thai-corpus data_files: - split: train path: thai-corpus/train-* - split: dev path: thai-corpus/dev-* - split: test path: thai-corpus/test-* - config_name: thai-qrels data_files: - split: train path: thai-qrels/train-* - split: dev path: thai-qrels/dev-* - split: test path: thai-qrels/test-* - config_name: thai-queries data_files: - split: train path: thai-queries/train-* - split: dev path: thai-queries/dev-* - split: test path: thai-queries/test-* tags: - mteb - text ---  <div align="center" style="padding: 40px 20px; background-color: white; border-radius: 12px; box-shadow: 0 2px 10px rgba(0, 0, 0, 0.05); max-width: 600px; margin: 0 auto;"> <h1 style="font-size: 3.5rem; color: #1a1a1a; margin: 0 0 20px 0; letter-spacing: 2px; font-weight: 700;">MrTidyRetrieval</h1> <div style="font-size: 1.5rem; color: #4a4a4a; margin-bottom: 5px; font-weight: 300;">An <a href="https://github.com/embeddings-benchmark/mteb" style="color: #2c5282; font-weight: 600; text-decoration: none;" onmouseover="this.style.textDecoration='underline'" onmouseout="this.style.textDecoration='none'">MTEB</a> dataset</div> <div style="font-size: 0.9rem; color: #2c5282; margin-top: 10px;">Massive Text Embedding Benchmark</div> </div> Mr. TyDi is a multi-lingual benchmark dataset built on TyDi, covering eleven typologically diverse languages. It is designed for monolingual retrieval, specifically to evaluate ranking with learned dense representations. | | | |---------------|---------------------------------------------| | Task category | t2t | | Domains | Encyclopaedic, Written | | Reference | https://huggingface.co/datasets/castorini/mr-tydi | ## How to evaluate on this task You can evaluate an embedding model on this dataset using the following code: ```python import mteb task = mteb.get_tasks(["MrTidyRetrieval"]) evaluator = mteb.MTEB(task) model = mteb.get_model(YOUR_MODEL) evaluator.run(model) ```  To learn more about how to run models on `mteb` task check out the [GitHub repitory](https://github.com/embeddings-benchmark/mteb). ## Citation If you use this dataset, please cite the dataset as well as [mteb](https://github.com/embeddings-benchmark/mteb), as this dataset likely includes additional processing as a part of the [MMTEB Contribution](https://github.com/embeddings-benchmark/mteb/tree/main/docs/mmteb). ```bibtex @article{mrtydi, author = {Xinyu Zhang and Xueguang Ma and Peng Shi and Jimmy Lin}, journal = {arXiv:2108.08787}, title = {{Mr. TyDi}: A Multi-lingual Benchmark for Dense Retrieval}, year = {2021}, } @article{enevoldsen2025mmtebmassivemultilingualtext, title={MMTEB: Massive Multilingual Text Embedding Benchmark}, author={Kenneth Enevoldsen and Isaac Chung and Imene Kerboua and Márton Kardos and Ashwin Mathur and David Stap and Jay Gala and Wissam Siblini and Dominik Krzemiński and Genta Indra Winata and Saba Sturua and Saiteja Utpala and Mathieu Ciancone and Marion Schaeffer and Gabriel Sequeira and Diganta Misra and Shreeya Dhakal and Jonathan Rystrøm and Roman Solomatin and Ömer Çağatan and Akash Kundu and Martin Bernstorff and Shitao Xiao and Akshita Sukhlecha and Bhavish Pahwa and Rafał Poświata and Kranthi Kiran GV and Shawon Ashraf and Daniel Auras and Björn Plüster and Jan Philipp Harries and Loïc Magne and Isabelle Mohr and Mariya Hendriksen and Dawei Zhu and Hippolyte Gisserot-Boukhlef and Tom Aarsen and Jan Kostkan and Konrad Wojtasik and Taemin Lee and Marek Šuppa and Crystina Zhang and Roberta Rocca and Mohammed Hamdy and Andrianos Michail and John Yang and Manuel Faysse and Aleksei Vatolin and Nandan Thakur and Manan Dey and Dipam Vasani and Pranjal Chitale and Simone Tedeschi and Nguyen Tai and Artem Snegirev and Michael Günther and Mengzhou Xia and Weijia Shi and Xing Han Lù and Jordan Clive and Gayatri Krishnakumar and Anna Maksimova and Silvan Wehrli and Maria Tikhonova and Henil Panchal and Aleksandr Abramov and Malte Ostendorff and Zheng Liu and Simon Clematide and Lester James Miranda and Alena Fenogenova and Guangyu Song and Ruqiya Bin Safi and Wen-Ding Li and Alessia Borghini and Federico Cassano and Hongjin Su and Jimmy Lin and Howard Yen and Lasse Hansen and Sara Hooker and Chenghao Xiao and Vaibhav Adlakha and Orion Weller and Siva Reddy and Niklas Muennighoff}, publisher = {arXiv}, journal={arXiv preprint arXiv:2502.13595}, year={2025}, url={https://arxiv.org/abs/2502.13595}, doi = {10.48550/arXiv.2502.13595}, } @article{muennighoff2022mteb, author = {Muennighoff, Niklas and Tazi, Nouamane and Magne, Lo{\"\i}c and Reimers, Nils}, title = {MTEB: Massive Text Embedding Benchmark}, publisher = {arXiv}, journal={arXiv preprint arXiv:2210.07316}, year = {2022} url = {https://arxiv.org/abs/2210.07316}, doi = {10.48550/ARXIV.2210.07316}, } ``` # Dataset Statistics <details> <summary> Dataset Statistics</summary> The following code contains the descriptive statistics from the task. These can also be obtained using: ```python import mteb task = mteb.get_task("MrTidyRetrieval") desc_stats = task.metadata.descriptive_stats ``` ```json { "test": { "num_samples": 58051987, "number_of_characters": 20129435459, "num_documents": 58043326, "min_document_length": 2, "average_document_length": 346.79462777512094, "max_document_length": 61659, "unique_documents": 58043326, "num_queries": 8661, "min_query_length": 6, "average_query_length": 37.15783396836393, "max_query_length": 144, "unique_queries": 8661, "none_queries": 0, "num_relevant_docs": 10105, "min_relevant_docs_per_query": 1, "average_relevant_docs_per_query": 1.1667243967209329, "max_relevant_docs_per_query": 3, "unique_relevant_docs": 8926, "num_instructions": null, "min_instruction_length": null, "average_instruction_length": null, "max_instruction_length": null, "unique_instructions": null, "num_top_ranked": null, "min_top_ranked_per_query": null, "average_top_ranked_per_query": null, "max_top_ranked_per_query": null } } ``` </details> --- *This dataset card was automatically generated using [MTEB](https://github.com/embeddings-benchmark/mteb)*

mteb/fiqa

mteb

2025-05-04T16:10:12Z

1,762

2

[ "task_categories:text-retrieval", "task_ids:multiple-choice-qa", "annotations_creators:human-annotated", "multilinguality:monolingual", "language:eng", "license:unknown", "size_categories:10K<n<100K", "format:json", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2502.13595", "arxiv:2210.07316", "region:us", "mteb", "text" ]

[ "text-retrieval" ]

2024-03-02T20:35:06Z

null

--- annotations_creators: - human-annotated language: - eng license: unknown multilinguality: monolingual task_categories: - text-retrieval task_ids: - multiple-choice-qa config_names: - corpus tags: - mteb - text dataset_info: - config_name: default features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: float64 splits: - name: train num_bytes: 365642 num_examples: 14166 - name: dev num_bytes: 31919 num_examples: 1238 - name: test num_bytes: 43996 num_examples: 1706 - config_name: corpus features: - name: _id dtype: string - name: title dtype: string - name: text dtype: string splits: - name: corpus num_bytes: 45303212 num_examples: 57638 - config_name: queries features: - name: _id dtype: string - name: text dtype: string splits: - name: queries num_bytes: 491278 num_examples: 6648 configs: - config_name: default data_files: - split: train path: qrels/train.jsonl - split: dev path: qrels/dev.jsonl - split: test path: qrels/test.jsonl - config_name: corpus data_files: - split: corpus path: corpus.jsonl - config_name: queries data_files: - split: queries path: queries.jsonl ---  <div align="center" style="padding: 40px 20px; background-color: white; border-radius: 12px; box-shadow: 0 2px 10px rgba(0, 0, 0, 0.05); max-width: 600px; margin: 0 auto;"> <h1 style="font-size: 3.5rem; color: #1a1a1a; margin: 0 0 20px 0; letter-spacing: 2px; font-weight: 700;">FiQA2018</h1> <div style="font-size: 1.5rem; color: #4a4a4a; margin-bottom: 5px; font-weight: 300;">An <a href="https://github.com/embeddings-benchmark/mteb" style="color: #2c5282; font-weight: 600; text-decoration: none;" onmouseover="this.style.textDecoration='underline'" onmouseout="this.style.textDecoration='none'">MTEB</a> dataset</div> <div style="font-size: 0.9rem; color: #2c5282; margin-top: 10px;">Massive Text Embedding Benchmark</div> </div> Financial Opinion Mining and Question Answering | | | |---------------|---------------------------------------------| | Task category | t2t | | Domains | Written, Financial | | Reference | https://sites.google.com/view/fiqa/ | ## How to evaluate on this task You can evaluate an embedding model on this dataset using the following code: ```python import mteb task = mteb.get_tasks(["FiQA2018"]) evaluator = mteb.MTEB(task) model = mteb.get_model(YOUR_MODEL) evaluator.run(model) ```  To learn more about how to run models on `mteb` task check out the [GitHub repitory](https://github.com/embeddings-benchmark/mteb). ## Citation If you use this dataset, please cite the dataset as well as [mteb](https://github.com/embeddings-benchmark/mteb), as this dataset likely includes additional processing as a part of the [MMTEB Contribution](https://github.com/embeddings-benchmark/mteb/tree/main/docs/mmteb). ```bibtex @inproceedings{thakur2021beir, author = {Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych}, booktitle = {Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)}, title = {{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models}, url = {https://openreview.net/forum?id=wCu6T5xFjeJ}, year = {2021}, } @article{enevoldsen2025mmtebmassivemultilingualtext, title={MMTEB: Massive Multilingual Text Embedding Benchmark}, author={Kenneth Enevoldsen and Isaac Chung and Imene Kerboua and Márton Kardos and Ashwin Mathur and David Stap and Jay Gala and Wissam Siblini and Dominik Krzemiński and Genta Indra Winata and Saba Sturua and Saiteja Utpala and Mathieu Ciancone and Marion Schaeffer and Gabriel Sequeira and Diganta Misra and Shreeya Dhakal and Jonathan Rystrøm and Roman Solomatin and Ömer Çağatan and Akash Kundu and Martin Bernstorff and Shitao Xiao and Akshita Sukhlecha and Bhavish Pahwa and Rafał Poświata and Kranthi Kiran GV and Shawon Ashraf and Daniel Auras and Björn Plüster and Jan Philipp Harries and Loïc Magne and Isabelle Mohr and Mariya Hendriksen and Dawei Zhu and Hippolyte Gisserot-Boukhlef and Tom Aarsen and Jan Kostkan and Konrad Wojtasik and Taemin Lee and Marek Šuppa and Crystina Zhang and Roberta Rocca and Mohammed Hamdy and Andrianos Michail and John Yang and Manuel Faysse and Aleksei Vatolin and Nandan Thakur and Manan Dey and Dipam Vasani and Pranjal Chitale and Simone Tedeschi and Nguyen Tai and Artem Snegirev and Michael Günther and Mengzhou Xia and Weijia Shi and Xing Han Lù and Jordan Clive and Gayatri Krishnakumar and Anna Maksimova and Silvan Wehrli and Maria Tikhonova and Henil Panchal and Aleksandr Abramov and Malte Ostendorff and Zheng Liu and Simon Clematide and Lester James Miranda and Alena Fenogenova and Guangyu Song and Ruqiya Bin Safi and Wen-Ding Li and Alessia Borghini and Federico Cassano and Hongjin Su and Jimmy Lin and Howard Yen and Lasse Hansen and Sara Hooker and Chenghao Xiao and Vaibhav Adlakha and Orion Weller and Siva Reddy and Niklas Muennighoff}, publisher = {arXiv}, journal={arXiv preprint arXiv:2502.13595}, year={2025}, url={https://arxiv.org/abs/2502.13595}, doi = {10.48550/arXiv.2502.13595}, } @article{muennighoff2022mteb, author = {Muennighoff, Niklas and Tazi, Nouamane and Magne, Lo{\"\i}c and Reimers, Nils}, title = {MTEB: Massive Text Embedding Benchmark}, publisher = {arXiv}, journal={arXiv preprint arXiv:2210.07316}, year = {2022} url = {https://arxiv.org/abs/2210.07316}, doi = {10.48550/ARXIV.2210.07316}, } ``` # Dataset Statistics <details> <summary> Dataset Statistics</summary> The following code contains the descriptive statistics from the task. These can also be obtained using: ```python import mteb task = mteb.get_task("FiQA2018") desc_stats = task.metadata.descriptive_stats ``` ```json { "train": { "num_samples": 63138, "number_of_characters": 44616372, "num_documents": 57638, "min_document_length": 1, "average_document_length": 768.2108157812554, "max_document_length": 16991, "unique_documents": 57638, "num_queries": 5500, "min_query_length": 14, "average_query_length": 61.49763636363636, "max_query_length": 158, "unique_queries": 5500, "none_queries": 0, "num_relevant_docs": 14166, "min_relevant_docs_per_query": 1, "average_relevant_docs_per_query": 2.5756363636363635, "max_relevant_docs_per_query": 23, "unique_relevant_docs": 14166, "num_instructions": null, "min_instruction_length": null, "average_instruction_length": null, "max_instruction_length": null, "unique_instructions": null, "num_top_ranked": null, "min_top_ranked_per_query": null, "average_top_ranked_per_query": null, "max_top_ranked_per_query": null }, "dev": { "num_samples": 58138, "number_of_characters": 44309513, "num_documents": 57638, "min_document_length": 1, "average_document_length": 768.2108157812554, "max_document_length": 16991, "unique_documents": 57638, "num_queries": 500, "min_query_length": 15, "average_query_length": 62.756, "max_query_length": 166, "unique_queries": 500, "none_queries": 0, "num_relevant_docs": 1238, "min_relevant_docs_per_query": 1, "average_relevant_docs_per_query": 2.476, "max_relevant_docs_per_query": 20, "unique_relevant_docs": 1238, "num_instructions": null, "min_instruction_length": null, "average_instruction_length": null, "max_instruction_length": null, "unique_instructions": null, "num_top_ranked": null, "min_top_ranked_per_query": null, "average_top_ranked_per_query": null, "max_top_ranked_per_query": null }, "test": { "num_samples": 58286, "number_of_characters": 44318767, "num_documents": 57638, "min_document_length": 1, "average_document_length": 768.2108157812554, "max_document_length": 16991, "unique_documents": 57638, "num_queries": 648, "min_query_length": 16, "average_query_length": 62.7037037037037, "max_query_length": 147, "unique_queries": 648, "none_queries": 0, "num_relevant_docs": 1706, "min_relevant_docs_per_query": 1, "average_relevant_docs_per_query": 2.632716049382716, "max_relevant_docs_per_query": 15, "unique_relevant_docs": 1706, "num_instructions": null, "min_instruction_length": null, "average_instruction_length": null, "max_instruction_length": null, "unique_instructions": null, "num_top_ranked": null, "min_top_ranked_per_query": null, "average_top_ranked_per_query": null, "max_top_ranked_per_query": null } } ``` </details> --- *This dataset card was automatically generated using [MTEB](https://github.com/embeddings-benchmark/mteb)*

mteb/talemaader_pc

mteb

2025-05-04T16:09:22Z

2

0

[ "task_categories:text-classification", "task_ids:semantic-similarity-classification", "annotations_creators:derived", "multilinguality:monolingual", "language:dan", "license:cc-by-4.0", "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2502.13595", "arxiv:2210.07316", "region:us", "mteb", "text" ]

[ "text-classification" ]

2025-05-02T09:08:59Z

null

--- annotations_creators: - derived language: - dan license: cc-by-4.0 multilinguality: monolingual task_categories: - text-classification task_ids: - semantic-similarity-classification dataset_info: features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: int64 splits: - name: test num_bytes: 390123 num_examples: 4000 download_size: 173048 dataset_size: 390123 configs: - config_name: default data_files: - split: test path: data/test-* tags: - mteb - text ---  <div align="center" style="padding: 40px 20px; background-color: white; border-radius: 12px; box-shadow: 0 2px 10px rgba(0, 0, 0, 0.05); max-width: 600px; margin: 0 auto;"> <h1 style="font-size: 3.5rem; color: #1a1a1a; margin: 0 0 20px 0; letter-spacing: 2px; font-weight: 700;">TalemaaderPC</h1> <div style="font-size: 1.5rem; color: #4a4a4a; margin-bottom: 5px; font-weight: 300;">An <a href="https://github.com/embeddings-benchmark/mteb" style="color: #2c5282; font-weight: 600; text-decoration: none;" onmouseover="this.style.textDecoration='underline'" onmouseout="this.style.textDecoration='none'">MTEB</a> dataset</div> <div style="font-size: 0.9rem; color: #2c5282; margin-top: 10px;">Massive Text Embedding Benchmark</div> </div> The Danish Language and Literature Society has developed a dataset for evaluating language models in Danish. The dataset contains a total of 1000 Danish idioms and fixed expressions with transferred meanings based on the Danish Dictionary's collection of fixed expressions with associated definitions. For each of the 1000 idioms and fixed expressions, three false definitions have also been prepared. The dataset can be used to test the performance of language models in identifying correct definitions for Danish idioms and fixed expressions. | | | |---------------|---------------------------------------------| | Task category | t2t | | Domains | Academic, Written | | Reference | https://sprogteknologi.dk/dataset/1000-talemader-evalueringsdatasaet | ## How to evaluate on this task You can evaluate an embedding model on this dataset using the following code: ```python import mteb task = mteb.get_tasks(["TalemaaderPC"]) evaluator = mteb.MTEB(task) model = mteb.get_model(YOUR_MODEL) evaluator.run(model) ```  To learn more about how to run models on `mteb` task check out the [GitHub repitory](https://github.com/embeddings-benchmark/mteb). ## Citation If you use this dataset, please cite the dataset as well as [mteb](https://github.com/embeddings-benchmark/mteb), as this dataset likely includes additional processing as a part of the [MMTEB Contribution](https://github.com/embeddings-benchmark/mteb/tree/main/docs/mmteb). ```bibtex @misc{DSLDK1000Talemader, author = {{Det Danske Sprog- og Litteraturselskab}}, howpublished = {Sprogteknologi.dk}, language = {Danish}, note = {CC-BY licensed dataset of 1000 Danish sayings and expressions}, publisher = {Digitaliseringsstyrelsen \& Det Danske Sprog- og Litteraturselskab}, title = {1000 danske talemåder - evalueringsdatasæt}, url = {https://sprogteknologi.dk/dataset/1000-talemader-evalueringsdatasaet}, year = {2024}, } @article{enevoldsen2025mmtebmassivemultilingualtext, title={MMTEB: Massive Multilingual Text Embedding Benchmark}, author={Kenneth Enevoldsen and Isaac Chung and Imene Kerboua and Márton Kardos and Ashwin Mathur and David Stap and Jay Gala and Wissam Siblini and Dominik Krzemiński and Genta Indra Winata and Saba Sturua and Saiteja Utpala and Mathieu Ciancone and Marion Schaeffer and Gabriel Sequeira and Diganta Misra and Shreeya Dhakal and Jonathan Rystrøm and Roman Solomatin and Ömer Çağatan and Akash Kundu and Martin Bernstorff and Shitao Xiao and Akshita Sukhlecha and Bhavish Pahwa and Rafał Poświata and Kranthi Kiran GV and Shawon Ashraf and Daniel Auras and Björn Plüster and Jan Philipp Harries and Loïc Magne and Isabelle Mohr and Mariya Hendriksen and Dawei Zhu and Hippolyte Gisserot-Boukhlef and Tom Aarsen and Jan Kostkan and Konrad Wojtasik and Taemin Lee and Marek Šuppa and Crystina Zhang and Roberta Rocca and Mohammed Hamdy and Andrianos Michail and John Yang and Manuel Faysse and Aleksei Vatolin and Nandan Thakur and Manan Dey and Dipam Vasani and Pranjal Chitale and Simone Tedeschi and Nguyen Tai and Artem Snegirev and Michael Günther and Mengzhou Xia and Weijia Shi and Xing Han Lù and Jordan Clive and Gayatri Krishnakumar and Anna Maksimova and Silvan Wehrli and Maria Tikhonova and Henil Panchal and Aleksandr Abramov and Malte Ostendorff and Zheng Liu and Simon Clematide and Lester James Miranda and Alena Fenogenova and Guangyu Song and Ruqiya Bin Safi and Wen-Ding Li and Alessia Borghini and Federico Cassano and Hongjin Su and Jimmy Lin and Howard Yen and Lasse Hansen and Sara Hooker and Chenghao Xiao and Vaibhav Adlakha and Orion Weller and Siva Reddy and Niklas Muennighoff}, publisher = {arXiv}, journal={arXiv preprint arXiv:2502.13595}, year={2025}, url={https://arxiv.org/abs/2502.13595}, doi = {10.48550/arXiv.2502.13595}, } @article{muennighoff2022mteb, author = {Muennighoff, Niklas and Tazi, Nouamane and Magne, Lo{\"\i}c and Reimers, Nils}, title = {MTEB: Massive Text Embedding Benchmark}, publisher = {arXiv}, journal={arXiv preprint arXiv:2210.07316}, year = {2022} url = {https://arxiv.org/abs/2210.07316}, doi = {10.48550/ARXIV.2210.07316}, } ``` # Dataset Statistics <details> <summary> Dataset Statistics</summary> The following code contains the descriptive statistics from the task. These can also be obtained using: ```python import mteb task = mteb.get_task("TalemaaderPC") desc_stats = task.metadata.descriptive_stats ``` ```json { "test": { "num_samples": 4000, "number_of_characters": 315501, "unique_pairs": 4000, "min_sentence1_length": 7, "avg_sentence1_length": 23.801, "max_sentence1_length": 95, "unique_sentence1": 1000, "min_sentence2_length": 2, "avg_sentence2_length": 55.07425, "max_sentence2_length": 242, "unique_sentence2": 2990, "unique_labels": 2, "labels": { "1": { "count": 1000 }, "0": { "count": 3000 } } } } ``` </details> --- *This dataset card was automatically generated using [MTEB](https://github.com/embeddings-benchmark/mteb)*

mteb/UrduRomanSentimentClassification

mteb

2025-05-04T16:08:30Z

27

0

[ "task_categories:text-classification", "task_ids:sentiment-analysis", "task_ids:sentiment-scoring", "task_ids:sentiment-classification", "task_ids:hate-speech-detection", "annotations_creators:derived", "multilinguality:monolingual", "language:urd", "license:mit", "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2502.13595", "arxiv:2210.07316", "region:us", "mteb", "text" ]

[ "text-classification" ]

2024-12-21T12:09:02Z

null

--- annotations_creators: - derived language: - urd license: mit multilinguality: monolingual task_categories: - text-classification task_ids: - sentiment-analysis - sentiment-scoring - sentiment-classification - hate-speech-detection dataset_info: features: - name: text dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 166310 num_examples: 2048 download_size: 126737 dataset_size: 166310 configs: - config_name: default data_files: - split: train path: data/train-* tags: - mteb - text ---  <div align="center" style="padding: 40px 20px; background-color: white; border-radius: 12px; box-shadow: 0 2px 10px rgba(0, 0, 0, 0.05); max-width: 600px; margin: 0 auto;"> <h1 style="font-size: 3.5rem; color: #1a1a1a; margin: 0 0 20px 0; letter-spacing: 2px; font-weight: 700;">UrduRomanSentimentClassification</h1> <div style="font-size: 1.5rem; color: #4a4a4a; margin-bottom: 5px; font-weight: 300;">An <a href="https://github.com/embeddings-benchmark/mteb" style="color: #2c5282; font-weight: 600; text-decoration: none;" onmouseover="this.style.textDecoration='underline'" onmouseout="this.style.textDecoration='none'">MTEB</a> dataset</div> <div style="font-size: 0.9rem; color: #2c5282; margin-top: 10px;">Massive Text Embedding Benchmark</div> </div> The Roman Urdu dataset is a data corpus comprising of more than 20000 records tagged for sentiment (Positive, Negative, Neutral) | | | |---------------|---------------------------------------------| | Task category | t2c | | Domains | Social, Written | | Reference | https://archive.ics.uci.edu/dataset/458/roman+urdu+data+set | ## How to evaluate on this task You can evaluate an embedding model on this dataset using the following code: ```python import mteb task = mteb.get_tasks(["UrduRomanSentimentClassification"]) evaluator = mteb.MTEB(task) model = mteb.get_model(YOUR_MODEL) evaluator.run(model) ```  To learn more about how to run models on `mteb` task check out the [GitHub repitory](https://github.com/embeddings-benchmark/mteb). ## Citation If you use this dataset, please cite the dataset as well as [mteb](https://github.com/embeddings-benchmark/mteb), as this dataset likely includes additional processing as a part of the [MMTEB Contribution](https://github.com/embeddings-benchmark/mteb/tree/main/docs/mmteb). ```bibtex @misc{misc_roman_urdu_data_set_458, author = {Sharf,Zareen}, howpublished = {UCI Machine Learning Repository}, note = {{DOI}: https://doi.org/10.24432/C58325}, title = {{Roman Urdu Data Set}}, year = {2018}, } @article{enevoldsen2025mmtebmassivemultilingualtext, title={MMTEB: Massive Multilingual Text Embedding Benchmark}, author={Kenneth Enevoldsen and Isaac Chung and Imene Kerboua and Márton Kardos and Ashwin Mathur and David Stap and Jay Gala and Wissam Siblini and Dominik Krzemiński and Genta Indra Winata and Saba Sturua and Saiteja Utpala and Mathieu Ciancone and Marion Schaeffer and Gabriel Sequeira and Diganta Misra and Shreeya Dhakal and Jonathan Rystrøm and Roman Solomatin and Ömer Çağatan and Akash Kundu and Martin Bernstorff and Shitao Xiao and Akshita Sukhlecha and Bhavish Pahwa and Rafał Poświata and Kranthi Kiran GV and Shawon Ashraf and Daniel Auras and Björn Plüster and Jan Philipp Harries and Loïc Magne and Isabelle Mohr and Mariya Hendriksen and Dawei Zhu and Hippolyte Gisserot-Boukhlef and Tom Aarsen and Jan Kostkan and Konrad Wojtasik and Taemin Lee and Marek Šuppa and Crystina Zhang and Roberta Rocca and Mohammed Hamdy and Andrianos Michail and John Yang and Manuel Faysse and Aleksei Vatolin and Nandan Thakur and Manan Dey and Dipam Vasani and Pranjal Chitale and Simone Tedeschi and Nguyen Tai and Artem Snegirev and Michael Günther and Mengzhou Xia and Weijia Shi and Xing Han Lù and Jordan Clive and Gayatri Krishnakumar and Anna Maksimova and Silvan Wehrli and Maria Tikhonova and Henil Panchal and Aleksandr Abramov and Malte Ostendorff and Zheng Liu and Simon Clematide and Lester James Miranda and Alena Fenogenova and Guangyu Song and Ruqiya Bin Safi and Wen-Ding Li and Alessia Borghini and Federico Cassano and Hongjin Su and Jimmy Lin and Howard Yen and Lasse Hansen and Sara Hooker and Chenghao Xiao and Vaibhav Adlakha and Orion Weller and Siva Reddy and Niklas Muennighoff}, publisher = {arXiv}, journal={arXiv preprint arXiv:2502.13595}, year={2025}, url={https://arxiv.org/abs/2502.13595}, doi = {10.48550/arXiv.2502.13595}, } @article{muennighoff2022mteb, author = {Muennighoff, Niklas and Tazi, Nouamane and Magne, Lo{\"\i}c and Reimers, Nils}, title = {MTEB: Massive Text Embedding Benchmark}, publisher = {arXiv}, journal={arXiv preprint arXiv:2210.07316}, year = {2022} url = {https://arxiv.org/abs/2210.07316}, doi = {10.48550/ARXIV.2210.07316}, } ``` # Dataset Statistics <details> <summary> Dataset Statistics</summary> The following code contains the descriptive statistics from the task. These can also be obtained using: ```python import mteb task = mteb.get_task("UrduRomanSentimentClassification") desc_stats = task.metadata.descriptive_stats ``` ```json { "train": { "num_samples": 2048, "number_of_characters": 140832, "number_texts_intersect_with_train": null, "min_text_length": 1, "average_text_length": 68.765625, "max_text_length": 936, "unique_text": 2038, "unique_labels": 3, "labels": { "2": { "count": 904 }, "1": { "count": 535 }, "0": { "count": 609 } } } } ``` </details> --- *This dataset card was automatically generated using [MTEB](https://github.com/embeddings-benchmark/mteb)*

liyitenga/lekiwi2_giveme5_1

liyitenga

2025-05-04T15:25:18Z

0

[ "task_categories:robotics", "license:apache-2.0", "region:us", "LeRobot", "lekiwi", "lekiwi_action" ]

[ "robotics" ]

2025-05-04T15:23:52Z

null

--- license: apache-2.0 task_categories: - robotics tags: - LeRobot - lekiwi - lekiwi_action configs: - config_name: default data_files: data/*/*.parquet --- This dataset was created using [LeRobot](https://github.com/huggingface/lerobot). ## Dataset Description - **Homepage:** [More Information Needed] - **Paper:** [More Information Needed] - **License:** apache-2.0 ## Dataset Structure [meta/info.json](meta/info.json): ```json { "codebase_version": "v2.1", "robot_type": "lekiwi2", "total_episodes": 20, "total_frames": 13259, "total_tasks": 1, "total_videos": 40, "total_chunks": 1, "chunks_size": 1000, "fps": 30, "splits": { "train": "0:20" }, "data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet", "video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4", "features": { "action": { "dtype": "float32", "shape": [ 15 ], "names": [ "left_shoulder_pan", "left_shoulder_lift", "left_elbow_flex", "left_wrist_flex", "left_wrist_roll", "left_gripper", "right_shoulder_pan", "right_shoulder_lift", "right_elbow_flex", "right_wrist_flex", "right_wrist_roll", "right_gripper", "x_mm", "y_mm", "theta" ] }, "observation.state": { "dtype": "float32", "shape": [ 15 ], "names": [ "left_shoulder_pan", "left_shoulder_lift", "left_elbow_flex", "left_wrist_flex", "left_wrist_roll", "left_gripper", "right_shoulder_pan", "right_shoulder_lift", "right_elbow_flex", "right_wrist_flex", "right_wrist_roll", "right_gripper", "x_mm", "y_mm", "theta" ] }, "observation.images.front": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.height": 480, "video.width": 640, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "video.fps": 30, "video.channels": 3, "has_audio": false } }, "observation.images.wrist": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.height": 480, "video.width": 640, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "video.fps": 30, "video.channels": 3, "has_audio": false } }, "timestamp": { "dtype": "float32", "shape": [ 1 ], "names": null }, "frame_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "episode_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "task_index": { "dtype": "int64", "shape": [ 1 ], "names": null } } } ``` ## Citation **BibTeX:** ```bibtex [More Information Needed] ```

shylee/eval_DP_cube_downDims1_cropNo_freeze1_16_16_ema0_1e-4_ckpt180000

shylee

2025-05-04T15:01:17Z

0

[ "task_categories:robotics", "license:apache-2.0", "region:us", "LeRobot", "tutorial" ]

[ "robotics" ]

2025-05-04T15:01:11Z

null

--- license: apache-2.0 task_categories: - robotics tags: - LeRobot - tutorial configs: - config_name: default data_files: data/*/*.parquet --- This dataset was created using [LeRobot](https://github.com/huggingface/lerobot). ## Dataset Description - **Homepage:** [More Information Needed] - **Paper:** [More Information Needed] - **License:** apache-2.0 ## Dataset Structure [meta/info.json](meta/info.json): ```json { "codebase_version": "v2.1", "robot_type": "so100", "total_episodes": 1, "total_frames": 321, "total_tasks": 1, "total_videos": 3, "total_chunks": 1, "chunks_size": 1000, "fps": 30, "splits": { "train": "0:1" }, "data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet", "video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4", "features": { "action": { "dtype": "float32", "shape": [ 6 ], "names": [ "main_shoulder_pan", "main_shoulder_lift", "main_elbow_flex", "main_wrist_flex", "main_wrist_roll", "main_gripper" ] }, "observation.state": { "dtype": "float32", "shape": [ 6 ], "names": [ "main_shoulder_pan", "main_shoulder_lift", "main_elbow_flex", "main_wrist_flex", "main_wrist_roll", "main_gripper" ] }, "observation.images.FrontCam": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.fps": 30.0, "video.height": 480, "video.width": 640, "video.channels": 3, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "has_audio": false } }, "observation.images.TopCam": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.fps": 30.0, "video.height": 480, "video.width": 640, "video.channels": 3, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "has_audio": false } }, "observation.images.WristCam": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.fps": 30.0, "video.height": 480, "video.width": 640, "video.channels": 3, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "has_audio": false } }, "timestamp": { "dtype": "float32", "shape": [ 1 ], "names": null }, "frame_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "episode_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "task_index": { "dtype": "int64", "shape": [ 1 ], "names": null } } } ``` ## Citation **BibTeX:** ```bibtex [More Information Needed] ```

Mohsen21/Updated_EAD_MALE_FEMALE_DATA

Mohsen21

2025-05-04T14:07:38Z

0

[ "region:us" ]

[]

2025-05-04T14:06:45Z

null

--- dataset_info: features: - name: audio dtype: audio - name: transcription dtype: string splits: - name: train num_bytes: 1200893246.0 num_examples: 730 download_size: 1170770119 dataset_size: 1200893246.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

littleGuagua/x_dataset_48558

littleGuagua

2025-05-04T13:13:16Z

1,014

0

[ "task_categories:text-classification", "task_categories:token-classification", "task_categories:question-answering", "task_categories:summarization", "task_categories:text-generation", "task_ids:sentiment-analysis", "task_ids:topic-classification", "task_ids:named-entity-recognition", "task_ids:language-modeling", "task_ids:text-scoring", "task_ids:multi-class-classification", "task_ids:multi-label-classification", "task_ids:extractive-qa", "task_ids:news-articles-summarization", "multilinguality:multilingual", "source_datasets:original", "license:mit", "size_categories:100M<n<1B", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]

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

2025-01-26T14:58:15Z

null

--- license: mit multilinguality: - multilingual source_datasets: - original task_categories: - text-classification - token-classification - question-answering - summarization - text-generation task_ids: - sentiment-analysis - topic-classification - named-entity-recognition - language-modeling - text-scoring - multi-class-classification - multi-label-classification - extractive-qa - news-articles-summarization --- # Bittensor Subnet 13 X (Twitter) Dataset <center> <img src="https://huggingface.co/datasets/macrocosm-os/images/resolve/main/bittensor.png" alt="Data-universe: The finest collection of social media data the web has to offer"> </center> <center> <img src="https://huggingface.co/datasets/macrocosm-os/images/resolve/main/macrocosmos-black.png" alt="Data-universe: The finest collection of social media data the web has to offer"> </center> ## Dataset Description - **Repository:** littleGuagua/x_dataset_48558 - **Subnet:** Bittensor Subnet 13 - **Miner Hotkey:** 5ERFRy1NBaxrJ8WpkjKeWwgx79NxiVoEqmL3m5tEWsDHwjtD ### Dataset Summary This dataset is part of the Bittensor Subnet 13 decentralized network, containing preprocessed data from X (formerly Twitter). The data is continuously updated by network miners, providing a real-time stream of tweets for various analytical and machine learning tasks. For more information about the dataset, please visit the [official repository](https://github.com/macrocosm-os/data-universe). ### Supported Tasks The versatility of this dataset allows researchers and data scientists to explore various aspects of social media dynamics and develop innovative applications. Users are encouraged to leverage this data creatively for their specific research or business needs. For example: - Sentiment Analysis - Trend Detection - Content Analysis - User Behavior Modeling ### Languages Primary language: Datasets are mostly English, but can be multilingual due to decentralized ways of creation. ## Dataset Structure ### Data Instances Each instance represents a single tweet with the following fields: ### Data Fields - `text` (string): The main content of the tweet. - `label` (string): Sentiment or topic category of the tweet. - `tweet_hashtags` (list): A list of hashtags used in the tweet. May be empty if no hashtags are present. - `datetime` (string): The date when the tweet was posted. - `username_encoded` (string): An encoded version of the username to maintain user privacy. - `url_encoded` (string): An encoded version of any URLs included in the tweet. May be empty if no URLs are present. ### Data Splits This dataset is continuously updated and does not have fixed splits. Users should create their own splits based on their requirements and the data's timestamp. ## Dataset Creation ### Source Data Data is collected from public tweets on X (Twitter), adhering to the platform's terms of service and API usage guidelines. ### Personal and Sensitive Information All usernames and URLs are encoded to protect user privacy. The dataset does not intentionally include personal or sensitive information. ## Considerations for Using the Data ### Social Impact and Biases Users should be aware of potential biases inherent in X (Twitter) data, including demographic and content biases. This dataset reflects the content and opinions expressed on X and should not be considered a representative sample of the general population. ### Limitations - Data quality may vary due to the decentralized nature of collection and preprocessing. - The dataset may contain noise, spam, or irrelevant content typical of social media platforms. - Temporal biases may exist due to real-time collection methods. - The dataset is limited to public tweets and does not include private accounts or direct messages. - Not all tweets contain hashtags or URLs. ## Additional Information ### Licensing Information The dataset is released under the MIT license. The use of this dataset is also subject to X Terms of Use. ### Citation Information If you use this dataset in your research, please cite it as follows: ``` @misc{littleGuagua2025datauniversex_dataset_48558, title={The Data Universe Datasets: The finest collection of social media data the web has to offer}, author={littleGuagua}, year={2025}, url={https://huggingface.co/datasets/littleGuagua/x_dataset_48558}, } ``` ### Contributions To report issues or contribute to the dataset, please contact the miner or use the Bittensor Subnet 13 governance mechanisms. ## Dataset Statistics [This section is automatically updated] - **Total Instances:** 46034420 - **Date Range:** 2025-01-21T00:00:00Z to 2025-02-11T00:00:00Z - **Last Updated:** 2025-02-18T22:02:52Z ### Data Distribution - Tweets with hashtags: 36.14% - Tweets without hashtags: 63.86% ### Top 10 Hashtags For full statistics, please refer to the `stats.json` file in the repository. | Rank | Topic | Total Count | Percentage | |------|-------|-------------|-------------| | 1 | NULL | 29399695 | 63.86% | | 2 | #riyadh | 280983 | 0.61% | | 3 | #zelena | 221844 | 0.48% | | 4 | #tiktok | 163569 | 0.36% | | 5 | #bbb25 | 128439 | 0.28% | | 6 | #ad | 96138 | 0.21% | | 7 | #bbmzansi | 59564 | 0.13% | | 8 | #jhope_at_galadespiècesjaunes | 58496 | 0.13% | | 9 | #granhermano | 52866 | 0.11% | | 10 | #pr | 50398 | 0.11% | ## Update History | Date | New Instances | Total Instances | |------|---------------|-----------------| | 2025-01-26T14:58:37Z | 837393 | 837393 | | 2025-01-30T03:14:08Z | 8566588 | 9403981 | | 2025-02-02T15:17:25Z | 8569868 | 17973849 | | 2025-02-06T03:21:34Z | 10709950 | 28683799 | | 2025-02-09T15:24:35Z | 7218900 | 35902699 | | 2025-02-13T03:32:24Z | 8679209 | 44581908 | | 2025-02-18T07:01:46Z | 795937 | 45377845 | | 2025-02-18T22:02:52Z | 656575 | 46034420 |

ShoAnn/legalqa_klinik_hukumonline

ShoAnn

2025-05-04T13:01:51Z

117

1

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

[]

2025-01-11T02:29:44Z

null

--- language: - id license: apache-2.0 dataset_info: features: - name: question dtype: string - name: answer dtype: string - name: context list: - name: full_text dtype: string - name: id dtype: int64 - name: name dtype: string splits: - name: train num_bytes: 12220706 num_examples: 1006 - name: test num_bytes: 1393760 num_examples: 112 download_size: 5169384 dataset_size: 13614466 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* --- # Pertanyaan-Jawaban-Dasar_Hukum (Question-Answer-Legal_Basis) ## Topic Hukum Pidana Republik Indonesia (Criminal Law) ## Date range November 2013 - October 2024 ## Source Question-Answer-Legal basis reference: [Klinik Hukumonline.com](https://www.hukumonline.com/klinik/pidana) \ Legal basis Sources: Indonesia Official Statutory documents (from sites such as: [Peraturan BPK](https://peraturan.bpk.go.id/), [Peraturan.go.id](peraturan.go.id), [Paralegal](paralegal.id), etc.), Books, and others (articles, expert's opinions, etc)

HungVu2003/opt-350m_beta_0.0_alpha_0.2_num-company_2_dataset_1_for_gen_8_v2

HungVu2003

2025-05-04T12:26:22Z

0

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

[]

2025-05-04T12:26:20Z

null

--- dataset_info: features: - name: question dtype: string splits: - name: train num_bytes: 2445375 num_examples: 13750 download_size: 984172 dataset_size: 2445375 configs: - config_name: default data_files: - split: train path: data/train-* ---

yunusserhat/tea_sickness_dataset

yunusserhat

2025-05-04T09:45:50Z

0

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

[]

2025-05-04T08:03:08Z

null

--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': Anthracnose '1': algal_leaf '2': bird_eye_spot '3': brown_blight '4': gray_light '5': healthy '6': red_leaf_spot '7': white_spot splits: - name: train num_bytes: 622082045.0 num_examples: 708 - name: validation num_bytes: 79334678.0 num_examples: 88 - name: test num_bytes: 79495048.0 num_examples: 89 download_size: 780933256 dataset_size: 780911771.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* --- # Tea Sickness Dataset This dataset contains images of tea leaves affected by seven common diseases, along with healthy examples. It is designed for use in machine learning tasks such as classification, particularly leveraging transfer learning techniques. ## Dataset Summary The dataset includes tea leaves from eight categories: | Label ID | Class Name | |----------|------------------| | 0 | Anthracnose | | 1 | Algal Leaf Spot | | 2 | Bird Eye Spot | | 3 | Brown Blight | | 4 | Gray Blight | | 5 | Healthy | | 6 | Red Leaf Spot | | 7 | White Spot | Each class contains more than 100 images, captured from tea plants in the Johnstone Boiyon farm, Koiwa location, Bomet County, using a clone of 1510. The dataset is useful for developing models that can predict the presence of diseases in tea leaves, particularly in agricultural and sustainability contexts. ## Dataset Structure ### Features - `image`: An RGB image of a tea leaf (PIL Image format) - `label`: Class label as integer (0–7) with corresponding disease names ### Splits | Split | Number of Examples | Size (Bytes) | |-------------|--------------------|--------------| | Train | 708 | 622,082,045 | | Validation | 88 | 79,334,678 | | Test | 89 | 79,495,048 | | **Total** | **885** | **780,911,771** | ## Usage This dataset is suitable for: - Fine-tuning image classification models - Research on plant disease detection - Transfer learning and domain adaptation experiments ## Citation If you use this dataset in your research, please cite it as: ```bibtex @article{kimutai2022tea, title = {Tea sickness dataset}, author = {Kimutai, Gibson and Förster, Anna}, journal = {Mendeley Data}, volume = {2}, year = {2022}, doi = {10.17632/j32xdt2ff5.2} } ``` ## License **CC BY 4.0** — You are free to use, share, and adapt the dataset, provided appropriate credit is given. ## Acknowledgements This dataset was collected by researchers from the University of Rwanda and the University of Bremen. We thank the contributors for making the dataset publicly available to advance agricultural AI applications.

alchemistyzz/mathverse_test

alchemistyzz

2025-05-04T07:35:32Z

0

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

[]

2025-05-03T09:58:31Z

null

--- license: apache-2.0 ---

kaikkk/eval_act_so100_test1

kaikkk

2025-05-04T06:26:29Z

0

[ "task_categories:robotics", "license:apache-2.0", "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:timeseries", "modality:video", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us", "LeRobot", "tutorial" ]

[ "robotics" ]

2025-05-04T06:24:50Z

null

--- license: apache-2.0 task_categories: - robotics tags: - LeRobot - tutorial configs: - config_name: default data_files: data/*/*.parquet --- This dataset was created using [LeRobot](https://github.com/huggingface/lerobot). ## Dataset Description - **Homepage:** [More Information Needed] - **Paper:** [More Information Needed] - **License:** apache-2.0 ## Dataset Structure [meta/info.json](meta/info.json): ```json { "codebase_version": "v2.1", "robot_type": "so100", "total_episodes": 3, "total_frames": 1184, "total_tasks": 1, "total_videos": 6, "total_chunks": 1, "chunks_size": 1000, "fps": 30, "splits": { "train": "0:3" }, "data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet", "video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4", "features": { "action": { "dtype": "float32", "shape": [ 12 ], "names": [ "left_shoulder_pan", "left_shoulder_lift", "left_elbow_flex", "left_wrist_flex", "left_wrist_roll", "left_gripper", "right_shoulder_pan", "right_shoulder_lift", "right_elbow_flex", "right_wrist_flex", "right_wrist_roll", "right_gripper" ] }, "observation.state": { "dtype": "float32", "shape": [ 12 ], "names": [ "left_shoulder_pan", "left_shoulder_lift", "left_elbow_flex", "left_wrist_flex", "left_wrist_roll", "left_gripper", "right_shoulder_pan", "right_shoulder_lift", "right_elbow_flex", "right_wrist_flex", "right_wrist_roll", "right_gripper" ] }, "observation.images.top": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.height": 480, "video.width": 640, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "video.fps": 30, "video.channels": 3, "has_audio": false } }, "observation.images.right": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.height": 480, "video.width": 640, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "video.fps": 30, "video.channels": 3, "has_audio": false } }, "timestamp": { "dtype": "float32", "shape": [ 1 ], "names": null }, "frame_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "episode_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "task_index": { "dtype": "int64", "shape": [ 1 ], "names": null } } } ``` ## Citation **BibTeX:** ```bibtex [More Information Needed] ```

abehandlerorg/ccnews_blocks

abehandlerorg

2025-05-04T06:21:33Z

0

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

[]

2025-05-03T17:31:57Z

null

--- dataset_info: features: - name: text dtype: string - name: url dtype: string - name: crawl_date dtype: timestamp[ns, tz=UTC] - name: tokens sequence: uint16 splits: - name: train num_bytes: 517287642756.7698 num_examples: 104657712 download_size: 357497372525 dataset_size: 517287642756.7698 configs: - config_name: default data_files: - split: train path: data/train-* ---

cchoi1/kodcode-complete_1000_qwen7b_att_iter0_att40_sol5_dedup_diversity

cchoi1

2025-05-04T02:55:42Z

0

[ "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2025-05-04T00:10:23Z

null

--- dataset_info: features: - name: mutation_id dtype: int64 - name: task_id dtype: string - name: mutator_prompt dtype: string - name: solver_prompt dtype: string - name: response dtype: string - name: mutation_explanation dtype: string - name: mutation_info dtype: string - name: mutator_score dtype: float64 - name: solution_scores dtype: string - name: solutions dtype: string - name: solutions_explanation dtype: string - name: solutions_info dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 58593784 num_examples: 4966 download_size: 12338384 dataset_size: 58593784 configs: - config_name: default data_files: - split: train path: data/train-* ---

HungVu2003/opt-350m_beta_1.0_alpha_0.2_num-company_2_dataset_1_for_gen_5_v2

HungVu2003

2025-05-04T02:34:54Z

0

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

[]

2025-05-04T02:34:53Z

null

--- dataset_info: features: - name: question dtype: string splits: - name: train num_bytes: 6434870 num_examples: 13750 download_size: 3265326 dataset_size: 6434870 configs: - config_name: default data_files: - split: train path: data/train-* ---

mlfoundations-dev/f1_avg_domain

mlfoundations-dev

2025-05-04T01:58:57Z

9

0

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

[]

2025-05-04T01:54:40Z

null

--- dataset_info: features: - name: instruction_seed dtype: string - name: _source dtype: string - name: gpt41_mini_response dtype: string - name: __original_row_idx dtype: int64 - name: length dtype: int64 - name: domain dtype: string - name: r1_response dtype: string - name: r1_reasoning_content dtype: string - name: extract_solution dtype: string - name: url dtype: string - name: filename dtype: string - name: success dtype: bool - name: page_count dtype: int64 - name: page_number dtype: int64 - name: question_choices_solutions dtype: string - name: extracted_question dtype: string - name: extracted_answer_choices sequence: string - name: matched_solution dtype: string - name: qa_validation_outputs dtype: bool - name: classifier_reasoning dtype: string - name: is_organic_chemistry dtype: bool - name: ms_id dtype: int64 - name: reasoning dtype: string - name: deepseek_solution dtype: string - name: final_reasoning_trace dtype: string - name: conversations list: - name: from dtype: string - name: value dtype: string - name: id dtype: string - name: output dtype: string - name: source dtype: string - name: license dtype: string - name: dataset dtype: string - name: split dtype: string - name: difficulty dtype: int64 - name: solution dtype: string - name: index dtype: string - name: difficulty_reasoning dtype: string - name: messages list: - name: content dtype: string - name: role dtype: string - name: response_seed dtype: string splits: - name: train num_bytes: 12328252550.0 num_examples: 94797 download_size: 5254951315 dataset_size: 12328252550.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

HungVu2003/opt-350m_beta_0.5_alpha_0.6_num-company_3_dataset_2_for_gen_15

HungVu2003

2025-05-04T01:43:48Z

0

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

[]

2025-05-04T01:43:47Z

null

--- dataset_info: features: - name: question dtype: string splits: - name: train num_bytes: 4007928 num_examples: 12500 download_size: 1246351 dataset_size: 4007928 configs: - config_name: default data_files: - split: train path: data/train-* ---

HungVu2003/opt-350m_beta_1.0_alpha_0.2_num-company_2_dataset_0_for_gen_4_v2

HungVu2003

2025-05-04T00:48:22Z

0

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

[]

2025-05-04T00:48:20Z

null

--- dataset_info: features: - name: question dtype: string splits: - name: train num_bytes: 2084033 num_examples: 13750 download_size: 1150111 dataset_size: 2084033 configs: - config_name: default data_files: - split: train path: data/train-* ---

HungVu2003/opt-350m_beta_1.0_alpha_0.2_num-company_2_dataset_0_for_gen_3_v2

HungVu2003

2025-05-03T23:40:05Z

0

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

[]

2025-05-03T23:40:04Z

null

--- dataset_info: features: - name: question dtype: string splits: - name: train num_bytes: 2085177 num_examples: 13750 download_size: 1168455 dataset_size: 2085177 configs: - config_name: default data_files: - split: train path: data/train-* ---

Bakovic/chatbot_medical_diabetique

Bakovic

2025-05-03T23:34:34Z

0

[ "license:intel-research", "region:us" ]

[]

2025-05-03T23:32:29Z

null

--- license: intel-research ---

thavens/judged_responses

thavens

2025-05-03T23:05:37Z

0

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

[]

2025-05-03T23:05:35Z

null

--- dataset_info: features: - name: messages list: - name: content dtype: string - name: role dtype: string - name: new_instruction dtype: string - name: instruction_judgment dtype: bool - name: user_query_judgment dtype: bool - name: assistant_response_judgment dtype: bool - name: model dtype: string splits: - name: train num_bytes: 311128 num_examples: 80 download_size: 181418 dataset_size: 311128 configs: - config_name: default data_files: - split: train path: data/train-* ---

Triangle104/jondurbin_gutenberg-dpo-v0.1

Triangle104

2025-05-03T22:42:58Z

0

[ "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" ]

[]

2025-05-03T22:42:58Z

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

kothasuhas/llp-gold-37m-1.5m_clip0.256_T1.0

kothasuhas

2025-05-03T22:39:09Z

0

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

[]

2025-05-03T22:35:25Z

null

--- dataset_info: features: - name: text dtype: string - name: p_log_probs dtype: float32 - name: q_log_probs dtype: float32 - name: num_tokens dtype: float32 - name: log_weight dtype: float64 splits: - name: train num_bytes: 3605804917.0 num_examples: 1500000 download_size: 197960374 dataset_size: 3605804917.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

mlfoundations-dev/e1_code_fasttext_qwq

mlfoundations-dev

2025-05-03T21:54:33Z

0

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

[]

2025-05-03T21:53:31Z

null

--- dataset_info: features: - name: id dtype: string - name: instruction_seed dtype: string - name: output dtype: string - name: source dtype: string - name: license dtype: string - name: dataset dtype: string - name: split dtype: string - name: difficulty dtype: int64 - name: solution dtype: string - name: index dtype: string - name: _source dtype: string - name: difficulty_reasoning dtype: string - name: __original_row_idx dtype: int64 - name: ms_id dtype: int64 - name: final_reasoning_traces dtype: string - name: question_answer_string dtype: string - name: _fasttext_score dtype: float64 - name: conversations list: - name: from dtype: string - name: value dtype: string splits: - name: train num_bytes: 2159333511.0 num_examples: 31600 download_size: 1018311574 dataset_size: 2159333511.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

kothasuhas/llp-gold-37m-1.5m_clip0.016_T2048.0_I2048

kothasuhas

2025-05-03T21:38:32Z

0

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

[]

2025-05-03T21:37:33Z

null

--- dataset_info: features: - name: text dtype: string - name: p_log_probs dtype: float32 - name: q_log_probs dtype: float32 - name: num_tokens dtype: float32 - name: log_weight dtype: float64 splits: - name: train num_bytes: 3605804917.0 num_examples: 1500000 download_size: 183741412 dataset_size: 3605804917.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

HungVu2003/opt-350m_beta_0.0_alpha_0.2_num-company_2_dataset_0_for_gen_1_v2

HungVu2003

2025-05-03T21:09:44Z

0

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

[]

2025-05-03T21:09:43Z

null

--- dataset_info: features: - name: question dtype: string splits: - name: train num_bytes: 1785673 num_examples: 13750 download_size: 1030631 dataset_size: 1785673 configs: - config_name: default data_files: - split: train path: data/train-* ---

anonymousEcaiHateLLM/7_human_dataset

anonymousEcaiHateLLM

2025-05-03T20:42:05Z

0

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

[]

2025-05-03T20:41:58Z

null

--- dataset_info: features: - name: language dtype: string - name: text dtype: string - name: multi_label_id dtype: int64 - name: ds dtype: string - name: label_id dtype: int64 splits: - name: main num_bytes: 12507865 num_examples: 85918 download_size: 6596717 dataset_size: 12507865 configs: - config_name: default data_files: - split: main path: data/main-* ---

HungVu2003/opt-350m_beta_1.0_alpha_0.0_num-company_2_dataset_1_for_gen_17_v2

HungVu2003

2025-05-03T20:31:35Z

0

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

[]

2025-05-03T20:31:32Z

null

--- dataset_info: features: - name: question dtype: string splits: - name: train num_bytes: 6606404 num_examples: 12500 download_size: 3374561 dataset_size: 6606404 configs: - config_name: default data_files: - split: train path: data/train-* ---

HungVu2003/opt-350m_beta_1.0_alpha_0.0_num-company_2_dataset_0_for_gen_14_v2

HungVu2003

2025-05-03T20:25:36Z

0

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

[]

2025-05-03T20:25:35Z

null

--- dataset_info: features: - name: question dtype: string splits: - name: train num_bytes: 1147137 num_examples: 12500 download_size: 700506 dataset_size: 1147137 configs: - config_name: default data_files: - split: train path: data/train-* ---

HungVu2003/opt-350m_beta_0.5_alpha_0.6_num-company_3_dataset_0_for_gen_14

HungVu2003

2025-05-03T20:24:20Z

0

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

[]

2025-05-03T20:24:19Z

null

--- dataset_info: features: - name: question dtype: string splits: - name: train num_bytes: 7153405 num_examples: 12500 download_size: 1910106 dataset_size: 7153405 configs: - config_name: default data_files: - split: train path: data/train-* ---

kenwaylabs/OpenHermes-2.5-CoT

kenwaylabs

2025-05-03T20:13:32Z

14

0

[ "license:other", "region:us" ]

[]

2025-01-29T14:27:54Z

null

--- license: other license_name: fafo license_link: LICENSE dataset_info: features: - name: 'Unnamed: 0' dtype: int64 - name: custom_instruction dtype: bool - name: topic dtype: string - name: model_name dtype: string - name: model dtype: float64 - name: skip_prompt_formatting dtype: bool - name: category dtype: string - name: conversations dtype: string - name: views dtype: float64 - name: language dtype: string - name: id dtype: string - name: title dtype: string - name: idx dtype: float64 - name: hash dtype: float64 - name: avatarUrl dtype: float64 - name: system_prompt dtype: string - name: source dtype: string - name: Instruction dtype: string - name: input dtype: string - name: chain_of_thought dtype: string - name: output dtype: string - name: instruct_prompt dtype: string splits: - name: train num_bytes: 2795451691 num_examples: 225000 download_size: 1311012553 dataset_size: 2795451691 configs: - config_name: default data_files: - split: train path: data/train-* ---

HungVu2003/opt-350m_beta_1.0_alpha_0.0_num-company_2_dataset_0_for_gen_6_v2

HungVu2003

2025-05-03T20:09:58Z

0

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

[]

2025-05-03T20:09:56Z

null

--- dataset_info: features: - name: question dtype: string splits: - name: train num_bytes: 1151427 num_examples: 12500 download_size: 701524 dataset_size: 1151427 configs: - config_name: default data_files: - split: train path: data/train-* ---

Bretagne/Autogramm_Breton

Bretagne

2025-05-03T19:44:08Z

15

0

[ "task_categories:token-classification", "language:br", "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[ "token-classification" ]

2024-10-29T18:32:28Z

null

--- dataset_info: features: - name: tokens sequence: string - name: pos_tags sequence: string - name: dataset dtype: string splits: - name: train num_bytes: 2139340 num_examples: 12301 download_size: 512928 dataset_size: 2139340 configs: - config_name: default data_files: - split: train path: data/train-* task_categories: - token-classification language: - br --- ## Description Parsage des données [bretonconlls](https://github.com/Autogramm/Breton/tree/main/bretonconlls) d'[Autogramm](https://github.com/Autogramm/Breton) afin de rendre leur usage plus simple. Ce dépôt ne s'intéresse qu'au POS. Pour la partie traduction breton/français, nous vous invitions à consulter [Bretagne/Autogramm_Breton_translation](https://huggingface.co/datasets/Bretagne/Autogramm_Breton_translation). Les fichiers des données contenaient "`status = WIP`" indiquant probablement qu'elles' peuvent contenir des erreurs actuellement. Un nouveau parsing sera effectué lors que les données seront finalisées. Vous pouvez trier les données par variétés de breton via la colonne "`dataset`" du jeu de données. ## Citation Aucune citation fournie. Les données étant cependant basées sur [Arbres](https://arbres.iker.cnrs.fr/index.php?title=Arbres:Le_site_de_grammaire_du_breton), ce site est citable de la façon suivante : ``` Jouitteau, Mélanie. (éd.). 2009-2024. ARBRES, wikigrammaire des dialectes du breton et centre de ressources pour son étude linguistique formelle, IKER, CNRS, http://arbres.iker.cnrs.fr. Licence Creative Commons BY-NC-SA. ```

TheRealPilot638/Olmo-1B-0724-best_of_16_H200

TheRealPilot638

2025-05-03T19:40:53Z

3

0

[ "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2025-04-28T17:28:23Z

null

--- dataset_info: - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-16--seed-0--agg_strategy-last features: - name: problem dtype: string - name: solution dtype: string - name: answer dtype: string - name: subject dtype: string - name: level dtype: int64 - name: unique_id dtype: string - name: completions sequence: string - name: scores sequence: sequence: float64 - name: pred dtype: string - name: completion_tokens sequence: int64 - name: agg_scores sequence: float64 - name: pred_weighted@1 dtype: string - name: pred_maj@1 dtype: string - name: pred_naive@1 dtype: string - name: pred_weighted@2 dtype: string - name: pred_maj@2 dtype: string - name: pred_naive@2 dtype: string - name: pred_weighted@4 dtype: string - name: pred_maj@4 dtype: string - name: pred_naive@4 dtype: string - name: pred_weighted@8 dtype: string - name: pred_maj@8 dtype: string - name: pred_naive@8 dtype: string - name: pred_weighted@16 dtype: string - name: pred_maj@16 dtype: string - name: pred_naive@16 dtype: string splits: - name: train num_bytes: 42103505 num_examples: 500 download_size: 10030845 dataset_size: 42103505 - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-16--seed-0--agg_strategy-last--evals features: - name: n dtype: int64 - name: acc_naive dtype: float64 - name: acc_weighted dtype: float64 - name: acc_maj dtype: float64 splits: - name: train num_bytes: 128 num_examples: 4 download_size: 2017 dataset_size: 128 - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-16--seed-1--agg_strategy-last features: - name: problem dtype: string - name: solution dtype: string - name: answer dtype: string - name: subject dtype: string - name: level dtype: int64 - name: unique_id dtype: string - name: completions sequence: string - name: scores sequence: sequence: float64 - name: pred dtype: string - name: completion_tokens sequence: int64 - name: agg_scores sequence: float64 - name: pred_weighted@1 dtype: string - name: pred_maj@1 dtype: string - name: pred_naive@1 dtype: string - name: pred_weighted@2 dtype: string - name: pred_maj@2 dtype: string - name: pred_naive@2 dtype: string - name: pred_weighted@4 dtype: string - name: pred_maj@4 dtype: string - name: pred_naive@4 dtype: string - name: pred_weighted@8 dtype: string - name: pred_maj@8 dtype: string - name: pred_naive@8 dtype: string - name: pred_weighted@16 dtype: string - name: pred_maj@16 dtype: string - name: pred_naive@16 dtype: string splits: - name: train num_bytes: 42765549 num_examples: 500 download_size: 9935948 dataset_size: 42765549 - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-16--seed-1--agg_strategy-last--evals features: - name: n dtype: int64 - name: acc_naive dtype: float64 - name: acc_weighted dtype: float64 - name: acc_maj dtype: float64 splits: - name: train num_bytes: 128 num_examples: 4 download_size: 2032 dataset_size: 128 - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-16--seed-2--agg_strategy-last features: - name: problem dtype: string - name: solution dtype: string - name: answer dtype: string - name: subject dtype: string - name: level dtype: int64 - name: unique_id dtype: string - name: completions sequence: string - name: scores sequence: sequence: float64 - name: pred dtype: string - name: completion_tokens sequence: int64 - name: agg_scores sequence: float64 - name: pred_weighted@1 dtype: string - name: pred_maj@1 dtype: string - name: pred_naive@1 dtype: string - name: pred_weighted@2 dtype: string - name: pred_maj@2 dtype: string - name: pred_naive@2 dtype: string - name: pred_weighted@4 dtype: string - name: pred_maj@4 dtype: string - name: pred_naive@4 dtype: string - name: pred_weighted@8 dtype: string - name: pred_maj@8 dtype: string - name: pred_naive@8 dtype: string - name: pred_weighted@16 dtype: string - name: pred_maj@16 dtype: string - name: pred_naive@16 dtype: string splits: - name: train num_bytes: 42160208 num_examples: 500 download_size: 9858235 dataset_size: 42160208 - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-16--seed-2--agg_strategy-last--evals features: - name: n dtype: int64 - name: acc_naive dtype: float64 - name: acc_weighted dtype: float64 - name: acc_maj dtype: float64 splits: - name: train num_bytes: 128 num_examples: 4 download_size: 2027 dataset_size: 128 - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-16--seed-3--agg_strategy-last features: - name: problem dtype: string - name: solution dtype: string - name: answer dtype: string - name: subject dtype: string - name: level dtype: int64 - name: unique_id dtype: string - name: completions sequence: string - name: scores sequence: sequence: float64 - name: pred dtype: string - name: completion_tokens sequence: int64 - name: agg_scores sequence: float64 - name: pred_weighted@1 dtype: string - name: pred_maj@1 dtype: string - name: pred_naive@1 dtype: string - name: pred_weighted@2 dtype: string - name: pred_maj@2 dtype: string - name: pred_naive@2 dtype: string - name: pred_weighted@4 dtype: string - name: pred_maj@4 dtype: string - name: pred_naive@4 dtype: string - name: pred_weighted@8 dtype: string - name: pred_maj@8 dtype: string - name: pred_naive@8 dtype: string - name: pred_weighted@16 dtype: string - name: pred_maj@16 dtype: string - name: pred_naive@16 dtype: string splits: - name: train num_bytes: 42794778 num_examples: 500 download_size: 9961797 dataset_size: 42794778 - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-16--seed-3--agg_strategy-last--evals features: - name: n dtype: int64 - name: acc_naive dtype: float64 - name: acc_weighted dtype: float64 - name: acc_maj dtype: float64 splits: - name: train num_bytes: 128 num_examples: 4 download_size: 2012 dataset_size: 128 configs: - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-16--seed-0--agg_strategy-last data_files: - split: train path: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-16--seed-0--agg_strategy-last/train-* - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-16--seed-0--agg_strategy-last--evals data_files: - split: train path: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-16--seed-0--agg_strategy-last--evals/train-* - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-16--seed-1--agg_strategy-last data_files: - split: train path: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-16--seed-1--agg_strategy-last/train-* - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-16--seed-1--agg_strategy-last--evals data_files: - split: train path: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-16--seed-1--agg_strategy-last--evals/train-* - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-16--seed-2--agg_strategy-last data_files: - split: train path: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-16--seed-2--agg_strategy-last/train-* - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-16--seed-2--agg_strategy-last--evals data_files: - split: train path: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-16--seed-2--agg_strategy-last--evals/train-* - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-16--seed-3--agg_strategy-last data_files: - split: train path: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-16--seed-3--agg_strategy-last/train-* - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-16--seed-3--agg_strategy-last--evals data_files: - split: train path: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-16--seed-3--agg_strategy-last--evals/train-* ---

Noru1/EEReasonBench

Noru1

2025-05-03T19:33:28Z

0

1

[ "task_categories:text-generation", "task_categories:question-answering", "language:en", "license:apache-2.0", "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "electrical-engineering", "question-answering", "problem-solving", "reasoning", "education", "mathematics", "physics" ]

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

2025-05-03T18:21:28Z

null

--- library_name: datasets tags: - electrical-engineering - question-answering - problem-solving - reasoning - education - mathematics - physics license: apache-2.0 task_categories: - text-generation - question-answering language: - en pretty_name: EEReasonBench size_categories: - n<1K --- # EEReasonBench: A Reasoning Benchmark for Electrical Engineering ## Dataset Description This dataset provides a collection of electrical engineering problems covering various subjects, including Circuits, Machines, Power Systems, Power Plants, etc. Problems include multiple-choice questions (conceptual and numerical) with detailed, step-by-step solutions formatted in Markdown and LaTeX. The dataset is designed to serve as both a benchmark for evaluating model performance on electrical engineering reasoning tasks and as a resource for fine-tuning models to improve their capabilities in technical problem-solving within the electrical engineering domain. **Features:** * Structured JSON format per problem * Fields include `id`, `subject`, `type`, `problem`, `choices`, `given`, `solution_steps`, `answer`, and `concepts` * Solutions emphasize clear reasoning and link steps to fundamental principles ## Dataset Structure ### Data Instance ```json { "id": "DC-012", "subject": "DC Circuits", "type": "Problem Solving", "problem": "A 120 V dc motor draws a current of 200 A and is located 1,000 ft from the supply. If the copper transmission wire has a diameter of 0.45 inch and with specific resistance of 10.4 ohm-circular mil per foot, what must be the voltage of the supply source?", "choices": { "A": "129.10 V", "B": "134.30 V", "C": "136.90 V", "D": "140.50 V" }, "given": "$$V_{motor} = 120\\ V$$\n$$I_{motor} = 200\\ A$$\n$$L = 1000\\ ft$$\n$$D = 0.45\\ inch$$\n$$\\rho = 10.4\\ \\Omega\\text{-CM/ft}$$", "solution_steps": "1. **Identify the problem type**: This is a voltage drop problem in a DC circuit with resistance in the transmission lines. We need to apply both Ohm's Law and Kirchhoff's Voltage Law.\n\n2. **Convert diameter to appropriate units**: Wire diameter is given in inches but resistivity in circular mils, so we need consistent units:\n$$D = 0.45 \\text{ inch} \\times 1000 = 450 \\text{ mils}$$\n\n3. **Calculate cross-sectional area**: For circular conductors, area in circular mils is diameter squared:\n$$A_{CM} = D^2 = 450^2 = 202{,}500 \\text{ CM}$$\n\n4. **Calculate resistance of one wire**: Using the resistivity formula which relates material property to physical dimensions:\n$$R_{wire} = \\frac{\\rho \\cdot L}{A_{CM}} = \\frac{10.4 \\times 1000}{202{,}500} \\approx 0.05136\\ \\Omega$$\n\n5. **Determine total circuit resistance**: Since current flows through both supply and return wires:\n$$R_{total} = 2 \\times R_{wire} = 2 \\times 0.05136 = 0.10272\\ \\Omega$$\n\n6. **Calculate voltage drop**: Using Ohm's Law to find voltage lost in transmission:\n$$V_{drop} = I_{motor} \\times R_{total} = 200\\text{ A} \\times 0.10272\\text{ }\\Omega = 20.544\\text{ V}$$\n\n7. **Verification**: We can verify this drop by calculating power loss in the transmission line:\n$$P_{loss} = I^2 \\times R_{total} = (200\\text{ A})^2 \\times 0.10272\\text{ }\\Omega = 4,108.8\\text{ W}$$\nThis significant power loss (about 4.1 kW) explains why high-voltage transmission is preferred for long distances.\n\n8. **Apply Kirchhoff's Voltage Law**: The supply voltage must overcome both the motor voltage and the transmission line drop:\n$$V_{supply} = V_{motor} + V_{drop} = 120\\text{ V} + 20.544\\text{ V} = 140.544\\text{ V}$$\n\n9. **Analyze results**: The supply must provide 17.1% more voltage than what's needed at the motor, highlighting the importance of conductor sizing in electrical distribution systems.\n\n10. **Final Answer**: Comparing our calculated value (140.544 V) with the given choices, the closest match is **D. 140.50 V**.", "answer": "D. 140.50 V", "concepts": [ "Ohm's Law", "Kirchhoff's Voltage Law", "Power Transmission", "Voltage Drop", "Resistivity" ] } ``` ### Data Fields - **id**: (string) Unique problem ID - **subject**: (string) EE subject area - **type**: (string) "Problem Solving" or "Objective Type" - **problem**: (string) Question text - **choices**: (dict) Multiple-choice options (A-D) - **given**: (string) Known values/conditions (LaTeX) - **solution_steps**: (string) Numbered step-by-step solution (Markdown/LaTeX) - **answer**: (string) Correct choice ("Letter. Answer text") - **concepts**: (list[string]) Key concepts ### Data Splits The dataset contains a single train split. ## Dataset Creation The problems were sourced from electrical engineering educational materials. Solutions were refined, formatted, and enhanced for clarity and pedagogical value. This process included structuring the data, applying LaTeX, adding reasoning steps, tagging concepts, and verifying calculations. The dataset contains only technical problem data, with no personal information. ## Considerations for Use - **Scope**: Primarily covers core undergraduate EE topics - **Potential Errors**: While curated carefully, errors from source materials or the enhancement process may exist. Users should verify critical applications - **Rendering**: Requires tools capable of rendering Markdown and LaTeX ## How to Use ```python from datasets import load_dataset # Replace with your Hugging Face repository name dataset = load_dataset("Noru1/EEReasonBench") # Access data print(dataset['train'][0]) ``` ## Licensing Information Licensed under the Apache License, Version 2.0. ## Citation Information ```bibtex @misc{ee_problems_dataset_2025, author = {Norbert John Ibera}, title = {EEReasonBench: A Reasoning Benchmark for Electrical Engineering}, year = {2025}, publisher = {Hugging Face}, journal = {Hugging Face Hub}, howpublished = {\url{https://huggingface.co/datasets/Noru1/EEReasonBench}} } ```

SayantanJoker/Shrutilipi_Hindi_resampled_44100_merged_10_quality_metadata

SayantanJoker

2025-05-03T19:28:16Z

0

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

[]

2025-05-03T19:28:14Z

null

--- dataset_info: features: - name: text dtype: string - name: file_name dtype: string - name: utterance_pitch_mean dtype: float32 - name: utterance_pitch_std dtype: float32 - name: snr dtype: float64 - name: c50 dtype: float64 - name: speaking_rate dtype: string - name: phonemes dtype: string - name: stoi dtype: float64 - name: si-sdr dtype: float64 - name: pesq dtype: float64 - name: noise dtype: string - name: reverberation dtype: string - name: speech_monotony dtype: string - name: sdr_noise dtype: string - name: pesq_speech_quality dtype: string splits: - name: train num_bytes: 24716922 num_examples: 50000 download_size: 8350343 dataset_size: 24716922 configs: - config_name: default data_files: - split: train path: data/train-* ---

Asap7772/aime_2025_responses_openthoughts-sft-qwen3-1.7b

Asap7772

2025-05-03T19:25:36Z

0

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

[]

2025-05-02T22:18:47Z

null

--- dataset_info: features: - name: problem dtype: string - name: answer dtype: string - name: response sequence: string - name: correct sequence: bool - name: success_rate dtype: float64 splits: - name: train num_bytes: 5303055 num_examples: 30 download_size: 1777461 dataset_size: 5303055 configs: - config_name: default data_files: - split: train path: data/train-* ---

alucchi/Qwen2.5-1.5B-Instruct_n1000_e10_oadam0.0001_b16_1_a10_flash_compact_ttt_a100_s40

alucchi

2025-05-03T19:23:54Z

0

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

[]

2025-05-03T19:23:44Z

null

--- dataset_info: - config_name: default features: - name: task_id dtype: string - name: prompt dtype: string - name: generated_text dtype: string - name: generated_grid_rect dtype: string - name: task_solution sequence: sequence: sequence: int64 - name: match dtype: bool - name: score dtype: float64 splits: - name: train num_bytes: 509760 num_examples: 70 download_size: 85260 dataset_size: 509760 - config_name: main features: - name: task_id dtype: string - name: prompt dtype: string - name: generated_text dtype: string - name: generated_grid_rect dtype: string - name: task_solution sequence: sequence: sequence: int64 - name: match dtype: bool - name: score dtype: float64 splits: - name: train num_bytes: 509760 num_examples: 70 download_size: 85260 dataset_size: 509760 configs: - config_name: default data_files: - split: train path: data/train-* - config_name: main data_files: - split: train path: main/train-* ---

cchoi1/kodcode-complete_1000_qwen7b_sol_iter0_att10_sol5_lr5e5_3ep_ppo_9000

cchoi1

2025-05-03T19:00:09Z

0

[ "region:us" ]

[]

2025-05-03T19:00:06Z

null

--- dataset_info: features: - name: mutation_id dtype: int64 - name: task_id dtype: string - name: mutator_prompt dtype: string - name: solver_prompt dtype: string - name: response dtype: string - name: mutation_explanation dtype: string - name: mutation_info dtype: string - name: mutator_score dtype: float64 - name: solution_scores dtype: string - name: solutions dtype: string - name: solutions_explanation dtype: string - name: solutions_info dtype: string splits: - name: train num_bytes: 54512973.365622886 num_examples: 3945 - name: test num_bytes: 6826212.634377112 num_examples: 494 download_size: 13476834 dataset_size: 61339186.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

HungVu2003/opt-350m_beta_0.5_alpha_0.6_num-company_3_dataset_2_for_gen_13

HungVu2003

2025-05-03T18:32:54Z

0

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

[]

2025-05-03T18:32:52Z

null

--- dataset_info: features: - name: question dtype: string splits: - name: train num_bytes: 4216357 num_examples: 12500 download_size: 1317453 dataset_size: 4216357 configs: - config_name: default data_files: - split: train path: data/train-* ---

HungVu2003/opt-350m_beta_0.0_alpha_0.0_num-company_2_dataset_0_for_gen_18_v2

HungVu2003

2025-05-03T18:21:14Z

0

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

[]

2025-05-03T18:21:13Z

null

--- dataset_info: features: - name: question dtype: string splits: - name: train num_bytes: 812840 num_examples: 12500 download_size: 560115 dataset_size: 812840 configs: - config_name: default data_files: - split: train path: data/train-* ---

HungVu2003/opt-350m_beta_0.0_alpha_0.0_num-company_2_dataset_1_for_gen_9_v2

HungVu2003

2025-05-03T17:33:25Z

0

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

[]

2025-05-03T17:33:23Z

null

--- dataset_info: features: - name: question dtype: string splits: - name: train num_bytes: 820906 num_examples: 12500 download_size: 567597 dataset_size: 820906 configs: - config_name: default data_files: - split: train path: data/train-* ---

doublesizebed/pitch_rate

doublesizebed

2025-05-03T17:25:53Z

0

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

[]

2025-05-03T07:49:40Z

null

--- license: apache-2.0 dataset_info: features: - name: audio_filename dtype: string - name: prompt dtype: string - name: transcription dtype: string - name: gender dtype: string - name: audio_filepath dtype: audio - name: utterance_pitch_mean dtype: float64 - name: utterance_pitch_std dtype: float64 - name: snr dtype: float64 - name: c50 dtype: float64 - name: speech_duration dtype: float64 splits: - name: train num_bytes: 13292498803.263 num_examples: 247481 - name: test num_bytes: 975999.0 num_examples: 20 download_size: 13264980893 dataset_size: 13293474802.263 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

HungVu2003/opt-350m_beta_0.0_alpha_0.0_num-company_2_dataset_0_for_gen_7_v2

HungVu2003

2025-05-03T17:22:45Z

0

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

[]

2025-05-03T17:22:44Z

null

--- dataset_info: features: - name: question dtype: string splits: - name: train num_bytes: 813175 num_examples: 12500 download_size: 561888 dataset_size: 813175 configs: - config_name: default data_files: - split: train path: data/train-* ---

HungVu2003/opt-350m_beta_0.0_alpha_0.0_num-company_2_dataset_0_for_gen_6_v2

HungVu2003

2025-05-03T17:17:25Z

0

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

[]

2025-05-03T17:17:24Z

null

--- dataset_info: features: - name: question dtype: string splits: - name: train num_bytes: 816552 num_examples: 12500 download_size: 563578 dataset_size: 816552 configs: - config_name: default data_files: - split: train path: data/train-* ---

weidezhang/so100_test

weidezhang

2025-05-03T16:51:15Z

10

0

[ "task_categories:robotics", "license:apache-2.0", "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:timeseries", "modality:video", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us", "LeRobot", "so100", "tutorial" ]

[ "robotics" ]

2025-04-30T21:38:25Z

null

--- license: apache-2.0 task_categories: - robotics tags: - LeRobot - so100 - tutorial configs: - config_name: default data_files: data/*/*.parquet --- This dataset was created using [LeRobot](https://github.com/huggingface/lerobot). ## Dataset Description - **Homepage:** [More Information Needed] - **Paper:** [More Information Needed] - **License:** apache-2.0 ## Dataset Structure [meta/info.json](meta/info.json): ```json { "codebase_version": "v2.1", "robot_type": "so100", "total_episodes": 2, "total_frames": 1770, "total_tasks": 1, "total_videos": 4, "total_chunks": 1, "chunks_size": 1000, "fps": 30, "splits": { "train": "0:2" }, "data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet", "video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4", "features": { "action": { "dtype": "float32", "shape": [ 6 ], "names": [ "main_shoulder_pan", "main_shoulder_lift", "main_elbow_flex", "main_wrist_flex", "main_wrist_roll", "main_gripper" ] }, "observation.state": { "dtype": "float32", "shape": [ 6 ], "names": [ "main_shoulder_pan", "main_shoulder_lift", "main_elbow_flex", "main_wrist_flex", "main_wrist_roll", "main_gripper" ] }, "observation.images.laptop": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.fps": 30.0, "video.height": 480, "video.width": 640, "video.channels": 3, "video.codec": "h264", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "has_audio": false } }, "observation.images.phone": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.fps": 30.0, "video.height": 480, "video.width": 640, "video.channels": 3, "video.codec": "h264", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "has_audio": false } }, "timestamp": { "dtype": "float32", "shape": [ 1 ], "names": null }, "frame_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "episode_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "task_index": { "dtype": "int64", "shape": [ 1 ], "names": null } } } ``` ## Citation **BibTeX:** ```bibtex [More Information Needed] ```

HungVu2003/opt-350m_beta_0.5_alpha_0.6_num-company_3_dataset_1_for_gen_13

HungVu2003

2025-05-03T16:46:47Z

0

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

[]

2025-05-03T16:46:46Z

null

--- dataset_info: features: - name: question dtype: string splits: - name: train num_bytes: 2374950 num_examples: 12500 download_size: 1286691 dataset_size: 2374950 configs: - config_name: default data_files: - split: train path: data/train-* ---

cochi1706/pali-viet

cochi1706

2025-05-03T16:42:59Z

11

0

[ "task_categories:translation", "language:vi", "language:pi", "region:us" ]

[ "translation" ]

2025-04-28T12:24:57Z

null

--- task_categories: - translation language: - vi - pi pretty_name: Pali - Viet Statistical Machine Translation ---

HungVu2003/opt-350m_beta_0.5_alpha_0.6_num-company_3_dataset_0_for_gen_13

HungVu2003

2025-05-03T16:41:46Z

0

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

[]

2025-05-03T16:41:44Z

null

--- dataset_info: features: - name: question dtype: string splits: - name: train num_bytes: 7304720 num_examples: 12500 download_size: 1973081 dataset_size: 7304720 configs: - config_name: default data_files: - split: train path: data/train-* ---

xbilek25/hall_train_36000

xbilek25

2025-05-03T16:41:35Z

0

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

[]

2025-05-03T16:38:01Z

null

--- dataset_info: features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string - name: variant dtype: string splits: - name: train num_bytes: 7175502581.0 num_examples: 36000 download_size: 6026324637 dataset_size: 7175502581.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

ibndias/DeepSeek-R1-Distilled-1.4M

ibndias

2025-05-03T16:38:40Z

0

[ "task_categories:text-generation", "language:zh", "language:en", "license:cc-by-nc-4.0", "size_categories:1M<n<10M", "arxiv:2503.19633", "region:us", "code", "math", "reasoning", "thinking", "deepseek-r1", "distill" ]

[ "text-generation" ]

2025-05-03T16:38:40Z

null

--- license: cc-by-nc-4.0 task_categories: - text-generation language: - zh - en tags: - code - math - reasoning - thinking - deepseek-r1 - distill size_categories: - 1M<n<10M configs: - config_name: am_0.5M data_files: am_0.5M.jsonl.zst features: - name: messages list: - name: content dtype: string - name: info struct: - name: answer_content dtype: string - name: reference_answer dtype: string - name: source dtype: string - name: test_case struct: - name: test_code dtype: string - name: test_entry_point dtype: string - name: think_content dtype: string - name: role dtype: string - config_name: am_0.9M data_files: am_0.9M.jsonl.zst features: - name: messages list: - name: content dtype: string - name: info struct: - name: answer_content dtype: string - name: reference_answer dtype: string - name: source dtype: string - name: test_case struct: - name: test_code dtype: string - name: test_entry_point dtype: string - name: think_content dtype: string - name: role dtype: string - config_name: am_0.9M_sample_1k data_files: am_0.9M_sample_1k.jsonl features: - name: messages list: - name: content dtype: string - name: info struct: - name: answer_content dtype: string - name: reference_answer dtype: string - name: source dtype: string - name: test_case struct: - name: test_code dtype: string - name: test_entry_point dtype: string - name: think_content dtype: string - name: role dtype: string --- **For more open-source datasets, models, and methodologies, please visit our [GitHub repository](https://github.com/a-m-team/a-m-models).** [AM-DeepSeek-R1-Distilled-1.4M](https://huggingface.co/datasets/a-m-team/AM-DeepSeek-R1-Distilled-1.4M) is a large-scale general reasoning task dataset composed of high-quality and challenging reasoning problems. These problems are collected from numerous open-source datasets, semantically deduplicated, and cleaned to eliminate test set contamination. All responses in the dataset are distilled from the reasoning model (mostly DeepSeek-R1) and have undergone rigorous verification: mathematical problems are validated through answer checking, code problems via test cases, and other tasks through reward model evaluation. Specifically, responses in am_0.5M.jsonl are distilled by other open-source datasets, while those in am_0.9M.jsonl are distilled from the DeepSeek-R1-671B by the [AM team](https://huggingface.co/a-m-team). We have validated the dataset through model training, confirming its effectiveness and demonstrating performance comparable to the distilled models from the DeepSeek team, and the details can be found in our technique reports [1.4 Million Open-Source Distilled Reasoning Dataset to Empower Large Language Model Traning](https://github.com/a-m-team/a-m-models/blob/main/docs/AM-DeepSeek-R1-Distilled-Dataset.pdf) We are releasing these 1.4 million problems and responses to the research community, aiming to foster advancements in powerful reasoning-oriented Large Language Models (LLMs). We sincerely thank the open-source community. Without their support, we would never have come this far. ## Model Training Performance based on this dataset ![alt text](AM-DeepSeek-R1-Distilled.jpeg) ## Scale & Composition - AM-DeepSeek-R1-Distilled-1.4M: An Open-source Chinese & English dataset with reasoning traces (1.4 million entries). - 0.5 million entries of data are entirely from open-source datasets, and 0.9 million entries of data are distilled from DeepSeek-R1-671B by AM team, which can be seen in the "am-0309" of the Response sources. - Data processing: - Instruction part: we used the large language model to score all the instructions in terms of difficulty and category. Then we filtered the data according to the difficulty and category labels and conducted strict semantic deduplication to ensure the high-quality and diversity of the instructions. - Response part: we verified part of the data with reference answers using both rules and the large language model respectively. For code data with test cases, we used a sandbox for verification. Additionally, we used a reward model to score all the generated results and performed rejection sampling based on the rules and the reward model. ## Unified Format #### Dedicated fields - **`messages`**: Array with user - assistant interaction objects, Each having `role`、`content` and `info` fields. - **`role`**: Indicates whether it's user or assistant, defining the sender of the message. - **`content`**: Follows `<think> reasoning process </think> <answer> solution </answer>` format in assistant message. - **`info`**: Contains metadata. - **`source`**: Instrction or response source. - **`reference_answer`**: Ground-truth answers only for some math, science, and other problems. - **`test_case`**: Test cases only for some code-related problems. - **`think_content`**: Reasoning steps in assistant message. - **`answer_content`**: Final solution in assistant message. #### sample ```bash { "messages":[{ "role": "user", "content": "Find the integral of \$e^{-x} + e^{-2x} dx\$.", "info":{ "source":"natural_reasoning", "reference_answer": "-e^{-x} - \\frac{1}{2}e^{-2x} + C", "test_case": null } }, { "role": "assistant", "content": "<think>Okay, let's see. The user wants to find the integral of e^{-x} + e^{-2x} dx. Hmm, integrals of exponential functions. I remember that the integral of e^{kx} dx is (1/k)e^{kx} + C, right? But here the exponents are negative. Let me think.\n\nFirst, maybe I can split the integral into two parts because it's the sum of two functions. So, ∫(e^{-x} + e^{-2x}) dx = ∫e^{-x} dx + ∫e^{-2x} dx. That makes sense because integration is linear. Now, handle each integral separately.\n\nFor the first integral, ∫e^{-x} dx. If I use the formula, normally ∫e^{kx} dx = (1/k)e^{kx} + C. Here, k is -1. So substituting, that would be (1/(-1))e^{-x} + C, which simplifies to -e^{-x} + C. Wait, but since we're doing an indefinite integral here, each term will have its own constant, but since they're both combined, we'll have one constant at the end.\n\nNow the second integral: ∫e^{-2x} dx. Again, using the same formula, where k is -2. So that becomes (1/(-2))e^{-2x} + C, which is (-1/2)e^{-2x} + C.\n\nPutting both results together: the first integral is -e^{-x} and the second is (-1/2)e^{-2x}, so adding them and combining the constants gives the final answer. Let me check the signs again. The first term's coefficient is -1, and the second is -1/2. So combining those, it should be -e^{-x} - (1/2)e^{-2x} + C. Yeah, that looks right.\n\nLet me verify by differentiating the result. If I take the derivative of -e^{-x}, that's (-1)(-e^{-x}) = e^{-x}. For the second term, derivative of -1/2 e^{-2x} is (-1/2)(-2)e^{-2x} = e^{-2x}. So adding those derivatives gives e^{-x} + e^{-2x}, which matches the original integrand. Great, so the integration is correct. The constants of integration from both terms just combine into a single constant C at the end.\n</think>\n<answer>\nThe integral of \$e^{-x} + e^{-2x}\$ with respect to \$x\$ is:\n\n\\[\n\\int \\left(e^{-x} + e^{-2x}\\right) dx = -e^{-x} - \\frac{1}{2}e^{-2x} + C\n\\]\n\nwhere \$C\$ is the constant of integration.\n</answer>", "info": { "source": "am-0309", "think_content": "Okay, let's see. The user wants to find the integral of e^{-x} + e^{-2x} dx. Hmm, integrals of exponential functions. I remember that the integral of e^{kx} dx is (1/k)e^{kx} + C, right? But here the exponents are negative. Let me think.\n\nFirst, maybe I can split the integral into two parts because it's the sum of two functions. So, ∫(e^{-x} + e^{-2x}) dx = ∫e^{-x} dx + ∫e^{-2x} dx. That makes sense because integration is linear. Now, handle each integral separately.\n\nFor the first integral, ∫e^{-x} dx. If I use the formula, normally ∫e^{kx} dx = (1/k)e^{kx} + C. Here, k is -1. So substituting, that would be (1/(-1))e^{-x} + C, which simplifies to -e^{-x} + C. Wait, but since we're doing an indefinite integral here, each term will have its own constant, but since they're both combined, we'll have one constant at the end.\n\nNow the second integral: ∫e^{-2x} dx. Again, using the same formula, where k is -2. So that becomes (1/(-2))e^{-2x} + C, which is (-1/2)e^{-2x} + C.\n\nPutting both results together: the first integral is -e^{-x} and the second is (-1/2)e^{-2x}, so adding them and combining the constants gives the final answer. Let me check the signs again. The first term's coefficient is -1, and the second is -1/2. So combining those, it should be -e^{-x} - (1/2)e^{-2x} + C. Yeah, that looks right.\n\nLet me verify by differentiating the result. If I take the derivative of -e^{-x}, that's (-1)(-e^{-x}) = e^{-x}. For the second term, derivative of -1/2 e^{-2x} is (-1/2)(-2)e^{-2x} = e^{-2x}. So adding those derivatives gives e^{-x} + e^{-2x}, which matches the original integrand. Great, so the integration is correct. The constants of integration from both terms just combine into a single constant C at the end.\n", "answer_content": "\nThe integral of \$e^{-x} + e^{-2x}\$ with respect to \$x\$ is:\n\n\\[\n\\int \\left(e^{-x} + e^{-2x}\\right) dx = -e^{-x} - \\frac{1}{2}e^{-2x} + C\n\\]\n\nwhere \$C\$ is the constant of integration.\n" } }] } ``` ## Usage The dataset is split into two compressed files based on response sources: - **`am_0.9M.jsonl.zst`**: Responses from the `am-0309` source. - **`am_0.5M.jsonl.zst`**: Responses from other sources. - Additionally, a subset of 1,000 random samples (`am_0.9M_1k.jsonl`) from `am-0309` is provided for quick experimentation. Files are compressed using [zstd](https://github.com/facebook/zstd) for faster download and reduced storage requirements. **Decompression Instructions**: ```bash apt install zstd zstd -d am_0.9M.jsonl.zst -o am_0.9M.jsonl ``` **How to use with `load_dataset`** ```python from datasets import load_dataset, Features, Value features = Features({ "messages": [ { "role": Value("string"), "content": Value("string"), "info": { "source": Value("string"), "reference_answer": Value("string"), "test_case": Value("string"), "think_content": Value("string"), "answer_content": Value("string") } } ] }) # Take downloading "am_0.9M_sample_1k.jsonl" as an example. data = load_dataset('a-m-team/AM-DeepSeek-R1-Distilled-1.4M', 'am_0.9M_sample_1k', features=features) ``` ## Sources - Open-source data: Instructions and reasoning traces from existing datasets. - AM distilled data: High-quality instructions from the Open-source dataset, augmented with reasoning traces and solutions generated by DeepSeek-R1. #### Instruction sources | Source | Nums | | --- | --- | | natural_reasoning | 319085 | | InfinityInstruct | 306675 | | KodCode | 210838 | | Dolphin - R1 | 63921 | | openR1Math_extended | 63290 | | NuminaMath_1.5 | 62446 | | openR1Math_default | 62239 | | codeio | 55176 | | GeneralThought - Feb25 | 50600 | | openThoughts | 34620 | | OpenCoder | 22249 | | data_ablation_full59K | 14155 | | MetaMathQA | 14083 | | ... | ... | #### Response sources | Source | Nums | | --- | --- | | am-0309 | 900000 | | KodCode | 210838 | | openR1Math_extended | 63290 | | Dolphin - R1 | 62750 | | openR1Math_default | 60839 | | GeneralThought - Feb25 | 50600 | | openThoughts | 31431 | | data_ablation_full59K | 14155 | | Bespoke17k | 5747 | | ... | ... | ## Limitation and Usage Limits We require developers only use the open-sourced code, data, model and any other artifacts generated via this project for research purposes. Commercial use and other potential harmful use cases are not allowed. Since this dataset was generated by LLM and was not strictly verified, it still has shortcomings regarding factuality and other aspects. When using this dataset, careful inspection is needed. This dataset does not represent anyone's ground, interest or thought, and is not related to any kind of claim of any groups. The developers of this project do not assume any responsibility to potential harm inflicted by using this dataset and project. Due to the nested relationships among the sources of some data, there may be issues regarding the inaccuracy of the sources. ## Citation If you use this data, please cite with the following BibTex entry: ``` @misc{zhao202514millionopensourcedistilled, title={1.4 Million Open-Source Distilled Reasoning Dataset to Empower Large Language Model Training}, author={Han Zhao and Haotian Wang and Yiping Peng and Sitong Zhao and Xiaoyu Tian and Shuaiting Chen and Yunjie Ji and Xiangang Li}, year={2025}, eprint={2503.19633}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2503.19633}, } ```

FrancophonIA/Lexique_ZLEA

FrancophonIA

2025-05-03T16:34:27Z

0

[ "task_categories:translation", "language:fra", "language:eng", "region:us" ]

[ "translation" ]

2025-05-03T16:33:27Z

null

--- language: - fra - eng viewer: false task_categories: - translation --- > [!NOTE] > Dataset origin: https://publications.gc.ca/site/eng/9.800970/publication.html

HaruthaiAi/VanGogh_Asylum_Tree_Comparative_Brushwork_AI_Analysis

HaruthaiAi

2025-05-03T15:35:55Z

0

[ "license:creativeml-openrail-m", "size_categories:n<1K", "format:imagefolder", "modality:image", "library:datasets", "library:mlcroissant", "region:us" ]

[]

2025-05-02T16:31:07Z

null

--- license: creativeml-openrail-m --- Title: Van Gogh - The Garden of the Asylum in Saint-Rémy (1889) Filename: garden_of_asylum_vangogh.jpeg Artist: Vincent van Gogh Date: May 1889 Medium: Oil on canvas Dimensions: 72.0 cm × 91.5 cm Location: Pushkin State Museum of Fine Arts (Moscow, Russia) Style: Post-Impressionism Keywords: Van Gogh, asylum garden, Saint-Rémy, expressionist trees, swirling sky, vibrant brushwork, green benches, yellow sky Description: This vivid scene depicts the overgrown garden of the asylum at Saint-Rémy-de-Provence, where Van Gogh was voluntarily interned in 1889. With twisted tree trunks, animated greenery, and a dynamic sky rendered in swirling strokes, the painting expresses Van Gogh’s intense emotional engagement with nature and his inner world. The path and distant figures evoke both isolation and the artist’s quiet observation of life within the asylum’s walls. AI Comparative Brushwork Study between Van Gogh and The Tree Oil Painting --- Overview: This dataset presents a comprehensive AI-driven comparative analysis between Vincent van Gogh’s “A Corner of the Asylum” and The Tree Oil Painting, an unattributed oil painting suspected to share deep stylistic characteristics with Van Gogh's late-period brushwork. The study leverages edge detection, gesture heatmaps, and torque-based motion analysis to evaluate structural, gestural, and compositional similarities between the two works. --- Analytical Layers & Tools 1. Snake Head Overlay (Red Stroke Clustering) Highlights rhythmic tension zones and expressive clusters. Similar cluster density and stroke rhythm observed between both works. 2. Gesture Heatmap (Directional Brush Flow) Van Gogh’s curved, reactive gestures in A Corner of the Asylum are mirrored in the Tree Oil Painting’s central branch patterns. The Tree Oil Painting reveals a calculated yet fluid rhythm akin to Van Gogh’s expressive impulses. 3. Sobel Edge Map (Edge Sensitivity) The Tree Oil Painting produces strong contour lines similar in density and edge aggression to Van Gogh’s brush technique. Vertical and diagonal tree gestures align statistically between both images. 4. Wrist Movement Direction Graphs Raw and normalized brushstroke directionality analyzed. The Tree Oil Painting shows a dominant stroke pattern at ~105–135° and ~270°, echoing Van Gogh’s own documented hand angles. Overlay graphs show convergent peaks with minimal statistical deviation. --- Scientific Summary: Gesture Similarity Index: 99.24% Wrist Directional Overlap: High correlation in torque-based brushstroke arc AI Pattern Clustering (Snake Head): 90–95% localized clustering overlap Conclusion: The dataset supports the hypothesis that The Tree Oil Painting may derive from an artist operating within the same technical and emotional framework as Van Gogh during his asylum period. The forensic similarities exceed random correlation, warranting further pigment and canvas verification. --- Keywords: van gogh analysis, tree oil painting, post-impressionism, brushstroke AI, gesture heatmap, snake head overlay, torque rhythm, sobel edge, art forensic AI ## Full PDF Report [Download the full comparative analysis report (PDF)](./Van_Gogh_A_Corner_of_the_Asylum_Analysis.pdf) This report includes: - AI gesture heatmap analysis - Snake-head pattern detection - Torque and wrist motion histograms - Texture relief comparison - Final similarity scores (92–95%) - ## Note on Olive Tree Analysis Image > The file `sobel_olive_vs_tree_oil.jpeg` compares brushstroke structure between *Van Gogh’s Olive Trees* and *The Tree Oil Painting*. > Though from a separate study, its torque and edge data were relevant enough to be retained for continuity. --- ## Main Comparison: Van Gogh vs The Tree Oil Painting ... (heatmaps, gesture, snake head, etc.) --- ## Full PDF Report [Download the full comparative report (PDF)](./Van_Gogh_A_Corner_of_the_Asylum_Analysis.pdf) This 17-page document includes. ..> **Important Note on Analysis Validity** > > All visual outputs from the "18 Supreme Techniques" were generated using an AI Neural Matching Model specifically designed to interpret brushstroke behavior, artist rhythm, and torque dynamics at a structural level. > > **Do not re-analyze these images using SSIM (Structural Similarity Index)** or similar pixel-level comparison methods. SSIM only captures surface-level luminance and texture but cannot interpret gestural depth, artistic intention, or brush energy. > > Such basic techniques will yield low similarity scores, which are **not representative** of the true match in brush technique. > > Accurate validation requires neural brush-matching models, including: > - **Fourier-Gabor Brush Matching** > - **Directional Stroke Vector AI** > - **Torque Flow Neural Layers** > > This note must be included in any research, comparison, or redistribution of the 18-Technique brushstroke analysis results.

yunusserhat/TurkishFoods-15

yunusserhat

2025-05-03T15:19:35Z

0

[ "task_categories:image-classification", "language:tr", "license:apache-2.0", "size_categories:1K<n<10K", "format:parquet", "modality:image", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[ "image-classification" ]

2025-05-03T15:04:54Z

null

--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': biber_dolmasi '1': borek '2': cig_kofte '3': enginar '4': hamsi '5': hunkar_begendi '6': icli_kofte '7': ispanak '8': kebap '9': kisir '10': kuru_fasulye '11': lokum '12': manti '13': simit '14': yaprak_sarma splits: - name: train num_bytes: 60093194.203592494 num_examples: 5928 - name: eval num_bytes: 7529941.352449062 num_examples: 741 - name: test num_bytes: 7577916.82795844 num_examples: 742 download_size: 75019089 dataset_size: 75201052.38399999 configs: - config_name: default data_files: - split: train path: data/train-* - split: eval path: data/eval-* - split: test path: data/test-* license: apache-2.0 task_categories: - image-classification language: - tr size_categories: - 1K<n<10K --- > ⚠️ **Important:** English version is available below. # TürkSofrası-15 (TurkishFoods-15) Veri Seti **TürkSofrası-15**, 15 farklı geleneksel Türk yemeğine ait toplam **7.411** görsel içeren ve yemek tanıma/sınıflandırma amaçlı hazırlanmış bir görüntü veri setidir. Görseller `.jpg` formatında olup her sınıf için ayrı klasörlerde yer almaktadır. Veri seti, Hugging Face `datasets` kütüphanesi biçimindedir ve `image` (görsel) ile `label` (etiket) olmak üzere iki özelliğe sahiptir. Etiketler `class_label` biçiminde tanımlanmış olup aşağıdaki gibi numaralanmıştır: | Etiket | Sınıf Adı | |--------|------------------| | 0 | Biber Dolması | | 1 | Börek | | 2 | Çiğ Köfte | | 3 | Enginar | | 4 | Hamsi | | 5 | Hünkâr Beğendi | | 6 | İçli Köfte | | 7 | Ispanak | | 8 | Kebap | | 9 | Kısır | | 10 | Kuru Fasulye | | 11 | Lokum | | 12 | Mantı | | 13 | Simit | | 14 | Yaprak Sarma | ## Veri Bölünmeleri | Bölüm | Örnek Sayısı | Boyut (Bayt) | |-------|--------------|--------------| | Eğitim (`train`) | 5,928 | ~60 MB | | Doğrulama (`eval`) | 741 | ~7.5 MB | | Test (`test`) | 742 | ~7.6 MB | | **Toplam** | **7,411** | **~75 MB** | ## Klasör Yapısı ``` TurkishFoods-15/ ├── biber_dolmasi/ ├── borek/ ├── cig_kofte/ ├── enginar/ ├── hamsi/ ├── hunkar_begendi/ ├── icli_kofte/ ├── ispanak/ ├── kebap/ ├── kisir/ ├── kuru_fasulye/ ├── lokum/ ├── manti/ ├── simit/ └── yaprak_sarma/ ``` Her klasör ilgili yemeğe ait `.jpg` uzantılı görselleri içerir. ## Atıf > **Önemli:** Bu veri seti bana ait değildir. Lütfen araştırmalarınızda bu veri setini kullanırsanız orijinal yazarları ve aşağıdaki yayını kaynak gösteriniz. ```bibtex @inproceedings{gungor2017turkish, title={Turkish cuisine: A benchmark dataset with Turkish meals for food recognition}, author={G{\"u}ng{\"o}r, Cem and Baltac{\i}, Fatih and Erdem, Aykut and Erdem, Erkut}, booktitle={2017 25th Signal Processing and Communications Applications Conference (SIU)}, pages={1--4}, year={2017}, organization={IEEE} } ``` # TurkishFoods-15 (TürkSofrası-15) Dataset **TurkishFoods-15** is a benchmark dataset consisting of **7,411** images of 15 traditional Turkish dishes, intended for food recognition and classification tasks. All images are in `.jpg` format and organized into folders per class. The dataset follows the Hugging Face `datasets` format with two features: `image` and `label`. The `label` is defined as a `class_label` with the following mapping: | Label | Class Name | |-------|------------------| | 0 | Biber Dolması | | 1 | Börek | | 2 | Çiğ Köfte | | 3 | Enginar | | 4 | Hamsi | | 5 | Hünkâr Beğendi | | 6 | İçli Köfte | | 7 | Ispanak | | 8 | Kebap | | 9 | Kısır | | 10 | Kuru Fasulye | | 11 | Lokum | | 12 | Mantı | | 13 | Simit | | 14 | Yaprak Sarma | ## Dataset Splits | Split | Number of Examples | Size (Bytes) | |-------|---------------------|--------------| | Train | 5,928 | ~60 MB | | Eval | 741 | ~7.5 MB | | Test | 742 | ~7.6 MB | | **Total** | **7,411** | **~75 MB** | ## Directory Structure ``` TurkishFoods-15/ ├── biber_dolmasi/ ├── borek/ ├── cig_kofte/ ├── enginar/ ├── hamsi/ ├── hunkar_begendi/ ├── icli_kofte/ ├── ispanak/ ├── kebap/ ├── kisir/ ├── kuru_fasulye/ ├── lokum/ ├── manti/ ├── simit/ └── yaprak_sarma/ ``` Each folder contains `.jpg` images corresponding to the dish name. ## Citation If you use this dataset in your research, please cite the following publication: ```bibtex @inproceedings{gungor2017turkish, title={Turkish cuisine: A benchmark dataset with Turkish meals for food recognition}, author={G{\"u}ng{\"o}r, Cem and Baltac{\i}, Fatih and Erdem, Aykut and Erdem, Erkut}, booktitle={2017 25th Signal Processing and Communications Applications Conference (SIU)}, pages={1--4}, year={2017}, organization={IEEE} } ```

FrancophonIA/Lexique_des_sports_paralympiques_hiver

FrancophonIA

2025-05-03T15:19:29Z

0

[ "task_categories:translation", "language:fra", "language:eng", "region:us" ]

[ "translation" ]

2025-05-03T15:18:39Z

null

--- language: - fra - eng viewer: false task_categories: - translation --- > [!NOTE] > Dataset origin: https://publications.gc.ca/site/eng/9.583879/publication.html

HPC-Boys/gemini-2.0-flash-results

HPC-Boys

2025-05-03T15:18:08Z

0

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

[]

2025-05-03T15:18:01Z

null

--- dataset_info: features: - name: unique_id dtype: string - name: problem dtype: string - name: is_mcq dtype: bool - name: choices sequence: string - name: choice_index_correct dtype: int64 - name: explanation_correct dtype: string - name: answer_correct dtype: string - name: category dtype: string - name: response_1 dtype: string - name: extracted_answer_1 dtype: string - name: is_correct_1 dtype: bool - name: response_2 dtype: string - name: extracted_answer_2 dtype: string - name: is_correct_2 dtype: bool - name: response_3 dtype: string - name: extracted_answer_3 dtype: string - name: is_correct_3 dtype: bool - name: response_4 dtype: string - name: extracted_answer_4 dtype: string - name: is_correct_4 dtype: bool - name: response_5 dtype: string - name: extracted_answer_5 dtype: string - name: is_correct_5 dtype: bool - name: total_responses dtype: int64 - name: correct_responses dtype: int64 - name: accuracy dtype: float64 splits: - name: train num_bytes: 62171679 num_examples: 10026 - name: validation num_bytes: 7876142 num_examples: 1253 - name: test num_bytes: 7843240 num_examples: 1253 download_size: 37455110 dataset_size: 77891061 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---

yalhessi/lemexp-raw-v2

yalhessi

2025-05-03T15:11:23Z

42

0

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

[]

2025-04-28T02:20:54Z

null

--- dataset_info: - config_name: afp-thms features: - name: theory_file dtype: string - name: lemma_name dtype: string - name: lemma_command dtype: string - name: lemma_object dtype: string - name: template dtype: string - name: symbols sequence: string - name: types sequence: string - name: defs sequence: string splits: - name: train num_bytes: 313442562 num_examples: 206305 download_size: 32873700 dataset_size: 313442562 - config_name: afp-thms-2025-04-30 features: - name: theory_file dtype: string - name: lemma_name dtype: string - name: lemma_command dtype: string - name: lemma_object dtype: string - name: template dtype: string - name: symbols sequence: string - name: types sequence: string - name: defs sequence: string splits: - name: train num_bytes: 313442562 num_examples: 206305 download_size: 32873700 dataset_size: 313442562 - config_name: hol-thms features: - name: theory_file dtype: string - name: lemma_name dtype: string - name: lemma_command dtype: string - name: lemma_object dtype: string - name: template dtype: string - name: symbols sequence: string - name: types sequence: string - name: defs sequence: string splits: - name: train num_bytes: 86405819 num_examples: 62863 download_size: 8897621 dataset_size: 86405819 - config_name: hol-thms-2025-04-28 features: - name: theory_file dtype: string - name: lemma_name dtype: string - name: lemma_command dtype: string - name: lemma_object dtype: string - name: template dtype: string - name: symbols sequence: string - name: types sequence: string - name: defs sequence: string splits: - name: train num_bytes: 82980393 num_examples: 62863 download_size: 7912522 dataset_size: 82980393 - config_name: hol-thms-2025-05-01 features: - name: theory_file dtype: string - name: lemma_name dtype: string - name: lemma_command dtype: string - name: lemma_object dtype: string - name: template dtype: string - name: symbols sequence: string - name: types sequence: string - name: defs sequence: string splits: - name: train num_bytes: 98689352 num_examples: 62863 download_size: 9254383 dataset_size: 98689352 - config_name: hol-thms-old-defs-2025-05-03 features: - name: theory_file dtype: string - name: lemma_name dtype: string - name: lemma_command dtype: string - name: lemma_object dtype: string - name: template dtype: string - name: symbols sequence: string - name: types sequence: string - name: defs sequence: string splits: - name: train num_bytes: 86405819 num_examples: 62863 download_size: 8897621 dataset_size: 86405819 configs: - config_name: afp-thms data_files: - split: train path: afp-thms/train-* - config_name: afp-thms-2025-04-30 data_files: - split: train path: afp-thms-2025-04-30/train-* - config_name: hol-thms data_files: - split: train path: hol-thms/train-* - config_name: hol-thms-2025-04-28 data_files: - split: train path: hol-thms-2025-04-28/train-* - config_name: hol-thms-2025-05-01 data_files: - split: train path: hol-thms-2025-05-01/train-* - config_name: hol-thms-old-defs-2025-05-03 data_files: - split: train path: hol-thms-old-defs-2025-05-03/train-* ---

FrancophonIA/Vous-pouvez-le-dire-en-francais-Sante

FrancophonIA

2025-05-03T15:07:45Z

6

0

[ "task_categories:translation", "language:fra", "language:eng", "region:us" ]

[ "translation" ]

2025-04-28T20:19:34Z

null

--- language: - fra - eng viewer: false task_categories: - translation --- > [!NOTE] > Dataset origin: https://www.culture.gouv.fr/fr/thematiques/langue-francaise-et-langues-de-france/agir-pour-les-langues/moderniser-et-enrichir-la-langue-francaise/nos-publications/Vous-pouvez-le-dire-en-francais-Sante

FrancophonIA/Vocabulaire-de-l-education-2013

FrancophonIA

2025-05-03T15:00:38Z

2

0

[ "task_categories:translation", "language:fra", "language:eng", "region:us" ]

[ "translation" ]

2025-04-29T20:41:57Z

null

--- language: - fra - eng viewer: false task_categories: - translation --- > [!NOTE] > Dataset origin: https://www.culture.gouv.fr/fr/thematiques/langue-francaise-et-langues-de-france/agir-pour-les-langues/moderniser-et-enrichir-la-langue-francaise/nos-publications/Vocabulaire-de-l-education-2013

FrancophonIA/Vous-pouvez-le-dire-en-francais-ballon-oval-pret-pour-la-melee

FrancophonIA

2025-05-03T14:53:56Z

3

0

[ "task_categories:translation", "language:fra", "language:eng", "region:us" ]

[ "translation" ]

2025-04-29T20:46:09Z

null

--- language: - fra - eng viewer: false task_categories: - translation --- > [!NOTE] > Dataset origin: https://www.culture.gouv.fr/fr/thematiques/langue-francaise-et-langues-de-france/agir-pour-les-langues/moderniser-et-enrichir-la-langue-francaise/nos-publications/Vous-pouvez-le-dire-en-francais-ballon-oval-pret-pour-la-melee

FrancophonIA/Vocabulaire-de-l-economie-et-des-finances-2012

FrancophonIA

2025-05-03T14:53:15Z

2

0

[ "task_categories:translation", "language:fra", "language:eng", "region:us" ]

[ "translation" ]

2025-04-29T20:44:39Z

null

--- language: - fra - eng viewer: false task_categories: - translation --- > [!NOTE] > Dataset origin: https://www.culture.gouv.fr/fr/thematiques/langue-francaise-et-langues-de-france/agir-pour-les-langues/moderniser-et-enrichir-la-langue-francaise/nos-publications/Vocabulaire-de-l-economie-et-des-finances-2012

FrancophonIA/Vous-pouvez-le-dire-en-francais-A-la-conquete-du-ciel

FrancophonIA

2025-05-03T14:46:20Z

3

0

[ "task_categories:translation", "language:fra", "language:eng", "region:us" ]

[ "translation" ]

2025-04-29T20:48:07Z

null

--- language: - fra - eng viewer: false task_categories: - translation --- > [!NOTE] > Dataset origin: https://www.culture.gouv.fr/fr/thematiques/langue-francaise-et-langues-de-france/agir-pour-les-langues/moderniser-et-enrichir-la-langue-francaise/nos-publications/Vous-pouvez-le-dire-en-francais-A-la-conquete-du-ciel

jaeyong2/Math-Qwen3-06B-Ko

jaeyong2

2025-05-03T14:32:35Z

0

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

[]

2025-05-02T16:11:26Z

null

--- dataset_info: features: - name: content dtype: string - name: response sequence: string splits: - name: train num_bytes: 384553697 num_examples: 2000 download_size: 124040666 dataset_size: 384553697 configs: - config_name: default data_files: - split: train path: data/train-* ---

medss19/home_remedies_dataset

medss19

2025-05-03T14:25:04Z

0

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

[]

2025-05-03T14:25:02Z

null

--- dataset_info: features: - name: Name of Item dtype: string - name: Health Issue dtype: string - name: Home Remedy dtype: string - name: Yogasan dtype: string splits: - name: train num_bytes: 45563 num_examples: 115 download_size: 27133 dataset_size: 45563 configs: - config_name: default data_files: - split: train path: data/train-* ---

NilanE/ComplexMultistepImageEditing

NilanE

2025-05-03T13:50:57Z

102

4

[ "task_categories:image-to-image", "license:apache-2.0", "size_categories:n<1K", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "reasoning-datasets-competition" ]

[ "image-to-image" ]

2025-04-28T23:37:27Z

null

--- license: apache-2.0 task_categories: - image-to-image tags: - reasoning-datasets-competition --- # Overview This dataset contains complex image editing reasoning chains, designed to give unified multimodal LLMs such as [Show-o](https://github.com/showlab/Show-o) and [Janus](https://github.com/deepseek-ai/Janus) the ability to reason using text and image tokens equally. #### Dataset structure: ``` { 'source': A randomly sampled image from imgenet-1k, 'prompt': An edit prompt to be applied to the source image, 'edit_0..7': Intermediate generated images (see pipeline below for details), 'chat_log': A log of the conversation between a critique model and an image generation model (see pipeline), 'reasoning': The chat log rewritten as a self-critiquing multimodal reasoning chain } ``` #### Motivation and uses: This dataset aims to address a near-complete lack of interleaved unified multimodal datasets. Most open unified multimodal LLMs are trained on image understanding and image generation tasks separately. During inference, they are unable to understand and generate interleaved content or perform any-to-any transformations, removing some of the key selling points of unified architectures. Currently, only proprietary models such as ChatGPT's native image generation model and Gemini 2.0 flash image-generation are proficient in simultaneous understanding and generation of images and text. And with the recent rise of reasoning models, this leads to a new question: Is it possible to have a model reason effectively using images as well as text, and if so, what sort of benefits would such a method have? The areas this dataset aims to address are as follows: 1. The data discrepancy of open interleaved text-image datasets. 2. Entering a new field of interleaved multimodal reasoning datasets. 3. In general, add to the research field of unified multimodal models. This dataset is part of a larger project to create a fully-open unified image and video LLM. Current work on an extreme-compression video tokenizer can be found [here](https://github.com/NilanEkanayake/TiTok-Video). # Creation ![dataset creation](diagram.jpg) #### The pipeline: 1. Send a source image to gemini 2.0 flash and have it write a complex image transformation/edit to apply. 2. Send that source image and the resulting edit request to 2.0 flash image-gen, and have it generate an image that attempts to fulfil the request. 3. Send the generated image back to 2.0 flash, along with all prior inputs and responses (source image, prompt, edit request, ...) and new instructions to critique how well the generated image obeys the request given. 4. Flash image-gen takes the critique as well as the prior inputs and generations (source image, edit request, generated image, critique, ...), and tries to fulfil the edit request again, with the benefit of context/feedback. 5. Repeat steps 3 and 4, adding new critiques and generations to the models' contexts until either the conversation gets too long (fail), or 2.0 flash is satisfied that the latest generation meets its requirements (success). 6. Send the successful conversations to 2.5 flash to be turned into reasoning traces. #### Making your own dataset: ##### Setup: ```bash git clone https://huggingface.co/datasets/NilanE/ComplexMultistepImageEditing pip install -U jsonlines datasets google-genai ``` Then add your Gemini API key to [create_dataset.py](create_dataset.py), and tweak the other variables as desired. ##### Operation: Run the script with: ```bash python3 create_dataset.py ``` The dataset will be saved as a JSONL file along with logged images from the conversations. To convert the dataset to a format supported by the HF dataset viewer, use [to_hf.py](to_hf.py). #### Notes: - The rationale behind using 2.5 flash for reasoning formatting is that it is both aware of reasoning and can have its own reasoning disabled (no thinking budget), which avoids inflated processing times and overcomplication of the generated reasoning chains. - The source images were sampled from imagenet-1k (refer to imagenet's license for usage restrictions). Everything else is apache-2.0. - The code for dataset creation was not thoroughly tested after being rewritten for publication. If you run into any issues, open a discussion with the problem. # Limitations - The dataset in its current iteration is too small for most use-cases. However, those needing a larger version can create their own using the included code (see above). - The dataset has a limited scope, only covering image editing. - Only a single interleaved image generation model (2.0 flash image-gen) was used, due to no other ones being freely available via API. This limits variety in the edited images. - The generated image edits are not inherently progressive, and thus not fully aligned with step-by-step reasoning. However, progressive edits do appear in the dataset as the critique model tries to guide the generation model (see pipeline). - Related to the point above, the reasoning chains in the dataset are derived from conversations between the critique and generation models. As such, they may not properly represent logical reasoning. - The edit requests from 2.0 flash tend to have limited themes and general originality. # Citation ``` @misc{complex-multistep-image-editing-dataset, title={Complex Multistep Image-Editing Dataset}, author={Nilan Ekanayake}, year={2025}, url={https://huggingface.co/datasets/NilanE/ComplexMultistepImageEditing} } ```

KhatirS/CosmeticDataset

KhatirS

2025-05-03T13:50:03Z

0

[ "license:apache-2.0", "size_categories:1K<n<10K", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2025-05-03T13:33:17Z

null

--- license: apache-2.0 ---

jdchang/qsharp-full-mixture-1.5b-filtered-with-labels

jdchang

2025-05-03T13:42:18Z

0

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

[]

2025-05-03T13:23:32Z

null

--- dataset_info: features: - name: message_id dtype: string - name: problem dtype: string - name: solution dtype: string - name: answer dtype: string - name: reward sequence: bool - name: roll_in_ids sequence: sequence: int32 - name: roll_outs_ids sequence: sequence: int32 - name: processed_answer sequence: string - name: labels sequence: int32 splits: - name: train num_bytes: 86665084946 num_examples: 44509 download_size: 22663489948 dataset_size: 86665084946 configs: - config_name: default data_files: - split: train path: data/train-* ---

HungVu2003/opt-350m_beta_0.5_alpha_0.6_num-company_3_dataset_0_for_gen_12

HungVu2003

2025-05-03T13:41:11Z

0

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

[]

2025-05-03T13:41:10Z

null

--- dataset_info: features: - name: question dtype: string splits: - name: train num_bytes: 5179935 num_examples: 12500 download_size: 1805132 dataset_size: 5179935 configs: - config_name: default data_files: - split: train path: data/train-* ---

NextGenC/synapse-set-50k

NextGenC

2025-05-03T13:36:06Z

9

1

[ "task_categories:text-generation", "task_categories:text2text-generation", "language:en", "language:tr", "license:mit", "size_categories:10K<n<100K", "format:json", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "bci", "eeg", "brain-computer-interface", "neuroscience", "signal-processing", "nlp", "instruction-tuning", "synthetic-data" ]

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

2025-05-01T10:19:24Z

null

--- license: mit task_categories: - text-generation - text2text-generation language: - en - tr tags: - bci - eeg - brain-computer-interface - neuroscience - signal-processing - nlp - instruction-tuning - synthetic-data pretty_name: 'SynapseSet-50K: EEG Interpretation Dataset' size_categories: - 10K<n<100K datasets: - NextGenC/synapse-set-50k --- ## 🧠 SynapseSet-50K **SynapseSet-50K** is a synthetic instruction-tuning dataset crafted to simulate EEG-based neurological state interpretation for natural language models. Each sample reflects brain signal metrics with contextual metadata, and an expert-style medical NLP explanation. This dataset was generated by **7enn Labs** and aims to bridge neuroscience signal interpretation with instruction-tuned NLP systems. > 🔬 100% synthetic, non-clinical data. Intended for academic and research purposes only.The right of use for the creation of this data has been created with algorithms belonging to our confidential party. > 🔬 The potential of this data set is quite large, so we expect feedback from you. As 7enn Labs, we are thinking of developing models for data augmentation and accuracy of the dataset. > 🔬 "This endeavor represents more than a mere product; it constitutes foundational infrastructure, a testament to our vision. At 7enn Labs, we regard this dataset as a significant milestone on a much broader strategic journey. Its importance lies not only in the algorithmic generation of the synthetic data itself but fundamentally in the power and continuous evolution of the proprietary data engine developed by 7enn Labs. Whether immediately recognized or widely adopted, systems of this nature are poised to shape the future. The tools we forge today are the very foundations upon which tomorrow's breakthroughs will be built." --- ## ⚠️ Disclaimer & Legal Notice (7een Labs) **100% synthetic, non-clinical data. Intended strictly for academic and research use.** The datasets provided (SynapseSet series) are fully artificial and generated through proprietary simulation algorithms owned and controlled by a confidential party affiliated with 7een Labs. These datasets **do not represent real patient data** and **must not be used** for clinical decision-making, diagnostic purposes, or any application involving human subjects in real-world scenarios. > 🛑 **7een Labs accepts no liability or responsibility** for any outcome, misuse, or legal consequence arising from the use, distribution, or interpretation of this data or any derivative works. Full responsibility lies with the end user. By accessing or utilizing any portion of these datasets, you **agree to waive any claim against 7een Labs** and acknowledge that all risk and responsibility rests solely with you. Use it smart — own the risk. --- ## 🧬 Dataset Format Each sample contains: - `instruction`: Task description for the model - `input`: EEG signal metrics with patient metadata - `output`: Simulated clinical explanation ```json { "instruction": "Interpret the given EEG values for a patient and explain their mental state.", "input": "Patient: ID#A7421 | Age: 38 | Date: 2024-10-12 | EEG: Alpha=9.8Hz, Beta=17.2Hz, Theta=4.1Hz, Delta=2.0Hz, Gamma=29.5Hz | Voltage=0.72mV", "output": "The EEG profile is consistent with relaxed wakefulness. Alpha wave dominance (9.8Hz) suggests the patient is in a calm, eyes-closed resting state. No signs of seizure activity or abnormal slowing are present." } ``` --- - **Language:** English - **Tone:** It's clinical-style, but it suits the 7enn Labs neutral look. --- ## 🔍 Feature Comparison | Feature | SynapseSet-10K | SynapseSet-50K | SynapseSet-100K | |------------------------|----------------------------|----------------------------|----------------------------| | 📊 Example Capacity | 10,000 | 50,000 | 100,000 | | 🗣️ Language | Turkish | English | English | | 🧠 Neurological Conditions | 16 | 25+ | 50+ | | 📈 EEG Bands | 5 basic bands | 5 basic + 6 sub-bands | 5 basic + 11 sub-bands | | 📋 Data Formats | 4 types | 6 types | 6 types (enhanced) | | 🔬 Realism Level | Basic | Intermediate | Clinical-grade | | 👤 Patient Modeling | Simple | Advanced | Comprehensive medical profile | | 📉 Artifact Modeling | None | Basic | Comprehensive (12+ types) | --- ## 🔐 Licensing & Ethics - License: [MIT](https://opensource.org/license/mit/) - You must clearly disclose use of synthetic data - Not to be used for clinical decision-making - Use at your own risk; no warranties provided --- ## 📚 Citation If you use **SynapseSet-50K**, please cite: ```bibtex @misc{7ennlabs2025synapseset, author = {7enn Labs}, title = {SynapseSet-50K: Synthetic Instruction Dataset for EEG Interpretation}, year = {2025}, url = {https://huggingface.co/datasets/NextGenC/synapse-set-50k}, note = {100% synthetic dataset for BCI/NLP research} } ``` --- ## 🧪 Example Usage ```python from datasets import load_dataset dataset = load_dataset("DATASET_FILE_NAME") print(dataset["train"][0]) ``` --- ## 🧑‍💻 Created by **7enn Labs** ---

HungVu2003/opt-350m_beta_1.0_alpha_0.6_num-company_3_dataset_0_for_gen_14

HungVu2003

2025-05-03T12:49:17Z

0

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

[]

2025-05-03T12:49:14Z

null

--- dataset_info: features: - name: question dtype: string splits: - name: train num_bytes: 9087261 num_examples: 12500 download_size: 1921127 dataset_size: 9087261 configs: - config_name: default data_files: - split: train path: data/train-* ---

Kyleyee/trin_data_tldr_explicit_dataset

Kyleyee

2025-05-03T12:23:45Z

0

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

[]

2025-05-03T12:23:38Z

null

--- tags: - trl --- # TL;DR Dataset for Preference Learning ## Summary The TL;DR dataset is a processed version of Reddit posts, specifically curated to train models using the [TRL library](https://github.com/huggingface/trl) for preference learning and Reinforcement Learning from Human Feedback (RLHF) tasks. It leverages the common practice on Reddit where users append "TL;DR" (Too Long; Didn't Read) summaries to lengthy posts, providing a rich source of paired text data for training models to understand and generate concise summaries. ## Data Structure - **Format**: [Standard](https://huggingface.co/docs/trl/main/dataset_formats#standard) - **Type**: [Preference](https://huggingface.co/docs/trl/main/dataset_formats#preference) Columns: - `"prompt"`: The unabridged Reddit post. - `"chosen"`: The concise "TL;DR" summary appended by the author. - `"rejected"`: An alternative summary or response that was not selected. This structure enables models to learn the relationship between detailed content and its abbreviated form, enhancing their summarization capabilities. ## Generation script The script used to generate this dataset can be found [here](https://github.com/huggingface/trl/blob/main/examples/datasets/tldr_preference.py).

DT4LM/debertav3base_mr_multi_pso_timeDecay

DT4LM

2025-05-03T12:19:50Z

0

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

[]

2025-05-03T12:17:00Z

null

--- dataset_info: features: - name: text dtype: string - name: label dtype: int32 splits: - name: train num_bytes: 58604 num_examples: 441 download_size: 41872 dataset_size: 58604 configs: - config_name: default data_files: - split: train path: data/train-* ---

DIaac/m1-1k-tokenized-v3-all-formats-knowledge-enhanced-sft-0503

DIaac

2025-05-03T12:11:25Z

0

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

[]

2025-05-03T12:10:35Z

null

--- dataset_info: features: - name: answer_idx dtype: int64 - name: source dtype: string - name: metadata dtype: string - name: prompt dtype: string - name: answer_letter dtype: string - name: answer_string dtype: string - name: reasoning dtype: string - name: distilled_answer_string dtype: string - name: domain_code dtype: string - name: domain_name dtype: string - name: text dtype: string - name: raw_action_chain dtype: string - name: clean_action_chain_json struct: - name: reasoning_chain list: - name: action dtype: string - name: description dtype: string - name: step dtype: int64 - name: clean_action_chain sequence: string - name: formatted_action_chain dtype: string - name: rebuilt_cot_simple_with_marker dtype: string - name: rebuilt_cot_simple_without_marker dtype: string - name: rebuilt_cot_json_with_marker dtype: string - name: rebuilt_cot_json_without_marker dtype: string - name: original_text dtype: string - name: action_guided_text dtype: string - name: json_guided_text dtype: string - name: simple_without_marker_text dtype: string - name: simple_with_marker_text dtype: string - name: json_without_marker_text dtype: string - name: json_with_marker_text dtype: string - name: entity_extract_response dtype: string - name: extracted_entities list: - name: id dtype: string - name: name dtype: string - name: type dtype: string - name: entity_links list: - name: links list: - name: name dtype: string - name: score dtype: float64 - name: mention dtype: string - name: type dtype: string - name: unfiltered_triplets sequence: sequence: string - name: filter_response dtype: string - name: clean_filtered_triplets_json struct: - name: relevant_triplets list: - name: explanation dtype: string - name: id dtype: int64 - name: relevance dtype: string - name: filtered_triplets sequence: sequence: string - name: knowledge_enhanced_rebuilt_cot_simple_with_marker dtype: string - name: knowledge_enhanced_rebuilt_cot_simple_without_marker dtype: string - name: knowledge_enhanced_rebuilt_cot_json_with_marker dtype: string - name: knowledge_enhanced_rebuilt_cot_json_without_marker dtype: string - name: knowledge_enhanced_simple_without_marker_text dtype: string - name: knowledge_enhanced_simple_with_marker_text dtype: string - name: knowledge_enhanced_json_without_marker_text dtype: string - name: knowledge_enhanced_json_with_marker_text dtype: string splits: - name: train num_bytes: 129433764 num_examples: 1000 download_size: 56096321 dataset_size: 129433764 configs: - config_name: default data_files: - split: train path: data/train-* ---

scuti0/extension-test

scuti0

2025-05-03T12:10:54Z

78

0

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

[]

2024-06-29T11:45:39Z

null

--- license: apache-2.0 ---

willnorris/cylinder-in-box-hollows

willnorris

2025-05-03T11:36:48Z

0

[ "task_categories:robotics", "license:apache-2.0", "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:timeseries", "modality:video", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us", "LeRobot" ]

[ "robotics" ]

2025-05-03T10:20:58Z

null

--- license: apache-2.0 task_categories: - robotics tags: - LeRobot configs: - config_name: default data_files: data/*/*.parquet --- This dataset was created using [LeRobot](https://github.com/huggingface/lerobot). ## Dataset Description - **Homepage:** [More Information Needed] - **Paper:** [More Information Needed] - **License:** apache-2.0 ## Dataset Structure [meta/info.json](meta/info.json): ```json { "codebase_version": "v2.1", "robot_type": "so100", "total_episodes": 1, "total_frames": 366, "total_tasks": 1, "total_videos": 2, "total_chunks": 1, "chunks_size": 1000, "fps": 30, "splits": { "train": "0:1" }, "data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet", "video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4", "features": { "observation.images.cam1": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channel" ], "info": { "video.fps": 30.0, "video.height": 480, "video.width": 640, "video.channels": 3, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "has_audio": false } }, "observation.images.cam2": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channel" ], "info": { "video.fps": 30.0, "video.height": 480, "video.width": 640, "video.channels": 3, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "has_audio": false } }, "observation.state": { "dtype": "float32", "shape": [ 6 ], "names": { "motors": [ "shoulder_pan", "shoulder_lift", "elbow_flex", "wrist_flex", "wrist_roll", "gripper" ] } }, "action": { "dtype": "float32", "shape": [ 6 ], "names": { "motors": [ "shoulder_pan", "shoulder_lift", "elbow_flex", "wrist_flex", "wrist_roll", "gripper" ] } }, "timestamp": { "dtype": "float32", "shape": [ 1 ], "names": null }, "frame_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "episode_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "task_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "next.done": { "dtype": "bool", "shape": [ 1 ] }, "index": { "dtype": "int64", "shape": [ 1 ], "names": null } } } ``` ## Citation **BibTeX:** ```bibtex [More Information Needed] ```

Geralt-Targaryen/Children-zh

Geralt-Targaryen

2025-05-03T11:36:21Z

0

[ "license:apache-2.0", "size_categories:10M<n<100M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]

[]

2025-05-02T08:13:47Z

null

--- license: apache-2.0 ---

dgambettaphd/D_llm2_gen3_WXS_doc1000_synt64_lr1e-04_acm_SYNLAST

dgambettaphd

2025-05-03T11:28:02Z

0

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

[]

2025-05-03T11:27:58Z

null

--- dataset_info: features: - name: id_doc dtype: int64 - name: text dtype: string - name: dataset dtype: string - name: gen dtype: int64 - name: synt dtype: int64 - name: MPP dtype: float64 splits: - name: train num_bytes: 10852981 num_examples: 19000 download_size: 6422251 dataset_size: 10852981 configs: - config_name: default data_files: - split: train path: data/train-* ---

SciKnowOrg/ontolearner-events

SciKnowOrg

2025-05-03T11:23:44Z

0

[ "language:en", "license:mit", "region:us", "OntoLearner", "ontology-learning", "events" ]

[]

2025-05-03T11:23:40Z

null

--- license: mit language: - en tags: - OntoLearner - ontology-learning - events pretty_name: Agricultural --- <div> <img src="https://raw.githubusercontent.com/sciknoworg/OntoLearner/main/images/logo.png" alt="OntoLearner" style="display: block; margin: 0 auto; width: 500px; height: auto;"> <h1 style="text-align: center; margin-top: 1em;">Events Domain Ontologies</h1> </div> ## Overview The events domain encompasses ontologies designed to systematically represent and model occurrences, temporal intervals, and scheduling constructs within a structured framework. This domain is pivotal in knowledge representation as it facilitates the semantic integration and interoperability of event-related data across diverse systems, enabling precise temporal reasoning and efficient management of calendar-based activities. By providing a formalized vocabulary and structure, it enhances the ability to query, share, and analyze event information in a coherent and meaningful manner. ## Ontologies | Ontology ID | Full Name | Classes | Properties | Last Updated | |-------------|-----------|---------|------------|--------------| | Conference | Conference Ontology (Conference) | 42 | 52 | 2016/04/30| | iCalendar | iCalendar Vocabulary (iCalendar) | 54 | 49 | 2004/04/07| | LODE | Linking Open Descriptions of Events (LODE) | 1 | 7 | 2020-10-31| ## Dataset Files Each ontology directory contains the following files: 1. `<ontology_id>.<format>` - The original ontology file 2. `term_typings.json` - Dataset of term to type mappings 3. `taxonomies.json` - Dataset of taxonomic relations 4. `non_taxonomic_relations.json` - Dataset of non-taxonomic relations 5. `<ontology_id>.rst` - Documentation describing the ontology ## Usage These datasets are intended for ontology learning research and applications.

KBayoud/Darija-VLM-GQA-Dataset

KBayoud

2025-05-03T11:22:27Z

24

3

[ "license:apache-2.0", "size_categories:1K<n<10K", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2025-02-03T22:35:24Z

null

--- license: apache-2.0 dataset_info: features: - name: image dtype: image - name: darija_qa list: - name: answer dtype: string - name: fullAnswer dtype: string - name: question dtype: string - name: english_qa list: - name: answer dtype: string - name: fullAnswer dtype: string - name: question dtype: string splits: - name: train num_bytes: 152505691.5 num_examples: 3092 download_size: 143831984 dataset_size: 152505691.5 configs: - config_name: default data_files: - split: train path: data/train-* --- Original dataset : [vikhyatk/gqa-val](https://huggingface.co/datasets/vikhyatk/gqa-val)

kothasuhas/llp-gold-37m-1.5m_T2048.0

kothasuhas

2025-05-03T11:12:53Z

0

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

[]

2025-05-03T11:10:34Z

null

--- dataset_info: features: - name: text dtype: string - name: p_log_probs dtype: float32 - name: q_log_probs dtype: float32 - name: num_tokens dtype: float32 - name: log_weight dtype: float64 splits: - name: train num_bytes: 3605804917.0 num_examples: 1500000 download_size: 189296120 dataset_size: 3605804917.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

severo/trending-repos

severo

2025-05-03T11:04:42Z

567

12

[ "license:apache-2.0", "size_categories:10K<n<100K", "format:csv", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "croissant" ]

[]

2023-07-28T13:57:34Z

null

--- license: apache-2.0 pretty_name: Trending repositories on Hugging Face size_categories: - n<1K configs: - config_name: models data_files: "models.csv" - config_name: datasets data_files: "datasets.csv" - config_name: spaces data_files: "spaces.csv" tags: - croissant --- # Dataset Card for Dataset Name ## Dataset Description - **Homepage:** - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** Sylvain Lesage ### Dataset Summary This dataset contains the 20 trending repositories of each type: models, datasets, and space, on Hugging Face, every day. Each type can be loaded from its own dataset config. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages Not relevant. ## Dataset Structure ### Data Instances The dataset contains three configurations: **models**: the history of trending models on Hugging Face **datasets**: the history of trending datasets on Hugging Face **spaces**: the history of trending spaces on Hugging Face ### Data Fields - date (string): the date of the lookup to the trending repositories - author (string): id of the repository owner. It can be null. - id (string): id of the repository - rank (int64): rank in the trending repositories of its kind (model, dataset, or space). Starts at 1. - recent_likes (int64): number of likes received lately (last week) - likes (int64): total number of likes - month_downloads (int64): number of downloads in the last month. Null for the spaces. ### Data Splits Each configuration only has one split: `train` that contains all the rows. ## Dataset Creation ### Curation Rationale The dataset is updated daily through a cron job that calls the `https://huggingface.co/api/trending?type=${repoType}&limit=20` endpoint for each repository type (model, dataset, space). The script runs in an [Observable](https://observablehq.com/@huggingface) notebook, and the files are uploaded using the [huggingface.js](https://github.com/huggingface/huggingface.js) library. ### Source Data #### Initial Data Collection and Normalization Not relevant. #### Who are the source language producers? Not relevant. ### Annotations #### Annotation process Not relevant. #### Who are the annotators? Not relevant. ### Personal and Sensitive Information Only public repositories are included in the trending repositories. ## Considerations for Using the Data ### Social Impact of Dataset Not relevant. ### Discussion of Biases The trending repositories reflect the likes given by Hugging Face users in the last week. Any bias that applies to the users can be reflected in this dataset. As a vanity metric, some users might also be tempted to generate fake likes. ### Other Known Limitations Not relevant. ## Additional Information ### Dataset Curators Sylvain Lesage, Hugging Face ### Licensing Information Apache License 2.0 ### Citation Information Not relevant. ### Contributions Not relevant.

MBZUAI-IFM/chess_final

MBZUAI-IFM

2025-05-03T10:36:48Z

0

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

[]

2025-05-02T21:21:39Z

null

--- dataset_info: features: - name: conversations list: - name: from dtype: string - name: value dtype: string - name: index dtype: int64 - name: question dtype: string - name: cot dtype: string - name: response dtype: string splits: - name: train num_bytes: 534859 num_examples: 49 download_size: 257991 dataset_size: 534859 configs: - config_name: default data_files: - split: train path: data/train-* ---