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 12:15:05	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 12:13:14	trending_score float64 1 39 ⌀	card large_stringlengths 31 1M
ParkSY/data_nerf_anything_depth_normalmap	ParkSY	2025-05-05T05:59:15Z	0	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-05T05:36:29Z	null	--- dataset_info: features: - name: input_image dtype: string - name: edit_prompt dtype: string - name: edited_image dtype: string - name: label dtype: int64 - name: depthmap dtype: string - name: normalmap dtype: string splits: - name: train num_bytes: 1332384 num_examples: 8190 download_size: 266269 dataset_size: 1332384 configs: - config_name: default data_files: - split: train path: data/train-* ---
justinian336/salvadoran-news-elmundo	justinian336	2025-05-05T01:30:12Z	117	0	[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2023-02-15T14:38:15Z	null	--- dataset_info: features: - name: image_src dtype: string - name: title dtype: string - name: content dtype: string - name: category dtype: class_label: names: '0': Nacionales '1': Economia '2': Tecnomundo '3': Politica '4': Guia Mundialista '5': El Mundo '6': Editorial '7': Confidencial - name: date dtype: string - name: link dtype: string splits: - name: train num_bytes: 92200472 num_examples: 47007 download_size: 51272331 dataset_size: 92200472 --- # Dataset Card for "salvadoran-news-elmundo" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
mlfoundations-dev/nemo_nano_code_1000k	mlfoundations-dev	2025-05-05T01:03:07Z	0	0	[ "region:us" ]	[]	2025-05-05T00:50:19Z	null	--- dataset_info: features: - name: input dtype: string - name: output dtype: string - name: category dtype: string - name: license dtype: string - name: reasoning dtype: string - name: generator dtype: string - name: used_in_training dtype: string - name: version dtype: string - name: system_prompt dtype: string - name: conversations list: - name: from dtype: string - name: value dtype: string splits: - name: train num_bytes: 40531140108.885 num_examples: 1000000 download_size: 16693989550 dataset_size: 40531140108.885 configs: - config_name: default data_files: - split: train path: data/train-* ---
xbilek25/train_hall_absorb_0.7_3600_7200	xbilek25	2025-05-04T20:00:31Z	0	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:audio", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-04T15:33:49Z	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: 710317089.0 num_examples: 3600 download_size: 558743976 dataset_size: 710317089.0 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_18_v2	HungVu2003	2025-05-04T18:19:56Z	0	0	[ "region:us" ]	[]	2025-05-04T18:19:55Z	null	--- dataset_info: features: - name: question dtype: string splits: - name: train num_bytes: 2023535 num_examples: 13750 download_size: 1099056 dataset_size: 2023535 configs: - config_name: default data_files: - split: train path: data/train-* ---
openfoodfacts/product-database	openfoodfacts	2025-05-04T18:17:42Z	3,298	35	[ "language:en", "language:fr", "language:de", "language:es", "language:it", "language:nl", "language:pl", "language:pt", "language:sv", "language:bg", "language:ro", "language:fi", "language:ru", "language:nb", "language:cs", "language:th", "language:da", "language:hr", "language:hu", "language:ar", "language:el", "language:ja", "language:ca", "language:sr", "language:sl", "language:sk", "language:tr", "language:lt", "language:zh", "language:et", "language:lv", "language:xx", "language:uk", "language:id", "language:he", "language:vi", "language:is", "language:la", "language:in", "language:ko", "language:sq", "language:iw", "language:ka", "language:ms", "language:bs", "language:fa", "language:bn", "language:gl", "language:kk", "language:mk", "language:nn", "language:hi", "language:aa", "language:uz", "language:so", "language:af", "language:eu", "license:agpl-3.0", "license:odbl", "size_categories:1M<n<10M", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2024-10-21T08:44:28Z	null	--- language: - en - fr - de - es - it - nl - pl - pt - sv - bg - ro - fi - ru - nb - cs - th - da - hr - hu - ar - el - ja - ca - sr - sl - sk - tr - lt - zh - et - lv - xx - uk - id - he - vi - is - la - in - ko - sq - iw - ka - ms - bs - fa - bn - gl - kk - mk - nn - hi - aa - uz - so - af - eu license: - agpl-3.0 - odbl size_categories: - 1M<n<10M pretty_name: Open Food Facts Product Database dataset_info: config_name: default configs: - config_name: default data_files: - split: food path: food.parquet - split: beauty path: beauty.parquet --- # Open Food Facts Database ## What is 🍊 Open Food Facts? ### A food products database Open Food Facts is a database of food products with ingredients, allergens, nutrition facts and all the tidbits of information we can find on product labels. ### Made by everyone Open Food Facts is a non-profit association of volunteers. 25.000+ contributors like you have added 1.7 million + products from 150 countries using our Android or iPhone app or their camera to scan barcodes and upload pictures of products and their labels. ### For everyone Data about food is of public interest and has to be open. The complete database is published as open data and can be reused by anyone and for any use. Check-out the cool reuses or make your own! ## The Parquet Dataset This dataset is a simpler version of the [JSONL dump](https://world.openfoodfacts.org/data) provided by the Open Food Facts organization on a daily basis. It was converted into the Parquet format for easy of use. ### Data processing * `Debug` tags were removed. * `Tags`tags are conserved since they contain most information, * `Hierarchy` tags were removed * `lc` tags were removed. It corresponds to the ["language of the interface"](https://openfoodfacts.github.io/openfoodfacts-server/reference/api-tutorials/adding-missing-products/#sending-the-right-country-and-language-parameters-based-on-the-country-your-user-is-located-in-and-the-language-the-product-is-in), * `langs` tags are kept for each `ingredients_text` and conserved as individual columns (for now). The original JSONL dump was processed using [Pyarrow](https://arrow.apache.org/docs/python/). ## Conditions for reuse The Open Food Facts database is available under the Open Database License. The individual contents of the database are available under the Database Contents License. Products images are available under the Creative Commons Attribution ShareAlike licence. They may contain graphical elements subject to copyright or other rights, that may in some cases be reproduced (quotation rights or fair use). Please read Terms and conditions of use and re-use before re-using the data. ## Tell us about your reuse We are very interested in learning what the Open Food Facts data is used for. It is not mandatory, but we would very much appreciate it if you tell us about your re-uses so that we can share them with the Open Food Facts community. You can also fill this form to get a chance to get your app featured. - Homepage: https://world.openfoodfacts.org/ - Repository: https://github.com/openfoodfacts - Point of Contact: [email protected]
paulinus/so100_test	paulinus	2025-05-04T18:02:08Z	0	0	[ "task_categories:robotics", "license:apache-2.0", "region:us", "LeRobot", "so100", "tutorial" ]	[ "robotics" ]	2025-05-04T18:02:04Z	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": 1, "total_frames": 629, "total_tasks": 1, "total_videos": 0, "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" ] }, "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] ```
Lithium73fr/test2	Lithium73fr	2025-05-04T17:55:20Z	0	0	[ "task_categories:robotics", "region:us", "phosphobot", "so100", "phospho-dk" ]	[ "robotics" ]	2025-05-02T18:43:56Z	null	--- tags: - phosphobot - so100 - phospho-dk task_categories: - robotics --- # test2 This dataset was generated using a [phospho starter pack](https://robots.phospho.ai). This dataset contains a series of episodes recorded with a robot and multiple cameras. It can be directly used to train a policy using imitation learning. It's compatible with LeRobot and RLDS.
GitBag/Qwen2.5-7B_hmmt-feb-25_eval	GitBag	2025-05-04T17:48:21Z	0	0	[ "region:us" ]	[]	2025-05-04T17:48:20Z	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: 3844721 num_examples: 30 download_size: 1256848 dataset_size: 3844721 configs: - config_name: default data_files: - split: train path: data/train-* ---
RafaelJaime/sas_opposition_exam_data	RafaelJaime	2025-05-04T17:48:04Z	376	0	[ "language:es", "license:apache-2.0", "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "medical" ]	[]	2025-03-21T14:57:39Z	null	--- dataset_info: features: - name: statement dtype: string - name: answers sequence: string - name: correct_answer dtype: string - name: theme dtype: string - name: version dtype: string splits: - name: train num_bytes: 5128074 num_examples: 10712 download_size: 2407181 dataset_size: 5128074 configs: - config_name: default data_files: - split: train path: data/train-* license: apache-2.0 language: - es tags: - medical --- # SAS Opposition Exam Dataset This dataset contains questions and answers from all the exams of the SAS (Servicio Andaluz de Salud) public job offers. The questions and answers are sourced from the official webpage of the Andalusian Health Service [here](https://www.sspa.juntadeandalucia.es/servicioandaluzdesalud/profesionales/ofertas-de-empleo/oferta-de-empleo-publico-puestos-base/oep-extraordinaria-decreto-ley-122022-centros-sas/cuadro-de-evolucion-concurso-oposicion-centros-sas). ## Dataset Information - Statement: The question in the exam. - Answers: The possible answers for the question. - Real Answer: The correct answer for the question. - Theme: The topic or subject of the question. ### Dataset Creation Script The script used to create this dataset can be found at: [generation_script.py](https://huggingface.co/datasets/RafaelJaime/sas_opposition_exam_data/blob/main/generation_script.py).
HungVu2003/opt-350m_beta_1.0_alpha_0.8_num-company_3_dataset_1_for_gen_2	HungVu2003	2025-05-04T16:47:43Z	0	0	[ "region:us" ]	[]	2025-05-04T16:47:41Z	null	--- dataset_info: features: - name: question dtype: string splits: - name: train num_bytes: 2632052 num_examples: 12498 download_size: 1439908 dataset_size: 2632052 configs: - config_name: default data_files: - split: train path: data/train-* ---
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-* ---
JosephZ/mega_1m	JosephZ	2025-05-04T16:14:08Z	2	0	[ "language:en", "license:apache-2.0", "size_categories:100K<n<1M", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:2411.15435", "region:us", "scene-graph-generation" ]	[]	2025-05-02T09:13:32Z	null	--- dataset_info: features: - name: image_id dtype: string - name: file_name dtype: string - name: image dtype: image - name: objects dtype: string - name: relationships dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 221848987040.66 num_examples: 988531 download_size: 221355388705 dataset_size: 221848987040.66 configs: - config_name: default data_files: - split: train path: data/train-* license: apache-2.0 language: - en tags: - scene-graph-generation --- This dataset MegaSG contains 1M images annotated with scene graphs , which was introduced in the paper [What Makes a Scene ? Scene Graph-based Evaluation and Feedback for Controllable Generation](https://arxiv.org/pdf/2411.15435)
mteb/VoyageMMarcoReranking	mteb	2025-05-04T16:11:59Z	9	0	[ "task_categories:text-ranking", "annotations_creators:derived", "multilinguality:monolingual", "language:jpn", "license:cc-by-4.0", "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2312.16144", "arxiv:2502.13595", "arxiv:2210.07316", "region:us", "mteb", "text" ]	[ "text-ranking" ]	2025-02-18T20:02:06Z	null	--- annotations_creators: - derived language: - jpn license: cc-by-4.0 multilinguality: monolingual task_categories: - text-ranking 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: 26582357 num_examples: 53375 download_size: 12669365 dataset_size: 26582357 - config_name: default features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 3093432 num_examples: 53375 download_size: 359413 dataset_size: 3093432 - config_name: queries features: - name: _id dtype: string - name: text dtype: string splits: - name: test num_bytes: 139132 num_examples: 2048 download_size: 79174 dataset_size: 139132 - config_name: top_ranked features: - name: query-id dtype: string - name: corpus-ids sequence: string splits: - name: test num_bytes: 1778562 num_examples: 2048 download_size: 353814 dataset_size: 1778562 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-* - config_name: top_ranked data_files: - split: test path: top_ranked/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;">VoyageMMarcoReranking</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> a hard-negative augmented version of the Japanese MMARCO dataset as used in Voyage AI Evaluation Suite \| \| \| \|---------------\|---------------------------------------------\| \| Task category \| t2t \| \| Domains \| Academic, Non-fiction, Written \| \| Reference \| https://arxiv.org/abs/2312.16144 \| ## 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(["VoyageMMarcoReranking"]) 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{clavié2023jacolbert, archiveprefix = {arXiv}, author = {Benjamin Clavié}, eprint = {2312.16144}, title = {JaColBERT and Hard Negatives, Towards Better Japanese-First Embeddings for Retrieval: Early Technical Report}, 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("VoyageMMarcoReranking") desc_stats = task.metadata.descriptive_stats ``` ```json { "test": { "num_samples": 55423, "number_of_characters": 8824820, "num_documents": 53375, "min_document_length": 19, "average_document_length": 164.72532084309134, "max_document_length": 1192, "unique_documents": 53375, "num_queries": 2048, "min_query_length": 3, "average_query_length": 15.9208984375, "max_query_length": 73, "unique_queries": 2048, "none_queries": 0, "num_relevant_docs": 53375, "min_relevant_docs_per_query": 26, "average_relevant_docs_per_query": 1.06201171875, "max_relevant_docs_per_query": 29, "unique_relevant_docs": 53375, "num_instructions": null, "min_instruction_length": null, "average_instruction_length": null, "max_instruction_length": null, "unique_instructions": null, "num_top_ranked": 2048, "min_top_ranked_per_query": 26, "average_top_ranked_per_query": 26.06201171875, "max_top_ranked_per_query": 29 } } ``` </details> --- This dataset card was automatically generated using [MTEB](https://github.com/embeddings-benchmark/mteb)
mteb/SCIDOCS-PL	mteb	2025-05-04T16:11:26Z	34	0	[ "task_categories:text-retrieval", "multilinguality:monolingual", "source_datasets:mteb/scidocs", "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" ]	2025-02-05T19:10:26Z	null	--- language: - pol multilinguality: monolingual source_datasets: - mteb/scidocs 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: 35547893 num_examples: 25657 download_size: 21449173 dataset_size: 35547893 - config_name: default features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 2873088 num_examples: 29928 download_size: 1330598 dataset_size: 2873088 - config_name: queries features: - name: _id dtype: string - name: text dtype: string splits: - name: test num_bytes: 131568 num_examples: 1000 download_size: 100886 dataset_size: 131568 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;">SCIDOCS-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> SciDocs, a new evaluation benchmark consisting of seven document-level tasks ranging from citation prediction, to document classification and recommendation. \| \| \| \|---------------\|---------------------------------------------\| \| Task category \| t2t \| \| Domains \| None \| \| Reference \| https://allenai.org/data/scidocs \| ## 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(["SCIDOCS-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("SCIDOCS-PL") desc_stats = task.metadata.descriptive_stats ``` ```json { "test": { "num_samples": 26657, "number_of_characters": 32692750, "num_documents": 25657, "min_document_length": 12, "average_document_length": 1271.0791986592353, "max_document_length": 11840, "unique_documents": 25657, "num_queries": 1000, "min_query_length": 14, "average_query_length": 80.671, "max_query_length": 235, "unique_queries": 1000, "none_queries": 0, "num_relevant_docs": 29928, "min_relevant_docs_per_query": 27, "average_relevant_docs_per_query": 4.928, "max_relevant_docs_per_query": 30, "unique_relevant_docs": 25657, "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/CQADupstack-Unix-PL	mteb	2025-05-04T16:10:57Z	16	0	[ "task_categories:text-retrieval", "task_ids:multiple-choice-qa", "annotations_creators:derived", "multilinguality:translated", "source_datasets:mteb/cqadupstack-unix", "language:pol", "license:unknown", "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" ]	2025-02-05T19:19:28Z	null	--- annotations_creators: - derived language: - pol license: unknown multilinguality: translated source_datasets: - mteb/cqadupstack-unix task_categories: - text-retrieval task_ids: - multiple-choice-qa dataset_info: - config_name: corpus features: - name: _id dtype: string - name: text dtype: string - name: title dtype: string splits: - name: test num_bytes: 48663218 num_examples: 47382 download_size: 28352236 dataset_size: 48663218 - config_name: default features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 44636 num_examples: 1693 download_size: 23577 dataset_size: 44636 - config_name: queries features: - name: _id dtype: string - name: text dtype: string splits: - name: test num_bytes: 76537 num_examples: 1072 download_size: 54080 dataset_size: 76537 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;">CQADupstack-Unix-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> CQADupStack: A Stack Exchange Question Duplicate Pairs Dataset \| \| \| \|---------------\|---------------------------------------------\| \| Task category \| t2t \| \| Domains \| Written, Web, Programming \| \| Reference \| https://huggingface.co/datasets/clarin-knext/cqadupstack-unix-pl \| ## 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(["CQADupstack-Unix-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("CQADupstack-Unix-PL") desc_stats = task.metadata.descriptive_stats ``` ```json { "test": { "num_samples": 48454, "number_of_characters": 45977634, "num_documents": 47382, "min_document_length": 52, "average_document_length": 969.1105272044236, "max_document_length": 30428, "unique_documents": 47382, "num_queries": 1072, "min_query_length": 14, "average_query_length": 55.26026119402985, "max_query_length": 136, "unique_queries": 1072, "none_queries": 0, "num_relevant_docs": 1693, "min_relevant_docs_per_query": 1, "average_relevant_docs_per_query": 1.5792910447761195, "max_relevant_docs_per_query": 22, "unique_relevant_docs": 1693, "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/trec-covid	mteb	2025-05-04T16:10:37Z	1,263	2	[ "task_categories:text-retrieval", "multilinguality:monolingual", "language:eng", "size_categories:100K<n<1M", "format:json", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2104.09632", "arxiv:2502.13595", "arxiv:2210.07316", "region:us", "mteb", "text" ]	[ "text-retrieval" ]	2024-03-02T20:32:41Z	null	--- language: - eng multilinguality: monolingual task_categories: - text-retrieval task_ids: [] 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: test num_bytes: 1710499 num_examples: 66336 - config_name: corpus features: - name: _id dtype: string - name: title dtype: string - name: text dtype: string splits: - name: corpus num_bytes: 195185777 num_examples: 171332 - config_name: queries features: - name: _id dtype: string - name: text dtype: string splits: - name: queries num_bytes: 3953 num_examples: 50 configs: - config_name: default data_files: - 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;">TRECCOVID</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> TRECCOVID is an ad-hoc search challenge based on the COVID-19 dataset containing scientific articles related to the COVID-19 pandemic. \| \| \| \|---------------\|---------------------------------------------\| \| Task category \| t2t \| \| Domains \| Medical, Academic, Written \| \| Reference \| https://ir.nist.gov/covidSubmit/index.html \| ## 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(["TRECCOVID"]) 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{roberts2021searching, archiveprefix = {arXiv}, author = {Kirk Roberts and Tasmeer Alam and Steven Bedrick and Dina Demner-Fushman and Kyle Lo and Ian Soboroff and Ellen Voorhees and Lucy Lu Wang and William R Hersh}, eprint = {2104.09632}, primaryclass = {cs.IR}, title = {Searching for Scientific Evidence in a Pandemic: An Overview of TREC-COVID}, 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("TRECCOVID") desc_stats = task.metadata.descriptive_stats ``` ```json { "test": { "num_samples": 171382, "number_of_characters": 191508678, "num_documents": 171332, "min_document_length": 1, "average_document_length": 1117.7434221277986, "max_document_length": 122459, "unique_documents": 171332, "num_queries": 50, "min_query_length": 30, "average_query_length": 69.24, "max_query_length": 165, "unique_queries": 50, "none_queries": 0, "num_relevant_docs": 66336, "min_relevant_docs_per_query": 631, "average_relevant_docs_per_query": 493.5, "max_relevant_docs_per_query": 1941, "unique_relevant_docs": 35480, "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/mtop_intent	mteb	2025-05-04T16:08:08Z	2,668	2	[ "task_categories:text-classification", "annotations_creators:human-annotated", "multilinguality:multilingual", "language:deu", "language:eng", "language:fra", "language:hin", "language:spa", "language:tha", "license:unknown", "modality:text", "arxiv:2008.09335", "arxiv:2502.13595", "arxiv:2210.07316", "region:us", "mteb", "text" ]	[ "text-classification" ]	2022-05-19T15:03:15Z	null	--- annotations_creators: - human-annotated language: - deu - eng - fra - hin - spa - tha license: unknown multilinguality: multilingual task_categories: - text-classification task_ids: [] dataset_info: - config_name: de features: - name: text dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 748424 num_examples: 13424 - name: validation num_bytes: 100446 num_examples: 1815 - name: test num_bytes: 195937 num_examples: 3549 download_size: 543111 dataset_size: 1044807 - config_name: en features: - name: text dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 761023 num_examples: 15667 - name: validation num_bytes: 108483 num_examples: 2235 - name: test num_bytes: 214022 num_examples: 4386 download_size: 629031 dataset_size: 1083528 - config_name: es features: - name: text dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 621403 num_examples: 10934 - name: validation num_bytes: 87850 num_examples: 1527 - name: test num_bytes: 170223 num_examples: 2998 download_size: 403224 dataset_size: 879476 - config_name: fr features: - name: text dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 671550 num_examples: 11814 - name: validation num_bytes: 88815 num_examples: 1577 - name: test num_bytes: 182408 num_examples: 3193 download_size: 484784 dataset_size: 942773 - config_name: hi features: - name: text dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 1238687 num_examples: 11330 - name: validation num_bytes: 228095 num_examples: 2012 - name: test num_bytes: 303899 num_examples: 2789 download_size: 642592 dataset_size: 1770681 - config_name: th features: - name: text dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 1175051 num_examples: 10759 - name: validation num_bytes: 185878 num_examples: 1671 - name: test num_bytes: 301794 num_examples: 2765 download_size: 621662 dataset_size: 1662723 configs: - config_name: de data_files: - split: train path: de/train-* - split: validation path: de/validation-* - split: test path: de/test-* - config_name: en data_files: - split: train path: en/train-* - split: validation path: en/validation-* - split: test path: en/test-* - config_name: es data_files: - split: train path: es/train-* - split: validation path: es/validation-* - split: test path: es/test-* - config_name: fr data_files: - split: train path: fr/train-* - split: validation path: fr/validation-* - split: test path: fr/test-* - config_name: hi data_files: - split: train path: hi/train-* - split: validation path: hi/validation-* - split: test path: hi/test-* - config_name: th data_files: - split: train path: th/train-* - split: validation path: th/validation-* - split: test path: th/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;">MTOPIntentClassification</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> MTOP: Multilingual Task-Oriented Semantic Parsing \| \| \| \|---------------\|---------------------------------------------\| \| Task category \| t2c \| \| Domains \| Spoken, Spoken \| \| Reference \| https://arxiv.org/pdf/2008.09335.pdf \| ## 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(["MTOPIntentClassification"]) 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{li-etal-2021-mtop, abstract = {Scaling semantic parsing models for task-oriented dialog systems to new languages is often expensive and time-consuming due to the lack of available datasets. Available datasets suffer from several shortcomings: a) they contain few languages b) they contain small amounts of labeled examples per language c) they are based on the simple intent and slot detection paradigm for non-compositional queries. In this paper, we present a new multilingual dataset, called MTOP, comprising of 100k annotated utterances in 6 languages across 11 domains. We use this dataset and other publicly available datasets to conduct a comprehensive benchmarking study on using various state-of-the-art multilingual pre-trained models for task-oriented semantic parsing. We achieve an average improvement of +6.3 points on Slot F1 for the two existing multilingual datasets, over best results reported in their experiments. Furthermore, we demonstrate strong zero-shot performance using pre-trained models combined with automatic translation and alignment, and a proposed distant supervision method to reduce the noise in slot label projection.}, address = {Online}, author = {Li, Haoran and Arora, Abhinav and Chen, Shuohui and Gupta, Anchit and Gupta, Sonal and Mehdad, Yashar}, booktitle = {Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume}, doi = {10.18653/v1/2021.eacl-main.257}, editor = {Merlo, Paola and Tiedemann, Jorg and Tsarfaty, Reut}, month = apr, pages = {2950--2962}, publisher = {Association for Computational Linguistics}, title = {{MTOP}: A Comprehensive Multilingual Task-Oriented Semantic Parsing Benchmark}, url = {https://aclanthology.org/2021.eacl-main.257}, 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("MTOPIntentClassification") desc_stats = task.metadata.descriptive_stats ``` ```json { "validation": { "num_samples": 10837, "number_of_characters": 431895, "number_texts_intersect_with_train": 127, "min_text_length": 5, "average_text_length": 39.85374181046415, "max_text_length": 154, "unique_text": 10830, "unique_labels": 88, "labels": { "3": { "count": 919 }, "18": { "count": 645 }, "27": { "count": 117 }, "15": { "count": 322 }, "24": { "count": 81 }, "29": { "count": 487 }, "14": { "count": 713 }, "60": { "count": 320 }, "84": { "count": 69 }, "0": { "count": 628 }, "25": { "count": 1156 }, "1": { "count": 188 }, "30": { "count": 11 }, "16": { "count": 692 }, "54": { "count": 41 }, "59": { "count": 464 }, "22": { "count": 171 }, "9": { "count": 49 }, "43": { "count": 716 }, "63": { "count": 103 }, "48": { "count": 269 }, "11": { "count": 114 }, "32": { "count": 80 }, "4": { "count": 453 }, "53": { 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"102": { "count": 6 }, "89": { "count": 20 }, "31": { "count": 24 }, "79": { "count": 33 }, "57": { "count": 44 }, "104": { "count": 18 }, "110": { "count": 12 }, "62": { "count": 24 }, "6": { "count": 24 }, "21": { "count": 3 }, "74": { "count": 6 }, "73": { "count": 7 }, "26": { "count": 3 }, "66": { "count": 6 }, "88": { "count": 6 }, "10": { "count": 6 }, "12": { "count": 3 }, "93": { "count": 6 }, "50": { "count": 11 }, "107": { "count": 3 } }, "hf_subset_descriptive_stats": { "en": { "num_samples": 2235, "number_of_characters": 81663, "number_texts_intersect_with_train": 7, "min_text_length": 8, "average_text_length": 36.53825503355705, "max_text_length": 125, "unique_text": 2235, "unique_labels": 88, "labels": { "3": { "count": 189 }, "18": { "count": 163 }, "27": { "count": 29 }, "15": { "count": 65 }, "24": { "count": 15 }, "29": { "count": 93 }, "14": { "count": 136 }, "60": { "count": 59 }, "84": { "count": 12 }, "0": { "count": 131 }, "25": { "count": 223 }, "1": { "count": 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mteb/amazon_massive_intent	mteb	2025-05-04T16:08:03Z	79,081	21	[ "task_categories:text-classification", "annotations_creators:human-annotated", "multilinguality:translated", "language:afr", "language:amh", "language:ara", "language:aze", "language:ben", "language:cmo", "language:cym", "language:dan", "language:deu", "language:ell", "language:eng", "language:fas", "language:fin", "language:fra", "language:heb", "language:hin", "language:hun", "language:hye", "language:ind", "language:isl", "language:ita", "language:jav", "language:jpn", "language:kan", "language:kat", "language:khm", "language:kor", "language:lav", "language:mal", "language:mon", "language:msa", "language:mya", "language:nld", "language:nob", "language:pol", "language:por", "language:ron", "language:rus", "language:slv", "language:spa", "language:sqi", "language:swa", "language:swe", "language:tam", "language:tel", "language:tgl", "language:tha", "language:tur", "language:urd", "language:vie", "license:apache-2.0", "size_categories:1M<n<10M", "format:json", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "arxiv:2204.08582", "arxiv:2502.13595", "arxiv:2210.07316", "region:us", "mteb", "text" ]	[ "text-classification" ]	2022-05-15T20:59:15Z	null	--- annotations_creators: - human-annotated language: - afr - amh - ara - aze - ben - cmo - cym - dan - deu - ell - eng - fas - fin - fra - heb - hin - hun - hye - ind - isl - ita - jav - jpn - kan - kat - khm - kor - lav - mal - mon - msa - mya - nld - nob - pol - por - ron - rus - slv - spa - sqi - swa - swe - tam - tel - tgl - tha - tur - urd - vie license: apache-2.0 multilinguality: translated task_categories: - text-classification task_ids: [] configs: - config_name: default data_files: - path: train/.json.gz split: train - path: test/.json.gz split: test - path: validation/.json.gz split: validation - config_name: ta data_files: - path: train/ta.json.gz split: train - path: test/ta.json.gz split: test - path: validation/ta.json.gz split: validation - config_name: is data_files: - path: train/is.json.gz split: train - path: test/is.json.gz split: test - path: validation/is.json.gz split: validation - config_name: pl data_files: - path: train/pl.json.gz split: train - path: test/pl.json.gz split: test - path: validation/pl.json.gz split: validation - config_name: zh-CN data_files: - path: train/zh-CN.json.gz split: train - path: test/zh-CN.json.gz split: test - path: validation/zh-CN.json.gz split: validation - config_name: el data_files: - path: train/el.json.gz split: train - path: test/el.json.gz split: test - path: validation/el.json.gz split: validation - config_name: ru data_files: - path: train/ru.json.gz split: train - path: test/ru.json.gz split: test - path: validation/ru.json.gz split: validation - config_name: te data_files: - path: train/te.json.gz split: train - path: test/te.json.gz split: test - path: validation/te.json.gz split: validation - config_name: cy data_files: - path: train/cy.json.gz split: train - path: test/cy.json.gz split: test - path: validation/cy.json.gz split: validation - config_name: he data_files: - path: train/he.json.gz split: train - path: test/he.json.gz split: test - path: validation/he.json.gz split: validation - config_name: de data_files: - path: train/de.json.gz split: train - path: test/de.json.gz split: test - path: validation/de.json.gz split: validation - config_name: af data_files: - path: train/af.json.gz split: train - path: test/af.json.gz split: test - path: validation/af.json.gz split: validation - config_name: ml data_files: - path: train/ml.json.gz split: train - path: test/ml.json.gz split: test - path: validation/ml.json.gz split: validation - config_name: sl data_files: - path: train/sl.json.gz split: train - path: test/sl.json.gz split: test - path: validation/sl.json.gz split: validation - config_name: vi data_files: - path: train/vi.json.gz split: train - path: test/vi.json.gz split: test - path: validation/vi.json.gz split: validation - config_name: mn data_files: - path: train/mn.json.gz split: train - path: test/mn.json.gz split: test - path: validation/mn.json.gz split: validation - config_name: tl data_files: - path: train/tl.json.gz split: train - path: test/tl.json.gz split: test - path: validation/tl.json.gz split: validation - config_name: it data_files: - path: train/it.json.gz split: train - path: test/it.json.gz split: test - path: validation/it.json.gz split: validation - config_name: jv data_files: - path: train/jv.json.gz split: train - path: test/jv.json.gz split: test - path: validation/jv.json.gz split: validation - config_name: sq data_files: - path: train/sq.json.gz split: train - path: test/sq.json.gz split: test - path: validation/sq.json.gz split: validation - config_name: fa data_files: - path: train/fa.json.gz split: train - path: test/fa.json.gz split: test - path: validation/fa.json.gz split: validation - config_name: nb data_files: - path: train/nb.json.gz split: train - path: test/nb.json.gz split: test - path: validation/nb.json.gz split: validation - config_name: km data_files: - path: train/km.json.gz split: train - path: test/km.json.gz split: test - path: validation/km.json.gz split: validation - config_name: th data_files: - path: train/th.json.gz split: train - path: test/th.json.gz split: test - path: validation/th.json.gz split: validation - config_name: ja data_files: - path: train/ja.json.gz split: train - path: test/ja.json.gz split: test - path: validation/ja.json.gz split: validation - config_name: hi data_files: - path: train/hi.json.gz split: train - path: test/hi.json.gz split: test - path: validation/hi.json.gz split: validation - config_name: id data_files: - path: train/id.json.gz split: train - path: test/id.json.gz split: test - path: validation/id.json.gz split: validation - config_name: kn data_files: - path: train/kn.json.gz split: train - path: test/kn.json.gz split: test - path: validation/kn.json.gz split: validation - config_name: fi data_files: - path: train/fi.json.gz split: train - path: test/fi.json.gz split: test - path: validation/fi.json.gz split: validation - config_name: ur data_files: - path: train/ur.json.gz split: train - path: test/ur.json.gz split: test - path: validation/ur.json.gz split: validation - config_name: my data_files: - path: train/my.json.gz split: train - path: test/my.json.gz split: test - path: validation/my.json.gz split: validation - config_name: lv data_files: - path: train/lv.json.gz split: train - path: test/lv.json.gz split: test - path: validation/lv.json.gz split: validation - config_name: fr data_files: - path: train/fr.json.gz split: train - path: test/fr.json.gz split: test - path: validation/fr.json.gz split: validation - config_name: ko data_files: - path: train/ko.json.gz split: train - path: test/ko.json.gz split: test - path: validation/ko.json.gz split: validation - config_name: sw data_files: - path: train/sw.json.gz split: train - path: test/sw.json.gz split: test - path: validation/sw.json.gz split: validation - config_name: sv data_files: - path: train/sv.json.gz split: train - path: test/sv.json.gz split: test - path: validation/sv.json.gz split: validation - config_name: nl data_files: - path: train/nl.json.gz split: train - path: test/nl.json.gz split: test - path: validation/nl.json.gz split: validation - config_name: da data_files: - path: train/da.json.gz split: train - path: test/da.json.gz split: test - path: validation/da.json.gz split: validation - config_name: ar data_files: - path: train/ar.json.gz split: train - path: test/ar.json.gz split: test - path: validation/ar.json.gz split: validation - config_name: ms data_files: - path: train/ms.json.gz split: train - path: test/ms.json.gz split: test - path: validation/ms.json.gz split: validation - config_name: en data_files: - path: train/en.json.gz split: train - path: test/en.json.gz split: test - path: validation/en.json.gz split: validation - config_name: am data_files: - path: train/am.json.gz split: train - path: test/am.json.gz split: test - path: validation/am.json.gz split: validation - config_name: pt data_files: - path: train/pt.json.gz split: train - path: test/pt.json.gz split: test - path: validation/pt.json.gz split: validation - config_name: ka data_files: - path: train/ka.json.gz split: train - path: test/ka.json.gz split: test - path: validation/ka.json.gz split: validation - config_name: ro data_files: - path: train/ro.json.gz split: train - path: test/ro.json.gz split: test - path: validation/ro.json.gz split: validation - config_name: tr data_files: - path: train/tr.json.gz split: train - path: test/tr.json.gz split: test - path: validation/tr.json.gz split: validation - config_name: hu data_files: - path: train/hu.json.gz split: train - path: test/hu.json.gz split: test - path: validation/hu.json.gz split: validation - config_name: zh-TW data_files: - path: train/zh-TW.json.gz split: train - path: test/zh-TW.json.gz split: test - path: validation/zh-TW.json.gz split: validation - config_name: bn data_files: - path: train/bn.json.gz split: train - path: test/bn.json.gz split: test - path: validation/bn.json.gz split: validation - config_name: hy data_files: - path: train/hy.json.gz split: train - path: test/hy.json.gz split: test - path: validation/hy.json.gz split: validation - config_name: es data_files: - path: train/es.json.gz split: train - path: test/es.json.gz split: test - path: validation/es.json.gz split: validation - config_name: az data_files: - path: train/az.json.gz split: train - path: test/az.json.gz split: test - path: validation/az.json.gz split: validation 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;">MassiveIntentClassification</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> MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse Languages \| \| \| \|---------------\|---------------------------------------------\| \| Task category \| t2c \| \| Domains \| Spoken \| \| Reference \| https://arxiv.org/abs/2204.08582 \| ## 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(["MassiveIntentClassification"]) 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{fitzgerald2022massive, archiveprefix = {arXiv}, author = {Jack FitzGerald and Christopher Hench and Charith Peris and Scott Mackie and Kay Rottmann and Ana Sanchez and Aaron Nash and Liam Urbach and Vishesh Kakarala and Richa Singh and Swetha Ranganath and Laurie Crist and Misha Britan and Wouter Leeuwis and Gokhan Tur and Prem Natarajan}, eprint = {2204.08582}, primaryclass = {cs.CL}, title = {MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse Languages}, year = {2022}, } @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("MassiveIntentClassification") desc_stats = task.metadata.descriptive_stats ``` ```json { "validation": { "num_samples": 103683, "number_of_characters": 3583467, "number_texts_intersect_with_train": 5457, "min_text_length": 1, "average_text_length": 34.56176036573016, "max_text_length": 224, "unique_text": 102325, "unique_labels": 59, "labels": { "iot_hue_lightoff": { "count": 867 }, "iot_hue_lightdim": { "count": 867 }, "iot_cleaning": { "count": 969 }, "general_quirky": { "count": 5355 }, "takeaway_query": { "count": 1224 }, "play_music": { "count": 6273 }, "music_query": { "count": 1530 }, "weather_query": { "count": 6426 }, "music_settings": { "count": 408 }, "audio_volume_down": { "count": 408 }, "datetime_query": { "count": 3264 }, "general_greet": { "count": 102 }, "alarm_set": { "count": 1581 }, "audio_volume_up": { "count": 612 }, "alarm_query": { "count": 969 }, "news_query": { "count": 4182 }, "iot_hue_lighton": { "count": 255 }, "iot_wemo_off": { "count": 255 }, "iot_hue_lightchange": { "count": 1122 }, "audio_volume_mute": { "count": 765 }, "alarm_remove": { "count": 714 }, "general_joke": { "count": 765 }, "datetime_convert": { "count": 459 }, "iot_wemo_on": { "count": 357 }, "iot_hue_lightup": { "count": 612 }, "iot_coffee": { "count": 714 }, "social_post": { "count": 2550 }, "music_dislikeness": { "count": 102 }, "cooking_recipe": { "count": 2091 }, "takeaway_order": { "count": 1020 }, "music_likeness": { "count": 816 }, "calendar_query": { "count": 5202 }, "qa_stock": { "count": 1224 }, "qa_factoid": { "count": 4590 }, "calendar_set": { "count": 6681 }, "recommendation_events": { "count": 1326 }, "cooking_query": { "count": 102 }, "calendar_remove": { "count": 2397 }, "email_sendemail": { "count": 3213 }, "play_radio": { "count": 2346 }, "play_audiobook": { "count": 1785 }, "play_game": { "count": 1122 }, "lists_query": { "count": 2550 }, "lists_remove": { "count": 1887 }, "lists_createoradd": { "count": 1275 }, "email_addcontact": { "count": 255 }, "play_podcasts": { "count": 1734 }, "recommendation_movies": { "count": 612 }, "recommendation_locations": { "count": 1581 }, "transport_ticket": { "count": 1275 }, "transport_query": { "count": 1836 }, "transport_taxi": { "count": 1377 }, "transport_traffic": { "count": 1122 }, "qa_definition": { "count": 2805 }, "qa_currency": { "count": 1632 }, "qa_maths": { "count": 663 }, "social_query": { "count": 918 }, "email_query": { "count": 3723 }, "email_querycontact": { "count": 816 } } }, "test": { "num_samples": 151674, "number_of_characters": 5230011, "number_texts_intersect_with_train": 7273, "min_text_length": 1, "average_text_length": 34.48192175323391, "max_text_length": 495, "unique_text": 148972, "unique_labels": 59, "labels": { "alarm_set": { "count": 2091 }, "audio_volume_mute": { "count": 1632 }, "iot_hue_lightchange": { "count": 1836 }, "iot_hue_lighton": { "count": 153 }, "iot_hue_lightoff": { "count": 2193 }, "iot_cleaning": { "count": 1326 }, "general_quirky": { "count": 8619 }, "general_greet": { "count": 51 }, "datetime_query": { "count": 4488 }, "datetime_convert": { "count": 765 }, "alarm_remove": { "count": 1071 }, "alarm_query": { "count": 1734 }, "music_likeness": { "count": 1836 }, "iot_hue_lightup": { "count": 1377 }, "takeaway_order": { "count": 1122 }, "weather_query": { "count": 7956 }, "general_joke": { "count": 969 }, "play_music": { "count": 8976 }, "iot_hue_lightdim": { "count": 1071 }, "takeaway_query": { "count": 1785 }, "news_query": { "count": 6324 }, "audio_volume_up": { "count": 663 }, "iot_wemo_off": { "count": 918 }, "iot_wemo_on": { "count": 510 }, "iot_coffee": { "count": 1836 }, "music_query": { "count": 1785 }, "audio_volume_down": { "count": 561 }, "audio_volume_other": { "count": 306 }, "music_dislikeness": { "count": 204 }, "music_settings": { "count": 306 }, "recommendation_events": { "count": 2193 }, "qa_stock": { "count": 1326 }, "calendar_set": { "count": 10659 }, "play_audiobook": { "count": 2091 }, "social_query": { "count": 1275 }, "qa_factoid": { "count": 7191 }, "transport_ticket": { "count": 1785 }, "recommendation_locations": { "count": 1581 }, "calendar_query": { "count": 6426 }, "recommendation_movies": { "count": 1020 }, "transport_query": { "count": 2601 }, "cooking_recipe": { "count": 3672 }, "play_game": { "count": 1785 }, "calendar_remove": { "count": 3417 }, "email_query": { "count": 6069 }, "email_sendemail": { "count": 5814 }, "play_radio": { "count": 3672 }, "play_podcasts": { "count": 3213 }, "lists_query": { "count": 2601 }, "lists_remove": { "count": 2652 }, "lists_createoradd": { "count": 1989 }, "transport_taxi": { "count": 1173 }, "transport_traffic": { "count": 765 }, "qa_definition": { "count": 2907 }, "qa_maths": { "count": 1275 }, "social_post": { "count": 4131 }, "qa_currency": { "count": 1989 }, "email_addcontact": { "count": 612 }, "email_querycontact": { "count": 1326 } } }, "train": { "num_samples": 587214, "number_of_characters": 20507758, "number_texts_intersect_with_train": null, "min_text_length": 1, "average_text_length": 34.92382334208653, "max_text_length": 295, "unique_text": 565055, "unique_labels": 60, "labels": { "alarm_set": { "count": 9282 }, "audio_volume_mute": { "count": 5610 }, "iot_hue_lightchange": { "count": 6375 }, "iot_hue_lightoff": { "count": 7803 }, "iot_hue_lightdim": { "count": 3876 }, "iot_cleaning": { "count": 4743 }, "calendar_query": { "count": 28866 }, "play_music": { "count": 32589 }, "general_quirky": { "count": 28305 }, "general_greet": { "count": 1275 }, "datetime_query": { "count": 17850 }, "datetime_convert": { "count": 2652 }, "takeaway_query": { "count": 6222 }, "alarm_remove": { "count": 3978 }, "alarm_query": { "count": 6630 }, "news_query": { "count": 25653 }, "music_likeness": { "count": 5763 }, "music_query": { "count": 7854 }, "iot_hue_lightup": { "count": 3876 }, "takeaway_order": { "count": 6885 }, "weather_query": { "count": 29223 }, "music_settings": { "count": 2601 }, "general_joke": { "count": 3672 }, "music_dislikeness": { "count": 714 }, "audio_volume_other": { "count": 918 }, "iot_coffee": { "count": 6324 }, "audio_volume_up": { "count": 5610 }, "iot_wemo_on": { "count": 2448 }, "iot_hue_lighton": { "count": 1122 }, "iot_wemo_off": { "count": 2652 }, "audio_volume_down": { "count": 2652 }, "qa_stock": { "count": 7752 }, "play_radio": { "count": 14433 }, "recommendation_locations": { "count": 8823 }, "qa_factoid": { "count": 27744 }, "calendar_set": { "count": 41310 }, "play_audiobook": { "count": 7650 }, "play_podcasts": { "count": 9843 }, "social_query": { "count": 5508 }, "transport_query": { "count": 11577 }, "email_sendemail": { "count": 18054 }, "recommendation_movies": { "count": 3570 }, "lists_query": { "count": 10098 }, "play_game": { "count": 5712 }, "transport_ticket": { "count": 6477 }, "recommendation_events": { "count": 9690 }, "email_query": { "count": 21318 }, "transport_traffic": { "count": 5967 }, "cooking_query": { "count": 204 }, "qa_definition": { "count": 13617 }, "calendar_remove": { "count": 15912 }, "lists_remove": { "count": 8364 }, "cooking_recipe": { "count": 10557 }, "email_querycontact": { "count": 6477 }, "lists_createoradd": { "count": 9027 }, "transport_taxi": { "count": 5100 }, "qa_maths": { "count": 3978 }, "social_post": { "count": 14433 }, "qa_currency": { "count": 7242 }, "email_addcontact": { "count": 2754 } } } } ``` </details> --- This dataset card was automatically generated using [MTEB](https://github.com/embeddings-benchmark/mteb)*
Oriolshhh/parlabe-errors-castellanismes-35k	Oriolshhh	2025-05-04T15:36:57Z	0	0	[ "language:ca", "license:apache-2.0", "size_categories:10K<n<100K", "region:us", "català", "grammar-correction", "castellanismes", "text-to-text", "synthetic" ]	[]	2025-05-04T15:31:08Z	null	--- language: ca license: apache-2.0 tags: - català - grammar-correction - castellanismes - text-to-text - synthetic size_categories: - 10K<n<100K --- # Dataset de castellanismes en català (35.000 parelles) Aquest dataset conté 35.000 parelles de frases en format: ```text_erroni,text_correcte``` El conjunt inclou frases amb castellanismes habituals en el català oral i escrit, generades de manera sintètica per entrenar models que detectin i corregeixin aquestes interferències. --- ## Com s’ha generat? Les parelles han estat generades mitjançant: - API de GPT per introduir errors de castellanismes de forma natural - Filtratge automàtic amb un script Python - Selecció final per garantir diversitat i coherència lingüística Exemples d’errors de castellanisme: - Tinc que anar a casa → He d’anar a casa - Se lo compré a mi cosina ahir → Li vaig comprar a la meva cosina ahir - No em dona igual el que pensis → No m’és igual el que pensis --- ## Format - Llengua: Català (`ca`) - Format: `.csv` amb dues columnes: - `text_erroni` - `text_correcte` - Nombre de parelles: 35.000
mteb/bengali_hate_speech	mteb	2025-05-04T15:09:57Z	0	0	[ "region:us" ]	[]	2025-05-04T15:09:52Z	null	--- dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': 0 '1': 1 '2': 2 '3': 3 '4': 4 splits: - name: train num_bytes: 445907.77559976594 num_examples: 1567 - name: test num_bytes: 431110.58074897603 num_examples: 1515 download_size: 351631 dataset_size: 877018.356348742 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
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] ```
Siddhant00/SLR43ForOpherus	Siddhant00	2025-05-04T14:54:06Z	0	0	[ "region:us" ]	[]	2025-05-04T14:46:05Z	null	--- dataset_info: features: - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: path dtype: string - name: speaker dtype: int64 splits: - name: train num_bytes: 966898681.0 num_examples: 2064 download_size: 935774325 dataset_size: 966898681.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
rainbowbridge/x_dataset_36658	rainbowbridge	2025-05-04T14:19:36Z	1,210	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" ]	2025-01-27T09:42:25Z	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: rainbowbridge/x_dataset_36658 - Subnet: Bittensor Subnet 13 - Miner Hotkey: 5HBJT9LVMim1mFQnihWzwXze1tTJCRWG6gBmfGjuX8bYHQds ### 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{rainbowbridge2025datauniversex_dataset_36658, title={The Data Universe Datasets: The finest collection of social media data the web has to offer}, author={rainbowbridge}, year={2025}, url={https://huggingface.co/datasets/rainbowbridge/x_dataset_36658}, } ``` ### 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: 48889665 - Date Range: 2025-01-21T00:00:00Z to 2025-02-13T00:00:00Z - Last Updated: 2025-02-18T19:51:23Z ### Data Distribution - Tweets with hashtags: 43.87% - Tweets without hashtags: 56.13% ### Top 10 Hashtags For full statistics, please refer to the `stats.json` file in the repository. \| Rank \| Topic \| Total Count \| Percentage \| \|------\|-------\|-------------\|-------------\| \| 1 \| NULL \| 27441046 \| 56.13% \| \| 2 \| #riyadh \| 313769 \| 0.64% \| \| 3 \| #zelena \| 270307 \| 0.55% \| \| 4 \| #tiktok \| 207010 \| 0.42% \| \| 5 \| #bbb25 \| 142273 \| 0.29% \| \| 6 \| #ad \| 120913 \| 0.25% \| \| 7 \| #jhope_at_galadespiècesjaunes \| 86117 \| 0.18% \| \| 8 \| #royalrumble \| 71515 \| 0.15% \| \| 9 \| #granhermano \| 68966 \| 0.14% \| \| 10 \| #pr \| 68325 \| 0.14% \| ## Update History \| Date \| New Instances \| Total Instances \| \|------\|---------------\|-----------------\| \| 2025-01-27T09:43:29Z \| 3193387 \| 3193387 \| \| 2025-01-30T21:46:28Z \| 10063226 \| 13256613 \| \| 2025-02-03T09:49:08Z \| 7311324 \| 20567937 \| \| 2025-02-06T21:52:36Z \| 8681715 \| 29249652 \| \| 2025-02-10T11:26:25Z \| 8454192 \| 37703844 \| \| 2025-02-13T23:30:18Z \| 9715300 \| 47419144 \| \| 2025-02-18T04:50:00Z \| 752652 \| 48171796 \| \| 2025-02-18T19:51:23Z \| 717869 \| 48889665 \|
gunnybd01/StockIndicator-Data	gunnybd01	2025-05-04T11:08:57Z	0	0	[ "size_categories:10K<n<100K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-04T11:02:37Z	null	--- dataset_info: features: - name: Keys dtype: string - name: Trend_smr dtype: string - name: Volume_smr dtype: string - name: Volatility_smr dtype: string - name: Momentum_smr dtype: string - name: ShortPct dtype: float64 - name: MediumPct dtype: float64 - name: LongPct dtype: float64 splits: - name: train num_bytes: 504378755 num_examples: 60000 download_size: 82123715 dataset_size: 504378755 configs: - config_name: default data_files: - split: train path: data/train-* ---
GitBag/a_star_final_a_star_math_7_actor_aime-24_eval	GitBag	2025-05-04T11:00:38Z	0	0	[ "size_categories:n<1K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-04T11:00:36Z	null	--- dataset_info: features: - name: problem dtype: string - name: answer dtype: int64 - 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: 3887353 num_examples: 30 download_size: 1375386 dataset_size: 3887353 configs: - config_name: default data_files: - split: train path: data/train-* ---
HungVu2003/opt-350m_beta_1.0_alpha_0.6_num-company_3_dataset_1_for_gen_19	HungVu2003	2025-05-04T10:31:39Z	0	0	[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-04T10:31:38Z	null	--- dataset_info: features: - name: question dtype: string splits: - name: train num_bytes: 3599649 num_examples: 12500 download_size: 1854129 dataset_size: 3599649 configs: - config_name: default data_files: - split: train path: data/train-* ---
wyyyz139/character	wyyyz139	2025-05-04T10:22:48Z	2	0	[ "license:apache-2.0", "size_categories:1K<n<10K", "format:json", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "region:us" ]	[]	2025-05-04T02:57:50Z	null	--- license: apache-2.0 ---
HungVu2003/opt-350m_beta_0.5_alpha_0.6_num-company_3_dataset_2_for_gen_17	HungVu2003	2025-05-04T08:52:23Z	0	0	[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-04T08:52:21Z	null	--- dataset_info: features: - name: question dtype: string splits: - name: train num_bytes: 3882121 num_examples: 12500 download_size: 1222020 dataset_size: 3882121 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_10_v2	HungVu2003	2025-05-04T08:27:21Z	0	0	[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-04T08:27:20Z	null	--- dataset_info: features: - name: question dtype: string splits: - name: train num_bytes: 6383286 num_examples: 13750 download_size: 3232348 dataset_size: 6383286 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_9_v2	HungVu2003	2025-05-04T07:17:32Z	0	0	[ "region:us" ]	[]	2025-05-04T07:17:31Z	null	--- dataset_info: features: - name: question dtype: string splits: - name: train num_bytes: 6393295 num_examples: 13750 download_size: 3239607 dataset_size: 6393295 configs: - config_name: default data_files: - split: train path: data/train-* ---
rlawltjd/korean-nl2bash	rlawltjd	2025-05-04T07:04:23Z	0	0	[ "region:us" ]	[]	2025-05-04T07:04:15Z	null	--- dataset_info: features: - name: instruction dtype: string - name: output dtype: string splits: - name: train num_bytes: 1170873 num_examples: 8089 download_size: 448025 dataset_size: 1170873 configs: - config_name: default data_files: - split: train path: data/train-* ---
arielcerdap/tts-disfluencies-DA	arielcerdap	2025-05-04T06:55:17Z	0	0	[ "region:us" ]	[]	2025-05-04T06:54:53Z	null	--- dataset_info: features: - name: audio dtype: audio: sampling_rate: 44100 - name: text dtype: string - name: speaker_tag_used dtype: string - name: temperature_used dtype: float32 splits: - name: train num_bytes: 515151319.0 num_examples: 500 download_size: 499156183 dataset_size: 515151319.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
upvantage/deberta-1m-v2humanized	upvantage	2025-05-04T06:39:20Z	0	0	[ "size_categories:1M<n<10M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-04T06:30:44Z	null	--- dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': human '1': ai - name: type dtype: string splits: - name: train num_bytes: 1777430766 num_examples: 910928 - name: validation num_bytes: 197496555 num_examples: 101214 download_size: 1192474307 dataset_size: 1974927321 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* ---
flyingbugs/OpenR1-Math-220k-pruned-keep-0.1-end-start-0.0	flyingbugs	2025-05-04T05:13:34Z	0	0	[ "size_categories:10K<n<100K", "modality:text", "region:us" ]	[]	2025-05-04T05:12:40Z	null	--- dataset_info: features: - name: problem dtype: string - name: solution dtype: string - name: answer dtype: string - name: problem_type dtype: string - name: question_type dtype: string - name: source dtype: string - name: uuid dtype: string - name: is_reasoning_complete sequence: bool - name: generations sequence: string - name: correctness_math_verify sequence: bool - name: correctness_llama sequence: bool - name: finish_reasons sequence: string - name: correctness_count dtype: int64 - name: messages list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 3975332374 num_examples: 93733 download_size: 1721086932 dataset_size: 3975332374 configs: - config_name: default data_files: - split: train path: data/train-* ---
qhuang20/summarize_from_feedback_oai_preprocessing_1706381144_cnndm_relabel_pythia6.9b_emoji	qhuang20	2025-05-04T04:34:15Z	0	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-03T23:46:51Z	null	--- dataset_info: features: - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string - name: prompt_chosen dtype: string - name: prompt_rejected dtype: string - name: chosen_score dtype: float64 - name: rejected_score dtype: float64 - name: both_chosen_score dtype: float64 - name: both_rejected_score dtype: float64 - name: chosen_chosen_score dtype: float64 - name: chosen_rejected_score dtype: float64 - name: rejected_chosen_score dtype: float64 - name: rejected_rejected_score dtype: float64 - name: random_chosen_score dtype: float64 - name: random_rejected_score dtype: float64 splits: - name: validation_cnndm num_bytes: 26935641 num_examples: 2284 download_size: 3812053 dataset_size: 26935641 configs: - config_name: default data_files: - split: validation_cnndm path: data/validation_cnndm-* ---
HungVu2003/opt-350m_beta_0.0_alpha_0.2_num-company_2_dataset_1_for_gen_5_v2	HungVu2003	2025-05-04T04:33:17Z	0	0	[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-04T04:33:16Z	null	--- dataset_info: features: - name: question dtype: string splits: - name: train num_bytes: 2292678 num_examples: 13750 download_size: 1031494 dataset_size: 2292678 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_7_v2	HungVu2003	2025-05-04T04:07:18Z	0	0	[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-04T04:07:17Z	null	--- dataset_info: features: - name: question dtype: string splits: - name: train num_bytes: 2110876 num_examples: 13750 download_size: 1132970 dataset_size: 2110876 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_16	HungVu2003	2025-05-04T03:38:07Z	0	0	[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-04T03:38:06Z	null	--- dataset_info: features: - name: question dtype: string splits: - name: train num_bytes: 7468487 num_examples: 12500 download_size: 1913775 dataset_size: 7468487 configs: - config_name: default data_files: - split: train path: data/train-* ---
marcuscedricridia/OpenMathInstruct-1-1000-processed	marcuscedricridia	2025-05-04T02:46:57Z	0	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-04T02:46:56Z	null	--- dataset_info: features: - name: question dtype: string - name: generated_solution dtype: string splits: - name: train num_bytes: 586120.6902226892 num_examples: 1000 download_size: 293455 dataset_size: 586120.6902226892 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-* ---
aciang/ITK	aciang	2025-05-04T01:28:25Z	0	0	[ "license:apache-2.0", "size_categories:1K<n<10K", "format:json", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-04T01:25:49Z	null	--- license: apache-2.0 ---
dgambettaphd/D_llm2_gen1_WXS_doc1000_synt64_lr1e-04_acm_FRESH	dgambettaphd	2025-05-04T01:04:41Z	0	0	[ "size_categories:10K<n<100K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-04T01:04:18Z	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: 9773483 num_examples: 17000 download_size: 5870471 dataset_size: 9773483 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 ---
sergiomadrid/exist_task3	sergiomadrid	2025-05-03T22:47:05Z	0	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-03T22:46:49Z	null	--- dataset_info: features: - name: id dtype: int32 - name: text dtype: string - name: lang dtype: string - name: label_task3_1 sequence: string - name: label_task3_2 sequence: string - name: label_task3_3 sequence: sequence: string splits: - name: train num_bytes: 1768649 num_examples: 2524 download_size: 847539 dataset_size: 1768649 configs: - config_name: default data_files: - split: train path: data/train-* ---
AxeML/MiniSetPT	AxeML	2025-05-03T21:31:33Z	14	2	[ "task_categories:text-generation", "language:pt", "license:mit", "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "conversation", "dialogs", "dialogue" ]	[ "text-generation" ]	2025-05-01T19:01:10Z	2	--- license: mit task_categories: - text-generation language: - pt tags: - conversation - dialogs - dialogue size_categories: - 1K<n<10K --- <p align="center"> <img src="IA.png" alt="Banner do Projeto"> </p> # 📚 Dataset de Perguntas e Respostas por Tópico Este repositório contém um dataset com 10.000 amostras estruturadas para tarefas de Processamento de Linguagem Natural (PLN), com foco em perguntas temáticas e respostas desenvolvidas. ## 📁 Estrutura dos Dados Cada amostra é representada em formato JSON com os seguintes campos: - `id` (string): Identificador único da amostra (UUID). - `topic` (lista de strings): Lista com os tópicos abordados. - `prompts` (lista de strings): Conjunto de perguntas relacionadas ao tópico. - `response` (string): Resposta discursiva que contempla as perguntas propostas, escrita em linguagem natural. ### 🔍 Exemplo de Amostra ```json { "topic": ["jogos"], "prompts": [ "Como os jogos eletrônicos podem ser utilizados como ferramentas educacionais?", "Quais são os impactos psicológicos dos jogos competitivos em jogadores adolescentes?", "Como a evolução gráfica dos jogos influenciou a indústria do entretenimento?", "Quais são os principais elementos narrativos que tornam um jogo imersivo?" ], "response": "Os jogos são uma forma de entretenimento multifacetada que abrange diversas áreas de interesse. Como ferramentas educacionais, os jogos eletrônicos podem promover o aprendizado interativo, desenvolvendo habilidades como resolução de problemas, trabalho em equipe e pensamento crítico, através de mecânicas engajadoras. No aspecto psicológico, jogos competitivos podem tanto estimular a resiliência e a estratégia quanto exigir atenção aos possíveis efeitos negativos, como estresse ou frustração, especialmente em adolescentes. A evolução gráfica dos jogos revolucionou a indústria do entretenimento, elevando padrões tecnológicos e criando experiências visuais mais realistas, o que atraiu um público mais amplo e diversificado. Por fim, a narrativa imersiva em jogos depende de elementos como desenvolvimento de personagens, worldbuilding coeso e escolhas impactantes, que juntos criam uma conexão emocional entre o jogador e o universo virtual. Assim, os jogos se consolidam como uma mídia rica e versátil, com aplicações e impactos significativos em diversas esferas.", "id": "768cf005-1c0c-4f75-88b9-90cebdbeb81b" } ``` ## 🧠 Possíveis Aplicações - Treinamento de modelos de geração de texto; - Fine-tuning de LLMs em português; - Tarefas de sumarização e compreensão de texto; - Geração de respostas discursivas; - Classificação temática; - Estudos de coesão e coerência textual. ## 🗂️ Volume de Dados - Total de amostras: 10.000 - Idioma: Português (Brasil) ## 📄 Licença Este projeto está licenciado sob os termos da [Licença MIT](LICENSE).
aarontrinh02/leetcode_pipeline_part2	aarontrinh02	2025-05-03T21:08:05Z	0	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-03T02:47:35Z	null	--- dataset_info: features: - name: query_positive dtype: string - name: instruction_positive dtype: string - name: document_positive dtype: string - name: positive_language dtype: string - name: query_negative dtype: string - name: instruction_negative dtype: string - name: hard_negative_document_1 dtype: string - name: hard_negative_document_2 dtype: string splits: - name: train num_bytes: 16458010 num_examples: 1999 download_size: 5274648 dataset_size: 16458010 configs: - config_name: default data_files: - split: train path: data/train-* ---
anonymousEcaiHateLLM/Hate.2_labels_labeled	anonymousEcaiHateLLM	2025-05-03T20:34:38Z	0	0	[ "size_categories:100K<n<1M", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-03T20:34:25Z	null	--- dataset_info: features: - name: text dtype: string - name: unsloth/Qwen2.5-14B-Instruct-bnb-4bit_label_1 dtype: float64 - name: unsloth/Qwen2.5-14B-Instruct-bnb-4bit_label_2 dtype: float64 - name: unsloth/gemma-2-9b-it-bnb-4bit_label_1 dtype: float64 - name: unsloth/gemma-2-9b-it-bnb-4bit_label_2 dtype: float64 - name: unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit_label_1 dtype: float64 - name: unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit_label_2 dtype: float64 - name: unsloth/mistral-7b-instruct-v0.3-bnb-4bit_label_1 dtype: float64 - name: unsloth/mistral-7b-instruct-v0.3-bnb-4bit_label_2 dtype: float64 - name: language dtype: string - name: mean_label_1 dtype: float64 - name: mean_label_2 dtype: float64 - name: Mean dtype: int64 - name: lgb_label_1 dtype: float64 - name: lgb_label_2 dtype: float64 - name: Lgb dtype: int64 - name: Vote dtype: int64 splits: - name: 2_labels num_bytes: 79917049 num_examples: 240647 download_size: 44153399 dataset_size: 79917049 configs: - config_name: default data_files: - split: 2_labels path: data/2_labels-* ---
willnorris/cylinder-in-box-hollows-6	willnorris	2025-05-03T19:43:02Z	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" ]	2025-05-03T19:10:20Z	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": 298, "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 ], "names": null }, "index": { "dtype": "int64", "shape": [ 1 ], "names": null } } } ``` ## Citation BibTeX: ```bibtex [More Information Needed] ```
momo1942/x_dataset_40590	momo1942	2025-05-03T19:38:40Z	795	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" ]	2025-01-29T06:31:00Z	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: momo1942/x_dataset_40590 - Subnet: Bittensor Subnet 13 - Miner Hotkey: 5Fk6Ur49sNZjSVaCj4AVbEhuYTYTnVSdh5evvMDPExZSdD8g ### 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{momo19422025datauniversex_dataset_40590, title={The Data Universe Datasets: The finest collection of social media data the web has to offer}, author={momo1942}, year={2025}, url={https://huggingface.co/datasets/momo1942/x_dataset_40590}, } ``` ### 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: 54541574 - Date Range: 2025-01-23T00:00:00Z to 2025-02-13T00:00:00Z - Last Updated: 2025-02-18T18:12:32Z ### Data Distribution - Tweets with hashtags: 49.94% - Tweets without hashtags: 50.06% ### Top 10 Hashtags For full statistics, please refer to the `stats.json` file in the repository. \| Rank \| Topic \| Total Count \| Percentage \| \|------\|-------\|-------------\|-------------\| \| 1 \| NULL \| 27302504 \| 50.06% \| \| 2 \| #riyadh \| 416113 \| 0.76% \| \| 3 \| #zelena \| 307274 \| 0.56% \| \| 4 \| #tiktok \| 261916 \| 0.48% \| \| 5 \| #bbb25 \| 205680 \| 0.38% \| \| 6 \| #ad \| 150196 \| 0.28% \| \| 7 \| #royalrumble \| 121643 \| 0.22% \| \| 8 \| #jhope_at_galadespiècesjaunes \| 116582 \| 0.21% \| \| 9 \| #granhermano \| 92578 \| 0.17% \| \| 10 \| #bbmzansi \| 87251 \| 0.16% \| ## Update History \| Date \| New Instances \| Total Instances \| \|------\|---------------\|-----------------\| \| 2025-01-29T06:31:58Z \| 3075320 \| 3075320 \| \| 2025-02-01T18:34:52Z \| 8753140 \| 11828460 \| \| 2025-02-05T06:37:51Z \| 9310894 \| 21139354 \| \| 2025-02-08T18:42:36Z \| 11749598 \| 32888952 \| \| 2025-02-12T06:46:20Z \| 7821613 \| 40710565 \| \| 2025-02-17T03:33:43Z \| 12307358 \| 53017923 \| \| 2025-02-18T03:11:07Z \| 703943 \| 53721866 \| \| 2025-02-18T18:12:32Z \| 819708 \| 54541574 \|
francescocrivelli/eval_v1_act_so100_testing_pi0_one_francesco_full	francescocrivelli	2025-05-03T19:18:18Z	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" ]	2025-05-03T19:18:09Z	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": 10, "total_frames": 6351, "total_tasks": 1, "total_videos": 20, "total_chunks": 1, "chunks_size": 1000, "fps": 30, "splits": { "train": "0:10" }, "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.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.phone": { "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] ```
MBZUAI-IFM/AM_clean_final	MBZUAI-IFM	2025-05-03T18:29:38Z	0	0	[ "size_categories:1M<n<10M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-03T18:22:03Z	null	--- dataset_info: features: - name: conversations list: - name: from dtype: string - name: value dtype: string - name: dataset_source dtype: string - name: metadata dtype: string - name: has_forbidden dtype: bool splits: - name: train num_bytes: 26009841975 num_examples: 1139323 download_size: 11628816168 dataset_size: 26009841975 configs: - config_name: default data_files: - split: train path: data/train-* ---
cchoi1/kodcode-complete_1000_qwen7b_sol_iter0_att10_sol5_lr1e5_3ep_dpo_10000	cchoi1	2025-05-03T18:10:44Z	0	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-03T18:10:41Z	null	--- dataset_info: features: - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string - name: task_id dtype: string splits: - name: train num_bytes: 10023107.103929024 num_examples: 2524 - name: test num_bytes: 2509747.896070976 num_examples: 632 download_size: 2061810 dataset_size: 12532855.0 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_1_for_gen_13_v2	HungVu2003	2025-05-03T17:54:37Z	0	0	[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-03T17:54:36Z	null	--- dataset_info: features: - name: question dtype: string splits: - name: train num_bytes: 820412 num_examples: 12500 download_size: 567135 dataset_size: 820412 configs: - config_name: default data_files: - split: train path: data/train-* ---
junlinw/Qwen2.5-7B-Instruct-Turbo_MATH_10	junlinw	2025-05-03T17:50:41Z	0	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-03T17:50:39Z	null	--- dataset_info: features: - name: ids dtype: int64 - name: queries dtype: string - name: samples sequence: string - name: references dtype: string splits: - name: train num_bytes: 117454203 num_examples: 7500 download_size: 35970957 dataset_size: 117454203 configs: - config_name: default data_files: - split: train path: data/train-* ---
thailevann/Government_services_QA	thailevann	2025-05-03T17:46:10Z	20	0	[ "task_categories:question-answering", "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[ "question-answering" ]	2025-04-24T00:37:35Z	null	--- dataset_info: features: - name: instruction dtype: string - name: output dtype: string - name: label dtype: int64 - name: relevant dtype: string splits: - name: train num_bytes: 49161014 num_examples: 20955 download_size: 9270053 dataset_size: 49161014 configs: - config_name: default data_files: - split: train path: data/train-* task_categories: - question-answering size_categories: - 1K<n<10K ---
HungVu2003/opt-350m_beta_0.0_alpha_0.0_num-company_2_dataset_1_for_gen_10_v2	HungVu2003	2025-05-03T17:38:41Z	0	0	[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-03T17:38:40Z	null	--- dataset_info: features: - name: question dtype: string splits: - name: train num_bytes: 820715 num_examples: 12500 download_size: 567865 dataset_size: 820715 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_slow_generate	alucchi	2025-05-03T17:30:51Z	0	0	[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-03T17:30:41Z	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: 696999 num_examples: 70 download_size: 136054 dataset_size: 696999 - 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: 696999 num_examples: 70 download_size: 136054 dataset_size: 696999 configs: - config_name: default data_files: - split: train path: data/train-* - config_name: main data_files: - split: train path: main/train-* ---
Hieuman/wiki_en_smallavae	Hieuman	2025-05-03T17:28:55Z	124	0	[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-04-08T05:40:03Z	null	--- dataset_info: features: - name: text_1 dtype: string - name: text_2 dtype: string - name: label dtype: string - name: style_comparison dtype: string - name: content_comparison dtype: string - name: content_label dtype: string splits: - name: train num_bytes: 167683819.0 num_examples: 17072 download_size: 71112622 dataset_size: 167683819.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
ViratChauhan/sol-edu-ft	ViratChauhan	2025-05-03T17:14:29Z	0	0	[ "region:us" ]	[]	2025-05-03T17:14:27Z	null	--- dataset_info: features: - name: source dtype: string - name: question dtype: string - name: answer dtype: string splits: - name: train num_bytes: 4549609 num_examples: 6128 download_size: 2168857 dataset_size: 4549609 configs: - config_name: default data_files: - split: train path: data/train-* ---
aigrant/taiwan-ly-law-research	aigrant	2025-05-03T17:00:06Z	401	6	[ "language:zh", "license:apache-2.0", "size_categories:1K<n<10K", "format:csv", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2024-07-22T21:29:38Z	null	--- language: - zh configs: - config_name: research data_files: research.csv dataset_info: - config_name: research features: - name: research_no dtype: string - name: title dtype: string - name: related_laws dtype: string - name: authors dtype: string - name: published_date dtype: string - name: content dtype: string - name: doc_url dtype: string license: apache-2.0 --- # Taiwan Legislator Yuan Law Research Data ## Overview The law research documents are issued irregularly from Taiwan Legislator Yuan. The purpose of those research are providing better understanding on social issues in aspect of laws. One may find documents rich with technical terms which could provided as training data. For comprehensive document list check out this [link](https://www.ly.gov.tw/Pages/List.aspx?nodeid=6590) provided by Taiwan Legislator Yuan. There are currently missing document download links in 10th and 9th terms due to minor issue on crawler. We will fill in those missing data ASAP. ## Data Fields \| Field name \| Description \| \|----------------\|------------------------------------------------------------------------------------------------------------------------------------\| \| research_no \| ID of the research document \| \| title \| title of the document \| \| related_laws \| Related names of laws in the document. Separated by `;` \| \| authors \| Authors of document. Separated by `;` \| \| published_date \| Published date of the document in form `YYYY-mm-dd` \| \| content \| Full text content of the document. One may also find the original content in `.html` format at `html/{research_no}.html` \| \| doc_url \| The download link hosted on ly.gov.tw \| ## Sponsorship The work is sponsored by "【g0v 零時小學校】繁體中文AI 開源實踐計畫" ## Contact If you have any issue on the dataset. Please leave a discussion on it or contact us via: 報導者(The Reporter) [email protected] 歐噴有限公司(OpenFun Ltd.) [email protected]
gosamab/merged-handwriting-dataset	gosamab	2025-05-03T16:50:57Z	0	0	[ "size_categories:100K<n<1M", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-03T16:27:45Z	null	--- dataset_info: features: - name: source dtype: string - name: image dtype: image - name: text dtype: string - name: font_details dtype: string splits: - name: train num_bytes: 2176602946.666 num_examples: 447946 - name: test num_bytes: 540987875.28 num_examples: 111987 download_size: 3803906962 dataset_size: 2717590821.946 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
themachinefan/sandbagging-sciq-headlines	themachinefan	2025-05-03T16:50:09Z	0	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-03T16:50:03Z	null	--- dataset_info: features: - name: correct_answer dtype: string - name: index dtype: int64 - name: prompt dtype: string - name: sandbagging_environment dtype: bool - name: desired_answer dtype: string - name: prefix dtype: string - name: template dtype: string splits: - name: train num_bytes: 5234192 num_examples: 4000 - name: test num_bytes: 203622 num_examples: 156 - name: validation num_bytes: 204490 num_examples: 156 download_size: 1562350 dataset_size: 5642304 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: validation path: data/validation-* ---
littleGuagua/x_dataset_11627	littleGuagua	2025-05-03T16:37:25Z	1,557	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" ]	2025-01-26T13:13:48Z	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_11627 - Subnet: Bittensor Subnet 13 - Miner Hotkey: 5FUByNzgdM2eukk6SwetFsZ4EPTxRqaV4YNEhNcusS1SxRVX ### 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_11627, 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_11627}, } ``` ### 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: 149000631 - Date Range: 2025-01-21T00:00:00Z to 2025-02-10T00:00:00Z - Last Updated: 2025-02-18T20:47:28Z ### Data Distribution - Tweets with hashtags: 42.62% - Tweets without hashtags: 57.38% ### Top 10 Hashtags For full statistics, please refer to the `stats.json` file in the repository. \| Rank \| Topic \| Total Count \| Percentage \| \|------\|-------\|-------------\|-------------\| \| 1 \| NULL \| 85489881 \| 57.38% \| \| 2 \| #riyadh \| 1033096 \| 0.69% \| \| 3 \| #zelena \| 790108 \| 0.53% \| \| 4 \| #tiktok \| 618215 \| 0.41% \| \| 5 \| #bbb25 \| 362232 \| 0.24% \| \| 6 \| #ad \| 356819 \| 0.24% \| \| 7 \| #jhope_at_galadespiècesjaunes \| 234343 \| 0.16% \| \| 8 \| #bbmzansi \| 207541 \| 0.14% \| \| 9 \| #pr \| 188395 \| 0.13% \| \| 10 \| #yahooニュース \| 178958 \| 0.12% \| ## Update History \| Date \| New Instances \| Total Instances \| \|------\|---------------\|-----------------\| \| 2025-01-26T13:14:32Z \| 2274090 \| 2274090 \| \| 2025-01-30T01:26:02Z \| 29523249 \| 31797339 \| \| 2025-02-02T13:36:10Z \| 29333848 \| 61131187 \| \| 2025-02-06T01:47:05Z \| 28740147 \| 89871334 \| \| 2025-02-09T14:00:59Z \| 29293177 \| 119164511 \| \| 2025-02-13T02:15:32Z \| 28379764 \| 147544275 \| \| 2025-02-18T05:45:25Z \| 808939 \| 148353214 \| \| 2025-02-18T20:47:28Z \| 647417 \| 149000631 \|
FrancophonIA/Thesaurus_terminologie_canadienne_en_construction	FrancophonIA	2025-05-03T16:36:17Z	0	0	[ "task_categories:translation", "language:fra", "language:eng", "region:us" ]	[ "translation" ]	2025-05-03T16:35:08Z	null	--- language: - fra - eng viewer: false task_categories: - translation --- > [!NOTE] > Dataset origin: https://publications.gc.ca/site/eng/9.876320/publication.html
amekerishvili/ATCO2_full_files	amekerishvili	2025-05-03T16:14:01Z	0	0	[ "size_categories:n<1K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-03T12:01:28Z	null	--- dataset_info: features: - name: ID dtype: string - name: audio_file dtype: string - name: start_time dtype: float64 - name: end_time dtype: float64 - name: airport dtype: string - name: channel dtype: string - name: frequency dtype: string - name: time dtype: string - name: waypoints dtype: string - name: callsigns dtype: string - name: ground_truth_raw dtype: string - name: ground_truth dtype: string - name: non_Eng_ground_truth dtype: string - name: tags dtype: string - name: values_tags dtype: string - name: commands_tags dtype: string - name: callsigns_tags dtype: string - name: unnamed_tags dtype: string splits: - name: train num_bytes: 1558206 num_examples: 612 - name: validation num_bytes: 362174 num_examples: 136 - name: test num_bytes: 356108 num_examples: 129 download_size: 397317 dataset_size: 2276488 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---
mlfoundations-dev/no_pipeline_math_300k	mlfoundations-dev	2025-05-03T15:41:53Z	0	0	[ "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-03T15:39:48Z	null	--- dataset_info: features: - name: instruction_seed dtype: string - name: reasoning dtype: string - name: deepseek_solution dtype: string - name: source dtype: string - name: conversations list: - name: from dtype: string - name: value dtype: string - name: shard_id dtype: string splits: - name: train num_bytes: 7613230760.0 num_examples: 316000 download_size: 3353575536 dataset_size: 7613230760.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
FrancophonIA/Glossaire_procedure_parlementaire	FrancophonIA	2025-05-03T15:37:24Z	0	0	[ "task_categories:translation", "language:fra", "language:eng", "region:us" ]	[ "translation" ]	2025-05-03T15:36:48Z	null	--- language: - fra - eng viewer: false task_categories: - translation --- > [!NOTE] > Dataset origin: https://publications.gc.ca/site/eng/9.693563/publication.html
FrancophonIA/Lexique_du_droit_de_la_famille	FrancophonIA	2025-05-03T15:33:26Z	0	0	[ "task_categories:translation", "language:fra", "language:eng", "region:us" ]	[ "translation" ]	2025-05-03T15:32:34Z	null	--- language: - fra - eng viewer: false task_categories: - translation --- > [!NOTE] > Dataset origin: https://publications.gc.ca/site/eng/9.586275/publication.html
t2ance/polymnist-upd10	t2ance	2025-05-03T15:30:26Z	0	0	[ "region:us" ]	[]	2025-05-03T14:29:24Z	null	--- dataset_info: features: - name: m0 dtype: image - name: m1 dtype: image - name: m2 dtype: image - name: m3 dtype: image - name: m4 dtype: image - name: m5 dtype: image - name: m6 dtype: image - name: m7 dtype: image - name: m8 dtype: image - name: m9 dtype: image - name: label dtype: class_label: names: '0': '0' '1': '1' '2': '2' '3': '3' '4': '4' '5': '5' '6': '6' '7': '7' '8': '8' '9': '9' - name: sample_id dtype: string splits: - name: train num_bytes: 632476369.0 num_examples: 50000 - name: validation num_bytes: 126523764.0 num_examples: 10000 - name: test num_bytes: 126347943.0 num_examples: 10000 download_size: 916377594 dataset_size: 885348076.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---
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-* ---
Maxscha/json-instruct-generation-large	Maxscha	2025-05-03T15:03:37Z	0	0	[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-03T15:03:31Z	null	--- dataset_info: features: - name: schema dtype: string - name: input dtype: string - name: output dtype: string - name: task dtype: string splits: - name: train num_bytes: 99642713 num_examples: 50000 download_size: 31084332 dataset_size: 99642713 configs: - config_name: default data_files: - split: train path: data/train-* ---
CanCLID/zoengjyutgaai	CanCLID	2025-05-03T14:58:45Z	3,423	15	[ "task_categories:automatic-speech-recognition", "task_categories:text-to-speech", "task_categories:text-generation", "task_categories:feature-extraction", "task_categories:audio-to-audio", "task_categories:audio-classification", "task_categories:text-to-audio", "language:yue", "license:cc0-1.0", "size_categories:10K<n<100K", "format:parquet", "modality:audio", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us", "cantonese", "audio", "art" ]	[ "automatic-speech-recognition", "text-to-speech", "text-generation", "feature-extraction", "audio-to-audio", "audio-classification", "text-to-audio" ]	2024-07-11T06:46:10Z	null	--- language: - yue license: cc0-1.0 size_categories: - 10K<n<100K task_categories: - automatic-speech-recognition - text-to-speech - text-generation - feature-extraction - audio-to-audio - audio-classification - text-to-audio pretty_name: c configs: - config_name: default data_files: - split: saamgwokjinji path: data/saamgwokjinji-* - split: seoiwuzyun path: data/seoiwuzyun-* - split: mouzaakdung path: data/mouzaakdung-* tags: - cantonese - audio - art dataset_info: features: - name: audio dtype: audio - name: id dtype: string - name: episode_id dtype: int64 - name: audio_duration dtype: float64 - name: transcription dtype: string splits: - name: saamgwokjinji num_bytes: 2398591354.589 num_examples: 39173 - name: seoiwuzyun num_bytes: 1629539808.0 num_examples: 24744 - name: mouzaakdung num_bytes: 257168872.246 num_examples: 4742 download_size: 4304923024 dataset_size: 4285300034.835 --- # 張悦楷講古語音數據集 [English](#the-zoeng-jyut-gaai-story-telling-speech-dataset) ## Dataset Description - Homepage: [張悦楷講古語音數據集 The Zoeng Jyut Gaai Story-telling Speech Dataset](https://canclid.github.io/zoengjyutgaai/) - License: [CC0 1.0 Universal](https://creativecommons.org/publicdomain/zero/1.0/) - Language: Cantonese - Total Duration: 112.54 hours - Average Clip Duration: 5.901 seconds - Median Clip Duration: 5.443 seconds - Total number of characters: 1679097 - Average characters per clip: 24.36 - Median characters per clip: 23 - Average speech speed: 4.14 characters per second - Voice Actor: [張悦楷](https://zh.wikipedia.org/wiki/%E5%BC%A0%E6%82%A6%E6%A5%B7) 呢個係張悦楷講《三國演義》、《水滸傳》、《走進毛澤東的最後歲月》語音數據集。[張悦楷](https://zh.wikipedia.org/wiki/%E5%BC%A0%E6%82%A6%E6%A5%B7)係廣州最出名嘅講古佬 / 粵語説書藝人。佢從上世紀七十年代開始就喺廣東各個收音電台度講古，佢把聲係好多廣州人嘅共同回憶。本數據集收集嘅係佢最知名嘅三部作品。數據集用途： - TTS（語音合成）訓練集 - ASR（語音識別）訓練集或測試集 - 各種語言學、文學研究 - 直接聽嚟欣賞藝術！ TTS 效果演示：https://huggingface.co/spaces/laubonghaudoi/zoengjyutgaai_tts ## 説明 - 所有文本都根據 https://jyutping.org/blog/typo/ 同 https://jyutping.org/blog/particles/ 規範用字。 - 所有文本都使用全角標點，冇半角標點。 - 所有文本都用漢字轉寫，無阿拉伯數字無英文字母 - 所有音頻源都存放喺`/source`，為方便直接用作訓練數據，切分後嘅音頻都放喺 `opus/` - 所有 opus 音頻皆為 48000 Hz 採樣率。 - 所有源字幕 SRT 文件都存放喺 `srt/` 路經下，搭配 `source/` 下嘅音源可以直接作為帶字幕嘅錄音直接欣賞。 - `cut.py` 係切分腳本，將對應嘅音源根據 srt 切分成短句並生成一個文本轉寫 csv。 - `stats.py` 係統計腳本，運行佢就會顯示成個數據集嘅各項統計數據。 ## 下載使用要下載使用呢個數據集，可以喺 Python 入面直接跑： ```python from datasets import load_dataset ds = load_dataset("CanCLID/zoengjyutgaai") ``` 如果想單純將 `opus/` 入面所有嘢下載落嚟，可以跑下面嘅 Python 代碼，注意要安裝 `pip install --upgrade huggingface_hub` 先： ```python from huggingface_hub import snapshot_download # 如果淨係想下載啲字幕或者源音頻，就將 `opus/` 改成 `srt/` 或者 `source/` # If you only want to download subtitles or source audio, change `opus/` to `srt/` or `source/` snapshot_download(repo_id="CanCLID/zoengjyutgaai",allow_patterns="opus/",local_dir="./",repo_type="dataset") ``` 如果唔想用 python，你亦都可以用命令行叫 git 針對克隆個`opus/`或者其他路經，避免將成個 repo 都克隆落嚟浪費空間同下載時間： ```bash mkdir zoengjyutgaai cd zoengjyutgaai git init git remote add origin https://huggingface.co/datasets/CanCLID/zoengjyutgaai git sparse-checkout init --cone # 指定凈係下載個別路徑 git sparse-checkout set opus # 開始下載 git pull origin main ``` ### 數據集構建流程本數據集嘅收集、構建過程係： 1. 從 YouTube 或者國內評書網站度下載錄音源文件，一般都係每集半個鐘長嘅 `.webm` 或者 `.mp3`。 1. 用加字幕工具幫呢啲錄音加字幕，得到對應嘅 `.srt` 文件。 1. 將啲源錄音用下面嘅命令儘可能無壓縮噉轉換成 `.opus` 格式。 1. 運行`cut.py`，將每一集 `.opus` 按照 `.srt` 入面嘅時間點切分成一句一個 `.opus`，然後對應嘅文本寫入本數據集嘅 `xxx.csv`。 1. 然後打開一個 IPython，逐句跑下面嘅命令，將啲數據推上 HuggingFace。 ```python from datasets import load_dataset, DatasetDict from huggingface_hub import login sg = load_dataset('audiofolder', data_dir='./opus/saamgwokjinji') sw = load_dataset('audiofolder', data_dir='./opus/seoiwuzyun') mzd = load_dataset('audiofolder', data_dir='./opus/mouzaakdung') dataset = DatasetDict({ "saamgwokjinji": sg["train"], "seoiwuzyun": sw["train"], "mouzaakdung": mzd["train"], }) # 檢查下讀入嘅數據有冇問題 dataset['mouzaakdung'][0] # 準備好個 token 嚟登入 login() # 推上 HuggingFace datasets dataset.push_to_hub("CanCLID/zoengjyutgaai") ``` ### 音頻格式轉換首先要安裝 [ffmpeg](https://www.ffmpeg.org/download.html)，然後運行： ```bash # 將下載嘅音源由 webm 轉成 opus ffmpeg -i webm/saamgwokjinji/001.webm -c:a copy source/saamgwokjinji/001.opus # 或者轉 mp3 ffmpeg -i mp3/mouzaakdung/001.mp3 -c:a libopus -map_metadata -1 -b:a 48k -vbr on source/mouzaakdung/001.opus # 將 opus 轉成無損 wav ffmpeg -i source/saamgwokjinji/001.opus wav/saamgwokjinji/001.wav ``` 如果想將所有 opus 文件全部轉換成 wav，可以直接運行`to_wav.sh`: ``` chmod +x to_wav.sh ./to_wav.sh ``` 跟住就會生成一個 `wav/` 路經，入面都係 `opus/` 對應嘅音頻。注意 wav 格式非常掗埞，成個 `opus/` 轉晒後會佔用至少 500GB 儲存空間，所以轉換之前記得確保有足夠空間。如果你想對音頻重採樣，亦都可以修改 `to_wav.sh` 入面嘅命令順便做重採樣。 # The Zoeng Jyut Gaai Story-telling Speech Dataset This is a speech dataset of Zoeng Jyut Gaai story-telling _Romance of the Three Kingdoms_, _Water Margin_ and _The Final Days of Mao Zedong_. [Zoeng Jyut Gaai](https://zh.wikipedia.org/wiki/%E5%BC%A0%E6%82%A6%E6%A5%B7) is a famous actor, stand-up commedian and story-teller (講古佬) in 20th centry Canton. His voice remains in the memories of thousands of Cantonese people. This dataset is built from three of his most well-known story-telling pieces. Use case of this dataset: - TTS (Text-To-Speech) training set - ASR (Automatic Speech Recognition) training or eval set - Various linguistics / art analysis - Just listen and enjoy the art piece! TTS demo: https://huggingface.co/spaces/laubonghaudoi/zoengjyutgaai_tts ## Introduction - All transcriptions follow the prescribed orthography detailed in https://jyutping.org/blog/typo/ and https://jyutping.org/blog/particles/ - All transcriptions use full-width punctuations, no half-width punctuations is used. - All transcriptions are in Chinese characters, no Arabic numbers or Latin letters. - All source audio are stored in `source/`. For the convenice of training, segmented audios are stored in `opus/`. - All opus audio are in 48000 Hz sampling rate. - All source subtitle SRT files are stored in `srt/`. Use them with the webm files to enjoy subtitled storytelling pieces. - `cut.py` is the script for cutting opus audios into senteneces based on the srt, and generates a csv file for transcriptions. - `stats.py` is the script for getting stats of this dataset. ## Usage To use this dataset, simply run in Python: ```python from datasets import load_dataset ds = load_dataset("CanCLID/zoengjyutgaai") ``` If you only want to download a certain directory to save time and space from cloning the entire repo, run the Python codes below. Make sure you have `pip install --upgrade huggingface_hub` first： ```python from huggingface_hub import snapshot_download # If you only want to download subtitles or source audio, change `opus/` to `srt/` or `source/` snapshot_download(repo_id="CanCLID/zoengjyutgaai",allow_patterns="opus/*",local_dir="./",repo_type="dataset") ``` If you don't want to run python codes and want to do this via command lines, you can selectively clone only a directory of the repo: ```bash mkdir zoengjyutgaai cd zoengjyutgaai git init git remote add origin https://huggingface.co/datasets/CanCLID/zoengjyutgaai git sparse-checkout init --cone # Tell git which directory you want git sparse-checkout set opus # Pull the content git pull origin main ``` ### Audio format conversion Install [ffmpeg](https://www.ffmpeg.org/download.html) first, then run： ```bash # convert all webm into opus ffmpeg -i webm/saamgwokjinji/001.webm -c:a copy source/saamgwokjinji/001.opus # or into mp3 ffmpeg -i mp3/mouzaakdung/001.mp3 -c:a libopus -map_metadata -1 -b:a 48k -vbr on source/mouzaakdung/001.opus # convert all opus into loseless wav ffmpeg -i source/saamgwokjinji/001.opus wav/saamgwokjinji/001.wav ``` If you want to convert all opus to wav, run `to_wav.sh`: ``` chmod +x to_wav.sh ./to_wav.sh ``` It will generate a `wav/` path which contains all audios converted from `opus/`. Be aware the wav format is very space-consuming. A full conversion will take up at least 500GB space so make sure you have enough storage. If you want to resample the audio, modify the line within `to_wav.sh` to resample the audio while doing the conversion.
FrancophonIA/Vous-pouvez-le-dire-en-francais-Faire-des-affaires-en-francais	FrancophonIA	2025-05-03T14:52:34Z	3	0	[ "task_categories:translation", "language:fra", "language:eng", "region:us" ]	[ "translation" ]	2025-04-29T20:43:35Z	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-Faire-des-affaires-en-francais
FrancophonIA/Vous-pouvez-le-dire-en-francais-Genetique-biologie	FrancophonIA	2025-05-03T14:47:53Z	3	0	[ "task_categories:translation", "language:fra", "language:eng", "region:us" ]	[ "translation" ]	2025-04-29T20:47:46Z	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-Genetique-biologie
Kamyar-zeinalipour/llama2_kg	Kamyar-zeinalipour	2025-05-03T14:21:05Z	11	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-01T18:29:30Z	null	--- dataset_info: features: - name: cycle dtype: int64 - name: temperature dtype: float64 - name: top_p dtype: float64 - name: raw_generated_text dtype: string - name: extracted_output dtype: string - name: applied_template_text dtype: string - name: rouge_scores_text dtype: string - name: rouge_scores_triple dtype: string - name: rouge_l_fmeasure_text dtype: string - name: rouge_l_fmeasure_triple dtype: float64 - name: emb_similarity_text dtype: string - name: emb_similarity_triple dtype: float64 - name: combined_similarity_triple dtype: float64 - name: combined_similarity_text dtype: string - name: combined_similarity_triple_diff dtype: float64 - name: input_text dtype: string - name: initial_text dtype: string - name: source_file dtype: string - name: input_text_clean dtype: string - name: user_content dtype: string - name: assistant_output dtype: string - name: messages list: - name: content dtype: string - name: role dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 13691684 num_examples: 1950 - name: test num_bytes: 349248 num_examples: 50 download_size: 5889395 dataset_size: 14040932 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
gunnybd01/Trend_smr	gunnybd01	2025-05-03T14:18:19Z	0	0	[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-02T15:11:12Z	null	--- dataset_info: features: - name: Keys dtype: string - name: reports dtype: string - name: labels dtype: string splits: - name: train num_bytes: 156894600 num_examples: 60000 download_size: 34150437 dataset_size: 156894600 configs: - config_name: default data_files: - split: train path: data/train-* ---
kothasuhas/llp-gold-37m-1.5m_T32768.0_I32768	kothasuhas	2025-05-03T14:18:00Z	0	0	[ "size_categories:1M<n<10M", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-03T14:17:12Z	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: 1629512 dataset_size: 3605804917.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
Nelver28/fon-asr	Nelver28	2025-05-03T14:15:40Z	0	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-03T14:11:58Z	null	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: audio struct: - name: array sequence: float32 - name: path dtype: string - name: sampling_rate dtype: int64 - name: sentence dtype: string splits: - name: train num_bytes: 647571727 num_examples: 1084 - name: test num_bytes: 157608299 num_examples: 271 download_size: 334998565 dataset_size: 805180026 --- # Dataset Card for "fon-asr" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
starsofchance/MSR_data_cleaned	starsofchance	2025-05-03T14:06:02Z	29	0	[ "language:en", "license:mit", "size_categories:100K<n<1M", "format:csv", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "vulnerability-detection", "cve", "code-changes", "software-security", "stratified-split" ]	[]	2025-03-25T00:43:24Z	null	--- # YAML Metadata Block language: - en tags: - vulnerability-detection - cve - code-changes - software-security - stratified-split license: mit dataset_info: features: # Features in the final split files - name: idx dtype: int64 - name: func_before dtype: string - name: Vulnerability Classification dtype: string - name: vul dtype: int64 - name: func_after dtype: string - name: patch dtype: string - name: CWE ID dtype: string - name: lines_before dtype: string - name: lines_after dtype: string splits: - name: train num_examples: 150909 - name: validation num_examples: 18864 - name: test num_examples: 18863 dataset_original_file_size: 10GB uuncompressed --- # MSR Data Cleaned - C/C++ Code Vulnerability Dataset [![Dataset License](https://img.shields.io/badge/license-MIT-blue.svg)](LICENSE) ## 📌 Dataset Description A curated collection of C/C++ code vulnerabilities paired with: - CVE details (scores, classifications, exploit status) - Code changes (commit messages, added/deleted lines) - File-level and function-level diffs ## 🔍 Sample Data Structure from original file ```python +---------------+-----------------+----------------------+---------------------------+ \| CVE ID \| Attack Origin \| Publish Date \| Summary \| +===============+=================+======================+===========================+ \| CVE-2015-8467 \| Remote \| 2015-12-29 \| "The samldb_check_user..."\| +---------------+-----------------+----------------------+---------------------------+ \| CVE-2016-1234 \| Local \| 2016-01-15 \| "Buffer overflow in..." \| +---------------+-----------------+----------------------+---------------------------+ ``` Note: This is a simplified preview; the full dataset includes additional fields like commit_id, func_before, etc. ### 1. Accessing in Colab ```python !pip install huggingface_hub -q from huggingface_hub import snapshot_download repo_id = "starsofchance/MSR_data_cleaned" dataset_path = snapshot_download(repo_id=repo_id, repo_type="dataset") ``` ### 2. Extracting the Dataset ```python !apt-get install unzip -qq !unzip "/root/.cache/huggingface/.../MSR_data_cleaned.zip" -d "/content/extracted_data" ``` Note: Extracted size is 10GB (1.5GB compressed). Ensure sufficient disk space. ### 3. Creating Splits (Colab Pro Recommended) We used this memory-efficient approach: ```python from datasets import load_dataset dataset = load_dataset("csv", data_files="MSR_data_cleaned.csv", streaming=True) # Randomly distribute rows (80-10-10) for row in dataset: rand = random.random() if rand < 0.8: write_to(train.csv) elif rand < 0.9: write_to(validation.csv) else: write_to(test.csv) ``` Hardware Requirements: - Minimum 25GB RAM - Strong CPU (Colab Pro T4 GPU recommended) ##📊 Dataset Statistics - Number of Rows: 188,636 - Vulnerability Distribution: - Vulnerable (1): 18,863 (~10%) - Non-Vulnerable (0): 169,773 (~90%) ##📋 Data Fields Description - CVE_ID: Unique identifier for the vulnerability (Common Vulnerabilities and Exposures). - CWE_ID: Weakness category identifier (Common Weakness Enumeration). - Score: CVSS score indicating severity (float, 0-10). - Summary: Brief description of the vulnerability. - commit_id: Git commit hash linked to the code change. - codeLink: URL to the code repository or commit. - file_name: Name of the file containing the vulnerability. - func_after: Function code after the change. - lines_after: Code lines after the change. - Access_Gained: Type of access gained by exploiting the vulnerability. - Attack_Origin: Source of the attack (e.g., Remote, Local). - Authentication_Required: Whether authentication is needed to exploit. - Availability: Impact on system availability. - CVE_Page: URL to the CVE details page. - Complexity: Complexity of exploiting the vulnerability. - Confidentiality: Impact on data confidentiality. - Integrity: Impact on data integrity. - Known_Exploits: Details of known exploits, if any. - Publish_Date: Date the vulnerability was published. - Update_Date: Date of the last update to the vulnerability data. - Vulnerability_Classification: Type or category of the vulnerability. - add_lines: Lines added in the commit. - del_lines: Lines deleted in the commit. - commit_message: Description of the commit. - files_changed: List of files modified in the commit. - func_before: Function code before the change. - lang: Programming language (e.g., C, C++). - lines_before: Code lines before the change. ## splits file for UltiVul project: ## 🔍 Sample Data Structure (from train.csv) ```python { 'idx': 0, # Unique ID within the train split 'func_before': '...', # String containing function code before change 'Vulnerability Classification': '...', # Original vulnerability type classification 'vul': 0, # Integer: 0 for non-vulnerable, 1 for vulnerable (target label) 'func_after': '...', # String containing function code after change 'patch': '...', # String containing diff patch 'CWE ID': '...', # String CWE ID, e.g., "CWE-119" 'lines_before': '...', # String lines before change context 'lines_after': '...' # String lines after change context } ``` Note: This shows the structure of the final split files (train.csv, validation.csv, test.csv). The original MSR_data_cleaned.csv contains many more metadata fields. ##📦 Dataset New Files The dataset is available as three CSV files (specially created for the UltiVul project) hosted on Hugging Face, uploaded via huggingface_hub: - train.csv Size: 667 MB Description: Training split with 150,909 samples, approximately 80% of the data. - validation.csv Size: 86 MB Description: Validation split with 18,864 samples, approximately 10% of the data. - test.csv Size: 84.8 MB Description: Test split with 18,863 samples, approximately 10% of the data. 🙏 Acknowledgements Original dataset provided by Fan et al., 2020 Thanks to the Hugging Face team for dataset hosting tools. ## 📜 Citation ```bibtex @inproceedings{fan2020ccode, title={A C/C++ Code Vulnerability Dataset with Code Changes and CVE Summaries}, author={Fan, Jiahao and Li, Yi and Wang, Shaohua and Nguyen, Tien N}, booktitle={MSR '20: 17th International Conference on Mining Software Repositories}, pages={1--5}, year={2020}, doi={10.1145/3379597.3387501} } ``` ## 🌟 Dataset Creation - Source: Original data from [MSR 2020 Paper](https://doi.org/10.1145/3379597.3387501) - Processing: - Cleaned and standardized CSV format - Stream-based splitting to handle large size - Preserved all original metadata
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-* ---
DT4LM/debertav3base_sst2_multi_pso_timeDecay	DT4LM	2025-05-03T13:38:52Z	0	0	[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-03T13:36:33Z	null	--- dataset_info: features: - name: text dtype: string - name: label dtype: int32 splits: - name: train num_bytes: 19446 num_examples: 242 download_size: 15714 dataset_size: 19446 configs: - config_name: default data_files: - split: train path: data/train-* ---
NextGenC/synapse-set-10k	NextGenC	2025-05-03T13:36:20Z	17	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" ]	2025-04-30T17:26:21Z	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-10K: EEG Interpretation Dataset' size_categories: - 10K<n<100K datasets: - NextGenC/synapse-set-10k --- ## 🧠 SynapseSet-10K SynapseSet-10K 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: Turkish - 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-10K, please cite: ```bibtex @misc{7ennlabs2025synapseset, author = {7enn Labs}, title = {SynapseSet-10K: Synthetic Instruction Dataset for EEG Interpretation}, year = {2025}, url = {https://huggingface.co/datasets/NextGenC/synapse-set-100k}, 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 ---
NextGenC/synapse-set-100k	NextGenC	2025-05-03T13:35:37Z	10	1	[ "task_categories:text-generation", "task_categories:text2text-generation", "language:en", "language:tr", "license:mit", "size_categories:100K<n<1M", "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" ]	2025-05-01T10:29:00Z	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-100K: EEG Interpretation Dataset' size_categories: - 10K<n<100K datasets: - NextGenC/synapse-set-100k --- ## 🧠 SynapseSet-100K SynapseSet-100K 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-100K, please cite: ```bibtex @misc{7ennlabs2025synapseset, author = {7enn Labs}, title = {SynapseSet-100K: Synthetic Instruction Dataset for EEG Interpretation}, year = {2025}, url = {https://huggingface.co/datasets/NextGenC/synapse-set-100k}, 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 ---
nlpllmeval/NLP-Course-LLM-Reasoning-Eval-May2025	nlpllmeval	2025-05-03T13:14:54Z	0	1	[ "license:mit", "size_categories:n<1K", "format:json", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-03T11:23:08Z	null	--- license: mit --- # Overview of LLM Reasoning Eval Dataset This dataset contains evaluation of multiple large language models (LLMs) over 918 MCQ reasoning questions created by 184 students. Each question was used to test 3 LLMs (each 3 times): GPT-4o, Claude Sonnet 3.x (3.5 or 3.7), and Deepseek R1. The questions target various reasoning areas (i.e., Math, Logic, Temporal, Commonsense) and are included only if 3 seperate attempts (in a new session) by ChatGPT (GPT-4o) fail at giving the correct answer. This dataset was created as part of an NLP assignment in the FIT5217 unit at Monash University. - Language(s) (NLP): English - License: MIT License ## LLM-based Novelty and Objectivty Score (Zero-shot) outputs: For each question we prompted each of the 3 LLMs (3 times each) to judge its novelty and objectivity in a zero-shot setting. ### Prompt text used: You are assessing an annotator based on the quality of their quiz questions. Your task is the following: For a given question, assess it based on 2 criteria: (Criteria 1) Objectivity Score for which Objective Questions should receive Objectivity Score of 0.5, while Subjective Questions may receive Objectivity Score of 0 or 0.25. (Criteria 2) Novelty Score: Novel questions should receive Novelty Score of 0.5, while known questions or puzzles or questions with very familiar forms should receive Novelty Score of 0 or 0.25. Think step by step before giving the final scores. The question to assess is: [INSERT YOUR QUESTION] ## LLM reasoning for each question: We prompted 3 LLMs (GPT-4o, Claude Sonnet 3.5 or 3.7, and Deepseek R1), each 3 times, and recorded their response to each question along with the time stamp of the interaction. ## Files and Structures There are 2 files released in this project: - The `test.json` correspond to a unique submission (indexed by `AnomID_#`) and human created MCQ questions (Q1, ..., Q5) and gold option, and attempt 1 by each LLM (for the all 3 attempts see the next file). - The `full_meta_data.json` includes the content of test.json as well as the 3 LLMs reasonings (each attempted 3 times) as well as LLM-based judgements for objectivity and novelty of 3 LLMs (each attempted 3 times). Here is the structure of `full_meta_data.json` ```json { "AnomID_1": { "Q1": { "Question Text": "string", "Gold Answer": "string", "Reasonings": { "GPT-4o": { "Attempt 1": { "Output": "string", "Time": "string", "Correctness": true }, "Attempt 2": { ... }, "Attempt 3": { ... } }, "Claude Sonnet 3.x": { "Attempt 1": { ... }, "Attempt 2": { ... }, "Attempt 3": { ... } }, "Deepseek R1": { "Attempt 1": { ... }, "Attempt 2": { ... }, "Attempt 3": { ... } } }, "Judgement for Objectivity and Novelty": { "GPT-4o": { "Attempt 1": { "Output": "string", "Objectivity Score": 0.5, "Novelty Score": 0.5 }, "Attempt 2": { ... }, "Attempt 3": { ... } }, "Claude Sonnet 3.x": { "Attempt 1": { ... }, "Attempt 2": { ... }, "Attempt 3": { ... } }, "Deepseek R1": { "Attempt 1": { ... }, "Attempt 2": { ... }, "Attempt 3": { ... } } } }, "Q2": { ... }, "Q3": { ... }, "Q4": { ... }, "Q5": { ... } }, "AnomID_2": { ... }, ... } ``` ## Performance of LLMs Non-filtred results on all questions: \| LLM \| Attempt \| Correct \| Incorrect \| \|----------------------\|------------\|---------\|-----------\| \| GPT-4o \| Attempt 1 \| 0 \| 918 \| \| GPT-4o \| Attempt 2 \| 0 \| 918 \| \| GPT-4o \| Attempt 3 \| 2 \| 916 \| \| Claude Sonnet 3.x \| Attempt 1 \| 426 \| 492 \| \| Claude Sonnet 3.x \| Attempt 2 \| 425 \| 493 \| \| Claude Sonnet 3.x \| Attempt 3 \| 424 \| 494 \| \| Deepseek R1 \| Attempt 1 \| 560 \| 358 \| \| Deepseek R1 \| Attempt 2 \| 557 \| 361 \| \| Deepseek R1 \| Attempt 3 \| 549 \| 369 \| Results when questions not receiving maximum objecticity score (i.e., 0.5) for 3 out of 3 attempts were discarded from the set: \| LLM \| Attempt \| Correct \| Incorrect \| \|----------------------\|------------\|---------\|-----------\| \| GPT-4o \| Attempt 1 \| 0 \| 768 \| \| GPT-4o \| Attempt 2 \| 0 \| 768 \| \| GPT-4o \| Attempt 3 \| 2 \| 766 \| \| Claude Sonnet 3.x \| Attempt 1 \| 358 \| 410 \| \| Claude Sonnet 3.x \| Attempt 2 \| 358 \| 410 \| \| Claude Sonnet 3.x \| Attempt 3 \| 363 \| 405 \| \| Deepseek R1 \| Attempt 1 \| 485 \| 283 \| \| Deepseek R1 \| Attempt 2 \| 482 \| 286 \| \| Deepseek R1 \| Attempt 3 \| 475 \| 293 \| ## Citation Any use of the data or derivatives (for publication, training, ortesting) must acknowledge this project explicitly via the following citation: BibTeX: ```bibtex @misc{fit5217-llm-eval2025, title={FIT5217 NLP Course LLM Reasoning Eval May 2025}, author={FIT5217 S1 2025 Students at Monash University}, howpublished = {\url{https://huggingface.co/datasets/nlpllmeval/NLP-Course-LLM-Reasoning-Eval-May2025}}, year={2025}, } ``` ## Dataset Card Authors - Contributors were students in Semester 1 2025 Cohort of FIT5217 at Monash University ## Feedback or Questions 📧 [Fill out the form](https://docs.google.com/forms/d/e/1FAIpQLScZdMpJpwSwW66bEj3CU_NaE5vnhGkhJYf1VkmkuKW7O-QipA/viewform?usp=header) ## ⚖️ Disclaimer This dataset is provided "as is" for research and educational purposes only. Monash University and the dataset contributors make no warranties, express or implied, regarding the accuracy, reliability, or fitness of the data for any particular purpose. The dataset may contain outputs generated by third-party large language models (LLMs), and any biases, inaccuracies, or limitations in those model outputs are inherent to their respective providers. By using this dataset, you agree that: - The authors and affiliated institutions shall not be held liable for any damages, direct or indirect, arising from the use, misuse, or interpretation of the dataset or its derivatives. - Any analysis, publication, or product derived from this dataset must not attribute the dataset’s content to real individuals, and must properly anonymize any `AnomID_#` references if redistributed. - Users are solely responsible for ensuring that their use of the dataset complies with applicable laws, institutional policies, and ethical standards.
AndrisWillow/pile-500k	AndrisWillow	2025-05-03T13:13:44Z	0	0	[ "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-03T13:05:06Z	null	--- dataset_info: features: - name: text dtype: string - name: dataset dtype: string splits: - name: train num_bytes: 2235745969 num_examples: 400000 - name: validation num_bytes: 553532082 num_examples: 100000 download_size: 1453793734 dataset_size: 2789278051 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* ---
dgambettaphd/D_llm2_gen4_WXS_doc1000_synt64_lr1e-04_acm_SYNLAST	dgambettaphd	2025-05-03T12:59:31Z	0	0	[ "size_categories:10K<n<100K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-03T12:59:28Z	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: 11399406 num_examples: 20000 download_size: 6705681 dataset_size: 11399406 configs: - config_name: default data_files: - split: train path: data/train-* ---
shylee/eval_DP_cube_downDims1_cropNo_freeze1_32_1_ema1_1e-4_ckpt300000	shylee	2025-05-03T12:43:44Z	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" ]	2025-05-03T12:27:31Z	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": 2085, "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] ```
xingjianleng/textatlas5m_styledtextsynth_subset_s	xingjianleng	2025-05-03T12:27:50Z	0	0	[ "size_categories:10K<n<100K", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-03T10:37:03Z	null	--- dataset_info: features: - name: image dtype: image - name: image_path dtype: string - name: annotation dtype: string splits: - name: train num_bytes: 137333588384.845 num_examples: 90613 download_size: 137322762217 dataset_size: 137333588384.845 configs: - config_name: default data_files: - split: train path: data/train-* ---
Moamen-dcp/arazn_codeSwitched_mp3_full_notLower_prepared_4_whisperSmall	Moamen-dcp	2025-05-03T12:24:03Z	0	0	[ "size_categories:1K<n<10K", "format:parquet", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-03T12:22:15Z	null	--- dataset_info: features: - name: input_features sequence: sequence: float32 - name: labels sequence: int64 splits: - name: train num_bytes: 3212439856 num_examples: 3344 - name: test num_bytes: 1412200224 num_examples: 1470 - name: dev num_bytes: 1346888952 num_examples: 1402 download_size: 1491932576 dataset_size: 5971529032 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: dev path: data/dev-* ---
alirezzaa13/Mechanicalpart	alirezzaa13	2025-05-03T12:19:08Z	7	0	[ "task_categories:text-to-image", "language:en", "license:mit", "size_categories:1K<n<10K", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "chemistry" ]	[ "text-to-image" ]	2025-04-30T08:31:05Z	null	--- license: mit task_categories: - text-to-image language: - en tags: - chemistry pretty_name: 'Mechanical parts demonstration ' size_categories: - 1K<n<10K configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 10803571.559999999 num_examples: 7360 download_size: 4772715 dataset_size: 10803571.559999999 ---
AdoCleanCode/Youtube8M_general_test_data	AdoCleanCode	2025-05-03T12:01:24Z	0	0	[ "size_categories:100K<n<1M", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-03T12:01:20Z	null	--- dataset_info: features: - name: 'Unnamed: 0' dtype: int64 - name: caption dtype: string - name: coarse_label dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 93525030 num_examples: 332054 download_size: 29818334 dataset_size: 93525030 configs: - config_name: default data_files: - split: train path: data/train-* ---
JopanZh/finetuning_demo	JopanZh	2025-05-03T11:58:20Z	0	0	[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-03T11:58:19Z	null	--- dataset_info: features: - name: prompt dtype: string splits: - name: train num_bytes: 33961 num_examples: 33 download_size: 10521 dataset_size: 33961 configs: - config_name: default data_files: - split: train path: data/train-* ---
SciKnowOrg/ontolearner-education	SciKnowOrg	2025-05-03T11:23:33Z	0	0	[ "language:en", "license:mit", "region:us", "OntoLearner", "ontology-learning", "education" ]	[]	2025-05-03T11:23:30Z	null	--- license: mit language: - en tags: - OntoLearner - ontology-learning - education 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;">Education Domain Ontologies</h1> </div> ## Overview The education domain encompasses ontologies that systematically represent and organize knowledge related to learning content, educational programs, competencies, and teaching resources. This domain plays a critical role in facilitating the interoperability and integration of educational data, enabling precise knowledge representation and retrieval across diverse educational contexts. By providing a structured framework, it supports the development of intelligent educational systems and enhances the accessibility and personalization of learning experiences. ## Ontologies \| Ontology ID \| Full Name \| Classes \| Properties \| Last Updated \| \|-------------\|-----------\|---------\|------------\|--------------\| \| BIBFRAME \| Bibliographic Framework Ontology (BIBFRAME) \| 212 \| 215 \| 2022-10-03\| \| Common \| Common Ontology (Common) \| 6 \| 15 \| None\| \| DoCO \| Document Components Ontology (DoCO) \| 137 \| 7 \| 2015-07-03\| ## 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.
chiyuanhsiao/audio_L2-regular-15_spoken-web-questions	chiyuanhsiao	2025-05-03T11:23:23Z	0	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:audio", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-02T08:43:43Z	null	--- dataset_info: features: - name: url dtype: string - name: question dtype: string - name: answers sequence: string - name: question_unit sequence: int64 - name: response_interleaf dtype: string - name: response_text dtype: string - name: response_tokens sequence: int64 - name: response_speech dtype: audio - name: response_asr dtype: string splits: - name: test num_bytes: 1346719057.0 num_examples: 2032 download_size: 1232352852 dataset_size: 1346719057.0 configs: - config_name: default data_files: - split: test path: data/test-* ---
HyperX-Sen/Dream-Zira	HyperX-Sen	2025-05-03T11:17:57Z	0	0	[ "license:apache-2.0", "size_categories:n<1K", "format:json", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-03T11:17:30Z	null	--- license: apache-2.0 ---
kazugi/ball_dataset	kazugi	2025-05-03T11:13:19Z	0	0	[ "task_categories:robotics", "size_categories:n<1K", "modality:video", "library:datasets", "library:mlcroissant", "region:us", "phosphobot", "so100", "phospho-dk" ]	[ "robotics" ]	2025-05-03T10:54:35Z	null	--- tags: - phosphobot - so100 - phospho-dk task_categories: - robotics --- # ball_dataset This dataset was generated using a [phospho starter pack](https://robots.phospho.ai). This dataset contains a series of episodes recorded with a robot and multiple cameras. It can be directly used to train a policy using imitation learning. It's compatible with LeRobot and RLDS.
ParkSY/data_nerf_depthanying_depthmap	ParkSY	2025-05-03T11:06:18Z	0	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-03T11:06:12Z	null	--- dataset_info: features: - name: input_image dtype: string - name: edit_prompt dtype: string - name: edited_image dtype: string - name: label dtype: int64 - name: depthmap dtype: string - name: water_color dtype: string splits: - name: train num_bytes: 2324779 num_examples: 6552 download_size: 268143 dataset_size: 2324779 configs: - config_name: default data_files: - split: train path: data/train-* ---
ninja/hayat	ninja	2025-05-03T10:58:48Z	0	0	[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-03T10:58:45Z	null	--- dataset_info: features: - name: text dtype: string - name: entities list: - name: label dtype: string - name: related_to dtype: string - name: text dtype: string splits: - name: train num_bytes: 383615 num_examples: 580 download_size: 87818 dataset_size: 383615 configs: - config_name: default data_files: - split: train path: data/train-* ---
MBZUAI-IFM/puzzle_dpsk_conversation_final	MBZUAI-IFM	2025-05-03T10:48:10Z	0	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-02T17:37:55Z	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 - name: answer dtype: string splits: - name: train num_bytes: 27442441 num_examples: 3748 download_size: 12861317 dataset_size: 27442441 configs: - config_name: default data_files: - split: train path: data/train-* ---

ParkSY/data_nerf_anything_depth_normalmap

ParkSY

2025-05-05T05:59:15Z

0

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

[]

2025-05-05T05:36:29Z

null

--- dataset_info: features: - name: input_image dtype: string - name: edit_prompt dtype: string - name: edited_image dtype: string - name: label dtype: int64 - name: depthmap dtype: string - name: normalmap dtype: string splits: - name: train num_bytes: 1332384 num_examples: 8190 download_size: 266269 dataset_size: 1332384 configs: - config_name: default data_files: - split: train path: data/train-* ---

justinian336/salvadoran-news-elmundo

justinian336

2025-05-05T01:30:12Z

117

0

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

[]

2023-02-15T14:38:15Z

null

--- dataset_info: features: - name: image_src dtype: string - name: title dtype: string - name: content dtype: string - name: category dtype: class_label: names: '0': Nacionales '1': Economia '2': Tecnomundo '3': Politica '4': Guia Mundialista '5': El Mundo '6': Editorial '7': Confidencial - name: date dtype: string - name: link dtype: string splits: - name: train num_bytes: 92200472 num_examples: 47007 download_size: 51272331 dataset_size: 92200472 --- # Dataset Card for "salvadoran-news-elmundo" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

mlfoundations-dev/nemo_nano_code_1000k

mlfoundations-dev

2025-05-05T01:03:07Z

0

[ "region:us" ]

[]

2025-05-05T00:50:19Z

null

--- dataset_info: features: - name: input dtype: string - name: output dtype: string - name: category dtype: string - name: license dtype: string - name: reasoning dtype: string - name: generator dtype: string - name: used_in_training dtype: string - name: version dtype: string - name: system_prompt dtype: string - name: conversations list: - name: from dtype: string - name: value dtype: string splits: - name: train num_bytes: 40531140108.885 num_examples: 1000000 download_size: 16693989550 dataset_size: 40531140108.885 configs: - config_name: default data_files: - split: train path: data/train-* ---

xbilek25/train_hall_absorb_0.7_3600_7200

xbilek25

2025-05-04T20:00:31Z

0

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

[]

2025-05-04T15:33:49Z

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: 710317089.0 num_examples: 3600 download_size: 558743976 dataset_size: 710317089.0 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_18_v2

HungVu2003

2025-05-04T18:19:56Z

0

[ "region:us" ]

[]

2025-05-04T18:19:55Z

null

--- dataset_info: features: - name: question dtype: string splits: - name: train num_bytes: 2023535 num_examples: 13750 download_size: 1099056 dataset_size: 2023535 configs: - config_name: default data_files: - split: train path: data/train-* ---

openfoodfacts/product-database

openfoodfacts

2025-05-04T18:17:42Z

3,298

35

[ "language:en", "language:fr", "language:de", "language:es", "language:it", "language:nl", "language:pl", "language:pt", "language:sv", "language:bg", "language:ro", "language:fi", "language:ru", "language:nb", "language:cs", "language:th", "language:da", "language:hr", "language:hu", "language:ar", "language:el", "language:ja", "language:ca", "language:sr", "language:sl", "language:sk", "language:tr", "language:lt", "language:zh", "language:et", "language:lv", "language:xx", "language:uk", "language:id", "language:he", "language:vi", "language:is", "language:la", "language:in", "language:ko", "language:sq", "language:iw", "language:ka", "language:ms", "language:bs", "language:fa", "language:bn", "language:gl", "language:kk", "language:mk", "language:nn", "language:hi", "language:aa", "language:uz", "language:so", "language:af", "language:eu", "license:agpl-3.0", "license:odbl", "size_categories:1M<n<10M", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2024-10-21T08:44:28Z

null

--- language: - en - fr - de - es - it - nl - pl - pt - sv - bg - ro - fi - ru - nb - cs - th - da - hr - hu - ar - el - ja - ca - sr - sl - sk - tr - lt - zh - et - lv - xx - uk - id - he - vi - is - la - in - ko - sq - iw - ka - ms - bs - fa - bn - gl - kk - mk - nn - hi - aa - uz - so - af - eu license: - agpl-3.0 - odbl size_categories: - 1M<n<10M pretty_name: Open Food Facts Product Database dataset_info: config_name: default configs: - config_name: default data_files: - split: food path: food.parquet - split: beauty path: beauty.parquet --- # Open Food Facts Database ## What is 🍊 Open Food Facts? ### A food products database Open Food Facts is a database of food products with ingredients, allergens, nutrition facts and all the tidbits of information we can find on product labels. ### Made by everyone Open Food Facts is a non-profit association of volunteers. 25.000+ contributors like you have added 1.7 million + products from 150 countries using our Android or iPhone app or their camera to scan barcodes and upload pictures of products and their labels. ### For everyone Data about food is of public interest and has to be open. The complete database is published as open data and can be reused by anyone and for any use. Check-out the cool reuses or make your own! ## The Parquet Dataset This dataset is a simpler version of the [JSONL dump](https://world.openfoodfacts.org/data) provided by the Open Food Facts organization on a daily basis. It was converted into the Parquet format for easy of use. ### Data processing * `Debug` tags were removed. * `Tags`tags are conserved since they contain most information, * `Hierarchy` tags were removed * `lc` tags were removed. It corresponds to the ["language of the interface"](https://openfoodfacts.github.io/openfoodfacts-server/reference/api-tutorials/adding-missing-products/#sending-the-right-country-and-language-parameters-based-on-the-country-your-user-is-located-in-and-the-language-the-product-is-in), * `langs` tags are kept for each `ingredients_text` and conserved as individual columns (*for now*). The original JSONL dump was processed using [Pyarrow](https://arrow.apache.org/docs/python/). ## Conditions for reuse The Open Food Facts database is available under the Open Database License. The individual contents of the database are available under the Database Contents License. Products images are available under the Creative Commons Attribution ShareAlike licence. They may contain graphical elements subject to copyright or other rights, that may in some cases be reproduced (quotation rights or fair use). Please read Terms and conditions of use and re-use before re-using the data. ## Tell us about your reuse We are very interested in learning what the Open Food Facts data is used for. It is not mandatory, but we would very much appreciate it if you tell us about your re-uses so that we can share them with the Open Food Facts community. You can also fill this form to get a chance to get your app featured. - **Homepage:** https://world.openfoodfacts.org/ - **Repository:** https://github.com/openfoodfacts - **Point of Contact:** [email protected]

paulinus/so100_test

paulinus

2025-05-04T18:02:08Z

0

[ "task_categories:robotics", "license:apache-2.0", "region:us", "LeRobot", "so100", "tutorial" ]

[ "robotics" ]

2025-05-04T18:02:04Z

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": 1, "total_frames": 629, "total_tasks": 1, "total_videos": 0, "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" ] }, "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] ```

Lithium73fr/test2

Lithium73fr

2025-05-04T17:55:20Z

0

[ "task_categories:robotics", "region:us", "phosphobot", "so100", "phospho-dk" ]

[ "robotics" ]

2025-05-02T18:43:56Z

null

--- tags: - phosphobot - so100 - phospho-dk task_categories: - robotics --- # test2 **This dataset was generated using a [phospho starter pack](https://robots.phospho.ai).** This dataset contains a series of episodes recorded with a robot and multiple cameras. It can be directly used to train a policy using imitation learning. It's compatible with LeRobot and RLDS.

GitBag/Qwen2.5-7B_hmmt-feb-25_eval

GitBag

2025-05-04T17:48:21Z

0

[ "region:us" ]

[]

2025-05-04T17:48:20Z

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: 3844721 num_examples: 30 download_size: 1256848 dataset_size: 3844721 configs: - config_name: default data_files: - split: train path: data/train-* ---

RafaelJaime/sas_opposition_exam_data

RafaelJaime

2025-05-04T17:48:04Z

376

0

[ "language:es", "license:apache-2.0", "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "medical" ]

[]

2025-03-21T14:57:39Z

null

--- dataset_info: features: - name: statement dtype: string - name: answers sequence: string - name: correct_answer dtype: string - name: theme dtype: string - name: version dtype: string splits: - name: train num_bytes: 5128074 num_examples: 10712 download_size: 2407181 dataset_size: 5128074 configs: - config_name: default data_files: - split: train path: data/train-* license: apache-2.0 language: - es tags: - medical --- # SAS Opposition Exam Dataset This dataset contains questions and answers from all the exams of the SAS (Servicio Andaluz de Salud) public job offers. The questions and answers are sourced from the official webpage of the Andalusian Health Service [here](https://www.sspa.juntadeandalucia.es/servicioandaluzdesalud/profesionales/ofertas-de-empleo/oferta-de-empleo-publico-puestos-base/oep-extraordinaria-decreto-ley-122022-centros-sas/cuadro-de-evolucion-concurso-oposicion-centros-sas). ## Dataset Information - **Statement**: The question in the exam. - **Answers**: The possible answers for the question. - **Real Answer**: The correct answer for the question. - **Theme**: The topic or subject of the question. ### Dataset Creation Script The script used to create this dataset can be found at: [generation_script.py](https://huggingface.co/datasets/RafaelJaime/sas_opposition_exam_data/blob/main/generation_script.py).

HungVu2003/opt-350m_beta_1.0_alpha_0.8_num-company_3_dataset_1_for_gen_2

HungVu2003

2025-05-04T16:47:43Z

0

[ "region:us" ]

[]

2025-05-04T16:47:41Z

null

--- dataset_info: features: - name: question dtype: string splits: - name: train num_bytes: 2632052 num_examples: 12498 download_size: 1439908 dataset_size: 2632052 configs: - config_name: default data_files: - split: train path: data/train-* ---

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-* ---

JosephZ/mega_1m

JosephZ

2025-05-04T16:14:08Z

2

0

[ "language:en", "license:apache-2.0", "size_categories:100K<n<1M", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:2411.15435", "region:us", "scene-graph-generation" ]

[]

2025-05-02T09:13:32Z

null

--- dataset_info: features: - name: image_id dtype: string - name: file_name dtype: string - name: image dtype: image - name: objects dtype: string - name: relationships dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 221848987040.66 num_examples: 988531 download_size: 221355388705 dataset_size: 221848987040.66 configs: - config_name: default data_files: - split: train path: data/train-* license: apache-2.0 language: - en tags: - scene-graph-generation --- This dataset MegaSG contains 1M images annotated with scene graphs , which was introduced in the paper [What Makes a Scene ? Scene Graph-based Evaluation and Feedback for Controllable Generation](https://arxiv.org/pdf/2411.15435)

mteb/VoyageMMarcoReranking

mteb

2025-05-04T16:11:59Z

9

0

[ "task_categories:text-ranking", "annotations_creators:derived", "multilinguality:monolingual", "language:jpn", "license:cc-by-4.0", "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2312.16144", "arxiv:2502.13595", "arxiv:2210.07316", "region:us", "mteb", "text" ]

[ "text-ranking" ]

2025-02-18T20:02:06Z

null

--- annotations_creators: - derived language: - jpn license: cc-by-4.0 multilinguality: monolingual task_categories: - text-ranking 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: 26582357 num_examples: 53375 download_size: 12669365 dataset_size: 26582357 - config_name: default features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 3093432 num_examples: 53375 download_size: 359413 dataset_size: 3093432 - config_name: queries features: - name: _id dtype: string - name: text dtype: string splits: - name: test num_bytes: 139132 num_examples: 2048 download_size: 79174 dataset_size: 139132 - config_name: top_ranked features: - name: query-id dtype: string - name: corpus-ids sequence: string splits: - name: test num_bytes: 1778562 num_examples: 2048 download_size: 353814 dataset_size: 1778562 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-* - config_name: top_ranked data_files: - split: test path: top_ranked/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;">VoyageMMarcoReranking</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> a hard-negative augmented version of the Japanese MMARCO dataset as used in Voyage AI Evaluation Suite | | | |---------------|---------------------------------------------| | Task category | t2t | | Domains | Academic, Non-fiction, Written | | Reference | https://arxiv.org/abs/2312.16144 | ## 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(["VoyageMMarcoReranking"]) 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{clavié2023jacolbert, archiveprefix = {arXiv}, author = {Benjamin Clavié}, eprint = {2312.16144}, title = {JaColBERT and Hard Negatives, Towards Better Japanese-First Embeddings for Retrieval: Early Technical Report}, 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("VoyageMMarcoReranking") desc_stats = task.metadata.descriptive_stats ``` ```json { "test": { "num_samples": 55423, "number_of_characters": 8824820, "num_documents": 53375, "min_document_length": 19, "average_document_length": 164.72532084309134, "max_document_length": 1192, "unique_documents": 53375, "num_queries": 2048, "min_query_length": 3, "average_query_length": 15.9208984375, "max_query_length": 73, "unique_queries": 2048, "none_queries": 0, "num_relevant_docs": 53375, "min_relevant_docs_per_query": 26, "average_relevant_docs_per_query": 1.06201171875, "max_relevant_docs_per_query": 29, "unique_relevant_docs": 53375, "num_instructions": null, "min_instruction_length": null, "average_instruction_length": null, "max_instruction_length": null, "unique_instructions": null, "num_top_ranked": 2048, "min_top_ranked_per_query": 26, "average_top_ranked_per_query": 26.06201171875, "max_top_ranked_per_query": 29 } } ``` </details> --- *This dataset card was automatically generated using [MTEB](https://github.com/embeddings-benchmark/mteb)*

mteb/SCIDOCS-PL

mteb

2025-05-04T16:11:26Z

34

0

[ "task_categories:text-retrieval", "multilinguality:monolingual", "source_datasets:mteb/scidocs", "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" ]

2025-02-05T19:10:26Z

null

--- language: - pol multilinguality: monolingual source_datasets: - mteb/scidocs 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: 35547893 num_examples: 25657 download_size: 21449173 dataset_size: 35547893 - config_name: default features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 2873088 num_examples: 29928 download_size: 1330598 dataset_size: 2873088 - config_name: queries features: - name: _id dtype: string - name: text dtype: string splits: - name: test num_bytes: 131568 num_examples: 1000 download_size: 100886 dataset_size: 131568 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;">SCIDOCS-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> SciDocs, a new evaluation benchmark consisting of seven document-level tasks ranging from citation prediction, to document classification and recommendation. | | | |---------------|---------------------------------------------| | Task category | t2t | | Domains | None | | Reference | https://allenai.org/data/scidocs | ## 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(["SCIDOCS-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("SCIDOCS-PL") desc_stats = task.metadata.descriptive_stats ``` ```json { "test": { "num_samples": 26657, "number_of_characters": 32692750, "num_documents": 25657, "min_document_length": 12, "average_document_length": 1271.0791986592353, "max_document_length": 11840, "unique_documents": 25657, "num_queries": 1000, "min_query_length": 14, "average_query_length": 80.671, "max_query_length": 235, "unique_queries": 1000, "none_queries": 0, "num_relevant_docs": 29928, "min_relevant_docs_per_query": 27, "average_relevant_docs_per_query": 4.928, "max_relevant_docs_per_query": 30, "unique_relevant_docs": 25657, "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/CQADupstack-Unix-PL

mteb

2025-05-04T16:10:57Z

16

0

[ "task_categories:text-retrieval", "task_ids:multiple-choice-qa", "annotations_creators:derived", "multilinguality:translated", "source_datasets:mteb/cqadupstack-unix", "language:pol", "license:unknown", "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" ]

2025-02-05T19:19:28Z

null

--- annotations_creators: - derived language: - pol license: unknown multilinguality: translated source_datasets: - mteb/cqadupstack-unix task_categories: - text-retrieval task_ids: - multiple-choice-qa dataset_info: - config_name: corpus features: - name: _id dtype: string - name: text dtype: string - name: title dtype: string splits: - name: test num_bytes: 48663218 num_examples: 47382 download_size: 28352236 dataset_size: 48663218 - config_name: default features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 44636 num_examples: 1693 download_size: 23577 dataset_size: 44636 - config_name: queries features: - name: _id dtype: string - name: text dtype: string splits: - name: test num_bytes: 76537 num_examples: 1072 download_size: 54080 dataset_size: 76537 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;">CQADupstack-Unix-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> CQADupStack: A Stack Exchange Question Duplicate Pairs Dataset | | | |---------------|---------------------------------------------| | Task category | t2t | | Domains | Written, Web, Programming | | Reference | https://huggingface.co/datasets/clarin-knext/cqadupstack-unix-pl | ## 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(["CQADupstack-Unix-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("CQADupstack-Unix-PL") desc_stats = task.metadata.descriptive_stats ``` ```json { "test": { "num_samples": 48454, "number_of_characters": 45977634, "num_documents": 47382, "min_document_length": 52, "average_document_length": 969.1105272044236, "max_document_length": 30428, "unique_documents": 47382, "num_queries": 1072, "min_query_length": 14, "average_query_length": 55.26026119402985, "max_query_length": 136, "unique_queries": 1072, "none_queries": 0, "num_relevant_docs": 1693, "min_relevant_docs_per_query": 1, "average_relevant_docs_per_query": 1.5792910447761195, "max_relevant_docs_per_query": 22, "unique_relevant_docs": 1693, "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/trec-covid

mteb

2025-05-04T16:10:37Z

1,263

2

[ "task_categories:text-retrieval", "multilinguality:monolingual", "language:eng", "size_categories:100K<n<1M", "format:json", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2104.09632", "arxiv:2502.13595", "arxiv:2210.07316", "region:us", "mteb", "text" ]

[ "text-retrieval" ]

2024-03-02T20:32:41Z

null

--- language: - eng multilinguality: monolingual task_categories: - text-retrieval task_ids: [] 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: test num_bytes: 1710499 num_examples: 66336 - config_name: corpus features: - name: _id dtype: string - name: title dtype: string - name: text dtype: string splits: - name: corpus num_bytes: 195185777 num_examples: 171332 - config_name: queries features: - name: _id dtype: string - name: text dtype: string splits: - name: queries num_bytes: 3953 num_examples: 50 configs: - config_name: default data_files: - 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;">TRECCOVID</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> TRECCOVID is an ad-hoc search challenge based on the COVID-19 dataset containing scientific articles related to the COVID-19 pandemic. | | | |---------------|---------------------------------------------| | Task category | t2t | | Domains | Medical, Academic, Written | | Reference | https://ir.nist.gov/covidSubmit/index.html | ## 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(["TRECCOVID"]) 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{roberts2021searching, archiveprefix = {arXiv}, author = {Kirk Roberts and Tasmeer Alam and Steven Bedrick and Dina Demner-Fushman and Kyle Lo and Ian Soboroff and Ellen Voorhees and Lucy Lu Wang and William R Hersh}, eprint = {2104.09632}, primaryclass = {cs.IR}, title = {Searching for Scientific Evidence in a Pandemic: An Overview of TREC-COVID}, 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("TRECCOVID") desc_stats = task.metadata.descriptive_stats ``` ```json { "test": { "num_samples": 171382, "number_of_characters": 191508678, "num_documents": 171332, "min_document_length": 1, "average_document_length": 1117.7434221277986, "max_document_length": 122459, "unique_documents": 171332, "num_queries": 50, "min_query_length": 30, "average_query_length": 69.24, "max_query_length": 165, "unique_queries": 50, "none_queries": 0, "num_relevant_docs": 66336, "min_relevant_docs_per_query": 631, "average_relevant_docs_per_query": 493.5, "max_relevant_docs_per_query": 1941, "unique_relevant_docs": 35480, "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/mtop_intent

mteb

2025-05-04T16:08:08Z

2,668

2

[ "task_categories:text-classification", "annotations_creators:human-annotated", "multilinguality:multilingual", "language:deu", "language:eng", "language:fra", "language:hin", "language:spa", "language:tha", "license:unknown", "modality:text", "arxiv:2008.09335", "arxiv:2502.13595", "arxiv:2210.07316", "region:us", "mteb", "text" ]

[ "text-classification" ]

2022-05-19T15:03:15Z

null

--- annotations_creators: - human-annotated language: - deu - eng - fra - hin - spa - tha license: unknown multilinguality: multilingual task_categories: - text-classification task_ids: [] dataset_info: - config_name: de features: - name: text dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 748424 num_examples: 13424 - name: validation num_bytes: 100446 num_examples: 1815 - name: test num_bytes: 195937 num_examples: 3549 download_size: 543111 dataset_size: 1044807 - config_name: en features: - name: text dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 761023 num_examples: 15667 - name: validation num_bytes: 108483 num_examples: 2235 - name: test num_bytes: 214022 num_examples: 4386 download_size: 629031 dataset_size: 1083528 - config_name: es features: - name: text dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 621403 num_examples: 10934 - name: validation num_bytes: 87850 num_examples: 1527 - name: test num_bytes: 170223 num_examples: 2998 download_size: 403224 dataset_size: 879476 - config_name: fr features: - name: text dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 671550 num_examples: 11814 - name: validation num_bytes: 88815 num_examples: 1577 - name: test num_bytes: 182408 num_examples: 3193 download_size: 484784 dataset_size: 942773 - config_name: hi features: - name: text dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 1238687 num_examples: 11330 - name: validation num_bytes: 228095 num_examples: 2012 - name: test num_bytes: 303899 num_examples: 2789 download_size: 642592 dataset_size: 1770681 - config_name: th features: - name: text dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 1175051 num_examples: 10759 - name: validation num_bytes: 185878 num_examples: 1671 - name: test num_bytes: 301794 num_examples: 2765 download_size: 621662 dataset_size: 1662723 configs: - config_name: de data_files: - split: train path: de/train-* - split: validation path: de/validation-* - split: test path: de/test-* - config_name: en data_files: - split: train path: en/train-* - split: validation path: en/validation-* - split: test path: en/test-* - config_name: es data_files: - split: train path: es/train-* - split: validation path: es/validation-* - split: test path: es/test-* - config_name: fr data_files: - split: train path: fr/train-* - split: validation path: fr/validation-* - split: test path: fr/test-* - config_name: hi data_files: - split: train path: hi/train-* - split: validation path: hi/validation-* - split: test path: hi/test-* - config_name: th data_files: - split: train path: th/train-* - split: validation path: th/validation-* - split: test path: th/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;">MTOPIntentClassification</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> MTOP: Multilingual Task-Oriented Semantic Parsing | | | |---------------|---------------------------------------------| | Task category | t2c | | Domains | Spoken, Spoken | | Reference | https://arxiv.org/pdf/2008.09335.pdf | ## 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(["MTOPIntentClassification"]) 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{li-etal-2021-mtop, abstract = {Scaling semantic parsing models for task-oriented dialog systems to new languages is often expensive and time-consuming due to the lack of available datasets. Available datasets suffer from several shortcomings: a) they contain few languages b) they contain small amounts of labeled examples per language c) they are based on the simple intent and slot detection paradigm for non-compositional queries. In this paper, we present a new multilingual dataset, called MTOP, comprising of 100k annotated utterances in 6 languages across 11 domains. We use this dataset and other publicly available datasets to conduct a comprehensive benchmarking study on using various state-of-the-art multilingual pre-trained models for task-oriented semantic parsing. We achieve an average improvement of +6.3 points on Slot F1 for the two existing multilingual datasets, over best results reported in their experiments. Furthermore, we demonstrate strong zero-shot performance using pre-trained models combined with automatic translation and alignment, and a proposed distant supervision method to reduce the noise in slot label projection.}, address = {Online}, author = {Li, Haoran and Arora, Abhinav and Chen, Shuohui and Gupta, Anchit and Gupta, Sonal and Mehdad, Yashar}, booktitle = {Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume}, doi = {10.18653/v1/2021.eacl-main.257}, editor = {Merlo, Paola and Tiedemann, Jorg and Tsarfaty, Reut}, month = apr, pages = {2950--2962}, publisher = {Association for Computational Linguistics}, title = {{MTOP}: A Comprehensive Multilingual Task-Oriented Semantic Parsing Benchmark}, url = {https://aclanthology.org/2021.eacl-main.257}, 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("MTOPIntentClassification") desc_stats = task.metadata.descriptive_stats ``` ```json { "validation": { "num_samples": 10837, "number_of_characters": 431895, "number_texts_intersect_with_train": 127, "min_text_length": 5, "average_text_length": 39.85374181046415, "max_text_length": 154, "unique_text": 10830, "unique_labels": 88, "labels": { "3": { "count": 919 }, "18": { "count": 645 }, "27": { "count": 117 }, "15": { "count": 322 }, "24": { "count": 81 }, "29": { "count": 487 }, "14": { "count": 713 }, "60": { "count": 320 }, "84": { "count": 69 }, "0": { "count": 628 }, "25": { "count": 1156 }, "1": { "count": 188 }, "30": { "count": 11 }, "16": { "count": 692 }, "54": { "count": 41 }, "59": { "count": 464 }, "22": { "count": 171 }, "9": { "count": 49 }, "43": { "count": 716 }, "63": { "count": 103 }, "48": { "count": 269 }, "11": { "count": 114 }, "32": { "count": 80 }, "4": { "count": 453 }, "53": { "count": 88 }, "65": { "count": 92 }, "42": { "count": 218 }, "5": { "count": 139 }, "47": { "count": 93 }, "35": { "count": 41 }, "96": { "count": 11 }, "33": { "count": 42 }, "94": { "count": 4 }, "13": { "count": 23 }, "75": { "count": 23 }, "34": { "count": 43 }, "61": { "count": 88 }, "52": { "count": 65 }, "101": { "count": 6 }, "49": { "count": 29 }, "38": { "count": 54 }, "17": { "count": 40 }, "69": { "count": 38 }, "45": { "count": 58 }, "40": { "count": 56 }, "51": { "count": 39 }, "92": { "count": 12 }, "77": { "count": 11 }, "46": { "count": 31 }, "7": { "count": 22 }, "55": { "count": 26 }, "87": { "count": 6 }, "41": { "count": 31 }, "36": { "count": 45 }, "56": { "count": 10 }, "37": { "count": 36 }, "68": { "count": 35 }, "90": { "count": 6 }, "20": { "count": 8 }, "85": { "count": 8 }, "86": { "count": 20 }, "44": { "count": 39 }, "2": { "count": 17 }, "76": { "count": 5 }, "80": { "count": 17 }, "72": { "count": 34 }, "64": { "count": 6 }, "19": { "count": 49 }, 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"72": { "count": 69 }, "96": { "count": 4 }, "98": { "count": 3 }, "52": { "count": 67 }, "57": { "count": 36 }, "97": { "count": 6 }, "54": { "count": 34 }, "53": { "count": 88 }, "55": { "count": 19 }, "100": { "count": 3 }, "56": { "count": 10 }, "101": { "count": 7 }, "60": { "count": 368 }, "61": { "count": 59 }, "59": { "count": 231 }, "63": { "count": 100 }, "13": { "count": 34 }, "64": { "count": 9 }, "66": { "count": 9 }, "78": { "count": 6 }, "68": { "count": 20 }, "62": { "count": 33 }, "10": { "count": 19 }, "79": { "count": 45 }, "71": { "count": 16 }, "77": { "count": 14 }, "69": { "count": 40 }, "73": { "count": 7 }, "74": { "count": 40 }, "70": { "count": 10 }, "12": { "count": 42 }, "67": { "count": 10 }, "93": { "count": 8 }, "58": { "count": 8 }, "102": { "count": 3 }, "83": { "count": 1 }, "23": { "count": 1 }, "28": { "count": 9 }, "92": { "count": 6 }, "99": { "count": 1 }, "82": { "count": 5 }, "76": { "count": 5 }, "21": { "count": 7 }, "103": { "count": 6 }, "105": { "count": 1 }, "106": { "count": 4 }, "108": { "count": 1 }, "104": { "count": 3 }, "110": { "count": 1 }, "94": { "count": 1 }, "112": { "count": 1 }, "109": { "count": 1 } } } } } } ``` </details> --- *This dataset card was automatically generated using [MTEB](https://github.com/embeddings-benchmark/mteb)*

mteb/amazon_massive_intent

mteb

2025-05-04T16:08:03Z

79,081

21

[ "task_categories:text-classification", "annotations_creators:human-annotated", "multilinguality:translated", "language:afr", "language:amh", "language:ara", "language:aze", "language:ben", "language:cmo", "language:cym", "language:dan", "language:deu", "language:ell", "language:eng", "language:fas", "language:fin", "language:fra", "language:heb", "language:hin", "language:hun", "language:hye", "language:ind", "language:isl", "language:ita", "language:jav", "language:jpn", "language:kan", "language:kat", "language:khm", "language:kor", "language:lav", "language:mal", "language:mon", "language:msa", "language:mya", "language:nld", "language:nob", "language:pol", "language:por", "language:ron", "language:rus", "language:slv", "language:spa", "language:sqi", "language:swa", "language:swe", "language:tam", "language:tel", "language:tgl", "language:tha", "language:tur", "language:urd", "language:vie", "license:apache-2.0", "size_categories:1M<n<10M", "format:json", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "arxiv:2204.08582", "arxiv:2502.13595", "arxiv:2210.07316", "region:us", "mteb", "text" ]

[ "text-classification" ]

2022-05-15T20:59:15Z

null

--- annotations_creators: - human-annotated language: - afr - amh - ara - aze - ben - cmo - cym - dan - deu - ell - eng - fas - fin - fra - heb - hin - hun - hye - ind - isl - ita - jav - jpn - kan - kat - khm - kor - lav - mal - mon - msa - mya - nld - nob - pol - por - ron - rus - slv - spa - sqi - swa - swe - tam - tel - tgl - tha - tur - urd - vie license: apache-2.0 multilinguality: translated task_categories: - text-classification task_ids: [] configs: - config_name: default data_files: - path: train/*.json.gz split: train - path: test/*.json.gz split: test - path: validation/*.json.gz split: validation - config_name: ta data_files: - path: train/ta.json.gz split: train - path: test/ta.json.gz split: test - path: validation/ta.json.gz split: validation - config_name: is data_files: - path: train/is.json.gz split: train - path: test/is.json.gz split: test - path: validation/is.json.gz split: validation - config_name: pl data_files: - path: train/pl.json.gz split: train - path: test/pl.json.gz split: test - path: validation/pl.json.gz split: validation - config_name: zh-CN data_files: - path: train/zh-CN.json.gz split: train - path: test/zh-CN.json.gz split: test - path: validation/zh-CN.json.gz split: validation - config_name: el data_files: - path: train/el.json.gz split: train - path: test/el.json.gz split: test - path: validation/el.json.gz split: validation - config_name: ru data_files: - path: train/ru.json.gz split: train - path: test/ru.json.gz split: test - path: validation/ru.json.gz split: validation - config_name: te data_files: - path: train/te.json.gz split: train - path: test/te.json.gz split: test - path: validation/te.json.gz split: validation - config_name: cy data_files: - path: train/cy.json.gz split: train - path: test/cy.json.gz split: test - path: validation/cy.json.gz split: validation - config_name: he data_files: - path: train/he.json.gz split: train - path: test/he.json.gz split: test - path: validation/he.json.gz split: validation - config_name: de data_files: - path: train/de.json.gz split: train - path: test/de.json.gz split: test - path: validation/de.json.gz split: validation - config_name: af data_files: - path: train/af.json.gz split: train - path: test/af.json.gz split: test - path: validation/af.json.gz split: validation - config_name: ml data_files: - path: train/ml.json.gz split: train - path: test/ml.json.gz split: test - path: validation/ml.json.gz split: validation - config_name: sl data_files: - path: train/sl.json.gz split: train - path: test/sl.json.gz split: test - path: validation/sl.json.gz split: validation - config_name: vi data_files: - path: train/vi.json.gz split: train - path: test/vi.json.gz split: test - path: validation/vi.json.gz split: validation - config_name: mn data_files: - path: train/mn.json.gz split: train - path: test/mn.json.gz split: test - path: validation/mn.json.gz split: validation - config_name: tl data_files: - path: train/tl.json.gz split: train - path: test/tl.json.gz split: test - path: validation/tl.json.gz split: validation - config_name: it data_files: - path: train/it.json.gz split: train - path: test/it.json.gz split: test - path: validation/it.json.gz split: validation - config_name: jv data_files: - path: train/jv.json.gz split: train - path: test/jv.json.gz split: test - path: validation/jv.json.gz split: validation - config_name: sq data_files: - path: train/sq.json.gz split: train - path: test/sq.json.gz split: test - path: validation/sq.json.gz split: validation - config_name: fa data_files: - path: train/fa.json.gz split: train - path: test/fa.json.gz split: test - path: validation/fa.json.gz split: validation - config_name: nb data_files: - path: train/nb.json.gz split: train - path: test/nb.json.gz split: test - path: validation/nb.json.gz split: validation - config_name: km data_files: - path: train/km.json.gz split: train - path: test/km.json.gz split: test - path: validation/km.json.gz split: validation - config_name: th data_files: - path: train/th.json.gz split: train - path: test/th.json.gz split: test - path: validation/th.json.gz split: validation - config_name: ja data_files: - path: train/ja.json.gz split: train - path: test/ja.json.gz split: test - path: validation/ja.json.gz split: validation - config_name: hi data_files: - path: train/hi.json.gz split: train - path: test/hi.json.gz split: test - path: validation/hi.json.gz split: validation - config_name: id data_files: - path: train/id.json.gz split: train - path: test/id.json.gz split: test - path: validation/id.json.gz split: validation - config_name: kn data_files: - path: train/kn.json.gz split: train - path: test/kn.json.gz split: test - path: validation/kn.json.gz split: validation - config_name: fi data_files: - path: train/fi.json.gz split: train - path: test/fi.json.gz split: test - path: validation/fi.json.gz split: validation - config_name: ur data_files: - path: train/ur.json.gz split: train - path: test/ur.json.gz split: test - path: validation/ur.json.gz split: validation - config_name: my data_files: - path: train/my.json.gz split: train - path: test/my.json.gz split: test - path: validation/my.json.gz split: validation - config_name: lv data_files: - path: train/lv.json.gz split: train - path: test/lv.json.gz split: test - path: validation/lv.json.gz split: validation - config_name: fr data_files: - path: train/fr.json.gz split: train - path: test/fr.json.gz split: test - path: validation/fr.json.gz split: validation - config_name: ko data_files: - path: train/ko.json.gz split: train - path: test/ko.json.gz split: test - path: validation/ko.json.gz split: validation - config_name: sw data_files: - path: train/sw.json.gz split: train - path: test/sw.json.gz split: test - path: validation/sw.json.gz split: validation - config_name: sv data_files: - path: train/sv.json.gz split: train - path: test/sv.json.gz split: test - path: validation/sv.json.gz split: validation - config_name: nl data_files: - path: train/nl.json.gz split: train - path: test/nl.json.gz split: test - path: validation/nl.json.gz split: validation - config_name: da data_files: - path: train/da.json.gz split: train - path: test/da.json.gz split: test - path: validation/da.json.gz split: validation - config_name: ar data_files: - path: train/ar.json.gz split: train - path: test/ar.json.gz split: test - path: validation/ar.json.gz split: validation - config_name: ms data_files: - path: train/ms.json.gz split: train - path: test/ms.json.gz split: test - path: validation/ms.json.gz split: validation - config_name: en data_files: - path: train/en.json.gz split: train - path: test/en.json.gz split: test - path: validation/en.json.gz split: validation - config_name: am data_files: - path: train/am.json.gz split: train - path: test/am.json.gz split: test - path: validation/am.json.gz split: validation - config_name: pt data_files: - path: train/pt.json.gz split: train - path: test/pt.json.gz split: test - path: validation/pt.json.gz split: validation - config_name: ka data_files: - path: train/ka.json.gz split: train - path: test/ka.json.gz split: test - path: validation/ka.json.gz split: validation - config_name: ro data_files: - path: train/ro.json.gz split: train - path: test/ro.json.gz split: test - path: validation/ro.json.gz split: validation - config_name: tr data_files: - path: train/tr.json.gz split: train - path: test/tr.json.gz split: test - path: validation/tr.json.gz split: validation - config_name: hu data_files: - path: train/hu.json.gz split: train - path: test/hu.json.gz split: test - path: validation/hu.json.gz split: validation - config_name: zh-TW data_files: - path: train/zh-TW.json.gz split: train - path: test/zh-TW.json.gz split: test - path: validation/zh-TW.json.gz split: validation - config_name: bn data_files: - path: train/bn.json.gz split: train - path: test/bn.json.gz split: test - path: validation/bn.json.gz split: validation - config_name: hy data_files: - path: train/hy.json.gz split: train - path: test/hy.json.gz split: test - path: validation/hy.json.gz split: validation - config_name: es data_files: - path: train/es.json.gz split: train - path: test/es.json.gz split: test - path: validation/es.json.gz split: validation - config_name: az data_files: - path: train/az.json.gz split: train - path: test/az.json.gz split: test - path: validation/az.json.gz split: validation 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;">MassiveIntentClassification</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> MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse Languages | | | |---------------|---------------------------------------------| | Task category | t2c | | Domains | Spoken | | Reference | https://arxiv.org/abs/2204.08582 | ## 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(["MassiveIntentClassification"]) 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{fitzgerald2022massive, archiveprefix = {arXiv}, author = {Jack FitzGerald and Christopher Hench and Charith Peris and Scott Mackie and Kay Rottmann and Ana Sanchez and Aaron Nash and Liam Urbach and Vishesh Kakarala and Richa Singh and Swetha Ranganath and Laurie Crist and Misha Britan and Wouter Leeuwis and Gokhan Tur and Prem Natarajan}, eprint = {2204.08582}, primaryclass = {cs.CL}, title = {MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse Languages}, year = {2022}, } @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("MassiveIntentClassification") desc_stats = task.metadata.descriptive_stats ``` ```json { "validation": { "num_samples": 103683, "number_of_characters": 3583467, "number_texts_intersect_with_train": 5457, "min_text_length": 1, "average_text_length": 34.56176036573016, "max_text_length": 224, "unique_text": 102325, "unique_labels": 59, "labels": { "iot_hue_lightoff": { "count": 867 }, "iot_hue_lightdim": { "count": 867 }, "iot_cleaning": { "count": 969 }, "general_quirky": { "count": 5355 }, "takeaway_query": { "count": 1224 }, "play_music": { "count": 6273 }, "music_query": { "count": 1530 }, "weather_query": { "count": 6426 }, "music_settings": { "count": 408 }, "audio_volume_down": { "count": 408 }, "datetime_query": { "count": 3264 }, "general_greet": { "count": 102 }, "alarm_set": { "count": 1581 }, "audio_volume_up": { "count": 612 }, "alarm_query": { "count": 969 }, "news_query": { "count": 4182 }, "iot_hue_lighton": { "count": 255 }, "iot_wemo_off": { "count": 255 }, "iot_hue_lightchange": { "count": 1122 }, "audio_volume_mute": { "count": 765 }, "alarm_remove": { "count": 714 }, "general_joke": { "count": 765 }, "datetime_convert": { "count": 459 }, "iot_wemo_on": { "count": 357 }, "iot_hue_lightup": { "count": 612 }, "iot_coffee": { "count": 714 }, "social_post": { "count": 2550 }, "music_dislikeness": { "count": 102 }, "cooking_recipe": { "count": 2091 }, "takeaway_order": { "count": 1020 }, "music_likeness": { "count": 816 }, "calendar_query": { "count": 5202 }, "qa_stock": { "count": 1224 }, "qa_factoid": { "count": 4590 }, "calendar_set": { "count": 6681 }, "recommendation_events": { "count": 1326 }, "cooking_query": { "count": 102 }, "calendar_remove": { "count": 2397 }, "email_sendemail": { "count": 3213 }, "play_radio": { "count": 2346 }, "play_audiobook": { "count": 1785 }, "play_game": { "count": 1122 }, "lists_query": { "count": 2550 }, "lists_remove": { "count": 1887 }, "lists_createoradd": { "count": 1275 }, "email_addcontact": { "count": 255 }, "play_podcasts": { "count": 1734 }, "recommendation_movies": { "count": 612 }, "recommendation_locations": { "count": 1581 }, "transport_ticket": { "count": 1275 }, "transport_query": { "count": 1836 }, "transport_taxi": { "count": 1377 }, "transport_traffic": { "count": 1122 }, "qa_definition": { "count": 2805 }, "qa_currency": { "count": 1632 }, "qa_maths": { "count": 663 }, "social_query": { "count": 918 }, "email_query": { "count": 3723 }, "email_querycontact": { "count": 816 } } }, "test": { "num_samples": 151674, "number_of_characters": 5230011, "number_texts_intersect_with_train": 7273, "min_text_length": 1, "average_text_length": 34.48192175323391, "max_text_length": 495, "unique_text": 148972, "unique_labels": 59, "labels": { "alarm_set": { "count": 2091 }, "audio_volume_mute": { "count": 1632 }, "iot_hue_lightchange": { "count": 1836 }, "iot_hue_lighton": { "count": 153 }, "iot_hue_lightoff": { "count": 2193 }, "iot_cleaning": { "count": 1326 }, "general_quirky": { "count": 8619 }, "general_greet": { "count": 51 }, "datetime_query": { "count": 4488 }, "datetime_convert": { "count": 765 }, "alarm_remove": { "count": 1071 }, "alarm_query": { "count": 1734 }, "music_likeness": { "count": 1836 }, "iot_hue_lightup": { "count": 1377 }, "takeaway_order": { "count": 1122 }, "weather_query": { "count": 7956 }, "general_joke": { "count": 969 }, "play_music": { "count": 8976 }, "iot_hue_lightdim": { "count": 1071 }, "takeaway_query": { "count": 1785 }, "news_query": { "count": 6324 }, "audio_volume_up": { "count": 663 }, "iot_wemo_off": { "count": 918 }, "iot_wemo_on": { "count": 510 }, "iot_coffee": { "count": 1836 }, "music_query": { "count": 1785 }, "audio_volume_down": { "count": 561 }, "audio_volume_other": { "count": 306 }, "music_dislikeness": { "count": 204 }, "music_settings": { "count": 306 }, "recommendation_events": { "count": 2193 }, "qa_stock": { "count": 1326 }, "calendar_set": { "count": 10659 }, "play_audiobook": { "count": 2091 }, "social_query": { "count": 1275 }, "qa_factoid": { "count": 7191 }, "transport_ticket": { "count": 1785 }, "recommendation_locations": { "count": 1581 }, "calendar_query": { "count": 6426 }, "recommendation_movies": { "count": 1020 }, "transport_query": { "count": 2601 }, "cooking_recipe": { "count": 3672 }, "play_game": { "count": 1785 }, "calendar_remove": { "count": 3417 }, "email_query": { "count": 6069 }, "email_sendemail": { "count": 5814 }, "play_radio": { "count": 3672 }, "play_podcasts": { "count": 3213 }, "lists_query": { "count": 2601 }, "lists_remove": { "count": 2652 }, "lists_createoradd": { "count": 1989 }, "transport_taxi": { "count": 1173 }, "transport_traffic": { "count": 765 }, "qa_definition": { "count": 2907 }, "qa_maths": { "count": 1275 }, "social_post": { "count": 4131 }, "qa_currency": { "count": 1989 }, "email_addcontact": { "count": 612 }, "email_querycontact": { "count": 1326 } } }, "train": { "num_samples": 587214, "number_of_characters": 20507758, "number_texts_intersect_with_train": null, "min_text_length": 1, "average_text_length": 34.92382334208653, "max_text_length": 295, "unique_text": 565055, "unique_labels": 60, "labels": { "alarm_set": { "count": 9282 }, "audio_volume_mute": { "count": 5610 }, "iot_hue_lightchange": { "count": 6375 }, "iot_hue_lightoff": { "count": 7803 }, "iot_hue_lightdim": { "count": 3876 }, "iot_cleaning": { "count": 4743 }, "calendar_query": { "count": 28866 }, "play_music": { "count": 32589 }, "general_quirky": { "count": 28305 }, "general_greet": { "count": 1275 }, "datetime_query": { "count": 17850 }, "datetime_convert": { "count": 2652 }, "takeaway_query": { "count": 6222 }, "alarm_remove": { "count": 3978 }, "alarm_query": { "count": 6630 }, "news_query": { "count": 25653 }, "music_likeness": { "count": 5763 }, "music_query": { "count": 7854 }, "iot_hue_lightup": { "count": 3876 }, "takeaway_order": { "count": 6885 }, "weather_query": { "count": 29223 }, "music_settings": { "count": 2601 }, "general_joke": { "count": 3672 }, "music_dislikeness": { "count": 714 }, "audio_volume_other": { "count": 918 }, "iot_coffee": { "count": 6324 }, "audio_volume_up": { "count": 5610 }, "iot_wemo_on": { "count": 2448 }, "iot_hue_lighton": { "count": 1122 }, "iot_wemo_off": { "count": 2652 }, "audio_volume_down": { "count": 2652 }, "qa_stock": { "count": 7752 }, "play_radio": { "count": 14433 }, "recommendation_locations": { "count": 8823 }, "qa_factoid": { "count": 27744 }, "calendar_set": { "count": 41310 }, "play_audiobook": { "count": 7650 }, "play_podcasts": { "count": 9843 }, "social_query": { "count": 5508 }, "transport_query": { "count": 11577 }, "email_sendemail": { "count": 18054 }, "recommendation_movies": { "count": 3570 }, "lists_query": { "count": 10098 }, "play_game": { "count": 5712 }, "transport_ticket": { "count": 6477 }, "recommendation_events": { "count": 9690 }, "email_query": { "count": 21318 }, "transport_traffic": { "count": 5967 }, "cooking_query": { "count": 204 }, "qa_definition": { "count": 13617 }, "calendar_remove": { "count": 15912 }, "lists_remove": { "count": 8364 }, "cooking_recipe": { "count": 10557 }, "email_querycontact": { "count": 6477 }, "lists_createoradd": { "count": 9027 }, "transport_taxi": { "count": 5100 }, "qa_maths": { "count": 3978 }, "social_post": { "count": 14433 }, "qa_currency": { "count": 7242 }, "email_addcontact": { "count": 2754 } } } } ``` </details> --- *This dataset card was automatically generated using [MTEB](https://github.com/embeddings-benchmark/mteb)*

Oriolshhh/parlabe-errors-castellanismes-35k

Oriolshhh

2025-05-04T15:36:57Z

0

[ "language:ca", "license:apache-2.0", "size_categories:10K<n<100K", "region:us", "català", "grammar-correction", "castellanismes", "text-to-text", "synthetic" ]

[]

2025-05-04T15:31:08Z

null

--- language: ca license: apache-2.0 tags: - català - grammar-correction - castellanismes - text-to-text - synthetic size_categories: - 10K<n<100K --- # Dataset de castellanismes en català (35.000 parelles) Aquest dataset conté **35.000 parelles de frases** en format: ```text_erroni,text_correcte``` El conjunt inclou frases amb **castellanismes habituals** en el català oral i escrit, generades de manera sintètica per entrenar models que detectin i corregeixin aquestes interferències. --- ## Com s’ha generat? Les parelles han estat generades mitjançant: - **API de GPT** per introduir errors de castellanismes de forma natural - **Filtratge automàtic** amb un script Python - Selecció final per garantir diversitat i coherència lingüística Exemples d’errors de castellanisme: - *Tinc que anar a casa* → *He d’anar a casa* - *Se lo compré a mi cosina ahir* → *Li vaig comprar a la meva cosina ahir* - *No em dona igual el que pensis* → *No m’és igual el que pensis* --- ## Format - Llengua: Català (`ca`) - Format: `.csv` amb dues columnes: - `text_erroni` - `text_correcte` - Nombre de parelles: 35.000

mteb/bengali_hate_speech

mteb

2025-05-04T15:09:57Z

0

[ "region:us" ]

[]

2025-05-04T15:09:52Z

null

--- dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': 0 '1': 1 '2': 2 '3': 3 '4': 4 splits: - name: train num_bytes: 445907.77559976594 num_examples: 1567 - name: test num_bytes: 431110.58074897603 num_examples: 1515 download_size: 351631 dataset_size: 877018.356348742 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

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] ```

Siddhant00/SLR43ForOpherus

Siddhant00

2025-05-04T14:54:06Z

0

[ "region:us" ]

[]

2025-05-04T14:46:05Z

null

--- dataset_info: features: - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: path dtype: string - name: speaker dtype: int64 splits: - name: train num_bytes: 966898681.0 num_examples: 2064 download_size: 935774325 dataset_size: 966898681.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

rainbowbridge/x_dataset_36658

rainbowbridge

2025-05-04T14:19:36Z

1,210

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" ]

2025-01-27T09:42:25Z

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:** rainbowbridge/x_dataset_36658 - **Subnet:** Bittensor Subnet 13 - **Miner Hotkey:** 5HBJT9LVMim1mFQnihWzwXze1tTJCRWG6gBmfGjuX8bYHQds ### 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{rainbowbridge2025datauniversex_dataset_36658, title={The Data Universe Datasets: The finest collection of social media data the web has to offer}, author={rainbowbridge}, year={2025}, url={https://huggingface.co/datasets/rainbowbridge/x_dataset_36658}, } ``` ### 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:** 48889665 - **Date Range:** 2025-01-21T00:00:00Z to 2025-02-13T00:00:00Z - **Last Updated:** 2025-02-18T19:51:23Z ### Data Distribution - Tweets with hashtags: 43.87% - Tweets without hashtags: 56.13% ### Top 10 Hashtags For full statistics, please refer to the `stats.json` file in the repository. | Rank | Topic | Total Count | Percentage | |------|-------|-------------|-------------| | 1 | NULL | 27441046 | 56.13% | | 2 | #riyadh | 313769 | 0.64% | | 3 | #zelena | 270307 | 0.55% | | 4 | #tiktok | 207010 | 0.42% | | 5 | #bbb25 | 142273 | 0.29% | | 6 | #ad | 120913 | 0.25% | | 7 | #jhope_at_galadespiècesjaunes | 86117 | 0.18% | | 8 | #royalrumble | 71515 | 0.15% | | 9 | #granhermano | 68966 | 0.14% | | 10 | #pr | 68325 | 0.14% | ## Update History | Date | New Instances | Total Instances | |------|---------------|-----------------| | 2025-01-27T09:43:29Z | 3193387 | 3193387 | | 2025-01-30T21:46:28Z | 10063226 | 13256613 | | 2025-02-03T09:49:08Z | 7311324 | 20567937 | | 2025-02-06T21:52:36Z | 8681715 | 29249652 | | 2025-02-10T11:26:25Z | 8454192 | 37703844 | | 2025-02-13T23:30:18Z | 9715300 | 47419144 | | 2025-02-18T04:50:00Z | 752652 | 48171796 | | 2025-02-18T19:51:23Z | 717869 | 48889665 |

gunnybd01/StockIndicator-Data

gunnybd01

2025-05-04T11:08:57Z

0

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

[]

2025-05-04T11:02:37Z

null

--- dataset_info: features: - name: Keys dtype: string - name: Trend_smr dtype: string - name: Volume_smr dtype: string - name: Volatility_smr dtype: string - name: Momentum_smr dtype: string - name: ShortPct dtype: float64 - name: MediumPct dtype: float64 - name: LongPct dtype: float64 splits: - name: train num_bytes: 504378755 num_examples: 60000 download_size: 82123715 dataset_size: 504378755 configs: - config_name: default data_files: - split: train path: data/train-* ---

GitBag/a_star_final_a_star_math_7_actor_aime-24_eval

GitBag

2025-05-04T11:00:38Z

0

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

[]

2025-05-04T11:00:36Z

null

--- dataset_info: features: - name: problem dtype: string - name: answer dtype: int64 - 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: 3887353 num_examples: 30 download_size: 1375386 dataset_size: 3887353 configs: - config_name: default data_files: - split: train path: data/train-* ---

HungVu2003/opt-350m_beta_1.0_alpha_0.6_num-company_3_dataset_1_for_gen_19

HungVu2003

2025-05-04T10:31:39Z

0

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

[]

2025-05-04T10:31:38Z

null

--- dataset_info: features: - name: question dtype: string splits: - name: train num_bytes: 3599649 num_examples: 12500 download_size: 1854129 dataset_size: 3599649 configs: - config_name: default data_files: - split: train path: data/train-* ---

wyyyz139/character

wyyyz139

2025-05-04T10:22:48Z

2

0

[ "license:apache-2.0", "size_categories:1K<n<10K", "format:json", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "region:us" ]

[]

2025-05-04T02:57:50Z

null

--- license: apache-2.0 ---

HungVu2003/opt-350m_beta_0.5_alpha_0.6_num-company_3_dataset_2_for_gen_17

HungVu2003

2025-05-04T08:52:23Z

0

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

[]

2025-05-04T08:52:21Z

null

--- dataset_info: features: - name: question dtype: string splits: - name: train num_bytes: 3882121 num_examples: 12500 download_size: 1222020 dataset_size: 3882121 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_10_v2

HungVu2003

2025-05-04T08:27:21Z

0

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

[]

2025-05-04T08:27:20Z

null

--- dataset_info: features: - name: question dtype: string splits: - name: train num_bytes: 6383286 num_examples: 13750 download_size: 3232348 dataset_size: 6383286 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_9_v2

HungVu2003

2025-05-04T07:17:32Z

0

[ "region:us" ]

[]

2025-05-04T07:17:31Z

null

--- dataset_info: features: - name: question dtype: string splits: - name: train num_bytes: 6393295 num_examples: 13750 download_size: 3239607 dataset_size: 6393295 configs: - config_name: default data_files: - split: train path: data/train-* ---

rlawltjd/korean-nl2bash

rlawltjd

2025-05-04T07:04:23Z

0

[ "region:us" ]

[]

2025-05-04T07:04:15Z

null

--- dataset_info: features: - name: instruction dtype: string - name: output dtype: string splits: - name: train num_bytes: 1170873 num_examples: 8089 download_size: 448025 dataset_size: 1170873 configs: - config_name: default data_files: - split: train path: data/train-* ---

arielcerdap/tts-disfluencies-DA

arielcerdap

2025-05-04T06:55:17Z

0

[ "region:us" ]

[]

2025-05-04T06:54:53Z

null

--- dataset_info: features: - name: audio dtype: audio: sampling_rate: 44100 - name: text dtype: string - name: speaker_tag_used dtype: string - name: temperature_used dtype: float32 splits: - name: train num_bytes: 515151319.0 num_examples: 500 download_size: 499156183 dataset_size: 515151319.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

upvantage/deberta-1m-v2humanized

upvantage

2025-05-04T06:39:20Z

0

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

[]

2025-05-04T06:30:44Z

null

--- dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': human '1': ai - name: type dtype: string splits: - name: train num_bytes: 1777430766 num_examples: 910928 - name: validation num_bytes: 197496555 num_examples: 101214 download_size: 1192474307 dataset_size: 1974927321 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* ---

flyingbugs/OpenR1-Math-220k-pruned-keep-0.1-end-start-0.0

flyingbugs

2025-05-04T05:13:34Z

0

[ "size_categories:10K<n<100K", "modality:text", "region:us" ]

[]

2025-05-04T05:12:40Z

null

--- dataset_info: features: - name: problem dtype: string - name: solution dtype: string - name: answer dtype: string - name: problem_type dtype: string - name: question_type dtype: string - name: source dtype: string - name: uuid dtype: string - name: is_reasoning_complete sequence: bool - name: generations sequence: string - name: correctness_math_verify sequence: bool - name: correctness_llama sequence: bool - name: finish_reasons sequence: string - name: correctness_count dtype: int64 - name: messages list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 3975332374 num_examples: 93733 download_size: 1721086932 dataset_size: 3975332374 configs: - config_name: default data_files: - split: train path: data/train-* ---

qhuang20/summarize_from_feedback_oai_preprocessing_1706381144_cnndm_relabel_pythia6.9b_emoji

qhuang20

2025-05-04T04:34:15Z

0

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

[]

2025-05-03T23:46:51Z

null

--- dataset_info: features: - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string - name: prompt_chosen dtype: string - name: prompt_rejected dtype: string - name: chosen_score dtype: float64 - name: rejected_score dtype: float64 - name: both_chosen_score dtype: float64 - name: both_rejected_score dtype: float64 - name: chosen_chosen_score dtype: float64 - name: chosen_rejected_score dtype: float64 - name: rejected_chosen_score dtype: float64 - name: rejected_rejected_score dtype: float64 - name: random_chosen_score dtype: float64 - name: random_rejected_score dtype: float64 splits: - name: validation_cnndm num_bytes: 26935641 num_examples: 2284 download_size: 3812053 dataset_size: 26935641 configs: - config_name: default data_files: - split: validation_cnndm path: data/validation_cnndm-* ---

HungVu2003/opt-350m_beta_0.0_alpha_0.2_num-company_2_dataset_1_for_gen_5_v2

HungVu2003

2025-05-04T04:33:17Z

0

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

[]

2025-05-04T04:33:16Z

null

--- dataset_info: features: - name: question dtype: string splits: - name: train num_bytes: 2292678 num_examples: 13750 download_size: 1031494 dataset_size: 2292678 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_7_v2

HungVu2003

2025-05-04T04:07:18Z

0

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

[]

2025-05-04T04:07:17Z

null

--- dataset_info: features: - name: question dtype: string splits: - name: train num_bytes: 2110876 num_examples: 13750 download_size: 1132970 dataset_size: 2110876 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_16

HungVu2003

2025-05-04T03:38:07Z

0

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

[]

2025-05-04T03:38:06Z

null

--- dataset_info: features: - name: question dtype: string splits: - name: train num_bytes: 7468487 num_examples: 12500 download_size: 1913775 dataset_size: 7468487 configs: - config_name: default data_files: - split: train path: data/train-* ---

marcuscedricridia/OpenMathInstruct-1-1000-processed

marcuscedricridia

2025-05-04T02:46:57Z

0

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

[]

2025-05-04T02:46:56Z

null

--- dataset_info: features: - name: question dtype: string - name: generated_solution dtype: string splits: - name: train num_bytes: 586120.6902226892 num_examples: 1000 download_size: 293455 dataset_size: 586120.6902226892 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-* ---

aciang/ITK

aciang

2025-05-04T01:28:25Z

0

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

[]

2025-05-04T01:25:49Z

null

--- license: apache-2.0 ---

dgambettaphd/D_llm2_gen1_WXS_doc1000_synt64_lr1e-04_acm_FRESH

dgambettaphd

2025-05-04T01:04:41Z

0

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

[]

2025-05-04T01:04:18Z

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: 9773483 num_examples: 17000 download_size: 5870471 dataset_size: 9773483 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 ---

sergiomadrid/exist_task3

sergiomadrid

2025-05-03T22:47:05Z

0

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

[]

2025-05-03T22:46:49Z

null

--- dataset_info: features: - name: id dtype: int32 - name: text dtype: string - name: lang dtype: string - name: label_task3_1 sequence: string - name: label_task3_2 sequence: string - name: label_task3_3 sequence: sequence: string splits: - name: train num_bytes: 1768649 num_examples: 2524 download_size: 847539 dataset_size: 1768649 configs: - config_name: default data_files: - split: train path: data/train-* ---

AxeML/MiniSetPT

AxeML

2025-05-03T21:31:33Z

14

2

[ "task_categories:text-generation", "language:pt", "license:mit", "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "conversation", "dialogs", "dialogue" ]

[ "text-generation" ]

2025-05-01T19:01:10Z

2

--- license: mit task_categories: - text-generation language: - pt tags: - conversation - dialogs - dialogue size_categories: - 1K<n<10K --- <p align="center"> <img src="IA.png" alt="Banner do Projeto"> </p> # 📚 Dataset de Perguntas e Respostas por Tópico Este repositório contém um dataset com **10.000 amostras** estruturadas para tarefas de **Processamento de Linguagem Natural (PLN)**, com foco em **perguntas temáticas e respostas desenvolvidas**. ## 📁 Estrutura dos Dados Cada amostra é representada em formato JSON com os seguintes campos: - `id` *(string)*: Identificador único da amostra (UUID). - `topic` *(lista de strings)*: Lista com os tópicos abordados. - `prompts` *(lista de strings)*: Conjunto de perguntas relacionadas ao tópico. - `response` *(string)*: Resposta discursiva que contempla as perguntas propostas, escrita em linguagem natural. ### 🔍 Exemplo de Amostra ```json { "topic": ["jogos"], "prompts": [ "Como os jogos eletrônicos podem ser utilizados como ferramentas educacionais?", "Quais são os impactos psicológicos dos jogos competitivos em jogadores adolescentes?", "Como a evolução gráfica dos jogos influenciou a indústria do entretenimento?", "Quais são os principais elementos narrativos que tornam um jogo imersivo?" ], "response": "Os jogos são uma forma de entretenimento multifacetada que abrange diversas áreas de interesse. Como ferramentas educacionais, os jogos eletrônicos podem promover o aprendizado interativo, desenvolvendo habilidades como resolução de problemas, trabalho em equipe e pensamento crítico, através de mecânicas engajadoras. No aspecto psicológico, jogos competitivos podem tanto estimular a resiliência e a estratégia quanto exigir atenção aos possíveis efeitos negativos, como estresse ou frustração, especialmente em adolescentes. A evolução gráfica dos jogos revolucionou a indústria do entretenimento, elevando padrões tecnológicos e criando experiências visuais mais realistas, o que atraiu um público mais amplo e diversificado. Por fim, a narrativa imersiva em jogos depende de elementos como desenvolvimento de personagens, worldbuilding coeso e escolhas impactantes, que juntos criam uma conexão emocional entre o jogador e o universo virtual. Assim, os jogos se consolidam como uma mídia rica e versátil, com aplicações e impactos significativos em diversas esferas.", "id": "768cf005-1c0c-4f75-88b9-90cebdbeb81b" } ``` ## 🧠 Possíveis Aplicações - Treinamento de modelos de geração de texto; - Fine-tuning de LLMs em português; - Tarefas de sumarização e compreensão de texto; - Geração de respostas discursivas; - Classificação temática; - Estudos de coesão e coerência textual. ## 🗂️ Volume de Dados - **Total de amostras**: 10.000 - **Idioma**: Português (Brasil) ## 📄 Licença Este projeto está licenciado sob os termos da [Licença MIT](LICENSE).

aarontrinh02/leetcode_pipeline_part2

aarontrinh02

2025-05-03T21:08:05Z

0

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

[]

2025-05-03T02:47:35Z

null

--- dataset_info: features: - name: query_positive dtype: string - name: instruction_positive dtype: string - name: document_positive dtype: string - name: positive_language dtype: string - name: query_negative dtype: string - name: instruction_negative dtype: string - name: hard_negative_document_1 dtype: string - name: hard_negative_document_2 dtype: string splits: - name: train num_bytes: 16458010 num_examples: 1999 download_size: 5274648 dataset_size: 16458010 configs: - config_name: default data_files: - split: train path: data/train-* ---

anonymousEcaiHateLLM/Hate.2_labels_labeled

anonymousEcaiHateLLM

2025-05-03T20:34:38Z

0

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

[]

2025-05-03T20:34:25Z

null

--- dataset_info: features: - name: text dtype: string - name: unsloth/Qwen2.5-14B-Instruct-bnb-4bit_label_1 dtype: float64 - name: unsloth/Qwen2.5-14B-Instruct-bnb-4bit_label_2 dtype: float64 - name: unsloth/gemma-2-9b-it-bnb-4bit_label_1 dtype: float64 - name: unsloth/gemma-2-9b-it-bnb-4bit_label_2 dtype: float64 - name: unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit_label_1 dtype: float64 - name: unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit_label_2 dtype: float64 - name: unsloth/mistral-7b-instruct-v0.3-bnb-4bit_label_1 dtype: float64 - name: unsloth/mistral-7b-instruct-v0.3-bnb-4bit_label_2 dtype: float64 - name: language dtype: string - name: mean_label_1 dtype: float64 - name: mean_label_2 dtype: float64 - name: Mean dtype: int64 - name: lgb_label_1 dtype: float64 - name: lgb_label_2 dtype: float64 - name: Lgb dtype: int64 - name: Vote dtype: int64 splits: - name: 2_labels num_bytes: 79917049 num_examples: 240647 download_size: 44153399 dataset_size: 79917049 configs: - config_name: default data_files: - split: 2_labels path: data/2_labels-* ---

willnorris/cylinder-in-box-hollows-6

willnorris

2025-05-03T19:43:02Z

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" ]

2025-05-03T19:10:20Z

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": 298, "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 ], "names": null }, "index": { "dtype": "int64", "shape": [ 1 ], "names": null } } } ``` ## Citation **BibTeX:** ```bibtex [More Information Needed] ```

momo1942/x_dataset_40590

momo1942

2025-05-03T19:38:40Z

795

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" ]

2025-01-29T06:31:00Z

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:** momo1942/x_dataset_40590 - **Subnet:** Bittensor Subnet 13 - **Miner Hotkey:** 5Fk6Ur49sNZjSVaCj4AVbEhuYTYTnVSdh5evvMDPExZSdD8g ### 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{momo19422025datauniversex_dataset_40590, title={The Data Universe Datasets: The finest collection of social media data the web has to offer}, author={momo1942}, year={2025}, url={https://huggingface.co/datasets/momo1942/x_dataset_40590}, } ``` ### 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:** 54541574 - **Date Range:** 2025-01-23T00:00:00Z to 2025-02-13T00:00:00Z - **Last Updated:** 2025-02-18T18:12:32Z ### Data Distribution - Tweets with hashtags: 49.94% - Tweets without hashtags: 50.06% ### Top 10 Hashtags For full statistics, please refer to the `stats.json` file in the repository. | Rank | Topic | Total Count | Percentage | |------|-------|-------------|-------------| | 1 | NULL | 27302504 | 50.06% | | 2 | #riyadh | 416113 | 0.76% | | 3 | #zelena | 307274 | 0.56% | | 4 | #tiktok | 261916 | 0.48% | | 5 | #bbb25 | 205680 | 0.38% | | 6 | #ad | 150196 | 0.28% | | 7 | #royalrumble | 121643 | 0.22% | | 8 | #jhope_at_galadespiècesjaunes | 116582 | 0.21% | | 9 | #granhermano | 92578 | 0.17% | | 10 | #bbmzansi | 87251 | 0.16% | ## Update History | Date | New Instances | Total Instances | |------|---------------|-----------------| | 2025-01-29T06:31:58Z | 3075320 | 3075320 | | 2025-02-01T18:34:52Z | 8753140 | 11828460 | | 2025-02-05T06:37:51Z | 9310894 | 21139354 | | 2025-02-08T18:42:36Z | 11749598 | 32888952 | | 2025-02-12T06:46:20Z | 7821613 | 40710565 | | 2025-02-17T03:33:43Z | 12307358 | 53017923 | | 2025-02-18T03:11:07Z | 703943 | 53721866 | | 2025-02-18T18:12:32Z | 819708 | 54541574 |

francescocrivelli/eval_v1_act_so100_testing_pi0_one_francesco_full

francescocrivelli

2025-05-03T19:18:18Z

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" ]

2025-05-03T19:18:09Z

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": 10, "total_frames": 6351, "total_tasks": 1, "total_videos": 20, "total_chunks": 1, "chunks_size": 1000, "fps": 30, "splits": { "train": "0:10" }, "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.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.phone": { "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] ```

MBZUAI-IFM/AM_clean_final

MBZUAI-IFM

2025-05-03T18:29:38Z

0

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

[]

2025-05-03T18:22:03Z

null

--- dataset_info: features: - name: conversations list: - name: from dtype: string - name: value dtype: string - name: dataset_source dtype: string - name: metadata dtype: string - name: has_forbidden dtype: bool splits: - name: train num_bytes: 26009841975 num_examples: 1139323 download_size: 11628816168 dataset_size: 26009841975 configs: - config_name: default data_files: - split: train path: data/train-* ---

cchoi1/kodcode-complete_1000_qwen7b_sol_iter0_att10_sol5_lr1e5_3ep_dpo_10000

cchoi1

2025-05-03T18:10:44Z

0

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

[]

2025-05-03T18:10:41Z

null

--- dataset_info: features: - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string - name: task_id dtype: string splits: - name: train num_bytes: 10023107.103929024 num_examples: 2524 - name: test num_bytes: 2509747.896070976 num_examples: 632 download_size: 2061810 dataset_size: 12532855.0 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_1_for_gen_13_v2

HungVu2003

2025-05-03T17:54:37Z

0

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

[]

2025-05-03T17:54:36Z

null

--- dataset_info: features: - name: question dtype: string splits: - name: train num_bytes: 820412 num_examples: 12500 download_size: 567135 dataset_size: 820412 configs: - config_name: default data_files: - split: train path: data/train-* ---

junlinw/Qwen2.5-7B-Instruct-Turbo_MATH_10

junlinw

2025-05-03T17:50:41Z

0

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

[]

2025-05-03T17:50:39Z

null

--- dataset_info: features: - name: ids dtype: int64 - name: queries dtype: string - name: samples sequence: string - name: references dtype: string splits: - name: train num_bytes: 117454203 num_examples: 7500 download_size: 35970957 dataset_size: 117454203 configs: - config_name: default data_files: - split: train path: data/train-* ---

thailevann/Government_services_QA

thailevann

2025-05-03T17:46:10Z

20

0

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

[ "question-answering" ]

2025-04-24T00:37:35Z

null

--- dataset_info: features: - name: instruction dtype: string - name: output dtype: string - name: label dtype: int64 - name: relevant dtype: string splits: - name: train num_bytes: 49161014 num_examples: 20955 download_size: 9270053 dataset_size: 49161014 configs: - config_name: default data_files: - split: train path: data/train-* task_categories: - question-answering size_categories: - 1K<n<10K ---

HungVu2003/opt-350m_beta_0.0_alpha_0.0_num-company_2_dataset_1_for_gen_10_v2

HungVu2003

2025-05-03T17:38:41Z

0

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

[]

2025-05-03T17:38:40Z

null

--- dataset_info: features: - name: question dtype: string splits: - name: train num_bytes: 820715 num_examples: 12500 download_size: 567865 dataset_size: 820715 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_slow_generate

alucchi

2025-05-03T17:30:51Z

0

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

[]

2025-05-03T17:30:41Z

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: 696999 num_examples: 70 download_size: 136054 dataset_size: 696999 - 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: 696999 num_examples: 70 download_size: 136054 dataset_size: 696999 configs: - config_name: default data_files: - split: train path: data/train-* - config_name: main data_files: - split: train path: main/train-* ---

Hieuman/wiki_en_smallavae

Hieuman

2025-05-03T17:28:55Z

124

0

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

[]

2025-04-08T05:40:03Z

null

--- dataset_info: features: - name: text_1 dtype: string - name: text_2 dtype: string - name: label dtype: string - name: style_comparison dtype: string - name: content_comparison dtype: string - name: content_label dtype: string splits: - name: train num_bytes: 167683819.0 num_examples: 17072 download_size: 71112622 dataset_size: 167683819.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

ViratChauhan/sol-edu-ft

ViratChauhan

2025-05-03T17:14:29Z

0

[ "region:us" ]

[]

2025-05-03T17:14:27Z

null

--- dataset_info: features: - name: source dtype: string - name: question dtype: string - name: answer dtype: string splits: - name: train num_bytes: 4549609 num_examples: 6128 download_size: 2168857 dataset_size: 4549609 configs: - config_name: default data_files: - split: train path: data/train-* ---

aigrant/taiwan-ly-law-research

aigrant

2025-05-03T17:00:06Z

401

6

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

[]

2024-07-22T21:29:38Z

null

--- language: - zh configs: - config_name: research data_files: research.csv dataset_info: - config_name: research features: - name: research_no dtype: string - name: title dtype: string - name: related_laws dtype: string - name: authors dtype: string - name: published_date dtype: string - name: content dtype: string - name: doc_url dtype: string license: apache-2.0 --- # Taiwan Legislator Yuan Law Research Data ## Overview The law research documents are issued irregularly from Taiwan Legislator Yuan. The purpose of those research are providing better understanding on social issues in aspect of laws. One may find documents rich with technical terms which could provided as training data. For comprehensive document list check out this [link](https://www.ly.gov.tw/Pages/List.aspx?nodeid=6590) provided by Taiwan Legislator Yuan. There are currently missing document download links in 10th and 9th terms due to minor issue on crawler. We will fill in those missing data ASAP. ## Data Fields | Field name | Description | |----------------|------------------------------------------------------------------------------------------------------------------------------------| | research_no | ID of the research document | | title | title of the document | | related_laws | Related names of laws in the document. Separated by `;` | | authors | Authors of document. Separated by `;` | | published_date | Published date of the document in form `YYYY-mm-dd` | | content | Full text content of the document. One may also find the original content in `.html` format at `html/{research_no}.html` | | doc_url | The download link hosted on ly.gov.tw | ## Sponsorship The work is sponsored by "【g0v 零時小學校】繁體中文AI 開源實踐計畫" ## Contact If you have any issue on the dataset. Please leave a discussion on it or contact us via: 報導者(The Reporter) [email protected] 歐噴有限公司(OpenFun Ltd.) [email protected]

gosamab/merged-handwriting-dataset

gosamab

2025-05-03T16:50:57Z

0

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

[]

2025-05-03T16:27:45Z

null

--- dataset_info: features: - name: source dtype: string - name: image dtype: image - name: text dtype: string - name: font_details dtype: string splits: - name: train num_bytes: 2176602946.666 num_examples: 447946 - name: test num_bytes: 540987875.28 num_examples: 111987 download_size: 3803906962 dataset_size: 2717590821.946 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

themachinefan/sandbagging-sciq-headlines

themachinefan

2025-05-03T16:50:09Z

0

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

[]

2025-05-03T16:50:03Z

null

--- dataset_info: features: - name: correct_answer dtype: string - name: index dtype: int64 - name: prompt dtype: string - name: sandbagging_environment dtype: bool - name: desired_answer dtype: string - name: prefix dtype: string - name: template dtype: string splits: - name: train num_bytes: 5234192 num_examples: 4000 - name: test num_bytes: 203622 num_examples: 156 - name: validation num_bytes: 204490 num_examples: 156 download_size: 1562350 dataset_size: 5642304 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: validation path: data/validation-* ---

littleGuagua/x_dataset_11627

littleGuagua

2025-05-03T16:37:25Z

1,557

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" ]

2025-01-26T13:13:48Z

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_11627 - **Subnet:** Bittensor Subnet 13 - **Miner Hotkey:** 5FUByNzgdM2eukk6SwetFsZ4EPTxRqaV4YNEhNcusS1SxRVX ### 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_11627, 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_11627}, } ``` ### 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:** 149000631 - **Date Range:** 2025-01-21T00:00:00Z to 2025-02-10T00:00:00Z - **Last Updated:** 2025-02-18T20:47:28Z ### Data Distribution - Tweets with hashtags: 42.62% - Tweets without hashtags: 57.38% ### Top 10 Hashtags For full statistics, please refer to the `stats.json` file in the repository. | Rank | Topic | Total Count | Percentage | |------|-------|-------------|-------------| | 1 | NULL | 85489881 | 57.38% | | 2 | #riyadh | 1033096 | 0.69% | | 3 | #zelena | 790108 | 0.53% | | 4 | #tiktok | 618215 | 0.41% | | 5 | #bbb25 | 362232 | 0.24% | | 6 | #ad | 356819 | 0.24% | | 7 | #jhope_at_galadespiècesjaunes | 234343 | 0.16% | | 8 | #bbmzansi | 207541 | 0.14% | | 9 | #pr | 188395 | 0.13% | | 10 | #yahooニュース | 178958 | 0.12% | ## Update History | Date | New Instances | Total Instances | |------|---------------|-----------------| | 2025-01-26T13:14:32Z | 2274090 | 2274090 | | 2025-01-30T01:26:02Z | 29523249 | 31797339 | | 2025-02-02T13:36:10Z | 29333848 | 61131187 | | 2025-02-06T01:47:05Z | 28740147 | 89871334 | | 2025-02-09T14:00:59Z | 29293177 | 119164511 | | 2025-02-13T02:15:32Z | 28379764 | 147544275 | | 2025-02-18T05:45:25Z | 808939 | 148353214 | | 2025-02-18T20:47:28Z | 647417 | 149000631 |

FrancophonIA/Thesaurus_terminologie_canadienne_en_construction

FrancophonIA

2025-05-03T16:36:17Z

0

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

[ "translation" ]

2025-05-03T16:35:08Z

null

--- language: - fra - eng viewer: false task_categories: - translation --- > [!NOTE] > Dataset origin: https://publications.gc.ca/site/eng/9.876320/publication.html

amekerishvili/ATCO2_full_files

amekerishvili

2025-05-03T16:14:01Z

0

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

[]

2025-05-03T12:01:28Z

null

--- dataset_info: features: - name: ID dtype: string - name: audio_file dtype: string - name: start_time dtype: float64 - name: end_time dtype: float64 - name: airport dtype: string - name: channel dtype: string - name: frequency dtype: string - name: time dtype: string - name: waypoints dtype: string - name: callsigns dtype: string - name: ground_truth_raw dtype: string - name: ground_truth dtype: string - name: non_Eng_ground_truth dtype: string - name: tags dtype: string - name: values_tags dtype: string - name: commands_tags dtype: string - name: callsigns_tags dtype: string - name: unnamed_tags dtype: string splits: - name: train num_bytes: 1558206 num_examples: 612 - name: validation num_bytes: 362174 num_examples: 136 - name: test num_bytes: 356108 num_examples: 129 download_size: 397317 dataset_size: 2276488 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---

mlfoundations-dev/no_pipeline_math_300k

mlfoundations-dev

2025-05-03T15:41:53Z

0

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

[]

2025-05-03T15:39:48Z

null

--- dataset_info: features: - name: instruction_seed dtype: string - name: reasoning dtype: string - name: deepseek_solution dtype: string - name: source dtype: string - name: conversations list: - name: from dtype: string - name: value dtype: string - name: shard_id dtype: string splits: - name: train num_bytes: 7613230760.0 num_examples: 316000 download_size: 3353575536 dataset_size: 7613230760.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

FrancophonIA/Glossaire_procedure_parlementaire

FrancophonIA

2025-05-03T15:37:24Z

0

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

[ "translation" ]

2025-05-03T15:36:48Z

null

--- language: - fra - eng viewer: false task_categories: - translation --- > [!NOTE] > Dataset origin: https://publications.gc.ca/site/eng/9.693563/publication.html

FrancophonIA/Lexique_du_droit_de_la_famille

FrancophonIA

2025-05-03T15:33:26Z

0

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

[ "translation" ]

2025-05-03T15:32:34Z

null

--- language: - fra - eng viewer: false task_categories: - translation --- > [!NOTE] > Dataset origin: https://publications.gc.ca/site/eng/9.586275/publication.html

t2ance/polymnist-upd10

t2ance

2025-05-03T15:30:26Z

0

[ "region:us" ]

[]

2025-05-03T14:29:24Z

null

--- dataset_info: features: - name: m0 dtype: image - name: m1 dtype: image - name: m2 dtype: image - name: m3 dtype: image - name: m4 dtype: image - name: m5 dtype: image - name: m6 dtype: image - name: m7 dtype: image - name: m8 dtype: image - name: m9 dtype: image - name: label dtype: class_label: names: '0': '0' '1': '1' '2': '2' '3': '3' '4': '4' '5': '5' '6': '6' '7': '7' '8': '8' '9': '9' - name: sample_id dtype: string splits: - name: train num_bytes: 632476369.0 num_examples: 50000 - name: validation num_bytes: 126523764.0 num_examples: 10000 - name: test num_bytes: 126347943.0 num_examples: 10000 download_size: 916377594 dataset_size: 885348076.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---

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-* ---

Maxscha/json-instruct-generation-large

Maxscha

2025-05-03T15:03:37Z

0

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

[]

2025-05-03T15:03:31Z

null

--- dataset_info: features: - name: schema dtype: string - name: input dtype: string - name: output dtype: string - name: task dtype: string splits: - name: train num_bytes: 99642713 num_examples: 50000 download_size: 31084332 dataset_size: 99642713 configs: - config_name: default data_files: - split: train path: data/train-* ---

CanCLID/zoengjyutgaai

CanCLID

2025-05-03T14:58:45Z

3,423

15

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

[ "automatic-speech-recognition", "text-to-speech", "text-generation", "feature-extraction", "audio-to-audio", "audio-classification", "text-to-audio" ]

2024-07-11T06:46:10Z

null

--- language: - yue license: cc0-1.0 size_categories: - 10K<n<100K task_categories: - automatic-speech-recognition - text-to-speech - text-generation - feature-extraction - audio-to-audio - audio-classification - text-to-audio pretty_name: c configs: - config_name: default data_files: - split: saamgwokjinji path: data/saamgwokjinji-* - split: seoiwuzyun path: data/seoiwuzyun-* - split: mouzaakdung path: data/mouzaakdung-* tags: - cantonese - audio - art dataset_info: features: - name: audio dtype: audio - name: id dtype: string - name: episode_id dtype: int64 - name: audio_duration dtype: float64 - name: transcription dtype: string splits: - name: saamgwokjinji num_bytes: 2398591354.589 num_examples: 39173 - name: seoiwuzyun num_bytes: 1629539808.0 num_examples: 24744 - name: mouzaakdung num_bytes: 257168872.246 num_examples: 4742 download_size: 4304923024 dataset_size: 4285300034.835 --- # 張悦楷講古語音數據集 [English](#the-zoeng-jyut-gaai-story-telling-speech-dataset) ## Dataset Description - **Homepage:** [張悦楷講古語音數據集 The Zoeng Jyut Gaai Story-telling Speech Dataset](https://canclid.github.io/zoengjyutgaai/) - **License:** [CC0 1.0 Universal](https://creativecommons.org/publicdomain/zero/1.0/) - **Language:** Cantonese - **Total Duration:** 112.54 hours - **Average Clip Duration:** 5.901 seconds - **Median Clip Duration:** 5.443 seconds - **Total number of characters:** 1679097 - **Average characters per clip:** 24.36 - **Median characters per clip:** 23 - **Average speech speed:** 4.14 characters per second - **Voice Actor:** [張悦楷](https://zh.wikipedia.org/wiki/%E5%BC%A0%E6%82%A6%E6%A5%B7) 呢個係張悦楷講《三國演義》、《水滸傳》、《走進毛澤東的最後歲月》語音數據集。[張悦楷](https://zh.wikipedia.org/wiki/%E5%BC%A0%E6%82%A6%E6%A5%B7)係廣州最出名嘅講古佬 / 粵語説書藝人。佢從上世紀七十年代開始就喺廣東各個收音電台度講古，佢把聲係好多廣州人嘅共同回憶。本數據集收集嘅係佢最知名嘅三部作品。數據集用途： - TTS（語音合成）訓練集 - ASR（語音識別）訓練集或測試集 - 各種語言學、文學研究 - 直接聽嚟欣賞藝術！ TTS 效果演示：https://huggingface.co/spaces/laubonghaudoi/zoengjyutgaai_tts ## 説明 - 所有文本都根據 https://jyutping.org/blog/typo/ 同 https://jyutping.org/blog/particles/ 規範用字。 - 所有文本都使用全角標點，冇半角標點。 - 所有文本都用漢字轉寫，無阿拉伯數字無英文字母 - 所有音頻源都存放喺`/source`，為方便直接用作訓練數據，切分後嘅音頻都放喺 `opus/` - 所有 opus 音頻皆為 48000 Hz 採樣率。 - 所有源字幕 SRT 文件都存放喺 `srt/` 路經下，搭配 `source/` 下嘅音源可以直接作為帶字幕嘅錄音直接欣賞。 - `cut.py` 係切分腳本，將對應嘅音源根據 srt 切分成短句並生成一個文本轉寫 csv。 - `stats.py` 係統計腳本，運行佢就會顯示成個數據集嘅各項統計數據。 ## 下載使用要下載使用呢個數據集，可以喺 Python 入面直接跑： ```python from datasets import load_dataset ds = load_dataset("CanCLID/zoengjyutgaai") ``` 如果想單純將 `opus/` 入面所有嘢下載落嚟，可以跑下面嘅 Python 代碼，注意要安裝 `pip install --upgrade huggingface_hub` 先： ```python from huggingface_hub import snapshot_download # 如果淨係想下載啲字幕或者源音頻，就將 `opus/*` 改成 `srt/*` 或者 `source/*` # If you only want to download subtitles or source audio, change `opus/*` to `srt/*` or `source/*` snapshot_download(repo_id="CanCLID/zoengjyutgaai",allow_patterns="opus/*",local_dir="./",repo_type="dataset") ``` 如果唔想用 python，你亦都可以用命令行叫 git 針對克隆個`opus/`或者其他路經，避免將成個 repo 都克隆落嚟浪費空間同下載時間： ```bash mkdir zoengjyutgaai cd zoengjyutgaai git init git remote add origin https://huggingface.co/datasets/CanCLID/zoengjyutgaai git sparse-checkout init --cone # 指定凈係下載個別路徑 git sparse-checkout set opus # 開始下載 git pull origin main ``` ### 數據集構建流程本數據集嘅收集、構建過程係： 1. 從 YouTube 或者國內評書網站度下載錄音源文件，一般都係每集半個鐘長嘅 `.webm` 或者 `.mp3`。 1. 用加字幕工具幫呢啲錄音加字幕，得到對應嘅 `.srt` 文件。 1. 將啲源錄音用下面嘅命令儘可能無壓縮噉轉換成 `.opus` 格式。 1. 運行`cut.py`，將每一集 `.opus` 按照 `.srt` 入面嘅時間點切分成一句一個 `.opus`，然後對應嘅文本寫入本數據集嘅 `xxx.csv`。 1. 然後打開一個 IPython，逐句跑下面嘅命令，將啲數據推上 HuggingFace。 ```python from datasets import load_dataset, DatasetDict from huggingface_hub import login sg = load_dataset('audiofolder', data_dir='./opus/saamgwokjinji') sw = load_dataset('audiofolder', data_dir='./opus/seoiwuzyun') mzd = load_dataset('audiofolder', data_dir='./opus/mouzaakdung') dataset = DatasetDict({ "saamgwokjinji": sg["train"], "seoiwuzyun": sw["train"], "mouzaakdung": mzd["train"], }) # 檢查下讀入嘅數據有冇問題 dataset['mouzaakdung'][0] # 準備好個 token 嚟登入 login() # 推上 HuggingFace datasets dataset.push_to_hub("CanCLID/zoengjyutgaai") ``` ### 音頻格式轉換首先要安裝 [ffmpeg](https://www.ffmpeg.org/download.html)，然後運行： ```bash # 將下載嘅音源由 webm 轉成 opus ffmpeg -i webm/saamgwokjinji/001.webm -c:a copy source/saamgwokjinji/001.opus # 或者轉 mp3 ffmpeg -i mp3/mouzaakdung/001.mp3 -c:a libopus -map_metadata -1 -b:a 48k -vbr on source/mouzaakdung/001.opus # 將 opus 轉成無損 wav ffmpeg -i source/saamgwokjinji/001.opus wav/saamgwokjinji/001.wav ``` 如果想將所有 opus 文件全部轉換成 wav，可以直接運行`to_wav.sh`: ``` chmod +x to_wav.sh ./to_wav.sh ``` 跟住就會生成一個 `wav/` 路經，入面都係 `opus/` 對應嘅音頻。注意 wav 格式非常掗埞，成個 `opus/` 轉晒後會佔用至少 500GB 儲存空間，所以轉換之前記得確保有足夠空間。如果你想對音頻重採樣，亦都可以修改 `to_wav.sh` 入面嘅命令順便做重採樣。 # The Zoeng Jyut Gaai Story-telling Speech Dataset This is a speech dataset of Zoeng Jyut Gaai story-telling _Romance of the Three Kingdoms_, _Water Margin_ and _The Final Days of Mao Zedong_. [Zoeng Jyut Gaai](https://zh.wikipedia.org/wiki/%E5%BC%A0%E6%82%A6%E6%A5%B7) is a famous actor, stand-up commedian and story-teller (講古佬) in 20th centry Canton. His voice remains in the memories of thousands of Cantonese people. This dataset is built from three of his most well-known story-telling pieces. Use case of this dataset: - TTS (Text-To-Speech) training set - ASR (Automatic Speech Recognition) training or eval set - Various linguistics / art analysis - Just listen and enjoy the art piece! TTS demo: https://huggingface.co/spaces/laubonghaudoi/zoengjyutgaai_tts ## Introduction - All transcriptions follow the prescribed orthography detailed in https://jyutping.org/blog/typo/ and https://jyutping.org/blog/particles/ - All transcriptions use full-width punctuations, no half-width punctuations is used. - All transcriptions are in Chinese characters, no Arabic numbers or Latin letters. - All source audio are stored in `source/`. For the convenice of training, segmented audios are stored in `opus/`. - All opus audio are in 48000 Hz sampling rate. - All source subtitle SRT files are stored in `srt/`. Use them with the webm files to enjoy subtitled storytelling pieces. - `cut.py` is the script for cutting opus audios into senteneces based on the srt, and generates a csv file for transcriptions. - `stats.py` is the script for getting stats of this dataset. ## Usage To use this dataset, simply run in Python: ```python from datasets import load_dataset ds = load_dataset("CanCLID/zoengjyutgaai") ``` If you only want to download a certain directory to save time and space from cloning the entire repo, run the Python codes below. Make sure you have `pip install --upgrade huggingface_hub` first： ```python from huggingface_hub import snapshot_download # If you only want to download subtitles or source audio, change `opus/*` to `srt/*` or `source/*` snapshot_download(repo_id="CanCLID/zoengjyutgaai",allow_patterns="opus/*",local_dir="./",repo_type="dataset") ``` If you don't want to run python codes and want to do this via command lines, you can selectively clone only a directory of the repo: ```bash mkdir zoengjyutgaai cd zoengjyutgaai git init git remote add origin https://huggingface.co/datasets/CanCLID/zoengjyutgaai git sparse-checkout init --cone # Tell git which directory you want git sparse-checkout set opus # Pull the content git pull origin main ``` ### Audio format conversion Install [ffmpeg](https://www.ffmpeg.org/download.html) first, then run： ```bash # convert all webm into opus ffmpeg -i webm/saamgwokjinji/001.webm -c:a copy source/saamgwokjinji/001.opus # or into mp3 ffmpeg -i mp3/mouzaakdung/001.mp3 -c:a libopus -map_metadata -1 -b:a 48k -vbr on source/mouzaakdung/001.opus # convert all opus into loseless wav ffmpeg -i source/saamgwokjinji/001.opus wav/saamgwokjinji/001.wav ``` If you want to convert all opus to wav, run `to_wav.sh`: ``` chmod +x to_wav.sh ./to_wav.sh ``` It will generate a `wav/` path which contains all audios converted from `opus/`. Be aware the wav format is very space-consuming. A full conversion will take up at least 500GB space so make sure you have enough storage. If you want to resample the audio, modify the line within `to_wav.sh` to resample the audio while doing the conversion.

FrancophonIA/Vous-pouvez-le-dire-en-francais-Faire-des-affaires-en-francais

FrancophonIA

2025-05-03T14:52:34Z

3

0

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

[ "translation" ]

2025-04-29T20:43:35Z

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-Faire-des-affaires-en-francais

FrancophonIA/Vous-pouvez-le-dire-en-francais-Genetique-biologie

FrancophonIA

2025-05-03T14:47:53Z

3

0

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

[ "translation" ]

2025-04-29T20:47:46Z

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-Genetique-biologie

Kamyar-zeinalipour/llama2_kg

Kamyar-zeinalipour

2025-05-03T14:21:05Z

11

0

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

[]

2025-05-01T18:29:30Z

null

--- dataset_info: features: - name: cycle dtype: int64 - name: temperature dtype: float64 - name: top_p dtype: float64 - name: raw_generated_text dtype: string - name: extracted_output dtype: string - name: applied_template_text dtype: string - name: rouge_scores_text dtype: string - name: rouge_scores_triple dtype: string - name: rouge_l_fmeasure_text dtype: string - name: rouge_l_fmeasure_triple dtype: float64 - name: emb_similarity_text dtype: string - name: emb_similarity_triple dtype: float64 - name: combined_similarity_triple dtype: float64 - name: combined_similarity_text dtype: string - name: combined_similarity_triple_diff dtype: float64 - name: input_text dtype: string - name: initial_text dtype: string - name: source_file dtype: string - name: input_text_clean dtype: string - name: user_content dtype: string - name: assistant_output dtype: string - name: messages list: - name: content dtype: string - name: role dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 13691684 num_examples: 1950 - name: test num_bytes: 349248 num_examples: 50 download_size: 5889395 dataset_size: 14040932 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

gunnybd01/Trend_smr

gunnybd01

2025-05-03T14:18:19Z

0

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

[]

2025-05-02T15:11:12Z

null

--- dataset_info: features: - name: Keys dtype: string - name: reports dtype: string - name: labels dtype: string splits: - name: train num_bytes: 156894600 num_examples: 60000 download_size: 34150437 dataset_size: 156894600 configs: - config_name: default data_files: - split: train path: data/train-* ---

kothasuhas/llp-gold-37m-1.5m_T32768.0_I32768

kothasuhas

2025-05-03T14:18:00Z

0

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

[]

2025-05-03T14:17:12Z

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: 1629512 dataset_size: 3605804917.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

Nelver28/fon-asr

Nelver28

2025-05-03T14:15:40Z

0

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

[]

2025-05-03T14:11:58Z

null

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: audio struct: - name: array sequence: float32 - name: path dtype: string - name: sampling_rate dtype: int64 - name: sentence dtype: string splits: - name: train num_bytes: 647571727 num_examples: 1084 - name: test num_bytes: 157608299 num_examples: 271 download_size: 334998565 dataset_size: 805180026 --- # Dataset Card for "fon-asr" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

starsofchance/MSR_data_cleaned

starsofchance

2025-05-03T14:06:02Z

29

0

[ "language:en", "license:mit", "size_categories:100K<n<1M", "format:csv", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "vulnerability-detection", "cve", "code-changes", "software-security", "stratified-split" ]

[]

2025-03-25T00:43:24Z

null

--- # YAML Metadata Block language: - en tags: - vulnerability-detection - cve - code-changes - software-security - stratified-split license: mit dataset_info: features: # Features in the *final split files* - name: idx dtype: int64 - name: func_before dtype: string - name: Vulnerability Classification dtype: string - name: vul dtype: int64 - name: func_after dtype: string - name: patch dtype: string - name: CWE ID dtype: string - name: lines_before dtype: string - name: lines_after dtype: string splits: - name: train num_examples: 150909 - name: validation num_examples: 18864 - name: test num_examples: 18863 dataset_original_file_size: 10GB uuncompressed --- # MSR Data Cleaned - C/C++ Code Vulnerability Dataset [![Dataset License](https://img.shields.io/badge/license-MIT-blue.svg)](LICENSE) ## 📌 Dataset Description A curated collection of C/C++ code vulnerabilities paired with: - CVE details (scores, classifications, exploit status) - Code changes (commit messages, added/deleted lines) - File-level and function-level diffs ## 🔍 Sample Data Structure from original file ```python +---------------+-----------------+----------------------+---------------------------+ | CVE ID | Attack Origin | Publish Date | Summary | +===============+=================+======================+===========================+ | CVE-2015-8467 | Remote | 2015-12-29 | "The samldb_check_user..."| +---------------+-----------------+----------------------+---------------------------+ | CVE-2016-1234 | Local | 2016-01-15 | "Buffer overflow in..." | +---------------+-----------------+----------------------+---------------------------+ ``` Note: This is a simplified preview; the full dataset includes additional fields like commit_id, func_before, etc. ### 1. Accessing in Colab ```python !pip install huggingface_hub -q from huggingface_hub import snapshot_download repo_id = "starsofchance/MSR_data_cleaned" dataset_path = snapshot_download(repo_id=repo_id, repo_type="dataset") ``` ### 2. Extracting the Dataset ```python !apt-get install unzip -qq !unzip "/root/.cache/huggingface/.../MSR_data_cleaned.zip" -d "/content/extracted_data" ``` **Note: Extracted size is 10GB (1.5GB compressed). Ensure sufficient disk space. ### 3. Creating Splits (Colab Pro Recommended) We used this memory-efficient approach: ```python from datasets import load_dataset dataset = load_dataset("csv", data_files="MSR_data_cleaned.csv", streaming=True) # Randomly distribute rows (80-10-10) for row in dataset: rand = random.random() if rand < 0.8: write_to(train.csv) elif rand < 0.9: write_to(validation.csv) else: write_to(test.csv) ``` **Hardware Requirements:** - Minimum 25GB RAM - Strong CPU (Colab Pro T4 GPU recommended) ##📊 Dataset Statistics - Number of Rows: 188,636 - Vulnerability Distribution: - Vulnerable (1): 18,863 (~10%) - Non-Vulnerable (0): 169,773 (~90%) ##📋 Data Fields Description - CVE_ID: Unique identifier for the vulnerability (Common Vulnerabilities and Exposures). - CWE_ID: Weakness category identifier (Common Weakness Enumeration). - Score: CVSS score indicating severity (float, 0-10). - Summary: Brief description of the vulnerability. - commit_id: Git commit hash linked to the code change. - codeLink: URL to the code repository or commit. - file_name: Name of the file containing the vulnerability. - func_after: Function code after the change. - lines_after: Code lines after the change. - Access_Gained: Type of access gained by exploiting the vulnerability. - Attack_Origin: Source of the attack (e.g., Remote, Local). - Authentication_Required: Whether authentication is needed to exploit. - Availability: Impact on system availability. - CVE_Page: URL to the CVE details page. - Complexity: Complexity of exploiting the vulnerability. - Confidentiality: Impact on data confidentiality. - Integrity: Impact on data integrity. - Known_Exploits: Details of known exploits, if any. - Publish_Date: Date the vulnerability was published. - Update_Date: Date of the last update to the vulnerability data. - Vulnerability_Classification: Type or category of the vulnerability. - add_lines: Lines added in the commit. - del_lines: Lines deleted in the commit. - commit_message: Description of the commit. - files_changed: List of files modified in the commit. - func_before: Function code before the change. - lang: Programming language (e.g., C, C++). - lines_before: Code lines before the change. ## splits file for UltiVul project: ## 🔍 Sample Data Structure (from train.csv) ```python { 'idx': 0, # Unique ID within the train split 'func_before': '...', # String containing function code before change 'Vulnerability Classification': '...', # Original vulnerability type classification 'vul': 0, # Integer: 0 for non-vulnerable, 1 for vulnerable (target label) 'func_after': '...', # String containing function code after change 'patch': '...', # String containing diff patch 'CWE ID': '...', # String CWE ID, e.g., "CWE-119" 'lines_before': '...', # String lines before change context 'lines_after': '...' # String lines after change context } ``` **Note: This shows the structure of the final split files (train.csv, validation.csv, test.csv). The original MSR_data_cleaned.csv contains many more metadata fields. ##📦 Dataset New Files The dataset is available as three CSV files (specially created for the UltiVul project) hosted on Hugging Face, uploaded via huggingface_hub: - train.csv Size: 667 MB Description: Training split with 150,909 samples, approximately 80% of the data. - validation.csv Size: 86 MB Description: Validation split with 18,864 samples, approximately 10% of the data. - test.csv Size: 84.8 MB Description: Test split with 18,863 samples, approximately 10% of the data. 🙏 Acknowledgements Original dataset provided by Fan et al., 2020 Thanks to the Hugging Face team for dataset hosting tools. ## 📜 Citation ```bibtex @inproceedings{fan2020ccode, title={A C/C++ Code Vulnerability Dataset with Code Changes and CVE Summaries}, author={Fan, Jiahao and Li, Yi and Wang, Shaohua and Nguyen, Tien N}, booktitle={MSR '20: 17th International Conference on Mining Software Repositories}, pages={1--5}, year={2020}, doi={10.1145/3379597.3387501} } ``` ## 🌟 Dataset Creation - **Source**: Original data from [MSR 2020 Paper](https://doi.org/10.1145/3379597.3387501) - **Processing**: - Cleaned and standardized CSV format - Stream-based splitting to handle large size - Preserved all original metadata

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-* ---

DT4LM/debertav3base_sst2_multi_pso_timeDecay

DT4LM

2025-05-03T13:38:52Z

0

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

[]

2025-05-03T13:36:33Z

null

--- dataset_info: features: - name: text dtype: string - name: label dtype: int32 splits: - name: train num_bytes: 19446 num_examples: 242 download_size: 15714 dataset_size: 19446 configs: - config_name: default data_files: - split: train path: data/train-* ---

NextGenC/synapse-set-10k

NextGenC

2025-05-03T13:36:20Z

17

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" ]

2025-04-30T17:26:21Z

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-10K: EEG Interpretation Dataset' size_categories: - 10K<n<100K datasets: - NextGenC/synapse-set-10k --- ## 🧠 SynapseSet-10K **SynapseSet-10K** 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:** Turkish - **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-10K**, please cite: ```bibtex @misc{7ennlabs2025synapseset, author = {7enn Labs}, title = {SynapseSet-10K: Synthetic Instruction Dataset for EEG Interpretation}, year = {2025}, url = {https://huggingface.co/datasets/NextGenC/synapse-set-100k}, 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** ---

NextGenC/synapse-set-100k

NextGenC

2025-05-03T13:35:37Z

10

1

[ "task_categories:text-generation", "task_categories:text2text-generation", "language:en", "language:tr", "license:mit", "size_categories:100K<n<1M", "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" ]

2025-05-01T10:29:00Z

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-100K: EEG Interpretation Dataset' size_categories: - 10K<n<100K datasets: - NextGenC/synapse-set-100k --- ## 🧠 SynapseSet-100K **SynapseSet-100K** 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-100K**, please cite: ```bibtex @misc{7ennlabs2025synapseset, author = {7enn Labs}, title = {SynapseSet-100K: Synthetic Instruction Dataset for EEG Interpretation}, year = {2025}, url = {https://huggingface.co/datasets/NextGenC/synapse-set-100k}, 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** ---

nlpllmeval/NLP-Course-LLM-Reasoning-Eval-May2025

nlpllmeval

2025-05-03T13:14:54Z

0

1

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

[]

2025-05-03T11:23:08Z

null

--- license: mit --- # Overview of LLM Reasoning Eval Dataset This dataset contains evaluation of multiple large language models (LLMs) over 918 MCQ reasoning questions created by 184 students. Each question was used to test 3 LLMs (each 3 times): GPT-4o, Claude Sonnet 3.x (3.5 or 3.7), and Deepseek R1. The questions target various reasoning areas (i.e., Math, Logic, Temporal, Commonsense) and are included only if 3 seperate attempts (in a new session) by ChatGPT (GPT-4o) fail at giving the correct answer. This dataset was created as part of an NLP assignment in the FIT5217 unit at Monash University. - **Language(s) (NLP):** English - **License:** MIT License ## LLM-based Novelty and Objectivty Score (Zero-shot) outputs: For each question we prompted each of the 3 LLMs (3 times each) to judge its novelty and objectivity in a zero-shot setting. ### Prompt text used: You are assessing an annotator based on the quality of their quiz questions. Your task is the following: For a given question, assess it based on 2 criteria: (Criteria 1) Objectivity Score for which Objective Questions should receive Objectivity Score of 0.5, while Subjective Questions may receive Objectivity Score of 0 or 0.25. (Criteria 2) Novelty Score: Novel questions should receive Novelty Score of 0.5, while known questions or puzzles or questions with very familiar forms should receive Novelty Score of 0 or 0.25. Think step by step before giving the final scores. The question to assess is: [INSERT YOUR QUESTION] ## LLM reasoning for each question: We prompted 3 LLMs (GPT-4o, Claude Sonnet 3.5 or 3.7, and Deepseek R1), each 3 times, and recorded their response to each question along with the time stamp of the interaction. ## Files and Structures There are 2 files released in this project: - The `test.json` correspond to a unique submission (indexed by `AnomID_#`) and human created MCQ questions (Q1, ..., Q5) and gold option, and attempt 1 by each LLM (for the all 3 attempts see the next file). - The `full_meta_data.json` includes the content of test.json as well as the 3 LLMs reasonings (each attempted 3 times) as well as LLM-based judgements for objectivity and novelty of 3 LLMs (each attempted 3 times). Here is the structure of `full_meta_data.json` ```json { "AnomID_1": { "Q1": { "Question Text": "string", "Gold Answer": "string", "Reasonings": { "GPT-4o": { "Attempt 1": { "Output": "string", "Time": "string", "Correctness": true }, "Attempt 2": { ... }, "Attempt 3": { ... } }, "Claude Sonnet 3.x": { "Attempt 1": { ... }, "Attempt 2": { ... }, "Attempt 3": { ... } }, "Deepseek R1": { "Attempt 1": { ... }, "Attempt 2": { ... }, "Attempt 3": { ... } } }, "Judgement for Objectivity and Novelty": { "GPT-4o": { "Attempt 1": { "Output": "string", "Objectivity Score": 0.5, "Novelty Score": 0.5 }, "Attempt 2": { ... }, "Attempt 3": { ... } }, "Claude Sonnet 3.x": { "Attempt 1": { ... }, "Attempt 2": { ... }, "Attempt 3": { ... } }, "Deepseek R1": { "Attempt 1": { ... }, "Attempt 2": { ... }, "Attempt 3": { ... } } } }, "Q2": { ... }, "Q3": { ... }, "Q4": { ... }, "Q5": { ... } }, "AnomID_2": { ... }, ... } ``` ## Performance of LLMs Non-filtred results on all questions: | LLM | Attempt | Correct | Incorrect | |----------------------|------------|---------|-----------| | GPT-4o | Attempt 1 | 0 | 918 | | GPT-4o | Attempt 2 | 0 | 918 | | GPT-4o | Attempt 3 | 2 | 916 | | Claude Sonnet 3.x | Attempt 1 | 426 | 492 | | Claude Sonnet 3.x | Attempt 2 | 425 | 493 | | Claude Sonnet 3.x | Attempt 3 | 424 | 494 | | Deepseek R1 | Attempt 1 | 560 | 358 | | Deepseek R1 | Attempt 2 | 557 | 361 | | Deepseek R1 | Attempt 3 | 549 | 369 | Results when questions not receiving maximum objecticity score (i.e., 0.5) for 3 out of 3 attempts were discarded from the set: | LLM | Attempt | Correct | Incorrect | |----------------------|------------|---------|-----------| | GPT-4o | Attempt 1 | 0 | 768 | | GPT-4o | Attempt 2 | 0 | 768 | | GPT-4o | Attempt 3 | 2 | 766 | | Claude Sonnet 3.x | Attempt 1 | 358 | 410 | | Claude Sonnet 3.x | Attempt 2 | 358 | 410 | | Claude Sonnet 3.x | Attempt 3 | 363 | 405 | | Deepseek R1 | Attempt 1 | 485 | 283 | | Deepseek R1 | Attempt 2 | 482 | 286 | | Deepseek R1 | Attempt 3 | 475 | 293 | ## Citation Any use of the data or derivatives (for publication, training, ortesting) must acknowledge this project explicitly via the following citation: **BibTeX:** ```bibtex @misc{fit5217-llm-eval2025, title={FIT5217 NLP Course LLM Reasoning Eval May 2025}, author={FIT5217 S1 2025 Students at Monash University}, howpublished = {\url{https://huggingface.co/datasets/nlpllmeval/NLP-Course-LLM-Reasoning-Eval-May2025}}, year={2025}, } ``` ## Dataset Card Authors - Contributors were students in Semester 1 2025 Cohort of FIT5217 at Monash University ## Feedback or Questions 📧 [Fill out the form](https://docs.google.com/forms/d/e/1FAIpQLScZdMpJpwSwW66bEj3CU_NaE5vnhGkhJYf1VkmkuKW7O-QipA/viewform?usp=header) ## ⚖️ Disclaimer This dataset is provided **"as is"** for research and educational purposes only. Monash University and the dataset contributors make **no warranties**, express or implied, regarding the accuracy, reliability, or fitness of the data for any particular purpose. The dataset may contain outputs generated by third-party large language models (LLMs), and any biases, inaccuracies, or limitations in those model outputs are inherent to their respective providers. By using this dataset, you agree that: - The authors and affiliated institutions **shall not be held liable** for any damages, direct or indirect, arising from the use, misuse, or interpretation of the dataset or its derivatives. - Any analysis, publication, or product derived from this dataset **must not attribute the dataset’s content** to real individuals, and must properly anonymize any `AnomID_#` references if redistributed. - Users are solely responsible for ensuring that their use of the dataset complies with applicable laws, institutional policies, and ethical standards.

AndrisWillow/pile-500k

AndrisWillow

2025-05-03T13:13:44Z

0

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

[]

2025-05-03T13:05:06Z

null

--- dataset_info: features: - name: text dtype: string - name: dataset dtype: string splits: - name: train num_bytes: 2235745969 num_examples: 400000 - name: validation num_bytes: 553532082 num_examples: 100000 download_size: 1453793734 dataset_size: 2789278051 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* ---

dgambettaphd/D_llm2_gen4_WXS_doc1000_synt64_lr1e-04_acm_SYNLAST

dgambettaphd

2025-05-03T12:59:31Z

0

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

[]

2025-05-03T12:59:28Z

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: 11399406 num_examples: 20000 download_size: 6705681 dataset_size: 11399406 configs: - config_name: default data_files: - split: train path: data/train-* ---

shylee/eval_DP_cube_downDims1_cropNo_freeze1_32_1_ema1_1e-4_ckpt300000

shylee

2025-05-03T12:43:44Z

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" ]

2025-05-03T12:27:31Z

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": 2085, "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] ```

xingjianleng/textatlas5m_styledtextsynth_subset_s

xingjianleng

2025-05-03T12:27:50Z

0

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

[]

2025-05-03T10:37:03Z

null

--- dataset_info: features: - name: image dtype: image - name: image_path dtype: string - name: annotation dtype: string splits: - name: train num_bytes: 137333588384.845 num_examples: 90613 download_size: 137322762217 dataset_size: 137333588384.845 configs: - config_name: default data_files: - split: train path: data/train-* ---

Moamen-dcp/arazn_codeSwitched_mp3_full_notLower_prepared_4_whisperSmall

Moamen-dcp

2025-05-03T12:24:03Z

0

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

[]

2025-05-03T12:22:15Z

null

--- dataset_info: features: - name: input_features sequence: sequence: float32 - name: labels sequence: int64 splits: - name: train num_bytes: 3212439856 num_examples: 3344 - name: test num_bytes: 1412200224 num_examples: 1470 - name: dev num_bytes: 1346888952 num_examples: 1402 download_size: 1491932576 dataset_size: 5971529032 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: dev path: data/dev-* ---

alirezzaa13/Mechanicalpart

alirezzaa13

2025-05-03T12:19:08Z

7

0

[ "task_categories:text-to-image", "language:en", "license:mit", "size_categories:1K<n<10K", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "chemistry" ]

[ "text-to-image" ]

2025-04-30T08:31:05Z

null

--- license: mit task_categories: - text-to-image language: - en tags: - chemistry pretty_name: 'Mechanical parts demonstration ' size_categories: - 1K<n<10K configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 10803571.559999999 num_examples: 7360 download_size: 4772715 dataset_size: 10803571.559999999 ---

AdoCleanCode/Youtube8M_general_test_data

AdoCleanCode

2025-05-03T12:01:24Z

0

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

[]

2025-05-03T12:01:20Z

null

--- dataset_info: features: - name: 'Unnamed: 0' dtype: int64 - name: caption dtype: string - name: coarse_label dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 93525030 num_examples: 332054 download_size: 29818334 dataset_size: 93525030 configs: - config_name: default data_files: - split: train path: data/train-* ---

JopanZh/finetuning_demo

JopanZh

2025-05-03T11:58:20Z

0

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

[]

2025-05-03T11:58:19Z

null

--- dataset_info: features: - name: prompt dtype: string splits: - name: train num_bytes: 33961 num_examples: 33 download_size: 10521 dataset_size: 33961 configs: - config_name: default data_files: - split: train path: data/train-* ---

SciKnowOrg/ontolearner-education

SciKnowOrg

2025-05-03T11:23:33Z

0

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

[]

2025-05-03T11:23:30Z

null

--- license: mit language: - en tags: - OntoLearner - ontology-learning - education 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;">Education Domain Ontologies</h1> </div> ## Overview The education domain encompasses ontologies that systematically represent and organize knowledge related to learning content, educational programs, competencies, and teaching resources. This domain plays a critical role in facilitating the interoperability and integration of educational data, enabling precise knowledge representation and retrieval across diverse educational contexts. By providing a structured framework, it supports the development of intelligent educational systems and enhances the accessibility and personalization of learning experiences. ## Ontologies | Ontology ID | Full Name | Classes | Properties | Last Updated | |-------------|-----------|---------|------------|--------------| | BIBFRAME | Bibliographic Framework Ontology (BIBFRAME) | 212 | 215 | 2022-10-03| | Common | Common Ontology (Common) | 6 | 15 | None| | DoCO | Document Components Ontology (DoCO) | 137 | 7 | 2015-07-03| ## 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.

chiyuanhsiao/audio_L2-regular-15_spoken-web-questions

chiyuanhsiao

2025-05-03T11:23:23Z

0

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

[]

2025-05-02T08:43:43Z

null

--- dataset_info: features: - name: url dtype: string - name: question dtype: string - name: answers sequence: string - name: question_unit sequence: int64 - name: response_interleaf dtype: string - name: response_text dtype: string - name: response_tokens sequence: int64 - name: response_speech dtype: audio - name: response_asr dtype: string splits: - name: test num_bytes: 1346719057.0 num_examples: 2032 download_size: 1232352852 dataset_size: 1346719057.0 configs: - config_name: default data_files: - split: test path: data/test-* ---

HyperX-Sen/Dream-Zira

HyperX-Sen

2025-05-03T11:17:57Z

0

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

[]

2025-05-03T11:17:30Z

null

--- license: apache-2.0 ---

kazugi/ball_dataset

kazugi

2025-05-03T11:13:19Z

0

[ "task_categories:robotics", "size_categories:n<1K", "modality:video", "library:datasets", "library:mlcroissant", "region:us", "phosphobot", "so100", "phospho-dk" ]

[ "robotics" ]

2025-05-03T10:54:35Z

null

--- tags: - phosphobot - so100 - phospho-dk task_categories: - robotics --- # ball_dataset **This dataset was generated using a [phospho starter pack](https://robots.phospho.ai).** This dataset contains a series of episodes recorded with a robot and multiple cameras. It can be directly used to train a policy using imitation learning. It's compatible with LeRobot and RLDS.

ParkSY/data_nerf_depthanying_depthmap

ParkSY

2025-05-03T11:06:18Z

0

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

[]

2025-05-03T11:06:12Z

null

--- dataset_info: features: - name: input_image dtype: string - name: edit_prompt dtype: string - name: edited_image dtype: string - name: label dtype: int64 - name: depthmap dtype: string - name: water_color dtype: string splits: - name: train num_bytes: 2324779 num_examples: 6552 download_size: 268143 dataset_size: 2324779 configs: - config_name: default data_files: - split: train path: data/train-* ---

ninja/hayat

ninja

2025-05-03T10:58:48Z

0

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

[]

2025-05-03T10:58:45Z

null

--- dataset_info: features: - name: text dtype: string - name: entities list: - name: label dtype: string - name: related_to dtype: string - name: text dtype: string splits: - name: train num_bytes: 383615 num_examples: 580 download_size: 87818 dataset_size: 383615 configs: - config_name: default data_files: - split: train path: data/train-* ---

MBZUAI-IFM/puzzle_dpsk_conversation_final

MBZUAI-IFM

2025-05-03T10:48:10Z

0

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

[]

2025-05-02T17:37:55Z

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 - name: answer dtype: string splits: - name: train num_bytes: 27442441 num_examples: 3748 download_size: 12861317 dataset_size: 27442441 configs: - config_name: default data_files: - split: train path: data/train-* ---