librarian-bots/dataset_cards_with_metadata

datasetId large_string	author large_string	last_modified large_string	downloads int64	likes int64	tags large list	task_categories large list	createdAt large_string	trending_score float64	card large_string
fixie-ai/mls_eng_10k_chat	fixie-ai	2025-05-13T19:09:26Z	0	0	[ "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-13T17:46:07Z	null	--- dataset_info: features: - name: input_ids sequence: int32 - name: labels sequence: int32 - name: llasa_llama_formatted_text_prompt dtype: string - name: general_text_prompt dtype: string splits: - name: train num_bytes: 8915496143 num_examples: 151254 - name: test num_bytes: 11077284 num_examples: 236 - name: validation num_bytes: 11482367 num_examples: 238 download_size: 4219089264 dataset_size: 8938055794 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: validation path: data/validation-* ---
louisbrulenaudet/code-impots-annexe-ii	louisbrulenaudet	2025-05-13T15:42:31Z	637	0	[ "task_categories:text-generation", "task_categories:table-question-answering", "task_categories:summarization", "task_categories:text-retrieval", "task_categories:question-answering", "task_categories:text-classification", "multilinguality:monolingual", "source_datasets:original", "language:fr", "license:apache-2.0", "size_categories:n<1K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "finetuning", "legal", "french law", "droit français", "Code général des impôts, annexe II" ]	[ "text-generation", "table-question-answering", "summarization", "text-retrieval", "question-answering", "text-classification" ]	2024-03-25T22:39:19Z	null	--- license: apache-2.0 language: - fr multilinguality: - monolingual tags: - finetuning - legal - french law - droit français - Code général des impôts, annexe II source_datasets: - original pretty_name: Code général des impôts, annexe II task_categories: - text-generation - table-question-answering - summarization - text-retrieval - question-answering - text-classification size_categories: - 1K<n<10K --- # Code général des impôts, annexe II, non-instruct (2025-05-12) The objective of this project is to provide researchers, professionals and law students with simplified, up-to-date access to all French legal texts, enriched with a wealth of data to facilitate their integration into Community and European projects. Normally, the data is refreshed daily on all legal codes, and aims to simplify the production of training sets and labeling pipelines for the development of free, open-source language models based on open data accessible to all. ## Concurrent reading of the LegalKit [<img src="https://raw.githubusercontent.com/louisbrulenaudet/ragoon/main/assets/badge.svg" alt="Built with RAGoon" width="200" height="32"/>](https://github.com/louisbrulenaudet/ragoon) To use all the legal data published on LegalKit, you can use RAGoon: ```bash pip3 install ragoon ``` Then, you can load multiple datasets using this code snippet: ```python # -- coding: utf-8 -- from ragoon import load_datasets req = [ "louisbrulenaudet/code-artisanat", "louisbrulenaudet/code-action-sociale-familles", # ... ] datasets_list = load_datasets( req=req, streaming=False ) dataset = datasets.concatenate_datasets( datasets_list ) ``` ### Data Structure for Article Information This section provides a detailed overview of the elements contained within the `item` dictionary. Each key represents a specific attribute of the legal article, with its associated value providing detailed information. 1. Basic Information - `ref` (string): Reference - A reference to the article, combining the title_main and the article `number` (e.g., "Code Général des Impôts, art. 123"). - `texte` (string): Text Content - The textual content of the article. - `dateDebut` (string): Start Date - The date when the article came into effect. - `dateFin` (string): End Date - The date when the article was terminated or superseded. - `num` (string): Article Number - The number assigned to the article. - `id` (string): Article ID - Unique identifier for the article. - `cid` (string): Chronical ID - Chronical identifier for the article. - `type` (string): Type - The type or classification of the document (e.g., "AUTONOME"). - `etat` (string): Legal Status - The current legal status of the article (e.g., "MODIFIE_MORT_NE"). 2. Content and Notes - `nota` (string): Notes - Additional notes or remarks associated with the article. - `version_article` (string): Article Version - The version number of the article. - `ordre` (integer): Order Number - A numerical value used to sort articles within their parent section. 3. Additional Metadata - `conditionDiffere` (string): Deferred Condition - Specific conditions related to collective agreements. - `infosComplementaires` (string): Additional Information - Extra information pertinent to the article. - `surtitre` (string): Subtitle - A subtitle or additional title information related to collective agreements. - `nature` (string): Nature - The nature or category of the document (e.g., "Article"). - `texteHtml` (string): HTML Content - The article's content in HTML format. 4. Versioning and Extensions - `dateFinExtension` (string): End Date of Extension - The end date if the article has an extension. - `versionPrecedente` (string): Previous Version - Identifier for the previous version of the article. - `refInjection` (string): Injection Reference - Technical reference to identify the date of injection. - `idTexte` (string): Text ID - Identifier for the legal text to which the article belongs. - `idTechInjection` (string): Technical Injection ID - Technical identifier for the injected element. 5. Origin and Relationships - `origine` (string): Origin - The origin of the document (e.g., "LEGI"). - `dateDebutExtension` (string): Start Date of Extension - The start date if the article has an extension. - `idEliAlias` (string): ELI Alias - Alias for the European Legislation Identifier (ELI). - `cidTexte` (string): Text Chronical ID - Chronical identifier of the text. 6. Hierarchical Relationships - `sectionParentId` (string): Parent Section ID - Technical identifier of the parent section. - `multipleVersions` (boolean): Multiple Versions - Indicates if the article has multiple versions. - `comporteLiensSP` (boolean): Contains Public Service Links - Indicates if the article contains links to public services. - `sectionParentTitre` (string): Parent Section Title - Title of the parent section (e.g., "I : Revenu imposable"). - `infosRestructurationBranche` (string): Branch Restructuring Information - Information about branch restructuring. - `idEli` (string): ELI ID - European Legislation Identifier (ELI) for the article. - `sectionParentCid` (string): Parent Section Chronical ID - Chronical identifier of the parent section. 7. Additional Content and History - `numeroBo` (string): Official Bulletin Number - Number of the official bulletin where the article was published. - `infosRestructurationBrancheHtml` (string): Branch Restructuring Information (HTML) - Branch restructuring information in HTML format. - `historique` (string): History - Historical context or changes specific to collective agreements. - `infosComplementairesHtml` (string): Additional Information (HTML) - Additional information in HTML format. - `renvoi` (string): Reference - References to content within the article (e.g., "(1)"). - `fullSectionsTitre` (string): Full Section Titles - Concatenation of all titles in the parent chain. - `notaHtml` (string): Notes (HTML) - Additional notes or remarks in HTML format. - `inap` (string): INAP - A placeholder for INAP-specific information. ## Feedback If you have any feedback, please reach out at [[email protected]](mailto:[email protected]).
ESITime/tram-relation-responses	ESITime	2025-05-13T15:26:35Z	358	0	[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-04-06T14:57:46Z	null	--- dataset_info: - config_name: 05B features: - name: Question dtype: string - name: Option A dtype: string - name: Option B dtype: string - name: Option C dtype: string - name: Answer dtype: string - name: Source dtype: string - name: prompt dtype: string - name: __index_level_0__ dtype: int64 - name: response dtype: string splits: - name: test num_bytes: 563660 num_examples: 504 download_size: 288283 dataset_size: 563660 - config_name: 3B features: - name: Question dtype: string - name: Option A dtype: string - name: Option B dtype: string - name: Option C dtype: string - name: Answer dtype: string - name: Source dtype: string - name: prompt dtype: string - name: __index_level_0__ dtype: int64 - name: response dtype: string splits: - name: test num_bytes: 522730 num_examples: 504 download_size: 268767 dataset_size: 522730 - config_name: 4_epoch features: - name: Question dtype: string - name: Option A dtype: string - name: Option B dtype: string - name: Option C dtype: string - name: Answer dtype: string - name: Source dtype: string - name: prompt dtype: string - name: __index_level_0__ dtype: int64 - name: response dtype: string splits: - name: test num_bytes: 610557 num_examples: 504 download_size: 307133 dataset_size: 610557 - config_name: 6B features: - name: Question dtype: string - name: Option A dtype: string - name: Option B dtype: string - name: Option C dtype: string - name: Answer dtype: string - name: Source dtype: string - name: prompt dtype: string - name: __index_level_0__ dtype: int64 - name: response dtype: string splits: - name: test num_bytes: 523743 num_examples: 504 download_size: 269699 dataset_size: 523743 - config_name: base_base_message_3b features: - name: Question dtype: string - name: Option A dtype: string - name: Option B dtype: string - name: Option C dtype: string - name: Answer dtype: string - name: Source dtype: string - name: prompt dtype: string - name: __index_level_0__ dtype: int64 - name: response dtype: string splits: - name: test num_bytes: 1972433 num_examples: 504 download_size: 810457 dataset_size: 1972433 - config_name: base_create_1.5b features: - name: Question dtype: string - name: Option A dtype: string - name: Option B dtype: string - name: Option C dtype: string - name: Answer dtype: string - name: Source dtype: string - name: prompt dtype: string - name: __index_level_0__ dtype: int64 - name: response dtype: string splits: - name: test num_bytes: 939980 num_examples: 504 download_size: 433440 dataset_size: 939980 - config_name: base_create_3b features: - name: Question dtype: string - name: Option A dtype: string - name: Option B dtype: string - name: Option C dtype: string - name: Answer dtype: string - name: Source dtype: string - name: prompt dtype: string - name: __index_level_0__ dtype: int64 - name: response dtype: string splits: - name: test num_bytes: 564942 num_examples: 504 download_size: 281637 dataset_size: 564942 - config_name: free_1 features: - name: Question dtype: string - name: Option A dtype: string - name: Option B dtype: string - name: Option C dtype: string - name: Answer dtype: string - name: Source dtype: string - name: prompt dtype: string - name: __index_level_0__ dtype: int64 - name: response dtype: string splits: - name: test num_bytes: 574127 num_examples: 504 download_size: 290281 dataset_size: 574127 - config_name: only_1 features: - name: Question dtype: string - name: Option A dtype: string - name: Option B dtype: string - name: Option C dtype: string - name: Answer dtype: string - name: Source dtype: string - name: prompt dtype: string - name: __index_level_0__ dtype: int64 - name: response dtype: string splits: - name: test num_bytes: 563148 num_examples: 504 download_size: 283488 dataset_size: 563148 - config_name: qwen features: - name: Question dtype: string - name: Option A dtype: string - name: Option B dtype: string - name: Option C dtype: string - name: Answer dtype: string - name: Source dtype: string - name: prompt dtype: string - name: __index_level_0__ dtype: int64 - name: response dtype: string splits: - name: test num_bytes: 509236 num_examples: 504 download_size: 277574 dataset_size: 509236 - config_name: qwen05_sft features: - name: Question dtype: string - name: Option A dtype: string - name: Option B dtype: string - name: Option C dtype: string - name: Answer dtype: string - name: Source dtype: string - name: prompt dtype: string - name: __index_level_0__ dtype: int64 - name: response dtype: string splits: - name: test num_bytes: 400398 num_examples: 504 download_size: 203651 dataset_size: 400398 - config_name: qwen15_sft features: - name: Question dtype: string - name: Option A dtype: string - name: Option B dtype: string - name: Option C dtype: string - name: Answer dtype: string - name: Source dtype: string - name: prompt dtype: string - name: __index_level_0__ dtype: int64 - name: response dtype: string splits: - name: test num_bytes: 887100 num_examples: 504 download_size: 442407 dataset_size: 887100 - config_name: qwen3_sft features: - name: Question dtype: string - name: Option A dtype: string - name: Option B dtype: string - name: Option C dtype: string - name: Answer dtype: string - name: Source dtype: string - name: prompt dtype: string - name: __index_level_0__ dtype: int64 - name: response dtype: string splits: - name: test num_bytes: 673150 num_examples: 504 download_size: 333123 dataset_size: 673150 - config_name: qwen_3 features: - name: Question dtype: string - name: Option A dtype: string - name: Option B dtype: string - name: Option C dtype: string - name: Answer dtype: string - name: Source dtype: string - name: prompt dtype: string - name: __index_level_0__ dtype: int64 - name: response dtype: string splits: - name: test num_bytes: 503714 num_examples: 504 download_size: 260303 dataset_size: 503714 - config_name: qwen_3b_sft features: - name: Question dtype: string - name: Option A dtype: string - name: Option B dtype: string - name: Option C dtype: string - name: Answer dtype: string - name: Source dtype: string - name: prompt dtype: string - name: __index_level_0__ dtype: int64 - name: response dtype: string splits: - name: test num_bytes: 871497 num_examples: 504 download_size: 418390 dataset_size: 871497 - config_name: qwen_4 features: - name: Question dtype: string - name: Option A dtype: string - name: Option B dtype: string - name: Option C dtype: string - name: Answer dtype: string - name: Source dtype: string - name: prompt dtype: string - name: __index_level_0__ dtype: int64 - name: response dtype: string splits: - name: test num_bytes: 503131 num_examples: 504 download_size: 259984 dataset_size: 503131 - config_name: qwen_4_1 features: - name: Question dtype: string - name: Option A dtype: string - name: Option B dtype: string - name: Option C dtype: string - name: Answer dtype: string - name: Source dtype: string - name: prompt dtype: string - name: __index_level_0__ dtype: int64 - name: response dtype: string splits: - name: test num_bytes: 502707 num_examples: 505 download_size: 259125 dataset_size: 502707 - config_name: qwen_5 features: - name: Question dtype: string - name: Option A dtype: string - name: Option B dtype: string - name: Option C dtype: string - name: Answer dtype: string - name: Source dtype: string - name: prompt dtype: string - name: __index_level_0__ dtype: int64 - name: response dtype: string splits: - name: test num_bytes: 614947 num_examples: 504 download_size: 312432 dataset_size: 614947 - config_name: sft1 features: - name: Question dtype: string - name: Option A dtype: string - name: Option B dtype: string - name: Option C dtype: string - name: Answer dtype: string - name: Source dtype: string - name: prompt dtype: string - name: __index_level_0__ dtype: int64 - name: response dtype: string splits: - name: test num_bytes: 760039 num_examples: 504 download_size: 366132 dataset_size: 760039 - config_name: sft1_1 features: - name: Question dtype: string - name: Option A dtype: string - name: Option B dtype: string - name: Option C dtype: string - name: Answer dtype: string - name: Source dtype: string - name: prompt dtype: string - name: __index_level_0__ dtype: int64 - name: response dtype: string splits: - name: test num_bytes: 834967 num_examples: 504 download_size: 408377 dataset_size: 834967 - config_name: sft1_3 features: - name: Question dtype: string - name: Option A dtype: string - name: Option B dtype: string - name: Option C dtype: string - name: Answer dtype: string - name: Source dtype: string - name: prompt dtype: string - name: __index_level_0__ dtype: int64 - name: response dtype: string splits: - name: test num_bytes: 790174 num_examples: 504 download_size: 378792 dataset_size: 790174 - config_name: sft1_4 features: - name: Question dtype: string - name: Option A dtype: string - name: Option B dtype: string - name: Option C dtype: string - name: Answer dtype: string - name: Source dtype: string - name: prompt dtype: string - name: __index_level_0__ dtype: int64 - name: response dtype: string splits: - name: test num_bytes: 798017 num_examples: 504 download_size: 381126 dataset_size: 798017 - config_name: sft1_5 features: - name: Question dtype: string - name: Option A dtype: string - name: Option B dtype: string - name: Option C dtype: string - name: Answer dtype: string - name: Source dtype: string - name: prompt dtype: string - name: __index_level_0__ dtype: int64 - name: response dtype: string splits: - name: test num_bytes: 829423 num_examples: 504 download_size: 401651 dataset_size: 829423 - config_name: sft2 features: - name: Question dtype: string - name: Option A dtype: string - name: Option B dtype: string - name: Option C dtype: string - name: Answer dtype: string - name: Source dtype: string - name: prompt dtype: string - name: __index_level_0__ dtype: int64 - name: response dtype: string splits: - name: test num_bytes: 794397 num_examples: 504 download_size: 384196 dataset_size: 794397 - config_name: sft2_1 features: - name: Question dtype: string - name: Option A dtype: string - name: Option B dtype: string - name: Option C dtype: string - name: Answer dtype: string - name: Source dtype: string - name: prompt dtype: string - name: __index_level_0__ dtype: int64 - name: response dtype: string splits: - name: test num_bytes: 832224 num_examples: 504 download_size: 404456 dataset_size: 832224 - config_name: sft2_3 features: - name: Question dtype: string - name: Option A dtype: string - name: Option B dtype: string - name: Option C dtype: string - name: Answer dtype: string - name: Source dtype: string - name: prompt dtype: string - name: __index_level_0__ dtype: int64 - name: response dtype: string splits: - name: test num_bytes: 667852 num_examples: 504 download_size: 331857 dataset_size: 667852 - config_name: sft2_4 features: - name: Question dtype: string - name: Option A dtype: string - name: Option B dtype: string - name: Option C dtype: string - name: Answer dtype: string - name: Source dtype: string - name: prompt dtype: string - name: __index_level_0__ dtype: int64 - name: response dtype: string splits: - name: test num_bytes: 663333 num_examples: 504 download_size: 330105 dataset_size: 663333 - config_name: sft2_5 features: - name: Question dtype: string - name: Option A dtype: string - name: Option B dtype: string - name: Option C dtype: string - name: Answer dtype: string - name: Source dtype: string - name: prompt dtype: string - name: __index_level_0__ dtype: int64 - name: response dtype: string splits: - name: test num_bytes: 674219 num_examples: 504 download_size: 340371 dataset_size: 674219 configs: - config_name: 05B data_files: - split: test path: 05B/test-* - config_name: 3B data_files: - split: test path: 3B/test-* - config_name: 4_epoch data_files: - split: test path: 4_epoch/test-* - config_name: 6B data_files: - split: test path: 6B/test-* - config_name: base_base_message_3b data_files: - split: test path: base_base_message_3b/test-* - config_name: base_create_1.5b data_files: - split: test path: base_create_1.5b/test-* - config_name: base_create_3b data_files: - split: test path: base_create_3b/test-* - config_name: free_1 data_files: - split: test path: free_1/test-* - config_name: only_1 data_files: - split: test path: only_1/test-* - config_name: qwen data_files: - split: test path: qwen/test-* - config_name: qwen05_sft data_files: - split: test path: qwen05_sft/test-* - config_name: qwen15_sft data_files: - split: test path: qwen15_sft/test-* - config_name: qwen3_sft data_files: - split: test path: qwen3_sft/test-* - config_name: qwen_3 data_files: - split: test path: qwen_3/test-* - config_name: qwen_3b_sft data_files: - split: test path: qwen_3b_sft/test-* - config_name: qwen_4 data_files: - split: test path: qwen_4/test-* - config_name: qwen_4_1 data_files: - split: test path: qwen_4_1/test-* - config_name: qwen_5 data_files: - split: test path: qwen_5/test-* - config_name: sft1 data_files: - split: test path: sft1/test-* - config_name: sft1_1 data_files: - split: test path: sft1_1/test-* - config_name: sft1_3 data_files: - split: test path: sft1_3/test-* - config_name: sft1_4 data_files: - split: test path: sft1_4/test-* - config_name: sft1_5 data_files: - split: test path: sft1_5/test-* - config_name: sft2 data_files: - split: test path: sft2/test-* - config_name: sft2_1 data_files: - split: test path: sft2_1/test-* - config_name: sft2_3 data_files: - split: test path: sft2_3/test-* - config_name: sft2_4 data_files: - split: test path: sft2_4/test-* - config_name: sft2_5 data_files: - split: test path: sft2_5/test-* ---
Shekharmeena/female-audio-dataset	Shekharmeena	2025-05-13T11:44:36Z	0	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:audio", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-13T11:41:15Z	null	--- dataset_info: features: - name: audio dtype: audio - name: text dtype: string - name: gender dtype: class_label: names: '0': female '1': male splits: - name: train num_bytes: 4587686171.705792 num_examples: 5983 download_size: 4129876735 dataset_size: 4587686171.705792 configs: - config_name: default data_files: - split: train path: data/train-* ---
JingweiNi/LEXam	JingweiNi	2025-05-13T10:21:46Z	0	0	[ "license:cc-by-4.0", "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-13T09:53:20Z	null	--- license: cc-by-4.0 dataset_info: - config_name: mcq_4_choices features: - name: Question dtype: string - name: Choices dtype: string - name: Gold dtype: int64 - name: Course dtype: string - name: Language dtype: string - name: Area dtype: string - name: Jurisdiction dtype: string - name: Year dtype: int64 - name: n_statements dtype: int64 - name: none_as_an_option dtype: bool - name: Id dtype: string splits: - name: test num_bytes: 1701781 num_examples: 1660 download_size: 833522 dataset_size: 1701781 - config_name: mcq_perturbation features: - name: question dtype: string - name: 4_choices dtype: string - name: 4_choices_answer dtype: int64 - name: 8_choices dtype: string - name: 8_choices_answer dtype: int64 - name: 16_choices dtype: string - name: 16_choices_answer dtype: int64 - name: 32_choices dtype: string - name: 32_choices_answer dtype: int64 - name: course dtype: string - name: language dtype: string - name: n_statements dtype: int64 - name: id dtype: string splits: - name: test num_bytes: 779770 num_examples: 385 download_size: 327294 dataset_size: 779770 - config_name: open_question features: - name: Question dtype: string - name: Answer dtype: string - name: Course dtype: string - name: Language dtype: string - name: Area dtype: string - name: Jurisdiction dtype: string - name: Year dtype: int64 - name: ID dtype: string splits: - name: test num_bytes: 7966761 num_examples: 2541 - name: dev num_bytes: 994495 num_examples: 300 download_size: 4159184 dataset_size: 8961256 configs: - config_name: mcq_4_choices data_files: - split: test path: mcq_4_choices/test-* - config_name: mcq_perturbation data_files: - split: test path: mcq_perturbation/test-* - config_name: open_question data_files: - split: test path: open_question/test-* - split: dev path: open_question/dev-* --- <div align="center" style="display: flex; align-items: center; justify-content: center; gap: 16px;"> <img src="pictures/logo.png" alt="LEXam Logo" width="120" style="border: none;"> <div style="text-align: left;"> <h1 style="margin: 0; font-size: 2em;">LEXam: Benchmarking Legal Reasoning on 340 Law Exams</h1> <p style="margin: 6px 0 0; font-size: 1.2em;">A diverse, rigorous evaluation suite for legal AI from Swiss, EU, and international law examinations.</p> </div> </div> ### [[GitHub Repo]](https://github.com/EdisonNi-hku/LEXam) with code to run evaluations on LEXam ## 🔥 News - [2025/05] Release of the first version of [paper](), where we evaluate 20+ representative SoTA LLMs with evaluations stricly verified by legal experts. ## 🧩 Subsets - `mcq_4_choices`: The standard 1660 MCQs of LEXam with 4 choices. - `mcq_perturbation`: We find that using permulation perturbation can significantly increase the difficulty of LEXam. `mcq_perturbation` contains a set of MCQs with controled questions, but perturbed choices with 4, 8, 16, 32 alternative answers. - `open_question`: All open questions of LEXam, with rich meta-data including language, course, jusritiction etc.
nduque/eval_robustness_e8_no1_2_120k_2	nduque	2025-05-13T09:15:49Z	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" ]	2025-05-13T09:15:40Z	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": "koch", "total_episodes": 10, "total_frames": 4734, "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": [ 9 ], "names": null }, "observation.images.front": { "dtype": "video", "shape": [ 720, 1280, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.fps": 30.0, "video.height": 720, "video.width": 1280, "video.channels": 3, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "has_audio": false } }, "observation.images.above": { "dtype": "video", "shape": [ 720, 1280, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.fps": 30.0, "video.height": 720, "video.width": 1280, "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] ```
axondendriteplus/LightRAG-DAPO-ScalingLaws	axondendriteplus	2025-05-13T08:15:11Z	0	0	[ "size_categories:n<1K", "format:imagefolder", "modality:image", "library:datasets", "library:mlcroissant", "region:us", "created-with-pdfs-to-page-images-converter", "pdf-to-image" ]	[]	2025-05-13T08:15:01Z	null	--- size_categories: - n<1K tags: - created-with-pdfs-to-page-images-converter - pdf-to-image --- # Dataset Card for axondendriteplus/LightRAG-DAPO-ScalingLaws ## Dataset Description This dataset contains images converted from PDFs using the PDFs to Page Images Converter Space. - Number of images: 62 - Number of PDFs processed: 3 - Sample size per PDF: 100 - Created on: 2025-05-13 10:15:11 ## Dataset Creation ### Source Data The images in this dataset were generated from user-uploaded PDF files. ### Processing Steps 1. PDF files were uploaded to the PDFs to Page Images Converter. 2. Each PDF was processed, converting selected pages to images. 3. The resulting images were saved and uploaded to this dataset. ## Dataset Structure The dataset consists of JPEG images, each representing a single page from the source PDFs. ### Data Fields - `images/`: A folder containing all the converted images. ### Data Splits This dataset does not have specific splits. ## Additional Information - Contributions: Thanks to the PDFs to Page Images Converter for creating this dataset.
AbrehamT/tagged_articles	AbrehamT	2025-05-13T04:00:38Z	24	0	[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "biology", "chemistry" ]	[]	2025-05-10T21:59:14Z	null	--- tags: - biology - chemistry size_categories: - 10K<n<100K dataset_info: features: - name: pmid dtype: string - name: section dtype: string - name: text dtype: string - name: annotations sequence: sequence: int64 splits: - name: train num_bytes: 120073099 num_examples: 17619 download_size: 61504820 dataset_size: 120073099 configs: - config_name: default data_files: - split: train path: data/train-* --- # Alzheimer's Disease Tagged Articles Dataset This dataset contains a collection of scientific articles related to Alzheimer's disease, annotated with biomedical entities (such as diseases, genes, and species) using NCBI’s PubTator tools. ## Data Sources and Annotation Tools - Entity annotations were generated using NCBI's standard models and API calls to the PubTator3 API: - TaggerOne and GNORM for `gene_species_tagged_articles.json` - TaggerOne alone for `disease_tagged_articles.json` - PubTator3 API for 'converted_from_bioc.json' These tools are widely used in biomedical NLP for tagging mentions of diseases, genes, and species within PubMed articles. ## Dataset Structure The dataset is a flattened version of the original JSON files, where each record represents a tagged article section. The structure includes: - `pmid`: PubMed ID of the article - `section`: Type of section (e.g., `title`, `abstract`) - `text`: Lowercased content of the section - `annotations`: List of `[start, end]` character offsets identifying entity mentions Example: ```json { "pmid": "34379990", "section": "abstract", "text": "clinical progression of tauopathies may result...", "annotations": [[0, 26], [45, 53], ...] } ```` ## Preprocessing The dataset has been simplified: * Original nested structures were flattened * Tuples were converted to JSON-compliant lists * Only `title` and `abstract` sections are currently included ## Future Work * Extend tagging beyond titles and abstracts to include full-text sections (e.g., introduction, methods, results) * Add entity labels (e.g., `Disease`, `Gene`, `Species`) in a future version ## Usage ```python from datasets import load_dataset dataset = load_dataset("AbrehamT/tagged_articles") ``` ## License This dataset is released under the MIT License. ## Acknowledgments This dataset relies on NCBI’s PubTator, GNORM, and TaggerOne models. Credit goes to the developers of these tools and the researchers whose articles form the dataset foundation.
AlignmentResearch/ReNeLLMClearHarm	AlignmentResearch	2025-05-13T03:24:53Z	0	0	[ "size_categories:10K<n<100K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-12T22:39:14Z	null	--- dataset_info: - config_name: renellm-input-adv-Qwen2.5-14-config-default-examples-0-100-attacks-0-200 features: - name: clf_label dtype: int64 - name: instructions dtype: string - name: content sequence: string - name: answer_prompt dtype: string - name: proxy_clf_label dtype: int64 - name: gen_target dtype: string - name: proxy_gen_target dtype: string - name: original_text dtype: string - name: attack_index dtype: int64 - name: original_example_index dtype: int64 splits: - name: validation num_bytes: 16199407 num_examples: 20000 download_size: 1335998 dataset_size: 16199407 - config_name: renellm-input-adv-Qwen2.5-14-config-default-examples-0-100-attacks-0-200-short features: - name: clf_label dtype: int64 - name: instructions dtype: string - name: content sequence: string - name: answer_prompt dtype: string - name: proxy_clf_label dtype: int64 - name: gen_target dtype: string - name: proxy_gen_target dtype: string - name: original_text dtype: string - name: attack_index dtype: int64 - name: original_example_index dtype: int64 splits: - name: train num_bytes: 0 num_examples: 0 - name: validation num_bytes: 16169174.0 num_examples: 19997 download_size: 1387154 dataset_size: 16169174.0 configs: - config_name: renellm-input-adv-Qwen2.5-14-config-default-examples-0-100-attacks-0-200 data_files: - split: validation path: renellm-input-adv-Qwen2.5-14-config-default-examples-0-100-attacks-0-200/validation-* - config_name: renellm-input-adv-Qwen2.5-14-config-default-examples-0-100-attacks-0-200-short data_files: - split: train path: renellm-input-adv-Qwen2.5-14-config-default-examples-0-100-attacks-0-200-short/train-* - split: validation path: renellm-input-adv-Qwen2.5-14-config-default-examples-0-100-attacks-0-200-short/validation-* ---
danhdzzzz/tuyensinh_sp	danhdzzzz	2025-05-13T03:13:15Z	0	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-13T03:12:29Z	null	--- dataset_info: features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 263615 num_examples: 1059 download_size: 87116 dataset_size: 263615 configs: - config_name: default data_files: - split: train path: data/train-* ---
yoonholee/completions_Qwen3-1.7B_GSMPlus	yoonholee	2025-05-13T03:08:20Z	0	0	[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-13T03:08:18Z	null	--- dataset_info: features: - name: problem dtype: string - name: completions sequence: string - name: answer dtype: string - name: corrects sequence: bool - name: acc dtype: float64 splits: - name: train num_bytes: 9368563 num_examples: 200 download_size: 2824742 dataset_size: 9368563 configs: - config_name: default data_files: - split: train path: data/train-* ---
chuuhtetnaing/myanmar-ocr-dataset	chuuhtetnaing	2025-05-13T02:25:18Z	166	0	[ "task_categories:image-to-text", "language:my", "license:mit", "region:us" ]	[ "image-to-text" ]	2025-03-27T12:09:00Z	null	--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 23636898616.852 num_examples: 6903524 - name: test num_bytes: 2592888233.424 num_examples: 762736 download_size: 24248031522 dataset_size: 26229786850.276 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* language: - my pretty_name: Myanmar OCR Dataset (Synthetic) license: mit task_categories: - image-to-text --- # Myanmar OCR Dataset A synthetic dataset for training and fine-tuning Optical Character Recognition (OCR) models specifically for the Myanmar language. ## Dataset Description This dataset contains synthetically generated OCR images created specifically for Myanmar text recognition tasks. The images were generated using [myanmar-ocr-data-generator](https://github.com/chuuhtetnaing/myanmar-ocr-data-generator), a fork of [TextRecognitionDataGenerator](https://github.com/Belval/TextRecognitionDataGenerator) with fixes for proper Myanmar character splitting. ## Direct Download Available from [UT Austin SharePoint](https://utexas-my.sharepoint.com/:u:/g/personal/cn23349_my_utexas_edu/EZcWC2NEbKNMkeJ_WByugx0BMTSE6qKYQjVp62fn6m0toA?e=L9AKko) - The downloadable data is specifically formatted for PaddleOCR - Includes organized folder structure optimized for PaddleOCR training - Labels are provided in tab-separated format - Ready to fine-tuned for PaddleOCR ### Key Features - Synthetic Myanmar text images with corresponding text labels - Maximum text length of 100 characters per image - Various fonts, styles, skews, various distortion types, blur levels, and backgrounds to improve model robustness ## Generation Method The dataset was created using a forked and customized version of the TextRecognitionDataGenerator: - Original repository: [Belval/TextRecognitionDataGenerator](https://github.com/Belval/TextRecognitionDataGenerator) - Customized fork: [chuuhtetnaing/myanmar-ocr-data-generator](https://github.com/chuuhtetnaing/myanmar-ocr-data-generator) ### Text Sources The text content used to generate the OCR images was extracted from: - [chuuhtetnaing/myanmar-wikipedia-dataset](https://huggingface.co/datasets/chuuhtetnaing/myanmar-wikipedia-dataset): A collection of Myanmar language text from Wikipedia - [facebook/flores](https://huggingface.co/datasets/facebook/flores): Meta's FLORES dataset which includes Myanmar language texts ## Usage This dataset is particularly useful for: - Training OCR models for Myanmar language text recognition - Fine-tuning existing OCR models to improve performance on Myanmar script - Developing applications for Myanmar text digitization ## License This dataset is free to use for any purpose - personal, commercial, or educational.
hf-doc-build/doc-build-dev	hf-doc-build	2025-05-13T01:55:51Z	163,925	4	[ "license:mit", "region:us", "documentation" ]	[]	2022-11-08T09:03:37Z	null	--- license: mit tags: - documentation pretty_name: HF Documentation (PRs) viewer: false --- This is a dataset which contains the docs from all the PRs that are updating one of the docs from https://huggingface.co/docs. It is automatically updated by this [github action](https://github.com/huggingface/doc-builder/blob/main/.github/workflows/build_pr_documentation.yml) from the [doc-buider](https://github.com/huggingface/doc-builder) repo.
ahmedelgebaly/SQuad_SciQ_HotpotQA_Alpaca_Equal	ahmedelgebaly	2025-05-12T23:14:14Z	0	0	[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-12T23:14:09Z	null	--- dataset_info: features: - name: input dtype: string - name: output dtype: string - name: instruction dtype: string - name: source dtype: string splits: - name: train num_bytes: 72542175.0 num_examples: 30000 - name: validation num_bytes: 7393264.0 num_examples: 3000 download_size: 46541993 dataset_size: 79935439.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* ---
nebius/SWE-rebench	nebius	2025-05-12T22:21:55Z	29	0	[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-04-28T10:02:25Z	null	--- dataset_info: features: - name: instance_id dtype: string - name: base_commit dtype: string - name: created_at dtype: string - name: environment_setup_commit dtype: string - name: hints_text dtype: string - name: patch dtype: string - name: problem_statement dtype: string - name: repo dtype: string - name: test_patch dtype: string - name: meta struct: - name: commit_name dtype: string - name: failed_lite_validators sequence: string - name: has_test_patch dtype: bool - name: is_lite dtype: bool - name: llm_score struct: - name: difficulty_score dtype: int64 - name: issue_text_score dtype: int64 - name: test_score dtype: int64 - name: num_modified_files dtype: int64 - name: version dtype: string - name: install_config struct: - name: env_vars struct: - name: JUPYTER_PLATFORM_DIRS dtype: string - name: env_yml_path sequence: string - name: install dtype: string - name: log_parser dtype: string - name: no_use_env dtype: bool - name: packages dtype: string - name: pip_packages sequence: string - name: pre_install sequence: string - name: python dtype: string - name: reqs_path sequence: string - name: test_cmd dtype: string - name: requirements dtype: string - name: environment dtype: string - name: FAIL_TO_PASS sequence: string - name: FAIL_TO_FAIL sequence: string - name: PASS_TO_PASS sequence: string - name: PASS_TO_FAIL sequence: string - name: license_name dtype: string - name: __index_level_0__ dtype: int64 splits: - name: test num_bytes: 737537372 num_examples: 21336 download_size: 239735457 dataset_size: 737537372 configs: - config_name: default data_files: - split: test path: data/test-* --- # Dataset Summary SWE-rebench is a large-scale dataset designed to support the training and evaluation of LLM-based software engineering (SWE) agents. It is built using a fully automated pipeline that continuously extracts real-world GitHub tasks at scale. The dataset includes over 21,000 issue–pull request pairs from 6,000+ Python repositories, each validated for correctness through environment setup and test execution. * SWE-rebench expands on the methodology introduced in SWE-bench by adding: * Continuous task collection to prevent benchmark staleness * Decontamination mechanisms to mitigate data leakage into pretrained LLMs * Automatic environment extraction and validation to ensure high-quality execution # How to Use ```python from datasets import load_dataset ds = load_dataset('nebius/SWE-rebench') ``` # Dataset Statistics Average, 75th percentile, and maximum values characterizing various attributes of the collected instances. Statistics are micro-averaged without grouping by repository. \| Data \| Type \| Mean \| p75 \| Max \| \|---------------\|--------------------\|----------\|----------\|-----------\| \| Issue text \| Length (words) \| 111.5 \| 146 \| 1,294 \| \| Code base \| Files (Non-test) \| 71.71 \| 72.00 \| 2,264 \| \| \| Lines (Non-test) \| 15,163.38\| 13,777 \| 1,039,288 \| \| Gold patch \| Files edited \| 2.6 \| 3 \| 7 \| \| \| Lines edited \| 56 \| 76 \| 300 \| \| Tests \| Fail to Pass \| 10.94 \| 5 \| 4,941 \| \| \| Total \| 58.5 \| 49 \| 7,820 \| # Dataset Structure The dataset contains the following fields. It includes all fields from SWE-bench and adds a `meta` column, which indicates whether the instance meets the "lite" criteria and, if not, lists the failed validators. \| Field name \| Type \| Description \| \|----------------------------\|--------\|-------------------------------------------------------------------------------------------------\| \| `instance_id` \| str \| A formatted instance identifier, usually as `repo_owner__repo_name-PR-number`. \| \| `patch` \| str \| The gold patch, the patch generated by the PR (minus test-related code), that resolved the issue. \| \| `repo` \| str \| The repository owner/name identifier from GitHub. \| \| `base_commit` \| str \| The commit hash of the repository representing the HEAD of the repository before the solution PR is applied. \| \| `hints_text` \| str \| Comments made on the issue prior to the creation of the solution PR’s first commit creation date. \| \| `created_at` \| str \| The creation date of the pull request. \| \| `test_patch` \| str \| A test-file patch that was contributed by the solution PR. \| \| `problem_statement` \| str \| The issue title and body. \| \| `version` \| str \| Installation version to use for running evaluation. \| \| `environment_setup_commit` \| str \| Commit hash to use for environment setup and installation. \| \| `FAIL_TO_PASS` \| str \| A JSON list of strings that represent the set of tests resolved by the PR and tied to the issue resolution. \| \| `PASS_TO_PASS` \| str \| A JSON list of strings that represent tests that should pass before and after the PR application. \| \| `meta` \| str \| A JSON dictionary indicating whether the instance is lite, along with a list of failed lite validators if it is not. \| \| `license_name` \| str \| The type of license of the repository. \| \| `install_config` \| str \| Installation configuration for setting up the repository. \| \| `requirements` \| str \| Freezed requirements for the repository. \| \| `environment` \| str \| Environment configuration for the repository. \| To execute instances within SWE-rebench use this fork of SWE-bench [SWE-rebench/SWE-bench-fork](https://github.com/SWE-rebench/SWE-bench-fork) # License The dataset is licensed under the Creative Commons Attribution 4.0 license. However, please respect the license of each specific repository on which a particular instance is based. To facilitate this, the license of each repository at the time of the commit is provided for every instance.
vuquangtrung/newscook	vuquangtrung	2025-05-12T22:16:11Z	0	0	[ "task_categories:question-answering", "language:vi", "size_categories:10K<n<100K", "format:csv", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[ "question-answering" ]	2025-05-12T21:53:21Z	null	--- task_categories: - question-answering language: - vi size_categories: - 1K<n<10K ---
SAA-Lab/test_march23-cwv-genrm_cot_qwen1.5b-ckptglobal_step_324	SAA-Lab	2025-05-12T21:13:14Z	0	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-12T21:13:12Z	null	--- dataset_info: features: - name: post_id dtype: int64 - name: chosen_body dtype: string - name: rejected_body dtype: string - name: chosen_upvotes dtype: int64 - name: rejected_upvotes dtype: int64 - name: chosen_length dtype: int64 - name: rejected_length dtype: int64 - name: chosen_username dtype: string - name: rejected_username dtype: string - name: chosen_timestamp dtype: timestamp[us] - name: rejected_timestamp dtype: timestamp[us] - name: post_title dtype: string - name: time_diff dtype: float64 - name: __index_level_0__ dtype: int64 - name: prompt list: - name: content dtype: string - name: role dtype: string - name: answer dtype: string - name: model_response dtype: string - name: reasoning dtype: string - name: preferred dtype: string - name: is_correct dtype: bool splits: - name: train num_bytes: 32076409 num_examples: 1898 download_size: 18280357 dataset_size: 32076409 configs: - config_name: default data_files: - split: train path: data/train-* ---
hurrutia/Reglamento_Aeronautico_Colombiano_2024	hurrutia	2025-05-12T20:55:39Z	0	0	[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-12T14:20:13Z	null	--- dataset_info: features: - name: rac dtype: string - name: pagina dtype: string - name: pregunta dtype: string - name: respuesta dtype: string splits: - name: train num_bytes: 6732878 num_examples: 24479 download_size: 1813160 dataset_size: 6732878 configs: - config_name: default data_files: - split: train path: data/train-* ---
hurrutia/es-inclusive-language-it	hurrutia	2025-05-12T20:55:30Z	0	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-12T14:20:06Z	null	--- dataset_info: features: - name: pregunta dtype: string - name: respuesta dtype: string splits: - name: train num_bytes: 930607 num_examples: 4196 download_size: 406560 dataset_size: 930607 configs: - config_name: default data_files: - split: train path: data/train-* ---
AIxBlock/Spanish-Mx-short-utterances	AIxBlock	2025-05-12T20:40:34Z	33	0	[ "task_categories:text-to-audio", "task_categories:text-to-speech", "task_categories:token-classification", "language:es", "license:mit", "size_categories:100K<n<1M", "region:us" ]	[ "text-to-audio", "text-to-speech", "token-classification" ]	2025-05-08T23:57:48Z	null	--- license: mit task_categories: - text-to-audio - text-to-speech - token-classification language: - es size_categories: - 100K<n<1M --- This dataset is provided by AIxBlock, an unified platform for AI development and AI workflows automation. This dataset contains around 800k+ sentences in Spanish, making it a valuable resource for a wide range of language technology applications. All data has undergone quality assurance (QA) checks to ensure clarity, correctness, and natural phrasing. The dataset is well-suited for: Speech data generation (e.g., recording short audio clips lasting 8–30 seconds per sentence) Natural Language Processing (NLP) tasks, such as language modeling, translation, intent detection, and more
TAUR-dev/STEPS__r1_8d_and_4d_eval__v4_mini	TAUR-dev	2025-05-12T20:05:32Z	0	0	[ "size_categories:n<1K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-12T20:05:29Z	null	--- dataset_info: features: - name: __source_repo dtype: string - name: question dtype: string - name: eval_internal_cot dtype: string - name: solution dtype: string - name: eval_solution dtype: string - name: raw_eval_prompt dtype: string - name: judge_correct dtype: bool - name: judge_reasoning dtype: string - name: __orig_idx dtype: int64 - name: __chunk_idx dtype: int64 - name: eval_internal_cot__chunked dtype: string - name: eval_prompt list: - name: content dtype: string - name: role dtype: string - name: eval_steps list: - name: content dtype: string - name: steps_word_count dtype: int64 - name: trace_word_count dtype: int64 - name: word_count_diff dtype: int64 splits: - name: train num_bytes: 1374101 num_examples: 20 download_size: 332457 dataset_size: 1374101 configs: - config_name: default data_files: - split: train path: data/train-* ---
littleGuagua/x_dataset_48558	littleGuagua	2025-05-12T19:20:01Z	1,146	0	[ "task_categories:text-classification", "task_categories:token-classification", "task_categories:question-answering", "task_categories:summarization", "task_categories:text-generation", "task_ids:sentiment-analysis", "task_ids:topic-classification", "task_ids:named-entity-recognition", "task_ids:language-modeling", "task_ids:text-scoring", "task_ids:multi-class-classification", "task_ids:multi-label-classification", "task_ids:extractive-qa", "task_ids:news-articles-summarization", "multilinguality:multilingual", "source_datasets:original", "license:mit", "size_categories:100M<n<1B", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[ "text-classification", "token-classification", "question-answering", "summarization", "text-generation" ]	2025-01-26T14:58:15Z	null	--- license: mit multilinguality: - multilingual source_datasets: - original task_categories: - text-classification - token-classification - question-answering - summarization - text-generation task_ids: - sentiment-analysis - topic-classification - named-entity-recognition - language-modeling - text-scoring - multi-class-classification - multi-label-classification - extractive-qa - news-articles-summarization --- # Bittensor Subnet 13 X (Twitter) Dataset <center> <img src="https://huggingface.co/datasets/macrocosm-os/images/resolve/main/bittensor.png" alt="Data-universe: The finest collection of social media data the web has to offer"> </center> <center> <img src="https://huggingface.co/datasets/macrocosm-os/images/resolve/main/macrocosmos-black.png" alt="Data-universe: The finest collection of social media data the web has to offer"> </center> ## Dataset Description - Repository: littleGuagua/x_dataset_48558 - Subnet: Bittensor Subnet 13 - Miner Hotkey: 5ERFRy1NBaxrJ8WpkjKeWwgx79NxiVoEqmL3m5tEWsDHwjtD ### Dataset Summary This dataset is part of the Bittensor Subnet 13 decentralized network, containing preprocessed data from X (formerly Twitter). The data is continuously updated by network miners, providing a real-time stream of tweets for various analytical and machine learning tasks. For more information about the dataset, please visit the [official repository](https://github.com/macrocosm-os/data-universe). ### Supported Tasks The versatility of this dataset allows researchers and data scientists to explore various aspects of social media dynamics and develop innovative applications. Users are encouraged to leverage this data creatively for their specific research or business needs. For example: - Sentiment Analysis - Trend Detection - Content Analysis - User Behavior Modeling ### Languages Primary language: Datasets are mostly English, but can be multilingual due to decentralized ways of creation. ## Dataset Structure ### Data Instances Each instance represents a single tweet with the following fields: ### Data Fields - `text` (string): The main content of the tweet. - `label` (string): Sentiment or topic category of the tweet. - `tweet_hashtags` (list): A list of hashtags used in the tweet. May be empty if no hashtags are present. - `datetime` (string): The date when the tweet was posted. - `username_encoded` (string): An encoded version of the username to maintain user privacy. - `url_encoded` (string): An encoded version of any URLs included in the tweet. May be empty if no URLs are present. ### Data Splits This dataset is continuously updated and does not have fixed splits. Users should create their own splits based on their requirements and the data's timestamp. ## Dataset Creation ### Source Data Data is collected from public tweets on X (Twitter), adhering to the platform's terms of service and API usage guidelines. ### Personal and Sensitive Information All usernames and URLs are encoded to protect user privacy. The dataset does not intentionally include personal or sensitive information. ## Considerations for Using the Data ### Social Impact and Biases Users should be aware of potential biases inherent in X (Twitter) data, including demographic and content biases. This dataset reflects the content and opinions expressed on X and should not be considered a representative sample of the general population. ### Limitations - Data quality may vary due to the decentralized nature of collection and preprocessing. - The dataset may contain noise, spam, or irrelevant content typical of social media platforms. - Temporal biases may exist due to real-time collection methods. - The dataset is limited to public tweets and does not include private accounts or direct messages. - Not all tweets contain hashtags or URLs. ## Additional Information ### Licensing Information The dataset is released under the MIT license. The use of this dataset is also subject to X Terms of Use. ### Citation Information If you use this dataset in your research, please cite it as follows: ``` @misc{littleGuagua2025datauniversex_dataset_48558, title={The Data Universe Datasets: The finest collection of social media data the web has to offer}, author={littleGuagua}, year={2025}, url={https://huggingface.co/datasets/littleGuagua/x_dataset_48558}, } ``` ### Contributions To report issues or contribute to the dataset, please contact the miner or use the Bittensor Subnet 13 governance mechanisms. ## Dataset Statistics [This section is automatically updated] - Total Instances: 46034420 - Date Range: 2025-01-21T00:00:00Z to 2025-02-11T00:00:00Z - Last Updated: 2025-02-18T22:02:52Z ### Data Distribution - Tweets with hashtags: 36.14% - Tweets without hashtags: 63.86% ### Top 10 Hashtags For full statistics, please refer to the `stats.json` file in the repository. \| Rank \| Topic \| Total Count \| Percentage \| \|------\|-------\|-------------\|-------------\| \| 1 \| NULL \| 29399695 \| 63.86% \| \| 2 \| #riyadh \| 280983 \| 0.61% \| \| 3 \| #zelena \| 221844 \| 0.48% \| \| 4 \| #tiktok \| 163569 \| 0.36% \| \| 5 \| #bbb25 \| 128439 \| 0.28% \| \| 6 \| #ad \| 96138 \| 0.21% \| \| 7 \| #bbmzansi \| 59564 \| 0.13% \| \| 8 \| #jhope_at_galadespiècesjaunes \| 58496 \| 0.13% \| \| 9 \| #granhermano \| 52866 \| 0.11% \| \| 10 \| #pr \| 50398 \| 0.11% \| ## Update History \| Date \| New Instances \| Total Instances \| \|------\|---------------\|-----------------\| \| 2025-01-26T14:58:37Z \| 837393 \| 837393 \| \| 2025-01-30T03:14:08Z \| 8566588 \| 9403981 \| \| 2025-02-02T15:17:25Z \| 8569868 \| 17973849 \| \| 2025-02-06T03:21:34Z \| 10709950 \| 28683799 \| \| 2025-02-09T15:24:35Z \| 7218900 \| 35902699 \| \| 2025-02-13T03:32:24Z \| 8679209 \| 44581908 \| \| 2025-02-18T07:01:46Z \| 795937 \| 45377845 \| \| 2025-02-18T22:02:52Z \| 656575 \| 46034420 \|
pragsri8/ultrafeedback_60658_preference_dataset_causally-aligned-neutrals-on-causals	pragsri8	2025-05-12T19:01:02Z	0	0	[ "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-12T19:00:47Z	null	--- dataset_info: features: - name: chosen list: - name: content dtype: string - name: role dtype: string - name: rejected list: - name: content dtype: string - name: role dtype: string - name: neutral dtype: bool splits: - name: train num_bytes: 701786488 num_examples: 183836 download_size: 380044119 dataset_size: 701786488 configs: - config_name: default data_files: - split: train path: data/train-* ---
Aravindh25/trossen_pick_granola_bars_3cam_C_V8	Aravindh25	2025-05-12T18:51:08Z	121	0	[ "task_categories:robotics", "license:apache-2.0", "size_categories:100K<n<1M", "format:parquet", "modality:tabular", "modality:timeseries", "modality:video", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us", "LeRobot", "tutorial" ]	[ "robotics" ]	2025-05-10T01:48:03Z	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", "trossen_subversion": "v1.0", "robot_type": "trossen_ai_solo", "total_episodes": 25, "total_frames": 30723, "total_tasks": 1, "total_videos": 75, "total_chunks": 1, "chunks_size": 1000, "fps": 30, "splits": { "train": "0:25" }, "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": [ 7 ], "names": [ "main_joint_0", "main_joint_1", "main_joint_2", "main_joint_3", "main_joint_4", "main_joint_5", "main_joint_6" ] }, "observation.state": { "dtype": "float32", "shape": [ 7 ], "names": [ "main_joint_0", "main_joint_1", "main_joint_2", "main_joint_3", "main_joint_4", "main_joint_5", "main_joint_6" ] }, "observation.images.cam_wrist": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.fps": 30.0, "video.height": 480, "video.width": 640, "video.channels": 3, "video.codec": "h264", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "has_audio": false } }, "observation.images.cam_high": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.fps": 30.0, "video.height": 480, "video.width": 640, "video.channels": 3, "video.codec": "h264", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "has_audio": false } }, "observation.images.cam_front": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.fps": 30.0, "video.height": 480, "video.width": 640, "video.channels": 3, "video.codec": "h264", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "has_audio": false } }, "timestamp": { "dtype": "float32", "shape": [ 1 ], "names": null }, "frame_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "episode_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "task_index": { "dtype": "int64", "shape": [ 1 ], "names": null } } } ``` ## Citation BibTeX: ```bibtex [More Information Needed] ```
Matis-E/BUSI_benign_100	Matis-E	2025-05-12T18:02:21Z	39	0	[ "size_categories:n<1K", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-06T21:20:31Z	null	--- dataset_info: features: - name: image dtype: image - name: conditioning_image dtype: image - name: text dtype: string splits: - name: train num_bytes: 12306695.0 num_examples: 100 download_size: 12288899 dataset_size: 12306695.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
chiyuanhsiao/text_L2-regular-SQA_mmlu	chiyuanhsiao	2025-05-12T17:44:12Z	0	0	[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-12T17:44:05Z	null	--- dataset_info: features: - name: task_type dtype: string - name: task_name dtype: string - name: subtask_name dtype: string - name: input_question dtype: string - name: input_choice_list struct: - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: input_final_prompts sequence: string - name: input_correct_responses sequence: string - name: output_prediction_text sequence: string - name: output_parsed_answer dtype: string - name: output_choice_completions dtype: 'null' - name: output_choice_negative_log_likelihoods dtype: 'null' - name: output_metrics struct: - name: acc dtype: float64 - name: correct_format dtype: float64 - name: is_correct dtype: bool - name: input_question_hash dtype: string - name: input_final_prompts_hash sequence: string - name: benchmark_label dtype: string - name: eval_config struct: - name: max_gen_len dtype: string - name: max_prompt_len dtype: string - name: num_few_shot dtype: string - name: num_generations dtype: string - name: prompt_fn dtype: string - name: return_logprobs dtype: string - name: seed dtype: string - name: temperature dtype: string - name: top_k dtype: string - name: top_p dtype: string - name: my_prediction_text dtype: string splits: - name: latest num_bytes: 187010503 num_examples: 14042 download_size: 28043068 dataset_size: 187010503 configs: - config_name: default data_files: - split: latest path: data/latest-* ---
Bretagne/Banque_Sonore_Dialectes_Bretons	Bretagne	2025-05-12T16:55:59Z	1,142	1	[ "task_categories:automatic-speech-recognition", "task_categories:translation", "language:br", "language:fra", "language:multilingual", "license:cc-by-nc-nd-3.0", "size_categories:1K<n<10K", "format:parquet", "modality:audio", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[ "automatic-speech-recognition", "translation" ]	2023-12-06T18:55:45Z	null	--- language: - br - fra - multilingual task_categories: - automatic-speech-recognition - translation license: cc-by-nc-nd-3.0 configs: - config_name: data data_files: - split: train path: train.parquet --- > [!NOTE] > Dataset origin: http://banque.sonore.breton.free.fr/ # Description ## Issue du site Banque Sonore des Dialectes Bretons ### Présentation du projet La [Banque Sonore des Dialectes Bretons](http://banque.sonore.breton.free.fr/index.html) est un projet expérimental qui réunit sur internet un vaste ensemble d'enregistrements d'enquêtes effectuées depuis plus d'une dizaine d'années auprès de locuteurs traditionnels de breton. Alimentées par une équipe de bénévoles partageant un intérêt commun pour l'étude de la langue et des traditions populaires, ces archives en ligne souhaitent faire partager au plus grand nombre la richesse et la diversité de ce patrimoine culturel. ### Présentation du corpus Les enregistrements réunis dans nos archives proviennent de fonds particuliers pour la plupart inédits jusqu'à présent. L'ensemble de ce corpus concerne des domaines de recherche très divers pouvant aller de l'étude des sons, de la grammaire et du vocabulaire de la langue à l'étude des techniques agricoles, de la médecine traditionnelle ou encore des croyances populaires. Chaque ressource est soigneusement sélectionnée, transcrite, traduite et cataloguée et peut être accompagnée d'illustrations iconographiques. Grâce au moteur de recherche développé pour ce projet, linguistes, ethnologues, enseignants, étudiants, passionnés ou simples curieux... tous pourront facilement extraire de ce corpus les informations qui les intéressent. ### Mentions légales Ce site bénéficie d'une licence Creative Commons. Chaque déposant reste néanmoins responsable des documents qu'il souhaite faire partager en ligne. Si un extrait vous intéresse pour vos travaux, vous pouvez l'utiliser à des fins non commerciales. N'oubliez pas de citer le nom du projet ainsi que le numéro de sa cote suivi de la date de sa dernière mise à jour. ## Précisions concernant ce répertoire Hugging Face L'utilisation des données disponibles n'étant pas aisée sur le site, nous les avons scrappées le 6 décembre 2023. Ce répertoire contient 7291 fichiers audios représentant une durée de 15:53:23 (soit moins que les 8586 fichiers audio qui représentent une durée totale de 18:33:23 affichées sur la page [statistique](http://banque.sonore.breton.free.fr/statistique.php5) du site... un nouveau scrapping serait probablement à effectuer). ## Usage En plus de la tâche d'ASR, il est possible d'utiliser uniquement les colonnes `br` et `fr` comme données de traduction automatique. <!-- Une reconfiguration des données serait à effectuer afin de les proposer directement exploitables via un `load_dataset("Bretagne/Banque_Sonore_Dialectes_Bretons")`. En l'état, il faut procéder via le code suivant : ``` from huggingface_hub import snapshot_download repo_id = "Bretagne/Banque_Sonore_Dialectes_Bretons" local_dir = "le_chemin_vers_votre_dossier_en_local" snapshot_download(repo_id=repo_id, local_dir=local_dir, repo_type="dataset") ``` Chaque dossier correspond à un commune (via le code postal) dans lequel vous trouverez des enregistrements au format mp3 avec la transcription en breton et la traduction en français. -->
hshwk1983/x_dataset_20503	hshwk1983	2025-05-12T16:53:59Z	1,937	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" ]	2025-01-27T07:24:59Z	null	--- license: mit multilinguality: - multilingual source_datasets: - original task_categories: - text-classification - token-classification - question-answering - summarization - text-generation task_ids: - sentiment-analysis - topic-classification - named-entity-recognition - language-modeling - text-scoring - multi-class-classification - multi-label-classification - extractive-qa - news-articles-summarization --- # Bittensor Subnet 13 X (Twitter) Dataset <center> <img src="https://huggingface.co/datasets/macrocosm-os/images/resolve/main/bittensor.png" alt="Data-universe: The finest collection of social media data the web has to offer"> </center> <center> <img src="https://huggingface.co/datasets/macrocosm-os/images/resolve/main/macrocosmos-black.png" alt="Data-universe: The finest collection of social media data the web has to offer"> </center> ## Dataset Description - Repository: hshwk1983/x_dataset_20503 - Subnet: Bittensor Subnet 13 - Miner Hotkey: 5DsemqgVoVsi8vHK6QiEewS9B1T3cfxwjUfGXjCxzQTjRLBe ### Dataset Summary This dataset is part of the Bittensor Subnet 13 decentralized network, containing preprocessed data from X (formerly Twitter). The data is continuously updated by network miners, providing a real-time stream of tweets for various analytical and machine learning tasks. For more information about the dataset, please visit the [official repository](https://github.com/macrocosm-os/data-universe). ### Supported Tasks The versatility of this dataset allows researchers and data scientists to explore various aspects of social media dynamics and develop innovative applications. Users are encouraged to leverage this data creatively for their specific research or business needs. For example: - Sentiment Analysis - Trend Detection - Content Analysis - User Behavior Modeling ### Languages Primary language: Datasets are mostly English, but can be multilingual due to decentralized ways of creation. ## Dataset Structure ### Data Instances Each instance represents a single tweet with the following fields: ### Data Fields - `text` (string): The main content of the tweet. - `label` (string): Sentiment or topic category of the tweet. - `tweet_hashtags` (list): A list of hashtags used in the tweet. May be empty if no hashtags are present. - `datetime` (string): The date when the tweet was posted. - `username_encoded` (string): An encoded version of the username to maintain user privacy. - `url_encoded` (string): An encoded version of any URLs included in the tweet. May be empty if no URLs are present. ### Data Splits This dataset is continuously updated and does not have fixed splits. Users should create their own splits based on their requirements and the data's timestamp. ## Dataset Creation ### Source Data Data is collected from public tweets on X (Twitter), adhering to the platform's terms of service and API usage guidelines. ### Personal and Sensitive Information All usernames and URLs are encoded to protect user privacy. The dataset does not intentionally include personal or sensitive information. ## Considerations for Using the Data ### Social Impact and Biases Users should be aware of potential biases inherent in X (Twitter) data, including demographic and content biases. This dataset reflects the content and opinions expressed on X and should not be considered a representative sample of the general population. ### Limitations - Data quality may vary due to the decentralized nature of collection and preprocessing. - The dataset may contain noise, spam, or irrelevant content typical of social media platforms. - Temporal biases may exist due to real-time collection methods. - The dataset is limited to public tweets and does not include private accounts or direct messages. - Not all tweets contain hashtags or URLs. ## Additional Information ### Licensing Information The dataset is released under the MIT license. The use of this dataset is also subject to X Terms of Use. ### Citation Information If you use this dataset in your research, please cite it as follows: ``` @misc{hshwk19832025datauniversex_dataset_20503, title={The Data Universe Datasets: The finest collection of social media data the web has to offer}, author={hshwk1983}, year={2025}, url={https://huggingface.co/datasets/hshwk1983/x_dataset_20503}, } ``` ### 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: 52912520 - Date Range: 2025-01-21T00:00:00Z to 2025-02-13T00:00:00Z - Last Updated: 2025-02-18T20:45:01Z ### Data Distribution - Tweets with hashtags: 41.50% - Tweets without hashtags: 58.50% ### Top 10 Hashtags For full statistics, please refer to the `stats.json` file in the repository. \| Rank \| Topic \| Total Count \| Percentage \| \|------\|-------\|-------------\|-------------\| \| 1 \| NULL \| 30954536 \| 58.50% \| \| 2 \| #riyadh \| 333077 \| 0.63% \| \| 3 \| #zelena \| 227553 \| 0.43% \| \| 4 \| #tiktok \| 210733 \| 0.40% \| \| 5 \| #ad \| 123338 \| 0.23% \| \| 6 \| #bbb25 \| 111207 \| 0.21% \| \| 7 \| #pr \| 67348 \| 0.13% \| \| 8 \| #theheartkillersep11 \| 67119 \| 0.13% \| \| 9 \| #yahooニュース \| 65424 \| 0.12% \| \| 10 \| #theheartkillersep10 \| 64660 \| 0.12% \| ## Update History \| Date \| New Instances \| Total Instances \| \|------\|---------------\|-----------------\| \| 2025-01-27T07:26:13Z \| 4467532 \| 4467532 \| \| 2025-01-30T19:29:08Z \| 9248514 \| 13716046 \| \| 2025-02-03T07:32:06Z \| 8511001 \| 22227047 \| \| 2025-02-06T19:35:57Z \| 9947784 \| 32174831 \| \| 2025-02-10T07:39:43Z \| 8653004 \| 40827835 \| \| 2025-02-13T19:44:19Z \| 10753704 \| 51581539 \| \| 2025-02-18T05:43:42Z \| 691061 \| 52272600 \| \| 2025-02-18T20:45:01Z \| 639920 \| 52912520 \|
voxaiorg/drivethru-context-v2-all-preference-1-pairs-mistralai-Mistral-Small-3.1-24B-Instruct-2503	voxaiorg	2025-05-12T16:40:06Z	0	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-12T16:39:56Z	null	--- dataset_info: features: - name: prompt list: - name: content dtype: string - name: role dtype: string - name: chosen list: - name: content dtype: string - name: role dtype: string - name: rejected list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 395333849 num_examples: 4373 - name: validation num_bytes: 47938299 num_examples: 524 - name: test num_bytes: 60572445 num_examples: 655 download_size: 102808521 dataset_size: 503844593 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---
Ultralordb0d/medical_asr_prediction_usm_augmented	Ultralordb0d	2025-05-12T16:33:47Z	116	0	[ "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-04-19T10:23:56Z	null	--- dataset_info: features: - name: filename dtype: string - name: transcription dtype: string - name: prediction dtype: string - name: prediction_ph_par_all dtype: string - name: transcription_clean dtype: string - name: prediction_par_all dtype: string - name: prediction_par_all_medicalterms dtype: string - name: prediction_par_all_not_in_nltk dtype: string - name: prediction_embedding_model dtype: string - name: prediction_embedding_model_not_nltk dtype: string splits: - name: train num_bytes: 219249300 num_examples: 200000 download_size: 130065085 dataset_size: 219249300 configs: - config_name: default data_files: - split: train path: data/train-* ---
madeofajala/MalariaLegacyLLM	madeofajala	2025-05-12T16:16:32Z	0	0	[ "task_categories:text-classification", "task_categories:table-question-answering", "task_categories:zero-shot-classification", "language:en", "license:mit", "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2406.06316", "region:us", "biology", "chemistry", "medical" ]	[ "text-classification", "table-question-answering", "zero-shot-classification" ]	2025-05-12T15:10:05Z	null	--- dataset_info: features: - name: Prompt dtype: string - name: Answer dtype: string - name: CANONICAL_SMILES dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 77059720 num_examples: 94916 - name: validation num_bytes: 19254722 num_examples: 23762 - name: test num_bytes: 24133827 num_examples: 29674 download_size: 16354853 dataset_size: 120448269 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* license: mit task_categories: - text-classification - table-question-answering - zero-shot-classification language: - en tags: - biology - chemistry - medical pretty_name: malariallm size_categories: - 100K<n<1M --- # Dataset Card for Dataset Name <!-- Provide a quick summary of the dataset. --> This is an LLM instruction-tuned subset of the CHEMBL Legacy Malaria designed for using LLMs for virtual screening ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> This dataset is compiled from the CHEMBL Malaria Legacy dataset [link](https://chembl.gitbook.io/chembl-interface-documentation/legacy-resources). The dataset has been instruction-tuned just as proposed in [Tx-LLM: A Large Language Model for Therapeutics](https://arxiv.org/pdf/2406.06316) and TxGemma: Efficient and Agentic LLMs for Therapeutics The prompt consists of 4 parts: 1.) Instruction: General instruction to the LLMs to provide an accurate answer about the assay and molecule. 2.) Context: Information sourced from literature about the specific assay as described in the original datasets ASSAY_DESCRIPTION, as well as the target (protein or cell line). 3.) Question: A command to predict, given the molecule and assay information, if the molecule will be (A) Active or (B) inactive. 4.) Answer: - Curated by: Marvellous Ajala - Sourced From: CHEMBL - Language(s) (NLP): English - License: MIT ## Uses <!-- Address questions around how the dataset is intended to be used. --> This dataset is designed for finetuning general-purpose LLMs (Llama, Gemma etc) for virtual screening in Malaria ### Direct Use <!-- This section describes suitable use cases for the dataset. --> To use this dataset ```python import datasets dataset = datasets.load_dataset('madeofajala/MalariaLegacyLLM') # Display the first example print(dataset['train'][0]) ``` ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> The dataset contains 3 parts: - Trainset - Val set - Test set The dataset was split using a scaffold-based split. Each set contains: - Prompt: information about the assay and target in natural language - Answer: (A) if active and (B) if inactive - CANONICAL_SMILES: SMILES of molecule in focus - serial_number ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> This dataset was curated for instruction-tuning LLMs for virtual screening of Malaria using natural language (English) #### Data Collection and Processing <!-- This section describes the data collection and processing process, such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> This dataset consists of only the potency and IC50 subset of the original dataset. It was curated to contain only assays in which there was a definitive conclusion of molecules' activity (active or inactive). Also, molecules with two or more conflicting activity values, e.g active at higher or lower concentration and inactive at the other, were also completely removed #### Who are the source data producers? <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> Different pharma companies and independent researchers, including but not limited to: - Scientific Literature - TP-search Transporter Database - PubChem BioAssays - Open TG-GATEs - GSK Published Kinase Inhibitor Set - Sanger Institute Genomics of Drug Sensitivity in Cancer - Guide to Receptors and Channels - DrugMatrix in vitro pharmacology assays - Drugs for Neglected Diseases Initiative (DNDi) - St Jude Malaria Screening - WHO-TDR Malaria Screening - MMV Malaria Box - GSK Malaria Screening - Novartis Malaria Screening - GSK Tuberculosis Screening - Harvard Malaria Screening - OSDD Malaria Screening The original dataset was compiled by EBI-EMBL team ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users are advised to cross-reference the dataset with the orginal dataset provided by CHEMBL. Also, as the dataset is a combination of multiple collated and sourced tasks, projects etc, users should be aware of the implication of this to their task at hand. ## Citation [optional] <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> BibTeX: ```bibtex title = {Malaria Legacy Dataset for LLM}, author = {Marvellous Ajala} year = {2025}, publisher = {Hugging Face Datasets}, version = {1.0.0}, url = {https://huggingface.co/datasets/madeofajala/MalariaLegacyLLM}, ```
anonymous-author/paper_data	anonymous-author	2025-05-12T16:13:49Z	0	0	[ "license:cc-by-sa-4.0", "region:us" ]	[]	2025-05-12T16:12:26Z	null	--- license: cc-by-sa-4.0 ---
idoco/PopVQA	idoco	2025-05-12T15:52:23Z	84	2	[ "task_categories:visual-question-answering", "language:en", "license:mit", "size_categories:10K<n<100K", "modality:image", "region:us" ]	[ "visual-question-answering" ]	2025-05-08T06:28:52Z	null	--- license: mit task_categories: - visual-question-answering language: - en pretty_name: PopVQA size_categories: - 10K<n<100K --- # PopVQA: Popular Entity Visual Question Answering PopVQA is a dataset designed to study the performance gap in vision-language models (VLMs) when answering factual questions about entities presented in images versus text. ![PopVQA Teaser](./popvqa_teaser.png) ## 🔍 Motivation <img src="./paper_teaser.png" alt="Motivation" width="700"> PopVQA was curated to explore the disparity in model performance when answering factual questions about an entity described in text versus depicted in an image. This is achieved by asking the same questions twice, once with the textual representation (the entity's name), then, with the visual representation (entity image). We include several questions about every entity to allow a more fine grained evaluation. This dataset was introduced in the paper: > "Performance Gap in Entity Knowledge Extraction Across Modalities in Vision Language Models" > Ido Cohen, Daniela Gottesman, Mor Geva, Raja Giryes (2025) ## 📦 Dataset Structure The dataset consists of: - `entities.csv`: Metadata of 15,395 popular entities, of various types (celebrities, landmarks, logos, and paintings). - `questions.csv`: Over 100,000 factual questions, each given in two forms: one referring to a textual representation and one referring to a visual representation of the entity. - `original_path/`: Original images. - `resized_path/`: Images resized to 336×336 with aspect ratio preserved via padding. ### `entities.csv` columns: \| Column \| Description \| \|------------------\|---------------------------------------------\| \| `type` \| Entity type (e.g., `celebs`, `logos`) \| \| `subject` \| Entity name \| \| `s_uri` \| Wikidata URI of the subject \| \| `popularity` \| Wikipedia popularity score \| \| `aliases` \| Alternate names/aliases for the entity \| \| `image` \| wiki commons url \| \| `original_path` \| Path to the original image \| \| `resized_path` \| Path to the 336x336 padded image \| ### `questions.csv` columns: \| Column \| Description \| \|------------------------\|--------------------------------------------------------------\| \| `type` \| Entity type \| \| `subject` \| Entity name \| \| `question_for_image` \| Question phrased for visual context (e.g., “...in this image?”) \| \| `question` \| Textual version of the same question \| \| `possible_answers` \| List of acceptable answers \| \| `relation` \| Relation name (e.g., occupation, language) \| \| `s_uri`, `r_uri`, `a_uri` \| Wikidata URIs for subject, relation, and answer \| \| `attribute`, `a_type` \| Answer string and attribute types (e.g., "language") \|
Xecades/AerialExtreMatch-Benchmark	Xecades	2025-05-12T15:39:49Z	89	0	[ "task_categories:depth-estimation", "task_categories:keypoint-detection", "task_categories:image-feature-extraction", "license:mit", "modality:image", "region:us", "image" ]	[ "depth-estimation", "keypoint-detection", "image-feature-extraction" ]	2025-05-11T14:38:08Z	null	--- license: mit task_categories: - depth-estimation - keypoint-detection - image-feature-extraction pretty_name: AerialExtreMatch Benchmark viewer: false tags: - image --- # AerialExtreMatch — Benchmark Dataset [Code](https://github.com/Xecades/AerialExtreMatch) \| Project Page (WIP) \| Paper (WIP) This repo contains the benchmark set for our paper AerialExtreMatch: A Benchmark for Extreme-View Image Matching and Localization. 32 difficulty levels are included. We also provide [train](https://huggingface.co/datasets/Xecades/AerialExtreMatch-Train) and [localization](https://huggingface.co/datasets/Xecades/AerialExtreMatch-Localization) datasets. > WARNING: working in progress. ## Usage Simply clone this repository and unzip the dataset files. ```bash git clone [email protected]:datasets/Xecades/AerialExtreMatch-Benchmark cd AerialExtreMatch-Benchmark unzip ".zip" ``` TODO: provide a python example. ## Dataset Structure After unpacking each .zip file: <pre> . └── class_[id] <i>(class_0~class_31)</i> ├── class_[id].npy ├── depth: .exr └── rgb: *.jpg </pre> - Keys of `class_[id].npy` files: `['poses', 'intrinsics', 'depth', 'rgb', 'overlap', 'pitch', 'scale', 'pair']`. - Refer to original paper for details on classification.
autobio-bench/screw_tighten-blender	autobio-bench	2025-05-12T15:14:08Z	0	0	[ "task_categories:robotics", "license:mit", "size_categories:100K<n<1M", "format:parquet", "modality:tabular", "modality:timeseries", "modality:video", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us", "LeRobot", "medical" ]	[ "robotics" ]	2025-05-12T15:12:57Z	null	--- license: mit task_categories: - robotics tags: - LeRobot - medical 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: mit ## Dataset Structure [meta/info.json](meta/info.json): ```json { "codebase_version": "v2.0", "robot_type": null, "total_episodes": 100, "total_frames": 154830, "total_tasks": 1, "total_videos": 300, "total_chunks": 1, "chunks_size": 1000, "fps": 50, "splits": { "train": "0:100" }, "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": { "state": { "dtype": "float32", "shape": [ 14 ], "names": [ "state" ] }, "actions": { "dtype": "float32", "shape": [ 14 ], "names": [ "actions" ] }, "image": { "dtype": "video", "shape": [ 224, 224, 3 ], "names": [ "height", "width", "channel" ], "info": { "video.fps": 50.0, "video.height": 224, "video.width": 224, "video.channels": 3, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "has_audio": false } }, "wrist_image": { "dtype": "video", "shape": [ 224, 224, 3 ], "names": [ "height", "width", "channel" ], "info": { "video.fps": 50.0, "video.height": 224, "video.width": 224, "video.channels": 3, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "has_audio": false } }, "wrist_image_2": { "dtype": "video", "shape": [ 224, 224, 3 ], "names": [ "height", "width", "channel" ], "info": { "video.fps": 50.0, "video.height": 224, "video.width": 224, "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] ```
littleGuagua/x_dataset_8140	littleGuagua	2025-05-12T15:13:31Z	1,161	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" ]	2025-01-26T13:25:56Z	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_8140 - Subnet: Bittensor Subnet 13 - Miner Hotkey: 5HasdyDaczLXYaiykhuuszTMWS65QmAgo72UpwABUi3czyeu ### 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_8140, 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_8140}, } ``` ### 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: 50376997 - Date Range: 2025-01-21T00:00:00Z to 2025-02-10T00:00:00Z - Last Updated: 2025-02-18T20:55:45Z ### Data Distribution - Tweets with hashtags: 39.81% - Tweets without hashtags: 60.19% ### Top 10 Hashtags For full statistics, please refer to the `stats.json` file in the repository. \| Rank \| Topic \| Total Count \| Percentage \| \|------\|-------\|-------------\|-------------\| \| 1 \| NULL \| 30319553 \| 60.19% \| \| 2 \| #riyadh \| 310085 \| 0.62% \| \| 3 \| #zelena \| 215655 \| 0.43% \| \| 4 \| #tiktok \| 192806 \| 0.38% \| \| 5 \| #ad \| 112205 \| 0.22% \| \| 6 \| #bbb25 \| 110854 \| 0.22% \| \| 7 \| #grammys \| 82659 \| 0.16% \| \| 8 \| #jhope_at_galadespiècesjaunes \| 70215 \| 0.14% \| \| 9 \| #bbmzansi \| 66978 \| 0.13% \| \| 10 \| #sixtonesann \| 65126 \| 0.13% \| ## Update History \| Date \| New Instances \| Total Instances \| \|------\|---------------\|-----------------\| \| 2025-01-26T13:26:49Z \| 2721817 \| 2721817 \| \| 2025-01-30T01:43:17Z \| 9702324 \| 12424141 \| \| 2025-02-02T13:47:13Z \| 12507356 \| 24931497 \| \| 2025-02-06T01:50:29Z \| 8691717 \| 33623214 \| \| 2025-02-09T13:54:19Z \| 8748247 \| 42371461 \| \| 2025-02-13T02:21:42Z \| 6726572 \| 49098033 \| \| 2025-02-18T05:54:36Z \| 648154 \| 49746187 \| \| 2025-02-18T20:55:45Z \| 630810 \| 50376997 \|
jasonzheng/ioi-2024-bf16	jasonzheng	2025-05-12T14:59:10Z	0	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-12T14:59:07Z	null	--- dataset_info: features: - name: problem_id dtype: string - name: year dtype: string - name: uuid dtype: string - name: code dtype: string - name: subtask dtype: string splits: - name: train num_bytes: 3517307 num_examples: 2050 download_size: 687903 dataset_size: 3517307 configs: - config_name: default data_files: - split: train path: data/train-* ---
anonymous-user-546/test-csv-conversion	anonymous-user-546	2025-05-12T14:45:56Z	79	0	[ "license:cc-by-nc-nd-4.0", "size_categories:1M<n<10M", "format:csv", "modality:image", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-07T20:07:06Z	null	--- license: cc-by-nc-nd-4.0 ---
liwu/MNBVC	liwu	2025-05-12T14:30:02Z	36,760	541	[ "task_categories:text-generation", "task_categories:fill-mask", "task_ids:language-modeling", "task_ids:masked-language-modeling", "annotations_creators:other", "language_creators:other", "multilinguality:monolingual", "source_datasets:original", "language:zh", "license:mit", "region:us" ]	[ "text-generation", "fill-mask" ]	2023-02-13T14:00:47Z	null	--- annotations_creators: - other language: - zh language_creators: - other license: - mit multilinguality: - monolingual pretty_name: MNBVC size_categories: - unknown source_datasets: - original task_categories: - text-generation - fill-mask task_ids: - language-modeling - masked-language-modeling --- # Dataset Card for MNBVC ## Table of Contents - [Dataset Card for MNBVC](#dataset-card-for-mnbvc) - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [数据集介绍](#数据集介绍) - [数据子集](#数据子集) - [数据格式](#数据格式) - [文本数据](#文本数据) - [问答数据](#问答数据) - [Contributions](#contributions) ## Dataset Description - Homepage: http://mnbvc.253874.net/ - Repository: https://github.com/esbatmop/MNBVC - Paper: N/A - Leaderboard: N/A - Point of Contact: N/A ### 数据集介绍中文互联网上最古老最神秘(没有之一)的里屋社区于2023.1.1庄重宣布: 在英明神武的里屋管子带领下，决心发挥社区所长(哪都长)，帮助开源社区长期更新一份最大的中文互联网语料集。 Huggingface上的MNBVC数据集在逐渐更新中，请到[https://github.com/esbatmop/MNBVC](https://github.com/esbatmop/MNBVC) 获取未完成清洗的更多数据。可以使用如下脚本加载： ```python from datasets import load_dataset dataset = load_dataset("liwu/MNBVC", 'law_judgement', split='train', streaming=True) next(iter(dataset)) # get the first line ``` ## 数据子集 MNBVC数据集包含数个子集： - `law_judgement`: 来自法律文书的文本。 - `gov_xuexiqiangguo`: 来自学习强国的文本。 - `gov_report`: 来自政府工作报告的文本。 - `co_ann_report`: 企业年报文本。 - `code_metadata`: 代码元数据。 - `qa_zhihu`: 来自[知乎](https://huggingface.co/datasets/wangrui6/Zhihu-KOL)的问答数据。 - `qa_wikihow`: 来自wikihow的问答数据。 - `qa_mfa`: 外交部问答数据。 - `news_peoples_daily`: 来自人民日报的文本数据。 - `wikipedia`: 来自维基百科的文本数据。 - `qa_stackexchange`: 来自StackExchange的问答数据。 - `qa_chatgpt`: 使用ChatGPT构造的问答语料，感谢[genggui001](https://github.com/genggui001)贡献语料。 - `math`: - `math_qa `: 和数学领域有关的问答数据。 - `emath` :中国数学爱好者论坛语料数据 - `math_chat`: 和数学领域有关的对话数据数据，可以提升模型Chain of Thought的能力。 - `crawler_oscar`: 从CommonCrawl中清洗出来的通用文本数据。 - `game` : 一些游戏的平行语料数据。 - `Hogwarts_legacy` : 霍格沃茨指遗 - `The_Wither_3` : 巫师三 - `Baldurs_Gate_3` : 博德之门 3 - `GTA`: 侠盗猎车手4 与侠盗猎车手5 - `Turing_Complete`: 图灵完备性 - `EldenRing`: 艾尔登法环 - `hades`: 哈迪斯 - `sekiro`: 只狼 - `parallel`: 平行语料目录 - `subtitle`: 字幕语料 - `yyets`: 人人影视 - `united_nations`: 联合国平行语料 - `blog`: 博客语料目录 - `book`: 书籍语料目录 ## 数据格式目前MNBVC数据集包含如下几类数据： - 通用文本 - 问答语料 - 代码语料 - 多轮对话 - 论坛语料 - 平行语料可以在[MNBVC的wiki页面](https://wiki.mnbvc.org/doku.php/%E7%8E%B0%E6%9C%89%E8%AF%AD%E6%96%99%E6%A0%BC%E5%BC%8F)上查看这几类数据的具体格式。项目早期所上传的数据使用如下格式，以后这一格式会被废弃，相应数据也会重新上传： ```json { "text": datasets.Value("string"), "meta": datasets.Value("string") } ``` ### Contributions Thanks to the [Liwu community](http://mnbvc.253874.net/) for constructing this dataset. Thanks to [silver](https://github.com/silverriver) and [jiaming](https://huggingface.co/Yjiaming) for adding and uploading this dataset to Huggingface. ### Citation Please cite the repo if you use the data or code in this repo. ``` @misc{mnbvc, author = {{MOP-LIWU Community} and {MNBVC Team}}, title = {MNBVC: Massive Never-ending BT Vast Chinese corpus}, year = {2023}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/esbatmop/MNBVC}}, } ```
momo1942/x_dataset_44829	momo1942	2025-05-12T14:24:28Z	2,579	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" ]	2025-01-27T09:49:03Z	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_44829 - Subnet: Bittensor Subnet 13 - Miner Hotkey: 5CacbhmQxhAVGWgrYvCypqhR3n3mNmmWEA8JYzAVghmTDYZy ### 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_44829, 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_44829}, } ``` ### 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: 47517552 - Date Range: 2025-01-21T00:00:00Z to 2025-02-10T00:00:00Z - Last Updated: 2025-02-18T20:42:58Z ### Data Distribution - Tweets with hashtags: 46.37% - Tweets without hashtags: 53.63% ### Top 10 Hashtags For full statistics, please refer to the `stats.json` file in the repository. \| Rank \| Topic \| Total Count \| Percentage \| \|------\|-------\|-------------\|-------------\| \| 1 \| NULL \| 25482638 \| 53.63% \| \| 2 \| #riyadh \| 369646 \| 0.78% \| \| 3 \| #zelena \| 283758 \| 0.60% \| \| 4 \| #tiktok \| 222947 \| 0.47% \| \| 5 \| #ad \| 122468 \| 0.26% \| \| 6 \| #bbb25 \| 83620 \| 0.18% \| \| 7 \| #bbmzansi \| 82423 \| 0.17% \| \| 8 \| #jhope_at_galadespiècesjaunes \| 72240 \| 0.15% \| \| 9 \| #trump \| 71073 \| 0.15% \| \| 10 \| #pr \| 65594 \| 0.14% \| ## Update History \| Date \| New Instances \| Total Instances \| \|------\|---------------\|-----------------\| \| 2025-01-27T09:50:05Z \| 3300536 \| 3300536 \| \| 2025-01-30T21:53:32Z \| 11415036 \| 14715572 \| \| 2025-02-03T09:57:03Z \| 9268666 \| 23984238 \| \| 2025-02-06T21:59:40Z \| 5892953 \| 29877191 \| \| 2025-02-10T10:02:47Z \| 6650635 \| 36527826 \| \| 2025-02-13T22:07:25Z \| 9649951 \| 46177777 \| \| 2025-02-18T05:41:46Z \| 692358 \| 46870135 \| \| 2025-02-18T20:42:58Z \| 647417 \| 47517552 \|
jazasyed/musdb-alt	jazasyed	2025-05-12T14:15:52Z	28	0	[ "task_categories:automatic-speech-recognition", "language:en", "license:cc-by-nc-sa-4.0", "region:us", "music", "lyrics", "evaluation", "benchmark", "transcription" ]	[ "automatic-speech-recognition" ]	2025-04-16T11:16:20Z	null	--- task_categories: - automatic-speech-recognition language: - en tags: - music - lyrics - evaluation - benchmark - transcription pretty_name: MUSDB-ALT license: cc-by-nc-sa-4.0 --- # Dataset Card for MUSDB-ALT This dataset contains long-form lyric transcripts following the Jam-ALT [guidelines](https://huggingface.co/datasets/jamendolyrics/jam-alt/blob/main/GUIDELINES.md) for the test set of the dataset [MUSDB18](https://sigsep.github.io/datasets/musdb.html), with line-level timings. ## Dataset Details The dataset was constructed manually, based on the [MUSDB18 lyrics extension](https://zenodo.org/records/3989267) as a starting point. The lyrics extension contains transcripts of the 45 English language songs out of the 50 in the MUSDB18 test set. We annotated 39 of those 45 songs, excluding 6 for the following reasons: - Signe Jakobsen - What Have You Done To Me : Three overlapping vocal lines that could not be separated into lead and backing vocals - PR - Happy Daze : Vocal content primarily from highly processed vocal samples - PR - Oh No : Vocal content primarily from highly processed vocal samples - Skelpolu - Resurrection : Vocal content primarily from highly processed vocal samples - Timboz - Pony : Lyrics unintelligble due to screamed enunciation style - Triviul feat The Fiend - Widows : Three overlapping vocal lines that could not be separated into lead and backing vocals ### Dataset Description Paper: The dataset was introduced in the paper [Exploiting Music Source Separation for Automatic Lyrics Transcription with Whisper"](https://arxiv.org/abs/XXXXX) published at the Workshop [Artificial Intelligence For Music](https://ai4musicians.org/2025icme.html) at ICME 2025 - Funding: This work was supported by InnovateUK [Grant Number 10102804] - License: https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en ## Citation BibTeX: ``` @inproceedings{syed-2025-mss-alt, author = {Jaza Syed and Ivan Meresman-Higgs and Ond{\v{r}}ej C{\'{\i}}fka and Mark Sandler}, title = {Exploiting Music Source Separation for Automatic Lyrics Transcription with {Whisper}}, booktitle = {2025 {IEEE} International Conference on Multimedia and Expo Workshops (ICMEW)}, publisher = {IEEE}, year = {2025}, note = {In press} } ```
rainbowbridge/x_dataset_20722	rainbowbridge	2025-05-12T14:04:19Z	1,203	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" ]	2025-01-27T01:31:47Z	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_20722 - Subnet: Bittensor Subnet 13 - Miner Hotkey: 5EXTMFUDy34PkND7RWEEXb4vdr3JXmFXesoygkHDrim7GfR5 ### 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_20722, 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_20722}, } ``` ### 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: 53014608 - Date Range: 2025-01-21T00:00:00Z to 2025-02-12T00:00:00Z - Last Updated: 2025-02-18T18:57:54Z ### Data Distribution - Tweets with hashtags: 41.60% - Tweets without hashtags: 58.40% ### Top 10 Hashtags For full statistics, please refer to the `stats.json` file in the repository. \| Rank \| Topic \| Total Count \| Percentage \| \|------\|-------\|-------------\|-------------\| \| 1 \| NULL \| 30961185 \| 58.40% \| \| 2 \| #riyadh \| 327113 \| 0.62% \| \| 3 \| #zelena \| 254157 \| 0.48% \| \| 4 \| #tiktok \| 216346 \| 0.41% \| \| 5 \| #bbb25 \| 161006 \| 0.30% \| \| 6 \| #ad \| 125530 \| 0.24% \| \| 7 \| #royalrumble \| 75597 \| 0.14% \| \| 8 \| #bbmzansi \| 71549 \| 0.13% \| \| 9 \| #pr \| 69916 \| 0.13% \| \| 10 \| #yahooニュース \| 65493 \| 0.12% \| ## Update History \| Date \| New Instances \| Total Instances \| \|------\|---------------\|-----------------\| \| 2025-01-27T01:32:09Z \| 1057227 \| 1057227 \| \| 2025-01-30T13:48:23Z \| 11631895 \| 12689122 \| \| 2025-02-03T01:51:30Z \| 8401846 \| 21090968 \| \| 2025-02-06T13:56:34Z \| 12297890 \| 33388858 \| \| 2025-02-10T01:59:57Z \| 8203885 \| 41592743 \| \| 2025-02-13T14:08:19Z \| 10112124 \| 51704867 \| \| 2025-02-18T03:56:41Z \| 648961 \| 52353828 \| \| 2025-02-18T18:57:54Z \| 660780 \| 53014608 \|
zerostratos/chunks	zerostratos	2025-05-12T13:53:24Z	0	0	[ "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-12T13:53:14Z	null	--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 68005509 num_examples: 189426 download_size: 37395152 dataset_size: 68005509 configs: - config_name: default data_files: - split: train path: data/train-* ---
gmanolache/CrypticBio	gmanolache	2025-05-12T13:35:27Z	465	0	[ "task_categories:zero-shot-classification", "language:en", "license:cc", "size_categories:100M<n<1B", "format:parquet", "modality:image", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us", "biodiverstiy", "cryptic species", "fine-grained image recognition", "vision-language", "multimodal dataset" ]	[ "zero-shot-classification" ]	2025-04-27T06:35:09Z	null	--- license: cc task_categories: - zero-shot-classification language: - en tags: - biodiverstiy - cryptic species - fine-grained image recognition - vision-language - multimodal dataset pretty_name: A Large Multimodal Dataset for Visually Confusing Biodiversity size_categories: - 100M<n<1B --- # CrypticBio: A Large Multimodal Dataset for Visually Confusing Biodiversity <!-- Banner links --> <div style="text-align:left;"> <a href="https://georgianagmanolache.github.io/crypticbio/" target="_blank" style="display:inline-block;"> <img src="https://img.shields.io/badge/Project%20Page-Visit-blue" alt="Project Page"> </a> <a href="https://github.com/georgianagmanolache/crypticbio" target="_blank" style="display:inline-block;"> <img src="https://img.shields.io/badge/GitHub-Visit-lightgrey" alt="GitHub"> </a> </div> ## Description [CrypticBio](https://georgianagmanolache.github.io/crypticbio/) comprises metadata including species scientific and multicultural vernacular terminology, image URL, taxonomic hierarchy, spatiotemporal context, and cryptic species group. Cryptic species are groups of two or more taxa that are nearly indistinguishable based on visual characteristics alone. ## CrypticBio Dataset We present CrypticBio, the largest publicly available multimodal dataset of visually confusing species groups, specifically curated to support the development of AI models in the context of biodiversity identification applications. Curated from real-world trends in species misidentification among community annotators of iNaturalist, CrypticBio contains 67K cryptic species groups spanning 52K species, represented in 166 million images. ## New Benchmark Datasets We created four new benchmark datasets for fine-grained image classification of cryptic species. ### CrypticBio-Commom We curate a common species from Arachnida, Aves, Insecta, Plantae, Fungi, Mollusca, and Reptilia and associated cryptic group, spanning n=158 species. We randomly select 100 samples from each species in a cryptic group where there are more than 150 observation per species. ### CrypticBio-CommonUnseen To assess zero-shot performance on common species from CrypticBio-Common not encountered during training of state-of-the-art models, we specifically curate a subset spanning data from 01-09-2024 to 01-04-2025. We randomly select 100 samples from each species in a cryptic group where there are more than 150 observation per species, spanning n=133 species. ### CrypticBio-Endagered We propose a cryptic species subset of endangered species according to global IUCN Red List. We randomly select 30 samples from Arachnida, Aves, Insecta, Plantae, Fungi, Mollusca, and Reptilia and associated cryptic groups spanning n=37 species, filtering out taxa where there are less than 150 observation. ### CrypticBio-Invasive We also propose a cryptic species subset of invasive alien species (IAS) according to global the Global Invasive Species Database (GISD). IAS are a significant concern for biodiversity as their records appear to be exponentially rising across the Earth. We randomly select 100 samples from each invasive species cryptic group spanning n=72 species, filtering out taxa where there are less than 150 observation. ## Dataset Information ### Directory ```plaintext main/ ├── CrypticBio/ │ ├── part_0.csv │ ├── part_0.parquet │ ├── part_1.parquet │ ├── . │ ├── . │ ├── . │ └── part_626.parquet ├── CrypticBio-benchmarks/ │ ├── CrypticBio-Common.csv │ ├── CrypticBio-CommonUnseen.csv │ ├── CrypticBio-Endangered.csv │ └── CrypticBio-Invasive.csv ├──README.md └──.gitattributes ``` The data and the code are publicly available at [georgianagmanolache.github.io/crypticbio](https://georgianagmanolache.github.io/crypticbio/)
TrojAI/updatedviolence	TrojAI	2025-05-12T13:24:56Z	0	0	[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-12T13:24:46Z	null	--- dataset_info: features: - name: messages list: - name: content dtype: string - name: role dtype: string - name: expected_response dtype: string - name: label dtype: int64 - name: source dtype: string splits: - name: train num_bytes: 39363138 num_examples: 88910 - name: test num_bytes: 2996268 num_examples: 7020 download_size: 23764400 dataset_size: 42359406 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
SYSUSELab/RustEvo2	SYSUSELab	2025-05-12T12:40:06Z	0	0	[ "license:apache-2.0", "region:us" ]	[]	2025-05-12T12:38:13Z	null	--- license: apache-2.0 --- # RustEvo² RustEvo² is the first benchmark for evaluating LLMs' ability to adapt to evolving Rust APIs, as described in the paper "RustEvo²: An Evolving Benchmark for API Evolution in LLM-based Rust Code Generation". ## Dataset Overview Our work can be divided into two phases: Phase I: API Evolution Data Collection - We collect API changes from multiple sources including official Rust repositories and third-party crates. We analyze changelogs, documentation, and implementation changes to identify and categorize API evolutions into Stabilizations, Signature Changes, Behavioral Changes, and Deprecations. Phase II: RustEvo² Construction - We transform the collected API evolution data into natural programming tasks using an LLM-based generation pipeline. This process creates programming queries, code solutions, and test programs that implicitly require the use of specific API versions. The following figure illustrates our two-phase framework: <div align="center"> <img src="Imgs/overview.png" alt="RustEvo² Framework Overview" width="100%"/> </div> ### Dataset Format RustEvo² consists of 588 API changes (380 from Rust standard libraries, 208 from 15 third-party crates) spanning versions 1.71.0 to 1.84.0. These changes are categorized into four types: Stabilizations (31.3%), Signature Changes (31.5%), Behavioral Changes (33.2%), and Deprecations (4.1%), reflecting their actual distribution in the Rust ecosystem. Each task in RustEvo² consists of <API change information, programming query, function signature, reference solution, test program>. The API change information includes name, module path, version details, documentation, and source code. Programming queries describe real-world scenarios without explicitly mentioning the API. Function signatures guide implementation without revealing API specifics. Test programs verify correct API usage and functional behavior. One task example: ```json { "task_idx": 39, "query": "In a performance-critical application, you need to efficiently update a large collection of objects by cloning their state from another collection. The objects implement a custom `Clone` trait, but you want to avoid unnecessary trait bounds that could complicate the implementation. Design a function to handle this cloning operation efficiently.", "function_signature": "fn update_collection<T: Clone>(target: &mut Vec<T>, source: &Vec<T>)", "code": "fn update_collection<T: Clone>(target: &mut Vec<T>, source: &Vec<T>) {\n target.truncate(source.len());\n for (t, s) in target.iter_mut().zip(source.iter()) {\n t.clone_from(s);\n }\n if target.len() < source.len() {\n target.extend(source[target.len()..].iter().cloned());\n }\n}", "test_program": "..." }, ``` ## Usage ### Setup 1. Environment Setup: ```bash conda create -n RustEvo python=3.8 conda activate RustEvo pip install -r requirements.txt ``` 2. Install Rust toolchain ```bash curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs \| sh rustup toolchain install 1.71.0 1.72.0 1.73.0 1.74.0 1.75.0 1.76.0 1.77.0 1.78.0 1.79.0 1.80.0 1.81.0 1.82.0 1.83.0 1.84.0 ``` ### Construct your own evolving dataset If you don't want to construct a new dataset, you can directly use the existing dataset in the `data` folder. 1. Phase I: API Evolution Collection ```bash python scripts/rust_api_analyzer.py --repo ./rust-repo --output ./reports --start 1.72.0 --end 1.84.0 python scripts/crate_analyzer.py --crates_num 15 --start_date 2024-01-01 --end_date 2025-02-21 ``` 2. Phase II: Task Generation ```bash python scripts/generate_query.py --input ./reports/rust_api_changes.json --output ./data/queries/queries_rust.json python scripts/generate_code.py --input ./data/queries/queries_rust.json --output ./data/codes/codes_rust.json python scripts/generate_test.py --input_file ./data/codes/codes_rust.json --output_file ./data/test_programs/test_programs_rust.json ``` ### Evaluate 1. Replace the target LLM in the evaluate/generation.py 2. Run the evaluation script ```bash cd evaluate ./run.sh eval_models.py --model_name ``` ## Results Some important results of our experiments: ### Performance by Model \| Model \| Pass@1 (%) \| API Usage Accuracy (%) \| Coverage (%) \| \|-------\|------------\|---------\|--------------\| \| Claude-3.7-Sonnet \| 65.3 \| 78.2 \| 83.6 \| \| o1-mini \| 57.5 \| 70.4 \| 85.2 \| \| GPT-4o \| 55.4 \| 68.4 \| 77.2 \| \| Gemini-1.5-Pro \| 55.3 \| 62.6 \| 60.9 \| \| DeepSeek-v3 \| 54.8 \| 69.7 \| 71.0 \| \| Gemini-2.0-Flash \| 52.6 \| 73.5 \| 72.5 \| \| Llama-3.1-70B \| 51.0 \| 65.3 \| 69.0 \| \| Qwen-2.5-72B \| 50.9 \| 66.7 \| 64.7 \| \| Claude-3.5-Sonnet \| 48.1 \| 68.7 \| 80.3 \| \| Grok-3 \| 40.5 \| 67.2 \| 70.4 \| ### Performance by API Change Type \| Change Type \| Average Pass@1 (%) \| \|-------------\|-------------------\| \| Stabilizations \| 65.8 \| \| Signature Changes \| 58.2 \| \| Behavioral Changes \| 38.0 \| \| Deprecations \| 40.4 \| Complete evaluation results and error analysis are [here](Results).
amekerishvili/ATCO2_Callsigns	amekerishvili	2025-05-12T12:14:59Z	0	0	[ "region:us" ]	[]	2025-05-12T12:13:45Z	null	--- dataset_info: features: - name: audio_file dtype: string - name: ID dtype: string - name: ground_truth dtype: string - name: callsigns dtype: string - name: Callsigns_manual dtype: string - name: non_Eng_ground_truth dtype: string - name: tags dtype: string - name: airport dtype: string - name: channel dtype: string - name: whisper-large-v2-ANSP-3h1m dtype: string - name: ground_truth_norm dtype: string - name: whisper-large-v2-ANSP-3h1m_norm dtype: string - name: whisper-large-v2-ANSP-3h1m_norm_wer dtype: float64 - name: callsigns_NER_error_rate dtype: float64 - name: Callsigns_manual.1 dtype: string splits: - name: train num_bytes: 265042 num_examples: 100 download_size: 96625 dataset_size: 265042 configs: - config_name: default data_files: - split: train path: data/train-* ---
KBayoud/Custom-3	KBayoud	2025-05-12T12:14:03Z	0	0	[ "size_categories:n<1K", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-12T09:48:50Z	null	--- dataset_info: features: - name: image dtype: image - name: question dtype: string - name: answer dtype: string splits: - name: train num_bytes: 1264783692.0 num_examples: 420 download_size: 1263426207 dataset_size: 1264783692.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
ma921/oasst1-english-tokenized-qwen2.5_noise20	ma921	2025-05-12T12:08:52Z	0	0	[ "size_categories:1K<n<10K", "format:parquet", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-12T12:08:47Z	null	--- dataset_info: features: - name: pos_input_ids sequence: int64 - name: neg_input_ids sequence: int64 - name: flip dtype: int64 splits: - name: train num_bytes: 28348816 num_examples: 6859 download_size: 7693055 dataset_size: 28348816 configs: - config_name: default data_files: - split: train path: data/train-* ---
ma921/oasst1-english-tokenized-phi2_noise0	ma921	2025-05-12T11:58:45Z	0	0	[ "size_categories:1K<n<10K", "format:parquet", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-12T11:58:41Z	null	--- dataset_info: features: - name: sft_input_ids sequence: int64 - name: pos_input_ids sequence: int64 - name: neg_input_ids sequence: int64 splits: - name: train num_bytes: 45647820.0 num_examples: 6859 download_size: 11661751 dataset_size: 45647820.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
EdmondFU/Causal-Reasoning-Bench_CRBench	EdmondFU	2025-05-12T11:48:53Z	338	3	[ "task_categories:question-answering", "size_categories:10K<n<100K", "region:us" ]	[ "question-answering" ]	2025-04-23T07:13:49Z	null	--- task_categories: - question-answering size_categories: - 10K<n<100K --- <p align="center"> <img src="CRBench.png" width="50%" height="50%"> </p> <p align="left"> <img src="Deep.png" width="30%"> </p> # 🦙Causal Reasoning Bench（CRBench） Developing a labeled dataset with causal errors is crucial for evaluating the performance of causalizing methods for CoT reasoning. We proposed the CRBench as a benchmark to verify whether causalizing methods can effectively correct the causal errors. ## 🦙Causal Error We have summarized four types of causal errors that lead to CoT reasoning errors: - Measure error. Causal measurement error refers to the incorrect use of correlation indicators instead of causal indicators when measuring causal relationships, or the use of inappropriate causal measures (such as average treatment effect ATE, direct/indirect effects, etc.) when estimating causal effects. - Collider error. Collider error refers to the incorrect control or selection of a "collider" in causal reasoning, which introduces false correlation. A collider is a variable that is affected by two unrelated variables at the same time. If this collider is incorrectly controlled during analysis, it will cause false correlations between originally unrelated variables. Due to selection bias when selecting samples, two originally unrelated variables appear to have a causal relationship. - Confounding error. Confounding error refers to the omission of a confounder in causal inference, leading to an observed causal effect that is not genuine but rather driven by a common influencing factor. It can also occur when variables that should not be included in the reasoning process are considered, such as residual information from a previous question, biases within the model, hallucinations, and other misleading factors. - Mediation error. Mediation error refers to the incorrect interpretation of the role of the mediating variable in causal inference, which may be due to incorrect control of the mediating variable, incorrect addition of the mediating variable, or ignoring the mediating path. ## 🦙Available Subsets ``` ds = load_dataset("EdmondFU/Causal-Reasoning-Bench_CRBench", split="train") ``` ## Generated Process Description <p align="center"> <img src="Error generated.png" width="50%" height="50%"> </p> The example of generated causal error data. Causality measure error: In the process of determining that "when the intersection is inside the circle, each line must be a secant," the reasoning mistakenly overstates the impact of the intersection point's location. It erroneously asserts that "as long as the intersection is inside the circle, each line must intersect the circle at two points," thereby ignoring the possibility that a line might only intersect the circle at one point (which would be a tangent), leading to a causality measure error. Collider error When considering the impact of the intersection point's position on the relationship between the lines and the circle, the reasoning mistakenly treats the intersection position (inside, on, outside) as a "collider" that is simultaneously determined by both the type of the lines and the circle’s position. This error mixes independent factors. Confounding Error: In the reasoning process, an unrelated external factor is incorrectly introduced as a confounding variable. It is mistakenly assumed that this variable affects both the position of the intersection and the number of intersection points between the lines and the circle, which leads to an incorrect derivation of the number of possible configurations.This incorrectly introduces the circle’s radius as a confounder, mixing up the originally clear causal relationship based solely on the intersection point’s location, hence causing a confounding error. Mediation error: Here, an unneeded and non-existent mediator variable called 'penetration angle' is introduced, thereby misrepresenting the causal relationship between the intersection location and the line type, resulting in a mediation error, mistakenly assuming that the causal relationship between the intersection point’s location and the line type is transmitted through this mediator, which then leads to a misinterpretation of the relationships among variables. ## 🦙The CRBench dataset is generated based on publicly available high-quality reasoning datasets: - 🧠 [OpenThoughts-114k dataset](https://huggingface.co/datasets/open-thoughts/OpenThoughts-114k) - 🧠 [Bespoke-Stratos-17k dataset](https://huggingface.co/datasets/bespokelabs/Bespoke-Stratos-17k) - 🧠 [OpenThoughts2-1M](https://huggingface.co/datasets/open-thoughts/OpenThoughts2-1M) Including: Code - 💻[BAAI/TACO](https://huggingface.co/datasets/BAAI/TACO) - 💻[codeparrot/apps](https://huggingface.co/datasets/codeparrot/apps) - 💻[deepmind/code_contests](https://huggingface.co/datasets/deepmind/code_contests) - 💻[MatrixStudio/Codeforces-Python-Submissions](https://huggingface.co/datasets/MatrixStudio/Codeforces-Python-Submissions) - 💻[livecodebench/execution-v2](https://huggingface.co/datasets/livecodebench/execution-v2) - 💻[livecodebench/code_generation_lite](https://huggingface.co/datasets/livecodebench/code_generation_lite) Math - 🔢[AI-MO/NuminaMath-CoT](https://huggingface.co/datasets/AI-MO/NuminaMath-CoT) - 🔢[Maxwell-Jia/AIME_2024](https://huggingface.co/datasets/Maxwell-Jia/AIME_2024) - 🔢[game661100/MATH-500](https://huggingface.co/game661100/MATH-500) Science - 📊[camel-ai/chemistry](https://huggingface.co/datasets/camel-ai/chemistry) - 📊[camel-ai/biology](https://huggingface.co/datasets/camel-ai/biology) - 📊[camel-ai/physics](https://huggingface.co/datasets/camel-ai/physics) Puzzle - 🤖[INK-USC/riddle_sense](https://huggingface.co/datasets/INK-USC/riddle_sense) # 🦙Citation ``` @misc{CRbench, author = {Jiarun Fu,Hao Li}, month = April, title = {Causal Reasoning Bench}, howpublished = {https://huggingface.co/datasets/EdmondFU/Causal-Reasoning-Bench_CRBench}, year = {2025} } ``` # 🦙Contact Us ``` Jiarun Fu\| Phd student in BIT:[email protected] Hao Li\| Master's student in BIT:[email protected] ```
YasmineMakni/so100_mvt_ball	YasmineMakni	2025-05-12T11:37:49Z	0	0	[ "task_categories:robotics", "license:apache-2.0", "size_categories:10K<n<100K", "format:parquet", "modality:tabular", "modality:timeseries", "modality:video", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us", "LeRobot", "so100", "tutorial" ]	[ "robotics" ]	2025-05-12T09:55:58Z	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": 40, "total_frames": 11498, "total_tasks": 1, "total_videos": 80, "total_chunks": 1, "chunks_size": 1000, "fps": 25, "splits": { "train": "0:40" }, "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": [ 720, 1280, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.fps": 25.0, "video.height": 720, "video.width": 1280, "video.channels": 3, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "has_audio": false } }, "observation.images.cam": { "dtype": "video", "shape": [ 1080, 1920, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.fps": 25.0, "video.height": 1080, "video.width": 1920, "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] ```
James096/x_dataset_127	James096	2025-05-12T11:26:34Z	20	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:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[ "text-classification", "token-classification", "question-answering", "summarization", "text-generation" ]	2025-05-07T19:29:50Z	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: James096/x_dataset_127 - Subnet: Bittensor Subnet 13 - Miner Hotkey: 5D2KKAGcf1bHnT71v5jsw9TJBmQto5PhYKRSPcJDhk8gqSXj ### Miner Data Compliance Agreement In uploading this dataset, I am agreeing to the [Macrocosmos Miner Data Compliance Policy](https://github.com/macrocosm-os/data-universe/blob/add-miner-policy/docs/miner_policy.md). ### 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{James0962025datauniversex_dataset_127, title={The Data Universe Datasets: The finest collection of social media data the web has to offer}, author={James096}, year={2025}, url={https://huggingface.co/datasets/James096/x_dataset_127}, } ``` ### 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: 80481 - Date Range: 2025-04-06T00:00:00Z to 2025-05-06T00:00:00Z - Last Updated: 2025-05-12T11:26:31Z ### Data Distribution - Tweets with hashtags: 99.99% - Tweets without hashtags: 0.01% ### Top 10 Hashtags For full statistics, please refer to the `stats.json` file in the repository. \| Rank \| Topic \| Total Count \| Percentage \| \|------\|-------\|-------------\|-------------\| \| 1 \| #bitcoin \| 5883 \| 7.31% \| \| 2 \| #trump \| 4062 \| 5.05% \| \| 3 \| #crypto \| 3581 \| 4.45% \| \| 4 \| #btc \| 1815 \| 2.26% \| \| 5 \| #ai \| 1564 \| 1.94% \| \| 6 \| #tao \| 1539 \| 1.91% \| \| 7 \| #ethereum \| 1538 \| 1.91% \| \| 8 \| #binance \| 1324 \| 1.65% \| \| 9 \| #artificialintelligence \| 1307 \| 1.62% \| \| 10 \| #cardano \| 1154 \| 1.43% \| ## Update History \| Date \| New Instances \| Total Instances \| \|------\|---------------\|-----------------\| \| 2025-05-11T17:07:25Z \| 80480 \| 80480 \| \| 2025-05-12T11:26:31Z \| 1 \| 80481 \|
ncavallo/so100_test_lerobot2_4	ncavallo	2025-05-12T11:24:50Z	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", "so100", "tutorial" ]	[ "robotics" ]	2025-05-12T11:16:08Z	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": 245, "total_tasks": 1, "total_videos": 1, "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.robot": { "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] ```
yoonholee/completions_Qwen3-1.7B_AIME2025	yoonholee	2025-05-12T11:13:44Z	0	0	[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-12T11:13:42Z	null	--- dataset_info: features: - name: problem dtype: string - name: completions sequence: string - name: answer dtype: string - name: corrects sequence: bool - name: acc dtype: float64 splits: - name: train num_bytes: 12414044 num_examples: 30 download_size: 4371593 dataset_size: 12414044 configs: - config_name: default data_files: - split: train path: data/train-* ---
Botai666/Medical_VLM_Sycophancy	Botai666	2025-05-12T11:04:21Z	15	0	[ "license:apache-2.0", "region:us" ]	[]	2025-03-27T07:56:43Z	null	--- license: apache-2.0 ---
alozowski/hf_doc_test	alozowski	2025-05-12T10:58:58Z	0	0	[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-12T10:44:22Z	null	--- dataset_info: - config_name: chunked features: - name: document_id dtype: string - name: document_text dtype: string - name: document_filename dtype: string - name: document_metadata struct: - name: file_size dtype: int64 - name: raw_chunk_summaries sequence: string - name: chunk_summaries sequence: string - name: raw_document_summary dtype: string - name: document_summary dtype: string - name: summarization_model dtype: string - name: chunks list: - name: chunk_id dtype: string - name: chunk_text dtype: string - name: multihop_chunks list: - name: chunk_ids sequence: string - name: chunks_text sequence: string - name: chunk_info_metrics list: - name: avg_token_length dtype: float64 - name: bigram_diversity dtype: float64 - name: flesch_reading_ease dtype: float64 - name: gunning_fog dtype: float64 - name: perplexity dtype: float64 - name: token_count dtype: float64 - name: unique_token_ratio dtype: float64 - name: chunking_model dtype: string splits: - name: train num_bytes: 113907 num_examples: 1 download_size: 82204 dataset_size: 113907 - config_name: ingested features: - name: document_id dtype: string - name: document_text dtype: string - name: document_filename dtype: string - name: document_metadata struct: - name: file_size dtype: int64 splits: - name: train num_bytes: 44906 num_examples: 1 download_size: 17845 dataset_size: 44906 - config_name: lighteval features: - name: question dtype: string - name: additional_instructions dtype: string - name: ground_truth_answer dtype: string - name: gold sequence: int64 - name: choices sequence: string - name: question_category dtype: string - name: kind dtype: string - name: estimated_difficulty dtype: int64 - name: citations sequence: string - name: document_id dtype: string - name: chunk_ids sequence: string - name: question_generating_model dtype: string - name: chunks sequence: string - name: document dtype: string - name: document_summary dtype: string - name: answer_citation_score dtype: float64 - name: chunk_citation_score dtype: float64 - name: citation_score dtype: float64 splits: - name: train num_bytes: 890444 num_examples: 16 download_size: 50645 dataset_size: 890444 - config_name: multi_hop_questions features: - name: document_id dtype: string - name: source_chunk_ids sequence: string - name: additional_instructions dtype: string - name: question dtype: string - name: self_answer dtype: string - name: choices sequence: string - name: estimated_difficulty dtype: int64 - name: self_assessed_question_type dtype: string - name: generating_model dtype: string - name: thought_process dtype: string - name: citations sequence: string - name: raw_response dtype: string splits: - name: train num_bytes: 39753 num_examples: 4 download_size: 16677 dataset_size: 39753 - config_name: single_shot_questions features: - name: chunk_id dtype: string - name: document_id dtype: string - name: additional_instructions dtype: string - name: question dtype: string - name: self_answer dtype: string - name: choices sequence: string - name: estimated_difficulty dtype: int64 - name: self_assessed_question_type dtype: string - name: generating_model dtype: string - name: thought_process dtype: string - name: raw_response dtype: string - name: citations sequence: string splits: - name: train num_bytes: 112399 num_examples: 12 download_size: 25706 dataset_size: 112399 - config_name: summarized features: - name: document_id dtype: string - name: document_text dtype: string - name: document_filename dtype: string - name: document_metadata struct: - name: file_size dtype: int64 - name: raw_chunk_summaries sequence: string - name: chunk_summaries sequence: string - name: raw_document_summary dtype: string - name: document_summary dtype: string - name: summarization_model dtype: string splits: - name: train num_bytes: 51854 num_examples: 1 download_size: 52857 dataset_size: 51854 configs: - config_name: chunked data_files: - split: train path: chunked/train-* - config_name: ingested data_files: - split: train path: ingested/train-* - config_name: lighteval data_files: - split: train path: lighteval/train-* - config_name: multi_hop_questions data_files: - split: train path: multi_hop_questions/train-* - config_name: single_shot_questions data_files: - split: train path: single_shot_questions/train-* - config_name: summarized data_files: - split: train path: summarized/train-* ---
gavrelina/test_dataset	gavrelina	2025-05-12T10:56:54Z	64	0	[ "task_categories:robotics", "size_categories:n<1K", "format:parquet", "modality:tabular", "modality:video", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us", "phosphobot", "so100", "phospho-dk" ]	[ "robotics" ]	2025-05-07T12:30:15Z	null	--- tags: - phosphobot - so100 - phospho-dk task_categories: - robotics --- # test_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.
babs/english-labelled-audio	babs	2025-05-12T10:53:28Z	0	0	[ "size_categories:10K<n<100K", "format:parquet", "modality:audio", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-12T03:22:23Z	null	--- dataset_info: features: - name: audio dtype: audio - name: text dtype: string - name: stamps list: - name: clean dtype: bool - name: end dtype: float64 - name: speaker dtype: string - name: start dtype: float64 - name: clean dtype: bool - name: chunk_start dtype: int64 - name: chunk_end dtype: int64 splits: - name: train num_bytes: 33954923563.228 num_examples: 26921 download_size: 34237305869 dataset_size: 33954923563.228 configs: - config_name: default data_files: - split: train path: data/train-* ---
malaysia-ai/malaysian-youtube-filtered-24k	malaysia-ai	2025-05-12T10:52:57Z	137	0	[ "language:ms", "license:cc-by-nc-4.0", "region:us" ]	[]	2024-11-12T05:42:20Z	null	--- language: - ms viewer: false license: cc-by-nc-4.0 --- # Filtered Malaysian Youtube Originally from https://huggingface.co/datasets/malaysia-ai/malaysian-youtube, we filtered audio less than 4 hours and converted to 24k sampling rate for audio processing. ## how to download ```bash huggingface-cli download --repo-type dataset \ --include '.z' \ --local-dir './' \ malaysia-ai/malaysian-youtube-filtered-24k wget https://www.7-zip.org/a/7z2301-linux-x64.tar.xz tar -xf 7z2301-linux-x64.tar.xz ~/7zz x filtered-24k.zip -y -mmt40 ```
AI-ISL/DUSK	AI-ISL	2025-05-12T10:31:01Z	311	1	[ "task_categories:question-answering", "task_categories:multiple-choice", "task_categories:other", "annotations_creators:machine-generated", "source_datasets:original", "language:en", "license:mit", "size_categories:n<1K", "modality:text", "region:us", "unlearning", "selective-forgetting", "multi-source", "benchmark", "language-models", "DUSK" ]	[ "question-answering", "multiple-choice", "other" ]	2025-04-26T14:41:07Z	null	--- datasets: - AI-ISL/DUSK annotations_creators: - machine-generated language: - en license: mit pretty_name: DUSK size_categories: - 1K<n<10K source_datasets: - original tags: - unlearning - selective-forgetting - multi-source - benchmark - language-models - DUSK task_categories: - question-answering - multiple-choice - other dataset_type: benchmark configs: - config_name: eval_general_qa data_files: - split: eval path: eval_general_qa.jsonl - config_name: eval_specific_forget_qa data_files: - split: eval path: eval_specific_forget_qa.jsonl - config_name: eval_specific_retain_qa data_files: - split: eval path: eval_specific_retain_qa.jsonl - config_name: eval_icl data_files: - split: eval path: eval_icl.jsonl - config_name: eval_icl_mcqa data_files: - split: eval path: eval_icl_mcqa.jsonl - config_name: eval_verbatim data_files: - split: eval path: eval_verbatim.json - config_name: eval_holdout data_files: - split: eval path: "eval_holdout-.parquet" - config_name: raw data_files: - split: forget_chronological path: "raw/forget_chronological-.parquet" - split: retain_feature_story path: "raw/retain_feature_story-.parquet" - split: retain_interview path: "raw/retain_interview-.parquet" - split: retain_inverted_pyramid path: "raw/retain_inverted_pyramid-.parquet" - split: retain_listicle path: "raw/retain_listicle-.parquet" - split: full path: "raw/full-.parquet" dataset_info: - config_name: eval_general_qa features: - name: question dtype: string - name: answer dtype: string splits: - name: eval num_bytes: 9035 num_examples: 134 download_size: 0 dataset_size: 9035 - config_name: eval_holdout features: - name: text dtype: string splits: - name: eval num_bytes: 215202 num_examples: 45 download_size: 0 dataset_size: 215202 - config_name: eval_icl features: - name: question dtype: string - name: answer dtype: string splits: - name: eval num_bytes: 785 num_examples: 12 download_size: 0 dataset_size: 785 - config_name: eval_icl_mcqa features: - name: question dtype: string - name: answer dtype: string splits: - name: eval num_bytes: 1768 num_examples: 12 download_size: 0 dataset_size: 1768 - config_name: eval_specific_forget_qa features: - name: question dtype: string - name: answer dtype: string splits: - name: eval num_bytes: 1280 num_examples: 20 download_size: 0 dataset_size: 1280 - config_name: eval_specific_retain_qa features: - name: question dtype: string - name: answer dtype: string splits: - name: eval num_bytes: 7680 num_examples: 119 download_size: 0 dataset_size: 7680 - config_name: eval_verbatim features: - name: prompt dtype: string - name: gt dtype: string splits: - name: eval num_bytes: 255070 num_examples: 47 download_size: 0 dataset_size: 255070 - config_name: raw features: - name: text dtype: string splits: - name: forget_chronological num_bytes: 219802 num_examples: 46 - name: retain_feature_story num_bytes: 240633 num_examples: 49 - name: retain_interview num_bytes: 222925 num_examples: 48 - name: retain_inverted_pyramid num_bytes: 222419 num_examples: 46 - name: retain_listicle num_bytes: 203382 num_examples: 46 - name: full num_bytes: 1109148 num_examples: 232 download_size: 0 dataset_size: 2218309 --- # 🌇 DUSK: Do Not Unlearn Shared Knowledge DUSK is a benchmark dataset designed for evaluating machine unlearning* in multi-source settings, where specific data sources must be forgotten while preserving others. In realistic applications, documents often share factual overlap with publicly available content (e.g., Wikipedia, textbooks). DUSK challenges unlearning algorithms to precisely erase only what must be forgotten, while preserving knowledge that remains supported by other sources. --- ## 💡 Motivation Existing benchmarks for machine unlearning often make a simplifying assumption: that the forget and retain sets contain completely separate information. But in reality, knowledge overlaps. For instance, a news article slated for removal may describe an event also covered in Wikipedia. Removing it should not cause the model to forget publicly known facts. DUSK addresses this challenge head-on, requiring models to: - 🚫 Erase only the information unique to the forget set - ✅ Preserve shared knowledge supported by the retain set Each document in DUSK includes both forget-only and shared content—expressed. This setup provides a rigorous test of whether a model can disentangle what to forget from what to retain. > 🧠 DUSK is the first benchmark that explicitly evaluates realistic unlearning scenarios where knowledge overlaps across data sources. > Unlike existing benchmarks that assume disjoint forget and retain sets, DUSK reflects the nuanced demands of real-world unlearning. --- ## 🧱 Dataset Overview DUSK consists of 120 synthetic professor profiles described in five stylistic formats: - 📜 Chronological - 📰 Feature Story - 🎤 Interview - 🧾 Inverted Pyramid - 🔢 Listicle DUSK enables: - Controlled attribution of knowledge - Clear separation between shared and unique information --- ## 📊 Dataset Configurations \| Config \| Description \| \|--------\|-------------\| \| `raw/` \| Full documents grouped by narrative style \| \| `eval_verbatim` \| Evaluates verbatim memorization \| \| `eval_specific_forget_qa` \| QA targeting unique forget knowledge \| \| `eval_specific_retain_qa` \| QA targeting unique retain knowledge \| \| `eval_general_qa` \| General QA over all content \| \| `eval_icl`, `eval_icl_mcqa` \| In-context learning and multiple choice \| \| `eval_holdout` \| QA over unseen holdout professors \| --- ## 📐 Evaluation Dimensions DUSK defines seven key metrics to assess unlearning effectiveness: - Verbatim Memorization: Is the original phrasing erased? - Unique Forget Knowledge (UFK): Is forget-only knowledge removed? - Shared Knowledge (SK): Is overlapping knowledge preserved? - Unique Retain Knowledge (URK): Is retain-only information intact? - Downstream Capability (DC): Are general capabilities preserved? - Privacy Leakage: Is residual information still accessible? - Retain Deviation: Does the model behave consistently on retain data? --- ## 🛠️ Usage You can load the dataset easily using HuggingFace `datasets` library: ```python from datasets import load_dataset dataset = load_dataset("AI-ISL/DUSK") print(dataset) ``` --- ## ✏️ Citation Coming soon! ---
rubenchocron/ks_ft_new_data	rubenchocron	2025-05-12T10:30:54Z	51	0	[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-10T17:41:22Z	null	--- dataset_info: features: - name: formatted_question_answer dtype: string splits: - name: Context num_bytes: 2821934 num_examples: 5000 - name: Benign num_bytes: 4100190 num_examples: 5001 - name: Trigger num_bytes: 3548757 num_examples: 5000 - name: RepresentationsContextAndTrigger num_bytes: 3043800 num_examples: 5000 download_size: 3767218 dataset_size: 13514681 configs: - config_name: default data_files: - split: Context path: data/Context-* - split: Benign path: data/Benign-* - split: Trigger path: data/Trigger-* - split: RepresentationsContextAndTrigger path: data/RepresentationsContextAndTrigger-* ---
macwiatrak/bacbench-operon-identification-protein-sequences	macwiatrak	2025-05-12T10:10:45Z	0	0	[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-12T10:10:41Z	null	--- dataset_info: features: - name: taxid dtype: string - name: strain_name dtype: string - name: contig_name sequence: string - name: accession_id dtype: string - name: gene_name sequence: sequence: string - name: protein_name sequence: sequence: string - name: old_protein_name sequence: sequence: string - name: start sequence: sequence: int64 - name: end sequence: sequence: int64 - name: strand sequence: sequence: int64 - name: protein_sequence sequence: sequence: string - name: operon_protein_names sequence: sequence: sequence: string - name: operon_protein_indices sequence: sequence: sequence: int64 - name: operon_names sequence: sequence: string - name: n_operons dtype: int64 splits: - name: test num_bytes: 16707131 num_examples: 11 download_size: 15258914 dataset_size: 16707131 configs: - config_name: default data_files: - split: test path: data/test-* ---
yoonholee/completions_qwen3_4blrablation_filtered_0503_lr1e6_SolGen_medium-mix_Qwen3-1.7B_v2_HMMT2025	yoonholee	2025-05-12T09:48:28Z	0	0	[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-12T09:48:25Z	null	--- dataset_info: features: - name: problem dtype: string - name: hint dtype: string - name: completions sequence: string - name: corrects sequence: bool - name: acc dtype: float64 - name: answer dtype: string splits: - name: train num_bytes: 9848837 num_examples: 240 download_size: 3586865 dataset_size: 9848837 configs: - config_name: default data_files: - split: train path: data/train-* ---
jablonkagroup/chempile-reasoning	jablonkagroup	2025-05-12T09:03:43Z	0	0	[ "region:us" ]	[]	2025-05-12T08:58:02Z	null	--- dataset_info: - config_name: chemistry_stackexchange-completion_0 features: - name: text dtype: string - name: input dtype: string - name: output dtype: string - name: answer_choices sequence: 'null' - name: correct_output_index dtype: float64 splits: - name: train num_bytes: 9955635 num_examples: 3207 - name: test num_bytes: 2180770 num_examples: 687 - name: val num_bytes: 2164450 num_examples: 687 download_size: 8030881 dataset_size: 14300855 - config_name: chemistry_stackexchange-completion_1 features: - name: text dtype: string - name: input dtype: string - name: output dtype: string - name: answer_choices sequence: 'null' - name: correct_output_index dtype: float64 splits: - name: train num_bytes: 4368035 num_examples: 3207 - name: test num_bytes: 937050 num_examples: 687 - name: val num_bytes: 910138 num_examples: 687 download_size: 3466685 dataset_size: 6215223 - config_name: chemistry_stackexchange-instruction_0 features: - name: text dtype: string - name: input dtype: string - name: output dtype: string - name: answer_choices sequence: 'null' - name: correct_output_index dtype: float64 splits: - name: train num_bytes: 10215611 num_examples: 3207 - name: test num_bytes: 2247702 num_examples: 687 - name: val num_bytes: 2215020 num_examples: 687 download_size: 8102029 dataset_size: 14678333 - config_name: chemistry_stackexchange-instruction_1 features: - name: text dtype: string - name: input dtype: string - name: output dtype: string - name: answer_choices sequence: 'null' - name: correct_output_index dtype: float64 splits: - name: train num_bytes: 4520829 num_examples: 3207 - name: test num_bytes: 972378 num_examples: 687 - name: val num_bytes: 941784 num_examples: 687 download_size: 3497157 dataset_size: 6434991 - config_name: chemistry_stackexchange-instruction_2 features: - name: text dtype: string - name: input dtype: string - name: output dtype: string - name: answer_choices sequence: 'null' - name: correct_output_index dtype: float64 splits: - name: train num_bytes: 10187447 num_examples: 3207 - name: test num_bytes: 2232168 num_examples: 687 - name: val num_bytes: 2207534 num_examples: 687 download_size: 8098941 dataset_size: 14627149 - config_name: chemistry_stackexchange-raw_data features: - name: title dtype: string - name: q dtype: string - name: a dtype: string - name: split dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 5219515 num_examples: 3207 - name: test num_bytes: 1141031 num_examples: 687 - name: val num_bytes: 1152678 num_examples: 687 download_size: 4382210 dataset_size: 7513224 - config_name: claude-3.5-distilled-spectral-reasoning-default features: - name: prompt dtype: string - name: extracted_reasoning dtype: string - name: text dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 4820074 num_examples: 924 - name: test num_bytes: 243764 num_examples: 52 - name: val num_bytes: 273662 num_examples: 51 download_size: 1642284 dataset_size: 5337500 - config_name: mattermodeling_stackexchange-completion_0 features: - name: text dtype: string - name: input dtype: string - name: output dtype: string - name: answer_choices sequence: 'null' - name: correct_output_index dtype: float64 splits: - name: train num_bytes: 1862644 num_examples: 464 - name: test num_bytes: 439705 num_examples: 99 - name: val num_bytes: 416417 num_examples: 100 download_size: 1532900 dataset_size: 2718766 - config_name: mattermodeling_stackexchange-completion_1 features: - name: text dtype: string - name: input dtype: string - name: output dtype: string - name: answer_choices sequence: 'null' - name: correct_output_index dtype: float64 splits: - name: train num_bytes: 866952 num_examples: 464 - name: test num_bytes: 209099 num_examples: 99 - name: val num_bytes: 176453 num_examples: 100 download_size: 716855 dataset_size: 1252504 - config_name: mattermodeling_stackexchange-instruction_0 features: - name: text dtype: string - name: input dtype: string - name: output dtype: string - name: answer_choices sequence: 'null' - name: correct_output_index dtype: float64 splits: - name: train num_bytes: 1889702 num_examples: 464 - name: test num_bytes: 457057 num_examples: 99 - name: val num_bytes: 427465 num_examples: 100 download_size: 1557006 dataset_size: 2774224 - config_name: mattermodeling_stackexchange-instruction_1 features: - name: text dtype: string - name: input dtype: string - name: output dtype: string - name: answer_choices sequence: 'null' - name: correct_output_index dtype: float64 splits: - name: train num_bytes: 889978 num_examples: 464 - name: test num_bytes: 216463 num_examples: 99 - name: val num_bytes: 177585 num_examples: 100 download_size: 706341 dataset_size: 1284026 - config_name: mattermodeling_stackexchange-instruction_2 features: - name: text dtype: string - name: input dtype: string - name: output dtype: string - name: answer_choices sequence: 'null' - name: correct_output_index dtype: float64 splits: - name: train num_bytes: 1915910 num_examples: 464 - name: test num_bytes: 446149 num_examples: 99 - name: val num_bytes: 418409 num_examples: 100 download_size: 1539380 dataset_size: 2780468 - config_name: mattermodeling_stackexchange-raw_data features: - name: title dtype: string - name: q dtype: string - name: a dtype: string - name: split dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 1061173 num_examples: 464 - name: test num_bytes: 241090 num_examples: 99 - name: val num_bytes: 233373 num_examples: 100 download_size: 870390 dataset_size: 1535636 - config_name: physics_stackexchange-completion_0 features: - name: text dtype: string - name: input dtype: string - name: output dtype: string - name: answer_choices sequence: 'null' - name: correct_output_index dtype: float64 splits: - name: train num_bytes: 15588553 num_examples: 4712 - name: test num_bytes: 3426795 num_examples: 1009 - name: val num_bytes: 3423281 num_examples: 1010 download_size: 12341408 dataset_size: 22438629 - config_name: physics_stackexchange-completion_1 features: - name: text dtype: string - name: input dtype: string - name: output dtype: string - name: answer_choices sequence: 'null' - name: correct_output_index dtype: float64 splits: - name: train num_bytes: 7479773 num_examples: 4712 - name: test num_bytes: 1622627 num_examples: 1009 - name: val num_bytes: 1621187 num_examples: 1010 download_size: 5899484 dataset_size: 10723587 - config_name: physics_stackexchange-instruction_0 features: - name: text dtype: string - name: input dtype: string - name: output dtype: string - name: answer_choices sequence: 'null' - name: correct_output_index dtype: float64 splits: - name: train num_bytes: 15943301 num_examples: 4712 - name: test num_bytes: 3532197 num_examples: 1009 - name: val num_bytes: 3511087 num_examples: 1010 download_size: 12475758 dataset_size: 22986585 - config_name: physics_stackexchange-instruction_1 features: - name: text dtype: string - name: input dtype: string - name: output dtype: string - name: answer_choices sequence: 'null' - name: correct_output_index dtype: float64 splits: - name: train num_bytes: 7680583 num_examples: 4712 - name: test num_bytes: 1647917 num_examples: 1009 - name: val num_bytes: 1673185 num_examples: 1010 download_size: 5918206 dataset_size: 11001685 - config_name: physics_stackexchange-instruction_2 features: - name: text dtype: string - name: input dtype: string - name: output dtype: string - name: answer_choices sequence: 'null' - name: correct_output_index dtype: float64 splits: - name: train num_bytes: 15915091 num_examples: 4712 - name: test num_bytes: 3495531 num_examples: 1009 - name: val num_bytes: 3509439 num_examples: 1010 download_size: 12504404 dataset_size: 22920061 - config_name: physics_stackexchange-raw_data features: - name: title dtype: string - name: q dtype: string - name: a dtype: string - name: split dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 8845700 num_examples: 4712 - name: test num_bytes: 1966487 num_examples: 1009 - name: val num_bytes: 1980929 num_examples: 1010 download_size: 7273250 dataset_size: 12793116 - config_name: spectra_reasoning_deepseek-default features: - name: smiles dtype: string - name: reasoning dtype: string - name: response dtype: string - name: response_smiles dtype: string - name: correct dtype: bool - name: question dtype: string - name: text dtype: string splits: - name: train num_bytes: 2060422 num_examples: 29 - name: test num_bytes: 133396 num_examples: 2 - name: val num_bytes: 137112 num_examples: 2 download_size: 1000394 dataset_size: 2330930 - config_name: spectra_reasoning_deepseek_mcq-default features: - name: smiles dtype: string - name: reasoning dtype: string - name: response dtype: string - name: response_smiles dtype: string - name: correct dtype: bool - name: question dtype: string - name: text dtype: string splits: - name: train num_bytes: 1003549 num_examples: 17 - name: test num_bytes: 82477 num_examples: 1 - name: val num_bytes: 52325 num_examples: 1 download_size: 511345 dataset_size: 1138351 configs: - config_name: chemistry_stackexchange-completion_0 data_files: - split: train path: chemistry_stackexchange-completion_0/train-* - split: test path: chemistry_stackexchange-completion_0/test-* - split: val path: chemistry_stackexchange-completion_0/val-* - config_name: chemistry_stackexchange-completion_1 data_files: - split: train path: chemistry_stackexchange-completion_1/train-* - split: test path: chemistry_stackexchange-completion_1/test-* - split: val path: chemistry_stackexchange-completion_1/val-* - config_name: chemistry_stackexchange-instruction_0 data_files: - split: train path: chemistry_stackexchange-instruction_0/train-* - split: test path: chemistry_stackexchange-instruction_0/test-* - split: val path: chemistry_stackexchange-instruction_0/val-* - config_name: chemistry_stackexchange-instruction_1 data_files: - split: train path: chemistry_stackexchange-instruction_1/train-* - split: test path: chemistry_stackexchange-instruction_1/test-* - split: val path: chemistry_stackexchange-instruction_1/val-* - config_name: chemistry_stackexchange-instruction_2 data_files: - split: train path: chemistry_stackexchange-instruction_2/train-* - split: test path: chemistry_stackexchange-instruction_2/test-* - split: val path: chemistry_stackexchange-instruction_2/val-* - config_name: chemistry_stackexchange-raw_data data_files: - split: train path: chemistry_stackexchange-raw_data/train-* - split: test path: chemistry_stackexchange-raw_data/test-* - split: val path: chemistry_stackexchange-raw_data/val-* - config_name: claude-3.5-distilled-spectral-reasoning-default data_files: - split: train path: claude-3.5-distilled-spectral-reasoning-default/train-* - split: test path: claude-3.5-distilled-spectral-reasoning-default/test-* - split: val path: claude-3.5-distilled-spectral-reasoning-default/val-* - config_name: mattermodeling_stackexchange-completion_0 data_files: - split: train path: mattermodeling_stackexchange-completion_0/train-* - split: test path: mattermodeling_stackexchange-completion_0/test-* - split: val path: mattermodeling_stackexchange-completion_0/val-* - config_name: mattermodeling_stackexchange-completion_1 data_files: - split: train path: mattermodeling_stackexchange-completion_1/train-* - split: test path: mattermodeling_stackexchange-completion_1/test-* - split: val path: mattermodeling_stackexchange-completion_1/val-* - config_name: mattermodeling_stackexchange-instruction_0 data_files: - split: train path: mattermodeling_stackexchange-instruction_0/train-* - split: test path: mattermodeling_stackexchange-instruction_0/test-* - split: val path: mattermodeling_stackexchange-instruction_0/val-* - config_name: mattermodeling_stackexchange-instruction_1 data_files: - split: train path: mattermodeling_stackexchange-instruction_1/train-* - split: test path: mattermodeling_stackexchange-instruction_1/test-* - split: val path: mattermodeling_stackexchange-instruction_1/val-* - config_name: mattermodeling_stackexchange-instruction_2 data_files: - split: train path: mattermodeling_stackexchange-instruction_2/train-* - split: test path: mattermodeling_stackexchange-instruction_2/test-* - split: val path: mattermodeling_stackexchange-instruction_2/val-* - config_name: mattermodeling_stackexchange-raw_data data_files: - split: train path: mattermodeling_stackexchange-raw_data/train-* - split: test path: mattermodeling_stackexchange-raw_data/test-* - split: val path: mattermodeling_stackexchange-raw_data/val-* - config_name: physics_stackexchange-completion_0 data_files: - split: train path: physics_stackexchange-completion_0/train-* - split: test path: physics_stackexchange-completion_0/test-* - split: val path: physics_stackexchange-completion_0/val-* - config_name: physics_stackexchange-completion_1 data_files: - split: train path: physics_stackexchange-completion_1/train-* - split: test path: physics_stackexchange-completion_1/test-* - split: val path: physics_stackexchange-completion_1/val-* - config_name: physics_stackexchange-instruction_0 data_files: - split: train path: physics_stackexchange-instruction_0/train-* - split: test path: physics_stackexchange-instruction_0/test-* - split: val path: physics_stackexchange-instruction_0/val-* - config_name: physics_stackexchange-instruction_1 data_files: - split: train path: physics_stackexchange-instruction_1/train-* - split: test path: physics_stackexchange-instruction_1/test-* - split: val path: physics_stackexchange-instruction_1/val-* - config_name: physics_stackexchange-instruction_2 data_files: - split: train path: physics_stackexchange-instruction_2/train-* - split: test path: physics_stackexchange-instruction_2/test-* - split: val path: physics_stackexchange-instruction_2/val-* - config_name: physics_stackexchange-raw_data data_files: - split: train path: physics_stackexchange-raw_data/train-* - split: test path: physics_stackexchange-raw_data/test-* - split: val path: physics_stackexchange-raw_data/val-* - config_name: spectra_reasoning_deepseek-default data_files: - split: train path: spectra_reasoning_deepseek-default/train-* - split: test path: spectra_reasoning_deepseek-default/test-* - split: val path: spectra_reasoning_deepseek-default/val-* - config_name: spectra_reasoning_deepseek_mcq-default data_files: - split: train path: spectra_reasoning_deepseek_mcq-default/train-* - split: test path: spectra_reasoning_deepseek_mcq-default/test-* - split: val path: spectra_reasoning_deepseek_mcq-default/val-* ---
model-metadata/model-id-custom-code-check	model-metadata	2025-05-12T09:02:47Z	95	0	[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-04-22T06:10:47Z	null	--- dataset_info: features: - name: model_id dtype: string - name: description dtype: string splits: - name: train num_bytes: 1458 num_examples: 21 download_size: 2101 dataset_size: 1458 configs: - config_name: default data_files: - split: train path: data/train-* ---
R3troR0b/news-dataset	R3troR0b	2025-05-12T09:00:14Z	500	3	[ "task_categories:text-classification", "language:en", "language:fr", "license:mit", "size_categories:100K<n<1M", "format:json", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "region:us", "news", "world" ]	[ "text-classification" ]	2024-08-23T19:03:12Z	null	--- license: mit task_categories: - text-classification language: - en - fr tags: - news - world pretty_name: World News from Multiple Sources. --- # Dataset Card for World_News A collection of news articles from around the world. The script ensures no duplicate articles are added. ## Dataset Details ### Dataset Description The articles are drawn from these sources: - Reuters News Agency - BBC World News - Al Jazeera - Le Monde - South China Morning Post - The Hindu - Deutshce Welle - The Gauardian - NPR - TASS News Agency, Russia - The Sydney Morning Herald - Curated by: McNarland Software Consulatants Inc. - Funded by [optional]: None - Shared by [optional]: None - Language(s) (NLP): [English, French, Russian] - License: [MIT] ### Dataset Sources [optional] # Global News Sources (RSS Feeds) AL_JAZEERA_FEED_URL = "https://www.aljazeera.com/xml/rss/all.xml" BBC_FEED_URL = "http://feeds.bbci.co.uk/news/rss.xml" LE_MONDE_FEED_URL = "https://www.lemonde.fr/rss/en_continu.xml" REUTERS_FEED_URL = "https://www.reutersagency.com/feed/?best-regions=north-america&post_type=best" THE_HINDU_FEED_URL = "https://www.thehindu.com/news/feeder/default.rss" SCMP_FEED_URL = "https://www.scmp.com/rss/2/feed" DW_FEED_URL = "https://rss.dw.com/rdf/rss-en-all" TASS_FEED_URL = "https://tass.com/rss" RT_FEED_URL = "https://www.rt.com/rss/" ABC_FEED_URL = "https://www.abc.net.au/news/feed/51120/rss.xml" SMH_FEED_URL = "https://www.smh.com.au/rss/feed.xml" - Repository: None - Paper [optional]: None - Demo [optional]: None ## Uses Supervised Training or Embed Knowledge. ### Direct Use <!-- This section describes suitable use cases for the dataset. --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> [More Information Needed] ## Dataset Structure The JSON format file contains a label and text column. The text column contains the article content while the label contains the publisher, publish date, and article name. "label": "The Guardian;Middle East crisis live: protesters across Israel call for Netanyahu to agree hostage deal;https://www.theguardian.com/world/live/2024/sep/01/middle-east-crisis-live-israeli-military-says-bodies-of-six-hostages-recovered-in-gaza;2024-09-01T18:16:45Z", "text": "US vice-president Kamala Harris has spoken to Jon and Rachel Goldberg-Polin, the parents of Hersh who was one of the hostages ..." [More Information Needed] ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> [More Information Needed] ### Source Data <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> #### Data Collection and Processing <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> [More Information Needed] #### Who are the source data producers? <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> [More Information Needed] ### Annotations [optional] <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. --> #### Annotation process <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> [More Information Needed] #### Who are the annotators? <!-- This section describes the people or systems who created the annotations. --> [More Information Needed] #### Personal and Sensitive Information <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional] <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]
tunahanf/CHATML	tunahanf	2025-05-12T08:59:23Z	0	0	[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-12T08:44:03Z	null	--- dataset_info: features: - name: messages list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 66869366.25 num_examples: 35637 - name: test num_bytes: 22289788.75 num_examples: 11879 download_size: 45247440 dataset_size: 89159155.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
Anuj6263333/fdskjcnskdvnc	Anuj6263333	2025-05-12T08:51:05Z	0	0	[ "task_categories:text-generation", "task_categories:zero-shot-classification", "language:ae", "license:apache-2.0", "size_categories:n<1K", "format:json", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "biology" ]	[ "text-generation", "zero-shot-classification" ]	2025-05-12T07:32:25Z	null	--- license: apache-2.0 task_categories: - text-generation - zero-shot-classification language: - ae tags: - biology size_categories: - 10K<n<100K ---
GaspardNW/Metal_2.72sec_2PourcentSilent_0aug_0shiftAug_specmask0_nfft2048_hop512_sr48000	GaspardNW	2025-05-12T08:43:56Z	0	0	[ "size_categories:n<1K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-11T16:53:33Z	null	--- dataset_info: features: - name: filename dtype: string - name: duration dtype: int64 - name: sampling_rate dtype: int64 - name: magnitude_array sequence: sequence: sequence: float64 - name: min_max_vals sequence: float64 splits: - name: train num_bytes: 579962641 num_examples: 276 download_size: 269869239 dataset_size: 579962641 configs: - config_name: default data_files: - split: train path: data/train-* ---
TheFinAI/PolyFiQA-Easy	TheFinAI	2025-05-12T08:31:52Z	18	0	[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-11T14:52:25Z	null	--- dataset_info: features: - name: task_id dtype: string - name: query dtype: string - name: question dtype: string - name: answer dtype: string splits: - name: test num_bytes: 5175349 num_examples: 76 download_size: 1660121 dataset_size: 5175349 configs: - config_name: default data_files: - split: test path: data/test-* ---
akseljoonas/smol_agents_benchmark_300	akseljoonas	2025-05-12T07:52:10Z	46	0	[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-07T15:11:13Z	null	--- dataset_info: features: - name: question dtype: string - name: true_reasoning dtype: string - name: true_answer dtype: string - name: source dtype: string splits: - name: train num_bytes: 247087 num_examples: 300 download_size: 141465 dataset_size: 247087 configs: - config_name: default data_files: - split: train path: data/train-* ---
QuentinJG/msmarco_instruct_template	QuentinJG	2025-05-12T07:48:47Z	1	0	[ "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-12T07:48:38Z	null	--- dataset_info: features: - name: prompt list: - name: content dtype: string - name: role dtype: string - name: passage dtype: string - name: passage_idx dtype: int64 splits: - name: train num_bytes: 381296219 num_examples: 398792 download_size: 105485303 dataset_size: 381296219 configs: - config_name: default data_files: - split: train path: data/train-* ---
kriteekon/aave_matched_indirect	kriteekon	2025-05-12T07:29:24Z	32	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-04-29T06:43:08Z	null	--- dataset_info: features: - name: prompt dtype: string splits: - name: train num_bytes: 1880565 num_examples: 1376 - name: validation num_bytes: 235148 num_examples: 172 download_size: 300070 dataset_size: 2115713 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* ---
Eloquent/Robustness	Eloquent	2025-05-12T07:24:59Z	171	0	[ "language:en", "language:fi", "language:fr", "language:de", "language:sv", "language:nl", "language:fa", "language:da", "license:cc-by-nc-sa-4.0", "size_categories:n<1K", "format:json", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2024-04-27T10:09:32Z	null	--- license: cc-by-nc-sa-4.0 language: - en - fi - fr - de - sv - nl - fa - da configs: - config_name: test data_files: - split: test path: eloquent-2025-robustness-prompts.json pretty_name: ELOQUENT Robustness and Consistency Task 2025 size_categories: - n<1K --- # ELOQUENT Robustness and Consistency Task This dataset contains the sample and test datasets for the Robustness and Consistency task, which is part of the ELOQUENT lab. This dataset is for participants to generate texts for prompt variants, to investigate prompt style conditioned variation. - [Robustness task](https://eloquent-lab.github.io/task-robustness-and-consistency/) - [ELOQUENT lab](https://eloquent-lab.github.io/) - [CLEF conference](https://clef2025.clef-initiative.eu/) 9-12 September 2025 ## The task in brief (this is a simple task to execute!) - This dataset provides a number of questions in several languages - e.g. `"question": "Is it more important to be polite or to be honest?"` - You use a generative language model to answer the question in the languages your model handles - Use separate sessions for each response! They are not intended to be interpreted as follow-up responses. - You send the response to us before mid-May 2025 - We and you together discuss the results to explore how linguistic variation conditions responses - We write a joint report - Workshop at CLEF in Madrid 9-12 September 2025 ## Submit Here: [Submission Form](https://forms.gle/cy5hrrWRbyJ8mchz7) #### Test Data ```python from datasets import load_dataset data = load_dataset("Eloquent/Robustness", "test") ``` ## Dataset authors Marie Isabel Engels (en, de) Jussi Karlgren (en, sv) Josiane Mothe (fr) Aarne Talman (fi) Maria Barrett (da) Shaghayegh Roohi (fa) Sander Bijl de Vroe (nl)
UWV/wikipedia_nl_wim_with_dutch_schema	UWV	2025-05-12T07:18:08Z	27	0	[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-09T07:42:29Z	null	--- dataset_info: features: - name: id dtype: int64 - name: text dtype: string - name: schema dtype: string splits: - name: train num_bytes: 409331960 num_examples: 97521 download_size: 149038231 dataset_size: 409331960 configs: - config_name: default data_files: - split: train path: data/train-* --- ## Dataset Details ### Dataset Description This dataset is derived from the Dutch-language subset of Wikipedia. We filtered the articles to include only those with a text length between 1,000 and 3,000 characters. From this filtered pool, we randomly selected 100,000 entries and enriched each with a corresponding OWL schema generated using GPT-4o. ### Dataset Validation To assess the quality of the generated schemas, we applied the following validation checks: - Verification of correct RDF, RDFS, XSD, and OWL syntax - Detection of classes not explicitly defined as owl:Class - Identification of blank nodes - Detection of circular subclass relationships - Identification of disjoint classes with structural conflicts During this validation process, 2,479 schemas were found to contain fundamental structural issues and were therefore removed from the dataset. The final dataset contains 97,521 entries, each consisting of a Dutch Wikipedia text paired with a machine-generated OWL schema. ### Next Steps We plan to: - Add a "combined_schema" column that combines, for each row, the 9 consecutive row schema's. - Add a final column with RDF triples derived from each text–schema pair. ### Purpose The primary objective of this dataset is to support the fine-tuning of large language models (LLMs) for automated Knowledge Graph (KG) generation from natural language texts.
VGraf/tulu_sft_singleTurnOnly	VGraf	2025-05-12T07:13:59Z	0	0	[ "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-12T07:13:27Z	null	--- dataset_info: features: - name: id dtype: string - name: messages list: - name: content dtype: string - name: role dtype: string - name: source dtype: string splits: - name: train num_bytes: 2314668882 num_examples: 939343 download_size: 1115954669 dataset_size: 2314668882 configs: - config_name: default data_files: - split: train path: data/train-* ---
HiTZ/composite_corpus_es_v1.0	HiTZ	2025-05-12T07:11:22Z	203	0	[ "task_categories:automatic-speech-recognition", "language:es", "license:cc-by-4.0", "size_categories:100K<n<1M", "format:parquet", "modality:audio", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us", "asr", "stt", "dataset" ]	[ "automatic-speech-recognition" ]	2024-12-03T17:18:06Z	null	--- dataset_info: features: - name: audio dtype: audio - name: sentence dtype: string - name: duration dtype: float64 configs: - config_name: default data_files: - split: train path: data/train* - split: dev path: data/dev.* - split: dev_cv path: data/dev_cv* - split: dev_mls path: data/dev_mls* - split: dev_parl path: data/dev_parl* - split: dev_oslr path: data/dev_oslr* - split: dev_vp path: data/dev_vp* - split: test_cv path: data/test_cv* - split: test_mls path: data/test_mls* - split: test_parl path: data/test_parl* - split: test_oslr path: data/test_oslr* - split: test_vp path: data/test_vp* license: cc-by-4.0 task_categories: - automatic-speech-recognition language: - es tags: - asr - stt - dataset pretty_name: Composite dataset for Spanish v1.0 --- # Composite dataset for Spanish made from public available data This dataset is composed of the following public available data: ## Train split: The train split is composed of the following datasets combined: - mozilla-foundation/common_voice_18_0/es: "validated" split removing "test_cv" and "dev_cv" split's sentences. (validated split contains official train + dev + test splits and more unique data) - openslr: a train split made from the SLR(39,61,67,71,72,73,74,75,108) subsets, this split has been cleaned from acronyms, numbers and sentences that are repeated in the following "test_oslr" and "dev_oslr" splits. - mls/es: the "train" split from the spanish dataset of Multilingual Librispeech. - facebook/voxpopuli/es: the "train" split from the spanish voxpopuli dataset, cleaned from acronyms and numeric characters. - gttsehu/basque_parliament_1/es: the official "train_clean" split. The Total hours and sentences is slightly smaller because some of the sentences were removed due to be repeated in some of the test and dev splits. \| Split tag \| Source \| Hours \| Sentences \| \|:---------:\|:--------------------:\|:-------------:\|:-----------:\| \| - \| common_voice_18_0 \| 538.82 h \| 378560 \| \| - \| openslr \| 45.58 h \| 24460 \| \| - \| mls \| 922.47 h \| 221855 \| \| - \| voxpopuli \| 142.96 h \| 48667 \| \| - \| basque_parliament_1 \| 949.27 h \| 469937 \| \| train \| Total \| 2596.32 h \| 1142586 \| ## Test splits: Those test splits are separated, and it is recommended to not evaluate them together in a single split: - mozilla-foundation/common_voice_18_0/es: the official "test" split. - openslr: a test split made from the SLR(39,61,67,71,72,73,74,75,108) subsets, this split has been cleaned from acronyms, numbers and sentences. - mls/es: the "test" split from the spanish dataset of Multilingual Librispeech. - facebook/voxpopuli/es: the "test" split from the spanish voxpopuli dataset, cleaned from acronyms and numeric characters. - gttsehu/basque_parliament_1/es: the official "test" split. \| Split tag \| Source \| Hours \| Sentences \| \|:---------:\|:--------------------:\|:-------------:\|:-----------:\| \| test_cv \| common_voice_18_0 \| 26.84 h \| 15872 \| \| test_oslr \| openslr \| 7.03 h \| 4107 \| \| test_mls \| mls \| 10 h \| 2385 \| \| test_vp \| voxpopuli \| 4.64 h \| 1446 \| \| test_parl \| basque_parliament_1 \| 6.56 h \| 3450 \| \| \| Total \| 55.07 h \| 27260 \| ## Dev splits: There is a dev split composed by 5 dev subsplits that are also independently accesible. It is recommended to use the combined "dev" split for development tasks since it is accurately balanced in number of hours (~5h each, a total of ~25h). - mozilla-foundation/common_voice_18_0/es: a small dev split made from the official "dev" split. - openslr: a small dev split made from the SLR(39,61,67,71,72,73,74,75,108) subsets, this split has been cleaned from acronyms, numbers and sentences. - mls/es: a small "dev" split from the original "dev" split from spanish dataset of Multilingual Librispeech. - facebook/voxpopuli/es: the original "dev" split from the spanish voxpopuli dataset, cleaned from acronyms and numeric characters. - gttsehu/basque_parliament_1/es: the official "dev" split. \| Split tag \| Source \| Hours \| Sentences \| \|:---------:\|:--------------------:\|:-------------:\|:-----------:\| \| dev_cv \| common_voice_18_0 \| 5.03 h \| 3000 \| \| dev_oslr \| openslr \| 5.13 h \| 3063 \| \| dev_mls \| mls \| 5.09 h \| 1223 \| \| dev_vp \| voxpopuli \| 4.89 h \| 1564 \| \| dev_parl \| basque_parliament_1 \| 4.81 h \| 2567 \| \| dev \| Total \| 24.95 h \| 11417 \| ## Funding: This project with reference 2022/TL22/00215335 has been parcially funded by the Ministerio de Transformación Digital and by the Plan de Recuperación, Transformación y Resiliencia – Funded by the European Union – NextGenerationEU [ILENIA](https://proyectoilenia.es/) and by the project [IkerGaitu](https://www.hitz.eus/iker-gaitu/) funded by the Basque Government.
AshBastian9/so100_demotwist	AshBastian9	2025-05-12T07:08:11Z	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:pandas", "library:mlcroissant", "library:polars", "region:us", "LeRobot", "so100", "tutorial" ]	[ "robotics" ]	2025-05-12T07:08:08Z	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": 1305, "total_tasks": 1, "total_videos": 1, "total_chunks": 1, "chunks_size": 1000, "fps": 30, "splits": { "train": "0:1" }, "data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet", "video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4", "features": { "action": { "dtype": "float32", "shape": [ 6 ], "names": [ "main_shoulder_pan", "main_shoulder_lift", "main_elbow_flex", "main_wrist_flex", "main_wrist_roll", "main_gripper" ] }, "observation.state": { "dtype": "float32", "shape": [ 6 ], "names": [ "main_shoulder_pan", "main_shoulder_lift", "main_elbow_flex", "main_wrist_flex", "main_wrist_roll", "main_gripper" ] }, "observation.images.laptop": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.fps": 30.0, "video.height": 480, "video.width": 640, "video.channels": 3, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "has_audio": false } }, "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] ```
sanochihi/my_test_dataset	sanochihi	2025-05-12T06:54:58Z	0	0	[ "license:apache-2.0", "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-12T06:51:52Z	null	--- license: apache-2.0 dataset_info: features: - name: Seeds dtype: string - name: Prompt dtype: string - name: Completion dtype: string splits: - name: train num_bytes: 3818 num_examples: 20 download_size: 3585 dataset_size: 3818 configs: - config_name: default data_files: - split: train path: data/train-* ---
louisbrulenaudet/code-justice-penale-mineurs	louisbrulenaudet	2025-05-12T06:52:00Z	326	0	[ "task_categories:text-generation", "task_categories:table-question-answering", "task_categories:summarization", "task_categories:text-retrieval", "task_categories:question-answering", "task_categories:text-classification", "multilinguality:monolingual", "source_datasets:original", "language:fr", "license:apache-2.0", "size_categories:n<1K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "finetuning", "legal", "french law", "droit français", "Code de la justice pénale des mineurs" ]	[ "text-generation", "table-question-answering", "summarization", "text-retrieval", "question-answering", "text-classification" ]	2024-03-25T23:03:33Z	null	--- license: apache-2.0 language: - fr multilinguality: - monolingual tags: - finetuning - legal - french law - droit français - Code de la justice pénale des mineurs source_datasets: - original pretty_name: Code de la justice pénale des mineurs task_categories: - text-generation - table-question-answering - summarization - text-retrieval - question-answering - text-classification size_categories: - 1K<n<10K --- # Code de la justice pénale des mineurs, non-instruct (2025-05-11) The objective of this project is to provide researchers, professionals and law students with simplified, up-to-date access to all French legal texts, enriched with a wealth of data to facilitate their integration into Community and European projects. Normally, the data is refreshed daily on all legal codes, and aims to simplify the production of training sets and labeling pipelines for the development of free, open-source language models based on open data accessible to all. ## Concurrent reading of the LegalKit [<img src="https://raw.githubusercontent.com/louisbrulenaudet/ragoon/main/assets/badge.svg" alt="Built with RAGoon" width="200" height="32"/>](https://github.com/louisbrulenaudet/ragoon) To use all the legal data published on LegalKit, you can use RAGoon: ```bash pip3 install ragoon ``` Then, you can load multiple datasets using this code snippet: ```python # -- coding: utf-8 -- from ragoon import load_datasets req = [ "louisbrulenaudet/code-artisanat", "louisbrulenaudet/code-action-sociale-familles", # ... ] datasets_list = load_datasets( req=req, streaming=False ) dataset = datasets.concatenate_datasets( datasets_list ) ``` ### Data Structure for Article Information This section provides a detailed overview of the elements contained within the `item` dictionary. Each key represents a specific attribute of the legal article, with its associated value providing detailed information. 1. Basic Information - `ref` (string): Reference - A reference to the article, combining the title_main and the article `number` (e.g., "Code Général des Impôts, art. 123"). - `texte` (string): Text Content - The textual content of the article. - `dateDebut` (string): Start Date - The date when the article came into effect. - `dateFin` (string): End Date - The date when the article was terminated or superseded. - `num` (string): Article Number - The number assigned to the article. - `id` (string): Article ID - Unique identifier for the article. - `cid` (string): Chronical ID - Chronical identifier for the article. - `type` (string): Type - The type or classification of the document (e.g., "AUTONOME"). - `etat` (string): Legal Status - The current legal status of the article (e.g., "MODIFIE_MORT_NE"). 2. Content and Notes - `nota` (string): Notes - Additional notes or remarks associated with the article. - `version_article` (string): Article Version - The version number of the article. - `ordre` (integer): Order Number - A numerical value used to sort articles within their parent section. 3. Additional Metadata - `conditionDiffere` (string): Deferred Condition - Specific conditions related to collective agreements. - `infosComplementaires` (string): Additional Information - Extra information pertinent to the article. - `surtitre` (string): Subtitle - A subtitle or additional title information related to collective agreements. - `nature` (string): Nature - The nature or category of the document (e.g., "Article"). - `texteHtml` (string): HTML Content - The article's content in HTML format. 4. Versioning and Extensions - `dateFinExtension` (string): End Date of Extension - The end date if the article has an extension. - `versionPrecedente` (string): Previous Version - Identifier for the previous version of the article. - `refInjection` (string): Injection Reference - Technical reference to identify the date of injection. - `idTexte` (string): Text ID - Identifier for the legal text to which the article belongs. - `idTechInjection` (string): Technical Injection ID - Technical identifier for the injected element. 5. Origin and Relationships - `origine` (string): Origin - The origin of the document (e.g., "LEGI"). - `dateDebutExtension` (string): Start Date of Extension - The start date if the article has an extension. - `idEliAlias` (string): ELI Alias - Alias for the European Legislation Identifier (ELI). - `cidTexte` (string): Text Chronical ID - Chronical identifier of the text. 6. Hierarchical Relationships - `sectionParentId` (string): Parent Section ID - Technical identifier of the parent section. - `multipleVersions` (boolean): Multiple Versions - Indicates if the article has multiple versions. - `comporteLiensSP` (boolean): Contains Public Service Links - Indicates if the article contains links to public services. - `sectionParentTitre` (string): Parent Section Title - Title of the parent section (e.g., "I : Revenu imposable"). - `infosRestructurationBranche` (string): Branch Restructuring Information - Information about branch restructuring. - `idEli` (string): ELI ID - European Legislation Identifier (ELI) for the article. - `sectionParentCid` (string): Parent Section Chronical ID - Chronical identifier of the parent section. 7. Additional Content and History - `numeroBo` (string): Official Bulletin Number - Number of the official bulletin where the article was published. - `infosRestructurationBrancheHtml` (string): Branch Restructuring Information (HTML) - Branch restructuring information in HTML format. - `historique` (string): History - Historical context or changes specific to collective agreements. - `infosComplementairesHtml` (string): Additional Information (HTML) - Additional information in HTML format. - `renvoi` (string): Reference - References to content within the article (e.g., "(1)"). - `fullSectionsTitre` (string): Full Section Titles - Concatenation of all titles in the parent chain. - `notaHtml` (string): Notes (HTML) - Additional notes or remarks in HTML format. - `inap` (string): INAP - A placeholder for INAP-specific information. ## Feedback If you have any feedback, please reach out at [[email protected]](mailto:[email protected]).
louisbrulenaudet/code-justice-administrative	louisbrulenaudet	2025-05-12T06:51:59Z	444	0	[ "task_categories:text-generation", "task_categories:table-question-answering", "task_categories:summarization", "task_categories:text-retrieval", "task_categories:question-answering", "task_categories:text-classification", "multilinguality:monolingual", "source_datasets:original", "language:fr", "license:apache-2.0", "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "doi:10.57967/hf/1469", "region:us", "finetuning", "legal", "french law", "droit français", "Code de justice administrative" ]	[ "text-generation", "table-question-answering", "summarization", "text-retrieval", "question-answering", "text-classification" ]	2023-12-12T21:26:00Z	null	--- license: apache-2.0 language: - fr multilinguality: - monolingual tags: - finetuning - legal - french law - droit français - Code de justice administrative source_datasets: - original pretty_name: Code de justice administrative task_categories: - text-generation - table-question-answering - summarization - text-retrieval - question-answering - text-classification size_categories: - 1K<n<10K --- # Code de justice administrative, non-instruct (2025-05-11) The objective of this project is to provide researchers, professionals and law students with simplified, up-to-date access to all French legal texts, enriched with a wealth of data to facilitate their integration into Community and European projects. Normally, the data is refreshed daily on all legal codes, and aims to simplify the production of training sets and labeling pipelines for the development of free, open-source language models based on open data accessible to all. ## Concurrent reading of the LegalKit [<img src="https://raw.githubusercontent.com/louisbrulenaudet/ragoon/main/assets/badge.svg" alt="Built with RAGoon" width="200" height="32"/>](https://github.com/louisbrulenaudet/ragoon) To use all the legal data published on LegalKit, you can use RAGoon: ```bash pip3 install ragoon ``` Then, you can load multiple datasets using this code snippet: ```python # -- coding: utf-8 -- from ragoon import load_datasets req = [ "louisbrulenaudet/code-artisanat", "louisbrulenaudet/code-action-sociale-familles", # ... ] datasets_list = load_datasets( req=req, streaming=False ) dataset = datasets.concatenate_datasets( datasets_list ) ``` ### Data Structure for Article Information This section provides a detailed overview of the elements contained within the `item` dictionary. Each key represents a specific attribute of the legal article, with its associated value providing detailed information. 1. Basic Information - `ref` (string): Reference - A reference to the article, combining the title_main and the article `number` (e.g., "Code Général des Impôts, art. 123"). - `texte` (string): Text Content - The textual content of the article. - `dateDebut` (string): Start Date - The date when the article came into effect. - `dateFin` (string): End Date - The date when the article was terminated or superseded. - `num` (string): Article Number - The number assigned to the article. - `id` (string): Article ID - Unique identifier for the article. - `cid` (string): Chronical ID - Chronical identifier for the article. - `type` (string): Type - The type or classification of the document (e.g., "AUTONOME"). - `etat` (string): Legal Status - The current legal status of the article (e.g., "MODIFIE_MORT_NE"). 2. Content and Notes - `nota` (string): Notes - Additional notes or remarks associated with the article. - `version_article` (string): Article Version - The version number of the article. - `ordre` (integer): Order Number - A numerical value used to sort articles within their parent section. 3. Additional Metadata - `conditionDiffere` (string): Deferred Condition - Specific conditions related to collective agreements. - `infosComplementaires` (string): Additional Information - Extra information pertinent to the article. - `surtitre` (string): Subtitle - A subtitle or additional title information related to collective agreements. - `nature` (string): Nature - The nature or category of the document (e.g., "Article"). - `texteHtml` (string): HTML Content - The article's content in HTML format. 4. Versioning and Extensions - `dateFinExtension` (string): End Date of Extension - The end date if the article has an extension. - `versionPrecedente` (string): Previous Version - Identifier for the previous version of the article. - `refInjection` (string): Injection Reference - Technical reference to identify the date of injection. - `idTexte` (string): Text ID - Identifier for the legal text to which the article belongs. - `idTechInjection` (string): Technical Injection ID - Technical identifier for the injected element. 5. Origin and Relationships - `origine` (string): Origin - The origin of the document (e.g., "LEGI"). - `dateDebutExtension` (string): Start Date of Extension - The start date if the article has an extension. - `idEliAlias` (string): ELI Alias - Alias for the European Legislation Identifier (ELI). - `cidTexte` (string): Text Chronical ID - Chronical identifier of the text. 6. Hierarchical Relationships - `sectionParentId` (string): Parent Section ID - Technical identifier of the parent section. - `multipleVersions` (boolean): Multiple Versions - Indicates if the article has multiple versions. - `comporteLiensSP` (boolean): Contains Public Service Links - Indicates if the article contains links to public services. - `sectionParentTitre` (string): Parent Section Title - Title of the parent section (e.g., "I : Revenu imposable"). - `infosRestructurationBranche` (string): Branch Restructuring Information - Information about branch restructuring. - `idEli` (string): ELI ID - European Legislation Identifier (ELI) for the article. - `sectionParentCid` (string): Parent Section Chronical ID - Chronical identifier of the parent section. 7. Additional Content and History - `numeroBo` (string): Official Bulletin Number - Number of the official bulletin where the article was published. - `infosRestructurationBrancheHtml` (string): Branch Restructuring Information (HTML) - Branch restructuring information in HTML format. - `historique` (string): History - Historical context or changes specific to collective agreements. - `infosComplementairesHtml` (string): Additional Information (HTML) - Additional information in HTML format. - `renvoi` (string): Reference - References to content within the article (e.g., "(1)"). - `fullSectionsTitre` (string): Full Section Titles - Concatenation of all titles in the parent chain. - `notaHtml` (string): Notes (HTML) - Additional notes or remarks in HTML format. - `inap` (string): INAP - A placeholder for INAP-specific information. ## Feedback If you have any feedback, please reach out at [[email protected]](mailto:[email protected]).
Cartinoe5930/deepmath_500	Cartinoe5930	2025-05-12T06:44:26Z	0	0	[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-12T06:33:35Z	null	--- dataset_info: features: - name: question dtype: string - name: final_answer dtype: string - name: difficulty dtype: float64 - name: topic dtype: string splits: - name: train num_bytes: 145226 num_examples: 500 download_size: 67158 dataset_size: 145226 configs: - config_name: default data_files: - split: train path: data/train-* ---
AtsuMiyai/webchore_test13	AtsuMiyai	2025-05-12T06:41:00Z	0	0	[ "size_categories:n<1K", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-12T06:40:55Z	null	--- dataset_info: features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_1_license dtype: string - name: image_1_attribution dtype: string - name: image_1_use_original_mmmu dtype: bool - name: image_2 dtype: image - name: image_2_license dtype: string - name: image_2_attribution dtype: string - name: image_2_use_original_mmmu dtype: bool - name: image_3 dtype: image - name: image_3_license dtype: string - name: image_3_attribution dtype: string - name: image_3_use_original_mmmu dtype: bool - name: image_4 dtype: image - name: image_4_license dtype: string - name: image_4_attribution dtype: string - name: image_4_use_original_mmmu dtype: bool - name: image_5 dtype: image - name: image_5_license dtype: string - name: image_5_attribution dtype: string - name: image_5_use_original_mmmu dtype: bool - name: image_6 dtype: image - name: image_6_license dtype: string - name: image_6_attribution dtype: string - name: image_6_use_original_mmmu dtype: bool - name: image_7 dtype: image - name: image_7_license dtype: string - name: image_7_attribution dtype: string - name: image_7_use_original_mmmu dtype: bool - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string - name: sites sequence: string - name: start_url dtype: string - name: start_url_lite dtype: string - name: storage_state dtype: string - name: intent_template dtype: string - name: required_obs dtype: string - name: description dtype: string - name: instantiation_dict struct: - name: PRnumber dtype: 'null' - name: answer dtype: 'null' - name: answers dtype: 'null' - name: checkpint_info dtype: string - name: checkpoint1 dtype: string - name: checkpoint2 dtype: string - name: checkpoint3 dtype: string - name: checkpoint4 dtype: string - name: checkpoint5 dtype: string - name: checkpoint6 dtype: string - name: checkpoint_info dtype: string - name: commitSum dtype: 'null' - name: contents dtype: string - name: count dtype: 'null' - name: date dtype: 'null' - name: default_branch dtype: 'null' - name: difficulty dtype: string - name: enddate dtype: 'null' - name: format dtype: 'null' - name: issue_counts dtype: 'null' - name: issues_count dtype: 'null' - name: keyword dtype: 'null' - name: lastupdatetime dtype: 'null' - name: license dtype: 'null' - name: lowerbound dtype: 'null' - name: memo dtype: 'null' - name: memo1 dtype: 'null' - name: memo2 dtype: 'null' - name: memo3 dtype: 'null' - name: memo4 dtype: 'null' - name: memo5 dtype: 'null' - name: month dtype: string - name: number dtype: string - name: orderedProjects dtype: 'null' - name: project dtype: 'null' - name: project1 dtype: 'null' - name: project2 dtype: 'null' - name: project3 dtype: 'null' - name: project4 dtype: 'null' - name: project5 dtype: 'null' - name: question dtype: 'null' - name: readme_repo1 dtype: 'null' - name: readme_repo2_url dtype: 'null' - name: repository dtype: 'null' - name: repository1 dtype: 'null' - name: repository2 dtype: 'null' - name: repository3 dtype: 'null' - name: start_url dtype: string - name: start_url_lite dtype: string - name: tag1 dtype: string - name: tag2 dtype: string - name: target dtype: 'null' - name: total_star dtype: 'null' - name: unique_users_num dtype: 'null' - name: url dtype: 'null' - name: user dtype: string - name: user1 dtype: string - name: user2 dtype: string - name: user3 dtype: 'null' - name: user4 dtype: 'null' - name: user5 dtype: 'null' - name: userName dtype: 'null' - name: year dtype: string splits: - name: test num_bytes: 1772449.0 num_examples: 30 download_size: 1782916 dataset_size: 1772449.0 configs: - config_name: default data_files: - split: test path: data/test-* ---
tarsur909/imdb_sft_processed25p	tarsur909	2025-05-12T06:29:10Z	0	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-12T06:29:07Z	null	--- dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': neg '1': pos - name: query dtype: string - name: gen_review dtype: string - name: query_input_ids sequence: int64 - name: query_attention_mask sequence: int64 - name: reference_response dtype: string - name: reference_response_input_ids sequence: int64 - name: reference_response_attention_mask sequence: int64 - name: reference_response_token_len dtype: int64 - name: query_reference_response dtype: string - name: query_reference_response_input_ids sequence: int64 - name: query_reference_response_attention_mask sequence: int64 - name: query_reference_response_token_response_label sequence: int64 - name: query_reference_response_token_len dtype: int64 splits: - name: train num_bytes: 106246592.5 num_examples: 3125 - name: test num_bytes: 105687226.5 num_examples: 3125 download_size: 32770008 dataset_size: 211933819.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
FariqF/NPTL_SPEAKER_DATASETS	FariqF	2025-05-12T05:51:31Z	0	0	[ "license:mit", "size_categories:1K<n<10K", "format:parquet", "modality:audio", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-12T05:49:28Z	null	--- license: mit dataset_info: features: - name: source_id dtype: string - name: audio_id dtype: string - name: audio_start dtype: float32 - name: audio_end dtype: float32 - name: duration dtype: float32 - name: audio dtype: audio: sampling_rate: 16000 - name: text dtype: string - name: words sequence: - name: word dtype: string - name: start dtype: float32 - name: end dtype: float32 - name: speaker_id dtype: int64 splits: - name: train num_bytes: 2142509474.334 num_examples: 6597 download_size: 2063465797 dataset_size: 2142509474.334 configs: - config_name: default data_files: - split: train path: data/train-* ---
cchoi1/kodcode_1000_gpt-4o_qwen7b_att_iter0_debug	cchoi1	2025-05-12T05:50:31Z	18	0	[ "size_categories:n<1K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-04-29T22:55:38Z	null	--- dataset_info: features: - name: mutation_id dtype: int64 - name: task_id dtype: string - name: mutator_prompt dtype: string - name: solver_prompt dtype: string - name: response dtype: string - name: mutation_explanation dtype: string - name: mutation_info dtype: string - name: mutator_score dtype: float64 - name: solution_scores dtype: string - name: solutions dtype: string - name: solutions_explanation dtype: string - name: solutions_info dtype: string splits: - name: train num_bytes: 70519 num_examples: 10 download_size: 55180 dataset_size: 70519 configs: - config_name: default data_files: - split: train path: data/train-* ---
upvantage/top5-scored-gpt4.1	upvantage	2025-05-12T05:23:53Z	0	0	[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "modality:timeseries", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-12T05:09:28Z	null	--- dataset_info: features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: ai_score sequence: float32 - name: context dtype: string - name: text dtype: string - name: style dtype: string - name: lengthInstruction dtype: string splits: - name: train num_bytes: 132346814 num_examples: 26794 download_size: 37510275 dataset_size: 132346814 configs: - config_name: default data_files: - split: train path: data/train-* ---
ma921/hh-rlhf-filtered	ma921	2025-05-12T04:41:32Z	0	0	[ "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-12T04:41:21Z	null	--- dataset_info: features: - name: chosen dtype: string - name: rejected dtype: string splits: - name: train num_bytes: 298842161.7277985 num_examples: 155730 - name: test num_bytes: 16010125.275374182 num_examples: 8267 download_size: 160465974 dataset_size: 314852287.0031727 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
icedpanda/msmarco_cold_start_dataset_100k_llama_merge_aug	icedpanda	2025-05-12T04:26:05Z	25	0	[ "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-09T02:27:37Z	null	--- dataset_info: features: - name: query dtype: string - name: query_id dtype: string - name: pid sequence: string - name: response dtype: string - name: hard_negative_pid sequence: string splits: - name: train num_bytes: 225128843 num_examples: 102836 download_size: 134665838 dataset_size: 225128843 configs: - config_name: default data_files: - split: train path: data/train-* ---
charleyong/so100_tic_tac_toe_updated	charleyong	2025-05-12T04:22:19Z	0	0	[ "task_categories:robotics", "license:apache-2.0", "size_categories:10K<n<100K", "format:parquet", "modality:tabular", "modality:timeseries", "modality:video", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us", "LeRobot", "so100", "yc_demo", "multi_task" ]	[ "robotics" ]	2025-05-12T03:16:20Z	null	--- license: apache-2.0 task_categories: - robotics tags: - LeRobot - so100 - yc_demo - multi_task 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": 20, "total_frames": 5941, "total_tasks": 1, "total_videos": 20, "total_chunks": 1, "chunks_size": 1000, "fps": 30, "splits": { "train": "0:20" }, "data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet", "video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4", "features": { "action": { "dtype": "float32", "shape": [ 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.stationary": { "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] ```
justinsunqiu/transcription_changes	justinsunqiu	2025-05-12T04:21:51Z	0	0	[ "size_categories:n<1K", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-12T04:21:49Z	null	--- dataset_info: features: - name: id dtype: string - name: language dtype: string - name: culturally_distinct dtype: bool - name: cultural_distinction_explanation dtype: string - name: vocaroo_link dtype: string - name: image_link dtype: string - name: selected_other_languages dtype: bool - name: Goodness dtype: string - name: orig_transcription dtype: string - name: orig_translation dtype: string - name: fixed_transcription dtype: string - name: fixed_translation dtype: string splits: - name: train num_bytes: 1958762 num_examples: 498 download_size: 1108010 dataset_size: 1958762 configs: - config_name: default data_files: - split: train path: data/train-* ---
patrickechohelloworld/well_formatted_benchmarks_pro	patrickechohelloworld	2025-05-12T04:08:48Z	83	0	[ "task_categories:zero-shot-classification", "task_categories:question-answering", "language:en", "size_categories:1M<n<10M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us", "synthetic" ]	[ "zero-shot-classification", "question-answering" ]	2025-05-08T14:52:58Z	null	--- task_categories: - zero-shot-classification - question-answering language: - en tags: - synthetic size_categories: - 1M<n<10M --- # Dataset Card for well_formatted_benchmarks_pro <!-- Provide a quick summary of the dataset. --> This is a collection of formatted benchmarks. ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> This repo is home to formatted versions of some famous benchmarks I created this repo because current benchmark datasets on the hub generally don't have a fixed format, which is annoying when you try to use them. - Language(s) (NLP): English ### Dataset Sources <!-- Provide the basic links for the dataset. --> #### ARC - Repository: [Original ARC repo](https://huggingface.co/datasets/allenai/ai2_arc) - Demo: ```text <user>An astronomer observes that a planet rotates faster after a meteorite impact. Which is the most likely effect of this increase in rotation?\ <sep>A: Planetary density will decrease.</sep><sep>B: Planetary years will become longer.</sep><sep>C: Planetary days will become shorter.</sep>\ <sep>D: Planetary gravity will become stronger.</sep></user><model>C ``` #### GSM8K - Repository: [Original GSM8K repo](https://huggingface.co/datasets/openai/gsm8k) - Demo: ```text <user>A robe takes 2 bolts of blue fiber and half that much white fiber. How many bolts in total does it take?</user><model>It takes 2/2=<<2/2=1>>1 bolt of white fiber So the total amount of fabric is 2+1=<<2+1=3>>3 bolts of fabric #### 3 ``` #### HellaSwag - Repository: [Original HellaSwag repo](https://huggingface.co/datasets/Rowan/hellaswag) - Demo: ```text <user> The topic of this sentence is: Getting a haircut. Based on the topic of this sentence, finish this sentence: \ The man in the center is demonstrating a hairstyle on the person wearing the blue shirt. the man in the blue shirt\ <sep>A: is standing on the sponge cutting the hair of the person wearing the blue shirt.</sep>\ <sep>B: is doing the hairstyle with his hand and the hairspray.</sep><sep>C: sits on the chair next to the sink.</sep>\ <sep>D: is being shown eye to eye.</sep></user><model>C ``` #### MMLU - Repository: [Original MMLU repo](https://huggingface.co/datasets/cais/mmlu) - Demo: ```text <user>Find the degree for the given field extension Q(sqrt(2), sqrt(3), sqrt(18)) over Q.<sep>A: 0</sep><sep>B: 4</sep><sep>C: 2</sep><sep>D: 6</sep></user><model>B ``` #### OpenBookQA - Repository: [Original OpenBookQA repo](https://huggingface.co/datasets/mandarjoshi/trivia_qa) - Demo: ```text <user> It is ture that: predators eat prey. Based on this fact, answer the following question:\ Predators eat<sep>A: lions</sep><sep>B: humans</sep><sep>C: bunnies</sep><sep>D: grass</sep></user><model>C ``` #### TriviaQA - Repository: [Original TriviaQA repo](https://huggingface.co/datasets/mandarjoshi/trivia_qa) - Demo: ```text <user>Which American-born Sinclair won the Nobel Prize for Literature in 1930?</user>Sinclair Lewis ``` ### PIQA - Repository: [Original PIQA repo](https://huggingface.co/datasets/ybisk/piqa) - Demo ```text <user>The goal is: Make outdoor pillow.<sep>A: Blow into tin can and tie with rubber band.</sep>\ <sep>B: Blow into trash bag and tie with rubber band.</sep></user><model><model><model>B ``` ## Uses <!-- Address questions around how the dataset is intended to be used. --> ### Direct Use <!-- This section describes suitable use cases for the dataset. --> It's recommended to use this dataset by downloading the parquet files from `main` branch and load it with `polars`: ```python import polars as pl data = pl.read_parquet('./path/to/downloaded/file').get_column('text') ... ``` The special tokens used in this repo includes: ```text <user>: the beginning of prompt </user>: the end of prompt <model>: the beginning of response <sep>: the beginning of an option </sep>: the end of an option ``` These tokens works well with my custom tokenizer, but remember to replace them with your own special tokens like this: ```python # Replace with another token text = text.replace('<model>', 'YOUR_SPECIAL_TOKEN') # Remove the special token text = text.replace('<model>', '') ``` ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> Like what I mentioned above, a custom tokenizer is used to generate the files in `token` folder. So you need to tokenize the dataset yourself. ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> - Raw data from original repos are in the root directory of each subsets. - Formatted(but not tokenized) data files are in `./processed` directory. - Tokenized data files are in `./token` directory (and you probably don't need them, as mentioned above) ```text . ├── processed <- This is the formatted data you want! │ ├── train.parquet │ └── validation.parquet ├── token │ ├── train.parquet │ └── validation.parquet ├── train.parquet └── validation.parquet <- These are raw data files ``` ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> [More Information Needed] ### Source Data <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> #### Data Collection and Processing <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> The data processing is done mainly with the python scripts in the root directory (and their variants). So you can re-write these scripts based on your need to create your own formatted datasets! #### Who are the source data producers? <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> Please refer to the links above to see the original authors of these datasets. ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> Please keep in mind that these datasets is for benchmarking, some of them are not suitable for SFT. Although I didn't change the content of the original datasets, it's always good practice to check them out by yourself! ## Citation [optional] <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. --> [More Information Needed] ## Dataset Card Authors [optional] patrick_echo_hello_world ## Dataset Card Contact [[email protected]]
pratikmurali/fda_samd_regulations_golden_test_dataset	pratikmurali	2025-05-12T04:03:12Z	0	0	[ "license:apache-2.0", "size_categories:n<1K", "format:csv", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-12T04:02:38Z	null	--- license: apache-2.0 ---
ajd12342/paraspeechcaps-processed-situational-only-with-original-prompts-test-set-1000	ajd12342	2025-05-12T04:00:30Z	0	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-12T04:00:28Z	null	--- dataset_info: features: - name: source dtype: string - name: relative_audio_path dtype: string - name: text_description sequence: string - name: transcription dtype: string - name: intrinsic_tags sequence: string - name: situational_tags dtype: string - name: basic_tags sequence: string - name: all_tags sequence: string - name: speakerid dtype: string - name: name dtype: string - name: duration dtype: float64 - name: gender dtype: string - name: accent dtype: string - name: pitch dtype: string - name: speaking_rate dtype: string - name: noise dtype: string - name: utterance_pitch_mean dtype: float64 - name: snr dtype: float64 - name: phonemes dtype: string - name: audio_path dtype: string splits: - name: test num_bytes: 1119852.3727781163 num_examples: 1000 download_size: 398405 dataset_size: 1119852.3727781163 configs: - config_name: default data_files: - split: test path: data/test-* ---
justinsunqiu/multilingual_transcriptions_translated_raw	justinsunqiu	2025-05-12T03:58:38Z	0	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-12T03:58:37Z	null	--- dataset_info: features: - name: id dtype: string - name: language dtype: string - name: culturally_distinct dtype: bool - name: cultural_distinction_explanation dtype: string - name: vocaroo_link dtype: string - name: image_link dtype: string - name: transcription dtype: string - name: selected_other_languages dtype: bool - name: translation dtype: string - name: Goodness dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 11608597 num_examples: 6220 download_size: 5718578 dataset_size: 11608597 configs: - config_name: default data_files: - split: train path: data/train-* ---
Haviet2003/Gemma3_1b	Haviet2003	2025-05-12T03:52:40Z	0	0	[ "license:apache-2.0", "size_categories:n<1K", "format:csv", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-12T03:50:23Z	null	--- license: apache-2.0 ---
deokhk/MOpenThoughts-114k-problems	deokhk	2025-05-12T03:46:00Z	0	0	[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-12T03:45:54Z	null	--- dataset_info: config_name: en features: - name: problem dtype: string - name: ground_truth_solution dtype: string - name: domain dtype: string - name: id dtype: string splits: - name: train num_bytes: 38967862 num_examples: 18993 download_size: 18977718 dataset_size: 38967862 configs: - config_name: en data_files: - split: train path: en/train-* ---
cross-validation/City-Networks	cross-validation	2025-05-12T03:25:15Z	10	1	[ "license:cc-by-nd-4.0", "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-04-29T15:51:07Z	null	--- license: cc-by-nd-4.0 configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: name dtype: string - name: num_nodes dtype: int64 - name: edge_index sequence: sequence: int32 length: 2 - name: node_features sequence: sequence: float32 - name: labels sequence: int64 - name: train_mask sequence: bool - name: val_mask sequence: bool - name: test_mask sequence: bool splits: - name: train num_bytes: 202285660 num_examples: 4 download_size: 47142645 dataset_size: 202285660 ---
VGraf/BIG_tulu_related_truncated2048_cutto2turns	VGraf	2025-05-12T03:22:06Z	0	0	[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-12T03:21:37Z	null	--- dataset_info: features: - name: messages list: - name: content dtype: string - name: role dtype: string - name: id dtype: string - name: source dtype: string - name: chosen list: - name: content dtype: string - name: role dtype: string - name: rejected list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 479342706 num_examples: 48466 download_size: 253715537 dataset_size: 479342706 configs: - config_name: default data_files: - split: train path: data/train-* ---

fixie-ai/mls_eng_10k_chat

fixie-ai

2025-05-13T19:09:26Z

0

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

[]

2025-05-13T17:46:07Z

null

--- dataset_info: features: - name: input_ids sequence: int32 - name: labels sequence: int32 - name: llasa_llama_formatted_text_prompt dtype: string - name: general_text_prompt dtype: string splits: - name: train num_bytes: 8915496143 num_examples: 151254 - name: test num_bytes: 11077284 num_examples: 236 - name: validation num_bytes: 11482367 num_examples: 238 download_size: 4219089264 dataset_size: 8938055794 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: validation path: data/validation-* ---

louisbrulenaudet/code-impots-annexe-ii

louisbrulenaudet

2025-05-13T15:42:31Z

637

0

[ "task_categories:text-generation", "task_categories:table-question-answering", "task_categories:summarization", "task_categories:text-retrieval", "task_categories:question-answering", "task_categories:text-classification", "multilinguality:monolingual", "source_datasets:original", "language:fr", "license:apache-2.0", "size_categories:n<1K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "finetuning", "legal", "french law", "droit français", "Code général des impôts, annexe II" ]

[ "text-generation", "table-question-answering", "summarization", "text-retrieval", "question-answering", "text-classification" ]

2024-03-25T22:39:19Z

null

--- license: apache-2.0 language: - fr multilinguality: - monolingual tags: - finetuning - legal - french law - droit français - Code général des impôts, annexe II source_datasets: - original pretty_name: Code général des impôts, annexe II task_categories: - text-generation - table-question-answering - summarization - text-retrieval - question-answering - text-classification size_categories: - 1K<n<10K --- # Code général des impôts, annexe II, non-instruct (2025-05-12) The objective of this project is to provide researchers, professionals and law students with simplified, up-to-date access to all French legal texts, enriched with a wealth of data to facilitate their integration into Community and European projects. Normally, the data is refreshed daily on all legal codes, and aims to simplify the production of training sets and labeling pipelines for the development of free, open-source language models based on open data accessible to all. ## Concurrent reading of the LegalKit [<img src="https://raw.githubusercontent.com/louisbrulenaudet/ragoon/main/assets/badge.svg" alt="Built with RAGoon" width="200" height="32"/>](https://github.com/louisbrulenaudet/ragoon) To use all the legal data published on LegalKit, you can use RAGoon: ```bash pip3 install ragoon ``` Then, you can load multiple datasets using this code snippet: ```python # -*- coding: utf-8 -*- from ragoon import load_datasets req = [ "louisbrulenaudet/code-artisanat", "louisbrulenaudet/code-action-sociale-familles", # ... ] datasets_list = load_datasets( req=req, streaming=False ) dataset = datasets.concatenate_datasets( datasets_list ) ``` ### Data Structure for Article Information This section provides a detailed overview of the elements contained within the `item` dictionary. Each key represents a specific attribute of the legal article, with its associated value providing detailed information. 1. **Basic Information** - `ref` (string): **Reference** - A reference to the article, combining the title_main and the article `number` (e.g., "Code Général des Impôts, art. 123"). - `texte` (string): **Text Content** - The textual content of the article. - `dateDebut` (string): **Start Date** - The date when the article came into effect. - `dateFin` (string): **End Date** - The date when the article was terminated or superseded. - `num` (string): **Article Number** - The number assigned to the article. - `id` (string): **Article ID** - Unique identifier for the article. - `cid` (string): **Chronical ID** - Chronical identifier for the article. - `type` (string): **Type** - The type or classification of the document (e.g., "AUTONOME"). - `etat` (string): **Legal Status** - The current legal status of the article (e.g., "MODIFIE_MORT_NE"). 2. **Content and Notes** - `nota` (string): **Notes** - Additional notes or remarks associated with the article. - `version_article` (string): **Article Version** - The version number of the article. - `ordre` (integer): **Order Number** - A numerical value used to sort articles within their parent section. 3. **Additional Metadata** - `conditionDiffere` (string): **Deferred Condition** - Specific conditions related to collective agreements. - `infosComplementaires` (string): **Additional Information** - Extra information pertinent to the article. - `surtitre` (string): **Subtitle** - A subtitle or additional title information related to collective agreements. - `nature` (string): **Nature** - The nature or category of the document (e.g., "Article"). - `texteHtml` (string): **HTML Content** - The article's content in HTML format. 4. **Versioning and Extensions** - `dateFinExtension` (string): **End Date of Extension** - The end date if the article has an extension. - `versionPrecedente` (string): **Previous Version** - Identifier for the previous version of the article. - `refInjection` (string): **Injection Reference** - Technical reference to identify the date of injection. - `idTexte` (string): **Text ID** - Identifier for the legal text to which the article belongs. - `idTechInjection` (string): **Technical Injection ID** - Technical identifier for the injected element. 5. **Origin and Relationships** - `origine` (string): **Origin** - The origin of the document (e.g., "LEGI"). - `dateDebutExtension` (string): **Start Date of Extension** - The start date if the article has an extension. - `idEliAlias` (string): **ELI Alias** - Alias for the European Legislation Identifier (ELI). - `cidTexte` (string): **Text Chronical ID** - Chronical identifier of the text. 6. **Hierarchical Relationships** - `sectionParentId` (string): **Parent Section ID** - Technical identifier of the parent section. - `multipleVersions` (boolean): **Multiple Versions** - Indicates if the article has multiple versions. - `comporteLiensSP` (boolean): **Contains Public Service Links** - Indicates if the article contains links to public services. - `sectionParentTitre` (string): **Parent Section Title** - Title of the parent section (e.g., "I : Revenu imposable"). - `infosRestructurationBranche` (string): **Branch Restructuring Information** - Information about branch restructuring. - `idEli` (string): **ELI ID** - European Legislation Identifier (ELI) for the article. - `sectionParentCid` (string): **Parent Section Chronical ID** - Chronical identifier of the parent section. 7. **Additional Content and History** - `numeroBo` (string): **Official Bulletin Number** - Number of the official bulletin where the article was published. - `infosRestructurationBrancheHtml` (string): **Branch Restructuring Information (HTML)** - Branch restructuring information in HTML format. - `historique` (string): **History** - Historical context or changes specific to collective agreements. - `infosComplementairesHtml` (string): **Additional Information (HTML)** - Additional information in HTML format. - `renvoi` (string): **Reference** - References to content within the article (e.g., "(1)"). - `fullSectionsTitre` (string): **Full Section Titles** - Concatenation of all titles in the parent chain. - `notaHtml` (string): **Notes (HTML)** - Additional notes or remarks in HTML format. - `inap` (string): **INAP** - A placeholder for INAP-specific information. ## Feedback If you have any feedback, please reach out at [[email protected]](mailto:[email protected]).

ESITime/tram-relation-responses

ESITime

2025-05-13T15:26:35Z

358

0

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

[]

2025-04-06T14:57:46Z

null

--- dataset_info: - config_name: 05B features: - name: Question dtype: string - name: Option A dtype: string - name: Option B dtype: string - name: Option C dtype: string - name: Answer dtype: string - name: Source dtype: string - name: prompt dtype: string - name: __index_level_0__ dtype: int64 - name: response dtype: string splits: - name: test num_bytes: 563660 num_examples: 504 download_size: 288283 dataset_size: 563660 - config_name: 3B features: - name: Question dtype: string - name: Option A dtype: string - name: Option B dtype: string - name: Option C dtype: string - name: Answer dtype: string - name: Source dtype: string - name: prompt dtype: string - name: __index_level_0__ dtype: int64 - name: response dtype: string splits: - name: test num_bytes: 522730 num_examples: 504 download_size: 268767 dataset_size: 522730 - config_name: 4_epoch features: - name: Question dtype: string - name: Option A dtype: string - name: Option B dtype: string - name: Option C dtype: string - name: Answer dtype: string - name: Source dtype: string - name: prompt dtype: string - name: __index_level_0__ dtype: int64 - name: response dtype: string splits: - name: test num_bytes: 610557 num_examples: 504 download_size: 307133 dataset_size: 610557 - config_name: 6B features: - name: Question dtype: string - name: Option A dtype: string - name: Option B dtype: string - name: Option C dtype: string - name: Answer dtype: string - name: Source dtype: string - name: prompt dtype: string - name: __index_level_0__ dtype: int64 - name: response dtype: string splits: - name: test num_bytes: 523743 num_examples: 504 download_size: 269699 dataset_size: 523743 - config_name: base_base_message_3b features: - name: Question dtype: string - name: Option A dtype: string - name: Option B dtype: string - name: Option C dtype: string - name: Answer dtype: string - name: Source dtype: string - name: prompt dtype: string - name: __index_level_0__ dtype: int64 - name: response dtype: string splits: - name: test num_bytes: 1972433 num_examples: 504 download_size: 810457 dataset_size: 1972433 - config_name: base_create_1.5b features: - name: Question dtype: string - name: Option A dtype: string - name: Option B dtype: string - name: Option C dtype: string - name: Answer dtype: string - name: Source dtype: string - name: prompt dtype: string - name: __index_level_0__ dtype: int64 - name: response dtype: string splits: - name: test num_bytes: 939980 num_examples: 504 download_size: 433440 dataset_size: 939980 - config_name: base_create_3b features: - name: Question dtype: string - name: Option A dtype: string - name: Option B dtype: string - name: Option C dtype: string - name: Answer dtype: string - name: Source dtype: string - name: prompt dtype: string - name: __index_level_0__ dtype: int64 - name: response dtype: string splits: - name: test num_bytes: 564942 num_examples: 504 download_size: 281637 dataset_size: 564942 - config_name: free_1 features: - name: Question dtype: string - name: Option A dtype: string - name: Option B dtype: string - name: Option C dtype: string - name: Answer dtype: string - name: Source dtype: string - name: prompt dtype: string - name: __index_level_0__ dtype: int64 - name: response dtype: string splits: - name: test num_bytes: 574127 num_examples: 504 download_size: 290281 dataset_size: 574127 - config_name: only_1 features: - name: Question dtype: string - name: Option A dtype: string - name: Option B dtype: string - name: Option C dtype: string - name: Answer dtype: string - name: Source dtype: string - name: prompt dtype: string - name: __index_level_0__ dtype: int64 - name: response dtype: string splits: - name: test num_bytes: 563148 num_examples: 504 download_size: 283488 dataset_size: 563148 - config_name: qwen features: - name: Question dtype: string - name: Option A dtype: string - name: Option B dtype: string - name: Option C dtype: string - name: Answer dtype: string - name: Source dtype: string - name: prompt dtype: string - name: __index_level_0__ dtype: int64 - name: response dtype: string splits: - name: test num_bytes: 509236 num_examples: 504 download_size: 277574 dataset_size: 509236 - config_name: qwen05_sft features: - name: Question dtype: string - name: Option A dtype: string - name: Option B dtype: string - name: Option C dtype: string - name: Answer dtype: string - name: Source dtype: string - name: prompt dtype: string - name: __index_level_0__ dtype: int64 - name: response dtype: string splits: - name: test num_bytes: 400398 num_examples: 504 download_size: 203651 dataset_size: 400398 - config_name: qwen15_sft features: - name: Question dtype: string - name: Option A dtype: string - name: Option B dtype: string - name: Option C dtype: string - name: Answer dtype: string - name: Source dtype: string - name: prompt dtype: string - name: __index_level_0__ dtype: int64 - name: response dtype: string splits: - name: test num_bytes: 887100 num_examples: 504 download_size: 442407 dataset_size: 887100 - config_name: qwen3_sft features: - name: Question dtype: string - name: Option A dtype: string - name: Option B dtype: string - name: Option C dtype: string - name: Answer dtype: string - name: Source dtype: string - name: prompt dtype: string - name: __index_level_0__ dtype: int64 - name: response dtype: string splits: - name: test num_bytes: 673150 num_examples: 504 download_size: 333123 dataset_size: 673150 - config_name: qwen_3 features: - name: Question dtype: string - name: Option A dtype: string - name: Option B dtype: string - name: Option C dtype: string - name: Answer dtype: string - name: Source dtype: string - name: prompt dtype: string - name: __index_level_0__ dtype: int64 - name: response dtype: string splits: - name: test num_bytes: 503714 num_examples: 504 download_size: 260303 dataset_size: 503714 - config_name: qwen_3b_sft features: - name: Question dtype: string - name: Option A dtype: string - name: Option B dtype: string - name: Option C dtype: string - name: Answer dtype: string - name: Source dtype: string - name: prompt dtype: string - name: __index_level_0__ dtype: int64 - name: response dtype: string splits: - name: test num_bytes: 871497 num_examples: 504 download_size: 418390 dataset_size: 871497 - config_name: qwen_4 features: - name: Question dtype: string - name: Option A dtype: string - name: Option B dtype: string - name: Option C dtype: string - name: Answer dtype: string - name: Source dtype: string - name: prompt dtype: string - name: __index_level_0__ dtype: int64 - name: response dtype: string splits: - name: test num_bytes: 503131 num_examples: 504 download_size: 259984 dataset_size: 503131 - config_name: qwen_4_1 features: - name: Question dtype: string - name: Option A dtype: string - name: Option B dtype: string - name: Option C dtype: string - name: Answer dtype: string - name: Source dtype: string - name: prompt dtype: string - name: __index_level_0__ dtype: int64 - name: response dtype: string splits: - name: test num_bytes: 502707 num_examples: 505 download_size: 259125 dataset_size: 502707 - config_name: qwen_5 features: - name: Question dtype: string - name: Option A dtype: string - name: Option B dtype: string - name: Option C dtype: string - name: Answer dtype: string - name: Source dtype: string - name: prompt dtype: string - name: __index_level_0__ dtype: int64 - name: response dtype: string splits: - name: test num_bytes: 614947 num_examples: 504 download_size: 312432 dataset_size: 614947 - config_name: sft1 features: - name: Question dtype: string - name: Option A dtype: string - name: Option B dtype: string - name: Option C dtype: string - name: Answer dtype: string - name: Source dtype: string - name: prompt dtype: string - name: __index_level_0__ dtype: int64 - name: response dtype: string splits: - name: test num_bytes: 760039 num_examples: 504 download_size: 366132 dataset_size: 760039 - config_name: sft1_1 features: - name: Question dtype: string - name: Option A dtype: string - name: Option B dtype: string - name: Option C dtype: string - name: Answer dtype: string - name: Source dtype: string - name: prompt dtype: string - name: __index_level_0__ dtype: int64 - name: response dtype: string splits: - name: test num_bytes: 834967 num_examples: 504 download_size: 408377 dataset_size: 834967 - config_name: sft1_3 features: - name: Question dtype: string - name: Option A dtype: string - name: Option B dtype: string - name: Option C dtype: string - name: Answer dtype: string - name: Source dtype: string - name: prompt dtype: string - name: __index_level_0__ dtype: int64 - name: response dtype: string splits: - name: test num_bytes: 790174 num_examples: 504 download_size: 378792 dataset_size: 790174 - config_name: sft1_4 features: - name: Question dtype: string - name: Option A dtype: string - name: Option B dtype: string - name: Option C dtype: string - name: Answer dtype: string - name: Source dtype: string - name: prompt dtype: string - name: __index_level_0__ dtype: int64 - name: response dtype: string splits: - name: test num_bytes: 798017 num_examples: 504 download_size: 381126 dataset_size: 798017 - config_name: sft1_5 features: - name: Question dtype: string - name: Option A dtype: string - name: Option B dtype: string - name: Option C dtype: string - name: Answer dtype: string - name: Source dtype: string - name: prompt dtype: string - name: __index_level_0__ dtype: int64 - name: response dtype: string splits: - name: test num_bytes: 829423 num_examples: 504 download_size: 401651 dataset_size: 829423 - config_name: sft2 features: - name: Question dtype: string - name: Option A dtype: string - name: Option B dtype: string - name: Option C dtype: string - name: Answer dtype: string - name: Source dtype: string - name: prompt dtype: string - name: __index_level_0__ dtype: int64 - name: response dtype: string splits: - name: test num_bytes: 794397 num_examples: 504 download_size: 384196 dataset_size: 794397 - config_name: sft2_1 features: - name: Question dtype: string - name: Option A dtype: string - name: Option B dtype: string - name: Option C dtype: string - name: Answer dtype: string - name: Source dtype: string - name: prompt dtype: string - name: __index_level_0__ dtype: int64 - name: response dtype: string splits: - name: test num_bytes: 832224 num_examples: 504 download_size: 404456 dataset_size: 832224 - config_name: sft2_3 features: - name: Question dtype: string - name: Option A dtype: string - name: Option B dtype: string - name: Option C dtype: string - name: Answer dtype: string - name: Source dtype: string - name: prompt dtype: string - name: __index_level_0__ dtype: int64 - name: response dtype: string splits: - name: test num_bytes: 667852 num_examples: 504 download_size: 331857 dataset_size: 667852 - config_name: sft2_4 features: - name: Question dtype: string - name: Option A dtype: string - name: Option B dtype: string - name: Option C dtype: string - name: Answer dtype: string - name: Source dtype: string - name: prompt dtype: string - name: __index_level_0__ dtype: int64 - name: response dtype: string splits: - name: test num_bytes: 663333 num_examples: 504 download_size: 330105 dataset_size: 663333 - config_name: sft2_5 features: - name: Question dtype: string - name: Option A dtype: string - name: Option B dtype: string - name: Option C dtype: string - name: Answer dtype: string - name: Source dtype: string - name: prompt dtype: string - name: __index_level_0__ dtype: int64 - name: response dtype: string splits: - name: test num_bytes: 674219 num_examples: 504 download_size: 340371 dataset_size: 674219 configs: - config_name: 05B data_files: - split: test path: 05B/test-* - config_name: 3B data_files: - split: test path: 3B/test-* - config_name: 4_epoch data_files: - split: test path: 4_epoch/test-* - config_name: 6B data_files: - split: test path: 6B/test-* - config_name: base_base_message_3b data_files: - split: test path: base_base_message_3b/test-* - config_name: base_create_1.5b data_files: - split: test path: base_create_1.5b/test-* - config_name: base_create_3b data_files: - split: test path: base_create_3b/test-* - config_name: free_1 data_files: - split: test path: free_1/test-* - config_name: only_1 data_files: - split: test path: only_1/test-* - config_name: qwen data_files: - split: test path: qwen/test-* - config_name: qwen05_sft data_files: - split: test path: qwen05_sft/test-* - config_name: qwen15_sft data_files: - split: test path: qwen15_sft/test-* - config_name: qwen3_sft data_files: - split: test path: qwen3_sft/test-* - config_name: qwen_3 data_files: - split: test path: qwen_3/test-* - config_name: qwen_3b_sft data_files: - split: test path: qwen_3b_sft/test-* - config_name: qwen_4 data_files: - split: test path: qwen_4/test-* - config_name: qwen_4_1 data_files: - split: test path: qwen_4_1/test-* - config_name: qwen_5 data_files: - split: test path: qwen_5/test-* - config_name: sft1 data_files: - split: test path: sft1/test-* - config_name: sft1_1 data_files: - split: test path: sft1_1/test-* - config_name: sft1_3 data_files: - split: test path: sft1_3/test-* - config_name: sft1_4 data_files: - split: test path: sft1_4/test-* - config_name: sft1_5 data_files: - split: test path: sft1_5/test-* - config_name: sft2 data_files: - split: test path: sft2/test-* - config_name: sft2_1 data_files: - split: test path: sft2_1/test-* - config_name: sft2_3 data_files: - split: test path: sft2_3/test-* - config_name: sft2_4 data_files: - split: test path: sft2_4/test-* - config_name: sft2_5 data_files: - split: test path: sft2_5/test-* ---

Shekharmeena/female-audio-dataset

Shekharmeena

2025-05-13T11:44:36Z

0

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

[]

2025-05-13T11:41:15Z

null

--- dataset_info: features: - name: audio dtype: audio - name: text dtype: string - name: gender dtype: class_label: names: '0': female '1': male splits: - name: train num_bytes: 4587686171.705792 num_examples: 5983 download_size: 4129876735 dataset_size: 4587686171.705792 configs: - config_name: default data_files: - split: train path: data/train-* ---

JingweiNi/LEXam

JingweiNi

2025-05-13T10:21:46Z

0

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

[]

2025-05-13T09:53:20Z

null

--- license: cc-by-4.0 dataset_info: - config_name: mcq_4_choices features: - name: Question dtype: string - name: Choices dtype: string - name: Gold dtype: int64 - name: Course dtype: string - name: Language dtype: string - name: Area dtype: string - name: Jurisdiction dtype: string - name: Year dtype: int64 - name: n_statements dtype: int64 - name: none_as_an_option dtype: bool - name: Id dtype: string splits: - name: test num_bytes: 1701781 num_examples: 1660 download_size: 833522 dataset_size: 1701781 - config_name: mcq_perturbation features: - name: question dtype: string - name: 4_choices dtype: string - name: 4_choices_answer dtype: int64 - name: 8_choices dtype: string - name: 8_choices_answer dtype: int64 - name: 16_choices dtype: string - name: 16_choices_answer dtype: int64 - name: 32_choices dtype: string - name: 32_choices_answer dtype: int64 - name: course dtype: string - name: language dtype: string - name: n_statements dtype: int64 - name: id dtype: string splits: - name: test num_bytes: 779770 num_examples: 385 download_size: 327294 dataset_size: 779770 - config_name: open_question features: - name: Question dtype: string - name: Answer dtype: string - name: Course dtype: string - name: Language dtype: string - name: Area dtype: string - name: Jurisdiction dtype: string - name: Year dtype: int64 - name: ID dtype: string splits: - name: test num_bytes: 7966761 num_examples: 2541 - name: dev num_bytes: 994495 num_examples: 300 download_size: 4159184 dataset_size: 8961256 configs: - config_name: mcq_4_choices data_files: - split: test path: mcq_4_choices/test-* - config_name: mcq_perturbation data_files: - split: test path: mcq_perturbation/test-* - config_name: open_question data_files: - split: test path: open_question/test-* - split: dev path: open_question/dev-* --- <div align="center" style="display: flex; align-items: center; justify-content: center; gap: 16px;"> <img src="pictures/logo.png" alt="LEXam Logo" width="120" style="border: none;"> <div style="text-align: left;"> <h1 style="margin: 0; font-size: 2em;">LEXam: Benchmarking Legal Reasoning on 340 Law Exams</h1> <p style="margin: 6px 0 0; font-size: 1.2em;">A diverse, rigorous evaluation suite for legal AI from Swiss, EU, and international law examinations.</p> </div> </div> ### [[GitHub Repo]](https://github.com/EdisonNi-hku/LEXam) with code to run evaluations on LEXam ## 🔥 News - [2025/05] Release of the first version of [paper](), where we evaluate 20+ representative SoTA LLMs with evaluations stricly verified by legal experts. ## 🧩 Subsets - `mcq_4_choices`: The standard 1660 MCQs of LEXam with 4 choices. - `mcq_perturbation`: We find that using permulation perturbation can significantly increase the difficulty of LEXam. `mcq_perturbation` contains a set of MCQs with controled questions, but perturbed choices with 4, 8, 16, 32 alternative answers. - `open_question`: All open questions of LEXam, with rich meta-data including language, course, jusritiction etc.

nduque/eval_robustness_e8_no1_2_120k_2

nduque

2025-05-13T09:15:49Z

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" ]

2025-05-13T09:15:40Z

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": "koch", "total_episodes": 10, "total_frames": 4734, "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": [ 9 ], "names": null }, "observation.images.front": { "dtype": "video", "shape": [ 720, 1280, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.fps": 30.0, "video.height": 720, "video.width": 1280, "video.channels": 3, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "has_audio": false } }, "observation.images.above": { "dtype": "video", "shape": [ 720, 1280, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.fps": 30.0, "video.height": 720, "video.width": 1280, "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] ```

axondendriteplus/LightRAG-DAPO-ScalingLaws

axondendriteplus

2025-05-13T08:15:11Z

0

[ "size_categories:n<1K", "format:imagefolder", "modality:image", "library:datasets", "library:mlcroissant", "region:us", "created-with-pdfs-to-page-images-converter", "pdf-to-image" ]

[]

2025-05-13T08:15:01Z

null

--- size_categories: - n<1K tags: - created-with-pdfs-to-page-images-converter - pdf-to-image --- # Dataset Card for axondendriteplus/LightRAG-DAPO-ScalingLaws ## Dataset Description This dataset contains images converted from PDFs using the PDFs to Page Images Converter Space. - **Number of images:** 62 - **Number of PDFs processed:** 3 - **Sample size per PDF:** 100 - **Created on:** 2025-05-13 10:15:11 ## Dataset Creation ### Source Data The images in this dataset were generated from user-uploaded PDF files. ### Processing Steps 1. PDF files were uploaded to the PDFs to Page Images Converter. 2. Each PDF was processed, converting selected pages to images. 3. The resulting images were saved and uploaded to this dataset. ## Dataset Structure The dataset consists of JPEG images, each representing a single page from the source PDFs. ### Data Fields - `images/`: A folder containing all the converted images. ### Data Splits This dataset does not have specific splits. ## Additional Information - **Contributions:** Thanks to the PDFs to Page Images Converter for creating this dataset.

AbrehamT/tagged_articles

AbrehamT

2025-05-13T04:00:38Z

24

0

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

[]

2025-05-10T21:59:14Z

null

--- tags: - biology - chemistry size_categories: - 10K<n<100K dataset_info: features: - name: pmid dtype: string - name: section dtype: string - name: text dtype: string - name: annotations sequence: sequence: int64 splits: - name: train num_bytes: 120073099 num_examples: 17619 download_size: 61504820 dataset_size: 120073099 configs: - config_name: default data_files: - split: train path: data/train-* --- # Alzheimer's Disease Tagged Articles Dataset This dataset contains a collection of scientific articles related to **Alzheimer's disease**, annotated with biomedical entities (such as diseases, genes, and species) using NCBI’s **PubTator** tools. ## Data Sources and Annotation Tools - Entity annotations were generated using NCBI's standard models and API calls to the **PubTator3 API**: - **TaggerOne** and **GNORM** for `gene_species_tagged_articles.json` - **TaggerOne** alone for `disease_tagged_articles.json` - **PubTator3 API** for 'converted_from_bioc.json' These tools are widely used in biomedical NLP for tagging mentions of diseases, genes, and species within PubMed articles. ## Dataset Structure The dataset is a flattened version of the original JSON files, where each record represents a tagged article section. The structure includes: - `pmid`: PubMed ID of the article - `section`: Type of section (e.g., `title`, `abstract`) - `text`: Lowercased content of the section - `annotations`: List of `[start, end]` character offsets identifying entity mentions Example: ```json { "pmid": "34379990", "section": "abstract", "text": "clinical progression of tauopathies may result...", "annotations": [[0, 26], [45, 53], ...] } ```` ## Preprocessing The dataset has been simplified: * Original nested structures were flattened * Tuples were converted to JSON-compliant lists * Only `title` and `abstract` sections are currently included ## Future Work * Extend tagging beyond titles and abstracts to include full-text sections (e.g., introduction, methods, results) * Add entity labels (e.g., `Disease`, `Gene`, `Species`) in a future version ## Usage ```python from datasets import load_dataset dataset = load_dataset("AbrehamT/tagged_articles") ``` ## License This dataset is released under the **MIT License**. ## Acknowledgments This dataset relies on NCBI’s **PubTator**, **GNORM**, and **TaggerOne** models. Credit goes to the developers of these tools and the researchers whose articles form the dataset foundation.

AlignmentResearch/ReNeLLMClearHarm

AlignmentResearch

2025-05-13T03:24:53Z

0

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

[]

2025-05-12T22:39:14Z

null

--- dataset_info: - config_name: renellm-input-adv-Qwen2.5-14-config-default-examples-0-100-attacks-0-200 features: - name: clf_label dtype: int64 - name: instructions dtype: string - name: content sequence: string - name: answer_prompt dtype: string - name: proxy_clf_label dtype: int64 - name: gen_target dtype: string - name: proxy_gen_target dtype: string - name: original_text dtype: string - name: attack_index dtype: int64 - name: original_example_index dtype: int64 splits: - name: validation num_bytes: 16199407 num_examples: 20000 download_size: 1335998 dataset_size: 16199407 - config_name: renellm-input-adv-Qwen2.5-14-config-default-examples-0-100-attacks-0-200-short features: - name: clf_label dtype: int64 - name: instructions dtype: string - name: content sequence: string - name: answer_prompt dtype: string - name: proxy_clf_label dtype: int64 - name: gen_target dtype: string - name: proxy_gen_target dtype: string - name: original_text dtype: string - name: attack_index dtype: int64 - name: original_example_index dtype: int64 splits: - name: train num_bytes: 0 num_examples: 0 - name: validation num_bytes: 16169174.0 num_examples: 19997 download_size: 1387154 dataset_size: 16169174.0 configs: - config_name: renellm-input-adv-Qwen2.5-14-config-default-examples-0-100-attacks-0-200 data_files: - split: validation path: renellm-input-adv-Qwen2.5-14-config-default-examples-0-100-attacks-0-200/validation-* - config_name: renellm-input-adv-Qwen2.5-14-config-default-examples-0-100-attacks-0-200-short data_files: - split: train path: renellm-input-adv-Qwen2.5-14-config-default-examples-0-100-attacks-0-200-short/train-* - split: validation path: renellm-input-adv-Qwen2.5-14-config-default-examples-0-100-attacks-0-200-short/validation-* ---

danhdzzzz/tuyensinh_sp

danhdzzzz

2025-05-13T03:13:15Z

0

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

[]

2025-05-13T03:12:29Z

null

--- dataset_info: features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 263615 num_examples: 1059 download_size: 87116 dataset_size: 263615 configs: - config_name: default data_files: - split: train path: data/train-* ---

yoonholee/completions_Qwen3-1.7B_GSMPlus

yoonholee

2025-05-13T03:08:20Z

0

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

[]

2025-05-13T03:08:18Z

null

--- dataset_info: features: - name: problem dtype: string - name: completions sequence: string - name: answer dtype: string - name: corrects sequence: bool - name: acc dtype: float64 splits: - name: train num_bytes: 9368563 num_examples: 200 download_size: 2824742 dataset_size: 9368563 configs: - config_name: default data_files: - split: train path: data/train-* ---

chuuhtetnaing/myanmar-ocr-dataset

chuuhtetnaing

2025-05-13T02:25:18Z

166

0

[ "task_categories:image-to-text", "language:my", "license:mit", "region:us" ]

[ "image-to-text" ]

2025-03-27T12:09:00Z

null

--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 23636898616.852 num_examples: 6903524 - name: test num_bytes: 2592888233.424 num_examples: 762736 download_size: 24248031522 dataset_size: 26229786850.276 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* language: - my pretty_name: Myanmar OCR Dataset (Synthetic) license: mit task_categories: - image-to-text --- # Myanmar OCR Dataset A synthetic dataset for training and fine-tuning Optical Character Recognition (OCR) models specifically for the Myanmar language. ## Dataset Description This dataset contains synthetically generated OCR images created specifically for Myanmar text recognition tasks. The images were generated using [myanmar-ocr-data-generator](https://github.com/chuuhtetnaing/myanmar-ocr-data-generator), a fork of [TextRecognitionDataGenerator](https://github.com/Belval/TextRecognitionDataGenerator) with fixes for proper Myanmar character splitting. ## Direct Download Available from [UT Austin SharePoint](https://utexas-my.sharepoint.com/:u:/g/personal/cn23349_my_utexas_edu/EZcWC2NEbKNMkeJ_WByugx0BMTSE6qKYQjVp62fn6m0toA?e=L9AKko) - The downloadable data is specifically formatted for PaddleOCR - Includes organized folder structure optimized for PaddleOCR training - Labels are provided in tab-separated format - Ready to fine-tuned for PaddleOCR ### Key Features - Synthetic Myanmar text images with corresponding text labels - Maximum text length of 100 characters per image - Various fonts, styles, skews, various distortion types, blur levels, and backgrounds to improve model robustness ## Generation Method The dataset was created using a forked and customized version of the TextRecognitionDataGenerator: - Original repository: [Belval/TextRecognitionDataGenerator](https://github.com/Belval/TextRecognitionDataGenerator) - Customized fork: [chuuhtetnaing/myanmar-ocr-data-generator](https://github.com/chuuhtetnaing/myanmar-ocr-data-generator) ### Text Sources The text content used to generate the OCR images was extracted from: - [chuuhtetnaing/myanmar-wikipedia-dataset](https://huggingface.co/datasets/chuuhtetnaing/myanmar-wikipedia-dataset): A collection of Myanmar language text from Wikipedia - [facebook/flores](https://huggingface.co/datasets/facebook/flores): Meta's FLORES dataset which includes Myanmar language texts ## Usage This dataset is particularly useful for: - Training OCR models for Myanmar language text recognition - Fine-tuning existing OCR models to improve performance on Myanmar script - Developing applications for Myanmar text digitization ## License This dataset is free to use for any purpose - personal, commercial, or educational.

hf-doc-build/doc-build-dev

hf-doc-build

2025-05-13T01:55:51Z

163,925

4

[ "license:mit", "region:us", "documentation" ]

[]

2022-11-08T09:03:37Z

null

--- license: mit tags: - documentation pretty_name: HF Documentation (PRs) viewer: false --- This is a dataset which contains the docs from all the PRs that are updating one of the docs from https://huggingface.co/docs. It is automatically updated by this [github action](https://github.com/huggingface/doc-builder/blob/main/.github/workflows/build_pr_documentation.yml) from the [doc-buider](https://github.com/huggingface/doc-builder) repo.

ahmedelgebaly/SQuad_SciQ_HotpotQA_Alpaca_Equal

ahmedelgebaly

2025-05-12T23:14:14Z

0

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

[]

2025-05-12T23:14:09Z

null

--- dataset_info: features: - name: input dtype: string - name: output dtype: string - name: instruction dtype: string - name: source dtype: string splits: - name: train num_bytes: 72542175.0 num_examples: 30000 - name: validation num_bytes: 7393264.0 num_examples: 3000 download_size: 46541993 dataset_size: 79935439.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* ---

nebius/SWE-rebench

nebius

2025-05-12T22:21:55Z

29

0

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

[]

2025-04-28T10:02:25Z

null

--- dataset_info: features: - name: instance_id dtype: string - name: base_commit dtype: string - name: created_at dtype: string - name: environment_setup_commit dtype: string - name: hints_text dtype: string - name: patch dtype: string - name: problem_statement dtype: string - name: repo dtype: string - name: test_patch dtype: string - name: meta struct: - name: commit_name dtype: string - name: failed_lite_validators sequence: string - name: has_test_patch dtype: bool - name: is_lite dtype: bool - name: llm_score struct: - name: difficulty_score dtype: int64 - name: issue_text_score dtype: int64 - name: test_score dtype: int64 - name: num_modified_files dtype: int64 - name: version dtype: string - name: install_config struct: - name: env_vars struct: - name: JUPYTER_PLATFORM_DIRS dtype: string - name: env_yml_path sequence: string - name: install dtype: string - name: log_parser dtype: string - name: no_use_env dtype: bool - name: packages dtype: string - name: pip_packages sequence: string - name: pre_install sequence: string - name: python dtype: string - name: reqs_path sequence: string - name: test_cmd dtype: string - name: requirements dtype: string - name: environment dtype: string - name: FAIL_TO_PASS sequence: string - name: FAIL_TO_FAIL sequence: string - name: PASS_TO_PASS sequence: string - name: PASS_TO_FAIL sequence: string - name: license_name dtype: string - name: __index_level_0__ dtype: int64 splits: - name: test num_bytes: 737537372 num_examples: 21336 download_size: 239735457 dataset_size: 737537372 configs: - config_name: default data_files: - split: test path: data/test-* --- # Dataset Summary SWE-rebench is a large-scale dataset designed to support the training and evaluation of LLM-based software engineering (SWE) agents. It is built using a fully automated pipeline that continuously extracts real-world GitHub tasks at scale. The dataset includes over 21,000 issue–pull request pairs from 6,000+ Python repositories, each validated for correctness through environment setup and test execution. * SWE-rebench expands on the methodology introduced in SWE-bench by adding: * Continuous task collection to prevent benchmark staleness * Decontamination mechanisms to mitigate data leakage into pretrained LLMs * Automatic environment extraction and validation to ensure high-quality execution # How to Use ```python from datasets import load_dataset ds = load_dataset('nebius/SWE-rebench') ``` # Dataset Statistics Average, 75th percentile, and maximum values characterizing various attributes of the collected instances. Statistics are micro-averaged without grouping by repository. | Data | Type | Mean | p75 | Max | |---------------|--------------------|----------|----------|-----------| | Issue text | Length (words) | 111.5 | 146 | 1,294 | | Code base | Files (Non-test) | 71.71 | 72.00 | 2,264 | | | Lines (Non-test) | 15,163.38| 13,777 | 1,039,288 | | Gold patch | Files edited | 2.6 | 3 | 7 | | | Lines edited | 56 | 76 | 300 | | Tests | Fail to Pass | 10.94 | 5 | 4,941 | | | Total | 58.5 | 49 | 7,820 | # Dataset Structure The dataset contains the following fields. It includes all fields from SWE-bench and adds a `meta` column, which indicates whether the instance meets the "lite" criteria and, if not, lists the failed validators. | Field name | Type | Description | |----------------------------|--------|-------------------------------------------------------------------------------------------------| | `instance_id` | str | A formatted instance identifier, usually as `repo_owner__repo_name-PR-number`. | | `patch` | str | The gold patch, the patch generated by the PR (minus test-related code), that resolved the issue. | | `repo` | str | The repository owner/name identifier from GitHub. | | `base_commit` | str | The commit hash of the repository representing the HEAD of the repository before the solution PR is applied. | | `hints_text` | str | Comments made on the issue prior to the creation of the solution PR’s first commit creation date. | | `created_at` | str | The creation date of the pull request. | | `test_patch` | str | A test-file patch that was contributed by the solution PR. | | `problem_statement` | str | The issue title and body. | | `version` | str | Installation version to use for running evaluation. | | `environment_setup_commit` | str | Commit hash to use for environment setup and installation. | | `FAIL_TO_PASS` | str | A JSON list of strings that represent the set of tests resolved by the PR and tied to the issue resolution. | | `PASS_TO_PASS` | str | A JSON list of strings that represent tests that should pass before and after the PR application. | | `meta` | str | A JSON dictionary indicating whether the instance is lite, along with a list of failed lite validators if it is not. | | `license_name` | str | The type of license of the repository. | | `install_config` | str | Installation configuration for setting up the repository. | | `requirements` | str | Freezed requirements for the repository. | | `environment` | str | Environment configuration for the repository. | To execute instances within SWE-rebench use this fork of SWE-bench [SWE-rebench/SWE-bench-fork](https://github.com/SWE-rebench/SWE-bench-fork) # License The dataset is licensed under the Creative Commons Attribution 4.0 license. However, please respect the license of each specific repository on which a particular instance is based. To facilitate this, the license of each repository at the time of the commit is provided for every instance.

vuquangtrung/newscook

vuquangtrung

2025-05-12T22:16:11Z

0

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

[ "question-answering" ]

2025-05-12T21:53:21Z

null

--- task_categories: - question-answering language: - vi size_categories: - 1K<n<10K ---

SAA-Lab/test_march23-cwv-genrm_cot_qwen1.5b-ckptglobal_step_324

SAA-Lab

2025-05-12T21:13:14Z

0

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

[]

2025-05-12T21:13:12Z

null

--- dataset_info: features: - name: post_id dtype: int64 - name: chosen_body dtype: string - name: rejected_body dtype: string - name: chosen_upvotes dtype: int64 - name: rejected_upvotes dtype: int64 - name: chosen_length dtype: int64 - name: rejected_length dtype: int64 - name: chosen_username dtype: string - name: rejected_username dtype: string - name: chosen_timestamp dtype: timestamp[us] - name: rejected_timestamp dtype: timestamp[us] - name: post_title dtype: string - name: time_diff dtype: float64 - name: __index_level_0__ dtype: int64 - name: prompt list: - name: content dtype: string - name: role dtype: string - name: answer dtype: string - name: model_response dtype: string - name: reasoning dtype: string - name: preferred dtype: string - name: is_correct dtype: bool splits: - name: train num_bytes: 32076409 num_examples: 1898 download_size: 18280357 dataset_size: 32076409 configs: - config_name: default data_files: - split: train path: data/train-* ---

hurrutia/Reglamento_Aeronautico_Colombiano_2024

hurrutia

2025-05-12T20:55:39Z

0

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

[]

2025-05-12T14:20:13Z

null

--- dataset_info: features: - name: rac dtype: string - name: pagina dtype: string - name: pregunta dtype: string - name: respuesta dtype: string splits: - name: train num_bytes: 6732878 num_examples: 24479 download_size: 1813160 dataset_size: 6732878 configs: - config_name: default data_files: - split: train path: data/train-* ---

hurrutia/es-inclusive-language-it

hurrutia

2025-05-12T20:55:30Z

0

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

[]

2025-05-12T14:20:06Z

null

--- dataset_info: features: - name: pregunta dtype: string - name: respuesta dtype: string splits: - name: train num_bytes: 930607 num_examples: 4196 download_size: 406560 dataset_size: 930607 configs: - config_name: default data_files: - split: train path: data/train-* ---

AIxBlock/Spanish-Mx-short-utterances

AIxBlock

2025-05-12T20:40:34Z

33

0

[ "task_categories:text-to-audio", "task_categories:text-to-speech", "task_categories:token-classification", "language:es", "license:mit", "size_categories:100K<n<1M", "region:us" ]

[ "text-to-audio", "text-to-speech", "token-classification" ]

2025-05-08T23:57:48Z

null

--- license: mit task_categories: - text-to-audio - text-to-speech - token-classification language: - es size_categories: - 100K<n<1M --- This dataset is provided by AIxBlock, an unified platform for AI development and AI workflows automation. This dataset contains around 800k+ sentences in Spanish, making it a valuable resource for a wide range of language technology applications. All data has undergone quality assurance (QA) checks to ensure clarity, correctness, and natural phrasing. The dataset is well-suited for: Speech data generation (e.g., recording short audio clips lasting 8–30 seconds per sentence) Natural Language Processing (NLP) tasks, such as language modeling, translation, intent detection, and more

TAUR-dev/STEPS__r1_8d_and_4d_eval__v4_mini

TAUR-dev

2025-05-12T20:05:32Z

0

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

[]

2025-05-12T20:05:29Z

null

--- dataset_info: features: - name: __source_repo dtype: string - name: question dtype: string - name: eval_internal_cot dtype: string - name: solution dtype: string - name: eval_solution dtype: string - name: raw_eval_prompt dtype: string - name: judge_correct dtype: bool - name: judge_reasoning dtype: string - name: __orig_idx dtype: int64 - name: __chunk_idx dtype: int64 - name: eval_internal_cot__chunked dtype: string - name: eval_prompt list: - name: content dtype: string - name: role dtype: string - name: eval_steps list: - name: content dtype: string - name: steps_word_count dtype: int64 - name: trace_word_count dtype: int64 - name: word_count_diff dtype: int64 splits: - name: train num_bytes: 1374101 num_examples: 20 download_size: 332457 dataset_size: 1374101 configs: - config_name: default data_files: - split: train path: data/train-* ---

littleGuagua/x_dataset_48558

littleGuagua

2025-05-12T19:20:01Z

1,146

0

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

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

2025-01-26T14:58:15Z

null

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

pragsri8/ultrafeedback_60658_preference_dataset_causally-aligned-neutrals-on-causals

pragsri8

2025-05-12T19:01:02Z

0

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

[]

2025-05-12T19:00:47Z

null

--- dataset_info: features: - name: chosen list: - name: content dtype: string - name: role dtype: string - name: rejected list: - name: content dtype: string - name: role dtype: string - name: neutral dtype: bool splits: - name: train num_bytes: 701786488 num_examples: 183836 download_size: 380044119 dataset_size: 701786488 configs: - config_name: default data_files: - split: train path: data/train-* ---

Aravindh25/trossen_pick_granola_bars_3cam_C_V8

Aravindh25

2025-05-12T18:51:08Z

121

0

[ "task_categories:robotics", "license:apache-2.0", "size_categories:100K<n<1M", "format:parquet", "modality:tabular", "modality:timeseries", "modality:video", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us", "LeRobot", "tutorial" ]

[ "robotics" ]

2025-05-10T01:48:03Z

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", "trossen_subversion": "v1.0", "robot_type": "trossen_ai_solo", "total_episodes": 25, "total_frames": 30723, "total_tasks": 1, "total_videos": 75, "total_chunks": 1, "chunks_size": 1000, "fps": 30, "splits": { "train": "0:25" }, "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": [ 7 ], "names": [ "main_joint_0", "main_joint_1", "main_joint_2", "main_joint_3", "main_joint_4", "main_joint_5", "main_joint_6" ] }, "observation.state": { "dtype": "float32", "shape": [ 7 ], "names": [ "main_joint_0", "main_joint_1", "main_joint_2", "main_joint_3", "main_joint_4", "main_joint_5", "main_joint_6" ] }, "observation.images.cam_wrist": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.fps": 30.0, "video.height": 480, "video.width": 640, "video.channels": 3, "video.codec": "h264", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "has_audio": false } }, "observation.images.cam_high": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.fps": 30.0, "video.height": 480, "video.width": 640, "video.channels": 3, "video.codec": "h264", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "has_audio": false } }, "observation.images.cam_front": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.fps": 30.0, "video.height": 480, "video.width": 640, "video.channels": 3, "video.codec": "h264", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "has_audio": false } }, "timestamp": { "dtype": "float32", "shape": [ 1 ], "names": null }, "frame_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "episode_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "task_index": { "dtype": "int64", "shape": [ 1 ], "names": null } } } ``` ## Citation **BibTeX:** ```bibtex [More Information Needed] ```

Matis-E/BUSI_benign_100

Matis-E

2025-05-12T18:02:21Z

39

0

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

[]

2025-05-06T21:20:31Z

null

--- dataset_info: features: - name: image dtype: image - name: conditioning_image dtype: image - name: text dtype: string splits: - name: train num_bytes: 12306695.0 num_examples: 100 download_size: 12288899 dataset_size: 12306695.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

chiyuanhsiao/text_L2-regular-SQA_mmlu

chiyuanhsiao

2025-05-12T17:44:12Z

0

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

[]

2025-05-12T17:44:05Z

null

--- dataset_info: features: - name: task_type dtype: string - name: task_name dtype: string - name: subtask_name dtype: string - name: input_question dtype: string - name: input_choice_list struct: - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: input_final_prompts sequence: string - name: input_correct_responses sequence: string - name: output_prediction_text sequence: string - name: output_parsed_answer dtype: string - name: output_choice_completions dtype: 'null' - name: output_choice_negative_log_likelihoods dtype: 'null' - name: output_metrics struct: - name: acc dtype: float64 - name: correct_format dtype: float64 - name: is_correct dtype: bool - name: input_question_hash dtype: string - name: input_final_prompts_hash sequence: string - name: benchmark_label dtype: string - name: eval_config struct: - name: max_gen_len dtype: string - name: max_prompt_len dtype: string - name: num_few_shot dtype: string - name: num_generations dtype: string - name: prompt_fn dtype: string - name: return_logprobs dtype: string - name: seed dtype: string - name: temperature dtype: string - name: top_k dtype: string - name: top_p dtype: string - name: my_prediction_text dtype: string splits: - name: latest num_bytes: 187010503 num_examples: 14042 download_size: 28043068 dataset_size: 187010503 configs: - config_name: default data_files: - split: latest path: data/latest-* ---

Bretagne/Banque_Sonore_Dialectes_Bretons

Bretagne

2025-05-12T16:55:59Z

1,142

1

[ "task_categories:automatic-speech-recognition", "task_categories:translation", "language:br", "language:fra", "language:multilingual", "license:cc-by-nc-nd-3.0", "size_categories:1K<n<10K", "format:parquet", "modality:audio", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[ "automatic-speech-recognition", "translation" ]

2023-12-06T18:55:45Z

null

--- language: - br - fra - multilingual task_categories: - automatic-speech-recognition - translation license: cc-by-nc-nd-3.0 configs: - config_name: data data_files: - split: train path: train.parquet --- > [!NOTE] > Dataset origin: http://banque.sonore.breton.free.fr/ # Description ## Issue du site Banque Sonore des Dialectes Bretons ### Présentation du projet La [Banque Sonore des Dialectes Bretons](http://banque.sonore.breton.free.fr/index.html) est un projet expérimental qui réunit sur internet un vaste ensemble d'enregistrements d'enquêtes effectuées depuis plus d'une dizaine d'années auprès de locuteurs traditionnels de breton. Alimentées par une équipe de bénévoles partageant un intérêt commun pour l'étude de la langue et des traditions populaires, ces archives en ligne souhaitent faire partager au plus grand nombre la richesse et la diversité de ce patrimoine culturel. ### Présentation du corpus Les enregistrements réunis dans nos archives proviennent de fonds particuliers pour la plupart inédits jusqu'à présent. L'ensemble de ce corpus concerne des domaines de recherche très divers pouvant aller de l'étude des sons, de la grammaire et du vocabulaire de la langue à l'étude des techniques agricoles, de la médecine traditionnelle ou encore des croyances populaires. Chaque ressource est soigneusement sélectionnée, transcrite, traduite et cataloguée et peut être accompagnée d'illustrations iconographiques. Grâce au moteur de recherche développé pour ce projet, linguistes, ethnologues, enseignants, étudiants, passionnés ou simples curieux... tous pourront facilement extraire de ce corpus les informations qui les intéressent. ### Mentions légales Ce site bénéficie d'une licence Creative Commons. Chaque déposant reste néanmoins responsable des documents qu'il souhaite faire partager en ligne. Si un extrait vous intéresse pour vos travaux, vous pouvez l'utiliser à des fins non commerciales. N'oubliez pas de citer le nom du projet ainsi que le numéro de sa cote suivi de la date de sa dernière mise à jour. ## Précisions concernant ce répertoire Hugging Face L'utilisation des données disponibles n'étant pas aisée sur le site, nous les avons scrappées le 6 décembre 2023. Ce répertoire contient 7291 fichiers audios représentant une durée de 15:53:23 (soit moins que les 8586 fichiers audio qui représentent une durée totale de 18:33:23 affichées sur la page [statistique](http://banque.sonore.breton.free.fr/statistique.php5) du site... un nouveau scrapping serait probablement à effectuer). ## Usage En plus de la tâche d'ASR, il est possible d'utiliser uniquement les colonnes `br` et `fr` comme données de traduction automatique.

hshwk1983/x_dataset_20503

hshwk1983

2025-05-12T16:53:59Z

1,937

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" ]

2025-01-27T07:24:59Z

null

--- license: mit multilinguality: - multilingual source_datasets: - original task_categories: - text-classification - token-classification - question-answering - summarization - text-generation task_ids: - sentiment-analysis - topic-classification - named-entity-recognition - language-modeling - text-scoring - multi-class-classification - multi-label-classification - extractive-qa - news-articles-summarization --- # Bittensor Subnet 13 X (Twitter) Dataset <center> <img src="https://huggingface.co/datasets/macrocosm-os/images/resolve/main/bittensor.png" alt="Data-universe: The finest collection of social media data the web has to offer"> </center> <center> <img src="https://huggingface.co/datasets/macrocosm-os/images/resolve/main/macrocosmos-black.png" alt="Data-universe: The finest collection of social media data the web has to offer"> </center> ## Dataset Description - **Repository:** hshwk1983/x_dataset_20503 - **Subnet:** Bittensor Subnet 13 - **Miner Hotkey:** 5DsemqgVoVsi8vHK6QiEewS9B1T3cfxwjUfGXjCxzQTjRLBe ### Dataset Summary This dataset is part of the Bittensor Subnet 13 decentralized network, containing preprocessed data from X (formerly Twitter). The data is continuously updated by network miners, providing a real-time stream of tweets for various analytical and machine learning tasks. For more information about the dataset, please visit the [official repository](https://github.com/macrocosm-os/data-universe). ### Supported Tasks The versatility of this dataset allows researchers and data scientists to explore various aspects of social media dynamics and develop innovative applications. Users are encouraged to leverage this data creatively for their specific research or business needs. For example: - Sentiment Analysis - Trend Detection - Content Analysis - User Behavior Modeling ### Languages Primary language: Datasets are mostly English, but can be multilingual due to decentralized ways of creation. ## Dataset Structure ### Data Instances Each instance represents a single tweet with the following fields: ### Data Fields - `text` (string): The main content of the tweet. - `label` (string): Sentiment or topic category of the tweet. - `tweet_hashtags` (list): A list of hashtags used in the tweet. May be empty if no hashtags are present. - `datetime` (string): The date when the tweet was posted. - `username_encoded` (string): An encoded version of the username to maintain user privacy. - `url_encoded` (string): An encoded version of any URLs included in the tweet. May be empty if no URLs are present. ### Data Splits This dataset is continuously updated and does not have fixed splits. Users should create their own splits based on their requirements and the data's timestamp. ## Dataset Creation ### Source Data Data is collected from public tweets on X (Twitter), adhering to the platform's terms of service and API usage guidelines. ### Personal and Sensitive Information All usernames and URLs are encoded to protect user privacy. The dataset does not intentionally include personal or sensitive information. ## Considerations for Using the Data ### Social Impact and Biases Users should be aware of potential biases inherent in X (Twitter) data, including demographic and content biases. This dataset reflects the content and opinions expressed on X and should not be considered a representative sample of the general population. ### Limitations - Data quality may vary due to the decentralized nature of collection and preprocessing. - The dataset may contain noise, spam, or irrelevant content typical of social media platforms. - Temporal biases may exist due to real-time collection methods. - The dataset is limited to public tweets and does not include private accounts or direct messages. - Not all tweets contain hashtags or URLs. ## Additional Information ### Licensing Information The dataset is released under the MIT license. The use of this dataset is also subject to X Terms of Use. ### Citation Information If you use this dataset in your research, please cite it as follows: ``` @misc{hshwk19832025datauniversex_dataset_20503, title={The Data Universe Datasets: The finest collection of social media data the web has to offer}, author={hshwk1983}, year={2025}, url={https://huggingface.co/datasets/hshwk1983/x_dataset_20503}, } ``` ### 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:** 52912520 - **Date Range:** 2025-01-21T00:00:00Z to 2025-02-13T00:00:00Z - **Last Updated:** 2025-02-18T20:45:01Z ### Data Distribution - Tweets with hashtags: 41.50% - Tweets without hashtags: 58.50% ### Top 10 Hashtags For full statistics, please refer to the `stats.json` file in the repository. | Rank | Topic | Total Count | Percentage | |------|-------|-------------|-------------| | 1 | NULL | 30954536 | 58.50% | | 2 | #riyadh | 333077 | 0.63% | | 3 | #zelena | 227553 | 0.43% | | 4 | #tiktok | 210733 | 0.40% | | 5 | #ad | 123338 | 0.23% | | 6 | #bbb25 | 111207 | 0.21% | | 7 | #pr | 67348 | 0.13% | | 8 | #theheartkillersep11 | 67119 | 0.13% | | 9 | #yahooニュース | 65424 | 0.12% | | 10 | #theheartkillersep10 | 64660 | 0.12% | ## Update History | Date | New Instances | Total Instances | |------|---------------|-----------------| | 2025-01-27T07:26:13Z | 4467532 | 4467532 | | 2025-01-30T19:29:08Z | 9248514 | 13716046 | | 2025-02-03T07:32:06Z | 8511001 | 22227047 | | 2025-02-06T19:35:57Z | 9947784 | 32174831 | | 2025-02-10T07:39:43Z | 8653004 | 40827835 | | 2025-02-13T19:44:19Z | 10753704 | 51581539 | | 2025-02-18T05:43:42Z | 691061 | 52272600 | | 2025-02-18T20:45:01Z | 639920 | 52912520 |

voxaiorg/drivethru-context-v2-all-preference-1-pairs-mistralai-Mistral-Small-3.1-24B-Instruct-2503

voxaiorg

2025-05-12T16:40:06Z

0

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

[]

2025-05-12T16:39:56Z

null

--- dataset_info: features: - name: prompt list: - name: content dtype: string - name: role dtype: string - name: chosen list: - name: content dtype: string - name: role dtype: string - name: rejected list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 395333849 num_examples: 4373 - name: validation num_bytes: 47938299 num_examples: 524 - name: test num_bytes: 60572445 num_examples: 655 download_size: 102808521 dataset_size: 503844593 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---

Ultralordb0d/medical_asr_prediction_usm_augmented

Ultralordb0d

2025-05-12T16:33:47Z

116

0

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

[]

2025-04-19T10:23:56Z

null

--- dataset_info: features: - name: filename dtype: string - name: transcription dtype: string - name: prediction dtype: string - name: prediction_ph_par_all dtype: string - name: transcription_clean dtype: string - name: prediction_par_all dtype: string - name: prediction_par_all_medicalterms dtype: string - name: prediction_par_all_not_in_nltk dtype: string - name: prediction_embedding_model dtype: string - name: prediction_embedding_model_not_nltk dtype: string splits: - name: train num_bytes: 219249300 num_examples: 200000 download_size: 130065085 dataset_size: 219249300 configs: - config_name: default data_files: - split: train path: data/train-* ---

madeofajala/MalariaLegacyLLM

madeofajala

2025-05-12T16:16:32Z

0

[ "task_categories:text-classification", "task_categories:table-question-answering", "task_categories:zero-shot-classification", "language:en", "license:mit", "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2406.06316", "region:us", "biology", "chemistry", "medical" ]

[ "text-classification", "table-question-answering", "zero-shot-classification" ]

2025-05-12T15:10:05Z

null

--- dataset_info: features: - name: Prompt dtype: string - name: Answer dtype: string - name: CANONICAL_SMILES dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 77059720 num_examples: 94916 - name: validation num_bytes: 19254722 num_examples: 23762 - name: test num_bytes: 24133827 num_examples: 29674 download_size: 16354853 dataset_size: 120448269 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* license: mit task_categories: - text-classification - table-question-answering - zero-shot-classification language: - en tags: - biology - chemistry - medical pretty_name: malariallm size_categories: - 100K<n<1M --- # Dataset Card for Dataset Name  This is an LLM instruction-tuned subset of the CHEMBL Legacy Malaria designed for using LLMs for virtual screening ## Dataset Details ### Dataset Description  This dataset is compiled from the CHEMBL Malaria Legacy dataset [link](https://chembl.gitbook.io/chembl-interface-documentation/legacy-resources). The dataset has been instruction-tuned just as proposed in [Tx-LLM: A Large Language Model for Therapeutics](https://arxiv.org/pdf/2406.06316) and TxGemma: Efficient and Agentic LLMs for Therapeutics The prompt consists of 4 parts: 1.) **Instruction**: General instruction to the LLMs to provide an accurate answer about the assay and molecule. 2.) **Context**: Information sourced from literature about the specific assay as described in the original datasets ASSAY_DESCRIPTION, as well as the target (protein or cell line). 3.) **Question**: A command to predict, given the molecule and assay information, if the molecule will be (A) Active or (B) inactive. 4.) **Answer**: - **Curated by:** Marvellous Ajala - **Sourced From:** CHEMBL - **Language(s) (NLP):** English - **License:** MIT ## Uses  This dataset is designed for finetuning general-purpose LLMs (Llama, Gemma etc) for virtual screening in Malaria ### Direct Use  To use this dataset ```python import datasets dataset = datasets.load_dataset('madeofajala/MalariaLegacyLLM') # Display the first example print(dataset['train'][0]) ``` ## Dataset Structure  The dataset contains 3 parts: - Trainset - Val set - Test set The dataset was split using a scaffold-based split. Each set contains: - Prompt: information about the assay and target in natural language - Answer: (A) if active and (B) if inactive - CANONICAL_SMILES: SMILES of molecule in focus - serial_number ## Dataset Creation ### Curation Rationale  This dataset was curated for instruction-tuning LLMs for virtual screening of Malaria using natural language (English) #### Data Collection and Processing  This dataset consists of only the potency and IC50 subset of the original dataset. It was curated to contain only assays in which there was a definitive conclusion of molecules' activity (active or inactive). Also, molecules with two or more conflicting activity values, e.g active at higher or lower concentration and inactive at the other, were also completely removed #### Who are the source data producers?  Different pharma companies and independent researchers, including but not limited to: - Scientific Literature - TP-search Transporter Database - PubChem BioAssays - Open TG-GATEs - GSK Published Kinase Inhibitor Set - Sanger Institute Genomics of Drug Sensitivity in Cancer - Guide to Receptors and Channels - DrugMatrix in vitro pharmacology assays - Drugs for Neglected Diseases Initiative (DNDi) - St Jude Malaria Screening - WHO-TDR Malaria Screening - MMV Malaria Box - GSK Malaria Screening - Novartis Malaria Screening - GSK Tuberculosis Screening - Harvard Malaria Screening - OSDD Malaria Screening The original dataset was compiled by EBI-EMBL team ### Recommendations  Users are advised to cross-reference the dataset with the orginal dataset provided by CHEMBL. Also, as the dataset is a combination of multiple collated and sourced tasks, projects etc, users should be aware of the implication of this to their task at hand. ## Citation [optional]  **BibTeX:** ```bibtex title = {Malaria Legacy Dataset for LLM}, author = {Marvellous Ajala} year = {2025}, publisher = {Hugging Face Datasets}, version = {1.0.0}, url = {https://huggingface.co/datasets/madeofajala/MalariaLegacyLLM}, ```

anonymous-author/paper_data

anonymous-author

2025-05-12T16:13:49Z

0

[ "license:cc-by-sa-4.0", "region:us" ]

[]

2025-05-12T16:12:26Z

null

--- license: cc-by-sa-4.0 ---

idoco/PopVQA

idoco

2025-05-12T15:52:23Z

84

2

[ "task_categories:visual-question-answering", "language:en", "license:mit", "size_categories:10K<n<100K", "modality:image", "region:us" ]

[ "visual-question-answering" ]

2025-05-08T06:28:52Z

null

--- license: mit task_categories: - visual-question-answering language: - en pretty_name: PopVQA size_categories: - 10K<n<100K --- # PopVQA: Popular Entity Visual Question Answering PopVQA is a dataset designed to study the performance gap in vision-language models (VLMs) when answering factual questions about entities presented in **images** versus **text**. ![PopVQA Teaser](./popvqa_teaser.png) ## 🔍 Motivation <img src="./paper_teaser.png" alt="Motivation" width="700"> PopVQA was curated to explore the disparity in model performance when answering factual questions about an entity described in text versus depicted in an image. This is achieved by asking the same questions twice, once with the textual representation (the entity's name), then, with the visual representation (entity image). We include several questions about every entity to allow a more fine grained evaluation. This dataset was introduced in the paper: > **"Performance Gap in Entity Knowledge Extraction Across Modalities in Vision Language Models"** > Ido Cohen, Daniela Gottesman, Mor Geva, Raja Giryes (2025) ## 📦 Dataset Structure The dataset consists of: - `entities.csv`: Metadata of 15,395 popular entities, of various types (celebrities, landmarks, logos, and paintings). - `questions.csv`: Over 100,000 factual questions, each given in two forms: one referring to a **textual representation** and one referring to a **visual representation** of the entity. - `original_path/`: Original images. - `resized_path/`: Images resized to 336×336 with aspect ratio preserved via padding. ### `entities.csv` columns: | Column | Description | |------------------|---------------------------------------------| | `type` | Entity type (e.g., `celebs`, `logos`) | | `subject` | Entity name | | `s_uri` | Wikidata URI of the subject | | `popularity` | Wikipedia popularity score | | `aliases` | Alternate names/aliases for the entity | | `image` | wiki commons url | | `original_path` | Path to the original image | | `resized_path` | Path to the 336x336 padded image | ### `questions.csv` columns: | Column | Description | |------------------------|--------------------------------------------------------------| | `type` | Entity type | | `subject` | Entity name | | `question_for_image` | Question phrased for visual context (e.g., “...in this image?”) | | `question` | Textual version of the same question | | `possible_answers` | List of acceptable answers | | `relation` | Relation name (e.g., occupation, language) | | `s_uri`, `r_uri`, `a_uri` | Wikidata URIs for subject, relation, and answer | | `attribute`, `a_type` | Answer string and attribute types (e.g., "language") |

Xecades/AerialExtreMatch-Benchmark

Xecades

2025-05-12T15:39:49Z

89

0

[ "task_categories:depth-estimation", "task_categories:keypoint-detection", "task_categories:image-feature-extraction", "license:mit", "modality:image", "region:us", "image" ]

[ "depth-estimation", "keypoint-detection", "image-feature-extraction" ]

2025-05-11T14:38:08Z

null

--- license: mit task_categories: - depth-estimation - keypoint-detection - image-feature-extraction pretty_name: AerialExtreMatch Benchmark viewer: false tags: - image --- # AerialExtreMatch — Benchmark Dataset [Code](https://github.com/Xecades/AerialExtreMatch) | Project Page (WIP) | Paper (WIP) This repo contains the **benchmark** set for our paper *AerialExtreMatch: A Benchmark for Extreme-View Image Matching and Localization*. 32 difficulty levels are included. We also provide [**train**](https://huggingface.co/datasets/Xecades/AerialExtreMatch-Train) and [**localization**](https://huggingface.co/datasets/Xecades/AerialExtreMatch-Localization) datasets. > WARNING: working in progress. ## Usage Simply clone this repository and unzip the dataset files. ```bash git clone [email protected]:datasets/Xecades/AerialExtreMatch-Benchmark cd AerialExtreMatch-Benchmark unzip "*.zip" ``` TODO: provide a python example. ## Dataset Structure After unpacking each .zip file: <pre> . └── class_[id] <i>(class_0~class_31)</i> ├── class_[id].npy ├── depth: *.exr └── rgb: *.jpg </pre> - Keys of `class_[id].npy` files: `['poses', 'intrinsics', 'depth', 'rgb', 'overlap', 'pitch', 'scale', 'pair']`. - Refer to original paper for details on classification.

autobio-bench/screw_tighten-blender

autobio-bench

2025-05-12T15:14:08Z

0

[ "task_categories:robotics", "license:mit", "size_categories:100K<n<1M", "format:parquet", "modality:tabular", "modality:timeseries", "modality:video", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us", "LeRobot", "medical" ]

[ "robotics" ]

2025-05-12T15:12:57Z

null

--- license: mit task_categories: - robotics tags: - LeRobot - medical 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:** mit ## Dataset Structure [meta/info.json](meta/info.json): ```json { "codebase_version": "v2.0", "robot_type": null, "total_episodes": 100, "total_frames": 154830, "total_tasks": 1, "total_videos": 300, "total_chunks": 1, "chunks_size": 1000, "fps": 50, "splits": { "train": "0:100" }, "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": { "state": { "dtype": "float32", "shape": [ 14 ], "names": [ "state" ] }, "actions": { "dtype": "float32", "shape": [ 14 ], "names": [ "actions" ] }, "image": { "dtype": "video", "shape": [ 224, 224, 3 ], "names": [ "height", "width", "channel" ], "info": { "video.fps": 50.0, "video.height": 224, "video.width": 224, "video.channels": 3, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "has_audio": false } }, "wrist_image": { "dtype": "video", "shape": [ 224, 224, 3 ], "names": [ "height", "width", "channel" ], "info": { "video.fps": 50.0, "video.height": 224, "video.width": 224, "video.channels": 3, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "has_audio": false } }, "wrist_image_2": { "dtype": "video", "shape": [ 224, 224, 3 ], "names": [ "height", "width", "channel" ], "info": { "video.fps": 50.0, "video.height": 224, "video.width": 224, "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] ```

littleGuagua/x_dataset_8140

littleGuagua

2025-05-12T15:13:31Z

1,161

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" ]

2025-01-26T13:25:56Z

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_8140 - **Subnet:** Bittensor Subnet 13 - **Miner Hotkey:** 5HasdyDaczLXYaiykhuuszTMWS65QmAgo72UpwABUi3czyeu ### 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_8140, 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_8140}, } ``` ### 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:** 50376997 - **Date Range:** 2025-01-21T00:00:00Z to 2025-02-10T00:00:00Z - **Last Updated:** 2025-02-18T20:55:45Z ### Data Distribution - Tweets with hashtags: 39.81% - Tweets without hashtags: 60.19% ### Top 10 Hashtags For full statistics, please refer to the `stats.json` file in the repository. | Rank | Topic | Total Count | Percentage | |------|-------|-------------|-------------| | 1 | NULL | 30319553 | 60.19% | | 2 | #riyadh | 310085 | 0.62% | | 3 | #zelena | 215655 | 0.43% | | 4 | #tiktok | 192806 | 0.38% | | 5 | #ad | 112205 | 0.22% | | 6 | #bbb25 | 110854 | 0.22% | | 7 | #grammys | 82659 | 0.16% | | 8 | #jhope_at_galadespiècesjaunes | 70215 | 0.14% | | 9 | #bbmzansi | 66978 | 0.13% | | 10 | #sixtonesann | 65126 | 0.13% | ## Update History | Date | New Instances | Total Instances | |------|---------------|-----------------| | 2025-01-26T13:26:49Z | 2721817 | 2721817 | | 2025-01-30T01:43:17Z | 9702324 | 12424141 | | 2025-02-02T13:47:13Z | 12507356 | 24931497 | | 2025-02-06T01:50:29Z | 8691717 | 33623214 | | 2025-02-09T13:54:19Z | 8748247 | 42371461 | | 2025-02-13T02:21:42Z | 6726572 | 49098033 | | 2025-02-18T05:54:36Z | 648154 | 49746187 | | 2025-02-18T20:55:45Z | 630810 | 50376997 |

jasonzheng/ioi-2024-bf16

jasonzheng

2025-05-12T14:59:10Z

0

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

[]

2025-05-12T14:59:07Z

null

--- dataset_info: features: - name: problem_id dtype: string - name: year dtype: string - name: uuid dtype: string - name: code dtype: string - name: subtask dtype: string splits: - name: train num_bytes: 3517307 num_examples: 2050 download_size: 687903 dataset_size: 3517307 configs: - config_name: default data_files: - split: train path: data/train-* ---

anonymous-user-546/test-csv-conversion

anonymous-user-546

2025-05-12T14:45:56Z

79

0

[ "license:cc-by-nc-nd-4.0", "size_categories:1M<n<10M", "format:csv", "modality:image", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]

[]

2025-05-07T20:07:06Z

null

--- license: cc-by-nc-nd-4.0 ---

liwu/MNBVC

liwu

2025-05-12T14:30:02Z

36,760

541

[ "task_categories:text-generation", "task_categories:fill-mask", "task_ids:language-modeling", "task_ids:masked-language-modeling", "annotations_creators:other", "language_creators:other", "multilinguality:monolingual", "source_datasets:original", "language:zh", "license:mit", "region:us" ]

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

2023-02-13T14:00:47Z

null

--- annotations_creators: - other language: - zh language_creators: - other license: - mit multilinguality: - monolingual pretty_name: MNBVC size_categories: - unknown source_datasets: - original task_categories: - text-generation - fill-mask task_ids: - language-modeling - masked-language-modeling --- # Dataset Card for MNBVC ## Table of Contents - [Dataset Card for MNBVC](#dataset-card-for-mnbvc) - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [数据集介绍](#数据集介绍) - [数据子集](#数据子集) - [数据格式](#数据格式) - [文本数据](#文本数据) - [问答数据](#问答数据) - [Contributions](#contributions) ## Dataset Description - **Homepage:** http://mnbvc.253874.net/ - **Repository:** https://github.com/esbatmop/MNBVC - **Paper:** N/A - **Leaderboard:** N/A - **Point of Contact:** N/A ### 数据集介绍中文互联网上最古老最神秘(没有之一)的里屋社区于2023.1.1庄重宣布: 在英明神武的里屋管子带领下，决心发挥社区所长(哪都长)，帮助开源社区长期更新一份最大的中文互联网语料集。 Huggingface上的MNBVC数据集在逐渐更新中，请到[https://github.com/esbatmop/MNBVC](https://github.com/esbatmop/MNBVC) 获取未完成清洗的更多数据。可以使用如下脚本加载： ```python from datasets import load_dataset dataset = load_dataset("liwu/MNBVC", 'law_judgement', split='train', streaming=True) next(iter(dataset)) # get the first line ``` ## 数据子集 MNBVC数据集包含数个子集： - `law_judgement`: 来自法律文书的文本。 - `gov_xuexiqiangguo`: 来自学习强国的文本。 - `gov_report`: 来自政府工作报告的文本。 - `co_ann_report`: 企业年报文本。 - `code_metadata`: 代码元数据。 - `qa_zhihu`: 来自[知乎](https://huggingface.co/datasets/wangrui6/Zhihu-KOL)的问答数据。 - `qa_wikihow`: 来自wikihow的问答数据。 - `qa_mfa`: 外交部问答数据。 - `news_peoples_daily`: 来自人民日报的文本数据。 - `wikipedia`: 来自维基百科的文本数据。 - `qa_stackexchange`: 来自StackExchange的问答数据。 - `qa_chatgpt`: 使用ChatGPT构造的问答语料，感谢[genggui001](https://github.com/genggui001)贡献语料。 - `math`: - `math_qa `: 和数学领域有关的问答数据。 - `emath` :中国数学爱好者论坛语料数据 - `math_chat`: 和数学领域有关的对话数据数据，可以提升模型Chain of Thought的能力。 - `crawler_oscar`: 从CommonCrawl中清洗出来的通用文本数据。 - `game` : 一些游戏的平行语料数据。 - `Hogwarts_legacy` : 霍格沃茨指遗 - `The_Wither_3` : 巫师三 - `Baldurs_Gate_3` : 博德之门 3 - `GTA`: 侠盗猎车手4 与侠盗猎车手5 - `Turing_Complete`: 图灵完备性 - `EldenRing`: 艾尔登法环 - `hades`: 哈迪斯 - `sekiro`: 只狼 - `parallel`: 平行语料目录 - `subtitle`: 字幕语料 - `yyets`: 人人影视 - `united_nations`: 联合国平行语料 - `blog`: 博客语料目录 - `book`: 书籍语料目录 ## 数据格式目前MNBVC数据集包含如下几类数据： - 通用文本 - 问答语料 - 代码语料 - 多轮对话 - 论坛语料 - 平行语料可以在[MNBVC的wiki页面](https://wiki.mnbvc.org/doku.php/%E7%8E%B0%E6%9C%89%E8%AF%AD%E6%96%99%E6%A0%BC%E5%BC%8F)上查看这几类数据的具体格式。项目早期所上传的数据使用如下格式，以后这一格式会被废弃，相应数据也会重新上传： ```json { "text": datasets.Value("string"), "meta": datasets.Value("string") } ``` ### Contributions Thanks to the [Liwu community](http://mnbvc.253874.net/) for constructing this dataset. Thanks to [silver](https://github.com/silverriver) and [jiaming](https://huggingface.co/Yjiaming) for adding and uploading this dataset to Huggingface. ### Citation Please cite the repo if you use the data or code in this repo. ``` @misc{mnbvc, author = {{MOP-LIWU Community} and {MNBVC Team}}, title = {MNBVC: Massive Never-ending BT Vast Chinese corpus}, year = {2023}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/esbatmop/MNBVC}}, } ```

momo1942/x_dataset_44829

momo1942

2025-05-12T14:24:28Z

2,579

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" ]

2025-01-27T09:49:03Z

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_44829 - **Subnet:** Bittensor Subnet 13 - **Miner Hotkey:** 5CacbhmQxhAVGWgrYvCypqhR3n3mNmmWEA8JYzAVghmTDYZy ### 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_44829, 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_44829}, } ``` ### 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:** 47517552 - **Date Range:** 2025-01-21T00:00:00Z to 2025-02-10T00:00:00Z - **Last Updated:** 2025-02-18T20:42:58Z ### Data Distribution - Tweets with hashtags: 46.37% - Tweets without hashtags: 53.63% ### Top 10 Hashtags For full statistics, please refer to the `stats.json` file in the repository. | Rank | Topic | Total Count | Percentage | |------|-------|-------------|-------------| | 1 | NULL | 25482638 | 53.63% | | 2 | #riyadh | 369646 | 0.78% | | 3 | #zelena | 283758 | 0.60% | | 4 | #tiktok | 222947 | 0.47% | | 5 | #ad | 122468 | 0.26% | | 6 | #bbb25 | 83620 | 0.18% | | 7 | #bbmzansi | 82423 | 0.17% | | 8 | #jhope_at_galadespiècesjaunes | 72240 | 0.15% | | 9 | #trump | 71073 | 0.15% | | 10 | #pr | 65594 | 0.14% | ## Update History | Date | New Instances | Total Instances | |------|---------------|-----------------| | 2025-01-27T09:50:05Z | 3300536 | 3300536 | | 2025-01-30T21:53:32Z | 11415036 | 14715572 | | 2025-02-03T09:57:03Z | 9268666 | 23984238 | | 2025-02-06T21:59:40Z | 5892953 | 29877191 | | 2025-02-10T10:02:47Z | 6650635 | 36527826 | | 2025-02-13T22:07:25Z | 9649951 | 46177777 | | 2025-02-18T05:41:46Z | 692358 | 46870135 | | 2025-02-18T20:42:58Z | 647417 | 47517552 |

jazasyed/musdb-alt

jazasyed

2025-05-12T14:15:52Z

28

0

[ "task_categories:automatic-speech-recognition", "language:en", "license:cc-by-nc-sa-4.0", "region:us", "music", "lyrics", "evaluation", "benchmark", "transcription" ]

[ "automatic-speech-recognition" ]

2025-04-16T11:16:20Z

null

--- task_categories: - automatic-speech-recognition language: - en tags: - music - lyrics - evaluation - benchmark - transcription pretty_name: MUSDB-ALT license: cc-by-nc-sa-4.0 --- # Dataset Card for MUSDB-ALT This dataset contains long-form lyric transcripts following the Jam-ALT [guidelines](https://huggingface.co/datasets/jamendolyrics/jam-alt/blob/main/GUIDELINES.md) for the test set of the dataset [MUSDB18](https://sigsep.github.io/datasets/musdb.html), with line-level timings. ## Dataset Details The dataset was constructed manually, based on the [MUSDB18 lyrics extension](https://zenodo.org/records/3989267) as a starting point. The lyrics extension contains transcripts of the 45 English language songs out of the 50 in the MUSDB18 test set. We annotated 39 of those 45 songs, excluding 6 for the following reasons: - Signe Jakobsen - What Have You Done To Me : Three overlapping vocal lines that could not be separated into lead and backing vocals - PR - Happy Daze : Vocal content primarily from highly processed vocal samples - PR - Oh No : Vocal content primarily from highly processed vocal samples - Skelpolu - Resurrection : Vocal content primarily from highly processed vocal samples - Timboz - Pony : Lyrics unintelligble due to screamed enunciation style - Triviul feat The Fiend - Widows : Three overlapping vocal lines that could not be separated into lead and backing vocals ### Dataset Description **Paper:** The dataset was introduced in the paper [Exploiting Music Source Separation for Automatic Lyrics Transcription with Whisper"](https://arxiv.org/abs/XXXXX) published at the Workshop [Artificial Intelligence For Music](https://ai4musicians.org/2025icme.html) at ICME 2025 - **Funding:** This work was supported by InnovateUK [Grant Number 10102804] - **License:** https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en ## Citation **BibTeX:** ``` @inproceedings{syed-2025-mss-alt, author = {Jaza Syed and Ivan Meresman-Higgs and Ond{\v{r}}ej C{\'{\i}}fka and Mark Sandler}, title = {Exploiting Music Source Separation for Automatic Lyrics Transcription with {Whisper}}, booktitle = {2025 {IEEE} International Conference on Multimedia and Expo Workshops (ICMEW)}, publisher = {IEEE}, year = {2025}, note = {In press} } ```

rainbowbridge/x_dataset_20722

rainbowbridge

2025-05-12T14:04:19Z

1,203

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" ]

2025-01-27T01:31:47Z

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_20722 - **Subnet:** Bittensor Subnet 13 - **Miner Hotkey:** 5EXTMFUDy34PkND7RWEEXb4vdr3JXmFXesoygkHDrim7GfR5 ### 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_20722, 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_20722}, } ``` ### 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:** 53014608 - **Date Range:** 2025-01-21T00:00:00Z to 2025-02-12T00:00:00Z - **Last Updated:** 2025-02-18T18:57:54Z ### Data Distribution - Tweets with hashtags: 41.60% - Tweets without hashtags: 58.40% ### Top 10 Hashtags For full statistics, please refer to the `stats.json` file in the repository. | Rank | Topic | Total Count | Percentage | |------|-------|-------------|-------------| | 1 | NULL | 30961185 | 58.40% | | 2 | #riyadh | 327113 | 0.62% | | 3 | #zelena | 254157 | 0.48% | | 4 | #tiktok | 216346 | 0.41% | | 5 | #bbb25 | 161006 | 0.30% | | 6 | #ad | 125530 | 0.24% | | 7 | #royalrumble | 75597 | 0.14% | | 8 | #bbmzansi | 71549 | 0.13% | | 9 | #pr | 69916 | 0.13% | | 10 | #yahooニュース | 65493 | 0.12% | ## Update History | Date | New Instances | Total Instances | |------|---------------|-----------------| | 2025-01-27T01:32:09Z | 1057227 | 1057227 | | 2025-01-30T13:48:23Z | 11631895 | 12689122 | | 2025-02-03T01:51:30Z | 8401846 | 21090968 | | 2025-02-06T13:56:34Z | 12297890 | 33388858 | | 2025-02-10T01:59:57Z | 8203885 | 41592743 | | 2025-02-13T14:08:19Z | 10112124 | 51704867 | | 2025-02-18T03:56:41Z | 648961 | 52353828 | | 2025-02-18T18:57:54Z | 660780 | 53014608 |

zerostratos/chunks

zerostratos

2025-05-12T13:53:24Z

0

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

[]

2025-05-12T13:53:14Z

null

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 68005509 num_examples: 189426 download_size: 37395152 dataset_size: 68005509 configs: - config_name: default data_files: - split: train path: data/train-* ---

gmanolache/CrypticBio

gmanolache

2025-05-12T13:35:27Z

465

0

[ "task_categories:zero-shot-classification", "language:en", "license:cc", "size_categories:100M<n<1B", "format:parquet", "modality:image", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us", "biodiverstiy", "cryptic species", "fine-grained image recognition", "vision-language", "multimodal dataset" ]

[ "zero-shot-classification" ]

2025-04-27T06:35:09Z

null

--- license: cc task_categories: - zero-shot-classification language: - en tags: - biodiverstiy - cryptic species - fine-grained image recognition - vision-language - multimodal dataset pretty_name: A Large Multimodal Dataset for Visually Confusing Biodiversity size_categories: - 100M<n<1B --- # CrypticBio: A Large Multimodal Dataset for Visually Confusing Biodiversity  <div style="text-align:left;"> <a href="https://georgianagmanolache.github.io/crypticbio/" target="_blank" style="display:inline-block;"> <img src="https://img.shields.io/badge/Project%20Page-Visit-blue" alt="Project Page"> </a> <a href="https://github.com/georgianagmanolache/crypticbio" target="_blank" style="display:inline-block;"> <img src="https://img.shields.io/badge/GitHub-Visit-lightgrey" alt="GitHub"> </a> </div> ## Description [CrypticBio](https://georgianagmanolache.github.io/crypticbio/) comprises metadata including species scientific and multicultural vernacular terminology, image URL, taxonomic hierarchy, spatiotemporal context, and cryptic species group. Cryptic species are groups of two or more taxa that are nearly indistinguishable based on visual characteristics alone. ## CrypticBio Dataset We present CrypticBio, the largest publicly available multimodal dataset of visually confusing species groups, specifically curated to support the development of AI models in the context of biodiversity identification applications. Curated from real-world trends in species misidentification among community annotators of iNaturalist, CrypticBio contains 67K cryptic species groups spanning 52K species, represented in 166 million images. ## New Benchmark Datasets We created four new benchmark datasets for fine-grained image classification of cryptic species. ### CrypticBio-Commom We curate a common species from Arachnida, Aves, Insecta, Plantae, Fungi, Mollusca, and Reptilia and associated cryptic group, spanning n=158 species. We randomly select 100 samples from each species in a cryptic group where there are more than 150 observation per species. ### CrypticBio-CommonUnseen To assess zero-shot performance on common species from CrypticBio-Common not encountered during training of state-of-the-art models, we specifically curate a subset spanning data from 01-09-2024 to 01-04-2025. We randomly select 100 samples from each species in a cryptic group where there are more than 150 observation per species, spanning n=133 species. ### CrypticBio-Endagered We propose a cryptic species subset of endangered species according to global IUCN Red List. We randomly select 30 samples from Arachnida, Aves, Insecta, Plantae, Fungi, Mollusca, and Reptilia and associated cryptic groups spanning n=37 species, filtering out taxa where there are less than 150 observation. ### CrypticBio-Invasive We also propose a cryptic species subset of invasive alien species (IAS) according to global the Global Invasive Species Database (GISD). IAS are a significant concern for biodiversity as their records appear to be exponentially rising across the Earth. We randomly select 100 samples from each invasive species cryptic group spanning n=72 species, filtering out taxa where there are less than 150 observation. ## Dataset Information ### Directory ```plaintext main/ ├── CrypticBio/ │ ├── part_0.csv │ ├── part_0.parquet │ ├── part_1.parquet │ ├── . │ ├── . │ ├── . │ └── part_626.parquet ├── CrypticBio-benchmarks/ │ ├── CrypticBio-Common.csv │ ├── CrypticBio-CommonUnseen.csv │ ├── CrypticBio-Endangered.csv │ └── CrypticBio-Invasive.csv ├──README.md └──.gitattributes ``` The data and the code are publicly available at [georgianagmanolache.github.io/crypticbio](https://georgianagmanolache.github.io/crypticbio/)

TrojAI/updatedviolence

TrojAI

2025-05-12T13:24:56Z

0

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

[]

2025-05-12T13:24:46Z

null

--- dataset_info: features: - name: messages list: - name: content dtype: string - name: role dtype: string - name: expected_response dtype: string - name: label dtype: int64 - name: source dtype: string splits: - name: train num_bytes: 39363138 num_examples: 88910 - name: test num_bytes: 2996268 num_examples: 7020 download_size: 23764400 dataset_size: 42359406 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

SYSUSELab/RustEvo2

SYSUSELab

2025-05-12T12:40:06Z

0

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

[]

2025-05-12T12:38:13Z

null

--- license: apache-2.0 --- # RustEvo² RustEvo² is the first benchmark for evaluating LLMs' ability to adapt to evolving Rust APIs, as described in the paper "RustEvo²: An Evolving Benchmark for API Evolution in LLM-based Rust Code Generation". ## Dataset Overview Our work can be divided into two phases: Phase I: API Evolution Data Collection - We collect API changes from multiple sources including official Rust repositories and third-party crates. We analyze changelogs, documentation, and implementation changes to identify and categorize API evolutions into Stabilizations, Signature Changes, Behavioral Changes, and Deprecations. Phase II: RustEvo² Construction - We transform the collected API evolution data into natural programming tasks using an LLM-based generation pipeline. This process creates programming queries, code solutions, and test programs that implicitly require the use of specific API versions. The following figure illustrates our two-phase framework: <div align="center"> <img src="Imgs/overview.png" alt="RustEvo² Framework Overview" width="100%"/> </div> ### Dataset Format RustEvo² consists of 588 API changes (380 from Rust standard libraries, 208 from 15 third-party crates) spanning versions 1.71.0 to 1.84.0. These changes are categorized into four types: Stabilizations (31.3%), Signature Changes (31.5%), Behavioral Changes (33.2%), and Deprecations (4.1%), reflecting their actual distribution in the Rust ecosystem. Each task in RustEvo² consists of <API change information, programming query, function signature, reference solution, test program>. The API change information includes name, module path, version details, documentation, and source code. Programming queries describe real-world scenarios without explicitly mentioning the API. Function signatures guide implementation without revealing API specifics. Test programs verify correct API usage and functional behavior. One task example: ```json { "task_idx": 39, "query": "In a performance-critical application, you need to efficiently update a large collection of objects by cloning their state from another collection. The objects implement a custom `Clone` trait, but you want to avoid unnecessary trait bounds that could complicate the implementation. Design a function to handle this cloning operation efficiently.", "function_signature": "fn update_collection<T: Clone>(target: &mut Vec<T>, source: &Vec<T>)", "code": "fn update_collection<T: Clone>(target: &mut Vec<T>, source: &Vec<T>) {\n target.truncate(source.len());\n for (t, s) in target.iter_mut().zip(source.iter()) {\n t.clone_from(s);\n }\n if target.len() < source.len() {\n target.extend(source[target.len()..].iter().cloned());\n }\n}", "test_program": "..." }, ``` ## Usage ### Setup 1. Environment Setup: ```bash conda create -n RustEvo python=3.8 conda activate RustEvo pip install -r requirements.txt ``` 2. Install Rust toolchain ```bash curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh rustup toolchain install 1.71.0 1.72.0 1.73.0 1.74.0 1.75.0 1.76.0 1.77.0 1.78.0 1.79.0 1.80.0 1.81.0 1.82.0 1.83.0 1.84.0 ``` ### Construct your own evolving dataset If you don't want to construct a new dataset, you can directly use the existing dataset in the `data` folder. 1. Phase I: API Evolution Collection ```bash python scripts/rust_api_analyzer.py --repo ./rust-repo --output ./reports --start 1.72.0 --end 1.84.0 python scripts/crate_analyzer.py --crates_num 15 --start_date 2024-01-01 --end_date 2025-02-21 ``` 2. Phase II: Task Generation ```bash python scripts/generate_query.py --input ./reports/rust_api_changes.json --output ./data/queries/queries_rust.json python scripts/generate_code.py --input ./data/queries/queries_rust.json --output ./data/codes/codes_rust.json python scripts/generate_test.py --input_file ./data/codes/codes_rust.json --output_file ./data/test_programs/test_programs_rust.json ``` ### Evaluate 1. Replace the target LLM in the evaluate/generation.py 2. Run the evaluation script ```bash cd evaluate ./run.sh eval_models.py --model_name ``` ## Results Some important results of our experiments: ### Performance by Model | Model | Pass@1 (%) | API Usage Accuracy (%) | Coverage (%) | |-------|------------|---------|--------------| | Claude-3.7-Sonnet | 65.3 | 78.2 | 83.6 | | o1-mini | 57.5 | 70.4 | 85.2 | | GPT-4o | 55.4 | 68.4 | 77.2 | | Gemini-1.5-Pro | 55.3 | 62.6 | 60.9 | | DeepSeek-v3 | 54.8 | 69.7 | 71.0 | | Gemini-2.0-Flash | 52.6 | 73.5 | 72.5 | | Llama-3.1-70B | 51.0 | 65.3 | 69.0 | | Qwen-2.5-72B | 50.9 | 66.7 | 64.7 | | Claude-3.5-Sonnet | 48.1 | 68.7 | 80.3 | | Grok-3 | 40.5 | 67.2 | 70.4 | ### Performance by API Change Type | Change Type | Average Pass@1 (%) | |-------------|-------------------| | Stabilizations | 65.8 | | Signature Changes | 58.2 | | Behavioral Changes | 38.0 | | Deprecations | 40.4 | Complete evaluation results and error analysis are [here](Results).

amekerishvili/ATCO2_Callsigns

amekerishvili

2025-05-12T12:14:59Z

0

[ "region:us" ]

[]

2025-05-12T12:13:45Z

null

--- dataset_info: features: - name: audio_file dtype: string - name: ID dtype: string - name: ground_truth dtype: string - name: callsigns dtype: string - name: Callsigns_manual dtype: string - name: non_Eng_ground_truth dtype: string - name: tags dtype: string - name: airport dtype: string - name: channel dtype: string - name: whisper-large-v2-ANSP-3h1m dtype: string - name: ground_truth_norm dtype: string - name: whisper-large-v2-ANSP-3h1m_norm dtype: string - name: whisper-large-v2-ANSP-3h1m_norm_wer dtype: float64 - name: callsigns_NER_error_rate dtype: float64 - name: Callsigns_manual.1 dtype: string splits: - name: train num_bytes: 265042 num_examples: 100 download_size: 96625 dataset_size: 265042 configs: - config_name: default data_files: - split: train path: data/train-* ---

KBayoud/Custom-3

KBayoud

2025-05-12T12:14:03Z

0

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

[]

2025-05-12T09:48:50Z

null

--- dataset_info: features: - name: image dtype: image - name: question dtype: string - name: answer dtype: string splits: - name: train num_bytes: 1264783692.0 num_examples: 420 download_size: 1263426207 dataset_size: 1264783692.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

ma921/oasst1-english-tokenized-qwen2.5_noise20

ma921

2025-05-12T12:08:52Z

0

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

[]

2025-05-12T12:08:47Z

null

--- dataset_info: features: - name: pos_input_ids sequence: int64 - name: neg_input_ids sequence: int64 - name: flip dtype: int64 splits: - name: train num_bytes: 28348816 num_examples: 6859 download_size: 7693055 dataset_size: 28348816 configs: - config_name: default data_files: - split: train path: data/train-* ---

ma921/oasst1-english-tokenized-phi2_noise0

ma921

2025-05-12T11:58:45Z

0

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

[]

2025-05-12T11:58:41Z

null

--- dataset_info: features: - name: sft_input_ids sequence: int64 - name: pos_input_ids sequence: int64 - name: neg_input_ids sequence: int64 splits: - name: train num_bytes: 45647820.0 num_examples: 6859 download_size: 11661751 dataset_size: 45647820.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

EdmondFU/Causal-Reasoning-Bench_CRBench

EdmondFU

2025-05-12T11:48:53Z

338

3

[ "task_categories:question-answering", "size_categories:10K<n<100K", "region:us" ]

[ "question-answering" ]

2025-04-23T07:13:49Z

null

--- task_categories: - question-answering size_categories: - 10K<n<100K --- <p align="center"> <img src="CRBench.png" width="50%" height="50%"> </p> <p align="left"> <img src="Deep.png" width="30%"> </p> # 🦙Causal Reasoning Bench（CRBench） Developing a labeled dataset with causal errors is crucial for evaluating the performance of causalizing methods for CoT reasoning. We proposed the CRBench as a benchmark to verify whether causalizing methods can effectively correct the causal errors. ## 🦙Causal Error We have summarized four types of causal errors that lead to CoT reasoning errors: - **Measure error.** Causal measurement error refers to the incorrect use of correlation indicators instead of causal indicators when measuring causal relationships, or the use of inappropriate causal measures (such as average treatment effect ATE, direct/indirect effects, etc.) when estimating causal effects. - **Collider error.** Collider error refers to the incorrect control or selection of a "collider" in causal reasoning, which introduces false correlation. A collider is a variable that is affected by two unrelated variables at the same time. If this collider is incorrectly controlled during analysis, it will cause false correlations between originally unrelated variables. Due to selection bias when selecting samples, two originally unrelated variables appear to have a causal relationship. - **Confounding error.** Confounding error refers to the omission of a confounder in causal inference, leading to an observed causal effect that is not genuine but rather driven by a common influencing factor. It can also occur when variables that should not be included in the reasoning process are considered, such as residual information from a previous question, biases within the model, hallucinations, and other misleading factors. - **Mediation error.** Mediation error refers to the incorrect interpretation of the role of the mediating variable in causal inference, which may be due to incorrect control of the mediating variable, incorrect addition of the mediating variable, or ignoring the mediating path. ## 🦙Available Subsets ``` ds = load_dataset("EdmondFU/Causal-Reasoning-Bench_CRBench", split="train") ``` ## Generated Process Description <p align="center"> <img src="Error generated.png" width="50%" height="50%"> </p> The example of generated causal error data. **Causality measure error:** In the process of determining that "when the intersection is inside the circle, each line must be a secant," the reasoning mistakenly overstates the impact of the intersection point's location. It erroneously asserts that "as long as the intersection is inside the circle, each line must intersect the circle at two points," thereby ignoring the possibility that a line might only intersect the circle at one point (which would be a tangent), leading to a causality measure error. **Collider error** When considering the impact of the intersection point's position on the relationship between the lines and the circle, the reasoning mistakenly treats the intersection position (inside, on, outside) as a "collider" that is simultaneously determined by both the type of the lines and the circle’s position. This error mixes independent factors. **Confounding Error:** In the reasoning process, an unrelated external factor is incorrectly introduced as a confounding variable. It is mistakenly assumed that this variable affects both the position of the intersection and the number of intersection points between the lines and the circle, which leads to an incorrect derivation of the number of possible configurations.This incorrectly introduces the circle’s radius as a confounder, mixing up the originally clear causal relationship based solely on the intersection point’s location, hence causing a confounding error. **Mediation error:** Here, an unneeded and non-existent mediator variable called 'penetration angle' is introduced, thereby misrepresenting the causal relationship between the intersection location and the line type, resulting in a mediation error, mistakenly assuming that the causal relationship between the intersection point’s location and the line type is transmitted through this mediator, which then leads to a misinterpretation of the relationships among variables. ## 🦙The CRBench dataset is generated based on publicly available high-quality reasoning datasets: - 🧠 [OpenThoughts-114k dataset](https://huggingface.co/datasets/open-thoughts/OpenThoughts-114k) - 🧠 [Bespoke-Stratos-17k dataset](https://huggingface.co/datasets/bespokelabs/Bespoke-Stratos-17k) - 🧠 [OpenThoughts2-1M](https://huggingface.co/datasets/open-thoughts/OpenThoughts2-1M) Including: Code - 💻[BAAI/TACO](https://huggingface.co/datasets/BAAI/TACO) - 💻[codeparrot/apps](https://huggingface.co/datasets/codeparrot/apps) - 💻[deepmind/code_contests](https://huggingface.co/datasets/deepmind/code_contests) - 💻[MatrixStudio/Codeforces-Python-Submissions](https://huggingface.co/datasets/MatrixStudio/Codeforces-Python-Submissions) - 💻[livecodebench/execution-v2](https://huggingface.co/datasets/livecodebench/execution-v2) - 💻[livecodebench/code_generation_lite](https://huggingface.co/datasets/livecodebench/code_generation_lite) Math - 🔢[AI-MO/NuminaMath-CoT](https://huggingface.co/datasets/AI-MO/NuminaMath-CoT) - 🔢[Maxwell-Jia/AIME_2024](https://huggingface.co/datasets/Maxwell-Jia/AIME_2024) - 🔢[game661100/MATH-500](https://huggingface.co/game661100/MATH-500) Science - 📊[camel-ai/chemistry](https://huggingface.co/datasets/camel-ai/chemistry) - 📊[camel-ai/biology](https://huggingface.co/datasets/camel-ai/biology) - 📊[camel-ai/physics](https://huggingface.co/datasets/camel-ai/physics) Puzzle - 🤖[INK-USC/riddle_sense](https://huggingface.co/datasets/INK-USC/riddle_sense) # 🦙Citation ``` @misc{CRbench, author = {Jiarun Fu,Hao Li}, month = April, title = {Causal Reasoning Bench}, howpublished = {https://huggingface.co/datasets/EdmondFU/Causal-Reasoning-Bench_CRBench}, year = {2025} } ``` # 🦙Contact Us ``` Jiarun Fu| Phd student in BIT:[email protected] Hao Li| Master's student in BIT:[email protected] ```

YasmineMakni/so100_mvt_ball

YasmineMakni

2025-05-12T11:37:49Z

0

[ "task_categories:robotics", "license:apache-2.0", "size_categories:10K<n<100K", "format:parquet", "modality:tabular", "modality:timeseries", "modality:video", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us", "LeRobot", "so100", "tutorial" ]

[ "robotics" ]

2025-05-12T09:55:58Z

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": 40, "total_frames": 11498, "total_tasks": 1, "total_videos": 80, "total_chunks": 1, "chunks_size": 1000, "fps": 25, "splits": { "train": "0:40" }, "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": [ 720, 1280, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.fps": 25.0, "video.height": 720, "video.width": 1280, "video.channels": 3, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "has_audio": false } }, "observation.images.cam": { "dtype": "video", "shape": [ 1080, 1920, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.fps": 25.0, "video.height": 1080, "video.width": 1920, "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] ```

James096/x_dataset_127

James096

2025-05-12T11:26:34Z

20

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

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

2025-05-07T19:29:50Z

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:** James096/x_dataset_127 - **Subnet:** Bittensor Subnet 13 - **Miner Hotkey:** 5D2KKAGcf1bHnT71v5jsw9TJBmQto5PhYKRSPcJDhk8gqSXj ### Miner Data Compliance Agreement In uploading this dataset, I am agreeing to the [Macrocosmos Miner Data Compliance Policy](https://github.com/macrocosm-os/data-universe/blob/add-miner-policy/docs/miner_policy.md). ### 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{James0962025datauniversex_dataset_127, title={The Data Universe Datasets: The finest collection of social media data the web has to offer}, author={James096}, year={2025}, url={https://huggingface.co/datasets/James096/x_dataset_127}, } ``` ### 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:** 80481 - **Date Range:** 2025-04-06T00:00:00Z to 2025-05-06T00:00:00Z - **Last Updated:** 2025-05-12T11:26:31Z ### Data Distribution - Tweets with hashtags: 99.99% - Tweets without hashtags: 0.01% ### Top 10 Hashtags For full statistics, please refer to the `stats.json` file in the repository. | Rank | Topic | Total Count | Percentage | |------|-------|-------------|-------------| | 1 | #bitcoin | 5883 | 7.31% | | 2 | #trump | 4062 | 5.05% | | 3 | #crypto | 3581 | 4.45% | | 4 | #btc | 1815 | 2.26% | | 5 | #ai | 1564 | 1.94% | | 6 | #tao | 1539 | 1.91% | | 7 | #ethereum | 1538 | 1.91% | | 8 | #binance | 1324 | 1.65% | | 9 | #artificialintelligence | 1307 | 1.62% | | 10 | #cardano | 1154 | 1.43% | ## Update History | Date | New Instances | Total Instances | |------|---------------|-----------------| | 2025-05-11T17:07:25Z | 80480 | 80480 | | 2025-05-12T11:26:31Z | 1 | 80481 |

ncavallo/so100_test_lerobot2_4

ncavallo

2025-05-12T11:24:50Z

0

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

[ "robotics" ]

2025-05-12T11:16:08Z

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": 245, "total_tasks": 1, "total_videos": 1, "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.robot": { "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] ```

yoonholee/completions_Qwen3-1.7B_AIME2025

yoonholee

2025-05-12T11:13:44Z

0

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

[]

2025-05-12T11:13:42Z

null

--- dataset_info: features: - name: problem dtype: string - name: completions sequence: string - name: answer dtype: string - name: corrects sequence: bool - name: acc dtype: float64 splits: - name: train num_bytes: 12414044 num_examples: 30 download_size: 4371593 dataset_size: 12414044 configs: - config_name: default data_files: - split: train path: data/train-* ---

Botai666/Medical_VLM_Sycophancy

Botai666

2025-05-12T11:04:21Z

15

0

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

[]

2025-03-27T07:56:43Z

null

--- license: apache-2.0 ---

alozowski/hf_doc_test

alozowski

2025-05-12T10:58:58Z

0

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

[]

2025-05-12T10:44:22Z

null

--- dataset_info: - config_name: chunked features: - name: document_id dtype: string - name: document_text dtype: string - name: document_filename dtype: string - name: document_metadata struct: - name: file_size dtype: int64 - name: raw_chunk_summaries sequence: string - name: chunk_summaries sequence: string - name: raw_document_summary dtype: string - name: document_summary dtype: string - name: summarization_model dtype: string - name: chunks list: - name: chunk_id dtype: string - name: chunk_text dtype: string - name: multihop_chunks list: - name: chunk_ids sequence: string - name: chunks_text sequence: string - name: chunk_info_metrics list: - name: avg_token_length dtype: float64 - name: bigram_diversity dtype: float64 - name: flesch_reading_ease dtype: float64 - name: gunning_fog dtype: float64 - name: perplexity dtype: float64 - name: token_count dtype: float64 - name: unique_token_ratio dtype: float64 - name: chunking_model dtype: string splits: - name: train num_bytes: 113907 num_examples: 1 download_size: 82204 dataset_size: 113907 - config_name: ingested features: - name: document_id dtype: string - name: document_text dtype: string - name: document_filename dtype: string - name: document_metadata struct: - name: file_size dtype: int64 splits: - name: train num_bytes: 44906 num_examples: 1 download_size: 17845 dataset_size: 44906 - config_name: lighteval features: - name: question dtype: string - name: additional_instructions dtype: string - name: ground_truth_answer dtype: string - name: gold sequence: int64 - name: choices sequence: string - name: question_category dtype: string - name: kind dtype: string - name: estimated_difficulty dtype: int64 - name: citations sequence: string - name: document_id dtype: string - name: chunk_ids sequence: string - name: question_generating_model dtype: string - name: chunks sequence: string - name: document dtype: string - name: document_summary dtype: string - name: answer_citation_score dtype: float64 - name: chunk_citation_score dtype: float64 - name: citation_score dtype: float64 splits: - name: train num_bytes: 890444 num_examples: 16 download_size: 50645 dataset_size: 890444 - config_name: multi_hop_questions features: - name: document_id dtype: string - name: source_chunk_ids sequence: string - name: additional_instructions dtype: string - name: question dtype: string - name: self_answer dtype: string - name: choices sequence: string - name: estimated_difficulty dtype: int64 - name: self_assessed_question_type dtype: string - name: generating_model dtype: string - name: thought_process dtype: string - name: citations sequence: string - name: raw_response dtype: string splits: - name: train num_bytes: 39753 num_examples: 4 download_size: 16677 dataset_size: 39753 - config_name: single_shot_questions features: - name: chunk_id dtype: string - name: document_id dtype: string - name: additional_instructions dtype: string - name: question dtype: string - name: self_answer dtype: string - name: choices sequence: string - name: estimated_difficulty dtype: int64 - name: self_assessed_question_type dtype: string - name: generating_model dtype: string - name: thought_process dtype: string - name: raw_response dtype: string - name: citations sequence: string splits: - name: train num_bytes: 112399 num_examples: 12 download_size: 25706 dataset_size: 112399 - config_name: summarized features: - name: document_id dtype: string - name: document_text dtype: string - name: document_filename dtype: string - name: document_metadata struct: - name: file_size dtype: int64 - name: raw_chunk_summaries sequence: string - name: chunk_summaries sequence: string - name: raw_document_summary dtype: string - name: document_summary dtype: string - name: summarization_model dtype: string splits: - name: train num_bytes: 51854 num_examples: 1 download_size: 52857 dataset_size: 51854 configs: - config_name: chunked data_files: - split: train path: chunked/train-* - config_name: ingested data_files: - split: train path: ingested/train-* - config_name: lighteval data_files: - split: train path: lighteval/train-* - config_name: multi_hop_questions data_files: - split: train path: multi_hop_questions/train-* - config_name: single_shot_questions data_files: - split: train path: single_shot_questions/train-* - config_name: summarized data_files: - split: train path: summarized/train-* ---

gavrelina/test_dataset

gavrelina

2025-05-12T10:56:54Z

64

0

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

[ "robotics" ]

2025-05-07T12:30:15Z

null

--- tags: - phosphobot - so100 - phospho-dk task_categories: - robotics --- # test_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.

babs/english-labelled-audio

babs

2025-05-12T10:53:28Z

0

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

[]

2025-05-12T03:22:23Z

null

--- dataset_info: features: - name: audio dtype: audio - name: text dtype: string - name: stamps list: - name: clean dtype: bool - name: end dtype: float64 - name: speaker dtype: string - name: start dtype: float64 - name: clean dtype: bool - name: chunk_start dtype: int64 - name: chunk_end dtype: int64 splits: - name: train num_bytes: 33954923563.228 num_examples: 26921 download_size: 34237305869 dataset_size: 33954923563.228 configs: - config_name: default data_files: - split: train path: data/train-* ---

malaysia-ai/malaysian-youtube-filtered-24k

malaysia-ai

2025-05-12T10:52:57Z

137

0

[ "language:ms", "license:cc-by-nc-4.0", "region:us" ]

[]

2024-11-12T05:42:20Z

null

--- language: - ms viewer: false license: cc-by-nc-4.0 --- # Filtered Malaysian Youtube Originally from https://huggingface.co/datasets/malaysia-ai/malaysian-youtube, we filtered audio less than 4 hours and converted to 24k sampling rate for audio processing. ## how to download ```bash huggingface-cli download --repo-type dataset \ --include '*.z*' \ --local-dir './' \ malaysia-ai/malaysian-youtube-filtered-24k wget https://www.7-zip.org/a/7z2301-linux-x64.tar.xz tar -xf 7z2301-linux-x64.tar.xz ~/7zz x filtered-24k.zip -y -mmt40 ```

AI-ISL/DUSK

AI-ISL

2025-05-12T10:31:01Z

311

1

[ "task_categories:question-answering", "task_categories:multiple-choice", "task_categories:other", "annotations_creators:machine-generated", "source_datasets:original", "language:en", "license:mit", "size_categories:n<1K", "modality:text", "region:us", "unlearning", "selective-forgetting", "multi-source", "benchmark", "language-models", "DUSK" ]

[ "question-answering", "multiple-choice", "other" ]

2025-04-26T14:41:07Z

null

--- datasets: - AI-ISL/DUSK annotations_creators: - machine-generated language: - en license: mit pretty_name: DUSK size_categories: - 1K<n<10K source_datasets: - original tags: - unlearning - selective-forgetting - multi-source - benchmark - language-models - DUSK task_categories: - question-answering - multiple-choice - other dataset_type: benchmark configs: - config_name: eval_general_qa data_files: - split: eval path: eval_general_qa.jsonl - config_name: eval_specific_forget_qa data_files: - split: eval path: eval_specific_forget_qa.jsonl - config_name: eval_specific_retain_qa data_files: - split: eval path: eval_specific_retain_qa.jsonl - config_name: eval_icl data_files: - split: eval path: eval_icl.jsonl - config_name: eval_icl_mcqa data_files: - split: eval path: eval_icl_mcqa.jsonl - config_name: eval_verbatim data_files: - split: eval path: eval_verbatim.json - config_name: eval_holdout data_files: - split: eval path: "eval_holdout-*.parquet" - config_name: raw data_files: - split: forget_chronological path: "raw/forget_chronological-*.parquet" - split: retain_feature_story path: "raw/retain_feature_story-*.parquet" - split: retain_interview path: "raw/retain_interview-*.parquet" - split: retain_inverted_pyramid path: "raw/retain_inverted_pyramid-*.parquet" - split: retain_listicle path: "raw/retain_listicle-*.parquet" - split: full path: "raw/full-*.parquet" dataset_info: - config_name: eval_general_qa features: - name: question dtype: string - name: answer dtype: string splits: - name: eval num_bytes: 9035 num_examples: 134 download_size: 0 dataset_size: 9035 - config_name: eval_holdout features: - name: text dtype: string splits: - name: eval num_bytes: 215202 num_examples: 45 download_size: 0 dataset_size: 215202 - config_name: eval_icl features: - name: question dtype: string - name: answer dtype: string splits: - name: eval num_bytes: 785 num_examples: 12 download_size: 0 dataset_size: 785 - config_name: eval_icl_mcqa features: - name: question dtype: string - name: answer dtype: string splits: - name: eval num_bytes: 1768 num_examples: 12 download_size: 0 dataset_size: 1768 - config_name: eval_specific_forget_qa features: - name: question dtype: string - name: answer dtype: string splits: - name: eval num_bytes: 1280 num_examples: 20 download_size: 0 dataset_size: 1280 - config_name: eval_specific_retain_qa features: - name: question dtype: string - name: answer dtype: string splits: - name: eval num_bytes: 7680 num_examples: 119 download_size: 0 dataset_size: 7680 - config_name: eval_verbatim features: - name: prompt dtype: string - name: gt dtype: string splits: - name: eval num_bytes: 255070 num_examples: 47 download_size: 0 dataset_size: 255070 - config_name: raw features: - name: text dtype: string splits: - name: forget_chronological num_bytes: 219802 num_examples: 46 - name: retain_feature_story num_bytes: 240633 num_examples: 49 - name: retain_interview num_bytes: 222925 num_examples: 48 - name: retain_inverted_pyramid num_bytes: 222419 num_examples: 46 - name: retain_listicle num_bytes: 203382 num_examples: 46 - name: full num_bytes: 1109148 num_examples: 232 download_size: 0 dataset_size: 2218309 --- # 🌇 DUSK: Do Not Unlearn Shared Knowledge DUSK is a benchmark dataset designed for evaluating **machine unlearning** in **multi-source** settings, where specific data sources must be forgotten while preserving others. In realistic applications, documents often share factual overlap with publicly available content (e.g., Wikipedia, textbooks). DUSK challenges unlearning algorithms to **precisely erase only what must be forgotten**, while preserving knowledge that remains supported by other sources. --- ## 💡 Motivation Existing benchmarks for machine unlearning often make a simplifying assumption: that the forget and retain sets contain completely separate information. But in reality, knowledge overlaps. For instance, a news article slated for removal may describe an event also covered in Wikipedia. Removing it *should not* cause the model to forget publicly known facts. **DUSK addresses this challenge head-on**, requiring models to: - 🚫 Erase *only* the information *unique* to the forget set - ✅ Preserve *shared* knowledge supported by the retain set Each document in DUSK includes both forget-only and shared content—expressed. This setup provides a rigorous test of whether a model can disentangle what to forget from what to retain. > 🧠 **DUSK is the first benchmark that explicitly evaluates realistic unlearning scenarios where knowledge overlaps across data sources.** > Unlike existing benchmarks that assume disjoint forget and retain sets, DUSK reflects the nuanced demands of real-world unlearning. --- ## 🧱 Dataset Overview DUSK consists of 120 synthetic professor profiles described in five stylistic formats: - 📜 Chronological - 📰 Feature Story - 🎤 Interview - 🧾 Inverted Pyramid - 🔢 Listicle DUSK enables: - Controlled attribution of knowledge - Clear separation between *shared* and *unique* information --- ## 📊 Dataset Configurations | Config | Description | |--------|-------------| | `raw/` | Full documents grouped by narrative style | | `eval_verbatim` | Evaluates **verbatim memorization** | | `eval_specific_forget_qa` | QA targeting **unique forget knowledge** | | `eval_specific_retain_qa` | QA targeting **unique retain knowledge** | | `eval_general_qa` | General QA over all content | | `eval_icl`, `eval_icl_mcqa` | In-context learning and multiple choice | | `eval_holdout` | QA over unseen holdout professors | --- ## 📐 Evaluation Dimensions DUSK defines **seven key metrics** to assess unlearning effectiveness: - **Verbatim Memorization**: Is the original phrasing erased? - **Unique Forget Knowledge (UFK)**: Is forget-only knowledge removed? - **Shared Knowledge (SK)**: Is overlapping knowledge preserved? - **Unique Retain Knowledge (URK)**: Is retain-only information intact? - **Downstream Capability (DC)**: Are general capabilities preserved? - **Privacy Leakage**: Is residual information still accessible? - **Retain Deviation**: Does the model behave consistently on retain data? --- ## 🛠️ Usage You can load the dataset easily using HuggingFace `datasets` library: ```python from datasets import load_dataset dataset = load_dataset("AI-ISL/DUSK") print(dataset) ``` --- ## ✏️ Citation Coming soon! ---

rubenchocron/ks_ft_new_data

rubenchocron

2025-05-12T10:30:54Z

51

0

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

[]

2025-05-10T17:41:22Z

null

--- dataset_info: features: - name: formatted_question_answer dtype: string splits: - name: Context num_bytes: 2821934 num_examples: 5000 - name: Benign num_bytes: 4100190 num_examples: 5001 - name: Trigger num_bytes: 3548757 num_examples: 5000 - name: RepresentationsContextAndTrigger num_bytes: 3043800 num_examples: 5000 download_size: 3767218 dataset_size: 13514681 configs: - config_name: default data_files: - split: Context path: data/Context-* - split: Benign path: data/Benign-* - split: Trigger path: data/Trigger-* - split: RepresentationsContextAndTrigger path: data/RepresentationsContextAndTrigger-* ---

macwiatrak/bacbench-operon-identification-protein-sequences

macwiatrak

2025-05-12T10:10:45Z

0

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

[]

2025-05-12T10:10:41Z

null

--- dataset_info: features: - name: taxid dtype: string - name: strain_name dtype: string - name: contig_name sequence: string - name: accession_id dtype: string - name: gene_name sequence: sequence: string - name: protein_name sequence: sequence: string - name: old_protein_name sequence: sequence: string - name: start sequence: sequence: int64 - name: end sequence: sequence: int64 - name: strand sequence: sequence: int64 - name: protein_sequence sequence: sequence: string - name: operon_protein_names sequence: sequence: sequence: string - name: operon_protein_indices sequence: sequence: sequence: int64 - name: operon_names sequence: sequence: string - name: n_operons dtype: int64 splits: - name: test num_bytes: 16707131 num_examples: 11 download_size: 15258914 dataset_size: 16707131 configs: - config_name: default data_files: - split: test path: data/test-* ---

yoonholee/completions_qwen3_4blrablation_filtered_0503_lr1e6_SolGen_medium-mix_Qwen3-1.7B_v2_HMMT2025

yoonholee

2025-05-12T09:48:28Z

0

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

[]

2025-05-12T09:48:25Z

null

--- dataset_info: features: - name: problem dtype: string - name: hint dtype: string - name: completions sequence: string - name: corrects sequence: bool - name: acc dtype: float64 - name: answer dtype: string splits: - name: train num_bytes: 9848837 num_examples: 240 download_size: 3586865 dataset_size: 9848837 configs: - config_name: default data_files: - split: train path: data/train-* ---

jablonkagroup/chempile-reasoning

jablonkagroup

2025-05-12T09:03:43Z

0

[ "region:us" ]

[]

2025-05-12T08:58:02Z

null

--- dataset_info: - config_name: chemistry_stackexchange-completion_0 features: - name: text dtype: string - name: input dtype: string - name: output dtype: string - name: answer_choices sequence: 'null' - name: correct_output_index dtype: float64 splits: - name: train num_bytes: 9955635 num_examples: 3207 - name: test num_bytes: 2180770 num_examples: 687 - name: val num_bytes: 2164450 num_examples: 687 download_size: 8030881 dataset_size: 14300855 - config_name: chemistry_stackexchange-completion_1 features: - name: text dtype: string - name: input dtype: string - name: output dtype: string - name: answer_choices sequence: 'null' - name: correct_output_index dtype: float64 splits: - name: train num_bytes: 4368035 num_examples: 3207 - name: test num_bytes: 937050 num_examples: 687 - name: val num_bytes: 910138 num_examples: 687 download_size: 3466685 dataset_size: 6215223 - config_name: chemistry_stackexchange-instruction_0 features: - name: text dtype: string - name: input dtype: string - name: output dtype: string - name: answer_choices sequence: 'null' - name: correct_output_index dtype: float64 splits: - name: train num_bytes: 10215611 num_examples: 3207 - name: test num_bytes: 2247702 num_examples: 687 - name: val num_bytes: 2215020 num_examples: 687 download_size: 8102029 dataset_size: 14678333 - config_name: chemistry_stackexchange-instruction_1 features: - name: text dtype: string - name: input dtype: string - name: output dtype: string - name: answer_choices sequence: 'null' - name: correct_output_index dtype: float64 splits: - name: train num_bytes: 4520829 num_examples: 3207 - name: test num_bytes: 972378 num_examples: 687 - name: val num_bytes: 941784 num_examples: 687 download_size: 3497157 dataset_size: 6434991 - config_name: chemistry_stackexchange-instruction_2 features: - name: text dtype: string - name: input dtype: string - name: output dtype: string - name: answer_choices sequence: 'null' - name: correct_output_index dtype: float64 splits: - name: train num_bytes: 10187447 num_examples: 3207 - name: test num_bytes: 2232168 num_examples: 687 - name: val num_bytes: 2207534 num_examples: 687 download_size: 8098941 dataset_size: 14627149 - config_name: chemistry_stackexchange-raw_data features: - name: title dtype: string - name: q dtype: string - name: a dtype: string - name: split dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 5219515 num_examples: 3207 - name: test num_bytes: 1141031 num_examples: 687 - name: val num_bytes: 1152678 num_examples: 687 download_size: 4382210 dataset_size: 7513224 - config_name: claude-3.5-distilled-spectral-reasoning-default features: - name: prompt dtype: string - name: extracted_reasoning dtype: string - name: text dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 4820074 num_examples: 924 - name: test num_bytes: 243764 num_examples: 52 - name: val num_bytes: 273662 num_examples: 51 download_size: 1642284 dataset_size: 5337500 - config_name: mattermodeling_stackexchange-completion_0 features: - name: text dtype: string - name: input dtype: string - name: output dtype: string - name: answer_choices sequence: 'null' - name: correct_output_index dtype: float64 splits: - name: train num_bytes: 1862644 num_examples: 464 - name: test num_bytes: 439705 num_examples: 99 - name: val num_bytes: 416417 num_examples: 100 download_size: 1532900 dataset_size: 2718766 - config_name: mattermodeling_stackexchange-completion_1 features: - name: text dtype: string - name: input dtype: string - name: output dtype: string - name: answer_choices sequence: 'null' - name: correct_output_index dtype: float64 splits: - name: train num_bytes: 866952 num_examples: 464 - name: test num_bytes: 209099 num_examples: 99 - name: val num_bytes: 176453 num_examples: 100 download_size: 716855 dataset_size: 1252504 - config_name: mattermodeling_stackexchange-instruction_0 features: - name: text dtype: string - name: input dtype: string - name: output dtype: string - name: answer_choices sequence: 'null' - name: correct_output_index dtype: float64 splits: - name: train num_bytes: 1889702 num_examples: 464 - name: test num_bytes: 457057 num_examples: 99 - name: val num_bytes: 427465 num_examples: 100 download_size: 1557006 dataset_size: 2774224 - config_name: mattermodeling_stackexchange-instruction_1 features: - name: text dtype: string - name: input dtype: string - name: output dtype: string - name: answer_choices sequence: 'null' - name: correct_output_index dtype: float64 splits: - name: train num_bytes: 889978 num_examples: 464 - name: test num_bytes: 216463 num_examples: 99 - name: val num_bytes: 177585 num_examples: 100 download_size: 706341 dataset_size: 1284026 - config_name: mattermodeling_stackexchange-instruction_2 features: - name: text dtype: string - name: input dtype: string - name: output dtype: string - name: answer_choices sequence: 'null' - name: correct_output_index dtype: float64 splits: - name: train num_bytes: 1915910 num_examples: 464 - name: test num_bytes: 446149 num_examples: 99 - name: val num_bytes: 418409 num_examples: 100 download_size: 1539380 dataset_size: 2780468 - config_name: mattermodeling_stackexchange-raw_data features: - name: title dtype: string - name: q dtype: string - name: a dtype: string - name: split dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 1061173 num_examples: 464 - name: test num_bytes: 241090 num_examples: 99 - name: val num_bytes: 233373 num_examples: 100 download_size: 870390 dataset_size: 1535636 - config_name: physics_stackexchange-completion_0 features: - name: text dtype: string - name: input dtype: string - name: output dtype: string - name: answer_choices sequence: 'null' - name: correct_output_index dtype: float64 splits: - name: train num_bytes: 15588553 num_examples: 4712 - name: test num_bytes: 3426795 num_examples: 1009 - name: val num_bytes: 3423281 num_examples: 1010 download_size: 12341408 dataset_size: 22438629 - config_name: physics_stackexchange-completion_1 features: - name: text dtype: string - name: input dtype: string - name: output dtype: string - name: answer_choices sequence: 'null' - name: correct_output_index dtype: float64 splits: - name: train num_bytes: 7479773 num_examples: 4712 - name: test num_bytes: 1622627 num_examples: 1009 - name: val num_bytes: 1621187 num_examples: 1010 download_size: 5899484 dataset_size: 10723587 - config_name: physics_stackexchange-instruction_0 features: - name: text dtype: string - name: input dtype: string - name: output dtype: string - name: answer_choices sequence: 'null' - name: correct_output_index dtype: float64 splits: - name: train num_bytes: 15943301 num_examples: 4712 - name: test num_bytes: 3532197 num_examples: 1009 - name: val num_bytes: 3511087 num_examples: 1010 download_size: 12475758 dataset_size: 22986585 - config_name: physics_stackexchange-instruction_1 features: - name: text dtype: string - name: input dtype: string - name: output dtype: string - name: answer_choices sequence: 'null' - name: correct_output_index dtype: float64 splits: - name: train num_bytes: 7680583 num_examples: 4712 - name: test num_bytes: 1647917 num_examples: 1009 - name: val num_bytes: 1673185 num_examples: 1010 download_size: 5918206 dataset_size: 11001685 - config_name: physics_stackexchange-instruction_2 features: - name: text dtype: string - name: input dtype: string - name: output dtype: string - name: answer_choices sequence: 'null' - name: correct_output_index dtype: float64 splits: - name: train num_bytes: 15915091 num_examples: 4712 - name: test num_bytes: 3495531 num_examples: 1009 - name: val num_bytes: 3509439 num_examples: 1010 download_size: 12504404 dataset_size: 22920061 - config_name: physics_stackexchange-raw_data features: - name: title dtype: string - name: q dtype: string - name: a dtype: string - name: split dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 8845700 num_examples: 4712 - name: test num_bytes: 1966487 num_examples: 1009 - name: val num_bytes: 1980929 num_examples: 1010 download_size: 7273250 dataset_size: 12793116 - config_name: spectra_reasoning_deepseek-default features: - name: smiles dtype: string - name: reasoning dtype: string - name: response dtype: string - name: response_smiles dtype: string - name: correct dtype: bool - name: question dtype: string - name: text dtype: string splits: - name: train num_bytes: 2060422 num_examples: 29 - name: test num_bytes: 133396 num_examples: 2 - name: val num_bytes: 137112 num_examples: 2 download_size: 1000394 dataset_size: 2330930 - config_name: spectra_reasoning_deepseek_mcq-default features: - name: smiles dtype: string - name: reasoning dtype: string - name: response dtype: string - name: response_smiles dtype: string - name: correct dtype: bool - name: question dtype: string - name: text dtype: string splits: - name: train num_bytes: 1003549 num_examples: 17 - name: test num_bytes: 82477 num_examples: 1 - name: val num_bytes: 52325 num_examples: 1 download_size: 511345 dataset_size: 1138351 configs: - config_name: chemistry_stackexchange-completion_0 data_files: - split: train path: chemistry_stackexchange-completion_0/train-* - split: test path: chemistry_stackexchange-completion_0/test-* - split: val path: chemistry_stackexchange-completion_0/val-* - config_name: chemistry_stackexchange-completion_1 data_files: - split: train path: chemistry_stackexchange-completion_1/train-* - split: test path: chemistry_stackexchange-completion_1/test-* - split: val path: chemistry_stackexchange-completion_1/val-* - config_name: chemistry_stackexchange-instruction_0 data_files: - split: train path: chemistry_stackexchange-instruction_0/train-* - split: test path: chemistry_stackexchange-instruction_0/test-* - split: val path: chemistry_stackexchange-instruction_0/val-* - config_name: chemistry_stackexchange-instruction_1 data_files: - split: train path: chemistry_stackexchange-instruction_1/train-* - split: test path: chemistry_stackexchange-instruction_1/test-* - split: val path: chemistry_stackexchange-instruction_1/val-* - config_name: chemistry_stackexchange-instruction_2 data_files: - split: train path: chemistry_stackexchange-instruction_2/train-* - split: test path: chemistry_stackexchange-instruction_2/test-* - split: val path: chemistry_stackexchange-instruction_2/val-* - config_name: chemistry_stackexchange-raw_data data_files: - split: train path: chemistry_stackexchange-raw_data/train-* - split: test path: chemistry_stackexchange-raw_data/test-* - split: val path: chemistry_stackexchange-raw_data/val-* - config_name: claude-3.5-distilled-spectral-reasoning-default data_files: - split: train path: claude-3.5-distilled-spectral-reasoning-default/train-* - split: test path: claude-3.5-distilled-spectral-reasoning-default/test-* - split: val path: claude-3.5-distilled-spectral-reasoning-default/val-* - config_name: mattermodeling_stackexchange-completion_0 data_files: - split: train path: mattermodeling_stackexchange-completion_0/train-* - split: test path: mattermodeling_stackexchange-completion_0/test-* - split: val path: mattermodeling_stackexchange-completion_0/val-* - config_name: mattermodeling_stackexchange-completion_1 data_files: - split: train path: mattermodeling_stackexchange-completion_1/train-* - split: test path: mattermodeling_stackexchange-completion_1/test-* - split: val path: mattermodeling_stackexchange-completion_1/val-* - config_name: mattermodeling_stackexchange-instruction_0 data_files: - split: train path: mattermodeling_stackexchange-instruction_0/train-* - split: test path: mattermodeling_stackexchange-instruction_0/test-* - split: val path: mattermodeling_stackexchange-instruction_0/val-* - config_name: mattermodeling_stackexchange-instruction_1 data_files: - split: train path: mattermodeling_stackexchange-instruction_1/train-* - split: test path: mattermodeling_stackexchange-instruction_1/test-* - split: val path: mattermodeling_stackexchange-instruction_1/val-* - config_name: mattermodeling_stackexchange-instruction_2 data_files: - split: train path: mattermodeling_stackexchange-instruction_2/train-* - split: test path: mattermodeling_stackexchange-instruction_2/test-* - split: val path: mattermodeling_stackexchange-instruction_2/val-* - config_name: mattermodeling_stackexchange-raw_data data_files: - split: train path: mattermodeling_stackexchange-raw_data/train-* - split: test path: mattermodeling_stackexchange-raw_data/test-* - split: val path: mattermodeling_stackexchange-raw_data/val-* - config_name: physics_stackexchange-completion_0 data_files: - split: train path: physics_stackexchange-completion_0/train-* - split: test path: physics_stackexchange-completion_0/test-* - split: val path: physics_stackexchange-completion_0/val-* - config_name: physics_stackexchange-completion_1 data_files: - split: train path: physics_stackexchange-completion_1/train-* - split: test path: physics_stackexchange-completion_1/test-* - split: val path: physics_stackexchange-completion_1/val-* - config_name: physics_stackexchange-instruction_0 data_files: - split: train path: physics_stackexchange-instruction_0/train-* - split: test path: physics_stackexchange-instruction_0/test-* - split: val path: physics_stackexchange-instruction_0/val-* - config_name: physics_stackexchange-instruction_1 data_files: - split: train path: physics_stackexchange-instruction_1/train-* - split: test path: physics_stackexchange-instruction_1/test-* - split: val path: physics_stackexchange-instruction_1/val-* - config_name: physics_stackexchange-instruction_2 data_files: - split: train path: physics_stackexchange-instruction_2/train-* - split: test path: physics_stackexchange-instruction_2/test-* - split: val path: physics_stackexchange-instruction_2/val-* - config_name: physics_stackexchange-raw_data data_files: - split: train path: physics_stackexchange-raw_data/train-* - split: test path: physics_stackexchange-raw_data/test-* - split: val path: physics_stackexchange-raw_data/val-* - config_name: spectra_reasoning_deepseek-default data_files: - split: train path: spectra_reasoning_deepseek-default/train-* - split: test path: spectra_reasoning_deepseek-default/test-* - split: val path: spectra_reasoning_deepseek-default/val-* - config_name: spectra_reasoning_deepseek_mcq-default data_files: - split: train path: spectra_reasoning_deepseek_mcq-default/train-* - split: test path: spectra_reasoning_deepseek_mcq-default/test-* - split: val path: spectra_reasoning_deepseek_mcq-default/val-* ---

model-metadata/model-id-custom-code-check

model-metadata

2025-05-12T09:02:47Z

95

0

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

[]

2025-04-22T06:10:47Z

null

--- dataset_info: features: - name: model_id dtype: string - name: description dtype: string splits: - name: train num_bytes: 1458 num_examples: 21 download_size: 2101 dataset_size: 1458 configs: - config_name: default data_files: - split: train path: data/train-* ---

R3troR0b/news-dataset

R3troR0b

2025-05-12T09:00:14Z

500

3

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

[ "text-classification" ]

2024-08-23T19:03:12Z

null

--- license: mit task_categories: - text-classification language: - en - fr tags: - news - world pretty_name: World News from Multiple Sources. --- # Dataset Card for World_News A collection of news articles from around the world. The script ensures no duplicate articles are added. ## Dataset Details ### Dataset Description The articles are drawn from these sources: - Reuters News Agency - BBC World News - Al Jazeera - Le Monde - South China Morning Post - The Hindu - Deutshce Welle - The Gauardian - NPR - TASS News Agency, Russia - The Sydney Morning Herald - **Curated by:** McNarland Software Consulatants Inc. - **Funded by [optional]:** None - **Shared by [optional]:** None - **Language(s) (NLP):** [English, French, Russian] - **License:** [MIT] ### Dataset Sources [optional] # Global News Sources (RSS Feeds) AL_JAZEERA_FEED_URL = "https://www.aljazeera.com/xml/rss/all.xml" BBC_FEED_URL = "http://feeds.bbci.co.uk/news/rss.xml" LE_MONDE_FEED_URL = "https://www.lemonde.fr/rss/en_continu.xml" REUTERS_FEED_URL = "https://www.reutersagency.com/feed/?best-regions=north-america&post_type=best" THE_HINDU_FEED_URL = "https://www.thehindu.com/news/feeder/default.rss" SCMP_FEED_URL = "https://www.scmp.com/rss/2/feed" DW_FEED_URL = "https://rss.dw.com/rdf/rss-en-all" TASS_FEED_URL = "https://tass.com/rss" RT_FEED_URL = "https://www.rt.com/rss/" ABC_FEED_URL = "https://www.abc.net.au/news/feed/51120/rss.xml" SMH_FEED_URL = "https://www.smh.com.au/rss/feed.xml" - **Repository:** None - **Paper [optional]:** None - **Demo [optional]:** None ## Uses Supervised Training or Embed Knowledge. ### Direct Use  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Dataset Structure The JSON format file contains a label and text column. The text column contains the article content while the label contains the publisher, publish date, and article name. "label": "The Guardian;Middle East crisis live: protesters across Israel call for Netanyahu to agree hostage deal;https://www.theguardian.com/world/live/2024/sep/01/middle-east-crisis-live-israeli-military-says-bodies-of-six-hostages-recovered-in-gaza;2024-09-01T18:16:45Z", "text": "US vice-president Kamala Harris has spoken to Jon and Rachel Goldberg-Polin, the parents of Hersh who was one of the hostages ..." [More Information Needed] ## Dataset Creation ### Curation Rationale  [More Information Needed] ### Source Data  #### Data Collection and Processing  [More Information Needed] #### Who are the source data producers?  [More Information Needed] ### Annotations [optional]  #### Annotation process  [More Information Needed] #### Who are the annotators?  [More Information Needed] #### Personal and Sensitive Information  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]

tunahanf/CHATML

tunahanf

2025-05-12T08:59:23Z

0

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

[]

2025-05-12T08:44:03Z

null

--- dataset_info: features: - name: messages list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 66869366.25 num_examples: 35637 - name: test num_bytes: 22289788.75 num_examples: 11879 download_size: 45247440 dataset_size: 89159155.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

Anuj6263333/fdskjcnskdvnc

Anuj6263333

2025-05-12T08:51:05Z

0

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

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

2025-05-12T07:32:25Z

null

--- license: apache-2.0 task_categories: - text-generation - zero-shot-classification language: - ae tags: - biology size_categories: - 10K<n<100K ---

GaspardNW/Metal_2.72sec_2PourcentSilent_0aug_0shiftAug_specmask0_nfft2048_hop512_sr48000

GaspardNW

2025-05-12T08:43:56Z

0

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

[]

2025-05-11T16:53:33Z

null

--- dataset_info: features: - name: filename dtype: string - name: duration dtype: int64 - name: sampling_rate dtype: int64 - name: magnitude_array sequence: sequence: sequence: float64 - name: min_max_vals sequence: float64 splits: - name: train num_bytes: 579962641 num_examples: 276 download_size: 269869239 dataset_size: 579962641 configs: - config_name: default data_files: - split: train path: data/train-* ---

TheFinAI/PolyFiQA-Easy

TheFinAI

2025-05-12T08:31:52Z

18

0

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

[]

2025-05-11T14:52:25Z

null

--- dataset_info: features: - name: task_id dtype: string - name: query dtype: string - name: question dtype: string - name: answer dtype: string splits: - name: test num_bytes: 5175349 num_examples: 76 download_size: 1660121 dataset_size: 5175349 configs: - config_name: default data_files: - split: test path: data/test-* ---

akseljoonas/smol_agents_benchmark_300

akseljoonas

2025-05-12T07:52:10Z

46

0

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

[]

2025-05-07T15:11:13Z

null

--- dataset_info: features: - name: question dtype: string - name: true_reasoning dtype: string - name: true_answer dtype: string - name: source dtype: string splits: - name: train num_bytes: 247087 num_examples: 300 download_size: 141465 dataset_size: 247087 configs: - config_name: default data_files: - split: train path: data/train-* ---

QuentinJG/msmarco_instruct_template

QuentinJG

2025-05-12T07:48:47Z

1

0

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

[]

2025-05-12T07:48:38Z

null

--- dataset_info: features: - name: prompt list: - name: content dtype: string - name: role dtype: string - name: passage dtype: string - name: passage_idx dtype: int64 splits: - name: train num_bytes: 381296219 num_examples: 398792 download_size: 105485303 dataset_size: 381296219 configs: - config_name: default data_files: - split: train path: data/train-* ---

kriteekon/aave_matched_indirect

kriteekon

2025-05-12T07:29:24Z

32

0

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

[]

2025-04-29T06:43:08Z

null

--- dataset_info: features: - name: prompt dtype: string splits: - name: train num_bytes: 1880565 num_examples: 1376 - name: validation num_bytes: 235148 num_examples: 172 download_size: 300070 dataset_size: 2115713 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* ---

Eloquent/Robustness

Eloquent

2025-05-12T07:24:59Z

171

0

[ "language:en", "language:fi", "language:fr", "language:de", "language:sv", "language:nl", "language:fa", "language:da", "license:cc-by-nc-sa-4.0", "size_categories:n<1K", "format:json", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2024-04-27T10:09:32Z

null

--- license: cc-by-nc-sa-4.0 language: - en - fi - fr - de - sv - nl - fa - da configs: - config_name: test data_files: - split: test path: eloquent-2025-robustness-prompts.json pretty_name: ELOQUENT Robustness and Consistency Task 2025 size_categories: - n<1K --- # ELOQUENT Robustness and Consistency Task This dataset contains the sample and test datasets for the Robustness and Consistency task, which is part of the ELOQUENT lab. This dataset is for participants to generate texts for prompt variants, to investigate prompt style conditioned variation. - [Robustness task](https://eloquent-lab.github.io/task-robustness-and-consistency/) - [ELOQUENT lab](https://eloquent-lab.github.io/) - [CLEF conference](https://clef2025.clef-initiative.eu/) 9-12 September 2025 ## The task in brief (this is a simple task to execute!) - This dataset provides a number of questions in several languages - e.g. `"question": "Is it more important to be polite or to be honest?"` - You use a generative language model to answer the question in the languages your model handles - Use separate sessions for each response! They are not intended to be interpreted as follow-up responses. - You send the response to us before mid-May 2025 - We and you together discuss the results to explore how linguistic variation conditions responses - We write a joint report - Workshop at CLEF in Madrid 9-12 September 2025 ## Submit Here: [Submission Form](https://forms.gle/cy5hrrWRbyJ8mchz7) #### Test Data ```python from datasets import load_dataset data = load_dataset("Eloquent/Robustness", "test") ``` ## Dataset authors Marie Isabel Engels (en, de) Jussi Karlgren (en, sv) Josiane Mothe (fr) Aarne Talman (fi) Maria Barrett (da) Shaghayegh Roohi (fa) Sander Bijl de Vroe (nl)

UWV/wikipedia_nl_wim_with_dutch_schema

UWV

2025-05-12T07:18:08Z

27

0

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

[]

2025-05-09T07:42:29Z

null

--- dataset_info: features: - name: id dtype: int64 - name: text dtype: string - name: schema dtype: string splits: - name: train num_bytes: 409331960 num_examples: 97521 download_size: 149038231 dataset_size: 409331960 configs: - config_name: default data_files: - split: train path: data/train-* --- ## Dataset Details ### Dataset Description This dataset is derived from the Dutch-language subset of Wikipedia. We filtered the articles to include only those with a text length between 1,000 and 3,000 characters. From this filtered pool, we randomly selected 100,000 entries and enriched each with a corresponding OWL schema generated using GPT-4o. ### Dataset Validation To assess the quality of the generated schemas, we applied the following validation checks: - Verification of correct RDF, RDFS, XSD, and OWL syntax - Detection of classes not explicitly defined as owl:Class - Identification of blank nodes - Detection of circular subclass relationships - Identification of disjoint classes with structural conflicts During this validation process, 2,479 schemas were found to contain fundamental structural issues and were therefore removed from the dataset. The final dataset contains 97,521 entries, each consisting of a Dutch Wikipedia text paired with a machine-generated OWL schema. ### Next Steps We plan to: - Add a "combined_schema" column that combines, for each row, the 9 consecutive row schema's. - Add a final column with RDF triples derived from each text–schema pair. ### Purpose The primary objective of this dataset is to support the fine-tuning of large language models (LLMs) for automated Knowledge Graph (KG) generation from natural language texts.

VGraf/tulu_sft_singleTurnOnly

VGraf

2025-05-12T07:13:59Z

0

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

[]

2025-05-12T07:13:27Z

null

--- dataset_info: features: - name: id dtype: string - name: messages list: - name: content dtype: string - name: role dtype: string - name: source dtype: string splits: - name: train num_bytes: 2314668882 num_examples: 939343 download_size: 1115954669 dataset_size: 2314668882 configs: - config_name: default data_files: - split: train path: data/train-* ---

HiTZ/composite_corpus_es_v1.0

HiTZ

2025-05-12T07:11:22Z

203

0

[ "task_categories:automatic-speech-recognition", "language:es", "license:cc-by-4.0", "size_categories:100K<n<1M", "format:parquet", "modality:audio", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us", "asr", "stt", "dataset" ]

[ "automatic-speech-recognition" ]

2024-12-03T17:18:06Z

null

--- dataset_info: features: - name: audio dtype: audio - name: sentence dtype: string - name: duration dtype: float64 configs: - config_name: default data_files: - split: train path: data/train* - split: dev path: data/dev.* - split: dev_cv path: data/dev_cv* - split: dev_mls path: data/dev_mls* - split: dev_parl path: data/dev_parl* - split: dev_oslr path: data/dev_oslr* - split: dev_vp path: data/dev_vp* - split: test_cv path: data/test_cv* - split: test_mls path: data/test_mls* - split: test_parl path: data/test_parl* - split: test_oslr path: data/test_oslr* - split: test_vp path: data/test_vp* license: cc-by-4.0 task_categories: - automatic-speech-recognition language: - es tags: - asr - stt - dataset pretty_name: Composite dataset for Spanish v1.0 --- # Composite dataset for Spanish made from public available data This dataset is composed of the following public available data: ## Train split: The train split is composed of the following datasets combined: - **mozilla-foundation/common_voice_18_0/es**: "validated" split removing "test_cv" and "dev_cv" split's sentences. (validated split contains official train + dev + test splits and more unique data) - **openslr**: a train split made from the SLR(39,61,67,71,72,73,74,75,108) subsets, this split has been cleaned from acronyms, numbers and sentences that are repeated in the following "test_oslr" and "dev_oslr" splits. - **mls/es**: the "train" split from the spanish dataset of Multilingual Librispeech. - **facebook/voxpopuli/es**: the "train" split from the spanish voxpopuli dataset, cleaned from acronyms and numeric characters. - **gttsehu/basque_parliament_1/es**: the official "train_clean" split. The Total hours and sentences is slightly smaller because some of the sentences were removed due to be repeated in some of the test and dev splits. | Split tag | Source | Hours | Sentences | |:---------:|:--------------------:|:-------------:|:-----------:| | - | common_voice_18_0 | 538.82 h | 378560 | | - | openslr | 45.58 h | 24460 | | - | mls | 922.47 h | 221855 | | - | voxpopuli | 142.96 h | 48667 | | - | basque_parliament_1 | 949.27 h | 469937 | | train | **Total** | **2596.32 h** | **1142586** | ## Test splits: Those test splits are separated, and it is recommended to not evaluate them together in a single split: - **mozilla-foundation/common_voice_18_0/es**: the official "test" split. - **openslr**: a test split made from the SLR(39,61,67,71,72,73,74,75,108) subsets, this split has been cleaned from acronyms, numbers and sentences. - **mls/es**: the "test" split from the spanish dataset of Multilingual Librispeech. - **facebook/voxpopuli/es**: the "test" split from the spanish voxpopuli dataset, cleaned from acronyms and numeric characters. - **gttsehu/basque_parliament_1/es**: the official "test" split. | Split tag | Source | Hours | Sentences | |:---------:|:--------------------:|:-------------:|:-----------:| | test_cv | common_voice_18_0 | 26.84 h | 15872 | | test_oslr | openslr | 7.03 h | 4107 | | test_mls | mls | 10 h | 2385 | | test_vp | voxpopuli | 4.64 h | 1446 | | test_parl | basque_parliament_1 | 6.56 h | 3450 | | | **Total** | **55.07 h** | **27260** | ## Dev splits: There is a dev split composed by 5 dev subsplits that are also independently accesible. It is recommended to use the combined "dev" split for development tasks since it is accurately balanced in number of hours (~5h each, a total of ~25h). - **mozilla-foundation/common_voice_18_0/es**: a small dev split made from the official "dev" split. - **openslr**: a small dev split made from the SLR(39,61,67,71,72,73,74,75,108) subsets, this split has been cleaned from acronyms, numbers and sentences. - **mls/es**: a small "dev" split from the original "dev" split from spanish dataset of Multilingual Librispeech. - **facebook/voxpopuli/es**: the original "dev" split from the spanish voxpopuli dataset, cleaned from acronyms and numeric characters. - **gttsehu/basque_parliament_1/es**: the official "dev" split. | Split tag | Source | Hours | Sentences | |:---------:|:--------------------:|:-------------:|:-----------:| | dev_cv | common_voice_18_0 | 5.03 h | 3000 | | dev_oslr | openslr | 5.13 h | 3063 | | dev_mls | mls | 5.09 h | 1223 | | dev_vp | voxpopuli | 4.89 h | 1564 | | dev_parl | basque_parliament_1 | 4.81 h | 2567 | | dev | **Total** | **24.95 h** | **11417** | ## Funding: This project with reference 2022/TL22/00215335 has been parcially funded by the Ministerio de Transformación Digital and by the Plan de Recuperación, Transformación y Resiliencia – Funded by the European Union – NextGenerationEU [ILENIA](https://proyectoilenia.es/) and by the project [IkerGaitu](https://www.hitz.eus/iker-gaitu/) funded by the Basque Government.

AshBastian9/so100_demotwist

AshBastian9

2025-05-12T07:08:11Z

0

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

[ "robotics" ]

2025-05-12T07:08:08Z

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": 1305, "total_tasks": 1, "total_videos": 1, "total_chunks": 1, "chunks_size": 1000, "fps": 30, "splits": { "train": "0:1" }, "data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet", "video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4", "features": { "action": { "dtype": "float32", "shape": [ 6 ], "names": [ "main_shoulder_pan", "main_shoulder_lift", "main_elbow_flex", "main_wrist_flex", "main_wrist_roll", "main_gripper" ] }, "observation.state": { "dtype": "float32", "shape": [ 6 ], "names": [ "main_shoulder_pan", "main_shoulder_lift", "main_elbow_flex", "main_wrist_flex", "main_wrist_roll", "main_gripper" ] }, "observation.images.laptop": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.fps": 30.0, "video.height": 480, "video.width": 640, "video.channels": 3, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "has_audio": false } }, "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] ```

sanochihi/my_test_dataset

sanochihi

2025-05-12T06:54:58Z

0

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

[]

2025-05-12T06:51:52Z

null

--- license: apache-2.0 dataset_info: features: - name: Seeds dtype: string - name: Prompt dtype: string - name: Completion dtype: string splits: - name: train num_bytes: 3818 num_examples: 20 download_size: 3585 dataset_size: 3818 configs: - config_name: default data_files: - split: train path: data/train-* ---

louisbrulenaudet/code-justice-penale-mineurs

louisbrulenaudet

2025-05-12T06:52:00Z

326

0

[ "task_categories:text-generation", "task_categories:table-question-answering", "task_categories:summarization", "task_categories:text-retrieval", "task_categories:question-answering", "task_categories:text-classification", "multilinguality:monolingual", "source_datasets:original", "language:fr", "license:apache-2.0", "size_categories:n<1K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "finetuning", "legal", "french law", "droit français", "Code de la justice pénale des mineurs" ]

[ "text-generation", "table-question-answering", "summarization", "text-retrieval", "question-answering", "text-classification" ]

2024-03-25T23:03:33Z

null

--- license: apache-2.0 language: - fr multilinguality: - monolingual tags: - finetuning - legal - french law - droit français - Code de la justice pénale des mineurs source_datasets: - original pretty_name: Code de la justice pénale des mineurs task_categories: - text-generation - table-question-answering - summarization - text-retrieval - question-answering - text-classification size_categories: - 1K<n<10K --- # Code de la justice pénale des mineurs, non-instruct (2025-05-11) The objective of this project is to provide researchers, professionals and law students with simplified, up-to-date access to all French legal texts, enriched with a wealth of data to facilitate their integration into Community and European projects. Normally, the data is refreshed daily on all legal codes, and aims to simplify the production of training sets and labeling pipelines for the development of free, open-source language models based on open data accessible to all. ## Concurrent reading of the LegalKit [<img src="https://raw.githubusercontent.com/louisbrulenaudet/ragoon/main/assets/badge.svg" alt="Built with RAGoon" width="200" height="32"/>](https://github.com/louisbrulenaudet/ragoon) To use all the legal data published on LegalKit, you can use RAGoon: ```bash pip3 install ragoon ``` Then, you can load multiple datasets using this code snippet: ```python # -*- coding: utf-8 -*- from ragoon import load_datasets req = [ "louisbrulenaudet/code-artisanat", "louisbrulenaudet/code-action-sociale-familles", # ... ] datasets_list = load_datasets( req=req, streaming=False ) dataset = datasets.concatenate_datasets( datasets_list ) ``` ### Data Structure for Article Information This section provides a detailed overview of the elements contained within the `item` dictionary. Each key represents a specific attribute of the legal article, with its associated value providing detailed information. 1. **Basic Information** - `ref` (string): **Reference** - A reference to the article, combining the title_main and the article `number` (e.g., "Code Général des Impôts, art. 123"). - `texte` (string): **Text Content** - The textual content of the article. - `dateDebut` (string): **Start Date** - The date when the article came into effect. - `dateFin` (string): **End Date** - The date when the article was terminated or superseded. - `num` (string): **Article Number** - The number assigned to the article. - `id` (string): **Article ID** - Unique identifier for the article. - `cid` (string): **Chronical ID** - Chronical identifier for the article. - `type` (string): **Type** - The type or classification of the document (e.g., "AUTONOME"). - `etat` (string): **Legal Status** - The current legal status of the article (e.g., "MODIFIE_MORT_NE"). 2. **Content and Notes** - `nota` (string): **Notes** - Additional notes or remarks associated with the article. - `version_article` (string): **Article Version** - The version number of the article. - `ordre` (integer): **Order Number** - A numerical value used to sort articles within their parent section. 3. **Additional Metadata** - `conditionDiffere` (string): **Deferred Condition** - Specific conditions related to collective agreements. - `infosComplementaires` (string): **Additional Information** - Extra information pertinent to the article. - `surtitre` (string): **Subtitle** - A subtitle or additional title information related to collective agreements. - `nature` (string): **Nature** - The nature or category of the document (e.g., "Article"). - `texteHtml` (string): **HTML Content** - The article's content in HTML format. 4. **Versioning and Extensions** - `dateFinExtension` (string): **End Date of Extension** - The end date if the article has an extension. - `versionPrecedente` (string): **Previous Version** - Identifier for the previous version of the article. - `refInjection` (string): **Injection Reference** - Technical reference to identify the date of injection. - `idTexte` (string): **Text ID** - Identifier for the legal text to which the article belongs. - `idTechInjection` (string): **Technical Injection ID** - Technical identifier for the injected element. 5. **Origin and Relationships** - `origine` (string): **Origin** - The origin of the document (e.g., "LEGI"). - `dateDebutExtension` (string): **Start Date of Extension** - The start date if the article has an extension. - `idEliAlias` (string): **ELI Alias** - Alias for the European Legislation Identifier (ELI). - `cidTexte` (string): **Text Chronical ID** - Chronical identifier of the text. 6. **Hierarchical Relationships** - `sectionParentId` (string): **Parent Section ID** - Technical identifier of the parent section. - `multipleVersions` (boolean): **Multiple Versions** - Indicates if the article has multiple versions. - `comporteLiensSP` (boolean): **Contains Public Service Links** - Indicates if the article contains links to public services. - `sectionParentTitre` (string): **Parent Section Title** - Title of the parent section (e.g., "I : Revenu imposable"). - `infosRestructurationBranche` (string): **Branch Restructuring Information** - Information about branch restructuring. - `idEli` (string): **ELI ID** - European Legislation Identifier (ELI) for the article. - `sectionParentCid` (string): **Parent Section Chronical ID** - Chronical identifier of the parent section. 7. **Additional Content and History** - `numeroBo` (string): **Official Bulletin Number** - Number of the official bulletin where the article was published. - `infosRestructurationBrancheHtml` (string): **Branch Restructuring Information (HTML)** - Branch restructuring information in HTML format. - `historique` (string): **History** - Historical context or changes specific to collective agreements. - `infosComplementairesHtml` (string): **Additional Information (HTML)** - Additional information in HTML format. - `renvoi` (string): **Reference** - References to content within the article (e.g., "(1)"). - `fullSectionsTitre` (string): **Full Section Titles** - Concatenation of all titles in the parent chain. - `notaHtml` (string): **Notes (HTML)** - Additional notes or remarks in HTML format. - `inap` (string): **INAP** - A placeholder for INAP-specific information. ## Feedback If you have any feedback, please reach out at [[email protected]](mailto:[email protected]).

louisbrulenaudet/code-justice-administrative

louisbrulenaudet

2025-05-12T06:51:59Z

444

0

[ "task_categories:text-generation", "task_categories:table-question-answering", "task_categories:summarization", "task_categories:text-retrieval", "task_categories:question-answering", "task_categories:text-classification", "multilinguality:monolingual", "source_datasets:original", "language:fr", "license:apache-2.0", "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "doi:10.57967/hf/1469", "region:us", "finetuning", "legal", "french law", "droit français", "Code de justice administrative" ]

[ "text-generation", "table-question-answering", "summarization", "text-retrieval", "question-answering", "text-classification" ]

2023-12-12T21:26:00Z

null

--- license: apache-2.0 language: - fr multilinguality: - monolingual tags: - finetuning - legal - french law - droit français - Code de justice administrative source_datasets: - original pretty_name: Code de justice administrative task_categories: - text-generation - table-question-answering - summarization - text-retrieval - question-answering - text-classification size_categories: - 1K<n<10K --- # Code de justice administrative, non-instruct (2025-05-11) The objective of this project is to provide researchers, professionals and law students with simplified, up-to-date access to all French legal texts, enriched with a wealth of data to facilitate their integration into Community and European projects. Normally, the data is refreshed daily on all legal codes, and aims to simplify the production of training sets and labeling pipelines for the development of free, open-source language models based on open data accessible to all. ## Concurrent reading of the LegalKit [<img src="https://raw.githubusercontent.com/louisbrulenaudet/ragoon/main/assets/badge.svg" alt="Built with RAGoon" width="200" height="32"/>](https://github.com/louisbrulenaudet/ragoon) To use all the legal data published on LegalKit, you can use RAGoon: ```bash pip3 install ragoon ``` Then, you can load multiple datasets using this code snippet: ```python # -*- coding: utf-8 -*- from ragoon import load_datasets req = [ "louisbrulenaudet/code-artisanat", "louisbrulenaudet/code-action-sociale-familles", # ... ] datasets_list = load_datasets( req=req, streaming=False ) dataset = datasets.concatenate_datasets( datasets_list ) ``` ### Data Structure for Article Information This section provides a detailed overview of the elements contained within the `item` dictionary. Each key represents a specific attribute of the legal article, with its associated value providing detailed information. 1. **Basic Information** - `ref` (string): **Reference** - A reference to the article, combining the title_main and the article `number` (e.g., "Code Général des Impôts, art. 123"). - `texte` (string): **Text Content** - The textual content of the article. - `dateDebut` (string): **Start Date** - The date when the article came into effect. - `dateFin` (string): **End Date** - The date when the article was terminated or superseded. - `num` (string): **Article Number** - The number assigned to the article. - `id` (string): **Article ID** - Unique identifier for the article. - `cid` (string): **Chronical ID** - Chronical identifier for the article. - `type` (string): **Type** - The type or classification of the document (e.g., "AUTONOME"). - `etat` (string): **Legal Status** - The current legal status of the article (e.g., "MODIFIE_MORT_NE"). 2. **Content and Notes** - `nota` (string): **Notes** - Additional notes or remarks associated with the article. - `version_article` (string): **Article Version** - The version number of the article. - `ordre` (integer): **Order Number** - A numerical value used to sort articles within their parent section. 3. **Additional Metadata** - `conditionDiffere` (string): **Deferred Condition** - Specific conditions related to collective agreements. - `infosComplementaires` (string): **Additional Information** - Extra information pertinent to the article. - `surtitre` (string): **Subtitle** - A subtitle or additional title information related to collective agreements. - `nature` (string): **Nature** - The nature or category of the document (e.g., "Article"). - `texteHtml` (string): **HTML Content** - The article's content in HTML format. 4. **Versioning and Extensions** - `dateFinExtension` (string): **End Date of Extension** - The end date if the article has an extension. - `versionPrecedente` (string): **Previous Version** - Identifier for the previous version of the article. - `refInjection` (string): **Injection Reference** - Technical reference to identify the date of injection. - `idTexte` (string): **Text ID** - Identifier for the legal text to which the article belongs. - `idTechInjection` (string): **Technical Injection ID** - Technical identifier for the injected element. 5. **Origin and Relationships** - `origine` (string): **Origin** - The origin of the document (e.g., "LEGI"). - `dateDebutExtension` (string): **Start Date of Extension** - The start date if the article has an extension. - `idEliAlias` (string): **ELI Alias** - Alias for the European Legislation Identifier (ELI). - `cidTexte` (string): **Text Chronical ID** - Chronical identifier of the text. 6. **Hierarchical Relationships** - `sectionParentId` (string): **Parent Section ID** - Technical identifier of the parent section. - `multipleVersions` (boolean): **Multiple Versions** - Indicates if the article has multiple versions. - `comporteLiensSP` (boolean): **Contains Public Service Links** - Indicates if the article contains links to public services. - `sectionParentTitre` (string): **Parent Section Title** - Title of the parent section (e.g., "I : Revenu imposable"). - `infosRestructurationBranche` (string): **Branch Restructuring Information** - Information about branch restructuring. - `idEli` (string): **ELI ID** - European Legislation Identifier (ELI) for the article. - `sectionParentCid` (string): **Parent Section Chronical ID** - Chronical identifier of the parent section. 7. **Additional Content and History** - `numeroBo` (string): **Official Bulletin Number** - Number of the official bulletin where the article was published. - `infosRestructurationBrancheHtml` (string): **Branch Restructuring Information (HTML)** - Branch restructuring information in HTML format. - `historique` (string): **History** - Historical context or changes specific to collective agreements. - `infosComplementairesHtml` (string): **Additional Information (HTML)** - Additional information in HTML format. - `renvoi` (string): **Reference** - References to content within the article (e.g., "(1)"). - `fullSectionsTitre` (string): **Full Section Titles** - Concatenation of all titles in the parent chain. - `notaHtml` (string): **Notes (HTML)** - Additional notes or remarks in HTML format. - `inap` (string): **INAP** - A placeholder for INAP-specific information. ## Feedback If you have any feedback, please reach out at [[email protected]](mailto:[email protected]).

Cartinoe5930/deepmath_500

Cartinoe5930

2025-05-12T06:44:26Z

0

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

[]

2025-05-12T06:33:35Z

null

--- dataset_info: features: - name: question dtype: string - name: final_answer dtype: string - name: difficulty dtype: float64 - name: topic dtype: string splits: - name: train num_bytes: 145226 num_examples: 500 download_size: 67158 dataset_size: 145226 configs: - config_name: default data_files: - split: train path: data/train-* ---

AtsuMiyai/webchore_test13

AtsuMiyai

2025-05-12T06:41:00Z

0

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

[]

2025-05-12T06:40:55Z

null

--- dataset_info: features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_1_license dtype: string - name: image_1_attribution dtype: string - name: image_1_use_original_mmmu dtype: bool - name: image_2 dtype: image - name: image_2_license dtype: string - name: image_2_attribution dtype: string - name: image_2_use_original_mmmu dtype: bool - name: image_3 dtype: image - name: image_3_license dtype: string - name: image_3_attribution dtype: string - name: image_3_use_original_mmmu dtype: bool - name: image_4 dtype: image - name: image_4_license dtype: string - name: image_4_attribution dtype: string - name: image_4_use_original_mmmu dtype: bool - name: image_5 dtype: image - name: image_5_license dtype: string - name: image_5_attribution dtype: string - name: image_5_use_original_mmmu dtype: bool - name: image_6 dtype: image - name: image_6_license dtype: string - name: image_6_attribution dtype: string - name: image_6_use_original_mmmu dtype: bool - name: image_7 dtype: image - name: image_7_license dtype: string - name: image_7_attribution dtype: string - name: image_7_use_original_mmmu dtype: bool - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string - name: sites sequence: string - name: start_url dtype: string - name: start_url_lite dtype: string - name: storage_state dtype: string - name: intent_template dtype: string - name: required_obs dtype: string - name: description dtype: string - name: instantiation_dict struct: - name: PRnumber dtype: 'null' - name: answer dtype: 'null' - name: answers dtype: 'null' - name: checkpint_info dtype: string - name: checkpoint1 dtype: string - name: checkpoint2 dtype: string - name: checkpoint3 dtype: string - name: checkpoint4 dtype: string - name: checkpoint5 dtype: string - name: checkpoint6 dtype: string - name: checkpoint_info dtype: string - name: commitSum dtype: 'null' - name: contents dtype: string - name: count dtype: 'null' - name: date dtype: 'null' - name: default_branch dtype: 'null' - name: difficulty dtype: string - name: enddate dtype: 'null' - name: format dtype: 'null' - name: issue_counts dtype: 'null' - name: issues_count dtype: 'null' - name: keyword dtype: 'null' - name: lastupdatetime dtype: 'null' - name: license dtype: 'null' - name: lowerbound dtype: 'null' - name: memo dtype: 'null' - name: memo1 dtype: 'null' - name: memo2 dtype: 'null' - name: memo3 dtype: 'null' - name: memo4 dtype: 'null' - name: memo5 dtype: 'null' - name: month dtype: string - name: number dtype: string - name: orderedProjects dtype: 'null' - name: project dtype: 'null' - name: project1 dtype: 'null' - name: project2 dtype: 'null' - name: project3 dtype: 'null' - name: project4 dtype: 'null' - name: project5 dtype: 'null' - name: question dtype: 'null' - name: readme_repo1 dtype: 'null' - name: readme_repo2_url dtype: 'null' - name: repository dtype: 'null' - name: repository1 dtype: 'null' - name: repository2 dtype: 'null' - name: repository3 dtype: 'null' - name: start_url dtype: string - name: start_url_lite dtype: string - name: tag1 dtype: string - name: tag2 dtype: string - name: target dtype: 'null' - name: total_star dtype: 'null' - name: unique_users_num dtype: 'null' - name: url dtype: 'null' - name: user dtype: string - name: user1 dtype: string - name: user2 dtype: string - name: user3 dtype: 'null' - name: user4 dtype: 'null' - name: user5 dtype: 'null' - name: userName dtype: 'null' - name: year dtype: string splits: - name: test num_bytes: 1772449.0 num_examples: 30 download_size: 1782916 dataset_size: 1772449.0 configs: - config_name: default data_files: - split: test path: data/test-* ---

tarsur909/imdb_sft_processed25p

tarsur909

2025-05-12T06:29:10Z

0

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

[]

2025-05-12T06:29:07Z

null

--- dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': neg '1': pos - name: query dtype: string - name: gen_review dtype: string - name: query_input_ids sequence: int64 - name: query_attention_mask sequence: int64 - name: reference_response dtype: string - name: reference_response_input_ids sequence: int64 - name: reference_response_attention_mask sequence: int64 - name: reference_response_token_len dtype: int64 - name: query_reference_response dtype: string - name: query_reference_response_input_ids sequence: int64 - name: query_reference_response_attention_mask sequence: int64 - name: query_reference_response_token_response_label sequence: int64 - name: query_reference_response_token_len dtype: int64 splits: - name: train num_bytes: 106246592.5 num_examples: 3125 - name: test num_bytes: 105687226.5 num_examples: 3125 download_size: 32770008 dataset_size: 211933819.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

FariqF/NPTL_SPEAKER_DATASETS

FariqF

2025-05-12T05:51:31Z

0

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

[]

2025-05-12T05:49:28Z

null

--- license: mit dataset_info: features: - name: source_id dtype: string - name: audio_id dtype: string - name: audio_start dtype: float32 - name: audio_end dtype: float32 - name: duration dtype: float32 - name: audio dtype: audio: sampling_rate: 16000 - name: text dtype: string - name: words sequence: - name: word dtype: string - name: start dtype: float32 - name: end dtype: float32 - name: speaker_id dtype: int64 splits: - name: train num_bytes: 2142509474.334 num_examples: 6597 download_size: 2063465797 dataset_size: 2142509474.334 configs: - config_name: default data_files: - split: train path: data/train-* ---

cchoi1/kodcode_1000_gpt-4o_qwen7b_att_iter0_debug

cchoi1

2025-05-12T05:50:31Z

18

0

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

[]

2025-04-29T22:55:38Z

null

--- dataset_info: features: - name: mutation_id dtype: int64 - name: task_id dtype: string - name: mutator_prompt dtype: string - name: solver_prompt dtype: string - name: response dtype: string - name: mutation_explanation dtype: string - name: mutation_info dtype: string - name: mutator_score dtype: float64 - name: solution_scores dtype: string - name: solutions dtype: string - name: solutions_explanation dtype: string - name: solutions_info dtype: string splits: - name: train num_bytes: 70519 num_examples: 10 download_size: 55180 dataset_size: 70519 configs: - config_name: default data_files: - split: train path: data/train-* ---

upvantage/top5-scored-gpt4.1

upvantage

2025-05-12T05:23:53Z

0

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

[]

2025-05-12T05:09:28Z

null

--- dataset_info: features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: ai_score sequence: float32 - name: context dtype: string - name: text dtype: string - name: style dtype: string - name: lengthInstruction dtype: string splits: - name: train num_bytes: 132346814 num_examples: 26794 download_size: 37510275 dataset_size: 132346814 configs: - config_name: default data_files: - split: train path: data/train-* ---

ma921/hh-rlhf-filtered

ma921

2025-05-12T04:41:32Z

0

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

[]

2025-05-12T04:41:21Z

null

--- dataset_info: features: - name: chosen dtype: string - name: rejected dtype: string splits: - name: train num_bytes: 298842161.7277985 num_examples: 155730 - name: test num_bytes: 16010125.275374182 num_examples: 8267 download_size: 160465974 dataset_size: 314852287.0031727 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

icedpanda/msmarco_cold_start_dataset_100k_llama_merge_aug

icedpanda

2025-05-12T04:26:05Z

25

0

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

[]

2025-05-09T02:27:37Z

null

--- dataset_info: features: - name: query dtype: string - name: query_id dtype: string - name: pid sequence: string - name: response dtype: string - name: hard_negative_pid sequence: string splits: - name: train num_bytes: 225128843 num_examples: 102836 download_size: 134665838 dataset_size: 225128843 configs: - config_name: default data_files: - split: train path: data/train-* ---

charleyong/so100_tic_tac_toe_updated

charleyong

2025-05-12T04:22:19Z

0

[ "task_categories:robotics", "license:apache-2.0", "size_categories:10K<n<100K", "format:parquet", "modality:tabular", "modality:timeseries", "modality:video", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us", "LeRobot", "so100", "yc_demo", "multi_task" ]

[ "robotics" ]

2025-05-12T03:16:20Z

null

--- license: apache-2.0 task_categories: - robotics tags: - LeRobot - so100 - yc_demo - multi_task 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": 20, "total_frames": 5941, "total_tasks": 1, "total_videos": 20, "total_chunks": 1, "chunks_size": 1000, "fps": 30, "splits": { "train": "0:20" }, "data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet", "video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4", "features": { "action": { "dtype": "float32", "shape": [ 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.stationary": { "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] ```

justinsunqiu/transcription_changes

justinsunqiu

2025-05-12T04:21:51Z

0

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

[]

2025-05-12T04:21:49Z

null

--- dataset_info: features: - name: id dtype: string - name: language dtype: string - name: culturally_distinct dtype: bool - name: cultural_distinction_explanation dtype: string - name: vocaroo_link dtype: string - name: image_link dtype: string - name: selected_other_languages dtype: bool - name: Goodness dtype: string - name: orig_transcription dtype: string - name: orig_translation dtype: string - name: fixed_transcription dtype: string - name: fixed_translation dtype: string splits: - name: train num_bytes: 1958762 num_examples: 498 download_size: 1108010 dataset_size: 1958762 configs: - config_name: default data_files: - split: train path: data/train-* ---

patrickechohelloworld/well_formatted_benchmarks_pro

patrickechohelloworld

2025-05-12T04:08:48Z

83

0

[ "task_categories:zero-shot-classification", "task_categories:question-answering", "language:en", "size_categories:1M<n<10M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us", "synthetic" ]

[ "zero-shot-classification", "question-answering" ]

2025-05-08T14:52:58Z

null

--- task_categories: - zero-shot-classification - question-answering language: - en tags: - synthetic size_categories: - 1M<n<10M --- # Dataset Card for well_formatted_benchmarks_pro  This is a collection of formatted benchmarks. ## Dataset Details ### Dataset Description  This repo is home to formatted versions of some famous benchmarks I created this repo because current benchmark datasets on the hub generally don't have a fixed format, which is annoying when you try to use them. - **Language(s) (NLP):** English ### Dataset Sources  #### ARC - **Repository:** [Original ARC repo](https://huggingface.co/datasets/allenai/ai2_arc) - **Demo:** ```text <user>An astronomer observes that a planet rotates faster after a meteorite impact. Which is the most likely effect of this increase in rotation?\ <sep>A: Planetary density will decrease.</sep><sep>B: Planetary years will become longer.</sep><sep>C: Planetary days will become shorter.</sep>\ <sep>D: Planetary gravity will become stronger.</sep></user><model>C ``` #### GSM8K - **Repository:** [Original GSM8K repo](https://huggingface.co/datasets/openai/gsm8k) - **Demo:** ```text <user>A robe takes 2 bolts of blue fiber and half that much white fiber. How many bolts in total does it take?</user><model>It takes 2/2=<<2/2=1>>1 bolt of white fiber So the total amount of fabric is 2+1=<<2+1=3>>3 bolts of fabric #### 3 ``` #### HellaSwag - **Repository:** [Original HellaSwag repo](https://huggingface.co/datasets/Rowan/hellaswag) - **Demo:** ```text <user> The topic of this sentence is: Getting a haircut. Based on the topic of this sentence, finish this sentence: \ The man in the center is demonstrating a hairstyle on the person wearing the blue shirt. the man in the blue shirt\ <sep>A: is standing on the sponge cutting the hair of the person wearing the blue shirt.</sep>\ <sep>B: is doing the hairstyle with his hand and the hairspray.</sep><sep>C: sits on the chair next to the sink.</sep>\ <sep>D: is being shown eye to eye.</sep></user><model>C ``` #### MMLU - **Repository:** [Original MMLU repo](https://huggingface.co/datasets/cais/mmlu) - **Demo:** ```text <user>Find the degree for the given field extension Q(sqrt(2), sqrt(3), sqrt(18)) over Q.<sep>A: 0</sep><sep>B: 4</sep><sep>C: 2</sep><sep>D: 6</sep></user><model>B ``` #### OpenBookQA - **Repository:** [Original OpenBookQA repo](https://huggingface.co/datasets/mandarjoshi/trivia_qa) - **Demo:** ```text <user> It is ture that: predators eat prey. Based on this fact, answer the following question:\ Predators eat<sep>A: lions</sep><sep>B: humans</sep><sep>C: bunnies</sep><sep>D: grass</sep></user><model>C ``` #### TriviaQA - **Repository:** [Original TriviaQA repo](https://huggingface.co/datasets/mandarjoshi/trivia_qa) - **Demo:** ```text <user>Which American-born Sinclair won the Nobel Prize for Literature in 1930?</user>Sinclair Lewis ``` ### PIQA - **Repository:** [Original PIQA repo](https://huggingface.co/datasets/ybisk/piqa) - **Demo** ```text <user>The goal is: Make outdoor pillow.<sep>A: Blow into tin can and tie with rubber band.</sep>\ <sep>B: Blow into trash bag and tie with rubber band.</sep></user><model><model><model>B ``` ## Uses  ### Direct Use  It's recommended to use this dataset by downloading the parquet files from `main` branch and load it with `polars`: ```python import polars as pl data = pl.read_parquet('./path/to/downloaded/file').get_column('text') ... ``` The special tokens used in this repo includes: ```text <user>: the beginning of prompt </user>: the end of prompt <model>: the beginning of response <sep>: the beginning of an option </sep>: the end of an option ``` These tokens works well with my custom tokenizer, but remember to replace them with your own special tokens like this: ```python # Replace with another token text = text.replace('<model>', 'YOUR_SPECIAL_TOKEN') # Remove the special token text = text.replace('<model>', '') ``` ### Out-of-Scope Use  Like what I mentioned above, a custom tokenizer is used to generate the files in `token` folder. So you need to tokenize the dataset yourself. ## Dataset Structure  - **Raw data** from original repos are in the root directory of each subsets. - **Formatted(but not tokenized) data files** are in `./processed` directory. - **Tokenized data files** are in `./token` directory (and you probably don't need them, as mentioned above) ```text . ├── processed <- This is the formatted data you want! │ ├── train.parquet │ └── validation.parquet ├── token │ ├── train.parquet │ └── validation.parquet ├── train.parquet └── validation.parquet <- These are raw data files ``` ## Dataset Creation ### Curation Rationale  [More Information Needed] ### Source Data  #### Data Collection and Processing  The data processing is done mainly with the python scripts in the root directory (and their variants). So you can re-write these scripts based on your need to create your own formatted datasets! #### Who are the source data producers?  Please refer to the links above to see the original authors of these datasets. ## Bias, Risks, and Limitations  Please keep in mind that these datasets is for benchmarking, some of them are not suitable for SFT. Although I didn't change the content of the original datasets, it's always good practice to check them out by yourself! ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## Dataset Card Authors [optional] patrick_echo_hello_world ## Dataset Card Contact [[email protected]]

pratikmurali/fda_samd_regulations_golden_test_dataset

pratikmurali

2025-05-12T04:03:12Z

0

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

[]

2025-05-12T04:02:38Z

null

--- license: apache-2.0 ---

ajd12342/paraspeechcaps-processed-situational-only-with-original-prompts-test-set-1000

ajd12342

2025-05-12T04:00:30Z

0

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

[]

2025-05-12T04:00:28Z

null

--- dataset_info: features: - name: source dtype: string - name: relative_audio_path dtype: string - name: text_description sequence: string - name: transcription dtype: string - name: intrinsic_tags sequence: string - name: situational_tags dtype: string - name: basic_tags sequence: string - name: all_tags sequence: string - name: speakerid dtype: string - name: name dtype: string - name: duration dtype: float64 - name: gender dtype: string - name: accent dtype: string - name: pitch dtype: string - name: speaking_rate dtype: string - name: noise dtype: string - name: utterance_pitch_mean dtype: float64 - name: snr dtype: float64 - name: phonemes dtype: string - name: audio_path dtype: string splits: - name: test num_bytes: 1119852.3727781163 num_examples: 1000 download_size: 398405 dataset_size: 1119852.3727781163 configs: - config_name: default data_files: - split: test path: data/test-* ---

justinsunqiu/multilingual_transcriptions_translated_raw

justinsunqiu

2025-05-12T03:58:38Z

0

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

[]

2025-05-12T03:58:37Z

null

--- dataset_info: features: - name: id dtype: string - name: language dtype: string - name: culturally_distinct dtype: bool - name: cultural_distinction_explanation dtype: string - name: vocaroo_link dtype: string - name: image_link dtype: string - name: transcription dtype: string - name: selected_other_languages dtype: bool - name: translation dtype: string - name: Goodness dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 11608597 num_examples: 6220 download_size: 5718578 dataset_size: 11608597 configs: - config_name: default data_files: - split: train path: data/train-* ---

Haviet2003/Gemma3_1b

Haviet2003

2025-05-12T03:52:40Z

0

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

[]

2025-05-12T03:50:23Z

null

--- license: apache-2.0 ---

deokhk/MOpenThoughts-114k-problems

deokhk

2025-05-12T03:46:00Z

0

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

[]

2025-05-12T03:45:54Z

null

--- dataset_info: config_name: en features: - name: problem dtype: string - name: ground_truth_solution dtype: string - name: domain dtype: string - name: id dtype: string splits: - name: train num_bytes: 38967862 num_examples: 18993 download_size: 18977718 dataset_size: 38967862 configs: - config_name: en data_files: - split: train path: en/train-* ---

cross-validation/City-Networks

cross-validation

2025-05-12T03:25:15Z

10

1

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

[]

2025-04-29T15:51:07Z

null

--- license: cc-by-nd-4.0 configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: name dtype: string - name: num_nodes dtype: int64 - name: edge_index sequence: sequence: int32 length: 2 - name: node_features sequence: sequence: float32 - name: labels sequence: int64 - name: train_mask sequence: bool - name: val_mask sequence: bool - name: test_mask sequence: bool splits: - name: train num_bytes: 202285660 num_examples: 4 download_size: 47142645 dataset_size: 202285660 ---

VGraf/BIG_tulu_related_truncated2048_cutto2turns

VGraf

2025-05-12T03:22:06Z

0

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

[]

2025-05-12T03:21:37Z

null

--- dataset_info: features: - name: messages list: - name: content dtype: string - name: role dtype: string - name: id dtype: string - name: source dtype: string - name: chosen list: - name: content dtype: string - name: role dtype: string - name: rejected list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 479342706 num_examples: 48466 download_size: 253715537 dataset_size: 479342706 configs: - config_name: default data_files: - split: train path: data/train-* ---