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_id
string | id
string | author
string | cardData
string | disabled
bool | gated
null | lastModified
timestamp[ns] | likes
int64 | trendingScore
float64 | private
bool | sha
string | description
string | downloads
int64 | downloadsAllTime
int64 | tags
sequence | createdAt
timestamp[ns] | paperswithcode_id
string | citation
string |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
68138ea21a0ce2640a9b7b19 | nvidia/Nemotron-CrossThink | nvidia | {"license": "cc-by-4.0", "language": ["en"], "size_categories": ["10M<n<100M"], "task_categories": ["question-answering", "text-generation"], "pretty_name": "Nemotron-CrossThink", "dataset_info": {"splits": [{"name": "train_qa", "num_bytes": 353793822, "num_examples": 187496}, {"name": "train_math", "num_bytes": 260680780, "num_examples": 99880}], "download_size": 614474602, "dataset_size": 614474602}, "tags": ["text", "nvidia"], "configs": [{"config_name": "default", "data_files": [{"split": "train_qa", "path": "Data/Nemotron-CrossThink-QA.jsonl"}, {"split": "train_math", "path": "Data/Nemotron-CrossThink-Math.jsonl"}]}]} | false | null | 2025-05-01T17:12:10 | 71 | 69 | false | a4ce9a3b9434c5f231e2cbe30696d9a721c11d69 |
Nemotron-CrossThink: Scaling Self-Learning beyond Math Reasoning
Author: Syeda Nahida Akter, Shrimai Prabhumoye, Matvei Novikov, Seungju Han, Ying Lin, Evelina Bakhturina, Eric Nyberg, Yejin Choi,
Mostofa Patwary, Mohammad Shoeybi, Bryan Catanzaro
[Paper][Blog]
Dataset Description
Nemotron-CrossThink is a multi-domain reinforcement learning (RL) dataset designed to improve general-purpose
and mathematical reasoning in large language models (LLMs).
The dataset… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-CrossThink. | 5,539 | 5,539 | [
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"arxiv:2504.13941",
"arxiv:2406.20094",
"region:us",
"text",
"nvidia"
] | 2025-05-01T15:09:22 | null | null |
68072cc4cce05035af98207e | nvidia/OpenMathReasoning | nvidia | {"language": ["en"], "license": "cc-by-4.0", "size_categories": ["1M<n<10M"], "task_categories": ["question-answering", "text-generation"], "pretty_name": "OpenMathReasoning", "tags": ["math", "nvidia"], "configs": [{"config_name": "default", "data_files": [{"split": "cot", "path": "data/cot-*"}, {"split": "tir", "path": "data/tir-*"}, {"split": "genselect", "path": "data/genselect-*"}]}], "dataset_info": {"features": [{"name": "expected_answer", "dtype": "string"}, {"name": "problem_type", "dtype": "string"}, {"name": "problem_source", "dtype": "string"}, {"name": "generation_model", "dtype": "string"}, {"name": "pass_rate_72b_tir", "dtype": "string"}, {"name": "problem", "dtype": "string"}, {"name": "generated_solution", "dtype": "string"}, {"name": "inference_mode", "dtype": "string"}], "splits": [{"name": "cot", "num_bytes": 71638774515, "num_examples": 3201061}, {"name": "tir", "num_bytes": 35467270369, "num_examples": 1703010}, {"name": "genselect", "num_bytes": 6981053721, "num_examples": 565620}], "download_size": 49370957110, "dataset_size": 114087098605}} | false | null | 2025-04-24T04:13:32 | 198 | 48 | false | 47ea246374954205b6b9c8a7077b9bb0fd58b11a |
OpenMathReasoning
OpenMathReasoning is a large-scale math reasoning dataset for training large language models (LLMs).
This dataset contains
540K unique mathematical problems sourced from AoPS forums,
3.2M long chain-of-thought (CoT) solutions
1.7M long tool-integrated reasoning (TIR) solutions
566K samples that select the most promising solution out of many candidates (GenSelect)
We used Qwen2.5-32B-Instruct to preprocess problems, and
DeepSeek-R1 and QwQ-32B to generate… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/OpenMathReasoning. | 30,057 | 30,057 | [
"task_categories:question-answering",
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"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2504.16891",
"region:us",
"math",
"nvidia"
] | 2025-04-22T05:44:36 | null | null |
680a774a49e5373a4f68ffea | rajpurkarlab/ReXGradient-160K | rajpurkarlab | {"title": "ReXGradient-160K Dataset", "license": "other", "license_name": "rexgradient", "extra_gated_prompt": "## ReXGradient-160K Non-Commercial Data Access and Use Agreement\n\nBy accessing the ReXGradient-160K Data Repository, as hosted by President and Fellows of Harvard College on behalf of Harvard Medical School (\"Harvard\"), you (\"YOU\" or \"YOUR\") agree to the following terms of this agreement (\"Agreement\"):\n\n1. Acceptance of this Agreement\n\nBy downloading or otherwise accessing the data available from the ReXGradient-160K Data Repository (\"Data\"), which Data is available for non-commercial research and non-clinical purposes only, YOU (i) accept and agree to the terms of this Agreement, and (ii) further acknowledge that YOU may have an obligation to confirm that YOUR use of the Data under these terms is consistent with YOUR obligations to YOUR institution.\n\n2. Attribution\n\nData made available via the ReXGradient-160K Data Repository was provided to Harvard by Gradient Health. YOU agree to recognize (i) the ReXGradient-160K Data Repository and (ii) the contribution of Gradient Health as the source of the Data in all written, visual, or oral public disclosures concerning YOUR research using the Data, as appropriate in accordance with scholarly standards. \n\n3. Use of the Data\n\nUse of the Data is subject to YOUR compliance with all of the terms and conditions of this Agreement. No commercial use of the Data is permitted.\n\nRepresentations and Covenants:\n\nA. YOU represent and covenant that:\n\n1. YOU are not bound by any pre-existing legal obligations or other applicable laws that prevent YOU from downloading or using the Data;\n2. YOU will access and use the Data in compliance with all applicable laws, rules, and regulations, as well as all professional and ethical standards applicable to scientific research and YOUR research project, including without limitation, all applicable requirements pertaining to human subjects research and animal research;\n3. YOU agree not to use the Data in connection with the diagnosis or treatment of human subjects.\n4. YOU agree to establish appropriate administrative, technical, and physical safeguards to prevent unauthorized use of or access to the Data and comply with any other requirements relating to safeguarding of the Data that the ReXGradient-160K Data Repository may require from time to time;\n5. YOU will only use and download that portion of the Data that is necessary for use in YOUR research project; downloading of the entire ReXGradient-160K Data Repository or portions beyond what is needed for YOUR research project is strictly prohibited. YOU will only share the Data with those authorized employees, fellows, students and agents who have a need to access such Data for purposes of working on YOUR research project, and whose obligations of use are consistent with the terms of this Agreement.\n6. YOU will retain control over the Data YOU access and download.\n7. YOU will not, and will not permit any third party to, at any time, directly or indirectly: (i) disclose, distribute, release, sublicense, sell, rent, lease, loan, or otherwise grant access to the Data to any third party, except other users expressly authorized by Harvard to access and use the Data; (ii) publish or display any compilation or directory of the Data; or (iii) knowingly use the Data in any manner or for any purpose that infringes, misappropriates, or otherwise violates any intellectual property right or other right of any person, or that violates any applicable law.\n8. YOU will use the Data only for scientific research purposes and shall not use the Data for any commercial or clinical activities; \n9. YOU will regularly check the ReXGradient-160K Data Repository for any updates to Data pertinent to YOUR research project, and will only use the updated version of such Data, and will promptly destroy all outdated Data in YOUR possession; and\n10. YOU shall not use the Data except as authorized under this Agreement. \n\nB. YOU further covenant that YOU will not:\n\n1. obtain information from the Data that results in YOU or any third party(ies) directly or indirectly identifying any research subjects with the aid of other information acquired elsewhere;\n2. produce connections or links among the information included in Harvard's datasets (including information in the Data), or between the information included in Harvard's datasets (including information in the Data) and other third-party information that could be used to identify any individuals or organizations, not limited to research subjects;\n3. extract information from the Data that could aid YOU in gaining knowledge about or obtaining any means of contacting any subjects already known to YOU; or\n4. use the Data, either alone or in concert with any other information, to make any effort to identify or contact individuals who are or may be the sources of Data.\n\n4. Disclaimer\n\nTHE DATA IS PROVIDED \"AS IS\" AND \"AS AVAILABLE\" AND WITH ALL FAULTS AND DEFECTS. HARVARD MAKES NO, AND HEREBY DISCLAIMS ALL, REPRESENTATIONS AND WARRANTIES OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, WARRANTIES OF MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE, OR THAT THE USE OF THE DATA WILL NOT INFRINGE OR VIOLATE ANY PATENT, COPYRIGHT, TRADEMARK OR OTHER THIRD PARTY RIGHT, AND ANY WARRANTIES IMPLIED BY ANY COURSE OF PERFORMANCE OR DEALING.\n\nWITHOUT LIMITING THE FOREGOING, HARVARD MAKES NO WARRANTY AS TO THE ACCURACY, COMPLETENESS, RELIABILITY, ORIGINALITY, AVAILABILITY, OR SECURITY OF THE DATA, THAT THE DATA WILL MEET YOUR REQUIREMENTS OR PROVIDE PARTICULAR BENEFITS, OR THAT THE DATA AND ANY RELATED FILES WILL BE FREE FROM DEFECTS, ERRORS, VIRUSES OR OTHER HARMFUL COMPONENTS. YOUR USE OF THE DATA IS SOLELY AT YOUR OWN RISK.\n\n5. Limitation of Liability\n\nIN NO EVENT SHALL HARVARD BE LIABLE UNDER CONTRACT, TORT, STRICT LIABILITY, NEGLIGENCE OR ANY OTHER LEGAL THEORY WITH RESPECT TO THE DATA (I) FOR ANY DIRECT DAMAGES, OR (II) FOR ANY LOST PROFITS OR SPECIAL, INDIRECT, INCIDENTAL, PUNITIVE, OR CONSEQUENTIAL DAMAGES OF ANY KIND WHATSOEVER.\n\n6. Liability\n\nExcept to the extent prohibited by law, YOU assume all liability for damages which may arise from YOUR use, storage, download, disclosure, or disposal of the Data. Harvard will not be liable to YOU or any other person or entity for any loss, claim, liability, or demand made by YOU, or made against YOU by any other party, or otherwise relating to or arising in connection with the use of the Data by YOU, except to the extent permitted by law when caused by the gross negligence or willful misconduct of Harvard. No indemnification for any loss, claim, damage, or liability is intended or provided by either party under this Agreement.\n\n7. Governing Law; Venue\n\nThis Agreement shall be governed by and interpreted in accordance with the laws of the Commonwealth of Massachusetts (excluding the conflict of laws and rules thereof). All disputes under this Agreement will be resolved in the applicable state or federal courts of Massachusetts. YOU consent to the jurisdiction of such courts and waive any jurisdictional or venue defenses otherwise available.\n\n8. Integration and Severability\n\nThis Agreement represents the entire agreement between YOU and Harvard with respect to the subject matter hereof, and supersedes all prior or contemporaneous representations or understandings (whether oral, written or electronic) between YOU and Harvard with respect to the subject matter hereof. If any provision of this Agreement is or becomes invalid or is ruled invalid by any court of competent jurisdiction or is deemed unenforceable, it is the intention of the parties that the remainder of this Agreement shall not be affected.\n\n9. Reporting Requirement\n\nShould YOU (i) inadvertently receive identifiable information or otherwise identify a subject, or (ii) become aware of any use or disclosure of the Data not provided for or permitted by this Agreement, YOU shall immediately notify Harvard via email to [[email protected]](mailto:[email protected]) with **\"ReXGradient-160K\" in the subject line**, and follow Harvard's reasonable written instructions, which may include return or destruction of Data.\n\n10. Ownership\n\nHarvard shall retain ownership of any rights it may have in the Data, and YOU do not obtain any rights in the Data other than as set forth in this Agreement.\n\n11. Use of Harvard Name\n\nExcept as expressly provided in this Agreement, YOU shall not use or register Harvard's name (alone or as part of another name) or any logos, seals, insignia or other words, names, symbols or devices that identify Harvard, including any Harvard school, unit, division or affiliate (\"Harvard Names\") for any purpose in connection with this Agreement except with the prior written approval of, and in accordance with restrictions required by, Harvard. The foregoing notwithstanding, YOU may respond to legitimate business inquiries with factual information regarding the existence and purpose of the relationship that is the subject of this Agreement, without written permission from Harvard. Without limiting the foregoing, YOU shall cease all use of Harvard Names permitted under this Agreement on the termination or expiration of this Agreement except as otherwise approved by Harvard. \n\nExcept as expressly provided in Section II. Attribution of this Agreement, YOU shall not use Gradient Health's name, trademarks, or other logos in any publicity, advertising, or news release without the prior written approval of an authorized representative of Gradient Health. \n\n12. Term and Termination\n\nYOU may use the Data for the duration of YOUR research project as allowed under the permitted uses in Article III of this Agreement. Either party may terminate this agreement for any reason at any time, however, YOUR obligations that by their nature are intended to persist following termination shall remain in effect even after termination. Upon termination, YOU shall immediately cease use of the Data and destroy Data in YOUR possession and, if requested by Harvard, certify to Harvard as to its destruction.\n\nHarvard may limit, suspend or terminate YOUR access to Data at any time if Harvard believes YOU have violated the terms of this Agreement or otherwise acted negligently with respect to the Data or that continued use of or access to the Data by YOU presents significant risk to the individuals who are the subjects in the Data.\n\n13. Miscellaneous\n\nYOU may not assign, transfer or delegate any of YOUR rights and obligations hereunder without consent of the other party. No agency, partnership, joint venture, or employment relationship is created as a result of the Agreement. Neither Party shall have authority to make any statements, representations or commitments of any kind on behalf of the other Party, or to take any action which shall be binding on the other Party.\n\nNo modification or waiver of any provision of this Agreement shall be valid unless in writing and executed by duly-authorized representatives of both parties. A failure by one of the parties to this Agreement to assert its rights hereunder shall not be deemed a waiver of such rights. No such failure or waiver in writing by any one of the parties hereto with respect to any rights shall extend to or affect any subsequent breach or impair any right consequent thereon.\n\nReXGradient-160K Non-Commercial Data Access and Use Agreement v.1, April 7, 2025\n", "extra_gated_fields": {"Name": "text", "Institution": "text", "Email": "text", "By_checking_this_box": {"type": "checkbox", "description": "By checking this box, YOU acknowledge that YOU have read and are effectively signing this Non-Commercial Data Access and Use Agreement [https://huggingface.co/datasets/rajpurkarlab/ReXGradient-160K] and agreeing to its terms and conditions"}}, "configs": [{"config_name": "metadata", "data_files": [{"split": "train", "path": "metadata/train_metadata.csv"}, {"split": "validation", "path": "metadata/valid_metadata.csv"}, {"split": "test", "path": "metadata/test_metadata.csv"}]}]} | false | null | 2025-05-05T23:46:09 | 46 | 45 | false | 272f3ec1b4c3b26d6ed94bf41875bb1e1827e80b |
Overview
ReXGradient-160K is the largest publicly available multi-site chest X-ray dataset, containing 273,004 unique chest X-ray images from 160,000 radiological studies, collected from 109,487 unique patients across 3 U.S. health systems (79 medical sites). This comprehensive dataset includes multiple images per study and detailed radiology reports, making it particularly valuable for the development and evaluation of AI systems for medical imaging and automated report generation… See the full description on the dataset page: https://huggingface.co/datasets/rajpurkarlab/ReXGradient-160K. | 762 | 767 | [
"license:other",
"size_categories:100K<n<1M",
"format:csv",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2505.00228",
"region:us"
] | 2025-04-24T17:39:22 | null | null |
67ec47948647cfa17739af7a | nvidia/OpenCodeReasoning | nvidia | {"license": "cc-by-4.0", "size_categories": ["100K<n<1M"], "pretty_name": "OpenCodeReasoning", "dataset_info": [{"config_name": "split_0", "features": [{"name": "id", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "license", "dtype": "string"}, {"name": "dataset", "dtype": "string"}, {"name": "split", "dtype": "string"}, {"name": "difficulty", "dtype": "string"}, {"name": "solution", "dtype": "string"}], "splits": [{"name": "split_0", "num_bytes": 28108469190, "num_examples": 567850}]}, {"config_name": "split_1", "features": [{"name": "id", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "license", "dtype": "string"}, {"name": "dataset", "dtype": "string"}, {"name": "split", "dtype": "string"}, {"name": "difficulty", "dtype": "string"}, {"name": "solution", "dtype": "string"}, {"name": "index", "dtype": "string"}], "splits": [{"name": "split_1", "num_bytes": 4722811278, "num_examples": 167405}]}], "configs": [{"config_name": "split_0", "data_files": [{"split": "split_0", "path": "split_0/train-*"}]}, {"config_name": "split_1", "data_files": [{"split": "split_1", "path": "split_1/train-*"}]}], "task_categories": ["text-generation"], "tags": ["synthetic"]} | false | null | 2025-05-04T23:54:22 | 367 | 44 | false | 20a1ca19c0d050fe9057fc08339d6b370ec1c67a |
OpenCodeReasoning: Advancing Data Distillation for Competitive Coding
Data Overview
OpenCodeReasoning is the largest reasoning-based synthetic dataset to date for coding, comprises 735,255 samples in Python across 28,319 unique competitive programming
questions. OpenCodeReasoning is designed for supervised fine-tuning (SFT).
Technical Report - Discover the methodology and technical details behind OpenCodeReasoning.
Github Repo - Access the complete pipeline used to… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/OpenCodeReasoning. | 17,975 | 18,549 | [
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] | 2025-04-01T20:07:48 | null | null |
67d3479522a51de18affff22 | nvidia/Llama-Nemotron-Post-Training-Dataset | nvidia | {"license": "cc-by-4.0", "configs": [{"config_name": "SFT", "data_files": [{"split": "code", "path": "SFT/code/*.jsonl"}, {"split": "math", "path": "SFT/math/*.jsonl"}, {"split": "science", "path": "SFT/science/*.jsonl"}, {"split": "chat", "path": "SFT/chat/*.jsonl"}, {"split": "safety", "path": "SFT/safety/*.jsonl"}], "default": true}, {"config_name": "RL", "data_files": [{"split": "instruction_following", "path": "RL/instruction_following/*.jsonl"}]}]} | false | null | 2025-05-08T17:51:50 | 470 | 38 | false | ab2a40d258a6a4d9d4c277d702aeea445081766c |
Llama-Nemotron-Post-Training-Dataset-v1.1 Release
Update [4/8/2025]:
v1.1: We are releasing an additional 2.2M Math and 500K Code Reasoning Data in support of our release of Llama-3.1-Nemotron-Ultra-253B-v1. 🎉
Data Overview
This dataset is a compilation of SFT and RL data that supports improvements of math, code, general reasoning, and instruction following capabilities of the original Llama instruct model, in support of NVIDIA’s release of… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Llama-Nemotron-Post-Training-Dataset. | 11,338 | 11,349 | [
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"library:dask",
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"arxiv:2505.00949",
"region:us"
] | 2025-03-13T21:01:09 | null | null |
680788ed6b18c3643d14f39e | DMindAI/DMind_Benchmark | DMindAI | {"configs": [{"config_name": "objective_normal", "data_files": [{"split": "Tokenomist", "path": ["test_data/objective/Tokenomist.csv"]}, {"split": "Fundamentals", "path": ["test_data/objective/Blockchain_Fundamentals_benchmark.csv"]}, {"split": "DAO", "path": ["test_data/objective/DAO2.csv"]}, {"split": "Defi", "path": ["test_data/objective/Defi_benchmark.csv"]}, {"split": "MEME", "path": ["test_data/objective/MEME_Benchmark_modified.csv"]}, {"split": "NFT", "path": ["test_data/objective/NFT_Benchmark_modified.csv"]}, {"split": "Security", "path": ["test_data/objective/Security_Benchmark_modified.csv"]}, {"split": "Smart_contract", "path": ["test_data/objective/SmartContracts_benchmark.csv"]}]}, {"config_name": "objective_infrastructure", "data_files": [{"split": "Infrastructrue", "path": ["test_data/objective/Binfra_benchmark.csv"]}]}, {"config_name": "subjective_normal", "data_files": [{"split": "Tokenomist", "path": ["test_data/subjective/Token.jsonl"]}, {"split": "Fundamentals", "path": ["test_data/subjective/Blockchain_Fundamentals_benchmark.jsonl"]}, {"split": "DAO", "path": ["test_data/subjective/DAO.jsonl"]}, {"split": "Defi", "path": ["test_data/subjective/Defi.jsonl"]}, {"split": "MEME", "path": ["test_data/subjective/MEME.jsonl"]}, {"split": "NFT", "path": ["test_data/subjective/NFT.jsonl"]}, {"split": "Security", "path": ["test_data/subjective/Security.jsonl"]}, {"split": "Smart_contract", "path": ["test_data/subjective/smart_contract.jsonl"]}]}, {"config_name": "subjective_infrastructure", "data_files": [{"split": "Infrastructure", "path": ["test_data/subjective/Infra.jsonl"]}]}]} | false | null | 2025-05-08T18:37:56 | 31 | 31 | false | 6fd0dc14acd70a06e26b0cb58025686ab7ee8283 |
🔍 DMind Benchmark
A comprehensive framework for evaluating large language models (LLMs) on blockchain, cryptocurrency, and Web3 knowledge across multiple domains.
| Paper | Dataset |
Latest LLM Leaderboard In Web3
📊 Overview
This project provides tools to benchmark AI models on their understanding of blockchain concepts through both objective (multiple-choice) and subjective (open-ended) questions. The framework covers various domains including:
🧱… See the full description on the dataset page: https://huggingface.co/datasets/DMindAI/DMind_Benchmark. | 1,189 | 1,189 | [
"size_categories:1K<n<10K",
"modality:text",
"arxiv:2504.16116",
"region:us"
] | 2025-04-22T12:17:49 | null | null |
63990f21cc50af73d29ecfa3 | fka/awesome-chatgpt-prompts | fka | {"license": "cc0-1.0", "tags": ["ChatGPT"], "task_categories": ["question-answering"], "size_categories": ["100K<n<1M"]} | false | null | 2025-01-06T00:02:53 | 7,769 | 26 | false | 68ba7694e23014788dcc8ab5afe613824f45a05c | 🧠 Awesome ChatGPT Prompts [CSV dataset]
This is a Dataset Repository of Awesome ChatGPT Prompts
View All Prompts on GitHub
License
CC-0
| 14,564 | 156,255 | [
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"region:us",
"ChatGPT"
] | 2022-12-13T23:47:45 | null | null |
6798d7adece6b7910c594f1a | BramVanroy/CommonCrawl-CreativeCommons | BramVanroy | {"license": "cc", "task_categories": ["text-generation"], "task_ids": ["language-modeling"], "pretty_name": "Common Crawl Creative Commons Corpus (C5)", "language": ["afr", "deu", "eng", "fra", "fry", "ita", "nld", "spa", "af", "de", "en", "fr", "fy", "it", "nl", "es"], "configs": [{"config_name": "v1", "data_files": ["data/CC-MAIN-2019-30/**/*.parquet", "data/CC-MAIN-2020-05/**/*.parquet", "data/CC-MAIN-2022-05/**/*.parquet", "data/CC-MAIN-2023-06/**/*.parquet", "data/CC-MAIN-2024-46/**/*.parquet", "data/CC-MAIN-2024-51/**/*.parquet", "data/CC-MAIN-2025-05/**/*.parquet"]}, {"config_name": "default", "data_files": "data/**/*.parquet"}, {"config_name": "afr", "data_files": "data/**/afr/*.parquet"}, {"config_name": "deu", "data_files": "data/**/deu/*.parquet"}, {"config_name": "eng", "data_files": "data/**/eng/*.parquet"}, {"config_name": "spa", "data_files": "data/**/spa/*.parquet"}, {"config_name": "fra", 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The Common Crawl Creative Commons Corpus (C5)
Raw CommonCrawl crawls, annotated with Creative Commons license information
C5 is an effort to collect Creative Commons-licensed web data in one place.
The licensing information is extracted from the web pages based on whether they link to Creative Commons licenses either overtly in a tags (like in the footer of Wikipedia) or in metadata fields indicating deliberate Creative Commons publication. However, false positives may occur! See… See the full description on the dataset page: https://huggingface.co/datasets/BramVanroy/CommonCrawl-CreativeCommons. | 1,247 | 3,673 | [
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"language:fra",
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"language:es",
"license:cc",
"size_categories:100M<n<1B",
"modality:text",
"doi:10.57967/hf/5340",
"region:us"
] | 2025-01-28T13:12:13 | null | null |
6819bf075435fed9f1a20705 | maya-research/IndicVault | maya-research | {"license": "mit", "task_categories": ["question-answering", "text-generation"], "language": ["hi", "te", "en"], "configs": [{"config_name": "Hindi", "data_files": "Hindi100k.json"}, {"config_name": "Hinglish", "data_files": "Hinglish100k.json"}, {"config_name": "Telugu", "data_files": "Telugu100k.json"}], "size_categories": ["10K<n<100K"]} | false | null | 2025-05-06T09:33:00 | 26 | 26 | false | 6f89850d5dbae85afbe3976d46db3ef944e446b4 |
Indic Vault — everyday Indian language QA pairs, tuned for chatbots & voice agents.
🧾 Overview
Indic Vault is a high-quality, instruction-tuned dataset featuring question-answer pairs crafted in the contemporary, everyday language spoken across India in 2025. Unlike traditional datasets that lean heavily on formal or outdated linguistic styles, Indic Vault captures the authentic, colloquial expressions used in daily conversations, making it ideal for building AI… See the full description on the dataset page: https://huggingface.co/datasets/maya-research/IndicVault. | 235 | 235 | [
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"format:json",
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"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2025-05-06T07:49:27 | null | null |
6813224fb6dbda9fd92c0c1b | tokyotech-llm/swallow-code | tokyotech-llm | {"license": "llama3.3", "task_categories": ["text-generation"], "language": ["en", "ja"], "tags": ["code"], "pretty_name": "swallowcode", "size_categories": ["10M<n<100M"], "modalities": ["Text"], "formats": ["json"], "configs": [{"config_name": "swallow-code", "data_files": [{"split": "train", "path": "train/train-*"}]}, {"config_name": "exp1-the-stack-v2", "data_files": [{"split": "train", "path": "ablation/exp1-the-stack-v2-train-smol-ids-python/jsonl/train-*"}]}, {"config_name": "exp2-syntax-error", "data_files": [{"split": "train", "path": "ablation/exp2-syntax-error-filtered/jsonl/train-*"}]}, {"config_name": "exp3-linter", "data_files": [{"split": "train", "path": "ablation/exp3-linter-filtered/jsonl/train-*"}]}, {"config_name": "exp4-comment-lang", "data_files": [{"split": "train", "path": "ablation/exp4-code_comment_ja_or_en/jsonl/train-*"}]}, {"config_name": "exp5-sgcr", "data_files": [{"split": "train", "path": "ablation/exp5-sgcr/jsonl/train-*"}]}, {"config_name": "exp6-llm-scoring", "data_files": [{"split": "train", "path": "ablation/exp6-llm-based-scoring/jsonl/train-*"}]}, {"config_name": "exp7", "data_files": [{"split": "train", "path": "ablation/exp7/jsonl/train-*"}]}, {"config_name": "exp10-direct-sgcr", "data_files": [{"split": "train", "path": "ablation/exp10-direct-sgcr/jsonl/train-*"}]}, {"config_name": "exp11-scor", "data_files": [{"split": "train", "path": "ablation/exp11-scor/jsonl/train-*"}]}]} | false | null | 2025-05-08T04:09:25 | 25 | 25 | false | a2c3e7b472c9a1c4ac18ab140752e7db50a50570 |
SwallowCode
Resources
🐙 GitHub: Explore the project repository, including pipeline code and prompts at rioyokotalab/swallow-code-math.
📑 arXiv: Read our paper for detailed methodology and results at arXiv:2505.02881.
🤗 Sister Dataset: Discover SwallowMath, our companion dataset for mathematical reasoning.
What is it?
💻 SwallowCode is a high-quality code dataset comprising approximately 16.1 billion tokens of Python code, derived from… See the full description on the dataset page: https://huggingface.co/datasets/tokyotech-llm/swallow-code. | 920 | 920 | [
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"library:datasets",
"library:dask",
"library:mlcroissant",
"arxiv:2505.02881",
"region:us",
"code"
] | 2025-05-01T07:27:11 | null | null |
676f70846bf205795346d2be | FreedomIntelligence/medical-o1-reasoning-SFT | FreedomIntelligence | {"license": "apache-2.0", "task_categories": ["question-answering", "text-generation"], "language": ["en", "zh"], "tags": ["medical", "biology"], "configs": [{"config_name": "en", "data_files": "medical_o1_sft.json"}, {"config_name": "zh", "data_files": "medical_o1_sft_Chinese.json"}, {"config_name": "en_mix", "data_files": "medical_o1_sft_mix.json"}, {"config_name": "zh_mix", "data_files": "medical_o1_sft_mix_Chinese.json"}]} | false | null | 2025-04-22T15:11:21 | 688 | 21 | false | fc2c9e8a37b38f38da6d449564a8c350b244aef4 |
News
[2025/04/22] We split the data and kept only the medical SFT dataset (medical_o1_sft.json). The file medical_o1_sft_mix.json contains a mix of medical and general instruction data.
[2025/02/22] We released the distilled dataset from Deepseek-R1 based on medical verifiable problems. You can use it to initialize your models with the reasoning chain from Deepseek-R1.
[2024/12/25] We open-sourced the medical reasoning dataset for SFT, built on medical verifiable problems and an LLM… See the full description on the dataset page: https://huggingface.co/datasets/FreedomIntelligence/medical-o1-reasoning-SFT. | 11,897 | 64,852 | [
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"library:polars",
"arxiv:2412.18925",
"region:us",
"medical",
"biology"
] | 2024-12-28T03:29:08 | null | null |
66212f29fb07c3e05ad0432e | HuggingFaceFW/fineweb | HuggingFaceFW | {"license": "odc-by", "task_categories": ["text-generation"], "language": ["en"], "pretty_name": "FineWeb", "size_categories": ["n>1T"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/*/*"}]}, {"config_name": "sample-10BT", "data_files": [{"split": "train", "path": "sample/10BT/*"}]}, {"config_name": "sample-100BT", "data_files": [{"split": "train", "path": "sample/100BT/*"}]}, {"config_name": "sample-350BT", "data_files": [{"split": "train", "path": "sample/350BT/*"}]}, {"config_name": "CC-MAIN-2024-51", "data_files": [{"split": "train", "path": "data/CC-MAIN-2024-51/*"}]}, {"config_name": "CC-MAIN-2024-46", "data_files": [{"split": "train", "path": "data/CC-MAIN-2024-46/*"}]}, {"config_name": "CC-MAIN-2024-42", "data_files": [{"split": "train", "path": "data/CC-MAIN-2024-42/*"}]}, {"config_name": "CC-MAIN-2024-38", "data_files": [{"split": "train", "path": 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🍷 FineWeb
15 trillion tokens of the finest data the 🌐 web has to offer
What is it?
The 🍷 FineWeb dataset consists of more than 15T tokens of cleaned and deduplicated english web data from CommonCrawl. The data processing pipeline is optimized for LLM performance and ran on the 🏭 datatrove library, our large scale data processing library.
🍷 FineWeb was originally meant to be a fully open replication of 🦅 RefinedWeb, with a release of the full dataset under… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceFW/fineweb. | 853,759 | 3,234,694 | [
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] | 2024-04-18T14:33:13 | null | null |
680f79161cf05743d38266f6 | nyuuzyou/svgfind | nyuuzyou | {"pretty_name": "SVGFind Icons", "size_categories": ["1M<n<10M"], "task_categories": ["image-classification", "text-to-image"], "annotations_creators": ["found"], "language": ["en"], "multilinguality": ["monolingual"], "source_datasets": ["original"], "configs": [{"config_name": "default", "data_files": [{"split": "creativecommons", "path": "svgfind-CREATIVECOMMONS_*.jsonl.zst"}, {"split": "publicdomain", "path": "svgfind-PUBLICDOMAIN.jsonl.zst"}], "default": true}], "tags": ["image", "vector-graphics", "icons", "svg"], "license": "other"} | false | null | 2025-04-28T14:50:27 | 26 | 15 | false | ca3fc28a42b0e3dc81725e5700ce893524703772 |
Dataset Card for SVGFind Icons
Dataset Summary
This dataset contains a large collection of Scalable Vector Graphics (SVG) icons sourced from SVGFind.com. The icons cover a wide range of categories and styles, suitable for user interfaces, web development, presentations, and potentially for training vector graphics or icon classification models. Each icon is provided under either a Creative Commons license or is in the Public Domain, as clearly indicated in its metadata.… See the full description on the dataset page: https://huggingface.co/datasets/nyuuzyou/svgfind. | 684 | 684 | [
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] | 2025-04-28T12:48:22 | null | null |
681b4f1dab6b347478b74d0f | miscovery/Math_CoT_Arabic_English_Reasoning | miscovery | {"license": "mit", "task_categories": ["question-answering", "text-generation", "fill-mask"], "language": ["en", "ar"], "pretty_name": "Math CoT Arabic English Reasoning", "size_categories": ["1K<n<10K"], "tags": ["code"]} | false | null | 2025-05-07T12:28:42 | 14 | 14 | false | 193d4c2d4cbc1c4a5572bd8f5df50d69e177bac9 |
Math CoT Arabic English Dataset
A high-quality, bilingual (English & Arabic) dataset for Chain-of-Thought (COT) reasoning in mathematics and related disciplines, developed by Miscovery AI.
Overview
Math-COT is a unique dataset designed to facilitate and benchmark the development of chain-of-thought reasoning capabilities in language models across mathematical domains. With meticulously crafted examples, explicit reasoning steps, and bilingual support, this dataset offers… See the full description on the dataset page: https://huggingface.co/datasets/miscovery/Math_CoT_Arabic_English_Reasoning. | 25 | 25 | [
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] | 2025-05-07T12:16:29 | null | null |
680eefdef99a20ddfc931d13 | quotientai/HalluMix | quotientai | {"license": "apache-2.0", "language": ["en"], "tags": ["hallucination-evaluation", "benchmark"], "pretty_name": "HalluMix"} | false | null | 2025-05-02T14:49:58 | 13 | 13 | false | 3e3a82e813f7c8c45257f10c29234063f95e7625 |
Introducing HalluMix: A Task-Agnostic, Multi-Domain Benchmark for Detecting Hallucinations in Real-World Scenarios
✉️ Contact: {deanna, mike, freddie, julia}@quotientai.co 📜 Paper: HalluMix: A Task-Agnostic, Multi-Domain Benchmark for Real-World Hallucination Detection, Emery et al (2025)
As large language models (LLMs) are increasingly adopted in critical industries, ensuring their outputs are factually grounded has emerged as a major concern. One prominent issue is "hallucination… See the full description on the dataset page: https://huggingface.co/datasets/quotientai/HalluMix. | 534 | 534 | [
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"hallucination-evaluation",
"benchmark"
] | 2025-04-28T03:02:54 | null | null |
6812390f2fb2d588a9f7503a | deepseek-ai/DeepSeek-ProverBench | deepseek-ai | null | false | null | 2025-04-30T14:54:40 | 26 | 13 | false | 3b9f067088e5e005fab91434ddc05a903e0a6252 |
1. Introduction
We introduce DeepSeek-Prover-V2, an open-source large language model designed for formal theorem proving in Lean 4, with initialization data collected through a recursive theorem proving pipeline powered by DeepSeek-V3. The cold-start training procedure begins by prompting DeepSeek-V3 to decompose complex problems into a series of subgoals. The proofs of resolved subgoals… See the full description on the dataset page: https://huggingface.co/datasets/deepseek-ai/DeepSeek-ProverBench. | 1,920 | 1,920 | [
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] | 2025-04-30T14:51:59 | null | null |
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Dataset Card for The Cauldron
Dataset description
The Cauldron is part of the Idefics2 release.
It is a massive collection of 50 vision-language datasets (training sets only) that were used for the fine-tuning of the vision-language model Idefics2.
Load the dataset
To load the dataset, install the library datasets with pip install datasets. Then,
from datasets import load_dataset
ds = load_dataset("HuggingFaceM4/the_cauldron", "ai2d")
to download and load the… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceM4/the_cauldron. | 627,727 | 2,709,708 | [
"size_categories:1M<n<10M",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:1603.07396",
"arxiv:2206.01718",
"arxiv:2208.05358",
"arxiv:1612.06890",
"arxiv:2310.00367",
"arxiv:1710.07300",
"arxiv:2312.12241",
"arxiv:1912.03098",
"arxiv:2211.08545",
"arxiv:2306.05425",
"arxiv:1709.00103",
"arxiv:2003.12462",
"arxiv:1612.00837",
"arxiv:2205.00363",
"arxiv:2403.09029",
"arxiv:2405.02246",
"region:us"
] | 2024-04-11T17:53:57 | null | null |
68029f72cbf4fea90c2db08e | Mxode/Chinese-Instruct | Mxode | {"configs": [{"config_name": "stem_zh", "data_files": [{"split": "train", "path": "stem_zh/**/*"}]}, {"config_name": "infinity-instruct", "data_files": [{"split": "train", "path": "infinity-instruct/**/*"}]}, {"config_name": "firefly", "data_files": [{"split": "train", "path": "firefly/**/*"}]}, {"config_name": "magpie", "data_files": [{"split": "train", "path": "magpie/**/*"}]}, {"config_name": "dpsk-r1-distil", "default": true, "data_files": [{"split": "train", "path": "dpsk-r1-distil/**/*"}]}, {"config_name": "coig-cqia", "data_files": [{"split": "train", "path": "coig-cqia/**/*"}]}, {"config_name": "disc-law", "data_files": [{"split": "train", "path": "disc-law/**/*"}]}, {"config_name": "neo_sft_phase2", "data_files": [{"split": "train", "path": "neo_sft_phase2/**/*"}]}, {"config_name": "chinese-medical", "data_files": [{"split": "train", "path": "chinese-medical/**/*"}]}, {"config_name": "chinese-reasoning-distil", "data_files": [{"split": "train", "path": "chinese-reasoning-distil/**/*"}]}, {"config_name": "psycho-10k-dpsk-r1", "data_files": [{"split": "train", "path": "psycho-10k-dpsk-r1/**/*"}]}, {"config_name": "sof-c-zh", "data_files": [{"split": "train", "path": "sof-c-zh/**/*"}]}, {"config_name": "industryinstruction", "data_files": [{"split": "train", "path": "industryinstruction/**/*"}]}, {"config_name": "Chinese-QA-AFAF", "data_files": [{"split": "train", "path": "Chinese-QA-AFAF/**/*"}]}], "license": "cc-by-sa-4.0", "task_categories": ["text-generation", "question-answering"], "language": ["zh"]} | false | null | 2025-05-02T10:50:50 | 20 | 12 | false | 3530b685d752eb5f1f6d7f1d3aa8e719ade128f5 |
中文指令微调数据集
💻 Github Repo
本项目旨在构建一个高质量、多领域、大规模的中文指令微调数据集，目前仍在施工中 🚧💦
具体构成
dpsk-r1-distil：中文 DeepSeek-R1 蒸馏数据集，来自 Congliu/Chinese-DeepSeek-R1-Distill-data-110k，根据打分质量做了筛选，提取了最终的回答，未包含思考过程。
chinese-reasoning-distil：中文推理蒸馏数据集，来自 Mxode/Chinese-Reasoning-Distil-Data，提取了最终的回答，未包含思考过程。
firefly：中文通用指令微调数据集，指令取自 Mxode/Firefly-1.1M-Rephrased，其本身已经相较于原 Firefly 数据集做了改进优化，此处再次针对所有优化的指令用更强的模型（如 DeepSeek-V2.5）做了新的回答。
stem_zh：中文 STEM 指令微调数据集，指令取自… See the full description on the dataset page: https://huggingface.co/datasets/Mxode/Chinese-Instruct. | 2,254 | 2,254 | [
"task_categories:text-generation",
"task_categories:question-answering",
"language:zh",
"license:cc-by-sa-4.0",
"size_categories:1M<n<10M",
"format:json",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2025-04-18T18:52:34 | null | null |
680b9e1a53d9d56430b73e2c | Qwen/PolyMath | Qwen | {"configs": [{"config_name": "ar", "data_files": [{"split": "top", "path": "ar/top.parquet"}, {"split": "high", "path": "ar/high.parquet"}, {"split": "medium", "path": "ar/medium.parquet"}, {"split": "low", "path": "ar/low.parquet"}]}, {"config_name": "bn", "data_files": [{"split": "top", "path": "bn/top.parquet"}, {"split": "high", "path": "bn/high.parquet"}, {"split": "medium", "path": "bn/medium.parquet"}, {"split": "low", "path": "bn/low.parquet"}]}, {"config_name": "de", "data_files": [{"split": "top", "path": "de/top.parquet"}, {"split": "high", "path": "de/high.parquet"}, {"split": "medium", "path": "de/medium.parquet"}, {"split": "low", "path": "de/low.parquet"}]}, {"config_name": "en", "data_files": [{"split": "top", "path": "en/top.parquet"}, {"split": "high", "path": "en/high.parquet"}, {"split": "medium", "path": "en/medium.parquet"}, {"split": "low", "path": "en/low.parquet"}]}, {"config_name": "es", "data_files": [{"split": "top", "path": "es/top.parquet"}, {"split": "high", "path": "es/high.parquet"}, {"split": "medium", "path": "es/medium.parquet"}, {"split": "low", "path": "es/low.parquet"}]}, {"config_name": "fr", "data_files": [{"split": "top", "path": "fr/top.parquet"}, {"split": "high", "path": "fr/high.parquet"}, {"split": "medium", "path": "fr/medium.parquet"}, {"split": "low", "path": "fr/low.parquet"}]}, {"config_name": "id", "data_files": [{"split": "top", "path": "id/top.parquet"}, {"split": "high", "path": "id/high.parquet"}, {"split": "medium", "path": "id/medium.parquet"}, {"split": "low", "path": "id/low.parquet"}]}, {"config_name": "it", "data_files": [{"split": "top", "path": "it/top.parquet"}, {"split": "high", "path": "it/high.parquet"}, {"split": "medium", "path": "it/medium.parquet"}, {"split": "low", "path": "it/low.parquet"}]}, {"config_name": "ja", "data_files": [{"split": "top", "path": "ja/top.parquet"}, {"split": "high", "path": "ja/high.parquet"}, {"split": "medium", "path": "ja/medium.parquet"}, {"split": "low", "path": "ja/low.parquet"}]}, {"config_name": "ko", "data_files": [{"split": "top", "path": "ko/top.parquet"}, {"split": "high", "path": "ko/high.parquet"}, {"split": "medium", "path": "ko/medium.parquet"}, {"split": "low", "path": "ko/low.parquet"}]}, {"config_name": "ms", "data_files": [{"split": "top", "path": "ms/top.parquet"}, {"split": "high", "path": "ms/high.parquet"}, {"split": "medium", "path": "ms/medium.parquet"}, {"split": "low", "path": "ms/low.parquet"}]}, {"config_name": "pt", "data_files": [{"split": "top", "path": "pt/top.parquet"}, {"split": "high", "path": "pt/high.parquet"}, {"split": "medium", "path": "pt/medium.parquet"}, {"split": "low", "path": "pt/low.parquet"}]}, {"config_name": "ru", "data_files": [{"split": "top", "path": "ru/top.parquet"}, {"split": "high", "path": "ru/high.parquet"}, {"split": "medium", "path": "ru/medium.parquet"}, {"split": "low", "path": "ru/low.parquet"}]}, {"config_name": "sw", "data_files": [{"split": "top", "path": "sw/top.parquet"}, {"split": "high", "path": "sw/high.parquet"}, {"split": "medium", "path": "sw/medium.parquet"}, {"split": "low", "path": "sw/low.parquet"}]}, {"config_name": "te", "data_files": [{"split": "top", "path": "te/top.parquet"}, {"split": "high", "path": "te/high.parquet"}, {"split": "medium", "path": "te/medium.parquet"}, {"split": "low", "path": "te/low.parquet"}]}, {"config_name": "th", "data_files": [{"split": "top", "path": "th/top.parquet"}, {"split": "high", "path": "th/high.parquet"}, {"split": "medium", "path": "th/medium.parquet"}, {"split": "low", "path": "th/low.parquet"}]}, {"config_name": "vi", "data_files": [{"split": "top", "path": "vi/top.parquet"}, {"split": "high", "path": "vi/high.parquet"}, {"split": "medium", "path": "vi/medium.parquet"}, {"split": "low", "path": "vi/low.parquet"}]}, {"config_name": "zh", "data_files": [{"split": "top", "path": "zh/top.parquet"}, {"split": "high", "path": "zh/high.parquet"}, {"split": "medium", "path": "zh/medium.parquet"}, {"split": "low", "path": "zh/low.parquet"}]}]} | false | null | 2025-04-29T08:00:02 | 12 | 12 | false | 5cbffba3e86f73c201ee6910fcc115ef6c7ec25f |
PolyMath: Evaluating Mathematical Reasoning in Multilingual Contexts
PolyMath is a multilingual mathematical reasoning benchmark covering 18 languages and 4 easy-to-hard difficulty levels. Our benchmark ensures difficulty comprehensiveness, language diversity, and high-quality translation, making it a highly discriminative multilingual mathematical benchmark in the era of reasoning LLMs.
📈 Broad Difficulty Range: PolyMath defines and partitions… See the full description on the dataset page: https://huggingface.co/datasets/Qwen/PolyMath. | 406 | 406 | [
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"region:us"
] | 2025-04-25T14:37:14 | null | null |
680c9fa16d68dd7030e1f324 | nvidia/When2Call | nvidia | {"license": "cc-by-4.0", "configs": [{"config_name": "test", "data_files": [{"split": "llm_judge", "path": "test/when2call_test_llm_judge.jsonl"}, {"split": "mcq", "path": "test/when2call_test_mcq.jsonl"}], "default": true}, {"config_name": "train_sft", "data_files": [{"split": "train", "path": "train/when2call_train_sft.jsonl"}]}, {"config_name": "train_pref", "data_files": [{"split": "train", "path": "train/when2call_train_pref.jsonl"}]}], "task_categories": ["text-generation"], "language": ["en"], "tags": ["function-calling", "tool-calling", "synthetic", "nvidia"]} | false | null | 2025-04-29T00:14:10 | 20 | 12 | false | 0582f7749df63a96fdc3070932e83e72396ace53 |
When2Call
💾 Github | 📄 Paper
Dataset Description:
When2Call is a benchmark designed to evaluate tool-calling decision-making for large language models (LLMs), including when to generate a tool call, when to ask follow-up questions, when to admit the question can't be answered with the tools provided, and what to do if the question seems to require tool use but a tool call can't be made.
We find that state-of-the-art tool-calling LMs show significant room for… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/When2Call. | 669 | 669 | [
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"library:mlcroissant",
"library:polars",
"region:us",
"function-calling",
"tool-calling",
"synthetic",
"nvidia"
] | 2025-04-26T08:56:01 | null | null |
67aa021ced8d8663d42505cc | open-r1/OpenR1-Math-220k | open-r1 | {"license": "apache-2.0", "language": ["en"], "configs": [{"config_name": "all", "data_files": [{"split": "train", "path": "all/train-*"}]}, {"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}, {"config_name": "extended", "data_files": [{"split": "train", "path": "extended/train-*"}]}], "dataset_info": [{"config_name": "all", "features": [{"name": "problem", "dtype": "string"}, {"name": "solution", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "problem_type", "dtype": "string"}, {"name": "question_type", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "uuid", "dtype": "string"}, {"name": "is_reasoning_complete", "sequence": "bool"}, {"name": "generations", "sequence": "string"}, {"name": "correctness_math_verify", "sequence": "bool"}, {"name": "correctness_llama", "sequence": "bool"}, {"name": "finish_reasons", "sequence": "string"}, {"name": "correctness_count", "dtype": "int64"}, {"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 9734110026, "num_examples": 225129}], "download_size": 4221672067, "dataset_size": 9734110026}, {"config_name": "default", "features": [{"name": "problem", "dtype": "string"}, {"name": "solution", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "problem_type", "dtype": "string"}, {"name": "question_type", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "uuid", "dtype": "string"}, {"name": "is_reasoning_complete", "sequence": "bool"}, {"name": "generations", "sequence": "string"}, {"name": "correctness_math_verify", "sequence": "bool"}, {"name": "correctness_llama", "sequence": "bool"}, {"name": "finish_reasons", "sequence": "string"}, {"name": "correctness_count", "dtype": "int64"}, {"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 4964543659, "num_examples": 93733}], "download_size": 2149897914, "dataset_size": 4964543659}, {"config_name": "extended", "features": [{"name": "problem", "dtype": "string"}, {"name": "solution", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "problem_type", "dtype": "string"}, {"name": "question_type", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "uuid", "dtype": "string"}, {"name": "is_reasoning_complete", "sequence": "bool"}, {"name": "generations", "sequence": "string"}, {"name": "correctness_math_verify", "sequence": "bool"}, {"name": "correctness_llama", "sequence": "bool"}, {"name": "finish_reasons", "sequence": "string"}, {"name": "correctness_count", "dtype": "int64"}, {"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 4769566550, "num_examples": 131396}], "download_size": 2063936457, "dataset_size": 4769566550}]} | false | null | 2025-02-18T11:45:27 | 575 | 10 | false | e4e141ec9dea9f8326f4d347be56105859b2bd68 |
OpenR1-Math-220k
Dataset description
OpenR1-Math-220k is a large-scale dataset for mathematical reasoning. It consists of 220k math problems with two to four reasoning traces generated by DeepSeek R1 for problems from NuminaMath 1.5.
The traces were verified using Math Verify for most samples and Llama-3.3-70B-Instruct as a judge for 12% of the samples, and each problem contains at least one reasoning trace with a correct answer.
The dataset consists of two splits:… See the full description on the dataset page: https://huggingface.co/datasets/open-r1/OpenR1-Math-220k. | 28,393 | 116,734 | [
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"library:mlcroissant",
"library:polars",
"region:us"
] | 2025-02-10T13:41:48 | null | null |
67fce65dd1ec7d15ba6a2da3 | zwhe99/DeepMath-103K | zwhe99 | {"license": "mit", "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "final_answer", "dtype": "string"}, {"name": "difficulty", "dtype": "float64"}, {"name": "topic", "dtype": "string"}, {"name": "r1_solution_1", "dtype": "string"}, {"name": "r1_solution_2", "dtype": "string"}, {"name": "r1_solution_3", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 4963981308, "num_examples": 103110}], "download_size": 2135828159, "dataset_size": 4963981308}, "task_categories": ["text-generation", "text2text-generation"], "language": ["en"], "tags": ["math", "reasoning", "rl"], "pretty_name": "deepmath-103k", "size_categories": ["100K<n<1M"]} | false | null | 2025-05-08T07:20:43 | 169 | 10 | false | b24ee6e700c72e9bf9c134ad88363c0c9d260f70 |
DeepMath-103K
🔥 News
May 8, 2025: We found that 48 samples contained hints that revealed the answers. The relevant questions have now been revised to remove the leaked answers.
April 14, 2025: We release DeepMath-103K, a large-scale dataset featuring challenging, verifiable, and decontaminated math problems tailored for RL and SFT. We open source:… See the full description on the dataset page: https://huggingface.co/datasets/zwhe99/DeepMath-103K. | 21,510 | 21,510 | [
"task_categories:text-generation",
"task_categories:text2text-generation",
"language:en",
"license:mit",
"size_categories:100K<n<1M",
"format:parquet",
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"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2504.11456",
"region:us",
"math",
"reasoning",
"rl"
] | 2025-04-14T10:41:33 | null | null |
6797e648de960c48ff034e54 | open-thoughts/OpenThoughts-114k | open-thoughts | {"dataset_info": [{"config_name": "default", "features": [{"name": "system", "dtype": "string"}, {"name": "conversations", "list": [{"name": "from", "dtype": "string"}, {"name": "value", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 2635015668, "num_examples": 113957}], "download_size": 1078777193, "dataset_size": 2635015668}, {"config_name": "metadata", "features": [{"name": "problem", "dtype": "string"}, {"name": "deepseek_reasoning", "dtype": "string"}, {"name": "deepseek_solution", "dtype": "string"}, {"name": "ground_truth_solution", "dtype": "string"}, {"name": "domain", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "test_cases", "dtype": "string"}, {"name": "starter_code", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 5525214077.699433, "num_examples": 113957}], "download_size": 2469729724, "dataset_size": 5525214077.699433}], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}, {"config_name": "metadata", "data_files": [{"split": "train", "path": "metadata/train-*"}]}], "tags": ["curator", "synthetic"], "license": "apache-2.0"} | false | null | 2025-04-06T23:31:24 | 702 | 9 | false | a5996b0064b4ddd42c6e9a7302eeec0618cb7b63 |
Open-Thoughts-114k
Open synthetic reasoning dataset with 114k high-quality examples covering math, science, code, and puzzles!
Inspect the content with rich formatting with Curator Viewer.
Available Subsets
default subset containing ready-to-train data used to finetune the OpenThinker-7B and OpenThinker-32B models:
ds = load_dataset("open-thoughts/OpenThoughts-114k", split="train")
metadata subset containing extra columns used in dataset construction:… See the full description on the dataset page: https://huggingface.co/datasets/open-thoughts/OpenThoughts-114k. | 23,746 | 178,838 | [
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"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us",
"curator",
"synthetic"
] | 2025-01-27T20:02:16 | null | null |
680a5ef919f25e184a85c785 | Rapidata/text-2-image-Rich-Human-Feedback-32k | Rapidata | {"dataset_info": {"features": [{"name": "image_name", "dtype": "image"}, {"name": "sentence", "dtype": "string"}, {"name": "word_scores", "dtype": "string"}, {"name": "alignment_score_norm", "dtype": "float32"}, {"name": "coherence_score_norm", "dtype": "float32"}, {"name": "style_score_norm", "dtype": "float32"}, {"name": "alignment_heatmap", "dtype": {"array2_d": {"shape": [1024, 1024], "dtype": "float32"}}}, {"name": "coherence_heatmap", "dtype": {"array2_d": {"shape": [1024, 1024], "dtype": "float32"}}}, {"name": "alignment_score", "dtype": "float32"}, {"name": "coherence_score", "dtype": "float32"}, {"name": "style_score", "dtype": "float32"}], "splits": [{"name": "train", "num_bytes": 116617124714.976, "num_examples": 32528}], "download_size": 91216762385, "dataset_size": 116617124714.976}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "tags": ["heatmap", "t2i", "human", "feedback", "rich", "annotation", "open-image-preferences"], "license": "apache-2.0", "language": ["en"], "pretty_name": "Text to image - Rich Annotation", "size_categories": ["10K<n<100K"]} | false | null | 2025-04-29T11:28:30 | 21 | 9 | false | c4b7ca19097c0143c8f072df33af8c5e54425467 |
Building upon Google's research Rich Human Feedback for Text-to-Image Generation, and the
smaller, previous version of this dataset, we have collected over 3.7 million responses from 307'415 individual humans for the open-image-preference-v1 dataset using Rapidata via the Python API. Collection took less than 2 weeks.
If you get value from this dataset and would like to see more in the future, please consider liking it ♥️
Overview
We asked humans to evaluate AI-generated images… See the full description on the dataset page: https://huggingface.co/datasets/Rapidata/text-2-image-Rich-Human-Feedback-32k. | 1,181 | 1,181 | [
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"heatmap",
"t2i",
"human",
"feedback",
"rich",
"annotation",
"open-image-preferences"
] | 2025-04-24T15:55:37 | null | null |
681139f166e75cf56190c341 | SWE-bench/SWE-smith-trajectories | SWE-bench | {"dataset_info": {"features": [{"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "instance_id", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 479188645, "num_examples": 5016}], "download_size": 146906769, "dataset_size": 479188645}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "license": "mit", "language": ["en"], "tags": ["code", "agents", "software-engineering"], "size_categories": ["1K<n<10K"]} | false | null | 2025-05-07T16:10:02 | 11 | 9 | false | 07a0c81a9f38e0bb2b004f2ce8ea8a04dc7f0f3d |
SWE-smith Trajectories
Code
•
Paper
•
Site
This dataset contains the 5017 trajectories we fine-tuned Qwen 2.5 Coder Instruct on, leading to
SWE-agent-LM-32B, a coding LM agent that
achieve 40.2% on SWE-bench Verified (no verifiers or multiple rollouts, just 1 attempt per instance).
Trajectories were generated by running SWE-agent + Claude 3.7 Sonnet on task instances from
the SWE-smith dataset.
| 353 | 353 | [
"language:en",
"license:mit",
"size_categories:1K<n<10K",
"format:parquet",
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"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2504.21798",
"region:us",
"code",
"agents",
"software-engineering"
] | 2025-04-29T20:43:29 | null | null |
68132a23ef52a53ef9351750 | tokyotech-llm/swallow-math | tokyotech-llm | {"license": "llama3.3", "task_categories": ["text-generation"], "language": ["en"], "tags": ["math"], "pretty_name": "swallowmath", "size_categories": ["1M<n<10M"]} | false | null | 2025-05-07T03:15:18 | 9 | 9 | false | f0ecf05b746254933ebaded21d6f96763757df46 |
SwallowMath
Resources
🐙 GitHub: Explore the project repository, including pipeline code and prompts at rioyokotalab/swallow-code-math.
📑 arXiv: Read our paper for detailed methodology and results at arXiv:2505.02881.
🤗 Sister Dataset: Discover SwallowCode, our companion dataset for code generation.
What is it?
SwallowMath is a high-quality mathematical dataset comprising approximately 2.3 billion tokens derived from the FineMath-4+ dataset through an… See the full description on the dataset page: https://huggingface.co/datasets/tokyotech-llm/swallow-math. | 762 | 762 | [
"task_categories:text-generation",
"language:en",
"license:llama3.3",
"size_categories:1M<n<10M",
"format:json",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"arxiv:2505.02881",
"region:us",
"math"
] | 2025-05-01T08:00:35 | null | null |
649444227853dd12c3bbadd8 | Amod/mental_health_counseling_conversations | Amod | {"license": "openrail", "task_categories": ["text-generation", "question-answering"], "language": ["en"], "tags": ["medical"], "size_categories": ["1K<n<10K"]} | false | null | 2024-04-05T08:30:03 | 370 | 8 | false | 4672e03c7f1a7b2215eb4302b83ca50449ce2553 |
Amod/mental_health_counseling_conversations
Dataset Summary
This dataset is a collection of questions and answers sourced from two online counseling and therapy platforms. The questions cover a wide range of mental health topics, and the answers are provided by qualified psychologists. The dataset is intended to be used for fine-tuning language models to improve their ability to provide mental health advice.
Supported Tasks and Leaderboards
The dataset supports… See the full description on the dataset page: https://huggingface.co/datasets/Amod/mental_health_counseling_conversations. | 3,903 | 69,149 | [
"task_categories:text-generation",
"task_categories:question-answering",
"language:en",
"license:openrail",
"size_categories:1K<n<10K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"doi:10.57967/hf/1581",
"region:us",
"medical"
] | 2023-06-22T12:52:50 | null | null |
681133a1f664a34e36208c4b | SWE-bench/SWE-smith | SWE-bench | {"language": ["en"], "license": "mit", "size_categories": ["10K<n<100K"], "task_categories": ["text-generation"], "dataset_info": {"features": [{"name": "instance_id", "dtype": "string"}, {"name": "repo", "dtype": "string"}, {"name": "patch", "dtype": "string"}, {"name": "FAIL_TO_PASS", "sequence": "string"}, {"name": "PASS_TO_PASS", "sequence": "string"}, {"name": "created_at", "dtype": "string"}, {"name": "image_name", "dtype": "string"}, {"name": "base_commit", "dtype": "string"}, {"name": "problem_statement", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 5040247353, "num_examples": 50137}], "download_size": 253578293, "dataset_size": 5040247353}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "tags": ["code", "agents", "software-engineering"]} | false | null | 2025-05-08T16:25:32 | 12 | 8 | false | 699b53400d3855206a0fbf3ff4beaf1a52f4f232 |
SWE-smith Dataset
Code
•
Paper
•
Site
The SWE-smith Dataset is a training dataset of 50137 task instances from 128 GitHub repositories, collected using the SWE-smith toolkit.
It is the largest dataset to date for training software engineering agents.
All SWE-smith task instances come with an executable environment.
To learn more about how to use this dataset to train Language Models for Software Engineering, please refer to the documentation.
| 561 | 561 | [
"task_categories:text-generation",
"language:en",
"license:mit",
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2504.21798",
"region:us",
"code",
"agents",
"software-engineering"
] | 2025-04-29T20:16:33 | null | null |
End of preview.
NEW Changes Feb 27th
Added new fields on the
modelssplit:downloadsAllTime,safetensors,ggufAdded new field on the
datasetssplit:downloadsAllTimeAdded new split:
paperswhich is all of the Daily Papers
Updated Daily
- Downloads last month
- 5,817
Size of downloaded dataset files:
17.5 MB
Size of the auto-converted Parquet files:
17.5 MB
Number of rows:
7,822
Data Sourcing report
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