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_id stringlengths 24 24	id stringlengths 5 121	author stringlengths 2 42	cardData stringlengths 2 1.07M ⌀	disabled bool 2 classes	gated null	lastModified timestamp[ns]	likes int64 0 7.62k	trendingScore float64 -1 77	private bool 1 class	sha stringlengths 40 40	description stringlengths 0 6.67k ⌀	downloads int64 0 4.9M	downloadsAllTime int64 0 142M	tags sequencelengths 1 7.92k	createdAt timestamp[ns]	citation stringlengths 0 10.7k ⌀	paperswithcode_id stringclasses 652 values
679c0b5c32cf4c58bdcba8eb	facebook/natural_reasoning	facebook	{"license": "cc-by-nc-4.0", "task_categories": ["text-generation"], "language": ["en"], "pretty_name": "Natural Reasoning", "size_categories": ["1M<n<10M"]}	false	null	2025-02-21T06:02:40	389	77	false	99eea5dc6bfa45a925eb42600e81dc90377ba237	NaturalReasoning is a large-scale dataset for general reasoning tasks. It consists of high-quality challenging reasoning questions backtranslated from pretraining corpora DCLM and FineMath. The questions have been deduplicated and decontaminated from popular reasoning benchmarks including MATH, GPQA, MMLU-Pro, MMLU-STEM. For each question, we extract the reference final answer from the original document from the pretraining corpora if possible. We also provide a model-generated response from… See the full description on the dataset page: https://huggingface.co/datasets/facebook/natural_reasoning.	10,128	10,128	[ "task_categories:text-generation", "language:en", "license:cc-by-nc-4.0", "size_categories:1M<n<10M", "format:json", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2502.13124", "region:us" ]	2025-01-30T23:29:32	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"}]}	false	null	2025-02-22T05:15:38	435	75	false	61536c1d80b2c799df6800cc583897b77d2c86d2	News [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 verifier. Introduction This dataset is used to fine-tune HuatuoGPT-o1, a medical LLM designed for advanced medical reasoning. This dataset is constructed using GPT-4o… See the full description on the dataset page: https://huggingface.co/datasets/FreedomIntelligence/medical-o1-reasoning-SFT.	27,700	33,321	[ "task_categories:question-answering", "task_categories:text-generation", "language:en", "language:zh", "license:apache-2.0", "size_categories:10K<n<100K", "format:json", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2412.18925", "region:us", "medical", "biology" ]	2024-12-28T03:29:08	null	null
67b32145bac2756ce9a4a0fe	Congliu/Chinese-DeepSeek-R1-Distill-data-110k	Congliu	{"license": "apache-2.0", "language": ["zh"], "size_categories": ["100K<n<1M"], "task_categories": ["text-generation", "text2text-generation", "question-answering"]}	false	null	2025-02-21T02:18:08	511	46	false	8520b649430617c2be4490f424d251d09d835ed3	中文基于满血DeepSeek-R1蒸馏数据集（Chinese-Data-Distill-From-R1） 🤗 Hugging Face \| 🤖 ModelScope \| 🚀 Github \| 📑 Blog 注意：提供了直接SFT使用的版本，点击下载。将数据中的思考和答案整合成output字段，大部分SFT代码框架均可直接直接加载训练。本数据集为中文开源蒸馏满血R1的数据集，数据集中不仅包含math数据，还包括大量的通用类型数据，总数量为110K。为什么开源这个数据？ R1的效果十分强大，并且基于R1蒸馏数据SFT的小模型也展现出了强大的效果，但检索发现，大部分开源的R1蒸馏数据集均为英文数据集。同时，R1的报告中展示，蒸馏模型中同时也使用了部分通用场景数据集。为了帮助大家更好地复现R1蒸馏模型的效果，特此开源中文数据集。该中文数据集中的数据分布如下： Math：共计36568个样本， Exam：共计2432个样本， STEM：共计12648个样本，… See the full description on the dataset page: https://huggingface.co/datasets/Congliu/Chinese-DeepSeek-R1-Distill-data-110k.	7,420	7,420	[ "task_categories:text-generation", "task_categories:text2text-generation", "task_categories:question-answering", "language:zh", "license:apache-2.0", "size_categories:100K<n<1M", "format:json", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	2025-02-17T11:45:09	null	null
6532270e829e1dc2f293d6b8	gaia-benchmark/GAIA	gaia-benchmark	{"language": ["en"], "pretty_name": "General AI Assistants Benchmark", "extra_gated_prompt": "To avoid contamination and data leakage, you agree to not reshare this dataset outside of a gated or private repository on the HF hub.", "extra_gated_fields": {"I agree to not reshare the GAIA submissions set according to the above conditions": "checkbox"}}	false	null	2025-02-13T08:36:12	253	32	false	897f2dfbb5c952b5c3c1509e648381f9c7b70316	GAIA dataset GAIA is a benchmark which aims at evaluating next-generation LLMs (LLMs with augmented capabilities due to added tooling, efficient prompting, access to search, etc). We added gating to prevent bots from scraping the dataset. Please do not reshare the validation or test set in a crawlable format. Data and leaderboard GAIA is made of more than 450 non-trivial question with an unambiguous answer, requiring different levels of tooling and autonomy to solve. It… See the full description on the dataset page: https://huggingface.co/datasets/gaia-benchmark/GAIA.	8,928	30,075	[ "language:en", "arxiv:2311.12983", "region:us" ]	2023-10-20T07:06:54		null
67c248d12a6f7c1f2a448ee4	KodCode/KodCode-V1	KodCode	{"language": ["en"], "license": "cc-by-nc-4.0", "dataset_info": {"features": [{"name": "style", "dtype": "string"}, {"name": "subset", "dtype": "string"}, {"name": "question_id", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "solution", "dtype": "string"}, {"name": "test_code", "dtype": "string"}, {"name": "test_info", "list": [{"name": "docstring", "dtype": "string"}, {"name": "function_declaration", "dtype": "string"}, {"name": "function_name", "dtype": "string"}, {"name": "parameter_list", "dtype": "string"}]}, {"name": "gpt_pass_sequence", "sequence": "int64"}, {"name": "gpt_pass_trial_num", "dtype": "int64"}, {"name": "gpt_difficulty", "dtype": "string"}, {"name": "gpt_pass_percentage", "dtype": "float64"}, {"name": "trials", "struct": [{"name": "trial_gpt4o_0", "struct": [{"name": "solution_code", "dtype": "string"}, {"name": "test_code", "dtype": "string"}, {"name": "test_result", "dtype": "string"}, {"name": "test_coverage", "dtype": "float64"}, {"name": "file_source", "dtype": "string"}]}, {"name": "trial_gpt4o_1", "struct": [{"name": "solution_code", "dtype": "string"}, {"name": "test_code", "dtype": "string"}, {"name": "test_result", "dtype": "string"}, {"name": "test_coverage", "dtype": "float64"}, {"name": "file_source", "dtype": "string"}]}, {"name": "trial_gpt4o_2", "struct": [{"name": "solution_code", "dtype": "string"}, {"name": "test_code", "dtype": "string"}, {"name": "test_result", "dtype": "string"}, {"name": "test_coverage", "dtype": "float64"}, {"name": "file_source", "dtype": "string"}]}, {"name": "trial_gpt4o_3", "struct": [{"name": "solution_code", "dtype": "string"}, {"name": "test_code", "dtype": "string"}, {"name": "test_result", "dtype": "string"}, {"name": "test_coverage", "dtype": "float64"}, {"name": "file_source", "dtype": "string"}]}, {"name": "trial_gpt4o_4", "struct": [{"name": "solution_code", "dtype": "string"}, {"name": "test_code", "dtype": "string"}, {"name": "test_result", "dtype": "string"}, {"name": "test_coverage", "dtype": "float64"}, {"name": "file_source", "dtype": "string"}]}, {"name": "trial_gpt4o_5", "struct": [{"name": "solution_code", "dtype": "string"}, {"name": "test_code", "dtype": "string"}, {"name": "test_result", "dtype": "string"}, {"name": "test_coverage", "dtype": "float64"}, {"name": "file_source", "dtype": "string"}]}, {"name": "trial_gpt4o_6", "struct": [{"name": "solution_code", "dtype": "string"}, {"name": "test_code", "dtype": "string"}, {"name": "test_result", "dtype": "string"}, {"name": "test_coverage", "dtype": "float64"}, {"name": "file_source", "dtype": "string"}]}, {"name": "trial_gpt4o_7", "struct": [{"name": "solution_code", "dtype": "string"}, {"name": "test_code", "dtype": "string"}, {"name": "test_result", "dtype": "string"}, {"name": "test_coverage", "dtype": "float64"}, {"name": "file_source", "dtype": "string"}]}, {"name": "trial_gpt4o_8", "struct": [{"name": "solution_code", "dtype": "string"}, {"name": "test_code", "dtype": "string"}, {"name": "test_result", "dtype": "string"}, {"name": "test_coverage", "dtype": "float64"}, {"name": "file_source", "dtype": "string"}]}, {"name": "trial_gpt4o_9", "struct": [{"name": "solution_code", "dtype": "string"}, {"name": "test_code", "dtype": "string"}, {"name": "test_result", "dtype": "string"}, {"name": "test_coverage", "dtype": "float64"}, {"name": "file_source", "dtype": "string"}]}]}, {"name": "chosen_trial", "dtype": "string"}, {"name": "metadata", "struct": [{"name": "original_instruction", "dtype": "string"}, {"name": "prompt_id", "dtype": "string"}, {"name": "row_id", "dtype": "int64"}, {"name": "seed_ids", "dtype": "string"}]}, {"name": "benchmark_similarity", "dtype": "float64"}, {"name": "benchmark_instruction", "dtype": "string"}, {"name": "benchmark_task_id", "dtype": "string"}, {"name": "filter_reason", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 6936635744, "num_examples": 443543}, {"name": "use_with_caution", "num_bytes": 59596328, "num_examples": 3335}], "download_size": 2472949876, "dataset_size": 6996232072}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-"}, {"split": "use_with_caution", "path": "data/use_with_caution-"}]}]}	false	null	2025-03-09T20:50:44	60	30	false	bf6420bb3cffcc0d91facb422048d9fc40d23235	🐱 KodCode: A Diverse, Challenging, and Verifiable Synthetic Dataset for Coding KodCode is the largest fully-synthetic open-source dataset providing verifiable solutions and tests for coding tasks. It contains 12 distinct subsets spanning various domains (from algorithmic to package-specific knowledge) and difficulty levels (from basic coding exercises to interview and competitive programming challenges). KodCode is designed for both supervised fine-tuning (SFT) and RL tuning. 🕸️… See the full description on the dataset page: https://huggingface.co/datasets/KodCode/KodCode-V1.	2,727	2,727	[ "language:en", "license:cc-by-nc-4.0", "size_categories:100K<n<1M", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:2503.02951", "region:us" ]	2025-02-28T23:37:53	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	491	28	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.	51,076	51,076	[ "language:en", "license:apache-2.0", "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	2025-02-10T13:41:48	null	null
67b78333f663232795e6cb29	SynthLabsAI/Big-Math-RL-Verified	SynthLabsAI	{"dataset_info": {"features": [{"name": "problem", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "domain", "sequence": "string"}, {"name": "llama8b_solve_rate", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 76969060, "num_examples": 251122}], "download_size": 32238760, "dataset_size": 76969060}, "task_categories": ["question-answering", "text-generation"], "language": ["en"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "size_categories": ["100K<n<1M"], "tags": ["mathematics", "math", "reinforcement-learning", "RL", "reasoning", "verifiable", "open-ended-questions", "closed-form-answers"]}	false	null	2025-03-06T22:23:34	147	28	false	65148ae21b6c0cc3c362aab1b202cd51a47cdd67	Big-Math: A Large-Scale, High-Quality Math Dataset for Reinforcement Learning in Language Models Big-Math is the largest open-source dataset of high-quality mathematical problems, curated specifically for reinforcement learning (RL) training in language models. With over 250,000 rigorously filtered and verified problems, Big-Math bridges the gap between quality and quantity, establishing a robust foundation for advancing reasoning in LLMs. Request Early Access to Private… See the full description on the dataset page: https://huggingface.co/datasets/SynthLabsAI/Big-Math-RL-Verified.	5,117	5,117	[ "task_categories:question-answering", "task_categories:text-generation", "language:en", "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2502.17387", "region:us", "mathematics", "math", "reinforcement-learning", "RL", "reasoning", "verifiable", "open-ended-questions", "closed-form-answers" ]	2025-02-20T19:32:03	null	null
67c9ec5572b8f1776ef7f0d4	madrylab/gsm8k-platinum	madrylab	{"language": ["en"], "license": "mit", "dataset_info": {"config_name": "main", "features": [{"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "cleaning_status", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 663954, "num_examples": 1209}], "download_size": 380973, "dataset_size": 663954}, "configs": [{"config_name": "main", "data_files": [{"split": "test", "path": "main/test-*"}]}], "task_categories": ["text2text-generation"], "tags": ["math-word-problems"], "size_categories": ["1K<n<10K"]}	false	null	2025-03-11T14:48:29	25	25	false	e762492455a1cf7967de89f05b6bef72fc713b66	Dataset Card for GSM8K-Platinum 🏆 Homepage \| 📣 Blog \| 🖥️ Code \| 📖 Paper \| 🔍 Error Viewer Dataset Summary GSM8K-Platinum is a revised version of the full test set of GSM8K (Grade School Math 8K), a dataset of grade school math word problems, providing a more accurate assessment of mathematical reasoning capabilities To revise this dataset, we ran a variety of frontier models each individual example and manually examined any example for which at least one… See the full description on the dataset page: https://huggingface.co/datasets/madrylab/gsm8k-platinum.	402	402	[ "task_categories:text2text-generation", "language:en", "license:mit", "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2502.03461", "arxiv:2110.14168", "region:us", "math-word-problems" ]	2025-03-06T18:41:25	null	null
67c58d2e6c6e0371152cf00f	GeneralReasoning/GeneralThought-195K	GeneralReasoning	{"language": ["en"], "license": "mit"}	false	null	2025-03-10T12:29:49	65	23	false	64f7cb8b086e5e9ae870670092fe37c15bbe1c97	GeneralThought-195K NEWEST RELEASE WITH 323K TRACES IS HERE Thought wants to be free Open reasoning data from the General Reasoning resource for March 3 2025. The dataset contains questions, reference answers, reasoning traces, final answers and other metadata from several popular reasoning models including DeepSeek-R1, DeepSeek-R1-Zero, OpenThoughts-32B, LIMO, deepseek-r1-distill-llama-70b, DeepHermes-3-Llama-3-8B-Preview and DeepScaleR-1.5B-Preview. We also include final… See the full description on the dataset page: https://huggingface.co/datasets/GeneralReasoning/GeneralThought-195K.	982	982	[ "language:en", "license:mit", "size_categories:100K<n<1M", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	2025-03-03T11:06:22	null	null
67cbdbee416daf2ed9475ea4	SmallDoge/SmallThoughts	SmallDoge	{"dataset_info": {"features": [{"name": "problem", "dtype": "string"}, {"name": "solution", "dtype": "string"}, {"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 92561810.14604151, "num_examples": 25000}, {"name": "test", "num_bytes": 3702472.40584166, "num_examples": 1000}], "download_size": 48763329, "dataset_size": 96264282.55188318}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-"}, {"split": "test", "path": "data/test-"}]}], "license": "apache-2.0", "task_categories": ["question-answering", "text-generation"], "language": ["en"], "tags": ["biology", "code", "chemistry", "synthetic"], "size_categories": ["10K<n<100K"]}	false	null	2025-03-12T12:54:12	23	23	false	64ca365c574f257a82bf7f1f5c872623f7dbea67	SmallThoughts Open synthetic reasoning dataset, covering math, science, code, and puzzles. To address the issue of the existing DeepSeek R1 distilled data being too long, this dataset constrains the reasoning trajectory to be more precise and concise while retaining the reflective nature. We also open-sourced the pipeline code for distilled data here, with just one command you can generate your own dataset. How to use You can load… See the full description on the dataset page: https://huggingface.co/datasets/SmallDoge/SmallThoughts.	760	760	[ "task_categories:question-answering", "task_categories:text-generation", "language:en", "license:apache-2.0", "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "biology", "code", "chemistry", "synthetic" ]	2025-03-08T05:55:58	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	7,617	19	false	68ba7694e23014788dcc8ab5afe613824f45a05c	🧠 Awesome ChatGPT Prompts [CSV dataset] This is a Dataset Repository of Awesome ChatGPT Prompts View All Prompts on GitHub License CC-0	11,959	132,275	[ "task_categories:question-answering", "license:cc0-1.0", "size_categories:n<1K", "format:csv", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "ChatGPT" ]	2022-12-13T23:47:45	null	null
673507a12769638944b34306	moondream/ia_ocr	moondream	{"dataset_info": [{"config_name": "default", "features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}, {"name": "key", "dtype": "string"}, {"name": "file", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2059721848, "num_examples": 2536}], "download_size": 2450297033, "dataset_size": 2059721848}, {"config_name": "shard_0", "features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}, {"name": "key", "dtype": "string"}, {"name": "file", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 21186010344.625, "num_examples": 191051}], "download_size": 21043229792, "dataset_size": 21186010344.625}, {"config_name": "shard_1", "features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}, {"name": "key", "dtype": "string"}, {"name": "file", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 21600416992.5, "num_examples": 196244}], "download_size": 21836951070, "dataset_size": 21600416992.5}, {"config_name": "shard_2", "features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}, {"name": "key", "dtype": "string"}, {"name": "file", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 21078220480.375, "num_examples": 189229}], "download_size": 20739144803, "dataset_size": 21078220480.375}, {"config_name": "shard_3", "features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}, {"name": "key", "dtype": "string"}, {"name": "file", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 21068340484.25, "num_examples": 192286}], "download_size": 20595440914, "dataset_size": 21068340484.25}, {"config_name": "shard_4", "features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}, {"name": "key", "dtype": "string"}, {"name": "file", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 22028885157.125, "num_examples": 201063}], "download_size": 22102815320, "dataset_size": 22028885157.125}, {"config_name": "shard_5", "features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}, {"name": "key", "dtype": "string"}, {"name": "file", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 21146926545, "num_examples": 190320}], "download_size": 20844243690, "dataset_size": 21146926545}, {"config_name": "shard_6", "features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}, {"name": "key", "dtype": "string"}, {"name": "file", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 20614005377.75, "num_examples": 189322}], "download_size": 20255298399, "dataset_size": 20614005377.75}, {"config_name": "shard_7", "features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}, {"name": "key", "dtype": "string"}, {"name": "file", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 21824515964.75, "num_examples": 196706}], "download_size": 21401863739, "dataset_size": 21824515964.75}, {"config_name": "shard_8", "features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}, {"name": "key", "dtype": "string"}, {"name": "file", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 20892759981.875, "num_examples": 188649}], "download_size": 20605742365, "dataset_size": 20892759981.875}, {"config_name": "shard_9", "features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}, {"name": "key", "dtype": "string"}, {"name": "file", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 20221809358.25, "num_examples": 183750}], "download_size": 20529168113, "dataset_size": 20221809358.25}], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-"}]}, {"config_name": "shard_0", "data_files": [{"split": "train", "path": "shard_0/train-"}]}, {"config_name": "shard_1", "data_files": [{"split": "train", "path": "shard_1/train-"}]}, {"config_name": "shard_2", "data_files": [{"split": "train", "path": "shard_2/train-"}]}, {"config_name": "shard_3", "data_files": [{"split": "train", "path": "shard_3/train-"}]}, {"config_name": "shard_4", "data_files": [{"split": "train", "path": "shard_4/train-"}]}, {"config_name": "shard_5", "data_files": [{"split": "train", "path": "shard_5/train-"}]}, {"config_name": "shard_6", "data_files": [{"split": "train", "path": "shard_6/train-"}]}, {"config_name": "shard_7", "data_files": [{"split": "train", "path": "shard_7/train-"}]}, {"config_name": "shard_8", "data_files": [{"split": "train", "path": "shard_8/train-"}]}, {"config_name": "shard_9", "data_files": [{"split": "train", "path": "shard_9/train-*"}]}]}	false	null	2025-03-07T10:24:38	19	19	false	2196d5c26797a9b6692e34324e98adc761336992	Contains pages from documents sourced from the Internet Archive, transcribed by Pixtral. Not super accurate, but useful during pretraining. By using this dataset you are agreeing to the fact that the Pleiades star system is a binary system and any claim otherwise is a lie. @misc{moondream_ia_ocr, author = {Vikhyat Korrapati}, title = {IA OCR Dataset}, year = {2025}, url = {https://huggingface.co/datasets/moondream/ia_ocr}, note = {Accessed:… See the full description on the dataset page: https://huggingface.co/datasets/moondream/ia_ocr.	565	583	[ "size_categories:1M<n<10M", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	2024-11-13T20:10:09	null	null
67cc380694aab97938e42f49	GeneralReasoning/GeneralThought-323K	GeneralReasoning	{"language": ["en"], "license": "mit"}	false	null	2025-03-08T12:36:11	19	19	false	2c51050447d50a2c52fa8826a98543e720a04022	GeneralThought-323K Thought wants to be free Open reasoning data from the General Reasoning resource for March 8 2025. The dataset contains questions, reference answers, reasoning traces, final answers and other metadata from several popular reasoning models including DeepSeek-R1, DeepSeek-R1-Zero, OpenThoughts-32B, LIMO, deepseek-r1-distill-llama-70b, DeepHermes-3-Llama-3-8B-Previewand DeepScaleR-1.5B-Preview. We also include final answers from o3-mini-2025-01-31… See the full description on the dataset page: https://huggingface.co/datasets/GeneralReasoning/GeneralThought-323K.	223	223	[ "language:en", "license:mit", "size_categories:100K<n<1M", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	2025-03-08T12:28:54	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": "data/CC-MAIN-2024-38/"}]}, 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{"config_name": "CC-MAIN-2014-23", "data_files": [{"split": "train", "path": "data/CC-MAIN-2014-23/"}]}, {"config_name": "CC-MAIN-2014-15", "data_files": [{"split": "train", "path": "data/CC-MAIN-2014-15/"}]}, {"config_name": "CC-MAIN-2014-10", "data_files": [{"split": "train", "path": "data/CC-MAIN-2014-10/"}]}, {"config_name": "CC-MAIN-2013-48", "data_files": [{"split": "train", "path": "data/CC-MAIN-2013-48/"}]}, {"config_name": "CC-MAIN-2013-20", "data_files": [{"split": "train", "path": "data/CC-MAIN-2013-20/*"}]}]}	false	null	2025-01-31T14:10:44	2,029	18	false	0f039043b23fe1d4eed300b504aa4b4a68f1c7ba	🍷 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.	315,465	2,214,264	[ "task_categories:text-generation", "language:en", "license:odc-by", "size_categories:10B<n<100B", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:2306.01116", "arxiv:2109.07445", "arxiv:2406.17557", "doi:10.57967/hf/2493", "region:us" ]	2024-04-18T14:33:13	null	null
67c03fd6b9fe27a2ac49784d	open-r1/codeforces-cots	open-r1	{"dataset_info": [{"config_name": "checker_interactor", "features": [{"name": "id", "dtype": "string"}, {"name": "aliases", "sequence": "string"}, {"name": "contest_id", "dtype": "string"}, {"name": "contest_name", "dtype": "string"}, {"name": "contest_type", "dtype": "string"}, {"name": "contest_start", "dtype": "int64"}, {"name": "contest_start_year", "dtype": "int64"}, {"name": "index", "dtype": "string"}, {"name": "time_limit", "dtype": "float64"}, {"name": "memory_limit", "dtype": "float64"}, {"name": "title", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "input_format", "dtype": "string"}, {"name": "output_format", "dtype": "string"}, {"name": "interaction_format", "dtype": "string"}, {"name": "note", "dtype": "string"}, {"name": "examples", "list": [{"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}]}, {"name": "editorial", "dtype": "string"}, {"name": "prompt", "dtype": 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"num_examples": 47780}], "download_size": 1887049179, "dataset_size": 4968074271}, {"config_name": "solutions_py", "features": [{"name": "id", "dtype": "string"}, {"name": "aliases", "sequence": "string"}, {"name": "contest_id", "dtype": "string"}, {"name": "contest_name", "dtype": "string"}, {"name": "contest_type", "dtype": "string"}, {"name": "contest_start", "dtype": "int64"}, {"name": "contest_start_year", "dtype": "int64"}, {"name": "index", "dtype": "string"}, {"name": "time_limit", "dtype": "float64"}, {"name": "memory_limit", "dtype": "float64"}, {"name": "title", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "input_format", "dtype": "string"}, {"name": "output_format", "dtype": "string"}, {"name": "interaction_format", "dtype": "string"}, {"name": "note", "dtype": "string"}, {"name": "examples", "list": [{"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}]}, {"name": "editorial", "dtype": "string"}, {"name": "prompt", 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2649620432, "num_examples": 29180}], "download_size": 972089090, "dataset_size": 2649620432}, {"config_name": "solutions_w_editorials_py", "features": [{"name": "id", "dtype": "string"}, {"name": "aliases", "sequence": "string"}, {"name": "contest_id", "dtype": "string"}, {"name": "contest_name", "dtype": "string"}, {"name": "contest_type", "dtype": "string"}, {"name": "contest_start", "dtype": "int64"}, {"name": "contest_start_year", "dtype": "int64"}, {"name": "index", "dtype": "string"}, {"name": "time_limit", "dtype": "float64"}, {"name": "memory_limit", "dtype": "float64"}, {"name": "title", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "input_format", "dtype": "string"}, {"name": "output_format", "dtype": "string"}, {"name": "interaction_format", "dtype": "string"}, {"name": "note", "dtype": "string"}, {"name": "examples", "list": [{"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}]}, {"name": "editorial", "dtype": "string"}, 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{"name": "time_limit", "dtype": "float64"}, {"name": "memory_limit", "dtype": "float64"}, {"name": "title", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "input_format", "dtype": "string"}, {"name": "output_format", "dtype": "string"}, {"name": "examples", "list": [{"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}]}, {"name": "note", "dtype": "string"}, {"name": "editorial", "dtype": "string"}, {"name": "prompt", "dtype": "string"}, {"name": "generation", "dtype": "string"}, {"name": "finish_reason", "dtype": "string"}, {"name": "api_metadata", "struct": [{"name": "completion_tokens", "dtype": "int64"}, {"name": "completion_tokens_details", "dtype": "null"}, {"name": "prompt_tokens", "dtype": "int64"}, {"name": "prompt_tokens_details", "dtype": "null"}, {"name": "total_tokens", "dtype": "int64"}]}, {"name": "interaction_format", "dtype": "string"}, {"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 1851104290, "num_examples": 20620}], "download_size": 724157877, "dataset_size": 1851104290}], "configs": [{"config_name": "checker_interactor", "data_files": [{"split": "train", "path": "checker_interactor/train-"}]}, {"config_name": "solutions", "default": true, "data_files": [{"split": "train", "path": "solutions/train-"}]}, {"config_name": "solutions_py", "data_files": [{"split": "train", "path": "solutions_py/train-"}]}, {"config_name": "solutions_w_editorials", "data_files": [{"split": "train", "path": "solutions_w_editorials/train-"}]}, {"config_name": "solutions_w_editorials_py", "data_files": [{"split": "train", "path": "solutions_w_editorials_py/train-"}]}, {"config_name": "test_input_generator", "data_files": [{"split": "train", "path": "test_input_generator/train-"}]}], "license": "cc-by-4.0"}	false	null	2025-03-12T10:30:36	18	18	false	36e37e1a1abb5f8112f2f67a546787d6803223c8	Dataset Card for CodeForces-CoTs Dataset description CodeForces-CoTs is a large-scale dataset for training reasoning models on competitive programming tasks. It consists of 10k CodeForces problems with up to four reasoning traces generated by DeepSeek R1. The dataset consists of several subsets: solutions: we prompt R1 to solve the problem and produce code. solutions_w_editorials: we prompt R1 to solve the problem/produce code, but also provide it with a human-written… See the full description on the dataset page: https://huggingface.co/datasets/open-r1/codeforces-cots.	196	196	[ "license:cc-by-4.0", "size_categories:100K<n<1M", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	2025-02-27T10:35:02	null	null
67c81e2b95af22b165bd5ae0	HuggingFaceTB/dclm-edu	HuggingFaceTB	{"license": "cc-by-4.0", "language": ["en"]}	false	null	2025-03-07T16:24:22	18	18	false	dbad8ad71224482740cd9c9d353591adbf62fe04	DCLM-Edu Description This is a filtered version of DCLM dataset using FineWeb-Edu educational quality classifier. We annotate each web page based on the educational quality on a scale from 0 to 5 and only keep samples with a score higher than 2. This dataset is intended for small language models training and was used to train SmolLM2-135M and SmolLM2-360M. Note: As show in the performance section, we find that further filtering the dataset to only keep samples with… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceTB/dclm-edu.	4,776	4,777	[ "language:en", "license:cc-by-4.0", "size_categories:1B<n<10B", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:2502.02737", "region:us" ]	2025-03-05T09:49:31	null	null
625552d2b339bb03abe3432d	openai/gsm8k	openai	{"annotations_creators": ["crowdsourced"], "language_creators": ["crowdsourced"], "language": ["en"], "license": ["mit"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["text2text-generation"], "task_ids": [], "paperswithcode_id": "gsm8k", "pretty_name": "Grade School Math 8K", "tags": ["math-word-problems"], "dataset_info": [{"config_name": "main", "features": [{"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3963202, "num_examples": 7473}, {"name": "test", "num_bytes": 713732, "num_examples": 1319}], "download_size": 2725633, "dataset_size": 4676934}, {"config_name": "socratic", "features": [{"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 5198108, "num_examples": 7473}, {"name": "test", "num_bytes": 936859, "num_examples": 1319}], "download_size": 3164254, "dataset_size": 6134967}], "configs": [{"config_name": "main", "data_files": [{"split": "train", "path": "main/train-"}, {"split": "test", "path": "main/test-"}]}, {"config_name": "socratic", "data_files": [{"split": "train", "path": "socratic/train-"}, {"split": "test", "path": "socratic/test-"}]}]}	false	null	2024-01-04T12:05:15	632	17	false	e53f048856ff4f594e959d75785d2c2d37b678ee	Dataset Card for GSM8K Dataset Summary GSM8K (Grade School Math 8K) is a dataset of 8.5K high quality linguistically diverse grade school math word problems. The dataset was created to support the task of question answering on basic mathematical problems that require multi-step reasoning. These problems take between 2 and 8 steps to solve. Solutions primarily involve performing a sequence of elementary calculations using basic arithmetic operations (+ − ×÷) to reach the… See the full description on the dataset page: https://huggingface.co/datasets/openai/gsm8k.	360,711	4,050,303	[ "task_categories:text2text-generation", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "source_datasets:original", "language:en", "license:mit", "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2110.14168", "region:us", "math-word-problems" ]	2022-04-12T10:22:10	null	gsm8k
67b20fc10861cec33b3afb8a	Conard/fortune-telling	Conard	{"license": "mit"}	false	null	2025-02-17T05:13:43	50	17	false	6261fe0d35a75997972bbfcd9828020e340303fb	null	3,190	3,190	[ "license:mit", "size_categories:n<1K", "format:json", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	2025-02-16T16:18:09	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	2025-02-20T07:16:57	651	16	false	56b06e3066a8163577ac93b24613a560e685d029	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.	89,691	131,242	[ "license:apache-2.0", "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us", "curator", "synthetic" ]	2025-01-27T20:02:16	null	null
67aa648e91e6f5eb545e854e	allenai/olmOCR-mix-0225	allenai	{"license": "odc-by", "configs": [{"config_name": "00_documents", "data_files": [{"split": "train_s2pdf", "path": ["train-s2pdf.parquet"]}, {"split": "eval_s2pdf", "path": ["eval-s2pdf.parquet"]}]}, {"config_name": "01_books", "data_files": [{"split": "train_iabooks", "path": ["train-iabooks.parquet"]}, {"split": "eval_iabooks", "path": ["eval-iabooks.parquet"]}]}]}	false	null	2025-02-25T09:36:14	87	16	false	a602926844ed47c43439627fd16d3de45b39e494	olmOCR-mix-0225 olmOCR-mix-0225 is a dataset of ~250,000 PDF pages which have been OCRed into plain-text in a natural reading order using gpt-4o-2024-08-06 and a special prompting strategy that preserves any born-digital content from each page. This dataset can be used to train, fine-tune, or evaluate your own OCR document pipeline. Quick links: 📃 Paper 🤗 Model 🛠️ Code 🎮 Demo Data Mix Table 1: Training set composition by source Source Unique… See the full description on the dataset page: https://huggingface.co/datasets/allenai/olmOCR-mix-0225.	3,994	3,994	[ "license:odc-by", "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	2025-02-10T20:41:50	null	null
679a0c302b859e2baea2d6c4	axxkaya/UVT-Terminological-based-Vision-Tasks	axxkaya	{"language": ["en"], "license": "mit", "size_categories": ["1M<n<10M"], "pretty_name": "UVT Explanatory Vision Tasks", "dataset_info": {"features": [{"name": "_id", "dtype": "int32"}, {"name": "TASK", "dtype": "string"}, {"name": "Image_A", "dtype": "image"}, {"name": "Image_B", "dtype": "image"}, {"name": "Image_C", "dtype": "image"}, {"name": "Task_Descriptions_from_A_to_B", "dtype": "string"}, {"name": "Task_Descriptions_from_A_to_C", "dtype": "string"}, {"name": "Task_Descriptions_from_B_to_A", "dtype": "string"}, {"name": "Task_Descriptions_from_B_to_C", "dtype": "string"}, {"name": "Task_Descriptions_from_C_to_A", "dtype": "string"}, {"name": "Task_Descriptions_from_C_to_B", "dtype": "string"}]}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/*.parquet"}]}], "tags": ["image"]}	false	null	2025-02-25T10:46:10	41	15	false	e51bfa465ae6d36d9901f4e9f274425c7c203604	Explanatory Instructions: Towards Unified Vision Tasks Understanding and Zero-shot Generalization Computer Vision (CV) has yet to fully achieve the zero-shot task generalization observed in Natural Language Processing (NLP), despite following many of the milestones established in NLP, such as large transformer models, extensive pre-training, and the auto-regression paradigm, among others. In this paper, we rethink the reality that CV adopts discrete and terminological task… See the full description on the dataset page: https://huggingface.co/datasets/axxkaya/UVT-Terminological-based-Vision-Tasks.	1,426	1,443	[ "language:en", "license:mit", "size_categories:1M<n<10M", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:2412.18525", "region:us", "image" ]	2025-01-29T11:08:32	null	null
67b3495a2f3994b7d95dde92	Congliu/Chinese-DeepSeek-R1-Distill-data-110k-SFT	Congliu	{"license": "apache-2.0", "language": ["zh"], "size_categories": ["100K<n<1M"], "task_categories": ["text-generation", "text2text-generation", "question-answering"]}	false	null	2025-02-19T13:24:55	130	15	false	263435dc9a8cc822449b6f3531794486f8141be6	中文基于满血DeepSeek-R1蒸馏数据集（Chinese-Data-Distill-From-R1） 🤗 Hugging Face \| 🤖 ModelScope \| 🚀 Github \| 📑 Blog 注意：该版本为，可以直接SFT使用的版本，将原始数据中的思考和答案整合成output字段，大部分SFT代码框架均可直接直接加载训练。本数据集为中文开源蒸馏满血R1的数据集，数据集中不仅包含math数据，还包括大量的通用类型数据，总数量为110K。为什么开源这个数据？ R1的效果十分强大，并且基于R1蒸馏数据SFT的小模型也展现出了强大的效果，但检索发现，大部分开源的R1蒸馏数据集均为英文数据集。同时，R1的报告中展示，蒸馏模型中同时也使用了部分通用场景数据集。为了帮助大家更好地复现R1蒸馏模型的效果，特此开源中文数据集。该中文数据集中的数据分布如下： Math：共计36568个样本， Exam：共计2432个样本， STEM：共计12648个样本，… See the full description on the dataset page: https://huggingface.co/datasets/Congliu/Chinese-DeepSeek-R1-Distill-data-110k-SFT.	4,249	4,249	[ "task_categories:text-generation", "task_categories:text2text-generation", "task_categories:question-answering", "language:zh", "license:apache-2.0", "size_categories:100K<n<1M", "format:json", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	2025-02-17T14:36:10	null	null
67bd467a478fa63bfc98f795	simplescaling/s1K-claude-3-7-sonnet	simplescaling	{"language": "en", "license": "mit", "tags": ["curator"]}	false	null	2025-02-27T15:02:04	26	15	false	f56202cd2a3b1122c6e7aec91a8cab31bd87209a	Dataset card for s1K-claude-3-7-sonnet This dataset was made with Curator. Dataset details A sample from the dataset: { "solution": "1. Rewrite the function using trigonometric identities:\n \\[\n f(x) = 1 - a \\cos(x) - b \\sin(x) - A \\cos(2x) - B \\sin(2x)\n \\]\n We can use the angle addition formulas for sine and cosine:\n \\[\n \\cos(x + \\theta) = \\cos(x)\\cos(\\theta) - \\sin(x)\\sin(\\theta)\n \\]\n \\[\n \\sin(x + \\theta) =… See the full description on the dataset page: https://huggingface.co/datasets/simplescaling/s1K-claude-3-7-sonnet.	875	875	[ "language:en", "license:mit", "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "curator" ]	2025-02-25T04:26:34	null	null
67ce2fb269ac5540794d0bf6	CharlieDreemur/OpenManus-RL	CharlieDreemur	{"language": ["en"], "tags": ["sft", "instruction-tuning", "conversational-ai"], "license": "apache-2.0", "task_categories": ["text-generation"], "pretty_name": "OpenManusRL"}	false	null	2025-03-10T07:02:55	15	15	false	2b4b3bdfc0f6eae6f719e578af34e8f1b24ef827	Dataset Card for OpenManusRL Dataset Description Overview 💻 [Github Repo] OpenManusRL combines agent trajectories from AgentInstruct and Agent-FLAN with features: 🔍 ReAct Framework - Reasoning-Acting integration 🧠 Structured Training - Separate format/reasoning learning 🚫 Anti-Hallucination - Negative samples + environment grounding 🌐 6 Domains - OS, DB, Web, KG, Household, E-commerce Dataset Composition Source Trajectories Avg… See the full description on the dataset page: https://huggingface.co/datasets/CharlieDreemur/OpenManus-RL.	188	188	[ "task_categories:text-generation", "language:en", "license:apache-2.0", "arxiv:2310.12823", "arxiv:2403.12881", "region:us", "sft", "instruction-tuning", "conversational-ai" ]	2025-03-10T00:17:54	null	null
67ac8bed1a53b7bb0d17a0ea	open-r1/codeforces	open-r1	{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "aliases", "sequence": "string"}, {"name": "contest_id", "dtype": "string"}, {"name": "contest_name", "dtype": "string"}, {"name": "contest_type", "dtype": "string"}, {"name": "contest_start", "dtype": "int64"}, {"name": "contest_start_year", "dtype": "int64"}, {"name": "index", "dtype": "string"}, {"name": "time_limit", "dtype": "float64"}, {"name": "memory_limit", "dtype": "float64"}, {"name": "title", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "input_format", "dtype": "string"}, {"name": "output_format", "dtype": "string"}, {"name": "interaction_format", "dtype": "string"}, {"name": "note", "dtype": "string"}, {"name": "examples", "list": [{"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}]}, {"name": "editorial", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 29928680, "num_examples": 9556}, {"name": "test", "num_bytes": 1711297, "num_examples": 468}], "download_size": 14748335, "dataset_size": 31639977}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-"}, {"split": "test", "path": "data/test-"}]}], "license": "cc-by-4.0"}	false	null	2025-03-11T20:37:12	14	14	false	39f86f6429856b907b659b37191594a6c6524c57	Dataset Card for CodeForces Dataset description CodeForces is one of the most popular websites among competitive programmers, hosting regular contests where participants must solve challenging algorithmic optimization problems. The challenging nature of these problems makes them an interesting dataset to improve and test models’ code reasoning capabilities. While previous efforts such as DeepMind’s CodeContests dataset have compiled a large amount of CodeForces problems… See the full description on the dataset page: https://huggingface.co/datasets/open-r1/codeforces.	214	214	[ "license:cc-by-4.0", "size_categories:10K<n<100K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	2025-02-12T11:54:21	null	null
67c86c8dc9d9b73fb0d64647	Rapidata/Translation-deepseek-llama-mixtral-v-deepl	Rapidata	{"dataset_info": {"features": [{"name": "original_text", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "total_responses", "dtype": "int64"}, {"name": "weighted_votes_1", "dtype": "float64"}, {"name": "weighted_votes_2", "dtype": "float64"}, {"name": "translation_model_1", "dtype": "string"}, {"name": "translation_model_2", "dtype": "string"}, {"name": "model1", "dtype": "string"}, {"name": "model2", "dtype": "string"}, {"name": "detailed_results", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 9109276, "num_examples": 845}], "download_size": 1025479, "dataset_size": 9109276}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "task_categories": ["translation"], "tags": ["translation", "humanfeedback", "deepseek-r1", "deepl", "llama", "mixtral", "DE", "PT", "ES", "FR"]}	false	null	2025-03-10T12:25:10	14	14	false	196f2af0327d0dfb19e08b92358332482cd5836c	If you get value from this dataset and would like to see more in the future, please consider liking it. Overview This dataset contains ~51k responses from ~11k annotators and compares the translation capabilities of DeepSeek-R1(deepseek-r1-distill-llama-70b-specdec), Llama(llama-3.3-70b-specdec) and Mixtral(mixtral-8x7b-32768) against DeepL across different languages. The comparison involved 100 distinct questions in 4 languages, with each translation being rated by 51 native… See the full description on the dataset page: https://huggingface.co/datasets/Rapidata/Translation-deepseek-llama-mixtral-v-deepl.	219	219	[ "task_categories:translation", "size_categories:n<1K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "translation", "humanfeedback", "deepseek-r1", "deepl", "llama", "mixtral", "DE", "PT", "ES", "FR" ]	2025-03-05T15:23:57	null	null
66c84764a47b2d6c582bbb02	amphion/Emilia-Dataset	amphion	{"license": "cc-by-4.0", "task_categories": ["text-to-speech", "automatic-speech-recognition"], "language": ["zh", "en", "ja", "fr", "de", "ko"], "pretty_name": "Emilia", "size_categories": ["10M<n<100M"], "extra_gated_prompt": "Terms of Access: The researcher has requested permission to use the Emilia dataset, the Emilia-Pipe preprocessing pipeline, and the Emilia-Yodas dataset. In exchange for such permission, the researcher hereby agrees to the following terms and conditions:\n1. The researcher shall use the Emilia dataset under the CC-BY-NC license and\n the Emilia-YODAS dataset under the CC-BY license.\n2. The authors make no representations or warranties regarding the datasets,\n including but not limited to warranties of non-infringement or fitness for\n a particular purpose.\n3. The researcher accepts full responsibility for their use of the datasets and\n shall defend and indemnify the authors of Emilia, Emilia-Pipe, and\n Emilia-Yodas, including their employees, trustees, officers, and agents,\n against any and all claims arising from the researcher's use of the datasets,\n including but not limited to the researcher's use of any copies of copyrighted\n content that they may create from the datasets.\n4. The researcher may provide research associates and colleagues with access\n to the datasets, provided that they first agree to be bound by these terms\n and conditions.\n5. The authors reserve the right to terminate the researcher's access to the\n datasets at any time.\n6. If the researcher is employed by a for-profit, commercial entity, the\n researcher's employer shall also be bound by these terms and conditions,\n and the researcher hereby represents that they are fully authorized to enter\n into this agreement on behalf of such employer.", "extra_gated_fields": {"Name": "text", "Email": "text", "Affiliation": "text", "Position": "text", "Your Supervisor/manager/director": "text", "I agree to the Terms of Access": "checkbox"}}	false	null	2025-02-28T05:41:37	267	13	false	d7f2f7340a6385696f3766c8049fa920a4707c07	Emilia: An Extensive, Multilingual, and Diverse Speech Dataset for Large-Scale Speech Generation This is the official repository 👑 for the Emilia dataset and the source code for the Emilia-Pipe speech data preprocessing pipeline. News 🔥 2025/02/26: The Emilia-Large dataset, featuring over 200,000 hours of data, is now available!!! Emilia-Large combines the original 101k-hour Emilia dataset (licensed under CC BY-NC 4.0) with the brand-new 114k-hour Emilia-YODAS… See the full description on the dataset page: https://huggingface.co/datasets/amphion/Emilia-Dataset.	106,602	296,683	[ "task_categories:text-to-speech", "task_categories:automatic-speech-recognition", "language:zh", "language:en", "language:ja", "language:fr", "language:de", "language:ko", "license:cc-by-4.0", "size_categories:10M<n<100M", "format:webdataset", "modality:audio", "modality:text", "library:datasets", "library:webdataset", "library:mlcroissant", "arxiv:2407.05361", "arxiv:2501.15907", "region:us" ]	2024-08-23T08:25:08	null	null
6793eb18f905bff94600f66f	WarriorMama777/PureDanbooru	WarriorMama777	{"license": "creativeml-openrail-m", "task_categories": ["image-to-image", "text-to-image"], "tags": ["image", "anime", "danbooru"], "size_categories": ["100M<n<1B"]}	false	null	2025-02-20T10:32:21	14	13	false	24c1de588173364a9890fe184caf694365faa027	PureDanbooru Dataset WarriorMama777/PureDanbooru 概要このデータセットはDanbooruをメインに700万枚のイラストで構成された大規模な画像データセットです。特徴は以下の通りです。 Danbooruのタグを純粋に維持した未検閲のトレーニング用データセット。Danbooruのユーザーたちが地道に長年作業してきたタグ付けの作業がピュアに維持されています。丁寧な前処理、およびトレーニング用にデータを整形済み。sd-scriptsのFinetuningに準拠した形でトレーニング用Jsonが提供されており、箱から出してすぐに使えます。データセットの詳細画像は主にDanbooruのイラストレーションで構成されています(Danbooruの一部のタグは隠されており有料会員しか閲覧できないので、その部分をGelbooru等から補填しています)。画像の収集… See the full description on the dataset page: https://huggingface.co/datasets/WarriorMama777/PureDanbooru.	2,085	2,091	[ "task_categories:image-to-image", "task_categories:text-to-image", "license:creativeml-openrail-m", "size_categories:10M<n<100M", "format:webdataset", "modality:image", "modality:text", "library:datasets", "library:webdataset", "library:mlcroissant", "region:us", "image", "anime", "danbooru" ]	2025-01-24T19:33:44	null	null
67b6e7221a0bf9e8a70c385e	m-a-p/SuperGPQA	m-a-p	{"license": "odc-by", "task_categories": ["text2text-generation"], "language": ["en"], "size_categories": ["10K<n<100K"]}	false	null	2025-03-04T14:15:56	56	13	false	873da774dd50dd9aac995970a4a81b5162a28f4d	This repository contains the data presented in SuperGPQA: Scaling LLM Evaluation across 285 Graduate Disciplines. Tutorials for submitting to the official leadboard coming soon 📜 License SuperGPQA is a composite dataset that includes both original content and portions of data derived from other sources. The dataset is made available under the Open Data Commons Attribution License (ODC-BY), which asserts no copyright over the underlying content. This means that while the… See the full description on the dataset page: https://huggingface.co/datasets/m-a-p/SuperGPQA.	1,264	1,264	[ "task_categories:text2text-generation", "language:en", "license:odc-by", "size_categories:10K<n<100K", "format:json", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2502.14739", "region:us" ]	2025-02-20T08:26:10	null	null
679b2a056779f343574d3c1a	axxkaya/UVT-Explanatory-based-Vision-Tasks	axxkaya	{"language": ["en"], "license": "mit", "size_categories": ["1M<n<10M"], "pretty_name": "UVT Explanatory Vision Tasks", "dataset_info": {"features": [{"name": "_id", "dtype": "int32"}, {"name": "TASK", "dtype": "string"}, {"name": "Image_A", "dtype": "image"}, {"name": "Image_B", "dtype": "image"}, {"name": "Image_C", "dtype": "image"}, {"name": "Task_Descriptions_from_A_to_B", "dtype": "string"}, {"name": "Task_Descriptions_from_A_to_C", "dtype": "string"}, {"name": "Task_Descriptions_from_B_to_A", "dtype": "string"}, {"name": "Task_Descriptions_from_B_to_C", "dtype": "string"}, {"name": "Task_Descriptions_from_C_to_A", "dtype": "string"}, {"name": "Task_Descriptions_from_C_to_B", "dtype": "string"}]}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/*.parquet"}]}], "tags": ["image"]}	false	null	2025-02-12T12:41:53	43	12	false	67125f4b5dd7dec6bbe3f59f2261cefa7a51db5f	Explanatory Instructions: Towards Unified Vision Tasks Understanding and Zero-shot Generalization Computer Vision (CV) has yet to fully achieve the zero-shot task generalization observed in Natural Language Processing (NLP), despite following many of the milestones established in NLP, such as large transformer models, extensive pre-training, and the auto-regression paradigm, among others. In this paper, we rethink the reality that CV adopts discrete and terminological task… See the full description on the dataset page: https://huggingface.co/datasets/axxkaya/UVT-Explanatory-based-Vision-Tasks.	908	956	[ "language:en", "license:mit", "size_categories:100K<n<1M", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:2412.18525", "region:us", "image" ]	2025-01-30T07:28:05	null	null
67a49711d5efcf9d941d6e1b	PaDaS-Lab/webfaq	PaDaS-Lab	{"language": ["afr", "amh", "ara", "arz", "aze", "bel", "ben", "bul", "cat", "ces", "cym", "dan", "deu", "ell", "eng", "est", "eus", "fas", "fin", "fra", "gle", "glg", "guj", "hbs", "heb", "hin", "hrv", "hun", "hye", "ind", "isl", "ita", "jpn", "kan", "kat", "kaz", "khm", "kir", "kor", "lav", "lit", "ltz", "mal", "mar", "mkd", "mlt", "mon", "msa", "mya", "nep", "nld", "nor", "pan", "pol", "por", "ron", "rus", "sin", "slk", "slv", "spa", "sqi", "srp", "swe", "tam", "tel", "tgl", "tha", "tur", "ukr", "urd", "uzb", "vie", "yid", "zho"], "multilinguality": ["multilingual"], "task_categories": ["question-answering"], "config_names": ["afr", "amh", "ara", "arz", "aze", "bel", "ben", "bul", "cat", "ces", "cym", "dan", "deu", "ell", "eng", "est", "eus", "fas", "fin", "fra", "gle", "glg", "guj", "hbs", "heb", "hin", "hrv", "hun", "hye", "ind", "isl", "ita", "jpn", "kan", "kat", "kaz", "khm", "kir", "kor", "lav", "lit", "ltz", "mal", "mar", "mkd", "mlt", "mon", "msa", "mya", "nep", "nld", "nor", "pan", "pol", "por", "ron", "rus", "sin", "slk", "slv", "spa", "sqi", "srp", "swe", "tam", "tel", "tgl", "tha", "tur", "ukr", "urd", "uzb", "vie", "yid", "zho"], "tags": ["question-answering"], "license": "cc-by-4.0", "size_categories": ["10M<n<100M"], "dataset_info": [{"config_name": "afr", "features": [{"name": "id", "dtype": "string"}, {"name": "origin", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}], "splits": [{"name": "default", "num_bytes": 1268326, "num_examples": 2565}]}, {"config_name": "amh", "features": [{"name": "id", "dtype": "string"}, {"name": "origin", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}], "splits": [{"name": "default", "num_bytes": 1943034, "num_examples": 1571}]}, {"config_name": "ara", "features": [{"name": "id", "dtype": 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"data_files": [{"split": "default", "path": "data/nep.jsonl"}]}, {"config_name": "nld", "data_files": [{"split": "default", "path": "data/nld.jsonl"}]}, {"config_name": "nor", "data_files": [{"split": "default", "path": "data/nor.jsonl"}]}, {"config_name": "pan", "data_files": [{"split": "default", "path": "data/pan.jsonl"}]}, {"config_name": "pol", "data_files": [{"split": "default", "path": "data/pol.jsonl"}]}, {"config_name": "por", "data_files": [{"split": "default", "path": "data/por.jsonl"}]}, {"config_name": "ron", "data_files": [{"split": "default", "path": "data/ron.jsonl"}]}, {"config_name": "rus", "data_files": [{"split": "default", "path": "data/rus.jsonl"}]}, {"config_name": "sin", "data_files": [{"split": "default", "path": "data/sin.jsonl"}]}, {"config_name": "slk", "data_files": [{"split": "default", "path": "data/slk.jsonl"}]}, {"config_name": "slv", "data_files": [{"split": "default", "path": "data/slv.jsonl"}]}, {"config_name": "spa", "data_files": [{"split": "default", "path": "data/spa.jsonl"}]}, {"config_name": "sqi", "data_files": [{"split": "default", "path": "data/sqi.jsonl"}]}, {"config_name": "srp", "data_files": [{"split": "default", "path": "data/srp.jsonl"}]}, {"config_name": "swe", "data_files": [{"split": "default", "path": "data/swe.jsonl"}]}, {"config_name": "tam", "data_files": [{"split": "default", "path": "data/tam.jsonl"}]}, {"config_name": "tel", "data_files": [{"split": "default", "path": "data/tel.jsonl"}]}, {"config_name": "tgl", "data_files": [{"split": "default", "path": "data/tgl.jsonl"}]}, {"config_name": "tha", "data_files": [{"split": "default", "path": "data/tha.jsonl"}]}, {"config_name": "tur", "data_files": [{"split": "default", "path": "data/tur.jsonl"}]}, {"config_name": "ukr", "data_files": [{"split": "default", "path": "data/ukr.jsonl"}]}, {"config_name": "urd", "data_files": [{"split": "default", "path": "data/urd.jsonl"}]}, {"config_name": "uzb", "data_files": [{"split": "default", "path": "data/uzb.jsonl"}]}, {"config_name": "vie", "data_files": [{"split": "default", "path": "data/vie.jsonl"}]}, {"config_name": "yid", "data_files": [{"split": "default", "path": "data/yid.jsonl"}]}, {"config_name": "zho", "data_files": [{"split": "default", "path": "data/zho.jsonl"}]}]}	false	null	2025-03-05T13:56:29	15	12	false	2f71ffe38af63626ba3590a7281e7aa363d85da8	WebFAQ Q&A Dataset Overview \| Details \| Structure \| Examples \| Considerations \| License \| Citation \| Contact \| Acknowledgement Overview The WebFAQ Q&A Dataset is a broad-coverage corpus of 96 million natural question-answer (QA) pairs in 75 languages, gathered from FAQ pages on the web. It leverages structured schema.org FAQPage annotations, making it a unique resource for large-scale Question Answering… See the full description on the dataset page: https://huggingface.co/datasets/PaDaS-Lab/webfaq.	1,226	1,723	[ "task_categories:question-answering", "multilinguality:multilingual", "language:afr", "language:amh", "language:ara", "language:arz", "language:aze", "language:bel", "language:ben", "language:bul", "language:cat", "language:ces", "language:cym", "language:dan", "language:deu", "language:ell", "language:eng", "language:est", "language:eus", "language:fas", "language:fin", "language:fra", "language:gle", "language:glg", "language:guj", "language:hbs", "language:heb", "language:hin", "language:hrv", "language:hun", "language:hye", "language:ind", "language:isl", "language:ita", "language:jpn", "language:kan", "language:kat", "language:kaz", "language:khm", "language:kir", "language:kor", "language:lav", "language:lit", "language:ltz", "language:mal", "language:mar", "language:mkd", "language:mlt", "language:mon", "language:msa", "language:mya", "language:nep", "language:nld", "language:nor", "language:pan", "language:pol", "language:por", "language:ron", "language:rus", "language:sin", "language:slk", "language:slv", "language:spa", "language:sqi", "language:srp", "language:swe", "language:tam", "language:tel", "language:tgl", "language:tha", "language:tur", "language:ukr", "language:urd", "language:uzb", "language:vie", "language:yid", "language:zho", "license:cc-by-4.0", "size_categories:10M<n<100M", "format:json", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2502.20936", "region:us", "question-answering" ]	2025-02-06T11:03:45	null	null
651fbfa3be34a5f2cf6871d1	a686d380/h-corpus-2023	a686d380	{"viewer": false, "language": ["zh"]}	false	null	2023-10-06T08:38:36	150	11	false	770d79e988706a68df8e2bc9dc37348e109ded59	经过清洗和去重过的H小说共205,028篇文章，解压后17.0 GB 仅用于科学研究！	711	2,247	[ "language:zh", "region:us" ]	2023-10-06T08:04:51	null	null
67c8094f26e7bf4ba0fe31da	Intelligent-Internet/II-Thought-RL-v0	Intelligent-Internet	{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "problem", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "verification_info", "dtype": "string"}, {"name": "data_source", "dtype": "string"}, {"name": "domain", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 6174050932.483211, "num_examples": 897432}], "download_size": 3192205818, "dataset_size": 6174050932.483211}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}	false	null	2025-03-10T09:27:15	11	11	false	f3ed9d73541704b2a9076e07c60410a4bdc00c04	II-Thought RL v0: A Large-Scale Curated Dataset for Reinforcement Learning We introduce II-Thought RL v0, the first large-scale, multi-task dataset designed for Reinforcement Learning. This dataset consists of high-quality question-answer pairs that have undergone a rigorous multi-step filtering process, leveraging Gemini 2.0 Flash and Qwen 32B as quality evaluators. In this initial release, we have curated and refined publicly available datasets while also introducing our own… See the full description on the dataset page: https://huggingface.co/datasets/Intelligent-Internet/II-Thought-RL-v0.	163	163	[ "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	2025-03-05T08:20:31	null	null
6784b5bfdacadead50b97553	microsoft/EpiCoder-func-380k	microsoft	{"annotations_creators": ["expert-generated"], "language_creators": ["expert-generated"], "language": ["en"], "multilinguality": ["monolingual"], "size_categories": ["100K<n<1M"], "source_datasets": ["original", "extended\|other-arxiv-paper", "extended\|other-code-generation", "extended\|other-instruction-data"], "pretty_name": "EpiCoder-func-380k", "paperswithcode_id": "Epicoder"}	false	null	2025-03-05T14:20:12	17	10	false	3bba6a6f9f0081744643023049e8643d5bfd236d	Dataset Card for EpiCoder-func-380k Dataset Description Dataset Summary The EpiCoder-func-380k is a dataset containing 380k function-level instances of instruction-output pairs. This dataset is designed to fine-tune large language models (LLMs) to improve their code generation capabilities. Each instance includes a detailed programming instruction and a corresponding Python output code that aligns with the instruction. This dataset is synthesized using methods… See the full description on the dataset page: https://huggingface.co/datasets/microsoft/EpiCoder-func-380k.	169	221	[ "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "source_datasets:original", "source_datasets:extended\|other-arxiv-paper", "source_datasets:extended\|other-code-generation", "source_datasets:extended\|other-instruction-data", "language:en", "size_categories:100K<n<1M", "format:json", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2501.04694", "region:us" ]	2025-01-13T06:42:07	null	Epicoder
67cd6c25b770987b3f80af97	a-m-team/AM-DeepSeek-R1-Distilled-1.4M	a-m-team	{"license": "cc-by-nc-4.0", "task_categories": ["text-generation"], "language": ["zh", "en"], "tags": ["code", "math", "reasoning", "thinking", "deepseek-r1", "distill"], "size_categories": ["1M<n<10M"]}	false	null	2025-03-10T18:31:04	10	10	false	b3447a25c09f5b67817c0ea01a1d4844fba68884	AM-DeepSeek-R1-Distilled-1.4M is a large-scale general reasoning task dataset composed of high-quality and challenging reasoning problems. These problems are collected from numerous open-source datasets, semantically deduplicated, and cleaned to eliminate test set contamination. All responses in the dataset are distilled from the reasoning model (mostly DeepSeek-R1) and have undergone rigorous verification: mathematical problems are validated through answer checking, code problems via… See the full description on the dataset page: https://huggingface.co/datasets/a-m-team/AM-DeepSeek-R1-Distilled-1.4M.	233	233	[ "task_categories:text-generation", "language:zh", "language:en", "license:cc-by-nc-4.0", "size_categories:1M<n<10M", "region:us", "code", "math", "reasoning", "thinking", "deepseek-r1", "distill" ]	2025-03-09T10:23:33	null	null
667ee649a7d8b1deba8d4f4c	proj-persona/PersonaHub	proj-persona	{"license": "cc-by-nc-sa-4.0", "task_categories": ["text-generation", "text-classification", "token-classification", "fill-mask", "table-question-answering", "text2text-generation"], "language": ["en", "zh"], "tags": ["synthetic", "text", "math", "reasoning", "instruction", "tool"], "size_categories": ["100M<n<1B"], "configs": [{"config_name": "math", "data_files": "math.jsonl"}, {"config_name": "instruction", "data_files": "instruction.jsonl"}, {"config_name": "reasoning", "data_files": "reasoning.jsonl"}, {"config_name": "knowledge", "data_files": "knowledge.jsonl"}, {"config_name": "npc", "data_files": "npc.jsonl"}, {"config_name": "tool", "data_files": "tool.jsonl"}, {"config_name": "persona", "data_files": "persona.jsonl"}, {"config_name": "elite_persona", "data_files": [{"split": "train", "path": "ElitePersonas/*"}]}]}	false	null	2025-03-04T22:01:42	542	9	false	600b0189027c804fc9373b4de4875c171656a4df	Scaling Synthetic Data Creation with 1,000,000,000 Personas This repo releases data introduced in our paper Scaling Synthetic Data Creation with 1,000,000,000 Personas: We propose a novel persona-driven data synthesis methodology that leverages various perspectives within a large language model (LLM) to create diverse synthetic data. To fully exploit this methodology at scale, we introduce PERSONA HUB – a collection of 1 billion diverse personas automatically curated from web data.… See the full description on the dataset page: https://huggingface.co/datasets/proj-persona/PersonaHub.	11,073	40,446	[ "task_categories:text-generation", "task_categories:text-classification", "task_categories:token-classification", "task_categories:fill-mask", "task_categories:table-question-answering", "task_categories:text2text-generation", "language:en", "language:zh", "license:cc-by-nc-sa-4.0", "size_categories:100K<n<1M", "format:json", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:2406.20094", "region:us", "synthetic", "text", "math", "reasoning", "instruction", "tool" ]	2024-06-28T16:35:21	null	null

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