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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
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	345	80	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.	8,251	8,251	[ "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
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	483	76	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.	6,558	6,558	[ "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
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	390	67	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.	26,448	30,365	[ "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
67c58d2e6c6e0371152cf00f	GeneralReasoning/GeneralThought-195K	GeneralReasoning	{"language": ["en"], "license": "mit"}	false	null	2025-03-03T11:20:23	52	52	false	5b28bacb11bea72af3c4a776d4d0db486ff10899	GeneralThought-195K 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-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-195K.	497	497	[ "language:en", "license:mit", "size_categories:100K<n<1M", "modality:tabular", "modality:text", "region:us" ]	2025-03-03T11:06:22	null	null
67c248d12a6f7c1f2a448ee4	KodCode/KodCode-V1	KodCode	{"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-"}]}], "language": ["en"], "license": "cc"}	false	null	2025-03-06T09:18:18	50	45	false	0350cf9d8d66005e4962d5fc2d224c438740f517	🐱 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.	1,812	1,812	[ "language:en", "license:cc", "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
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	133	41	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.	4,253	4,253	[ "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
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	475	34	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.	44,397	44,397	[ "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
67c312a816933ef3357d9588	dvilasuero/natural-science-reasoning	dvilasuero	{"license": "apache-2.0", "task_categories": ["text-generation"], "language": ["en"], "tags": ["synthetic", "science"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "topics", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "llama70B", "dtype": "string"}, {"name": "llama8B", "dtype": "string"}, {"name": "r1-response", "dtype": "string"}, {"name": "compare-llamas", "dtype": "string"}, {"name": "compare-r1", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1791963, "num_examples": 100}], "download_size": 794550, "dataset_size": 1791963}}	false	null	2025-03-02T11:12:11	33	33	false	2c6c0e648ff84dfa6b68035d36bbbf8e33aa0b89	Natural Sciences Reasoning: the "smolest" reasoning dataset A smol-scale open dataset for reasoning tasks using Hugging Face Inference Endpoints. While intentionally limited in scale, this resource prioritizes: Reproducible pipeline for reasoning tasks using a variety of models (Deepseek V3, Deepsek-R1, Llama70B-Instruct, etc.) Knowledge sharing for domains other than Math and Code reasoning In this repo, you can find: The prompts and the pipeline (see the config file). The… See the full description on the dataset page: https://huggingface.co/datasets/dvilasuero/natural-science-reasoning.	447	450	[ "task_categories:text-generation", "language:en", "license:apache-2.0", "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "synthetic", "science" ]	2025-03-01T13:59:04	null	null
67bfc6ed21e5f4fcc4af2b1d	voidful/fineweb-zhtw	voidful	{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "metadata", "struct": [{"name": "dump", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "date", "dtype": "timestamp[s]"}, {"name": "file_path", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "language_score", "dtype": "float64"}, {"name": "language_script", "dtype": "string"}, {"name": "minhash_cluster_size", "dtype": "int64"}, {"name": "top_langs", "dtype": "string"}, {"name": "avg_words_per_line", "dtype": "float64"}]}], "splits": [{"name": "train", "num_bytes": 160689800579, "num_examples": 48058113}], "download_size": 107457281288, "dataset_size": 160689800579}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "license": "odc-by", "language": ["zh"], "pretty_name": "z"}	false	null	2025-03-04T09:38:21	35	31	false	588aa4d7fe6bce8d3bf1fc6f2abff3652ecdbb90	Fineweb-zhtw Overview / 概覽 This repository contains the Fineweb-zhtw dataset, a large-scale collection of Traditional Chinese text data mined from the web. It is built upon the HuggingFaceFW/fineweb-2 dataset with modifications provided by mtkresearch/fineweb-zhtw. 本專案提供 Fineweb-zhtw 資料集，為大規模的繁體中文網路文本資料。此資料集基於 HuggingFaceFW/fineweb-2 並經由 mtkresearch/fineweb-zhtw 進行修改。 https://github.com/voidful/fineweb-zhtw/tree/main Dataset Details / 資料集細節 Data Size: 107… See the full description on the dataset page: https://huggingface.co/datasets/voidful/fineweb-zhtw.	1,668	1,668	[ "language:zh", "license:odc-by", "size_categories:10M<n<100M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:2411.16387", "region:us" ]	2025-02-27T01:59:09	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	124	30	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.	3,678	3,678	[ "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
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	261	24	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.	101,080	286,606	[ "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
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	646	24	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.	100,141	127,333	[ "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
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	51	24	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.	880	880	[ "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
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	2,022	22	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.	318,497	2,188,782	[ "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
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	624	21	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.	364,276	4,012,310	[ "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	gsm8k	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	74	20	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.	2,749	2,749	[ "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
67c179456a31b8fe773e591c	di-zhang-fdu/R1-Vision-Reasoning-Instructions	di-zhang-fdu	{"dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "gt", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "correct", "dtype": "bool"}, {"name": "image", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 637594547, "num_examples": 167128}], "download_size": 377536732, "dataset_size": 637594547}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}	false	null	2025-03-06T12:48:45	20	20	false	5462a20d854aede546c58a1934fc46112955c0a6	VRI-160K: A dataset for Vision Reasoning Instruction Tuning Images Images data can be access from https://huggingface.co/datasets/Xkev/LLaVA-CoT-100k Data Source Raw data can be access from https://huggingface.co/datasets/di-zhang-fdu/llava-cot-100k-r1-format for GRPO training Citations @misc {di_zhang_2025, author = { {Di Zhang} }, title = { R1-Vision-Reasoning-Instructions (Revision 49c1686) }, year = 2025, url… See the full description on the dataset page: https://huggingface.co/datasets/di-zhang-fdu/R1-Vision-Reasoning-Instructions.	578	578	[ "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:2411.18203", "doi:10.57967/hf/4686", "region:us" ]	2025-02-28T08:52:21	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	7,606	18	false	68ba7694e23014788dcc8ab5afe613824f45a05c	🧠 Awesome ChatGPT Prompts [CSV dataset] This is a Dataset Repository of Awesome ChatGPT Prompts View All Prompts on GitHub License CC-0	11,817	130,836	[ "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
67b20fc10861cec33b3afb8a	Conard/fortune-telling	Conard	{"license": "mit"}	false	null	2025-02-17T05:13:43	40	18	false	6261fe0d35a75997972bbfcd9828020e340303fb	null	2,533	2,533	[ "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
67b960eb8e9228b80838bfe6	TIGER-Lab/TheoremExplainBench	TIGER-Lab	{"license": "mit", "pretty_name": "THB", "dataset_info": {"features": [{"name": "subject", "dtype": "string"}, {"name": "difficulty", "dtype": "string"}, {"name": "theorem", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "subfield", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 71677, "num_examples": 240}], "download_size": 39223, "dataset_size": 71677}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}	false	null	2025-03-01T05:29:42	22	16	false	4d992a7ff28a338774fb489ac735632e68dc47c0	TheoremExplainBench TheoremExplainBench is a dataset designed to evaluate and improve the ability of large language models (LLMs) to understand and explain mathematical and scientific theorems across multiple domains, through long-form multimodal content (e.g. Manim Videos). It consists of 240 theorems, categorized by difficulty and subject area to enable structured benchmarking. Dataset Details Curated by: Max Ku, Thomas Chong Language(s) (NLP): English License:… See the full description on the dataset page: https://huggingface.co/datasets/TIGER-Lab/TheoremExplainBench.	686	686	[ "license:mit", "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2502.19400", "region:us" ]	2025-02-22T05:30:19	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	234	15	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.	9,804	28,641	[ "language:en", "arxiv:2311.12983", "region:us" ]	2023-10-20T07:06:54	null
6795e2882ec68b4193d4dbf2	EricLu/SCP-116K	EricLu	{"license": "cc-by-nc-sa-4.0", "task_categories": ["text-generation", "question-answering"], "language": ["en"], "size_categories": ["100K<n<1M"], "tags": ["chemistry", "biology", "medical"]}	false	null	2025-02-07T07:02:55	69	15	false	9099221d2085cdba381bba3761addb43303592ba	Dataset Card for SCP-116K Dataset Description Paper SCP-116K: A High-Quality Problem-Solution Dataset and a Generalized Pipeline for Automated Extraction in the Higher Education Science Domain Dataset Summary SCP-116K is a large-scale dataset containing 116,756 high-quality scientific problem-solution pairs, automatically extracted from web crawled documents. The dataset covers multiple scientific disciplines including physics, chemistry… See the full description on the dataset page: https://huggingface.co/datasets/EricLu/SCP-116K.	959	1,279	[ "task_categories:text-generation", "task_categories:question-answering", "language:en", "license:cc-by-nc-sa-4.0", "size_categories:100K<n<1M", "format:json", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2501.15587", "region:us", "chemistry", "biology", "medical" ]	2025-01-26T07:21:44	null	null
67bd467a478fa63bfc98f795	simplescaling/s1K-claude-3-7-sonnet	simplescaling	{"language": "en", "license": "mit", "tags": ["curator"]}	false	null	2025-02-27T15:02:04	19	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.	563	563	[ "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
67c9ec5572b8f1776ef7f0d4	madrylab/gsm8k-platinum	madrylab	{"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-*"}]}]}	false	null	2025-03-07T20:23:57	15	15	false	c2730837b1f4e49f4120f7a0248513190de9828a	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.	150	150	[ "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" ]	2025-03-06T18:41:25	null	null
67c4ca68e5db835a91419043	CohereForAI/AyaVisionBench	CohereForAI	{"dataset_info": [{"config_name": "arb_Arab", "features": [{"name": "image", "sequence": "image"}, {"name": "image_source", "dtype": "string"}, {"name": "image_source_category", "dtype": "string"}, {"name": "index", "dtype": "int64"}, {"name": "question", "dtype": "string"}, {"name": "language", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 58113436, "num_examples": 135}], "download_size": 58055195, "dataset_size": 58113436}, {"config_name": "ces_Latn", "features": [{"name": "image", "sequence": "image"}, {"name": "image_source", "dtype": "string"}, {"name": "image_source_category", "dtype": "string"}, {"name": "index", "dtype": "int64"}, {"name": "question", "dtype": "string"}, {"name": "language", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 58099974, "num_examples": 135}], "download_size": 58050976, "dataset_size": 58099974}, {"config_name": "deu_Latn", "features": [{"name": "image", 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"kor_Hang", "data_files": [{"split": "test", "path": "kor_Hang/test-"}]}, {"config_name": "nld_Latn", "data_files": [{"split": "test", "path": "nld_Latn/test-"}]}, {"config_name": "pes_Arab", "data_files": [{"split": "test", "path": "pes_Arab/test-"}]}, {"config_name": "pol_Latn", "data_files": [{"split": "test", "path": "pol_Latn/test-"}]}, {"config_name": "por_Latn", "data_files": [{"split": "test", "path": "por_Latn/test-"}]}, {"config_name": "ron_Latn", "data_files": [{"split": "test", "path": "ron_Latn/test-"}]}, {"config_name": "rus_Cyrl", "data_files": [{"split": "test", "path": "rus_Cyrl/test-"}]}, {"config_name": "spa_Latn", "data_files": [{"split": "test", "path": "spa_Latn/test-"}]}, {"config_name": "tur_Latn", "data_files": [{"split": "test", "path": "tur_Latn/test-"}]}, {"config_name": "ukr_Cyrl", "data_files": [{"split": "test", "path": "ukr_Cyrl/test-"}]}, {"config_name": "vie_Latn", "data_files": [{"split": "test", "path": "vie_Latn/test-"}]}, {"config_name": "zho_Hans", "data_files": [{"split": "test", "path": "zho_Hans/test-*"}]}], "license": "apache-2.0", "language": ["en", "fr", "de", "es", "it", "pt", "ja", "ko", "zh", "ar", "el", "fa", "pl", "id", "cs", "he", "hi", "nl", "ro", "ru", "tr", "uk", "vi"]}	false	null	2025-03-04T07:58:13	14	14	false	2dd498f62b203d95a1971610e2a62a28e6e49a55	Dataset Card for Aya Vision Benchmark Dataset Details The Aya Vision Benchmark is designed to evaluate vision-language models in real-world multilingual scenarios. It spans 23 languages and 9 distinct task categories, with 15 samples per category, resulting in 135 image-question pairs per language. Each question requires visual context for the answer and covers languages that half of the world's population speaks, making this dataset particularly suited for… See the full description on the dataset page: https://huggingface.co/datasets/CohereForAI/AyaVisionBench.	1,616	1,616	[ "language:en", "language:fr", "language:de", "language:es", "language:it", "language:pt", "language:ja", "language:ko", "language:zh", "language:ar", "language:el", "language:fa", "language:pl", "language:id", "language:cs", "language:he", "language:hi", "language:nl", "language:ro", "language:ru", "language:tr", "language:uk", "language:vi", "license:apache-2.0", "size_categories:1K<n<10K", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	2025-03-02T21:15:20	null	null
67c4d0fb63797e32fba52447	CohereForAI/m-WildVision	CohereForAI	{"dataset_info": [{"config_name": "arb_Arab", "features": [{"name": "image", "sequence": "image"}, {"name": "instruction", "dtype": "string"}, {"name": "question_id", "dtype": "string"}, {"name": "language", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 137486834, "num_examples": 500}], "download_size": 134791124, "dataset_size": 137486834}, {"config_name": "ces_Latn", "features": [{"name": "image", "sequence": "image"}, {"name": "instruction", "dtype": "string"}, {"name": "question_id", "dtype": "string"}, {"name": "language", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 137446520, "num_examples": 500}], "download_size": 134785755, "dataset_size": 137446520}, {"config_name": "deu_Latn", "features": [{"name": "image", "sequence": "image"}, {"name": "instruction", "dtype": "string"}, {"name": "question_id", "dtype": "string"}, {"name": "language", "dtype": "string"}], "splits": [{"name": 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{"config_name": "kor_Hang", "data_files": [{"split": "test", "path": "kor_Hang/test-"}]}, {"config_name": "nld_Latn", "data_files": [{"split": "test", "path": "nld_Latn/test-"}]}, {"config_name": "pes_Arab", "data_files": [{"split": "test", "path": "pes_Arab/test-"}]}, {"config_name": "pol_Latn", "data_files": [{"split": "test", "path": "pol_Latn/test-"}]}, {"config_name": "por_Latn", "data_files": [{"split": "test", "path": "por_Latn/test-"}]}, {"config_name": "ron_Latn", "data_files": [{"split": "test", "path": "ron_Latn/test-"}]}, {"config_name": "rus_Cyrl", "data_files": [{"split": "test", "path": "rus_Cyrl/test-"}]}, {"config_name": "spa_Latn", "data_files": [{"split": "test", "path": "spa_Latn/test-"}]}, {"config_name": "tur_Latn", "data_files": [{"split": "test", "path": "tur_Latn/test-"}]}, {"config_name": "ukr_Cyrl", "data_files": [{"split": "test", "path": "ukr_Cyrl/test-"}]}, {"config_name": "vie_Latn", "data_files": [{"split": "test", "path": "vie_Latn/test-"}]}, {"config_name": "zho_Hans", "data_files": [{"split": "test", "path": "zho_Hans/test-*"}]}], "language": ["en", "fr", "de", "es", "it", "pt", "ja", "ko", "zh", "ar", "el", "fa", "pl", "id", "cs", "he", "hi", "nl", "ro", "ru", "tr", "uk", "vi"], "license": "apache-2.0"}	false	null	2025-03-04T07:57:34	14	14	false	bf7d7b6149c4d5fa0aa3487bbc4c327f65d843be	Dataset Card for m-WildVision Dataset Details The m-WildVision dataset is a multilingual multimodal LLM evaluation set covering 23 languages. It was created by translating prompts from the original English-only WildVision (vision_bench_0617) test set. The original prompts, developed by Lu et al. (2024) , consist of 500 challenging user queries sourced from the WildVision-Arena platform. The authors demonstrated that these prompts enable automatic LLM judge… See the full description on the dataset page: https://huggingface.co/datasets/CohereForAI/m-WildVision.	844	844	[ "language:en", "language:fr", "language:de", "language:es", "language:it", "language:pt", "language:ja", "language:ko", "language:zh", "language:ar", "language:el", "language:fa", "language:pl", "language:id", "language:cs", "language:he", "language:hi", "language:nl", "language:ro", "language:ru", "language:tr", "language:uk", "language:vi", "license:apache-2.0", "size_categories:10K<n<100K", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2406.11069", "region:us" ]	2025-03-02T21:43:23	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	13	13	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.	158	176	[ "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
67a557ba9330ead027242110	simplescaling/s1K-1.1	simplescaling	{"language": "en", "license": "mit", "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "solution", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "cot_type", "dtype": "string"}, {"name": "source_type", "dtype": "string"}, {"name": "metadata", "dtype": "string"}, {"name": "gemini_thinking_trajectory", "dtype": "string"}, {"name": "gemini_attempt", "dtype": "string"}, {"name": "deepseek_thinking_trajectory", "dtype": "string"}, {"name": "deepseek_attempt", "dtype": "string"}, {"name": "gemini_grade", "dtype": "string"}, {"name": "gemini_grade_reason", "dtype": "string"}, {"name": "deepseek_grade", "dtype": "string"}, {"name": "deepseek_grade_reason", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 48313304, "num_examples": 1000}], "download_size": 22323185, "dataset_size": 48313304}, "tags": ["curator"]}	false	null	2025-02-27T18:09:26	77	13	false	96c411f1fe4c49d20f0e2a1565f61e1a28b0b84d	Dataset Card for s1K Dataset Summary s1K-1.1 consists of the same 1,000 questions as in s1K but with traces instead generated by DeepSeek r1. We find that these traces lead to much better performance. Usage # pip install -q datasets from datasets import load_dataset ds = load_dataset("simplescaling/s1K-1.1")["train"] ds[0] Dataset Structure Data Instances An example looks as follows: { 'solution': '1. **Rewrite the function using… See the full description on the dataset page: https://huggingface.co/datasets/simplescaling/s1K-1.1.	5,963	6,000	[ "language:en", "license:mit", "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2501.19393", "region:us", "curator" ]	2025-02-07T00:45:46	null	null
67b95b604a1673b790a5dde5	FreedomIntelligence/Medical-R1-Distill-Data-Chinese	FreedomIntelligence	{"license": "apache-2.0", "task_categories": ["question-answering", "text-generation"], "language": ["en", "zh"], "tags": ["medical", "biology"], "configs": [{"config_name": "zh", "data_files": "medical_r1_distill_sft_Chinese.json"}]}	false	null	2025-02-22T06:56:41	24	13	false	a9b43c2bbfec1487db5c2aeb9f49b82c4b562b19	Introduction This dataset is an SFT dataset distilled from Deepseek-R1 (Full Power Version), based on Chinese medical verifiable problems from HuatuoGPT-o1. The distillation originates from the native Deepseek-R1 API requests. We hope this distilled dataset can help initialize your models with the reasoning chain from R1. You can also use our previously built medical verified long reasoning chains based on GPT-4o on medical-o1-reasoning-SFT. For details, see our paper and GitHub… See the full description on the dataset page: https://huggingface.co/datasets/FreedomIntelligence/Medical-R1-Distill-Data-Chinese.	680	680	[ "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" ]	2025-02-22T05:06:40	null	null
67c2955fbe05f412aa264278	KodCode/KodCode-V1-SFT-R1	KodCode	{"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": "r1_pass_sequence", "sequence": "int64"}, {"name": "r1_pass_trial_num", "dtype": "int64"}, {"name": "r1_correctness", "dtype": "string"}, {"name": "r1_solution", "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": "conversations", "list": [{"name": "from", "dtype": "string"}, {"name": "value", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 5020832193, "num_examples": 245937}, {"name": "incorrect", "num_bytes": 4942343914, "num_examples": 192557}, {"name": "use_with_caution", "num_bytes": 94651247, "num_examples": 4439}], "download_size": 4213334184, "dataset_size": 10057827354}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-"}, {"split": "incorrect", "path": "data/incorrect-"}, {"split": "use_with_caution", "path": "data/use_with_caution-*"}]}], "license": "cc", "language": ["en"], "size_categories": ["100K<n<1M"]}	false	null	2025-03-06T09:14:38	15	13	false	dec2d25d58d4ece3187c659e23ca4aab90c9ad97	🐱 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-SFT-R1.	1,995	1,995	[ "language:en", "license:cc", "size_categories:100K<n<1M", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:2503.02951", "region:us" ]	2025-03-01T05:04:31	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": 10792019, "num_examples": 746}], "download_size": 1059070, "dataset_size": 10792019}, "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	2025-03-06T12:54:47	13	13	false	5b6fd66927a483f6e36cba46dab365654c1001f3	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.	102	102	[ "task_categories:translation", "size_categories:1K<n<10K", "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
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	28	12	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.	1,239	1,256	[ "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
67b940cfd6bb21aa160b5520	FreedomIntelligence/Medical-R1-Distill-Data	FreedomIntelligence	{"license": "apache-2.0", "task_categories": ["question-answering", "text-generation"], "language": ["en", "zh"], "tags": ["medical", "biology"], "configs": [{"config_name": "en", "data_files": "medical_r1_distill_sft.json"}]}	false	null	2025-02-22T06:55:02	22	12	false	3491deecebe1973a2d7370b824f4b41be29dcf1a	Introduction This dataset is an SFT dataset distilled from Deepseek-R1 (Full Power Version), based on medical verifiable problems from HuatuoGPT-o1. The Chinese version of the dataset is available at FreedomIntelligence/Medical-R1-Distill-Data-Chinese. The distillation originates from the native Deepseek-R1 API requests. We hope this distilled dataset can help initialize your models with the reasoning chain from R1. You can also use our previously built medical verified long… See the full description on the dataset page: https://huggingface.co/datasets/FreedomIntelligence/Medical-R1-Distill-Data.	590	590	[ "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" ]	2025-02-22T03:13:19	null	null
67c81e2b95af22b165bd5ae0	HuggingFaceTB/dclm-edu	HuggingFaceTB	{"license": "cc-by-4.0", "language": ["en"]}	false	null	2025-03-07T16:24:22	12	12	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.	540	541	[ "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
6655eb19d17e141dcb546ed5	HuggingFaceFW/fineweb-edu	HuggingFaceFW	{"license": "odc-by", "task_categories": ["text-generation"], "language": ["en"], "pretty_name": "FineWeb-Edu", "size_categories": ["n>1T"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data//"}], "features": [{"name": "text", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "dump", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "date", "dtype": "string"}, {"name": "file_path", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "language_score", "dtype": "float64"}, {"name": "token_count", "dtype": "int64"}, {"name": "score", "dtype": "float64"}, {"name": "int_score", "dtype": "int64"}]}, {"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": 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"path": "data/CC-MAIN-2014-52/"}]}, {"config_name": "CC-MAIN-2014-49", "data_files": [{"split": "train", "path": "data/CC-MAIN-2014-49/"}]}, {"config_name": "CC-MAIN-2014-42", "data_files": [{"split": "train", "path": "data/CC-MAIN-2014-42/"}]}, {"config_name": "CC-MAIN-2014-41", "data_files": [{"split": "train", "path": "data/CC-MAIN-2014-41/"}]}, {"config_name": "CC-MAIN-2014-35", "data_files": [{"split": "train", "path": "data/CC-MAIN-2014-35/"}]}, {"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	2025-01-31T15:56:54	648	11	false	4863ab07d7520451e6f73e2912ad8bfee7d97c11	📚 FineWeb-Edu 1.3 trillion tokens of the finest educational data the 🌐 web has to offer Paper: https://arxiv.org/abs/2406.17557 What is it? 📚 FineWeb-Edu dataset consists of 1.3T tokens and 5.4T tokens (FineWeb-Edu-score-2) of educational web pages filtered from 🍷 FineWeb dataset. This is the 1.3 trillion version. To enhance FineWeb's quality, we developed an educational quality classifier using annotations generated by LLama3-70B-Instruct. We then… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu.	492,869	2,999,861	[ "task_categories:text-generation", "language:en", "license:odc-by", "size_categories:1B<n<10B", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:2406.17557", "arxiv:2404.14219", "arxiv:2401.10020", "arxiv:2109.07445", "doi:10.57967/hf/2497", "region:us" ]	2024-05-28T14:32:57	null	null
67374c18c32c765810f748f6	HuggingFaceH4/MATH-500	HuggingFaceH4	{"task_categories": ["text-generation"], "language": ["en"], "pretty_name": "MATH-500"}	false	null	2024-11-15T13:36:00	119	11	false	ff5b20257d8185524591543f8ff5993951537bb8	Dataset Card for MATH-500 This dataset contains a subset of 500 problems from the MATH benchmark that OpenAI created in their Let's Verify Step by Step paper. See their GitHub repo for the source file: https://github.com/openai/prm800k/tree/main?tab=readme-ov-file#math-splits	45,442	70,108	[ "task_categories:text-generation", "language:en", "size_categories:n<1K", "format:json", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	2024-11-15T13:26:48	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	31	11	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.	668	705	[ "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
67c67c88c7cd2413e2c8be4d	qihoo360/Light-R1-SFTData	qihoo360	{"license": "apache-2.0"}	false	null	2025-03-05T13:29:23	11	11	false	2c5e4670b5bb44d202bfb66770c9e63fe37d30aa	Light-R1: Surpassing R1-Distill from Scratch* with $1000 through Curriculum SFT & DPO *from models without long COT GitHub page Here are the two-stage SFT data we used to train Light-R1-32B. Simply refer to stage1-76k.json and stage2-3k.json Model Trained From Release Date AIME24 AIME25 DeepSeek-R1-Distill-Llama-70B Llama-3.3-70B-Instruct 25.1.20 70.0 54.1 DeepSeek-R1-Distill-Qwen-32B Qwen2.5-32B 25.1.20 72.6 54.9 LIMO (32B) Qwen2.5-32B-Instruct 25.2.4 56.3 47.1… See the full description on the dataset page: https://huggingface.co/datasets/qihoo360/Light-R1-SFTData.	182	204	[ "license:apache-2.0", "region:us" ]	2025-03-04T04:07:36	null	null
641debae1d05404efd046a4f	yahma/alpaca-cleaned	yahma	{"license": "cc-by-4.0", "language": ["en"], "tags": ["instruction-finetuning"], "pretty_name": "Alpaca-Cleaned", "task_categories": ["text-generation"]}	false	null	2023-04-10T20:29:06	653	10	false	12567cabf869d7c92e573c7c783905fc160e9639	Dataset Card for Alpaca-Cleaned Repository: https://github.com/gururise/AlpacaDataCleaned Dataset Description This is a cleaned version of the original Alpaca Dataset released by Stanford. The following issues have been identified in the original release and fixed in this dataset: Hallucinations: Many instructions in the original dataset had instructions referencing data on the internet, which just caused GPT3 to hallucinate an answer. "instruction":"Summarize the… See the full description on the dataset page: https://huggingface.co/datasets/yahma/alpaca-cleaned.	23,244	600,713	[ "task_categories:text-generation", "language:en", "license:cc-by-4.0", "size_categories:10K<n<100K", "format:json", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "instruction-finetuning" ]	2023-03-24T18:27:58	null	null
64382440c212a363c3ac15c8	OpenAssistant/oasst1	OpenAssistant	{"license": "apache-2.0", "dataset_info": {"features": [{"name": "message_id", "dtype": "string"}, {"name": "parent_id", "dtype": "string"}, {"name": "user_id", "dtype": "string"}, {"name": "created_date", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "role", "dtype": "string"}, {"name": "lang", "dtype": "string"}, {"name": "review_count", "dtype": "int32"}, {"name": "review_result", "dtype": "bool"}, {"name": "deleted", "dtype": "bool"}, {"name": "rank", "dtype": "int32"}, {"name": "synthetic", "dtype": "bool"}, {"name": "model_name", "dtype": "string"}, {"name": "detoxify", "struct": [{"name": "toxicity", "dtype": "float64"}, {"name": "severe_toxicity", "dtype": "float64"}, {"name": "obscene", "dtype": "float64"}, {"name": "identity_attack", "dtype": "float64"}, {"name": "insult", "dtype": "float64"}, {"name": "threat", "dtype": "float64"}, {"name": "sexual_explicit", "dtype": "float64"}]}, {"name": "message_tree_id", "dtype": "string"}, {"name": "tree_state", "dtype": "string"}, {"name": "emojis", "sequence": [{"name": "name", "dtype": "string"}, {"name": "count", "dtype": "int32"}]}, {"name": "labels", "sequence": [{"name": "name", "dtype": "string"}, {"name": "value", "dtype": "float64"}, {"name": "count", "dtype": "int32"}]}], "splits": [{"name": "train", "num_bytes": 100367999, "num_examples": 84437}, {"name": "validation", "num_bytes": 5243405, "num_examples": 4401}], "download_size": 41596430, "dataset_size": 105611404}, "language": ["en", "es", "ru", "de", "pl", "th", "vi", "sv", "bn", "da", "he", "it", "fa", "sk", "id", "nb", "el", "nl", "hu", "eu", "zh", "eo", "ja", "ca", "cs", "bg", "fi", "pt", "tr", "ro", "ar", "uk", "gl", "fr", "ko"], "tags": ["human-feedback"], "size_categories": ["100K<n<1M"], "pretty_name": "OpenAssistant Conversations"}	false	null	2023-05-02T13:21:21	1,357	10	false	fdf72ae0827c1cda404aff25b6603abec9e3399b	OpenAssistant Conversations Dataset (OASST1) Dataset Summary In an effort to democratize research on large-scale alignment, we release OpenAssistant Conversations (OASST1), a human-generated, human-annotated assistant-style conversation corpus consisting of 161,443 messages in 35 different languages, annotated with 461,292 quality ratings, resulting in over 10,000 fully annotated conversation trees. The corpus is a product of a worldwide crowd-sourcing effort… See the full description on the dataset page: https://huggingface.co/datasets/OpenAssistant/oasst1.	10,220	245,685	[ "language:en", "language:es", "language:ru", "language:de", "language:pl", "language:th", "language:vi", "language:sv", "language:bn", "language:da", "language:he", "language:it", "language:fa", "language:sk", "language:id", "language:nb", "language:el", "language:nl", "language:hu", "language:eu", "language:zh", "language:eo", "language:ja", "language:ca", "language:cs", "language:bg", "language:fi", "language:pt", "language:tr", "language:ro", "language:ar", "language:uk", "language:gl", "language:fr", "language:ko", "license:apache-2.0", "size_categories:10K<n<100K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2304.07327", "region:us", "human-feedback" ]	2023-04-13T15:48:16	null	null
673463fabe618c1a378d99c6	qgyd2021/chinese_porn_novel	qgyd2021	{"language": ["zh"], "size_categories": ["100M<n<1B"], "task_categories": ["text-generation"], "tags": ["art"], "dataset_info": {"config_name": "xbookcn_short_story", "features": [{"name": "source", "dtype": "string"}, {"name": "category", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "content", "dtype": "string"}, {"name": "content_length", "dtype": "uint32"}, {"name": "url", "dtype": "string"}, {"name": "summary1", "dtype": "string"}, {"name": "summary2", "dtype": "string"}, {"name": "summary3", "dtype": "string"}, {"name": "summary4", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1167355353, "num_examples": 627195}], "download_size": 721183317, "dataset_size": 1167355353}, "configs": [{"config_name": "xbookcn_short_story", "data_files": [{"split": "train", "path": "xbookcn_short_story/train-*"}], "default": true}]}	false	null	2024-11-13T11:06:27	59	10	false	170c125e168cf58400ad3b31300c88ed8a1c978a	Chinese Porn Novel https://huggingface.co/docs/hub/en/datasets-adding datasets-cli convert_to_parquet qgyd2021/chinese_porn_novel --trust_remote_code SQ小说, 用于制作特殊的 GPT 语言模型. 将每篇小说切分 chunk, 用 Qwen-instruct 对 chunk 进行4个摘要, 4个摘要的 prompt {content} 对于此文本，根据文本的长度输出3到7个具有代表性的简短句子来描述其内容。每个句子控制在10字左右，不要有序号等，每行一句。 {content} 对于此文本，根据文本的长度输出2到4个具有代表性的简短句子来描述其内容。每个句子控制在15字左右，不要有序号等，每行一句。 {content} 对于此文本，根据文本的长度输出2到4个具有代表性的简短句子来概括其内容。每个句子控制在10字左右，不要有序号等，每行一句。… See the full description on the dataset page: https://huggingface.co/datasets/qgyd2021/chinese_porn_novel.	1,373	1,994	[ "task_categories:text-generation", "language:zh", "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us", "art" ]	2024-11-13T08:31:54	null	null

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