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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 7.63k	trendingScore float64 -1 160	private bool 1 class	sha stringlengths 40 40	description stringlengths 0 6.67k ⌀	downloads int64 0 4.8M	downloadsAllTime int64 0 142M	tags sequencelengths 1 7.92k	createdAt timestamp[ns]	paperswithcode_id stringclasses 652 values	citation stringlengths 0 10.7k ⌀
67d3479522a51de18affff22	nvidia/Llama-Nemotron-Post-Training-Dataset-v1	nvidia	{"license": "cc-by-4.0", "configs": [{"config_name": "SFT", "data_files": [{"split": "code", "path": "SFT/code/.jsonl"}, {"split": "math", "path": "SFT/math/.jsonl"}, {"split": "science", "path": "SFT/science/.jsonl"}, {"split": "chat", "path": "SFT/chat/.jsonl"}, {"split": "safety", "path": "SFT/safety/.jsonl"}], "default": true}, {"config_name": "RL", "data_files": [{"split": "instruction_following", "path": "RL/instruction_following/.jsonl"}]}]}	false	null	2025-03-18T15:56:14	160	160	false	ed905e6239c9d191e4c965a403dde07a5383b5eb	Llama-Nemotron-Post-Training-Dataset-v1 Release Data Overview This dataset is a compilation of SFT and RL data that supports improvements of math, code, general reasoning, and instruction following capabilities of the original Llama instruct model, in support of NVIDIA’s release of Llama-3.3-Nemotron-Super-49B-v1 and Llama-3.1-Nemotron-Nano-8B-v1. Llama-3.3-Nemotron-Super-49B-v1 is a large language model (LLM) which is a derivative of Meta’s Llama-3.3-70B-Instruct (AKA… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Llama-Nemotron-Post-Training-Dataset-v1.	2,656	2,665	[ "license:cc-by-4.0", "size_categories:10M<n<100M", "format:json", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	2025-03-13T21:01:09	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	517	63	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.	29,123	42,759	[ "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
67d97c4be2b27852325fd8e2	nvidia/PhysicalAI-Robotics-GR00T-X-Embodiment-Sim	nvidia	{"license": "cc-by-4.0"}	false	null	2025-03-21T15:02:34	60	60	false	9cd48351839af877ff365fa8bf06e1cf9e57d539	PhysicalAI-Robotics-GR00T-X-Embodiment-Sim Github Repo: Isaac GR00T N1 We provide a set of datasets used for post-training of GR00T N1. Each dataset is a collection of trajectories from different robot embodiments and tasks. Cross-embodied bimanual manipulation: 9k trajectories Dataset Name #trajectories bimanual_panda_gripper.Threading 1000 bimanual_panda_hand.LiftTray 1000 bimanual_panda_gripper.ThreePieceAssembly 1000… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/PhysicalAI-Robotics-GR00T-X-Embodiment-Sim.	4,472	4,472	[ "license:cc-by-4.0", "region:us" ]	2025-03-18T13:59:39	null	null
67c0cda5c0b7a236a5f070e3	glaiveai/reasoning-v1-20m	glaiveai	{"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "response", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 177249016911, "num_examples": 22199375}], "download_size": 87247205094, "dataset_size": 177249016911}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "license": "apache-2.0", "task_categories": ["text-generation"], "language": ["en"], "size_categories": ["10M<n<100M"]}	false	null	2025-03-19T13:21:37	57	55	false	da6bb3d0ff8fd8ea5abacee8519762ca6aaf367e	We are excited to release a synthetic reasoning dataset containing 22mil+ general reasoning questions and responses generated using deepseek-ai/DeepSeek-R1-Distill-Llama-70B. While there have been multiple efforts to build open reasoning datasets for math and code tasks, we noticed a lack of large datasets containing reasoning traces for diverse non code/math topics like social and natural sciences, education, creative writing and general conversations, which is why we decided to release this… See the full description on the dataset page: https://huggingface.co/datasets/glaiveai/reasoning-v1-20m.	2,077	2,077	[ "task_categories:text-generation", "language:en", "license:apache-2.0", "size_categories:10M<n<100M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	2025-02-27T20:40:05	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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{"name": "passed_test_count", "dtype": "null"}, {"name": "programmingLanguage", "dtype": "string"}, {"name": "programming_language", "dtype": "string"}, {"name": "submission_id", "dtype": "string"}, {"name": "verdict", "dtype": "string"}]}, {"name": "failed_solutions", "list": [{"name": "code", "dtype": "string"}, {"name": "passedTestCount", "dtype": "int64"}, {"name": "programmingLanguage", "dtype": "string"}, {"name": "verdict", "dtype": "string"}]}, {"name": "generated_tests", "struct": [{"name": "input", "sequence": "string"}, {"name": "output", "sequence": "string"}]}, {"name": "private_tests", "struct": [{"name": "input", "sequence": "string"}, {"name": "output", "sequence": "string"}]}, {"name": "problem_type", "dtype": "string"}, {"name": "public_tests", "struct": [{"name": "input", "sequence": "string"}, {"name": "output", "sequence": "string"}]}, {"name": "public_tests_ms", "list": [{"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 1499075280, "num_examples": 9796}], "download_size": 466078291, "dataset_size": 1499075280}, {"config_name": "test_input_generator", "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": "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_decontaminated", "data_files": [{"split": "train", "path": "solutions_decontaminated/train-"}]}, {"config_name": "solutions_py", "data_files": [{"split": "train", "path": "solutions_py/train-"}]}, {"config_name": "solutions_py_decontaminated", "data_files": [{"split": "train", "path": "solutions_py_decontaminated/train-"}]}, {"config_name": "solutions_w_editorials", "data_files": [{"split": "train", "path": "solutions_w_editorials/train-"}]}, {"config_name": "solutions_w_editorials_decontaminated", "data_files": [{"split": "train", "path": "solutions_w_editorials_decontaminated/train-"}]}, {"config_name": "solutions_w_editorials_py", "data_files": [{"split": "train", "path": "solutions_w_editorials_py/train-"}]}, {"config_name": "solutions_w_editorials_py_decontaminated", "data_files": [{"split": "train", "path": "solutions_w_editorials_py_decontaminated/train-"}]}, {"config_name": "test_input_generator", "data_files": [{"split": "train", "path": "test_input_generator/train-"}]}], "license": "cc-by-4.0"}	false	null	2025-03-17T11:29:08	90	51	false	5f9671cf3779c3c709bd9f6f61b38ef3f061d5c8	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 five reasoning traces generated by DeepSeek R1. We did not filter the traces for correctness, but found that around 84% of the Python ones pass the public tests. The dataset consists of several subsets: solutions: we prompt R1 to solve the problem and produce code.… See the full description on the dataset page: https://huggingface.co/datasets/open-r1/codeforces-cots.	5,014	5,014	[ "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
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	572	44	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.	8,780	9,428	[ "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
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	440	37	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.	13,663	13,663	[ "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
67d7eeec9830e5c1e2a8f708	BytedTsinghua-SIA/DAPO-Math-17k	BytedTsinghua-SIA	{"license": "apache-2.0", "task_categories": ["text-generation"], "language": ["en"], "tags": ["math"], "pretty_name": "DAPO-Math-17k", "size_categories": ["1M<n<10M"]}	false	null	2025-03-18T07:47:04	31	31	false	9f6440001c15da8e7c7516fdbb3d2ce49de711de	This dataset actually only contains ~17k unique prompts and was duplicated by ~100x by accident.	1,269	1,269	[ "task_categories:text-generation", "language:en", "license:apache-2.0", "size_categories:1M<n<10M", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "math" ]	2025-03-17T09:44:12	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	520	27	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.	54,489	65,803	[ "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
67b20fc10861cec33b3afb8a	Conard/fortune-telling	Conard	{"license": "mit"}	false	null	2025-02-17T05:13:43	81	27	false	6261fe0d35a75997972bbfcd9828020e340303fb	null	4,988	5,001	[ "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
67c122a87c100c8caa21c89d	TIGER-Lab/VisualWebInstruct	TIGER-Lab	{"language": ["en"], "license": "apache-2.0", "size_categories": ["100K<n<1M"], "task_categories": ["question-answering", "visual-question-answering"], "pretty_name": "VisualWebInstruct", "tags": ["math", "science"], "configs": [{"config_name": "example", "data_files": [{"split": "train", "path": "data/train-*"}]}, {"config_name": "conversation", "data_files": [{"split": "train", "path": "mixed_conversation.parquet"}]}, {"config_name": "visualwebinstruct", "data_files": [{"split": "train", "path": "visualwebinstruct_qa.parquet"}]}]}	false	null	2025-03-21T07:54:25	25	22	false	e060d8237917237f19e1ea592efb4de90aaed171	VisualWebInstruct: Scaling up Multimodal Instruction Data through Web Search VisualWebInstruct is a large-scale, diverse multimodal instruction dataset designed to enhance vision-language models' reasoning capabilities. The dataset contains approximately 900K question-answer (QA) pairs, with 40% consisting of visual QA pairs associated with 163,743 unique images, while the remaining 60% are text-only QA pairs. Links GitHub Repository Research Paper Project Website… See the full description on the dataset page: https://huggingface.co/datasets/TIGER-Lab/VisualWebInstruct.	752	772	[ "task_categories:question-answering", "task_categories:visual-question-answering", "language:en", "license:apache-2.0", "size_categories:1M<n<10M", "format:parquet", "modality:image", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2503.10582", "region:us", "math", "science" ]	2025-02-28T02:42:48	null	null
67c5a5ba52976b223005f88b	DropletX/DropletVideo-10M	DropletX	{"license": "cc-by-nc-sa-4.0", "task_categories": ["image-to-video", "text-to-video"], "language": ["en"], "size_categories": ["10M<n<100M"], "extra_gated_prompt": "You agree to not use the data to conduct experiments that cause harm to human subjects.", "extra_gated_fields": {"Name": "text", "Company/Organization": "text", "E-Mail": "text", "Job title": "text"}}	false	null	2025-03-19T16:15:37	24	21	false	3f5bc6339a46d3b9b2a4469c081b2d37f881d6ee	🔍 Dataset Note: DropletVideo-1M is the premium subset of DropletVideo-10M, filtered with aesthetic score > 4.51 and image quality score > 7.51. ✈️ Introduction The challenge of spatiotemporal consistency has long existed in the field of video generation. We have released the open-source dataset DropletVideo-10M —the world's largest video generation dataset with spatiotemporal consistency. It… See the full description on the dataset page: https://huggingface.co/datasets/DropletX/DropletVideo-10M.	257	257	[ "task_categories:image-to-video", "task_categories:text-to-video", "language:en", "license:cc-by-nc-sa-4.0", "size_categories:10M<n<100M", "format:csv", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:2503.06053", "region:us" ]	2025-03-03T12:51:06	null	null
67c8270b5999e7df91a854da	yaak-ai/L2D	yaak-ai	{"license": "apache-2.0", "task_categories": ["robotics"], "tags": ["LeRobot"], "configs": [{"config_name": "default", "data_files": "data//.parquet"}]}	false	null	2025-03-10T18:34:05	25	20	false	49115405b552802c9838d4a4c85a4ed947f901b3	This dataset was created using LeRobot. Dataset Structure meta/info.json: { "codebase_version": "v2.1", "robot_type": "KIA Niro EV 2023", "total_episodes": 100, "total_frames": 28519, "total_tasks": 1, "total_videos": 700, "total_chunks": 1, "chunks_size": 1000, "fps": 10, "splits": { "train": "0:100" }, "data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet", "video_path":… See the full description on the dataset page: https://huggingface.co/datasets/yaak-ai/L2D.	1,247	1,247	[ "task_categories:robotics", "license:apache-2.0", "size_categories:10K<n<100K", "format:parquet", "modality:tabular", "modality:text", "modality:timeseries", "modality:video", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us", "LeRobot" ]	2025-03-05T10:27:23	null	null
67d967709b5f9bcc5eef92e1	HuggingFaceTB/stack-edu	HuggingFaceTB	{"dataset_info": [{"config_name": "C", "features": [{"name": "blob_id", "dtype": "large_string"}, {"name": "language", "dtype": "large_string"}, {"name": "repo_name", "dtype": "large_string"}, {"name": "path", "dtype": "large_string"}, {"name": "src_encoding", "dtype": "large_string"}, {"name": "length_bytes", "dtype": "int64"}, {"name": "score", "dtype": "float64"}, {"name": "int_score", "dtype": "int64"}, {"name": "detected_licenses", "large_list": "large_string"}, {"name": "license_type", "dtype": "large_string"}], "splits": [{"name": "train", "num_bytes": 1100442974, "num_examples": 5848375}], "download_size": 571816053, "dataset_size": 1100442974}, {"config_name": "CSharp", "features": [{"name": "blob_id", "dtype": "large_string"}, {"name": "language", "dtype": "large_string"}, {"name": "repo_name", "dtype": "large_string"}, {"name": "path", "dtype": "large_string"}, {"name": "src_encoding", "dtype": "large_string"}, {"name": "length_bytes", "dtype": "int64"}, {"name": "score", "dtype": "float64"}, {"name": "int_score", "dtype": "int64"}, {"name": "detected_licenses", "large_list": "large_string"}, {"name": "license_type", "dtype": "large_string"}], "splits": [{"name": "train", "num_bytes": 2392066248, "num_examples": 11425016}], "download_size": 1232015539, "dataset_size": 2392066248}, {"config_name": "Cpp", "features": [{"name": "blob_id", "dtype": "large_string"}, {"name": "language", "dtype": "large_string"}, {"name": "repo_name", "dtype": "large_string"}, {"name": "path", "dtype": "large_string"}, {"name": "src_encoding", "dtype": "large_string"}, {"name": "length_bytes", "dtype": "int64"}, {"name": "score", "dtype": "float64"}, {"name": "int_score", "dtype": "int64"}, {"name": "detected_licenses", "large_list": "large_string"}, {"name": "license_type", "dtype": "large_string"}], "splits": [{"name": "train", "num_bytes": 3167426435, "num_examples": 16246746}], "download_size": 1632803797, 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"data_files": [{"split": "train", "path": "C/train-"}]}, {"config_name": "CSharp", "data_files": [{"split": "train", "path": "CSharp/train-"}]}, {"config_name": "Cpp", "data_files": [{"split": "train", "path": "Cpp/train-"}]}, {"config_name": "Go", "data_files": [{"split": "train", "path": "Go/train-"}]}, {"config_name": "Java", "data_files": [{"split": "train", "path": "Java/train-"}]}, {"config_name": "JavaScript", "data_files": [{"split": "train", "path": "JavaScript/train-"}]}, {"config_name": "Markdown", "data_files": [{"split": "train", "path": "Markdown/train-"}]}, {"config_name": "PHP", "data_files": [{"split": "train", "path": "PHP/train-"}]}, {"config_name": "Python", "data_files": [{"split": "train", "path": "Python/train-"}]}, {"config_name": "Ruby", "data_files": [{"split": "train", "path": "Ruby/train-"}]}, {"config_name": "Rust", "data_files": [{"split": "train", "path": "Rust/train-"}]}, {"config_name": "SQL", "data_files": [{"split": "train", "path": "SQL/train-"}]}, {"config_name": "Shell", "data_files": [{"split": "train", "path": "Shell/train-"}]}, {"config_name": "Swift", "data_files": [{"split": "train", "path": "Swift/train-"}]}, {"config_name": "TypeScript", "data_files": [{"split": "train", "path": "TypeScript/train-*"}]}]}	false	null	2025-03-20T13:51:54	20	20	false	eeec5caac5cc3758a18f1d3ba4416837a9ba814c	💻 Stack-Edu Stack-Edu is a 125B token dataset of educational code filtered from The Stack v2, precisely the curated training corpus of StarCoder2 models denoted StarCoder2Data. It is intended for Language Models training. This dataset was curated using a classifier-based filtering strategy, inspired by 📚 FineWeb-Edu, to retain only the highest-quality educational programming content. Stack-Edu shows consistent improvement over StarCoder2data on all the programming languages on… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceTB/stack-edu.	245	245	[ "size_categories:100M<n<1B", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:2402.19173", "arxiv:2502.02737", "region:us" ]	2025-03-18T12:30:40	null	null
67c5ffdbf2e146eac1f0edb9	DropletX/DropletVideo-1M	DropletX	{"license": "cc-by-nc-sa-4.0", "task_categories": ["image-to-video", "text-to-video"], "language": ["en"], "size_categories": ["10M<n<100M"], "extra_gated_prompt": "You agree to not use the data to conduct experiments that cause harm to human subjects.", "extra_gated_fields": {"Name": "text", "Company/Organization": "text", "E-Mail": "text", "Job title": "text"}}	false	null	2025-03-19T16:16:33	19	19	false	c7f9e1e130fe2858a1ecb46d979d474114ca1baa	🔍 Dataset Note: DropletVideo-1M is the premium subset of DropletVideo-10M, filtered with aesthetic score > 4.51 and image quality score > 7.51. ✈️ Introduction The challenge of spatiotemporal consistency has long existed in the field of video generation. We have released the open-source dataset DropletVideo-10M —the world's largest video generation dataset with spatiotemporal consistency. It… See the full description on the dataset page: https://huggingface.co/datasets/DropletX/DropletVideo-1M.	153	153	[ "task_categories:image-to-video", "task_categories:text-to-video", "language:en", "license:cc-by-nc-sa-4.0", "size_categories:n<1K", "format:imagefolder", "modality:image", "library:datasets", "library:mlcroissant", "arxiv:2503.06053", "region:us" ]	2025-03-03T19:15:39	null	null
67d305619f485955bf117049	nvidia/HelpSteer3	nvidia	{"license": "cc-by-4.0", "language": ["en", "zh", "ko", "fr", "es", "ru", "ja", "de", "it", "pt", "pl", "id", "nl", "vi"], "pretty_name": "HelpSteer3", "size_categories": ["10K<n<100K"], "tags": ["human-feedback"], "configs": [{"config_name": "preference", "default": true, "data_files": [{"split": "train", "path": "preference/train.jsonl.gz"}, {"split": "validation", "path": "preference/validation.jsonl.gz"}]}, {"config_name": "feedback", "data_files": [{"split": "train", "path": "feedback/train.jsonl.gz"}, {"split": "validation", "path": "feedback/validation.jsonl.gz"}]}, {"config_name": "edit", "data_files": [{"split": "train", "path": "edit/train.jsonl.gz"}, {"split": "validation", "path": "edit/validation.jsonl.gz"}]}, {"config_name": "edit_quality", "data_files": [{"split": "train", "path": "edit_quality/train.jsonl.gz"}, {"split": "validation", "path": "edit_quality/validation.jsonl.gz"}]}]}	false	null	2025-03-18T19:51:32	19	19	false	7366103dbb732074dcf866560d2431d0ae8c9b1d	HelpSteer3 HelpSteer3 is an open-source Helpfulness Dataset (CC-BY-4.0) that supports aligning models to become more helpful in responding to user prompts. When used to tune Llama 3.3 70B Instruct Models to perform a novel approach to Inference Time Scaling (ITS) for open-ended, general-domain tasks, we achieve as high as 93.4% on Arena Hard, which makes it No. 1 on the benchmark as of 18 Mar 2025. See details on the paper at https://arxiv.org/abs/2503.04378. Models were trained… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/HelpSteer3.	299	299	[ "language:en", "language:zh", "language:ko", "language:fr", "language:es", "language:ru", "language:ja", "language:de", "language:it", "language:pt", "language:pl", "language:id", "language:nl", "language:vi", "license:cc-by-4.0", "size_categories:10K<n<100K", "format:json", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2503.04378", "region:us", "human-feedback" ]	2025-03-13T16:18:41	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	2,048	17	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.	289,851	2,277,984	[ "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
67d6cac12e36db434b2be97e	manycore-research/SpatialLM-Testset	manycore-research	{"license": "cc-by-nc-4.0", "configs": [{"config_name": "default", "data_files": [{"split": "test", "path": "test.csv"}]}]}	false	null	2025-03-19T15:05:46	17	17	false	3a5c44deac7ac1de370c5341d2748250cbbf52e3	SpatialLM Testset We provide a test set of 107 preprocessed point clouds and their corresponding GT layouts, point clouds are reconstructed from RGB videos using MASt3R-SLAM. SpatialLM-Testset is quite challenging compared to prior clean RGBD scan datasets due to the noises and occlusions in the point clouds reconstructed from monocular RGB videos. Folder Structure Outlines of the dataset files: project-root/ ├── pcd/*.ply… See the full description on the dataset page: https://huggingface.co/datasets/manycore-research/SpatialLM-Testset.	1,122	1,122	[ "license:cc-by-nc-4.0", "size_categories:n<1K", "format:csv", "modality:3d", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	2025-03-16T12:57:37	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	650	16	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.	346,976	4,158,772	[ "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
67dabea523ec1d597d1e0012	MaziyarPanahi/Llama-Nemotron-Post-Training-Dataset-v1-ShareGPT	MaziyarPanahi	null	false	null	2025-03-19T14:19:33	16	16	false	eb68620950785989802be1a80760ba34533a8f1d	Llama-Nemotron-Post-Training-Dataset-v1 in ShareGPT Format This dataset is a conversion of NVIDIA's Llama-Nemotron-Post-Training-Dataset-v1 into the ShareGPT format while preserving the original splits and columns. Format Each example contains all original fields plus a messages array: { "input": "original input text", "output": "original output text", ... (other original columns) ..., "messages": [ {"role": "user", "content": "User message"}, {"role":… See the full description on the dataset page: https://huggingface.co/datasets/MaziyarPanahi/Llama-Nemotron-Post-Training-Dataset-v1-ShareGPT.	337	337	[ "size_categories:10M<n<100M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	2025-03-19T12:55:01	null	null
67cba813ef7ed9b8e2a948c7	canopylabs/zac-sample-dataset	canopylabs	{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "audio", "dtype": {"audio": {"sampling_rate": 48000}}}], "splits": [{"name": "train", "num_bytes": 13147142.424794896, "num_examples": 20}], "download_size": 10349037, "dataset_size": 13147142.424794896}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}	false	null	2025-03-08T02:14:46	15	15	false	5464e5b186dab0d49049eca0b28774ad9371fc89	null	277	277	[ "size_categories:n<1K", "format:parquet", "modality:audio", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	2025-03-08T02:14:43	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"}]}, {"name": "system_prompt", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 207497599, "num_examples": 50000}, {"name": "test", "num_bytes": 4533192, "num_examples": 1000}], "download_size": 82841801, "dataset_size": 212030791}, "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	2025-03-14T13:21:53	42	15	false	e7b425e9e659c3827af4c89cbe10e080fea3f038	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.	3,801	3,801	[ "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
67ce2fb269ac5540794d0bf6	CharlieDreemur/OpenManus-RL	CharlieDreemur	{"language": ["en"], "tags": ["sft", "instruction-tuning", "conversational-ai"], "license": "apache-2.0", "task_categories": ["text-generation"], "pretty_name": "OpenManusRL", "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "conversations", "list": [{"name": "role", "dtype": "string"}, {"name": "content", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 277895199, "num_examples": 48927}], "download_size": 73312767, "dataset_size": 277895199}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}	false	null	2025-03-15T01:29:38	36	15	false	b102de3f0a2e40221fc923ed9f34756251fc666c	Dataset Card for OpenManusRL Dataset Description Overview 💻 [Github Repo] OpenManusRL combines agent trajectories from AgentInstruct, Agent-FLAN and AgentTraj-L(AgentGym) 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 Overview Source… See the full description on the dataset page: https://huggingface.co/datasets/CharlieDreemur/OpenManus-RL.	1,241	1,241	[ "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", "arxiv:2310.12823", "arxiv:2403.12881", "arxiv:2406.04151", "region:us", "sft", "instruction-tuning", "conversational-ai" ]	2025-03-10T00:17:54	null	null
66a145d28f0d2327e07fc119	cfahlgren1/hub-stats	cfahlgren1	{"license": "apache-2.0", "configs": [{"config_name": "models", "data_files": "models.parquet"}, {"config_name": "datasets", "data_files": "datasets.parquet"}, {"config_name": "spaces", "data_files": "spaces.parquet"}, {"config_name": "posts", "data_files": "posts.parquet"}, {"config_name": "papers", "data_files": "daily_papers.parquet"}]}	false	null	2025-03-20T23:40:59	39	14	false	3d2e26d6322f9b0961cf72141cd7019acb53ebc6	NEW Changes Feb 27th Added new fields on the models split: downloadsAllTime, safetensors, gguf Added new field on the datasets split: downloadsAllTime Added new split: papers which is all of the Daily Papers Updated Daily	7,811	16,992	[ "license:apache-2.0", "size_categories:1M<n<10M", "format:parquet", "modality:image", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	2024-07-24T18:20:02	null	null
67abc2c2d6edf5606aa5c0d7	facebook/collaborative_agent_bench	facebook	{"license": "other", "extra_gated_prompt": "## License", "extra_gated_fields": {"First Name": "text", "Last Name": "text", "Date of birth": "date_picker", "Country": "country", "Affiliation": "text", "I accept the terms and conditions": "checkbox", "geo": "ip_location"}, "extra_gated_description": "SWEET-RL Research License and Acceptable Use Policy", "extra_gated_button_content": "I Accept Self-taught Evaluator Research License and AUP"}	false	null	2025-03-20T04:17:14	14	14	false	cf3526da25989b53f105fe9b74c1174a3e19c548	This dataset is released as part of SWEET-RL: Training Multi-Turn LLM Agents on Collaborative Reasoning Tasks research project. Please refer to our project materials here for training and evaluation details. Citation If you use data, model, or code from this work, please cite with the following BibTex entry: @misc{zhou2025sweetrltrainingmultiturnllm, title={SWEET-RL: Training Multi-Turn LLM Agents on Collaborative Reasoning Tasks}, author={Yifei Zhou and Song Jiang and… See the full description on the dataset page: https://huggingface.co/datasets/facebook/collaborative_agent_bench.	10	10	[ "license:other", "arxiv:2503.15478", "region:us" ]	2025-02-11T21:36:02	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	7,634	13	false	68ba7694e23014788dcc8ab5afe613824f45a05c	🧠 Awesome ChatGPT Prompts [CSV dataset] This is a Dataset Repository of Awesome ChatGPT Prompts View All Prompts on GitHub License CC-0	12,400	136,198	[ "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
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	272	12	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.	9,693	33,774	[ "language:en", "arxiv:2311.12983", "region:us" ]	2023-10-20T07:06:54	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	148	12	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.	5,314	5,612	[ "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
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	24	12	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.	720	720	[ "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
67d421a66af8d6a03083eb69	GeneralReasoning/GeneralThought-430K	GeneralReasoning	{"language": ["en"], "license": "mit"}	false	null	2025-03-14T13:04:04	20	12	false	9f2b46abdf8e3ba2faf650541242d4bd8ac22892	GeneralThought-430K Thought wants to be free Open reasoning data from the General Reasoning resource for March 14 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-430K.	630	630	[ "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" ]	2025-03-14T12:31:34	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	99	10	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.	7,380	9,112	[ "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
621ffdd236468d709f182a80	allenai/c4	allenai	{"pretty_name": "C4", "annotations_creators": ["no-annotation"], "language_creators": ["found"], "language": ["af", "am", "ar", "az", "be", "bg", "bn", "ca", "ceb", "co", "cs", "cy", "da", "de", "el", "en", "eo", "es", "et", "eu", "fa", "fi", "fil", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "haw", "he", "hi", "hmn", "ht", "hu", "hy", "id", "ig", "is", "it", "iw", "ja", "jv", "ka", "kk", "km", "kn", "ko", "ku", "ky", "la", "lb", "lo", "lt", "lv", "mg", "mi", "mk", "ml", "mn", "mr", "ms", "mt", "my", "ne", "nl", "no", "ny", "pa", "pl", "ps", "pt", "ro", "ru", "sd", "si", "sk", "sl", "sm", "sn", "so", "sq", "sr", "st", "su", "sv", "sw", "ta", "te", "tg", "th", "tr", "uk", "und", "ur", "uz", "vi", "xh", "yi", "yo", "zh", "zu"], "language_bcp47": ["bg-Latn", "el-Latn", "hi-Latn", "ja-Latn", "ru-Latn", "zh-Latn"], "license": ["odc-by"], "multilinguality": ["multilingual"], "size_categories": ["n<1K", "1K<n<10K", "10K<n<100K", "100K<n<1M", 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"multilingual/c4-yi-validation..json.gz"}]}, {"config_name": "yo", "data_files": [{"split": "train", "path": "multilingual/c4-yo..json.gz"}, {"split": "validation", "path": "multilingual/c4-yo-validation..json.gz"}]}, {"config_name": "zh", "data_files": [{"split": "train", "path": "multilingual/c4-zh..json.gz"}, {"split": "validation", "path": "multilingual/c4-zh-validation..json.gz"}]}, {"config_name": "zh-Latn", "data_files": [{"split": "train", "path": "multilingual/c4-zh-Latn..json.gz"}, {"split": "validation", "path": "multilingual/c4-zh-Latn-validation..json.gz"}]}, {"config_name": "zu", "data_files": [{"split": "train", "path": "multilingual/c4-zu..json.gz"}, {"split": "validation", "path": "multilingual/c4-zu-validation..json.gz"}]}]}	false	null	2024-01-09T19:14:03	390	9	false	1588ec454efa1a09f29cd18ddd04fe05fc8653a2	C4 Dataset Summary A colossal, cleaned version of Common Crawl's web crawl corpus. Based on Common Crawl dataset: "https://commoncrawl.org". This is the processed version of Google's C4 dataset We prepared five variants of the data: en, en.noclean, en.noblocklist, realnewslike, and multilingual (mC4). For reference, these are the sizes of the variants: en: 305GB en.noclean: 2.3TB en.noblocklist: 380GB realnewslike: 15GB multilingual (mC4): 9.7TB (108 subsets, one… See the full description on the dataset page: https://huggingface.co/datasets/allenai/c4.	408,564	5,320,428	[ "task_categories:text-generation", "task_categories:fill-mask", "task_ids:language-modeling", "task_ids:masked-language-modeling", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:multilingual", "source_datasets:original", "language:af", "language:am", "language:ar", "language:az", "language:be", "language:bg", "language:bn", "language:ca", "language:ceb", "language:co", "language:cs", "language:cy", "language:da", "language:de", "language:el", "language:en", "language:eo", "language:es", "language:et", "language:eu", "language:fa", "language:fi", "language:fil", "language:fr", "language:fy", "language:ga", "language:gd", "language:gl", "language:gu", "language:ha", "language:haw", "language:he", "language:hi", "language:hmn", "language:ht", "language:hu", "language:hy", "language:id", "language:ig", "language:is", "language:it", "language:iw", "language:ja", "language:jv", "language:ka", "language:kk", "language:km", "language:kn", "language:ko", "language:ku", "language:ky", "language:la", "language:lb", "language:lo", "language:lt", "language:lv", "language:mg", "language:mi", "language:mk", "language:ml", "language:mn", "language:mr", "language:ms", "language:mt", "language:my", "language:ne", "language:nl", "language:no", "language:ny", "language:pa", "language:pl", "language:ps", "language:pt", "language:ro", "language:ru", "language:sd", "language:si", "language:sk", "language:sl", "language:sm", "language:sn", "language:so", "language:sq", "language:sr", "language:st", "language:su", "language:sv", "language:sw", "language:ta", "language:te", "language:tg", "language:th", "language:tr", "language:uk", "language:und", "language:ur", "language:uz", "language:vi", "language:xh", "language:yi", "language:yo", "language:zh", "language:zu", "license:odc-by", "size_categories:10B<n<100B", "modality:text", "arxiv:1910.10683", "region:us" ]	2022-03-02T23:29:22	c4	null
649444227853dd12c3bbadd8	Amod/mental_health_counseling_conversations	Amod	{"license": "openrail", "task_categories": ["text-generation", "question-answering"], "language": ["en"], "tags": ["medical"], "size_categories": ["1K<n<10K"]}	false	null	2024-04-05T08:30:03	333	9	false	4672e03c7f1a7b2215eb4302b83ca50449ce2553	Amod/mental_health_counseling_conversations Dataset Summary This dataset is a collection of questions and answers sourced from two online counseling and therapy platforms. The questions cover a wide range of mental health topics, and the answers are provided by qualified psychologists. The dataset is intended to be used for fine-tuning language models to improve their ability to provide mental health advice. Supported Tasks and Leaderboards The… See the full description on the dataset page: https://huggingface.co/datasets/Amod/mental_health_counseling_conversations.	4,998	62,357	[ "task_categories:text-generation", "task_categories:question-answering", "language:en", "license:openrail", "size_categories:1K<n<10K", "format:json", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "doi:10.57967/hf/1581", "region:us", "medical" ]	2023-06-22T12:52:50	null	null
66fcfe8ac529fcdfbd3696f4	SylvanL/Traditional-Chinese-Medicine-Dataset-SFT	SylvanL	{"license": "apache-2.0", "task_categories": ["table-question-answering"], "language": ["zh"], "tags": ["medical"], "size_categories": ["1B<n<10B"]}	false	null	2024-10-26T10:47:40	66	9	false	5ba2abbea72d757a1fd70a683193452c35b36f83	启古纳今，厚德精术数据介绍非网络来源的高质量中医数据集-指令微调 High-Quality Traditional Chinese Medicine Dataset from Non-Internet Sources - SFT/IFT 该数据集经过大量人力和资源的投入精心构建，以共建LLM高质量中文社区为己任。包含约1GB的中医各个领域临床案例、名家典籍、医学百科，名词解释等优质问答内容，涵盖全面，配比均衡。数据集主要由非网络来源的内部数据构成，并99%为简体中文内容，内容质量优异，信息密度可观。该数据集的数据源与SylvanL/Traditional-Chinese-Medicine-Dataset-Pretrain中的内容存在一定关联，但不高度重叠。在二者的构建过程中，存在着一定的循序渐进与互为补充的逻辑. 该数据集可以独立使用，但建议先使用配套的预训练数据集对模型进行继续预训练后，再使用该数据集进行进一步的指令微调。… See the full description on the dataset page: https://huggingface.co/datasets/SylvanL/Traditional-Chinese-Medicine-Dataset-SFT.	1,964	3,789	[ "task_categories:table-question-answering", "language:zh", "license:apache-2.0", "size_categories:1M<n<10M", "format:json", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "region:us", "medical" ]	2024-10-02T08:04:26	null	null
666513f121aa69e38699e6d3	UCSC-VLAA/MedTrinity-25M	UCSC-VLAA	{"language": ["en"], "size_categories": ["10M<n<100M"], "task_categories": ["question-answering"], "dataset_info": [{"config_name": "25M_full", "features": [{"name": "id", "dtype": "string"}, {"name": "file_name", "dtype": "string"}, {"name": "caption", "dtype": "string"}, {"name": "source", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 25234102586, "num_examples": 24760560}], "download_size": 7353330306, "dataset_size": 25234102586}, {"config_name": "default", "features": [{"name": "image", "dtype": "image"}, {"name": "id", "dtype": "string"}, {"name": "caption", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 4781050841.25, "num_examples": 161630}], "download_size": 8300138103, "dataset_size": 4781050841.25}], "configs": [{"config_name": "25M_full", "data_files": [{"split": "train", "path": "25M_full/train-"}]}, {"config_name": "25M_demo", "data_files": [{"split": "train", "path": "data/train-"}]}], "tags": ["medical"]}	false	null	2024-10-11T00:47:43	133	8	false	89e5c684794e5c4cc1af9e8f1a7798af7c937dbf	Tutorial of using Medtrinity-25M MedTrinity-25M, a comprehensive, large-scale multimodal dataset for medicine, covering over 25 million images across 10 modalities, with multigranular annotations for more than 65 diseases. These enriched annotations encompass both global textual information, such as disease/lesion type, modality, region-specific descriptions, and inter-regional relationships, as well as detailed local annotations for regions of interest (ROIs), including… See the full description on the dataset page: https://huggingface.co/datasets/UCSC-VLAA/MedTrinity-25M.	2,545	9,704	[ "task_categories:question-answering", "language:en", "size_categories:10M<n<100M", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:2408.02900", "region:us", "medical" ]	2024-06-09T02:31:13	null	null
67954a35c16b74e280f72f15	ServiceNow-AI/R1-Distill-SFT	ServiceNow-AI	{"license": "cc-by-nc-sa-4.0", "configs": [{"config_name": "v0", "data_files": [{"split": "train", "path": "v0/train-"}]}, {"config_name": "v1", "data_files": [{"split": "train", "path": "v1/train-"}]}], "dataset_info": [{"config_name": "v0", "features": [{"name": "id", "dtype": "string"}, {"name": "reannotated_assistant_content", "dtype": "string"}, {"name": "problem", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "solution", "dtype": "string"}, {"name": "verified", "dtype": "null"}, {"name": "quality_metrics", "dtype": "null"}], "splits": [{"name": "train", "num_bytes": 1279431141, "num_examples": 171647}], "download_size": 554111459, "dataset_size": 1279431141}, {"config_name": "v1", "features": [{"name": "id", "dtype": "string"}, {"name": "reannotated_assistant_content", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "reannotated_messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "source_dataset", "dtype": "string"}, {"name": "verified", "dtype": "null"}, {"name": "quality_metrics", "dtype": "null"}], "splits": [{"name": "train", "num_bytes": 25783989151, "num_examples": 1679162}], "download_size": 11128580062, "dataset_size": 25783989151}]}	false	null	2025-02-08T22:46:58	282	8	false	16e851e107d928b9069dcce428a2d3d7154e5353	🔉 𝗦𝗟𝗔𝗠 𝗹𝗮𝗯 - 𝗥𝟭-𝗗𝗶𝘀𝘁𝗶𝗹𝗹-𝗦𝗙𝗧 Dataset Lewis Tunstall, Ed Beeching, Loubna Ben Allal, Clem Delangue 🤗 and others at Hugging Face announced today that they are - 𝗼𝗽𝗲𝗻𝗹𝘆 𝗿𝗲𝗽𝗿𝗼𝗱𝘂𝗰𝗶𝗻𝗴 𝗥𝟭 🔥 We at 𝗦𝗟𝗔𝗠 𝗹𝗮𝗯 (ServiceNow Language Models) have been cooking up something as well. Inspired by Open-r1, we have decided to open source the data stage-by-stage to support the open source community. 𝗕𝗼𝗼𝗸𝗺𝗮𝗿𝗸 this page! KEY DETAILS: ⚗️ Distilled… See the full description on the dataset page: https://huggingface.co/datasets/ServiceNow-AI/R1-Distill-SFT.	4,026	10,669	[ "license:cc-by-nc-sa-4.0", "size_categories:1M<n<10M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	2025-01-25T20:31:49	null	null
67a404bc8c6d42c5ec097433	Anthropic/EconomicIndex	Anthropic	{"license": "mit", "pretty_name": "EconomicIndex", "tags": ["text"], "viewer": true, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "onet_task_mappings.csv"}]}]}	false	null	2025-02-10T19:28:32	198	8	false	218b35116baa43c55beffe61f243bd81f5f84cf8	Overview This directory contains ONET task mapping and automation vs. augmentation data from "Which Economic Tasks are Performed with AI? Evidence from Millions of Claude Conversations." The data and provided analysis are described below. Please see our blog post and paper for further visualizations and complete analysis. Data SOC_Structure.csv - Standard Occupational Classification (SOC) system hierarchy from the U.S. Department of Labor ONET database… See the full description on the dataset page: https://huggingface.co/datasets/Anthropic/EconomicIndex.	2,892	7,942	[ "license:mit", "size_categories:1K<n<10K", "format:csv", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "text" ]	2025-02-06T00:39:24	null	null
67bc34742281367a6b4a5bb7	jmhb/microvqa	jmhb	{"language": ["en"], "license": "cc-by-sa-4.0", "size_categories": ["1K<n<10K"], "task_categories": ["visual-question-answering", "multiple-choice"], "pretty_name": "MicroVQA", "dataset_info": {"features": [{"name": "key_question", "dtype": "int64"}, {"name": "key_image", "dtype": "int64"}, {"name": "images_list", "sequence": "image"}, {"name": "question", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "correct_index", "dtype": "int64"}, {"name": "correct_answer", "dtype": "string"}, {"name": "question_0", "dtype": "string"}, {"name": "answer_0", "dtype": "string"}, {"name": "comments_0", "dtype": "string"}, {"name": "incorrect_answer_0", "dtype": "string"}, {"name": "question_1", "dtype": "string"}, {"name": "choices_1", "sequence": "string"}, {"name": "correct_index_1", "dtype": "int64"}, {"name": "question_2", "dtype": "string"}, {"name": "choices_2", "sequence": "string"}, {"name": "correct_index_2", "dtype": "int64"}, {"name": "question_3", "dtype": "string"}, {"name": "choices_3", "sequence": "string"}, {"name": "correct_index_3", "dtype": "int64"}, {"name": "task", "dtype": "int64"}, {"name": "task_str", "dtype": "string"}, {"name": "context_image_generation", "dtype": "string"}, {"name": "context_motivation", "dtype": "string"}, {"name": "images_source", "dtype": "string"}, {"name": "image_caption", "dtype": "string"}, {"name": "key_person", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 2354666072.188, "num_examples": 1042}], "download_size": 462156805, "dataset_size": 2354666072.188}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "tags": ["biology", "biomedical", "microscopy", "pathology", "vision-language", "question-answering", "scientific-research"]}	false	null	2025-03-18T17:04:39	11	8	false	e50938e2fd7299310896df749f74b5f6cc528ca5	MicroVQA: A Multimodal Reasoning Benchmark for Microscopy-Based Scientific Research (CVPR 2025) 🌐 Homepage / blog • 📝 arXiv • 🤗 HF Dataset • 💻 Code • 🏛 CC-BY-SA-4.0 MicroVQA is expert-curated benchmark for multimodal reasoning for microscopy-based scientific research, proposed in the paper MicroVQA: A Multimodal Reasoning Benchmark for Microscopy-Based Scientific Research. Paper abstract Scientific research demands sophisticated reasoning over multimodal… See the full description on the dataset page: https://huggingface.co/datasets/jmhb/microvqa.	766	766	[ "task_categories:visual-question-answering", "task_categories:multiple-choice", "language:en", "license:cc-by-sa-4.0", "size_categories:1K<n<10K", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:2503.13399", "region:us", "biology", "biomedical", "microscopy", "pathology", "vision-language", "question-answering", "scientific-research" ]	2025-02-24T08:57:24	null	null
67c248d12a6f7c1f2a448ee4	KodCode/KodCode-V1	KodCode	{"language": ["en"], "license": "cc-by-nc-4.0", "dataset_info": {"features": [{"name": "version", "dtype": "string"}, {"name": "style", "dtype": "string"}, {"name": "subset", "dtype": "string"}, {"name": "question_id", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "solution", "dtype": "string"}, {"name": "test", "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": "file_source", "dtype": "string"}, {"name": "solution_code", "dtype": "string"}, {"name": "test_code", "dtype": "string"}, {"name": "test_coverage", "dtype": "float64"}, {"name": "test_result", "dtype": "string"}]}, {"name": "trial_gpt4o_1", "struct": [{"name": "file_source", "dtype": "string"}, {"name": "solution_code", "dtype": "string"}, {"name": "test_code", "dtype": "string"}, {"name": "test_coverage", "dtype": "float64"}, {"name": "test_result", "dtype": "string"}]}, {"name": "trial_gpt4o_2", "struct": [{"name": "file_source", "dtype": "string"}, {"name": "solution_code", "dtype": "string"}, {"name": "test_code", "dtype": "string"}, {"name": "test_coverage", "dtype": "float64"}, {"name": "test_result", "dtype": "string"}]}, {"name": "trial_gpt4o_3", "struct": [{"name": "file_source", "dtype": "string"}, {"name": "solution_code", "dtype": "string"}, {"name": "test_code", "dtype": "string"}, {"name": "test_coverage", "dtype": "float64"}, {"name": "test_result", "dtype": "string"}]}, {"name": "trial_gpt4o_4", "struct": [{"name": "file_source", "dtype": "string"}, {"name": "solution_code", "dtype": "string"}, {"name": "test_code", "dtype": "string"}, {"name": "test_coverage", "dtype": "float64"}, {"name": "test_result", "dtype": "string"}]}, {"name": "trial_gpt4o_5", "struct": [{"name": "file_source", "dtype": "string"}, {"name": "solution_code", "dtype": "string"}, {"name": "test_code", "dtype": "string"}, {"name": "test_coverage", "dtype": "float64"}, {"name": "test_result", "dtype": "string"}]}, {"name": "trial_gpt4o_6", "struct": [{"name": "file_source", "dtype": "string"}, {"name": "solution_code", "dtype": "string"}, {"name": "test_code", "dtype": "string"}, {"name": "test_coverage", "dtype": "float64"}, {"name": "test_result", "dtype": "string"}]}, {"name": "trial_gpt4o_7", "struct": [{"name": "file_source", "dtype": "string"}, {"name": "solution_code", "dtype": "string"}, {"name": "test_code", "dtype": "string"}, {"name": "test_coverage", "dtype": "float64"}, {"name": "test_result", "dtype": "string"}]}, {"name": "trial_gpt4o_8", "struct": [{"name": "file_source", "dtype": "string"}, {"name": "solution_code", "dtype": "string"}, {"name": "test_code", "dtype": "string"}, {"name": "test_coverage", "dtype": "float64"}, {"name": "test_result", "dtype": "string"}]}, {"name": "trial_gpt4o_9", "struct": [{"name": "file_source", "dtype": "string"}, {"name": "solution_code", "dtype": "string"}, {"name": "test_code", "dtype": "string"}, {"name": "test_coverage", "dtype": "float64"}, {"name": "test_result", "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": 7436730246, "num_examples": 484097}, {"name": "use_with_caution", "num_bytes": 59623008, "num_examples": 3335}], "download_size": 2642644096, "dataset_size": 7496353254}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-"}, {"split": "use_with_caution", "path": "data/use_with_caution-"}]}], "tags": ["code"], "size_categories": ["100K<n<1M"]}	false	null	2025-03-17T07:56:27	76	8	false	97d81f0695ddd7c5c02bfe16436db9a17a4a21e5	🐱 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.	3,874	3,874	[ "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", "code" ]	2025-02-28T23:37:53	null	null
6664c00380c533842fb0c680	lmg-anon/vntl-leaderboard	lmg-anon	{"language": ["en", "ja"], "tags": ["benchmark", "leaderboard"], "task_categories": ["translation"], "pretty_name": "vntl-leaderboard", "size_categories": ["n<1K"], "configs": [{"config_name": "leaderboard", "data_files": "leaderboard.jsonl"}]}	false	null	2025-01-02T16:34:32	34	7	false	cf3d232d77458394857dbf8411de95fd3a894aef	VNTL Leaderboard The VNTL leaderboard ranks Large Language Models (LLMs) based on their performance in translating Japanese Visual Novels into English. Please be aware that the current results are preliminary and subject to change as new models are evaluated, or changes are done in the evaluation script. Comparison with Established Translation Tools For comparison, this table shows the scores for established translation tools. These include both widely available… See the full description on the dataset page: https://huggingface.co/datasets/lmg-anon/vntl-leaderboard.	3,518	8,332	[ "task_categories:translation", "language:en", "language:ja", "size_categories:n<1K", "format:json", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "benchmark", "leaderboard" ]	2024-06-08T20:33:07	null	null
66a53dc7d40a13036c5f2ebe	mlabonne/FineTome-100k	mlabonne	{"dataset_info": {"features": [{"name": "conversations", "list": [{"name": "from", "dtype": "string"}, {"name": "value", "dtype": "string"}]}, {"name": "source", "dtype": "string"}, {"name": "score", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 239650960.7474458, "num_examples": 100000}], "download_size": 116531415, "dataset_size": 239650960.7474458}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}	false	null	2024-07-29T09:52:30	182	7	false	c2343c1372ff31f51aa21248db18bffa3193efdb	FineTome-100k The FineTome dataset is a subset of arcee-ai/The-Tome (without arcee-ai/qwen2-72b-magpie-en), re-filtered using HuggingFaceFW/fineweb-edu-classifier. It was made for my article "Fine-tune Llama 3.1 Ultra-Efficiently with Unsloth".	16,517	79,582	[ "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	2024-07-27T18:34:47	null	null
67374c18c32c765810f748f6	HuggingFaceH4/MATH-500	HuggingFaceH4	{"task_categories": ["text-generation"], "language": ["en"], "pretty_name": "MATH-500"}	false	null	2024-11-15T13:36:00	132	7	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	57,939	97,688	[ "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
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	97	7	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.	5,283	5,285	[ "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
67cf999e2d0993b445bfd594	Gen-Verse/WideRange4D	Gen-Verse	{"task_categories": ["image-to-video"], "tags": ["4d-reconstruction", "gaussian-splatting"], "license": "unknown"}	false	null	2025-03-21T11:22:37	7	7	false	c747d56214a46fe41ba42acc412b324ef58e5466	WideRange4D: Enabling High-Quality 4D Reconstruction with Wide-Range Movements and Scenes Ling Yang1, Kaixin Zhu1, Juanxi Tian1, Bohan Zeng1, Mingbao Lin3, Hongjuan Pei2, Wentao Zhang1‡, Shuicheng Yan3‡ 1 Peking University 2 University of the Chinese Academy of Sciences 3 National University of Singapore * Equal Contributions. ‡ Corresponding Author. Example Github Page arXiv Paper @article{yang2025widerange4d, title={WideRange4D: Enabling High-Quality… See the full description on the dataset page: https://huggingface.co/datasets/Gen-Verse/WideRange4D.	3,493	3,493	[ "task_categories:image-to-video", "license:unknown", "size_categories:10K<n<100K", "modality:video", "library:datasets", "library:mlcroissant", "arxiv:2503.13435", "region:us", "4d-reconstruction", "gaussian-splatting" ]	2025-03-11T02:02:06	null	null
67d9fd082ad0bffeb5bbc771	HuggingFaceTB/issues-kaggle-notebooks	HuggingFaceTB	{"dataset_info": [{"config_name": "issues", "features": [{"name": "repo_name", "dtype": "string"}, {"name": "issue_id", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 30986711842, "num_examples": 15549682}], "download_size": 16370074732, "dataset_size": 30986711842}, {"config_name": "kaggle", "features": [{"name": "file_id", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 5209133899, "num_examples": 580195}], "download_size": 2222724371, "dataset_size": 5209133899}], "configs": [{"config_name": "issues", "data_files": [{"split": "train", "path": "issues/train-"}]}, {"config_name": "kaggle", "data_files": [{"split": "train", "path": "kaggle/train-"}]}]}	false	null	2025-03-19T20:00:18	7	7	false	ef882ad1ed8274340e8fc9bac087c903f2f75396	GitHub Issues & Kaggle Notebooks Description GitHub Issues & Kaggle Notebooks is a collection of two code datasets intended for language models training, they are sourced from GitHub issues and notebooks in Kaggle platform. These datasets are a modified part of the StarCoder2 model training corpus, precisely the bigcode/StarCoder2-Extras dataset. We reformat the samples to remove StarCoder2's special tokens and use natural text to delimit comments in issues and display… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceTB/issues-kaggle-notebooks.	62	62	[ "size_categories:10M<n<100M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:2402.19173", "region:us" ]	2025-03-18T23:08:56	null	null

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