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2025-07-31 13:13:09
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63990f21cc50af73d29ecfa3
|
fka/awesome-chatgpt-prompts
|
fka
|
{"license": "cc0-1.0", "tags": ["ChatGPT"], "task_categories": ["question-answering"], "size_categories": ["100K<n<1M"]}
| false |
False
| 2025-01-06T00:02:53 | 8,516 | 78 | false |
68ba7694e23014788dcc8ab5afe613824f45a05c
|
🧠 Awesome ChatGPT Prompts [CSV dataset]
This is a Dataset Repository of Awesome ChatGPT Prompts
View All Prompts on GitHub
License
CC-0
| 33,140 | 230,082 |
[
"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 |
687141e6df15b094718f28be
|
NousResearch/Hermes-3-Dataset
|
NousResearch
|
{"license": "apache-2.0"}
| false |
False
| 2025-07-11T17:43:25 | 265 | 52 | false |
b1fddbdcae4e6714889365d1e6ce266a45289cc9
| 6,664 | 6,664 |
[
"license:apache-2.0",
"size_categories:100K<n<1M",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2025-07-11T16:55:02 | null | null |
|
687a0c02efb93725cd663b85
|
MegaScience/MegaScience
|
MegaScience
|
{"language": ["en"], "license": "cc-by-nc-sa-4.0", "size_categories": ["1M<n<10M"], "task_categories": ["text-generation"], "tags": ["science", "reasoning"], "dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "subject", "dtype": "string"}, {"name": "reference_answer", "dtype": "string"}, {"name": "source", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3719840088, "num_examples": 1253230}], "download_size": 1878947811, "dataset_size": 3719840088}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
| false |
False
| 2025-07-24T04:55:24 | 60 | 44 | false |
8df5586005374acba25aecc4f5469ce30fec605c
|
MegaScience: Pushing the Frontiers of Post-Training Datasets for Science Reasoning
Code: https://github.com/GAIR-NLP/MegaScience
Project Page: https://huggingface.co/MegaScience
MegaScience is a large-scale mixture of high-quality open-source datasets consisting of 1.25 million instances. We first collect multiple public datasets, then conduct comprehensive ablation studies across different data selection methods to identify the optimal approach for each dataset, thereby… See the full description on the dataset page: https://huggingface.co/datasets/MegaScience/MegaScience.
| 3,737 | 3,737 |
[
"task_categories:text-generation",
"language:en",
"license:cc-by-nc-sa-4.0",
"size_categories:1M<n<10M",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2507.16812",
"region:us",
"science",
"reasoning"
] | 2025-07-18T08:55:30 | null | null |
68895c3182e38006a8e9aa94
|
nvidia/Nemotron-Post-Training-Dataset-v1
|
nvidia
|
{"dataset_info": {"features": [{"name": "uuid", "dtype": "string"}, {"name": "license", "dtype": "string"}, {"name": "generator", "dtype": "string"}, {"name": "version", "dtype": "string"}, {"name": "category", "dtype": "string"}, {"name": "reasoning", "dtype": "string"}, {"name": "messages", "list": [{"name": "role", "dtype": "string"}, {"name": "content", "dtype": "string"}, {"name": "tool_calls", "list": [{"name": "id", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "function", "struct": [{"name": "name", "dtype": "string"}, {"name": "arguments", "dtype": "string"}]}]}]}, {"name": "metadata", "dtype": "string"}], "splits": [{"name": "chat", "num_bytes": 3824039827, "num_examples": 746622}, {"name": "code", "num_bytes": 91391705833, "num_examples": 1896395}, {"name": "math", "num_bytes": 79173786238, "num_examples": 2044407}, {"name": "stem", "num_bytes": 329529074790, "num_examples": 20662167}, {"name": "tool", "num_bytes": 6395081261, "num_examples": 310051}], "download_size": 203373185595, "dataset_size": 510313687949}, "configs": [{"config_name": "default", "data_files": [{"split": "chat", "path": "data/chat-*"}, {"split": "code", "path": "data/code-*"}, {"split": "math", "path": "data/math-*"}, {"split": "stem", "path": "data/stem-*"}, {"split": "tool_calling", "path": "data/tool-*"}]}], "license": "cc-by-4.0"}
| false |
False
| 2025-07-31T07:28:58 | 42 | 42 | false |
06d0aef56fb542903a8d368d93ef54428cef0f61
|
Nemotron-Post-Training-Dataset-v1 Release
This dataset is a compilation of SFT data that supports improvements of math, code, stem, general reasoning, and tool calling capabilities of the original Llama instruct model Llama-3.3-Nemotron-Super-49B-v1.5.
Llama-3.3-Nemotron-Super-49B-v1.5 is an LLM which is a derivative of Meta Llama-3.3-70B-Instruct (AKA the reference model).
Llama-3.3-Nemotron-Super-49B-v1.5 offers a great tradeoff between model accuracy and efficiency. Efficiency… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-Post-Training-Dataset-v1.
| 582 | 582 |
[
"license:cc-by-4.0",
"size_categories:10M<n<100M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2505.00949",
"region:us"
] | 2025-07-29T23:41:37 | null | null |
685a3e532ffa3324700102d5
|
interstellarninja/hermes_reasoning_tool_use
|
interstellarninja
|
{"dataset_info": {"features": [{"name": "conversations", "list": [{"name": "from", "dtype": "string"}, {"name": "value", "dtype": "string"}]}, {"name": "tools", "dtype": "string"}, {"name": "task", "dtype": "string"}, {"name": "category", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "scenario_category", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 392137224, "num_examples": 51004}], "download_size": 128188655, "dataset_size": 392137224}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "license": "apache-2.0", "task_categories": ["question-answering"], "language": ["en"], "tags": ["tool-use", "json-mode", "reasoning", "rl"], "size_categories": ["10K<n<100K"]}
| false |
False
| 2025-07-23T11:19:25 | 76 | 36 | false |
cf5c4ed24134666ffb642fd34bc38fa9ff2ca909
| null | 1,369 | 1,541 |
[
"task_categories:question-answering",
"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",
"tool-use",
"json-mode",
"reasoning",
"rl"
] | 2025-06-24T05:57:39 | null | null |
684975418fb1ad8c76edc770
|
microsoft/rStar-Coder
|
microsoft
|
{"pretty_name": "rStar-Coder", "configs": [{"config_name": "synthetic_sft", "data_files": [{"split": "train", "path": "synthetic_sft/*.parquet"}]}, {"config_name": "synthetic_rl", "data_files": [{"split": "train", "path": "synthetic_rl/*.parquet"}]}, {"config_name": "synthetic_rl_testcase", "data_files": [{"split": "train", "path": "synthetic_rl_testcase/*.parquet"}]}, {"config_name": "seed_sft", "data_files": [{"split": "train", "path": "seed_sft/*.parquet"}]}, {"config_name": "seed_testcase", "data_files": [{"split": "train", "path": "seed_testcase/*.parquet"}]}], "license": "cc-by-4.0"}
| false |
False
| 2025-07-20T06:11:10 | 160 | 26 | false |
3a7a0a0636ec96e3c1ec42ebe79ade467caa040d
|
rStar-Coder Dataset
Project GitHub | Paper
Dataset Description
rStar-Coder is a large-scale competitive code problem dataset containing 418K programming problems, 580K long-reasoning solutions, and rich test cases of varying difficulty levels. This dataset aims to enhance code reasoning capabilities in large language models, particularly in handling competitive code problems.
Experiments on Qwen models (1.5B-14B) across various code reasoning benchmarks demonstrate… See the full description on the dataset page: https://huggingface.co/datasets/microsoft/rStar-Coder.
| 11,691 | 11,706 |
[
"license:cc-by-4.0",
"size_categories:1M<n<10M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2505.21297",
"region:us"
] | 2025-06-11T12:23:29 | null | null |
688710657650ffcfbe174277
|
zai-org/CC-Bench-trajectories
|
zai-org
|
{"license": "mit", "task_categories": ["text-generation"], "language": ["en", "zh"], "tags": ["code", "agent", "coding", "trajectory", "benchmark"], "size_categories": ["n<1K"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "train.parquet"}]}], "dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "task_id", "dtype": "int64"}, {"name": "trajectory", "dtype": "string"}, {"name": "model_name", "dtype": "string"}, {"name": "task_category", "dtype": "string"}, {"name": "user_messages", "dtype": "int64"}, {"name": "assistant_messages", "dtype": "int64"}, {"name": "total_input_tokens", "dtype": "int64"}, {"name": "total_output_tokens", "dtype": "int64"}, {"name": "total_tokens", "dtype": "int64"}, {"name": "tool_calls", "dtype": "int64"}, {"name": "tool_failures", "dtype": "int64"}, {"name": "failure_rate", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 21608817, "num_examples": 208}], "download_size": 21608817, "dataset_size": 21608817}}
| false |
False
| 2025-07-28T12:08:16 | 25 | 25 | false |
f6fd4b2c2c26cf3e1b6447c1749e24cb6699dd28
|
CC-Bench Trajectories Overview
To evaluate GLM-4.5's agentic coding capabilities in real-world scenarios, we build CC-Bench (using Claude Code as the agentic coding testbed) to conduct comprehensive testing against Claude-4-Sonnet, Kimi-K2, and Qwen3-Coder using 52 carefully designed coding tasks spanning multiple development domains. This dataset contains complete agentic trajectories of all 52 coding tasks with four models.
Test Dataset
Our evaluation dataset consists… See the full description on the dataset page: https://huggingface.co/datasets/zai-org/CC-Bench-trajectories.
| 2,681 | 2,681 |
[
"task_categories:text-generation",
"language:en",
"language:zh",
"license:mit",
"size_categories:n<1K",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us",
"code",
"agent",
"coding",
"trajectory",
"benchmark"
] | 2025-07-28T05:53:41 | null | null |
687c6f08386709ad79871f40
|
UCSC-VLAA/GPT-Image-Edit-1.5M
|
UCSC-VLAA
|
{"language": ["en"], "license": "cc-by-4.0", "size_categories": ["1M<n<10M"], "task_categories": ["image-to-image"], "pretty_name": "GPT-Image-Edit-1.5M", "tags": ["image", "image-editing", "instruction-tuning", "instruction-guided", "multimodal"], "library_name": "datasets"}
| false |
False
| 2025-07-30T16:38:38 | 24 | 22 | false |
b56063b84ae60196cfcb1d0bbc29502c3d0178cd
|
GPT-Image-Edit-1.5M A Million-Scale, GPT-Generated Image Dataset
📃Arxiv | 🌐 Project Page | 💻Github
GPT-Image-Edit-1.5M is a comprehensive image editing dataset that is built upon HQ-Edit, UltraEdit, OmniEdit and Complex-Edit, with all output images regenerated with GPT-Image-1.
📣 News
[2025.07.27] 🤗 We release GPT-Image-Edit, a state-of-the-art image editing model with 1.5M high-quality editing samples. All data, models, training code and evaluation code are… See the full description on the dataset page: https://huggingface.co/datasets/UCSC-VLAA/GPT-Image-Edit-1.5M.
| 7,967 | 7,967 |
[
"task_categories:image-to-image",
"language:en",
"license:cc-by-4.0",
"size_categories:1M<n<10M",
"format:webdataset",
"modality:image",
"modality:text",
"library:datasets",
"library:webdataset",
"library:mlcroissant",
"arxiv:2507.21033",
"region:us",
"image",
"image-editing",
"instruction-tuning",
"instruction-guided",
"multimodal"
] | 2025-07-20T04:22:32 | null | null |
68328f9074e873192976717f
|
multimodal-reasoning-lab/Zebra-CoT
|
multimodal-reasoning-lab
|
{"license": "cc-by-nc-4.0", "size_categories": ["100K<n<1M"], "task_categories": ["any-to-any", "image-text-to-text", "visual-question-answering"], "tags": ["visual-reasoning", "multimodal", "chain-of-thought"], "dataset_info": [{"config_name": "2D Visual Reasoning - Visual Jigsaw", "features": [{"name": "Question", "dtype": "string"}, {"name": "Text Reasoning Trace", "dtype": "string"}, {"name": "Final Answer", "dtype": "string"}, {"name": "problem_image_1", "dtype": "image"}, {"name": "reasoning_image_1", "dtype": "image"}, {"name": "reasoning_image_2", "dtype": "image"}, {"name": "reasoning_image_3", "dtype": "image"}, {"name": "reasoning_image_4", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 12582901580.818, "num_examples": 21899}], "download_size": 12050671761, "dataset_size": 12582901580.818}, {"config_name": "2D Visual Reasoning - Visual Search", "features": [{"name": "Question", "dtype": "string"}, {"name": "Text Reasoning Trace", "dtype": "string"}, {"name": "Final Answer", "dtype": "string"}, {"name": "problem_image_1", "dtype": "image"}, {"name": "reasoning_image_1", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 13219910500, "num_examples": 30000}], "download_size": 12844156433, "dataset_size": 13219910500}, {"config_name": "3D Visual Reasoning - Embodied CoT", "features": [{"name": "Question", "dtype": "string"}, {"name": "Text Reasoning Trace", "dtype": "string"}, {"name": "Final Answer", "dtype": "string"}, {"name": "problem_image_1", "dtype": "image"}, {"name": "problem_image_2", "dtype": "image"}, {"name": "reasoning_image_1", "dtype": "image"}, {"name": "reasoning_image_2", "dtype": "image"}, {"name": "reasoning_image_3", "dtype": "image"}, {"name": "reasoning_image_4", "dtype": "image"}, {"name": "reasoning_image_5", "dtype": "image"}, {"name": "reasoning_image_6", "dtype": "image"}, {"name": "reasoning_image_7", "dtype": "image"}, {"name": "reasoning_image_8", "dtype": "image"}, {"name": "reasoning_image_9", "dtype": "image"}, {"name": "reasoning_image_10", "dtype": "image"}, {"name": "reasoning_image_11", "dtype": "image"}, {"name": "reasoning_image_12", "dtype": "image"}, {"name": "reasoning_image_13", "dtype": "image"}, {"name": "reasoning_image_14", "dtype": "image"}, {"name": "reasoning_image_15", "dtype": "image"}, {"name": "reasoning_image_16", "dtype": "image"}, {"name": "reasoning_image_17", "dtype": "image"}, {"name": "reasoning_image_18", "dtype": "image"}, {"name": "reasoning_image_19", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 3951703486.138, "num_examples": 22666}], "download_size": 3915085114, "dataset_size": 3951703486.138}, {"config_name": "3D Visual Reasoning - Multi-Hop Objects Counting", "features": [{"name": "Question", "dtype": "string"}, {"name": "Text Reasoning Trace", "dtype": "string"}, {"name": "Final Answer", "dtype": "string"}, {"name": "problem_image_1", "dtype": "image"}, {"name": "reasoning_image_1", "dtype": "image"}, {"name": "reasoning_image_2", "dtype": "image"}, {"name": "reasoning_image_3", "dtype": "image"}, {"name": "reasoning_image_4", "dtype": "image"}, {"name": "reasoning_image_5", "dtype": "image"}, {"name": "reasoning_image_6", "dtype": "image"}, {"name": "reasoning_image_7", "dtype": "image"}, {"name": "reasoning_image_8", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 19515039955, "num_examples": 10000}], "download_size": 19790655896, "dataset_size": 19515039955}, {"config_name": "3D Visual Reasoning - Robot Planning", "features": [{"name": "Question", "dtype": "string"}, {"name": "Text Reasoning Trace", "dtype": "string"}, {"name": "Final Answer", "dtype": "string"}, {"name": "problem_image_1", "dtype": "image"}, {"name": "problem_image_2", "dtype": "image"}, {"name": "reasoning_image_1", "dtype": "image"}, {"name": "reasoning_image_2", "dtype": "image"}, {"name": "reasoning_image_3", "dtype": "image"}, {"name": "reasoning_image_4", "dtype": "image"}, {"name": "reasoning_image_5", "dtype": "image"}, {"name": "reasoning_image_6", "dtype": "image"}, {"name": "reasoning_image_7", "dtype": "image"}, {"name": "reasoning_image_8", "dtype": "image"}, {"name": "reasoning_image_9", "dtype": "image"}, {"name": "reasoning_image_10", "dtype": "image"}, {"name": "reasoning_image_11", "dtype": "image"}, {"name": "reasoning_image_12", "dtype": "image"}, {"name": "reasoning_image_13", "dtype": "image"}, {"name": "reasoning_image_14", "dtype": "image"}, {"name": "reasoning_image_15", "dtype": "image"}, {"name": "reasoning_image_16", "dtype": "image"}, {"name": "reasoning_image_17", "dtype": "image"}, {"name": "reasoning_image_18", "dtype": "image"}, {"name": "reasoning_image_19", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 1898502775.976, "num_examples": 6944}], "download_size": 1942223260, "dataset_size": 1898502775.976}, {"config_name": "Scientific Reasoning - 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Geometry", "features": [{"name": "Question", "dtype": "string"}, {"name": "Text Reasoning Trace", "dtype": "string"}, {"name": "Final Answer", "dtype": "string"}, {"name": "problem_image_1", "dtype": "image"}, {"name": "reasoning_image_1", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 166153915.182, "num_examples": 1058}], "download_size": 71915579, "dataset_size": 166153915.182}, {"config_name": "Scientific Reasoning - Graph Algorithms", "features": [{"name": "Question", "dtype": "string"}, {"name": "Text Reasoning Trace", "dtype": "string"}, {"name": "Final Answer", "dtype": "string"}, {"name": "problem_image_1", "dtype": "image"}, {"name": "reasoning_image_1", "dtype": "image"}, {"name": "reasoning_image_2", "dtype": "image"}, {"name": "reasoning_image_3", "dtype": "image"}, {"name": "reasoning_image_4", "dtype": "image"}, {"name": "reasoning_image_5", "dtype": "image"}, {"name": "reasoning_image_6", "dtype": "image"}, {"name": "reasoning_image_7", "dtype": "image"}, {"name": "reasoning_image_8", "dtype": "image"}, {"name": "reasoning_image_9", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 3137613949, "num_examples": 10000}], "download_size": 2795027626, "dataset_size": 3137613949}, {"config_name": "Scientific Reasoning - 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Checkers", "features": [{"name": "Question", "dtype": "string"}, {"name": "Text Reasoning Trace", "dtype": "string"}, {"name": "Final Answer", "dtype": "string"}, {"name": "problem_image_1", "dtype": "image"}, {"name": "reasoning_image_1", "dtype": "image"}, {"name": "reasoning_image_2", "dtype": "image"}, {"name": "reasoning_image_3", "dtype": "image"}, {"name": "reasoning_image_4", "dtype": "image"}, {"name": "reasoning_image_5", "dtype": "image"}, {"name": "reasoning_image_6", "dtype": "image"}, {"name": "reasoning_image_7", "dtype": "image"}, {"name": "reasoning_image_8", "dtype": "image"}, {"name": "reasoning_image_9", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 798412047.376, "num_examples": 2753}], "download_size": 784302007, "dataset_size": 798412047.376}, {"config_name": "Visual Logic & Strategic Games - Chess", "features": [{"name": "Question", "dtype": "string"}, {"name": "Text Reasoning Trace", "dtype": "string"}, {"name": "Final Answer", "dtype": "string"}, {"name": "problem_image_1", "dtype": "image"}, {"name": "reasoning_image_1", "dtype": "image"}, {"name": "reasoning_image_2", "dtype": "image"}, {"name": "reasoning_image_3", "dtype": "image"}, {"name": "reasoning_image_4", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 3914720265.394, "num_examples": 20483}], "download_size": 3872363943, "dataset_size": 3914720265.394}, {"config_name": "Visual Logic & Strategic Games - 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Maze", "features": [{"name": "Question", "dtype": "string"}, {"name": "Text Reasoning Trace", "dtype": "string"}, {"name": "Final Answer", "dtype": "string"}, {"name": "problem_image_1", "dtype": "image"}, {"name": "reasoning_image_1", "dtype": "image"}, {"name": "reasoning_image_2", "dtype": "image"}, {"name": "reasoning_image_3", "dtype": "image"}, {"name": "reasoning_image_4", "dtype": "image"}, {"name": "reasoning_image_5", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 5418428166, "num_examples": 20000}], "download_size": 5958257563, "dataset_size": 5418428166}, {"config_name": "Visual Logic & Strategic Games - RPM", "features": [{"name": "Question", "dtype": "string"}, {"name": "Text Reasoning Trace", "dtype": "string"}, {"name": "Final Answer", "dtype": "string"}, {"name": "problem_image_1", "dtype": "image"}, {"name": "reasoning_image_1", "dtype": "image"}, {"name": "reasoning_image_2", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 1113615987, "num_examples": 3000}], "download_size": 631931331, "dataset_size": 1113615987}, {"config_name": "Visual Logic & Strategic Games - Tetris", "features": [{"name": "Question", "dtype": "string"}, {"name": "Text Reasoning Trace", "dtype": "string"}, {"name": "Final Answer", "dtype": "string"}, {"name": "problem_image_1", "dtype": "image"}, {"name": "reasoning_image_1", "dtype": "image"}, {"name": "reasoning_image_2", "dtype": "image"}, {"name": "reasoning_image_3", "dtype": "image"}, {"name": "reasoning_image_4", "dtype": "image"}, {"name": "reasoning_image_5", "dtype": "image"}, {"name": "reasoning_image_6", "dtype": "image"}, {"name": "reasoning_image_7", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 2745762328, "num_examples": 10000}], "download_size": 1176544601, "dataset_size": 2745762328}], "configs": [{"config_name": "2D Visual Reasoning - Visual Jigsaw", "data_files": [{"split": "train", "path": "2D Visual Reasoning - Visual Jigsaw/train-*"}]}, {"config_name": "2D Visual Reasoning - Visual Search", "data_files": [{"split": "train", "path": "2D Visual Reasoning - Visual Search/train-*"}]}, {"config_name": "3D Visual Reasoning - Embodied CoT", "data_files": [{"split": "train", "path": "3D Visual Reasoning - Embodied CoT/train-*"}]}, {"config_name": "3D Visual Reasoning - Multi-Hop Objects Counting", "data_files": [{"split": "train", "path": "3D Visual Reasoning - Multi-Hop Objects Counting/train-*"}]}, {"config_name": "3D Visual Reasoning - Robot Planning", "data_files": [{"split": "train", "path": "3D Visual Reasoning - Robot Planning/train-*"}]}, {"config_name": "Scientific Reasoning - Chemistry", "data_files": [{"split": "train", "path": "Scientific Reasoning - Chemistry/train-*"}]}, {"config_name": "Scientific Reasoning - Competitive Programming", "data_files": [{"split": "train", "path": "Scientific Reasoning - Competitive Programming/train-*"}]}, {"config_name": "Scientific Reasoning - Geometry", "data_files": [{"split": "train", "path": "Scientific Reasoning - Geometry/train-*"}]}, {"config_name": "Scientific Reasoning - Graph Algorithms", "data_files": [{"split": "train", "path": "Scientific Reasoning - Graph Algorithms/train-*"}]}, {"config_name": "Scientific Reasoning - Physics", "data_files": [{"split": "train", "path": "Scientific Reasoning - Physics/train-*"}]}, {"config_name": "Visual Logic & Strategic Games - ARC-AGI", "data_files": [{"split": "train", "path": "Visual Logic & Strategic Games - ARC-AGI/train-*"}]}, {"config_name": "Visual Logic & Strategic Games - Checkers", "data_files": [{"split": "train", "path": "Visual Logic & Strategic Games - Checkers/train-*"}]}, {"config_name": "Visual Logic & Strategic Games - Chess", "data_files": [{"split": "train", "path": "Visual Logic & Strategic Games - Chess/train-*"}]}, {"config_name": "Visual Logic & Strategic Games - Ciphers", "data_files": [{"split": "train", "path": "Visual Logic & Strategic Games - Ciphers/train-*"}]}, {"config_name": "Visual Logic & Strategic Games - Connect Four", "data_files": [{"split": "train", "path": "Visual Logic & Strategic Games - Connect Four/train-*"}]}, {"config_name": "Visual Logic & Strategic Games - Maze", "data_files": [{"split": "train", "path": "Visual Logic & Strategic Games - Maze/train-*"}]}, {"config_name": "Visual Logic & Strategic Games - RPM", "data_files": [{"split": "train", "path": "Visual Logic & Strategic Games - RPM/train-*"}]}, {"config_name": "Visual Logic & Strategic Games - Tetris", "data_files": [{"split": "train", "path": "Visual Logic & Strategic Games - Tetris/train-*"}]}]}
| false |
False
| 2025-07-26T02:00:54 | 34 | 20 | false |
0be141b18cb0986c3fa79f77daaec562622f1b1d
|
Zebra‑CoT
A diverse large-scale dataset for interleaved vision‑language reasoning traces.
Dataset Description
Zebra‑CoT is a diverse large‑scale dataset with 182,384 samples containing logically coherent interleaved text‑image reasoning traces across four major categories: scientific reasoning, 2D visual reasoning, 3D visual reasoning, and visual logic & strategic games.
Dataset Structure
Each example in Zebra‑CoT consists of:
Problem statement:… See the full description on the dataset page: https://huggingface.co/datasets/multimodal-reasoning-lab/Zebra-CoT.
| 5,879 | 7,077 |
[
"task_categories:any-to-any",
"task_categories:image-text-to-text",
"task_categories:visual-question-answering",
"license:cc-by-nc-4.0",
"size_categories:100K<n<1M",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2507.16746",
"region:us",
"visual-reasoning",
"multimodal",
"chain-of-thought"
] | 2025-05-25T03:33:36 | null | null |
66e0b225bd62a1da48328722
|
common-pile/caselaw_access_project
|
common-pile
|
{"task_categories": ["text-generation"], "language": ["en"], "pretty_name": "Caselaw Access Project"}
| false |
False
| 2025-06-06T03:51:23 | 176 | 16 | false |
3c2cb5080b3a16a04d8d8d07b28eaec7c1ba7a90
|
Caselaw Access Project
Description
This dataset contains 6.7 million cases from the Caselaw Access Project and Court Listener.
The Caselaw Access Project consists of nearly 40 million pages of U.S. federal and state court decisions and judges’ opinions from the last 365 years.
In addition, Court Listener adds over 900 thousand cases scraped from 479 courts.
The Caselaw Access Project and Court Listener source legal data from a wide variety of resources such as the… See the full description on the dataset page: https://huggingface.co/datasets/common-pile/caselaw_access_project.
| 5,481 | 7,110 |
[
"task_categories:text-generation",
"language:en",
"size_categories:1M<n<10M",
"format:json",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"arxiv:2506.05209",
"region:us"
] | 2024-09-10T20:55:01 | null | null |
67d3479522a51de18affff22
|
nvidia/Llama-Nemotron-Post-Training-Dataset
|
nvidia
|
{"license": "cc-by-4.0", "configs": [{"config_name": "SFT", "data_files": [{"split": "code", "path": "SFT/code/*.jsonl"}, {"split": "math", "path": "SFT/math/*.jsonl"}, {"split": "science", "path": "SFT/science/*.jsonl"}, {"split": "chat", "path": "SFT/chat/*.jsonl"}, {"split": "safety", "path": "SFT/safety/*.jsonl"}], "default": true}, {"config_name": "RL", "data_files": [{"split": "instruction_following", "path": "RL/instruction_following/*.jsonl"}]}]}
| false |
False
| 2025-05-08T17:51:50 | 547 | 15 | false |
ab2a40d258a6a4d9d4c277d702aeea445081766c
|
Llama-Nemotron-Post-Training-Dataset-v1.1 Release
Update [4/8/2025]:
v1.1: We are releasing an additional 2.2M Math and 500K Code Reasoning Data in support of our release of Llama-3.1-Nemotron-Ultra-253B-v1. 🎉
Data Overview
This dataset is a compilation of SFT and RL data that supports improvements of math, code, general reasoning, and instruction following capabilities of the original Llama instruct model, in support of NVIDIA’s release of… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Llama-Nemotron-Post-Training-Dataset.
| 7,455 | 36,738 |
[
"license:cc-by-4.0",
"size_categories:1M<n<10M",
"format:json",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"arxiv:2505.00949",
"region:us"
] | 2025-03-13T21:01:09 | null | null |
6807af7004bb82059e072037
|
deepvk/NonverbalTTS
|
deepvk
|
{"tags": ["audio"], "license": "apache-2.0", "language": ["en"], "pretty_name": "NonverbalTTS", "size_categories": ["1K<n<10K"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "default/train/**"}, {"split": "dev", "path": "default/dev/**"}, {"split": "test", "path": "default/test/**"}, {"split": "other", "path": "default/other/**"}]}], "task_categories": ["text-to-speech"]}
| false |
False
| 2025-07-22T14:47:53 | 29 | 15 | false |
de245c4a2b70f564f85f84b421635d4f5d6ff2ea
|
NonverbalTTS Dataset 🎵🗣️
NonverbalTTS is a 17-hour open-access English speech corpus with aligned text annotations for nonverbal vocalizations (NVs) and emotional categories, designed to advance expressive text-to-speech (TTS) research.
Key Features ✨
17 hours of high-quality speech data
10 NV types: Breathing, laughter, sighing, sneezing, coughing, throat clearing, groaning, grunting, snoring, sniffing
8 emotion categories: Angry, disgusted, fearful, happy… See the full description on the dataset page: https://huggingface.co/datasets/deepvk/NonverbalTTS.
| 908 | 1,043 |
[
"task_categories:text-to-speech",
"language:en",
"license:apache-2.0",
"size_categories:1K<n<10K",
"format:parquet",
"modality:audio",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2507.13155",
"arxiv:2409.09546",
"region:us",
"audio"
] | 2025-04-22T15:02:08 | null | null |
67bb71f1aca0fe22d1e84b44
|
allenai/CoSyn-400K
|
allenai
|
{"license": "odc-by", "task_categories": ["visual-question-answering"], "dataset_info": [{"config_name": "chart", "features": [{"name": "id", "dtype": "string"}, {"name": "image", "dtype": "image"}, {"name": "qa_pairs", "sequence": [{"name": "question", "dtype": "string"}, {"name": "explanation", "dtype": "string"}, {"name": "answer", "dtype": "string"}]}, {"name": "metadata", "struct": [{"name": "figure_type", "dtype": "string"}, {"name": "persona", "dtype": "string"}, {"name": "topic", "dtype": "string"}]}, {"name": "data", "dtype": "string"}, {"name": "code", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 25262691844.136, "num_examples": 116814}, {"name": "validation", "num_bytes": 220083787.264, "num_examples": 1024}], "download_size": 24927449477, "dataset_size": 25482775631.4}, {"config_name": "chemical", "features": [{"name": "id", "dtype": "string"}, {"name": "image", "dtype": "image"}, {"name": "qa_pairs", "sequence": [{"name": "question", "dtype": "string"}, {"name": "explanation", "dtype": "string"}, {"name": "answer", "dtype": "string"}]}, {"name": "metadata", "struct": [{"name": "figure_type", "dtype": "string"}, {"name": "persona", "dtype": "string"}, {"name": "topic", "dtype": "string"}]}, {"name": "data", "dtype": "string"}, {"name": "code", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 282021984.062, "num_examples": 8942}, {"name": "validation", "num_bytes": 4186180, "num_examples": 128}], "download_size": 276447943, "dataset_size": 286208164.062}, {"config_name": "circuit", "features": [{"name": "id", "dtype": "string"}, {"name": "image", "dtype": "image"}, {"name": "qa_pairs", "sequence": [{"name": "question", "dtype": "string"}, {"name": "explanation", "dtype": "string"}, {"name": "answer", "dtype": "string"}]}, {"name": "metadata", "struct": [{"name": "figure_type", "dtype": "string"}, {"name": "persona", "dtype": "string"}, {"name": "topic", "dtype": "string"}]}, {"name": "data", "dtype": "string"}, {"name": "code", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 405803895.22, "num_examples": 10470}, {"name": "validation", "num_bytes": 5126755, "num_examples": 128}], "download_size": 392176815, "dataset_size": 410930650.22}, {"config_name": "diagram", "features": [{"name": "id", "dtype": "string"}, {"name": "image", "dtype": "image"}, {"name": "qa_pairs", "sequence": [{"name": "question", "dtype": "string"}, {"name": "explanation", "dtype": "string"}, {"name": "answer", "dtype": "string"}]}, {"name": "metadata", "struct": [{"name": "figure_type", "dtype": "string"}, {"name": "persona", "dtype": "string"}, {"name": "topic", "dtype": "string"}]}, {"name": "data", "dtype": "string"}, {"name": "code", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 6647512945.646, "num_examples": 34963}, {"name": "validation", "num_bytes": 194765398, "num_examples": 1024}], "download_size": 6695298322, "dataset_size": 6842278343.646}, {"config_name": "document", "features": [{"name": "id", "dtype": "string"}, {"name": "image", "dtype": "image"}, {"name": "qa_pairs", "sequence": [{"name": "question", "dtype": "string"}, {"name": "explanation", "dtype": "string"}, {"name": "answer", "dtype": "string"}]}, {"name": "metadata", "struct": [{"name": "figure_type", "dtype": "string"}, {"name": "persona", "dtype": "string"}, {"name": "topic", "dtype": "string"}]}, {"name": "data", "dtype": "string"}, {"name": "code", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 20408059180.798, "num_examples": 71282}, {"name": "validation", "num_bytes": 287297344.304, "num_examples": 1024}], "download_size": 20220923713, "dataset_size": 20695356525.102}, {"config_name": "graphic", "features": [{"name": "id", "dtype": "string"}, {"name": "image", "dtype": "image"}, {"name": "qa_pairs", "sequence": [{"name": "question", "dtype": "string"}, {"name": "explanation", "dtype": "string"}, {"name": "answer", "dtype": "string"}]}, {"name": "metadata", "struct": [{"name": "figure_type", "dtype": "string"}, {"name": "persona", "dtype": "string"}, {"name": "topic", "dtype": "string"}]}, {"name": "data", "dtype": "string"}, {"name": "code", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 401715264.464, "num_examples": 26968}, {"name": "validation", "num_bytes": 15527102.264, "num_examples": 1024}], "download_size": 360711845, "dataset_size": 417242366.728}, {"config_name": "math", "features": [{"name": "id", "dtype": "string"}, {"name": "image", "dtype": "image"}, {"name": "qa_pairs", "sequence": [{"name": "question", "dtype": "string"}, {"name": "explanation", "dtype": "string"}, {"name": "answer", "dtype": "string"}]}, {"name": "metadata", "struct": [{"name": "figure_type", "dtype": "string"}, {"name": "persona", "dtype": "string"}, {"name": "topic", "dtype": "string"}]}, {"name": "data", "dtype": "string"}, {"name": "code", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 6288774127.884, "num_examples": 66714}, {"name": "validation", "num_bytes": 97463564.56, "num_examples": 1024}], "download_size": 6245281939, "dataset_size": 6386237692.444}, {"config_name": "music", "features": [{"name": "id", "dtype": "string"}, {"name": "image", "dtype": "image"}, {"name": "qa_pairs", "sequence": [{"name": "question", "dtype": "string"}, {"name": "explanation", "dtype": "string"}, {"name": "answer", "dtype": "string"}]}, {"name": "metadata", "struct": [{"name": "figure_type", "dtype": "string"}, {"name": "persona", "dtype": "string"}, {"name": "topic", "dtype": "string"}]}, {"name": "data", "dtype": "string"}, {"name": "code", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 436496623.452, "num_examples": 11969}, {"name": "validation", "num_bytes": 4754704, "num_examples": 128}], "download_size": 397428056, "dataset_size": 441251327.452}, {"config_name": "nutrition", "features": [{"name": "id", "dtype": "string"}, {"name": "image", "dtype": "image"}, {"name": "qa_pairs", "sequence": [{"name": "question", "dtype": "string"}, {"name": "explanation", "dtype": "string"}, {"name": "answer", "dtype": "string"}]}, {"name": "metadata", "struct": [{"name": "figure_type", "dtype": "string"}, {"name": "persona", "dtype": "string"}, {"name": "topic", "dtype": "string"}]}, {"name": "data", "dtype": "string"}, {"name": "code", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1445696898.35, "num_examples": 6931}, {"name": "validation", "num_bytes": 27712685, "num_examples": 128}], "download_size": 1410256975, "dataset_size": 1473409583.35}, {"config_name": "table", "features": [{"name": "id", "dtype": "string"}, {"name": "image", "dtype": "image"}, {"name": "qa_pairs", "sequence": [{"name": "question", "dtype": "string"}, {"name": "explanation", "dtype": "string"}, {"name": "answer", "dtype": "string"}]}, {"name": "metadata", "struct": [{"name": "figure_type", "dtype": "string"}, {"name": "persona", "dtype": "string"}, {"name": "topic", "dtype": "string"}]}, {"name": "data", "dtype": "string"}, {"name": "code", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 7026511042.24, "num_examples": 46518}, {"name": "validation", "num_bytes": 152040498.064, "num_examples": 1024}], "download_size": 6918074537, "dataset_size": 7178551540.304}], "configs": [{"config_name": "chart", "data_files": [{"split": "train", "path": "chart/train-*"}, {"split": "validation", "path": "chart/validation-*"}]}, {"config_name": "chemical", "data_files": [{"split": "train", "path": "chemical/train-*"}, {"split": "validation", "path": "chemical/validation-*"}]}, {"config_name": "circuit", "data_files": [{"split": "train", "path": "circuit/train-*"}, {"split": "validation", "path": "circuit/validation-*"}]}, {"config_name": "diagram", "data_files": [{"split": "train", "path": "diagram/train-*"}, {"split": "validation", "path": "diagram/validation-*"}]}, {"config_name": "document", "data_files": [{"split": "train", "path": "document/train-*"}, {"split": "validation", "path": "document/validation-*"}]}, {"config_name": "graphic", "data_files": [{"split": "train", "path": "graphic/train-*"}, {"split": "validation", "path": "graphic/validation-*"}]}, {"config_name": "math", "data_files": [{"split": "train", "path": "math/train-*"}, {"split": "validation", "path": "math/validation-*"}]}, {"config_name": "music", "data_files": [{"split": "train", "path": "music/train-*"}, {"split": "validation", "path": "music/validation-*"}]}, {"config_name": "nutrition", "data_files": [{"split": "train", "path": "nutrition/train-*"}, {"split": "validation", "path": "nutrition/validation-*"}]}, {"config_name": "table", "data_files": [{"split": "train", "path": "table/train-*"}, {"split": "validation", "path": "table/validation-*"}]}]}
| false |
False
| 2025-02-28T19:14:42 | 32 | 13 | false |
86e46e1fd5e754d056169f0fb38f06c6997ff7de
|
CoSyn-400k
CoSyn-400k is a collection of synthetic question-answer pairs about very diverse range of computer-generated images.
The data was created by using the Claude large language model to generate code that can be executed to render an image,
and using GPT-4o mini to generate Q/A pairs based on the code (without using the rendered image).
The code used to generate this data is open source.
Synthetic pointing data is available in a seperate repo.
Quick links:
📃 CoSyn… See the full description on the dataset page: https://huggingface.co/datasets/allenai/CoSyn-400K.
| 2,172 | 16,916 |
[
"task_categories:visual-question-answering",
"license:odc-by",
"size_categories:100K<n<1M",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2502.14846",
"arxiv:2409.17146",
"region:us"
] | 2025-02-23T19:07:29 | null | null |
6837854ff36dbe5068b5d602
|
open-thoughts/OpenThoughts3-1.2M
|
open-thoughts
|
{"dataset_info": {"features": [{"name": "difficulty", "dtype": "int64"}, {"name": "source", "dtype": "string"}, {"name": "domain", "dtype": "string"}, {"name": "conversations", "list": [{"name": "from", "dtype": "string"}, {"name": "value", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 59763369750, "num_examples": 1200000}], "download_size": 28188197544, "dataset_size": 59763369750}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "license": "apache-2.0", "task_categories": ["text-generation"], "tags": ["reasoning", "mathematics", "code", "science"], "library_name": "datasets"}
| false |
False
| 2025-06-09T16:14:06 | 147 | 12 | false |
61bcf9d4eb38b30295efc2021227a63cc5bb34c8
|
paper |
dataset |
model
[!NOTE]
We have released a paper for OpenThoughts! See our paper here.
OpenThoughts3-1.2M
Open-source state-of-the-art reasoning dataset with 1.2M rows. 🚀
OpenThoughts3-1.2M is the third iteration in our line of OpenThoughts datasets, building on our previous OpenThoughts-114k and OpenThoughts2-1M.
This time around, we scale even further and generate our dataset in a much more systematic way -- OpenThoughts3-1.2M is the result of a… See the full description on the dataset page: https://huggingface.co/datasets/open-thoughts/OpenThoughts3-1.2M.
| 11,811 | 36,632 |
[
"task_categories:text-generation",
"license:apache-2.0",
"size_categories:1M<n<10M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2506.04178",
"region:us",
"reasoning",
"mathematics",
"code",
"science"
] | 2025-05-28T21:51:11 | null | null |
683fd7b68de3ffc58390f5e2
|
XenArcAI/MathX-5M
|
XenArcAI
|
{"license": "mit", "tags": ["Mathematics", "XenArcAI", "High-Performance-Math", "Sparse-Math-Optimization", "Deep-Learning-Mathematics", "Math-Reasoning-LLM", "Symbolic-Math", "Computational-Mathematics", "ML-Math", "HPC-AI", "Numerical-Computing"], "task_categories": ["question-answering", "text-generation"], "size_categories": ["50GB"]}
| false |
False
| 2025-07-26T05:19:46 | 53 | 12 | false |
718166a53a74e462705d55b0c9f9d40448a7ff20
|
XenArcAI
Note : This datset is the part of a lineup MathX by XenArcAI you can get a lots of datasets on this same linup main focus is to provide very high quality datasets for model training
and finetuning
This dataset is curated from high-quality public sources and enhanced with synthetic data from both closed and open-source models. It serves as a strong foundation for instruction-based model tuning and fine-tuning, offering one of the most refined and extensive corpora… See the full description on the dataset page: https://huggingface.co/datasets/XenArcAI/MathX-5M.
| 5,539 | 6,433 |
[
"task_categories:question-answering",
"task_categories:text-generation",
"license:mit",
"size_categories:1M<n<10M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us",
"Mathematics",
"XenArcAI",
"High-Performance-Math",
"Sparse-Math-Optimization",
"Deep-Learning-Mathematics",
"Math-Reasoning-LLM",
"Symbolic-Math",
"Computational-Mathematics",
"ML-Math",
"HPC-AI",
"Numerical-Computing"
] | 2025-06-04T05:20:54 | null | null |
68733036a88d572f1c84c9db
|
StyleXX/OmniStyle-150k
|
StyleXX
|
{"license": "apache-2.0"}
| false |
False
| 2025-07-23T08:00:36 | 13 | 12 | false |
b9264acb310d31e48b7115e958f1594226e63304
|
OmniStyle-150K Dataset
OmniStyle-150K is a high-quality triplet dataset specifically designed to support generalizable, controllable, and high-resolution image style transfer. Each triplet includes a content image, a style reference image, and the corresponding stylized result.
📦 Dataset Structure
OmniStyle-150K/: Stylized result images
content/: Original content images
style/: Style reference images
Each file in the OmniStyle-150K/ folder is named using the… See the full description on the dataset page: https://huggingface.co/datasets/StyleXX/OmniStyle-150k.
| 419 | 419 |
[
"license:apache-2.0",
"region:us"
] | 2025-07-13T04:04:06 | null | null |
6878963273bedf813f4fef37
|
spatialverse/InteriorGS
|
spatialverse
|
{"viewer": false, "license": "other", "license_name": "interiorgs-terms-of-use", "license_link": "https://kloudsim-usa-cos.kujiale.com/InteriorGS/InteriorGS_Terms_of_Use.pdf"}
| false |
auto
| 2025-07-25T06:38:13 | 12 | 12 | false |
f41811680802f1e9f95f9f44658b79751ce76c63
|
InteriorGS: 3D Gaussian Splatting Dataset of Semantically Labeled Indoor Scenes
A comprehensive indoor scene dataset featuring 3D Gaussian representations with semantic annotations and spatial occupancy information.
Sample from the InteriorGS dataset. The dataset provides high-quality 3D Gaussian Splatting (3DGS) representations along with instance-level semantic bounding boxes and occupancy maps indicating agent-accessible areas. The red and yellow trajectories… See the full description on the dataset page: https://huggingface.co/datasets/spatialverse/InteriorGS.
| 653 | 653 |
[
"license:other",
"region:us"
] | 2025-07-17T06:20:34 | null | null |
676f70846bf205795346d2be
|
FreedomIntelligence/medical-o1-reasoning-SFT
|
FreedomIntelligence
|
{"license": "apache-2.0", "task_categories": ["question-answering", "text-generation"], "language": ["en", "zh"], "tags": ["medical", "biology"], "configs": [{"config_name": "en", "data_files": "medical_o1_sft.json"}, {"config_name": "zh", "data_files": "medical_o1_sft_Chinese.json"}, {"config_name": "en_mix", "data_files": "medical_o1_sft_mix.json"}, {"config_name": "zh_mix", "data_files": "medical_o1_sft_mix_Chinese.json"}]}
| false |
False
| 2025-04-22T15:11:21 | 802 | 11 | false |
fc2c9e8a37b38f38da6d449564a8c350b244aef4
|
News
[2025/04/22] We split the data and kept only the medical SFT dataset (medical_o1_sft.json). The file medical_o1_sft_mix.json contains a mix of medical and general instruction data.
[2025/02/22] We released the distilled dataset from Deepseek-R1 based on medical verifiable problems. You can use it to initialize your models with the reasoning chain from Deepseek-R1.
[2024/12/25] We open-sourced the medical reasoning dataset for SFT, built on medical verifiable problems and an LLM… See the full description on the dataset page: https://huggingface.co/datasets/FreedomIntelligence/medical-o1-reasoning-SFT.
| 8,714 | 91,712 |
[
"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 |
67bc84052cedbdaed9ee5c82
|
atalaydenknalbant/rawg-games-dataset
|
atalaydenknalbant
|
{"license": "cc0-1.0", "task_categories": ["sentence-similarity", "summarization", "feature-extraction"], "tags": ["games", "video-games"]}
| false |
False
| 2025-07-22T01:33:53 | 25 | 11 | false |
e8c649971a9c36836ffd1bea1334184d247fd59d
|
Description
RAWG Games Dataset video game records data gathered directly from the RAWG API.
It includes essential fields such as game id, title, release date, rating, genres, platforms, descriptive tags,
Metacritic score, developers, publishers, playtime, and a detailed description. The data was collected to support
studies, trend analysis, and insights into the gaming industry. Each field is aligned with the specifications provided in the RAWG API documentation.… See the full description on the dataset page: https://huggingface.co/datasets/atalaydenknalbant/rawg-games-dataset.
| 398 | 1,177 |
[
"task_categories:sentence-similarity",
"task_categories:summarization",
"task_categories:feature-extraction",
"license:cc0-1.0",
"size_categories:100K<n<1M",
"format:parquet",
"modality:image",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us",
"games",
"video-games"
] | 2025-02-24T14:36:53 | null | null |
6858e379f9dc599076596798
|
facebook/seamless-interaction
|
facebook
|
{"license": "cc-by-nc-4.0", "configs": [{"config_name": "improvised", "data_files": [{"split": "dev", "path": ["improvised/dev/**/*"]}, {"split": "test", "path": ["improvised/test/**/*"]}, {"split": "train", "path": ["improvised/train/**/*"]}]}, {"config_name": "naturalistic", "data_files": [{"split": "dev", "path": ["naturalistic/dev/**/*"]}, {"split": "test", "path": ["naturalistic/test/**/*"]}, {"split": "train", "path": ["naturalistic/train/**/*"]}]}], "tags": ["webdataset", "audio", "video"], "pretty_name": "Seamless Interaction"}
| false |
False
| 2025-07-14T20:45:08 | 125 | 11 | false |
ba9e212ab927ba05bfd80778f53bf9de69f65e3b
|
Seamless Interaction Dataset
A large-scale multimodal dataset of 4,000+ hours of human interactions for AI research
🖼️ Blog
🌐 Website
🎮 Demo
📦 GitHub
📄 Paper
Human communication involves a complex interplay of verbal and nonverbal signals, essential for conveying meaning and achieving interpersonal goals.
The Seamless Interaction Dataset is a large-scale collection of over 4,000 hours of face-to-face interaction footage from more than 4,000 participants in… See the full description on the dataset page: https://huggingface.co/datasets/facebook/seamless-interaction.
| 157,632 | 166,368 |
[
"license:cc-by-nc-4.0",
"modality:audio",
"modality:video",
"library:webdataset",
"region:us",
"webdataset",
"audio",
"video"
] | 2025-06-23T05:17:45 | null | null |
686321460e836b7a4c5621fa
|
atalaydenknalbant/MathCaptcha10k
|
atalaydenknalbant
|
{"license": "cc-by-4.0", "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "ocr_text", "dtype": "string"}, {"name": "result", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 60582512, "num_examples": 10000}, {"name": "test", "num_bytes": 70989855.334, "num_examples": 11766}], "download_size": 132297385, "dataset_size": 131572367.334}, "task_categories": ["question-answering"], "tags": ["captcha", "math", "mathcaptcha", "math-captcha", "mvccaptcha"]}
| false |
False
| 2025-07-06T21:35:38 | 17 | 11 | false |
34d0caf9c175034bae863678c28128fc06ab1d61
|
Dataset Details
Dataset Name: MathCaptcha10k
Curated by: Atalay Denknalbant
License: Creative Commons Attribution 4.0 International (CC BY 4.0)
Repository: https://www.kaggle.com/datasets/atalaydenknalbant/mathcaptcha10k
Dataset Description
A corpus of 10 000 synthetic arithmetic‐captcha images rendered at 200×70 px. Each image contains exactly two base-10 numbers (1–2 digits), a single + or – operator, an = sign and a trailing question mark (e.g.… See the full description on the dataset page: https://huggingface.co/datasets/atalaydenknalbant/MathCaptcha10k.
| 1,865 | 1,934 |
[
"task_categories:question-answering",
"license:cc-by-4.0",
"size_categories:10K<n<100K",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us",
"captcha",
"math",
"mathcaptcha",
"math-captcha",
"mvccaptcha"
] | 2025-06-30T23:44:06 | null | null |
687ea4b5432984e8877a06ed
|
atalaydenknalbant/Kinetics-700
|
atalaydenknalbant
|
{"annotations_creators": ["other"], "language_creators": ["other"], "language": ["en"], "license": ["other"], "multilinguality": ["monolingual"], "pretty_name": "Kinetics-700", "tags": ["video", "action-recognition", "computer-vision", "large-scale", "research", "human-actions"], "dataset_info": {"features": [{"name": "video", "dtype": "video", "description": "Path to the video file."}, {"name": "label", "dtype": "string", "description": "Human action label for the video clip."}, {"name": "youtube_id", "dtype": "string", "description": "The YouTube ID of the source video."}, {"name": "start_time", "dtype": "int64", "description": "Start timestamp of the action clip within the YouTube video (in seconds)."}, {"name": "end_time", "dtype": "int64", "description": "End timestamp of the action clip within the YouTube video (in seconds)."}], "splits": [{"name": "train", "num_bytes": "737,862,498,037", "num_examples": 536499}, {"name": "val", "num_bytes": "50,623,801,874", "num_examples": 33966}, {"name": "test", "num_bytes": "147,390,516,680", "num_examples": 64535}]}, "citation": [{"doi": "10.1109/ICCV.2017.335", "text": "@inproceedings{kay2017kinetics,\n title={The Kinetics Human Action Video Dataset},\n author={Kay, Will and Carreira, Joaquin and Simonyan, Karen and Zhang, Brian and Hillier, Chloe and Vijayanarasimhan, Sudheendra and Viola, Fabio and Tim Green and Trevor Back and Paul Natsev and others},\n booktitle={Proceedings of the IEEE International Conference on Computer Vision},\n pages={6611--6619},\n year={2017}\n}"}, {"doi": "10.1109/CVPR.2019.00971", "text": "@inproceedings{carreira2019kinetics,\n title={A short note on Kinetics-700: a much larger dataset for human action recognition},\n author={Carreira, Joaquin and Chuan, Eric and Zisserman, Andrew},\n booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},\n pages={9503--9506},\n year={2019}\n}"}]}
| false |
False
| 2025-07-27T08:10:44 | 11 | 11 | false |
f3a2cb54af3d9eb6daee706535237af8aae10eca
|
🎬 Dataset Card for Kinetics-700
📦 🚨IMPORTANT Dataset Decompression for Kinetics-700🚨
To fully utilize the Kinetics-700 dataset, you must download and decompress all 22 zipped archives. This process is essential to access the complete video collection. Failure to decompress all archives will result in an incomplete dataset.
📝 Dataset Description
The Kinetics-700 dataset is a large scale collection of YouTube video URLs for human action recognition. It is an… See the full description on the dataset page: https://huggingface.co/datasets/atalaydenknalbant/Kinetics-700.
| 294 | 294 |
[
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"language:en",
"license:other",
"size_categories:100K<n<1M",
"modality:video",
"library:datasets",
"library:mlcroissant",
"region:us",
"video",
"action-recognition",
"computer-vision",
"large-scale",
"research",
"human-actions"
] | 2025-07-21T20:36:05 | null | null |
661823b590a8b6724f1c6534
|
HuggingFaceM4/the_cauldron
|
HuggingFaceM4
|
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"st_vqa", "data_files": [{"split": "train", "path": "st_vqa/train-*"}]}, {"config_name": "tabmwp", "data_files": [{"split": "train", "path": "tabmwp/train-*"}]}, {"config_name": "tallyqa", "data_files": [{"split": "train", "path": "tallyqa/train-*"}]}, {"config_name": "tat_qa", "data_files": [{"split": "train", "path": "tat_qa/train-*"}]}, {"config_name": "textcaps", "data_files": [{"split": "train", "path": "textcaps/train-*"}]}, {"config_name": "textvqa", "data_files": [{"split": "train", "path": "textvqa/train-*"}]}, {"config_name": "tqa", "data_files": [{"split": "train", "path": "tqa/train-*"}]}, {"config_name": "vistext", "data_files": [{"split": "train", "path": "vistext/train-*"}]}, {"config_name": "visual7w", "data_files": [{"split": "train", "path": "visual7w/train-*"}]}, {"config_name": "visualmrc", "data_files": [{"split": "train", "path": "visualmrc/train-*"}]}, {"config_name": "vqarad", "data_files": [{"split": "train", "path": "vqarad/train-*"}]}, {"config_name": "vqav2", "data_files": [{"split": "train", "path": "vqav2/train-*"}]}, {"config_name": "vsr", "data_files": [{"split": "train", "path": "vsr/train-*"}]}, {"config_name": "websight", "data_files": [{"split": "train", "path": "websight/train-*"}]}]}
| false |
False
| 2024-05-06T13:37:52 | 484 | 10 | false |
847a98a779b1652d65111daf20c972dfcd333605
|
Dataset Card for The Cauldron
Dataset description
The Cauldron is part of the Idefics2 release.
It is a massive collection of 50 vision-language datasets (training sets only) that were used for the fine-tuning of the vision-language model Idefics2.
Load the dataset
To load the dataset, install the library datasets with pip install datasets. Then,
from datasets import load_dataset
ds = load_dataset("HuggingFaceM4/the_cauldron", "ai2d")
to download and load the… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceM4/the_cauldron.
| 28,854 | 2,878,158 |
[
"size_categories:1M<n<10M",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:1603.07396",
"arxiv:2206.01718",
"arxiv:2208.05358",
"arxiv:1612.06890",
"arxiv:2310.00367",
"arxiv:1710.07300",
"arxiv:2312.12241",
"arxiv:1912.03098",
"arxiv:2211.08545",
"arxiv:2306.05425",
"arxiv:1709.00103",
"arxiv:2003.12462",
"arxiv:1612.00837",
"arxiv:2205.00363",
"arxiv:2403.09029",
"arxiv:2405.02246",
"region:us"
] | 2024-04-11T17:53:57 | null | null |
686176a165816f63e6edee56
|
theaidealab/workflows
|
theaidealab
|
nan
| false |
False
| 2025-07-29T15:56:41 | 13 | 10 | false |
6a48a73734ddf6edfe21d468e1bb5030caba680f
| null | 5,349 | 5,501 |
[
"region:us"
] | 2025-06-29T17:23:45 | null | null |
688a11828e02585787ed1ed2
|
Trendyol/Trendyol-Cybersecurity-Instruction-Tuning-Dataset
|
Trendyol
|
{"license": "apache-2.0", "task_categories": ["text-generation", "question-answering"], "language": ["en"], "tags": ["cybersecurity", "defensive-security", "instruction-tuning", "threat-intelligence", "incident-response", "security-operations"], "pretty_name": "Trendyol Cybersecurity Defense Dataset", "size_categories": ["10K<n<100K"], "dataset_info": {"version": "1.0.0"}}
| false |
False
| 2025-07-30T13:08:11 | 10 | 10 | false |
357544e7576607d88eaeac9b0adb07e9fd8bb2bb
|
Trendyol Cybersecurity Defense Instruction-Tuning Dataset (v2.0)
🚀 TL;DR
53,202 meticulously curated system/user/assistant instruction-tuning examples covering 200+ specialized cybersecurity domains. Built by the Trendyol Security Team for training state-of-the-art defensive security AI assistants. Expanded from 21K to 53K rows with comprehensive coverage of modern security challenges including cloud-native threats, AI/ML security, quantum computing risks… See the full description on the dataset page: https://huggingface.co/datasets/Trendyol/Trendyol-Cybersecurity-Instruction-Tuning-Dataset.
| 54 | 54 |
[
"task_categories:text-generation",
"task_categories:question-answering",
"language:en",
"license:apache-2.0",
"size_categories:10K<n<100K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us",
"cybersecurity",
"defensive-security",
"instruction-tuning",
"threat-intelligence",
"incident-response",
"security-operations"
] | 2025-07-30T12:35:14 | null | null |
68879040031998011dd7af28
|
Rapidata/text-2-video-human-preferences-genmo-mochi-1
|
Rapidata
|
{"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "video1", "dtype": "string"}, {"name": "video2", "dtype": "string"}, {"name": "weighted_results1_Alignment", "dtype": "float64"}, {"name": "weighted_results2_Alignment", "dtype": "float64"}, {"name": "detailedResults_Alignment", "list": [{"name": "userDetails", "struct": [{"name": "age", "dtype": "string"}, {"name": "country", "dtype": "string"}, {"name": "gender", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "occupation", "dtype": "string"}, {"name": "userScores", "struct": [{"name": "global", "dtype": "float64"}]}]}, {"name": "votedFor", "dtype": "string"}]}, {"name": "weighted_results1_Coherence", "dtype": "float64"}, {"name": "weighted_results2_Coherence", "dtype": "float64"}, {"name": "detailedResults_Coherence", "list": [{"name": "userDetails", "struct": [{"name": "age", "dtype": "string"}, {"name": "country", "dtype": "string"}, {"name": "gender", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "occupation", "dtype": "string"}, {"name": "userScores", "struct": [{"name": "global", "dtype": "float64"}]}]}, {"name": "votedFor", "dtype": "string"}]}, {"name": "weighted_results1_Preference", "dtype": "float64"}, {"name": "weighted_results2_Preference", "dtype": "float64"}, {"name": "detailedResults_Preference", "list": [{"name": "userDetails", "struct": [{"name": "age", "dtype": "string"}, {"name": "country", "dtype": "string"}, {"name": "gender", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "occupation", "dtype": "string"}, {"name": "userScores", "struct": [{"name": "global", "dtype": "float64"}]}]}, {"name": "votedFor", "dtype": "string"}]}, {"name": "file_name1", "dtype": "string"}, {"name": "file_name2", "dtype": "string"}, {"name": "model1", "dtype": "string"}, {"name": "model2", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 6301627, "num_examples": 1103}], "download_size": 653558, "dataset_size": 6301627}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "license": "apache-2.0", "task_categories": ["video-classification", "text-to-video", "text-classification"], "language": ["en"], "tags": ["videos", "t2v", "text-2-video", "text2video", "text-to-video", "human", "annotations", "preferences", "likert", "coherence", "alignment", "wan", "wan 2.1", "veo2", "veo", "pikka", "alpha", "sora", "hunyuan", "veo3", "mochi-1"], "pretty_name": "mochi-1 Human Preferences", "size_categories": ["1K<n<10K"]}
| false |
False
| 2025-07-28T15:09:22 | 9 | 9 | false |
9b8c6dbba6ba4e034adaa509550e53d81e3b7148
|
Rapidata Video Generation Genmo Mochi-1 Human Preference
In this dataset, ~60k human responses from ~20k human annotators were collected to evaluate mochi-1 video generation model on our benchmark. This dataset was collected in roughtly 30 min using the Rapidata Python API, accessible to anyone and ideal for large scale data annotation.
Explore our latest model rankings on our website.
If you get value from this dataset and would like to see more in the future, please… See the full description on the dataset page: https://huggingface.co/datasets/Rapidata/text-2-video-human-preferences-genmo-mochi-1.
| 105 | 105 |
[
"task_categories:video-classification",
"task_categories:text-to-video",
"task_categories:text-classification",
"language:en",
"license:apache-2.0",
"size_categories:1K<n<10K",
"format:parquet",
"modality:image",
"modality:tabular",
"modality:text",
"modality:video",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us",
"videos",
"t2v",
"text-2-video",
"text2video",
"text-to-video",
"human",
"annotations",
"preferences",
"likert",
"coherence",
"alignment",
"wan",
"wan 2.1",
"veo2",
"veo",
"pikka",
"alpha",
"sora",
"hunyuan",
"veo3",
"mochi-1"
] | 2025-07-28T14:59:12 | null | null |
688a19299ffcb1a7664ae936
|
Rapidata/text-2-video-human-preferences-seedance-1-pro
|
Rapidata
|
{"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "video1", "dtype": "string"}, {"name": "video2", "dtype": "string"}, {"name": "weighted_results1_Alignment", "dtype": "float64"}, {"name": "weighted_results2_Alignment", "dtype": "float64"}, {"name": "detailedResults_Alignment", "list": [{"name": "userDetails", "struct": [{"name": "age", "dtype": "string"}, {"name": "country", "dtype": "string"}, {"name": "gender", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "occupation", "dtype": "string"}, {"name": "userScores", "struct": [{"name": "global", "dtype": "float64"}]}]}, {"name": "votedFor", "dtype": "string"}]}, {"name": "weighted_results1_Coherence", "dtype": "float64"}, {"name": "weighted_results2_Coherence", "dtype": "float64"}, {"name": "detailedResults_Coherence", "list": [{"name": "userDetails", "struct": [{"name": "age", "dtype": "string"}, {"name": "country", "dtype": "string"}, {"name": "gender", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "occupation", "dtype": "string"}, {"name": "userScores", "struct": [{"name": "global", "dtype": "float64"}]}]}, {"name": "votedFor", "dtype": "string"}]}, {"name": "weighted_results1_Preference", "dtype": "float64"}, {"name": "weighted_results2_Preference", "dtype": "float64"}, {"name": "detailedResults_Preference", "list": [{"name": "userDetails", "struct": [{"name": "age", "dtype": "string"}, {"name": "country", "dtype": "string"}, {"name": "gender", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "occupation", "dtype": "string"}, {"name": "userScores", "struct": [{"name": "global", "dtype": "float64"}]}]}, {"name": "votedFor", "dtype": "string"}]}, {"name": "file_name1", "dtype": "string"}, {"name": "file_name2", "dtype": "string"}, {"name": "model1", "dtype": "string"}, {"name": "model2", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 6590910, "num_examples": 1092}], "download_size": 626884, "dataset_size": 6590910}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "license": "apache-2.0", "task_categories": ["video-classification", "text-to-video", "text-classification"], "language": ["en"], "tags": ["videos", "t2v", "text-2-video", "text2video", "text-to-video", "human", "annotations", "preferences", "likert", "coherence", "alignment", "wan", "wan 2.1", "veo2", "veo", "pikka", "alpha", "sora", "hunyuan", "veo3", "mochi-1", "seedance-1-pro", "seedance", "seedance 1"], "pretty_name": "seedance-1-pro Human Preferences", "size_categories": ["1K<n<10K"]}
| false |
False
| 2025-07-30T14:39:57 | 8 | 8 | false |
17d28d549b2719ffb4265c73deb4f41225e1e38b
|
Rapidata Video Generation Seedance 1 Pro Human Preference
In this dataset, ~60k human responses from ~20k human annotators were collected to evaluate Seedance 1 Pro video generation model on our benchmark. This dataset was collected in roughtly 30 min using the Rapidata Python API, accessible to anyone and ideal for large scale data annotation.
Explore our latest model rankings on our website.
If you get value from this dataset and would like to see more in the future, please… See the full description on the dataset page: https://huggingface.co/datasets/Rapidata/text-2-video-human-preferences-seedance-1-pro.
| 2 | 2 |
[
"task_categories:video-classification",
"task_categories:text-to-video",
"task_categories:text-classification",
"language:en",
"license:apache-2.0",
"size_categories:1K<n<10K",
"format:parquet",
"modality:image",
"modality:tabular",
"modality:text",
"modality:video",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us",
"videos",
"t2v",
"text-2-video",
"text2video",
"text-to-video",
"human",
"annotations",
"preferences",
"likert",
"coherence",
"alignment",
"wan",
"wan 2.1",
"veo2",
"veo",
"pikka",
"alpha",
"sora",
"hunyuan",
"veo3",
"mochi-1",
"seedance-1-pro",
"seedance",
"seedance 1"
] | 2025-07-30T13:07:53 | null | null |
688a32a17f12efa7e0295d03
|
Rapidata/text-2-video-human-preferences-kling-v2.1-master
|
Rapidata
|
{"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "video1", "dtype": "string"}, {"name": "video2", "dtype": "string"}, {"name": "weighted_results1_Alignment", "dtype": "float64"}, {"name": "weighted_results2_Alignment", "dtype": "float64"}, {"name": "detailedResults_Alignment", "list": [{"name": "userDetails", "struct": [{"name": "age", "dtype": "string"}, {"name": "country", "dtype": "string"}, {"name": "gender", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "occupation", "dtype": "string"}, {"name": "userScores", "struct": [{"name": "global", "dtype": "float64"}]}]}, {"name": "votedFor", "dtype": "string"}]}, {"name": "weighted_results1_Coherence", "dtype": "float64"}, {"name": "weighted_results2_Coherence", "dtype": "float64"}, {"name": "detailedResults_Coherence", "list": [{"name": "userDetails", "struct": [{"name": "age", "dtype": "string"}, {"name": "country", "dtype": "string"}, {"name": "gender", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "occupation", "dtype": "string"}, {"name": "userScores", "struct": [{"name": "global", "dtype": "float64"}]}]}, {"name": "votedFor", "dtype": "string"}]}, {"name": "weighted_results1_Preference", "dtype": "float64"}, {"name": "weighted_results2_Preference", "dtype": "float64"}, {"name": "detailedResults_Preference", "list": [{"name": "userDetails", "struct": [{"name": "age", "dtype": "string"}, {"name": "country", "dtype": "string"}, {"name": "gender", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "occupation", "dtype": "string"}, {"name": "userScores", "struct": [{"name": "global", "dtype": "float64"}]}]}, {"name": "votedFor", "dtype": "string"}]}, {"name": "file_name1", "dtype": "string"}, {"name": "file_name2", "dtype": "string"}, {"name": "model1", "dtype": "string"}, {"name": "model2", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 6789195, "num_examples": 1191}], "download_size": 657410, "dataset_size": 6789195}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "license": "apache-2.0", "task_categories": ["video-classification", "text-to-video", "text-classification"], "language": ["en"], "tags": ["videos", "t2v", "text-2-video", "text2video", "text-to-video", "human", "annotations", "preferences", "likert", "coherence", "alignment", "wan", "wan 2.1", "veo2", "veo", "pikka", "alpha", "sora", "hunyuan", "veo3", "mochi-1", "seedance-1-pro", "seedance", "seedance 1", "kling", "kling v2.1", "kling v2.1 master"], "pretty_name": "kling v2.1 master Human Preferences", "size_categories": ["1K<n<10K"]}
| false |
False
| 2025-07-30T15:45:21 | 8 | 8 | false |
c8d1327f9ce461d063dd415d42f8108146723e52
|
Rapidata Video Generation Kling v2.1 Master Human Preference
In this dataset, ~60k human responses from ~20k human annotators were collected to evaluate Kling v2.1 Master video generation model on our benchmark. This dataset was collected in roughtly 30 min using the Rapidata Python API, accessible to anyone and ideal for large scale data annotation.
Explore our latest model rankings on our website.
If you get value from this dataset and would like to see more in the future… See the full description on the dataset page: https://huggingface.co/datasets/Rapidata/text-2-video-human-preferences-kling-v2.1-master.
| 3 | 3 |
[
"task_categories:video-classification",
"task_categories:text-to-video",
"task_categories:text-classification",
"language:en",
"license:apache-2.0",
"size_categories:1K<n<10K",
"format:parquet",
"modality:image",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us",
"videos",
"t2v",
"text-2-video",
"text2video",
"text-to-video",
"human",
"annotations",
"preferences",
"likert",
"coherence",
"alignment",
"wan",
"wan 2.1",
"veo2",
"veo",
"pikka",
"alpha",
"sora",
"hunyuan",
"veo3",
"mochi-1",
"seedance-1-pro",
"seedance",
"seedance 1",
"kling",
"kling v2.1",
"kling v2.1 master"
] | 2025-07-30T14:56:33 | 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-2025-05", "data_files": [{"split": "train", "path": "data/CC-MAIN-2025-05/*"}]}, {"config_name": "CC-MAIN-2025-08", "data_files": [{"split": "train", "path": "data/CC-MAIN-2025-08/*"}]}, {"config_name": "CC-MAIN-2025-13", "data_files": [{"split": "train", "path": "data/CC-MAIN-2025-13/*"}]}, {"config_name": "CC-MAIN-2025-18", "data_files": [{"split": "train", "path": "data/CC-MAIN-2025-18/*"}]}, {"config_name": "CC-MAIN-2025-21", "data_files": [{"split": 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| false |
False
| 2025-07-11T20:16:53 | 2,273 | 7 | false |
9bb295ddab0e05d785b879661af7260fed5140fc
|
🍷 FineWeb
15 trillion tokens of the finest data the 🌐 web has to offer
What is it?
The 🍷 FineWeb dataset consists of more than 18.5T tokens (originally 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… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceFW/fineweb.
| 683,082 | 4,657,624 |
[
"task_categories:text-generation",
"language:en",
"license:odc-by",
"size_categories:10B<n<100B",
"modality:tabular",
"modality:text",
"arxiv:2306.01116",
"arxiv:2109.07445",
"arxiv:2406.17557",
"doi:10.57967/hf/2493",
"region:us"
] | 2024-04-18T14:33:13 | null | null |
66b5c35c854ad316cf7a8493
|
moondream/synthcat
|
moondream
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "elements", "sequence": [{"name": "role", "dtype": "string"}, {"name": "text", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 188454566523, "num_examples": 2000000}], "download_size": 188179589916, "dataset_size": 188454566523}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
| false |
False
| 2025-07-27T17:56:17 | 7 | 7 | false |
4594615c145cbbedbe2c5335d4f89eb2d5abdb45
|
Synthetically generated OCR samples. Similar to SynthDog, but more realistic text and larger scale.
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.
| 480 | 480 |
[
"size_categories:1M<n<10M",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2024-08-09T07:21:00 | null | null |
6791fcbb49c4df6d798ca7c9
|
cais/hle
|
cais
|
{"license": "mit", "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "image", "dtype": "string"}, {"name": "image_preview", "dtype": "image"}, {"name": "answer", "dtype": "string"}, {"name": "answer_type", "dtype": "string"}, {"name": "author_name", "dtype": "string"}, {"name": "rationale", "dtype": "string"}, {"name": "rationale_image", "dtype": "image"}, {"name": "raw_subject", "dtype": "string"}, {"name": "category", "dtype": "string"}, {"name": "canary", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 284205983, "num_examples": 2500}], "download_size": 274276147, "dataset_size": 284205983}, "configs": [{"config_name": "default", "data_files": [{"split": "test", "path": "data/test-*"}]}]}
| false |
auto
| 2025-05-20T21:28:17 | 438 | 7 | false |
021a3d71f516a7ac28ceb8d284969902edf1edeb
|
Humanity's Last Exam
🌐 Website | 📄 Paper | GitHub
Center for AI Safety & Scale AI
Humanity's Last Exam (HLE) is a multi-modal benchmark at the frontier of human knowledge, designed to be the final closed-ended academic benchmark of its kind with broad subject coverage. Humanity's Last Exam consists of 2,500 questions across dozens of subjects, including mathematics, humanities, and the natural sciences. HLE is developed globally by subject-matter experts and consists of… See the full description on the dataset page: https://huggingface.co/datasets/cais/hle.
| 13,364 | 49,828 |
[
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"modality:image",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2025-01-23T08:24:27 | 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 |
False
| 2025-02-18T11:45:27 | 623 | 7 | 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.
| 27,075 | 191,162 |
[
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"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2025-02-10T13:41:48 | 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", "reinforcement-learning"], "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 |
False
| 2025-07-02T20:43:57 | 71 | 7 | false |
69b73a4d1ebbf8b88278793a8028d253c5b214fe
|
HelpSteer3
HelpSteer3 is an open-source dataset (CC-BY-4.0) that supports aligning models to become more helpful in responding to user prompts.
HelpSteer3-Preference can be used to train Llama 3.3 Nemotron Super 49B v1 (for Generative RMs) and Llama 3.3 70B Instruct Models (for Bradley-Terry RMs) to produce Reward Models that score as high as 85.5% on RM-Bench and 78.6% on JudgeBench, which substantially surpass existing Reward Models on these benchmarks.
HelpSteer3-Feedback and… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/HelpSteer3.
| 3,213 | 11,151 |
[
"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:2410.16184",
"arxiv:2505.11475",
"arxiv:2503.04378",
"region:us",
"human-feedback",
"reinforcement-learning"
] | 2025-03-13T16:18:41 | null | null |
6878fe94cb3130b11ddfc192
|
iitolstykh/NHR-Edit
|
iitolstykh
|
{"language": ["en"], "license": "apache-2.0", "task_categories": ["image-to-image", "text-to-image"], "pretty_name": "NHR-Edit", "dataset_type": "image", "arxiv": 2507.14119, "tags": ["image-editing", "generative-ai", "triplet-mining"], "size_categories": ["100K<n<1M"]}
| false |
False
| 2025-07-23T13:03:07 | 20 | 7 | false |
b7404f4857ae87e07e6c8852dcf2572f6c70dc44
|
NoHumanRequired (NHR) Dataset for image editing
🌐 NHR Website |
📜 NHR Paper on arXiv |
💻 GitHub Repository |
🤗 BAGEL-NHR-Edit |
NHR-Edit is a training dataset for instruction-based image editing. Each sample consists of an input image, a natural language editing instruction, and the corresponding edited image. All samples are generated fully automatically using the NoHumanRequired pipeline, without any human annotation or filtering.
This dataset is… See the full description on the dataset page: https://huggingface.co/datasets/iitolstykh/NHR-Edit.
| 37,516 | 37,516 |
[
"task_categories:image-to-image",
"task_categories:text-to-image",
"language:en",
"license:apache-2.0",
"size_categories:1K<n<10K",
"format:imagefolder",
"modality:image",
"modality:text",
"library:datasets",
"library:mlcroissant",
"arxiv:2507.14119",
"region:us",
"image-editing",
"generative-ai",
"triplet-mining"
] | 2025-07-17T13:45:56 | null | null |
645e8da96320b0efe40ade7a
|
roneneldan/TinyStories
|
roneneldan
|
{"license": "cdla-sharing-1.0", "task_categories": ["text-generation"], "language": ["en"]}
| false |
False
| 2024-08-12T13:27:26 | 707 | 6 | false |
f54c09fd23315a6f9c86f9dc80f725de7d8f9c64
|
Dataset containing synthetically generated (by GPT-3.5 and GPT-4) short stories that only use a small vocabulary.
Described in the following paper: https://arxiv.org/abs/2305.07759.
The models referred to in the paper were trained on TinyStories-train.txt (the file tinystories-valid.txt can be used for validation loss). These models can be found on Huggingface, at roneneldan/TinyStories-1M/3M/8M/28M/33M/1Layer-21M.
Additional resources:
tinystories_all_data.tar.gz - contains a superset of… See the full description on the dataset page: https://huggingface.co/datasets/roneneldan/TinyStories.
| 33,799 | 732,622 |
[
"task_categories:text-generation",
"language:en",
"license:cdla-sharing-1.0",
"size_categories:1M<n<10M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2305.07759",
"region:us"
] | 2023-05-12T19:04:09 | null | null |
6879f16814f35d5cabe1926e
|
MegaScience/TextbookReasoning
|
MegaScience
|
{"language": ["en"], "license": "cc-by-nc-sa-4.0", "size_categories": ["100K<n<1M"], "task_categories": ["text-generation"], "library_name": "datasets", "tags": ["science", "reasoning", "scientific-reasoning", "question-answering", "education", "textbooks"], "dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "subject", "dtype": "string"}, {"name": "reference_answer", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 997341823, "num_examples": 651840}], "download_size": 532362586, "dataset_size": 997341823}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
| false |
False
| 2025-07-24T04:57:03 | 11 | 6 | false |
ca7ecbec76d01bff2e99f3dc17735b02f87d4e96
|
MegaScience: Pushing the Frontiers of Post-Training Datasets for Science Reasoning
Dataset Description
Scientific reasoning is critical for developing AI scientists and supporting human researchers in advancing the frontiers of natural science discovery. However, the open-source community has primarily focused on mathematics and coding while neglecting the scientific domain, largely due to the absence of open, large-scale, high-quality, verifiable scientific reasoning… See the full description on the dataset page: https://huggingface.co/datasets/MegaScience/TextbookReasoning.
| 1,039 | 1,039 |
[
"task_categories:text-generation",
"language:en",
"license:cc-by-nc-sa-4.0",
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2507.16812",
"region:us",
"science",
"reasoning",
"scientific-reasoning",
"question-answering",
"education",
"textbooks"
] | 2025-07-18T07:02:00 | null | null |
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"models": [
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"_id": "687de260234339fed21e768a",
"id": "Qwen/Qwen3-235B-A22B-Instruct-2507"
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],
"relation": "quantized"
}
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