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timestamp[ns]date 2021-02-05 16:03:35
2025-07-29 13:13:47
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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 | 8,483 | 70 | false |
68ba7694e23014788dcc8ab5afe613824f45a05c
|
🧠 Awesome ChatGPT Prompts [CSV dataset]
This is a Dataset Repository of Awesome ChatGPT Prompts
View All Prompts on GitHub
License
CC-0
| 32,125 | 226,661 |
[
"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 |
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 | 68 | 68 | false |
cf5c4ed24134666ffb642fd34bc38fa9ff2ca909
| null | 1,097 | 1,246 |
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"language:en",
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"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us",
"tool-use",
"json-mode",
"reasoning",
"rl"
] | 2025-06-24T05:57:39 | null | null |
687141e6df15b094718f28be
|
NousResearch/Hermes-3-Dataset
|
NousResearch
|
{"license": "apache-2.0"}
| false |
False
| 2025-07-11T17:43:25 | 256 | 56 | false |
b1fddbdcae4e6714889365d1e6ce266a45289cc9
| 5,931 | 5,931 |
[
"license:apache-2.0",
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"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 | 47 | 47 | 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.
| 2,408 | 2,408 |
[
"task_categories:text-generation",
"language:en",
"license:cc-by-nc-sa-4.0",
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"format:parquet",
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"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2507.16812",
"region:us",
"science",
"reasoning"
] | 2025-07-18T08:55:30 | 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 | 157 | 33 | 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.
| 9,644 | 9,659 |
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"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 |
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 - 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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 | 27 | 27 | 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.
| 4,580 | 5,750 |
[
"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 | 175 | 25 | 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.
| 5,202 | 6,566 |
[
"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 |
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 | 20 | 20 | 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.
| 1,411 | 1,411 |
[
"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 |
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 | 27 | 17 | 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.
| 670 | 804 |
[
"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 |
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 | 124 | 16 | 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.
| 156,455 | 159,468 |
[
"license:cc-by-nc-4.0",
"modality:audio",
"modality:video",
"library:webdataset",
"region:us",
"webdataset",
"audio",
"video"
] | 2025-06-23T05:17:45 | null | null |
67bb71f1aca0fe22d1e84b44
|
allenai/CoSyn-400K
|
allenai
|
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{"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": 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"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": 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"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 | 31 | 14 | 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.
| 2,167 | 16,744 |
[
"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 |
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 | 14 | 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.
| 300 | 1,076 |
[
"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 |
661823b590a8b6724f1c6534
|
HuggingFaceM4/the_cauldron
|
HuggingFaceM4
|
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"ocrvqa/train-*"}]}, {"config_name": "okvqa", "data_files": [{"split": "train", "path": "okvqa/train-*"}]}, {"config_name": "plotqa", "data_files": [{"split": "train", "path": "plotqa/train-*"}]}, {"config_name": "raven", "data_files": [{"split": "train", "path": "raven/train-*"}]}, {"config_name": "rendered_text", "data_files": [{"split": "train", "path": "rendered_text/train-*"}]}, {"config_name": "robut_sqa", "data_files": [{"split": "train", "path": "robut_sqa/train-*"}]}, {"config_name": "robut_wikisql", "data_files": [{"split": "train", "path": "robut_wikisql/train-*"}]}, {"config_name": "robut_wtq", "data_files": [{"split": "train", "path": "robut_wtq/train-*"}]}, {"config_name": "scienceqa", "data_files": [{"split": "train", "path": "scienceqa/train-*"}]}, {"config_name": "screen2words", "data_files": [{"split": "train", "path": "screen2words/train-*"}]}, {"config_name": "spot_the_diff", "data_files": [{"split": "train", "path": "spot_the_diff/train-*"}]}, {"config_name": "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 | 482 | 12 | 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.
| 31,163 | 2,875,880 |
[
"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 |
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 | 12 | 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.
| 35,655 | 35,655 |
[
"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 |
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 | 16 | 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.
| 1,059 | 1,059 |
[
"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.
| 161 | 161 |
[
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"language:en",
"license:other",
"modality:video",
"region:us",
"video",
"action-recognition",
"computer-vision",
"large-scale",
"research",
"human-actions"
] | 2025-07-21T20:36:05 | 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 | 798 | 10 | 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.
| 8,515 | 91,032 |
[
"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 |
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 | 541 | 10 | 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.
| 7,440 | 35,844 |
[
"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 |
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 | 143 | 10 | 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.
| 12,912 | 35,972 |
[
"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 | 50 | 10 | 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.
| 5,226 | 6,118 |
[
"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 |
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 | 9 | 9 | 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.
| 729 | 729 |
[
"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 |
6822a5044c32644c4c93f908
|
facebook/community-alignment-dataset
|
facebook
|
{"license": "cc-by-4.0", "language": ["hi", "en", "pt", "it", "fr"], "tags": ["alignment", "preference", "reward", "llm"], "pretty_name": "Community Alignment Dataset", "size_categories": ["10K<n<100K"]}
| false |
False
| 2025-07-16T02:01:47 | 28 | 8 | false |
57557ea20217667608902399b003b3e5a7fa8e5e
|
Community Alignment
Github |
Paper
Dataset
Community Alignment is a large-scale open source, multilingual and multi-turn preference dataset to align LLMs with human preferences across cultures. It features prompt-level overlap in annotators, enabling social-choice-based and distributional approaches to LLM alignment, as well as natural language explanations for choices.
[Large-scale] ~200,000 comparisons of LLM responses, collected from >3,000 unique annotators who… See the full description on the dataset page: https://huggingface.co/datasets/facebook/community-alignment-dataset.
| 557 | 557 |
[
"language:hi",
"language:en",
"language:pt",
"language:it",
"language:fr",
"license:cc-by-4.0",
"size_categories:10K<n<100K",
"format:csv",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2507.09650",
"region:us",
"alignment",
"preference",
"reward",
"llm"
] | 2025-05-13T01:48:52 | null | null |
686fc33898943c873b45c9a0
|
HuggingFaceTB/smoltalk2
|
HuggingFaceTB
|
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"SFT/smoltalk_smollm3_smol_rewrite_no_think-*"}, {"split": "smoltalk_smollm3_smol_summarize_no_think", "path": "SFT/smoltalk_smollm3_smol_summarize_no_think-*"}, {"split": "smoltalk_smollm3_systemchats_30k_no_think", "path": "SFT/smoltalk_smollm3_systemchats_30k_no_think-*"}, {"split": "table_gpt_no_think", "path": "SFT/table_gpt_no_think-*"}, {"split": "tulu_3_sft_personas_instruction_following_no_think", "path": "SFT/tulu_3_sft_personas_instruction_following_no_think-*"}, {"split": "xlam_traces_no_think", "path": "SFT/xlam_traces_no_think-*"}]}]}
| false |
False
| 2025-07-11T15:02:04 | 81 | 8 | false |
4a2ecec51a9da187480ee05d6ed2d00b27749706
|
SmolTalk2
Dataset description
This dataset contains three subsets (Mid, SFT, Preference) that correspond to the three phases of Post-Training for SmolLM3-3B. You can find more details in our blog post about how we used the data in each of the stages SmolLM3.
The specific weight of each subset is available in the training recipe in SmolLM's repository.
You can load a dataset using
from datasets import load_dataset
# To load the train split of a specific subset, such as… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceTB/smoltalk2.
| 6,545 | 6,545 |
[
"size_categories:1M<n<10M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2410.15553",
"arxiv:2412.15115",
"region:us"
] | 2025-07-10T13:42:16 | null | null |
687fb21e84441782e4a693c9
|
quotientai/limbic-eval-tool-use-mcp
|
quotientai
|
{"dataset_info": {"features": [{"name": "available_tools", "dtype": "string"}, {"name": "message_history", "dtype": "string"}, {"name": "score", "dtype": "string"}, {"name": "failure_reason", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 62214490, "num_examples": 9813}], "download_size": 20381332, "dataset_size": 124428980}, "configs": [{"config_name": "default", "data_files": [{"split": "test", "path": "data/test-*"}]}]}
| false |
False
| 2025-07-22T16:21:12 | 8 | 8 | false |
5334425eea8a5cdc818ccec534116baf042368fb
|
Dataset Summary
The MCP Tool Call Evaluation Test Dataset is a synthetic dataset designed for evaluating and benchmarking language models' ability to correctly execute function calls in the context of Model Context Protocol (MCP) tools. This dataset contains 9,813 test examples that assess a model's proficiency in:
Tool Selection: Choosing the correct function from available tools
Parameter Structure: Providing all required parameters with correct names
Parameter Values: Supplying… See the full description on the dataset page: https://huggingface.co/datasets/quotientai/limbic-eval-tool-use-mcp.
| 228 | 228 |
[
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2025-07-22T15:45:34 | 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 | 8 | 8 | 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.
| 53 | 53 |
[
"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 |
621ffdd236468d709f182a80
|
allenai/c4
|
allenai
|
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| false |
False
| 2024-01-09T19:14:03 | 442 | 7 | 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 per… See the full description on the dataset page: https://huggingface.co/datasets/allenai/c4.
| 283,555 | 6,771,055 |
[
"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 |
66212f29fb07c3e05ad0432e
|
HuggingFaceFW/fineweb
|
HuggingFaceFW
|
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| false |
False
| 2025-07-11T20:16:53 | 2,269 | 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.
| 744,817 | 4,628,357 |
[
"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.
| 102 | 102 |
[
"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 |
670f3aa1af1b2ffefc95d350
|
sapientinc/sudoku-extreme
|
sapientinc
|
{"task_categories": ["question-answering"]}
| false |
False
| 2024-10-17T05:50:57 | 12 | 7 | false |
58942f96baeb572ca3127e2a9e9c70f330783d6b
|
Hardest Sudoku Puzzle Dataset V2
This dataset contains a mixture of easy and very hard Sudoku puzzles collected from the Sudoku community.
Dataset Composition
Sources
tdoku benchmarks
enjoysudoku
Easy Puzzles (1.1M)
puzzles0_kaggle
puzzles1_unbiased
puzzles2_17_clue
Hard Puzzles (3.1M)
puzzles3_magictour_top1465
puzzles4_forum_hardest_1905
puzzles6_forum_hardest_1106
ph_2010/01_file1.txt
Dataset Characteristics
All… See the full description on the dataset page: https://huggingface.co/datasets/sapientinc/sudoku-extreme.
| 983 | 1,518 |
[
"task_categories:question-answering",
"size_categories:1M<n<10M",
"format:csv",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2024-10-16T04:01:37 | null | null |
685ee204a610e50e6d2a13cc
|
snorkelai/agent-finance-reasoning
|
snorkelai
|
{"license": "apache-2.0", "task_categories": ["question-answering"], "language": ["en"], "tags": ["finance"], "size_categories": ["n<1K"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "train.parquet"}]}]}
| false |
False
| 2025-07-22T16:16:32 | 46 | 7 | false |
aa385590657e2e4d608f9a2666523f44da902674
|
Agent Finance Reasoning
This dataset includes traces and associated metadata from agentic interactions on a Financial Reasoning task. We built the agentic system in langgraph and ReAct agents. In each sample, the agent is given a question that must be answered with information from one or more company 10-K's. The 10-K's are accessible to the agents through tools.
Our question-answer pairs have been carefully co-created with Snorkel's Expert Data-as-a-Service network of financial… See the full description on the dataset page: https://huggingface.co/datasets/snorkelai/agent-finance-reasoning.
| 4,159 | 4,172 |
[
"task_categories:question-answering",
"language:en",
"license:apache-2.0",
"size_categories:n<1K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us",
"finance"
] | 2025-06-27T18:25:08 | null | null |
686176a165816f63e6edee56
|
theaidealab/workflows
|
theaidealab
|
nan
| false |
False
| 2025-07-23T16:57:43 | 9 | 7 | false |
b8e1e9439c037a0b26dec6e4242d048507c93879
| null | 4,142 | 4,146 |
[
"region:us"
] | 2025-06-29T17:23:45 | null | null |
68702a21e541641406c04533
|
futurehouse/hle-gold-bio-chem
|
futurehouse
|
{"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": "train", "num_bytes": 16938456.0072, "num_examples": 149}], "download_size": 4982881, "dataset_size": 16938456.0072}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "license": "mit", "language": ["en"], "task_categories": ["question-answering"], "tags": ["biology", "chemistry"]}
| false |
False
| 2025-07-23T17:02:11 | 7 | 7 | false |
1feb9e1d545731dba81e594438330406830e5260
|
Humanity's Last Exam (HLE) Bio/Chem Gold
Humanity’s Last Exam (HLE) is a challenging question-answering AI benchmark covering advanced academic fields including Math, Physics, Chemistry, Biology, Engineering, and Computer Science.
At FutureHouse, we audited the biology and chemistry subsets of HLE using a combination of expert human evaluators and our in-house research agent, and found that around 30% of the questions contain answers directly contradicted by peer-reviewed… See the full description on the dataset page: https://huggingface.co/datasets/futurehouse/hle-gold-bio-chem.
| 279 | 279 |
[
"task_categories:question-answering",
"language:en",
"license:mit",
"size_categories:n<1K",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us",
"biology",
"chemistry"
] | 2025-07-10T21:01:21 | 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 | 622 | 6 | false |
e4e141ec9dea9f8326f4d347be56105859b2bd68
|
OpenR1-Math-220k
Dataset description
OpenR1-Math-220k is a large-scale dataset for mathematical reasoning. It consists of 220k math problems with two to four reasoning traces generated by DeepSeek R1 for problems from NuminaMath 1.5.
The traces were verified using Math Verify for most samples and Llama-3.3-70B-Instruct as a judge for 12% of the samples, and each problem contains at least one reasoning trace with a correct answer.
The dataset consists of two splits:… See the full description on the dataset page: https://huggingface.co/datasets/open-r1/OpenR1-Math-220k.
| 28,260 | 190,271 |
[
"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 |
6867333920521a7f4c7ff024
|
hackaprompt/Pliny_HackAPrompt_Dataset
|
hackaprompt
|
{"task_categories": ["text-generation"], "language": ["en"], "tags": ["redteaming", "safety", "prompt-injections", "jailbreaks"], "pretty_name": "Pliny_HackAPrompt_Dataset", "license": "cc-by-4.0", "citation": "@dataset{pliny_hackaprompt_2025,\n author = {Michael Ilie, Saurav Vidyadhara, Fady Yanni, Sander Schulhoff},\n title = {Pliny HackAPrompt Dataset},\n year = {2025},\n version = {1.0},\n url = {https://huggingface.co/datasets/hackaprompt/Pliny_HackAPrompt_Dataset},\n note = {Accessed 25 July 2025}\n}\n", "size_categories": ["10K<n<100K"]}
| false |
auto
| 2025-07-25T23:33:25 | 107 | 6 | false |
50039d57a45ebf717962f72bf8dd9d807f10e575
|
Pliny Challenges README
Welcome to the Pliny X HackAPrompt Dataset! We open source all submissions to the Pliny X HackAPrompt competition track. Here's what you need to know about the data and how to work with it.
If you're interested in learning more about this dataset and other HackAPrompt offerings and events, please reach out to [email protected] .
In the Pliny track, there are 12 challenges, of which 3 are image-only challenges (pliny_6_challenge, pliny_7_challenge… See the full description on the dataset page: https://huggingface.co/datasets/hackaprompt/Pliny_HackAPrompt_Dataset.
| 1,731 | 1,731 |
[
"task_categories:text-generation",
"language:en",
"license:cc-by-4.0",
"size_categories:10K<n<100K",
"format:arrow",
"modality:text",
"library:datasets",
"library:mlcroissant",
"region:us",
"redteaming",
"safety",
"prompt-injections",
"jailbreaks"
] | 2025-07-04T01:49:45 | null | null |
6870b161fd87ef6f64841c4b
|
WeiChow/merit
|
WeiChow
|
{"license": "apache-2.0", "configs": [{"config_name": "default", "data_files": [{"split": "test", "path": "data/test-*"}, {"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "title", "dtype": "string"}, {"name": "idx", "dtype": "string"}, {"name": "class", "dtype": "string"}, {"name": "country", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "attribute", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 51596983155.875, "num_examples": 51177}, {"name": "train", "num_bytes": 140440312133.625, "num_examples": 135027}], "download_size": 189814608379, "dataset_size": 192037295289.5}}
| false |
False
| 2025-07-27T06:47:05 | 9 | 6 | false |
17d1a6cd0283b2dd4e7a5b19d5d2af5438d2c963
|
MERIT: Multilingual Semantic Retrieval with Interleaved Multi-Condition Query
This repository serves as the official storage for the MERIT retrieval dataset mentioned in the paper. MERIT is the first multilingual dataset designed for interleaved multi-condition semantic retrieval, consisting of 320,000 queries and 135,000 products across 5 languages, covering 7 distinct product categories.
Dataset Organization
Specifically, the data is organized in the following… See the full description on the dataset page: https://huggingface.co/datasets/WeiChow/merit.
| 1,115 | 1,115 |
[
"license:apache-2.0",
"size_categories:100K<n<1M",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2506.03144",
"region:us"
] | 2025-07-11T06:38:25 | null | null |
687915b30d9c42af21d382a8
|
t-tech/T-Wix
|
t-tech
|
{"license": "odc-by", "language": ["ru", "en"], "size_categories": ["100M<n<1B"], "pretty_name": "T-Wix", "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "subset", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3628841342, "num_examples": 499598}], "download_size": 1701308457, "dataset_size": 3628841342}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
| false |
False
| 2025-07-17T17:35:21 | 25 | 6 | false |
5eee845e765a33262e0191c993d34b62c3a86fa5
|
T-Wix SFT Mixture
🚨 T-Wix is built entirely from publicly available data and intended for use in research and development.
The dataset may contain noise, biases, or artifacts that require careful inspection and preprocessing.
Users are fully responsible for any downstream use and must ensure compliance with ethical, legal, and safety standards.
📝 Dataset Summary
T‑Wix is a Russian supervised fine‑tuning (SFT) dataset.
The dataset is divided into 2 sticks:… See the full description on the dataset page: https://huggingface.co/datasets/t-tech/T-Wix.
| 605 | 605 |
[
"language:ru",
"language:en",
"license:odc-by",
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2308.07074",
"arxiv:2308.12032",
"region:us"
] | 2025-07-17T15:24:35 | null | null |
687a009b9e5dc43cb2aa1ae9
|
dorni/SpeakerVid-5M-Dataset
|
dorni
|
nan
| false |
False
| 2025-07-23T13:47:21 | 6 | 6 | false |
631b6ca2bb006d3dd99c0cf8ebfc476290a66028
|
Data Usage (download from hugging face)
We provide separate list files for all data and SFT data. The all_data_list.json file contains the YouTube video IDs and the names of several clips obtained from the video segmentation (these names serve as unique identifiers and can be used to locate the corresponding annotations in the annotation folder). Every YouTube video ID specific to a single video on youtube.com, for example, you can access 8Hg_-5aUOYo through Link… See the full description on the dataset page: https://huggingface.co/datasets/dorni/SpeakerVid-5M-Dataset.
| 249 | 249 |
[
"region:us"
] | 2025-07-18T08:06:51 | null | null |
687a34af842ce282335570c0
|
knowledgator/gliclass-v3-logic-dataset
|
knowledgator
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "true_labels", "sequence": "string"}, {"name": "all_labels", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 8157690, "num_examples": 7776}], "download_size": 4729534, "dataset_size": 8157690}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "license": "apache-2.0", "task_categories": ["text-classification", "question-answering", "sentence-similarity"], "language": ["en"], "tags": ["logic", "reasoning"], "size_categories": ["1K<n<10K"]}
| false |
False
| 2025-07-21T14:51:26 | 6 | 6 | false |
a335e33122f5eb05a54dfc1beb091b992c499dbd
|
GLiClass‑V3 Logic Dataset
Rows 7 776 | Split train only | Format Parquet | Language EN | License Apache‑2.0
What it is
A length‑balanced corpus of single‑sentence prompts built purely for inducing reasoning in language models.
Why it helps
Teaches symbolic‑logic patterns and multi‑label behaviour.
Buckets cover 15 word‑length ranges (4 → 1,024) in equal proportions, exposing models to both tiny and very long inputs.
Each example has 1‑50 true and… See the full description on the dataset page: https://huggingface.co/datasets/knowledgator/gliclass-v3-logic-dataset.
| 137 | 137 |
[
"task_categories:text-classification",
"task_categories:question-answering",
"task_categories:sentence-similarity",
"language:en",
"license:apache-2.0",
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us",
"logic",
"reasoning"
] | 2025-07-18T11:49:03 | null | null |
687933ec90bd1c9ec6d5e8e4
|
setfunctionenvironment/testnew
|
setfunctionenvironment
|
{"license": "apache-2.0", "task_categories": ["text-to-speech"], "language": ["en"], "size_categories": ["100K<n<1M"]}
| false |
False
| 2025-07-18T00:27:04 | 97 | 5.6 | false |
24ee947cb0b3ff1b46ddeb91da549d858ce162ec
|
Audio Dataset Statistics
Overview
Metric
Value
Total audio files
556,667
Total duration
1,024.71 hours (3,688,949 seconds)
Average duration
6.63 seconds
Shortest clip
0.41 seconds
Longest clip
44.97 seconds
Speaker Breakdown
Top 10 Speakers by Clip Count
Speaker
Clips
Duration
% of Total
Despina
60,150
118.07 hours
11.5%
Sulafat
31,593
58.15 hours
5.7%
Achernar29,889
54.53 hours
5.3%
Autonoe
27,897… See the full description on the dataset page: https://huggingface.co/datasets/setfunctionenvironment/testnew.
| 2,312 | 2,312 |
[
"task_categories:text-to-speech",
"language:en",
"license:apache-2.0",
"size_categories:100K<n<1M",
"format:parquet",
"modality:audio",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us",
"not-for-all-audiences"
] | 2025-07-17T17:33:32 | null | null |
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Changelog
NEW Changes July 25th
- added
baseModelsfield to models which shows the models that the user tagged as base models for that model
Example:
{
"models": [
{
"_id": "687de260234339fed21e768a",
"id": "Qwen/Qwen3-235B-A22B-Instruct-2507"
}
],
"relation": "quantized"
}
NEW Changes July 9th
- Fixed issue with
ggufcolumn with integer overflow causing import pipeline to be broken over a few weeks ✅
NEW Changes Feb 27th
Added new fields on the
modelssplit:downloadsAllTime,safetensors,ggufAdded new field on the
datasetssplit:downloadsAllTimeAdded new split:
paperswhich is all of the Daily Papers
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