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687141e6df15b094718f28be
|
NousResearch/Hermes-3-Dataset
|
NousResearch
|
{"license": "apache-2.0"}
| false |
False
| 2025-07-11T17:43:25 | 253 | 72 | false |
b1fddbdcae4e6714889365d1e6ce266a45289cc9
| 5,448 | 5,448 |
[
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"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 |
|
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 | 8,472 | 65 | false |
68ba7694e23014788dcc8ab5afe613824f45a05c
|
🧠 Awesome ChatGPT Prompts [CSV dataset]
This is a Dataset Repository of Awesome ChatGPT Prompts
View All Prompts on GitHub
License
CC-0
| 31,621 | 225,532 |
[
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"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 | 65 | 65 | false |
cf5c4ed24134666ffb642fd34bc38fa9ff2ca909
| null | 953 | 1,091 |
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"language:en",
"license:apache-2.0",
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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 |
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 | 155 | 44 | 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.
| 9,062 | 9,077 |
[
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"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2505.21297",
"region:us"
] | 2025-06-11T12:23:29 | 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 | 35 | 35 | 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.
| 1,741 | 1,741 |
[
"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 |
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 | 174 | 31 | 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.
| 5,069 | 6,354 |
[
"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 |
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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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 | 22 | 22 | 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.
| 3,929 | 5,015 |
[
"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 |
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 | 18 | 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.
| 156,121 | 158,539 |
[
"license:cc-by-nc-4.0",
"modality:audio",
"modality:video",
"library:webdataset",
"region:us",
"webdataset",
"audio",
"video"
] | 2025-06-23T05:17:45 | null | null |
686fc33898943c873b45c9a0
|
HuggingFaceTB/smoltalk2
|
HuggingFaceTB
|
{"dataset_info": [{"config_name": "Mid", "features": [{"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "source", "dtype": "string"}], "splits": [{"name": "Llama_Nemotron_Post_Training_Dataset_reasoning_r1", "num_bytes": 61605080860, "num_examples": 3644790}, {"name": "OpenThoughts3_1.2M", "num_bytes": 56341153994, "num_examples": 1135104}], "download_size": 53697026569, "dataset_size": 117946234854}, {"config_name": "Preference", "features": [{"name": "chosen", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "rejected", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "prompt", "dtype": "string"}, {"name": "chat_template_kwargs", "struct": [{"name": "custom_instructions", "dtype": "string"}, {"name": "enable_thinking", "dtype": "bool"}, {"name": "python_tools", "sequence": "string"}, {"name": "xml_tools", "sequence": "string"}]}, {"name": "source", "dtype": "string"}], "splits": [{"name": "llama_3.1_tulu_3_8b_preference_mixture_no_think", "num_bytes": 1471659085, "num_examples": 230501}, {"name": "tulu_3_8b_pref_mix_Qwen3_32B_Qwen3_0.6B_think", "num_bytes": 4563920395, "num_examples": 216385}], "download_size": 2599953933, "dataset_size": 6035579480}, {"config_name": "SFT", "features": [{"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "chat_template_kwargs", "struct": [{"name": "custom_instructions", "dtype": "string"}, {"name": "enable_thinking", "dtype": "bool"}, {"name": "python_tools", "sequence": "string"}, {"name": "xml_tools", "sequence": "string"}]}, {"name": "source", "dtype": "string"}], "splits": [{"name": "LongAlign_64k_Qwen3_32B_yarn_131k_think", "num_bytes": 520907823, "num_examples": 7526}, {"name": "OpenThoughts3_1.2M_think", "num_bytes": 56273098407, "num_examples": 1133524}, {"name": "aya_dataset_Qwen3_32B_think", "num_bytes": 60172886, "num_examples": 15222}, {"name": "multi_turn_reasoning_if_think", "num_bytes": 421909719, "num_examples": 28217}, {"name": "s1k_1.1_think", "num_bytes": 25598191, "num_examples": 835}, {"name": "smolagents_toolcalling_traces_think", "num_bytes": 200401637, "num_examples": 9079}, {"name": "smoltalk_everyday_convs_reasoning_Qwen3_32B_think", "num_bytes": 11727816, "num_examples": 2057}, {"name": "smoltalk_multilingual8_Qwen3_32B_think", "num_bytes": 1900647658, "num_examples": 244736}, {"name": "smoltalk_systemchats_Qwen3_32B_think", "num_bytes": 123542086, "num_examples": 27436}, {"name": "table_gpt_Qwen3_32B_think", "num_bytes": 77802435, "num_examples": 13201}, {"name": "LongAlign_64k_context_lang_annotated_lang_6_no_think", "num_bytes": 402317420, "num_examples": 6249}, {"name": "Mixture_of_Thoughts_science_no_think", "num_bytes": 130260467, "num_examples": 86110}, {"name": "OpenHermes_2.5_no_think", "num_bytes": 585433379, "num_examples": 384900}, {"name": "OpenThoughts3_1.2M_no_think_no_think", "num_bytes": 1215060567, "num_examples": 435193}, {"name": "hermes_function_calling_v1_no_think", "num_bytes": 45038786, "num_examples": 8961}, {"name": "smoltalk_multilingual_8languages_lang_5_no_think", "num_bytes": 564939737, "num_examples": 254047}, {"name": "smoltalk_smollm3_everyday_conversations_no_think", "num_bytes": 1955692, "num_examples": 2260}, {"name": "smoltalk_smollm3_explore_instruct_rewriting_no_think", "num_bytes": 14186608, "num_examples": 30391}, {"name": "smoltalk_smollm3_smol_magpie_ultra_no_think", "num_bytes": 2820888852, "num_examples": 406843}, {"name": "smoltalk_smollm3_smol_rewrite_no_think", "num_bytes": 89561602, "num_examples": 53262}, {"name": "smoltalk_smollm3_smol_summarize_no_think", "num_bytes": 229104725, "num_examples": 96061}, {"name": "smoltalk_smollm3_systemchats_30k_no_think", "num_bytes": 89680522, "num_examples": 33997}, {"name": "table_gpt_no_think", "num_bytes": 31127809, "num_examples": 13203}, {"name": "tulu_3_sft_personas_instruction_following_no_think", "num_bytes": 59890932, "num_examples": 29970}, {"name": "xlam_traces_no_think", "num_bytes": 96806049, "num_examples": 59962}], "download_size": 31444817219, "dataset_size": 65992061805}], "configs": [{"config_name": "Mid", "data_files": [{"split": "Llama_Nemotron_Post_Training_Dataset_reasoning_r1", "path": "Mid/Llama_Nemotron_Post_Training_Dataset_reasoning_r1-*"}, {"split": "OpenThoughts3_1.2M", "path": "Mid/OpenThoughts3_1.2M-*"}]}, {"config_name": "Preference", "data_files": [{"split": "llama_3.1_tulu_3_8b_preference_mixture_no_think", "path": "Preference/llama_3.1_tulu_3_8b_preference_mixture_no_think-*"}, {"split": "tulu_3_8b_pref_mix_Qwen3_32B_Qwen3_0.6B_think", "path": "Preference/tulu_3_8b_pref_mix_Qwen3_32B_Qwen3_0.6B_think-*"}]}, {"config_name": "SFT", "data_files": [{"split": "LongAlign_64k_Qwen3_32B_yarn_131k_think", "path": "SFT/LongAlign_64k_Qwen3_32B_yarn_131k_think-*"}, {"split": "OpenThoughts3_1.2M_think", "path": "SFT/OpenThoughts3_1.2M_think-*"}, {"split": "aya_dataset_Qwen3_32B_think", "path": "SFT/aya_dataset_Qwen3_32B_think-*"}, {"split": "multi_turn_reasoning_if_think", "path": "SFT/multi_turn_reasoning_if_think-*"}, {"split": "s1k_1.1_think", "path": "SFT/s1k_1.1_think-*"}, {"split": "smolagents_toolcalling_traces_think", "path": "SFT/smolagents_toolcalling_traces_think-*"}, {"split": "smoltalk_everyday_convs_reasoning_Qwen3_32B_think", "path": "SFT/smoltalk_everyday_convs_reasoning_Qwen3_32B_think-*"}, {"split": "smoltalk_multilingual8_Qwen3_32B_think", "path": "SFT/smoltalk_multilingual8_Qwen3_32B_think-*"}, {"split": "smoltalk_systemchats_Qwen3_32B_think", "path": "SFT/smoltalk_systemchats_Qwen3_32B_think-*"}, {"split": "table_gpt_Qwen3_32B_think", "path": "SFT/table_gpt_Qwen3_32B_think-*"}, {"split": "LongAlign_64k_context_lang_annotated_lang_6_no_think", "path": "SFT/LongAlign_64k_context_lang_annotated_lang_6_no_think-*"}, {"split": "Mixture_of_Thoughts_science_no_think", "path": "SFT/Mixture_of_Thoughts_science_no_think-*"}, {"split": "OpenHermes_2.5_no_think", "path": "SFT/OpenHermes_2.5_no_think-*"}, {"split": "OpenThoughts3_1.2M_no_think_no_think", "path": "SFT/OpenThoughts3_1.2M_no_think_no_think-*"}, {"split": "hermes_function_calling_v1_no_think", "path": "SFT/hermes_function_calling_v1_no_think-*"}, {"split": "smoltalk_multilingual_8languages_lang_5_no_think", "path": "SFT/smoltalk_multilingual_8languages_lang_5_no_think-*"}, {"split": "smoltalk_smollm3_everyday_conversations_no_think", "path": "SFT/smoltalk_smollm3_everyday_conversations_no_think-*"}, {"split": "smoltalk_smollm3_explore_instruct_rewriting_no_think", "path": "SFT/smoltalk_smollm3_explore_instruct_rewriting_no_think-*"}, {"split": "smoltalk_smollm3_smol_magpie_ultra_no_think", "path": "SFT/smoltalk_smollm3_smol_magpie_ultra_no_think-*"}, {"split": "smoltalk_smollm3_smol_rewrite_no_think", "path": "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 | 17 | 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.
| 5,963 | 5,963 |
[
"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 |
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 | 23 | 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.
| 612 | 745 |
[
"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 |
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 | 481 | 14 | 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.
| 31,467 | 2,874,899 |
[
"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 |
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 | 24 | 13 | 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.
| 272 | 1,047 |
[
"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 |
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 | 13 | 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.
| 12,647 | 35,549 |
[
"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 |
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": "train", "path": "data/CC-MAIN-2025-21/*"}]}, {"config_name": "CC-MAIN-2025-26", "data_files": [{"split": "train", "path": "data/CC-MAIN-2025-26/*"}]}, {"config_name": "CC-MAIN-2024-51", "data_files": [{"split": "train", "path": "data/CC-MAIN-2024-51/*"}]}, {"config_name": "CC-MAIN-2024-46", "data_files": [{"split": "train", "path": "data/CC-MAIN-2024-46/*"}]}, {"config_name": "CC-MAIN-2024-42", "data_files": [{"split": "train", "path": "data/CC-MAIN-2024-42/*"}]}, {"config_name": "CC-MAIN-2024-38", "data_files": [{"split": "train", "path": "data/CC-MAIN-2024-38/*"}]}, {"config_name": "CC-MAIN-2024-33", "data_files": [{"split": "train", "path": "data/CC-MAIN-2024-33/*"}]}, {"config_name": "CC-MAIN-2024-30", "data_files": [{"split": "train", "path": "data/CC-MAIN-2024-30/*"}]}, {"config_name": "CC-MAIN-2024-26", "data_files": [{"split": "train", "path": "data/CC-MAIN-2024-26/*"}]}, {"config_name": "CC-MAIN-2024-22", "data_files": [{"split": "train", "path": "data/CC-MAIN-2024-22/*"}]}, 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| false |
False
| 2025-07-11T20:16:53 | 2,269 | 12 | 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.
| 773,480 | 4,600,206 |
[
"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 |
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 | 797 | 12 | 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.
| 8,521 | 90,727 |
[
"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 |
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 | 12 | 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.
| 583 | 583 |
[
"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 |
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 | 28 | 11 | 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.
| 1,970 | 16,493 |
[
"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 |
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 | 16 | 11 | 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.
| 33,458 | 33,458 |
[
"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 |
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.
| 132 | 132 |
[
"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 |
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 | 539 | 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.
| 7,178 | 35,397 |
[
"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 |
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 | 48 | 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.
| 5,112 | 5,999 |
[
"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 |
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 | 9 | 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.
| 4,039 | 4,048 |
[
"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 |
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 | 14 | 9 | 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.
| 1,054 | 1,054 |
[
"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 |
687b5239efc13fcacd6156b4
|
OpenGVLab/Doc-750K
|
OpenGVLab
|
{"license": "mit", "task_categories": ["question-answering"]}
| false |
False
| 2025-07-22T10:24:06 | 10 | 8 | false |
b85d69113c65e77229d21ae787686d73e9c199ab
|
This dataset is in the paper Docopilot: Improving Multimodal Models for Document-Level Understanding.
Please refer to https://github.com/OpenGVLab/Docopilot for details.
FAQ
Unzipping Split Archives on Linux
If you encounter issues when unzipping the image archive on Linux, such as:
zip bomb warnings
bad zipfile offset errors
Please try the following solutions:
Zip Bomb Warning
Some systems may trigger a zip bomb detection warning due to the large number of small image… See the full description on the dataset page: https://huggingface.co/datasets/OpenGVLab/Doc-750K.
| 3,939 | 3,939 |
[
"task_categories:question-answering",
"license:mit",
"arxiv:2507.14675",
"region:us"
] | 2025-07-19T08:07:21 | 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.
| 192 | 192 |
[
"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 |
6532270e829e1dc2f293d6b8
|
gaia-benchmark/GAIA
|
gaia-benchmark
|
{"language": ["en"], "pretty_name": "General AI Assistants Benchmark", "extra_gated_prompt": "To avoid contamination and data leakage, you agree to not reshare this dataset outside of a gated or private repository on the HF hub.", "extra_gated_fields": {"I agree to not reshare the GAIA submissions set according to the above conditions": "checkbox"}}
| false |
auto
| 2025-02-13T08:36:12 | 396 | 7 | false |
897f2dfbb5c952b5c3c1509e648381f9c7b70316
|
GAIA dataset
GAIA is a benchmark which aims at evaluating next-generation LLMs (LLMs with augmented capabilities due to added tooling, efficient prompting, access to search, etc).
We added gating to prevent bots from scraping the dataset. Please do not reshare the validation or test set in a crawlable format.
Data and leaderboard
GAIA is made of more than 450 non-trivial question with an unambiguous answer, requiring different levels of tooling and autonomy to solve. It… See the full description on the dataset page: https://huggingface.co/datasets/gaia-benchmark/GAIA.
| 8,000 | 79,247 |
[
"language:en",
"arxiv:2311.12983",
"region:us"
] | 2023-10-20T07:06:54 | null |
|
670f3aa1af1b2ffefc95d350
|
sapientinc/sudoku-extreme
|
sapientinc
|
{"task_categories": ["question-answering"]}
| false |
False
| 2024-10-17T05:50:57 | 10 | 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.
| 821 | 1,353 |
[
"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 |
686176a165816f63e6edee56
|
theaidealab/workflows
|
theaidealab
|
nan
| false |
False
| 2025-07-23T16:57:43 | 9 | 7 | false |
b8e1e9439c037a0b26dec6e4242d048507c93879
| null | 4,025 | 4,025 |
[
"region:us"
] | 2025-06-29T17:23:45 | null | null |
6863b82d06d39ee584cd2be2
|
sapienzanlp/bookcoref
|
sapienzanlp
|
{"dataset_info": [{"config_name": "full", "features": [{"name": "doc_key", "dtype": "string"}, {"name": "gutenberg_key", "dtype": "string"}, {"name": "sentences", "sequence": {"sequence": "string"}}, {"name": "clusters", "sequence": {"sequence": {"sequence": "int64"}}}, {"name": "characters", "list": [{"name": "name", "dtype": "string"}, {"name": "mentions", "sequence": {"sequence": "int64"}}]}], "splits": [{"name": "train", "num_bytes": 118643409, "num_examples": 45}, {"name": "validation", "num_bytes": 5893208, "num_examples": 5}, {"name": "test", "num_bytes": 2732407, "num_examples": 3}], "download_size": 317560335, "dataset_size": 127269024}, {"config_name": "split", "features": [{"name": "doc_key", "dtype": "string"}, {"name": "gutenberg_key", "dtype": "string"}, {"name": "sentences", "sequence": {"sequence": "string"}}, {"name": "clusters", "sequence": {"sequence": {"sequence": "int64"}}}, {"name": "characters", "list": [{"name": "name", "dtype": "string"}, {"name": "mentions", "sequence": {"sequence": "int64"}}]}], "splits": [{"name": "train", "num_bytes": 118849212, "num_examples": 7544}, {"name": "validation", "num_bytes": 5905814, "num_examples": 398}, {"name": "test", "num_bytes": 2758250, "num_examples": 152}], "download_size": 317560335, "dataset_size": 127513276}], "language": ["en"], "pretty_name": "BOOKCOREF", "size_categories": ["10M<n<100M"], "tags": ["coreference-resolution"], "license": "cc-by-sa-4.0"}
| false |
False
| 2025-07-23T09:55:20 | 7 | 7 | false |
dd4f9c897ada5e2d9bc906f90ce1cd0a7767ca72
|
BookCoref is a large-scale dataset for coreference resolution, with a manually annotated test set and an automatically generated training set.
| 298 | 302 |
[
"language:en",
"license:cc-by-sa-4.0",
"size_categories:10M<n<100M",
"arxiv:2507.12075",
"region:us",
"coreference-resolution"
] | 2025-07-01T10:27:57 |
@inproceedings{
anonymous2025bookcoref,
title={{BOOKCOREF}: Coreference Resolution at Book Scale},
author={Martinelli, Giuliano and Bonomo, Tommaso and Huguet Cabot, Pere-Lluís and Navigli, Roberto},
booktitle={The 63rd Annual Meeting of the Association for Computational Linguistics},
year={2025},
url={https://openreview.net/forum?id=Jfpr2w2OR6}
}
| 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.
| 238 | 238 |
[
"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 |
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 | 7 | 7 | 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.
| 587 | 587 |
[
"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 |
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 | 95 | 6.4 | 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.
| 2,223 | 2,223 |
[
"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 |
621ffdd236468d709f182a80
|
allenai/c4
|
allenai
|
{"pretty_name": "C4", "annotations_creators": ["no-annotation"], "language_creators": ["found"], "language": ["af", "am", "ar", "az", "be", "bg", "bn", "ca", "ceb", "co", "cs", "cy", "da", "de", "el", "en", "eo", "es", "et", "eu", "fa", "fi", "fil", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "haw", "he", "hi", "hmn", "ht", "hu", "hy", "id", "ig", "is", "it", "iw", "ja", "jv", "ka", "kk", "km", "kn", "ko", "ku", "ky", "la", "lb", "lo", "lt", "lv", "mg", "mi", "mk", "ml", "mn", "mr", "ms", "mt", "my", "ne", "nl", "no", "ny", "pa", "pl", "ps", "pt", "ro", "ru", "sd", "si", "sk", "sl", "sm", "sn", "so", "sq", "sr", "st", "su", "sv", "sw", "ta", "te", "tg", "th", "tr", "uk", "und", "ur", "uz", "vi", "xh", "yi", "yo", "zh", "zu"], "language_bcp47": ["bg-Latn", "el-Latn", "hi-Latn", "ja-Latn", "ru-Latn", "zh-Latn"], "license": ["odc-by"], "multilinguality": ["multilingual"], "size_categories": ["n<1K", "1K<n<10K", "10K<n<100K", "100K<n<1M", "1M<n<10M", "10M<n<100M", "100M<n<1B", "1B<n<10B"], "source_datasets": ["original"], "task_categories": ["text-generation", "fill-mask"], "task_ids": ["language-modeling", "masked-language-modeling"], "paperswithcode_id": "c4", "dataset_info": [{"config_name": "en", "features": [{"name": "text", "dtype": "string"}, {"name": "timestamp", "dtype": "string"}, {"name": "url", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 828589180707, "num_examples": 364868892}, {"name": "validation", "num_bytes": 825767266, "num_examples": 364608}], "download_size": 326778635540, "dataset_size": 1657178361414}, {"config_name": "en.noblocklist", "features": [{"name": "text", "dtype": "string"}, {"name": "timestamp", "dtype": "string"}, {"name": "url", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1029628201361, "num_examples": 393391519}, {"name": "validation", "num_bytes": 1025606012, "num_examples": 393226}], "download_size": 406611392434, "dataset_size": 2059256402722}, {"config_name": "realnewslike", "features": [{"name": "text", "dtype": "string"}, {"name": "timestamp", "dtype": "string"}, {"name": "url", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 38165657946, "num_examples": 13799838}, {"name": "validation", "num_bytes": 37875873, "num_examples": 13863}], "download_size": 15419740744, "dataset_size": 76331315892}, {"config_name": "en.noclean", "features": [{"name": "text", "dtype": "string"}, {"name": "timestamp", "dtype": "string"}, {"name": "url", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 6715509699938, "num_examples": 1063805381}, {"name": "validation", "num_bytes": 6706356913, "num_examples": 1065029}], "download_size": 2430376268625, "dataset_size": 6722216056851}], "configs": [{"config_name": "en", "data_files": [{"split": "train", "path": "en/c4-train.*.json.gz"}, {"split": "validation", "path": "en/c4-validation.*.json.gz"}]}, {"config_name": "en.noblocklist", "data_files": [{"split": "train", "path": "en.noblocklist/c4-train.*.json.gz"}, {"split": "validation", "path": "en.noblocklist/c4-validation.*.json.gz"}]}, {"config_name": "en.noclean", "data_files": [{"split": "train", "path": "en.noclean/c4-train.*.json.gz"}, {"split": "validation", "path": "en.noclean/c4-validation.*.json.gz"}]}, {"config_name": "realnewslike", "data_files": [{"split": "train", "path": "realnewslike/c4-train.*.json.gz"}, {"split": "validation", "path": "realnewslike/c4-validation.*.json.gz"}]}, {"config_name": "multilingual", "data_files": [{"split": "train", "path": ["multilingual/c4-af.*.json.gz", "multilingual/c4-am.*.json.gz", "multilingual/c4-ar.*.json.gz", "multilingual/c4-az.*.json.gz", "multilingual/c4-be.*.json.gz", "multilingual/c4-bg.*.json.gz", "multilingual/c4-bg-Latn.*.json.gz", "multilingual/c4-bn.*.json.gz", "multilingual/c4-ca.*.json.gz", "multilingual/c4-ceb.*.json.gz", "multilingual/c4-co.*.json.gz", "multilingual/c4-cs.*.json.gz", "multilingual/c4-cy.*.json.gz", "multilingual/c4-da.*.json.gz", 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"path": "multilingual/c4-pl-validation.*.json.gz"}]}, {"config_name": "ps", "data_files": [{"split": "train", "path": "multilingual/c4-ps.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-ps-validation.*.json.gz"}]}, {"config_name": "pt", "data_files": [{"split": "train", "path": "multilingual/c4-pt.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-pt-validation.*.json.gz"}]}, {"config_name": "ro", "data_files": [{"split": "train", "path": "multilingual/c4-ro.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-ro-validation.*.json.gz"}]}, {"config_name": "ru", "data_files": [{"split": "train", "path": "multilingual/c4-ru.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-ru-validation.*.json.gz"}]}, {"config_name": "ru-Latn", "data_files": [{"split": "train", "path": "multilingual/c4-ru-Latn.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-ru-Latn-validation.*.json.gz"}]}, {"config_name": "sd", "data_files": [{"split": "train", "path": "multilingual/c4-sd.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-sd-validation.*.json.gz"}]}, {"config_name": "si", "data_files": [{"split": "train", "path": "multilingual/c4-si.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-si-validation.*.json.gz"}]}, {"config_name": "sk", "data_files": [{"split": "train", "path": "multilingual/c4-sk.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-sk-validation.*.json.gz"}]}, {"config_name": "sl", "data_files": [{"split": "train", "path": "multilingual/c4-sl.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-sl-validation.*.json.gz"}]}, {"config_name": "sm", "data_files": [{"split": "train", "path": "multilingual/c4-sm.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-sm-validation.*.json.gz"}]}, {"config_name": "sn", "data_files": [{"split": "train", "path": "multilingual/c4-sn.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-sn-validation.*.json.gz"}]}, {"config_name": "so", "data_files": [{"split": "train", "path": "multilingual/c4-so.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-so-validation.*.json.gz"}]}, {"config_name": "sq", "data_files": [{"split": "train", "path": "multilingual/c4-sq.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-sq-validation.*.json.gz"}]}, {"config_name": "sr", "data_files": [{"split": "train", "path": "multilingual/c4-sr.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-sr-validation.*.json.gz"}]}, {"config_name": "st", "data_files": [{"split": "train", "path": "multilingual/c4-st.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-st-validation.*.json.gz"}]}, {"config_name": "su", "data_files": [{"split": "train", "path": "multilingual/c4-su.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-su-validation.*.json.gz"}]}, {"config_name": "sv", "data_files": [{"split": "train", "path": "multilingual/c4-sv.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-sv-validation.*.json.gz"}]}, {"config_name": "sw", "data_files": [{"split": "train", "path": "multilingual/c4-sw.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-sw-validation.*.json.gz"}]}, {"config_name": "ta", "data_files": [{"split": "train", "path": "multilingual/c4-ta.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-ta-validation.*.json.gz"}]}, {"config_name": "te", "data_files": [{"split": "train", "path": "multilingual/c4-te.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-te-validation.*.json.gz"}]}, {"config_name": "tg", "data_files": [{"split": "train", "path": "multilingual/c4-tg.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-tg-validation.*.json.gz"}]}, {"config_name": "th", "data_files": [{"split": "train", "path": "multilingual/c4-th.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-th-validation.*.json.gz"}]}, {"config_name": "tr", "data_files": [{"split": "train", "path": "multilingual/c4-tr.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-tr-validation.*.json.gz"}]}, {"config_name": "uk", "data_files": [{"split": "train", "path": "multilingual/c4-uk.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-uk-validation.*.json.gz"}]}, {"config_name": "und", "data_files": [{"split": "train", "path": "multilingual/c4-und.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-und-validation.*.json.gz"}]}, {"config_name": "ur", "data_files": [{"split": "train", "path": "multilingual/c4-ur.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-ur-validation.*.json.gz"}]}, {"config_name": "uz", "data_files": [{"split": "train", "path": "multilingual/c4-uz.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-uz-validation.*.json.gz"}]}, {"config_name": "vi", "data_files": [{"split": "train", "path": "multilingual/c4-vi.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-vi-validation.*.json.gz"}]}, {"config_name": "xh", "data_files": [{"split": "train", "path": "multilingual/c4-xh.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-xh-validation.*.json.gz"}]}, {"config_name": "yi", "data_files": [{"split": "train", "path": "multilingual/c4-yi.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-yi-validation.*.json.gz"}]}, {"config_name": "yo", "data_files": [{"split": "train", "path": "multilingual/c4-yo.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-yo-validation.*.json.gz"}]}, {"config_name": "zh", "data_files": [{"split": "train", "path": "multilingual/c4-zh.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-zh-validation.*.json.gz"}]}, {"config_name": "zh-Latn", "data_files": [{"split": "train", "path": "multilingual/c4-zh-Latn.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-zh-Latn-validation.*.json.gz"}]}, {"config_name": "zu", "data_files": [{"split": "train", "path": "multilingual/c4-zu.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-zu-validation.*.json.gz"}]}]}
| false |
False
| 2024-01-09T19:14:03 | 441 | 6 | 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.
| 277,627 | 6,759,030 |
[
"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 | null |
c4
|
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 | 6 | 6 | 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.
| 38 | 38 |
[
"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 |
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 | 620 | 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.
| 27,982 | 189,559 |
[
"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 |
67ec47948647cfa17739af7a
|
nvidia/OpenCodeReasoning
|
nvidia
|
{"license": "cc-by-4.0", "size_categories": ["100K<n<1M"], "pretty_name": "OpenCodeReasoning", "dataset_info": [{"config_name": "split_0", "features": [{"name": "id", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "license", "dtype": "string"}, {"name": "dataset", "dtype": "string"}, {"name": "split", "dtype": "string"}, {"name": "difficulty", "dtype": "string"}, {"name": "solution", "dtype": "string"}], "splits": [{"name": "split_0", "num_bytes": 28108469190, "num_examples": 567850}]}, {"config_name": "split_1", "features": [{"name": "id", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "license", "dtype": "string"}, {"name": "dataset", "dtype": "string"}, {"name": "split", "dtype": "string"}, {"name": "difficulty", "dtype": "string"}, {"name": "solution", "dtype": "string"}, {"name": "index", "dtype": "string"}], "splits": [{"name": "split_1", "num_bytes": 4722811278, "num_examples": 167405}]}], "configs": [{"config_name": "split_0", "data_files": [{"split": "split_0", "path": "split_0/train-*"}]}, {"config_name": "split_1", "data_files": [{"split": "split_1", "path": "split_1/train-*"}]}], "task_categories": ["text-generation"], "tags": ["synthetic"]}
| false |
False
| 2025-05-04T23:54:22 | 488 | 6 | false |
20a1ca19c0d050fe9057fc08339d6b370ec1c67a
|
OpenCodeReasoning: Advancing Data Distillation for Competitive Coding
Data Overview
OpenCodeReasoning is the largest reasoning-based synthetic dataset to date for coding, comprises 735,255 samples in Python across 28,319 unique competitive programming
questions. OpenCodeReasoning is designed for supervised fine-tuning (SFT).
Technical Report - Discover the methodology and technical details behind OpenCodeReasoning.
Github Repo - Access the complete pipeline used to… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/OpenCodeReasoning.
| 3,940 | 35,069 |
[
"task_categories:text-generation",
"license:cc-by-4.0",
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2504.01943",
"region:us",
"synthetic"
] | 2025-04-01T20:07:48 | null | null |
68072cc4cce05035af98207e
|
nvidia/OpenMathReasoning
|
nvidia
|
{"language": ["en"], "license": "cc-by-4.0", "size_categories": ["1M<n<10M"], "task_categories": ["question-answering", "text-generation"], "pretty_name": "OpenMathReasoning", "tags": ["math", "nvidia"], "configs": [{"config_name": "default", "data_files": [{"split": "cot", "path": "data/cot-*"}, {"split": "tir", "path": "data/tir-*"}, {"split": "genselect", "path": "data/genselect-*"}, {"split": "additional_problems", "path": "data/additional_problems-*"}]}], "dataset_info": {"features": [{"name": "expected_answer", "dtype": "string"}, {"name": "problem_type", "dtype": "string"}, {"name": "problem_source", "dtype": "string"}, {"name": "generation_model", "dtype": "string"}, {"name": "pass_rate_72b_tir", "dtype": "string"}, {"name": "problem", "dtype": "string"}, {"name": "generated_solution", "dtype": "string"}, {"name": "inference_mode", "dtype": "string"}, {"name": "used_in_kaggle", "dtype": "bool"}], "splits": [{"name": "cot", "num_bytes": 71639174648, "num_examples": 3201061}, {"name": "tir", "num_bytes": 35746562996, "num_examples": 1718466}, {"name": "genselect", "num_bytes": 6981124435, "num_examples": 565620}, {"name": "additional_problems", "num_bytes": 66328865, "num_examples": 193170}], "download_size": 49585391985, "dataset_size": 114433190944}}
| false |
False
| 2025-05-27T18:43:44 | 315 | 6 | false |
d3d08664755704f422af97d43a7ff0ded4bd95df
|
OpenMathReasoning
OpenMathReasoning is a large-scale math reasoning dataset for training large language models (LLMs).
This dataset contains
306K unique mathematical problems sourced from AoPS forums with:
3.2M long chain-of-thought (CoT) solutions
1.7M long tool-integrated reasoning (TIR) solutions
566K samples that select the most promising solution out of many candidates (GenSelect)
Additional 193K problems sourced from AoPS forums (problems only, no solutions)
We used… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/OpenMathReasoning.
| 18,314 | 84,748 |
[
"task_categories:question-answering",
"task_categories:text-generation",
"language:en",
"license:cc-by-4.0",
"size_categories:1M<n<10M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2504.16891",
"region:us",
"math",
"nvidia"
] | 2025-04-22T05:44:36 | 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 | 26 | 6 | 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.
| 524 | 524 |
[
"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 |
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 | 106 | 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.
| 1,718 | 1,718 |
[
"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.
| 1,104 | 1,104 |
[
"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 |
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.
| 168 | 168 |
[
"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.
| 124 | 124 |
[
"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 |
621ffdd236468d709f181e5e
|
cais/mmlu
|
cais
|
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"professional_accounting/test-*"}, {"split": "validation", "path": "professional_accounting/validation-*"}, {"split": "dev", "path": "professional_accounting/dev-*"}]}, {"config_name": "professional_law", "data_files": [{"split": "test", "path": "professional_law/test-*"}, {"split": "validation", "path": "professional_law/validation-*"}, {"split": "dev", "path": "professional_law/dev-*"}]}, {"config_name": "professional_medicine", "data_files": [{"split": "test", "path": "professional_medicine/test-*"}, {"split": "validation", "path": "professional_medicine/validation-*"}, {"split": "dev", "path": "professional_medicine/dev-*"}]}, {"config_name": "professional_psychology", "data_files": [{"split": "test", "path": "professional_psychology/test-*"}, {"split": "validation", "path": "professional_psychology/validation-*"}, {"split": "dev", "path": "professional_psychology/dev-*"}]}, {"config_name": "public_relations", "data_files": [{"split": "test", "path": "public_relations/test-*"}, {"split": "validation", "path": "public_relations/validation-*"}, {"split": "dev", "path": "public_relations/dev-*"}]}, {"config_name": "security_studies", "data_files": [{"split": "test", "path": "security_studies/test-*"}, {"split": "validation", "path": "security_studies/validation-*"}, {"split": "dev", "path": "security_studies/dev-*"}]}, {"config_name": "sociology", "data_files": [{"split": "test", "path": "sociology/test-*"}, {"split": "validation", "path": "sociology/validation-*"}, {"split": "dev", "path": "sociology/dev-*"}]}, {"config_name": "us_foreign_policy", "data_files": [{"split": "test", "path": "us_foreign_policy/test-*"}, {"split": "validation", "path": "us_foreign_policy/validation-*"}, {"split": "dev", "path": "us_foreign_policy/dev-*"}]}, {"config_name": "virology", "data_files": [{"split": "test", "path": "virology/test-*"}, {"split": "validation", "path": "virology/validation-*"}, {"split": "dev", "path": "virology/dev-*"}]}, {"config_name": "world_religions", "data_files": [{"split": "test", "path": "world_religions/test-*"}, {"split": "validation", "path": "world_religions/validation-*"}, {"split": "dev", "path": "world_religions/dev-*"}]}]}
| false |
False
| 2024-03-08T20:36:26 | 515 | 5 | false |
c30699e8356da336a370243923dbaf21066bb9fe
|
Dataset Card for MMLU
Dataset Summary
Measuring Massive Multitask Language Understanding by Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt (ICLR 2021).
This is a massive multitask test consisting of multiple-choice questions from various branches of knowledge. The test spans subjects in the humanities, social sciences, hard sciences, and other areas that are important for some people to learn. This covers 57 tasks… See the full description on the dataset page: https://huggingface.co/datasets/cais/mmlu.
| 245,451 | 37,888,174 |
[
"task_categories:question-answering",
"task_ids:multiple-choice-qa",
"annotations_creators:no-annotation",
"language_creators:expert-generated",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:mit",
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2009.03300",
"arxiv:2005.00700",
"arxiv:2005.14165",
"arxiv:2008.02275",
"region:us"
] | 2022-03-02T23:29:22 | null |
mmlu
|
65377f5989dd48faca8f7cf1
|
HuggingFaceH4/ultrachat_200k
|
HuggingFaceH4
|
{"language": ["en"], "license": "mit", "size_categories": ["100K<n<1M"], "task_categories": ["text-generation"], "pretty_name": "UltraChat 200k", "configs": [{"config_name": "default", "data_files": [{"split": "train_sft", "path": "data/train_sft-*"}, {"split": "test_sft", "path": "data/test_sft-*"}, {"split": "train_gen", "path": "data/train_gen-*"}, {"split": "test_gen", "path": "data/test_gen-*"}]}], "dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "prompt_id", "dtype": "string"}, {"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}], "splits": [{"name": "train_sft", "num_bytes": 1397058554, "num_examples": 207865}, {"name": "test_sft", "num_bytes": 154695659, "num_examples": 23110}, {"name": "train_gen", "num_bytes": 1347396812, "num_examples": 256032}, {"name": "test_gen", "num_bytes": 148276089, "num_examples": 28304}], "download_size": 1624049723, "dataset_size": 3047427114}}
| false |
False
| 2024-10-16T11:52:27 | 558 | 5 | false |
8049631c405ae6576f93f445c6b8166f76f5505a
|
Dataset Card for UltraChat 200k
Dataset Description
This is a heavily filtered version of the UltraChat dataset and was used to train Zephyr-7B-β, a state of the art 7b chat model.
The original datasets consists of 1.4M dialogues generated by ChatGPT and spanning a wide range of topics. To create UltraChat 200k, we applied the following logic:
Selection of a subset of data for faster supervised fine tuning.
Truecasing of the dataset, as we observed around 5% of the data… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceH4/ultrachat_200k.
| 21,983 | 589,819 |
[
"task_categories:text-generation",
"language:en",
"license:mit",
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2305.14233",
"region:us"
] | 2023-10-24T08:24:57 | null | null |
6655eb19d17e141dcb546ed5
|
HuggingFaceFW/fineweb-edu
|
HuggingFaceFW
|
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"CC-MAIN-2015-27", "data_files": [{"split": "train", "path": "data/CC-MAIN-2015-27/*"}]}, {"config_name": "CC-MAIN-2015-22", "data_files": [{"split": "train", "path": "data/CC-MAIN-2015-22/*"}]}, {"config_name": "CC-MAIN-2015-18", "data_files": [{"split": "train", "path": "data/CC-MAIN-2015-18/*"}]}, {"config_name": "CC-MAIN-2015-14", "data_files": [{"split": "train", "path": "data/CC-MAIN-2015-14/*"}]}, {"config_name": "CC-MAIN-2015-11", "data_files": [{"split": "train", "path": "data/CC-MAIN-2015-11/*"}]}, {"config_name": "CC-MAIN-2015-06", "data_files": [{"split": "train", "path": "data/CC-MAIN-2015-06/*"}]}, {"config_name": "CC-MAIN-2014-52", "data_files": [{"split": "train", "path": "data/CC-MAIN-2014-52/*"}]}, {"config_name": "CC-MAIN-2014-49", "data_files": [{"split": "train", "path": "data/CC-MAIN-2014-49/*"}]}, {"config_name": "CC-MAIN-2014-42", "data_files": [{"split": "train", "path": "data/CC-MAIN-2014-42/*"}]}, {"config_name": "CC-MAIN-2014-41", "data_files": [{"split": "train", "path": "data/CC-MAIN-2014-41/*"}]}, {"config_name": "CC-MAIN-2014-35", "data_files": [{"split": "train", "path": "data/CC-MAIN-2014-35/*"}]}, {"config_name": "CC-MAIN-2014-23", "data_files": [{"split": "train", "path": "data/CC-MAIN-2014-23/*"}]}, {"config_name": "CC-MAIN-2014-15", "data_files": [{"split": "train", "path": "data/CC-MAIN-2014-15/*"}]}, {"config_name": "CC-MAIN-2014-10", "data_files": [{"split": "train", "path": "data/CC-MAIN-2014-10/*"}]}, {"config_name": "CC-MAIN-2013-48", "data_files": [{"split": "train", "path": "data/CC-MAIN-2013-48/*"}]}, {"config_name": "CC-MAIN-2013-20", "data_files": [{"split": "train", "path": "data/CC-MAIN-2013-20/*"}]}]}
| false |
False
| 2025-07-11T20:16:53 | 722 | 5 | false |
87f09149ef4734204d70ed1d046ddc9ca3f2b8f9
|
📚 FineWeb-Edu
1.3 trillion tokens of the finest educational data the 🌐 web has to offer
Paper: https://arxiv.org/abs/2406.17557
What is it?
📚 FineWeb-Edu dataset consists of 1.3T tokens and 5.4T tokens (FineWeb-Edu-score-2) of educational web pages filtered from 🍷 FineWeb dataset. This is the 1.3 trillion version.
To enhance FineWeb's quality, we developed an educational quality classifier using annotations generated by LLama3-70B-Instruct. We then… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu.
| 150,714 | 3,821,543 |
[
"task_categories:text-generation",
"language:en",
"license:odc-by",
"size_categories:1B<n<10B",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2406.17557",
"arxiv:2404.14219",
"arxiv:2401.10020",
"arxiv:2109.07445",
"doi:10.57967/hf/2497",
"region:us"
] | 2024-05-28T14:32:57 | null | null |
66d7ddc90d88366001af03ca
|
common-pile/arxiv_papers
|
common-pile
|
{"task_categories": ["text-generation"], "language": ["en"], "pretty_name": "ArXiv Papers"}
| false |
False
| 2025-06-06T03:50:34 | 9 | 5 | false |
963fe980c55b353980653f1a27c1dc0c8a2d7058
|
ArXiv Papers
Description
ArXiv is an online open-access repository of over 2.4 million scholarly papers covering fields such as computer science, mathematics, physics, quantitative biology, economics, and more.
When uploading papers, authors can choose from a variety of licenses.
This dataset includes text from all papers uploaded under CC BY, CC BY-SA, and CC0 licenses through a three-step pipeline:
first, the latex source files for openly licensed papers were… See the full description on the dataset page: https://huggingface.co/datasets/common-pile/arxiv_papers.
| 444 | 1,515 |
[
"task_categories:text-generation",
"language:en",
"size_categories:100K<n<1M",
"format:json",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"arxiv:2506.05209",
"region:us"
] | 2024-09-04T04:10:49 | null | null |
6718c7eb95693d6c54671278
|
marcelbinz/Psych-101
|
marcelbinz
|
{"license": "apache-2.0", "language": ["en"], "tags": ["Psychology"], "pretty_name": "Psych-101", "size_categories": ["100B<n<1T"]}
| false |
False
| 2024-11-02T16:43:37 | 100 | 5 | false |
611565c66395e2787cd7e3305149bb75dc138024
|
Dataset Summary
Psych-101 is a data set of natural language transcripts from human psychological experiments.
It comprises trial-by-trial data from 160 psychological experiments and 60,092 participants, making 10,681,650 choices.
Human choices are encapsuled in "<<" and ">>" tokens.
Paper: Centaur: a foundation model of human cognition
Point of Contact: Marcel Binz
Example Prompt
You will be presented with triplets of objects, which will be assigned to the keys D… See the full description on the dataset page: https://huggingface.co/datasets/marcelbinz/Psych-101.
| 2,129 | 3,875 |
[
"language:en",
"license:apache-2.0",
"size_categories:10K<n<100K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2410.20268",
"region:us",
"Psychology"
] | 2024-10-23T09:54:51 | null | null |
67374c18c32c765810f748f6
|
HuggingFaceH4/MATH-500
|
HuggingFaceH4
|
{"task_categories": ["text-generation"], "language": ["en"], "pretty_name": "MATH-500"}
| false |
False
| 2024-11-15T13:36:00 | 167 | 5 | false |
ff5b20257d8185524591543f8ff5993951537bb8
|
Dataset Card for MATH-500
This dataset contains a subset of 500 problems from the MATH benchmark that OpenAI created in their Let's Verify Step by Step paper. See their GitHub repo for the source file: https://github.com/openai/prm800k/tree/main?tab=readme-ov-file#math-splits
| 67,938 | 373,189 |
[
"task_categories:text-generation",
"language:en",
"size_categories:n<1K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2024-11-15T13:26:48 | null | null |
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 | 434 | 5 | 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.
| 12,309 | 48,068 |
[
"license:mit",
"size_categories:1K<n<10K",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2025-01-23T08:24:27 | null | null |
6815d50d798f92168c316cdd
|
microsoft/NextCoderDataset
|
microsoft
|
{"license": "mit", "task_categories": ["text-generation"], "language": ["en"], "tags": ["code", "microsoft", "nextcoder", "selekt"], "size_categories": ["100K<n<1M"]}
| false |
False
| 2025-07-08T15:02:15 | 35 | 5 | false |
924e23fa2fae2d2db568c3f8f18015b616a53715
|
NextCoderDataset
GitHub | Paper
NextCoder: Robust Adaptation of Code LMs to Diverse Code Edits (ICML'2025)
Data Overview
NextCoderdataset is the instruction-variant of synthetic dataset, used for training models on code-editing scenarios and compromised of around 381k (127k*3) samples across 8 different programming languages: Python,
Java, C++, C, Rust, Javascript, Go and Kotlin.
This is used to finetune the NextCoder family models using the novel… See the full description on the dataset page: https://huggingface.co/datasets/microsoft/NextCoderDataset.
| 1,399 | 1,401 |
[
"task_categories:text-generation",
"language:en",
"license:mit",
"size_categories:100K<n<1M",
"format:arrow",
"modality:text",
"library:datasets",
"library:mlcroissant",
"region:us",
"code",
"microsoft",
"nextcoder",
"selekt"
] | 2025-05-03T08:34:21 | null | null |
68168f8d5bef4371f27fae27
|
nvidia/OpenCodeReasoning-2
|
nvidia
|
{"license": "cc-by-4.0", "size_categories": ["100K<n<1M"], "pretty_name": "OpenCodeReasoning-2", "dataset_info": [{"config_name": "train", "features": [{"name": "question", "dtype": "string"}, {"name": "r1_generation", "dtype": "string"}, {"name": "qwq_critique", "dtype": "string"}, {"name": "solution", "dtype": "string"}, {"name": "judgement", "dtype": "string"}, {"name": "pass_rate", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "license", "dtype": "string"}, {"name": "dataset", "dtype": "string"}, {"name": "split", "dtype": "string"}, {"name": "difficulty", "dtype": "string"}, {"name": "index", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "question_id", "dtype": "string"}], "splits": [{"name": "python", "num_bytes": 72296224095, "num_examples": 1398166}, {"name": "cpp", "num_bytes": 44739645805, "num_examples": 1174475}]}], "configs": [{"config_name": "train", "data_files": [{"split": "python", "path": "train/python/train-*"}, {"split": "cpp", "path": "train/cpp/train-*"}]}], "task_categories": ["text-generation"], "tags": ["synthetic"]}
| false |
False
| 2025-05-17T00:19:15 | 34 | 5 | false |
eadf535931451525f3e5621d0f960c240bc62fd9
|
OpenCodeReasoning-2: A Large-scale Dataset for Reasoning in Code Generation and Critique
Dataset Description
OpenCodeReasoning-2 is the largest reasoning-based synthetic dataset to date for coding, comprising 1.4M samples in Python and 1.1M samples in C++ across 34,799 unique competitive programming questions.
OpenCodeReasoning-2 is designed for supervised fine-tuning (SFT) tasks of code completion and code critique.
Github Repo - Access the complete pipeline used to… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/OpenCodeReasoning-2.
| 2,063 | 5,078 |
[
"task_categories:text-generation",
"license:cc-by-4.0",
"size_categories:1M<n<10M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us",
"synthetic"
] | 2025-05-03T21:50:05 | null | null |
68243d59fa04f1d3600d7459
|
miromind-ai/MiroMind-M1-SFT-719K
|
miromind-ai
|
{"license": "apache-2.0", "language": ["en"], "base_model": ["deepseek-ai/DeepSeek-R1-Distill-Qwen-32B"]}
| false |
False
| 2025-07-22T01:44:22 | 7 | 5 | false |
0cfe6100fef757a885b521e2255178a7e742eece
|
MiroMind-M1
🧾 Overview
Training performance of MiroMind-M1-RL-7B on AIME24 and AIME25.
MiroMind-M1 is a fully open-source series of reasoning language models built on Qwen-2.5, focused on advancing mathematical reasoning. It is trained through supervised fine-tuning (SFT) on 719K curated problems and reinforcement learning with verifiable rewards (RLVR) on 62K challenging examples, using a context-aware multi-stage policy optimization method… See the full description on the dataset page: https://huggingface.co/datasets/miromind-ai/MiroMind-M1-SFT-719K.
| 661 | 734 |
[
"language:en",
"license:apache-2.0",
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2507.14683",
"region:us"
] | 2025-05-14T06:51:05 | null | null |
6826919bf87011387f780ef5
|
huuuuusy/FIOVA
|
huuuuusy
|
{"license": "cc-by-nc-4.0", "size_categories": ["1K<n<10K"], "pretty_name": "FIOVA"}
| false |
False
| 2025-05-19T08:33:27 | 6 | 5 | false |
23e9b81b6f81917051610f1d1736d5d9c20d0573
|
📽️ FIOVA: Five-In-One Video Annotations Benchmark
FIOVA (Five-In-One Video Annotations) is a human-centric benchmark designed to evaluate the alignment of long video descriptions generated by large vision-language models (LVLMs) with human perception. It comprises 3,002 real-world videos, each annotated independently by five human annotators, capturing diverse semantic perspectives and supporting rich evaluation.
🔍 Dataset Highlights
3,002 long videos (average 33.6… See the full description on the dataset page: https://huggingface.co/datasets/huuuuusy/FIOVA.
| 10 | 78 |
[
"license:cc-by-nc-4.0",
"size_categories:1K<n<10K",
"modality:text",
"region:us"
] | 2025-05-16T01:15:07 | null | null |
686c8945120ef62d5fc937ae
|
Caoza/PhysX-3D
|
Caoza
|
{"license": "gpl-3.0", "language": ["en"], "tags": ["Physical 3D Generation", "3D Vision", "3D"], "size_categories": ["1M<n<10M"], "task_categories": ["image-to-3d"]}
| false |
False
| 2025-07-21T07:33:53 | 15 | 5 | false |
ae5b68ad35c7b92a60f34e5838b1c667dc60d646
|
PhysXNet & PhysXNet-XL
This dataset aims to bridge the critical gap in physics-annotated 3D datasets. It is the first physics-grounded 3D dataset systematically annotated across five foundational dimensions: absolute scale, material, affordance, kinematics, and function description.
Dataset Details
Dataset Sources
Repository: PhysX-3D
Project page: PhysX-3D: Physical-Grounded 3D Asset Generation
Demo video: Video
Dataset Structure
PhysX… See the full description on the dataset page: https://huggingface.co/datasets/Caoza/PhysX-3D.
| 4,709 | 4,709 |
[
"task_categories:image-to-3d",
"language:en",
"license:gpl-3.0",
"size_categories:1M<n<10M",
"arxiv:2507.12465",
"region:us",
"Physical 3D Generation",
"3D Vision",
"3D"
] | 2025-07-08T02:58:13 | null | null |
686f58b04cce5bc141e17ce3
|
2077AIDataFoundation/VeriGUI
|
2077AIDataFoundation
|
{"license": "apache-2.0", "language": ["en"], "tags": ["Gui_Agent", "Benchmark"], "pretty_name": "VeriGUI", "size_categories": ["n<1K"], "configs": [{"config_name": "default", "data_files": [{"split": "test", "path": "data_studio.json"}]}]}
| false |
False
| 2025-07-23T12:09:35 | 9 | 5 | false |
85bba1f0053d50579dd7435f41e528801d675bf4
|
VeriGUI: Verifiable Long-Chain GUI Dataset
Overview
VeriGUI is a large-scale, human-annotated dataset designed to facilitate the development and evaluation of autonomous GUI agents capable of performing complex, long-horizon tasks in realistic computer environments. Unlike existing GUI datasets that focus on short-term interactions, VeriGUI emphasizes long-chain complexity and subtask-level verifiability to better reflect real-world human-computer interaction scenarios.… See the full description on the dataset page: https://huggingface.co/datasets/2077AIDataFoundation/VeriGUI.
| 551 | 551 |
[
"language:en",
"license:apache-2.0",
"size_categories:n<1K",
"format:json",
"modality:text",
"modality:video",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us",
"Gui_Agent",
"Benchmark"
] | 2025-07-10T06:07:44 | null | null |
6874f3d11d76632042390f16
|
peteromallet/InScene-Dataset
|
peteromallet
|
{"dataset_info": {"features": [{"name": "image_id", "dtype": "string"}, {"name": "control_image", "dtype": "image"}, {"name": "target_image", "dtype": "image"}, {"name": "prompt", "dtype": "string"}, {"name": "negative_prompt", "dtype": "string"}, {"name": "control_path", "dtype": "string"}, {"name": "target_path", "dtype": "string"}], "splits": [{"name": "validation", "num_bytes": 58746880, "num_examples": 79}, {"name": "train", "num_bytes": 303297873, "num_examples": 394}], "download_size": 605754167, "dataset_size": 362044753}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}]}]}
| false |
False
| 2025-07-15T09:46:14 | 20 | 5 | false |
458adbe1ca6a722eab07d8dc9c65d8658b73c9ba
| null | 751 | 751 |
[
"size_categories:n<1K",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2025-07-14T12:10:57 | null | null |
687743e8f5c6e9f87e383237
|
Alibaba-NLP/WebShaper
|
Alibaba-NLP
|
nan
| false |
False
| 2025-07-22T02:13:28 | 5 | 5 | false |
5dd17c63c2d0f86d245c262bfd7bf444200e126f
|
WebShaper: Agentically Data Synthesizing via Information-Seeking Formalization
Github: https://github.com/Alibaba-NLP/WebAgent
Paper: https://arxiv.org/pdf/2507.15061
TLTR
WebShaper is a synthesized training dataset for information-seeking (IS) task. It is based on our proposed task formalization of IS, and synthesized by our Expander Agent. WebShaper would cover a broader range of task forms, reasoning structure, and diversified knowledge.
Description… See the full description on the dataset page: https://huggingface.co/datasets/Alibaba-NLP/WebShaper.
| 313 | 313 |
[
"size_categories:n<1K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2507.15061",
"region:us"
] | 2025-07-16T06:17:12 | null | null |
687e4229603474ba510e1a29
|
DocReRank/FinHNQue-FinanceHardNegativeQueries
|
DocReRank
|
{"license": "cc-by-4.0", "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "positive_queries", "sequence": "string"}, {"name": "negative_queries", "sequence": {"sequence": "string"}}, {"name": "answer", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 5396420013.984, "num_examples": 21904}], "download_size": 5271554587, "dataset_size": 5396420013.984}}
| false |
False
| 2025-07-22T11:18:31 | 5 | 5 | false |
88260c6cfc37bf838ae1801e491bba2547564b13
|
Dataset Card for FinHNQue Dataset
Dataset Summary
The FinHNQue (Financial Hard Negative Queries) dataset was developed to address challenges in financial document retrieval, where models struggle with fine-grained distinctions such as numerical values, entity names, and time periods.Although the ColPali training set includes financial documents, performance on financial benchmarks remains notably lower due to these subtleties.
To overcome this, FinHNQue introduces… See the full description on the dataset page: https://huggingface.co/datasets/DocReRank/FinHNQue-FinanceHardNegativeQueries.
| 140 | 140 |
[
"license:cc-by-4.0",
"size_categories:10K<n<100K",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2505.22584",
"region:us"
] | 2025-07-21T13:35:37 | null | null |
68809d0f9c3401b217f4c84d
|
stepfun-ai/StepEval-Audio-Paralinguistic
|
stepfun-ai
|
nan
| false |
False
| 2025-07-23T10:09:06 | 5 | 5 | false |
7e031d41a9b51ede06f4bbd5d2f5352da444118e
|
StepEval-Audio-Paralinguistic Dataset
Overview
StepEval-Audio-Paralinguistic is a speech-to-speech benchmark designed to evaluate AI models' understanding of paralinguistic information in speech across 11 distinct dimensions. The dataset contains 550 carefully curated and annotated speech samples for assessing capabilities beyond semantic understanding.
Key Features
Comprehensive coverage: 11 paralinguistic dimensions with 50 samples each
Diverse sources:… See the full description on the dataset page: https://huggingface.co/datasets/stepfun-ai/StepEval-Audio-Paralinguistic.
| 481 | 481 |
[
"size_categories:n<1K",
"format:audiofolder",
"modality:audio",
"library:datasets",
"library:mlcroissant",
"region:us"
] | 2025-07-23T08:27:59 | null | null |
621ffdd236468d709f184284
|
wikimedia/wikipedia
|
wikimedia
|
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{"config_name": "20231101.zh-classical", "features": [{"name": "id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 14869227, "num_examples": 12708}], "download_size": 10098073, "dataset_size": 14869227}, {"config_name": "20231101.zh-min-nan", "features": [{"name": "id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 153672031, "num_examples": 432798}], "download_size": 37122048, "dataset_size": 153672031}, {"config_name": "20231101.zh-yue", "features": [{"name": "id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 109936351, "num_examples": 134140}], "download_size": 64950815, "dataset_size": 109936351}, {"config_name": "20231101.zu", "features": [{"name": "id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 7088246, "num_examples": 11561}], "download_size": 3792429, "dataset_size": 7088246}], "language_bcp47": ["be-tarask", "en-simple"]}
| false |
False
| 2024-01-09T09:40:51 | 878 | 4 | false |
b04c8d1ceb2f5cd4588862100d08de323dccfbaa
|
Dataset Card for Wikimedia Wikipedia
Dataset Summary
Wikipedia dataset containing cleaned articles of all languages.
The dataset is built from the Wikipedia dumps (https://dumps.wikimedia.org/)
with one subset per language, each containing a single train split.
Each example contains the content of one full Wikipedia article with cleaning to strip
markdown and unwanted sections (references, etc.).
All language subsets have already been processed for recent dump, and you… See the full description on the dataset page: https://huggingface.co/datasets/wikimedia/wikipedia.
| 52,849 | 1,284,837 |
[
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"license:cc-by-sa-3.0",
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"library:polars",
"region:us"
] | 2022-03-02T23:29:22 | null | null |
625552d2b339bb03abe3432d
|
openai/gsm8k
|
openai
|
{"annotations_creators": ["crowdsourced"], "language_creators": ["crowdsourced"], "language": ["en"], "license": ["mit"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["text2text-generation"], "task_ids": [], "paperswithcode_id": "gsm8k", "pretty_name": "Grade School Math 8K", "tags": ["math-word-problems"], "dataset_info": [{"config_name": "main", "features": [{"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3963202, "num_examples": 7473}, {"name": "test", "num_bytes": 713732, "num_examples": 1319}], "download_size": 2725633, "dataset_size": 4676934}, {"config_name": "socratic", "features": [{"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 5198108, "num_examples": 7473}, {"name": "test", "num_bytes": 936859, "num_examples": 1319}], "download_size": 3164254, "dataset_size": 6134967}], "configs": [{"config_name": "main", "data_files": [{"split": "train", "path": "main/train-*"}, {"split": "test", "path": "main/test-*"}]}, {"config_name": "socratic", "data_files": [{"split": "train", "path": "socratic/train-*"}, {"split": "test", "path": "socratic/test-*"}]}]}
| false |
False
| 2024-01-04T12:05:15 | 814 | 4 | false |
e53f048856ff4f594e959d75785d2c2d37b678ee
|
Dataset Card for GSM8K
Dataset Summary
GSM8K (Grade School Math 8K) is a dataset of 8.5K high quality linguistically diverse grade school math word problems. The dataset was created to support the task of question answering on basic mathematical problems that require multi-step reasoning.
These problems take between 2 and 8 steps to solve.
Solutions primarily involve performing a sequence of elementary calculations using basic arithmetic operations (+ − ×÷) to reach the… See the full description on the dataset page: https://huggingface.co/datasets/openai/gsm8k.
| 319,505 | 6,127,687 |
[
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:mit",
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2110.14168",
"region:us",
"math-word-problems"
] | 2022-04-12T10:22:10 | null |
gsm8k
|
627007d3becab9e2dcf15a40
|
ILSVRC/imagenet-1k
|
ILSVRC
|
{"annotations_creators": ["crowdsourced"], "language_creators": ["crowdsourced"], "language": ["en"], "license": ["other"], "license_details": "imagenet-agreement", "multilinguality": ["monolingual"], "paperswithcode_id": "imagenet-1k-1", "pretty_name": "ImageNet", "size_categories": ["1M<n<10M"], "source_datasets": ["original"], "task_categories": ["image-classification"], "task_ids": ["multi-class-image-classification"], "extra_gated_prompt": "By clicking on \u201cAccess repository\u201d below, you also agree to ImageNet Terms of Access:\n[RESEARCHER_FULLNAME] (the \"Researcher\") has requested permission to use the ImageNet database (the \"Database\") at Princeton University and Stanford University. In exchange for such permission, Researcher hereby agrees to the following terms and conditions:\n1. Researcher shall use the Database only for non-commercial research and educational purposes.\n2. Princeton University, Stanford University and Hugging Face make no representations or warranties regarding the Database, including but not limited to warranties of non-infringement or fitness for a particular purpose.\n3. Researcher accepts full responsibility for his or her use of the Database and shall defend and indemnify the ImageNet team, Princeton University, Stanford University and Hugging Face, including their employees, Trustees, officers and agents, against any and all claims arising from Researcher's use of the Database, including but not limited to Researcher's use of any copies of copyrighted images that he or she may create from the Database.\n4. Researcher may provide research associates and colleagues with access to the Database provided that they first agree to be bound by these terms and conditions.\n5. Princeton University, Stanford University and Hugging Face reserve the right to terminate Researcher's access to the Database at any time.\n6. If Researcher is employed by a for-profit, commercial entity, Researcher's employer shall also be bound by these terms and conditions, and Researcher hereby represents that he or she is fully authorized to enter into this agreement on behalf of such employer.\n7. The law of the State of New Jersey shall apply to all disputes under this agreement.", "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "tench, Tinca tinca", "1": "goldfish, Carassius auratus", "2": "great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias", "3": "tiger shark, Galeocerdo cuvieri", "4": "hammerhead, hammerhead shark", "5": "electric ray, crampfish, numbfish, torpedo", "6": "stingray", "7": "cock", "8": "hen", "9": "ostrich, Struthio camelus", "10": "brambling, Fringilla montifringilla", "11": "goldfinch, Carduelis carduelis", "12": "house finch, linnet, Carpodacus mexicanus", "13": "junco, snowbird", "14": "indigo bunting, indigo finch, indigo bird, Passerina cyanea", "15": "robin, American robin, Turdus migratorius", "16": "bulbul", "17": "jay", "18": "magpie", "19": "chickadee", "20": "water ouzel, dipper", "21": "kite", "22": "bald eagle, American eagle, Haliaeetus leucocephalus", "23": "vulture", "24": "great grey owl, great gray owl, Strix nebulosa", "25": "European fire salamander, Salamandra salamandra", "26": "common newt, Triturus vulgaris", "27": "eft", "28": "spotted salamander, Ambystoma maculatum", "29": "axolotl, mud puppy, Ambystoma mexicanum", "30": "bullfrog, Rana catesbeiana", "31": "tree frog, tree-frog", "32": "tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui", "33": "loggerhead, loggerhead turtle, Caretta caretta", "34": "leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea", "35": "mud turtle", "36": "terrapin", "37": "box turtle, box tortoise", "38": "banded gecko", "39": "common iguana, iguana, Iguana iguana", "40": "American chameleon, anole, Anolis carolinensis", "41": "whiptail, whiptail lizard", "42": "agama", "43": "frilled lizard, Chlamydosaurus kingi", "44": "alligator lizard", "45": "Gila monster, Heloderma suspectum", "46": "green lizard, Lacerta viridis", "47": "African chameleon, Chamaeleo chamaeleon", "48": "Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis", "49": "African crocodile, Nile crocodile, Crocodylus niloticus", "50": "American alligator, Alligator mississipiensis", "51": "triceratops", "52": "thunder snake, worm snake, Carphophis amoenus", "53": "ringneck snake, ring-necked snake, ring snake", "54": "hognose snake, puff adder, sand viper", "55": "green snake, grass snake", "56": "king snake, kingsnake", "57": "garter snake, grass snake", "58": "water snake", "59": "vine snake", "60": "night snake, Hypsiglena torquata", "61": "boa constrictor, Constrictor constrictor", "62": "rock python, rock snake, Python sebae", "63": "Indian cobra, Naja naja", "64": "green mamba", "65": "sea snake", "66": "horned viper, cerastes, sand viper, horned asp, Cerastes cornutus", "67": "diamondback, diamondback rattlesnake, Crotalus adamanteus", "68": "sidewinder, horned rattlesnake, Crotalus cerastes", "69": "trilobite", "70": "harvestman, daddy longlegs, Phalangium opilio", "71": "scorpion", "72": "black and gold garden spider, Argiope aurantia", "73": "barn spider, Araneus cavaticus", "74": "garden spider, Aranea diademata", "75": "black widow, Latrodectus mactans", "76": "tarantula", "77": "wolf spider, hunting spider", "78": "tick", "79": "centipede", "80": "black grouse", "81": "ptarmigan", "82": "ruffed grouse, partridge, Bonasa umbellus", "83": "prairie chicken, prairie grouse, prairie fowl", "84": "peacock", "85": "quail", "86": "partridge", "87": "African grey, African gray, Psittacus erithacus", "88": "macaw", "89": "sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita", "90": "lorikeet", "91": "coucal", "92": "bee eater", "93": "hornbill", "94": "hummingbird", "95": "jacamar", "96": "toucan", "97": "drake", "98": "red-breasted merganser, Mergus serrator", "99": "goose", "100": "black swan, Cygnus atratus", "101": "tusker", "102": "echidna, spiny anteater, anteater", "103": "platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus", "104": "wallaby, brush kangaroo", "105": "koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus", "106": "wombat", "107": "jellyfish", "108": "sea anemone, anemone", "109": "brain coral", "110": "flatworm, platyhelminth", "111": "nematode, nematode worm, roundworm", "112": "conch", "113": "snail", "114": "slug", "115": "sea slug, nudibranch", "116": "chiton, coat-of-mail shell, sea cradle, polyplacophore", "117": "chambered nautilus, pearly nautilus, nautilus", "118": "Dungeness crab, Cancer magister", "119": "rock crab, Cancer irroratus", "120": "fiddler crab", "121": "king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica", "122": "American lobster, Northern lobster, Maine lobster, Homarus americanus", "123": "spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish", "124": "crayfish, crawfish, crawdad, crawdaddy", "125": "hermit crab", "126": "isopod", "127": "white stork, Ciconia ciconia", "128": "black stork, Ciconia nigra", "129": "spoonbill", "130": "flamingo", "131": "little blue heron, Egretta caerulea", "132": "American egret, great white heron, Egretta albus", "133": "bittern", "134": "crane", "135": "limpkin, Aramus pictus", "136": "European gallinule, Porphyrio porphyrio", "137": "American coot, marsh hen, mud hen, water hen, Fulica americana", "138": "bustard", "139": "ruddy turnstone, Arenaria interpres", "140": "red-backed sandpiper, dunlin, Erolia alpina", "141": "redshank, Tringa totanus", "142": "dowitcher", "143": "oystercatcher, oyster catcher", "144": "pelican", "145": "king penguin, Aptenodytes patagonica", "146": "albatross, mollymawk", "147": "grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus", "148": "killer whale, killer, orca, grampus, sea wolf, Orcinus orca", "149": "dugong, Dugong dugon", "150": "sea lion", "151": "Chihuahua", "152": "Japanese spaniel", "153": "Maltese dog, Maltese terrier, Maltese", "154": "Pekinese, Pekingese, Peke", "155": "Shih-Tzu", "156": "Blenheim spaniel", "157": "papillon", "158": "toy terrier", "159": "Rhodesian ridgeback", "160": "Afghan hound, Afghan", "161": "basset, basset hound", "162": "beagle", "163": "bloodhound, sleuthhound", "164": "bluetick", "165": "black-and-tan coonhound", "166": "Walker hound, Walker foxhound", "167": "English foxhound", "168": "redbone", "169": "borzoi, Russian wolfhound", "170": "Irish wolfhound", "171": "Italian greyhound", "172": "whippet", "173": "Ibizan hound, Ibizan Podenco", "174": "Norwegian elkhound, elkhound", "175": "otterhound, otter hound", "176": "Saluki, gazelle hound", "177": "Scottish deerhound, deerhound", "178": "Weimaraner", "179": "Staffordshire bullterrier, Staffordshire bull terrier", "180": "American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier", "181": "Bedlington terrier", "182": "Border terrier", "183": "Kerry blue terrier", "184": "Irish terrier", "185": "Norfolk terrier", "186": "Norwich terrier", "187": "Yorkshire terrier", "188": "wire-haired fox terrier", "189": "Lakeland terrier", "190": "Sealyham terrier, Sealyham", "191": "Airedale, Airedale terrier", "192": "cairn, cairn terrier", "193": "Australian terrier", "194": "Dandie Dinmont, Dandie Dinmont terrier", "195": "Boston bull, Boston terrier", "196": "miniature schnauzer", "197": "giant schnauzer", "198": "standard schnauzer", "199": "Scotch terrier, Scottish terrier, Scottie", "200": "Tibetan terrier, chrysanthemum dog", "201": "silky terrier, Sydney silky", "202": "soft-coated wheaten terrier", "203": "West Highland white terrier", "204": "Lhasa, Lhasa apso", "205": "flat-coated retriever", "206": "curly-coated retriever", "207": "golden retriever", "208": "Labrador retriever", "209": "Chesapeake Bay retriever", "210": "German short-haired pointer", "211": "vizsla, Hungarian pointer", "212": "English setter", "213": "Irish setter, red setter", "214": "Gordon setter", "215": "Brittany spaniel", "216": "clumber, clumber spaniel", "217": "English springer, English springer spaniel", "218": "Welsh springer spaniel", "219": "cocker spaniel, English cocker spaniel, cocker", "220": "Sussex spaniel", "221": "Irish water spaniel", "222": "kuvasz", "223": "schipperke", "224": "groenendael", "225": "malinois", "226": "briard", "227": "kelpie", "228": "komondor", "229": "Old English sheepdog, bobtail", "230": "Shetland sheepdog, Shetland sheep dog, Shetland", "231": "collie", "232": "Border collie", "233": "Bouvier des Flandres, Bouviers des Flandres", "234": "Rottweiler", "235": "German shepherd, German shepherd dog, German police dog, alsatian", "236": "Doberman, Doberman pinscher", "237": "miniature pinscher", "238": "Greater Swiss Mountain dog", "239": "Bernese mountain dog", "240": "Appenzeller", "241": "EntleBucher", "242": "boxer", "243": "bull mastiff", "244": "Tibetan mastiff", "245": "French bulldog", "246": "Great Dane", "247": "Saint Bernard, St Bernard", "248": "Eskimo dog, husky", "249": "malamute, malemute, Alaskan malamute", "250": "Siberian husky", "251": "dalmatian, coach dog, carriage dog", "252": "affenpinscher, monkey pinscher, monkey dog", "253": "basenji", "254": "pug, pug-dog", "255": "Leonberg", "256": "Newfoundland, Newfoundland dog", "257": "Great Pyrenees", "258": "Samoyed, Samoyede", "259": "Pomeranian", "260": "chow, chow chow", "261": "keeshond", "262": "Brabancon griffon", "263": "Pembroke, Pembroke Welsh corgi", "264": "Cardigan, Cardigan Welsh corgi", "265": "toy poodle", "266": "miniature poodle", "267": "standard poodle", "268": "Mexican hairless", "269": "timber wolf, grey wolf, gray wolf, Canis lupus", "270": "white wolf, Arctic wolf, Canis lupus tundrarum", "271": "red wolf, maned wolf, Canis rufus, Canis niger", "272": "coyote, prairie wolf, brush wolf, Canis latrans", "273": "dingo, warrigal, warragal, Canis dingo", "274": "dhole, Cuon alpinus", "275": "African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus", "276": "hyena, hyaena", "277": "red fox, Vulpes vulpes", "278": "kit fox, Vulpes macrotis", "279": "Arctic fox, white fox, Alopex lagopus", "280": "grey fox, gray fox, Urocyon cinereoargenteus", "281": "tabby, tabby cat", "282": "tiger cat", "283": "Persian cat", "284": "Siamese cat, Siamese", "285": "Egyptian cat", "286": "cougar, puma, catamount, mountain lion, painter, panther, Felis concolor", "287": "lynx, catamount", "288": "leopard, Panthera pardus", "289": "snow leopard, ounce, Panthera uncia", "290": "jaguar, panther, Panthera onca, Felis onca", "291": "lion, king of beasts, Panthera leo", "292": "tiger, Panthera tigris", "293": "cheetah, chetah, Acinonyx jubatus", "294": "brown bear, bruin, Ursus arctos", "295": "American black bear, black bear, Ursus americanus, Euarctos americanus", "296": "ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus", "297": "sloth bear, Melursus ursinus, Ursus ursinus", "298": "mongoose", "299": "meerkat, mierkat", "300": "tiger beetle", "301": "ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle", "302": "ground beetle, carabid beetle", "303": "long-horned beetle, longicorn, longicorn beetle", "304": "leaf beetle, chrysomelid", "305": "dung beetle", "306": "rhinoceros beetle", "307": "weevil", "308": "fly", "309": "bee", "310": "ant, emmet, pismire", "311": "grasshopper, hopper", "312": "cricket", "313": "walking stick, walkingstick, stick insect", "314": "cockroach, roach", "315": "mantis, mantid", "316": "cicada, cicala", "317": "leafhopper", "318": "lacewing, lacewing fly", "319": "dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk", "320": "damselfly", "321": "admiral", "322": "ringlet, ringlet butterfly", "323": "monarch, monarch butterfly, milkweed butterfly, Danaus plexippus", "324": "cabbage butterfly", "325": "sulphur butterfly, sulfur butterfly", "326": "lycaenid, lycaenid butterfly", "327": "starfish, sea star", "328": "sea urchin", "329": "sea cucumber, holothurian", "330": "wood rabbit, cottontail, cottontail rabbit", "331": "hare", "332": "Angora, Angora rabbit", "333": "hamster", "334": "porcupine, hedgehog", "335": "fox squirrel, eastern fox squirrel, Sciurus niger", "336": "marmot", "337": "beaver", "338": "guinea pig, Cavia cobaya", "339": "sorrel", "340": "zebra", "341": "hog, pig, grunter, squealer, Sus scrofa", "342": "wild boar, boar, Sus scrofa", "343": "warthog", "344": "hippopotamus, hippo, river horse, Hippopotamus amphibius", "345": "ox", "346": "water buffalo, water ox, Asiatic buffalo, Bubalus bubalis", "347": "bison", "348": "ram, tup", "349": "bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis", "350": "ibex, Capra ibex", "351": "hartebeest", "352": "impala, Aepyceros melampus", "353": "gazelle", "354": "Arabian camel, dromedary, Camelus dromedarius", "355": "llama", "356": "weasel", "357": "mink", "358": "polecat, fitch, foulmart, foumart, Mustela putorius", "359": "black-footed ferret, ferret, Mustela nigripes", "360": "otter", "361": "skunk, polecat, wood pussy", "362": "badger", "363": "armadillo", "364": "three-toed sloth, ai, Bradypus tridactylus", "365": "orangutan, orang, orangutang, Pongo pygmaeus", "366": "gorilla, Gorilla gorilla", "367": "chimpanzee, chimp, Pan troglodytes", "368": "gibbon, Hylobates lar", "369": "siamang, Hylobates syndactylus, Symphalangus syndactylus", "370": "guenon, guenon monkey", "371": "patas, hussar monkey, Erythrocebus patas", "372": "baboon", "373": "macaque", "374": "langur", "375": "colobus, colobus monkey", "376": "proboscis monkey, Nasalis larvatus", "377": "marmoset", "378": "capuchin, ringtail, Cebus capucinus", "379": "howler monkey, howler", "380": "titi, titi monkey", "381": "spider monkey, Ateles geoffroyi", "382": "squirrel monkey, Saimiri sciureus", "383": "Madagascar cat, ring-tailed lemur, Lemur catta", "384": "indri, indris, Indri indri, Indri brevicaudatus", "385": "Indian elephant, Elephas maximus", "386": "African elephant, Loxodonta africana", "387": "lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens", "388": "giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca", "389": "barracouta, snoek", "390": "eel", "391": "coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch", "392": "rock beauty, Holocanthus tricolor", "393": "anemone fish", "394": "sturgeon", "395": "gar, garfish, garpike, billfish, Lepisosteus osseus", "396": "lionfish", "397": "puffer, pufferfish, blowfish, globefish", "398": "abacus", "399": "abaya", "400": "academic gown, academic robe, judge's robe", "401": "accordion, piano accordion, squeeze box", "402": "acoustic guitar", "403": "aircraft carrier, carrier, flattop, attack aircraft carrier", "404": "airliner", "405": "airship, dirigible", "406": "altar", "407": "ambulance", "408": "amphibian, amphibious vehicle", "409": "analog clock", "410": "apiary, bee house", "411": "apron", "412": "ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin", "413": "assault rifle, assault gun", "414": "backpack, back pack, knapsack, packsack, rucksack, haversack", "415": "bakery, bakeshop, bakehouse", "416": "balance beam, beam", "417": "balloon", "418": "ballpoint, ballpoint pen, ballpen, Biro", "419": "Band Aid", "420": "banjo", "421": "bannister, banister, balustrade, balusters, handrail", "422": "barbell", "423": "barber chair", "424": "barbershop", "425": "barn", "426": "barometer", "427": "barrel, cask", "428": "barrow, garden cart, lawn cart, wheelbarrow", "429": "baseball", "430": "basketball", "431": "bassinet", "432": "bassoon", "433": "bathing cap, swimming cap", "434": "bath towel", "435": "bathtub, bathing tub, bath, tub", "436": "beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon", "437": "beacon, lighthouse, beacon light, pharos", "438": "beaker", "439": "bearskin, busby, shako", "440": "beer bottle", "441": "beer glass", "442": "bell cote, bell cot", "443": "bib", "444": "bicycle-built-for-two, tandem bicycle, tandem", "445": "bikini, two-piece", "446": "binder, ring-binder", "447": "binoculars, field glasses, opera glasses", "448": "birdhouse", "449": "boathouse", "450": "bobsled, bobsleigh, bob", "451": "bolo tie, bolo, bola tie, bola", "452": "bonnet, poke bonnet", "453": "bookcase", "454": "bookshop, bookstore, bookstall", "455": "bottlecap", "456": "bow", "457": "bow tie, bow-tie, bowtie", "458": "brass, memorial tablet, plaque", "459": "brassiere, bra, bandeau", "460": "breakwater, groin, groyne, mole, bulwark, seawall, jetty", "461": "breastplate, aegis, egis", "462": "broom", "463": "bucket, pail", "464": "buckle", "465": "bulletproof vest", "466": "bullet train, bullet", "467": "butcher shop, meat market", "468": "cab, hack, taxi, taxicab", "469": "caldron, cauldron", "470": "candle, taper, wax light", "471": "cannon", "472": "canoe", "473": "can opener, tin opener", "474": "cardigan", "475": "car mirror", "476": "carousel, carrousel, merry-go-round, roundabout, whirligig", "477": "carpenter's kit, tool kit", "478": "carton", "479": "car wheel", "480": "cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM", "481": "cassette", "482": "cassette player", "483": "castle", "484": "catamaran", "485": "CD player", "486": "cello, violoncello", "487": "cellular telephone, cellular phone, cellphone, cell, mobile phone", "488": "chain", "489": "chainlink fence", "490": "chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour", "491": "chain saw, chainsaw", "492": "chest", "493": "chiffonier, commode", "494": "chime, bell, gong", "495": "china cabinet, china closet", "496": "Christmas stocking", "497": "church, church building", "498": "cinema, movie theater, movie theatre, movie house, picture palace", "499": "cleaver, meat cleaver, chopper", "500": "cliff dwelling", "501": "cloak", "502": "clog, geta, patten, sabot", "503": "cocktail shaker", "504": "coffee mug", "505": "coffeepot", "506": "coil, spiral, volute, whorl, helix", "507": "combination lock", "508": "computer keyboard, keypad", "509": "confectionery, confectionary, candy store", "510": "container ship, containership, container vessel", "511": "convertible", "512": "corkscrew, bottle screw", "513": "cornet, horn, trumpet, trump", "514": "cowboy boot", "515": "cowboy hat, ten-gallon hat", "516": "cradle", "517": "crane2", "518": "crash helmet", "519": "crate", "520": "crib, cot", "521": "Crock Pot", "522": "croquet ball", "523": "crutch", "524": "cuirass", "525": "dam, dike, dyke", "526": "desk", "527": "desktop computer", "528": "dial telephone, dial phone", "529": "diaper, nappy, napkin", "530": "digital clock", "531": "digital watch", "532": "dining table, board", "533": "dishrag, dishcloth", "534": "dishwasher, dish washer, dishwashing machine", "535": "disk brake, disc brake", "536": "dock, dockage, docking facility", "537": "dogsled, dog sled, dog sleigh", "538": "dome", "539": "doormat, welcome mat", "540": "drilling platform, offshore rig", "541": "drum, membranophone, tympan", "542": "drumstick", "543": "dumbbell", "544": "Dutch oven", "545": "electric fan, blower", "546": "electric guitar", "547": "electric locomotive", "548": "entertainment center", "549": "envelope", "550": "espresso maker", "551": "face powder", "552": "feather boa, boa", "553": "file, file cabinet, filing cabinet", "554": "fireboat", "555": "fire engine, fire truck", "556": "fire screen, fireguard", "557": "flagpole, flagstaff", "558": "flute, transverse flute", "559": "folding chair", "560": "football helmet", "561": "forklift", "562": "fountain", "563": "fountain pen", "564": "four-poster", "565": "freight car", "566": "French horn, horn", "567": "frying pan, frypan, skillet", "568": "fur coat", "569": "garbage truck, dustcart", "570": "gasmask, respirator, gas helmet", "571": "gas pump, gasoline pump, petrol pump, island dispenser", "572": "goblet", "573": "go-kart", "574": "golf ball", "575": "golfcart, golf cart", "576": "gondola", "577": "gong, tam-tam", "578": "gown", "579": "grand piano, grand", "580": "greenhouse, nursery, glasshouse", "581": "grille, radiator grille", "582": "grocery store, grocery, food market, market", "583": "guillotine", "584": "hair slide", "585": "hair spray", "586": "half track", "587": "hammer", "588": "hamper", "589": "hand blower, blow dryer, blow drier, hair dryer, hair drier", "590": "hand-held computer, hand-held microcomputer", "591": "handkerchief, hankie, hanky, hankey", "592": "hard disc, hard disk, fixed disk", "593": "harmonica, mouth organ, harp, mouth harp", "594": "harp", "595": "harvester, reaper", "596": "hatchet", "597": "holster", "598": "home theater, home theatre", "599": "honeycomb", "600": "hook, claw", "601": "hoopskirt, crinoline", "602": "horizontal bar, high bar", "603": "horse cart, horse-cart", "604": "hourglass", "605": "iPod", "606": "iron, smoothing iron", "607": "jack-o'-lantern", "608": "jean, blue jean, denim", "609": "jeep, landrover", "610": "jersey, T-shirt, tee shirt", "611": "jigsaw puzzle", "612": "jinrikisha, ricksha, rickshaw", "613": "joystick", "614": "kimono", "615": "knee pad", "616": "knot", "617": "lab coat, laboratory coat", "618": "ladle", "619": "lampshade, lamp shade", "620": "laptop, laptop computer", "621": "lawn mower, mower", "622": "lens cap, lens cover", "623": "letter opener, paper knife, paperknife", "624": "library", "625": "lifeboat", "626": "lighter, light, igniter, ignitor", "627": "limousine, limo", "628": "liner, ocean liner", "629": "lipstick, lip rouge", "630": "Loafer", "631": "lotion", "632": "loudspeaker, speaker, speaker unit, loudspeaker system, speaker system", "633": "loupe, jeweler's loupe", "634": "lumbermill, sawmill", "635": "magnetic compass", "636": "mailbag, postbag", "637": "mailbox, letter box", "638": "maillot", "639": "maillot, tank suit", "640": "manhole cover", "641": "maraca", "642": "marimba, xylophone", "643": "mask", "644": "matchstick", "645": "maypole", "646": "maze, labyrinth", "647": "measuring cup", "648": "medicine chest, medicine cabinet", "649": "megalith, megalithic structure", "650": "microphone, mike", "651": "microwave, microwave oven", "652": "military uniform", "653": "milk can", "654": "minibus", "655": "miniskirt, mini", "656": "minivan", "657": "missile", "658": "mitten", "659": "mixing bowl", "660": "mobile home, manufactured home", "661": "Model T", "662": "modem", "663": "monastery", "664": "monitor", "665": "moped", "666": "mortar", "667": "mortarboard", "668": "mosque", "669": "mosquito net", "670": "motor scooter, scooter", "671": "mountain bike, all-terrain bike, off-roader", "672": "mountain tent", "673": "mouse, computer mouse", "674": "mousetrap", "675": "moving van", "676": "muzzle", "677": "nail", "678": "neck brace", "679": "necklace", "680": "nipple", "681": "notebook, notebook computer", "682": "obelisk", "683": "oboe, hautboy, hautbois", "684": "ocarina, sweet potato", "685": "odometer, hodometer, mileometer, milometer", "686": "oil filter", "687": "organ, pipe organ", "688": "oscilloscope, scope, cathode-ray oscilloscope, CRO", "689": "overskirt", "690": "oxcart", "691": "oxygen mask", "692": "packet", "693": "paddle, boat paddle", "694": "paddlewheel, paddle wheel", "695": "padlock", "696": "paintbrush", "697": "pajama, pyjama, pj's, jammies", "698": "palace", "699": "panpipe, pandean pipe, syrinx", "700": "paper towel", "701": "parachute, chute", "702": "parallel bars, bars", "703": "park bench", "704": "parking meter", "705": "passenger car, coach, carriage", "706": "patio, terrace", "707": "pay-phone, pay-station", "708": "pedestal, plinth, footstall", "709": "pencil box, pencil case", "710": "pencil sharpener", "711": "perfume, essence", "712": "Petri dish", "713": "photocopier", "714": "pick, plectrum, plectron", "715": "pickelhaube", "716": "picket fence, paling", "717": "pickup, pickup truck", "718": "pier", "719": "piggy bank, penny bank", "720": "pill bottle", "721": "pillow", "722": "ping-pong ball", "723": "pinwheel", "724": "pirate, pirate ship", "725": "pitcher, ewer", "726": "plane, carpenter's plane, woodworking plane", "727": "planetarium", "728": "plastic bag", "729": "plate rack", "730": "plow, plough", "731": "plunger, plumber's helper", "732": "Polaroid camera, Polaroid Land camera", "733": "pole", "734": "police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria", "735": "poncho", "736": "pool table, billiard table, snooker table", "737": "pop bottle, soda bottle", "738": "pot, flowerpot", "739": "potter's wheel", "740": "power drill", "741": "prayer rug, prayer mat", "742": "printer", "743": "prison, prison house", "744": "projectile, missile", "745": "projector", "746": "puck, hockey puck", "747": "punching bag, punch bag, punching ball, punchball", "748": "purse", "749": "quill, quill pen", "750": "quilt, comforter, comfort, puff", "751": "racer, race car, racing car", "752": "racket, racquet", "753": "radiator", "754": "radio, wireless", "755": "radio telescope, radio reflector", "756": "rain barrel", "757": "recreational vehicle, RV, R.V.", "758": "reel", "759": "reflex camera", "760": "refrigerator, icebox", "761": "remote control, remote", "762": "restaurant, eating house, eating place, eatery", "763": "revolver, six-gun, six-shooter", "764": "rifle", "765": "rocking chair, rocker", "766": "rotisserie", "767": "rubber eraser, rubber, pencil eraser", "768": "rugby ball", "769": "rule, ruler", "770": "running shoe", "771": "safe", "772": "safety pin", "773": "saltshaker, salt shaker", "774": "sandal", "775": "sarong", "776": "sax, saxophone", "777": "scabbard", "778": "scale, weighing machine", "779": "school bus", "780": "schooner", "781": "scoreboard", "782": "screen, CRT screen", "783": "screw", "784": "screwdriver", "785": "seat belt, seatbelt", "786": "sewing machine", "787": "shield, buckler", "788": "shoe shop, shoe-shop, shoe store", "789": "shoji", "790": "shopping basket", "791": "shopping cart", "792": "shovel", "793": "shower cap", "794": "shower curtain", "795": "ski", "796": "ski mask", "797": "sleeping bag", "798": "slide rule, slipstick", "799": "sliding door", "800": "slot, one-armed bandit", "801": "snorkel", "802": "snowmobile", "803": "snowplow, snowplough", "804": "soap dispenser", "805": "soccer ball", "806": "sock", "807": "solar dish, solar collector, solar furnace", "808": "sombrero", "809": "soup bowl", "810": "space bar", "811": "space heater", "812": "space shuttle", "813": "spatula", "814": "speedboat", "815": "spider web, spider's web", "816": "spindle", "817": "sports car, sport car", "818": "spotlight, spot", "819": "stage", "820": "steam locomotive", "821": "steel arch bridge", "822": "steel drum", "823": "stethoscope", "824": "stole", "825": "stone wall", "826": "stopwatch, stop watch", "827": "stove", "828": "strainer", "829": "streetcar, tram, tramcar, trolley, trolley car", "830": "stretcher", "831": "studio couch, day bed", "832": "stupa, tope", "833": "submarine, pigboat, sub, U-boat", "834": "suit, suit of clothes", "835": "sundial", "836": "sunglass", "837": "sunglasses, dark glasses, shades", "838": "sunscreen, sunblock, sun blocker", "839": "suspension bridge", "840": "swab, swob, mop", "841": "sweatshirt", "842": "swimming trunks, bathing trunks", "843": "swing", "844": "switch, electric switch, electrical switch", "845": "syringe", "846": "table lamp", "847": "tank, army tank, armored combat vehicle, armoured combat vehicle", "848": "tape player", "849": "teapot", "850": "teddy, teddy bear", "851": "television, television system", "852": "tennis ball", "853": "thatch, thatched roof", "854": "theater curtain, theatre curtain", "855": "thimble", "856": "thresher, thrasher, threshing machine", "857": "throne", "858": "tile roof", "859": "toaster", "860": "tobacco shop, tobacconist shop, tobacconist", "861": "toilet seat", "862": "torch", "863": "totem pole", "864": "tow truck, tow car, wrecker", "865": "toyshop", "866": "tractor", "867": "trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi", "868": "tray", "869": "trench coat", "870": "tricycle, trike, velocipede", "871": "trimaran", "872": "tripod", "873": "triumphal arch", "874": "trolleybus, trolley coach, trackless trolley", "875": "trombone", "876": "tub, vat", "877": "turnstile", "878": "typewriter keyboard", "879": "umbrella", "880": "unicycle, monocycle", "881": "upright, upright piano", "882": "vacuum, vacuum cleaner", "883": "vase", "884": "vault", "885": "velvet", "886": "vending machine", "887": "vestment", "888": "viaduct", "889": "violin, fiddle", "890": "volleyball", "891": "waffle iron", "892": "wall clock", "893": "wallet, billfold, notecase, pocketbook", "894": "wardrobe, closet, press", "895": "warplane, military plane", "896": "washbasin, handbasin, washbowl, lavabo, wash-hand basin", "897": "washer, automatic washer, washing machine", "898": "water bottle", "899": "water jug", "900": "water tower", "901": "whiskey jug", "902": "whistle", "903": "wig", "904": "window screen", "905": "window shade", "906": "Windsor tie", "907": "wine bottle", "908": "wing", "909": "wok", "910": "wooden spoon", "911": "wool, woolen, woollen", "912": "worm fence, snake fence, snake-rail fence, Virginia fence", "913": "wreck", "914": "yawl", "915": "yurt", "916": "web site, website, internet site, site", "917": "comic book", "918": "crossword puzzle, crossword", "919": "street sign", "920": "traffic light, traffic signal, stoplight", "921": "book jacket, dust cover, dust jacket, dust wrapper", "922": "menu", "923": "plate", "924": "guacamole", "925": "consomme", "926": "hot pot, hotpot", "927": "trifle", "928": "ice cream, icecream", "929": "ice lolly, lolly, lollipop, popsicle", "930": "French loaf", "931": "bagel, beigel", "932": "pretzel", "933": "cheeseburger", "934": "hotdog, hot dog, red hot", "935": "mashed potato", "936": "head cabbage", "937": "broccoli", "938": "cauliflower", "939": "zucchini, courgette", "940": "spaghetti squash", "941": "acorn squash", "942": "butternut squash", "943": "cucumber, cuke", "944": "artichoke, globe artichoke", "945": "bell pepper", "946": "cardoon", "947": "mushroom", "948": "Granny Smith", "949": "strawberry", "950": "orange", "951": "lemon", "952": "fig", "953": "pineapple, ananas", "954": "banana", "955": "jackfruit, jak, jack", "956": "custard apple", "957": "pomegranate", "958": "hay", "959": "carbonara", "960": "chocolate sauce, chocolate syrup", "961": "dough", "962": "meat loaf, meatloaf", "963": "pizza, pizza pie", "964": "potpie", "965": "burrito", "966": "red wine", "967": "espresso", "968": "cup", "969": "eggnog", "970": "alp", "971": "bubble", "972": "cliff, drop, drop-off", "973": "coral reef", "974": "geyser", "975": "lakeside, lakeshore", "976": "promontory, headland, head, foreland", "977": "sandbar, sand bar", "978": "seashore, coast, seacoast, sea-coast", "979": "valley, vale", "980": "volcano", "981": "ballplayer, baseball player", "982": "groom, bridegroom", "983": "scuba diver", "984": "rapeseed", "985": "daisy", "986": "yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum", "987": "corn", "988": "acorn", "989": "hip, rose hip, rosehip", "990": "buckeye, horse chestnut, conker", "991": "coral fungus", "992": "agaric", "993": "gyromitra", "994": "stinkhorn, carrion fungus", "995": "earthstar", "996": "hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa", "997": "bolete", "998": "ear, spike, capitulum", "999": "toilet tissue, toilet paper, bathroom tissue"}}}}], "splits": [{"name": "test", "num_bytes": 13613661561, "num_examples": 100000}, {"name": "train", "num_bytes": 146956944242, "num_examples": 1281167}, {"name": "validation", "num_bytes": 6709003386, "num_examples": 50000}], "download_size": 166009941208, "dataset_size": 167279609189}}
| false |
auto
| 2024-07-16T13:30:57 | 536 | 4 | false |
4603483700ee984ea9debe3ddbfdeae86f6489eb
|
ILSVRC 2012, commonly known as 'ImageNet' is an image dataset organized according to the WordNet hierarchy. Each meaningful concept in WordNet, possibly described by multiple words or word phrases, is called a "synonym set" or "synset". There are more than 100,000 synsets in WordNet, majority of them are nouns (80,000+). ImageNet aims to provide on average 1000 images to illustrate each synset. Images of each concept are quality-controlled and human-annotated. In its completion, ImageNet hopes to offer tens of millions of cleanly sorted images for most of the concepts in the WordNet hierarchy. ImageNet 2012 is the most commonly used subset of ImageNet. This dataset spans 1000 object classes and contains 1,281,167 training images, 50,000 validation images and 100,000 test images
| 22,344 | 1,176,771 |
[
"task_categories:image-classification",
"task_ids:multi-class-image-classification",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:other",
"size_categories:1M<n<10M",
"arxiv:1409.0575",
"arxiv:1912.07726",
"arxiv:1811.12231",
"arxiv:2109.13228",
"region:us"
] | 2022-05-02T16:33:23 |
@article{imagenet15russakovsky,
Author = {Olga Russakovsky and Jia Deng and Hao Su and Jonathan Krause and Sanjeev Satheesh and Sean Ma and Zhiheng Huang and Andrej Karpathy and Aditya Khosla and Michael Bernstein and Alexander C. Berg and Li Fei-Fei},
Title = { {ImageNet Large Scale Visual Recognition Challenge} },
Year = {2015},
journal = {International Journal of Computer Vision (IJCV)},
doi = {10.1007/s11263-015-0816-y},
volume={115},
number={3},
pages={211-252}
}
|
imagenet-1k-1
|
6451a2ee41f3c769b91b2685
|
liuhaotian/LLaVA-Pretrain
|
liuhaotian
|
{"license": "other", "language": ["en"], "pretty_name": "LLaVA Pretrain"}
| false |
False
| 2023-07-06T08:47:38 | 199 | 4 | false |
70f9d1e5e1a697fe35830875cfc7de1dd590d727
|
LLaVA Visual Instruct Pretrain Dataset Card
Dataset details
Dataset type:
LLaVA Visual Instruct Pretrain LCS-558K is a subset of LAION/CC/SBU dataset, filtered with a more balanced concept coverage distribution.
Captions are also associated with BLIP synthetic caption for reference.
It is constructed for the pretraining stage for feature alignment in visual instruction tuning.
We aim to build large multimodal towards GPT-4 vision/language capability.
Dataset date:… See the full description on the dataset page: https://huggingface.co/datasets/liuhaotian/LLaVA-Pretrain.
| 1,521 | 18,179 |
[
"language:en",
"license:other",
"modality:image",
"region:us"
] | 2023-05-02T23:55:26 | null | null |
6480d02ee1421e205fdd7f2a
|
cerebras/SlimPajama-627B
|
cerebras
|
{"task_categories": ["text-generation"], "language": ["en"], "pretty_name": "SlimPajama-627B"}
| false |
False
| 2023-07-07T23:13:12 | 485 | 4 | false |
2d0accdd58c5d5511943ca1f5ff0e3eb5e293543
|
The dataset consists of 59166 jsonl files and is ~895GB compressed. It is a cleaned and deduplicated version of Together's RedPajama.
Check out our blog post explaining our methods, our code on GitHub, and join the discussion on the Cerebras Discord.
Getting Started
You can download the dataset using Hugging Face datasets:
from datasets import load_dataset
ds = load_dataset("cerebras/SlimPajama-627B")
Background
Today we are releasing SlimPajama – the largest… See the full description on the dataset page: https://huggingface.co/datasets/cerebras/SlimPajama-627B.
| 67,488 | 1,372,466 |
[
"task_categories:text-generation",
"language:en",
"arxiv:2306.01116",
"arxiv:2302.13971",
"region:us"
] | 2023-06-07T18:45:02 | null | null |
6525e2116b41932089f2b15d
|
ProlificAI/social-reasoning-rlhf
|
ProlificAI
|
{"license": "mit", "task_categories": ["text-generation"], "language": ["en"], "pretty_name": "Social Reasoning RLHF", "size_categories": ["1K<n<10K"], "tags": ["human-feedback", "rlhf"]}
| false |
False
| 2023-10-11T08:50:59 | 44 | 4 | false |
de1bee17491f41c5105d2ffc2254171ea4a65368
|
Dataset Summary
This repository provides access to a social reasoning dataset that aims to provide signal to how humans navigate social situations, how they reason about them and how they understand each other. It contains questions probing people's thinking and understanding of various social situations.
This dataset was created by collating a set of questions within the following social reasoning tasks:
understanding of emotions
intent recognition
social norms
social… See the full description on the dataset page: https://huggingface.co/datasets/ProlificAI/social-reasoning-rlhf.
| 118 | 9,749 |
[
"task_categories:text-generation",
"language:en",
"license:mit",
"size_categories:1K<n<10K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us",
"human-feedback",
"rlhf"
] | 2023-10-10T23:45:21 | null | null |
658570e3bae0736365b32de4
|
google/IFEval
|
google
|
{"license": "apache-2.0", "task_categories": ["text-generation"], "language": ["en"], "pretty_name": "IFEval"}
| false |
False
| 2024-08-14T08:21:56 | 77 | 4 | false |
966cd89545d6b6acfd7638bc708b98261ca58e84
|
Dataset Card for IFEval
Dataset Summary
This dataset contains the prompts used in the Instruction-Following Eval (IFEval) benchmark for large language models. It contains around 500 "verifiable instructions" such as "write in more than 400 words" and "mention the keyword of AI at least 3 times" which can be verified by heuristics. To load the dataset, run:
from datasets import load_dataset
ifeval = load_dataset("google/IFEval")
Supported Tasks and… See the full description on the dataset page: https://huggingface.co/datasets/google/IFEval.
| 21,015 | 146,694 |
[
"task_categories:text-generation",
"language:en",
"license:apache-2.0",
"size_categories:n<1K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2311.07911",
"region:us"
] | 2023-12-22T11:20:03 | null | null |
659683af994d0ef5817d8e42
|
HuggingFaceM4/WebSight
|
HuggingFaceM4
|
{"language": ["en"], "license": "cc-by-4.0", "size_categories": ["1M<n<10M"], "pretty_name": "WebSight", "dataset_info": [{"config_name": "v0.2", "features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}, {"name": "llm_generated_idea", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 368943620718.125, "num_examples": 1922671}], "download_size": 144861710051, "dataset_size": 368943620718.125}, {"config_name": "v0.1", "features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 35386660486.65, "num_examples": 822987}], "download_size": 31394170440, "dataset_size": 35386660486.65}], "configs": [{"config_name": "v0.2", "default": true, "data_files": [{"split": "train", "path": "v0.2/train-*"}]}, {"config_name": "v0.1", "data_files": [{"split": "train", "path": "data/train-*"}]}], "tags": ["code", "synthetic"]}
| false |
False
| 2024-03-26T15:37:29 | 358 | 4 | false |
b11f8172f89c992b56ac702319e02c428cca4a4e
|
Dataset Card for WebSight
Dataset Description
WebSight is a large synthetic dataset containing HTML/CSS codes representing synthetically generated English websites, each accompanied by a corresponding screenshot.
This dataset serves as a valuable resource for tasks such as generating UI codes from a screenshot.
It comes in two versions:
v0.1: Websites are coded with HTML + CSS. They do not include real images.
v0.2: Websites are coded with HTML + Tailwind CSS. They do… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceM4/WebSight.
| 5,739 | 120,772 |
[
"language:en",
"license:cc-by-4.0",
"size_categories:1M<n<10M",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2403.09029",
"region:us",
"code",
"synthetic"
] | 2024-01-04T10:08:47 | null | null |
65970a082d23530ec05b7d37
|
jtatman/stable-diffusion-prompts-stats-full-uncensored
|
jtatman
|
nan
| false |
False
| 2024-11-08T15:34:37 | 78 | 4 | false |
eeddd630cca52dbb9af5a4a40aa5748b565da36e
| null | 1,350 | 6,488 |
[
"size_categories:100K<n<1M",
"format:parquet",
"modality:image",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2024-01-04T19:42:00 | null | null |
65dc13085ca10be41fdd8b27
|
bigcode/the-stack-v2
|
bigcode
|
{"annotations_creators": [], "language_creators": ["crowdsourced", "expert-generated"], "language": ["code"], "license": ["other"], "multilinguality": ["multilingual"], "pretty_name": "The-Stack-v2", "size_categories": ["unknown"], "source_datasets": [], "task_categories": ["text-generation"], "task_ids": [], "extra_gated_prompt": "## Terms of Use for The Stack v2\n\nThe Stack v2 dataset is a collection of source code in over 600 programming languages. We ask that you read and acknowledge the following points before using the dataset:\n1. Downloading the dataset in bulk requires a an agreement with SoftwareHeritage and INRIA. Contact [[email protected]](mailto:[email protected]?subject=TheStackV2%20request%20for%20dataset%20access%20information) for more information.\n2. If you are using the dataset to train models you must adhere to the SoftwareHeritage [principles for language model training](https://www.softwareheritage.org/2023/10/19/swh-statement-on-llm-for-code/).\n3. The Stack v2 is a collection of source code from repositories with various licenses. Any use of all or part of the code gathered in The Stack v2 must abide by the terms of the original licenses, including attribution clauses when relevant. We facilitate this by providing provenance information for each data point.\n4. The Stack v2 is regularly updated to enact validated data removal requests. By clicking on \"Access repository\", you agree to update your own version of The Stack v2 to the most recent usable version.\n\nBy clicking on \"Access repository\" below, you accept that your contact information (email address and username) can be shared with the dataset maintainers as well.\n ", "extra_gated_fields": {"Email": "text", "I have read the License and agree with its terms": "checkbox"}, "dataset_info": {"features": [{"name": "blob_id", "dtype": "string"}, {"name": "directory_id", "dtype": "string"}, {"name": "path", "dtype": "string"}, {"name": "content_id", "dtype": "string"}, {"name": "detected_licenses", "sequence": "string"}, {"name": "license_type", "dtype": "string"}, {"name": "repo_name", "dtype": "string"}, {"name": "snapshot_id", "dtype": "string"}, {"name": "revision_id", "dtype": "string"}, {"name": "branch_name", "dtype": "string"}, {"name": "visit_date", "dtype": "timestamp[ns]"}, {"name": "revision_date", "dtype": "timestamp[ns]"}, {"name": 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"data/dircolors/*.parquet"}]}, {"config_name": "eC", "data_files": [{"split": "train", "path": "data/eC/*.parquet"}]}, {"config_name": "edn", "data_files": [{"split": "train", "path": "data/edn/*.parquet"}]}, {"config_name": "fish", "data_files": [{"split": "train", "path": "data/fish/*.parquet"}]}, {"config_name": "hoon", "data_files": [{"split": "train", "path": "data/hoon/*.parquet"}]}, {"config_name": "jq", "data_files": [{"split": "train", "path": "data/jq/*.parquet"}]}, {"config_name": "kvlang", "data_files": [{"split": "train", "path": "data/kvlang/*.parquet"}]}, {"config_name": "mIRC_Script", "data_files": [{"split": "train", "path": "data/mIRC_Script/*.parquet"}]}, {"config_name": "mcfunction", "data_files": [{"split": "train", "path": "data/mcfunction/*.parquet"}]}, {"config_name": "mupad", "data_files": [{"split": "train", "path": "data/mupad/*.parquet"}]}, {"config_name": "nanorc", "data_files": [{"split": "train", "path": "data/nanorc/*.parquet"}]}, {"config_name": "nesC", "data_files": [{"split": "train", "path": "data/nesC/*.parquet"}]}, {"config_name": "ooc", "data_files": [{"split": "train", "path": "data/ooc/*.parquet"}]}, {"config_name": "q", "data_files": [{"split": "train", "path": "data/q/*.parquet"}]}, {"config_name": "reStructuredText", "data_files": [{"split": "train", "path": "data/reStructuredText/*.parquet"}]}, {"config_name": "robots.txt", "data_files": [{"split": "train", "path": "data/robots.txt/*.parquet"}]}, {"config_name": "sed", "data_files": [{"split": "train", "path": "data/sed/*.parquet"}]}, {"config_name": "wdl", "data_files": [{"split": "train", "path": "data/wdl/*.parquet"}]}, {"config_name": "wisp", "data_files": [{"split": "train", "path": "data/wisp/*.parquet"}]}, {"config_name": "xBase", "data_files": [{"split": "train", "path": "data/xBase/*.parquet"}]}]}
| false |
auto
| 2024-04-23T15:52:32 | 393 | 4 | false |
7408bfbcfd48e5833d62fd3dba48afd20d109473
|
The Stack v2
The dataset consists of 4 versions:
bigcode/the-stack-v2: the full "The Stack v2" dataset <-- you are here
bigcode/the-stack-v2-dedup: based on the bigcode/the-stack-v2 but further near-deduplicated
bigcode/the-stack-v2-train-full-ids: based on the bigcode/the-stack-v2-dedup dataset but further filtered with heuristics and spanning 600+ programming languages. The data is grouped into repositories.bigcode/the-stack-v2-train-smol-ids: based on the… See the full description on the dataset page: https://huggingface.co/datasets/bigcode/the-stack-v2.
| 3,412 | 205,346 |
[
"task_categories:text-generation",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:multilingual",
"language:code",
"license:other",
"size_categories:1B<n<10B",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2402.19173",
"arxiv:2107.03374",
"arxiv:2207.14157",
"region:us"
] | 2024-02-26T04:26:48 | null | null |
65fc5a783bc54054aa2e6e62
|
gretelai/synthetic_text_to_sql
|
gretelai
|
{"license": "apache-2.0", "task_categories": ["question-answering", "table-question-answering", "text-generation"], "language": ["en"], "tags": ["synthetic", "SQL", "text-to-SQL", "code"], "size_categories": ["100K<n<1M"]}
| false |
False
| 2024-05-10T22:30:56 | 578 | 4 | false |
273a86f5f290e8d61b6767a9ff690c82bc990dc4
|
Image generated by DALL-E. See prompt for more details
synthetic_text_to_sql
gretelai/synthetic_text_to_sql is a rich dataset of high quality synthetic Text-to-SQL samples,
designed and generated using Gretel Navigator, and released under Apache 2.0.
Please see our release blogpost for more details.
The dataset includes:
105,851 records partitioned into 100,000 train and 5,851 test records
~23M total tokens, including ~12M SQL tokens
Coverage across 100 distinct… See the full description on the dataset page: https://huggingface.co/datasets/gretelai/synthetic_text_to_sql.
| 3,944 | 58,853 |
[
"task_categories:question-answering",
"task_categories:table-question-answering",
"task_categories:text-generation",
"language:en",
"license:apache-2.0",
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2306.05685",
"region:us",
"synthetic",
"SQL",
"text-to-SQL",
"code"
] | 2024-03-21T16:04:08 | null | null |
660e7b9b4636ce2b0e77b699
|
mozilla-foundation/common_voice_17_0
|
mozilla-foundation
|
{"pretty_name": "Common Voice Corpus 17.0", "annotations_creators": ["crowdsourced"], "language_creators": ["crowdsourced"], "language": ["ab", "af", "am", "ar", "as", "ast", "az", "ba", "bas", "be", "bg", "bn", "br", "ca", "ckb", "cnh", "cs", "cv", "cy", "da", "de", "dv", "dyu", "el", "en", "eo", "es", "et", "eu", "fa", "fi", "fr", "fy", "ga", "gl", "gn", "ha", "he", "hi", "hsb", "ht", "hu", "hy", "ia", "id", "ig", "is", "it", "ja", "ka", "kab", "kk", "kmr", "ko", "ky", "lg", "lij", "lo", "lt", "ltg", "lv", "mdf", "mhr", "mk", "ml", "mn", "mr", "mrj", "mt", "myv", "nan", "ne", "nhi", "nl", "nn", "nso", "oc", "or", "os", "pa", "pl", "ps", "pt", "quy", "rm", "ro", "ru", "rw", "sah", "sat", "sc", "sk", "skr", "sl", "sq", "sr", "sv", "sw", "ta", "te", "th", "ti", "tig", "tk", "tok", "tr", "tt", "tw", "ug", "uk", "ur", "uz", "vi", "vot", "yi", "yo", "yue", "zgh", "zh", "zu", "zza"], "language_bcp47": ["zh-CN", "zh-HK", "zh-TW", "sv-SE", "rm-sursilv", "rm-vallader", "pa-IN", "nn-NO", "ne-NP", "nan-tw", "hy-AM", "ga-IE", "fy-NL"], "license": ["cc0-1.0"], "multilinguality": ["multilingual"], "source_datasets": ["extended|common_voice"], "paperswithcode_id": "common-voice", "extra_gated_prompt": "By clicking on \u201cAccess repository\u201d below, you also agree to not attempt to determine the identity of speakers in the Common Voice dataset."}
| false |
auto
| 2024-06-16T13:50:23 | 320 | 4 | false |
b10d53980ef166bc24ce3358471c1970d7e6b5ec
|
Dataset Card for Common Voice Corpus 17.0
Dataset Summary
The Common Voice dataset consists of a unique MP3 and corresponding text file.
Many of the 31175 recorded hours in the dataset also include demographic metadata like age, sex, and accent
that can help improve the accuracy of speech recognition engines.
The dataset currently consists of 20408 validated hours in 124 languages, but more voices and languages are always added.
Take a look at the Languages page to… See the full description on the dataset page: https://huggingface.co/datasets/mozilla-foundation/common_voice_17_0.
| 34,184 | 595,801 |
[
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:multilingual",
"source_datasets:extended|common_voice",
"language:ab",
"language:af",
"language:am",
"language:ar",
"language:as",
"language:ast",
"language:az",
"language:ba",
"language:bas",
"language:be",
"language:bg",
"language:bn",
"language:br",
"language:ca",
"language:ckb",
"language:cnh",
"language:cs",
"language:cv",
"language:cy",
"language:da",
"language:de",
"language:dv",
"language:dyu",
"language:el",
"language:en",
"language:eo",
"language:es",
"language:et",
"language:eu",
"language:fa",
"language:fi",
"language:fr",
"language:fy",
"language:ga",
"language:gl",
"language:gn",
"language:ha",
"language:he",
"language:hi",
"language:hsb",
"language:ht",
"language:hu",
"language:hy",
"language:ia",
"language:id",
"language:ig",
"language:is",
"language:it",
"language:ja",
"language:ka",
"language:kab",
"language:kk",
"language:kmr",
"language:ko",
"language:ky",
"language:lg",
"language:lij",
"language:lo",
"language:lt",
"language:ltg",
"language:lv",
"language:mdf",
"language:mhr",
"language:mk",
"language:ml",
"language:mn",
"language:mr",
"language:mrj",
"language:mt",
"language:myv",
"language:nan",
"language:ne",
"language:nhi",
"language:nl",
"language:nn",
"language:nso",
"language:oc",
"language:or",
"language:os",
"language:pa",
"language:pl",
"language:ps",
"language:pt",
"language:quy",
"language:rm",
"language:ro",
"language:ru",
"language:rw",
"language:sah",
"language:sat",
"language:sc",
"language:sk",
"language:skr",
"language:sl",
"language:sq",
"language:sr",
"language:sv",
"language:sw",
"language:ta",
"language:te",
"language:th",
"language:ti",
"language:tig",
"language:tk",
"language:tok",
"language:tr",
"language:tt",
"language:tw",
"language:ug",
"language:uk",
"language:ur",
"language:uz",
"language:vi",
"language:vot",
"language:yi",
"language:yo",
"language:yue",
"language:zgh",
"language:zh",
"language:zu",
"language:zza",
"license:cc0-1.0",
"size_categories:10M<n<100M",
"modality:audio",
"modality:text",
"library:datasets",
"library:mlcroissant",
"arxiv:1912.06670",
"region:us"
] | 2024-04-04T10:06:19 |
@inproceedings{commonvoice:2020,
author = {Ardila, R. and Branson, M. and Davis, K. and Henretty, M. and Kohler, M. and Meyer, J. and Morais, R. and Saunders, L. and Tyers, F. M. and Weber, G.},
title = {Common Voice: A Massively-Multilingual Speech Corpus},
booktitle = {Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020)},
pages = {4211--4215},
year = 2020
}
|
common-voice
|
666a59145c3bb7e4a6c8d180
|
Salesforce/xlam-function-calling-60k
|
Salesforce
|
{"extra_gated_heading": "Acknowledge to follow corresponding license and cite APIGen to access the repository", "extra_gated_button_content": "Agree and access repository", "extra_gated_fields": {"First Name": "text", "Last Name": "text", "Country": "country", "Affiliation": "text"}, "license": "cc-by-4.0", "task_categories": ["question-answering", "text-generation", "reinforcement-learning"], "language": ["en"], "tags": ["function-calling", "LLM Agent", "code", "synthetic"], "size_categories": ["10K<n<100K"], "configs": [{"config_name": "dataset", "data_files": [{"split": "train", "path": "xlam_function_calling_60k.json"}]}]}
| false |
auto
| 2025-01-24T19:25:58 | 486 | 4 | false |
26d14ebfe18b1f7b524bd39b404b50af5dc97866
|
APIGen Function-Calling Datasets
Paper | Website | Models
This repo contains 60,000 data collected by APIGen, an automated data generation pipeline designed to produce verifiable high-quality datasets for function-calling applications. Each data in our dataset is verified through three hierarchical stages: format checking, actual function executions, and semantic verification, ensuring its reliability and correctness.
We conducted human evaluation over 600 sampled data points, and… See the full description on the dataset page: https://huggingface.co/datasets/Salesforce/xlam-function-calling-60k.
| 3,968 | 40,768 |
[
"task_categories:question-answering",
"task_categories:text-generation",
"task_categories:reinforcement-learning",
"language:en",
"license:cc-by-4.0",
"size_categories:10K<n<100K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2406.18518",
"region:us",
"function-calling",
"LLM Agent",
"code",
"synthetic"
] | 2024-06-13T02:27:32 | null | null |
667ee649a7d8b1deba8d4f4c
|
proj-persona/PersonaHub
|
proj-persona
|
{"license": "cc-by-nc-sa-4.0", "task_categories": ["text-generation", "text-classification", "token-classification", "fill-mask", "table-question-answering", "text2text-generation"], "language": ["en", "zh"], "tags": ["synthetic", "text", "math", "reasoning", "instruction", "tool"], "size_categories": ["100M<n<1B"], "configs": [{"config_name": "math", "data_files": "math.jsonl"}, {"config_name": "instruction", "data_files": "instruction.jsonl"}, {"config_name": "reasoning", "data_files": "reasoning.jsonl"}, {"config_name": "knowledge", "data_files": "knowledge.jsonl"}, {"config_name": "npc", "data_files": "npc.jsonl"}, {"config_name": "tool", "data_files": "tool.jsonl"}, {"config_name": "persona", "data_files": "persona.jsonl"}, {"config_name": "elite_persona", "data_files": [{"split": "train", "path": "ElitePersonas/*"}]}]}
| false |
False
| 2025-03-04T22:01:42 | 616 | 4 | false |
600b0189027c804fc9373b4de4875c171656a4df
|
Scaling Synthetic Data Creation with 1,000,000,000 Personas
This repo releases data introduced in our paper Scaling Synthetic Data Creation with 1,000,000,000 Personas:
We propose a novel persona-driven data synthesis methodology that leverages various perspectives within a large language model (LLM) to create diverse synthetic data. To fully exploit this methodology at scale, we introduce PERSONA HUB – a collection of 1 billion diverse personas automatically curated from web data.… See the full description on the dataset page: https://huggingface.co/datasets/proj-persona/PersonaHub.
| 17,752 | 80,228 |
[
"task_categories:text-generation",
"task_categories:text-classification",
"task_categories:token-classification",
"task_categories:fill-mask",
"task_categories:table-question-answering",
"language:en",
"language:zh",
"license:cc-by-nc-sa-4.0",
"size_categories:100K<n<1M",
"format:json",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2406.20094",
"region:us",
"synthetic",
"text",
"math",
"reasoning",
"instruction",
"tool"
] | 2024-06-28T16:35:21 | null | null |
6697abec43d9faa413ca745c
|
HuggingFaceM4/Docmatix
|
HuggingFaceM4
|
{"language": ["en"], "license": "mit", "size_categories": ["1M<n<10M"], "task_categories": ["visual-question-answering"], "pretty_name": "Docmatix", "tags": ["docvqa"], "configs": [{"config_name": "images", "data_files": [{"split": "train", "path": "data/train-*"}]}, {"config_name": "pdf", "data_files": [{"split": "train", "path": "pdf/train-*"}]}, {"config_name": "zero-shot-exp", "data_files": [{"split": "train", "path": "zero-shot-exp/train-*"}, {"split": "test", "path": "zero-shot-exp/test-*"}]}], "dataset_info": [{"config_name": "images", "features": [{"name": "images", "sequence": "image"}, {"name": "texts", "list": [{"name": "user", "dtype": "string"}, {"name": "assistant", "dtype": "string"}, {"name": "source", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 552957537722.77, "num_examples": 1273215}], "download_size": 159404414330, "dataset_size": 552957537722.77}, {"config_name": "pdf", "features": [{"name": "pdf", "dtype": "binary"}, {"name": "texts", "list": [{"name": "user", "dtype": "string"}, {"name": "assistant", "dtype": "string"}, {"name": "source", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 458612867150, "num_examples": 1273245}], "download_size": 431829972210, "dataset_size": 458612867150}, {"config_name": "zero-shot-exp", "features": [{"name": "images", "sequence": "image"}, {"name": "texts", "list": [{"name": "user", "dtype": "string"}, {"name": "assistant", "dtype": "string"}, {"name": "source", "dtype": "string"}]}], "splits": [{"name": "test", "num_bytes": 68900253, "num_examples": 200}, {"name": "train", "num_bytes": 578335690.5, "num_examples": 1700}], "download_size": 642963847, "dataset_size": 647235943.5}]}
| false |
False
| 2024-08-26T08:15:21 | 285 | 4 | false |
0725b65616e0e5f6024be10e38ddf8d8c48664fd
|
Dataset Card for Docmatix
Dataset description
Docmatix is part of the Idefics3 release (stay tuned).
It is a massive dataset for Document Visual Question Answering that was used for the fine-tuning of the vision-language model Idefics3.
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/Docmatix")
If you want the dataset to link to the pdf files… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceM4/Docmatix.
| 9,998 | 158,705 |
[
"task_categories:visual-question-answering",
"language:en",
"license:mit",
"size_categories:1M<n<10M",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2408.12637",
"region:us",
"docvqa"
] | 2024-07-17T11:33:00 | null | null |
66cd7bbefc6f503213a054e7
|
lmms-lab/LLaVA-Video-178K
|
lmms-lab
|
{"configs": [{"config_name": "0_30_s_academic_v0_1", "data_files": [{"split": "caption", "path": "0_30_s_academic_v0_1/*cap*.json"}, {"split": "open_ended", "path": "0_30_s_academic_v0_1/*oe*.json"}, {"split": "multi_choice", "path": "0_30_s_academic_v0_1/*mc*.json"}]}, {"config_name": "0_30_s_youtube_v0_1", "data_files": [{"split": "caption", "path": "0_30_s_youtube_v0_1/*cap*.json"}, {"split": "open_ended", "path": "0_30_s_youtube_v0_1/*oe*.json"}, {"split": "multi_choice", "path": "0_30_s_youtube_v0_1/*mc*.json"}]}, {"config_name": "0_30_s_activitynet", "data_files": [{"split": "open_ended", "path": "0_30_s_activitynet/*oe*.json"}]}, {"config_name": "0_30_s_perceptiontest", "data_files": [{"split": "multi_choice", "path": "0_30_s_perceptiontest/*mc*.json"}]}, {"config_name": "0_30_s_nextqa", "data_files": [{"split": "open_ended", "path": "0_30_s_nextqa/*oe*.json"}, {"split": "multi_choice", "path": "0_30_s_nextqa/*mc*.json"}]}, {"config_name": "30_60_s_academic_v0_1", "data_files": [{"split": "caption", "path": "30_60_s_academic_v0_1/*cap*.json"}, {"split": "open_ended", "path": "30_60_s_academic_v0_1/*oe*.json"}, {"split": "multi_choice", "path": "30_60_s_academic_v0_1/*mc*.json"}]}, {"config_name": "30_60_s_youtube_v0_1", "data_files": [{"split": "caption", "path": "30_60_s_youtube_v0_1/*cap*.json"}, {"split": "open_ended", "path": "30_60_s_youtube_v0_1/*oe*.json"}, {"split": "multi_choice", "path": "30_60_s_youtube_v0_1/*mc*.json"}]}, {"config_name": "30_60_s_activitynet", "data_files": [{"split": "open_ended", "path": "30_60_s_activitynet/*oe*.json"}]}, {"config_name": "30_60_s_perceptiontest", "data_files": [{"split": "multi_choice", "path": "30_60_s_perceptiontest/*mc*.json"}]}, {"config_name": "30_60_s_nextqa", "data_files": [{"split": "open_ended", "path": "30_60_s_nextqa/*oe*.json"}, {"split": "multi_choice", "path": "30_60_s_nextqa/*mc*.json"}]}, {"config_name": "1_2_m_youtube_v0_1", "data_files": [{"split": "caption", "path": "1_2_m_youtube_v0_1/*cap*.json"}, {"split": "open_ended", "path": "1_2_m_youtube_v0_1/*oe*.json"}, {"split": "multi_choice", "path": "1_2_m_youtube_v0_1/*mc*.json"}]}, {"config_name": "1_2_m_academic_v0_1", "data_files": [{"split": "caption", "path": "1_2_m_academic_v0_1/*cap*.json"}, {"split": "open_ended", "path": "1_2_m_academic_v0_1/*oe*.json"}, {"split": "multi_choice", "path": "1_2_m_academic_v0_1/*mc*.json"}]}, {"config_name": "1_2_m_activitynet", "data_files": [{"split": "open_ended", "path": "1_2_m_activitynet/*oe*.json"}]}, {"config_name": "1_2_m_nextqa", "data_files": [{"split": "open_ended", "path": "1_2_m_nextqa/*oe*.json"}, {"split": "multi_choice", "path": "1_2_m_nextqa/*mc*.json"}]}, {"config_name": "2_3_m_youtube_v0_1", "data_files": [{"split": "caption", "path": "2_3_m_youtube_v0_1/*cap*.json"}, {"split": "open_ended", "path": "2_3_m_youtube_v0_1/*oe*.json"}, {"split": "multi_choice", "path": "2_3_m_youtube_v0_1/*mc*.json"}]}, {"config_name": "2_3_m_academic_v0_1", "data_files": [{"split": "caption", "path": "2_3_m_academic_v0_1/*cap*.json"}, {"split": "open_ended", "path": "2_3_m_academic_v0_1/*oe*.json"}, {"split": "multi_choice", "path": "2_3_m_academic_v0_1/*mc*.json"}]}, {"config_name": "2_3_m_activitynet", "data_files": [{"split": "open_ended", "path": "2_3_m_activitynet/*oe*.json"}]}, {"config_name": "2_3_m_nextqa", "data_files": [{"split": "open_ended", "path": "2_3_m_nextqa/*oe*.json"}, {"split": "multi_choice", "path": "2_3_m_nextqa/*mc*.json"}]}, {"config_name": "llava_hound", "data_files": [{"split": "open_ended", "path": "llava_hound/sharegptvideo_qa_255k_processed.json"}]}], "language": ["en"], "task_categories": ["visual-question-answering", "video-text-to-text"], "tags": ["video"]}
| false |
False
| 2024-10-11T04:59:25 | 159 | 4 | false |
6d8c562dc26d70042a0d9704d1cae58c94b89098
|
Dataset Card for LLaVA-Video-178K
Uses
This dataset is used for the training of the LLaVA-Video model. We only allow the use of this dataset for academic research and education purpose. For OpenAI GPT-4 generated data, we recommend the users to check the OpenAI Usage Policy.
Data Sources
For the training of LLaVA-Video, we utilized video-language data from five primary sources:
LLaVA-Video-178K: This dataset includes 178,510 caption entries, 960,792 open-ended… See the full description on the dataset page: https://huggingface.co/datasets/lmms-lab/LLaVA-Video-178K.
| 39,655 | 229,674 |
[
"task_categories:visual-question-answering",
"task_categories:video-text-to-text",
"language:en",
"size_categories:1M<n<10M",
"modality:text",
"modality:video",
"arxiv:2410.02713",
"region:us",
"video"
] | 2024-08-27T07:09:50 | null | null |
676f70968756741d47c691df
|
FreedomIntelligence/medical-o1-verifiable-problem
|
FreedomIntelligence
|
{"license": "apache-2.0", "task_categories": ["question-answering", "text-generation"], "language": ["en"], "tags": ["medical", "biology"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "medical_o1_verifiable_problem.json"}]}]}
| false |
False
| 2024-12-30T02:56:46 | 108 | 4 | false |
46d5175eb74fdef3516d51d52e8c40db04bbdf35
|
Introduction
This dataset features open-ended medical problems designed to improve LLMs' medical reasoning. Each entry includes a open-ended question and a ground-truth answer based on challenging medical exams. The verifiable answers enable checking LLM outputs, refining their reasoning processes.
For details, see our paper and GitHub repository.
Citation
If you find our data useful, please consider citing our work!
@misc{chen2024huatuogpto1medicalcomplexreasoning… See the full description on the dataset page: https://huggingface.co/datasets/FreedomIntelligence/medical-o1-verifiable-problem.
| 1,015 | 5,878 |
[
"task_categories:question-answering",
"task_categories:text-generation",
"language:en",
"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"
] | 2024-12-28T03:29:26 | null | null |
67a4d31993e9cebec5433082
|
google/wmt24pp
|
google
|
{"license": "apache-2.0", "language": ["ar", "bg", "bn", "ca", "da", "de", "el", "es", "et", "fa", "fi", "fr", "gu", "he", "hi", "hr", "hu", "id", "is", "it", "ja", "kn", "ko", "lt", "lv", "ml", "mr", "nl", "no", "pa", "pl", "pt", "ro", "ru", "sk", "sl", "sr", "sv", "sw", "ta", "te", "th", "tr", "uk", "ur", "vi", "zh", "zu"], "task_categories": ["translation"], "size_categories": ["10K<n<100K"], "configs": [{"config_name": "en-ar_EG", "data_files": [{"split": "train", "path": "en-ar_EG.jsonl"}]}, {"config_name": "en-ar_SA", "data_files": [{"split": "train", "path": "en-ar_SA.jsonl"}]}, {"config_name": "en-bg_BG", "data_files": [{"split": "train", "path": "en-bg_BG.jsonl"}]}, {"config_name": "en-bn_IN", "data_files": [{"split": "train", "path": "en-bn_IN.jsonl"}]}, {"config_name": "en-ca_ES", "data_files": [{"split": "train", "path": "en-ca_ES.jsonl"}]}, {"config_name": "en-cs_CZ", "data_files": [{"split": "train", "path": "en-cs_CZ.jsonl"}]}, {"config_name": "en-da_DK", "data_files": [{"split": "train", "path": "en-da_DK.jsonl"}]}, {"config_name": "en-de_DE", "data_files": [{"split": "train", "path": "en-de_DE.jsonl"}]}, {"config_name": "en-el_GR", "data_files": [{"split": "train", "path": "en-el_GR.jsonl"}]}, {"config_name": "en-es_MX", "data_files": [{"split": "train", "path": "en-es_MX.jsonl"}]}, {"config_name": "en-et_EE", "data_files": [{"split": "train", "path": "en-et_EE.jsonl"}]}, {"config_name": "en-fa_IR", "data_files": [{"split": "train", "path": "en-fa_IR.jsonl"}]}, {"config_name": "en-fi_FI", "data_files": [{"split": "train", "path": "en-fi_FI.jsonl"}]}, {"config_name": "en-fil_PH", "data_files": [{"split": "train", "path": "en-fil_PH.jsonl"}]}, {"config_name": "en-fr_CA", "data_files": [{"split": "train", "path": "en-fr_CA.jsonl"}]}, {"config_name": "en-fr_FR", "data_files": [{"split": "train", "path": "en-fr_FR.jsonl"}]}, {"config_name": "en-gu_IN", "data_files": [{"split": "train", "path": "en-gu_IN.jsonl"}]}, {"config_name": "en-he_IL", "data_files": [{"split": "train", "path": "en-he_IL.jsonl"}]}, {"config_name": "en-hi_IN", "data_files": [{"split": "train", "path": "en-hi_IN.jsonl"}]}, {"config_name": "en-hr_HR", "data_files": [{"split": "train", "path": "en-hr_HR.jsonl"}]}, {"config_name": "en-hu_HU", "data_files": [{"split": "train", "path": "en-hu_HU.jsonl"}]}, {"config_name": "en-id_ID", "data_files": [{"split": "train", "path": "en-id_ID.jsonl"}]}, {"config_name": "en-is_IS", "data_files": [{"split": "train", "path": "en-is_IS.jsonl"}]}, {"config_name": "en-it_IT", "data_files": [{"split": "train", "path": "en-it_IT.jsonl"}]}, {"config_name": "en-ja_JP", "data_files": [{"split": "train", "path": "en-ja_JP.jsonl"}]}, {"config_name": "en-kn_IN", "data_files": [{"split": "train", "path": "en-kn_IN.jsonl"}]}, {"config_name": "en-ko_KR", "data_files": [{"split": "train", "path": "en-ko_KR.jsonl"}]}, {"config_name": "en-lt_LT", "data_files": [{"split": "train", "path": "en-lt_LT.jsonl"}]}, {"config_name": "en-lv_LV", "data_files": [{"split": "train", "path": "en-lv_LV.jsonl"}]}, {"config_name": "en-ml_IN", "data_files": [{"split": "train", "path": "en-ml_IN.jsonl"}]}, {"config_name": "en-mr_IN", "data_files": [{"split": "train", "path": "en-mr_IN.jsonl"}]}, {"config_name": "en-nl_NL", "data_files": [{"split": "train", "path": "en-nl_NL.jsonl"}]}, {"config_name": "en-no_NO", "data_files": [{"split": "train", "path": "en-no_NO.jsonl"}]}, {"config_name": "en-pa_IN", "data_files": [{"split": "train", "path": "en-pa_IN.jsonl"}]}, {"config_name": "en-pl_PL", "data_files": [{"split": "train", "path": "en-pl_PL.jsonl"}]}, {"config_name": "en-pt_BR", "data_files": [{"split": "train", "path": "en-pt_BR.jsonl"}]}, {"config_name": "en-pt_PT", "data_files": [{"split": "train", "path": "en-pt_PT.jsonl"}]}, {"config_name": "en-ro_RO", "data_files": [{"split": "train", "path": "en-ro_RO.jsonl"}]}, {"config_name": "en-ru_RU", "data_files": [{"split": "train", "path": "en-ru_RU.jsonl"}]}, {"config_name": "en-sk_SK", "data_files": [{"split": "train", "path": "en-sk_SK.jsonl"}]}, {"config_name": "en-sl_SI", "data_files": [{"split": "train", "path": "en-sl_SI.jsonl"}]}, {"config_name": "en-sr_RS", "data_files": [{"split": "train", "path": "en-sr_RS.jsonl"}]}, {"config_name": "en-sv_SE", "data_files": [{"split": "train", "path": "en-sv_SE.jsonl"}]}, {"config_name": "en-sw_KE", "data_files": [{"split": "train", "path": "en-sw_KE.jsonl"}]}, {"config_name": "en-sw_TZ", "data_files": [{"split": "train", "path": "en-sw_TZ.jsonl"}]}, {"config_name": "en-ta_IN", "data_files": [{"split": "train", "path": "en-ta_IN.jsonl"}]}, {"config_name": "en-te_IN", "data_files": [{"split": "train", "path": "en-te_IN.jsonl"}]}, {"config_name": "en-th_TH", "data_files": [{"split": "train", "path": "en-th_TH.jsonl"}]}, {"config_name": "en-tr_TR", "data_files": [{"split": "train", "path": "en-tr_TR.jsonl"}]}, {"config_name": "en-uk_UA", "data_files": [{"split": "train", "path": "en-uk_UA.jsonl"}]}, {"config_name": "en-ur_PK", "data_files": [{"split": "train", "path": "en-ur_PK.jsonl"}]}, {"config_name": "en-vi_VN", "data_files": [{"split": "train", "path": "en-vi_VN.jsonl"}]}, {"config_name": "en-zh_CN", "data_files": [{"split": "train", "path": "en-zh_CN.jsonl"}]}, {"config_name": "en-zh_TW", "data_files": [{"split": "train", "path": "en-zh_TW.jsonl"}]}, {"config_name": "en-zu_ZA", "data_files": [{"split": "train", "path": "en-zu_ZA.jsonl"}]}]}
| false |
False
| 2025-03-13T21:53:34 | 51 | 4 | false |
388a8a9cf4ddddad75d9e0549e840ea1f3d36f29
|
WMT24++
This repository contains the human translation and post-edit data for the 55 en->xx language pairs released in
the publication
WMT24++: Expanding the Language Coverage of WMT24 to 55 Languages & Dialects.
If you are interested in the MT/LLM system outputs and automatic metric scores, please see MTME.
If you are interested in the images of the source URLs for each document, please see here.
Schema
Each language pair is stored in its own jsonl file.
Each row is… See the full description on the dataset page: https://huggingface.co/datasets/google/wmt24pp.
| 1,944 | 19,680 |
[
"task_categories:translation",
"language:ar",
"language:bg",
"language:bn",
"language:ca",
"language:da",
"language:de",
"language:el",
"language:es",
"language:et",
"language:fa",
"language:fi",
"language:fr",
"language:gu",
"language:he",
"language:hi",
"language:hr",
"language:hu",
"language:id",
"language:is",
"language:it",
"language:ja",
"language:kn",
"language:ko",
"language:lt",
"language:lv",
"language:ml",
"language:mr",
"language:nl",
"language:no",
"language:pa",
"language:pl",
"language:pt",
"language:ro",
"language:ru",
"language:sk",
"language:sl",
"language:sr",
"language:sv",
"language:sw",
"language:ta",
"language:te",
"language:th",
"language:tr",
"language:uk",
"language:ur",
"language:vi",
"language:zh",
"language:zu",
"license:apache-2.0",
"size_categories:10K<n<100K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2502.12404",
"region:us"
] | 2025-02-06T15:19:53 | null | null |
67a66dbe0fdd554315266373
|
nuprl/reasoning-weekly
|
nuprl
|
{"dataset_info": {"features": [{"name": "ID", "dtype": "int64"}, {"name": "url", "dtype": "string"}, {"name": "date", "dtype": "string"}, {"name": "challenge", "dtype": "string"}, {"name": "answer", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 220997, "num_examples": 613}], "download_size": 115863, "dataset_size": 220997}, "configs": [{"config_name": "default", "data_files": [{"split": "test", "path": "data/test-*"}]}], "task_categories": ["question-answering"]}
| false |
False
| 2025-07-25T10:44:19 | 7 | 4 | false |
e94df62e4da3dd0e68afad79649d02e4178a2874
|
Dataset Card for Verbal Reasoning Challenge
Dataset Summary
The Verbal Reasoning Challenge is a dataset designed to evaluate the reasoning abilities of Large Language Models.
It is based on the "off-air challenges" from the NPR Sunday Puzzle Challenge, which are designed to be understandable by any adult in the United States.
With tasks that are out of distribution from code and mathematics, the benchmark assesses verbal reasoning skills, including logical reasoning… See the full description on the dataset page: https://huggingface.co/datasets/nuprl/reasoning-weekly.
| 102 | 1,013 |
[
"task_categories:question-answering",
"size_categories:n<1K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2502.01584",
"region:us"
] | 2025-02-07T20:31:58 | null | null |
67d97c4be2b27852325fd8e2
|
nvidia/PhysicalAI-Robotics-GR00T-X-Embodiment-Sim
|
nvidia
|
{"license": "cc-by-4.0", "task_categories": ["robotics"], "tags": ["robotics"]}
| false |
False
| 2025-07-11T09:54:08 | 140 | 4 | false |
9877fbe12979af12f5e499339eb14ff8c426abb9
|
PhysicalAI-Robotics-GR00T-X-Embodiment-Sim
Github Repo: Isaac GR00T N1
We provide a set of datasets used for post-training of GR00T N1. Each dataset is a collection of trajectories from different robot embodiments and tasks.
Cross-embodied bimanual manipulation: 9k trajectories
Dataset Name
#trajectories
bimanual_panda_gripper.Threading
1000
bimanual_panda_hand.LiftTray
1000
bimanual_panda_gripper.ThreePieceAssembly
1000… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/PhysicalAI-Robotics-GR00T-X-Embodiment-Sim.
| 507,704 | 2,102,288 |
[
"task_categories:robotics",
"license:cc-by-4.0",
"region:us",
"robotics"
] | 2025-03-18T13:59:39 | null | null |
680b9e1a53d9d56430b73e2c
|
Qwen/PolyMath
|
Qwen
|
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| false |
False
| 2025-05-16T09:29:32 | 34 | 4 | false |
71e0db902e0ece05e208dfd8b1695bd3d95cf130
|
PolyMath: Evaluating Mathematical Reasoning in Multilingual Contexts
PolyMath is a multilingual mathematical reasoning benchmark covering 18 languages and 4 easy-to-hard difficulty levels. Our benchmark ensures difficulty comprehensiveness, language diversity, and high-quality translation, making it a highly discriminative multilingual mathematical benchmark in the era of reasoning LLMs.
📈 Broad Difficulty Range: PolyMath defines and partitions… See the full description on the dataset page: https://huggingface.co/datasets/Qwen/PolyMath.
| 2,417 | 9,278 |
[
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2504.18428",
"region:us"
] | 2025-04-25T14:37:14 | null | null |
6820fb77b82e61bb50999662
|
open-r1/Mixture-of-Thoughts
|
open-r1
|
{"dataset_info": [{"config_name": "all", "features": [{"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "num_tokens", "dtype": "int64"}, {"name": "source", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 7062819826.825458, "num_examples": 349317}], "download_size": 3077653717, "dataset_size": 7062819826.825458}, {"config_name": "code", "features": [{"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "num_tokens", "dtype": "int64"}, {"name": "source", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3872656251.3167396, "num_examples": 83070}], "download_size": 1613338604, "dataset_size": 3872656251.3167396}, {"config_name": "math", "features": [{"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "num_tokens", "dtype": "int64"}, {"name": "source", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1599028646, "num_examples": 93733}], "download_size": 704448153, "dataset_size": 1599028646}, {"config_name": "science", "features": [{"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "num_tokens", "dtype": "int64"}, {"name": "source", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1590765326, "num_examples": 172514}], "download_size": 674333812, "dataset_size": 1590765326}], "configs": [{"config_name": "all", "data_files": [{"split": "train", "path": "all/train-*"}]}, {"config_name": "code", "data_files": [{"split": "train", "path": "code/train-*"}]}, {"config_name": "math", "data_files": [{"split": "train", "path": "math/train-*"}]}, {"config_name": "science", "data_files": [{"split": "train", "path": "science/train-*"}]}], "task_categories": ["text-generation"], "language": ["en"], "pretty_name": "Mixture of Thoughts", "size_categories": ["100K<n<1M"]}
| false |
False
| 2025-05-26T15:25:56 | 260 | 4 | false |
e55fa28006c0d0ec60fb3547520f775dd42d02cd
|
Dataset summary
Mixture-of-Thoughts is a curated dataset of 350k verified reasoning traces distilled from DeepSeek-R1. The dataset spans tasks in mathematics, coding, and science, and is designed to teach language models to reason step-by-step. It was used in the Open R1 project to train OpenR1-Distill-7B, an SFT model that replicates the reasoning capabilities of deepseek-ai/DeepSeek-R1-Distill-Qwen-7B from the same base model.
To load the dataset, run:
from datasets import… See the full description on the dataset page: https://huggingface.co/datasets/open-r1/Mixture-of-Thoughts.
| 7,831 | 52,463 |
[
"task_categories:text-generation",
"language:en",
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2504.21318",
"arxiv:2505.00949",
"region:us"
] | 2025-05-11T19:33:11 | null | null |
683fa649ee7dce90f5aafa46
|
a-m-team/AM-DeepSeek-R1-0528-Distilled
|
a-m-team
|
{"task_categories": ["text-generation"], "language": ["en", "zh"], "tags": ["reasoning"], "size_categories": ["1M<n<10M"]}
| false |
False
| 2025-06-09T14:42:53 | 88 | 4 | false |
8d94d36259328de72f619f2d42ea3fd13098d007
|
📘 Dataset Summary
This dataset is a high-quality reasoning corpus distilled from DeepSeek-R1-0528, an improved version of the DeepSeek-R1 large language model. Compared to its initial release, DeepSeek-R1-0528 demonstrates significant advances in reasoning, instruction following, and multi-turn dialogue. Motivated by these improvements, we collected and distilled a diverse set of 2.6 million queries across multiple domains, using DeepSeek-R1-0528 as the teacher.
A notable… See the full description on the dataset page: https://huggingface.co/datasets/a-m-team/AM-DeepSeek-R1-0528-Distilled.
| 3,560 | 14,216 |
[
"task_categories:text-generation",
"language:en",
"language:zh",
"size_categories:1M<n<10M",
"region:us",
"reasoning"
] | 2025-06-04T01:50:01 | null | null |
68675076b2b012e7c45741cc
|
KwaiVGI/VIVID-10M
|
KwaiVGI
|
{"license": "cc-by-nc-4.0", "task_categories": ["text-to-video", "text-to-image"], "size_categories": ["1M<n<10M"], "tags": ["video-editing", "image-editing"], "language": ["en"]}
| false |
False
| 2025-07-16T02:22:19 | 9 | 4 | false |
73036fbaa3fbc743746a57c62dbd658280f1ad4b
|
VIVID-10M
[project page] | [Paper] | [arXiv]
VIVID-10M is the first large-scale hybrid image-video local editing dataset aimed at reducing data construction and model training costs, comprising 9.7M samples that encompass a wide range of video editing tasks.
Data Index
The data index is located at four .csv files:
vivid-image-change.csv
vivid-image-remove.csv
vivid-video-change.csv
vivid-video-remove.csv
VIVID-Video splits contains the columns:
local_caption, #… See the full description on the dataset page: https://huggingface.co/datasets/KwaiVGI/VIVID-10M.
| 11,911 | 11,911 |
[
"task_categories:text-to-video",
"task_categories:text-to-image",
"language:en",
"license:cc-by-nc-4.0",
"size_categories:10M<n<100M",
"format:webdataset",
"modality:image",
"modality:text",
"library:datasets",
"library:webdataset",
"library:mlcroissant",
"arxiv:2411.15260",
"region:us",
"video-editing",
"image-editing"
] | 2025-07-04T03:54:30 | null | null |
6873ea40dc716fb6868f08d2
|
HongchengGao/TuringEyeTest
|
HongchengGao
|
{"dataset_info": {"features": [{"name": "idx", "dtype": "string"}, {"name": "Question", "dtype": "string"}, {"name": "Image", "dtype": "image"}, {"name": "Groundtruth", "dtype": "string"}, {"name": "Task", "dtype": "string"}], "splits": [{"name": "full", "num_bytes": 171585064, "num_examples": 490}, {"name": "HiddenText", "num_bytes": 107144174, "num_examples": 150}, {"name": "3DCaptcha", "num_bytes": 399420, "num_examples": 150}, {"name": "Colorblind", "num_bytes": 11984769, "num_examples": 150}, {"name": "ChineseLigature", "num_bytes": 52056701, "num_examples": 40}], "download_size": 343068512, "dataset_size": 343170128}, "configs": [{"config_name": "default", "data_files": [{"split": "full", "path": "data/full-*"}, {"split": "HiddenText", "path": "data/HiddenText-*"}, {"split": "3DCaptcha", "path": "data/3DCaptcha-*"}, {"split": "Colorblind", "path": "data/Colorblind-*"}, {"split": "ChineseLigature", "path": "data/ChineseLigature-*"}]}]}
| false |
False
| 2025-07-24T11:23:26 | 8 | 4 | false |
2cc637da14c5f167676ffde210048c4f2306bd55
|
Data Description
The Turing Eye Test (TET) as presented in Paper 'Pixels, Patterns, But No Poetry: To See the World Like Humans'.
This benchmark includes four tasks:
HiddenText: comprises scale-variant items where text is rendered as shapes within the figure, appearing as text when reduced and resolving into a complete image when magnified, which contains 150 images.
3DCaptcha: involves recognition challenges constructed with curved characters in the three-dimensional space, which… See the full description on the dataset page: https://huggingface.co/datasets/HongchengGao/TuringEyeTest.
| 254 | 254 |
[
"size_categories:n<1K",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2507.16863",
"region:us"
] | 2025-07-13T17:17:52 | 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 | 4 | 4 | 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.
| 120 | 120 |
[
"license:other",
"region:us"
] | 2025-07-17T06:20:34 | null | null |
687e20d3782236bd5eda4a25
|
DocReRank/ColHNQue-ColPaliHardNegativeQueries
|
DocReRank
|
{"license": "cc-by-4.0", "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "positive_queries", "sequence": "string"}, {"name": "negative_queries", "sequence": {"sequence": "string"}}, {"name": "answer", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 54125603838.375, "num_examples": 117693}], "download_size": 52210976943, "dataset_size": 54125603838.375}}
| false |
False
| 2025-07-22T11:18:06 | 4 | 4 | false |
3073963a9c4333259905116b9d76914c0fa06860
|
Dataset Card for ColHNQue Dataset
Dataset Summary
The ColHNQue (ColPaliHardNegativeQueries) dataset was introduced in the paper DocReRank: Single‑Page Hard Negative Query Generation for Training Multi‑Modal RAG Rerankers.
It addresses the limitations of document-level hard negative mining by generating hard negative queries at the page/image level. Given a page and its corresponding positive query, multiple negative queries are generated that are semantically similar but… See the full description on the dataset page: https://huggingface.co/datasets/DocReRank/ColHNQue-ColPaliHardNegativeQueries.
| 278 | 278 |
[
"license:cc-by-4.0",
"size_categories:100K<n<1M",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2505.22584",
"region:us"
] | 2025-07-21T11:13:23 | null | null |
687e459e730ee8988a5e312f
|
DocReRank/RephColHNQue-RephrasedColPaliHardNegativeQueries
|
DocReRank
|
{"license": "cc-by-4.0", "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "positive_queries", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 10815307, "num_examples": 117693}], "download_size": 6181402, "dataset_size": 10815307}}
| false |
False
| 2025-07-22T11:17:49 | 4 | 4 | false |
9e50cc8756f47135a016ed9da9cc56db9d37c473
|
Dataset Card for Reph-ColHNQue Dataset
Dataset Summary
The RephColHNQue (RephrasedColPaliHardNegativeQueries) dataset was introduced in the paper DocReRank: Single‑Page Hard Negative Query Generation for Training Multi‑Modal RAG Rerankers.
This dataset includes only the reprased poistive queires correspondign to ColHNQue dataset. See Project Page for more information. The queries were rephrased using
Columns
Column
Description
positive_queries… See the full description on the dataset page: https://huggingface.co/datasets/DocReRank/RephColHNQue-RephrasedColPaliHardNegativeQueries.
| 97 | 97 |
[
"license:cc-by-4.0",
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2505.22584",
"region:us"
] | 2025-07-21T13:50:22 | null | null |
688121084806c88daee0c668
|
ymsun99/X-MethaneWet
|
ymsun99
|
{"license": "cc-by-4.0", "task_categories": ["tabular-regression"], "tags": ["climate", "tabular"], "size_categories": ["n<1K"]}
| false |
False
| 2025-07-27T22:43:39 | 4 | 4 | false |
c445a98ffb0a99407cf7db7ca234901ae2e8cc98
|
🌍 X-MethaneWet: A Cross-Scale Global Wetland Methane Benchmark Dataset
X-MethaneWet is a first-of-its-kind cross-scale global wetland methane benchmark dataset designed to support research on methane (CH₄) emissions across temporal and spatial scales. It integrates physics-based model simulations and real-world observations to enable data-driven climate science and AI-powered modeling of wetland CH₄ fluxes.
This dataset integrates two complementary sources:
FLUXNET-CH₄: Real-world… See the full description on the dataset page: https://huggingface.co/datasets/ymsun99/X-MethaneWet.
| 118 | 118 |
[
"task_categories:tabular-regression",
"license:cc-by-4.0",
"size_categories:n<1K",
"modality:text",
"modality:tabular",
"arxiv:2505.18355",
"region:us",
"climate",
"tabular"
] | 2025-07-23T17:51:04 | null | null |
6882091170fcd3a1bc246d16
|
omersaidd/tts_mazlum_kiper_tur
|
omersaidd
|
{"license": "mit", "dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "audio", "dtype": "audio"}], "splits": [{"name": "train", "num_bytes": 11225757206.22, "num_examples": 9643}], "download_size": 8960083688, "dataset_size": 11225757206.22}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "language": ["tr"], "tags": ["Text-to-speech"]}
| false |
False
| 2025-07-25T09:38:53 | 4 | 4 | false |
f08fec04225fc25e5cf39e4ff20e3233f483e297
| null | 135 | 135 |
[
"language:tr",
"license:mit",
"size_categories:1K<n<10K",
"format:parquet",
"modality:audio",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us",
"Text-to-speech"
] | 2025-07-24T10:21:05 | null | null |
6885efd2e5db9634466eea68
|
CraneAILabs/UCCB
|
CraneAILabs
|
{"language": ["en", "ug", "lg", "nyn", "ach", "teo"], "license": "cc-by-nc-sa-4.0", "pretty_name": "Ugandan Cultural Context Benchmark (UCCB) Suite", "tags": ["uganda", "cultural-benchmark", "question-answering", "knowledge-base", "africa", "low-resource-languages", "bias-evaluation"], "size_categories": ["1K<n<10K"], "task_categories": ["question-answering", "text-classification"]}
| false |
False
| 2025-07-27T10:30:11 | 4 | 4 | false |
c08d96b5574d3c772b0ae7e61b0f278066f18134
|
Dataset Card for the Ugandan Cultural Context Benchmark (UCCB) Suite
Dataset Summary
The Ugandan Cultural Context Benchmark (UCCB) Suite is the first comprehensive question-answering dataset designed to evaluate the cultural understanding and reasoning abilities of Large Language Models (LLMs) concerning Uganda's multifaceted environment. The dataset contains 1,039 carefully curated question-answer pairs across 24 cultural domains.
The benchmark was created to… See the full description on the dataset page: https://huggingface.co/datasets/CraneAILabs/UCCB.
| 62 | 62 |
[
"task_categories:question-answering",
"task_categories:text-classification",
"language:en",
"language:ug",
"language:lg",
"language:nyn",
"language:ach",
"language:teo",
"license:cc-by-nc-sa-4.0",
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us",
"uganda",
"cultural-benchmark",
"question-answering",
"knowledge-base",
"africa",
"low-resource-languages",
"bias-evaluation"
] | 2025-07-27T09:22:26 | null | null |
621ffdd236468d709f181d5e
|
allenai/ai2_arc
|
allenai
|
{"annotations_creators": ["found"], "language_creators": ["found"], "language": ["en"], "license": ["cc-by-sa-4.0"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["question-answering"], "task_ids": ["open-domain-qa", "multiple-choice-qa"], "pretty_name": "Ai2Arc", "language_bcp47": ["en-US"], "dataset_info": [{"config_name": "ARC-Challenge", "features": [{"name": "id", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "choices", "sequence": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": "string"}]}, {"name": "answerKey", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 349760, "num_examples": 1119}, {"name": "test", "num_bytes": 375511, "num_examples": 1172}, {"name": "validation", "num_bytes": 96660, "num_examples": 299}], "download_size": 449460, "dataset_size": 821931}, {"config_name": "ARC-Easy", "features": [{"name": "id", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "choices", "sequence": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": "string"}]}, {"name": "answerKey", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 619000, "num_examples": 2251}, {"name": "test", "num_bytes": 657514, "num_examples": 2376}, {"name": "validation", "num_bytes": 157394, "num_examples": 570}], "download_size": 762935, "dataset_size": 1433908}], "configs": [{"config_name": "ARC-Challenge", "data_files": [{"split": "train", "path": "ARC-Challenge/train-*"}, {"split": "test", "path": "ARC-Challenge/test-*"}, {"split": "validation", "path": "ARC-Challenge/validation-*"}]}, {"config_name": "ARC-Easy", "data_files": [{"split": "train", "path": "ARC-Easy/train-*"}, {"split": "test", "path": "ARC-Easy/test-*"}, {"split": "validation", "path": "ARC-Easy/validation-*"}]}]}
| false |
False
| 2023-12-21T15:09:48 | 211 | 3 | false |
210d026faf9955653af8916fad021475a3f00453
|
Dataset Card for "ai2_arc"
Dataset Summary
A new dataset of 7,787 genuine grade-school level, multiple-choice science questions, assembled to encourage research in
advanced question-answering. The dataset is partitioned into a Challenge Set and an Easy Set, where the former contains
only questions answered incorrectly by both a retrieval-based algorithm and a word co-occurrence algorithm. We are also
including a corpus of over 14 million science sentences relevant to… See the full description on the dataset page: https://huggingface.co/datasets/allenai/ai2_arc.
| 162,463 | 9,335,956 |
[
"task_categories:question-answering",
"task_ids:open-domain-qa",
"task_ids:multiple-choice-qa",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:cc-by-sa-4.0",
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:1803.05457",
"region:us"
] | 2022-03-02T23:29:22 | null | null |
621ffdd236468d709f181e65
|
hotpotqa/hotpot_qa
|
hotpotqa
|
{"annotations_creators": ["crowdsourced"], "language": ["en"], "language_creators": ["found"], "license": ["cc-by-sa-4.0"], "multilinguality": ["monolingual"], "pretty_name": "HotpotQA", "size_categories": ["100K<n<1M"], "source_datasets": ["original"], "task_categories": ["question-answering"], "task_ids": [], "paperswithcode_id": "hotpotqa", "tags": ["multi-hop"], "dataset_info": [{"config_name": "distractor", "features": [{"name": "id", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "level", "dtype": "string"}, {"name": "supporting_facts", "sequence": [{"name": "title", "dtype": "string"}, {"name": "sent_id", "dtype": "int32"}]}, {"name": "context", "sequence": [{"name": "title", "dtype": "string"}, {"name": "sentences", "sequence": "string"}]}], "splits": [{"name": "train", "num_bytes": 552949315, "num_examples": 90447}, {"name": "validation", "num_bytes": 45716111, "num_examples": 7405}], "download_size": 612746344, "dataset_size": 598665426}, {"config_name": "fullwiki", "features": [{"name": "id", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "level", "dtype": "string"}, {"name": "supporting_facts", "sequence": [{"name": "title", "dtype": "string"}, {"name": "sent_id", "dtype": "int32"}]}, {"name": "context", "sequence": [{"name": "title", "dtype": "string"}, {"name": "sentences", "sequence": "string"}]}], "splits": [{"name": "train", "num_bytes": 552949315, "num_examples": 90447}, {"name": "validation", "num_bytes": 46848601, "num_examples": 7405}, {"name": "test", "num_bytes": 46000102, "num_examples": 7405}], "download_size": 660094672, "dataset_size": 645798018}]}
| false |
False
| 2024-01-18T11:05:40 | 137 | 3 | false |
087b2e421aa4e6999e5ec0cb486a1d5c35fc1d71
|
HotpotQA is a new dataset with 113k Wikipedia-based question-answer pairs with four key features:
(1) the questions require finding and reasoning over multiple supporting documents to answer;
(2) the questions are diverse and not constrained to any pre-existing knowledge bases or knowledge schemas;
(3) we provide sentence-level supporting facts required for reasoning, allowingQA systems to reason with strong supervisionand explain the predictions;
(4) we offer a new type of factoid comparison questions to testQA systems’ ability to extract relevant facts and perform necessary comparison.
| 10,642 | 505,303 |
[
"task_categories:question-answering",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:cc-by-sa-4.0",
"size_categories:100K<n<1M",
"arxiv:1809.09600",
"region:us",
"multi-hop"
] | 2022-03-02T23:29:22 |
@inproceedings{yang2018hotpotqa,
title={{HotpotQA}: A Dataset for Diverse, Explainable Multi-hop Question Answering},
author={Yang, Zhilin and Qi, Peng and Zhang, Saizheng and Bengio, Yoshua and Cohen, William W. and Salakhutdinov, Ruslan and Manning, Christopher D.},
booktitle={Conference on Empirical Methods in Natural Language Processing ({EMNLP})},
year={2018}
}
|
hotpotqa
|
621ffdd236468d709f181f09
|
Skylion007/openwebtext
|
Skylion007
|
{"annotations_creators": ["no-annotation"], "language_creators": ["found"], "language": ["en"], "license": ["cc0-1.0"], "multilinguality": ["monolingual"], "pretty_name": "OpenWebText", "size_categories": ["1M<n<10M"], "source_datasets": ["original"], "task_categories": ["text-generation", "fill-mask"], "task_ids": ["language-modeling", "masked-language-modeling"], "paperswithcode_id": "openwebtext", "dataset_info": {"features": [{"name": "text", "dtype": "string"}], "config_name": "plain_text", "splits": [{"name": "train", "num_bytes": 39769491688, "num_examples": 8013769}], "download_size": 12880189440, "dataset_size": 39769491688}}
| false |
False
| 2024-05-17T17:56:27 | 442 | 3 | false |
f3808c30e817981b845ec549c43e82bb467d8144
|
An open-source replication of the WebText dataset from OpenAI.
| 59,194 | 4,789,555 |
[
"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:monolingual",
"source_datasets:original",
"language:en",
"license:cc0-1.0",
"size_categories:1M<n<10M",
"region:us"
] | 2022-03-02T23:29:22 |
@misc{Gokaslan2019OpenWeb,
title={OpenWebText Corpus},
author={Aaron Gokaslan*, Vanya Cohen*, Ellie Pavlick, Stefanie Tellex},
howpublished{\\url{http://Skylion007.github.io/OpenWebTextCorpus}},
year={2019}
}
|
openwebtext
|
621ffdd236468d709f181fc6
|
EleutherAI/pile
|
EleutherAI
|
{"annotations_creators": ["no-annotation"], "language_creators": ["found"], "language": ["en"], "license": "other", "multilinguality": ["monolingual"], "pretty_name": "the Pile", "size_categories": ["100B<n<1T"], "source_datasets": ["original"], "task_categories": ["text-generation", "fill-mask"], "task_ids": ["language-modeling", "masked-language-modeling"], "paperswithcode_id": "the-pile"}
| false |
False
| 2023-05-03T15:58:14 | 438 | 3 | false |
148e1d5e8349977c76f673190424a2faf6980a1d
|
The Pile is a 825 GiB diverse, open source language modelling data set that consists of 22 smaller, high-quality
datasets combined together.
| 2,216 | 305,488 |
[
"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:monolingual",
"source_datasets:original",
"language:en",
"license:other",
"size_categories:100B<n<1T",
"arxiv:2201.07311",
"arxiv:2101.00027",
"region:us"
] | 2022-03-02T23:29:22 |
@misc{gao2020pile,
title={The Pile: An 800GB Dataset of Diverse Text for Language Modeling},
author={Leo Gao and Stella Biderman and Sid Black and Laurence Golding and Travis Hoppe and Charles Foster and Jason Phang and Horace He and Anish Thite and Noa Nabeshima and Shawn Presser and Connor Leahy},
year={2020},
eprint={2101.00027},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
|
the-pile
|
624d6002300264d00b63aceb
|
huggan/wikiart
|
huggan
|
{"license": "unknown", "license_details": "Data files \u00a9 Original Authors", "size_categories": ["10K<n<100K"], "task_categories": ["image-classification", "text-to-image", "image-to-text"], "tags": ["art"]}
| false |
False
| 2023-03-22T13:56:08 | 157 | 3 | false |
d559852d2b232e0fcf195e775866964f0564f2b5
|
Dataset Summary
Dataset containing 81,444 pieces of visual art from various artists, taken from WikiArt.org,
along with class labels for each image :
"artist" : 129 artist classes, including a "Unknown Artist" class
"genre" : 11 genre classes, including a "Unknown Genre" class
"style" : 27 style classes
On WikiArt.org, the description for the "Artworks by Genre" page reads :
A genre system divides artworks according to depicted themes and objects. A classical hierarchy of genres… See the full description on the dataset page: https://huggingface.co/datasets/huggan/wikiart.
| 2,780 | 49,500 |
[
"task_categories:image-classification",
"task_categories:text-to-image",
"task_categories:image-to-text",
"license:unknown",
"size_categories:10K<n<100K",
"format:parquet",
"modality:image",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us",
"art"
] | 2022-04-06T09:40:18 | null | null |
6274dfacbe455dadd1060ffb
|
openlifescienceai/medmcqa
|
openlifescienceai
|
{"annotations_creators": ["no-annotation"], "language_creators": ["expert-generated"], "language": ["en"], "license": ["apache-2.0"], "multilinguality": ["monolingual"], "size_categories": ["100K<n<1M"], "source_datasets": ["original"], "task_categories": ["question-answering", "multiple-choice"], "task_ids": ["multiple-choice-qa", "open-domain-qa"], "paperswithcode_id": "medmcqa", "pretty_name": "MedMCQA", "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "opa", "dtype": "string"}, {"name": "opb", "dtype": "string"}, {"name": "opc", "dtype": "string"}, {"name": "opd", "dtype": "string"}, {"name": "cop", "dtype": {"class_label": {"names": {"0": "a", "1": "b", "2": "c", "3": "d"}}}}, {"name": "choice_type", "dtype": "string"}, {"name": "exp", "dtype": "string"}, {"name": "subject_name", "dtype": "string"}, {"name": "topic_name", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 131903297, "num_examples": 182822}, {"name": "test", "num_bytes": 1399350, "num_examples": 6150}, {"name": "validation", "num_bytes": 2221428, "num_examples": 4183}], "download_size": 88311487, "dataset_size": 135524075}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}, {"split": "validation", "path": "data/validation-*"}]}]}
| false |
False
| 2024-01-04T14:32:02 | 168 | 3 | false |
91c6572c454088bf71b679ad90aa8dffcd0d5868
|
Dataset Card for MedMCQA
Dataset Summary
MedMCQA is a large-scale, Multiple-Choice Question Answering (MCQA) dataset designed to address real-world medical entrance exam questions.
MedMCQA has more than 194k high-quality AIIMS & NEET PG entrance exam MCQs covering 2.4k healthcare topics and 21 medical subjects are collected with an average token length of 12.77 and high topical diversity.
Each sample contains a question, correct answer(s), and other options which require… See the full description on the dataset page: https://huggingface.co/datasets/openlifescienceai/medmcqa.
| 7,516 | 224,246 |
[
"task_categories:question-answering",
"task_categories:multiple-choice",
"task_ids:multiple-choice-qa",
"task_ids:open-domain-qa",
"annotations_creators:no-annotation",
"language_creators:expert-generated",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:apache-2.0",
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2022-05-06T08:43:24 | null |
medmcqa
|
62fba7fda80632fbd479aa60
|
MLCommons/peoples_speech
|
MLCommons
|
{"annotations_creators": ["crowdsourced", "machine-generated"], "language_creators": ["crowdsourced", "machine-generated"], "language": ["en"], "license": ["cc-by-2.0", "cc-by-2.5", "cc-by-3.0", "cc-by-4.0", "cc-by-sa-3.0", "cc-by-sa-4.0"], "multilinguality": ["monolingual"], "size_categories": ["1T<n"], "source_datasets": ["original"], "task_categories": ["automatic-speech-recognition"], "task_ids": [], "pretty_name": "People's Speech", "tags": ["robust-speech-recognition", "noisy-speech-recognition", "speech-recognition"], "dataset_info": [{"config_name": "clean", "features": [{"name": "id", "dtype": "string"}, {"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}, {"name": "duration_ms", "dtype": "int32"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 401733771186.124, "num_examples": 1501271}, {"name": "validation", "num_bytes": 2459781412.24, "num_examples": 18622}, {"name": "test", "num_bytes": 4324307722.96, "num_examples": 34898}], "download_size": 398550700437, "dataset_size": 408517860321.32404}, {"config_name": "clean_sa", "features": [{"name": "id", "dtype": "string"}, {"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}, {"name": "duration_ms", "dtype": "int32"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 75267509124.558, "num_examples": 257093}, {"name": "validation", "num_bytes": 2075929254.254, "num_examples": 18622}, {"name": "test", "num_bytes": 3894954757.41, "num_examples": 34898}], "download_size": 72518549222, "dataset_size": 81238393136.222}, {"config_name": "dirty", "features": [{"name": "id", "dtype": "string"}, {"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}, {"name": "duration_ms", "dtype": "int32"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1569500875399.994, "num_examples": 5476898}, {"name": "validation", "num_bytes": 2641406179.2539997, "num_examples": 18622}, {"name": "test", "num_bytes": 5097236056.41, "num_examples": 34898}], "download_size": 1496747948260, "dataset_size": 1577239517635.6577}, {"config_name": "dirty_sa", "features": [{"name": "id", "dtype": "string"}, {"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}, {"name": "duration_ms", "dtype": "int32"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 163776914241.91, "num_examples": 548014}, {"name": "validation", "num_bytes": 2075929254.254, "num_examples": 18622}, {"name": "test", "num_bytes": 3894954757.41, "num_examples": 34898}], "download_size": 149326092074, "dataset_size": 169747798253.574}, {"config_name": "microset", "features": [{"name": "id", "dtype": "string"}, {"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}, {"name": "duration_ms", "dtype": "int32"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 92397066, "num_examples": 336}], "download_size": 90204303, "dataset_size": 92397066}, {"config_name": "test", "features": [{"name": "id", "dtype": "string"}, {"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}, {"name": "duration_ms", "dtype": "int32"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 3894954757.41, "num_examples": 34898}], "download_size": 4087772459, "dataset_size": 3894954757.41}, {"config_name": "validation", "features": [{"name": "id", "dtype": "string"}, {"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}, {"name": "duration_ms", "dtype": "int32"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "validation", "num_bytes": 2075929254.254, "num_examples": 18622}], "download_size": 2335244149, "dataset_size": 2075929254.254}], "configs": [{"config_name": "clean", "data_files": [{"split": "train", "path": "clean/train-*"}, {"split": "validation", "path": "clean/validation-*"}, {"split": "test", "path": "clean/test-*"}]}, {"config_name": "clean_sa", "data_files": [{"split": "train", "path": "clean_sa/train-*"}, {"split": "validation", "path": "clean_sa/validation-*"}, {"split": "test", "path": "clean_sa/test-*"}]}, {"config_name": "dirty", "data_files": [{"split": "train", "path": "dirty/train-*"}, {"split": "validation", "path": "dirty/validation-*"}, {"split": "test", "path": "dirty/test-*"}]}, {"config_name": "dirty_sa", "data_files": [{"split": "train", "path": "dirty_sa/train-*"}, {"split": "validation", "path": "dirty_sa/validation-*"}, {"split": "test", "path": "dirty_sa/test-*"}]}, {"config_name": "microset", "data_files": [{"split": "train", "path": "microset/train-*"}]}, {"config_name": "test", "data_files": [{"split": "test", "path": "test/test-*"}]}, {"config_name": "validation", "data_files": [{"split": "validation", "path": "validation/validation-*"}]}]}
| false |
False
| 2024-11-20T15:17:45 | 132 | 3 | false |
f10597c5d3d3a63f8b6827701297c3afdf178272
|
Dataset Card for People's Speech
Dataset Summary
The People's Speech Dataset is among the world's largest English speech recognition corpus today that is licensed for academic and commercial usage under CC-BY-SA and CC-BY 4.0. It includes 30,000+ hours of transcribed speech in English languages with a diverse set of speakers. This open dataset is large enough to train speech-to-text systems and crucially is available with a permissive license.
Supported Tasks… See the full description on the dataset page: https://huggingface.co/datasets/MLCommons/peoples_speech.
| 40,779 | 357,846 |
[
"task_categories:automatic-speech-recognition",
"annotations_creators:crowdsourced",
"annotations_creators:machine-generated",
"language_creators:crowdsourced",
"language_creators:machine-generated",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:cc-by-2.0",
"license:cc-by-2.5",
"license:cc-by-3.0",
"license:cc-by-4.0",
"license:cc-by-sa-3.0",
"license:cc-by-sa-4.0",
"size_categories:1M<n<10M",
"format:parquet",
"modality:audio",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2111.09344",
"region:us",
"robust-speech-recognition",
"noisy-speech-recognition",
"speech-recognition"
] | 2022-08-16T14:21:49 | null | null |
639244f571c51c43091df168
|
Anthropic/hh-rlhf
|
Anthropic
|
{"license": "mit", "tags": ["human-feedback"]}
| false |
False
| 2023-05-26T18:47:34 | 1,384 | 3 | false |
09be8c5bbc57cb3887f3a9732ad6aa7ec602a1fa
|
Dataset Card for HH-RLHF
Dataset Summary
This repository provides access to two different kinds of data:
Human preference data about helpfulness and harmlessness from Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback. These data are meant to train preference (or reward) models for subsequent RLHF training. These data are not meant for supervised training of dialogue agents. Training dialogue agents on these data is likely to lead… See the full description on the dataset page: https://huggingface.co/datasets/Anthropic/hh-rlhf.
| 15,795 | 1,617,948 |
[
"license:mit",
"size_categories:100K<n<1M",
"format:json",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2204.05862",
"region:us",
"human-feedback"
] | 2022-12-08T20:11:33 | null | null |
641debae1d05404efd046a4f
|
yahma/alpaca-cleaned
|
yahma
|
{"license": "cc-by-4.0", "language": ["en"], "tags": ["instruction-finetuning"], "pretty_name": "Alpaca-Cleaned", "task_categories": ["text-generation"]}
| false |
False
| 2023-04-10T20:29:06 | 704 | 3 | false |
12567cabf869d7c92e573c7c783905fc160e9639
|
Dataset Card for Alpaca-Cleaned
Repository: https://github.com/gururise/AlpacaDataCleaned
Dataset Description
This is a cleaned version of the original Alpaca Dataset released by Stanford. The following issues have been identified in the original release and fixed in this dataset:
Hallucinations: Many instructions in the original dataset had instructions referencing data on the internet, which just caused GPT3 to hallucinate an answer.
"instruction":"Summarize the… See the full description on the dataset page: https://huggingface.co/datasets/yahma/alpaca-cleaned.
| 23,638 | 713,367 |
[
"task_categories:text-generation",
"language:en",
"license:cc-by-4.0",
"size_categories:10K<n<100K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us",
"instruction-finetuning"
] | 2023-03-24T18:27:58 | null | null |
6457bc5798a8724fa6120362
|
tiiuae/falcon-refinedweb
|
tiiuae
|
{"dataset_info": {"features": [{"name": "content", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "timestamp", "dtype": "timestamp[s]"}, {"name": "dump", "dtype": "string"}, {"name": "segment", "dtype": "string"}, {"name": "image_urls", "sequence": {"sequence": "string"}}], "splits": [{"name": "train", "num_bytes": 2766953721769, "num_examples": 968000015}], "download_size": 466888198663, "dataset_size": 2766953721769}, "license": "odc-by", "task_categories": ["text-generation"], "language": ["en"], "pretty_name": "Falcon RefinedWeb", "size_categories": ["100B<n<1T"]}
| false |
False
| 2023-06-20T12:38:07 | 865 | 3 | false |
c735840575b629292b41da8dde11dcd523d4f91c
|
📀 Falcon RefinedWeb
Falcon RefinedWeb is a massive English web dataset built by TII and released under an ODC-By 1.0 license.
See the 📓 paper on arXiv for more details.
RefinedWeb is built through stringent filtering and large-scale deduplication of CommonCrawl; we found models trained on RefinedWeb to achieve performance in-line or better than models trained on curated datasets, while only relying on web data.
RefinedWeb is also "multimodal-friendly": it contains links and alt… See the full description on the dataset page: https://huggingface.co/datasets/tiiuae/falcon-refinedweb.
| 24,075 | 779,352 |
[
"task_categories:text-generation",
"language:en",
"license:odc-by",
"size_categories:100M<n<1B",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2306.01116",
"arxiv:2203.15556",
"arxiv:2107.06499",
"arxiv:2104.08758",
"arxiv:2109.07445",
"arxiv:1911.00359",
"arxiv:2112.11446",
"doi:10.57967/hf/0737",
"region:us"
] | 2023-05-07T14:57:27 | null | null |
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