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69688350f88007d02e2ed431
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ma-xu/fine-t2i
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ma-xu
|
{"license": "apache-2.0", "task_categories": ["image-to-text", "text-to-image"], "language": ["en"], "tags": ["text", "image", "image-generation", "t2i", "image caption"], "size_categories": ["1M<n<10M"]}
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28fdd5663ee202b5cafc01d6ed08a03f14957854
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Fine-T2I: An Open, Large-Scale, and Diverse Dataset for High-Quality T2I Fine-Tuning [arxiv]
by Xu Ma, Yitian Zhang,
Qihua Dong, Yun Fu
Northeastern Univeristy
Please see our [Dataset Explore] to view detailed samples (loading is slow, be patient).
🆕 What's New
[2026.02.20]: Fine-T2I reaches the #1 spot among Hugging Face Datasets Trending list ⭐️⭐️⭐️
[2026.02.16]: Fine-T2I tops the Hugging Face Datasets Trending list, reaching the #2 spot and #1… See the full description on the dataset page: https://huggingface.co/datasets/ma-xu/fine-t2i.
| 25,499
| 25,562
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[
"task_categories:image-to-text",
"task_categories:text-to-image",
"language:en",
"license:apache-2.0",
"size_categories:100K<n<1M",
"format:webdataset",
"modality:image",
"modality:text",
"library:datasets",
"library:webdataset",
"library:mlcroissant",
"arxiv:2602.09439",
"region:us",
"text",
"image",
"image-generation",
"t2i",
"image caption"
] | 2026-01-15T06:04:00
| null | null |
69811d0763152b4ea6afd82b
|
OpenResearcher/OpenResearcher-Dataset
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OpenResearcher
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"seed_57/train-*"}]}], "license": "mit"}
| false
|
False
| 2026-02-12T22:23:38
| 66
| 47
| false
|
447be0c730619d46e5eab75233a2fdab5eef5316
|
🤗 HuggingFace ｜
Blog ｜ Slack | WeChat
Overview
OpenResearcher is a fully open agentic large language model (30B-A3B) designed for long-horizon deep research scenarios. It achieves an impressive 54.8% accuracy on BrowseComp-Plus, surpassing performance of GPT-4.1, Claude-Opus-4, Gemini-2.5-Pro, DeepSeek-R1 and Tongyi-DeepResearch. It also demonstrates leading performance across a range of deep research benchmarks, including… See the full description on the dataset page: https://huggingface.co/datasets/OpenResearcher/OpenResearcher-Dataset.
| 6,813
| 6,813
|
[
"license:mit",
"size_categories:10K<n<100K",
"format:parquet",
"format:optimized-parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"region:us"
] | 2026-02-02T21:54:15
| null | null |
696789567b115954f1c68ab0
|
openbmb/UltraData-Math
|
openbmb
|
{"language": ["en", "zh"], "license": "apache-2.0", "size_categories": ["100B<n<1T"], "task_categories": ["text-generation"], "pretty_name": "UltraData-Math", "arxiv": "xxxx.xxxxx", "tags": ["llm", "pretraining", "math", "data-synthesis", "data-filtering", "high-quality", "mathematical-reasoning"], "configs": [{"config_name": "UltraData-Math-L3-Conversation-Synthetic", "data_files": "data/UltraData-Math-L3/Conversation-Synthetic/*.parquet"}, {"config_name": "UltraData-Math-L3-Multi-Style-Synthetic", "data_files": "data/UltraData-Math-L3/Multi-Style-Synthetic/*.parquet"}, {"config_name": "UltraData-Math-L3-QA-Synthetic", "data_files": "data/UltraData-Math-L3/QA-Synthetic/*.parquet"}, {"config_name": "UltraData-Math-L3-Textbook-Exercise-Synthetic", "data_files": "data/UltraData-Math-L3/Textbook-Exercise-Synthetic/*.parquet"}, {"config_name": "UltraData-Math-L2-preview", "data_files": "data/UltraData-Math-L2-preview/**/*.parquet"}, {"config_name": "UltraData-Math-L1", "data_files": "data/UltraData-Math-L1/**/*.parquet"}], "default_config_name": "UltraData-Math-L3-Conversation-Synthetic"}
| false
|
False
| 2026-02-10T03:18:19
| 231
| 41
| false
|
642faf937ef942e0c45b15ec37f33884ff43d369
|
UltraData-Math
🤗 Dataset | 💻 Source Code | 🇨🇳 中文 README
UltraData-Math is a large-scale, high-quality mathematical pre-training dataset totaling 290B+ tokens across three progressive tiers—L1 (170.5B tokens web corpus), L2 (33.7B tokens quality-selected), and L3 (88B tokens multi-format refined)—designed to systematically enhance mathematical reasoning in LLMs. It has been applied to the mathematical pre-training of the MiniCPM Series models.
🆕 What's New… See the full description on the dataset page: https://huggingface.co/datasets/openbmb/UltraData-Math.
| 39,051
| 39,087
|
[
"task_categories:text-generation",
"language:en",
"language:zh",
"license:apache-2.0",
"size_categories:100M<n<1B",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"region:us",
"llm",
"pretraining",
"math",
"data-synthesis",
"data-filtering",
"high-quality",
"mathematical-reasoning"
] | 2026-01-14T12:17:26
| null | null |
698c22cfff4308d71cbe4e5d
|
GD-ML/IntTravel_dataset
|
GD-ML
|
{"task_categories": ["other"], "tags": ["recommendation-system", "poi-recommendation", "mobility", "travel"], "size_categories": ["1B<n<10B"]}
| false
|
False
| 2026-02-20T11:41:45
| 63
| 41
| false
|
0bf911bb51b17876cc9239f465318b8acee1318f
|
IntTravel: A Real-World Dataset and Generative Framework for Integrated Multi-Task Travel Recommendation
Paper | GitHub
IntTravel is the first large-scale public dataset for integrated travel recommendation, including 4.1 billion interactions from 163 million users with 7.3 million POIs. Built upon this dataset, the authors introduce an end-to-end, decoder-only generative framework for multi-task recommendation. It incorporates information preservation, selection, and factorization… See the full description on the dataset page: https://huggingface.co/datasets/GD-ML/IntTravel_dataset.
| 884
| 884
|
[
"task_categories:other",
"size_categories:1B<n<10B",
"format:csv",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"arxiv:2602.11664",
"region:us",
"recommendation-system",
"poi-recommendation",
"mobility",
"travel"
] | 2026-02-11T06:33:51
| null | null |
6954b1c915215faed6aba004
|
nvidia/SAGE-10k
|
nvidia
|
{"license": "apache-2.0", "pretty_name": "SAGE-10k", "size_categories": ["10K<n<100K"], "task_categories": ["text-to-3d"], "language": ["en"], "tags": ["Scene-Generation", "Interactive-Scenes", "Embodied-AI", "Scene-Understanding", "Robotics"]}
| false
|
False
| 2026-02-11T03:54:29
| 60
| 40
| false
|
d425de49ff445fd3903d790a11461d23ffd0c7dd
|
SAGE-10k
SAGE-10k is a large-scale interactive indoor scene dataset featuring realistic layouts, generated by the agentic-driven pipeline introduced in "SAGE: Scalable Agentic 3D Scene Generation for Embodied AI". The dataset contains 10,000 diverse scenes spanning 50 room types and styles, along with 565K uniquely generated 3D objects.
🔑 Key Features
SAGE-10k integrates a wide variety of scenes, and particularly, preserves small items for… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/SAGE-10k.
| 11,366
| 11,445
|
[
"task_categories:text-to-3d",
"language:en",
"license:apache-2.0",
"size_categories:10K<n<100K",
"arxiv:2602.10116",
"region:us",
"Scene-Generation",
"Interactive-Scenes",
"Embodied-AI",
"Scene-Understanding",
"Robotics"
] | 2025-12-31T05:16:57
| null | null |
69046ac0bdcb40370ae08f99
|
google/MapTrace
|
google
|
{"license": "cc-by-4.0", "task_categories": ["image-to-text"], "language": ["en"], "tags": ["map"], "size_categories": ["1M<n<10M"]}
| false
|
False
| 2026-01-03T06:15:16
| 56
| 39
| false
|
00bae0d2d917fd12548a089285d633dadf1bc81c
|
MapTrace: A 2M-Sample Synthetic Dataset for Path Tracing on Maps
Dataset Format
The dataset contains 2M annotated paths designed to train models on route-tracing tasks.
Splits:
maptrace_parquet: Contains paths on more complex, stylized maps such as those found in brochures, park directories or shopping malls.
floormap_parquet: Contains paths on simpler, structured floor maps, typical of office buildings appartment complexes, or campus maps.
Each of these splits has… See the full description on the dataset page: https://huggingface.co/datasets/google/MapTrace.
| 711
| 16,253
|
[
"task_categories:image-to-text",
"language:en",
"license:cc-by-4.0",
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"arxiv:2512.19609",
"region:us",
"map"
] | 2025-10-31T07:52:32
| null | null |
69860046f0e3ab590f5dbe17
|
OpenMed/Medical-Reasoning-SFT-Mega
|
OpenMed
|
{"license": "apache-2.0", "task_categories": ["text-generation", "question-answering"], "language": ["en"], "tags": ["medical", "reasoning", "healthcare", "clinical", "chain-of-thought", "thinking", "sft", "mega", "combined"], "size_categories": ["1M<n<10M"]}
| false
|
False
| 2026-02-06T15:06:13
| 79
| 39
| false
|
7adced6ff1fa04a6b022932ef25c71e56011fed9
|
Medical-Reasoning-SFT-Mega
The ultimate medical reasoning dataset - combining 7 state-of-the-art AI models with fair distribution deduplication. 1.79 million unique samples with 3.78 billion tokens of medical chain-of-thought reasoning.
Dataset Overview
Metric
Value
Total Samples
1,789,998 (after deduplication)
Total Tokens
~3.78 Billion
Content Tokens
~2.22 Billion
Reasoning Tokens
~1.56 Billion
Samples with Reasoning
1,789,764 (100.0%)
Unique… See the full description on the dataset page: https://huggingface.co/datasets/OpenMed/Medical-Reasoning-SFT-Mega.
| 1,657
| 1,657
|
[
"task_categories:text-generation",
"task_categories:question-answering",
"language:en",
"license:apache-2.0",
"size_categories:1M<n<10M",
"format:parquet",
"format:optimized-parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"region:us",
"medical",
"reasoning",
"healthcare",
"clinical",
"chain-of-thought",
"thinking",
"sft",
"mega",
"combined"
] | 2026-02-06T14:52:54
| null | null |
698deae1442bc90631d18c91
|
AlicanKiraz0/Turkish-Finance-SFT-Dataset
|
AlicanKiraz0
|
{"license": "mit", "task_categories": ["question-answering"], "language": ["tr"], "tags": ["finance", "fintech"], "size_categories": ["1K<n<10K"]}
| false
|
False
| 2026-02-12T16:33:00
| 50
| 38
| false
|
d106acf808bfa59040b1290d360314d079ef1cb7
|
🇹🇷 Turkish Finance SFT Dataset
Türkçe Finans Alanına Özel Supervised Fine-Tuning (SFT) Dataseti
📋 Dataset Özeti
Bu dataset, Türkçe finans asistanı LLM'lerin eğitimi için özel olarak tasarlanmış, kapsamlı bir Supervised Fine-Tuning (SFT) veri setidir. Kripto para, borsa, teknik analiz, temel analiz, risk yönetimi ve finansal regülasyonlar dahil olmak üzere geniş bir yelpazede yaklaşık 10 milyon token boyutunda soru-cevap çifti verisi içermektedir.
Dataset, hem… See the full description on the dataset page: https://huggingface.co/datasets/AlicanKiraz0/Turkish-Finance-SFT-Dataset.
| 196
| 196
|
[
"task_categories:question-answering",
"language:tr",
"license:mit",
"size_categories:1K<n<10K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us",
"finance",
"fintech"
] | 2026-02-12T14:59:45
| null | null |
69853b3733f5e88402c36a18
|
Snowflake/AgentWorldModel-1K
|
Snowflake
|
{"license": "cc-by-4.0", "language": ["en"], "tags": ["agent", "tool-use", "reinforcement-learning", "mcp", "synthetic"], "pretty_name": "agent-world-model", "viewer": false}
| false
|
False
| 2026-02-17T18:21:16
| 47
| 35
| false
|
dde80a0283fe781bdc51656bce57063dc5650213
|
AgentWorldModel-1K
Agent World Model: Infinity Synthetic Environments for Agentic Reinforcement Learning
Zhaoyang Wang1,
Canwen Xu2,
Boyi Liu2,
Yite Wang2,
Siwei Han1,
Zhewei Yao2,
Huaxiu Yao1,
Yuxiong He2
1UNC-Chapel Hill 2Snowflake AI Research
Overview
AgentWorldModel-1K contains 1,000 fully synthetic, executable, SQL database-backed tool-use environments exposed via a unified MCP (Model Context Protocol) interface, designed for large-scale… See the full description on the dataset page: https://huggingface.co/datasets/Snowflake/AgentWorldModel-1K.
| 273
| 273
|
[
"language:en",
"license:cc-by-4.0",
"arxiv:2602.10090",
"region:us",
"agent",
"tool-use",
"reinforcement-learning",
"mcp",
"synthetic"
] | 2026-02-06T00:52:07
| null | null |
698b2c8b4c9e577aa3b1fa16
|
nohurry/Opus-4.6-Reasoning-3000x-filtered
|
nohurry
|
{"license": "apache-2.0"}
| false
|
False
| 2026-02-10T13:06:40
| 41
| 35
| false
|
80e9226ea6168634ee2d6c010c3da619af8ad542
|
Filtered from: https://huggingface.co/datasets/crownelius/Opus-4.6-Reasoning-3000x
The original dataset has 979 refusals, I removed these in this version.
| 281
| 281
|
[
"license:apache-2.0",
"size_categories:1K<n<10K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us"
] | 2026-02-10T13:03:07
| null | null |
69843b85036f5289e47c75b2
|
zai-org/terminal-bench-2-verified
|
zai-org
|
{"license": "apache-2.0"}
| false
|
False
| 2026-02-19T07:01:19
| 49
| 33
| false
|
0948df7324003d0ed4e0e8d51b16e95135450871
|
Terminal-Bench 2.0 Verified: Instruction & Environment Fix Version
中文版本
We conducted a comprehensive review of the entire Terminal-Bench 2.0 dataset and identified various issues. Both GLM-5 and Step 3.5-Flash were evaluated using this verified version.
This modified version addresses environment and instruction issues we discovered in Terminal-Bench 2.0. It includes two types of fixes:
Environment Fixes: Updated Dockerfiles and instructions to support Claude Code Agent runtime… See the full description on the dataset page: https://huggingface.co/datasets/zai-org/terminal-bench-2-verified.
| 5,527
| 5,527
|
[
"license:apache-2.0",
"region:us"
] | 2026-02-05T06:41:09
| null | null |
68cda1dc5626180aae4f99d3
|
allenai/molmospaces
|
allenai
|
{"license": ["odc-by", "cc-by-4.0"], "tags": ["robotics", "embodied ai", "grasps", "objects", "scenes", "benchmark"], "pretty_name": "MolmoSpaces", "size_categories": ["100K<n<1M"], "configs": [{"config_name": "isaac__objects__objaverse__20260128", "data_files": [{"split": "pkgs", "path": "isaac/objects/objaverse/20260128/pkgs-*"}]}, {"config_name": "isaac__scenes__holodeck-objaverse-train__20260128", "data_files": [{"split": "pkgs", "path": "isaac/scenes/holodeck-objaverse-train/20260128/pkgs-*"}]}, {"config_name": "isaac__scenes__holodeck-objaverse-val__20260128", "data_files": [{"split": "pkgs", "path": "isaac/scenes/holodeck-objaverse-val/20260128/pkgs-*"}]}, {"config_name": "isaac__scenes__procthor-10k-test__20260128", "data_files": [{"split": "pkgs", "path": "isaac/scenes/procthor-10k-test/20260128/pkgs-*"}]}, {"config_name": "isaac__scenes__procthor-10k-train__20260128", "data_files": [{"split": "pkgs", "path": "isaac/scenes/procthor-10k-train/20260128/pkgs-*"}]}, {"config_name": "isaac__scenes__procthor-10k-val__20260128", "data_files": [{"split": "pkgs", "path": "isaac/scenes/procthor-10k-val/20260128/pkgs-*"}]}, {"config_name": "isaac__scenes__procthor-objaverse-train__20260128", "data_files": [{"split": "pkgs", "path": "isaac/scenes/procthor-objaverse-train/20260128/pkgs-*"}]}, {"config_name": "isaac__scenes__procthor-objaverse-val__20260128", "data_files": [{"split": "pkgs", "path": "isaac/scenes/procthor-objaverse-val/20260128/pkgs-*"}]}, {"config_name": "isaac__objects__thor__20260128", "data_files": [{"split": "pkgs", "path": "isaac/objects/thor/20260128/pkgs-*"}]}, {"config_name": "isaac__scenes__ithor__20260121", "data_files": [{"split": "pkgs", "path": "isaac/scenes/ithor/20260121/pkgs-*"}]}, {"config_name": "mujoco__benchmarks__molmospaces-bench-v1__20260210", "data_files": [{"split": "pkgs", "path": "mujoco/benchmarks/molmospaces-bench-v1/20260210/pkgs-*"}]}, {"config_name": "mujoco__grasps__droid__20251116", "data_files": [{"split": "pkgs", "path": "mujoco/grasps/droid/20251116/pkgs-*"}]}, {"config_name": "mujoco__grasps__droid_objaverse__20251218", "data_files": [{"split": "pkgs", "path": "mujoco/grasps/droid_objaverse/20251218/pkgs-*"}]}, {"config_name": "mujoco__objects__objathor_metadata__20260129", "data_files": [{"split": "pkgs", "path": "mujoco/objects/objathor_metadata/20260129/pkgs-*"}]}, {"config_name": "mujoco__objects__objaverse__20260131", "data_files": [{"split": "pkgs", "path": "mujoco/objects/objaverse/20260131/pkgs-*"}]}, {"config_name": "mujoco__objects__thor__20251117", "data_files": [{"split": "pkgs", "path": "mujoco/objects/thor/20251117/pkgs-*"}]}, {"config_name": "mujoco__robots__floating_robotiq__20260208_retry4", "data_files": [{"split": "pkgs", "path": "mujoco/robots/floating_robotiq/20260208_retry4/pkgs-*"}]}, {"config_name": "mujoco__robots__floating_rum__20251110", "data_files": [{"split": "pkgs", "path": "mujoco/robots/floating_rum/20251110/pkgs-*"}]}, {"config_name": "mujoco__robots__franka_droid__20260127", "data_files": [{"split": "pkgs", "path": "mujoco/robots/franka_droid/20260127/pkgs-*"}]}, {"config_name": "mujoco__robots__rby1__20251224", "data_files": [{"split": "pkgs", "path": "mujoco/robots/rby1/20251224/pkgs-*"}]}, {"config_name": "mujoco__robots__rby1m__20251224", "data_files": [{"split": "pkgs", "path": "mujoco/robots/rby1m/20251224/pkgs-*"}]}, {"config_name": "mujoco__scenes__holodeck-objaverse-train__20251217", "data_files": [{"split": "pkgs", "path": "mujoco/scenes/holodeck-objaverse-train/20251217/pkgs-*"}]}, {"config_name": "mujoco__scenes__holodeck-objaverse-val__20251217", "data_files": [{"split": "pkgs", "path": "mujoco/scenes/holodeck-objaverse-val/20251217/pkgs-*"}]}, {"config_name": "mujoco__scenes__ithor__20251217", "data_files": [{"split": "pkgs", "path": "mujoco/scenes/ithor/20251217/pkgs-*"}]}, {"config_name": "mujoco__scenes__procthor-10k-test__20251121", "data_files": [{"split": "pkgs", "path": 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| false
|
manual
| 2026-02-16T10:28:57
| 38
| 29
| false
|
a66e5abaa54694bda4fe6cbaeed192a7608c39ea
|
MolmoSpaces
This respository contains asset data for MolmoSpaces, including
Objects
Robots
Scenes
Grasps
Benchmarks
Updates
[2026/02/16] - Isaac-compatible USD objects and scenes now also available!
Downloading
We recommend using the download.py script to list available data sources and extract
data from one or all sources to a local cache directory. To use the script you'll need a few dependecies
in your Python environment that can be installed, e.g., by:… See the full description on the dataset page: https://huggingface.co/datasets/allenai/molmospaces.
| 204
| 221
|
[
"license:odc-by",
"license:cc-by-4.0",
"size_categories:100K<n<1M",
"format:parquet",
"format:optimized-parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us",
"robotics",
"embodied ai",
"grasps",
"objects",
"scenes",
"benchmark"
] | 2025-09-19T18:33:00
| null | null |
698b7cc970fdb3cada1cfedb
|
TeichAI/Pony-Alpha-15k
|
TeichAI
|
nan
| false
|
False
| 2026-02-17T00:02:30
| 38
| 29
| false
|
be92242fba2b94331ce36d09fbe5bccdab2f6efe
|
Pony Alpha 15k
This is a reasoning dataset generated using the stealth model Pony Alpha, which ended up being GLM-5.
As the largest dataset we have made yet. The prompts from this dataset were almost all generated by GPT 5.1 and Gemini 3 (flash and pro).
The categories covered include academia, multi-lingual creative writing, finance, health, law, marketing/SEO, programming, philosophy, web dev, python scripting, and science.
Stats:
Cost: $ 0 (USD)
Tokens (input + output): 43.3 M
| 205
| 205
|
[
"size_categories:10K<n<100K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us"
] | 2026-02-10T18:45:29
| null | null |
6976f93884f077acd5e16ac1
|
Nanbeige/ToolMind-Web-QA
|
Nanbeige
|
{"license": "apache-2.0", "configs": [{"config_name": "test"}], "task_categories": ["text-generation"], "language": ["en"], "tags": ["synthetic", "deep search"], "pretty_name": "ToolMind-Web-QA"}
| false
|
False
| 2026-02-19T07:53:07
| 27
| 27
| false
|
2690dcdfdd82ab147aad4a65ab231d4e344f5cd0
|
Dataset Summary
ToolMind-Web-QA is a validated public dataset designed for research on search-augmented and long-horizon search agents.
The dataset contains 6k complex question-answer (QA) pairs synthesized from Wikipedia entity-relation knowledge graphs and also includes trajectories, averaged over 100 turns, constructed through advanced search agents.
The dataset emphasizes multi-hop reasoning, evidence-grounded answers, and search-oriented problem-solving.
Data… See the full description on the dataset page: https://huggingface.co/datasets/Nanbeige/ToolMind-Web-QA.
| 724
| 724
|
[
"task_categories:text-generation",
"language:en",
"license:apache-2.0",
"arxiv:2602.13367",
"region:us",
"synthetic",
"deep search"
] | 2026-01-26T05:18:48
| null | null |
698562bfcccb3e493c940fbe
|
Soul-AILab/VividHead
|
Soul-AILab
|
{"license": "apache-2.0", "task_categories": ["image-to-video"], "pretty_name": "VividHead", "size_categories": ["100K<n<1M"]}
| false
|
False
| 2026-02-12T09:20:28
| 51
| 24
| false
|
3a319838f8136a6f3f351c7a21293b51e95663c5
|
SoulX-FlashHead: Oracle-guided Generation of Infinite Real-time Streaming Talking Heads
Tan Yu*, Qian Qiao*✉, Le Shen*, Ke Zhou, Jincheng Hu, Dian Sheng, Bo Hu, Haoming Qin, Jun Gao, Changhai Zhou, Shunshun Yin, Siyuan Liu ✉
*Equal Contribution
✉Corresponding Author
VividHead Dataset
Highlights
🔥 Large-scale, high-quality talking-head dataset with 330K clips and 782 hours of head-cropped videos
🔥 Broad diversity across 15+ languages and a wide age range… See the full description on the dataset page: https://huggingface.co/datasets/Soul-AILab/VividHead.
| 3,164
| 3,164
|
[
"task_categories:image-to-video",
"license:apache-2.0",
"size_categories:100K<n<1M",
"arxiv:2602.07449",
"region:us"
] | 2026-02-06T03:40:47
| null | null |
698f0bb66f3403bf6c1b2c96
|
atreydesai/qgqa-gpt-5.2-20260213-041705
|
atreydesai
|
{"dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "options", "list": "string"}, {"name": "answer", "dtype": "string"}, {"name": "answer_index", "dtype": "int64"}, {"name": "category", "dtype": "string"}, {"name": "src", "dtype": "string"}, {"name": "subfield", "dtype": "string"}, {"name": "difficulty", "dtype": "string"}, {"name": "choices_answer", "list": "string"}, {"name": "choices_human", "list": "string"}, {"name": "legacy_choices_synthetic", "list": "string"}, {"name": "cond_model_q_a_scratch", "list": "string"}, {"name": "qa_options_randomized", "list": "string"}, {"name": "qa_correct_answer_letter", "dtype": "string"}, {"name": "qa_full_question", "dtype": "string"}, {"name": "qa_model_input", "dtype": "string"}, {"name": "qa_model_output", "dtype": "string"}, {"name": "cond_model_q_a_dhuman", "list": "string"}, {"name": "qadh_options_randomized", "list": "string"}, {"name": "qadh_correct_answer_letter", "dtype": "string"}, {"name": "qadh_full_question", "dtype": "string"}, {"name": "qadh_model_input", "dtype": "string"}, {"name": "qadh_model_output", "dtype": "string"}, {"name": "cond_model_q_a_dmodel", "list": "string"}, {"name": "qadm_options_randomized", "list": "string"}, {"name": "qadm_correct_answer_letter", "dtype": "string"}, {"name": "qadm_full_question", "dtype": "string"}, {"name": "qadm_model_input", "dtype": "string"}, {"name": "qadm_model_output", "dtype": "string"}, {"name": "is_calculation", "dtype": "bool"}, {"name": "id", "dtype": "string"}, {"name": "question_id", "dtype": "int64"}, {"name": "whitespace_bug_fixed", "dtype": "bool"}, {"name": "discipline", "dtype": "string"}, {"name": "dataset_type", "dtype": "string"}, {"name": "cot_content", "dtype": "string"}, {"name": "answer_letter", "dtype": "string"}, {"name": "labels", "list": "string"}], "splits": [{"name": "arc_challenge", "num_bytes": 5562595, "num_examples": 750}, {"name": "arc_easy", "num_bytes": 4791349, "num_examples": 750}, {"name": "mmlu_pro", "num_bytes": 13407549, "num_examples": 750}, {"name": "supergpqa", "num_bytes": 6149982, "num_examples": 750}], "download_size": 13674429, "dataset_size": 29911475}, "configs": [{"config_name": "default", "data_files": [{"split": "arc_challenge", "path": "data/arc_challenge-*"}, {"split": "arc_easy", "path": "data/arc_easy-*"}, {"split": "mmlu_pro", "path": "data/mmlu_pro-*"}, {"split": "supergpqa", "path": "data/supergpqa-*"}]}]}
| false
|
False
| 2026-02-13T11:32:10
| 35
| 24
| false
|
6b779dc2d9491ed7fdf60ddd862b6ffe82f8f117
| null | 46
| 46
|
[
"size_categories:1K<n<10K",
"format:parquet",
"format:optimized-parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us"
] | 2026-02-13T11:32:06
| null | null |
698a87c0d1963da4cb34d33e
|
nvidia/PhysicalAI-Robotics-Kitchen-Sim-Demos
|
nvidia
|
{"license": "cc-by-4.0", "task_categories": ["robotics"], "tags": ["robotics"], "viewer": false}
| false
|
False
| 2026-02-13T02:47:07
| 34
| 23
| false
|
0d5e2a6ea83224b77b7413a466b9ff57e3d795cd
|
PhysicalAI-Robotics-Kitchen-Sim-Demos
We provide a 600 hours of human-teleoperated demonstrations across 316 different tasks, totalling 55k trajectories.
The datasets are collected using Franka Panda robot with an Omron mobile base.
The datasets follow the LeRobot format. Here is an overview of important elements of each dataset:
Click to expand dataset structure
lerobot/
├── meta/ # Metadata files describing the dataset
│ ├── info.json… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/PhysicalAI-Robotics-Kitchen-Sim-Demos.
| 567
| 567
|
[
"task_categories:robotics",
"license:cc-by-4.0",
"region:us",
"robotics"
] | 2026-02-10T01:20:00
| null | null |
697cf3557b4ea8733c879c82
|
galaxyMindAiLabs/stem-reasoning-complex
|
galaxyMindAiLabs
|
{"license": "apache-2.0", "task_categories": ["text-generation", "question-answering"], "language": ["en", "zh"], "tags": ["stem", "biology", "physics", "chemistry", "math", "reasoning", "chain-of-thought", "sft"], "format": ["parquet"], "size_categories": ["100K<n<1M"]}
| false
|
False
| 2026-02-15T13:25:44
| 57
| 22
| false
|
f06fccd7f363cd1c93ea98741314b7123447ae21
|
STEM-Reasoning-Complex: High-Fidelity Scientific CoT Dataset
1. Dataset Summary
STEM-Reasoning-Complex is a curated collection of 118.225 high-quality samples designed for Supervised Fine-Tuning (SFT) and alignment of Large Language Models. The dataset focuses on four core disciplines: Biology, Mathematics, Physics, and Chemistry.
Unlike standard QA datasets, each entry provides a structured Chain-of-Thought (CoT) reasoning process, enabling models to learn "internal… See the full description on the dataset page: https://huggingface.co/datasets/galaxyMindAiLabs/stem-reasoning-complex.
| 459
| 459
|
[
"task_categories:text-generation",
"task_categories:question-answering",
"language:en",
"language:zh",
"license:apache-2.0",
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"region:us",
"stem",
"biology",
"physics",
"chemistry",
"math",
"reasoning",
"chain-of-thought",
"sft"
] | 2026-01-30T18:07:17
| null | null |
6986cb617ee2b3c146bd2432
|
openbmb/Ultra-FineWeb-L3
|
openbmb
|
{"language": ["en", "zh"], "license": "apache-2.0", "task_categories": ["text-generation"], "pretty_name": "Ultra-FineWeb-L3", "tags": ["llm", "pretraining", "web-data", "data-synthesis", "high-quality"], "configs": [{"config_name": "ultrafineweb_en_l3", "data_files": "data/ultrafineweb_en_l3/*.jsonl"}, {"config_name": "ultrafineweb_zh_l3", "data_files": "data/ultrafineweb_zh_l3/*.jsonl"}], "default_config_name": "ultrafineweb_en_l3"}
| false
|
False
| 2026-02-09T07:05:40
| 36
| 22
| false
|
86d7ba1fbec95adb7970f281d055e5f092b36619
|
Ultra-FineWeb-L3
Ultra-FineWeb-L3 is a high-quality refined web pre-training dataset, produced through multi-format synthesis and rewriting based on the UltraData L0-L4 Tiered Data Management Framework.
📚 Overview
Starting from quality-selected web data (Ultra-FineWeb), we apply LLM-driven synthesis and refinement to produce structured, high-quality content across multiple formats.
🏗️ Data Processing Pipeline
The L3 refinement process transforms raw web text… See the full description on the dataset page: https://huggingface.co/datasets/openbmb/Ultra-FineWeb-L3.
| 1,293
| 1,293
|
[
"task_categories:text-generation",
"language:en",
"language:zh",
"license:apache-2.0",
"size_categories:n<1K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us",
"llm",
"pretraining",
"web-data",
"data-synthesis",
"high-quality"
] | 2026-02-07T05:19:29
| null | null |
698ec679e8cbdf676dd7322d
|
deepgenteam/DeepGen-1.0
|
deepgenteam
|
{"license": "apache-2.0"}
| false
|
False
| 2026-02-13T10:44:53
| 24
| 22
| false
|
7f356c1b94bf9fe4a81147ec9cbfc8705cb022da
|
💡 DeepGen 1.0: A Lightweight Unified Multimodal Model for Advancing Image Generation and Editing
DeepGen 1.0 is a lightweight unified multimodal model with only 5B parameters (3B VLM + 2B DiT). It integrates five core capabilities—general image generation, general image editing, reasoning image generation, reasoning image editing, and text rendering—within a single model. Across multiple authoritative benchmarks, DeepGen 1.0 is competitive… See the full description on the dataset page: https://huggingface.co/datasets/deepgenteam/DeepGen-1.0.
| 679
| 679
|
[
"license:apache-2.0",
"size_categories:n<1K",
"format:imagefolder",
"modality:image",
"library:datasets",
"library:mlcroissant",
"arxiv:2602.12205",
"region:us"
] | 2026-02-13T06:36:41
| null | null |
68873481d4a41fe542ba35b7
|
uv-scripts/ocr
|
uv-scripts
|
{"viewer": false, "tags": ["uv-script", "ocr", "vision-language-model", "document-processing", "hf-jobs"]}
| false
|
False
| 2026-02-19T13:07:01
| 59
| 21
| false
|
c37f5fca2ad20a671fb514d99e9c2fc966a48055
|
OCR UV Scripts
Part of uv-scripts - ready-to-run ML tools powered by UV and HuggingFace Jobs.
13 OCR models from 0.9B to 8B parameters. Pick a model, point at your dataset, get markdown — no setup required.
🚀 Quick Start
Run OCR on any dataset without needing your own GPU:
# Quick test with 10 samples
hf jobs uv run --flavor l4x1 \
--secrets HF_TOKEN \
https://huggingface.co/datasets/uv-scripts/ocr/raw/main/glm-ocr.py \
your-input-dataset… See the full description on the dataset page: https://huggingface.co/datasets/uv-scripts/ocr.
| 681
| 2,990
|
[
"region:us",
"uv-script",
"ocr",
"vision-language-model",
"document-processing",
"hf-jobs"
] | 2025-07-28T08:27:45
| null | null |
696ca336d0d46e96ec4f1b40
|
OpenDriveLab-org/Kai0
|
OpenDriveLab-org
|
{"license": "cc-by-nc-sa-4.0", "task_categories": ["robotics"], "tags": ["LeRobot"], "configs": [{"config_name": "default", "data_files": "Task_A/base/data/chunk-000/episode_000000.parquet"}]}
| false
|
False
| 2026-02-14T19:31:50
| 30
| 19
| false
|
16e26997097db7374820286ba0403a9a8105a3df
|
KAI0
TODO
The advantage label will be coming soon.
Contents
About the Dataset
Load the Dataset
Download the Dataset
Dataset Structure
Folder hierarchy
Details
License and Citation
About the Dataset
~134 hours real world scenarios
Main Tasks
Task_A
Single task
Initial state: T-shirts are randomly tossed onto the table, presenting random crumpled configurations
Manipulation task: Operate the… See the full description on the dataset page: https://huggingface.co/datasets/OpenDriveLab-org/Kai0.
| 22,521
| 22,663
|
[
"task_categories:robotics",
"license:cc-by-nc-sa-4.0",
"size_categories:1K<n<10K",
"format:parquet",
"modality:tabular",
"modality:timeseries",
"modality:video",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us",
"LeRobot"
] | 2026-01-18T09:09:10
| null | null |
6981b559f11eace8fc8fcc78
|
commoncrawl/CommonLID
|
commoncrawl
|
{"configs": [{"config_name": "default", "data_files": [{"split": "test", "path": "commonlid_20251209.tsv.gz"}]}], "license": "other", "license_name": "common-crawl-tou", "license_link": "https://commoncrawl.org/terms-of-use", "library_name": "commonlid", "task_categories": ["text-classification"], "language": ["ace", "acf", "aeb", "afr", "amh", "apd", "ara", "arb", "arg", "ars", "ary", "arz", "asm", "aze", "azj", "bak", "bcl", "ben", "bik", "bre", "bul", "cat", "ces", "cmn", "crh", "deu", "ell", "eng", "est", "ext", "fas", "fil", "fin", "fra", "fro", "fry", "fuv", "gaz", "gcf", "gcr", "gla", "gle", "gom", "grc", "gug", "guj", "guw", "hau", "hbo", "heb", "hin", "ibo", "ind", "ita", "jav", "jpn", "kab", "kan", "kik", "kor", "lat", "lav", "lij", "lin", "ltg", "lug", "lvs", "mal", "mar", "mlg", "msa", "nld", "nso", "nyn", "oci", "orm", "ory", "pan", "pcm", "pol", "por", "rcf", "rus", "san", "sna", "sot", "spa", "swa", "swh", "tam", "tat", "tel", "tgl", "tha", "tuk", "tur", "ukr", "urd", "uzb", "uzs", "vec", "vie", "wuu", "xho", "yor", "yue", "zho", "zsm", "zul"], "pretty_name": "CommonLID", "tags": ["text"], "extra_gated_heading": "Protecting the integrity of CommonLID for evaluation", "extra_gated_fields": {"I am aware that CommonLID is intended for use as an evaluation dataset": "checkbox", "I agree not to re-host CommonLID in places where it could be picked up by web crawlers": "checkbox", "If I evaluate using CommonLID, I will ensure that its contents are not in the training data": "checkbox"}, "size_categories": ["100K<n<1M"]}
| false
|
auto
| 2026-02-10T00:07:47
| 24
| 19
| false
|
1ab8feb9fa051f7ad60e80f8592fac0d973ead9b
|
CommonLID
CommonLID is a community-created language identification (LID) benchmark. CommonLID consists of web text manually annotated for the language that it is written in. CommonLID contains annotations for 109 languages, where 78 of those languages have at least 100 lines of data.
The number of lines available for each language is provided in Appendix A of the preprint.
Dataset construction details
Method details are in our preprint: CommonLID: Re-evaluating… See the full description on the dataset page: https://huggingface.co/datasets/commoncrawl/CommonLID.
| 188
| 188
|
[
"task_categories:text-classification",
"language:ace",
"language:acf",
"language:aeb",
"language:afr",
"language:amh",
"language:apd",
"language:ara",
"language:arb",
"language:arg",
"language:ars",
"language:ary",
"language:arz",
"language:asm",
"language:aze",
"language:azj",
"language:bak",
"language:bcl",
"language:ben",
"language:bik",
"language:bre",
"language:bul",
"language:cat",
"language:ces",
"language:cmn",
"language:crh",
"language:deu",
"language:ell",
"language:eng",
"language:est",
"language:ext",
"language:fas",
"language:fil",
"language:fin",
"language:fra",
"language:fro",
"language:fry",
"language:fuv",
"language:gaz",
"language:gcf",
"language:gcr",
"language:gla",
"language:gle",
"language:gom",
"language:grc",
"language:gug",
"language:guj",
"language:guw",
"language:hau",
"language:hbo",
"language:heb",
"language:hin",
"language:ibo",
"language:ind",
"language:ita",
"language:jav",
"language:jpn",
"language:kab",
"language:kan",
"language:kik",
"language:kor",
"language:lat",
"language:lav",
"language:lij",
"language:lin",
"language:ltg",
"language:lug",
"language:lvs",
"language:mal",
"language:mar",
"language:mlg",
"language:msa",
"language:nld",
"language:nso",
"language:nyn",
"language:oci",
"language:orm",
"language:ory",
"language:pan",
"language:pcm",
"language:pol",
"language:por",
"language:rcf",
"language:rus",
"language:san",
"language:sna",
"language:sot",
"language:spa",
"language:swa",
"language:swh",
"language:tam",
"language:tat",
"language:tel",
"language:tgl",
"language:tha",
"language:tuk",
"language:tur",
"language:ukr",
"language:urd",
"language:uzb",
"language:uzs",
"language:vec",
"language:vie",
"language:wuu",
"language:xho",
"language:yor",
"language:yue",
"language:zho",
"language:zsm",
"language:zul",
"license:other",
"size_categories:100K<n<1M",
"format:csv",
"modality:image",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"arxiv:2601.18026",
"region:us",
"text"
] | 2026-02-03T08:44:09
| 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
| 2,666
| 18
| 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.
| 197,213
| 6,307,276
|
[
"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 |
67c92e867c6308c49ce2e98c
|
openbmb/Ultra-FineWeb
|
openbmb
|
{"language": ["en", "zh"], "license": "apache-2.0", "size_categories": ["n>1T"], "task_categories": ["text-generation"], "pretty_name": "Ultra-FineWeb", "tags": ["llm", "pretraining", "web-corpus", "data-filtering", "high-quality"], "configs": [{"config_name": "default", "data_files": [{"split": "en", "path": "data/ultrafineweb_en/*"}, {"split": "zh", "path": "data/ultrafineweb_zh/*"}], "features": [{"name": "content", "dtype": "string"}, {"name": "score", "dtype": "float"}, {"name": "source", "dtype": "string"}]}]}
| false
|
False
| 2025-12-10T14:20:21
| 326
| 18
| false
|
f5030f44ebe4fdc77e8cc9c6aee2aff1e33944f7
|
Ultra-FineWeb
📜 Ultra-FineWeb Technical Report | 📄 MiniCPM4 Paper | 💻 GitHub Repository | 🌐 MiniCPM4 Project Page
📚 Introduction
Ultra-FineWeb is a large-scale, high-quality, and efficiently-filtered dataset. We use the proposed efficient verification-based high-quality filtering pipeline to the FineWeb and Chinese FineWeb datasets (source data from Chinese FineWeb-edu-v2, which includes IndustryCorpus2, MiChao, WuDao, SkyPile, WanJuan, ChineseWebText… See the full description on the dataset page: https://huggingface.co/datasets/openbmb/Ultra-FineWeb.
| 55,135
| 442,293
|
[
"task_categories:text-generation",
"language:en",
"language:zh",
"license:apache-2.0",
"size_categories:1B<n<10B",
"modality:text",
"arxiv:2505.05427",
"arxiv:2506.07900",
"arxiv:2412.04315",
"region:us",
"llm",
"pretraining",
"web-corpus",
"data-filtering",
"high-quality"
] | 2025-03-06T05:11:34
| null | null |
67d45c3d35fc7f6d2ab224c8
|
allenai/olmOCR-bench
|
allenai
|
{"license": "odc-by", "tags": ["text"], "configs": [{"config_name": "olmocr-bench", "data_files": [{"split": "arxiv_math", "path": ["bench_data/arxiv_math.jsonl"]}, {"split": "headers_footers", "path": ["bench_data/headers_footers.jsonl"]}, {"split": "long_tiny_text", "path": ["bench_data/long_tiny_text.jsonl"]}, {"split": "multi_column", "path": ["bench_data/multi_column.jsonl"]}, {"split": "old_scans", "path": ["bench_data/old_scans.jsonl"]}, {"split": "old_scans_math", "path": ["bench_data/old_scans_math.jsonl"]}, {"split": "table_tests", "path": ["bench_data/table_tests.jsonl"]}]}], "language": ["en"], "pretty_name": "olmOCR-bench", "size_categories": ["1K<n<10K"]}
| false
|
False
| 2026-02-19T17:28:38
| 58
| 18
| false
|
54a96a6fb6a2bd3b297e59869491db4d3625b711
|
olmOCR-bench
olmOCR-bench is a dataset of 1,403 PDF files, plus 7,010 unit test cases that capture properties of the output that a good OCR system should have.
This benchmark evaluates the ability of OCR systems to accurately convert PDF documents to markdown format while preserving critical textual and structural information.
Quick links:
📃 Paper
🛠️ Code
🎮 Demo
Table 1. Distribution of Test Classes by Document Source
Document Source
Text Present
Text… See the full description on the dataset page: https://huggingface.co/datasets/allenai/olmOCR-bench.
| 2,745
| 30,796
|
[
"benchmark:official",
"benchmark:eval-yaml",
"language:en",
"license:odc-by",
"size_categories:1K<n<10K",
"modality:document",
"modality:text",
"arxiv:2502.18443",
"region:us",
"text"
] | 2025-03-14T16:41:33
| null | null |
698ccba293fade2777c7c5b7
|
allenai/olmix
|
allenai
|
{"license": "apache-2.0", "task_categories": ["other"], "pretty_name": "Olmix Swarm Datasets", "size_categories": ["n<1K"], "tags": ["data-mixing", "language-models", "pretraining", "mixture-optimization"]}
| false
|
False
| 2026-02-20T01:26:06
| 18
| 18
| false
|
33b58d580ba2b439fd9ede85a173188141586db3
|
Olmix Swarm Datasets
This repository contains proxy run swarm datasets for data mixing using Olmix. Each swarm consists of multiple proxy training runs with different domain mixture ratios and their corresponding evaluation metrics across various downstream tasks. The swarm datasets here are based on 30M parameter proxy models trained on 3B tokens.
For more details, see our paper: Olmix: A Framework for Data Mixing Throughout LM Development
Swarms
The dataset contains… See the full description on the dataset page: https://huggingface.co/datasets/allenai/olmix.
| 238
| 238
|
[
"task_categories:other",
"license:apache-2.0",
"size_categories:n<1K",
"arxiv:2602.12237",
"arxiv:2406.11794",
"arxiv:2502.02737",
"arxiv:2512.13961",
"region:us",
"data-mixing",
"language-models",
"pretraining",
"mixture-optimization"
] | 2026-02-11T18:34:10
| null | null |
66bb76014dbf7716986c7f86
|
princeton-nlp/SWE-bench_Verified
|
princeton-nlp
|
{"dataset_info": {"features": [{"name": "repo", "dtype": "string"}, {"name": "instance_id", "dtype": "string"}, {"name": "base_commit", "dtype": "string"}, {"name": "patch", "dtype": "string"}, {"name": "test_patch", "dtype": "string"}, {"name": "problem_statement", "dtype": "string"}, {"name": "hints_text", "dtype": "string"}, {"name": "created_at", "dtype": "string"}, {"name": "version", "dtype": "string"}, {"name": "FAIL_TO_PASS", "dtype": "string"}, {"name": "PASS_TO_PASS", "dtype": "string"}, {"name": "environment_setup_commit", "dtype": "string"}, {"name": "difficulty", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 7779763, "num_examples": 500}], "download_size": 2096679, "dataset_size": 7779763}, "configs": [{"config_name": "default", "data_files": [{"split": "test", "path": "data/test-*"}]}]}
| false
|
False
| 2025-02-18T23:48:55
| 294
| 17
| false
|
c104f840cc67f8b6eec6f759ebc8b2693d585d4a
|
Dataset Summary
SWE-bench Verified is a subset of 500 samples from the SWE-bench test set, which have been human-validated for quality. SWE-bench is a dataset that tests systems’ ability to solve GitHub issues automatically. See this post for more details on the human-validation process.
The dataset collects 500 test Issue-Pull Request pairs from popular Python repositories. Evaluation is performed by unit test verification using post-PR behavior as the reference solution.
The original… See the full description on the dataset page: https://huggingface.co/datasets/princeton-nlp/SWE-bench_Verified.
| 599,582
| 8,610,127
|
[
"size_categories:n<1K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2024-08-13T15:04:33
| null | null |
69829b0ed8d4403d656d7a0b
|
futurehouse/labbench2
|
futurehouse
|
{"license": "cc-by-sa-4.0", "size_categories": ["1K<n<10K"], "task_categories": ["question-answering"], "pretty_name": "LABBench2", "dataset_info": [{"config_name": "all", "features": [{"name": "id", "dtype": "string"}, {"name": "tag", "dtype": "string"}, {"name": "version", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "ideal", "dtype": "string"}, {"name": "files", "dtype": "string"}, {"name": "sources", "list": "string"}, {"name": "key_passage", "dtype": "string"}, {"name": "canary", "dtype": "string"}, {"name": "is_opensource", "dtype": "bool"}, {"name": "ground_truth", "dtype": "bool"}, {"name": "prompt_suffix", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "mode", "struct": [{"name": "file", "dtype": "bool"}, {"name": "retrieve", "dtype": "bool"}, {"name": "inject", "dtype": "bool"}]}, {"name": "validator_params", "dtype": "string"}, {"name": "answer_regex", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1378534, "num_examples": 1912}], "download_size": 476714, "dataset_size": 1378534}, {"config_name": "cloning", "features": [{"name": "id", "dtype": "string"}, {"name": "tag", "dtype": "string"}, {"name": "version", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "ideal", "dtype": "string"}, {"name": "files", "dtype": "string"}, {"name": "sources", "list": "string"}, {"name": "key_passage", "dtype": "string"}, {"name": "canary", "dtype": "string"}, {"name": "is_opensource", "dtype": "bool"}, {"name": "ground_truth", "dtype": "bool"}, {"name": "prompt_suffix", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "mode", "struct": [{"name": "file", "dtype": "bool"}, {"name": "retrieve", "dtype": "bool"}, {"name": "inject", "dtype": "bool"}]}, {"name": "validator_params", "dtype": "string"}, {"name": "answer_regex", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 38582, "num_examples": 14}], "download_size": 23236, "dataset_size": 38582}, 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{"config_name": "cloning", "data_files": [{"split": "train", "path": "cloning/train-*"}]}, {"config_name": "dbqa2", "data_files": [{"split": "train", "path": "dbqa2/train-*"}]}, {"config_name": "figqa2", "data_files": [{"split": "train", "path": "figqa2/train-*"}]}, {"config_name": "figqa2-img", "data_files": [{"split": "train", "path": "figqa2-img/train-*"}]}, {"config_name": "figqa2-pdf", "data_files": [{"split": "train", "path": "figqa2-pdf/train-*"}]}, {"config_name": "litqa3", "data_files": [{"split": "train", "path": "litqa3/train-*"}]}, {"config_name": "patentqa", "data_files": [{"split": "train", "path": "patentqa/train-*"}]}, {"config_name": "protocolqa2", "data_files": [{"split": "train", "path": "protocolqa2/train-*"}]}, {"config_name": "seqqa2", "data_files": [{"split": "train", "path": "seqqa2/train-*"}]}, {"config_name": "sourcequality", "data_files": [{"split": "train", "path": "sourcequality/train-*"}]}, {"config_name": "suppqa2", "data_files": [{"split": "train", "path": "suppqa2/train-*"}]}, {"config_name": "tableqa2", "data_files": [{"split": "train", "path": "tableqa2/train-*"}]}, {"config_name": "tableqa2-img", "data_files": [{"split": "train", "path": "tableqa2-img/train-*"}]}, {"config_name": "tableqa2-pdf", "data_files": [{"split": "train", "path": "tableqa2-pdf/train-*"}]}, {"config_name": "trialqa", "data_files": [{"split": "train", "path": "trialqa/train-*"}]}]}
| false
|
auto
| 2026-02-12T01:32:13
| 38
| 17
| false
|
b519de99a74872c0bf41567a1137e3464746d6fa
|
LABBench2
LABBench2 is a benchmark for measuring real-world capabilities of AI systems performing scientific research tasks. It is an evolution of the Language Agent Biology Benchmark (LAB-Bench), comprising nearly 1,900 tasks that measure similar capabilities but in more realistic contexts.
LABBench2 provides a meaningful jump in difficulty over LAB-Bench (model-specific accuracy differences range from −26% to −46% across subtasks), underscoring continued room for improvement.… See the full description on the dataset page: https://huggingface.co/datasets/futurehouse/labbench2.
| 1,571
| 1,571
|
[
"task_categories:question-answering",
"license:cc-by-sa-4.0",
"size_categories:1K<n<10K",
"format:parquet",
"format:optimized-parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"arxiv:2407.10362",
"region:us"
] | 2026-02-04T01:04:14
| null | null |
67323181adc3df46516a0611
|
nyuuzyou/suno
|
nyuuzyou
|
{"pretty_name": "Suno Music Generation Dataset", "size_categories": ["100K<n<1M"], "task_categories": ["audio-classification", "text-to-audio"], "annotations_creators": ["found"], "language": ["en", "ja", "multilingual"], "license": "cc0-1.0", "multilinguality": ["multilingual"], "source_datasets": ["original"], "tags": ["audio", "video", "image", "text"]}
| false
|
False
| 2026-02-03T08:50:56
| 132
| 16
| false
|
dd95495c415eea043c250f12da595de2ad4cad7f
|
Dataset Card for Suno.ai Music Generation
Dataset Summary
This dataset contains metadata for 659,788 songs generated by artificial intelligence on the suno.com platform, a service that generates music using artificial intelligence. The songs were discovered by search queries with words from the dwyl/english-words word list.
Languages
The dataset is multilingual with English as the primary language:
English (en): Primary language for metadata and most lyrics… See the full description on the dataset page: https://huggingface.co/datasets/nyuuzyou/suno.
| 361
| 2,611
|
[
"task_categories:audio-classification",
"task_categories:text-to-audio",
"annotations_creators:found",
"multilinguality:multilingual",
"source_datasets:original",
"language:en",
"language:ja",
"language:multilingual",
"license:cc0-1.0",
"size_categories:100K<n<1M",
"format:parquet",
"modality:image",
"modality:tabular",
"modality:text",
"modality:audio",
"modality:video",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us",
"audio",
"video",
"image",
"text"
] | 2024-11-11T16:32:01
| null | null |
697c5798f84f7047f7e9cd57
|
opencsg/Fineweb-Edu-Chinese-V2.2
|
opencsg
|
{"language": ["zh"], "license": "apache-2.0", "task_categories": ["text-generation", "question-answering"], "tags": ["education", "nlp", "sft", "synthetic", "deepseek"], "size_categories": ["10B<n<100B", "1M<n<10M"], "pretty_name": "Chinese Fineweb Edu V2.2", "configs": [{"config_name": "default", "data_files": [{"split": "sft_qa", "path": "sft/cleaned/*.jsonl"}, {"split": "sft_context", "path": "sft/*.jsonl"}]}, {"config_name": "pretrain", "data_files": [{"split": "score_4_5", "path": "4_5/*.parquet"}, {"split": "score_3_4", "path": "3_4/*.parquet"}, {"split": "score_2_3", "path": "2_3/*.parquet"}]}]}
| false
|
False
| 2026-02-02T11:54:10
| 61
| 16
| false
|
ab9e282ac7f146b52ea19df00c0225146b784129
|
Chinese Fineweb Edu Dataset V2.2 (Instruct & Pre-train)
[[中文]] | [[English]]
OpenCSG Community | 👾 GitHub | 📖 Technical Report
Dataset Introduction: Filling the Data Puzzle for Chinese Education LLMs
Chinese Fineweb Edu Dataset V2.2 is a rare high-quality dataset in the open-source community that covers the full process from Pre-training to Supervised Fine-Tuning (SFT) for the Chinese education domain.
This project aims to solve the core pain point of… See the full description on the dataset page: https://huggingface.co/datasets/opencsg/Fineweb-Edu-Chinese-V2.2.
| 54,020
| 54,020
|
[
"task_categories:text-generation",
"task_categories:question-answering",
"language:zh",
"license:apache-2.0",
"size_categories:10B<n<100B",
"arxiv:2501.08197",
"arxiv:2305.11206",
"arxiv:2305.15717",
"arxiv:2307.01850",
"arxiv:2307.08701",
"region:us",
"education",
"nlp",
"sft",
"synthetic",
"deepseek"
] | 2026-01-30T07:02:48
| null | null |
698ed246c13d5b918cb12f17
|
nvidia/PhysicalAI-Kitchen-Assets
|
nvidia
|
{"license": "cc-by-4.0", "viewer": false}
| false
|
False
| 2026-02-13T08:01:26
| 16
| 16
| false
|
474f169c579db89828cd99fd167f179b099fd85a
|
Simulation Kitchen Assets
Asset Description:
The Simulation Kitchen Assets are a collection of digital 3D assets intended for use in a simulated kitchen environment.
The assets are broadly divided into 2 categories: fixtures and objects.
The fixture assets are comprised of interactable kitchen appliances such as stoves, microwaves, and ovens.
The object assets consist of common kitchen objects such as saucepans and glass cups.
| 334
| 334
|
[
"license:cc-by-4.0",
"region:us"
] | 2026-02-13T07:27:02
| null | null |
6928ac839f54f92be8b78d70
|
TeichAI/claude-4.5-opus-high-reasoning-250x
|
TeichAI
|
nan
| false
|
False
| 2025-11-28T03:02:41
| 257
| 15
| false
|
742c86f88b66bf53cb5961a25e4360f5582f4a6e
|
This is a reasoning dataset created using Claude Opus 4.5 with a reasoning depth set to high. Some of these questions are from reedmayhew and the rest were generated.
The dataset is meant for creating distilled versions of Claude Opus 4.5 by fine-tuning already existing open-source LLMs.
Stats
Costs: $ 52.3 (USD)
Total tokens (input + output): 2.13 M
| 5,380
| 15,013
|
[
"size_categories:n<1K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2025-11-27T19:54:43
| null | null |
69836757bbb0f79b9472304c
|
perplexity-ai/draco
|
perplexity-ai
|
{"license": "mit", "language": ["en"], "tags": ["deep-research"], "pretty_name": "DRACO Benchmark"}
| false
|
False
| 2026-02-13T04:35:13
| 70
| 15
| false
|
c34113287e65fa53ce0c987c98053bdb9d96e94e
|
DRACO: a Cross-Domain Benchmark for Deep Research Accuracy, Completeness, and Objectivity
The DRACO Benchmark consists of complex, open-ended research tasks with expert-curated rubrics for evaluating deep research systems. Tasks span 10 domains and require drawing on information sources from 40 countries. Each task is paired with a detailed, task-specific rubric featuring an average of ~40 evaluation criteria across four axes: factual accuracy, breadth and depth of analysis… See the full description on the dataset page: https://huggingface.co/datasets/perplexity-ai/draco.
| 9,521
| 9,521
|
[
"language:en",
"license:mit",
"size_categories:n<1K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"arxiv:2602.11685",
"region:us",
"deep-research"
] | 2026-02-04T15:35:51
| null | null |
698dd2570db46090757245bc
|
markov-ai/computer-use
|
markov-ai
|
{"license": "apache-2.0", "task_categories": ["robotics", "image-to-text"], "tags": ["computer-use", "gui-agent", "osworld", "trajectories", "reinforcement-learning"], "size_categories": ["n<1K"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*.parquet"}]}]}
| false
|
False
| 2026-02-13T15:11:21
| 14
| 13
| false
|
de58c88b4b33dd03fa4d5d0f490748f576bd37b3
|
Computer Use Trajectories
Successful computer-use agent trajectories collected on OSWorld tasks.
Dataset Details
Rows: 160 (one per task trajectory)
Steps: 1,378 total across all trajectories (avg ~8.6 steps/task)
Agent: Gemini 3 Flash Preview with linearized accessibility-tree grounding
Score filter: Only trajectories with score = 1.0 (fully successful)
Domains
Domain
Tasks
Description
chrome
21
Web browsing tasks in Google Chrome
gimp
15
Image… See the full description on the dataset page: https://huggingface.co/datasets/markov-ai/computer-use.
| 121
| 121
|
[
"task_categories:robotics",
"task_categories:image-to-text",
"license:apache-2.0",
"size_categories:n<1K",
"format:parquet",
"format:optimized-parquet",
"modality:image",
"modality:text",
"modality:timeseries",
"modality:video",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"region:us",
"computer-use",
"gui-agent",
"osworld",
"trajectories",
"reinforcement-learning"
] | 2026-02-12T13:15:03
| null | null |
663b7fd5a4152b77b637ba11
|
TIGER-Lab/MMLU-Pro
|
TIGER-Lab
|
{"language": ["en"], "license": "mit", "size_categories": ["10K<n<100K"], "task_categories": ["question-answering"], "pretty_name": "MMLU-Pro", "tags": ["evaluation"], "configs": [{"config_name": "default", "data_files": [{"split": "test", "path": "data/test-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "question_id", "dtype": "int64"}, {"name": "question", "dtype": "string"}, {"name": "options", "sequence": "string"}, {"name": "answer", "dtype": "string"}, {"name": "answer_index", "dtype": "int64"}, {"name": "cot_content", "dtype": "string"}, {"name": "category", "dtype": "string"}, {"name": "src", "dtype": "string"}], "splits": [{"name": "validation", "num_bytes": 61242, "num_examples": 70}, {"name": "test", "num_bytes": 8714663, "num_examples": 12032}], "download_size": 121157475, "dataset_size": 8775905}}
| false
|
False
| 2026-01-19T03:18:14
| 431
| 12
| false
|
527feea0afed1de15a8c115abf7be4c912123315
|
MMLU-Pro Dataset
MMLU-Pro dataset is a more robust and challenging massive multi-task understanding dataset tailored to more rigorously benchmark large language models' capabilities. This dataset contains 12K complex questions across various disciplines.
|Github | 🏆Leaderboard | 📖Paper |
🚀 What's New
[2025.01.18] Fixed leading space issue in answer options (affected chemistry, physics, and other STEM subsets). This formatting inconsistency could have been… See the full description on the dataset page: https://huggingface.co/datasets/TIGER-Lab/MMLU-Pro.
| 83,348
| 1,199,480
|
[
"benchmark:official",
"task_categories:question-answering",
"language:en",
"license:mit",
"size_categories:10K<n<100K",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"arxiv:2406.01574",
"doi:10.57967/hf/2439",
"region:us",
"evaluation"
] | 2024-05-08T13:36:21
| null | null |
6655eb19d17e141dcb546ed5
|
HuggingFaceFW/fineweb-edu
|
HuggingFaceFW
|
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| false
|
False
| 2025-07-11T20:16:53
| 953
| 12
| 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.
| 262,345
| 5,942,775
|
[
"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:polars",
"library:mlcroissant",
"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 |
End of preview. Expand
in Data Studio
Changelog
NEW Changes July 25th
- added
baseModelsfield to models which shows the models that the user tagged as base models for that model
Example:
{
"models": [
{
"_id": "687de260234339fed21e768a",
"id": "Qwen/Qwen3-235B-A22B-Instruct-2507"
}
],
"relation": "quantized"
}
NEW Changes July 9th
- Fixed issue with
ggufcolumn with integer overflow causing import pipeline to be broken over a few weeks ✅
NEW Changes Feb 27th
Added new fields on the
modelssplit:downloadsAllTime,safetensors,ggufAdded new field on the
datasetssplit:downloadsAllTimeAdded new split:
paperswhich is all of the Daily Papers
Updated Daily
- Downloads last month
- 6,848
Size of downloaded dataset files:
1.76 GB
Size of the auto-converted Parquet files:
1.76 GB
Number of rows:
4,420,304