_id large_stringlengths 24 24 | id large_stringlengths 4 123 | author large_stringlengths 2 42 | cardData large_stringlengths 2 1.09M | disabled bool 1 class | gated large_stringclasses 3 values | lastModified timestamp[us]date 2021-02-05 16:03:35 2026-02-23 13:13:41 | likes int64 0 9.6k | trendingScore float64 0 84 | private bool 1 class | sha large_stringlengths 40 40 | description large_stringlengths 0 6.67k โ | downloads int64 0 1.88M | downloadsAllTime int64 0 143M | tags listlengths 1 7.92k | createdAt timestamp[us]date 2022-03-02 23:29:22 2026-02-23 13:13:39 | paperswithcode_id large_stringclasses 689 values | citation large_stringlengths 0 10.7k โ |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
698b2c8b4c9e577aa3b1fa16 | nohurry/Opus-4.6-Reasoning-3000x-filtered | nohurry | {"license": "apache-2.0"} | false | False | 2026-02-10T13:06:40 | 94 | 84 | 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.
| 737 | 737 | [
"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 |
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 | 84 | 67 | 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 hasโฆ See the full description on the dataset page: https://huggingface.co/datasets/google/MapTrace. | 924 | 16,590 | [
"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 |
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 | 91 | 53 | 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 โ no setup required.
๐ 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-datasetโฆ See the full description on the dataset page: https://huggingface.co/datasets/uv-scripts/ocr. | 757 | 3,105 | [
"region:us",
"uv-script",
"ocr",
"vision-language-model",
"document-processing",
"hf-jobs"
] | 2025-07-28T08:27:45 | 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 | 88 | 48 | 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:
๐ Paper
๐ ๏ธ Code
๐ฎ Demo
Table 1. Distribution of Test Classes by Document Source
Document Source
Text Present
Textโฆ See the full description on the dataset page: https://huggingface.co/datasets/allenai/olmOCR-bench. | 2,115 | 31,140 | [
"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 |
6993ef463a18b487423bd218 | ronantakizawa/github-top-code | ronantakizawa | {"license": "mit", "task_categories": ["text-generation"], "language": ["code"], "tags": ["code", "github", "source-code", "trending-developers", "software-engineering"], "size_categories": ["1M<n<10M"]} | false | False | 2026-02-23T01:41:46 | 46 | 46 | false | 7e85cf433fa8aac7ba3d3ff2b24b0cfee91a3985 |
GitHub Top Developer Source Code
A curated dataset of 1.3M+ source code files from GitHub's top ranked developers (2015-2025).
This dataset is based on the top ranked developers from this dataset: https://huggingface.co/datasets/ronantakizawa/github-top-developers
Dataset Summary
1.3M+ source code files from repositories across ~4,700 unique developers
80+ programming languages included (Python, JavaScript, TypeScript, Rust, Go, C/C++, Java, and more)
Source code only โโฆ See the full description on the dataset page: https://huggingface.co/datasets/ronantakizawa/github-top-code. | 191 | 191 | [
"task_categories:text-generation",
"language:code",
"license:mit",
"size_categories:1M<n<10M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"region:us",
"code",
"github",
"source-code",
"trending-developers",
"software-engineering"
] | 2026-02-17T04:32:06 | null | null |
69811d0763152b4ea6afd82b | OpenResearcher/OpenResearcher-Dataset | OpenResearcher | {"dataset_info": [{"config_name": "seed_42", "features": [{"name": "qid", "dtype": "int64"}, {"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "messages", "list": [{"name": "channel", "dtype": "string"}, {"name": "content", "list": [{"name": "channel_config", "struct": [{"name": "channel_required", "dtype": "bool"}, {"name": "valid_channels", "list": "string"}]}, {"name": "conversation_start_date", "dtype": "string"}, {"name": "knowledge_cutoff", "dtype": "string"}, {"name": "model_identity", "dtype": "string"}, {"name": "reasoning_effort", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "tools", "struct": [{"name": "browser", "struct": [{"name": "description", "dtype": "string"}, {"name": "name", "dtype": "string"}, {"name": "tools", "list": [{"name": "description", "dtype": "string"}, {"name": "name", "dtype": "string"}, {"name": "parameters", 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"int64"}], "splits": [{"name": "train", "num_bytes": 1150224058, "num_examples": 6102}], "download_size": 474643458, "dataset_size": 1150224058}, {"config_name": "seed_57", "features": [{"name": "qid", "dtype": "int64"}, {"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "messages", "list": [{"name": "channel", "dtype": "string"}, {"name": "content", "list": [{"name": "channel_config", "struct": [{"name": "channel_required", "dtype": "bool"}, {"name": "valid_channels", "list": "string"}]}, {"name": "conversation_start_date", "dtype": "string"}, {"name": "knowledge_cutoff", "dtype": "string"}, {"name": "model_identity", "dtype": "string"}, {"name": "reasoning_effort", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "tools", "struct": [{"name": "browser", "struct": [{"name": "description", "dtype": "string"}, {"name": "name", "dtype": "string"}, {"name": "tools", "list": [{"name": "description", "dtype": "string"}, {"name": "name", "dtype": "string"}, {"name": "parameters", "struct": [{"name": "properties", "struct": [{"name": "cursor", "struct": [{"name": "default", "dtype": "int64"}, {"name": "type", "dtype": "string"}]}, {"name": "id", "struct": [{"name": "default", "dtype": "int64"}, {"name": "type", "list": "string"}]}, {"name": "loc", "struct": [{"name": "default", "dtype": "int64"}, {"name": "type", "dtype": "string"}]}, {"name": "num_lines", "struct": [{"name": "default", "dtype": "int64"}, {"name": "type", "dtype": "string"}]}, {"name": "pattern", "struct": [{"name": "type", "dtype": "string"}]}, {"name": "query", "struct": [{"name": "type", "dtype": "string"}]}, {"name": "source", "struct": [{"name": "type", "dtype": "string"}]}, {"name": "topn", "struct": [{"name": "default", "dtype": "int64"}, {"name": "type", "dtype": "string"}]}, {"name": "view_source", "struct": [{"name": "default", "dtype": "bool"}, {"name": "type", "dtype": "string"}]}]}, {"name": "required", "list": "string"}, {"name": "type", "dtype": "string"}]}]}]}]}, {"name": "type", "dtype": "string"}]}, {"name": "content_type", "dtype": "string"}, {"name": "name", "dtype": "string"}, {"name": "recipient", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "latency_s", "dtype": "float64"}, {"name": "error", "dtype": "null"}, {"name": "attempts", "dtype": "int64"}, {"name": "status", "dtype": "string"}, {"name": "chunk_idx", "dtype": "int64"}, {"name": "num_chunks", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1131677120, "num_examples": 6102}], "download_size": 466987581, "dataset_size": 1131677120}], "configs": [{"config_name": "seed_42", "data_files": [{"split": "train", "path": "seed_42/train-*"}]}, {"config_name": "seed_43", "data_files": [{"split": "train", "path": "seed_43/train-*"}]}, {"config_name": "seed_44", "data_files": [{"split": "train", "path": "seed_44/train-*"}]}, {"config_name": "seed_45", "data_files": [{"split": "train", "path": "seed_45/train-*"}]}, {"config_name": "seed_46", "data_files": [{"split": "train", "path": "seed_46/train-*"}]}, {"config_name": "seed_47", "data_files": [{"split": "train", "path": "seed_47/train-*"}]}, {"config_name": "seed_48", "data_files": [{"split": "train", "path": "seed_48/train-*"}]}, {"config_name": "seed_49", "data_files": [{"split": "train", "path": "seed_49/train-*"}]}, {"config_name": "seed_50", "data_files": [{"split": "train", "path": "seed_50/train-*"}]}, {"config_name": "seed_51", "data_files": [{"split": "train", "path": "seed_51/train-*"}]}, {"config_name": "seed_52", "data_files": [{"split": "train", "path": "seed_52/train-*"}]}, {"config_name": "seed_53", "data_files": [{"split": "train", "path": "seed_53/train-*"}]}, {"config_name": "seed_54", "data_files": [{"split": "train", "path": "seed_54/train-*"}]}, {"config_name": "seed_55", "data_files": [{"split": "train", "path": "seed_55/train-*"}]}, {"config_name": "seed_56", "data_files": [{"split": "train", "path": "seed_56/train-*"}]}, {"config_name": "seed_57", "data_files": [{"split": "train", "path": "seed_57/train-*"}]}], "license": "mit"} | false | False | 2026-02-12T22:23:38 | 88 | 44 | false | 447be0c730619d46e5eab75233a2fdab5eef5316 |
๐ค HuggingFace ๏ฝ
Blog ๏ฝ 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, includingโฆ See the full description on the dataset page: https://huggingface.co/datasets/OpenResearcher/OpenResearcher-Dataset. | 11,132 | 11,132 | [
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"library:mlcroissant",
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] | 2026-02-02T21:54:15 | 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 | 61 | 42 | 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 ย 2Snowflake AI Research ย
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-scaleโฆ See the full description on the dataset page: https://huggingface.co/datasets/Snowflake/AgentWorldModel-1K. | 395 | 395 | [
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"license:cc-by-4.0",
"arxiv:2602.10090",
"region:us",
"agent",
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"reinforcement-learning",
"mcp",
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] | 2026-02-06T00:52:07 | null | null |
699976f2d9b39c5c7980eb37 | FINAL-Bench/Metacognitive | FINAL-Bench | {"language": ["en"], "license": "apache-2.0", "pretty_name": "FINAL Bench \u2014 Functional Metacognitive Reasoning Benchmark", "size_categories": ["n<1K"], "task_categories": ["text-generation", "question-answering"], "tags": ["functional-metacognition", "self-correction", "reasoning", "benchmark", "error-recovery", "declarative-procedural-gap", "cognitive-bias", "TICOS", "AGI-evaluation", "LLM-evaluation", "metacognition"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "FINAL_Bench_100.jsonl"}]}]} | false | False | 2026-02-22T16:25:52 | 39 | 39 | false | 474568d130e25830002dcddbdae4e554f8de8f24 |
FINAL Bench: Functional Metacognitive Reasoning Benchmark
"Not how much AI knows โ but whether it knows what it doesn't know, and can fix it."
Overview
FINAL Bench (Frontier Intelligence Nexus for AGI-Level Verification) is the first comprehensive benchmark for evaluating functional metacognition in Large Language Models (LLMs).
Unlike existing benchmarks (MMLU, HumanEval, GPQA) that measure only final-answer accuracy, FINAL Bench evaluates the entire pipeline of errorโฆ See the full description on the dataset page: https://huggingface.co/datasets/FINAL-Bench/Metacognitive. | 899 | 899 | [
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] | 2026-02-21T09:12:18 | null | null |
698b7cc970fdb3cada1cfedb | TeichAI/Pony-Alpha-15k | TeichAI | nan | false | False | 2026-02-17T00:02:30 | 50 | 38 | 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
| 342 | 342 | [
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"library:mlcroissant",
"region:us"
] | 2026-02-10T18:45:29 | null | null |
6928ac839f54f92be8b78d70 | TeichAI/claude-4.5-opus-high-reasoning-250x | TeichAI | nan | false | False | 2025-11-28T03:02:41 | 281 | 34 | 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
| 5,678 | 15,460 | [
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"modality:text",
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"library:mlcroissant",
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] | 2025-11-27T19:54:43 | 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 | 2026-02-20T15:02:34 | 243 | 32 | false | 7573d2644324bc6ea10586c8c26d781d9618efc7 |
UltraData-Math
๐ค Dataset | ๐ป Source Code | ๐จ๐ณ ไธญๆ README
UltraData-Math is a large-scale, high-quality mathematical pre-training dataset totaling 290B+ tokens across three progressive tiersโL1 (170.5B tokens web corpus), L2 (33.7B tokens quality-selected), and L3 (88B tokens multi-format refined)โdesigned to systematically enhance mathematical reasoning in LLMs. It has been applied to the mathematical pre-training of the MiniCPM Series models.
It was introduced in theโฆ See the full description on the dataset page: https://huggingface.co/datasets/openbmb/UltraData-Math. | 43,992 | 44,028 | [
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] | 2026-01-14T12:17:26 | 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 | 40 | 31 | 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.
Dataโฆ See the full description on the dataset page: https://huggingface.co/datasets/Nanbeige/ToolMind-Web-QA. | 1,732 | 1,732 | [
"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 |
69688350f88007d02e2ed431 | ma-xu/fine-t2i | 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"]} | false | False | 2026-02-20T06:41:40 | 86 | 30 | false | 28fdd5663ee202b5cafc01d6ed08a03f14957854 |
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).
๐ What's New
[2026.02.20]: Fine-T2I reaches the #1 spot among Hugging Face Datasets Trending list โญ๏ธโญ๏ธโญ๏ธ
[2026.02.16]: Fine-T2I tops the Hugging Face Datasets Trending list, reaching the #2 spot and #1โฆ See the full description on the dataset page: https://huggingface.co/datasets/ma-xu/fine-t2i. | 30,339 | 30,402 | [
"task_categories:image-to-text",
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"region:us",
"text",
"image",
"image-generation",
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"image caption"
] | 2026-01-15T06:04:00 | 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 | 34 | 28 | 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
Imageโฆ See the full description on the dataset page: https://huggingface.co/datasets/markov-ai/computer-use. | 164 | 164 | [
"task_categories:robotics",
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"computer-use",
"gui-agent",
"osworld",
"trajectories",
"reinforcement-learning"
] | 2026-02-12T13:15:03 | null | null |
6716146cfc14a25260d39431 | openfoodfacts/product-database | openfoodfacts | {"language": ["en", "fr", "de", "es", "it", "nl", "pl", "pt", "sv", "bg", "ro", "fi", "ru", "nb", "cs", "th", "da", "hr", "hu", "ar", "el", "ja", "ca", "sr", "sl", "sk", "tr", "lt", "zh", "et", "lv", "xx", "uk", "id", "he", "vi", "is", "la", "in", "ko", "sq", "iw", "ka", "ms", "bs", "fa", "bn", "gl", "kk", "mk", "nn", "hi", "aa", "uz", "so", "af", "eu"], "license": ["agpl-3.0", "odbl"], "size_categories": ["1M<n<10M"], "pretty_name": "Open Food Facts Product Database", "dataset_info": {"config_name": "default"}, "configs": [{"config_name": "default", "data_files": [{"split": "food", "path": "food.parquet"}, {"split": "beauty", "path": "beauty.parquet"}]}]} | false | False | 2026-02-22T16:01:32 | 100 | 27 | false | d69e9eeecc7789ce4bbf7f7b988287aee69e649e |
Open Food Facts Database
What is ๐ Open Food Facts?
A food products database
Open Food Facts is a database of food products with ingredients, allergens, nutrition facts and all the tidbits of information we can find on product labels.
Made by everyone
Open Food Facts is a non-profit association of volunteers. 25.000+ contributors like you have added 1.7 million + products from 150 countries using our Android or iPhone app or their camera to scanโฆ See the full description on the dataset page: https://huggingface.co/datasets/openfoodfacts/product-database. | 4,568 | 71,798 | [
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"region:us"
] | 2024-10-21T08:44:28 | 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 | 37 | 27 | 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-evaluatingโฆ See the full description on the dataset page: https://huggingface.co/datasets/commoncrawl/CommonLID. | 240 | 240 | [
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"library:polars",
"library:mlcroissant",
"arxiv:2601.18026",
"region:us",
"text"
] | 2026-02-03T08:44:09 | null | null |
698ec679e8cbdf676dd7322d | deepgenteam/DeepGen-1.0 | deepgenteam | {"license": "apache-2.0"} | false | False | 2026-02-13T10:44:53 | 33 | 27 | false | 7f356c1b94bf9fe4a81147ec9cbfc8705cb022da |
๐ก 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 capabilitiesโgeneral image generation, general image editing, reasoning image generation, reasoning image editing, and text renderingโwithin a single model. Across multiple authoritative benchmarks, DeepGen 1.0 is competitiveโฆ See the full description on the dataset page: https://huggingface.co/datasets/deepgenteam/DeepGen-1.0. | 1,776 | 1,776 | [
"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 |
698ed246c13d5b918cb12f17 | nvidia/PhysicalAI-Kitchen-Assets | nvidia | {"license": "cc-by-4.0", "viewer": false} | false | False | 2026-02-13T08:01:26 | 28 | 25 | 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.
| 442 | 442 | [
"license:cc-by-4.0",
"region:us"
] | 2026-02-13T07:27:02 | 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 | 2026-02-20T21:53:30 | 28 | 23 | false | 01615012a131d16ff5ecdae8ab044dec3b65d4be |
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 containsโฆ See the full description on the dataset page: https://huggingface.co/datasets/allenai/olmix. | 262 | 262 | [
"task_categories:other",
"license:apache-2.0",
"size_categories:n<1K",
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"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 |
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 | 33 | 21 | 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 theโฆ See the full description on the dataset page: https://huggingface.co/datasets/OpenDriveLab-org/Kai0. | 25,161 | 25,314 | [
"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 |
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 | 53 | 21 | false | d106acf808bfa59040b1290d360314d079ef1cb7 |
๐น๐ท Turkish Finance SFT Dataset
Tรผrkรงe Finans Alanฤฑna รzel Supervised Fine-Tuning (SFT) Dataseti
๐ Dataset รzeti
Bu dataset, Tรผrkรงe finans asistanฤฑ LLM'lerin eฤitimi iรงin รถzel olarak tasarlanmฤฑล, kapsamlฤฑ bir Supervised Fine-Tuning (SFT) veri setidir. Kripto para, borsa, teknik analiz, temel analiz, risk yรถnetimi ve finansal regรผlasyonlar dahil olmak รผzere geniล bir yelpazede yaklaลฤฑk 10 milyon token boyutunda soru-cevap รงifti verisi iรงermektedir.
Dataset, hemโฆ See the full description on the dataset page: https://huggingface.co/datasets/AlicanKiraz0/Turkish-Finance-SFT-Dataset. | 241 | 241 | [
"task_categories:question-answering",
"language:tr",
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"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us",
"finance",
"fintech"
] | 2026-02-12T14:59:45 | 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 | 37 | 20 | false | 6b779dc2d9491ed7fdf60ddd862b6ffe82f8f117 | null | 86 | 86 | [
"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 |
67335bb8f014ee49558ef3fe | PleIAs/common_corpus | PleIAs | {"language": ["en", "fr", "de", "zh", "it", "es", "ja", "pl", "la", "nl", "ru", "ar", "ko"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "common_corpus_1/subset_100_1.parquet"}]}]} | false | False | 2026-02-19T13:13:01 | 364 | 19 | false | b78a5c1dfe9a7f2ed3062f27e95a0af510d62ee6 |
Common Corpus
Full paper - ICLR 2026 oral
Common Corpus is the largest open and permissible licensed text dataset, comprising 2.27 trillion tokens (2,267,302,720,836 tokens). It is a diverse dataset, consisting of books, newspapers, scientific articles, government and legal documents, code, and more. Common Corpus has been created by Pleias in association with several partners.
Common Corpus differs from existing open datasets in that it is:
Truly Open: contains only data thatโฆ See the full description on the dataset page: https://huggingface.co/datasets/PleIAs/common_corpus. | 61,930 | 929,748 | [
"language:en",
"language:fr",
"language:de",
"language:zh",
"language:it",
"language:es",
"language:ja",
"language:pl",
"language:la",
"language:nl",
"language:ru",
"language:ar",
"language:ko",
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"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"arxiv:2410.22587",
"region:us"
] | 2024-11-12T13:44:24 | 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 | 63 | 19 | 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.
๐ Key Features
SAGE-10k integrates a wide variety of scenes, and particularly, preserves small items forโฆ See the full description on the dataset page: https://huggingface.co/datasets/nvidia/SAGE-10k. | 14,117 | 14,228 | [
"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 |
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 | 19 | 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%)
Uniqueโฆ See the full description on the dataset page: https://huggingface.co/datasets/OpenMed/Medical-Reasoning-SFT-Mega. | 1,895 | 1,895 | [
"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 |
69843b85036f5289e47c75b2 | zai-org/terminal-bench-2-verified | zai-org | {"license": "apache-2.0"} | false | False | 2026-02-19T07:01:19 | 52 | 18 | false | 0948df7324003d0ed4e0e8d51b16e95135450871 |
Terminal-Bench 2.0 Verified: Instruction & Environment Fix Version
ไธญๆ็ๆฌ
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 runtimeโฆ See the full description on the dataset page: https://huggingface.co/datasets/zai-org/terminal-bench-2-verified. | 5,645 | 5,645 | [
"license:apache-2.0",
"region:us"
] | 2026-02-05T06:41:09 | 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 | 62 | 16 | 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 "internalโฆ See the full description on the dataset page: https://huggingface.co/datasets/galaxyMindAiLabs/stem-reasoning-complex. | 499 | 499 | [
"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 |
698c22cfff4308d71cbe4e5d | GD-ML/IntTravel_dataset | GD-ML | {"task_categories": ["other"], "tags": ["recommendation-system", "poi-recommendation", "mobility", "travel"], "size_categories": ["1B<n<10B"], "configs": [{"config_name": "default", "data_files": [{"split": "interaction", "path": "interaction_1.csv"}, {"split": "user_info", "path": "user_info.csv"}, {"split": "poi_info", "path": "poi_info.csv"}, {"split": "poi_info_groupby_gid", "path": "poi_info_groupby_gid.csv"}]}], "dataset_info": {"features": [{"name": "user_id\ttimestamp\taction_type\tpoi_id\tgeographic_id\tadministrative_region_id\tweather\ttravel_mode\tvia_poi_id", "dtype": "string"}], "splits": [{"name": "interaction", "num_bytes": 2126134566, "num_examples": 41221617}], "download_size": 1535432558, "dataset_size": 2126134566}} | false | False | 2026-02-22T17:04:14 | 66 | 16 | false | 0b6f47087baa05efd011c365ca90cb872eeedeb2 | We are currently in the process of optimizing the data organization and continuously uploading a large-scale dataset. The data currently available is for reference purposes only. The README will be updated once the full dataset has been uploaded. Please stay tuned for updates.
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 recommendationโฆ See the full description on the dataset page: https://huggingface.co/datasets/GD-ML/IntTravel_dataset. | 1,852 | 1,852 | [
"task_categories:other",
"size_categories:1B<n<10B",
"arxiv:2602.11664",
"region:us",
"recommendation-system",
"poi-recommendation",
"mobility",
"travel"
] | 2026-02-11T06:33:51 | null | null |
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๐ท FineWeb
15 trillion tokens of the finest data the ๐ web has to offer
What is it?
The ๐ท 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 ๐ญ datatrove library, our large scale data processing library.
๐ท FineWeb was originally meant to be a fully open replication of ๐ฆ
RefinedWeb, with a releaseโฆ See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceFW/fineweb. | 169,524 | 6,320,303 | [
"task_categories:text-generation",
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"license:odc-by",
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"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 |
6993443f3002cd10f9b949e7 | VLR-CVC/DocVQA-2026 | VLR-CVC | {"task_categories": ["visual-question-answering", "document-question-answering", "image-text-to-text", "question-answering"], "language": ["en"], "tags": ["multimodal", "benchmark", "document-understanding"], "configs": [{"config_name": "default", "data_files": [{"split": "val", "path": "val.parquet"}]}]} | false | False | 2026-02-23T08:34:00 | 15 | 15 | false | 3cf7b9ab362ae4daa9d083d400a33e44d5232cb3 |
DocVQA 2026 | ICDAR2026 Competition on Multimodal Reasoning over Documents in Multiple Domains
Building upon previous DocVQA benchmarks, this evaluation dataset introduces challenging reasoning questions over a diverse collection of documents spanning eight domains, including business reports, scientific papers, slides, posters, maps, comics, infographics, and engineering drawings.By expanding coverage to new document domains and introducing richerโฆ See the full description on the dataset page: https://huggingface.co/datasets/VLR-CVC/DocVQA-2026. | 372 | 372 | [
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"task_categories:document-question-answering",
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"task_categories:question-answering",
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"size_categories:n<1K",
"format:parquet",
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"modality:image",
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"library:datasets",
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"library:polars",
"library:mlcroissant",
"region:us",
"multimodal",
"benchmark",
"document-understanding"
] | 2026-02-16T16:22:23 | 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 | 327 | 14 | false | f5030f44ebe4fdc77e8cc9c6aee2aff1e33944f7 |
Ultra-FineWeb
๐ Ultra-FineWeb Technical Report | ๐ MiniCPM4 Paper | ๐ป GitHub Repository | ๐ MiniCPM4 Project Page
๐ 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, ChineseWebTextโฆ See the full description on the dataset page: https://huggingface.co/datasets/openbmb/Ultra-FineWeb. | 57,726 | 445,264 | [
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"region:us",
"llm",
"pretraining",
"web-corpus",
"data-filtering",
"high-quality"
] | 2025-03-06T05:11:34 | 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": 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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:โฆ See the full description on the dataset page: https://huggingface.co/datasets/allenai/molmospaces. | 212 | 229 | [
"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 |
6982ffd3cbaa03fa2bd18900 | lm-provers/FineProofs-SFT | lm-provers | {"license": "apache-2.0", "task_categories": ["text-generation", "question-answering"], "language": ["en"], "tags": ["math", "reasoning", "olympiad", "proof-generation", "chain-of-thought"], "pretty_name": "FineProofs SFT Dataset", "size_categories": ["1K<n<10K"], "dataset_info": [{"config_name": "all", "features": [{"name": "problem", "dtype": "string"}, {"name": "reasoning_content", "dtype": "string"}, {"name": "proof", "dtype": "string"}, {"name": "category", "dtype": "string"}, {"name": "competition", "dtype": "string"}, {"name": "gemini-3-pro-grade", "dtype": "int64"}, {"name": "qwen3-4b-thinking-reward@128", "dtype": "float64"}, {"name": "source", "dtype": "string"}, {"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 1261177070, "num_examples": 7777}], "download_size": 559037559, "dataset_size": 1261177070}, {"config_name": "default", "features": [{"name": "problem", "dtype": "string"}, {"name": "reasoning_content", "dtype": "string"}, {"name": "proof", "dtype": "string"}, {"name": "category", "dtype": "string"}, {"name": "competition", "dtype": "string"}, {"name": "gemini-3-pro-grade", "dtype": "int64"}, {"name": "qwen3-4b-thinking-reward@128", "dtype": "float64"}, {"name": "source", "dtype": "string"}, {"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 694239300, "num_examples": 4281}], "download_size": 346092746, "dataset_size": 694239300}], "configs": [{"config_name": "all", "data_files": [{"split": "train", "path": "all/train-*"}]}, {"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | false | False | 2026-02-14T14:01:35 | 16 | 14 | false | 73661e62811cf2940a0d3f82788a4f4332204c2f |
FineProofs SFT
Dataset Description
FineProofs SFT is a high-quality supervised fine-tuning dataset containing mathematical Olympiad problems paired with chain-of-thought reasoning and formal proofs distilled from DeepSeek-Math-V2. The dataset comprises 7,777 samples (4,300 unique problems) sourced from international Olympiad competitions and Art of Problem Solving (AoPS), each annotated with:
Detailed reasoning traces (thinking content) generated byโฆ See the full description on the dataset page: https://huggingface.co/datasets/lm-provers/FineProofs-SFT. | 142 | 142 | [
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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 | 37 | 14 | 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.
๐ 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.
๐๏ธ Data Processing Pipeline
The L3 refinement process transforms raw web textโฆ See the full description on the dataset page: https://huggingface.co/datasets/openbmb/Ultra-FineWeb-L3. | 2,214 | 2,214 | [
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] | 2026-02-07T05:19:29 | null | null |
69932857f73a8ccc1e28ae4a | ajibawa-2023/Java-Code-Large | ajibawa-2023 | {"license": "mit", "task_categories": ["text-generation"], "language": ["en"], "tags": ["code", "java"], "size_categories": ["10M<n<100M"]} | false | False | 2026-02-16T17:49:08 | 14 | 14 | false | be47bf9c12862c51ea21b272596fca301ebe4712 | Java-Code-Large
Java-Code-Large is a large-scale corpus of publicly available Java source code comprising more than 15 million java codes. The dataset is designed to support research in large language model (LLM) pretraining, code intelligence, software engineering automation, and program analysis.
By providing a high-volume, language-specific corpus, Java-Code-Large enables systematic experimentation in Java-focused model training, domain adaptation, and downstream code understanding tasks.โฆ See the full description on the dataset page: https://huggingface.co/datasets/ajibawa-2023/Java-Code-Large. | 767 | 767 | [
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6996f9d7c633a08f9cec0674 | Solenopsisbot/real-slop | Solenopsisbot | {"license": "mit", "task_categories": ["text-generation"], "language": ["en"], "pretty_name": "Real Slop"} | false | False | 2026-02-21T02:02:48 | 14 | 14 | false | 868e146adc54a76862b936a9774697fa0cd79e50 |
REAL SLOP
About
This is a dataset containing 155k entries of real llm interactions across a variety of models.
Privacy and Filtering
This dataset was created with users knowing that all requests they sent would be monitored and logged. This dataset has undergone pretty heaving filtering of PII, maybe too much but oh well better safe than sorry.
Tools
Yeah so funny story... I kind of wasn't logging tool defenitions until partway into collecting thisโฆ See the full description on the dataset page: https://huggingface.co/datasets/Solenopsisbot/real-slop. | 83 | 96 | [
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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 | 298 | 13 | 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 systemsโ 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 originalโฆ See the full description on the dataset page: https://huggingface.co/datasets/princeton-nlp/SWE-bench_Verified. | 579,611 | 8,660,250 | [
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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": 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"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 | 42 | 13 | 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 โ26% to โ46% across subtasks), underscoring continued room for improvement.โฆ See the full description on the dataset page: https://huggingface.co/datasets/futurehouse/labbench2. | 1,632 | 1,632 | [
"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 |
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 | 53 | 12 | false | 3a319838f8136a6f3f351c7a21293b51e95663c5 |
SoulX-FlashHead: Oracle-guided Generation of Infinite Real-time Streaming Talking Heads
Tan Yu*, Qian Qiao*โ, Le Shen*, Ke Zhou, Jincheng Hu, Dian Sheng, Bo Hu, Haoming Qin, Jun Gao, Changhai Zhou, Shunshun Yin, Siyuan Liu โ
*Equal Contribution
โCorresponding Author
VividHead Dataset
Highlights
๐ฅ Large-scale, high-quality talking-head dataset with 330K clips and 782 hours of head-cropped videos
๐ฅ Broad diversity across 15+ languages and a wide age rangeโฆ See the full description on the dataset page: https://huggingface.co/datasets/Soul-AILab/VividHead. | 3,931 | 3,931 | [
"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 |
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 | 12 | 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/
โโโ meta/ # Metadata files describing the dataset
โ โโโ info.jsonโฆ See the full description on the dataset page: https://huggingface.co/datasets/nvidia/PhysicalAI-Robotics-Kitchen-Sim-Demos. | 834 | 834 | [
"task_categories:robotics",
"license:cc-by-4.0",
"region:us",
"robotics"
] | 2026-02-10T01:20:00 | null | null |
656523d6bfb751371817c448 | Idavidrein/gpqa | Idavidrein | {"license": "cc-by-4.0", "viewer": true, "extra_gated_prompt": "You agree to NOT reveal examples from this dataset in plain text or images online, to reduce the risk of leakage into foundation model training corpora.", "extra_gated_fields": {"I accept these terms": "checkbox"}, "configs": [{"config_name": "gpqa_extended", "data_files": "gpqa_extended.csv"}, {"config_name": "gpqa_main", "data_files": "gpqa_main.csv"}, {"config_name": "gpqa_diamond", "data_files": "gpqa_diamond.csv"}, {"config_name": "gpqa_experts", "data_files": "gpqa_experts.csv"}], "task_categories": ["question-answering", "text-generation"], "language": ["en"], "tags": ["open-domain-qa", "open-book-qa", "multiple-choice-qa"], "pretty_name": "GPQA", "size_categories": ["n<1K"]} | false | auto | 2026-01-22T19:14:08 | 368 | 10 | false | 5233cd1db58884ed0bf678c7c6be731722a23f84 |
Dataset Card for GPQA
GPQA is a multiple-choice, Q&A dataset of very hard questions written and validated by experts in biology, physics, and chemistry. When attempting questions out of their own domain (e.g., a physicist answers a chemistry question), these experts get only 34% accuracy, despite spending >30m with full access to Google.
We request that you do not reveal examples from this dataset in plain text or images online, to reduce the risk of leakage into foundation modelโฆ See the full description on the dataset page: https://huggingface.co/datasets/Idavidrein/gpqa. | 85,128 | 1,367,815 | [
"benchmark:official",
"task_categories:question-answering",
"task_categories:text-generation",
"language:en",
"license:cc-by-4.0",
"size_categories:1K<n<10K",
"format:csv",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"arxiv:2311.12022",
"region:us",
"open-domain-qa",
"open-book-qa",
"multiple-choice-qa"
] | 2023-11-27T23:18:46 | 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 | 2026-01-20T22:42:17 | 713 | 10 | false | 5a81a4c7271a2a2a312b9a690f0c2fde837e4c29 |
[!NOTE]
IMPORTANT: Please help us protect the integrity of this benchmark by not publicly sharing, re-uploading, or distributing the dataset.
Humanity's Last Exam
๐ Website | ๐ 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 ofโฆ See the full description on the dataset page: https://huggingface.co/datasets/cais/hle. | 33,943 | 179,019 | [
"benchmark:official",
"license:mit",
"size_categories:1K<n<10K",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us"
] | 2025-01-23T08:24:27 | null | null |
693e6fcdaae4bb2c40f58333 | teyler/epstein-files-20k | teyler | {"tags": ["epstein", "public-records", "government-documents", "legal-documents", "research", "information-retrieval"]} | false | False | 2025-12-14T08:05:35 | 18 | 10 | false | 1e669c107a8351eed3f28e99e727249d40b393ea |
Disclaimer
This dataset is a reupload of a previously circulating public dataset. The contents may include unverified, incomplete, disputed, or inaccurate information and should not be interpreted as factual, authoritative, or as proof of guilt for any individual.
This dataset is provided strictly for research, archival, and educational purposes, such as analysis of information propagation, data preservation, or media studies.
No claims are made regarding the accuracy, authenticityโฆ See the full description on the dataset page: https://huggingface.co/datasets/teyler/epstein-files-20k. | 1,246 | 1,356 | [
"size_categories:1M<n<10M",
"format:text",
"modality:text",
"library:datasets",
"library:mlcroissant",
"region:us",
"epstein",
"public-records",
"government-documents",
"legal-documents",
"research",
"information-retrieval"
] | 2025-12-14T08:05:33 | null | null |
69836757bbb0f79b9472304c | perplexity-ai/draco | perplexity-ai | {"license": "mit", "language": ["en"], "tags": ["deep-research"], "pretty_name": "DRACO Benchmark"} | false | False | 2026-02-20T23:02:24 | 71 | 10 | false | ce076749809027649ebd331bcb70f42bf720d387 |
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 analysisโฆ See the full description on the dataset page: https://huggingface.co/datasets/perplexity-ai/draco. | 9,593 | 9,593 | [
"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 |
699043a63c019221cf01e9f1 | nvidia/PhysicalAI-Robotics-GR00T-Teleop-GR1 | nvidia | {"license": "cc-by-nc-4.0"} | false | False | 2026-02-14T15:30:12 | 10 | 10 | false | 9f68429ad12535d30d1aae33b7945c79c830c8c4 |
Introduction
TL;DR: DreamDojo is a generalist robot world model pretrained on 44k hours of human egocentric data, showing unprecedented generalization to diverse objects and environments.
Project page: https://dreamdojo-world.github.io/
Paper: https://arxiv.org/abs/2602.06949
Code: https://github.com/NVIDIA/DreamDojo
How to Use
Check out https://github.com/NVIDIA/DreamDojo
Citation
@article{gao2026dreamdojo,
title={DreamDojo: A Generalist Robotโฆ See the full description on the dataset page: https://huggingface.co/datasets/nvidia/PhysicalAI-Robotics-GR00T-Teleop-GR1. | 2,147 | 2,147 | [
"license:cc-by-nc-4.0",
"size_categories:1M<n<10M",
"format:parquet",
"modality:tabular",
"modality:video",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"arxiv:2602.06949",
"region:us"
] | 2026-02-14T09:43:02 | null | null |
69935b413883cdc4e00959a0 | aprilrobotics/sample | aprilrobotics | {"license": "cc-by-4.0"} | false | False | 2026-02-17T12:11:33 | 10 | 10 | false | 2d6142d7215362b5a1a71c53dc689f3b5c262462 |
April Robotics Data Sample
This dataset contains 3D ground truth hand and finger annotations combined with egocentric and wrist recordings of industrial assembly operations captured in an active manufacturing environment. The data showcases our multimodal capture system consisting of head and wrist-mounted cameras and a sensorized glove that tracks high-quality, precise human hand motion and manipulation.
Dataset Summary
Property
Value
Total episodes
6
Framesโฆ See the full description on the dataset page: https://huggingface.co/datasets/aprilrobotics/sample. | 482 | 482 | [
"license:cc-by-4.0",
"size_categories:n<1K",
"modality:video",
"library:datasets",
"library:mlcroissant",
"region:us"
] | 2026-02-16T18:00:33 | null | null |
63990f21cc50af73d29ecfa3 | fka/prompts.chat | fka | {"license": "cc0-1.0", "tags": ["ChatGPT", "prompts", "AI", "GPT", "Claude", "Gemini", "Llama", "Mistral", "LLM", "prompt-engineering", "conversational-ai", "text-generation", "chatbot", "awesome-list"], "task_categories": ["question-answering", "text-generation"], "size_categories": ["100K<n<1M"]} | false | False | 2026-02-23T03:54:51 | 9,595 | 9 | false | fe7700b1638aa5eaff641ef89d018c852388eed9 |
a.k.a. Awesome ChatGPT Prompts
This is a Dataset Repository mirror of prompts.chat โ a social platform for AI prompts.
๐ข Notice
This Hugging Face dataset is a mirror. For the latest prompts, features, and community contributions, please visit:
๐ Website: prompts.chat
๐ฆ GitHub: github.com/f/awesome-chatgpt-prompts
About
prompts.chat is an open-source platform where users can share, discover, and collect AI prompts from the community. The project can beโฆ See the full description on the dataset page: https://huggingface.co/datasets/fka/prompts.chat. | 18,802 | 454,770 | [
"task_categories:question-answering",
"task_categories:text-generation",
"license:cc0-1.0",
"size_categories:1K<n<10K",
"format:csv",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us",
"ChatGPT",
"prompts",
"AI",
"GPT",
"Claude",
"Gemini",
"Llama",
"Mistral",
"LLM",
"prompt-engineering",
"conversational-ai",
"text-generation",
"chatbot",
"awesome-list"
] | 2022-12-13T23:47:45 | 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 | 134 | 9 | 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 lyricsโฆ See the full description on the dataset page: https://huggingface.co/datasets/nyuuzyou/suno. | 388 | 2,648 | [
"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 |
698f5e863a18b48742f06553 | MathArena/aime_2026 | MathArena | {"dataset_info": {"features": [{"name": "problem_idx", "dtype": "int64"}, {"name": "answer", "dtype": "int64"}, {"name": "problem", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 12291, "num_examples": 30}], "download_size": 10065, "dataset_size": 12291}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "license": "cc-by-nc-sa-4.0", "language": ["en"], "pretty_name": "AIME 2026", "size_categories": ["n<1K"]} | false | False | 2026-02-16T09:50:26 | 11 | 9 | false | 10b4e45b7a503075d4da8a0d57916a4f06ce6bd2 |
Homepage and repository
Homepage: https://matharena.ai/
Repository: https://github.com/eth-sri/matharena
Dataset Summary
This dataset contains the questions from AIME 2026 used for the MathArena Leaderboard
Data Fields
Below one can find the description of each field in the dataset.
problem_idx (int): Index of the problem in the competition
problem (str): Full problem statement
answer (str): Ground-truth answer to the question
Source Data
Theโฆ See the full description on the dataset page: https://huggingface.co/datasets/MathArena/aime_2026. | 673 | 673 | [
"benchmark:official",
"benchmark:eval-yaml",
"language:en",
"license:cc-by-nc-sa-4.0",
"size_categories:n<1K",
"format:parquet",
"format:optimized-parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us"
] | 2026-02-13T17:25:26 | null | null |
6991e2cd8be5bf8321a86939 | Zchu/REDSearcher_SFT_10K | Zchu | {"dataset_info": {"features": [{"name": "meta", "struct": [{"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "language", "dtype": "string"}]}, {"name": "messages", "list": [{"name": "role", "dtype": "string"}, {"name": "content", "dtype": "string"}]}, {"name": "system_prompt", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2152970840, "num_examples": 10001}], "download_size": 1017289914, "dataset_size": 2152970840}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | false | False | 2026-02-15T15:15:31 | 9 | 9 | false | a747c4b5ca7eb8f124b49f2503176c1b821ab035 | null | 167 | 167 | [
"size_categories:10K<n<100K",
"format:parquet",
"format:optimized-parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"region:us"
] | 2026-02-15T15:14:21 | null | null |
6995eab429908127acb95001 | ajibawa-2023/JavaScript-Code-Large | ajibawa-2023 | {"license": "mit", "task_categories": ["text-generation"], "language": ["en"], "tags": ["code", "javascript"], "size_categories": ["1M<n<10M"]} | false | False | 2026-02-18T20:04:18 | 9 | 9 | false | d78dea8c5dd737b71c7024e0e4b2ed82bee4b436 | JavaScript-Code-Large
JavaScript-Code-Large is a large-scale corpus of JavaScript source code comprising around 5 million JavaScript files. The dataset is designed to support research in large language model (LLM) pretraining, code intelligence, software engineering automation, and program analysis for the JavaScript ecosystem.
By providing a high-volume, language-specific corpus, JavaScript-Code-Large enables systematic experimentation in JavaScript-focused model training, domain adaptationโฆ See the full description on the dataset page: https://huggingface.co/datasets/ajibawa-2023/JavaScript-Code-Large. | 1,250 | 1,250 | [
"task_categories:text-generation",
"language:en",
"license:mit",
"size_categories:1M<n<10M",
"format:json",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"region:us",
"code",
"javascript"
] | 2026-02-18T16:37:08 | null | null |
65d8078da3c18e931627f12d | m-a-p/Code-Feedback | m-a-p | {"language": ["en"], "pipeline_tag": "text-generation", "tags": ["code"], "license": "apache-2.0", "task_categories": ["question-answering"], "size_categories": ["10K<n<100K"]} | false | False | 2024-02-26T05:45:12 | 226 | 8 | false | f411b16a97c910ac9acf8b0d0948e340aa77cc34 | OpenCodeInterpreter: Integrating Code Generation with Execution and Refinement
[๐ Homepage]
|
[๐ ๏ธCode]
Introduction
OpenCodeInterpreter is a family of open-source code generation systems designed to bridge the gap between large language models and advanced proprietary systems like the GPT-4 Code Interpreter. It significantly advances code generation capabilities by integrating execution and iterative refinement functionalities.
For further information and relatedโฆ See the full description on the dataset page: https://huggingface.co/datasets/m-a-p/Code-Feedback. | 1,205 | 27,157 | [
"task_categories:question-answering",
"language:en",
"license:apache-2.0",
"size_categories:10K<n<100K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2402.14658",
"region:us",
"code"
] | 2024-02-23T02:48:45 | 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 | 433 | 8 | 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 | ๐Leaderboard | ๐Paper |
๐ 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 beenโฆ See the full description on the dataset page: https://huggingface.co/datasets/TIGER-Lab/MMLU-Pro. | 82,330 | 1,207,552 | [
"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 |
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Changelog
NEW Changes July 25th
- added
baseModelsfield to models which shows the models that the user tagged as base models for that model
Example:
{
"models": [
{
"_id": "687de260234339fed21e768a",
"id": "Qwen/Qwen3-235B-A22B-Instruct-2507"
}
],
"relation": "quantized"
}
NEW Changes July 9th
- Fixed issue with
ggufcolumn with integer overflow causing import pipeline to be broken over a few weeks โ
NEW Changes Feb 27th
Added new fields on the
modelssplit:downloadsAllTime,safetensors,ggufAdded new field on the
datasetssplit:downloadsAllTimeAdded new split:
paperswhich is all of the Daily Papers
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Size of downloaded dataset files:
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Number of rows:
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