_id large_stringlengths 24 24 | id large_stringlengths 5 123 | author large_stringlengths 2 42 | cardData large_stringlengths 2 2.4M ⌀ | disabled bool 1
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values | lastModified timestamp[us]date 2021-02-05 16:03:35 2026-09-30 13:18:21 | likes int64 0 9.85k | trendingScore float64 0 552 | private bool 1
class | sha large_stringlengths 40 40 | description large_stringlengths 0 6.67k ⌀ | downloads int64 0 4.4M | downloadsAllTime int64 0 143M | mainSize float64 0 306,846B ⌀ | tags listlengths 1 7.92k | createdAt timestamp[us]date 2022-03-02 23:29:22 2026-09-30 13:17:16 | paperswithcode_id large_stringclasses 719
values | citation large_stringlengths 0 10.7k ⌀ |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
6ab65a7f909092518dca860f | XiaomiMiMo/MiMo-V2.6-RL-oss | XiaomiMiMo | {"license": "apache-2.0", "configs": [{"config_name": "code", "data_files": "code.parquet"}, {"config_name": "cyber", "data_files": "cyber.parquet"}, {"config_name": "general", "data_files": "general/train.parquet"}, {"config_name": "webdev", "data_files": "webdev.parquet"}, {"config_name": "music", "data_files": "musi... | false | False | 2026-09-26T01:40:41 | 565 | 552 | false | 639865fd3374018d6cb29b9fb82dd531406fcf5f |
Agentic RL Environments
RL training environments for LLM agents.
Domain
Task Family
Verifier
Code
Software engineering
Executable tests
Cyber
Vulnerability reproduction
Rule checks
General
Knowledge work
Rubric-based judging
Visual
Web development
Visual grading
Music
Symbolic music co... | 47,819 | 47,819 | 12,102,484,722 | [
"license:apache-2.0",
"size_categories:1K<n<10K",
"format:parquet",
"modality:document",
"modality:image",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us"
] | 2026-09-25T11:26:55 | null | null |
6aa321e46caea5109a90c179 | secemp9/arxiv-complete | secemp9 | {"license": "other", "license_name": "mixed-arxiv-author-licenses", "license_link": "LICENSE", "pretty_name": "arXiv Complete Corpus", "language": ["en"], "task_categories": ["text-generation", "text-retrieval"], "tags": ["arxiv", "scientific-papers", "latex", "preprints", "full-text"], "size_categories": ["10M<n<100M"... | false | False | 2026-09-19T20:39:46 | 513 | 96 | false | cee894837962fede5612cccf2a4c7cacf49b4c3a |
arXiv Complete Corpus
A snapshot of arXiv's metadata, version history, submission files and rendered
documents. It covers 3,148,796 papers and includes file contents, paths, sizes
and SHA-256 digests. Metadata comes from arXiv's OAI-PMH arXivRaw interface;
files come from the GCS mirror, S3 source archiv... | 114,683 | 114,683 | 16,076,057,281,538 | [
"task_categories:text-generation",
"task_categories:text-retrieval",
"language:en",
"license:other",
"size_categories:100M<n<1B",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"arxiv:2401.18030",
"region:... | 2026-09-10T21:32:20 | null | null |
6a981c1f3a639ff95e1342fa | MoreThought/Fable-5.1-Max-Reasoning-Filtered-10000x | MoreThought | {"license": "apache-2.0", "task_categories": ["text-generation", "question-answering"], "language": ["en"], "pretty_name": "The First And Best Fable 5.1 Reasoning Data", "tags": ["fable 5.1", "coding", "synthetic", "thinking", "think", "reason", "reasoning", "distill", "distillation", "agent", "agentic", "SFT", "CoT", ... | false | False | 2026-09-24T14:27:20 | 219 | 77 | false | 602d49c99c4af5530cc02327dd2d90827c84292a |
Dataset Description
This dataset contains 10,000 agentic coding and reasoning multi-turn high-quality traces generated by the new Fable 5.1 model using max reasoning effort.
It holds almost 500,000,000 tokens of step-by-step chain-of-thought programming across multiple complex domains.
It has also been ... | 4,177 | 4,177 | 2,594,907,881 | [
"task_categories:text-generation",
"task_categories:question-answering",
"language:en",
"license:apache-2.0",
"size_categories:10K<n<100K",
"format:json",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us",
"fable 5.1",
"coding",
"synthetic",
"thin... | 2026-09-02T12:52:47 | null | null |
6ab831e64edba2b438670785 | nisten/opus5-5-doctor-patient-conversations-all-human-diseases | nisten | {"license": "apache-2.0", "language": ["en"], "task_categories": ["question-answering", "text-generation"], "tags": ["medical", "healthcare", "clinical", "synthetic", "doctor-patient", "chatml", "rag", "conversational"], "size_categories": ["1K<n<10K"], "pretty_name": "Doctor-Patient Conversations \u2014 All Human Dise... | false | False | 2026-09-27T16:02:46 | 77 | 77 | false | 9277244e642a8ba02cb8c1d26408e5792932045b |
Opus-5.5 generated Doctor-Patient Conversations for All Human Diseases
The sequel to nisten/opus-doctor-patient-conversations-all-human-diseases (Opus 4.8). Same disease list, same 20-key schema, same ChatML conversations — regenerated from scratch with Claude Opus 5.5, one agent per disease, and held to... | 577 | 577 | 75,466,355 | [
"task_categories:question-answering",
"task_categories:text-generation",
"language:en",
"license:apache-2.0",
"size_categories:1K<n<10K",
"region:us",
"medical",
"healthcare",
"clinical",
"synthetic",
"doctor-patient",
"chatml",
"rag",
"conversational"
] | 2026-09-26T20:58:14 | null | null |
6a34c6065edc6bacb0b36213 | espnet/yodas3 | espnet | {"license": "cc-by-3.0", "task_categories": ["audio-to-audio", "automatic-speech-recognition", "text-to-speech", "translation"], "dataset_info": [{"config_name": "preview", "features": [{"name": "audio", "dtype": "audio"}, {"name": "lang", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "shard", "dtype"... | false | False | 2026-09-30T08:40:08 | 77 | 73 | false | b7ce8c5660698c0ec39545fa5db2217213189d1d |
YODAS v3
Paper
YODAS v3 is a large web-crawled dataset containing over 1.1 million hours of audio that were originally released under a CC-BY-3.0 license. The dataset contains audio in over 100 languages. YODAS v3 can be used for a variety of multi-modal tasks, including Automatic Speech Recognition, Tex... | 32,918 | 32,918 | 55,601,473,635,770 | [
"task_categories:audio-to-audio",
"task_categories:automatic-speech-recognition",
"task_categories:text-to-speech",
"task_categories:translation",
"license:cc-by-3.0",
"region:us"
] | 2026-06-19T04:31:02 | null | null |
621ffdd236468d709f184284 | wikimedia/wikipedia | wikimedia | {"language": ["ab", "ace", "ady", "af", "alt", "am", "ami", "an", "ang", "anp", "ar", "arc", "ary", "arz", "as", "ast", "atj", "av", "avk", "awa", "ay", "az", "azb", "ba", "ban", "bar", "bbc", "bcl", "be", "bg", "bh", "bi", "bjn", "blk", "bm", "bn", "bo", "bpy", "br", "bs", "bug", "bxr", "ca", "cbk", "cdo", "ce", "ceb"... | false | False | 2024-01-09T09:40:51 | 1,599 | 64 | false | b04c8d1ceb2f5cd4588862100d08de323dccfbaa |
Dataset Card for Wikimedia Wikipedia
Dataset Summary
Wikipedia dataset containing cleaned articles of all languages.
The dataset is built from the Wikipedia dumps (https://dumps.wikimedia.org/)
with one subset per language, each containing a single train split.
Each example contains the co... | 277,034 | 3,269,683 | 71,792,022,791 | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"language:ab",
"language:ace",
"language:ady",
"language:af",
"language:alt",
"language:am",
"language:ami",
"language:an",
"language:ang",
"language:anp",
"... | 2022-03-02T23:29:22 | null | null |
6ab4f16cb386daf00b9d61ee | FineEnvs/SmolDataEnvs | FineEnvs | {"license": "mit", "task_categories": ["question-answering", "table-question-answering"], "tags": ["smoldataenvs", "data-analysis", "agent", "rl-environment", "reinforcement-learning", "code-agent"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "pat... | false | False | 2026-09-30T05:24:11 | 63 | 63 | false | 6c439f075cf550793b1bfd7b887a41e51f3b23ab |
📈 SmolDataEnvs
5.5K+ RL tasks for hill-climbing small models in code and data science.
A 2B model on these tasks. Left: what it optimises. Right: 144 held-out tasks it never trains on.
Two runs over the same 5,000 tasks: shuffled against a curriculum ordered easiest to hardest.
Data-anal... | 3,375 | 3,375 | 4,436,042 | [
"task_categories:question-answering",
"task_categories:table-question-answering",
"license:mit",
"size_categories:1K<n<10K",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us",
"smoldataenvs",
"d... | 2026-09-24T09:46:20 | null | null |
6aaa4864f37373a4ce11d91a | LocalLLaMA/typed-decisions | LocalLLaMA | {"license": "apache-2.0", "language": ["en"], "pretty_name": "Typed Decisions", "size_categories": ["n<1K"], "task_categories": ["text-classification"], "tags": ["structured-decisions", "calibration", "probabilistic-classification", "system-one", "workflow-evaluation", "synthetic"], "configs": [{"config_name": "agent_t... | false | False | 2026-09-30T03:27:01 | 73 | 56 | false | e135720c8fdff7896a4e4068cff624a899d41597 |
Typed Decisions
A benchmark for typed probabilistic decisions. A model gets one piece of
unstructured state and answers five typed questions about it at once, and every
answer is a probability distribution, not a single label.
The schema follows the System One primitives (noul, choice, score) used by
Typ... | 19,901 | 19,901 | 2,167,016 | [
"task_categories:text-classification",
"language:en",
"license:apache-2.0",
"size_categories:1K<n<10K",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us",
"structured-decisions",
"calibration",
... | 2026-09-16T07:42:28 | null | null |
6aad2d99c1832da6f69ad2e0 | Harland/OmniVChat | Harland | {"license": "cc-by-nc-nd-4.0", "task_categories": ["video-text-to-text"], "language": ["en", "zh"], "tags": ["audio-visual", "omni", "benchmark", "rubric", "reinforcement-learning"], "size_categories": ["1K<n<10K"], "configs": [{"config_name": "single_turn", "data_files": "data/single_turn.jsonl"}, {"config_name": "mul... | false | False | 2026-09-21T04:52:52 | 50 | 45 | false | 96a77b5cc67f48d52996e7594756d6156681dae6 |
OmniVChat: Synthesizing, Benchmarking, and Training for Native Audio-Visual Dialogue
1 The Chinese University of Hong Kong 2 Alibaba Token Hub, Alibaba Group
3 Shanghai Jiao Tong University 4 Shanghai Innovation Institute 5 Zhejiang University
OmniVChat (Omni Video Chat) is the... | 3,632 | 3,632 | 29,707,414,986 | [
"task_categories:video-text-to-text",
"language:en",
"language:zh",
"license:cc-by-nc-nd-4.0",
"size_categories:1K<n<10K",
"format:json",
"modality:text",
"modality:video",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"arxiv:2609.21465",
"region:us",
"au... | 2026-09-18T12:24:57 | null | null |
6a5466cdd7c4631ab9b28b80 | genrobot2025/Gen-HumanEgo | genrobot2025 | "{\"license\": \"cc-by-sa-4.0\", \"task_categories\": [\"robotics\"], \"language\": [\"en\"], \"tags(...TRUNCATED) | false | auto | 2026-09-25T04:29:13 | 45 | 40 | false | 3cca54aeba0d9af3ebf4995b035d23b41f9387da | "\n\t\n\t\t\n\t\n\t\n\t\tGen-HumanEgo\n\t\n\n\n \n\n\n1,800+ hours of egocentric human demonstratio(...TRUNCATED) | 266,616 | 267,079 | 62,035,012,405,416 | ["task_categories:robotics","language:en","license:cc-by-sa-4.0","size_categories:n>1T","region:us",(...TRUNCATED) | 2026-07-13T04:17:17 | null | null |
End of preview. Expand in Data Studio
Changelog
NEW Changes March 11th 2026
- Added new split:
arxiv_papers, sourced from the Hugging Face/api/papersendpoint paperscontinues to point todaily_papers.parquet, which is the Daily Papers feed
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
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