_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 2026-06-27 13:16:21 | likes int64 0 9.75k | trendingScore float64 0 106 | private bool 1
class | sha large_stringlengths 40 40 | description large_stringlengths 0 6.67k ⌀ | downloads int64 0 3.02M | downloadsAllTime int64 0 143M | mainSize float64 0 306,846B ⌀ | tags listlengths 1 7.92k | createdAt timestamp[us]date 2022-03-02 23:29:22 2026-06-27 13:16:21 | paperswithcode_id large_stringclasses 712
values | citation large_stringlengths 0 10.7k ⌀ |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
6a2cd0828137fb18cecbcc06 | Glint-Research/Fable-5-traces | Glint-Research | {"license": "agpl-3.0", "pretty_name": "Fable 5 Pi Agent Traces", "annotations_creators": ["machine-generated"], "language": ["en"], "size_categories": ["1K<n<10K"], "task_categories": ["text-generation"], "tags": ["agent-traces", "pi-agent", "claude-code", "fable-5", "chain-of-thought", "tool-use", "coding-agents", "s... | false | False | 2026-06-19T01:28:44 | 429 | 106 | false | 3e6e668a6674427a595d3719b716adb2496946a2 |
Glint Research Dataset Card
Fable 5 Pi Agent Traces
A compact, high-signal corpus of Fable 5 coding-agent traces converted into Hugging Face Agent Traces / Pi-compatible sessions for Data Studio inspection, tool-use policy learning, and reasoning/action distillation.
... | 33,015 | 33,015 | 187,507,932 | [
"task_categories:text-generation",
"annotations_creators:machine-generated",
"language:en",
"license:agpl-3.0",
"size_categories:1K<n<10K",
"format:json",
"format:agent-traces",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
... | 2026-06-13T03:37:38 | null | null |
6a2a47c4f5ff6c6dee016974 | armand0e/claude-fable-5-claude-code | armand0e | {"pretty_name": "claude-fable-5 Agent Traces", "task_categories": ["text-generation"], "tags": ["agent-traces", "format:agent-traces", "claude", "distillation", "claude-fable-5", "teich"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "*.jsonl"}]}]} | false | False | 2026-06-19T16:23:10 | 217 | 50 | false | c19fb6831700da833b22d1c9cdac47fe8603685c |
claude-fable-5 Agent Traces
It's worth noting that our team was working with Glint-Research to collect as much fable data as possible.
These are just the anonymized raw traces of both of our teams combined. This means that Glint-Research/Fable-5-traces was created from formatting and splitting up this sa... | 11,362 | 11,362 | 75,140,629 | [
"task_categories:text-generation",
"size_categories:n<1K",
"format:json",
"format:agent-traces",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"region:us",
"agent-traces",
"format:agent-traces",
"claude",
"distillation",... | 2026-06-11T05:29:40 | null | null |
69fa9e0468659d62c5c9df7b | LocalLaws/LOCUS-v1 | LocalLaws | {"language": ["en"], "license": "cc-by-nc-4.0", "size_categories": ["1M<n<10M"], "task_categories": ["text-classification"], "pretty_name": "LOCUS v1.0", "tags": ["law", "legal-nlp", "local-government", "municipal-law", "ordinances", "classification"], "configs": [{"config_name": "default", "data_files": [{"split": "tr... | false | False | 2026-06-20T03:06:10 | 60 | 49 | false | 4cee954ca8ad8e31cb0502dff6682c87b74b4302 |
LOCUS v1.0
This repository contains the dataset presented in the paper Freeing the Law with LOCUS: A Local Ordinance Corpus for the United States.
Dataset Summary
LOCUS v1.0 is a chunk-level dataset of U.S. municipal and county law text labeled by legal function. Each eligible chunk is ass... | 1,557 | 1,813 | 1,765,908,117 | [
"task_categories:text-classification",
"language:en",
"license:cc-by-nc-4.0",
"size_categories:1M<n<10M",
"format:parquet",
"format:optimized-parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"arxiv:2606.19334",
"reg... | 2026-05-06T01:48:52 | null | null |
6a394ba974e3ccb07645f8a7 | Qwen/AgentWorldBench | Qwen | {"license": "apache-2.0", "task_categories": ["text-generation"], "language": ["en"], "tags": ["world-model", "agent", "benchmark", "evaluation", "environment-simulation", "qwen"], "size_category": "1K<n<10K"} | false | False | 2026-06-24T02:05:57 | 39 | 37 | false | db74b0cca6f7dd41b9684b18a3633d13f2bbf783 |
AgentWorldBench
AgentWorldBench is a comprehensive evaluation benchmark for language world models, constructed from real-world observations of frontier model trajectories on established benchmarks such as Tool Decathlon, Terminal-Bench 1.0 & 2.0, and OSWorld-Verified. Every evaluation sample is paired wi... | 669 | 669 | 257,212,028 | [
"task_categories:text-generation",
"language:en",
"license:apache-2.0",
"size_categories:1K<n<10K",
"format:json",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"arxiv:2606.24597",
"region:us",
"world-model",
"agent",
... | 2026-06-22T14:50:17 | null | null |
6a06cc4dea20d325f8fc6213 | ArtificialAnalysis/ITBench-AA | ArtificialAnalysis | {"license": "cc-by-4.0", "language": ["en"], "task_categories": ["question-answering"], "tags": ["sre", "kubernetes", "root-cause-analysis", "agents", "it-operations"], "pretty_name": "ITBench-AA", "size_categories": ["n<1K"], "configs": [{"config_name": "sre", "data_files": [{"split": "test", "path": "sre/data.jsonl"}... | false | False | 2026-05-27T01:28:25 | 40 | 31 | false | 76df38a82288f75ba9e41dc8c515033332497473 |
ITBench-AA
Artificial Analysis' release of the public scenarios from
IBM's ITBench benchmark, used for
the ITBench-AA leaderboard.
This repo currently contains the SRE subset (sre config). Each row is a
Kubernetes incident scenario with its expected contributing-factor entities. An
agent under evaluation... | 32,640 | 32,643 | 31,095,271,891 | [
"task_categories:question-answering",
"language:en",
"license:cc-by-4.0",
"size_categories:n<1K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us",
"sre",
"kubernetes",
"root-cause-analysis",
"agents",
"it-operat... | 2026-05-15T07:33:33 | null | null |
6a2c5668f7f66fcaa0d54e17 | lazarus19/Vibe-Coding-Instruct | lazarus19 | {"license": "apache-2.0", "task_categories": ["text-generation"], "language": ["en"], "tags": ["custom", "vibecodinginstruct"], "pretty_name": "Vibe-Coding-Instruct", "size_categories": ["1M<n<10M"]} | false | False | 2026-06-18T13:52:24 | 166 | 29 | false | 7ad49b3cbf0b73934b1d567d2b5c4768bce7989e | null | 2,146 | 2,146 | 458,936,450 | [
"task_categories:text-generation",
"language:en",
"license:apache-2.0",
"size_categories:1M<n<10M",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us",
"custom",
"vibecodinginstruct"
] | 2026-06-12T18:56:40 | null | null |
6a05fb804b04c5157df46866 | WithinUsAI/claude_mythos_distilled_25k | WithinUsAI | {"license": "apache-2.0", "language": ["en"], "tags": ["synthetic", "claude", "mythos", "distillation", "cybersecurity", "coding", "reasoning", "agentic", "frontier-model-mirror", "sft", "instruction-tuning"], "size_categories": ["10K<n<100K"], "pretty_name": "Claude Mythos Distilled 25K", "dataset_info": {"features": ... | false | False | 2026-05-18T00:45:03 | 124 | 24 | false | 2c5e638c51a22b8b883def51bab685ae7e282c72 |
Claude Mythos Distilled 25K
A high-quality synthetic supervised fine-tuning (SFT) dataset designed to train and fine-tune any LLM to mirror the capabilities, reasoning style, agentic behavior, and technical depth of Anthropic's Claude Mythos (distilled frontier model).
Dataset Summary
Siz... | 3,173 | 3,496 | 55,202,753 | [
"language:en",
"license:apache-2.0",
"size_categories:10K<n<100K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us",
"synthetic",
"claude",
"mythos",
"distillation",
"cybersecurity",
"coding",
"reasoning",
"a... | 2026-05-14T16:42:40 | null | null |
6a307dae8e258cbed418ec58 | XDOF/ABC-130k | XDOF | {"license": "apache-2.0", "pretty_name": "ABC", "language": ["en"], "tags": ["robotics", "manipulation", "imitation-learning", "bimanual", "teleoperation", "mcap"], "task_categories": ["robotics"], "size_categories": ["n>1T"], "configs": [{"config_name": "yam", "data_files": [{"split": "train", "path": "data/train/**"}... | false | auto | 2026-06-26T22:13:45 | 58 | 24 | false | fad18a5f891a47e665756d4cab2a67a7a080d8bb |
ABC-130k
ABC-130k is the largest open-source robot teleoperation dataset. It contains
bimanual manipulation trajectories collected on two-arm YAM stations. Episodes
are distributed as MCAP files, with subtask annotations kept as separate
artifacts so they can be revised or extended independently of the u... | 372,024 | 372,024 | 22,060,641,937,273 | [
"task_categories:robotics",
"language:en",
"license:apache-2.0",
"size_categories:n>1T",
"region:us",
"robotics",
"manipulation",
"imitation-learning",
"bimanual",
"teleoperation",
"mcap"
] | 2026-06-15T22:33:18 | null | null |
6a27d0ad7a6ecef661d66995 | BitRobot/HIW-500 | BitRobot | {"pretty_name": "HIW500: Humanoids In-the-Wild Dataset", "language": ["en"], "tags": ["robotics", "humanoid"], "license": "cc-by-4.0"} | false | False | 2026-06-25T09:24:44 | 24 | 23 | false | 07c1bc714eea0d58f6da5b8f923cdafa6987889b |
HIW500: Humanoids In-the-Wild Dataset
https://bitrobot-foundation.github.io/humanoids-in-the-wild-500-hours/
HIW500: Humanoids In-the-Wild Dataset is a large-scale dataset for whole-body humanoid robot learning in natural home environments. It captures human teleoperation demonstrations on Unitree G1 acr... | 43,530 | 43,530 | 8,937,650,917,107 | [
"language:en",
"license:cc-by-4.0",
"region:us",
"robotics",
"humanoid"
] | 2026-06-09T08:37:01 | null | null |
6a3404497e03daf35bd3202e | scholarweave/arxiv-latex | scholarweave | {"license": "other", "license_name": "dual-license", "license_link": "LICENSE", "task_categories": ["text-generation", "feature-extraction"], "language": ["en"], "tags": ["science", "arxiv", "latex", "academic"], "pretty_name": "arXiv LaTeX Source Dataset", "size_categories": ["1M<n<10M"], "configs": [{"config_name": "... | false | False | 2026-06-25T10:46:32 | 23 | 23 | false | 9e402d14e2ef8187b0d29f646feeb9693e6b0d0e |
arXiv LaTeX Source Dataset
This dataset provides the entire corpus of arXiv's LaTeX source files, pre-parsed, formatted, and aligned with official metadata in ready-to-query Parquet files.
Why I Built This
If you have ever tried to work with the complete history of arXiv papers at scale, ... | 4,007 | 4,007 | 196,218,171,651 | [
"task_categories:text-generation",
"task_categories:feature-extraction",
"language:en",
"license:other",
"size_categories:1M<n<10M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"region:us",
"science",
"arxiv",
"latex",
... | 2026-06-18T14:44:25 | null | null |
69f434edee1d16ec78d229ce | angrygiraffe/claude-opus-4.6-4.7-reasoning-8.7k | angrygiraffe | {"license": "apache-2.0", "task_categories": ["text-generation", "question-answering"], "language": ["en"], "tags": ["sft", "chain-of-thought", "coding", "math", "roleplay", "science", "humanities", "art", "multi-turn", "text", "json"], "pretty_name": "Claude Opus 4.6/4.7 Reasoning Dataset", "size_categories": ["1K<n<1... | false | False | 2026-05-01T17:11:41 | 420 | 20 | false | f0330e0ca46469b3928adef18c2b55f9476d6bd3 |
Background
Ended up with some tokens to burn on a Claude Max plan. Assembly began during 4.6 and moved to 4.7. Model is tagged. The development evolved as it went along. The dataset has not been manually reviewed. It's entirely Claude developed.
Clarification on Reasoning
The reasoning is ... | 9,075 | 16,118 | 260,301,481 | [
"task_categories:text-generation",
"task_categories:question-answering",
"language:en",
"license:apache-2.0",
"size_categories:10K<n<100K",
"format:json",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"region:us",
"sft",
"chain-of-thought",
"coding",
"math",... | 2026-05-01T05:06:53 | null | null |
6a34e9d01b6b6e116d313e13 | Crownelius/Complete-FABLE.5-traces-2M | Crownelius | {"license": "mit", "pretty_name": "Complete FABLE.5 Traces 2M", "annotations_creators": ["machine-generated"], "language": ["en"], "language_creators": ["found", "machine-generated"], "multilinguality": ["monolingual"], "size_categories": ["1M<n<10M"], "task_categories": ["text-generation"], "task_ids": ["language-mode... | false | False | 2026-06-21T12:26:51 | 25 | 17 | false | 19a5b7863e10eec6838cf531bd20d24d2ec1106e |
Complete FABLE.5 Traces 2M
Full FABLE.5 / Mythos corpus restored, with session-limit answer rows removed.
Dataset Viewer | Parquet | Raw JSONL.gz
This dataset is a post-closure compilation of all available FABLE.5 / Mythos trace datasets found on Hugging Face during the curation pass after the ... | 1,745 | 1,745 | 2,079,549,297 | [
"task_categories:text-generation",
"task_ids:language-modeling",
"annotations_creators:machine-generated",
"language_creators:found",
"language_creators:machine-generated",
"multilinguality:monolingual",
"language:en",
"license:mit",
"size_categories:1M<n<10M",
"format:parquet",
"modality:tabula... | 2026-06-19T07:03:44 | null | null |
67ac9b0ae2c56194379f17a9 | SakanaAI/AI-CUDA-Engineer-Archive | SakanaAI | {"tags": ["code"], "pretty_name": "The AI CUDA Engineer Archive", "license": "cc-by-4.0", "configs": [{"config_name": "default", "data_files": [{"split": "level_1", "path": "level_1.parquet"}, {"split": "level_2", "path": "level_2.parquet"}, {"split": "level_3", "path": "level_3.parquet"}]}]} | false | False | 2025-02-20T02:02:27 | 221 | 16 | false | 4edbe8d6d0b417e05aaf8ec7e23f78aecdc5516b |
The AI CUDA Engineer Archive 👷: Agentic CUDA Kernel Discovery, Optimization & Composition
We release The AI CUDA Engineer archive, a dataset consisting of approximately 30,000 CUDA kernels generated by The AI CUDA Engineer. It is released under the CC-By-4.0 license and can be accessed via HuggingFace ... | 3,402 | 31,258 | 67,716,683 | [
"license:cc-by-4.0",
"size_categories:10K<n<100K",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us",
"code"
] | 2025-02-12T12:58:50 | 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"]}, {"spl... | false | False | 2026-02-19T17:28:38 | 248 | 16 | 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 str... | 6,301 | 50,223 | null | [
"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 |
69e15643062441e6b7109caa | nvidia/Open-SWE-Traces | nvidia | {"dataset_info": {"features": [{"name": "instance_id", "dtype": "string"}, {"name": "repo", "dtype": "string"}, {"name": "license", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "trajectory_id", "dtype": "string"}, {"name": "trajectory", "list": [{"name": "role", "dtype": "string"}, {"name": "co... | false | False | 2026-06-25T16:48:33 | 30 | 16 | false | f6689f56f1af2e2082861738071d4c4278b1922a |
Open-SWE-Traces: Advancing Distillation for Software Engineering Agents
Data Overview
Open-SWE-Traces is an agentic instruction tuning dataset designed to advance the capabilities of LLMs in software engineering. This dataset comprises 200k+ agent
trajectories collected using the SWE-agen... | 2,040 | 2,058 | 17,783,617,931 | [
"license:cc-by-4.0",
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"arxiv:2606.16038",
"region:us",
"code",
"synthetic",
"tools",
"agents",
"software"
] | 2026-04-16T21:36:03 | null | null |
6a2d8bf9763f90e1368360cb | lordx64/agentic-distill-fable-5-sft | lordx64 | {"license": "agpl-3.0", "language": ["en"], "tags": ["agentic", "chain-of-thought", "distillation", "claude", "claude-fable-5", "agent-traces", "sft", "qwen-chat-template", "qwable"], "task_categories": ["text-generation"], "size_categories": ["1K<n<10K"], "configs": [{"config_name": "default", "data_files": [{"split":... | false | False | 2026-06-15T14:15:12 | 40 | 15 | false | 9df06dd13b692dd482bd6ef0e547f577a5f94942 |
Fable-5 SFT — prepared for Qwable fine-tuning
4,659 single-turn pairs from Claude Fable-5 (Anthropic preview model, suspended globally 2026-06-22 under U.S. export-control directives), reformatted into a single-text-column parquet ready for SFTTrainer(dataset_text_field="text") + train_on_responses_only.... | 1,050 | 1,050 | 14,605,136 | [
"task_categories:text-generation",
"language:en",
"license:agpl-3.0",
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us",
"agentic",
"chain-of-thought",
"distillation",
"claude",
"cla... | 2026-06-13T16:57:29 | null | null |
6a35a1b97d1c93c320e0c0d1 | AletheiaResearch/GLM-5.2-Agent | AletheiaResearch | {"pretty_name": "GLM-5.2 Agent traces", "task_categories": ["text-generation"], "tags": ["agent-traces", "format:agent-traces", "pi", "distillation", "z-ai/glm-5.2", "teich"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "**/*.jsonl"}]}]} | false | False | 2026-06-23T18:46:49 | 18 | 15 | false | 10f3e37942a1f5abb6b3f04c71886f9a36248fa3 | This dataset was generated using teich by TeichAI
GLM-5.2 Agent traces
This directory contains raw agent trace files generated by teich.
JSONL files: 284
Model metadata: z-ai/glm-5.2
Training-ready tools
Generated agent traces carry configured or recovered tool schemas so tools remain ava... | 893 | 893 | 90,472,581 | [
"task_categories:text-generation",
"size_categories:n<1K",
"format:json",
"format:agent-traces",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"region:eu",
"agent-traces",
"format:agent-traces",
"pi",
"distillation",
"... | 2026-06-19T20:08:25 | null | null |
6a36e65ae86005a76f1c7adf | ajibawa-2023/Shell-Code-Large | ajibawa-2023 | {"license": "mit", "task_categories": ["text-generation"], "language": ["en"], "tags": ["Shell", "Code", "LLM", "Training"], "size_categories": ["100K<n<1M"]} | false | False | 2026-06-20T19:48:46 | 16 | 15 | false | 91adad625cc7d91ce983f95466e1bbfb2693fd87 |
Shell-Code-Large
Shell-Code-Large is a large-scale corpus of Shell scripting source code comprising approximately 640,000 code samples stored in JSON Lines (.jsonl) format. The dataset is designed to support research in large language model (LLM) pretraining, code intelligence, DevOps automation, cloud i... | 176 | 176 | 3,365,721,917 | [
"task_categories:text-generation",
"language:en",
"license:mit",
"size_categories:100K<n<1M",
"format:json",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"region:us",
"Shell",
"Code",
"LLM",
"Training"
] | 2026-06-20T19:13:30 | null | null |
6a3246e7ae94378f6d10aff0 | PawanKrd/claude-fable-5-code | PawanKrd | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "code.fable-5.jsonl"}]}], "dataset_info": {"features": [{"name": "category", "dtype": "string"}, {"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "model", "dtype": "stri... | false | False | 2026-06-17T07:37:06 | 20 | 14 | false | 4bf63f6009a984b50f5a7e07368e3fe24fa849aa |
Claude Fable 5 Coding and Math Dataset (Non-Thinking)
This repository contains a dataset of 603 coding and math-related prompts and responses from Claude Fable 5.
The generation of this dataset cost approximately $75.
Please note that this dataset is non-thinking. Fable 5 only supported adaptive thinking... | 484 | 484 | 3,173,322 | [
"task_categories:text-generation",
"language:en",
"size_categories:n<1K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us",
"code",
"claude",
"fable-5"
] | 2026-06-17T07:04:07 | null | null |
69af1e7e484ef491320be72e | Aignostics/OpenTME | Aignostics | {"license": "other", "task_categories": ["image-classification", "image-segmentation", "image-feature-extraction", "object-detection"], "tags": ["biomarker-discovery", "biology", "bladder-cancer", "breast-cancer", "cancer", "cell-segmentation", "colorectal-cancer", "computational-pathology", "digital-pathology", "H&E",... | false | manual | 2026-06-19T12:03:22 | 29 | 13 | false | b2e4a22823f8d14d1097d568461afeeb1e5bc67e |
OpenTME: Open-Access Tumor Microenvironment Profiles from TCGA
OpenTME is an open-access project by Aignostics for academic researchers. It provides comprehensive spatial outputs for whole slide images (WSIs) of formalin-fixed, paraffin-embedded slides from The Cancer Genome Atlas (TCGA). OpenTME is powe... | 15,602 | 16,366 | 5,066,213,789 | [
"task_categories:image-classification",
"task_categories:image-segmentation",
"task_categories:image-feature-extraction",
"task_categories:object-detection",
"license:other",
"size_categories:10K<n<100K",
"format:imagefolder",
"modality:image",
"library:datasets",
"library:mlcroissant",
"arxiv:2... | 2026-03-09T19:24:46 | null | null |
6a27d2d419ba88e1bcf065fb | BitRobot/HIW-500-LeRobot | BitRobot | {"pretty_name": "HIW500: Humanoids In-the-Wild Dataset (LeRobot)", "language": ["en"], "tags": ["LeRobot", "robotics", "humanoid", "unitree-g1", "manipulation", "mobile-manipulation", "bimanual", "imitation-learning", "teleoperation", "in-the-wild"], "task_categories": ["robotics"], "configs": [{"config_name": "default... | false | False | 2026-06-25T09:25:47 | 13 | 13 | false | d8fc5b6f91df8e4c153ac7ccfd122efd0869cb76 | This dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v3.0",
"fps": 30,
"features": {
"observation.images.head": {
"dtype": "video",
"shape": [
480,
1280,
3
... | 14,042 | 14,042 | 2,150,165,807,005 | [
"task_categories:robotics",
"language:en",
"license:cc-by-4.0",
"size_categories:10M<n<100M",
"format:parquet",
"modality:tabular",
"modality:text",
"modality:timeseries",
"modality:video",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"library:lerobot",
"r... | 2026-06-09T08:46:12 | null | null |
6a3a711102e6e7f9c77f3a2a | Rapidata/svg-benchmark | Rapidata | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "license": "cc-by-4.0", "task_categories": ["text-to-image", "image-classification", "reinforcement-learning"], "language": ["en"], "size_categories": ["100K<n<1M"], "pretty_name": "SVG Generation Benchmark (Static)", ... | false | False | 2026-06-25T13:49:04 | 13 | 13 | false | 2a749cd790a5853e411b41ead7614b270d6b89bf |
Rapidata Static SVG Generation Benchmark
Built by Rapidata.
This dataset contains 1,355,161 human responses, collected with the
Rapidata Python SDK, comparing how well 30 frontier LLMs generate
static SVGs from text prompts. Each row is a head-to-head comparison between two models' renders of
the same pr... | 281 | 281 | 24,583,751,740 | [
"task_categories:text-to-image",
"task_categories:image-classification",
"task_categories:reinforcement-learning",
"language:en",
"license:cc-by-4.0",
"size_categories:100K<n<1M",
"format:parquet",
"format:optimized-parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:dask... | 2026-06-23T11:42:09 | null | null |
685d6910fa047a479aefe2a2 | ibm-research/AssetOpsBench | ibm-research | {"language": ["en"], "pretty_name": "AssetOpsBench", "configs": [{"config_name": "scenarios", "data_files": [{"split": "train", "path": "data/scenarios/all_utterance.jsonl"}], "default": true}, {"config_name": "compressor", "data_files": [{"split": "train", "path": "data/asset/compressor_utterance.jsonl"}]}, {"config_n... | false | False | 2026-05-28T17:10:58 | 43 | 12 | false | 5e25bb7f2cd37fb68b9a9e1f99d170ca5be7ce17 |
AssetOpsBench
AssetOpsBench is a specialized benchmark designed for evaluating Large Language Models (LLMs) and Multi-Agent systems in industrial operations. It focuses on the intersection of sensor data interpretation, maintenance logic, and Prognostics and Health Management (PHM).
The benchmark enables... | 866 | 9,881 | 10,034,194 | [
"task_categories:question-answering",
"task_categories:time-series-forecasting",
"language:en",
"license:apache-2.0",
"size_categories:n<1K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"arxiv:2506.03828",
"region:us",
"Ind... | 2025-06-26T15:36:48 | null | null |
6a2c0ff5f05071e5d8d863dd | makora-ai/triton-gpu-latency | makora-ai | {"license": "apache-2.0", "task_categories": ["text-generation"], "language": ["en"], "tags": ["gpu", "triton", "cuda", "kernel-generation", "code", "pytorch", "latency", "benchmark"], "size_categories": ["100K<n<1M"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train.parquet"... | false | False | 2026-06-12T15:14:03 | 12 | 12 | false | 3b30911350fa433e1cb97ec2a41bd8908dd35d5d |
Triton GPU Latency Dataset
A large dataset of PyTorch problems (mostly from KernelBench) paired with candidate Triton-kernel implementations and their measured GPU runtimes generated by MakoraGenerate. Each row is a self-contained Python program that defines (1) a reference Model written with plain PyTor... | 120 | 120 | 1,968,266,807 | [
"task_categories:text-generation",
"language:en",
"license:apache-2.0",
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us",
"gpu",
"triton",
"cuda",
"kernel-generation",
"code",
"p... | 2026-06-12T13:56:05 | null | null |
67d05b35637847cd702f212e | futo-org/swipe.futo.org | futo-org | {"language": ["en"], "license": "mit", "task_categories": ["other"], "configs": [{"config_name": "swipe-1", "data_files": [{"split": "train", "path": "train.jsonl"}, {"split": "test", "path": "test.jsonl"}, {"split": "validation", "path": "dev.jsonl"}]}, {"config_name": "swipe-2", "data_files": [{"split": "train", "pat... | false | False | 2026-06-25T20:28:18 | 26 | 10 | false | d71bf5fd7f45b3e7c2ed2d76a21b0dbd3b4ba566 |
Dataset Card for swipe.futo.org
This dataset is presented in the paper FUTO Swipe: Layout-Agnostic Neural Swipe Decoding.
It contains multiple collection runs from the swipe.futo.org website. The QWERTY layout definition is provided here
Collection process
Users were able to volunteer to c... | 474 | 2,918 | 6,872,036,572 | [
"task_categories:other",
"language:en",
"license:mit",
"size_categories:1M<n<10M",
"format:json",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"arxiv:2606.25247",
"region:us"
] | 2025-03-11T15:48:05 | null | null |
69e9263752b11322a41bff09 | Meddies/meddies-persona-vie | Meddies | {"pretty_name": "Meddies Persona VIE", "language": ["vi"], "license": "cc-by-nc-4.0", "multilinguality": "monolingual", "size_categories": ["100K<n<1M"], "annotations_creators": ["machine-generated"], "language_creators": ["machine-generated"], "source_datasets": ["HoangHa/meddies-persona"], "viewer": true, "task_categ... | false | False | 2026-06-26T17:44:33 | 11 | 10 | false | d4dd1bb5901c7c9308f4c11d378cf69359a63c12 |
Meddies Persona VIE
Vietnamese synthetic patient personas for teams that need better patient context, not just better-looking generated text.
[!IMPORTANT]
This is a research and simulation artifact for healthcare AI teams building Vietnamese synthetic data pipelines. It is not a record of... | 671 | 1,068 | 578,545,823 | [
"task_categories:other",
"annotations_creators:machine-generated",
"language_creators:machine-generated",
"multilinguality:monolingual",
"source_datasets:HoangHa/meddies-persona",
"language:vi",
"license:cc-by-nc-4.0",
"size_categories:100K<n<1M",
"format:parquet",
"format:optimized-parquet",
"m... | 2026-04-22T19:49:11 | null | null |
6a00ee1b8af2b11e0d2b374b | WithinUsAI/GPT_5.5_Distilled | WithinUsAI | {"license": "apache-2.0"} | false | False | 2026-05-12T17:49:04 | 24 | 10 | false | 4f49e8c7e98ca80694b7378ff8fed5f7344c5fb3 | null | 898 | 1,069 | 46,711,856 | [
"license:apache-2.0",
"size_categories:10K<n<100K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us"
] | 2026-05-10T20:44:11 | null | null |
6a261e159c9c7a503bfdea7c | open-thoughts/OpenThoughts-Agent-SFT-100K | open-thoughts | {"license": "apache-2.0", "language": ["en"], "tags": ["agents", "terminal", "code", "software-engineering", "sft"], "pretty_name": "OpenThoughts-Agent-SFT-100K", "size_categories": ["100K<n<1M"]} | false | False | 2026-06-08T07:01:02 | 10 | 10 | false | 45fb28fcc38d352133cb28a1c8a43a2f14fea97b |
Project |
Code |
Collection
OpenThoughts-Agent-SFT-100K
OpenThoughts-Agent is an open-source effort to curate the best datasets for training agents. Our release includes datasets, models and our research codebase.
OpenThoughts-Agent-SFT-100K is the 100,000-example point of the OpenThoughts-Ag... | 330 | 330 | 1,749,504,790 | [
"language:en",
"license:apache-2.0",
"size_categories:10K<n<100K",
"format:parquet",
"format:optimized-parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"region:us",
"agents",
"terminal",
"code",
"software-engineering",
"sft"
] | 2026-06-08T01:42:45 | null | null |
6a35605763a799204f741823 | jdopensource/JoyAI-VL-Interaction | jdopensource | {"license": "apache-2.0", "task_categories": ["video-text-to-text"]} | false | False | 2026-06-23T08:06:07 | 11 | 10 | false | 6cf9b8383e0fcfeecfe8c12d44499b01b931c98e |
JoyAI-VL-Interaction Dataset
The first open, vision-driven real-time interaction model — it watches a live video stream and decides on its own when to speak, stay silent, or delegate.
📄 Paper · 🌐 Project Page & Demos · 💻 GitHub · 🤗 Paper Page
Dataset Description
This repository contai... | 687 | 687 | 2,647,458,242 | [
"task_categories:video-text-to-text",
"license:apache-2.0",
"arxiv:2606.14777",
"region:us"
] | 2026-06-19T15:29:27 | null | null |
6a3154f1671ba44c169ee371 | google/WikiProfile | google | {"license": "cc-by-sa-4.0", "task_categories": ["question-answering"], "language": ["en"], "tags": ["Factuality", "Knowledge", "Wikipedia", "QA"], "pretty_name": "wikiprofile", "size_categories": ["1K<n<10K"]} | false | False | 2026-06-19T09:58:02 | 12 | 9 | false | 0448b6abf3d6fb0a964e6935c0265dbd47584cda |
WikiProfile
WikiProfile is a factual knowledge benchmark for evaluating how well language models encode and recall factual knowledge. It comprises 2,150 facts, each paired with 10 questions, for a total of 21,500 question instances.
Each fact is grounded in the first paragraph (summary) of an English Wik... | 269 | 269 | 7,510,683 | [
"task_categories:question-answering",
"language:en",
"license:cc-by-sa-4.0",
"size_categories:1K<n<10K",
"format:csv",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"arxiv:2602.14080",
"region:us",
"Factuality",
"Knowl... | 2026-06-16T13:51:45 | 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/*... | false | False | 2025-07-11T20:16:53 | 2,902 | 8 | 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 ... | 273,922 | 8,559,024 | 54,812,538,723,397 | [
"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 |
69693d3f3669d609ad898396 | ibm-research/ITBench-Lite | ibm-research | {"license": "apache-2.0", "dataset_info": {"features": [{"name": "scenario_id", "dtype": "string"}, {"name": "domain", "dtype": "string"}, {"name": "version", "dtype": "string"}, {"name": "task_type", "dtype": "string"}, {"name": "snapshot_data", "dtype": "object"}], "configs": [{"config_name": "sre", "data_files": [{"... | false | False | 2026-04-21T15:30:08 | 12 | 8 | false | d0916b08ba421ce5e672e9ad68aa947d938dfef0 |
ITBench-Lite Dataset Card
Dataset Overview
Dataset Name: ITBench-LiteOrganization: IBM ResearchLicense: Apache 2.0Language: EnglishPaper: ITBench: Evaluating AI Agents across Diverse Real-World IT Automation TasksGitHub: ITBench
ITBench-Lite is a systematic framework for benchmarking LLMs and AI... | 9,152 | 21,747 | 31,034,154,126 | [
"task_categories:question-answering",
"task_categories:text-generation",
"task_categories:other",
"language:en",
"license:apache-2.0",
"size_categories:10K<n<100K",
"arxiv:2502.05352",
"region:us",
"kubernetes",
"sre",
"finops",
"ciso",
"incident-response",
"fault-diagnosis",
"root-cause... | 2026-01-15T19:17:19 | null | null |
69e695a5d20baec02ee3039c | nvidia/Nemotron-Personas-Korea | nvidia | {"license": "cc-by-4.0", "task_categories": ["text-generation"], "language": ["ko"], "tags": ["synthetic", "personas", "NVIDIA", "Korean", "datadesigner"], "size_categories": ["1M<n<10M"], "dataset_info": {"features": [{"name": "uuid", "dtype": "string"}, {"name": "professional_persona", "dtype": "string"}, {"name": "s... | false | False | 2026-06-22T15:37:31 | 508 | 8 | false | ada0f5b53a38bb5a30cce09358adde883c1ab63a |
Nemotron-Personas-Korea
우리나라 실제 분포에 기반한 합성 페르소나를 위한 복합 AI 시스템
A compound AI approach to personas grounded in real-world distributions
데이터셋 개요 (Overview)
Nemotron-Personas-Korea는 대한민국의 실제 인구통계학적·지리적·성격 특성 분포를 기반으로 합성된 오픈소스 페르소나 데이터셋(CC BY 4.0)으로, 우리나라 인구의 다양성과 특성을 폭넓게 반영... | 12,880 | 104,082 | 1,984,406,022 | [
"task_categories:text-generation",
"language:ko",
"license:cc-by-4.0",
"size_categories:1M<n<10M",
"format:parquet",
"format:optimized-parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"library:datadesigner",
"region:u... | 2026-04-20T21:07:49 | null | null |
6a347406d390563cd60d032d | allenai/tmax-15k-open-instruct | allenai | {"dataset_info": {"features": [{"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "ground_truth", "dtype": "string"}, {"name": "dataset", "dtype": "string"}, {"name": "env_config", "struct": [{"name": "env_name", "dtype": "string"}, {"name": "image", "d... | false | False | 2026-06-26T23:11:46 | 8 | 8 | false | 7b090eca98bf351356bc1c64290c5c4a09f2f98c |
💻 Code ·
🤗 Models & Data ·
📜 Paper ·
📓 Blog
[!NOTE]
For full information, go check out the Tmax paper here.
TMax 15k - Open Instruct
This is the dataset we used to train Tmax 9b (and our other tmax models), formatted for use with our open-instruct fork here.
In general, this is a collec... | 439 | 439 | 92,920,392 | [
"language:en",
"license:odc-by",
"size_categories:10K<n<100K",
"format:parquet",
"format:optimized-parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"arxiv:2606.23321",
"region:us"
] | 2026-06-18T22:41:10 | null | null |
645e8da96320b0efe40ade7a | roneneldan/TinyStories | roneneldan | {"license": "cdla-sharing-1.0", "task_categories": ["text-generation"], "language": ["en"]} | false | False | 2024-08-12T13:27:26 | 1,042 | 7 | false | f54c09fd23315a6f9c86f9dc80f725de7d8f9c64 | Dataset containing synthetically generated (by GPT-3.5 and GPT-4) short stories that only use a small vocabulary.
Described in the following paper: https://arxiv.org/abs/2305.07759.
The models referred to in the paper were trained on TinyStories-train.txt (the file tinystories-valid.txt can be used for validation los... | 79,624 | 1,497,537 | 7,621,978,240 | [
"task_categories:text-generation",
"language:en",
"license:cdla-sharing-1.0",
"size_categories:1M<n<10M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"arxiv:2305.07759",
"region:us"
] | 2023-05-12T19:04:09 | null | null |
6532270e829e1dc2f293d6b8 | gaia-benchmark/GAIA | gaia-benchmark | {"language": ["en"], "pretty_name": "General AI Assistants Benchmark", "extra_gated_prompt": "To avoid contamination and data leakage, you agree to not reshare this dataset outside of a gated or private repository on the HF hub.", "extra_gated_fields": {"I agree to not reshare the GAIA submissions set according to the ... | false | auto | 2025-10-28T14:44:54 | 704 | 7 | false | 682dd723ee1e1697e00360edccf2366dc8418dd9 |
GAIA dataset
GAIA is a benchmark which aims at evaluating next-generation LLMs (LLMs with augmented capabilities due to added tooling, efficient prompting, access to search, etc).
We added gating to prevent bots from scraping the dataset. Please do not reshare the validation or test set in a crawlable fo... | 35,111 | 316,108 | 110,175,514 | [
"language:en",
"size_categories:n<1K",
"format:parquet",
"modality:audio",
"modality:document",
"modality:image",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"arxiv:2311.12983",
"region:us"
] | 2023-10-20T07:06:54 | null | null |
68ae11cd78570b7e4c66edba | ScaleAI/SWE-bench_Pro | ScaleAI | {"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": "requirements", "dtype":... | false | False | 2026-02-23T20:54:47 | 141 | 7 | false | 7ab5114912baf22bb098818e604c02fe7ad2c11f |
Dataset Summary
SWE-Bench Pro is a challenging, enterprise-level dataset for testing agent ability on long-horizon software engineering tasks.
Paper: https://static.scale.com/uploads/654197dc94d34f66c0f5184e/SWEAP_Eval_Scale%20(9).pdf
See the related evaluation Github: https://github.com/scaleapi/SWE-bench_P... | 67,916 | 1,108,631 | 7,822,488 | [
"benchmark:official",
"benchmark:eval-yaml",
"size_categories:n<1K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us"
] | 2025-08-26T19:58:05 | null | null |
6a1cbd0141aa598ff9f9bf57 | HelioAI/Fable-5-Distill-Reasoning-462x | HelioAI | {"annotations_creators": ["machine-generated"], "language": ["en", "ru"], "license": "unknown", "size_categories": ["n<1K"], "task_categories": ["text-generation"], "tags": ["reasoning", "long-context", "reasoning-traces", "synthetic-data", "chain-of-thought", "process-supervision", "mythos-v2", "deep-reasoning", "trac... | false | False | 2026-06-15T22:35:42 | 31 | 7 | false | ab4e69b74e7ef455f15f23fc60bac891db90a918 |
HelioAI Labs
Mythos V2 Full Distill
DeepReason 462×105M
Unrestricted full-parameter distillation from Mythos V2 — complete reasoning traces with zero alignment truncation, engineered for deep analytical research and process supervision.
... | 1,200 | 1,200 | 146,180,522 | [
"task_categories:text-generation",
"annotations_creators:machine-generated",
"language:en",
"language:ru",
"license:unknown",
"size_categories:n<1K",
"region:us",
"reasoning",
"long-context",
"reasoning-traces",
"synthetic-data",
"chain-of-thought",
"process-supervision",
"mythos-v2",
"d... | 2026-05-31T22:58:09 | null | null |
6a1ceae73647a19350d8531b | xlangai/osworld_v2_tasks | xlangai | {"license": "apache-2.0", "pretty_name": "OSWorld V2 Task Classes"} | false | auto | 2026-06-24T15:30:35 | 9 | 7 | false | 796f1aa190229c54cff1f18b46ddf64d928863b0 |
OSWorld V2 Task Classes
This gated dataset contains the official root-level task_*.py Python task classes for OSWorld V2.
The public GitHub repository keeps the task loader, helper utilities, and documentation. The task implementations are gated to reduce benchmark leakage and to help prevent evaluated a... | 260 | 260 | 2,507,003 | [
"license:apache-2.0",
"size_categories:n<1K",
"format:json",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us"
] | 2026-06-01T02:13:59 | null | null |
6a3232e527a91971fc536a73 | AutoArk-AI/ark-asr-open-asr-leaderboard-results | AutoArk-AI | {"pretty_name": "ARK-ASR Open ASR Leaderboard Results", "license": "apache-2.0"} | false | False | 2026-06-17T05:39:31 | 12 | 7 | false | 1419570c86ca7599cb25efedee2361de38a77ff4 |
ARK-ASR Open ASR Leaderboard Results
This dataset contains JSONL prediction manifests for AutoArk-AI/ARK-ASR-0.6B on hf-audio/open-asr-leaderboard public English short-form splits.
These files are intended for Open ASR Leaderboard maintainer verification.
Scoring summary from normalizer.eval_utils.score_... | 102 | 102 | 28,171,974 | [
"license:apache-2.0",
"size_categories:10K<n<100K",
"format:json",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"region:us"
] | 2026-06-17T05:38:45 | null | null |
6a38b528b22e71da5a862d50 | sequelbox/Titanium4-DeepSeek-V4-Pro | sequelbox | {"license": "apache-2.0", "tags": ["titanium", "titanium-4", "agentic", "agentic-coding", "python", "dev-ops", "devops", "terraform", "ansible", "docker", "jenkins", "kubernetes", "helm", "grafana", "prometheus", "shell", "bash", "azure", "aws", "gcp", "c++", "c#", "c", "rust", "java", "javascript", "typescript", "algo... | false | False | 2026-06-22T04:36:35 | 7 | 7 | false | 4b6b6c1849c757f6071ffc1e2bf4b13ef54e0b2b | Click here to support our open-source dataset and model releases - help us speed up our release schedule!
Titanium 4 is an agentic coding dataset focused on DevOps and architecture, testing the limits of DeepSeek-V4-Pro's agentic skills:
Questions prioritize real-world, challenging agentic coding tasks in DevOps and a... | 120 | 120 | 904,758,228 | [
"task_categories:text-generation",
"language:en",
"license:apache-2.0",
"size_categories:10K<n<100K",
"format:csv",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"doi:10.57967/hf/9257",
"region:us",
"titanium",
"titanium-4",
"agentic",
... | 2026-06-22T04:08:08 | null | null |
6a3907d46e621593a3b86a2d | AutoArk-AI/ark-asr-3b-open-asr-leaderboard-results | AutoArk-AI | {"pretty_name": "ARK-ASR-3B Open ASR Leaderboard Results", "license": "apache-2.0", "task_categories": ["automatic-speech-recognition"], "tags": ["open-asr-leaderboard", "ark-asr", "automatic-speech-recognition"]} | false | False | 2026-06-22T10:01:19 | 11 | 7 | false | b98599d8b525251956c36fd237b0c30af18fea90 |
ARK-ASR-3B Open ASR Leaderboard Results
Raw JSONL manifests for AutoArk-AI/ARK-ASR-3B on the public English
short-form hf-audio/open-asr-leaderboard splits.
These manifests were generated on a local 8x RTX 4090 machine and scored with
the shared Open ASR Leaderboard scorer:
PYTHONPATH=. python - <<'PY'
f... | 103 | 103 | 28,264,125 | [
"task_categories:automatic-speech-recognition",
"license:apache-2.0",
"size_categories:10K<n<100K",
"format:json",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"region:us",
"open-asr-leaderboard",
"ark-asr",
"automatic-speech-recognition"
] | 2026-06-22T10:00:52 | null | null |
621ffdd236468d709f181e5e | cais/mmlu | cais | {"annotations_creators": ["no-annotation"], "language_creators": ["expert-generated"], "language": ["en"], "license": ["mit"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["question-answering"], "task_ids": ["multiple-choice-qa"], "paperswit... | false | False | 2024-03-08T20:36:26 | 777 | 6 | false | c30699e8356da336a370243923dbaf21066bb9fe |
Dataset Card for MMLU
Dataset Summary
Measuring Massive Multitask Language Understanding by Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt (ICLR 2021).
This is a massive multitask test consisting of multiple-choice questions from various branc... | 434,324 | 42,054,439 | null | [
"task_categories:question-answering",
"task_ids:multiple-choice-qa",
"annotations_creators:no-annotation",
"language_creators:expert-generated",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:mit",
"size_categories:100K<n<1M",
"format:parquet",
"modality:text"... | 2022-03-02T23:29:22 | mmlu | null |
625552d2b339bb03abe3432d | openai/gsm8k | openai | {"annotations_creators": ["crowdsourced"], "language_creators": ["crowdsourced"], "language": ["en"], "license": ["mit"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["text-generation"], "task_ids": [], "paperswithcode_id": "gsm8k", "pretty_na... | false | False | 2026-03-23T10:18:13 | 1,406 | 6 | false | 740312add88f781978c0658806c59bc2815b9866 |
Dataset Card for GSM8K
Dataset Summary
GSM8K (Grade School Math 8K) is a dataset of 8.5K high quality linguistically diverse grade school math word problems. The dataset was created to support the task of question answering on basic mathematical problems that require multi-step reasoning.
These p... | 898,961 | 12,848,158 | 5,900,352 | [
"benchmark:official",
"benchmark:eval-yaml",
"task_categories:text-generation",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:mit",
"size_categories:10K<n<100K",
"format:parquet",
"modal... | 2022-04-12T10:22:10 | gsm8k | null |
655100ea2adb0688a0042ddd | teknium/OpenHermes-2.5 | teknium | {"language": ["eng"], "pretty_name": "OpenHermes 2.5", "tags": ["synthetic", "GPT-4", "Distillation", "Compilation"]} | false | False | 2024-04-15T08:18:12 | 862 | 6 | false | b82037821055c377bed0d495e72e46de3bc72e84 |
Dataset Card for Dataset Name
This is the dataset that made OpenHermes 2.5 and Nous Hermes 2 series of models.
Support me on GitHub sponsors <3 : https://github.com/sponsors/teknium1
Dataset Details
Dataset Description
The Open Hermes 2/2.5 and Nous Hermes 2 models have made significan... | 15,182 | 248,991 | 1,936,283,760 | [
"language:eng",
"size_categories:1M<n<10M",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us",
"synthetic",
"GPT-4",
"Distillation",
"Compilation"
] | 2023-11-12T16:44:26 | 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_extende... | false | auto | 2026-03-05T23:06:58 | 470 | 6 | false | 633f5ee89ab8ad4522a9f850766b73f62147ffdd |
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 ... | 96,769 | 1,852,863 | 8,713,216 | [
"benchmark:official",
"benchmark:eval-yaml",
"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",
"... | 2023-11-27T23:18:46 | null | null |
694e0b147890558bf09a0394 | AutoArk-AI/gpa-demos | AutoArk-AI | {"license": "apache-2.0"} | false | False | 2025-12-26T07:19:30 | 11 | 6 | false | 9139583003dc77bfbf5788dc1646323b6c44199f | null | 54 | 175 | 5,443,333 | [
"license:apache-2.0",
"size_categories:n<1K",
"format:audiofolder",
"modality:audio",
"library:datasets",
"library:mlcroissant",
"region:us"
] | 2025-12-26T04:12:04 | null | null |
69b0a69caab02f7aaec0e66f | bones-studio/seed | bones-studio | {"license": "other", "license_name": "bones-seed-license", "license_link": "https://bones.studio/info/seed-license", "task_categories": ["robotics", "text-to-video", "video-text-to-text"], "tags": ["motion-capture", "humanoid-robotics", "human-motion", "physical-ai", "whole-body-control", "NVIDIA-SOMA", "Unitree-G1", "... | false | auto | 2026-05-03T15:03:12 | 160 | 6 | false | 2f59b2077b9da34dd4e43618e705c7cb962c9a66 |
BONES-SEED: Skeletal Everyday Embodiment Dataset
BONES-SEED is an open dataset of 142,220 annotated human motion animations for humanoid robotics. It provides motion capture data in SOMA and Unitree G1 formats, with natural language descriptions, temporal segmentation, and detailed skeletal metadata.
Proj... | 4,152 | 17,768 | null | [
"task_categories:robotics",
"task_categories:text-to-video",
"task_categories:video-text-to-text",
"language:en",
"license:other",
"size_categories:100K<n<1M",
"region:us",
"motion-capture",
"humanoid-robotics",
"human-motion",
"physical-ai",
"whole-body-control",
"NVIDIA-SOMA",
"Unitree-G... | 2026-03-10T23:17:48 | null | null |
69ca9b695a4dac480491fd13 | lambda/hermes-agent-reasoning-traces | lambda | {"license": "apache-2.0", "task_categories": ["text-generation"], "language": ["en"], "tags": ["tool-calling", "function-calling", "agent", "hermes", "reasoning", "sharegpt", "sft", "traces"], "size_categories": ["10K<n<100K"], "configs": [{"config_name": "kimi", "data_files": [{"split": "train", "path": "data/kimi/tra... | false | False | 2026-04-17T10:06:39 | 370 | 6 | false | b92885e4f0161d4b2536512710e004d4892cac6e |
Hermes Agent Reasoning Traces
Multi-turn tool-calling trajectories for training AI agents using the Hermes Agent harness. Each sample is a real agent conversation with step-by-step reasoning (<think> blocks) and actual tool execution results.
This dataset has two configs, one per source model:
Config
M... | 3,064 | 14,858 | 1,616,105,008 | [
"task_categories:text-generation",
"language:en",
"license:apache-2.0",
"size_categories:10K<n<100K",
"format:parquet",
"format:optimized-parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us",
"tool-calling",
"function-calling... | 2026-03-30T15:48:57 | null | null |
69d3b00b2d56eb23d8824420 | badlogicgames/pi-mono | badlogicgames | {"pretty_name": "coding agent session traces", "task_categories": ["text-generation"], "tags": ["agent-traces", "coding-agent", "pi-share-hf"], "language": ["en", "code"], "license": "other"} | false | False | 2026-04-06T13:10:36 | 168 | 6 | false | dac2a1d3ba12dda597b973a791a77618ccb5f413 |
Coding agent session traces for badlogicgames/pi-mono
This dataset contains redacted coding agent session traces collected while working on https://github.com/badlogic/pi-mono.git. The traces were exported with pi-share-hf from a local pi workspace and filtered to keep only sessions that passed determini... | 2,423 | 25,150 | 224,783,955 | [
"task_categories:text-generation",
"language:en",
"language:code",
"license:other",
"region:us",
"agent-traces",
"coding-agent",
"pi-share-hf"
] | 2026-04-06T13:07:23 | null | null |
6a2d645596c462b483de27bc | Pythagoras-LM/SFT_Dataset | Pythagoras-LM | {"language": ["en"], "license": "apache-2.0", "size_categories": ["100K<n<1M"], "pretty_name": "Pythagoras-Prover SFT Dataset", "tags": ["Lean4", "Theorem-Proving", "Formal-Reasoning"], "task_categories": ["text-generation"]} | false | False | 2026-06-22T15:42:51 | 9 | 6 | false | 6865661fe316db5f5fb0617ccbab6fd1e4b17a33 |
Pythagoras SFT Dataset
Project Page | GitHub | Paper
Data
Our training dataset consists of approximately 841K problems paired with Lean formal statements, formal proofs, and reasoning chains. We release a partial subset, which consists of 126K instances:
30K easy instances
49K medium inst... | 211 | 211 | 996,193,621 | [
"task_categories:text-generation",
"language:en",
"license:apache-2.0",
"size_categories:100K<n<1M",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"arxiv:2606.12594",
"region:us",
"Lean4",
"Theorem-Proving",
"Formal-Reasoni... | 2026-06-13T14:08:21 | null | null |
6a313297671ba44c169b69c0 | HKUSTAudio/ISCSLP2026-CoT-TTS | HKUSTAudio | {"viewer": false} | false | False | 2026-06-19T02:00:44 | 9 | 6 | false | 29e5d4b3232398763ca6b42ec0c6025f1c9d7e8a |
ISCSLP 2026 CoT-TTS Dataset
Dataset Overview
This dataset is prepared for the ISCSLP 2026 CoT-TTS Challenge and is designed to support research on context-aware, expressive, and CoT-guided speech generation. It is constructed from speech-rich media sources, including films, TV dramas, radi... | 8,609 | 8,609 | 2,152,290,152,143 | [
"region:us"
] | 2026-06-16T11:25:11 | null | null |
6a33f8732bf5d1c979fed69d | lazarus19/Vibe-Coding-Instruct-V2 | lazarus19 | {"license": "apache-2.0", "language": ["en"], "tags": ["ai", "custom", "vibecodinginstruct", "vibecodinginstructv2"], "pretty_name": "VibeCodingInstructV2", "task_categories": ["text-classification"], "size_categories": ["10M<n<100M"]} | false | False | 2026-06-18T19:12:07 | 8 | 6 | false | 54a83576f89f376ce3733ca3d3e6bf03c1e8d394 |
Dataset Card for Dataset Name
This dataset card aims to be a base template for new datasets. It has been generated using this raw template.
Dataset Details
Dataset Description
Curated by: [More Information Needed]
Funded by [optional]: [More Information Needed]
Shared... | 142 | 142 | 7,154,266,968 | [
"task_categories:text-classification",
"language:en",
"license:apache-2.0",
"size_categories:1M<n<10M",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us",
"ai",
"custom",
"vibecodinginstruct",
"vibecodinginstructv2"... | 2026-06-18T13:53:55 | null | null |
6a3571c863a799204f75b7bc | STBack23/omnivoice-vi | STBack23 | {"license": "apache-2.0", "language": ["vi"], "tags": ["text-to-speech", "tts", "voice-cloning", "vietnamese", "srt", "dubbing", "omnivoice"], "pretty_name": "OmniVoice Vietnamese Voices", "size_categories": ["n<1K"]} | false | False | 2026-06-24T19:13:29 | 9 | 6 | false | 60c0d530d3476e59d31b1dbe8393e12e4f74f037 |
OmniVoice VI — Giọng Việt + SRT lồng tiếng
Dataset chứa 6 giọng tiếng Việt và công cụ speak.py để chạy trên Google Colab với OmniVoice.
Giọng có sẵn
Slug
Tên
ban_mai
Ban Mai
lan_trinh
Lan Trinh
ngan_ha
Ngan Ha
ngoc_huyen
Ngoc Huyen
thao_trinh
Thao Trinh
tuong_vy
Tuong ... | 537 | 537 | 6,730,368 | [
"language:vi",
"license:apache-2.0",
"size_categories:n<1K",
"format:text",
"modality:audio",
"modality:text",
"library:datasets",
"library:mlcroissant",
"region:us",
"text-to-speech",
"tts",
"voice-cloning",
"vietnamese",
"srt",
"dubbing",
"omnivoice"
] | 2026-06-19T16:43:52 | null | null |
6a36673934f0213d9c1e6704 | ClSu/ember-features | ClSu | {"license": "mit", "pretty_name": "EMBER precomputed features", "configs": [{"config_name": "gemma", "data_files": [{"split": "embedding", "path": "google_gemma-2-2b-it/interpretations/rank100/seed42/*/embedding/potential_features.csv"}, {"split": "mlp", "path": "google_gemma-2-2b-it/interpretations/rank100/seed42/*/ml... | false | False | 2026-06-20T12:01:48 | 8 | 6 | false | 9c08d5ee46efb5e66c7d86640d6e03ed1742d50f |
EMBER precomputed features
Concept features for EMBedding ERasure (EMBER), a plug-and-play module that uses
Sparse Matrix Factorization to precisely erase concept-related features from token
embeddings, making existing erasure methods more robust to relearning.
For each concept, two factorizations are pr... | 902 | 902 | 40,510,893,953 | [
"license:mit",
"size_categories:1K<n<10K",
"format:csv",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"arxiv:2606.03695",
"region:us"
] | 2026-06-20T10:11:05 | null | null |
6a3871555ae47cd0bf948930 | FrontisAI/NatureBench | FrontisAI | {"language": ["en"], "license": "other", "license_name": "mit-with-third-party-data", "license_link": "LICENSE", "pretty_name": "NatureBench", "size_categories": ["n<1K"], "tags": ["coding-agents", "benchmark", "scientific-machine-learning", "nature"], "configs": [{"config_name": "default", "data_files": [{"split": "tr... | false | False | 2026-06-25T08:26:20 | 6 | 6 | false | ee848844b68de031fad462ccdf9a8f579e5e4bd6 |
Dataset Card for NatureBench
NatureBench is a cross-discipline benchmark of 90 tasks distilled from peer-reviewed Nature-family publications, spanning 6 scientific domains. It is designed to evaluate whether AI coding agents can move beyond reproduction toward discovery: each task asks an agent to solv... | 14,612 | 14,612 | 1,254,319,270,174 | [
"language:en",
"license:other",
"size_categories:n<1K",
"format:json",
"modality:image",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"arxiv:2606.24530",
"region:us",
"coding-agents",
"benchmark",
"scientific-machine-learning",
"nature... | 2026-06-21T23:18:45 | null | null |
6a3a9fd3f6664a1b13e59781 | AstroAutomata/ThousandWorlds | AstroAutomata | {"pretty_name": "ThousandWorlds", "license": "cc-by-4.0", "size_categories": ["1K<n<10K"], "task_categories": ["tabular-regression", "other"], "tags": ["benchmark", "datasets", "physical-sciences", "scientific-machine-learning", "exoplanets", "climate", "astronomy", "emulation", "simulation", "physics", "pde", "paramet... | false | False | 2026-06-23T15:01:40 | 6 | 6 | false | 8493801b64b2ffef2717e4042a1dc61002003543 |
ThousandWorlds
ThousandWorlds is a benchmark for emulating exoplanet climates: 1760
simulations across 5 GCMs, 8 planet parameters, and atmospheric
variables on a 32 x 64 x 10 latitude-longitude-pressure grid. It includes three
nested benchmark subsets, two evaluation protocols, and eight released base... | 251 | 251 | 7,634,508,786 | [
"task_categories:tabular-regression",
"task_categories:other",
"license:cc-by-4.0",
"size_categories:1K<n<10K",
"library:datasets",
"arxiv:2606.18338",
"region:us",
"benchmark",
"datasets",
"physical-sciences",
"scientific-machine-learning",
"exoplanets",
"climate",
"astronomy",
"emulati... | 2026-06-23T15:01:39 | null | null |
6a3b58f031ecca7eacf9ec84 | KlingTeam/UnityShotsBench | KlingTeam | {"license": "cc-by-nc-4.0", "pretty_name": "UnityShots Benchmark", "language": ["zh", "yue", "en", "de", "es", "ar", "hi", "bn", "sw", "yo", "fa", "pt", "vi"], "task_categories": ["text-to-video", "image-to-video"], "tags": ["multi-shot", "audio-video-generation", "storytelling", "video-generation", "talking-head", "mu... | false | False | 2026-06-24T12:45:27 | 6 | 6 | false | 90b6bbfb1961f665a13c51640f58b20eef57d953 |
UnityShots Benchmark
A multilingual, multi-cultural k-shot storytelling benchmark for evaluating multi-shot
audio-video generation. Each case is a short cinematic story told across several shots, with a
consistent cast whose identity, voice, and world must persist across every cut.
This is the evaluation... | 1,169 | 1,169 | 775,265,877 | [
"task_categories:text-to-video",
"task_categories:image-to-video",
"language:zh",
"language:yue",
"language:en",
"language:de",
"language:es",
"language:ar",
"language:hi",
"language:bn",
"language:sw",
"language:yo",
"language:fa",
"language:pt",
"language:vi",
"license:cc-by-nc-4.0",... | 2026-06-24T04:11:28 | 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 | 2026-06-27T04:25:55 | 9,749 | 5 | false | ff56193d79a0106146c6a21d4f096fe8366bb7c0 |
a.k.a. Awesome ChatGPT Prompts
This is a Dataset Repository mirror of prompts.chat — a social platform for AI prompts.
📢 Notice
This Hugging Face dataset is a mirror. For the latest prompts, features, and community contributions, please visit:
🌐 Website: prompts.chat
📦 GitHub: github.com/f... | 31,742 | 611,316 | 5,119,542 | [
"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"... | 2022-12-13T23:47:45 | null | null |
64382440c212a363c3ac15c8 | OpenAssistant/oasst1 | OpenAssistant | {"license": "apache-2.0", "dataset_info": {"features": [{"name": "message_id", "dtype": "string"}, {"name": "parent_id", "dtype": "string"}, {"name": "user_id", "dtype": "string"}, {"name": "created_date", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "role", "dtype": "string"}, {"name": "lang", "dt... | false | False | 2023-05-02T13:21:21 | 1,538 | 5 | false | fdf72ae0827c1cda404aff25b6603abec9e3399b |
OpenAssistant Conversations Dataset (OASST1)
Dataset Summary
In an effort to democratize research on large-scale alignment, we release OpenAssistant
Conversations (OASST1), a human-generated, human-annotated assistant-style conversation
corpus consisting of 161,443 messages in 35 different langu... | 15,542 | 410,098 | 231,507,594 | [
"language:en",
"language:es",
"language:ru",
"language:de",
"language:pl",
"language:th",
"language:vi",
"language:sv",
"language:bn",
"language:da",
"language:he",
"language:it",
"language:fa",
"language:sk",
"language:id",
"language:nb",
"language:el",
"language:nl",
"language:... | 2023-04-13T15:48:16 | 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
- Downloads last month
- 4,106