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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
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 | 2026-06-29T15:10:20 | 521 | 87 | false | e05c417852fc59fd8da758e68b352732423ca0cb |
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.
... | 54,467 | 54,467 | 187,507,989 | [
"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 |
670befa7623c91990f914eb6 | mlabonne/open-perfectblend | mlabonne | {"dataset_info": {"features": [{"name": "conversations", "list": [{"name": "from", "dtype": "string"}, {"name": "value", "dtype": "string"}]}, {"name": "source", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2951380166, "num_examples": 1420909}], "download_size": 1483360321, "dataset_size": 2951380166}... | false | False | 2025-01-15T20:01:32 | 145 | 71 | false | af60f3c18201652a83a93f46fcfee1b646ba3df7 |
🎨 Open-PerfectBlend
Open-PerfectBlend is an open-source reproduction of the instruction dataset introduced in the paper "The Perfect Blend: Redefining RLHF with Mixture of Judges".
It's a solid general-purpose instruction dataset with chat, math, code, and instruction-following data.
Data s... | 3,406 | 15,136 | 1,483,365,514 | [
"license:apache-2.0",
"arxiv:2409.20370",
"region:us"
] | 2024-10-13T16:04:55 | 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 | 276 | 54 | 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... | 16,454 | 16,454 | 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 |
6a437ed52e089285573dcfd3 | markov-ai/gaming-500-hours | markov-ai | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "metadata.jsonl"}]}]} | false | False | 2026-06-30T11:56:39 | 50 | 50 | false | 5af703f2810306e7d75eb4394ae59591f1f6e8a2 |
Gaming Dataset (gaming-1) — 494.7 Hours
Native PC/console gameplay screen-recordings, organized by game. Each workflow
is one play session, trimmed to pure gameplay — login screens, launchers,
desktop, collection-app references, and any watching/streaming are removed.
In-game menus, lobbies, loading, and... | 11,275 | 11,275 | 1,598,371,626,719 | [
"size_categories:n<1K",
"format:json",
"modality:tabular",
"modality:text",
"modality:video",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us"
] | 2026-06-30T08:31:17 | null | null |
6a3543c41278d868e0c1bf12 | bcbl190626/SpanishBCBL | bcbl190626 | {"pretty_name": "DECOMEG: Brain Activity During Typing (MEG & EEG)", "license": "cc-by-nc-4.0", "language": ["es"], "tags": ["neuroscience", "meg", "eeg", "brain-computer-interface", "bci", "brain-to-text", "typing", "motor", "electrophysiology"], "task_categories": ["other"], "size_categories": ["100GB<n<1TB"], "modal... | false | False | 2026-06-29T11:56:46 | 49 | 49 | false | 88f9096c6ce3a3fb17cc7b8e3131ff7f96da5684 |
DECOMEG — Brain Activity During Typing (MEG & EEG)
Non-invasive brain recordings (magnetoencephalography, MEG; and electroencephalography, EEG)
of healthy adults typing briefly-memorized sentences on a QWERTY keyboard. This is the dataset
underlying Brain2Qwerty (Lévy et al., 2025) and its companion neur... | 3,970 | 3,970 | 280,382,552,015 | [
"task_categories:other",
"language:es",
"license:cc-by-nc-4.0",
"arxiv:2502.07429",
"region:us",
"neuroscience",
"meg",
"eeg",
"brain-computer-interface",
"bci",
"brain-to-text",
"typing",
"motor",
"electrophysiology"
] | 2026-06-19T13:27:32 | null | null |
6a3becf673d60eeb0376d121 | LiquidAI/ifstruct-v1.0 | LiquidAI | {"pretty_name": "IFStruct v1.0", "language": ["en"], "tags": ["structured-output", "json", "yaml", "instruction-following", "schema-following"], "task_categories": ["text-generation"], "size_categories": ["1K<n<10K"], "configs": [{"config_name": "default", "data_files": [{"split": "test", "path": "test.parquet"}]}], "l... | false | False | 2026-06-30T18:04:06 | 37 | 37 | false | 1454d7a76ff95188c3c5e4eccce49a70c2b5f665 |
IFStruct v1.0
Eval code: https://github.com/Liquid4All/ifstruct
IFStruct is a benchmark for structured-output compliance: can a model produce valid JSON/YAML that follows a requested schema, when the requirements are phrased the many different ways real users phrase them? It is scored without constrained... | 194 | 194 | 2,931,629 | [
"benchmark:official",
"benchmark:eval-yaml",
"task_categories:text-generation",
"language:en",
"license:apache-2.0",
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us",
"structured-output... | 2026-06-24T14:43:02 | 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 | 61 | 35 | 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 ... | 4,867 | 4,867 | 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 |
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 | 2026-06-30T13:15:30 | 55 | 30 | false | addba09fb37d139556703cd778115117766433bb |
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, ... | 18,289 | 18,289 | 285,237,128,051 | [
"task_categories:text-generation",
"task_categories:feature-extraction",
"language:en",
"license:other",
"size_categories:1M<n<10M",
"modality:text",
"region:us",
"science",
"arxiv",
"latex",
"academic"
] | 2026-06-18T14:44:25 | 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 | 87 | 20 | 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... | 2,138 | 2,810 | 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 | 2026-07-04T12:59:38 | 62 | 19 | false | 6b8d28437042434dcdd168434227ca0de408c5ba |
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... | 1,766 | 1,766 | 257,213,344 | [
"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 |
6a3b7528ee2af5bbf328b350 | ByteDance-Seed/EdgeBench | ByteDance-Seed | {"license": "cc-by-4.0", "task_categories": ["text-generation"], "language": ["en"], "pretty_name": "EdgeBench", "size_categories": ["n<1K"], "tags": ["benchmark", "code-agents", "evaluation", "long-horizon"], "configs": [{"config_name": "tasks", "data_files": "tasks.jsonl"}]} | false | False | 2026-07-04T11:11:00 | 19 | 19 | false | 3d6a8a78152b7d327cf815aedef4fc5265c360b1 |
Overview
EdgeBench is a benchmark of 134 real-world tasks for evaluating how autonomous AI agents learn from real-world environments. Instead of measuring one-shot performance, EdgeBench places agents in executable task environments with rea... | 1,321 | 1,321 | 5,363,889 | [
"task_categories:text-generation",
"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",
"benchmark",
"code-agents",
"evaluation",
"long-horizon"
] | 2026-06-24T06:11:52 | null | null |
6a421e32b1d98b18fb3edd74 | ginigen-ai/Metacognition-Bench | ginigen-ai | {"license": "apache-2.0", "task_categories": ["text-generation", "question-answering"], "language": ["en"], "tags": ["metacognition", "self-correction", "hallucination-detection", "reasoning", "benchmark", "trap-escape", "error-recovery", "metacognition-adapter", "aether"], "size_categories": ["n<1K"], "pretty_name": "... | false | False | 2026-07-03T07:40:14 | 27 | 18 | false | 5fe6c6198059f1244c22c20ee827accc518aeb65 |
Metacognition-Bench
"Not whether a model knows the answer — but whether it knows when it might be wrong, and can correct itself."
Metacognition-Bench is a curated benchmark of 300 metacognitive-trap problems that measure functional metacognition in Large Language Models: the ability to detect and rec... | 171 | 171 | 378,965 | [
"task_categories:text-generation",
"task_categories:question-answering",
"language:en",
"license:apache-2.0",
"size_categories:n<1K",
"region:us",
"metacognition",
"self-correction",
"hallucination-detection",
"reasoning",
"benchmark",
"trap-escape",
"error-recovery",
"metacognition-adapte... | 2026-06-29T07:26:42 | 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 | 140 | 15 | 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... | 3,862 | 4,557 | 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 |
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 | 55 | 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.... | 1,628 | 1,628 | 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 |
6a413a341831ca8ca806cd45 | prathoshap/vagdhenu-data | prathoshap | {"license": "cc-by-4.0", "task_categories": ["text-to-speech"], "language": ["sa"], "pretty_name": "V\u0101gdhenu \u2014 Sanskrit Chant Corpus", "size_categories": ["1K<n<10K"], "configs": [{"config_name": "style_a", "data_files": "style_a/**"}, {"config_name": "style_b", "data_files": "style_b/**"}]} | false | False | 2026-06-28T19:16:49 | 14 | 14 | false | adda7747c1ea05c45e6b789d23d6b6bbf550918d |
Vāgdhenu — Sanskrit Chant Corpus
A single-speaker Sanskrit chant (pārāyaṇa) recording corpus — classical ślokas chanted with tradition-faithful prosody and metrically-aware durations. Training data behind the Vāgdhenu Sanskrit Chant TTS.
~1,467 clips · ~5.3 hours · 24 kHz mono. One reciter (the author); ... | 2,373 | 2,373 | 922,873,637 | [
"task_categories:text-to-speech",
"language:sa",
"license:cc-by-4.0",
"size_categories:1K<n<10K",
"format:audiofolder",
"modality:audio",
"modality:text",
"library:datasets",
"library:mlcroissant",
"region:us"
] | 2026-06-28T15:13:56 | 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 | 2026-06-29T11:22:36 | 27 | 13 | false | 099319e447586f3e49ed3140c1d2ffe5cdf4647e |
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... | 989 | 989 | 24,583,752,698 | [
"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 |
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 | 1,419 | 12 | 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... | 942,813 | 13,087,773 | 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 |
6a27d0ad7a6ecef661d66995 | BitRobot/HIW-500 | BitRobot | {"pretty_name": "HIW-500: Humanoids In-the-Wild Dataset", "language": ["en"], "tags": ["robotics", "humanoid"], "license": "cc-by-4.0"} | false | False | 2026-06-29T13:47:34 | 39 | 12 | false | 2ca7ffcd85ec5212f81ae08491a4076bf48ea841 |
HIW-500: Humanoids In-the-Wild Dataset
https://bitrobot-foundation.github.io/humanoids-in-the-wild-500-hours/
HIW-500: 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 a... | 71,066 | 71,066 | 8,937,650,917,430 | [
"language:en",
"license:cc-by-4.0",
"region:us",
"robotics",
"humanoid"
] | 2026-06-09T08:37:01 | 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 | 2026-07-02T20:47:44 | 73 | 12 | false | 29136bc9b9e38d320b00ffcddbbe4cd0e3278c58 |
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... | 621,412 | 621,412 | 22,060,641,937,503 | [
"task_categories:robotics",
"language:en",
"license:apache-2.0",
"size_categories:n>1T",
"arxiv:2606.27375",
"region:us",
"robotics",
"manipulation",
"imitation-learning",
"bimanual",
"teleoperation",
"mcap"
] | 2026-06-15T22:33:18 | null | null |
6a352293225ebf635e87572e | DavydenkoGr/AFTER | DavydenkoGr | {"license": "apache-2.0", "language": ["en"], "pretty_name": "AFTER", "viewer": false, "tags": ["benchmark", "agents", "skill-evolution", "evaluation", "software-engineering", "data-science", "data-engineering", "infrastructure", "generative-ai", "project-management"]} | false | False | 2026-06-30T09:35:05 | 13 | 12 | false | 18287618ffe4173da683895176ec700f08080752 |
AFTER
A Benchmark for Skill Evolution Frameworks
Measuring whether agents can improve reusable skills, and whether those
improvements transfer across roles, tasks, and execution contexts.
📄 Abstract
AFTER is a benchmark for studying skill evolution: the abil... | 1,227 | 1,227 | 64,904,324 | [
"language:en",
"license:apache-2.0",
"arxiv:2606.23127",
"region:us",
"benchmark",
"agents",
"skill-evolution",
"evaluation",
"software-engineering",
"data-science",
"data-engineering",
"infrastructure",
"generative-ai",
"project-management"
] | 2026-06-19T11:05:55 | null | null |
6a3bf717fc9799bfca0ced29 | RekaAI/CS2-10k | RekaAI | {"license": "cc-by-nc-4.0", "pretty_name": "CS2-10k", "task_categories": ["other"], "tags": ["counter-strike", "cs2", "gaming", "egocentric", "first-person", "video", "world-models", "imitation-learning", "action-prediction", "webdataset"], "size_categories": ["100K<n<1M"], "configs": [{"config_name": "default", "data_... | false | False | 2026-06-29T17:14:11 | 13 | 11 | false | 5bff96ad139daec3b83a42590a024a7f6cff8cf7 |
CS2-10k: A Large-Scale Egocentric Counter-Strike 2 Dataset
CS2-10k is a large-scale egocentric gameplay dataset built from professional CS2 matches. It contains 600,000+ player-round videos spanning 10,000+ hours of first-person footage, paired with per-frame annotations covering keyboard state, m... | 57,487 | 57,487 | 63,484,680,387,407 | [
"task_categories:other",
"license:cc-by-nc-4.0",
"size_categories:100K<n<1M",
"modality:video",
"library:webdataset",
"region:us",
"counter-strike",
"cs2",
"gaming",
"egocentric",
"first-person",
"video",
"world-models",
"imitation-learning",
"action-prediction",
"webdataset"
] | 2026-06-24T15:26:15 | null | null |
6655eb19d17e141dcb546ed5 | HuggingFaceFW/fineweb-edu | HuggingFaceFW | {"license": "odc-by", "task_categories": ["text-generation"], "language": ["en"], "pretty_name": "FineWeb-Edu", "size_categories": ["n>1T"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/*/*"}], "features": [{"name": "text", "dtype": "string"}, {"name": "id", "dtype": "string"},... | false | False | 2025-07-11T20:16:53 | 1,175 | 10 | false | 87f09149ef4734204d70ed1d046ddc9ca3f2b8f9 |
📚 FineWeb-Edu
1.3 trillion tokens of the finest educational data the 🌐 web has to offer
Paper: https://arxiv.org/abs/2406.17557
What is it?
📚 FineWeb-Edu dataset consists of 1.3T tokens and 5.4T tokens (FineWeb-Edu-score-2) of educational web pages filtered from 🍷 FineWeb ... | 384,910 | 7,833,153 | 5,835,742,481,176 | [
"task_categories:text-generation",
"language:en",
"license:odc-by",
"size_categories:1B<n<10B",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"arxiv:2406.17557",
"arxiv:2404.14219",
"arxiv:2401.10020",
... | 2024-05-28T14:32:57 | null | null |
696e2528357a40707550b1c4 | google/WaxalNLP | google | {"language_creators": ["creator_1"], "language": ["ach", "aka", "amh", "bau", "dag", "dga", "ewe", "fat", "ful", "hau", "ibo", "kik", "kpo", "lin", "lug", "luo", "mas", "mlg", "nyn", "orm", "pcm", "sid", "sna", "sog", "swa", "tir", "twi", "wal", "yor"], "license": ["cc-by-sa-4.0", "cc-by-4.0"], "multilinguality": ["mul... | false | False | 2026-06-11T13:46:09 | 245 | 10 | false | e0a62aaebc61bd5bb8cac17a08d1b42c65551dd2 |
Waxal Datasets
The WAXAL dataset is a large-scale multilingual speech corpus for African languages, introduced in the paper WAXAL: A Large-Scale Multilingual African Language Speech Corpus.
Dataset Description
The Waxal project provides datasets for both Automated Speech Recognition (ASR)
... | 34,293 | 134,308 | 1,060,211,984,900 | [
"task_categories:automatic-speech-recognition",
"task_categories:text-to-speech",
"language_creators:creator_1",
"multilinguality:multilingual",
"source_datasets:UGSpeechData",
"source_datasets:DigitalUmuganda/AfriVoice",
"source_datasets:original",
"language:ach",
"language:aka",
"language:amh",
... | 2026-01-19T12:35:52 | null | null |
6a2c5668f7f66fcaa0d54e17 | CodeDevX/Vibe-Coding-Instruct | CodeDevX | {"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 | 177 | 10 | false | 7ad49b3cbf0b73934b1d567d2b5c4768bce7989e | null | 2,650 | 2,650 | 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 |
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 | 31 | 10 | 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... | 941 | 941 | 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 |
6a37fba2e45f47278427402a | macrodata/WGO-Bench | macrodata | {"pretty_name": "WGO-Bench", "task_categories": ["robotics", "video-classification"], "language": ["en"], "tags": ["temporal-segmentation", "subtask-annotation", "robotics", "robot-learning", "egocentric-video", "droid", "robocoin", "galaxea", "homer"], "license": "cc-by-nc-sa-4.0", "size_categories": ["n<1K"], "config... | false | False | 2026-06-28T16:50:51 | 10 | 10 | false | 795dc4429128cfcd288e2245f3ced674fb83322b |
WGO-Bench: What's Going On Benchmark
WGO-Bench is a small, manually annotated benchmark for evaluating how well vision-language models can turn robot and egocentric manipulation videos into timestamped subtask annotations.
Each row contains one video episode, a high-level task instruction, and gold subta... | 830 | 830 | 1,398,838,638 | [
"task_categories:robotics",
"task_categories:video-classification",
"language:en",
"license:cc-by-nc-sa-4.0",
"size_categories:n<1K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us",
"temporal-segmentation",
"sub... | 2026-06-21T14:56:34 | null | null |
67d45c3d35fc7f6d2ab224c8 | allenai/olmOCR-bench | allenai | {"license": "odc-by", "tags": ["text"], "configs": [{"config_name": "olmocr-bench", "data_files": [{"split": "arxiv_math", "path": ["bench_data/arxiv_math.jsonl"]}, {"split": "headers_footers", "path": ["bench_data/headers_footers.jsonl"]}, {"split": "long_tiny_text", "path": ["bench_data/long_tiny_text.jsonl"]}, {"spl... | false | False | 2026-02-19T17:28:38 | 258 | 9 | 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... | 8,004 | 53,529 | 356,940,588 | [
"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 |
6835e8703de5738a2e9af4ae | nvidia/PhysicalAI-Autonomous-Vehicles | nvidia | {"extra_gated_heading": "You must agree to the NVIDIA Autonomous Vehicle Dataset License Agreement to access this dataset.", "extra_gated_prompt": "### NVIDIA Autonomous Vehicle Dataset License Agreement\n\nThis NVIDIA Autonomous Vehicle Dataset License Agreement (\"Agreement\") is a legal agreement between you, whethe... | false | auto | 2026-05-06T21:55:22 | 934 | 9 | false | b719eea7f0a63619ef51ec7f54178af0937ef050 |
PHYSICAL AI AUTONOMOUS VEHICLES
The PhysicalAI-Autonomous-Vehicles dataset provides one of the largest, geographically diverse collections of multi-sensor data empowering AV researchers to build the next generation of Physical AI based end-to-end driving systems. This dataset is ready for commercial/non-com... | 208,571 | 2,578,882 | 133,214,352,118,097 | [
"license:other",
"region:us"
] | 2025-05-27T16:29:36 | 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 | 428 | 9 | 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 ... | 8,522 | 17,274 | 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 |
6a2a0f4091887681beb657d9 | sbintuitions/joyo-kanji-yomi-benchmark | sbintuitions | {"license": "mit", "task_categories": ["text-to-speech"], "language": ["ja"], "tags": ["tts-evaluation", "japanese", "kanji", "chinese-character", "pronunciation"], "pretty_name": "Joyo Kanji Yomi Benchmark", "size_categories": ["10K<n<100K"]} | false | False | 2026-06-29T02:37:04 | 9 | 9 | false | 2ae383bcdac88dacf89c9ab84f9990eec55fe5c9 |
Joyo Kanji Yomi Benchmark
A kanji-level pronunciation evaluation benchmark for Japanese TTS, covering all 2,136 Joyo kanji and their 4,378 readings with 13,095 native-speaker-verified test sentences.
Dataset Description
Each sample targets a specific kanji-reading pair. The sentence cont... | 64 | 64 | 2,725,671 | [
"task_categories:text-to-speech",
"language:ja",
"license:mit",
"size_categories:10K<n<100K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"arxiv:2606.25369",
"region:us",
"tts-evaluation",
"japanese",
"kanji",
"chinese-... | 2026-06-11T01:28:32 | null | null |
6a42c1990b85d2302a07c74a | Voxel51/SceneFun3D | Voxel51 | {"annotations_creators": ["expert-generated", "machine-generated"], "language": ["en"], "license": "cc-by-nc-sa-4.0", "pretty_name": "SceneFun3D", "size_categories": ["n<1K"], "splits": ["train", "val", "test"], "task_categories": ["object-detection"], "tags": ["fiftyone", "3d", "point-cloud", "fo3d", "group", "video",... | false | False | 2026-06-29T19:44:43 | 9 | 9 | false | 76803371aae67277cfa0cc29804db2368c6756ce |
Dataset Card for SceneFun3D
SceneFun3D is a 3D scene-understanding dataset of high-resolution Faro laser-scan point clouds of indoor environments, densely annotated with fine-grained
functional interactive elements (handles, knobs, buttons, switches, ...), their affordances, motion parameters, and free-... | 1,640 | 1,640 | 6,361,431,713 | [
"task_categories:object-detection",
"annotations_creators:expert-generated",
"annotations_creators:machine-generated",
"language:en",
"license:cc-by-nc-sa-4.0",
"size_categories:n<1K",
"modality:video",
"modality:3d",
"library:fiftyone",
"region:us",
"fiftyone",
"3d",
"point-cloud",
"fo3d"... | 2026-06-29T19:03:53 | null | null |
639244f571c51c43091df168 | Anthropic/hh-rlhf | Anthropic | {"license": "mit", "tags": ["human-feedback"]} | false | False | 2023-05-26T18:47:34 | 1,808 | 8 | false | 09be8c5bbc57cb3887f3a9732ad6aa7ec602a1fa |
Dataset Card for HH-RLHF
Dataset Summary
This repository provides access to two different kinds of data:
Human preference data about helpfulness and harmlessness from Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback. These data are meant to train preferenc... | 29,021 | 1,939,494 | 94,745,957 | [
"license:mit",
"size_categories:100K<n<1M",
"format:json",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2204.05862",
"region:us",
"human-feedback"
] | 2022-12-08T20:11:33 | null | null |
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 | 2,913 | 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 ... | 337,208 | 8,717,449 | 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 |
6a153f0b85e02991e81333d0 | uw-math-ai/math-graph | uw-math-ai | {"license": "cc-by-4.0", "language": ["en"], "task_categories": ["text-retrieval", "feature-extraction"], "tags": ["mathematics", "theorem-proving", "lean", "arxiv", "knowledge-graph", "dependency-graph", "autoformalization"], "size_categories": ["10M<n<100M"], "configs": [{"config_name": "paper_arxiv", "data_files": "... | false | False | 2026-06-28T21:56:05 | 8 | 8 | false | ced4ca9de1bd9e5b67aa09d1d515e270e438fa1e |
Math-Graph
Math-Graph is the dataset behind TheoremGraph, a unified, statement-level dependency
graph spanning both informal and formal mathematics. On the informal side it parses millions of
theorem-like environments from mathematics arXiv and recovers directed dependency edges within and
across papers;... | 134 | 263 | 13,632,661,483 | [
"task_categories:text-retrieval",
"task_categories:feature-extraction",
"language:en",
"license:cc-by-4.0",
"size_categories:10M<n<100M",
"format:csv",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"arxiv:2606.25363",
"r... | 2026-05-26T06:34:51 | 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", "glm-5.2", "teich"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "*.jsonl"}]}]} | false | False | 2026-07-01T19:20:23 | 27 | 8 | false | c0c098b3a1bdc8c0a4896ed92b31769bcd52ce61 | 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: 319
Model metadata: glm-5.2
Training-ready tools
Generated agent traces carry configured or recovered tool schemas so tools remain availabl... | 1,597 | 1,597 | 121,121,593 | [
"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 |
6a45413ea0d17027b983a4fa | OpenOneRec/Explorer_LLM_Rec_Competition | OpenOneRec | null | false | False | 2026-07-01T16:36:25 | 9 | 8 | false | 8b88c769d74801df49d41e47cb8653b90cbe1015 |
Explorer_LLM_Rec_Competition
This dataset contains user historical behaviors and content metadata, constructed from real user interaction histories. It covers behavior sequences across multiple domains for a single user and supports cross-domain recommendation, semantic-ID retrieval / generation, content... | 14,578 | 14,578 | 17,184,874,770 | [
"region:us"
] | 2026-07-01T16:33:02 | 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", "dtyp... | false | auto | 2026-01-20T22:42:17 | 850 | 7 | 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
🌐 Website | 📄 Paper | GitHub
Center for AI Safety & Scale AI
Humanity's Last Exam (HLE) is a multi-modal benchmark at the frontier of huma... | 28,760 | 364,324 | 274,282,300 | [
"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 |
68465f1ba516bd14fc146e1f | nvidia/Nemotron-Personas-USA | nvidia | {"license": "cc-by-4.0", "task_categories": ["text-generation"], "language": ["en"], "tags": ["synthetic", "personas", "NVIDIA", "datadesigner"], "size_categories": ["1M<n<10M"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "... | false | False | 2025-12-16T19:13:23 | 332 | 7 | false | 5b4cd35ab46490c1da1bd2b5a2324d6f871be180 |
Nemotron-Personas-USA
A compound AI approach to personas grounded in real-world distributions
v1.1 Update
The v1.1 update introduces the following changes:
leverage openai/gpt-oss-120b model instead of mistralai/Mixtral-8x22B-v0.1 model to improve data quality and diversity
increase the n... | 13,682 | 147,155 | 2,689,226,423 | [
"task_categories:text-generation",
"language:en",
"license:cc-by-4.0",
"size_categories:1M<n<10M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"library:datadesigner",
"region:us",
"synthetic",
"personas",
"NVIDIA",
"da... | 2025-06-09T04:12:11 | 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 | 175 | 7 | 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... | 2,337 | 25,348 | 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 |
69fc1f1a2042bc11f9fc0092 | agents-last-exam/agents-last-exam | agents-last-exam | {"license": "cc-by-4.0", "language": ["en"], "tags": ["computer-use-agents", "agent-benchmark", "benchmark", "evaluation"], "pretty_name": "Agents Last Exam \u2014 Task Card Metadata", "configs": [{"config_name": "default", "data_files": [{"split": "v1.0", "path": "task_cards.parquet"}]}]} | false | False | 2026-06-12T18:28:44 | 198 | 7 | false | b07f71f2b82477f02c8c4e1b885fa032e16aed86 |
Agents Last Exam — Task Card Metadata (v1.0)
A metadata-only release (v1.0) of 153 tasks from the Agents Last Exam (ALE)
benchmark for evaluating computer-use agents on long-horizon professional work.
The Agents Last Exam dataset family
ALE is published as three companion HuggingFace datas... | 8,314 | 8,358 | 194,603 | [
"language:en",
"license:cc-by-4.0",
"size_categories:n<1K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us",
"computer-use-agents",
"agent-benchmark",
"benchmark",
"evaluation"
] | 2026-05-07T05:11:54 | 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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