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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
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 | 89 | 88 | 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... | 15,126 | 15,126 | 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 |
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 | 562 | 77 | 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.
... | 64,153 | 64,153 | 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 |
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 | 2026-07-07T12:32:14 | 48 | 48 | false | 2e342ca5f2673fb1287411fdbadcdfde82b35682 |
IFStruct v1.0
[!Note]
📝 Blog post: https://www.liquid.ai/blog/ifstruct-v1.0
💻 GitHub: 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 diffe... | 285 | 285 | 2,931,798 | [
"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 |
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 | 158 | 40 | 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... | 4,042 | 15,892 | 1,483,365,514 | [
"license:apache-2.0",
"arxiv:2409.20370",
"region:us"
] | 2024-10-13T16:04:55 | 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 | 2026-07-07T08:20:31 | 35 | 35 | false | 92698fa5fae2ab0b3b658d0e96ce9f64f1bc5a62 |
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... | 3,034 | 3,034 | 5,364,010 | [
"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",
"arxiv:2607.05155",
"region:us",
"benchmark",
"code-agents",
"evaluation",
"long... | 2026-06-24T06:11:52 | 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 | 286 | 33 | 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... | 17,143 | 17,143 | 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 |
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": ["10K<n<100K"], "task_categories": ["text-generation"], "task_ids": ["language-mo... | false | False | 2026-07-07T06:28:23 | 73 | 32 | false | e4f555103f9a00179088702abe07ab02dda23dac |
Complete FABLE.5 Traces 2M
Provenance-cleaned FABLE.5 / Claude corpus — trimmed to content-verified traces only.
Dataset Viewer | Parquet
This dataset is a post-closure compilation of FABLE.5 / Claude trace datasets found on Hugging Face after the closure of Fable and Mythos. It is deduplicated... | 6,387 | 6,387 | 307,149,644 | [
"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:10K<n<100K",
"format:parquet",
"modality:tabu... | 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 | 61 | 27 | 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, ... | 23,592 | 23,592 | 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 |
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 | 57 | 25 | 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... | 4,477 | 4,477 | 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 |
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 | 20 | 18 | 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... | 58,902 | 58,902 | 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 |
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 | 1,430 | 16 | 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... | 966,257 | 13,183,850 | 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 |
6a423065974497b9b32a07db | kyutai/rocket-science | kyutai | {"license": "cc-by-nc-sa-4.0", "pretty_name": "Rocket Science", "language": ["en"], "size_categories": ["1M<n<10M"], "task_categories": ["other"], "tags": ["rocket-league", "world-model", "video", "reinforcement-learning", "multimodal", "game", "webdataset"], "extra_gated_prompt": "Rocket League \u00a9 Psyonix LLC / Ep... | false | auto | 2026-07-06T11:14:51 | 14 | 13 | false | ddd274d8392fce77cdcc71b28c94fa3bdf49cb25 |
Rocket Science
Time-aligned video, keyboard actions, game events, and per-frame game state for all four players, captured from a 2v2 Rocket League match.
This is the dataset behind MIRA, a real-time multiplayer world model trained to simulate Rocket League gameplay — by General Intuition and Kyutai, in c... | 963 | 963 | 29,235,516,908,974 | [
"task_categories:other",
"language:en",
"license:cc-by-nc-sa-4.0",
"size_categories:1M<n<10M",
"modality:video",
"library:webdataset",
"region:us",
"rocket-league",
"world-model",
"video",
"reinforcement-learning",
"multimodal",
"game",
"webdataset"
] | 2026-06-29T08:44:21 | 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 | 14 | 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... | 1,467 | 1,467 | 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 |
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 | 69 | 12 | 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... | 1,989 | 1,989 | 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 |
6a45413ea0d17027b983a4fa | OpenOneRec/Explorer_LLM_Rec_Competition | OpenOneRec | null | false | False | 2026-07-01T16:36:25 | 14 | 11 | 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... | 19,055 | 19,055 | 17,184,874,770 | [
"region:us"
] | 2026-07-01T16:33:02 | 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 | 1,179 | 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 ... | 381,702 | 7,870,076 | 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 | 246 | 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)
... | 39,388 | 142,908 | 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 |
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 | 143 | 10 | 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... | 3,953 | 4,798 | 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 |
639244f571c51c43091df168 | Anthropic/hh-rlhf | Anthropic | {"license": "mit", "tags": ["human-feedback"]} | false | False | 2023-05-26T18:47:34 | 1,812 | 9 | 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... | 29,814 | 1,943,419 | 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 |
697b4cc88c8b203d5e91290f | ruggsea/infini-news-corpus | ruggsea | {"license": "cc-by-4.0", "task_categories": ["text-generation", "text-classification", "text-retrieval"], "language": ["eng", "spa", "rus", "deu", "ita", "fra", "tur", "arb", "por", "hin", "jpn", "ell", "ron", "zho", "pol", "nld", "kor", "ukr", "vie", "swe", "hun", "bul", "ces", "ind", "fas", "tam", "arz", "nor", "urd"... | false | False | 2026-07-01T11:47:33 | 17 | 9 | false | 5b78199b86a838a5634b2d3267d72b98b8f71721 |
INFINI-NEWS Corpus
🔎 Search this corpus online: query it with sub-second full-text search and n-gram counts — in the browser or via a public, keyless REST API, no download required — at infini-news.uni-graz.at (API reference).
A multilingual news corpus extracted from
Common Crawl CC-News WARC files.
... | 29,991 | 130,503 | 1,807,488,896,832 | [
"task_categories:text-generation",
"task_categories:text-classification",
"task_categories:text-retrieval",
"annotations_creators:machine-generated",
"multilinguality:multilingual",
"source_datasets:original",
"language:eng",
"language:spa",
"language:rus",
"language:deu",
"language:ita",
"lan... | 2026-01-29T12:04:24 | 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 | 32 | 9 | 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... | 2,072 | 2,072 | 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 |
6a382f6fa519cd301493b37b | Syn4D/Syn4D | Syn4D | {"license": "cc-by-4.0", "arxiv": 2605.05207, "pretty_name": "Syn4D: A Multiview Synthetic 4D Dataset", "viewer": false} | false | False | 2026-07-04T21:14:00 | 10 | 9 | false | 599b301ca0d131d9b5532d8c0cb1d24e09dcb0e1 |
Syn4D: A Multiview Synthetic 4D Dataset
Syn4D is a synthetic 4D dataset with multi-view RGB videos, depth, masks, tracking geometry, and supporting object mesh metadata.
Layout
data/
syn4d_v1_stride_1/ # Syn4D V1, every frame can be a tracking reference frame
syn4d_v1_stride_... | 14,291 | 14,291 | 1,701,805,420,189 | [
"license:cc-by-4.0",
"arxiv:2605.05207",
"region:us"
] | 2026-06-21T18:37:35 | null | null |
6a2a1f91f76bc9ca45b048d1 | CMRobot/MotionDecode | CMRobot | {"dataset_info": {"license": "other", "license_name": "chingmu-terms", "license_link": "LICENSE", "language": ["en", "zh"], "pretty_name": "ChingMu Robot Motion Dataset", "tags": ["motion-capture", "humanoid-robotics", "imitation-learning", "optical-mocap", "bvh", "dexterous-hands", "whole-body-control"], "size_categor... | false | auto | 2026-07-07T03:27:26 | 9 | 8 | false | 148feb597136fe99760d9d89415fb9dfcfd0cefb |
ChingMu 1000-Hour Embodied Motion Dataset
High-precision optical motion capture data for humanoid robots, dexterous hands, embodied AI, and virtual production.
Duration
1000+ hours @ 120 Hz
**Scenarios **
15+ real-world scenes
**Tasks **
500+ standardized tasks
Objects
200+ tracked... | 1,180 | 1,180 | 17,543,386,896 | [
"region:us"
] | 2026-06-11T02:38:09 | 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 | 78 | 8 | 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... | 653,386 | 653,386 | 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 |
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 | 8 | 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); ... | 2,981 | 2,981 | 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 |
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 | 29 | 8 | 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... | 217 | 217 | 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 |
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
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