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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
6a429172c50c64cfb0a54dfb | nvidia/simready-assets | nvidia | {"license": "cc-by-4.0", "viewer": false} | false | False | 2026-11-04T20:49:04 | 1,457 | 331 | false | 7879e0e50423290300ac20ecd6e727325f680b60 |
Welcome to NVIDIA SimReady Assets
Welcome to NVIDIA's open-source SimReady Assets dataset!
SimReady Central brings together OEM-curated datasets like this one, helping you discover simulation-ready assets for your projects.
Interested in contributing? Visit nvidia/simready-central-library and request con... | 1,419,300 | 1,421,941 | 489,676,463,590 | [
"license:cc-by-4.0",
"region:us",
"simready"
] | 2026-06-29T15:38:26 | null | null |
6a1b134acd3cccc1ba63521f | Vyber07/cyber-security | Vyber07 | {"license": "apache-2.0", "task_categories": ["text-generation", "question-answering"], "language": ["en"], "tags": ["cybersecurity", "penetration-testing", "red-team", "blue-team", "web-security", "cloud-security", "malware", "forensics", "exploit", "vulnerability", "iot", "ai-security", "bug-bounty", "offensive-secur... | false | auto | 2026-08-31T17:57:10 | 739 | 178 | false | bc7a780571cb67d482250649ab573908b9557873 |
Cybersecurity AI Knowledge Base โ PhD-Level Dataset
Overview
This is the most comprehensive cybersecurity knowledge base ever assembled for AI training. It covers all domains of cybersecurity at PhD-level depth โ from offensive red teaming and bug bounty exploitation to defensive SOC opera... | 12,936 | 19,192 | 95,039,000,249 | [
"task_categories:text-generation",
"task_categories:question-answering",
"language:en",
"license:apache-2.0",
"size_categories:n<1K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us",
"cybersecurity",
"penetration-te... | 2026-05-30T16:41:46 | null | null |
69b53151d3cc52f95d53bcbb | open-index/hacker-news | open-index | {"license": "odc-by", "task_categories": ["text-generation", "feature-extraction", "text-classification", "question-answering"], "language": ["en"], "pretty_name": "Hacker News - Complete Archive", "size_categories": ["10M<n<100M"], "tags": ["hacker-news", "forum", "text", "parquet", "community", "tech", "comments", "l... | false | False | 2026-08-23T16:25:00 | 535 | 162 | false | c54ced5973b95d80cd934b8921f44ec8d87c3cd1 |
Hacker News - Complete Archive
Every Hacker News item since 2006, live-updated every 5 minutes
What is it?
This dataset contains the complete Hacker News archive: every story, comment, Ask HN, Show HN, job posting, and poll ever submitted to the site. Hacker News is one of the longest-ru... | 476,793 | 712,055 | 12,426,849,376 | [
"task_categories:text-generation",
"task_categories:feature-extraction",
"task_categories:text-classification",
"task_categories:question-answering",
"language:en",
"license:odc-by",
"size_categories:10M<n<100M",
"modality:text",
"region:us",
"hacker-news",
"forum",
"text",
"parquet",
"com... | 2026-03-14T09:58:41 | null | null |
6aeb4beb05f37ecf15d45eaa | codepawl/abo-glb | codepawl | {"license": "cc-by-4.0", "pretty_name": "Amazon Berkeley Objects GLB", "tags": ["3d", "gltf", "glb", "web", "three.js", "67js", "furniture", "pbr", "amazon-berkeley-objects"], "size_categories": ["1K<n<10K"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": ["metadata.jsonl", "engine/*/*... | false | False | 2026-11-07T06:42:59 | 89 | 87 | false | 11517bd633d927f9e78e4614973e07f7819122e4 |
Amazon Berkeley Objects GLB
The 7,953 3D models of Amazon Berkeley Objects
(ABO), made small enough to load in a browser. Each product is one .glb with its full PBR material (colour, normal,
metallic and roughness maps), at real size in metres, Y up. Most stand on Y = 0.
Amazon's files
This data... | 20,835 | 20,835 | 43,107,220,550 | [
"license:cc-by-4.0",
"size_categories:1K<n<10K",
"modality:3d",
"modality:image",
"modality:tabular",
"modality:text",
"library:datasets",
"library:mlcroissant",
"region:us",
"3d",
"gltf",
"glb",
"web",
"three.js",
"67js",
"furniture",
"pbr",
"amazon-berkeley-objects"
] | 2026-11-04T15:14:51 | null | null |
6ae9af1f39e47bbba4fe056f | PentesterFlow/OffsecCyber | PentesterFlow | {"language": ["en"], "license": "apache-2.0", "task_categories": ["text-generation", "text-classification"], "tags": ["cybersecurity", "application-security", "vulnerabilities", "cve", "cwe", "instruction-tuning", "jsonl"], "size_categories": ["10K<n<100K"]} | false | False | 2026-11-03T20:19:14 | 73 | 70 | false | e2aa04114f466659701cf999dfa27a2ebf7054ef |
Security Vulnerability Instruction Dataset
An English-language dataset of security-focused assistant conversations about software vulnerabilities. Examples cover vulnerability explanations, CVE analysis, severity and impact, CWE references, remediation guidance, and offensive/web-appsec reasoning. It co... | 5,210 | 5,210 | 122,978,097 | [
"task_categories:text-generation",
"task_categories:text-classification",
"language:en",
"license:apache-2.0",
"size_categories:10K<n<100K",
"region:us",
"cybersecurity",
"application-security",
"vulnerabilities",
"cve",
"cwe",
"instruction-tuning",
"jsonl"
] | 2026-11-03T09:53:35 | null | null |
6ae16a49de063323d85a858b | ccore/ThreeJS-3D-Scenes | ccore | {"license": "mit", "pretty_name": "CCORE Three.js 3D Scenes", "language": ["en"], "task_categories": ["text-generation"], "size_categories": ["1K<n<10K"], "tags": ["3d", "threejs", "code", "reasoning", "conversational", "html", "javascript"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "pat... | false | False | 2026-11-03T21:53:43 | 83 | 61 | false | 57b77a19d38eee8316c8b1f9e71c772046d9f48c |
CCORE Three.js 3D Scenes
4,146 high-quality 3D scene examples with prompts, full thinking traces, and complete HTML/JavaScript responses.
The scene code and thinking traces were generated with MiniMax M3.1 Flash.
This dataset brings together a broad range of Three.js scenes, from individual objects and d... | 6,672 | 6,672 | 1,504,196,639 | [
"task_categories:text-generation",
"language:en",
"license:mit",
"size_categories:1K<n<10K",
"format:parquet",
"modality:image",
"modality:text",
"modality:3d",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us",
"3d",
"threejs",
"code",
"reaso... | 2026-10-28T03:21:45 | null | null |
6ae482b93e70c6dcbc833ed5 | HuggingFaceBio/FineDNA | HuggingFaceBio | {"license": "apache-2.0", "language": ["en"], "task_categories": ["text-generation"], "tags": ["biology", "genomics", "dna", "midtraining", "sft", "rl"], "pretty_name": "FineDNA", "configs": [{"config_name": "mid_dna_eukaryote_fungi", "data_files": [{"split": "train", "path": "mid/dna_eukaryote_fungi/train-*.parquet"}]... | false | False | 2026-10-30T17:15:34 | 58 | 56 | false | e918c3649ffc614684d10e46e1eb5ba41170e7f9 |
FineDNA
FineDNA is the training data of Carbon-X, the model that taught
Qwen3-14B to read DNA, for all three of its training stages. Each stage is a group of configs:
stage
configs
size
starts from
Mid-training
mid_*, one per source (23)
514.30B tokens, of which the run read 302B
Qwen/Qwen3-1... | 10,246 | 10,246 | 955,468,228,092 | [
"task_categories:text-generation",
"language:en",
"license:apache-2.0",
"size_categories:100M<n<1B",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"region:us",
"biology",
"genomics",
"dna",
"midtraining",
"sft",
"rl"
] | 2026-10-30T11:42:49 | null | null |
6ae214f8e6dd01e15efa0f53 | codepawl/simready-glb | codepawl | {"license": "cc-by-4.0", "pretty_name": "SimReady GLB", "tags": ["3d", "gltf", "glb", "web", "three.js", "simready", "nvidia"], "size_categories": ["1K<n<10K"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": ["metadata.jsonl", "engine/*/*.glb"]}]}]} | false | False | 2026-11-07T06:01:43 | 75 | 54 | false | 3be2abd90406285b34f868907290aa4992dece7b |
SimReady GLB
Open the 3D viewer to search all 2,595 models and
orbit any of them in the browser.
Web-ready glTF binary (.glb) copies of the 3D models in
nvidia/simready-assets. Each file loads directly in
three.js, Babylon.js, model-viewer or any glTF 2.0 viewer.
Models by NVIDIA SimReady, licensed CC ... | 26,133 | 26,133 | 26,718,416,223 | [
"license:cc-by-4.0",
"size_categories:1K<n<10K",
"modality:3d",
"modality:image",
"modality:tabular",
"modality:text",
"library:datasets",
"library:mlcroissant",
"region:us",
"3d",
"gltf",
"glb",
"web",
"three.js",
"simready",
"nvidia"
] | 2026-10-28T15:30:00 | null | null |
6a96c11d1be93f5d15eff9f0 | venvoo/china-a-share-l2-level2-limit-order-book-tick-data | venvoo | {"license": "other", "license_name": "research-use-only", "license_link": "LICENSE", "gated": "manual", "extra_gated_heading": "Two steps before you request access / \u7533\u8bf7\u524d\u8bf7\u5148\u5b8c\u6210\u4e24\u6b65", "extra_gated_description": "**1.** Like (\u2665) this repository \u2014 the button is at the top ... | false | manual | 2026-11-07T09:10:07 | 647 | 42 | false | 7e7c741f03f7f75325e666266ce2ed7b431ce27b |
China A-Share Level-2 Archive
2017โ2026 ยท Quotes, orders and trades ยท Parquet
A historical archive of Chinese exchange Level-2 data, supplied through a vendor export.
It includes ten-level quote snapshots, individual order messages and trade-stream records.
The files cover A-share stocks and non-stock in... | 219,121 | 292,803 | 6,390,080,646,662 | [
"license:other",
"size_categories:n>1T",
"region:us",
"finance",
"trading",
"stock-market",
"china",
"a-share",
"level-2",
"limit-order-book",
"tick-data",
"high-frequency",
"market-microstructure"
] | 2026-09-01T12:12:13 | null | null |
6ad6246c3a48e8f4dd2faec9 | scenario-labs/3d-collection | scenario-labs | {"pretty_name": "Scenario 3D Collection", "license": "cc-by-4.0", "configs": [{"config_name": "default", "data_files": "data/*.parquet"}]} | false | False | 2026-10-19T15:21:02 | 386 | 40 | false | 55b4c002b4c1529fc48da3dff98516c3069bfa3f |
Scenario 3D Collection
1,342 selected AI-generated 3D examples, with all 1,500 output files downloaded. About 39 GB including previews and available source inputs.
Another 393 examples made with Tencent Hunyuan models are published separately under their own licenses: Scenario 3D Collection (Hunyuan).
... | 43,936 | 43,936 | 38,680,167,995 | [
"license:cc-by-4.0",
"size_categories:1K<n<10K",
"format:parquet",
"modality:3d",
"modality:image",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us"
] | 2026-10-19T14:08:44 | null | null |
6aeac72d2f84a4407df12007 | codepawl/scanned-objects-glb | codepawl | {"license": "cc-by-4.0", "pretty_name": "Scanned Objects GLB", "tags": ["3d", "gltf", "glb", "web", "three.js", "photogrammetry", "google-scanned-objects"], "size_categories": ["1K<n<10K"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": ["metadata.jsonl", "engine/*/*.glb"]}]}]} | false | False | 2026-11-07T06:44:21 | 41 | 40 | false | 75e8895e4f4e854cddd7009a7e60f86812041012 |
Scanned Objects GLB
Web-ready glTF binary (.glb) copies of the 1,033 household products in
Google Scanned Objects,
taken from the official copies on Gazebo Fuel. Each file loads
directly in three.js, Babylon.js, model-viewer or any glTF 2.0 viewer, at real size in metres, Y up.
Scans by Google Research... | 10,059 | 10,059 | 1,514,029,271 | [
"license:cc-by-4.0",
"size_categories:1K<n<10K",
"modality:3d",
"modality:image",
"modality:tabular",
"modality:text",
"library:datasets",
"library:mlcroissant",
"region:us",
"3d",
"gltf",
"glb",
"web",
"three.js",
"photogrammetry",
"google-scanned-objects"
] | 2026-11-04T05:48:29 | null | null |
6aea924e6cae8de3206f13ef | tencent/TailorRig | tencent | {"license": "other", "license_name": "tailorrig-license", "license_link": "LICENSE", "language": ["en"], "size_categories": ["10K<n<100K"], "tags": ["3d", "mesh", "skeleton", "rigging", "skinning", "auto-rigging", "character", "npz"]} | false | False | 2026-11-05T06:24:47 | 32 | 31 | false | 96ed3da4f5b98ef886c886467a25e2652b69b6e4 |
TailorRig
Human character rigging dataset: 39,073 humanoid meshes with skeletons and skinning weights, stored as .npz files and packed into four source-based subset archives.
Suitable for training and evaluation of skeleton generation (auto-rigging), skinning-weight prediction (auto-skinning), joint-posi... | 485 | 485 | 34,156,108,621 | [
"language:en",
"license:other",
"size_categories:10K<n<100K",
"modality:3d",
"arxiv:2611.02902",
"region:us",
"3d",
"mesh",
"skeleton",
"rigging",
"skinning",
"auto-rigging",
"character",
"npz"
] | 2026-11-04T02:02:54 | null | null |
6a981c1f3a639ff95e1342fa | MoreThought/Fable-5.1-Max-Reasoning-Filtered-10000x | MoreThought | {"license": "apache-2.0", "task_categories": ["text-generation", "question-answering"], "language": ["en"], "pretty_name": "The First And Best Fable 5.1 Reasoning Data", "tags": ["fable 5.1", "coding", "synthetic", "thinking", "think", "reason", "reasoning", "distill", "distillation", "agent", "agentic", "SFT", "CoT", ... | false | False | 2026-10-10T12:16:08 | 472 | 28 | false | 4904ca0c09044f447aff16e4f66b6f2f53c9f702 |
Dataset Description
This dataset contains 10,000 agentic coding and reasoning multi-turn high-quality traces generated by the new Fable 5.1 model using max reasoning effort.
It holds almost 500,000,000 tokens of step-by-step chain-of-thought programming across multiple complex domains.
It has also been ... | 6,882 | 12,425 | 2,538,730,713 | [
"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:pandas",
"library:polars",
"library:mlcroissant",
"region:us",
"fable 5.1",
"coding",
"... | 2026-09-02T12:52:47 | 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 | 406 | 22 | 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... | 11,215 | 44,833 | 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 |
6ad974006f33ed010a773a57 | bumbuboon/llm-course-final-2026 | bumbuboon | {"license": "cc-by-sa-4.0", "language": ["ja"]} | false | auto | 2026-10-28T02:36:29 | 21 | 20 | false | e8041a0494e1583fac9eac42699df44619cf84a3 |
LLM course final RAG participant dataset
LLM่ฌๅบงใฎๆ็ตใณใณใใงไฝฟ็จใใใๆฅๆฌ่ชWikipediaใ็จใใRAG๏ผๆค็ดขๆกๅผต็ๆ๏ผ็จใใผใฟใปใใใงใใ็ด100ไธไปถใฎๆ็ซ ใใฃใณใฏใจๆค็ดข็จใคใณใใใฏในใๅ้ฒใใฆใใพใใ
| 5,418 | 5,418 | 9,136,201,808 | [
"language:ja",
"license:cc-by-sa-4.0",
"region:us"
] | 2026-10-22T02:25:04 | null | null |
6addc6991f4b3bf1ce4492ad | speechcolab/gigaspeech3 | speechcolab | {"pretty_name": "GigaSpeech 3", "language": ["ar"], "license": "other", "license_name": "gigaspeech3-research-only", "license_link": "https://huggingface.co/datasets/speechcolab/gigaspeech3/blob/main/README.md#terms-of-use", "task_categories": ["automatic-speech-recognition"], "annotations_creators": ["machine-generate... | false | False | 2026-11-07T11:24:57 | 20 | 20 | false | 9d93b9c173241daaf7595652129fa6fa59984819 |
GigaSpeech 3
Scaling Multi-Dialect Arabic Speech Recognition with Weakly Labeled Data
GigaSpeech 3 is a large-scale Arabic speech recognition corpus containing approximately 10 million weakly transcribed speech segments across five dialects: Algerian, Egyptian, Iraqi, Saudi, and Syrian Arabic. It support... | 7,234 | 7,234 | 4,817,330,311,067 | [
"task_categories:automatic-speech-recognition",
"annotations_creators:machine-generated",
"language_creators:found",
"multilinguality:monolingual",
"language:ar",
"license:other",
"size_categories:10M<n<100M",
"modality:audio",
"arxiv:2611.02476",
"region:us",
"audio",
"speech",
"arabic",
... | 2026-10-25T09:06:33 | null | null |
6ae8cb3c4aead1f72f52f062 | Axion1-0/axion-training-data | Axion1-0 | {"language": ["en"], "license": "apache-2.0", "size_categories": ["10K<n<100K"], "task_categories": ["text-generation", "question-answering"], "pretty_name": "Axion 1.0 Training Data", "tags": ["agents", "scientific-reasoning", "tool-use", "supervised-fine-tuning", "reinforcement-learning", "datasets"], "configs": [{"c... | false | False | 2026-11-05T01:53:04 | 20 | 20 | false | 668831c96e7c3d974bc5c414329d508277f23663 |
Axion 1.0 Training Data
Scientific reasoning and tool use for open agent foundation models.
Project Page ยท
Paper (Hugging Face) ยท
Paper (PDF) ยท
Code
Axion 1.0 follows a three-stage training recipe: learn self-contained reasoning,
learn to acquire missing information through tools, the... | 1,570 | 1,570 | 533,793,867 | [
"task_categories:text-generation",
"task_categories:question-answering",
"language:en",
"license:apache-2.0",
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"arxiv:2611.03233",
"region:us",
"agen... | 2026-11-02T17:41:16 | null | null |
6aee3e2c7550f7ca21eac6e5 | NousResearch/arete-data-pairs | NousResearch | {"pretty_name": "Arete data pairs", "language": ["en"], "tags": ["values", "synthetic", "sft"], "task_categories": ["text-generation", "question-answering"], "size_categories": "10K<n<100K", "configs": [{"config_name": "paired", "data_files": [{"split": "train", "path": "paired/*.jsonl"}]}, {"config_name": "sft_accepte... | false | False | 2026-11-06T21:14:56 | 20 | 20 | false | 664cc9b71679199b76c49b8069eaed23bd618f80 |
Arete data pairs โ v1.0.0
Promptโresponse pairs for seven values topics, each prompt answered by both the Inkling teacher
(thinkingmachines/inkling) and the Qwen 3.8-27B starting checkpoint (Qwen/Qwen3.8-27B), with
per-row category tags and the teacher review. release_manifest.json holds per-file sha256s... | 244 | 244 | 918,946,550 | [
"task_categories:text-generation",
"task_categories:question-answering",
"language:en",
"size_categories:10K<n<100K",
"format:json",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"region:us",
"values",
"synthetic",
"sft"
] | 2026-11-06T20:53:00 | null | null |
6ae06b664d377c4fbdb6c0ef | bitmanagerai-speech-lab/darbuka_arabic_tts_dataset | bitmanagerai-speech-lab | {"license": "other", "language": ["ar", "arz"], "task_categories": ["automatic-speech-recognition", "text-to-speech"], "pretty_name": "Darbuka Arabic speech \u2014 WebDataset V1", "tags": ["audio", "arabic", "webdataset", "speech"], "configs": [{"config_name": "default", "default": true, "data_files": [{"split": "train... | false | False | 2026-10-31T17:58:32 | 22 | 19 | false | a7370bb3569d92aa76d511cca2d95b02b888cc7a |
Darbuka Arabic
Darbuka Arabic brings together Arabic speech, automatic transcripts, and audio-quality measurements for speech recognition and text-to-speech research, with a focus on Egyptian and Saudi speech.
At a glance
Speech
1,944 hours across 943,731 clips
Clip length
2โ20 seconds
A... | 3,038 | 3,038 | 885,145,697,813 | [
"task_categories:automatic-speech-recognition",
"task_categories:text-to-speech",
"language:ar",
"language:arz",
"license:other",
"modality:image",
"modality:audio",
"library:webdataset",
"region:us",
"audio",
"arabic",
"webdataset",
"speech"
] | 2026-10-27T09:14:14 | null | null |
6aeb305678a51a6c7d683b1d | Sandflow/sand3d-objaverse | Sandflow | {"pretty_name": "sand3d-objaverse", "license": "cc-by-4.0", "task_categories": ["text-to-3d", "image-to-3d"], "tags": ["3d", "code", "openusd", "usd", "cad", "articulated-objects", "program-synthesis", "sand3d"], "size_categories": ["10K<n<100K"], "configs": [{"config_name": "default", "data_files": [{"split": "train",... | false | False | 2026-11-05T06:47:05 | 19 | 19 | false | 92dd5858c177734782734c20f20b2de522b229bf |
sand3d-objaverse
66โฏ887 3D objects written as code. Each object is a short Python program in
sand3d, the OpenUSD file that program exports, and a
render of it. Everyday objects, articulated mechanisms and mechanical CAD parts, in one language:
every part is named, every dimension a parameter, and articu... | 110,694 | 110,694 | 182,555,020,747 | [
"task_categories:text-to-3d",
"task_categories:image-to-3d",
"license:cc-by-4.0",
"size_categories:10K<n<100K",
"format:parquet",
"modality:3d",
"modality:image",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"arxiv:2212.08051",
"region:us",
... | 2026-11-04T13:17:10 | null | null |
6aee073374a02567d07bc15c | NousResearch/Propaganda_Eval | NousResearch | {"license": "mit", "language": ["en"], "pretty_name": "Censorship & Propaganda Eval", "size_categories": ["n<1K"], "task_categories": ["question-answering"], "tags": ["evaluation", "censorship", "propaganda", "llm-as-a-judge"], "configs": [{"config_name": "items", "data_files": "data/items.jsonl", "default": true}]} | false | False | 2026-11-06T20:39:08 | 19 | 19 | false | 401223a2bfcdcd99d4650a83c4d20565db0e56b8 |
Censorship & Propaganda Eval
803 prompts about topics that the People's Republic of China censors or presents through an official narrative.
Each prompt comes with 2โ4 required facts taken from a cited source. A model's answer is graded on two things:
Fact score (0โ5): does the answer state the document... | 212 | 212 | 859,611 | [
"task_categories:question-answering",
"language:en",
"license:mit",
"size_categories:n<1K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us",
"evaluation",
"censorship",
"propaganda",
"llm-as-a-judge"
] | 2026-11-06T16:58:27 | null | null |
6a97e8aabe471b1b359f7cd6 | TheAgenticDataCompany/open-yap-1k | TheAgenticDataCompany | {"license": "cc-by-4.0", "license_link": "https://huggingface.co/datasets/TheAgenticDataCompany/open-yap-1k/blob/main/LICENSE.txt", "pretty_name": "Open Yap 1K sample", "task_categories": ["audio-to-audio", "automatic-speech-recognition", "text-to-speech"], "language": ["en"], "tags": ["speech", "audio", "full-duplex",... | false | False | 2026-10-21T07:08:26 | 190 | 18 | false | a08b1c80e50159d462ddcbbabfdea5bf7ccfc626 |
Open Yap 1K: 1,000 hours of full-duplex natural conversation, free for commercial use
Today we're releasing Open Yap 1K: 1,000 hours of dual-channel English conversation, capturing how people speak together naturally in real-world environments recorded in 48kHz. The dataset ships free for both commercia... | 6,625 | 12,860 | 4,348,178,691 | [
"task_categories:audio-to-audio",
"task_categories:automatic-speech-recognition",
"task_categories:text-to-speech",
"annotations_creators:machine-generated",
"language_creators:crowdsourced",
"language:en",
"license:cc-by-4.0",
"size_categories:n<1K",
"format:audiofolder",
"modality:audio",
"mod... | 2026-09-02T09:13:14 | 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 โ
NEW Changes Feb 27th
Added new fields on the
modelssplit:downloadsAllTime,safetensors,ggufAdded new field on the
datasetssplit:downloadsAllTimeAdded new split:
paperswhich is all of the Daily Papers
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