Datasets:

cfahlgren1
/

hub-stats

_id large_stringlengths 24 24	id large_stringlengths 5 123	author large_stringlengths 2 42	cardData large_stringlengths 2 2.4M ⌀	disabled bool 1 class	gated large_stringclasses 3 values	lastModified timestamp[us]date 2021-02-05 16:03:35 2026-09-23 13:19:13	likes int64 0 9.84k	trendingScore float64 0 395	private bool 1 class	sha large_stringlengths 40 40	description large_stringlengths 0 6.67k ⌀	downloads int64 0 3.3M	downloadsAllTime int64 0 143M	mainSize float64 0 306,846B ⌀	tags listlengths 1 7.92k	createdAt timestamp[us]date 2022-03-02 23:29:22 2026-09-23 13:18:14	paperswithcode_id large_stringclasses 718 values	citation large_stringlengths 0 10.7k ⌀
6aa321e46caea5109a90c179	secemp9/arxiv-complete	secemp9	{"license": "other", "license_name": "mixed-arxiv-author-licenses", "license_link": "LICENSE", "pretty_name": "arXiv Complete Corpus", "language": ["en"], "task_categories": ["text-generation", "text-retrieval"], "tags": ["arxiv", "scientific-papers", "latex", "preprints", "full-text"], "size_categories": ["10M<n<100M"...	false	False	2026-09-19T20:39:46	407	395	false	cee894837962fede5612cccf2a4c7cacf49b4c3a	arXiv Complete Corpus A snapshot of arXiv's metadata, version history, submission files and rendered documents. It covers 3,148,796 papers and includes file contents, paths, sizes and SHA-256 digests. Metadata comes from arXiv's OAI-PMH arXivRaw interface; files come from the GCS mirror, S3 source archiv...	51,110	51,110	16,076,057,281,538	[ "task_categories:text-generation", "task_categories:text-retrieval", "language:en", "license:other", "size_categories:100M<n<1B", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:polars", "library:mlcroissant", "arxiv:2401.18030", "region:...	2026-09-10T21:32:20	null	null
6a981c1f3a639ff95e1342fa	MoreThought/Fable-5.1-Max-Reasoning-Filtered-5000x	MoreThought	{"license": "apache-2.0", "task_categories": ["text-generation", "question-answering"], "language": ["en"], "pretty_name": "The First Fable 5.1 Reasoning Data", "tags": ["fable 5.1", "coding", "synthetic", "thinking", "think", "reason", "reasoning", "distill", "distillation", "agent", "agentic", "SFT", "CoT", "code", "...	false	False	2026-09-22T16:34:11	129	67	false	efa191ae1f3f59449a7041fa182d9bceb0dbc6b5	Dataset Description This dataset contains 5,000 agentic coding and reasoning traces generated by the new Fable 5.1 model using max reasoning effort. It holds almost 150,000,000 tokens of step-by-step chain-of-thought programming across multiple complex domains. It has also been deduplicated and filtered...	2,343	2,343	621,170,457	[ "task_categories:text-generation", "task_categories:question-answering", "language:en", "license:apache-2.0", "size_categories:1K<n<10K", "format:json", "library:datasets", "library:pandas", "library:polars", "library:mlcroissant", "region:us", "fable 5.1", "coding", "synthetic", "thinki...	2026-09-02T12:52:47	null	null
621ffdd236468d709f184284	wikimedia/wikipedia	wikimedia	{"language": ["ab", "ace", "ady", "af", "alt", "am", "ami", "an", "ang", "anp", "ar", "arc", "ary", "arz", "as", "ast", "atj", "av", "avk", "awa", "ay", "az", "azb", "ba", "ban", "bar", "bbc", "bcl", "be", "bg", "bh", "bi", "bjn", "blk", "bm", "bn", "bo", "bpy", "br", "bs", "bug", "bxr", "ca", "cbk", "cdo", "ce", "ceb"...	false	False	2024-01-09T09:40:51	1,524	50	false	b04c8d1ceb2f5cd4588862100d08de323dccfbaa	Dataset Card for Wikimedia Wikipedia Dataset Summary Wikipedia dataset containing cleaned articles of all languages. The dataset is built from the Wikipedia dumps (https://dumps.wikimedia.org/) with one subset per language, each containing a single train split. Each example contains the co...	272,107	3,204,708	71,792,022,791	[ "task_categories:text-generation", "task_categories:fill-mask", "task_ids:language-modeling", "task_ids:masked-language-modeling", "language:ab", "language:ace", "language:ady", "language:af", "language:alt", "language:am", "language:ami", "language:an", "language:ang", "language:anp", "...	2022-03-02T23:29:22	null	null
6a9560f9ba572a7516598144	openbmb/UltraData-SFT-Agent-2609	openbmb	{"language": ["en", "zh"], "license": "apache-2.0", "size_categories": ["100K<n<1M"], "task_categories": ["text-generation", "question-answering"], "pretty_name": "UltraData-SFT-Agent-2609", "tags": ["llm", "sft", "supervised-fine-tuning", "post-training", "agent", "tool-use", "function-calling", "code-agent", "search-...	false	False	2026-09-06T01:59:01	223	49	false	f684cc1a9f3e19f6f4929102cd9b06cc0b895b8a	UltraData-SFT-Agent-2609 📦 UltraData Collection \| 🌐 UltraData \| 🤗 MiniCPM5 Series English \| 中文 📚 Introduction UltraData-SFT-Agent-2609 is the L3 refined data for Agent instruction-tuning within UltraData's L0-L4 tiered data management framework. Built for the post-training...	22,006	22,006	54,221,444,130	[ "task_categories:text-generation", "task_categories:question-answering", "language:en", "language:zh", "license:apache-2.0", "size_categories:100K<n<1M", "format:json", "modality:text", "library:datasets", "library:dask", "library:polars", "library:mlcroissant", "arxiv:2602.09003", "region...	2026-08-31T11:09:45	null	null
6a9bd18840511abaeec4d6ad	zgcagi/ZGCM-1-Data	zgcagi	{"pretty_name": "ZGCM-1-Data", "language": ["zh", "en"], "task_categories": ["text-generation", "question-answering"], "license": "other", "size_categories": ["1B<n<10B"], "tags": ["pretraining", "midtraining", "supervised-fine-tuning", "code", "reasoning", "long-context"], "configs": [{"config_name": "zgcm-1-pretrain-...	false	auto	2026-09-18T18:33:33	60	42	false	20a6cf27f80d53a2c1fcc8f0dbb9bef7cdd144c3	A Fully Open and Extremely Efficient Foundation Model for Math and Agentic Search Zhongguancun Academy · Zhongguancun Institute of Artificial Intelligence 📄 Tech Report · 🤗 Model · 🤗 Data · 📊 Results · 💻 Training Code · 💬 WeChat Community Introduction ZGCM-1 is a 7.39B-parameter dense languag...	27,488	27,488	5,740,580,604,787	[ "task_categories:text-generation", "task_categories:question-answering", "language:zh", "language:en", "license:other", "size_categories:1B<n<10B", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:polars", "library:mlcroissant", "arxiv:2609.13356", "region:us...	2026-09-05T08:23:36	null	null
6aa213a3942797ca6668c162	Yootta/World-SimReady-Home	Yootta	{"viewer": false, "license": "cc-by-nc-sa-4.0", "pretty_name": "WorldSimReady-Home", "language": ["en"], "tags": ["robotics", "embodied-ai", "simulation", "openusd", "3d", "image", "video", "timeseries", "robot-manipulation", "simready"], "task_categories": ["robotics"], "extra_gated_prompt": "WorldSimReady-Home is ava...	false	auto	2026-09-20T03:50:21	73	40	false	f467ffe3101fe9a4c5b2f8c974082a36d55a96d4	WorldSimReady-Home Dataset description CAD-based SimReady assets Optimized CAD assets with configured collision and physical properties. Manually reviewed scenes Physics configuration reviewed for every household scene. Scalable task generation Batch simulation data across ro...	24,628	24,628	3,008,519,419,176	[ "task_categories:robotics", "language:en", "license:cc-by-nc-sa-4.0", "modality:3d", "modality:image", "modality:video", "modality:timeseries", "region:us", "robotics", "embodied-ai", "simulation", "openusd", "3d", "image", "video", "timeseries", "robot-manipulation", "simready" ]	2026-09-10T02:19:15	null	null
6a88290bf198e93508a91ba2	markov-ai/cad-1000-hours	markov-ai	null	false	False	2026-09-17T11:30:08	508	39	false	1e95e9c44eb7db9d583f484a133b7842e85a1e11	CAD-1K Open v2 - 1,018.1229 Hours 509 end-to-end, single-display Windows CAD task recordings across seven CAD software families. Each task contains: task_desc.json - task prompt, application, reference-input paths, and expected deliverables input_files/ - reference inputs named input.ext or input_N.ext ...	117,340	166,298	222,457,767,638	[ "modality:document", "modality:video", "region:us" ]	2026-08-21T10:31:39	null	null
682236304f2a298acff85b64	DeepMostInnovations/saas-sales-conversations	DeepMostInnovations	{"language": ["en"], "license": "apache-2.0", "task_categories": ["text-classification", "text-generation"], "tags": ["sales", "conversations", "synthetic", "saas", "b2b", "reinforcement-learning"], "pretty_name": "SaaS Sales Conversation Dataset", "size_categories": ["10K<n<100K"]}	false	False	2025-05-12T18:06:11	49	38	false	714f4544cdbc3f192e7f8ea93053815c8e5479cf	saas-sales-conversations Dataset Description This is a synthetic dataset of sales conversations for SaaS (Software as a Service) companies, designed for training sales conversion prediction models. The dataset was created following the methodology presented in "SalesRLAgent: A Reinforcement Learni...	795	4,819	7,166,718,134	[ "task_categories:text-classification", "task_categories:text-generation", "language:en", "license:apache-2.0", "size_categories:100K<n<1M", "format:csv", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2503.23303", ...	2025-05-12T17:56:00	null	null
6aa42d3e43a0c5ca08d92d92	eidon-ai/tracker-pov	eidon-ai	{"license": "cc-by-4.0", "pretty_name": "Eidon Tracker POV", "size_categories": ["10K<n<100K"], "task_categories": ["robotics", "video-classification"], "tags": ["egocentric", "imu", "manipulation", "activities-of-daily-living", "motion-capture", "embodied-ai"], "configs": [{"config_name": "recordings", "data_dir": "re...	false	False	2026-09-20T19:28:29	35	35	false	47c55ccc6d0308894ab482009c6fa91569057a08	Eidon Tracker POV 1,274 hours of egocentric video paired with 7-point IMU arm tracking, recorded during ordinary household work. Contributors wore a head-mounted camera and a seven-sensor IMU harness while doing real chores in their own homes: laundry, cleaning, dishes, cooking. Each recording pairs firs...	10,594	10,594	9,025,651,677,751	[ "task_categories:robotics", "task_categories:video-classification", "license:cc-by-4.0", "size_categories:10K<n<100K", "modality:tabular", "modality:text", "modality:video", "library:datasets", "library:mlcroissant", "region:us", "egocentric", "imu", "manipulation", "activities-of-daily-li...	2026-09-11T16:33:02	null	null
621ffdd236468d709f181e3f	nyu-mll/glue	nyu-mll	"{\"annotations_creators\": [\"other\"], \"language_creators\": [\"other\"], \"language\": [\"en\"],(...TRUNCATED)	false	False	2024-01-30T07:41:18	1,092	34	false	bcdcba79d07bc864c1c254ccfcedcce55bcc9a8c	"\n\t\n\t\t\n\t\n\t\n\t\tDataset Card for GLUE\n\t\n\n\n\t\n\t\t\n\t\n\t\n\t\tDataset Summary\n\t\n\(...TRUNCATED)	874,446	43,456,923	162,286,103	["task_categories:text-classification","task_ids:acceptability-classification","task_ids:natural-lan(...TRUNCATED)	2022-03-02T23:29:22	glue	null

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Changelog

NEW Changes March 11th 2026

Added new split: arxiv_papers, sourced from the Hugging Face /api/papers endpoint
papers continues to point to daily_papers.parquet, which is the Daily Papers feed

NEW Changes July 25th

added baseModels field 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 gguf column with integer overflow causing import pipeline to be broken over a few weeks ✅

NEW Changes Feb 27th

Added new fields on the models split: downloadsAllTime, safetensors, gguf
Added new field on the datasets split: downloadsAllTime
Added new split: papers which is all of the Daily Papers

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