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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
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 | 171 | 164 | 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... | 4,043 | 4,075 | 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 |
6a2cd0828137fb18cecbcc06 | Glint-Research/Fable-5-traces | Glint-Research | {"license": "agpl-3.0"} | false | False | 2026-06-13T22:39:38 | 126 | 125 | false | e2f05e8482b09c3ec3ab3cfd9cb14cec3bf47754 | A simple dataset of all the Fable 5 data we could get our hands on before it was taken away (no clue if it's coming back). Expect some fine-tuned models trained on this soon. Big thanks to the TeichAI team (weird thanking myself, lol) for providing 953 messages, while I added the CoT data.
| 41 | 41 | 69,839,480 | [
"license:agpl-3.0",
"size_categories:1K<n<10K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us"
] | 2026-06-13T03:37:38 | null | null |
69f7b3cc62d65c8f39fe7270 | stanford-vision-lab/gpic | stanford-vision-lab | {"viewer": false, "license": "mit", "language": ["en"]} | false | auto | 2026-06-04T19:45:37 | 129 | 55 | false | ab5a293b37a2d2e3d8228518c61b6ffbe4458c55 |
GPIC: A Giant Permissive Image Corpus for Visual Generation
Keshigeyan Chandrasegaran*1,
Kyle Sargent*1,
Suchir Agarwal1,
Michael Jang1,
Michael Poli1,2,
Juan Carlos Niebles1,4,
Justin Johnson3,
Jiaju... | 146,735 | 149,469 | 12,952,181,356,563 | [
"language:en",
"license:mit",
"arxiv:2605.30341",
"region:us"
] | 2026-05-03T20:45:00 | null | null |
66ec310ff6a692d629b2667b | wikimedia/structured-wikipedia | wikimedia | {"language": ["en", "fr"], "pretty_name": "Wikimedia Structured Contents Dataset", "tags": ["wikipedia", "wikimedia", "structured-data", "parquet", "knowledge-base", "references", "citations", "tables", "multilingual"], "configs": [{"config_name": "enwiki_namespace_0", "data_files": [{"split": "train", "path": "enwiki/... | false | False | 2026-05-19T12:54:16 | 367 | 51 | false | 417c267bb457fa645c22eb3b5c77764963194c70 |
Dataset Card for Wikimedia Structured Wikipedia
Quick Links
Wikimedia Enterprise
Structured Contents Documentation
Data Dictionary
Wikimedia Attribution Framework
Meta-Wiki Discussion
Dataset Summary
Pre-parsed English and French Wikipedia articles, extracted using the Wik... | 16,416 | 39,199 | 72,556,848,943 | [
"language:en",
"language:fr",
"license:cc-by-sa-4.0",
"size_categories:10M<n<100M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"region:us",
"wikipedia",
"wikimedia",
"structured-data",
"parquet",
"knowledge-base",
"... | 2024-09-19T14:11:27 | 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 | 370 | 37 | 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 not Clau... | 9,191 | 11,337 | null | [
"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 |
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 | 2026-06-14T06:43:41 | 38 | 37 | false | c5179776b887e8e5a2cc6b4b48b806a89fcc7224 |
claude-fable-5 Agent Traces
For training on this dataset I recommend using the teich package to convert to openai style chats. It parses and filters out things like hitting limits and model switches, etc. As well as knows the exact tool-schemas and descriptions for the tools used.
I encourage everyone to... | 163 | 163 | 75,140,097 | [
"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 |
6a18688b49129d13bb56ba50 | nvidia/Nemotron-Pretraining-Code-v3 | nvidia | {"license": "cc-by-4.0", "task_categories": ["text-generation"], "tags": ["text", "pre-training", "human", "legal", "Nemotron_3_Ultra"], "language": ["code"], "size_categories": ["100M<n<1B"], "configs": [{"config_name": "Nemotron-Code-Metadata", "data_files": [{"path": ["Nemotron-Code-Metadata/part_00000.parquet", "Ne... | false | False | 2026-06-04T05:22:40 | 46 | 28 | false | 9b42feaec991c69006452e6654d91a58a04d935a |
Nemotron-Pretraining-Code-v3
Dataset Description:
The Nemotron-Pretraining-Code-v3 dataset is part of the Nemotron Pretraining Data collection of pretraining datasets. Designed for the NVIDIA Nemotron 3 family of LLMs, this dataset is intended to improve the coding capabilities of LLMs.
Th... | 1,331 | 1,331 | 8,220,044,612 | [
"task_categories:text-generation",
"language:code",
"license:cc-by-4.0",
"size_categories:100M<n<1B",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us",
"text",
"pre-training",
"human",
"legal",
"Nemotron_3_Ult... | 2026-05-28T16:08:43 | null | null |
6a0eb43154ff1b9068f42571 | openbmb/UltraData-SFT-2605 | openbmb | {"language": ["en", "zh"], "license": "apache-2.0", "size_categories": ["10B<n<100B"], "task_categories": ["text-generation", "question-answering"], "pretty_name": "UltraData-SFT-2605", "tags": ["llm", "sft", "supervised-fine-tuning", "post-training", "deep-thinking", "reasoning", "instruction-following", "math", "code... | false | auto | 2026-05-28T17:18:14 | 342 | 22 | false | affda6aca75e7cff78e73f93ad08d4c3b01f097c |
UltraData-SFT-2605
📦 UltraData Collection |
🌐 UltraData |
🤗 MiniCPM5 Series
English |
中文
📚 Introduction
UltraData-SFT-2605 is the full set of core-domain SFT data used in the post-training of MiniCPM5-1B-SFT within the MiniCPM5-1B series, and a key representative of L3 ref... | 40,491 | 40,491 | 318,990,664,596 | [
"task_categories:text-generation",
"task_categories:question-answering",
"language:en",
"language:zh",
"license:apache-2.0",
"size_categories:10B<n<100B",
"arxiv:2602.09003",
"region:us",
"llm",
"sft",
"supervised-fine-tuning",
"post-training",
"deep-thinking",
"reasoning",
"instruction-... | 2026-05-21T07:28:49 | 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 | 53 | 21 | 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
Size: 25,00... | 1,372 | 1,378 | 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 |
6a1fb3f4aa35c86b3f202fe5 | nvidia/Nemotron-Personas-El-Salvador | nvidia | {"license": "cc-by-4.0", "task_categories": ["text-generation"], "language": ["es"], "tags": ["synthetic", "personas", "NVIDIA", "datadesigner", "sovereign-ai", "el-salvador"], "size_categories": ["100K<n<1M"], "dataset_info": {"features": [{"name": "uuid", "dtype": "string"}, {"name": "professional_persona", "dtype": ... | false | False | 2026-06-05T11:16:23 | 53 | 21 | false | 68f6a21c17973d6a34ac6354d50a31c1fbcec14f |
Nemotron-Personas-El-Salvador
Un enfoque de IA compuesta para personas en español salvadoreño ancladas en distribuciones del mundo real
A compound AI approach to Salvadoran Spanish personas grounded in real-world distributions
Resumen del conjunto de datos (Dataset Overvie... | 4,039 | 4,039 | 571,195,004 | [
"task_categories:text-generation",
"language:es",
"license:cc-by-4.0",
"size_categories:100K<n<1M",
"format:parquet",
"format:optimized-parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"library:datadesigner",
"region:us",
"synthetic",
... | 2026-06-03T04:56:20 | null | null |
6a21ab4879603ac03e5cef41 | nvidia/Nemotron-Personas-Vietnam | nvidia | {"license": "cc-by-4.0", "task_categories": ["text-generation"], "language": ["vi"], "tags": ["synthetic", "personas", "NVIDIA", "Vietnamese", "datadesigner"], "size_categories": ["100K<n<1M"], "dataset_info": {"features": [{"name": "uuid", "dtype": "string"}, {"name": "professional_persona", "dtype": "string"}, {"name... | false | False | 2026-06-05T12:32:08 | 47 | 21 | false | e39e5096428256b2edfa2f73351f7bab02d70424 |
Nemotron-Personas-Vietnam
Hệ thống AI kết hợp để tạo personas tổng hợp dựa trên phân bố thực tế của Việt Nam
A compound AI approach to personas grounded in real-world distributions
Tổng quan (Overview)
Nemotron-Personas-Vietnam là tập dữ liệu personas được cung cấp dưới... | 4,969 | 4,969 | 123,111,005 | [
"task_categories:text-generation",
"language:vi",
"license:cc-by-4.0",
"size_categories:100K<n<1M",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"library:datadesigner",
"region:us",
"synthetic",
"perso... | 2026-06-04T16:43:52 | null | null |
6a2044d8b379def1f184cba7 | liumindmind/Neko_Audio-80K_Short | liumindmind | null | false | False | 2026-06-08T16:16:43 | 21 | 20 | false | 87f4afc4159416ab2d4423affbf459ebd218810e | 8,192 | 8,192 | 98,087,679,090 | [
"size_categories:10K<n<100K",
"format:json",
"modality:audio",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us"
] | 2026-06-03T15:14:32 | null | null | |
6986cb617ee2b3c146bd2432 | openbmb/Ultra-FineWeb-L3 | openbmb | {"language": ["en", "zh"], "license": "apache-2.0", "size_categories": ["100B<n<1T"], "task_categories": ["text-generation"], "pretty_name": "Ultra-FineWeb-L3", "tags": ["llm", "pretraining", "data-synthesis", "data-filtering", "high-quality", "general-knowledge", "qa-generation", "multi-style-rewriting", "minicpm"], "... | false | False | 2026-05-28T09:03:52 | 290 | 19 | false | c68ab81ad03b2d2f476fa8ab3c72bed3528da359 |
Ultra-FineWeb-L3
📜 Ultra-FineWeb Technical Report |
📦 UltraData Collection |
🌐 UltraData |
🤗 MiniCPM5 Series
English |
中文
📚 Introduction
Ultra-FineWeb-L3 is the L3 refined data for general high-quality web data within UltraData's L0-L4 tiered data management framework. Moving... | 69,778 | 72,224 | 1,899,216,536,437 | [
"task_categories:text-generation",
"language:en",
"language:zh",
"license:apache-2.0",
"size_categories:1B<n<10B",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"arxiv:2505.05427",
"arxiv:2602.09003",
"region:us",
"llm",
... | 2026-02-07T05:19:29 | null | null |
69f11d665ba9bf615476d1fe | MEDHARVIX-SYSTEMS/bhasaflow-khasi-english-parallel-sample-v1 | MEDHARVIX-SYSTEMS | {"language": ["kha", "en"], "license": "other", "pretty_name": "BhasaFlow Khasi-English Parallel Sample v1", "size_categories": ["n<1K"], "task_categories": ["automatic-speech-recognition", "text-to-speech", "translation", "audio-classification"], "tags": ["khasi", "english", "parallel-corpus", "low-resource", "speech-... | false | False | 2026-04-28T20:50:14 | 23 | 18 | false | 6b93c3fc2e17952caf38dab7cf10a69c3bff3fea |
BhasaFlow Khasi-English Parallel Sample v1
A professionally curated, gold-standard parallel speech and text corpus for the Khasi language.
Published by Medharvix Systems Private Limited
Part of the BhasaFlow Low-Resource Language Technology Initiative
Overview
This repository contain... | 2,127 | 3,006 | 21,840,445 | [
"task_categories:automatic-speech-recognition",
"task_categories:text-to-speech",
"task_categories:translation",
"task_categories:audio-classification",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:multilingual",
"multilinguality:translation",
"sour... | 2026-04-28T20:49:42 | 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 | 2,885 | 17 | 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 ... | 441,628 | 8,469,824 | 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 |
69f12b06f0d634b0a5591342 | MEDHARVIX-SYSTEMS/bhasaflow-khasi-monolingual-corpus-v1 | MEDHARVIX-SYSTEMS | {"language": ["kha"], "license": "cc-by-nc-4.0", "task_categories": ["text-generation"], "tags": ["khasi", "monolingual", "low-resource", "northeast-india", "bhasaflow", "medharvix"], "size_categories": ["n<1K"], "pretty_name": "BhasaFlow Khasi Monolingual Corpus v1"} | false | False | 2026-04-30T18:22:56 | 22 | 17 | false | 0a41158d77500fc666a942dcf9b269d27c8ea35a |
BhasaFlow Khasi Monolingual Corpus v1
By Medharvix Systems Private Limited
Overview
A curated monolingual Khasi text corpus for language modeling, NLP research, and linguistic analysis, with a focus on preserving and digitizing low-resource languages of Northeast India.
Dataset Structure
... | 108 | 123 | 20,567 | [
"task_categories:text-generation",
"language:kha",
"license:cc-by-nc-4.0",
"size_categories:n<1K",
"format:csv",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us",
"khasi",
"monolingual",
"low-resource",
"northeast-india",
"bhas... | 2026-04-28T21:47:50 | null | null |
67c92e867c6308c49ce2e98c | openbmb/Ultra-FineWeb | openbmb | {"language": ["en", "zh"], "license": "apache-2.0", "size_categories": ["n>1T"], "task_categories": ["text-generation"], "pretty_name": "Ultra-FineWeb", "tags": ["llm", "pretraining", "web-corpus", "data-filtering", "high-quality"], "configs": [{"config_name": "default", "data_files": [{"split": "en", "path": "data/ult... | false | False | 2026-05-28T04:25:13 | 387 | 15 | false | 7ddd4170ce03e0afbd7d9b80d4bc0b8eebf877e4 |
Ultra-FineWeb
📜 Technical Report |
📦 UltraData Collection |
🌐 UltraData |
🤗 MiniCPM4 Series |
🤗 MiniCPM5 Series
English |
中文
📚 Introduction
Ultra-FineWeb is a large-scale, high-quality, and efficiently-filtered dataset. We use the proposed efficient verification-based high-q... | 86,142 | 625,472 | 9,733,108,790,509 | [
"task_categories:text-generation",
"language:en",
"language:zh",
"license:apache-2.0",
"size_categories:1B<n<10B",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"arxiv:2505.05427",
"arxiv:2602.09003",
"arxiv:2412.04315",
"... | 2025-03-06T05:11:34 | null | null |
69e1bed4cc8fb2e676e4aa7c | Jackrong/GLM-5.1-Reasoning-1M-Cleaned | Jackrong | {"license": "apache-2.0", "language": ["en", "zh"], "size_categories": ["100K<n<1M"], "task_categories": ["text-generation", "question-answering"], "tags": ["reasoning", "chain-of-thought", "instruction-tuning", "sft", "distillation", "glm", "glm-5.1", "cleaned"], "configs": [{"config_name": "main", "default": true, "d... | false | False | 2026-04-19T05:05:17 | 274 | 15 | false | f6d6ccafe40359d5ec2515ee25e92aac8cae9c3d |
GLM-5.1-Reasoning-1M-Cleaned
GLM-5.1-Reasoning-1M-Cleaned is a cleaned and reformatted derivative of Kassadin88/GLM-5.1-1000000x. It preserves the original four-subset layout (main, PHD-Science, Multilingual-STEM, Math) while converting every example into a unified SFT-ready schema with explicit conversatio... | 7,165 | 17,933 | 31,734,914,777 | [
"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:pandas",
"library:polars",
"library:mlcroissant",
"region:us",
"reasoning",... | 2026-04-17T05:02:12 | null | null |
69f12b04c8c909edcd887037 | MEDHARVIX-SYSTEMS/bhasaflow-khasi-english-parallel-corpus-v1 | MEDHARVIX-SYSTEMS | {"language": ["en", "kha"], "license": "cc-by-nc-4.0", "task_categories": ["translation"], "tags": ["khasi", "english", "parallel-corpus", "low-resource", "northeast-india", "bhasaflow", "medharvix"], "size_categories": ["n<1K"], "pretty_name": "BhasaFlow Khasi-English Parallel Corpus v1"} | false | False | 2026-04-30T18:22:53 | 19 | 15 | false | 5015205d62f72d94ebb58ef37cf3440833e702b3 |
BhasaFlow Khasi-English Parallel Corpus v1
By Medharvix Systems Private Limited
Overview
A curated parallel corpus of Khasi-English sentence pairs designed for machine translation research and development, with a focus on low-resource language technology for Northeast India.
Dataset Struct... | 134 | 152 | 32,326 | [
"task_categories:translation",
"language:en",
"language:kha",
"license:cc-by-nc-4.0",
"size_categories:n<1K",
"format:csv",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us",
"khasi",
"english",
"parallel-corpus",
"low-resource"... | 2026-04-28T21:47:48 | null | null |
6a2c5668f7f66fcaa0d54e17 | lazarus19/Vibe-Coding-Claude-Fable-5 | lazarus19 | null | false | False | 2026-06-12T18:57:00 | 15 | 15 | false | 57abac435453120f96b6e5d69ab411099db2dea0 | null | 14 | 14 | 458,936,274 | [
"size_categories:1M<n<10M",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us"
] | 2026-06-12T18:56:40 | null | null |
639244f571c51c43091df168 | Anthropic/hh-rlhf | Anthropic | {"license": "mit", "tags": ["human-feedback"]} | false | False | 2023-05-26T18:47:34 | 1,788 | 14 | 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... | 32,450 | 1,918,315 | 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 |
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 | 1,142 | 13 | 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 ... | 476,257 | 7,583,615 | 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 |
6a267c902681c53f9f209cbb | carpedkm/CustoMDiT | carpedkm | {"license": "cc-by-4.0", "task_categories": ["text-to-video"], "tags": ["video-customization", "identity-preserving", "open-domain", "diffusion-transformer"], "size_categories": ["1M<n<10M"]} | false | False | 2026-06-09T08:45:20 | 14 | 13 | false | 04609d5290c3dd8e1b6f8faf8fb20e57bfed2633 |
PexelsCustom-1M
A Comprehensive Ecosystem for Open-Domain Customized Video Generation (ICASSP 2026)
The first large-scale, publicly available dataset for customized video generation (CVG), providing 1,036,431 curated (identity, text, video) triplets across 8,373 identity categories from ~320K Pexels HD v... | 96 | 96 | 3,398,243,508 | [
"task_categories:text-to-video",
"license:cc-by-4.0",
"size_categories:1M<n<10M",
"format:csv",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us",
"video-customization",
"identity-preserving",
"open-domain",
"diffusion-transformer"
... | 2026-06-08T08:25:52 | null | null |
6a280ca340f6011352faa9af | redmadrobot-rnd/pii_benchmark | redmadrobot-rnd | {"license": "mit", "language": ["ru"], "pretty_name": "Russian PII NER Benchmark", "size_categories": ["1K<n<10K"], "task_categories": ["token-classification"], "tags": ["pii", "ner", "named-entity-recognition", "pii-detection", "privacy", "anonymization", "guardrails", "russian", "benchmark"]} | false | False | 2026-06-09T12:53:01 | 13 | 13 | false | f77ea831274daf980cc45c61a93c226be9d978d6 |
Russian PII NER Evaluation Dataset
Dataset Description
This dataset is designed for evaluating PII (Personally Identifiable
Information) detection and Named Entity Recognition (NER) systems on
Russian-language text. It targets guardrail and anonymization pipelines that
must reliably find p... | 122 | 122 | 3,233,396 | [
"task_categories:token-classification",
"language:ru",
"license:mit",
"size_categories:1K<n<10K",
"format:csv",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us",
"pii",
"ner",
"named-entity-recognition",
"pii-detection",
"priva... | 2026-06-09T12:52:51 | 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 | 1,386 | 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... | 900,664 | 12,407,613 | 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 |
69f0c6101cc98d8ac04c03cd | jasperai/monet | jasperai | {"license": "apache-2.0", "pretty_name": "MONET", "task_categories": ["text-to-image", "image-feature-extraction", "zero-shot-image-classification"], "language": ["en"], "size_categories": ["100M<n<1B"], "tags": ["multimodal", "image-text", "captioning", "text-to-image", "synthetic-data"], "configs": [{"config_name": "... | false | False | 2026-05-29T10:16:25 | 130 | 12 | false | 8ec2189db8fa36bb41ea29aff7341bc5092d7482 |
Dataset Card for MONET
MONET (Massive, Open, Non-redundant and Enriched Text-to-image dataset) is a large-scale, curated image-text dataset designed for training text-to-image (T2I) systems. It contains 104.9 million high-quality image-text pairs distilled from 2.9 billion raw pairs across nine heterogen... | 154,536 | 378,177 | 68,151,014,835,264 | [
"task_categories:text-to-image",
"task_categories:image-feature-extraction",
"task_categories:zero-shot-image-classification",
"language:en",
"license:apache-2.0",
"size_categories:100M<n<1B",
"arxiv:2605.21272",
"region:us",
"multimodal",
"image-text",
"captioning",
"text-to-image",
"synthe... | 2026-04-28T14:37:04 | null | null |
6a0bde24f8d23d4248aa0a23 | Jackrong/Claude-opus-4.7-TraceInversion-5000x | Jackrong | {"annotations_creators": ["machine-generated"], "language": ["en", "zh", "ko", "ru", "ja", "es"], "license": "apache-2.0", "size_categories": ["1K-10K"], "task_categories": ["text-generation"], "tags": ["reasoning", "trace-inversion", "synthetic-data", "chain-of-thought", "distillation", "claude-opus", "negentropy", "q... | false | False | 2026-05-19T10:20:17 | 56 | 12 | false | ab3b48f1d461ec40af924fd3163d2b9c8eaeb07c |
🌀 Claude-opus-4.7-TraceInversion-5000x
v1.0 Release
A High-Fidelity Reconstructed CoT Dataset Saturated with the 'Opus Deep Logic Style' via Trace Inversion
📊 5,000 Samples
🧬 Trace Inversion & Negentropy
🛠 SFT & DPO Ready
🔥 Claude 4.7-Max Distillation
🌐 English & ... | 1,850 | 1,850 | 96,499,026 | [
"task_categories:text-generation",
"annotations_creators:machine-generated",
"language:en",
"language:zh",
"language:ko",
"language:ru",
"language:ja",
"language:es",
"license:apache-2.0",
"size_categories:1K<n<10K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"... | 2026-05-19T03:51:00 | null | null |
6a133c47bf07261a98e49fd3 | zhifeixie/StreamAudio-2M | zhifeixie | {"pretty_name": "StreamAudio-2M", "license": "cc-by-4.0", "language": ["en", "zh"], "task_categories": ["audio-classification", "automatic-speech-recognition", "translation", "audio-to-audio"], "tags": ["audio", "streaming", "audio-understanding", "asr", "speech-translation", "voice-chat"], "configs": [{"config_name": ... | false | False | 2026-06-03T13:18:23 | 25 | 11 | false | 8f551eea2bc4d22b32e0220f4e3d84acb05ce3e9 |
StreamAudio-2M
Large-scale streaming-audio dataset for audio-LLM / audio-agent training. Each row is a
stream: a sequence of audio turns sharing one unified schema. ~2.28M unique audio clips
are organised into six task subsets.
Subsets
Subset
Rows
Description
Stream_Audio_Unders... | 2,696 | 2,696 | 757,095,037,384 | [
"task_categories:audio-classification",
"task_categories:automatic-speech-recognition",
"task_categories:translation",
"task_categories:audio-to-audio",
"language:en",
"language:zh",
"license:cc-by-4.0",
"size_categories:100K<n<1M",
"format:json",
"modality:tabular",
"modality:text",
"modality... | 2026-05-24T17:58:31 | null | null |
6a2298808c1cdb723e100845 | artefactory/ledger-long-context-multi-kpi | artefactory | {"configs": [{"config_name": "no_eval", "data_files": [{"split": "dev", "path": "no_eval/data.parquet"}]}, {"config_name": "eval", "data_files": [{"split": "eval", "path": "eval/data.parquet"}]}], "task_categories": ["table-question-answering"], "language": ["en"], "tags": ["financial-reports", "ocr", "kpi-extraction",... | false | False | 2026-06-06T21:48:46 | 11 | 11 | false | fcd8f3c6f0c82eb22a8263449fe86ee5b59b52b0 |
the LEDGER Long-Context Multi-KPI extraction datasets and benchmarks.
OCR'd annual reports with ground-truth KPI values for financial information extraction benchmarking.
Dataset Description
This dataset pairs OCR-extracted annual report text (from DeepSeek OCR) with structured KPI ground-... | 2,210 | 2,210 | 4,005,150,757 | [
"task_categories:table-question-answering",
"language:en",
"license:cc-by-4.0",
"size_categories:1K<n<10K",
"format:parquet",
"modality:image",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"doi:10.57967/hf/9070",
"regio... | 2026-06-05T09:36:00 | null | null |
67b6911c98d6df9360426b15 | whale99/Interaction2Code | whale99 | {"language": ["en"], "size_categories": ["n<1K"], "task_categories": ["image-text-to-text"]} | false | False | 2026-05-25T05:54:48 | 10 | 10 | false | 7a2d55fe666ddd319d26bdcb1dddb01c764a8602 |
Interaction2Code: Benchmarking MLLM-based Interactive Webpage Code Generation from Interactive Prototyping
Project Page | Paper | GitHub
Interaction2Code is the first systematic investigation and benchmark for Multimodal Large Language Models (MLLMs) in generating interactive webpages. While existing benchma... | 3,287 | 7,823 | 367,649,654 | [
"task_categories:image-text-to-text",
"language:en",
"size_categories:1K<n<10K",
"format:imagefolder",
"modality:image",
"library:datasets",
"library:mlcroissant",
"arxiv:2411.03292",
"region:us"
] | 2025-02-20T02:19:08 | null | null |
6a1de46d3a7ae8c9ef0850b2 | tahoebio/EmeraldBay | tahoebio | {"license": "cc-by-4.0", "tags": ["biology", "single-cell", "RNA", "drug-sensitivity", "perturbation", "chemistry"], "size_categories": ["1M<n<10M"], "configs": [{"config_name": "expression_data", "data_files": "expression_data/train-*", "default": true}, {"config_name": "gene_metadata", "data_files": "metadata/gene_me... | false | False | 2026-06-05T21:12:47 | 10 | 10 | false | f2a0be6b02f731553657f0115c345b20bb020ede |
Emerald Bay
Emerald Bay is a single-cell perturbation dataset of over 1.8M transcriptomic profiles spanning 52 cell lines and 91 drug
treatments, including combinations. Generated using Tahoe Therapeutics's MOSAIC high-throughput platform, it comprises a
curated set of anticancer agents applied at mult... | 691 | 691 | 57,714,710,155 | [
"license:cc-by-4.0",
"size_categories:1M<n<10M",
"format:parquet",
"format:optimized-parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"region:us",
"biology",
"single-cell",
"RNA",
"drug-sensitivity",
"perturbation",
"chemistry"
] | 2026-06-01T19:58:37 | null | null |
6a2299080fe9efc247e5cc9a | artefactory/ledger-long-context-KPI-QA | artefactory | {"configs": [{"config_name": "eval", "data_files": [{"split": "test", "path": "eval/data-*.parquet"}]}, {"config_name": "no_eval", "data_files": [{"split": "train", "path": "no_eval/data-*.parquet"}]}], "task_categories": ["question-answering", "text-generation"], "language": ["en"], "tags": ["finance", "financial-repo... | false | False | 2026-06-07T02:47:30 | 10 | 10 | false | 8f96dd4f171d2a7d04dcad009c6d7f83c3671628 |
LEDGER — Long-Context KPI Question Answering & Page Retrieval
This dataset is part of the LEDGER (Long-context Evaluation of Documents for
Grounded Extraction and Retrieval) benchmark.
It supports two of the three LEDGER tasks:
Page-level KPI retrieval — given a natural-language question about a financi... | 3,338 | 3,338 | 5,238,504,132 | [
"task_categories:question-answering",
"task_categories:text-generation",
"language:en",
"license:cc-by-4.0",
"size_categories:100K<n<1M",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"doi:10.57967/hf/9050"... | 2026-06-05T09:38:16 | null | null |
6a294060470b7ac939ed241b | victor/fable-5-boeing-747-trace | victor | {"pretty_name": "Fable 5 Boeing 747 - Claude Code session trace", "license": "mit", "tags": ["agent-traces", "claude-code", "threejs", "fable-5"], "configs": [{"config_name": "default", "data_files": "trace.jsonl"}]} | false | False | 2026-06-11T20:13:15 | 10 | 10 | false | e146afb46a99b3873a1a61e12454ba3cd2fff299 |
Fable 5 Boeing 747: Claude Code session trace
The full Claude Code (Fable 5) session transcript that built victor/fable-5-boeing-747, a procedural Boeing 747 in Three.js, from a single /goal prompt:
create the most realistic boeing 747 using THREEJS - use your vision capabilities to create a self verifi... | 269 | 269 | 31,577,223 | [
"license:mit",
"size_categories:n<1K",
"format:json",
"format:agent-traces",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us",
"agent-traces",
"claude-code",
"threejs",
"fable-5"
] | 2026-06-10T10:45:52 | null | null |
6a0ecad032e2c32583a37759 | VCLab-PolyU/GGT-100K | VCLab-PolyU | {"license": "cc-by-nc-nd-4.0"} | false | False | 2026-06-01T06:43:40 | 44 | 9 | false | f896935f85b79eb12c71163032d6826c7a4bb2d4 |
GGT-100K: Generative Ground Truth for Generalizable Real-World Image Restoration
Real-world LQ–HQ pairs from MFMs to expand IR generalization boundaries.
Xiangtao Kong1,2,* |
Jixin Zhao1,2,* |
Lingchen Sun1,2; |
Rongyuan Wu1,2; |
Lei Zhang1,2,†
1 The Hong Kong Polytechnic University2 OPPO Research Inst... | 2,725 | 2,725 | 383,112,874,268 | [
"license:cc-by-nc-nd-4.0",
"arxiv:2605.31039",
"region:us"
] | 2026-05-21T09:05:20 | null | null |
6a18489bb93f3af6ed8c5f50 | qualialabsAI/SmoothConv | qualialabsAI | {"language": "zh", "license": "cc-by-nc-4.0", "tags": ["speech", "conversational-speech", "chinese"], "pretty_name": "SmoothConv"} | false | False | 2026-06-12T04:48:12 | 9 | 9 | false | cd74b4fca285a66d6ac8c16228d0953ff1e0cda2 |
SmoothConv
SmoothConv is a high-quality Chinese multi-channel conversational speech dataset with expert human annotations, developed by ASLP@NPU and QualiaLabs as part of the SmoothConv–DuplexConv corpus family.
Companion dataset: DuplexConv on HuggingFace (2,000 hours, LLM-assisted ann... | 8,227 | 8,227 | 85,862,864,657 | [
"language:zh",
"license:cc-by-nc-4.0",
"arxiv:0000.00000",
"region:us",
"speech",
"conversational-speech",
"chinese"
] | 2026-05-28T13:52:27 | null | null |
6a18492ed0294b77f2b68667 | qualialabsAI/DuplexConv | qualialabsAI | {"language": "zh", "license": "cc-by-nc-4.0", "tags": ["speech", "conversational-speech", "chinese"], "pretty_name": "DuplexConv"} | false | False | 2026-06-12T04:47:34 | 9 | 9 | false | 0bb99da7ab7a2f6f86d6b23df92c9383e711d09a |
DuplexConv
DuplexConv is a large-scale Chinese multi-channel conversational speech dataset with LLM-assisted annotations, developed by ASLP@NPU and QualiaLabs as part of the SmoothConv–DuplexConv corpus family.
Companion dataset: SmoothConv on HuggingFace (100 hours, expert human annota... | 6,240 | 6,240 | 1,640,733,137,836 | [
"language:zh",
"license:cc-by-nc-4.0",
"arxiv:0000.00000",
"region:us",
"speech",
"conversational-speech",
"chinese"
] | 2026-05-28T13:54:54 | null | null |
6a229964bc73aafdd4e59b84 | artefactory/ledger-market-sentiment | artefactory | {"configs": [{"config_name": "letters", "data_files": [{"split": "train", "path": "letters/data.parquet"}]}, {"config_name": "eps_surprise", "data_files": [{"split": "train", "path": "eps_surprise/data.parquet"}]}, {"config_name": "stock_prices", "data_files": [{"split": "train", "path": "stock_prices/data-*.parquet"}]... | false | False | 2026-06-07T02:39:34 | 9 | 9 | false | 11d1ff69d81f106b97644d789d7603c375a02ec3 |
LEDGER Market Sentiment Prediction Data
Data used for the market sentiment prediction case study in the LEDGER paper,
linking CEO-letter rhetoric to EPS surprises and post-publication market reactions.
Dataset Description
This dataset supports research on whether the rhetoric in corporate ... | 93 | 93 | 803,974,540 | [
"task_categories:text-classification",
"task_categories:time-series-forecasting",
"language:en",
"license:cc-by-4.0",
"size_categories:100K<n<1M",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"region:us",
... | 2026-06-05T09:39:48 | null | null |
6a23da4d0f9d4b1aa8296e50 | armand0e/minimax-m3-claude-code-traces | armand0e | {"pretty_name": "Minimax M3 Claude Code Traces", "task_categories": ["text-generation"], "tags": ["agent-traces", "format:agent-traces", "claude-code", "distillation", "minimax/minimax-m3", "teich"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "*.jsonl"}]}]} | false | False | 2026-06-06T08:29:12 | 10 | 9 | false | 964cab9e833256be31f5a549378721a5a4b755b0 | This dataset was generated using teich by TeichAI
Prepare these datasets for supervised fine-tuning in just a few lines of code — see the Conversion section below.
Minimax M3 Claude Code Traces
This directory contains raw agent trace files generated by teich.
All assistant responses were generated by mi... | 637 | 637 | 14,875,393 | [
"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-code",
"distillat... | 2026-06-06T08:29:01 | null | null |
6a2647e21546853bde0ccb12 | zhiqix/PUM-MATH | zhiqix | {"license": "cc-by-4.0", "language": ["en"], "task_categories": ["text-generation", "text-classification"], "tags": ["reasoning", "mathematical-reasoning", "prefix-evaluation", "llm-evaluation", "pairwise-ranking", "utility-model"], "pretty_name": "PUM Prefix Pair Dataset"} | false | False | 2026-06-08T05:04:13 | 11 | 9 | false | a9cf649f0a7a3728ac85e584309d4a23148e308d |
PUM Prefix Pair Dataset
This dataset contains pairwise prefix preference examples for gain-based evaluation of LLM reasoning. Each example compares two partial reasoning prefixes for the same math problem and records which prefix is preferred according to outcome-grounded prefix utility.
The dataset is a... | 119 | 119 | 1,785,969,792 | [
"task_categories:text-generation",
"task_categories:text-classification",
"language:en",
"license:cc-by-4.0",
"size_categories:100K<n<1M",
"format:json",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"arxiv:2606.07190",
... | 2026-06-08T04:41:06 | null | null |
645e8da96320b0efe40ade7a | roneneldan/TinyStories | roneneldan | {"license": "cdla-sharing-1.0", "task_categories": ["text-generation"], "language": ["en"]} | false | False | 2024-08-12T13:27:26 | 1,025 | 8 | false | f54c09fd23315a6f9c86f9dc80f725de7d8f9c64 | Dataset containing synthetically generated (by GPT-3.5 and GPT-4) short stories that only use a small vocabulary.
Described in the following paper: https://arxiv.org/abs/2305.07759.
The models referred to in the paper were trained on TinyStories-train.txt (the file tinystories-valid.txt can be used for validation los... | 88,506 | 1,464,352 | 7,621,978,240 | [
"task_categories:text-generation",
"language:en",
"license:cdla-sharing-1.0",
"size_categories:1M<n<10M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"arxiv:2305.07759",
"region:us"
] | 2023-05-12T19:04:09 | null | null |
6841fee647554eb6e0b7203d | nvidia/PhysicalAI-Autonomous-Vehicles-NuRec | nvidia | {"extra_gated_heading": "You must agree to the NVIDIA Autonomous Vehicles NuRec Dataset License Agreement to access this dataset.", "extra_gated_prompt": "### NVIDIA Autonomous Vehicles NuRec Dataset License Agreement\n\nThis NVIDIA Autonomous Vehicles NuRec Dataset License Agreement (\"Agreement\") is a legal agreemen... | false | auto | 2026-06-13T13:30:36 | 181 | 8 | false | a448105cf2eb57526784443946470f0010d4feed |
task_categories:
- robotics
tags:
- physicalAI
🚀 News Update (October 22, 2025 - Many More Scenes and Better Ease of Use)!!
We have now:
Increased our number of NuRec scenes to 924!!
Added labels.json file for helping users who want to search by types of scenes based on: behavior, layou... | 17,209 | 108,154 | 2,894,456,497,687 | [
"license:other",
"region:us"
] | 2025-06-05T20:32:38 | null | null |
69836757bbb0f79b9472304c | perplexity-ai/draco | perplexity-ai | {"license": "mit", "language": ["en"], "tags": ["deep-research"], "pretty_name": "DRACO Benchmark"} | false | False | 2026-02-20T23:02:24 | 101 | 8 | false | ce076749809027649ebd331bcb70f42bf720d387 |
DRACO: a Cross-Domain Benchmark for Deep Research Accuracy, Completeness, and Objectivity
The DRACO Benchmark consists of complex, open-ended research tasks with expert-curated rubrics for evaluating deep research systems. Tasks span 10 domains and require drawing on information sources from 40 countries. Ea... | 444 | 11,354 | 920,807 | [
"language:en",
"license:mit",
"size_categories:n<1K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"arxiv:2602.11685",
"region:us",
"deep-research"
] | 2026-02-04T15:35:51 | null | null |
69fc64cba0322de6ab5c04c5 | SWE-Explore-Bench/SWE-Explore-Bench | SWE-Explore-Bench | {"license": "cc-by-nc-nd-4.0"} | false | False | 2026-06-08T15:41:51 | 8 | 8 | false | bdb0ae45d7c337d9e1dc3ebfe2a0af6bc7c1fbd9 |
SWE-Explore-Bench
SWE-Explore-Bench is the dataset for SWE-Explore: Benchmarking How Coding Agents Explore Repositories.
Citation
If you use SWE-Explore-Bench, please cite:
@misc{zhang2026sweexplore,
title = {{SWE-Explore}: Benchmarking How Coding Agents Explore Repositories},
author =... | 177 | 213 | 13,686,958 | [
"license:cc-by-nc-nd-4.0",
"size_categories:n<1K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"arxiv:2606.07297",
"region:us"
] | 2026-05-07T10:09:15 | null | null |
6a041ab186ebfeb767465f0b | zlab-princeton/i1-captions | zlab-princeton | {"configs": [{"config_name": "fluxreason", "data_files": [{"split": "train", "path": "fluxreason/train-*.parquet"}], "default": true}, {"config_name": "gptedit", "data_files": [{"split": "train", "path": "gptedit/train-*.parquet"}]}, {"config_name": "imagenet22k", "data_files": [{"split": "train", "path": "imagenet22k/... | false | False | 2026-06-12T02:01:04 | 9 | 8 | false | bb8c4a4da111c1e0b2a0afa53d381ec57b98ad19 | i1: A Simple and Fully Open Recipe for Strong Text-to-Image Models
Boya Zeng, Tianze Luo, Shu Pu, Jucheng Shen, Taiming Lu, Gabriel Sarch, Zhuang Liu
Princeton University
[arXiv][code][model][project page]
1. Overview
This dataset contains all captions used in our controlled experiments and the f... | 3,157 | 3,189 | 153,105,377,964 | [
"task_categories:text-to-image",
"size_categories:100M<n<1B",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"arxiv:2606.11289",
"region:us"
] | 2026-05-13T06:31:13 | null | null |
6a11091062d4b4e96a200f9c | armand0e/qwen3.7-max-pi-traces | armand0e | {"pretty_name": "Qwen3.7 Max Pi Traces", "task_categories": ["text-generation"], "tags": ["agent-traces", "format:agent-traces", "pi", "distillation", "qwen/qwen3.7-max", "teich"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "*.jsonl"}]}]} | false | False | 2026-05-23T02:58:23 | 90 | 8 | false | bae934b1c4285b6d2ac720b9c2a127dad9c1c39a | This dataset was generated using teich by TeichAI
Prepare these datasets for supervised fine-tuning in just a few lines of code — see the Conversion section below.
Qwen3.7 Max Pi Traces
This directory contains raw agent trace files generated by teich.
All assistant responses were generated by qwen/qwen3... | 8,815 | 8,815 | 9,927,137 | [
"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",
"pi",
"distillation",
"... | 2026-05-23T01:55:28 | 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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