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696ddc1ba806b4bfbcfc0224 | opendatalab/ChartVerse-SFT-1800K | opendatalab | {"license": "apache-2.0", "language": ["en"], "task_categories": ["visual-question-answering", "image-text-to-text"], "tags": ["chart", "reasoning", "vision-language", "multimodal", "chart-understanding", "CoT", "SFT", "large-scale"], "size_categories": ["1M<n<10M"]} | false | False | 2026-01-30T08:01:50 | 86 | 75 | false | 86fd98bdfac3e7fa2120748e7d6c597e7ee26cf8 | ChartVerse-SFT-1800K is an extended large-scale chart reasoning dataset with Chain-of-Thought (CoT) annotations, developed as part of the opendatalab/ChartVerse project. For more details about our method, datasets, and full model series, please visit our Project Page.
This dataset contains all verified correct samples ... | 1,969 | 1,969 | [
"task_categories:visual-question-answering",
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"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"arxiv:2601.13... | 2026-01-19T07:24:11 | null | null |
69524c8ad001e56220ced9bc | Alibaba-Apsara/Superior-Reasoning-SFT-gpt-oss-120b | Alibaba-Apsara | {"license": "cc-by-4.0", "task_categories": ["text-generation"], "language": ["en"], "tags": ["code", "math", "scientific-qa", "instruction-following", "reasoning", "thinking", "gpt-oss-120b", "distill"], "size_categories": ["435K"], "configs": [{"config_name": "stage1", "data_files": "Superior-Reasoning-SFT-gpt-oss-12... | false | False | 2026-01-15T06:39:55 | 303 | 66 | false | e9d54e2a3f376fd5c62cafd3c4c99b304cdda698 |
Superior-Reasoning-SFT-gpt-oss-120b
🚀 Overview
The Superior-Reasoning-SFT-gpt-oss-120b dataset is a high-quality, open-source collection containing 435K samples designed to democratize the training of high-performance Long Chain-of-Thought (Long-CoT) models. Unlike standard dis... | 29,428 | 29,429 | [
"task_categories:text-generation",
"language:en",
"license:cc-by-4.0",
"size_categories:100K<n<1M",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"arxiv:2601.09088",
"arxiv:2512.20908",
"region:us",
"code",
"math",
"scien... | 2025-12-29T09:40:26 | null | null |
696b2406e6c69ff4f49745f4 | sojuL/RubricHub_v1 | sojuL | {"license": "apache-2.0", "language": ["zh", "en"], "tags": ["medical", "science", "wirting", "isntruction", "chat", "general"], "pretty_name": "RubricHub", "size_categories": ["100K<n<1M"], "task_categories": ["text-generation", "reinforcement-learning", "question-answering"]} | false | False | 2026-01-20T07:16:51 | 142 | 63 | false | bec50742963ed3672391fecbcc4b60067b9fa8bc |
RubricHub_v1
RubricHub is a large-scale (approximately 110K), multi-domain dataset that provides high-quality rubric-based supervision for open-ended generation tasks. It is constructed via an automated coarse-to-fine rubric generation framework, which integrates principle-guided synthesis, multi-model aggre... | 732 | 732 | [
"task_categories:text-generation",
"task_categories:reinforcement-learning",
"task_categories:question-answering",
"language:zh",
"language:en",
"license:apache-2.0",
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"librar... | 2026-01-17T05:54:14 | null | null |
6965b354f2c297a7078582d4 | Qwen/DeepPlanning | Qwen | {"language": ["en", "zh"], "license": "apache-2.0", "viewer": false, "task_categories": ["text-generation"], "tags": ["planning", "llm-benchmark", "reasoning", "autonomous-agents"], "pretty_name": "DeepPlanning", "size_categories": ["1k<n<10k"]} | false | False | 2026-01-27T05:22:17 | 66 | 62 | false | 4769c4974f6a2ac026a725a9e99320727454ead8 |
DeepPlanning: Benchmarking Long-Horizon Agentic Planning with Verifiable Constraints
DeepPlanningBench is a challenging benchmark for evaluating long-horizon agentic planning capabilities of large language models (LLMs) with verifiable constraints. It features realistic multi-day travel planning and multi-pr... | 82 | 82 | [
"task_categories:text-generation",
"language:en",
"language:zh",
"license:apache-2.0",
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"library:mlcroissant",
"arxiv:2601.18137",
"region:us",
"planning",
"llm-benchmark",
"reasoni... | 2026-01-13T02:52:04 | null | null |
69660562d230db5333514344 | FOMO-MRI/FOMO300K | FOMO-MRI | {"license": "other", "license_name": "license", "tags": ["brain", "mri", "ssl", "foundation_model", "3d", "image"], "pretty_name": "FOMO-300K", "size_categories": ["100K<n<1M"], "task_categories": ["image-feature-extraction", "zero-shot-classification"], "viewer": false, "extra_gated_prompt": "\nThis collection of data... | false | auto | 2026-01-25T09:25:23 | 77 | 59 | false | 580083cd4f33b145d5ffdc57265915128e541ffe |
FOMO300K: Brain MRI Dataset for Large-Scale Self-Supervised Learning with Clinical Data
Dataset paper preprint: A large-scale heterogeneous 3D magnetic resonance brain imaging dataset for self-supervised learning.
https://arxiv.org/pdf/2506.14432v2.
Description
FOMO-300K is a large-scale data... | 21,863 | 21,863 | [
"task_categories:image-feature-extraction",
"task_categories:zero-shot-classification",
"license:other",
"size_categories:100K<n<1M",
"modality:3d",
"modality:image",
"arxiv:2506.14432",
"region:us",
"brain",
"mri",
"ssl",
"foundation_model",
"3d",
"image"
] | 2026-01-13T08:42:10 | null | null |
6967b2da7b115954f1c9327c | mercor/apex-agents | mercor | {"license": "cc-by-4.0", "language": ["en"], "tags": ["agents", "benchmarking", "finance", "legal", "management-consulting", "tool-use", "long-horizon"], "pretty_name": "apex-agents", "size_categories": ["n<1K"]} | false | False | 2026-01-22T00:33:03 | 66 | 58 | false | 602aae289ba9f4b74c27635e6f3a1738b000e5be |
APEX–Agents
APEX–Agents is a benchmark from Mercor for evaluating whether AI agents can execute long-horizon, cross-application professional services tasks. Tasks were created by investment banking analysts, management consultants, and corporate lawyers, and require agents to navigate realistic work environm... | 5,270 | 5,270 | [
"language:en",
"license:cc-by-4.0",
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"region:us",
"agents",
"benchmarking",
"finance",
"legal",
"management-consulting",
"tool-use",
"long-horizon"
] | 2026-01-14T15:14:34 | null | null |
6938038933eda94c0094c844 | raidium/RadImageNet-VQA | raidium | {"language": ["en"], "license": "apache-2.0", "size_categories": ["1K<n<10M"], "task_categories": ["visual-question-answering"], "tags": ["medical"], "pretty_name": "RadImageNet-VQA", "dataset_info": [{"config_name": "alignment", "features": [{"name": "image", "dtype": "image"}, {"name": "conversations", "list": [{"nam... | false | auto | 2025-12-19T10:06:57 | 61 | 49 | false | fe2154107adfd74f5b8218be6d2b3b127b668d32 |
RadImageNet-VQA: A Large-Scale CT and MRI Dataset for Radiologic Visual Question Answering
📖 Paper
Dataset Details
We introduce RadImageNet-VQA, a large-scale dataset designed for training and benchmarking radiologic VQA on CT and MRI exams. Built from the CT/MRI subset of RadImageNet... | 1,579 | 1,798 | [
"task_categories:visual-question-answering",
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"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us",
"medical"
] | 2025-12-09T11:10:01 | null | null |
69645867fd167898fdec27e6 | moonworks/lunara-aesthetic | moonworks | {"license": "apache-2.0", "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "prompt", "dtype": "string"}, {"name": "region", "dtype": "string"}, {"name": "category", "dtype": "string"}, {"name"... | false | False | 2026-01-22T08:40:29 | 74 | 48 | false | fcf45a62e226560ae63e60eb01c4d40372457965 |
Dataset Card for Moonworks Lunara Aesthetic Dataset
Sample Images
Dataset Summary
paper: https://arxiv.org/abs/2601.07941
The Lunara Aesthetic Dataset is a curated collection of 2,000 high-quality image–prompt pairs designed for controlle... | 5,911 | 5,911 | [
"task_categories:text-to-image",
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"library:polars",
"library:mlcroissant",
"arxiv:2601.07941",
"region:us",
"art"
] | 2026-01-12T02:11:51 | null | null |
6969078587ce326016ddda46 | lightonai/LightOnOCR-mix-0126 | lightonai | {"dataset_info": {"features": [{"name": "key", "dtype": "string"}, {"name": "page_idx", "dtype": "int64"}, {"name": "content", "dtype": "string"}, {"name": "metadata", "struct": [{"name": "element_counts", "struct": [{"name": "formulas", "dtype": "int64"}, {"name": "images", "dtype": "int64"}, {"name": "tables", "dtype... | false | False | 2026-01-26T16:29:46 | 106 | 46 | false | af0218b88fc337468d91f9c107ae33453f65cf30 |
LightOnOCR-mix-0126
LightOnOCR-mix-0126 is a large-scale OCR training dataset built via distillation: a strong vision–language model is prompted to produce naturally ordered full-page transcriptions (Markdown with LaTeX math spans and HTML tables) from rendered document pages. The dataset is designed as supe... | 1,384 | 1,384 | [
"task_categories:image-to-text",
"language:en",
"language:fr",
"language:de",
"language:es",
"language:it",
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"language:nl",
"language:zh",
"language:pt",
"language:bg",
"language:tr",
"language:ur",
"language:hi",
"language:th",
"language... | 2026-01-15T15:28:05 | null | null |
696a53dfe8359277ca69b28a | rootsautomation/pubmed-ocr | rootsautomation | {"language": ["en"], "license": "other", "size_categories": ["1M<n<10M"], "task_categories": ["image-to-text", "image-text-to-text"], "pretty_name": "PubMed-OCR", "arxiv": 2601.11425, "dataset_info": {"features": [{"name": "basename", "dtype": "string"}, {"name": "page", "dtype": "int32"}, {"name": "license", "dtype": ... | false | False | 2026-01-22T19:58:29 | 61 | 44 | false | d03682f1b9e4d1c2a4d48657063cc467a464363d |
PubMed-OCR: PMC Open Access OCR Annotations
PubMed-OCR is an OCR-centric corpus of scientific articles derived from PubMed Central Open Access PDFs. Each page is rendered to an image and annotated with Google Cloud Vision OCR, released in a compact JSON schema with word-, line-, and paragraph-level bounding ... | 2,632 | 2,632 | [
"task_categories:image-to-text",
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"arxiv:2601.11425",
"region:us",
"biology",
"... | 2026-01-16T15:06:07 | null | null |
67a404bc8c6d42c5ec097433 | Anthropic/EconomicIndex | Anthropic | {"language": "en", "pretty_name": "EconomicIndex", "tags": ["AI", "LLM", "Economic Impacts", "Anthropic"], "viewer": true, "license": "mit", "configs": [{"config_name": "release_2026_01_15", "data_files": [{"split": "raw_claude_ai", "path": "release_2026_01_15/data/intermediate/aei_raw_claude_ai_2025-11-13_to_2025-11-2... | false | False | 2026-01-15T23:52:53 | 444 | 43 | false | f7f2edfbbcf28329dd621fc8e3cc83d0d99b72eb |
The Anthropic Economic Index
Overview
The Anthropic Economic Index provides insights into how AI is being incorporated into real-world tasks across the modern economy.
Data Releases
This repository contains multiple data releases, each with its own documentation:
2026-01-15 Release: Up... | 6,836 | 41,131 | [
"language:en",
"license:mit",
"arxiv:2503.04761",
"region:us",
"AI",
"LLM",
"Economic Impacts",
"Anthropic"
] | 2025-02-06T00:39:24 | null | null |
67fce65dd1ec7d15ba6a2da3 | zwhe99/DeepMath-103K | zwhe99 | {"license": "mit", "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "final_answer", "dtype": "string"}, {"name": "difficulty", "dtype": "float64"}, {"name": "topic", "dtype": "string"}, {"... | false | False | 2025-05-29T03:37:07 | 341 | 43 | false | 5cf055d1fe3d7a2eb19719ac020211469736ae44 |
DeepMath-103K
🔥 News
May 8, 2025: We found that 48 samples contained hints that revealed the answers. The relevant questions have now been revised to remove the leaked answe... | 7,836 | 92,236 | [
"task_categories:text-generation",
"language:en",
"license:mit",
"size_categories:100K<n<1M",
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"library:polars",
"arxiv:2504.11456",
"region:us",
"math",
"reasoning",
"rl"
] | 2025-04-14T10:41:33 | null | null |
696e2528357a40707550b1c4 | google/WaxalNLP | google | {"license": ["cc-by-sa-4.0", "cc-by-4.0"], "annotation_creators": ["human-annotated", "crowdsourced"], "language_creators": ["creator_1"], "tags": ["audio", "automatic-speech-recognition", "text-to-speech"], "language": ["ach", "aka", "dag", "dga", "ewe", "fat", "ful", "hau", "ibo", "kpo", "lin", "lug", "mas", "mlg", "... | false | False | 2026-01-26T17:30:43 | 47 | 43 | false | 75c875b3ec0731682ad9a68dd1a784856eae1378 |
Waxal Datasets
Dataset Description
The Waxal project provides datasets for both Automated Speech Recognition (ASR)
and Text-to-Speech (TTS) for African languages. The goal of this dataset's
creation and release is to facilitate research that improves the accuracy and
fluency of speech and language... | 1,509 | 1,509 | [
"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:dag",
... | 2026-01-19T12:35:52 | null | null |
6978a37bcc1cd38620f46bbc | MiniMaxAI/role-play-bench | MiniMaxAI | {"language": ["zh", "en"], "license": "apache-2.0", "task_categories": ["text-generation"], "size_categories": ["1K<n<10K"], "configs": [{"config_name": "seeds_zh", "data_files": [{"split": "test", "path": "data/zh/seeds.parquet"}]}, {"config_name": "seeds_en", "data_files": [{"split": "test", "path": "data/en/seeds.pa... | false | False | 2026-01-28T04:01:11 | 40 | 40 | false | 3c1be2a56afbcaab19ae6b40b8a24429eae792f5 |
Role-play Benchmark
A comprehensive benchmark for evaluating Role-play Agents in Chinese and English scenarios.
Dataset Summary
Role-play Benchmark is designed to evaluate Role-play Agents' ability to deliver immersive role-play experiences through Situated Reenactment. Unlike traditional benchmar... | 117 | 117 | [
"task_categories:text-generation",
"language:zh",
"language:en",
"license:apache-2.0",
"size_categories:1K<n<10K",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us"
] | 2026-01-27T11:37:31 | null | null |
68ba0ffd343a84103b603c45 | Pageshift-Entertainment/LongPage | Pageshift-Entertainment | {"pretty_name": "LongPage", "dataset_name": "LongPage", "library_name": "datasets", "language": ["en"], "license": ["cc-by-4.0", "other"], "task_categories": ["text-generation"], "task_ids": ["language-modeling", "text2text-generation"], "size_categories": ["n<1K"], "source_datasets": ["original"], "annotations_creator... | false | False | 2026-01-20T14:01:26 | 139 | 37 | false | 27d907b6a9f92682110e68ef91f001b4812698d6 |
Overview 🚀📚
The first comprehensive dataset for training AI models to write complete novels with sophisticated reasoning.
🧠 Hierarchical Reasoning Architecture — Multi-layered planning traces including character archetypes, story arcs, world rules, and scene breakdowns. A complete cognitive roadmap for l... | 7,006 | 18,323 | [
"task_categories:text-generation",
"task_ids:language-modeling",
"task_ids:text2text-generation",
"annotations_creators:machine-generated",
"language_creators:found",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"license:other",
"size_categories:... | 2025-09-04T22:17:33 | null | null |
6976521d67df645b2b063143 | nvidia/Nemotron-Personas-Brazil | nvidia | {"license": "cc-by-4.0", "task_categories": ["text-generation"], "language": ["pt"], "tags": ["synthetic", "personas", "NVIDIA", "datadesigner"], "size_categories": ["1M<n<10M"], "dataset_info": {"features": [{"name": "uuid", "dtype": "string"}, {"name": "professional_persona", "dtype": "string"}, {"name": "sports_pers... | false | False | 2026-01-26T23:09:43 | 37 | 37 | false | 441be2bd83a829020452ba9242efd31d212ae602 |
Nemotron-Personas-Brazil
Abordagem de IA composta para geração de personas baseada em distribuições do mundo real
Visão Geral do Conjunto de Dados (Dataset Overview):
Nemotron-Personas-Brazil é um conjunto de dados (dataset) de código aberto (CC BY 4.0) composto por personas geradas sintetica... | 588 | 588 | [
"task_categories:text-generation",
"language:pt",
"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",
"library:datadesigner",
"region:us",
"synthetic",
... | 2026-01-25T17:25:49 | null | null |
68e91c24e825003e1c2aec1a | SWE-Arena/leaderboard_data | SWE-Arena | nan | false | False | 2026-01-24T19:43:10 | 36 | 35 | false | 8f7931b7b18b553f5a5d4d695d7a4fb0dfd08d81 | null | 844 | 1,827 | [
"region:us"
] | 2025-10-10T14:45:56 | null | null |
6960b100448a2a7a83c8f3fb | nyuuzyou/google-code-archive | nyuuzyou | {"annotations_creators": ["machine-generated"], "language_creators": ["found"], "language": ["code", "en"], "license": "other", "multilinguality": ["multilingual"], "pretty_name": "Google Code Archive Dataset", "size_categories": ["10M<n<100M"], "source_datasets": ["original"], "task_categories": ["text-generation"], "... | false | False | 2026-01-09T08:00:30 | 42 | 34 | false | 242084cfa56acf4af01fb76858ccfb8294ee2406 |
Google Code Archive Dataset
Dataset Description
This dataset was compiled from the Google Code Archive, a preserved snapshot of projects hosted on Google Code, Google's open-source project hosting service that operated from 2006 to 2016. Google Code was one of the major code hosting platforms of i... | 885 | 885 | [
"task_categories:text-generation",
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"multilinguality:multilingual",
"source_datasets:original",
"language:code",
"language:en",
"license:other",
"size_categories:10M<n<100M",
"format:parquet",
"modality:text",
"library:dataset... | 2026-01-09T07:40:48 | null | null |
695bd296a9cec4f412019cd2 | DeepGlint-AI/DanQing100M | DeepGlint-AI | {"license": "cc-by-4.0", "task_categories": ["zero-shot-image-classification", "image-to-text"], "language": ["zh"], "arxiv_id": 2601.10305, "dataset_info": {"features": [{"name": "url", "dtype": "string"}, {"name": "alt_text", "dtype": "string"}, {"name": "recaption", "dtype": "string"}], "splits": [{"name": "train", ... | false | False | 2026-01-20T07:14:23 | 46 | 33 | false | 508ac59f20e90f80b7bb3765c6741759e115c741 |
100M Chinese image-text pairs | 12TB dataset | 2024-2025 web data
DanQing: An Up-to-Date Large-Scale Chinese Vision-Language Pre-training Dataset
Project Page | Paper | Code
Hengyu Shen∗, Tiancheng Gu∗, Bin Qin, Lan Wu, Yuling Wu, Shuo Tan, Zelong Sun, Jun Wang, Nan Wu, Xiang An, Weidong Cai, Ziyong Feng‡, Ka... | 4,798 | 4,798 | [
"task_categories:zero-shot-image-classification",
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"language:zh",
"license:cc-by-4.0",
"size_categories:10M<n<100M",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"arxiv:2601.1... | 2026-01-05T15:02:46 | null | null |
633a585e593f7e38374056ec | bigcode/the-stack | bigcode | {"annotations_creators": [], "language_creators": ["crowdsourced", "expert-generated"], "language": ["code"], "license": ["other"], "multilinguality": ["multilingual"], "pretty_name": "The-Stack", "size_categories": ["unknown"], "source_datasets": [], "task_categories": ["text-generation"], "task_ids": [], "extra_gated... | false | auto | 2023-04-13T12:15:50 | 945 | 31 | false | 349a71353fd5868fb90b593ef09e311379da498a |
Dataset Card for The Stack
Changelog
Release
Description
v1.0
Initial release of the Stack. Included 30 programming languages and 18 permissive licenses. Note: Three included licenses (MPL/EPL/LGPL) are considered weak copyleft licenses. The resulting near-deduplicated dataset is 3TB i... | 16,631 | 339,242 | [
"task_categories:text-generation",
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"license:other",
"size_categories:100M<n<1B",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
... | 2022-10-03T03:34:54 | null | null |
695df55a4e351abe5277cca5 | UniParser/OmniScience | UniParser | {"license": "cc-by-nc-sa-4.0", "task_categories": ["image-to-text"], "extra_gated_heading": "Request Access to This Dataset", "extra_gated_description": "Please complete the required fields below to request access. Access will be automatically granted upon submission.", "extra_gated_fields": {"Full Name": {"type": "tex... | false | auto | 2026-01-22T02:55:43 | 104 | 31 | false | 9c9fdac9ea87b36e3889330463cd4aee2e81ce95 |
OmniScience: A Large-scale Dataset for Scientific Image Understanding
🚀 2026-01-21: The OmniScience dataset ranked Top 8 on Hugging Face Datasets Trending (Top 1 on Image Caption Filed). 🚀 2026-01-17: The OmniScience dataset surpassed 5,000 downloads within 5 days of its release. 🚀 2026-01-12: Official r... | 8,935 | 8,951 | [
"task_categories:image-to-text",
"license:cc-by-nc-sa-4.0",
"size_categories:1M<n<10M",
"format:parquet",
"format:optimized-parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"arxiv:2512.15098",
"region:us"
] | 2026-01-07T05:55:38 | null | null |
650a9248d26103b6eee3ea7b | lmsys/lmsys-chat-1m | lmsys | {"size_categories": ["1M<n<10M"], "task_categories": ["conversational"], "extra_gated_prompt": "You agree to the [LMSYS-Chat-1M Dataset License Agreement](https://huggingface.co/datasets/lmsys/lmsys-chat-1m#lmsys-chat-1m-dataset-license-agreement).", "extra_gated_fields": {"Name": "text", "Email": "text", "Affiliation"... | false | auto | 2024-07-27T09:28:42 | 825 | 30 | false | 200748d9d3cddcc9d782887541057aca0b18c5da |
LMSYS-Chat-1M: A Large-Scale Real-World LLM Conversation Dataset
This dataset contains one million real-world conversations with 25 state-of-the-art LLMs.
It is collected from 210K unique IP addresses in the wild on the Vicuna demo and Chatbot Arena website from April to August 2023.
Each sample includes a c... | 4,913 | 292,462 | [
"size_categories:1M<n<10M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"arxiv:2309.11998",
"region:us"
] | 2023-09-20T06:33:44 | null | null |
696a3aa73b9cc2d063e34382 | DAGroup-PKU/RoVid-X | DAGroup-PKU | {"language": ["en"], "license": "cc-by-4.0", "size_categories": ["n>1T"], "task_categories": ["image-to-video"], "tags": ["robotics video generation", "text-to-video", "image-to-video", "video-generation", "large-scale", "benchmark", "evaluation"]} | false | False | 2026-01-25T11:47:36 | 36 | 30 | false | 446d496e440683271f943ffd65fc1fb761818883 |
Rethinking Video Generation Model for the Embodied World
If you like our project, please give us a star ⭐ on GitHub for the latest update.
Key features
4M robotic video clips(10K+ hours) for large-scale video generation training.
1300+ fine-grained ... | 1,925 | 1,925 | [
"task_categories:image-to-video",
"language:en",
"license:cc-by-4.0",
"size_categories:n<1K",
"modality:video",
"library:datasets",
"library:mlcroissant",
"arxiv:2601.15282",
"region:us",
"robotics video generation",
"text-to-video",
"image-to-video",
"video-generation",
"large-scale",
"... | 2026-01-16T13:18:31 | null | null |
67bdc389c748a39392fe7bd7 | Neph0s/CoSER | Neph0s | {"license": "mit", "language": ["en"], "size_categories": ["100M<n<1000M"], "viewer": true, "default_split": "test"} | false | False | 2025-07-13T16:00:50 | 69 | 28 | false | 7cc80430f92532cda85df45015a4aca8ecc068d0 |
CoSER Dataset
Overview
CoSER is a high-quality dataset for role-playing LLMs, sourced from 771 renowned novels. The dataset contains authentic multi-turn, multi-character dialogues extracted from acclaimed literary works.
Key Features
Authentic Content: Unlike synthetic datasets, CoSER... | 1,429 | 12,778 | [
"language:en",
"license:mit",
"arxiv:2502.09082",
"region:us"
] | 2025-02-25T13:20:09 | null | null |
69676b65aeecdadc87f8da8e | facebook/action100m-preview | facebook | {"language": ["en"], "license": "fair-noncommercial-research-license", "size_categories": ["10M<n<100M"], "task_categories": ["video-classification", "video-text-to-text"], "tags": ["video", "action"], "arxiv": 2601.10592} | false | False | 2026-01-29T17:20:17 | 128 | 28 | false | 128d3edb9449334f89e65c806b16f35279ee50c9 |
Action100M: A Large-scale Video Action Dataset
Paper | GitHub
Action100M is a large-scale dataset constructed from 1.2M Internet instructional videos (14.6 years of duration), yielding ~100 million temporally localized segments with open-vocabulary action supervision and rich captions. It serves as a foundat... | 4,495 | 4,495 | [
"task_categories:video-classification",
"task_categories:video-text-to-text",
"language:en",
"license:fair-noncommercial-research-license",
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"modality:video",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissa... | 2026-01-14T10:09:41 | null | null |
67510376ab059ce38f2caaa3 | SWE-Arena/conversation_data | SWE-Arena | nan | false | False | 2026-01-25T17:18:10 | 28 | 27 | false | fb2d49c4f9667206dc37622e52af466f45a29d46 | null | 80 | 1,405 | [
"region:us"
] | 2024-12-05T01:35:50 | null | null |
68072cc4cce05035af98207e | nvidia/OpenMathReasoning | nvidia | {"language": ["en"], "license": "cc-by-4.0", "size_categories": ["1M<n<10M"], "task_categories": ["question-answering", "text-generation"], "pretty_name": "OpenMathReasoning", "tags": ["math", "nvidia"], "configs": [{"config_name": "default", "data_files": [{"split": "cot", "path": "data/cot-*"}, {"split": "tir", "path... | false | False | 2025-05-27T18:43:44 | 424 | 26 | false | d3d08664755704f422af97d43a7ff0ded4bd95df |
OpenMathReasoning
OpenMathReasoning is a large-scale math reasoning dataset for training large language models (LLMs).
This dataset contains
306K unique mathematical problems sourced from AoPS forums with:
3.2M long chain-of-thought (CoT) solutions
1.7M long tool-integrated reasoning (TIR) solutions
566K... | 11,573 | 153,538 | [
"task_categories:question-answering",
"task_categories:text-generation",
"language:en",
"license:cc-by-4.0",
"size_categories:1M<n<10M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2504.16891",
"region:us",
"math",
... | 2025-04-22T05:44:36 | null | null |
675103d813aa765a04ace26f | SWE-Arena/vote_data | SWE-Arena | nan | false | False | 2026-01-25T17:18:14 | 26 | 25 | false | 2efc84d5525a3fea718043670158dc0f1eb4b2e7 | null | 81 | 2,182 | [
"size_categories:n<1K",
"modality:text",
"region:us"
] | 2024-12-05T01:37:28 | null | null |
68f119703c5910443df36569 | SWE-Arena/bot_data | SWE-Arena | nan | false | False | 2026-01-22T04:55:09 | 26 | 25 | false | b7cc227d7f5ef4a237a25eb1f1f0c0f03c6721c4 | null | 148 | 1,417 | [
"size_categories:n<1K",
"modality:text",
"region:us"
] | 2025-10-16T16:12:32 | null | null |
621ffdd236468d709f181d5e | allenai/ai2_arc | allenai | {"annotations_creators": ["found"], "language_creators": ["found"], "language": ["en"], "license": ["cc-by-sa-4.0"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["question-answering"], "task_ids": ["open-domain-qa", "multiple-choice-qa"], "pre... | false | False | 2023-12-21T15:09:48 | 310 | 21 | false | 210d026faf9955653af8916fad021475a3f00453 |
Dataset Card for "ai2_arc"
Dataset Summary
A new dataset of 7,787 genuine grade-school level, multiple-choice science questions, assembled to encourage research in
advanced question-answering. The dataset is partitioned into a Challenge Set and an Easy Set, where the former contains
only questio... | 277,277 | 10,820,969 | [
"task_categories:question-answering",
"task_ids:open-domain-qa",
"task_ids:multiple-choice-qa",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:cc-by-sa-4.0",
"size_categories:1K<n<10K",
"format:parquet",... | 2022-03-02T23:29:22 | null | null |
696674bae19dee6669d689d8 | AvitoTech/BAT | AvitoTech | {"configs": [{"config_name": "fpa_campaigns", "data_files": "fpa/campaigns.csv"}, {"config_name": "fpa_stats", "data_files": "fpa/stats.csv"}, {"config_name": "vcg_campaigns", "data_files": "vcg/campaigns.csv"}, {"config_name": "vcg_stats", "data_files": "vcg/stats.csv"}]} | false | False | 2026-01-13T16:37:15 | 21 | 21 | false | cd15e02054d26a6f1534cab5a7897a7f1bd974b7 |
BAT Dataset
This dataset provides an alternative way to access the data from the BAT (BAT: Benchmark for Auto-bidding Task) autobidding benchmark.
Related Resources
GitHub Repository: avito-tech/bat-autobidding-benchmark
Paper: BAT: Benchmark for Auto-bidding Task
Dataset Description
... | 44 | 44 | [
"size_categories:10M<n<100M",
"format:csv",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us"
] | 2026-01-13T16:37:14 | null | null |
End of preview. Expand in Data Studio
Changelog
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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