_id large_stringlengths 24 24 | id large_stringlengths 4 123 | author large_stringlengths 2 42 | cardData large_stringlengths 2 1.09M ⌀ | disabled bool 1
class | gated large_stringclasses 3
values | lastModified timestamp[us]date 2021-02-05 16:03:35 2026-05-23 13:16:38 | likes int64 0 9.7k | trendingScore float64 0 79 | private bool 1
class | sha large_stringlengths 40 40 | description large_stringlengths 0 6.67k ⌀ | downloads int64 0 3.38M | downloadsAllTime int64 0 143M | mainSize float64 0 306,846B ⌀ | tags listlengths 1 7.92k | createdAt timestamp[us]date 2022-03-02 23:29:22 2026-05-23 13:15:48 | paperswithcode_id large_stringclasses 707
values | citation large_stringlengths 0 10.7k ⌀ |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
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 | 191 | 79 | 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... | 4,445 | 4,445 | 260,301,481 | [
"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 |
69f6ddc8a018b8e16911aa32 | GD-ML/TransitLM | GD-ML | {"license": "cc-by-nc-4.0", "task_categories": ["text-generation"], "language": ["zh"], "tags": ["transportation", "route-planning", "public-transit", "mobility", "instruction-tuning", "benchmark"], "pretty_name": "TransitLM"} | false | False | 2026-05-13T11:01:47 | 72 | 69 | false | 07e3e7ac735ebbc6c74284170b812e0224b31419 |
TransitLM: Dataset Release & Evaluation Protocol
Dataset Description
TransitLM is a dataset for public transit route planning in Chinese urban environments, designed to support training and evaluation of language models that generate structured transit routes from origin-destination information. T... | 814 | 814 | 52,971,509,752 | [
"task_categories:text-generation",
"language:zh",
"license:cc-by-nc-4.0",
"size_categories:100K<n<1M",
"format:csv",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"region:us",
"transportation",
"route-planning",
"public-... | 2026-05-03T05:31:52 | null | null |
6a01fb055a8a01e921f585a9 | TuringEnterprises/Open-MM-RL | TuringEnterprises | {"license": "mit", "language": ["en"], "pretty_name": "open-mm-rl", "size_categories": ["n<1K"], "tags": ["chemistry", "physics", "math", "biology", "science", "RL"], "task_categories": ["question-answering"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_... | false | False | 2026-05-13T07:32:18 | 203 | 61 | false | eceb05ae7ffe378f3a02884d93ab95a405f6db19 |
Dataset Summary
Open-MM-RL is a multimodal STEM reasoning dataset covering Physics, Mathematics, Biology, and Chemistry. It is designed for problems that require models to interpret visual information and combine it with step-by-step analytical reasoning.
Explore the full Open-MM-RL dataset (3,000 tasks comi... | 12,508 | 12,508 | 31,062,538 | [
"task_categories:question-answering",
"language:en",
"license:mit",
"size_categories:n<1K",
"format:parquet",
"format:optimized-parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us",
"chemistry",
"physics",... | 2026-05-11T15:51:33 | null | null |
69e08d8954823215aef2af15 | AlienKevin/SWE-ZERO-12M-trajectories | AlienKevin | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*.parquet"}]}], "license": "apache-2.0", "task_categories": ["text-generation"], "language": ["en"], "tags": ["swe-zero", "code", "agentic", "pre-training"], "size_categories": ["10M<n<100M"]} | false | False | 2026-05-14T23:54:23 | 103 | 44 | false | 44e028077c55e7255c328516c8bd76080fbb3840 |
SWE-ZERO 12M Trajectories
The largest agentic-coding trace dataset to date: 112 B tokens of execution-free agentic trajectories covering 122 K pull requests, 3 K repositories, and 16 programming languages.
Motivation
Agentic mid-training has become a standard ingredient for frontier coding models:... | 11,145 | 11,396 | 35,972,855,768 | [
"task_categories:text-generation",
"language:en",
"license:apache-2.0",
"size_categories:10M<n<100M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"region:us",
"swe-zero",
"code",
"agentic",
"pre-training"
] | 2026-04-16T07:19:37 | null | null |
6a0108e014d1344d73bbd7d1 | 5CD-AI/Viet-Handwriting-OCR-v2 | 5CD-AI | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1129241109, "num_examples": 59247}, {"name": "test", "num_bytes": 20676100, "num_examples": 1000}], "download_size": 1266117836, "dataset_size": 1149917209}, "configs": [{"... | false | auto | 2026-05-18T17:14:55 | 52 | 42 | false | dd8a098d14c358b969641916c63c5d46255c33f8 |
Dataset Overview
From our experience, OPEN-SOURCE DATASETS AND MODELS FROM THE GLOBAL COMMUNITY HAVE HELPED US GREATLY. However, we also learned that TO PUSH THE MODELS FURTHER, WE NEED MORE HIGH-QUALITY LOCAL DATA to DEVELOP STRONGER VIETNAMESE AI MODELS 💪.
This dataset consists of 60,248 Vietnamese 🇻🇳 h... | 477 | 477 | 1,266,126,485 | [
"task_categories:image-to-text",
"language:vi",
"size_categories:10K<n<100K",
"format:parquet",
"format:optimized-parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"arxiv:2408.12480",
"region:us",
"ocr",
"text-recogn... | 2026-05-10T22:38:24 | null | null |
69e7409e244b695efe87097a | PsiBotAI/SynData | PsiBotAI | {"language": ["en"], "license": "cc-by-4.0", "configs": [{"config_name": "all_clips", "data_files": [{"split": "train", "path": "viewer/clips.parquet"}]}]} | false | False | 2026-05-22T08:05:58 | 170 | 33 | false | 36ea3ecbe05cca833c8cb58cf186aa776d497d09 |
SynData
中文说明
Demo
If the video cannot be displayed in your environment, open it directly:
assets/syndata-demo.mp4
1. Overview
SynData is a next-generation large-scale real-world multimodal dataset newly released by PsiBot. It comprehensively covers key dimensions including vision,... | 170,338 | 170,529 | 29,260,329,421,214 | [
"language:en",
"license:cc-by-4.0",
"size_categories:100K<n<1M",
"format:parquet",
"modality:3d",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us"
] | 2026-04-21T09:17:18 | 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 | 134 | 31 | 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 Wikimedia E... | 2,916 | 24,680 | 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 |
69fed0efdacd5a79d81aba6b | TeichAI/DeepSeek-v4-Pro-Agent | TeichAI | {"pretty_name": "DeepSeek v4 Pro Agent Traces", "tags": ["agent-traces", "pi", "distillation", "deepseek/deepseek-v4-pro", "teich"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "*.jsonl"}]}], "task_categories": ["text-generation"], "language": ["en"]} | false | False | 2026-05-22T00:11:12 | 52 | 31 | false | abbf7b71b145633b60beb21ddc0d158582fd80dd | 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.
DeepSeek v4 Pro Agent Traces
This directory contains raw agent trace files generated by teich.
All assistant responses were generated by dee... | 3,119 | 3,119 | 279,544,893 | [
"task_categories:text-generation",
"language:en",
"size_categories:1K<n<10K",
"format:json",
"format:agent-traces",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"region:us",
"agent-traces",
"pi",
"distillation",
"deep... | 2026-05-09T06:15:11 | null | null |
69f6f0a419b2807b25b00086 | actava/chi-bench | actava | {"pretty_name": "\u03c7-Bench", "language": ["en"], "license": "apache-2.0", "size_categories": ["n<1K"], "task_categories": ["text-generation"], "tags": ["benchmark", "agents", "healthcare", "clinical", "prior-authorization", "care-management", "utilization-management", "long-horizon", "tool-use", "mcp"], "configs": [... | false | False | 2026-05-22T04:51:56 | 31 | 28 | false | e004e0a4ea9d254b128684b1d047c2ff9680280e |
Clinical Healthcare In-Situ Environment
Task fixtures for a long-horizon, policy-rich healthcare-workflow agent benchmark
What is in this dataset
CHI-Bench evaluates AI agents on end-to-end U.S. healthcare workflows across three long-horizon domains: provider prior authorization, payer ut... | 1,500 | 1,500 | 179,234,205 | [
"task_categories:text-generation",
"language:en",
"license:apache-2.0",
"size_categories:n<1K",
"format:json",
"modality:document",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"arxiv:2605.16679",
"region:us",
"benchm... | 2026-05-03T06:52:20 | null | null |
698f31c08d725e29501c0e3a | Qwen/WebWorldData | Qwen | {"license": "apache-2.0", "task_categories": ["text-generation"], "language": ["en", "zh"], "tags": ["WebWorld", "world-model", "web-agent", "browser-simulation", "a11y", "html", "xml", "markdown", "trajectories", "agent-training", "synthetic-data"], "pretty_name": "WebWorldData", "size_categories": ["1M<n<10M"]} | false | False | 2026-05-08T12:12:49 | 51 | 23 | false | e108c5f8e35445c9ddff71cde2a5b1fc4db4020c |
WebWorldData 🌐
Overview
WebWorldData is a large-scale dataset of 1.06M web interaction trajectories collected from the open web, designed for training browser world models. It is the training data behind the WebWorld model series.
Each trajectory consists of sequences of (sta... | 933 | 938 | 52,243,357,762 | [
"task_categories:text-generation",
"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",
"arxiv:2602.14721",
"region:us",
"WebWorld",
"world-model",
... | 2026-02-13T14:14:24 | 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 | 2,818 | 21 | 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 performa... | 1,005,470 | 8,178,850 | 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 |
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 | 220 | 20 | 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... | 11,173 | 13,213 | 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 |
69fc7badac92f4fc9ae29807 | HuggingFaceBio/carbon-pretraining-corpus | HuggingFaceBio | {"license": "other", "task_categories": ["text-generation"], "tags": ["biology", "genomics", "dna"], "pretty_name": "Carbon Pretraining Corpus", "size_categories": ["1T<n<10T"], "language": [], "configs": [{"config_name": "eukaryote_generator", "data_files": [{"split": "train", "path": "eukaryote_generator/**/*.parquet... | false | False | 2026-05-14T12:41:15 | 19 | 17 | false | cb4c13a78102933b3a6ac65734d326f7b431d9b7 |
🧬 Carbon Pretraining Corpus
Description
173M DNA & RNA sequences · 1.1 trillion nucleotides — the DNA pretraining mixture used to train Carbon, a genomic foundation model.
This dataset is a collection of data sources intended for training genomic foundation models, such as Carbon. It contains D... | 3,825 | 3,825 | 533,123,648,200 | [
"task_categories:text-generation",
"license:other",
"size_categories:100M<n<1B",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"arxiv:2502.07272",
"region:us",
"biology",
"genomics",
"dna"
] | 2026-05-07T11:46:53 | null | null |
6a085fa4944148ccb2d85ee5 | LukaDev13/Liminal-Dreamcore-1K | LukaDev13 | {"title": "Dreamcore", "emoji": "\ud83c\udf19", "colorFrom": "gray", "colorTo": "gray", "sdk": "other", "pinned": false, "license": "mit", "tags": ["ai-generated", "dreamcore", "aesthetic", "image-collection", "gpt-image"]} | false | False | 2026-05-18T22:03:11 | 18 | 17 | false | 29d8ecc0e0ac76dc5098851face10eb6848d85ca |
Dreamcore
A Collection of 1000 AI-Generated Dreamcore Aesthetic Images
All images in this collection are AI-generated.
Architecture
The generation pipeline behind this collection:
What Is Dreamcore?
Dreamcore is an internet aesthetic that captures the visual l... | 2,481 | 2,481 | 359,188,558 | [
"license:mit",
"modality:image",
"region:us",
"ai-generated",
"dreamcore",
"aesthetic",
"image-collection",
"gpt-image"
] | 2026-05-16T12:14:28 | null | null |
6a0b4ffa9e1b8ddf7861aa0c | zhifeixie/Voices-in-the-Wild-2M | zhifeixie | {"language": ["en", "zh"], "task_categories": ["automatic-speech-recognition"], "tags": ["audio", "speech", "asr", "robustness", "noisy-speech"], "pretty_name": "Voices in the Wild", "configs": [{"config_name": "default", "data_files": [{"split": "distortion", "path": "data/distortion-*.parquet"}, {"split": "distortion... | false | False | 2026-05-19T08:51:14 | 17 | 17 | false | 1c0278f88d3535110b4c9870739ba2e9d058ae90 |
Voices in the Wild
Voices in the Wild is an automatic speech recognition dataset for robustness training and evaluation under diverse acoustic conditions. Audio files use public sequential names and are grouped only by normalized acoustic subset.
Data Fields
file_name: relative path to the audio ... | 6,214 | 6,214 | 177,427,373,572 | [
"task_categories:automatic-speech-recognition",
"language:en",
"language:zh",
"modality:audio",
"region:us",
"audio",
"speech",
"asr",
"robustness",
"noisy-speech"
] | 2026-05-18T17:44:26 | 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 | 1,328 | 16 | false | 740312add88f781978c0658806c59bc2815b9866 |
Dataset Card for GSM8K
Dataset Summary
GSM8K (Grade School Math 8K) is a dataset of 8.5K high quality linguistically diverse grade school math word problems. The dataset was created to support the task of question answering on basic mathematical problems that require multi-step reasoning.
These p... | 957,053 | 11,756,267 | 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 |
6918abcd7b63899ef32fd37d | Modotte/CodeX-2M-Thinking | Modotte | {"license": "apache-2.0", "pretty_name": "CodeX-5M-Thinking", "dataset_name": "Modotte/CodeX-5M-Thinking", "size_categories": ["1M<n<10M"], "language": ["en"], "task_categories": ["text-generation", "question-answering"], "tags": ["Coding", "Code", "CodeX", "Modotte", "LLM-training", "synthetic", "curated", "benchmark"... | false | False | 2026-02-10T07:23:38 | 109 | 16 | false | f9a4622fe9ccaa71509beea80e3bc69739cbbfa2 |
Modotte
Note: This dataset is part of the lineup CodeX by Modotte. You can get lots of datasets in this same lineup, with the main focus on providing very high-quality datasets for model training and fine-tuning.
This dataset is fully synthetic, curated from high-quality public sources and enhanced... | 6,414 | 16,312 | 24,444,876,787 | [
"task_categories:text-generation",
"task_categories:question-answering",
"annotations_creators:machine-generated",
"annotations_creators:expert-verified",
"multilinguality:monolingual",
"source_datasets:Modotte internal synthetic generation",
"language:en",
"license:apache-2.0",
"size_categories:1M<... | 2025-11-15T16:35:25 | null | null |
69ea840a9a3a30e09b700a00 | ShadenA/MathNet | ShadenA | {"pretty_name": "MathNet v0 \u2014 Olympiad Math Reasoning & Retrieval", "license": "cc-by-4.0", "repository": "https://github.com/ShadeAlsha/MathNet", "contact_email": "shaden@mit.edu", "homepage": "https://mathnet.mit.edu", "task_categories": ["question-answering", "text-generation", "image-to-text"], "language": ["e... | false | False | 2026-04-27T23:48:47 | 82 | 14 | false | ae12e35eef0fc52bbbef270d6ef0f5b002252eb9 |
Quick Start · Overview · Tasks · Comparison · Dataset Stats · Data Sources · Pipeline · Schema · License · Citation
This is the official MathNet v0. A larger version v1 will be uploaded soon (more countires, problems and richer metadata). Schema is stable but field values may be revised in v1.
Qu... | 18,403 | 24,481 | 738,145,122 | [
"task_categories:question-answering",
"task_categories:text-generation",
"task_categories:image-to-text",
"language:en",
"language:pt",
"language:es",
"language:fr",
"language:it",
"language:sr",
"language:sl",
"language:de",
"language:zh",
"language:ro",
"language:ko",
"language:nl",
... | 2026-04-23T20:41:46 | null | null |
6a02c56f61fcafc28add25ba | alibaba-multimodal-industrial-ai/IndustryBench | alibaba-multimodal-industrial-ai | {"language": ["zh", "en", "ru", "vi"], "license": "mit", "task_categories": ["question-answering", "text-generation"], "pretty_name": "IndustryBench", "size_categories": ["1K<n<10K"]} | false | False | 2026-05-13T05:23:50 | 29 | 14 | false | 11ef6081abb6699f29d7eacb24829846fc879cfd |
IndustryBench: Probing the Industrial Knowledge Boundaries of LLMs
💻Github | 📝Paper
IndustryBench is a multi-lingual benchmark for evaluating the industrial domain knowledge of large language models. It comprises 2,049 expert-curated QA pairs spanning 12 industrial sectors, with human-reviewed translations... | 249 | 249 | 16,213,098 | [
"task_categories:question-answering",
"task_categories:text-generation",
"language:zh",
"language:en",
"language:ru",
"language:vi",
"license:mit",
"size_categories:1K<n<10K",
"format:csv",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"a... | 2026-05-12T06:15:11 | 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 | 1,086 | 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 data... | 625,053 | 7,247,587 | 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 |
69eae63acc97dccc4e14bfe5 | 5551z/VisCoR-55K | 5551z | {"dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 0, "num_examples": 54844}], "download_size": 0, "dataset_size": 0}} | false | False | 2026-04-30T10:51:23 | 40 | 13 | false | 98b8087267ba987bd9c2110b9d51f72f716a6430 |
VisCoR-55K Dataset
VisCoR-55K is a high-quality dataset for visual reasoning, spanning five categories: General, Reasoning, Math, Graph/Chart, and OCR.
This release contains three components:
VQA Samples: Original visual question-answer pairs.
Contrastive Counterparts: Matched contrastive VQA pairs construc... | 521 | 521 | 8,143,797,508 | [
"size_categories:10K<n<100K",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"arxiv:2603.02556",
"region:us"
] | 2026-04-24T03:40:42 | null | null |
69f1912bb578a2976aeb2503 | sensenova/SenseNova-SI-8M | sensenova | {"license": "apache-2.0", "language": ["en"], "pretty_name": "SenseNova-SI-8M", "size_categories": ["10M<n<100M"], "task_categories": ["visual-question-answering", "question-answering"], "configs": [{"config_name": "preview", "default": true, "data_files": [{"split": "train", "path": "SenseNova-SI-8M_1000samples.parque... | false | False | 2026-05-13T04:54:38 | 17 | 13 | false | 2f1c0b6136417f5e2423aff839086636858de3f0 | EN | 中文
SenseNova-SI-8M
🚀 This is the official full-scale training dataset of the SenseNova-SI series.SenseNova-SI-8M contains ~8.16 million carefully curated training samples spanning ~2.72 million unique images, organized under a rigorous taxonomy of spatial capabilitie... | 3,936 | 3,936 | 1,120,643,901,557 | [
"task_categories:visual-question-answering",
"task_categories:question-answering",
"language:en",
"license:apache-2.0",
"size_categories:1M<n<10M",
"format:parquet",
"format:optimized-parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"librar... | 2026-04-29T05:03:39 | null | null |
69e695a5d20baec02ee3039c | nvidia/Nemotron-Personas-Korea | nvidia | {"license": "cc-by-4.0", "task_categories": ["text-generation"], "language": ["ko"], "tags": ["synthetic", "personas", "NVIDIA", "Korean", "datadesigner"], "size_categories": ["1M<n<10M"], "dataset_info": {"features": [{"name": "uuid", "dtype": "string"}, {"name": "professional_persona", "dtype": "string"}, {"name": "s... | false | False | 2026-04-23T07:42:48 | 468 | 12 | false | d0a9272116a2ebf139b964ca72b8b8f604616689 |
Nemotron-Personas-Korea
우리나라 실제 분포에 기반한 합성 페르소나를 위한 복합 AI 시스템
A compound AI approach to personas grounded in real-world distributions
데이터셋 개요 (Overview)
Nemotron-Personas-Korea는 대한민국의 실제 인구통계학적·지리적·성격 특성 분포를 기반으로 합성된 오픈소스 페르소나 데이터셋(CC BY 4.0)으로, 우리나라 인구의 다양성과 특성을 폭넓게 반영하도록 설계되었... | 84,578 | 87,554 | 1,984,405,985 | [
"task_categories:text-generation",
"language:ko",
"license:cc-by-4.0",
"size_categories:1M<n<10M",
"format:parquet",
"format:optimized-parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"library:datadesigner",
"region:u... | 2026-04-20T21:07:49 | null | null |
69fd2ace3d600fa5f6587a10 | blanchon/opencs2_dataset | blanchon | {"license": "cc-by-4.0", "task_categories": ["video-classification", "reinforcement-learning", "other"], "language": ["en"], "tags": ["opencs2", "counter-strike-2", "torchcodec", "video", "audio", "parquet"], "pretty_name": "OpenCS2 - POV Renders", "configs": [{"config_name": "pov_rounds", "data_files": [{"split": "tra... | false | False | 2026-05-04T15:38:59 | 25 | 12 | false | 3934b59905159337b01eb174e33ce772f14506ad |
OpenCS2 - POV Renders
Browse with the OpenCS2 Viewer - every match, map and round, with all 10 player POVs synced on one timeline.
Tick-aligned Counter-Strike 2 POV training clips, rendered from
blanchon/cs2_dataset_demo. Each row
in the main table is one player's perspective for one round; ten POVs per r... | 22,921 | 22,921 | 10,628,527,328,690 | [
"task_categories:video-classification",
"task_categories:reinforcement-learning",
"task_categories:other",
"language:en",
"license:cc-by-4.0",
"size_categories:100K<n<1M",
"format:parquet",
"modality:tabular",
"modality:text",
"modality:video",
"modality:audio",
"library:datasets",
"library:... | 2026-05-08T00:14:06 | null | null |
69ca9b695a4dac480491fd13 | lambda/hermes-agent-reasoning-traces | lambda | {"license": "apache-2.0", "task_categories": ["text-generation"], "language": ["en"], "tags": ["tool-calling", "function-calling", "agent", "hermes", "reasoning", "sharegpt", "sft", "traces"], "size_categories": ["10K<n<100K"], "configs": [{"config_name": "kimi", "data_files": [{"split": "train", "path": "data/kimi/tra... | false | False | 2026-04-17T10:06:39 | 329 | 11 | false | b92885e4f0161d4b2536512710e004d4892cac6e |
Hermes Agent Reasoning Traces
Multi-turn tool-calling trajectories for training AI agents using the Hermes Agent harness. Each sample is a real agent conversation with step-by-step reasoning (<think> blocks) and actual tool execution results.
This dataset has two configs, one per source model:
Config
M... | 3,669 | 11,299 | 1,616,105,008 | [
"task_categories:text-generation",
"language:en",
"license:apache-2.0",
"size_categories:10K<n<100K",
"format:parquet",
"format:optimized-parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us",
"tool-calling",
"function-calling... | 2026-03-30T15:48:57 | null | null |
69ef6131ceb075c32613a27a | open-thoughts/AgentTrove | open-thoughts | {"license": "apache-2.0", "task_categories": ["text-generation"], "language": ["en"], "tags": ["agent", "code", "agentic-traces", "reinforcement-learning", "terminus-2", "harbor", "agent-traces"], "size_categories": ["1M<n<10M"]} | false | False | 2026-05-07T14:20:40 | 150 | 11 | false | b395a4307a2bc9950a90dc899438f149e115fc60 |
AgentTrove
AgentTrove is the largest open-source collection of agentic interaction traces to date, released by the OpenThoughts-Agent team. It contains 1,696,847 rows drawn from 219 source datasets spanning code repair, shell scripting, mathematical problem-solving, competitive programming, and general compu... | 11,074 | 11,074 | 19,552,366,847 | [
"task_categories:text-generation",
"language:en",
"license:apache-2.0",
"size_categories:1M<n<10M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"region:us",
"agent",
"code",
"agentic-traces",
"reinforcement-learning",
... | 2026-04-27T13:14:25 | null | null |
6a0343048a02fa22647f255a | tinixai/ocr_annual_financials | tinixai | {"language": ["vi"], "license": "cc-by-nc-4.0", "task_categories": ["document-question-answering", "text-generation"], "pretty_name": "TiniX Vietnam OCR Annual Financial Statements", "size_categories": ["10K<n<100K"]} | false | False | 2026-05-18T15:35:53 | 18 | 11 | false | dbe359d1a3e1470de802047bbd45f4b3cca1dabd |
📋 TiniX Vietnam OCR Annual Financial Statements (2015–2025)
📌 Overview
TiniX Vietnam OCR Annual Financial Statements là bộ dữ liệu văn bản OCR từ báo cáo tài chính thường niên của các doanh nghiệp niêm yết tại Việt Nam trong giai đoạn 2015–2025. Dữ liệu được thu thập và xử lý bởi TiniX AI bao gồ... | 7,427 | 7,427 | 194,170,299,184 | [
"task_categories:document-question-answering",
"task_categories:text-generation",
"language:vi",
"license:cc-by-nc-4.0",
"size_categories:10K<n<100K",
"modality:document",
"modality:text",
"library:datasets",
"library:mlcroissant",
"region:us"
] | 2026-05-12T15:11:00 | null | null |
6a0bde409f539ee2b902e024 | Jackrong/Claude-opus-4.6-TraceInversion-9000x | Jackrong | {"annotations_creators": ["machine-generated"], "language": ["en", "zh", "ko", "ja", "ru", "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 | 2026-05-19T10:20:02 | 11 | 11 | false | dcb98612aa4eb657cddec26ac2047e3f6c454ed3 |
🌀 Claude-opus-4.6-TraceInversion-9000x
v1.0 Release
A High-Fidelity Reconstructed CoT Dataset via Trace Inversion
📊 9,000 Samples
🧬 Trace Inversion & Negentropy
🛠 SFT & DPO Ready
🔥 Claude 4.6 Distillation
🌐 English & Multilingual
💡 What is Trace ... | 230 | 230 | 61,997,908 | [
"task_categories:text-generation",
"annotations_creators:machine-generated",
"language:en",
"language:zh",
"language:ko",
"language:ja",
"language:ru",
"language:es",
"license:apache-2.0",
"size_categories:1K<n<10K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"... | 2026-05-19T03:51:28 | 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
- Downloads last month
- 4,937