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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
dataset | garrethlee/comprehensive-arithmetic-problems | garrethlee | 2024-11-18 | 2026-05-13 | [] | mit | [] | [
"license:mit",
"size_categories:1M<n<10M",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 1M<n<10M | 22,019 | null | 1,600,000 | 65,249,693 | [] | |
dataset | xlangai/BRIGHT | xlangai | 2024-06-07 | 2025-03-01 | [
"en"
] | cc-by-4.0 | [
"text-retrieval"
] | [
"task_categories:text-retrieval",
"language:en",
"license:cc-by-4.0",
"size_categories:1M<n<10M",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2407.12883",
"region:us",
"text-retrieval",
"code",
"biology",
"ear... | 1M<n<10M | 21,902 | null | 1,347,120 | 604,467,844 | [
"2407.12883"
] |
# BRIGHT benchmark
BRIGHT is the first text retrieval benchmark that requires intensive reasoning to retrieve relevant documents.
The queries are collected from diverse domains (StackExchange, LeetCode, and math competitions), all sourced from realistic human data.
Experiments show that existing retrieval models perf... |
dataset | SciCode/SciCode-Domain-Code | SciCode | 2026-02-19 | 2026-02-19 | [
"code"
] | apache-2.0 | [
"text-generation"
] | [
"task_categories:text-generation",
"language:code",
"license:apache-2.0",
"size_categories:1M<n<10M",
"format:csv",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"region:us",
"code",
"scientific-computing",
"domain-speci... | 1M<n<10M | 21,774 | null | 155,855 | 123,450,692,143 | [] |
# DATA1: Domain-Specific Code Dataset
## Dataset Overview
DATA1 is a large-scale domain-specific code dataset focusing on code samples from interdisciplinary fields such as biology, chemistry, materials science, and related areas. The dataset is collected and organized from GitHub repositories, covering 178 differen... |
dataset | google-research-datasets/nq_open | google-research-datasets | 2022-03-02 | 2024-03-22 | [
"en"
] | cc-by-sa-3.0 | [
"question-answering"
] | [
"task_categories:question-answering",
"task_ids:open-domain-qa",
"annotations_creators:expert-generated",
"language_creators:other",
"multilinguality:monolingual",
"source_datasets:extended|natural_questions",
"language:en",
"license:cc-by-sa-3.0",
"size_categories:10K<n<100K",
"format:parquet",
... | 10K<n<100K | 21,432 | [
"monolingual"
] | 91,535 | 4,688,185 | [] |
# Dataset Card for nq_open
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
... |
dataset | opencsg/Fineweb-Edu-Chinese-V2.1 | opencsg | 2025-01-15 | 2026-01-28 | [
"zh"
] | apache-2.0 | [
"text-generation"
] | [
"task_categories:text-generation",
"language:zh",
"license:apache-2.0",
"size_categories:100M<n<1B",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2501.08197",
"region:us"
] | 100M<n<1B | 23,385 | null | 957,608,411 | 2,416,144,808,247 | [
"2501.08197"
] |
# **Chinese Fineweb Edu Dataset V2.1** [[中文]](#chinese) [[English]](#english)
<a id="english"></a>
<p align="center">
<img width="600px" alt="OpenCSG" src="./logo.png">
</p>
<p align="center"><a href="https://opencsg.com/models">[OpenCSG Community]</a> <a href="https://github.com/yuyijiong/fineweb-e... |
dataset | NJU-LINK/WebCompass | NJU-LINK | 2026-04-07 | 2026-05-18 | [
"en",
"zh"
] | apache-2.0 | [
"text-generation",
"image-text-to-text",
"video-text-to-text"
] | [
"task_categories:text-generation",
"task_categories:image-text-to-text",
"task_categories:video-text-to-text",
"language:en",
"language:zh",
"license:apache-2.0",
"size_categories:n<1K",
"format:json",
"modality:image",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",... | n<1K | 22,441 | null | 933 | 5,884,773,948 | [
"2604.18224"
] |
# WebCompass
A unified multimodal benchmark for evaluating LLMs' ability to generate, edit, and repair functional web pages. WebCompass spans three input modalities — text design documents, reference screenshots, and video demonstrations — and three task families — **generation**, **editing**, and **repair**.
**GitH... |
dataset | smolagents/gaia-traces | smolagents | 2025-04-03 | 2025-04-09 | [] | null | [] | [
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 1K<n<10K | 21,355 | null | 1,204 | 4,416,663 | [] | |
dataset | sentence-transformers/stsb | sentence-transformers | 2024-04-25 | 2024-04-25 | [
"en"
] | null | [
"feature-extraction",
"sentence-similarity"
] | [
"task_categories:feature-extraction",
"task_categories:sentence-similarity",
"multilinguality:monolingual",
"language:en",
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us",
"sentence-tran... | 1K<n<10K | 21,314 | [
"monolingual"
] | 8,628 | 724,705 | [] |
# Dataset Card for STSB
The Semantic Textual Similarity Benchmark (Cer et al., 2017) is a collection of sentence pairs drawn from news headlines, video and image captions, and natural language inference data.
Each pair is human-annotated with a similarity score from 1 to 5. However, for this variant, the similarity s... |
dataset | MohamedRashad/arabic-books | MohamedRashad | 2024-11-13 | 2024-11-28 | [
"ar"
] | gpl-3.0 | [
"text-generation"
] | [
"task_categories:text-generation",
"language:ar",
"license:gpl-3.0",
"size_categories:1K<n<10K",
"format:arrow",
"modality:text",
"library:datasets",
"library:mlcroissant",
"arxiv:2411.17835",
"region:us",
"arabic",
"ocr",
"books",
"text-extraction",
"language-modeling",
"vision-transf... | 1K<n<10K | 23,220 | null | 8,647 | 256,202,579,063 | [
"2411.17835"
] |
# Arabic Books
## Dataset Summary
The `arabic-books` dataset contains **8,500 rows of text**, each representing the full text of a single Arabic book. These texts were extracted using the [arabic-large-nougat](https://huggingface.co/MohamedRashad/arabic-large-nougat) model, showcasing the model’s capabilities in Ara... |
dataset | lmms-lab/ChartQA | lmms-lab | 2024-01-26 | 2024-03-08 | [] | null | [] | [
"size_categories:1K<n<10K",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2203.10244",
"region:us"
] | 1K<n<10K | 21,259 | null | 2,500 | 72,614,607 | [
"2203.10244"
] |
<p align="center" width="100%">
<img src="https://i.postimg.cc/g0QRgMVv/WX20240228-113337-2x.png" width="100%" height="80%">
</p>
# Large-scale Multi-modality Models Evaluation Suite
> Accelerating the development of large-scale multi-modality models (LMMs) with `lmms-eval`
🏠 [Homepage](https://lmms-lab.github.io... |
dataset | derek-thomas/ScienceQA | derek-thomas | 2023-02-10 | 2023-02-25 | [
"en"
] | cc-by-sa-4.0 | [
"multiple-choice",
"question-answering",
"other",
"visual-question-answering",
"text-classification"
] | [
"task_categories:multiple-choice",
"task_categories:question-answering",
"task_categories:other",
"task_categories:visual-question-answering",
"task_categories:text-classification",
"task_ids:multiple-choice-qa",
"task_ids:closed-domain-qa",
"task_ids:open-domain-qa",
"task_ids:visual-question-answe... | 10K<n<100K | 21,390 | [
"monolingual"
] | 21,208 | 626,493,224 | [
"2209.09513"
] |
# Dataset Card Creation Guide
## Table of Contents
- [Dataset Card Creation Guide](#dataset-card-creation-guide)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboa... |
dataset | evalplus/mbppplus | evalplus | 2024-01-23 | 2024-04-17 | [] | apache-2.0 | [] | [
"license:apache-2.0",
"size_categories:n<1K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | n<1K | 21,377 | null | 378 | 1,131,960 | [] | |
dataset | ai-hyz/MemoryAgentBench | ai-hyz | 2025-07-05 | 2025-10-07 | [] | mit | [
"question-answering",
"zero-shot-classification",
"summarization",
"text-classification",
"text-generation"
] | [
"task_categories:question-answering",
"task_categories:zero-shot-classification",
"task_categories:summarization",
"task_categories:text-classification",
"task_categories:text-generation",
"license:mit",
"size_categories:n<1K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pand... | n<1K | 21,358 | null | 146 | 76,576,260 | [
"2507.05257"
] | # 🚧 Update
- [x] (Sep 29th, 2025) We updated our paper, where we removed some in-efficient and high-cost samples. We also added a sub-sample of DetectiveQA.
- [x] (July 7th, 2025) We released the initial version of our datasets.
- [x] (July 22nd, 2025) We modify the datasets slightly, adding the keypoints in L... |
dataset | ARTPARK-IISc/Vaani | ARTPARK-IISc | 2024-09-30 | 2026-05-04 | [
"ne",
"as",
"ml",
"gu",
"or",
"en",
"ta",
"ur",
"te",
"kn",
"bn",
"hi"
] | cc-by-4.0 | [
"automatic-speech-recognition",
"text-to-speech",
"image-to-text",
"text-to-image"
] | [
"task_categories:automatic-speech-recognition",
"task_categories:text-to-speech",
"task_categories:image-to-text",
"task_categories:text-to-image",
"language:ne",
"language:as",
"language:ml",
"language:gu",
"language:or",
"language:en",
"language:ta",
"language:ur",
"language:te",
"langua... | 10M<n<100M | 22,291 | null | null | 3,640,833,083,173 | [
"2603.28714"
] | VAANI is an India-representative multi-modal multi-lingual dataset.
The current version (phase 1- 80 districts, phase 2- 85 districts) contains ~31,255 hours of spontaenous,image-prompted speech by 156K speakers across 165 districts, talking about 288K images covering 106 languages.
From this audio data, 2,043 hours o... |
dataset | justinsunyt/terminal-bench-2-leaderboard | justinsunyt | 2026-03-11 | 2026-03-11 | [] | apache-2.0 | [] | [
"license:apache-2.0",
"modality:text",
"region:us"
] | null | 22,629 | null | null | 4,018,672,364 | [] |
# Terminal-Bench 2.0 Leaderboard Submissions
This repository accepts leaderboard submissions for [Terminal-Bench 2.0](https://terminal-bench.org).
## How to Submit
1. [Fork this repository](https://huggingface.co/docs/hub/en/repositories-next-steps#duplicating-with-the-git-history-fork)
2. Create a new bran... |
dataset | nebius/SWE-rebench-V2 | nebius | 2026-02-04 | 2026-05-12 | [
"en"
] | cc-by-4.0 | [
"text-generation"
] | [
"task_categories:text-generation",
"language:en",
"license:cc-by-4.0",
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"arxiv:2602.23866",
"region:us",
"code",
"software-engineering",
"swe-ben... | 10K<n<100K | 21,245 | null | 32,079 | 428,865,577 | [
"2602.23866"
] |
# SWE-rebench-V2
## Dataset Summary
SWE-rebench-V2 is a curated dataset of software-engineering tasks derived from real GitHub issues and pull requests. The dataset contains 32,079 samples covering Python, Go, TypeScript, JavaScript, Rust, Java, PHP, Kotlin, Julia, Elixir, Scala, Swift, Dart, C, C++, C#, R, Clojure,... |
dataset | allenai/molmospaces | allenai | 2025-09-19 | 2026-05-28 | [] | odc-by | [] | [
"license:odc-by",
"license:cc-by-4.0",
"size_categories:1M<n<10M",
"modality:tabular",
"modality:text",
"arxiv:2602.11337",
"region:us",
"robotics",
"embodied ai",
"grasps",
"objects",
"scenes",
"benchmark"
] | 1M<n<10M | 21,562 | null | 1,003,624 | 13,083,439,196,452 | [
"2602.11337"
] |
# MolmoSpaces
This respository contains asset data for [MolmoSpaces](https://github.com/allenai/molmospaces), including
- Objects
- Robots
- Scenes
- Grasps
- Benchmarks
## Updates
- **[2026/05/28] - New mujoco scene versions (`ithor`, `procthor-10k-{train,val,test}`,
`procthor-objaverse-{train,val}`, and `holodeck-o... |
dataset | arcee-ai/distilabel-intel-orca-dpo-pairs-binarized | arcee-ai | 2024-05-18 | 2024-05-18 | [
"en"
] | apache-2.0 | [] | [
"language:en",
"license:apache-2.0",
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"library:distilabel",
"region:us",
"dpo",
"orpo",
"synthetic",
"distilabel"
] | 10K<n<100K | 21,148 | null | 12,859 | 30,887,867 | [] | This is the binarized version of distilabel Orca Pairs for DPO and ORPO.
Reference: https://huggingface.co/datasets/argilla/distilabel-intel-orca-dpo-pairs?row=0 |
dataset | CohereLabs/wikipedia-2023-11-embed-multilingual-v3 | CohereLabs | 2024-01-11 | 2026-03-25 | [] | null | [] | [
"size_categories:100M<n<1B",
"modality:text",
"region:us"
] | 100M<n<1B | 21,417 | null | 247,154,006 | 536,270,738,315 | [] |
# Multilingual Embeddings for Wikipedia in 300+ Languages
This dataset contains the [wikimedia/wikipedia](https://huggingface.co/datasets/wikimedia/wikipedia) dataset dump from 2023-11-01 from Wikipedia in all 300+ languages.
The individual articles have been chunked and embedded with the state-of-the-art multiling... |
dataset | samyakjain/MSRBackups | samyakjain | 2025-07-08 | 2025-07-09 | [] | null | [] | [
"size_categories:n<1K",
"modality:document",
"modality:text",
"library:datasets",
"library:mlcroissant",
"region:us"
] | n<1K | 25,644 | null | 104 | 7,754,894,004 | [] | |
dataset | JQL-AI/fw2_edu_scores | JQL-AI | 2025-07-30 | 2025-08-07 | [
"sq",
"bg",
"ca",
"cs",
"da",
"de",
"es",
"et",
"el",
"eu",
"fi",
"fr",
"gl",
"ga",
"hr",
"hu",
"hy",
"is",
"it",
"lv",
"lt",
"mk",
"nl",
"pl",
"pt",
"ro",
"sl",
"sk",
"sr",
"tr",
"sv",
"nb",
"nn"
] | null | [
"text-ranking"
] | [
"task_categories:text-ranking",
"language:sq",
"language:bg",
"language:ca",
"language:cs",
"language:da",
"language:de",
"language:es",
"language:et",
"language:el",
"language:eu",
"language:fi",
"language:fr",
"language:gl",
"language:ga",
"language:hr",
"language:hu",
"language:... | 1B<n<10B | 21,204 | null | 985,686,816 | 2,909,449,254,783 | [
"2505.22232"
] |
# Fineweb2-Edu-scores
## Dataset summary
FineWeb2-JQL-Education is a **model-annotated** language subset of [**FineWeb2**](https://huggingface.co/datasets/HuggingFaceFW/fineweb-2), spanning **36 languages**.
Our model-annotations allow for a filtering that achieves higher-quality training outcomes without excessive... |
dataset | mteb/arguana | mteb | 2024-03-02 | 2026-04-17 | [
"eng"
] | cc-by-sa-4.0 | [
"text-retrieval"
] | [
"benchmark:official",
"benchmark:eval-yaml",
"task_categories:text-retrieval",
"annotations_creators:derived",
"multilinguality:monolingual",
"source_datasets:mteb/arguana",
"language:eng",
"license:cc-by-sa-4.0",
"size_categories:10K<n<100K",
"format:json",
"modality:text",
"library:datasets"... | 10K<n<100K | 21,145 | [
"monolingual"
] | 11,486 | 11,736,896 | [
"2502.13595",
"2210.07316"
] | <!-- adapted from https://github.com/huggingface/huggingface_hub/blob/v0.30.2/src/huggingface_hub/templates/datasetcard_template.md -->
<div align="center" style="padding: 40px 20px; background-color: white; border-radius: 12px; box-shadow: 0 2px 10px rgba(0, 0, 0, 0.05); max-width: 600px; margin: 0 auto;">
<h1 styl... |
dataset | Alljoined/Alljoined-1.6M | Alljoined | 2025-05-13 | 2026-03-12 | [] | cc-by-nc-sa-4.0 | [] | [
"license:cc-by-nc-sa-4.0",
"size_categories:1M<n<10M",
"format:parquet",
"modality:image",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"library:webdataset",
"arxiv:2508.18571",
"region:us",
"webdataset"
] | 1M<n<10M | 21,190 | null | 1,686,693 | 136,083,294,813 | [
"2508.18571"
] |
## Alljoined 1.6M
- **Homepage:** https://alljoined.com
- **Repository:** https://github.com/Alljoined/Alljoined-1.6M
- **Paper:** https://arxiv.org/abs/2508.18571
- **Point of Contact:** team@alljoined.com
We present a new large-scale electroencephalography (EEG) dataset as part of the THINGS initiative, comprising... |
dataset | ait4x/polyu-storyworld-characters | ait4x | 2025-02-13 | 2026-05-01 | [] | mit | [] | [
"license:mit",
"size_categories:n<1K",
"format:imagefolder",
"modality:image",
"modality:text",
"library:datasets",
"library:mlcroissant",
"region:us"
] | n<1K | 21,144 | null | 625 | 970,231,670 | [] | |
dataset | heegyu/bbq | heegyu | 2023-07-14 | 2023-07-14 | [] | cc-by-4.0 | [] | [
"license:cc-by-4.0",
"size_categories:10K<n<100K",
"modality:tabular",
"modality:text",
"library:datasets",
"library:mlcroissant",
"region:us"
] | 10K<n<100K | 21,003 | null | 58,492 | 50,941,256 | [] | # BBQ
Repository for the Bias Benchmark for QA dataset.
https://github.com/nyu-mll/BBQ
Authors: Alicia Parrish, Angelica Chen, Nikita Nangia, Vishakh Padmakumar, Jason Phang, Jana Thompson, Phu Mon Htut, and Samuel R. Bowman.
## About BBQ (paper abstract)
It is well documented that NLP models learn social biases, bu... |
dataset | wecover/OPUS_GlobalVoices | wecover | 2024-01-31 | 2024-11-24 | [
"am",
"ar",
"bg",
"bn",
"ca",
"cs",
"da",
"de",
"el",
"en",
"eo",
"es",
"fa",
"fr",
"he",
"hi",
"hu",
"id",
"it",
"km",
"ko",
"ku",
"mg",
"mk",
"my",
"ne",
"nl",
"or",
"pa",
"pt",
"pl",
"ro",
"ru",
"sq",
"sr",
"sv",
"sw",
"tr",
"ur",
"zh"... | null | [] | [
"language:am",
"language:ar",
"language:bg",
"language:bn",
"language:ca",
"language:cs",
"language:da",
"language:de",
"language:el",
"language:en",
"language:eo",
"language:es",
"language:fa",
"language:fr",
"language:he",
"language:hi",
"language:hu",
"language:id",
"language:... | 10M<n<100M | 20,860 | null | 10,292,730 | 672,615,344 | [] | |
dataset | RUC-NLPIR/FlashRAG_datasets | RUC-NLPIR | 2024-07-16 | 2025-05-06 | [
"en"
] | cc-by-sa-4.0 | [
"question-answering",
"summarization",
"text2text-generation"
] | [
"task_categories:question-answering",
"task_categories:summarization",
"language:en",
"license:cc-by-sa-4.0",
"size_categories:1M<n<10M",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2405.13576",
"region:us"
] | 1M<n<10M | 20,803 | null | 4,986,104 | 32,074,270,902 | [
"2405.13576"
] |
# ⚡FlashRAG: A Python Toolkit for Efficient RAG Research
FlashRAG is a Python toolkit for the reproduction and development of Retrieval Augmented Generation (RAG) research. Our toolkit includes 36 pre-processed benchmark RAG datasets and 16 state-of-the-art RAG algorithms.
With FlashRAG and provided resources, you c... |
dataset | RLDF/RLDF-benchmark | RLDF | 2026-05-01 | 2026-05-02 | [] | cc-by-4.0 | [] | [
"license:cc-by-4.0",
"size_categories:n<1K",
"format:text",
"modality:image",
"modality:text",
"library:datasets",
"library:mlcroissant",
"region:us"
] | n<1K | 22,788 | null | 1 | 1,324,836,393 | [] | |
dataset | minpeter/xlam-function-calling-60k-parsed | minpeter | 2025-02-15 | 2025-05-28 | [
"en"
] | cc-by-4.0 | [
"text-generation"
] | [
"task_categories:text-generation",
"language:en",
"license:cc-by-4.0",
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 10K<n<100K | 20,714 | null | 60,000 | 26,064,516 | [] |
# [PARSED] APIGen Function-Calling Datasets (xLAM)
This dataset contains the _**full**_ data from the original [Salesforce/xlam-function-calling-60k](https://huggingface.co/datasets/Salesforce/xlam-function-calling-60k)
| Subset name | multi-turn | parallel | multiple definition | Last turn type | numb... |
dataset | alabulei/gaianet-test | alabulei | 2024-03-29 | 2024-09-11 | [] | apache-2.0 | [] | [
"license:apache-2.0",
"size_categories:n<1K",
"format:webdataset",
"modality:text",
"library:datasets",
"library:webdataset",
"library:mlcroissant",
"region:us"
] | n<1K | 20,649 | null | 1 | 3,455,998,141 | [] | |
dataset | OALL/details_princeton-nlp__Llama-3-8B-ProLong-512k-Instruct | OALL | 2024-09-19 | 2024-09-19 | [] | null | [] | [
"size_categories:100K<n<1M",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 100K<n<1M | 21,100 | null | 145,930 | 92,024,927 | [] |
# Dataset Card for Evaluation run of princeton-nlp/Llama-3-8B-ProLong-512k-Instruct
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [princeton-nlp/Llama-3-8B-ProLong-512k-Instruct](https://huggingface.co/princeton-nlp/Llama-3-8B-ProLong-512k-Instruct)... |
dataset | hatakeyama-llm-team/PMC | hatakeyama-llm-team | 2024-04-16 | 2024-10-01 | [] | null | [] | [
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | 100K<n<1M | 20,646 | null | 819,253 | 113,241,626,587 | [] |
# Data collected from [PMC](https://www.ncbi.nlm.nih.gov/pmc/tools/textmining/)
- Only CC-BY, CC-BY-SA licenses are included.
- For all records, check the jsonl files in the [data folder](https://huggingface.co/datasets/hatakeyama-llm-team/PMC/tree/main)
|
dataset | HuggingFaceM4/WebSight | HuggingFaceM4 | 2024-01-04 | 2024-03-26 | [
"en"
] | cc-by-4.0 | [] | [
"language:en",
"license:cc-by-4.0",
"size_categories:1M<n<10M",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2403.09029",
"region:us",
"code",
"synthetic"
] | 1M<n<10M | 20,704 | null | 2,745,658 | 317,180,101,480 | [
"2403.09029"
] | # Dataset Card for WebSight
## Dataset Description
WebSight is a large synthetic dataset containing HTML/CSS codes representing synthetically generated English websites, each accompanied by a corresponding screenshot.
This dataset serves as a valuable resource for tasks such as generating UI codes from a screenshot... |
dataset | ruggsea/infini-news-corpus | ruggsea | 2026-01-29 | 2026-05-14 | [
"eng",
"spa",
"rus",
"deu",
"ita",
"fra",
"tur",
"arb",
"por",
"hin",
"jpn",
"ell",
"ron",
"zho",
"pol",
"nld",
"kor",
"ukr",
"vie",
"swe",
"hun",
"bul",
"ces",
"ind",
"fas",
"tam",
"arz",
"nor",
"urd",
"ben",
"fin",
"slk",
"hrv",
"msa",
"est",
"... | cc-by-4.0 | [
"text-generation",
"text-classification",
"text-retrieval"
] | [
"task_categories:text-generation",
"task_categories:text-classification",
"task_categories:text-retrieval",
"annotations_creators:machine-generated",
"multilinguality:multilingual",
"source_datasets:original",
"language:eng",
"language:spa",
"language:rus",
"language:deu",
"language:ita",
"lan... | 100M<n<1B | 23,101 | [
"multilingual"
] | 1,357,027,742 | 1,807,488,896,525 | [
"2310.16248",
"2411.19638"
] |
# INFINI-NEWS Corpus
A multilingual news corpus extracted from
[Common Crawl CC-News WARC files](https://commoncrawl.org/blog/news-dataset-available).
One row per article, with body text extracted via
[trafilatura](https://github.com/adbar/trafilatura),
WARC provenance, and derived metadata (publish date, language, t... |
dataset | tiiuae/falcon-refinedweb | tiiuae | 2023-05-07 | 2023-06-20 | [
"en"
] | odc-by | [
"text-generation"
] | [
"task_categories:text-generation",
"language:en",
"license:odc-by",
"size_categories:100M<n<1B",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2306.01116",
"arxiv:2203.15556",
"arxiv:2107.06499",
"arxiv:2104.08758",
... | 100M<n<1B | 21,002 | null | 968,000,015 | 1,676,959,687,683 | [
"2306.01116",
"2203.15556",
"2107.06499",
"2104.08758",
"2109.07445",
"1911.00359",
"2112.11446"
] |
# 📀 Falcon RefinedWeb
**Falcon RefinedWeb is a massive English web dataset built by [TII](https://www.tii.ae) and released under an ODC-By 1.0 license.**
See the 📓 [paper on arXiv](https://arxiv.org/abs/2306.01116) for more details.
RefinedWeb is built through stringent filtering and large-scale deduplication of... |
dataset | mlfoundations/MINT-1T-PDF-CC-2023-23 | mlfoundations | 2024-07-12 | 2024-09-19 | [
"en"
] | cc-by-4.0 | [
"image-to-text",
"text-generation"
] | [
"task_categories:image-to-text",
"task_categories:text-generation",
"language:en",
"license:cc-by-4.0",
"size_categories:1M<n<10M",
"format:webdataset",
"modality:image",
"modality:text",
"library:datasets",
"library:webdataset",
"library:mlcroissant",
"arxiv:2406.11271",
"region:us",
"mul... | 1M<n<10M | 23,766 | null | 3,400 | 4,164,957,137,389 | [
"2406.11271"
] |
<h1 align="center">
🍃 MINT-1T:<br>Scaling Open-Source Multimodal Data by 10x:<br> A Multimodal Dataset with One Trillion Tokens
</h1>
🍃 MINT-1T is an open-source **M**ultimodal **INT**erleaved dataset with 1 trillion text tokens and 3.4 billion images, a 10x scale-up from existing open-source datasets. Additional... |
dataset | xiaoyu127/cs5242_add_on_dataset | xiaoyu127 | 2026-03-15 | 2026-03-15 | [] | mit | [] | [
"license:mit",
"size_categories:100K<n<1M",
"format:text",
"modality:image",
"modality:text",
"library:datasets",
"library:mlcroissant",
"region:us"
] | 100K<n<1M | 23,340 | null | 202,599 | 3,351,342,521 | [] | |
dataset | asahi417/seamless-align-enA-viA.speaker-embedding.xlsr-2b | asahi417 | 2024-06-11 | 2024-06-25 | [] | null | [] | [
"size_categories:100K<n<1M",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | 100K<n<1M | 20,988 | null | 120,785 | 1,304,488,706,549 | [] | |
dataset | MIN-Lab/minWM-data | MIN-Lab | 2026-05-12 | 2026-05-16 | [] | null | [] | [
"size_categories:1K<n<10K",
"format:json",
"modality:text",
"modality:video",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us"
] | 1K<n<10K | 21,757 | null | 6,400 | 526,529,876,176 | [] | |
dataset | palshub/phonemizer-dicts | palshub | 2026-04-13 | 2026-04-20 | [] | cc0-1.0 | [] | [
"license:cc0-1.0",
"size_categories:100K<n<1M",
"format:csv",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us"
] | 100K<n<1M | 20,521 | null | 124,373 | 6,368,767 | [] |
# Phonemizer Dicts
Pre-generated IPA dictionaries for GPL-free text-to-phonemes lookup.
## Files
- `en-us.tsv` — 124K English (US) words, tab-separated `word<TAB>IPA`
## Provenance
Generated by running espeak-ng over an English wordlist. The TSV is program output; espeak-ng source (GPL-3.0) is not redistributed her... |
dataset | PGLearn/PGLearn-Small-118_ieee | PGLearn | 2025-04-18 | 2025-04-18 | [] | cc-by-sa-4.0 | [
"tabular-regression"
] | [
"task_categories:tabular-regression",
"license:cc-by-sa-4.0",
"size_categories:100K<n<1M",
"format:parquet",
"modality:tabular",
"modality:text",
"modality:timeseries",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us",
"energy",
"optimization",
"op... | 100K<n<1M | 20,447 | null | 999,985 | 56,896,551,794 | [] | |
dataset | gaianet/vitalik.eth | gaianet | 2024-03-23 | 2024-03-23 | [] | apache-2.0 | [] | [
"license:apache-2.0",
"size_categories:1K<n<10K",
"format:text",
"modality:text",
"library:datasets",
"library:mlcroissant",
"region:us"
] | 1K<n<10K | 20,365 | null | 1,041 | 206,576,066 | [] |
Prepare Qdrant:
```
mkdir qdrant_storage
mkdir qdrant_snapshots
```
Start Qdrant:
```
docker run -d -p 6333:6333 -p 6334:6334 \
-v $(pwd)/qdrant_storage:/qdrant/storage:z \
-v $(pwd)/qdrant_snapshots:/qdrant/snapshots:z \
qdrant/qdrant
```
Create collection:
```
curl -X PUT 'http://localhost:6333/coll... |
dataset | spacenship/whiskeyClassification | spacenship | 2026-05-11 | 2026-05-11 | [] | null | [] | [
"modality:image",
"modality:text",
"region:us"
] | null | 22,173 | null | 10,000 | 80,805,396,112 | [] | |
dataset | autogluon/fev_datasets | autogluon | 2025-02-27 | 2026-01-28 | [] | other | [
"time-series-forecasting"
] | [
"task_categories:time-series-forecasting",
"task_ids:univariate-time-series-forecasting",
"task_ids:multivariate-time-series-forecasting",
"annotations_creators:no-annotation",
"source_datasets:original",
"license:other",
"size_categories:100K<n<1M",
"format:parquet",
"modality:tabular",
"modality... | 100K<n<1M | 20,492 | null | 127,836 | 657,693,152 | [
"2012.07436",
"2304.14343",
"2410.10393",
"2505.14766",
"2202.03224",
"2208.04360",
"2509.26468",
"2503.12107"
] |
## Forecast evaluation datasets
This repository contains time series datasets that can be used for evaluation of univariate & multivariate forecasting models.
The main focus of this repository is on datasets that reflect real-world forecasting scenarios, such as those involving covariates, missing values, and other ... |
dataset | jiyu9437/gaia_test | jiyu9437 | 2025-06-18 | 2025-08-11 | [] | null | [] | [
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 1K<n<10K | 20,352 | null | 2,558 | 852,241 | [] | |
dataset | MathLLMs/VoiceAssistant-Eval | MathLLMs | 2025-09-23 | 2025-10-21 | [] | mit | [
"question-answering",
"visual-question-answering",
"audio-to-audio",
"any-to-any",
"multiple-choice",
"text-generation"
] | [
"task_categories:question-answering",
"task_categories:visual-question-answering",
"task_categories:audio-to-audio",
"task_categories:any-to-any",
"task_categories:multiple-choice",
"task_categories:text-generation",
"license:mit",
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
... | 10K<n<100K | 20,435 | null | 10,497 | 9,491,289,312 | [
"2509.22651"
] |
# 🔥 VoiceAssistant-Eval: Benchmarking AI Assistants across Listening, Speaking, and Viewing
![MathQA](h... |
dataset | klnfdsf/yolo48-dataset | klnfdsf | 2026-05-16 | 2026-05-16 | [] | null | [] | [
"size_categories:100K<n<1M",
"format:text",
"modality:text",
"library:datasets",
"library:mlcroissant",
"region:us"
] | 100K<n<1M | 21,466 | null | 290,108 | 809,082,649 | [] | |
dataset | pscotti/mindeyev2 | pscotti | 2023-08-23 | 2024-10-22 | [] | null | [] | [
"size_categories:100K<n<1M",
"modality:text",
"region:us"
] | 100K<n<1M | 20,276 | null | 232,276 | 214,544,615,980 | [] | Using these files requires that you have already agreed to the Natural Scenes Dataset's Terms and Conditions: https://cvnlab.slite.page/p/IB6BSeW_7o/Terms-and-Conditions
Webdatasets only contain behavioral information, .tar filename numbering correspondings to the scanning session of the subject.
(Always use "new_tes... |
dataset | AI4Math/MathVista | AI4Math | 2023-10-15 | 2024-02-11 | [
"en",
"zh",
"fa"
] | cc-by-sa-4.0 | [
"multiple-choice",
"question-answering",
"visual-question-answering",
"text-classification"
] | [
"task_categories:multiple-choice",
"task_categories:question-answering",
"task_categories:visual-question-answering",
"task_categories:text-classification",
"task_ids:multiple-choice-qa",
"task_ids:closed-domain-qa",
"task_ids:open-domain-qa",
"task_ids:visual-question-answering",
"task_ids:multi-cl... | 1K<n<10K | 20,268 | [
"monolingual"
] | 6,141 | 1,754,088,814 | [
"2310.02255"
] | # Dataset Card for MathVista
- [Dataset Description](https://huggingface.co/datasets/AI4Math/MathVista/blob/main/README.md#dataset-description)
- [Paper Information](https://huggingface.co/datasets/AI4Math/MathVista/blob/main/README.md#paper-information)
- [Dataset Examples](https://huggingface.co/datasets/AI4Math/Mat... |
dataset | gaianet/ktx.finance | gaianet | 2024-03-23 | 2024-03-23 | [] | apache-2.0 | [] | [
"license:apache-2.0",
"size_categories:1K<n<10K",
"format:text",
"modality:text",
"library:datasets",
"library:mlcroissant",
"region:us"
] | 1K<n<10K | 20,342 | null | 1,124 | 205,970,446 | [] |
Prepare Qdrant:
```
mkdir qdrant_storage
mkdir qdrant_snapshots
```
Start Qdrant:
```
docker run -d -p 6333:6333 -p 6334:6334 \
-v $(pwd)/qdrant_storage:/qdrant/storage:z \
-v $(pwd)/qdrant_snapshots:/qdrant/snapshots:z \
qdrant/qdrant
```
Create collection:
```
curl -X PUT 'http://localhost:6333/coll... |
dataset | DSIMB/PATHOS-PLM-EMBEDDINGS | DSIMB | 2026-03-06 | 2026-05-15 | [
"en"
] | mit | [
"feature-extraction"
] | [
"task_categories:feature-extraction",
"language:en",
"license:mit",
"license:cc-by-nc-sa-4.0",
"license:other",
"size_categories:100M<n<1B",
"format:parquet",
"modality:text",
"modality:timeseries",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"region:us",
... | 100M<n<1B | 21,145 | null | 466,534,193 | 7,149,405,587,125 | [] |
# PATHOS PLM Embeddings
Precomputed protein language model (PLM) embeddings for missense substitutions and wild-type residues in 20,416 human SwissProt proteins. These embeddings are used by [PATHOS](https://github.com/DSIMB/PATHOS) to predict the pathogenicity of missense mutations.
Paper: http://dx.doi.org/10.1016... |
dataset | mlabonne/FineTome-100k | mlabonne | 2024-07-27 | 2024-07-29 | [] | null | [] | [
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 100K<n<1M | 20,326 | null | 100,000 | 116,534,704 | [] |
# FineTome-100k
The FineTome dataset is a subset of [arcee-ai/The-Tome](https://huggingface.co/datasets/arcee-ai/The-Tome) (without arcee-ai/qwen2-72b-magpie-en), re-filtered using [HuggingFaceFW/... |
dataset | codeparrot/github-code-clean | codeparrot | 2022-06-29 | 2022-07-05 | [] | apache-2.0 | [] | [
"license:apache-2.0",
"size_categories:10M<n<100M",
"modality:text",
"library:datasets",
"library:mlcroissant",
"region:us"
] | 10M<n<100M | 20,300 | null | 11,027,000 | 313,663,770,241 | [] | This is a cleaner version of [Github-code dataset](https://huggingface.co/datasets/codeparrot/github-code), we add the following filters:
* Average line length < 100
* Alpha numeric characters fraction > 0.25
* Remove auto-generated files (keyword search)
3.39M files are removed making up 2.94% of the dataset. |
dataset | ogutsevda/graph-pannuke | ogutsevda | 2026-02-13 | 2026-03-03 | [] | cc-by-nc-sa-4.0 | [
"graph-ml"
] | [
"task_categories:graph-ml",
"license:cc-by-nc-sa-4.0",
"size_categories:1K<n<10K",
"format:csv",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"arxiv:2603.00143",
"arxiv:2003.10778",
"region:us",
"histopathology",
"node-classification",
"py... | 1K<n<10K | 20,431 | null | 4,772 | 94,366,770 | [
"2603.00143",
"2003.10778"
] |
# Graph-PanNuke: A Cell-Graph Dataset for Nucleus Classification from PanNuke
<p align="center">
<img src="animation.gif" alt="Graph-PanNuke teaser – cell-graph construction from a histopathology patch" width="600"/>
</p>
**Graph-PanNuke** is a node-level classification dataset derived from the [PanNuke](https://w... |
dataset | sriom1/asset-yolo-dataset | sriom1 | 2026-03-09 | 2026-03-23 | [] | null | [] | [
"modality:image",
"modality:text",
"region:us"
] | null | 23,652 | null | null | 18,945,466,924 | [] | # Asset YOLO Dataset
Auto-annotated. 84 classes.
|
dataset | shash42/forecast-news | shash42 | 2026-02-15 | 2026-05-14 | [] | apache-2.0 | [] | [
"license:apache-2.0",
"size_categories:10M<n<100M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"region:us"
] | 10M<n<100M | 21,113 | null | 54,038,484 | 75,927,877,858 | [] | |
dataset | kitkatdafu/persona_in_pal | kitkatdafu | 2025-02-05 | 2025-02-18 | [
"en"
] | null | [] | [
"language:en",
"size_categories:1B<n<10B",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | 1B<n<10B | 20,054 | null | 1,200,000,000 | 39,702,680,767 | [] | |
dataset | wshen15/FalconGym_2.0 | wshen15 | 2026-03-11 | 2026-03-21 | [] | null | [] | [
"size_categories:1K<n<10K",
"modality:image",
"modality:text",
"region:us"
] | 1K<n<10K | 20,791 | null | 2,646 | 180,602,148,490 | [] | |
dataset | emozilla/pg19 | emozilla | 2023-08-08 | 2023-10-09 | [] | null | [] | [
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | 10K<n<100K | 19,664 | null | 13,834 | 7,064,848,503 | [] | # Dataset Card for "pg19"
Paraquet version of [pg19](https://huggingface.co/datasets/pg19)
Statistics (in # of characters): `total_len: 11425076324, average_len: 399450.2595622684` |
dataset | liyucheng/goodreads | liyucheng | 2025-02-10 | 2025-02-13 | [] | null | [] | [
"size_categories:100M<n<1B",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | 100M<n<1B | 20,035 | null | 231,768,118 | 9,446,308,672 | [] | |
dataset | eulermaxwell/AnalogRetriever-data | eulermaxwell | 2026-03-31 | 2026-03-31 | [] | null | [] | [
"size_categories:100K<n<1M",
"format:text",
"modality:image",
"modality:text",
"library:datasets",
"library:mlcroissant",
"region:us"
] | 100K<n<1M | 20,620 | null | 268,200 | 130,122,498 | [] | |
dataset | juliensimon/esa-rosetta-observations | juliensimon | 2026-03-25 | 2026-05-26 | [
"en"
] | other | [
"tabular-classification"
] | [
"task_categories:tabular-classification",
"language:en",
"license:other",
"size_categories:10M<n<100M",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"region:us",
"space",
"comet",
"67p",
"rosetta",
... | 10M<n<100M | 19,682 | null | 24,426,328 | 713,965,917 | [] |
# ESA Rosetta Observations
<div align="center">
<img src="banner.jpg" alt="Rosetta spacecraft approaching Comet 67P/Churyumov-Gerasimenko" width="400">
<p><em>Credit: NASA/ESA</em></p>
</div>
*Part of the [Solar System Datasets](https://huggingface.co/collections/juliensimon/solar-system-datasets-67dbfa3057e3824... |
dataset | SWE-bench/SWE-smith | SWE-bench | 2025-04-29 | 2025-12-14 | [
"en"
] | mit | [
"text-generation"
] | [
"task_categories:text-generation",
"language:en",
"license:mit",
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2504.21798",
"region:us",
"code",
"agents",
"software-engineering"
] | 10K<n<100K | 19,488 | null | 59,136 | 277,771,488 | [
"2504.21798"
] |
<div align="center">
<a href="https://swesmith.com/">
<img src="https://avatars.githubusercontent.com/u/189315905?s=200&v=4" alt="Logo" width="200">
<h1 align="center">SWE-smith Dataset</h1>
</a>
</div>
<p align="center">
<a href="https://github.com/SWE-bench/SWE-smith">Code</a>
•
<a href="https://huggingf... |
dataset | reasoning-proj/severity_ablation_math | reasoning-proj | 2025-09-19 | 2025-09-22 | [] | null | [] | [
"size_categories:10K<n<100K",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 10K<n<100K | 19,733 | null | 27,083 | 6,573,167,764 | [] | |
dataset | evolai/universal_qa | evolai | 2026-04-25 | 2026-04-25 | [] | null | [] | [
"size_categories:1M<n<10M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"region:us"
] | 1M<n<10M | 19,677 | null | 2,381,765 | 434,625,386 | [] | |
dataset | liang12121/dreamzero-egoverse-360-pretrain | liang12121 | 2026-04-30 | 2026-04-30 | [] | null | [] | [
"size_categories:10M<n<100M",
"format:parquet",
"modality:tabular",
"modality:text",
"modality:timeseries",
"modality:video",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"region:us"
] | 10M<n<100M | 22,123 | null | 90,070,330 | 352,447,343,080 | [] | |
dataset | TeraflopAI/SEC-EDGAR | TeraflopAI | 2025-11-13 | 2026-04-17 | [
"en"
] | apache-2.0 | [
"text-generation",
"text-classification"
] | [
"task_categories:text-generation",
"task_categories:text-classification",
"language:en",
"license:apache-2.0",
"size_categories:1M<n<10M",
"modality:text",
"region:us",
"finance",
"edgar",
"sec"
] | 1M<n<10M | 19,803 | null | 8,055,455 | 295,014,462,673 | [] | [Datamule](https://datamule.xyz/), [Teraflop AI](https://www.teraflopai.com/), and [Eventual](https://www.eventual.ai/) collaborated to release the SEC-EDGAR dataset.
The dataset contains 590 gbs of data, spanning 8 million samples and 43 billion tokens from all major filings in t... |
dataset | kisate-team/gemma-2b-suite-explanations | kisate-team | 2024-07-17 | 2024-07-29 | [] | apache-2.0 | [] | [
"license:apache-2.0",
"size_categories:1M<n<10M",
"modality:text",
"region:us"
] | 1M<n<10M | 19,486 | null | 1,146,810 | 52,026,034,466 | [] | |
dataset | OALL/details_SenseLLM__ReflectionCoder-DS-33B | OALL | 2024-09-17 | 2024-09-17 | [] | null | [] | [
"size_categories:100K<n<1M",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 100K<n<1M | 19,702 | null | 145,930 | 117,363,028 | [] |
# Dataset Card for Evaluation run of SenseLLM/ReflectionCoder-DS-33B
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [SenseLLM/ReflectionCoder-DS-33B](https://huggingface.co/SenseLLM/ReflectionCoder-DS-33B).
The dataset is composed of 136 configurati... |
dataset | Lazyup-crypto/fineweb-edu | Lazyup-crypto | 2026-01-09 | 2026-01-09 | [
"en"
] | odc-by | [
"text-generation"
] | [
"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",
... | 1B<n<10B | 19,602 | null | 1,435,543,039 | 5,835,742,481,176 | [
"2406.17557",
"2404.14219",
"2401.10020",
"2109.07445"
] |
# 📚 FineWeb-Edu
<center>
<img src="https://cdn-uploads.huggingface.co/production/uploads/61c141342aac764ce1654e43/wwRnEQydH9qdRtFofIE-A.png" alt="FineWeb-Edu: The finest collection of educational content the web has to offer">
</center>
> 1.3 trillion tokens of the finest educational data the 🌐 web has to offe... |
dataset | gauduc/ulatroi | gauduc | 2026-04-16 | 2026-04-21 | [
"vi",
"zh"
] | mit | [
"video-classification",
"automatic-speech-recognition",
"translation"
] | [
"task_categories:video-classification",
"task_categories:automatic-speech-recognition",
"task_categories:translation",
"language:vi",
"language:zh",
"license:mit",
"size_categories:n<1K",
"format:text",
"modality:text",
"library:datasets",
"library:mlcroissant",
"region:us",
"SLOB",
"multi... | n<1K | 19,229 | null | 170 | 707,980,805,364 | [] |
# 🧠 Project SLOB: Spontaneous Lifestyle & Observational Behaviors Dataset
## 📌 Abstract
Welcome to the primary data ingestion node for **Project SLOB**. This repository hosts a massive, high-fidelity multimodal dataset designed to train next-generation Artificial Intelligence in recognizing, analyzing, and predicti... |
dataset | tokyotech-llm/swallow-math-v2 | tokyotech-llm | 2025-10-19 | 2025-11-06 | [
"en"
] | apache-2.0 | [
"text-generation"
] | [
"task_categories:text-generation",
"language:en",
"license:apache-2.0",
"size_categories:10M<n<100M",
"format:json",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"arxiv:2505.02881",
"region:us",
"math"
] | 10M<n<100M | 19,453 | null | 2,632,280 | 1,034,428,470,695 | [
"2505.02881"
] |
# SwallowMath-v2
<img src="https://huggingface.co/datasets/tokyotech-llm/swallow-math/resolve/main/figures/swallow-code-math-log.png" alt="SwallowMath-v2 Icon" width="500">
### Resources
- 📑 **arXiv**: Read our paper for detailed methodology at [arXiv:2505.02881](https://arxiv.org/abs/2505.02881).
- 🤗 **Sister Da... |
dataset | H-Liu1997/BEAT2 | H-Liu1997 | 2024-02-10 | 2024-02-10 | [] | apache-2.0 | [] | [
"license:apache-2.0",
"size_categories:1K<n<10K",
"format:csv",
"modality:audio",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | 1K<n<10K | 19,556 | null | 4,108 | 20,317,984,205 | [] | |
dataset | facebook/voxpopuli | facebook | 2022-05-10 | 2026-01-30 | [
"en",
"de",
"fr",
"es",
"pl",
"it",
"ro",
"hu",
"cs",
"nl",
"fi",
"hr",
"sk",
"sl",
"et",
"lt"
] | cc0-1.0 | [
"automatic-speech-recognition"
] | [
"task_categories:automatic-speech-recognition",
"multilinguality:multilingual",
"language:en",
"language:de",
"language:fr",
"language:es",
"language:pl",
"language:it",
"language:ro",
"language:hu",
"language:cs",
"language:nl",
"language:fi",
"language:hr",
"language:sk",
"language:s... | 1M<n<10M | 19,241 | [
"multilingual"
] | 1,255,237 | 673,250,359,818 | [
"2101.00390"
] |
# Dataset Card for Voxpopuli
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structu... |
dataset | gaianet/london | gaianet | 2024-04-10 | 2024-05-04 | [] | apache-2.0 | [] | [
"license:apache-2.0",
"size_categories:n<1K",
"format:text",
"modality:text",
"library:datasets",
"library:mlcroissant",
"region:us"
] | n<1K | 19,160 | null | 661 | 274,598,756 | [] | |
dataset | wahlinski/handwritten_cross-outs | wahlinski | 2026-05-07 | 2026-05-08 | [
"en"
] | cc-by-4.0 | [
"image-classification",
"image-to-text"
] | [
"task_categories:image-classification",
"task_categories:image-to-text",
"language:en",
"license:cc-by-4.0",
"size_categories:10K<n<100K",
"format:parquet",
"format:optimized-parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissa... | 10K<n<100K | 20,616 | null | 31,420 | 1,760,283,380 | [] | # HTR with Cross-out Words Dataset
## Overview
This dataset consists of **handwritten word images** produced by **12 different authors**. It includes both **clean (non-crossed-out)** samples and **crossed-out words**, making it suitable for multiple handwriting-related research tasks.
The dataset introduces **7 disti... |
dataset | dunyiguo/terminal-bench-2-leaderboard | dunyiguo | 2026-02-13 | 2026-02-13 | [] | apache-2.0 | [] | [
"license:apache-2.0",
"modality:text",
"region:us"
] | null | 20,802 | null | null | 4,639,509,108 | [] |
# Terminal-Bench 2.0 Leaderboard Submissions
This repository accepts leaderboard submissions for [Terminal-Bench 2.0](https://terminal-bench.org).
## How to Submit
1. [Fork this repository](https://huggingface.co/docs/hub/en/repositories-next-steps#duplicating-with-the-git-history-fork)
2. Create a new bran... |
dataset | teknium/OpenHermes-2.5 | teknium | 2023-11-12 | 2024-04-15 | [
"eng"
] | null | [] | [
"language:eng",
"size_categories:1M<n<10M",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us",
"synthetic",
"GPT-4",
"Distillation",
"Compilation"
] | 1M<n<10M | 19,117 | null | 1,001,551 | 1,936,283,760 | [] |
# Dataset Card for Dataset Name
This is the dataset that made OpenHermes 2.5 and Nous Hermes 2 series of models.
Support me on GitHub sponsors <3 : https://github.com/sponsors/teknium1
## Dataset ... |
dataset | tinyBenchmarks/tinyMMLU | tinyBenchmarks | 2024-02-22 | 2024-07-08 | [
"en"
] | null | [
"question-answering"
] | [
"task_categories:question-answering",
"task_ids:multiple-choice-qa",
"annotations_creators:no-annotation",
"language_creators:expert-generated",
"multilinguality:monolingual",
"source_datasets:cais/mmlu",
"language:en",
"size_categories:n<1K",
"format:parquet",
"modality:text",
"library:datasets... | n<1K | 19,124 | [
"monolingual"
] | 385 | 569,025 | [
"2402.14992"
] | # tinyMMLU
Welcome to tinyMMLU! This dataset serves as a concise version of the [MMLU](https://huggingface.co/datasets/cais/mmlu) dataset, offering a subset of 100 data points selected from the original compilation.
tinyMMLU is designed to enable users to efficiently estimate the performance of a large language model... |
dataset | valentinafevu/productos-supermercado | valentinafevu | 2025-07-01 | 2025-07-01 | [] | null | [] | [
"size_categories:1K<n<10K",
"format:imagefolder",
"modality:image",
"modality:text",
"library:datasets",
"library:mlcroissant",
"region:us"
] | 1K<n<10K | 19,950 | null | 7,449 | 103,336,359 | [] | # Dataset de productos de supermercado
language:
- es
pretty_name: "Dataset de productos de supermercado"
tags:
- scrapping
- colombia
- supermarket |
dataset | sy1998/MLVU_dev | sy1998 | 2024-07-03 | 2025-03-15 | [] | null | [] | [
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"modality:video",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 1K<n<10K | 18,994 | null | 2,174 | 285,064,305,098 | [] | |
dataset | allenai/molmobot-data | allenai | 2026-03-05 | 2026-03-26 | [
"en"
] | odc-by | [] | [
"language:en",
"license:odc-by",
"size_categories:100K<n<1M",
"format:parquet",
"format:optimized-parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"arxiv:2603.16861",
"region:us",
"robotics",
"embodied ai",
"m... | 100K<n<1M | 19,327 | null | 324,497 | 10,319,735,195,109 | [
"2603.16861"
] |
# MolmoBot-data
Training episode data (actions, visual inputs, and other sensor data) for 8 tasks on 2 robotic platforms:
- DoorOpeningDataGenConfig
- RBY1OpenDataGenConfig
- RBY1PickDataGenConfig
- FrankaPickOmniCamConfig
- RBY1PickAndPlaceDataGenConfig
- FrankaPickAndPlaceOmniCamConfig
- FrankaPickAndPlaceColorOmni... |
dataset | hatemestinbejaia/ExperimentDATA_knowledge_distillation_vs_fine_tuning | hatemestinbejaia | 2024-10-15 | 2026-01-03 | [] | null | [] | [
"size_categories:100M<n<1B",
"modality:tabular",
"modality:text",
"region:us"
] | 100M<n<1B | 19,097 | null | 238,486,138 | 507,836,210,397 | [] | |
dataset | ExylosAi/table_spill_cleanup_bimanual | ExylosAi | 2026-05-05 | 2026-05-25 | [
"en"
] | apache-2.0 | [
"robotics"
] | [
"task_categories:robotics",
"language:en",
"license:apache-2.0",
"size_categories:10K<n<100K",
"format:parquet",
"modality:image",
"modality:tabular",
"modality:text",
"modality:video",
"modality:timeseries",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"r... | 10K<n<100K | 23,083 | null | 67,811 | 21,504,114,125 | [] | # Exylos Bimanual Table Spill Cleanup Sample
> A human-in-the-loop, multi-view bimanual robot manipulation dataset for tabletop spill cleanup: removing distractor objects and wiping spilled liquid from the surface with a sponge. Delivered in a LeRobot-compatible structure with synchronized video, state/action trajec... |
dataset | vdivyasharma/IndicSynth | vdivyasharma | 2025-06-06 | 2026-01-12 | [
"bn",
"gu",
"hi",
"kn",
"ml",
"mr",
"or",
"pa",
"sa",
"ta",
"te",
"ur"
] | cc-by-nc-4.0 | [
"audio-classification",
"text-to-speech",
"automatic-speech-recognition"
] | [
"task_categories:audio-classification",
"task_categories:text-to-speech",
"task_categories:automatic-speech-recognition",
"language:bn",
"language:gu",
"language:hi",
"language:kn",
"language:ml",
"language:mr",
"language:or",
"language:pa",
"language:sa",
"language:ta",
"language:te",
"... | 1M<n<10M | 19,000 | null | 1,912,665 | 845,217,878,136 | [] |
# IndicSynth: Indian Multilingual Audio Deepfake Detection & Anti-Spoofing Dataset
*A Large-Scale Multilingual Synthetic Speech Dataset for Low-Resource Indian Languages to facilitate audio deepfake detection and anti-spoofing research*
**🏆 Outstanding Paper Award, ACL 2025**
---
## 🧠 Overview
**IndicSynth** is... |
dataset | bertram-gilfoyle/CC-MAIN-2021-43-raw | bertram-gilfoyle | 2024-02-22 | 2024-02-22 | [] | null | [] | [
"size_categories:10M<n<100M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | 10M<n<100M | 18,979 | null | 20,875,977 | 108,966,311,698 | [] | |
dataset | ehovy/race | ehovy | 2022-03-02 | 2024-01-04 | [
"en"
] | other | [
"multiple-choice"
] | [
"task_categories:multiple-choice",
"task_ids:multiple-choice-qa",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:other",
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"libr... | 100K<n<1M | 18,843 | [
"monolingual"
] | 195,374 | 83,045,341 | [
"1704.04683"
] |
# Dataset Card for "race"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
-... |
dataset | zyang39/GAIA | zyang39 | 2025-02-21 | 2025-02-21 | [] | null | [] | [
"size_categories:n<1K",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | n<1K | 18,777 | null | 165 | 5,697,695 | [] | |
dataset | davidwickerhf/rechtspraak-opendata | davidwickerhf | 2026-04-25 | 2026-04-26 | [
"nl"
] | other | [] | [
"language:nl",
"license:other",
"size_categories:1M<n<10M",
"format:webdataset",
"modality:text",
"library:datasets",
"library:webdataset",
"library:mlcroissant",
"region:us",
"law",
"legal",
"netherlands",
"rechtspraak",
"raw-data",
"court-decisions"
] | 1M<n<10M | 21,131 | null | 968,100 | 7,849,507,817 | [] |
# Rechtspraak OpenData Archive
This dataset repository stores archival snapshots used by the Maastricht Rechtspraak data pipelines. The repository is organized as source-oriented data, not as a normalized analysis dataset.
## Repository Layout
- `raw_data/` contains raw Rechtspraak OpenData source snapshots as down... |
dataset | LucasFang/FLUX-Reason-6M | LucasFang | 2025-07-03 | 2026-02-02 | [] | apache-2.0 | [] | [
"license:apache-2.0",
"size_categories:1M<n<10M",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2509.09680",
"region:us"
] | 1M<n<10M | 18,984 | null | 5,890,279 | 882,376,828,155 | [
"2509.09680"
] |
***
# FLUX-Reason-6M
FLUX-Reason-6M is a massive, 6-million-scale text-to-image dataset engineered to instill complex reasoning capabilities in generative models. This dataset was created to bridge the performance gap between open-source and leading closed-source text-to-image systems.
This dataset contains:
* **... |
dataset | Joschka/big_bench_hard | Joschka | 2024-11-03 | 2025-07-19 | [
"en"
] | mit | [
"question-answering",
"text2text-generation",
"multiple-choice",
"text-generation"
] | [
"task_categories:question-answering",
"task_categories:multiple-choice",
"task_categories:text-generation",
"language:en",
"license:mit",
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2210.... | 1K<n<10K | 18,809 | null | 6,538 | 664,854 | [
"2210.09261",
"2206.04615",
"2507.11936"
] |
All rights and obligations of the dataset are with original authors of the paper/dataset.
I have merely made this dataset with a MIT licence available on HuggingFace.
# BIG-Bench Hard Dataset
This repository contains a copy of the [BIG-Bench Hard](https://arxiv.org/abs/2210.09261) dataset.
Small edits to the formatt... |
dataset | mansoorbaloch/chimera-bench | mansoorbaloch | 2026-03-09 | 2026-05-28 | [
"en"
] | cc-by-4.0 | [
"other"
] | [
"task_categories:other",
"language:en",
"license:cc-by-4.0",
"size_categories:1K<n<10K",
"format:csv",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us",
"antibody-design",
"protein-structure",
"benchmark",
"... | 1K<n<10K | 19,003 | null | 2,922 | 3,256,516,385 | [] |
# CHIMERA-Bench v1.0
A unified benchmark for epitope-specific antibody CDR sequence-structure co-design.
**Paper**: [CHIMERA-Bench: A Benchmark Dataset for Epitope-Specific Antibody Design](https://openreview.net/forum?id=PyZvVIJbSy) (ICLR 2026 GEM Workshop)
**Code**: [github.com/mansoorbaloch/chimera-bench](https:... |
dataset | fancyzhx/dbpedia_14 | fancyzhx | 2022-03-02 | 2024-01-22 | [
"en"
] | cc-by-sa-3.0 | [
"text-classification"
] | [
"task_categories:text-classification",
"task_ids:topic-classification",
"annotations_creators:machine-generated",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:cc-by-sa-3.0",
"size_categories:100K<n<1M",
"format:parquet",
"mo... | 100K<n<1M | 18,752 | [
"monolingual"
] | 630,000 | 119,433,190 | [
"1509.01626"
] |
# Dataset Card for DBpedia14
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
... |
dataset | OleehyO/latex-formulas-80M | OleehyO | 2024-11-05 | 2025-08-22 | [] | apache-2.0 | [] | [
"license:apache-2.0",
"size_categories:10M<n<100M",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | 10M<n<100M | 18,840 | null | 78,150,689 | 686,391,957,279 | [] |
For more details, please refer to the [𝐓𝐞𝐱𝐓𝐞𝐥𝐥𝐞𝐫 GitHub repository](https://github.com/OleehyO/TexTeller?tab=readme-ov-file).
- **IMPORTANT NOTE!!!** The handwritten subset of this dataset was collected entirely from existing open source work, which includes all test sets. If you want to use this subset for... |
dataset | Winniechen2002/TexasPokerRobot | Winniechen2002 | 2026-03-18 | 2026-05-06 | [] | cc-by-4.0 | [] | [
"license:cc-by-4.0",
"size_categories:1K<n<10K",
"format:csv",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us",
"robotics",
"robot-learning",
"imitation-learning",
"manipulation",
"tabular"
] | 1K<n<10K | 18,791 | null | 1,470 | 377,941,446,754 | [] |
# TexasPokerRobot
TexasPokerRobot is a robot manipulation dataset collected in a Texas poker tabletop environment. The raw episodes are stored as compressed NumPy `.npz` files, organized by action folder. This release adds a Hugging Face-compatible manifest at `data/train.csv` so the dataset has a standard loadable s... |
dataset | OpenGVLab/ShareGPT-4o | OpenGVLab | 2024-05-28 | 2024-08-17 | [
"en"
] | mit | [
"visual-question-answering",
"question-answering"
] | [
"task_categories:visual-question-answering",
"task_categories:question-answering",
"language:en",
"license:mit",
"size_categories:10K<n<100K",
"format:json",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 10K<n<100K | 19,538 | null | null | 4,898,680,602,776 | [] | |
dataset | BangumiBase/enennoshouboutaisannoshou | BangumiBase | 2025-08-07 | 2025-08-07 | [] | mit | [] | [
"license:mit",
"size_categories:1K<n<10K",
"modality:image",
"modality:text",
"region:us",
"art"
] | 1K<n<10K | 18,710 | null | null | 8,844,116,885 | [] |
# Bangumi Image Base of Enen No Shouboutai: San No Shou
This is the image base of bangumi Enen no Shouboutai: San no Shou, we detected 66 characters, 3443 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If y... |
dataset | hunarbatra/4DReasoner_v4_test | hunarbatra | 2026-05-04 | 2026-05-04 | [] | null | [] | [
"size_categories:1K<n<10K",
"format:parquet",
"modality:image",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us"
] | 1K<n<10K | 21,174 | null | 1,815 | 9,358,965,026 | [] | |
dataset | wmt/wmt19 | wmt | 2022-03-02 | 2024-04-04 | [
"cs",
"de",
"en",
"fi",
"fr",
"gu",
"kk",
"lt",
"ru",
"zh"
] | unknown | [
"translation"
] | [
"task_categories:translation",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:translation",
"source_datasets:extended|europarl_bilingual",
"source_datasets:extended|news_commentary",
"source_datasets:extended|opus_paracrawl",
"source_datasets:extended|un_multi",
"l... | 100M<n<1B | 18,613 | [
"translation"
] | 124,448,248 | 16,820,767,303 | [] |
# Dataset Card for "wmt19"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
... |
dataset | allenai/tulu-3-sft-mixture | allenai | 2024-11-08 | 2024-12-02 | [
"amh",
"arb",
"ary",
"ars",
"acq",
"arz",
"apc",
"ben",
"ceb",
"dan",
"deu",
"ell",
"eng",
"eus",
"fil",
"fin",
"fra",
"gle",
"guj",
"hat",
"hau",
"hin",
"hun",
"ibo",
"ind",
"ita",
"jav",
"jpn",
"kan",
"kir",
"kor",
"kur",
"lit",
"mal",
"mar",
"... | odc-by | [
"other"
] | [
"task_categories:other",
"annotations_creators:crowdsourced",
"annotations_creators:expert-generated",
"annotations_creators:machine-generated",
"multilinguality:multilingual",
"source_datasets:allenai/coconot",
"source_datasets:ai2-adapt-dev/flan_v2_converted",
"source_datasets:HuggingFaceH4/no_robot... | 100K<n<1M | 18,583 | [
"multilingual"
] | 939,343 | 1,412,964,994 | [] |
<img src="https://huggingface.co/datasets/allenai/blog-images/resolve/main/tulu-3/Tulu3-logo.png" alt="Tulu3 banner" width="400" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
# Tulu 3 SFT Mixture
*Note that this collection is licensed under ODC-BY-1.0 license; different licenses apply to subsets o... |
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