artifact_type large_stringclasses 2
values | artifact_name large_stringlengths 5 123 | org large_stringlengths 2 42 | created_at large_stringdate 2022-03-02 00:00:00 2026-05-31 00:00:00 | last_modified large_stringdate 2020-07-16 00:00:00 2026-05-31 00:00:00 | languages listlengths 0 7.91k | license large_stringclasses 81
values | task_categories listlengths 0 47 | tags listlengths 2 7.92k | size_category large_stringclasses 11
values | downloads int64 0 262M | multilinguality listlengths 0 52 ⌀ | num_dataset_rows float64 0 194B ⌀ | disk_size float64 6 306,846B ⌀ | arxiv_ids listlengths 0 440 | readme large_stringlengths 0 13.4M |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
dataset | builddotai/Egocentric-100K | builddotai | 2025-12-04 | 2026-02-16 | [] | apache-2.0 | [] | [
"license:apache-2.0",
"size_categories:1M<n<10M",
"format:webdataset",
"modality:text",
"modality:video",
"library:datasets",
"library:webdataset",
"library:mlcroissant",
"region:us",
"video",
"egocentric",
"robotics"
] | 1M<n<10M | 76,180 | null | null | 25,408,752,282,408 | [] |
Egocentric-100K is the largest dataset of manual labor. You can visualize the dataset [here](https://build.ai).
![image](https://cdn-uploads.huggingface.co/production/uploads/690d75303df78b... |
dataset | AI-Growth-Lab/patents_claims_1.5m_traim_test | AI-Growth-Lab | 2022-07-31 | 2022-07-31 | [] | null | [] | [
"size_categories:1M<n<10M",
"format:csv",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 1M<n<10M | 49,001 | null | 1,492,294 | 3,532,684,128 | [] | |
dataset | facebook/multilingual_librispeech | facebook | 2022-03-02 | 2024-08-12 | [
"de",
"nl",
"fr",
"it",
"es",
"pt",
"pl",
"en"
] | cc-by-4.0 | [
"automatic-speech-recognition",
"text-to-speech",
"text-to-audio"
] | [
"task_categories:automatic-speech-recognition",
"task_categories:text-to-speech",
"task_categories:text-to-audio",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:multilingual",
"source_datasets:original",
"language:de... | 1M<n<10M | 51,878 | [
"multilingual"
] | 1,489,998 | 462,280,319,329 | [
"2012.03411"
] |
# Dataset Card for MultiLingual LibriSpeech
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [How to use](#how-to-use)
- [Dataset Structure](#dataset-stru... |
dataset | NuTonic/sat-image-boundingbox-sft-full | NuTonic | 2026-04-23 | 2026-04-23 | [
"en"
] | apache-2.0 | [
"image-text-to-text"
] | [
"task_categories:image-text-to-text",
"language:en",
"license:apache-2.0",
"size_categories:100K<n<1M",
"format:json",
"modality:image",
"modality:text",
"modality:geospatial",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us",
"satellite",
"land-... | 100K<n<1M | 117,077 | null | 531,408 | 74,669,032,799 | [] |
# NU-TONIC raw SFT Full
**Satellite imagery** and aligned **land-cover** outputs packaged as **image–text** rows for fine-tuning in SFT format. JSONL user prompts name the modality (satellite imagery vs. overhead context) where it matters.
## Provenance
- **Locations:** GeoGuessr-style POIs (source: `stochastic/ran... |
dataset | opendatalab/Sci-Base | opendatalab | 2026-03-24 | 2026-05-27 | [
"en"
] | cc-by-4.0 | [] | [
"language:en",
"license:cc-by-4.0",
"size_categories:1M<n<10M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"region:us",
"chem",
"bio",
"climate",
"medical",
"material",
"earth",
"physics"
] | 1M<n<10M | 49,866 | null | 3,631,260 | 3,866,748,580,712 | [] |
# Sci-Base: The Largest AI-Ready Scientific Foundation Dataset
## 🌌 The Sciverse Data Foundation
[**Sciverse**](https://sciverse.space/) is a comprehensive, multi-layered scientific data foundation designed to provide the ultimate data infrastructure for the AI for Science (AI4S) community. As scientific research b... |
dataset | AudioLLMs/Multitask-National-Speech-Corpus-v1-extend | AudioLLMs | 2025-03-18 | 2025-03-31 | [] | null | [] | [
"size_categories:10M<n<100M",
"format:parquet",
"modality:audio",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | 10M<n<100M | 51,028 | null | 15,188,296 | 2,998,740,151,186 | [] | |
dataset | chonkie-ai/recipes | chonkie-ai | 2025-04-08 | 2025-10-07 | [] | apache-2.0 | [] | [
"license:apache-2.0",
"size_categories:n<1K",
"format:json",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"region:us"
] | n<1K | 49,387 | null | 57 | 264,095 | [] |
<div align="center">
# 🦛 Chonkie Recipes 🍳
> _Chonkie loves to cook up a storm in the kitchen_
</div>
This repository contains all the recipes that you can use with Chonkie to manage various documents, languages, and more.
## Usage
To use the ... |
dataset | klieret/swe-bench-dummy-test-dataset | klieret | 2025-07-01 | 2025-07-01 | [] | mit | [] | [
"license:mit",
"size_categories:n<1K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | n<1K | 48,042 | null | 1 | 3,024 | [] | |
dataset | gaia-benchmark/GAIA | gaia-benchmark | 2023-10-20 | 2025-10-28 | [
"en"
] | null | [] | [
"language:en",
"size_categories:n<1K",
"format:parquet",
"modality:audio",
"modality:document",
"modality:image",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"arxiv:2311.12983",
"region:us"
] | n<1K | 47,330 | null | null | 110,175,514 | [
"2311.12983"
] |
# GAIA dataset
GAIA is a benchmark which aims at evaluating next-generation LLMs (LLMs with augmented capabilities due to added tooling, efficient prompting, access to search, etc).
We added gating to prevent bots from scraping the dataset. Please do not reshare the validation or test set in a crawlable format.
## ... |
dataset | UniverseTBD/arxiv-abstracts-large | UniverseTBD | 2023-07-18 | 2023-07-18 | [
"en"
] | afl-3.0 | [
"text-generation"
] | [
"task_categories:text-generation",
"language:en",
"license:afl-3.0",
"size_categories:1M<n<10M",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 1M<n<10M | 47,556 | null | 2,292,057 | 3,815,312,463 | [] |
The arXiv Dataset is a comprehensive knowledge repository of 1.7 million scholarly articles drawn from the vast domains of physics, computer science, statistics, electrical engineering, quantitative biology, and economics among others. It provides open access to vital features such as article titles, authors, categori... |
dataset | pwc-archive/evaluation-tables | pwc-archive | 2025-08-18 | 2025-09-13 | [] | cc-by-sa-4.0 | [] | [
"license:cc-by-sa-4.0",
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | 1K<n<10K | 48,987 | null | 2,254 | 137,688,444 | [] |
> [!CAUTION]
> This dataset will not be updated. It corresponds to the last available public snapshot of the data, retrieved on July 28th, 2025.
>
|
dataset | mesolitica/fineweb-filter-malaysian-context | mesolitica | 2024-08-07 | 2024-08-13 | [
"en"
] | null | [] | [
"language:en",
"size_categories:10M<n<100M",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | 10M<n<100M | 55,922 | null | 98,697,427 | 358,744,736,828 | [] |
# HuggingFaceFW/fineweb filter Malaysian context
## What is it?
We filter the original 🍷 FineWeb dataset that consists more than **15T tokens** on simple Malaysian keywords.
Total tokens for the filtered dataset is 174102784199 tokens, **174B tokens**.
## How we do it?
1. We filter rows using `{'malay', 'malaysi... |
dataset | HuggingFaceFW/finepdfs | HuggingFaceFW | 2025-09-05 | 2026-04-03 | [
"aai",
"aak",
"aau",
"aaz",
"aba",
"abi",
"abk",
"abn",
"abq",
"abs",
"abt",
"abx",
"aby",
"abz",
"aca",
"acd",
"ace",
"acf",
"ach",
"acm",
"acn",
"acr",
"acu",
"ada",
"ade",
"adh",
"adi",
"adj",
"adl",
"ady",
"adz",
"aeb",
"aer",
"aeu",
"aey",
"... | odc-by | [
"text-generation"
] | [
"task_categories:text-generation",
"language:aai",
"language:aak",
"language:aau",
"language:aaz",
"language:aba",
"language:abi",
"language:abk",
"language:abn",
"language:abq",
"language:abs",
"language:abt",
"language:abx",
"language:aby",
"language:abz",
"language:aca",
"language... | 100M<n<1B | 54,855 | null | 476,178,356 | 5,375,643,185,924 | [
"2506.18421",
"2109.07445"
] | 
> Liberating 3T of the finest tokens from PDFs
## What is this?
As we run out of web pages to process, the natural question has always been: what to do next? Only a few knew about a data source that e... |
dataset | dinofamily529/malaysia_legal | dinofamily529 | 2025-02-19 | 2025-02-19 | [
"ms",
"en"
] | null | [] | [
"language:ms",
"language:en",
"size_categories:10K<n<100K",
"format:csv",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 10K<n<100K | 47,177 | null | 17,644 | 16,506,984,445 | [] | from datasets import load_dataset
ds = load_dataset("izardy/malaysia-kehakiman-ejudgement") |
dataset | llamaindex/ParseBench | llamaindex | 2026-04-09 | 2026-04-19 | [
"en"
] | apache-2.0 | [] | [
"benchmark:official",
"benchmark:eval-yaml",
"language:en",
"license:apache-2.0",
"size_categories:100K<n<1M",
"format:json",
"modality:document",
"modality:image",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"arxiv:2604.08538",
"region... | 100K<n<1M | 49,450 | null | 169,011 | 592,182,175 | [
"2604.08538"
] |
# ParseBench
**Quick links:** [\[🌐 Website\]](https://parsebench.ai) [\[📜 Paper\]](https://arxiv.org/abs/2604.08538) [\[💻 Code\]](https://github.com/run-llama/ParseBench)
**ParseBench** is a benchmark for evaluating document parsing systems on real-world enterprise... |
dataset | xlangai/DS-1000 | xlangai | 2024-04-09 | 2024-09-19 | [
"code"
] | cc-by-sa-4.0 | [
"text2text-generation"
] | [
"language:code",
"license:cc-by-sa-4.0",
"size_categories:1K<n<10K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us",
"code-generation"
] | 1K<n<10K | 46,366 | null | 1,000 | 3,443,137 | [] |
<h1 align="center"> DS-1000 in simplified format </h1>
🔥 Check the leaderboard from Eval-Arena on our [project page](https://ds1000-code-gen.github.io/).
See testing code and more information (also the original [fill-in-the-middle/Insertion format](https://github.com/xlang-ai/DS-1000/tree/original_format)) in the [... |
dataset | facebook/SACo-Gold | facebook | 2025-10-08 | 2025-11-17 | [
"en"
] | other | [] | [
"language:en",
"license:other",
"size_categories:n<1K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | n<1K | 46,417 | null | null | 487,037,405 | [] |
# Dataset Card for SA-Co/Gold
SA-Co/Gold is a benchmark for promptable concept segmentation (PCS) in images. The benchmark contains images paired with text labels (also referred as Noun Phrases aka NPs), each annotated exhaustively with masks on all object instances that match the label. SA-Co/Gold comprises 7 subset... |
dataset | LLM360/MegaMath | LLM360 | 2025-03-30 | 2025-04-09 | [
"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:2504.02807",
"region:us",
"math",
"code",
"pre-training",
"synthesis"
... | 100M<n<1B | 49,009 | null | 217,499,877 | 1,118,158,270,272 | [
"2504.02807"
] |
# MegaMath: Pushing the Limits of Open Math Copora
> Megamath is part of TxT360, curated by LLM360 Team.
<center><img src="teasor.png" alt="MegaMath Collection" /></center>
We introduce MegaMath, an open math pretraining dataset curated from diverse, math-focused sources, with over 300B tokens.
MegaMath is curated v... |
dataset | HuggingFaceFW/fineweb-2 | HuggingFaceFW | 2024-12-05 | 2025-10-27 | [
"aai",
"aak",
"aau",
"aaz",
"aba",
"abi",
"abk",
"abn",
"abq",
"abs",
"abt",
"abx",
"aby",
"abz",
"aca",
"acd",
"ace",
"acf",
"ach",
"acm",
"acn",
"acr",
"acu",
"ada",
"ade",
"adh",
"adi",
"adj",
"adl",
"ady",
"adz",
"aeb",
"aer",
"aeu",
"aey",
"... | odc-by | [
"text-generation"
] | [
"task_categories:text-generation",
"language:aai",
"language:aak",
"language:aau",
"language:aaz",
"language:aba",
"language:abi",
"language:abk",
"language:abn",
"language:abq",
"language:abs",
"language:abt",
"language:abx",
"language:aby",
"language:abz",
"language:aca",
"language... | 1B<n<10B | 53,699 | null | 4,484,929,995 | 20,150,082,682,632 | [
"2506.20920",
"2109.07445",
"2406.17557"
] | # 🥂 FineWeb2
<center>
<img src="https://huggingface.co/datasets/HuggingFaceFW/admin/resolve/main/fineweb-2-logo.png" alt="FineWeb 2: A sparkling update with 1000s of languages">
</center>
> A sparkling update with 1000s of languages
# Table of Contents
- [🥂 FineWeb2](#-fineweb2)
* [What is it?](#what-is-it)... |
dataset | hf-internal-testing/imagefolder_with_metadata | hf-internal-testing | 2023-08-24 | 2025-03-04 | [] | null | [] | [
"size_categories:n<1K",
"format:imagefolder",
"modality:image",
"modality:text",
"library:datasets",
"library:mlcroissant",
"region:us"
] | n<1K | 44,343 | null | 4 | 92,092 | [] | |
dataset | CharlesPing/fineweb-edu | CharlesPing | 2026-01-20 | 2026-01-20 | [
"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 | 46,652 | null | 3,496,736,741 | 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 | MMMU/MMMU_Pro | MMMU | 2024-08-27 | 2026-05-19 | [
"en"
] | apache-2.0 | [
"question-answering",
"visual-question-answering",
"multiple-choice"
] | [
"benchmark:official",
"task_categories:question-answering",
"task_categories:visual-question-answering",
"task_categories:multiple-choice",
"language:en",
"license:apache-2.0",
"size_categories:1K<n<10K",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:dask",
... | 1K<n<10K | 45,547 | null | 5,190 | 2,987,975,113 | [
"2409.02813"
] |
# MMMU-Pro (A More Robust Multi-discipline Multimodal Understanding Benchmark)
[**🌐 Homepage**](https://mmmu-benchmark.github.io/) | [**🏆 Leaderboard**](https://mmmu-benchmark.github.io/#leaderboard) | [**🤗 Dataset**](https://huggingface.co/datasets/MMMU/MMMU_Pro) | [**🤗 Paper**](https://huggingface.co/papers/24... |
dataset | hf-internal-testing/dataset_with_data_files | hf-internal-testing | 2023-08-24 | 2024-09-05 | [] | null | [] | [
"size_categories:n<1K",
"format:text",
"modality:text",
"library:datasets",
"library:mlcroissant",
"region:us"
] | n<1K | 43,763 | null | 20 | 770 | [] | |
dataset | ceval/ceval-exam | ceval | 2023-05-16 | 2025-07-27 | [
"zh"
] | cc-by-nc-sa-4.0 | [
"text-classification",
"multiple-choice",
"question-answering"
] | [
"task_categories:text-classification",
"task_categories:multiple-choice",
"task_categories:question-answering",
"language:zh",
"license:cc-by-nc-sa-4.0",
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polar... | 10K<n<100K | 45,834 | null | 13,948 | 3,738,399 | [
"2305.08322"
] |
C-Eval is a comprehensive Chinese evaluation suite for foundation models. It consists of 13948 multi-choice questions spanning 52 diverse disciplines and four difficulty levels. Please visit our [website](https://cevalbenchmark.com/) and [GitHub](https://github.com/SJTU-LIT/ceval/tree/main) or check our [paper](https:... |
dataset | mteb/sts12-sts | mteb | 2022-04-20 | 2026-02-24 | [
"eng"
] | unknown | [
"sentence-similarity"
] | [
"task_categories:sentence-similarity",
"task_ids:semantic-similarity-scoring",
"annotations_creators:human-annotated",
"multilinguality:monolingual",
"language:eng",
"license:unknown",
"size_categories:1K<n<10K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:po... | 1K<n<10K | 45,691 | [
"monolingual"
] | 5,342 | 258,906 | [
"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 | ma-xu/fine-t2i | ma-xu | 2026-01-15 | 2026-02-20 | [
"en"
] | apache-2.0 | [
"image-to-text",
"text-to-image"
] | [
"task_categories:image-to-text",
"task_categories:text-to-image",
"language:en",
"license:apache-2.0",
"size_categories:100K<n<1M",
"format:webdataset",
"modality:image",
"modality:text",
"library:datasets",
"library:webdataset",
"library:mlcroissant",
"arxiv:2602.09439",
"region:us",
"tex... | 100K<n<1M | 46,549 | null | 1,600 | 2,323,228,981,157 | [
"2602.09439"
] |
# Fine-T2I: An Open, Large-Scale, and Diverse Dataset for High-Quality T2I Fine-Tuning [[arxiv]](https://arxiv.org/abs/2602.09439)
by [Xu Ma](https://ma-xu.github.io/), [Yitian Zhang](https://bespontaneous.github.io/homepage/),
[Qihua Dong](https://dddraxxx.github.io/), [Yun Fu](http://www1.ece.neu.edu/~yunfu/) ... |
dataset | open-r1/OpenR1-Math-220k | open-r1 | 2025-02-10 | 2025-02-18 | [
"en"
] | apache-2.0 | [] | [
"language:en",
"license:apache-2.0",
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"region:us"
] | 100K<n<1M | 44,826 | null | 450,258 | 12,649,350,862 | [] |
# OpenR1-Math-220k
## Dataset description
OpenR1-Math-220k is a large-scale dataset for mathematical reasoning. It consists of 220k math problems with two to four reasoning traces generated by [DeepSeek R1](https://huggingface.co/deepseek-ai/DeepSeek-R1) for problems from NuminaMath 1.5.
The traces were verified us... |
dataset | Salesforce/lotsa_data | Salesforce | 2024-02-22 | 2025-01-21 | [] | apache-2.0 | [] | [
"license:apache-2.0",
"size_categories:1M<n<10M",
"format:arrow",
"modality:text",
"modality:timeseries",
"library:datasets",
"library:mlcroissant",
"arxiv:2402.02592",
"region:us"
] | 1M<n<10M | 48,249 | null | 1,346,185 | 924,720,648,419 | [
"2402.02592"
] | # LOTSA Data
The Large-scale Open Time Series Archive (LOTSA) is a collection of open time series datasets for time series forecasting.
It was collected for the purpose of pre-training Large Time Series Models.
See the [paper](https://arxiv.org/abs/2402.02592) and [codebase](https://github.com/SalesforceAIResearch/u... |
dataset | nvidia/Nemotron-Personas-Korea | nvidia | 2026-04-20 | 2026-04-23 | [
"ko"
] | cc-by-4.0 | [
"text-generation"
] | [
"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... | 1M<n<10M | 44,255 | null | 1,000,000 | 1,984,405,985 | [] | Nemotron-Personas-Korea
=========================================================================
<center>
<img src="images/nemotron_personas_korea_approach.png" alt="Nemotron-Personas-Korea" width="400px">
<p>
<em>우리나라 실제 분포에 기반한 합성 페르소나를 위한 복합 AI 시스템</em><br>
<em>A compound AI approach to personas grounde... |
dataset | google/WaxalNLP | google | 2026-01-19 | 2026-05-05 | [
"ach",
"aka",
"amh",
"bau",
"dag",
"dga",
"ewe",
"fat",
"ful",
"hau",
"ibo",
"kik",
"kpo",
"lin",
"lug",
"luo",
"mas",
"mlg",
"nyn",
"orm",
"pcm",
"sid",
"sna",
"sog",
"swa",
"tir",
"twi",
"wal",
"yor"
] | cc-by-sa-4.0 | [
"automatic-speech-recognition",
"text-to-speech"
] | [
"task_categories:automatic-speech-recognition",
"task_categories:text-to-speech",
"language_creators:creator_1",
"multilinguality:multilingual",
"source_datasets:UGSpeechData",
"source_datasets:DigitalUmuganda/AfriVoice",
"source_datasets:original",
"language:ach",
"language:aka",
"language:amh",
... | 1M<n<10M | 46,382 | [
"multilingual"
] | 2,559,274 | 1,060,211,964,357 | [
"2602.02734"
] |
# Waxal Datasets
The WAXAL dataset is a large-scale multilingual speech corpus for African languages, introduced in the paper [WAXAL: A Large-Scale Multilingual African Language Speech Corpus](https://huggingface.co/papers/2602.02734).
## Table of Contents
- [Dataset Description](#dataset-description)
- [AS... |
dataset | cogsci13/Amazon-Reviews-2023-Books-Meta | cogsci13 | 2024-04-18 | 2024-04-18 | [
"en"
] | null | [] | [
"language:en",
"size_categories:1M<n<10M",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2403.03952",
"region:us",
"recommendation",
"reviews"
] | 1M<n<10M | 43,991 | null | 4,448,181 | 7,830,883,327 | [
"2403.03952"
] | # Amazon Reviews 2023 (Books Only)
**This is a subset of Amazon Review 2023 dataset. Please visit [amazon-reviews-2023.github.io/](https://amazon-reviews-2023.github.io/) for more details, loading scripts, and preprocessed benchmark files.**
**[April 18, 2024]** Update
1. This dataset was created and pushed for the ... |
dataset | OpenGVLab/OmniCorpus-CC | OpenGVLab | 2024-08-30 | 2025-03-20 | [
"en"
] | cc-by-4.0 | [
"image-to-text",
"visual-question-answering"
] | [
"task_categories:image-to-text",
"task_categories:visual-question-answering",
"language:en",
"license:cc-by-4.0",
"size_categories:100M<n<1B",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2406.08418",
"region:us"
] | 100M<n<1B | 47,179 | null | 871,537,572 | 2,895,427,599,706 | [
"2406.08418"
] |
<p align="center">
<h1 align="center">🐳 OmniCorpus: A Unified Multimodal Corpus of 10 Billion-Level Images Interleaved with Text</h1>
</p>
> ⭐️ **NOTE:** Several parquet files were marked unsafe (viruses) by official scaning of hf, while they are reported safe by ClamAV and Virustotal.
> We found [many false posi... |
dataset | aadityabuilds/tree-distribution-shift | aadityabuilds | 2026-02-09 | 2026-03-02 | [] | null | [] | [
"size_categories:100K<n<1M",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"region:us"
] | 100K<n<1M | 54,484 | null | 466,940 | 182,780,110,523 | [] |
# Tree Distribution Shift — Satellite Tree Detection (COCO + HF Datasets)
This is a dataset containing ~30K COCO tree crown annotated satellite image tiles of 400x400 px dimensions. These annotations come from all states in India and California in the United States.
This dataset is organized as **configs** (distribut... |
dataset | autogluon/chronos_datasets | autogluon | 2024-06-22 | 2025-03-18 | [] | 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:10M<n<100M",
"format:parquet",
"modality:tabular",
"modalit... | 10M<n<100M | 43,904 | null | 1,482,125 | 894,729,566,924 | [
"2403.07815"
] |
# Chronos datasets
Time series datasets used for training and evaluation of the [Chronos](https://github.com/amazon-science/chronos-forecasting) forecasting models.
Note that some Chronos datasets (`ETTh`, `ETTm`, `brazilian_cities_temperature` and `spanish_energy_and_weather`) that rely on a custom builder script a... |
dataset | japanese-asr/whisper_transcriptions.reazonspeech.all | japanese-asr | 2024-08-30 | 2024-09-01 | [] | null | [] | [
"size_categories:10M<n<100M",
"format:parquet",
"modality:audio",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | 10M<n<100M | 43,370 | null | 21,911,172 | 2,473,175,397,750 | [] | |
dataset | cais/hle | cais | 2025-01-23 | 2026-01-20 | [] | mit | [] | [
"benchmark:official",
"license:mit",
"size_categories:1K<n<10K",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us"
] | 1K<n<10K | 41,938 | null | null | 274,282,300 | [] |
<div align="center">
> [!NOTE]
> IMPORTANT: Please help us protect the integrity of this benchmark by not publicly sharing, re-uploading, or distributing the dataset.
# Humanity's Last Exam
🌐 [Website](https://lastexam.ai) | 📄 [Paper](https://lastexam.ai/paper) | [GitHub](https://github.com/centerforaisafety/... |
dataset | farhanhubble/jfk-archives | farhanhubble | 2025-04-07 | 2025-04-10 | [] | mit | [
"question-answering"
] | [
"task_categories:question-answering",
"license:mit",
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us",
"legal"
] | 10K<n<100K | 41,442 | null | 56,300 | 56,509,267,978 | [] | # Dataset Card for JFK Archives
<!-- Provide a quick summary of the dataset. -->
This dataset is a collection of all records pertaining to the assassination of the
US president, John F. Kennedy, released until April 2025 through [archives.org](https://www.archives.gov/research/jfk)
by the US government.
## Dataset D... |
dataset | allenai/Dolci-Instruct-SFT | allenai | 2025-11-18 | 2026-02-03 | [
"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",
"language:amh",
"language:arb",
"language:ary",
"language:ars",
"language:acq",
"language:arz",
"language:apc",
"la... | 1M<n<10M | 41,087 | [
"multilingual"
] | 2,152,112 | 3,061,435,653 | [
"2512.13961"
] |
# Dolci Instruct SFT Mixture
*Note that this collection licensed under ODC-BY. It is intended for research and educational use in accordance with Ai2's [Responsible Use Guidelines](https://allenai.org/responsible-use).*
The Dolci Instruct SFT mixture was used to train [Olmo 3 7B Instruct SFT](https://huggingface.co/... |
dataset | ise-uiuc/Magicoder-OSS-Instruct-75K | ise-uiuc | 2023-12-03 | 2023-12-04 | [] | mit | [
"text-generation",
"conversational"
] | [
"task_categories:text-generation",
"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 | 43,002 | null | 75,197 | 203,203,865 | [] |
This is the **OSS-Instruct** dataset generated by `gpt-3.5-turbo-1106` developed by OpenAI. Please pay attention to OpenAI's usage policy when adopting this dataset: https://openai.com/policies/usage-policies.
|
dataset | princeton-nlp/SWE-bench | princeton-nlp | 2023-10-10 | 2025-03-03 | [] | null | [] | [
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2310.06770",
"region:us"
] | 10K<n<100K | 42,729 | null | 21,527 | 120,092,540 | [
"2310.06770"
] |
### Dataset Summary
SWE-bench is a dataset that tests systems’ ability to solve GitHub issues automatically. The dataset collects 2,294 Issue-Pull Request pairs from 12 popular Python repositories. Evaluation is performed by unit test verification using post-PR behavior as the reference solution.
The dataset was rele... |
dataset | ScalingIntelligence/KernelBench | ScalingIntelligence | 2024-11-19 | 2025-07-21 | [] | null | [] | [
"size_categories:n<1K",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | n<1K | 42,229 | null | 270 | 119,530 | [] |
# KernelBench
A benchmark designed to evaluate the ability of LLMs to generate efficient GPU kernels for optimizing neural network performance
## Version
[07-21-2025] This HF dataset version has been updated to v0.1
## Citation
```bibtex
@misc{ouyang2024kernelbench,
title={KernelBench: Can LLMs Write GPU Kerne... |
dataset | Anthropic/hh-rlhf | Anthropic | 2022-12-08 | 2023-05-26 | [] | mit | [] | [
"license:mit",
"size_categories:100K<n<1M",
"format:json",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2204.05862",
"region:us",
"human-feedback"
] | 100K<n<1M | 41,137 | null | 169,352 | 94,745,957 | [
"2204.05862"
] |
# Dataset Card for HH-RLHF
## Dataset Summary
This repository provides access to two different kinds of data:
1. Human preference data about helpfulness and harmlessness from [Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback](https://arxiv.org/abs/2204.05862). These data are... |
dataset | bigcode/the-stack-smol | bigcode | 2022-10-10 | 2023-05-02 | [
"code"
] | null | [
"text-generation"
] | [
"task_categories:text-generation",
"task_ids:language-modeling",
"language_creators:crowdsourced",
"multilinguality:multilingual",
"language:code",
"size_categories:100K<n<1M",
"format:json",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"reg... | 100K<n<1M | 40,979 | [
"multilingual"
] | null | 2,947,444,338 | [] |
## Dataset Description
A small subset (~0.1%) of [the-stack](https://huggingface.co/datasets/bigcode/the-stack) dataset, each programming language has 10,000 random samples from the original dataset. The dataset has 2.6GB of text (code).
#... |
dataset | allenai/scirepeval | allenai | 2023-10-19 | 2024-01-16 | [] | null | [] | [
"size_categories:10M<n<100M",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | 10M<n<100M | 41,851 | null | 12,375,686 | 27,435,525,805 | [] | |
dataset | openbmb/UltraData-Math | openbmb | 2026-01-14 | 2026-04-15 | [
"en",
"zh"
] | apache-2.0 | [
"text-generation"
] | [
"task_categories:text-generation",
"language:en",
"language:zh",
"license:apache-2.0",
"size_categories:100M<n<1B",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"arxiv:2602.09003",
"region:us",
"llm",
"pretraining",
"ma... | 100M<n<1B | 43,083 | null | 181,186,453 | 552,413,408,436 | [
"2602.09003"
] |
# UltraData-Math
<div align="center">
<img src="assets/ultradata-math-logo.png" width="600"/>
</div>
<p align="center">
<a href="https://huggingface.co/datasets/openbmb/UltraData-Math">🤗 Dataset</a> | <a href="https://github.com/UltraData-OpenBMB/UltraData-Math">💻 Source Code</a> | <a href="https://huggingface.c... |
dataset | PGLearn/PGLearn-Small-300_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 | 42,991 | null | 932,109 | 115,026,061,713 | [] | |
dataset | sunghong/CADS-dataset | sunghong | 2025-12-17 | 2025-12-17 | [] | other | [
"image-segmentation"
] | [
"task_categories:image-segmentation",
"license:other",
"size_categories:10K<n<100K",
"format:csv",
"modality:tabular",
"modality:text",
"modality:image",
"modality:3d",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"arxiv:2507.22953",
"region:us",
"medica... | 10K<n<100K | 75,382 | null | 22,011 | 805,945,739,906 | [
"2507.22953"
] |
# CADS: A Comprehensive Anatomical Dataset and Segmentation for Whole-Body Anatomy in Computed Tomography
<img src="https://raw.githubusercontent.com/murong-xu/CADS/refs/heads/main/resources/images/whole-body-parts-visualization.png" width="90%">
## Overview
CADS is a robust, fully automated framework for segmenting... |
dataset | ADSKAILab/Zero-To-CAD-1m | ADSKAILab | 2026-04-11 | 2026-05-03 | [
"en",
"code"
] | apache-2.0 | [
"text-to-3d",
"image-to-3d"
] | [
"task_categories:text-to-3d",
"task_categories:image-to-3d",
"language:en",
"language:code",
"license:apache-2.0",
"size_categories:100K<n<1M",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"arxiv:2604.24... | 100K<n<1M | 42,056 | null | 999,633 | 349,104,973,477 | [
"2604.24479"
] |
<p align="center">
<img src="assets/logo.png" alt="Zero-to-CAD" width="100%"/>
</p>
# Zero-to-CAD 1M
**One million executable, interpretable CAD construction sequences synthesized entirely without real-world data.**
<p align="center">
<img src="assets/agentic.png" alt="Zero-to-CAD agentic synthesis pipeline" wi... |
dataset | MrigLabIITRopar/GroMo25 | MrigLabIITRopar | 2026-03-12 | 2026-04-02 | [
"en"
] | cc-by-4.0 | [
"image-classification",
"text-to-image",
"image-to-text"
] | [
"task_categories:image-classification",
"task_categories:text-to-image",
"task_categories:image-to-text",
"language:en",
"license:cc-by-4.0",
"size_categories:100K<n<1M",
"format:csv",
"modality:image",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:polars"... | 100K<n<1M | 68,920 | null | 119,994 | 313,424,334,668 | [] |
# GroMo25: Multiview Time-Series Plant Image Dataset for Age Estimation and Leaf Counting
## Dataset Summary
**GroMo25** is a multiview, time-series plant image dataset designed for plant age estimation (in days) and leaf counting tasks in precision agriculture. It contains high-quality images of four crop species —... |
dataset | unimelb-nlp/wikiann | unimelb-nlp | 2022-03-02 | 2024-02-22 | [
"ace",
"af",
"als",
"am",
"an",
"ang",
"ar",
"arc",
"arz",
"as",
"ast",
"ay",
"az",
"ba",
"bar",
"be",
"bg",
"bh",
"bn",
"bo",
"br",
"bs",
"ca",
"cbk",
"cdo",
"ce",
"ceb",
"ckb",
"co",
"crh",
"cs",
"csb",
"cv",
"cy",
"da",
"de",
"diq",
"dv",
... | unknown | [
"token-classification"
] | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:machine-generated",
"language_creators:crowdsourced",
"multilinguality:multilingual",
"source_datasets:original",
"language:ace",
"language:af",
"language:als",
"language:am",
"language:an",
"lan... | 1M<n<10M | 43,698 | [
"multilingual"
] | 2,003,000 | 142,934,974 | [
"1902.00193"
] |
# Dataset Card for WikiANN
## 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 | thuml/Time-Series-Library | thuml | 2025-11-12 | 2025-11-12 | [
"en"
] | cc-by-4.0 | [
"time-series-forecasting"
] | [
"task_categories:time-series-forecasting",
"language:en",
"license:cc-by-4.0",
"size_categories:1M<n<10M",
"modality:tabular",
"modality:text",
"arxiv:2407.13278",
"region:us",
"time-series",
"forecasting",
"anomaly-detection",
"classification",
"TSLib"
] | 1M<n<10M | 43,142 | null | 1,732,380 | 3,871,190,812 | [
"2407.13278"
] |
# Time-Series-Library (TSLib)
TSLib is an open-source library for deep learning researchers, especially for deep time series analysis.
We provide a neat code base to evaluate advanced deep time series models or develop your model, which covers five mainstream tasks: **long- and short-term forecasting, imputation, a... |
dataset | openbmb/Ultra-FineWeb | openbmb | 2025-03-06 | 2026-05-28 | [
"en",
"zh"
] | apache-2.0 | [
"text-generation"
] | [
"task_categories:text-generation",
"language:en",
"language:zh",
"license:apache-2.0",
"size_categories:1B<n<10B",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"arxiv:2505.05427",
"arxiv:2602.09003",
"arxiv:2412.04315",
"... | 1B<n<10B | 54,801 | null | 1,290,261,453 | 9,733,108,790,509 | [
"2505.05427",
"2602.09003",
"2412.04315"
] |
# Ultra-FineWeb
<div align="center">
<img src="assets/ultra-fineweb-logo.png" width="600"/>
</div>
<p align="center">
<a href="https://arxiv.org/abs/2505.05427">📜 Technical Report</a> |
<a href="https://huggingface.co/collections/openbmb/ultradata">📦 UltraData Collection</a> |
<a href="https://ultradata.openbmb.... |
dataset | MrZilinXiao/MoCa_train_with_image | MrZilinXiao | 2025-07-08 | 2025-07-16 | [] | null | [] | [
"size_categories:1M<n<10M",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | 1M<n<10M | 40,954 | null | 2,506,117 | 534,437,452,690 | [] | |
dataset | allenai/dolma3_mix-150B-1025 | allenai | 2025-10-09 | 2026-01-15 | [
"en"
] | odc-by | [
"text-generation"
] | [
"task_categories:text-generation",
"language:en",
"license:odc-by",
"size_categories:10M<n<100M",
"format:json",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"arxiv:2512.13961",
"region:us"
] | 10M<n<100M | 41,045 | null | 895,074 | 110,586,450,778 | [
"2512.13961"
] |
<img alt="Logo for Dolma Mix" src="dolma-mix.png" width="234px" style="margin-left:'auto' margin-right:'auto' display:'block'">
# Dolma 3 Sample: 150B Mix
### Dataset Sources
Sample of data for 1Bx5C and 7Bx1B. For the full Dolma 3 pool, see: https://huggingface.co/datasets/allenai/dolma3
| Source | Type | Tokens ... |
dataset | lmms-lab/MME | lmms-lab | 2023-09-16 | 2023-12-23 | [] | null | [] | [
"size_categories:1K<n<10K",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | 1K<n<10K | 40,427 | null | 2,374 | 864,021,129 | [] |
# Evaluation Dataset for MME |
dataset | cardiffnlp/tweet_eval | cardiffnlp | 2022-03-02 | 2024-01-04 | [
"en"
] | unknown | [
"text-classification"
] | [
"task_categories:text-classification",
"task_ids:intent-classification",
"task_ids:multi-class-classification",
"task_ids:sentiment-classification",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"source_datasets:extended|other-tweet-datasets",
"language:en",... | 100K<n<1M | 40,314 | [
"monolingual"
] | 200,785 | 14,244,314 | [
"2010.12421"
] |
# Dataset Card for tweet_eval
## 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 | open-web-math/open-web-math | open-web-math | 2023-09-06 | 2023-10-17 | [] | null | [] | [
"size_categories:1M<n<10M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2310.06786",
"region:us"
] | 1M<n<10M | 39,918 | null | 6,315,233 | 27,432,202,217 | [
"2310.06786"
] |
<img src="imgs/OpenWebMath-left.png" width="300">
[Keiran Paster](https://keirp.com)\*, [Marco Dos Santos](https://marco-dossantos.github.io/)\*, [Zhangir Azerbayev](https://zhangir-azerbayev.github.io/), [Jimmy Ba](https://jimmylba.github.io/)
[GitHub ](https://github.com/keirp/OpenWebMath) | [ArXiv](https://arxiv.... |
dataset | lmms-lab/GQA | lmms-lab | 2023-12-26 | 2024-03-08 | [] | mit | [] | [
"license:mit",
"size_categories:10M<n<100M",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | 10M<n<100M | 39,954 | null | 24,206,801 | 30,092,686,653 | [] |
<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 | HuggingFaceTB/finemath | HuggingFaceTB | 2024-11-25 | 2025-02-06 | [] | odc-by | [] | [
"license:odc-by",
"size_categories:10M<n<100M",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2502.02737",
"doi:10.57967/hf/3847",
"region:us"
] | 10M<n<100M | 39,960 | null | 48,283,984 | 149,447,641,060 | [
"2502.02737"
] |
# 📐 FineMath
## What is it?
📐 FineMath consists of **34B tokens** (FineMath-3+) and **54B tokens** (FineMath-3+ with InfiMM-WebMath-3+) of mathematical educational content filtered from CommonCr... |
dataset | MERaLiON/Multitask-National-Speech-Corpus-v1 | MERaLiON | 2024-11-28 | 2025-01-21 | [] | null | [] | [
"size_categories:10M<n<100M",
"format:parquet",
"modality:audio",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2501.01034",
"region:us"
] | 10M<n<100M | 41,202 | null | 15,188,396 | 2,998,028,810,108 | [
"2501.01034"
] | Multitask-National-Speech-Corpus (MNSC v1) is derived from [IMDA's NSC Corpus](https://www.imda.gov.sg/how-we-can-help/national-speech-corpus).
MNSC is a multitask speech understanding dataset derived and further annotated from IMDA NSC Corpus. It focuses on the knowledge of Singapore's local accent, localised terms,... |
dataset | Revankumar/news_room_large_dataset | Revankumar | 2023-11-03 | 2023-11-03 | [] | mit | [] | [
"license:mit",
"size_categories:1M<n<10M",
"format:json",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 1M<n<10M | 40,437 | null | 1,212,740 | 5,789,155,007 | [] | |
dataset | hf-internal-testing/multi_dir_dataset | hf-internal-testing | 2023-08-24 | 2022-02-25 | [] | null | [] | [
"size_categories:n<1K",
"format:text",
"modality:text",
"library:datasets",
"library:mlcroissant",
"region:us"
] | n<1K | 39,859 | null | 4 | 1,637 | [] | |
dataset | lmms-lab/textvqa | lmms-lab | 2024-01-16 | 2024-03-08 | [] | null | [] | [
"size_categories:10K<n<100K",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | 10K<n<100K | 39,145 | null | 45,336 | 8,097,810,090 | [] |
<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 | google/RSRCC | google | 2026-04-15 | 2026-04-23 | [
"en"
] | null | [
"visual-question-answering",
"image-text-to-text",
"multiple-choice"
] | [
"task_categories:visual-question-answering",
"task_categories:image-text-to-text",
"task_categories:multiple-choice",
"language:en",
"size_categories:100K<n<1M",
"format:imagefolder",
"modality:image",
"modality:text",
"modality:geospatial",
"library:datasets",
"library:mlcroissant",
"arxiv:26... | 100K<n<1M | 47,243 | null | 126,131 | 1,748,085,591 | [
"2604.20623"
] |
# RSRCC (A Remote Sensing Regional Change Comprehension Benchmark Constructed via Retrieval-Augmented Best-of-N Ranking)
<p align="center">
<img src="data_exmaple-1.png" alt="Data Examples" width="900">
</p>
This repository hosts the **RSRCC** dataset introduced in [RSRCC paper](https://arxiv.org/pdf/2604.20623).
... |
dataset | CARD-Data/CARD-Germany-Batch2 | CARD-Data | 2025-11-18 | 2026-05-16 | [] | cc-by-4.0 | [
"depth-estimation",
"object-detection",
"image-to-3d"
] | [
"task_categories:depth-estimation",
"task_categories:object-detection",
"task_categories:image-to-3d",
"license:cc-by-4.0",
"size_categories:100K<n<1M",
"modality:image",
"modality:text",
"region:us",
"autonomous-driving",
"depth",
"stereo",
"lidar",
"computer-vision"
] | 100K<n<1M | 47,922 | null | null | 119,935,598,513 | [] |
# CARD – Germany 2 Days
A comprehensive multi-modal driving dataset with stereo cameras, LiDAR, and depth annotations.
## Dataset Structure
This dataset contains 22 sequences across different cities around Stuttgart area in Germany:
- **germany_batch2**: 22 sequences
Temporal consistency: Sequences are recorded a... |
dataset | kantor3/CADS-dataset | kantor3 | 2026-03-15 | 2026-03-15 | [] | other | [
"image-segmentation"
] | [
"task_categories:image-segmentation",
"license:other",
"size_categories:10K<n<100K",
"format:csv",
"modality:tabular",
"modality:text",
"modality:image",
"modality:3d",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"arxiv:2507.22953",
"region:us",
"medica... | 10K<n<100K | 66,258 | null | 11,961 | 803,261,262,120 | [
"2507.22953"
] |
# CADS: A Comprehensive Anatomical Dataset and Segmentation for Whole-Body Anatomy in Computed Tomography
<img src="https://raw.githubusercontent.com/murong-xu/CADS/refs/heads/main/resources/images/whole-body-parts-visualization.png" width="90%">
## Overview
CADS is a robust, fully automated framework for segmenting... |
dataset | asahi417/seamless-align-enA-hiA | asahi417 | 2024-05-28 | 2024-05-30 | [] | null | [] | [
"size_categories:100K<n<1M",
"format:parquet",
"modality:audio",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 100K<n<1M | 39,917 | null | 178,022 | 24,864,882,863 | [] | |
dataset | Open-Bee/Honey-Data-15M | Open-Bee | 2025-10-16 | 2026-03-10 | [
"en"
] | null | [
"image-text-to-text"
] | [
"task_categories:image-text-to-text",
"language:en",
"size_categories:10M<n<100M",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2510.13795",
"region:us",
"Bee-8B",
"Honey-Data-15M"
] | 10M<n<100M | 39,352 | null | 14,489,304 | 4,712,676,158,654 | [
"2510.13795"
] |
# Bee: A High-Quality Corpus and Full-Stack Suite to Unlock Advanced Fully Open MLLMs
[[🏠 Homepage](https://open-bee.github.io/)] [[📖 Arxiv Paper](https://arxiv.org/pdf/2510.13795)] [[🤗 Models & Datasets](https://huggingface.co/collections/Open-Bee/bee-8b-68ecbf10417810d90fbd9995)] [[💻 Code](https://github.com/Op... |
dataset | Open-Orca/OpenOrca | Open-Orca | 2023-06-15 | 2025-02-19 | [
"en"
] | mit | [
"conversational",
"text-classification",
"token-classification",
"table-question-answering",
"question-answering",
"zero-shot-classification",
"summarization",
"feature-extraction",
"text-generation",
"text2text-generation"
] | [
"task_categories:text-classification",
"task_categories:token-classification",
"task_categories:table-question-answering",
"task_categories:question-answering",
"task_categories:zero-shot-classification",
"task_categories:summarization",
"task_categories:feature-extraction",
"task_categories:text-gene... | 1M<n<10M | 38,918 | null | 2,942,029 | 4,099,123,187 | [
"2306.02707",
"2301.13688",
"2302.13971"
] | ## Table of Contents
- [Dataset Summary](#dataset-summary)
- [Dataset Attribution](#dataset-attribution)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [... |
dataset | SynthLabsAI/Big-Math-RL-Verified | SynthLabsAI | 2025-02-20 | 2025-03-25 | [
"en"
] | apache-2.0 | [
"question-answering",
"text-generation"
] | [
"task_categories:question-answering",
"task_categories:text-generation",
"language:en",
"license:apache-2.0",
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2502.17387",
"region:us",
"mat... | 100K<n<1M | 38,799 | null | null | 33,156,988 | [
"2502.17387"
] |
# Big-Math: A Large-Scale, High-Quality Math Dataset for Reinforcement Learning in Language Models
Big-Math is the largest open-source dataset of high-quality mathematical problems, curated specifically for reinforcement learning (RL) training in language models. With over 250,000 rigorously filtered and verified pr... |
dataset | a2015003713/military-aircraft-detection-dataset | a2015003713 | 2025-05-24 | 2026-05-15 | [] | null | [
"object-detection",
"image-classification",
"image-feature-extraction"
] | [
"task_categories:object-detection",
"task_categories:image-classification",
"task_categories:image-feature-extraction",
"size_categories:10K<n<100K",
"format:text",
"modality:image",
"modality:text",
"library:datasets",
"library:mlcroissant",
"region:us"
] | 10K<n<100K | 49,788 | null | 41,797 | 8,126,933,801 | [] | # Military Aircraft Detection Dataset
- Military aircraft detection dataset in COCO and YOLO format.
- The dataset contains 102 different military aircraft types.
- This dataset is synchronized from the original Kaggle dataset:
[https://www.kaggle.com/datasets/a2015003713/militaryaircraftdetectiondataset](https:/... |
dataset | argilla/databricks-dolly-15k-curated-en | argilla | 2023-05-30 | 2023-10-02 | [
"en"
] | null | [] | [
"language:en",
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 10K<n<100K | 39,022 | null | 15,015 | 15,565,154 | [] |
## Guidelines
In this dataset, you will find a collection of records that show a category, an instruction, a context and a response to that instruction. The aim of the project is to correct the instructions, intput and responses to make sure they are of the highest quality and that they match the task category that t... |
dataset | ieasybooks-org/shamela-waqfeya-library | ieasybooks-org | 2025-05-14 | 2025-05-14 | [
"ar"
] | mit | [
"image-to-text"
] | [
"task_categories:image-to-text",
"language:ar",
"license:mit",
"size_categories:1K<n<10K",
"format:csv",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 1K<n<10K | 49,927 | null | 4,667 | 117,011,856,842 | [] |
# Shamela Waqfeya Library
## 📖 Overview
Shamela Waqfeya is one of the primary online resources for Islamic books, similar to [Shamela](https://shamela.ws). It hosts more than 4,500 PDF books across over 40 categories.
In this dataset, we processed the original PDF files using Google Document AI APIs and extracted t... |
dataset | m-a-p/PIN-14M | m-a-p | 2024-04-12 | 2025-09-22 | [
"en",
"zh"
] | apache-2.0 | [] | [
"language:en",
"language:zh",
"license:apache-2.0",
"size_categories:10K<n<100K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2406.13923",
"region:us",
"multimodal",
"interleaved"
] | 10K<n<100K | 39,888 | null | 68,084 | 20,173,366,241,972 | [
"2406.13923"
] |
# PIN-14M
A mini version of "PIN: A Knowledge-Intensive Dataset for Paired and Interleaved Multimodal Documents"
Paper: https://arxiv.org/abs/2406.13923
This dataset contains **14M** samples in PIN format, with around **18.79** TB storage.
🚀 News
[ 2025.09.04 ] !NEW! 🔥 We have completed the final version of the... |
dataset | lmms-lab/POPE | lmms-lab | 2024-01-18 | 2024-05-23 | [] | null | [] | [
"size_categories:10K<n<100K",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2305.10355",
"region:us"
] | 10K<n<100K | 38,076 | null | 18,000 | 510,050,293 | [
"2305.10355"
] |
<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 | maritaca-ai/enem | maritaca-ai | 2023-11-24 | 2024-12-03 | [
"pt"
] | apache-2.0 | [
"visual-question-answering",
"multiple-choice"
] | [
"task_categories:visual-question-answering",
"task_categories:multiple-choice",
"language:pt",
"license:apache-2.0",
"size_categories:n<1K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2311.14169",
"arxiv:2303.17003",
... | n<1K | 38,127 | null | 540 | 744,072 | [
"2311.14169",
"2303.17003"
] |
The ENEM 2022, 2023 and 2024 datasets encompass all multiple-choice questions from the last two editions of the [Exame Nacional do Ensino Médio (ENEM)](https://www.gov.br/inep/pt-br/areas-de-atuacao/avaliacao-e-exames-educacionais/enem), the main standardized entrance examination adopted by Brazilian universities. The... |
dataset | OpenGVLab/MVBench | OpenGVLab | 2023-11-28 | 2024-10-18 | [
"en"
] | mit | [
"visual-question-answering",
"video-classification"
] | [
"task_categories:visual-question-answering",
"task_categories:video-classification",
"language:en",
"license:mit",
"size_categories:1K<n<10K",
"format:json",
"modality:image",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
... | 1K<n<10K | 38,078 | null | 4,000 | 17,331,064,311 | [
"2311.17005"
] | # MVBench
## Dataset Description
- **Repository:** [MVBench](https://github.com/OpenGVLab/Ask-Anything/blob/main/video_chat2/mvbench.ipynb)
- **Paper:** [2311.17005](https://arxiv.org/abs/2311.17005)
- **Point of Contact:** mailto:[kunchang li](likunchang@pjlab.org.cn)
## <span style="color: red;">Important Update<... |
dataset | epfml/FineWeb2-embedded | epfml | 2025-02-17 | 2025-02-19 | [
"ru",
"zh",
"de",
"ja",
"es",
"fr",
"it",
"pt",
"pl",
"nl",
"id",
"tr",
"cs",
"vi",
"sv",
"fa",
"ar",
"el",
"da",
"hu"
] | odc-by | [
"text-generation"
] | [
"task_categories:text-generation",
"language:ru",
"language:zh",
"language:de",
"language:ja",
"language:es",
"language:fr",
"language:it",
"language:pt",
"language:pl",
"language:nl",
"language:id",
"language:tr",
"language:cs",
"language:vi",
"language:sv",
"language:fa",
"langua... | 1B<n<10B | 39,200 | null | 3,980,470,502 | 28,307,087,016,509 | [
"2502.10361"
] | # FineWeb2-embedded
## Dataset summary
FineWeb2-embedded is an extension of the [**FineWeb2**](https://huggingface.co/datasets/HuggingFaceFW/fineweb-2) dataset, annotated with **document-level** [**XLM-RoBERTa**](https://huggingface.co/FacebookAI/xlm-roberta-base) **embeddings** for **20 languages**, making the datas... |
dataset | Maxwell-Jia/AIME_2024 | Maxwell-Jia | 2024-12-05 | 2025-02-18 | [
"en"
] | mit | [
"text-generation"
] | [
"task_categories:text-generation",
"language:en",
"license:mit",
"size_categories:n<1K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us",
"explanation-generation"
] | n<1K | 38,715 | null | 30 | 41,426 | [] |
# AIME 2024 Dataset
## Dataset Description
This dataset contains problems from the American Invitational Mathematics Examination (AIME) 2024. AIME is a prestigious high school mathematics competition known for its challenging mathematical problems.
## Dataset Details
- **Format**: JSONL
- **Size**: 30 records
- **... |
dataset | bigcode/bigcodebench | bigcode | 2024-06-05 | 2025-04-30 | [
"code"
] | apache-2.0 | [] | [
"language_creators:expert-generated",
"language:code",
"license:apache-2.0",
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2406.15877",
"region:us",
"croissant",
"code"
] | 1K<n<10K | 38,004 | null | 5,700 | 16,590,169 | [
"2406.15877"
] |
# BigCodeBench
<center>
<img src="https://github.com/bigcode-bench/bigcode-bench.github.io/blob/main/asset/bigcodebench_banner.svg?raw=true" alt="BigCodeBench">
</center>
## Dataset Description
- **Homepage:** https://bigcode-bench.github.io/
- **Repository:** https://github.com/bigcode-project/bigcodebench
- **Pape... |
dataset | baber/paul_graham_essays | baber | 2025-02-20 | 2025-02-20 | [] | null | [] | [
"size_categories:n<1K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | n<1K | 36,711 | null | 218 | 1,191,490 | [] | |
dataset | eaddario/imatrix-calibration | eaddario | 2025-01-29 | 2026-05-05 | [
"ar",
"de",
"en",
"es",
"fr",
"hi",
"id",
"it",
"ja",
"ms",
"nl",
"pl",
"pt",
"ru",
"th",
"tl",
"vi",
"zh"
] | mit | [
"text-generation"
] | [
"task_categories:text-generation",
"language:ar",
"language:de",
"language:en",
"language:es",
"language:fr",
"language:hi",
"language:id",
"language:it",
"language:ja",
"language:ms",
"language:nl",
"language:pl",
"language:pt",
"language:ru",
"language:th",
"language:tl",
"langua... | n<1K | 37,835 | null | 299 | 1,875,272,900 | [] |
# Importance Matrix Calibration Datasets
This repository provides calibration datasets used to generate importance matrices ([imatrix](https://github.com/ggml-org/llama.cpp/tree/master/tools/imatrix)), which are required to minimize errors when quantizing models with [LLaMA C++](https://github.com/ggml-org/llama.cpp)... |
dataset | common-pile/caselaw_access_project | common-pile | 2024-09-10 | 2025-06-06 | [
"en"
] | null | [
"text-generation"
] | [
"task_categories:text-generation",
"language:en",
"size_categories:1M<n<10M",
"format:json",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"arxiv:2506.05209",
"region:us"
] | 1M<n<10M | 37,140 | null | 351,619 | 24,262,715,368 | [
"2506.05209"
] | # Caselaw Access Project
## Description
This dataset contains 6.7 million cases from the Caselaw Access Project and Court Listener.
The Caselaw Access Project consists of nearly 40 million pages of U.S. federal and state court decisions and judges’ opinions from the last 365 years.
In addition, Court Listener adds ov... |
dataset | coastalcph/lex_glue | coastalcph | 2022-03-02 | 2024-01-04 | [
"en"
] | cc-by-4.0 | [
"question-answering",
"text-classification"
] | [
"task_categories:question-answering",
"task_categories:text-classification",
"task_ids:multi-class-classification",
"task_ids:multi-label-classification",
"task_ids:multiple-choice-qa",
"task_ids:topic-classification",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolin... | 100K<n<1M | 37,152 | [
"monolingual"
] | 236,714 | 563,999,548 | [
"2110.00976",
"2109.00904",
"1805.01217",
"2104.08671"
] |
# Dataset Card for "LexGLUE"
## 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 | lukaemon/bbh | lukaemon | 2023-02-01 | 2025-07-11 | [] | null | [] | [
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2210.09261",
"region:us"
] | 1K<n<10K | 37,364 | null | 6,511 | 659,027 | [
"2210.09261"
] | # BIG-bench Hard dataset
homepage: https://github.com/suzgunmirac/BIG-Bench-Hard
```
@article{suzgun2022challenging,
title={Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them},
author={Suzgun, Mirac and Scales, Nathan and Sch{\"a}rli, Nathanael and Gehrmann, Sebastian and Tay, Yi and Chung, H... |
dataset | CohereLabs/Global-MMLU | CohereLabs | 2024-12-01 | 2025-08-14 | [
"en",
"ar",
"bn",
"es",
"fr",
"hi",
"ru",
"de",
"id",
"it",
"ja",
"ko",
"pt",
"zh",
"yo",
"nl",
"ro",
"uk",
"vi",
"tr",
"pl",
"fa",
"cs",
"he",
"el",
"ms",
"fil",
"te",
"si",
"ne",
"ky",
"sv",
"lt",
"sr",
"mg",
"so",
"ha",
"am",
"sn",
"ig... | apache-2.0 | [] | [
"language:en",
"language:ar",
"language:bn",
"language:es",
"language:fr",
"language:hi",
"language:ru",
"language:de",
"language:id",
"language:it",
"language:ja",
"language:ko",
"language:pt",
"language:zh",
"language:yo",
"language:nl",
"language:ro",
"language:uk",
"language:... | 100K<n<1M | 37,451 | null | 601,734 | 199,723,528 | [
"2412.03304"
] |
# Dataset Summary
[Global-MMLU](https://arxiv.org/abs/2412.03304) 🌍 is a multilingual evaluation set spanning 42 languages, including English. This dataset combines machine translations for [MMLU](https://huggin... |
dataset | ieasybooks-org/waqfeya-library | ieasybooks-org | 2025-04-22 | 2025-05-14 | [
"ar"
] | mit | [
"image-to-text"
] | [
"task_categories:image-to-text",
"language:ar",
"license:mit",
"size_categories:10K<n<100K",
"format:csv",
"modality:image",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 10K<n<100K | 60,762 | null | 10,150 | 218,290,387,390 | [] |
# Waqfeya Library
## 📖 Overview
[Waqfeya](https://waqfeya.net) is one of the primary online resources for Islamic books, similar to [Shamela](https://shamela.ws). It hosts more than 10,000 PDF books across over 80 categories.
In this dataset, we processed the original PDF files using Google Document AI APIs and ext... |
dataset | ppxscal/arxiv-metadata-oai-snapshot | ppxscal | 2023-11-07 | 2023-11-07 | [] | null | [] | [
"size_categories:1M<n<10M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | 1M<n<10M | 36,156 | null | 2,318,918 | 1,946,715,068 | [] | # Dataset Card for "arxiv-metadata-oai-snapshot"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
dataset | facebook/xnli | facebook | 2022-03-02 | 2024-01-05 | [
"ar",
"bg",
"de",
"el",
"en",
"es",
"fr",
"hi",
"ru",
"sw",
"th",
"tr",
"ur",
"vi",
"zh"
] | null | [] | [
"language:ar",
"language:bg",
"language:de",
"language:el",
"language:en",
"language:es",
"language:fr",
"language:hi",
"language:ru",
"language:sw",
"language:th",
"language:tr",
"language:ur",
"language:vi",
"language:zh",
"size_categories:1M<n<10M",
"format:parquet",
"modality:t... | 1M<n<10M | 36,076 | null | 6,403,232 | 1,845,646,077 | [] |
# Dataset Card for "xnli"
## 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 | biglam/old_bailey_proceedings | biglam | 2022-07-16 | 2024-01-08 | [
"en"
] | cc-by-4.0 | [
"text-classification",
"text-generation"
] | [
"task_categories:text-classification",
"task_categories:text-generation",
"task_ids:multi-class-classification",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"language_creators:machine-generated",
"m... | 1K<n<10K | 36,037 | [
"monolingual"
] | 2,638 | 370,771,878 | [] | [Needs More Information]
# Dataset Card for Old Bailey Proceedings
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instanc... |
dataset | HPLT/HPLT2.0_cleaned | HPLT | 2024-10-19 | 2025-11-13 | [
"ace",
"af",
"als",
"am",
"ar",
"as",
"ast",
"awa",
"ayr",
"azb",
"azj",
"ba",
"bm",
"ban",
"be",
"bem",
"bn",
"bho",
"bjn",
"bo",
"bs",
"bug",
"bg",
"ca",
"ceb",
"cs",
"cjk",
"ckb",
"crh",
"cy",
"da",
"de",
"dik",
"dyu",
"dz",
"el",
"en",
"e... | cc0-1.0 | [
"fill-mask",
"text-generation"
] | [
"task_categories:fill-mask",
"task_categories:text-generation",
"task_ids:language-modeling",
"multilinguality:multilingual",
"language:ace",
"language:af",
"language:als",
"language:am",
"language:ar",
"language:as",
"language:ast",
"language:awa",
"language:ayr",
"language:azb",
"langu... | 1B<n<10B | 57,824 | [
"multilingual"
] | 9,028,716,523 | 25,508,705,703,938 | [
"2503.10267"
] |
**NB: HPLT2.0 is now superseded by a newer release:**
**[HPLT3.0](https://huggingface.co/datasets/HPLT/HPLT3.0)**
**We recommed switching to v3.0, unless you have a compelling reason to stay on 2.0.**
This is a large-scale collection of web-crawled documents in 191 world languages, produced by the [HPLT project](ht... |
dataset | Xnhyacinth/LongBench | Xnhyacinth | 2025-02-28 | 2025-09-15 | [
"en"
] | null | [] | [
"language:en",
"size_categories:1K<n<10K",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 1K<n<10K | 36,515 | null | 8,418 | 161,258,574 | [] | |
dataset | Williamsanderson/MedQA-Darija-MultiLingual | Williamsanderson | 2026-04-03 | 2026-05-06 | [
"ar",
"fr",
"en"
] | cc-by-4.0 | [
"question-answering",
"automatic-speech-recognition",
"text-to-speech"
] | [
"task_categories:question-answering",
"task_categories:automatic-speech-recognition",
"task_categories:text-to-speech",
"language:ar",
"language:fr",
"language:en",
"license:cc-by-4.0",
"size_categories:100K<n<1M",
"format:parquet",
"modality:audio",
"modality:text",
"library:datasets",
"lib... | 100K<n<1M | 54,183 | null | 267,627 | 18,152,658,390 | [] |
# MedQA-Darija-MultiLingual
**The largest open trilingual medical Q&A dataset with directly-playable speech audio for English, French, and Moroccan Darija.**
A research dataset for the [BRAIN HEALTH](https://github.com/Williamsanderson) initiative, designed for multilingual medical NLP, low-resource speech re... |
dataset | theelderemo/genius-lyrics-cleaned | theelderemo | 2026-03-09 | 2026-03-09 | [
"en"
] | mit | [
"text-generation"
] | [
"task_categories:text-generation",
"task_ids:language-modeling",
"source_datasets:carlosgdcj/genius-song-lyrics-with-language-information",
"language:en",
"license:mit",
"size_categories:1M<n<10M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"librar... | 1M<n<10M | 36,047 | null | 3,179,588 | 2,558,853,571 | [] |
<div align="center">
<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 900 80" width="100%">
<defs>
<linearGradient id="aat-g" x1="0%" y1="0%" x2="100%" y2="0%">
<stop offset="0%" stop-color="#7C3AED" stop-opacity="1"/>
<stop offset="55%" stop-color="#7C3AED" stop-opacity="0.3"/>
<stop offs... |
dataset | MLCommons/peoples_speech | MLCommons | 2022-08-16 | 2024-11-20 | [
"en"
] | cc-by-2.0 | [
"automatic-speech-recognition"
] | [
"task_categories:automatic-speech-recognition",
"annotations_creators:crowdsourced",
"annotations_creators:machine-generated",
"language_creators:crowdsourced",
"language_creators:machine-generated",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:cc-by-2.0",
"li... | 1M<n<10M | 38,427 | [
"monolingual"
] | 8,051,212 | 2,123,656,524,825 | [
"2111.09344"
] |
# Dataset Card for People's Speech
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data F... |
dataset | chrisrca/clash-royale-tv-replays | chrisrca | 2025-11-10 | 2025-12-09 | [] | mit | [
"feature-extraction"
] | [
"task_categories:feature-extraction",
"license:mit",
"size_categories:10K<n<100K",
"modality:image",
"modality:tabular",
"modality:text",
"region:us",
"clash-royale",
"replays",
"gaming",
"computer-vision",
"parquet",
"image-dataset",
"video-frames",
"mobile-gaming"
] | 10K<n<100K | 36,741 | null | 52,876 | 1,879,547,138,989 | [] |
# Clash Royale TV Replays
Frame-by-frame gameplay recordings (~10 fps) from Clash Royale's TV Royale, covering all 31 arenas. Automated recording using tools from our [github repository](https://github.com/chrisrca/CS541-Deep-Learning-Clash-Royale-Project/tree/emulation).
## Structure
```
arena_{XX}/{replay_uuid}/
... |
dataset | zai-org/LongBench-v2 | zai-org | 2024-12-18 | 2024-12-20 | [
"en"
] | apache-2.0 | [
"multiple-choice",
"question-answering",
"text-classification",
"table-question-answering"
] | [
"task_categories:multiple-choice",
"task_categories:question-answering",
"task_categories:text-classification",
"task_categories:table-question-answering",
"language:en",
"license:apache-2.0",
"size_categories:n<1K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"librar... | n<1K | 35,812 | null | 503 | 465,497,668 | [
"2412.15204"
] |
# LongBench v2: Towards Deeper Understanding and Reasoning on Realistic Long-context Multitasks
🌐 Project Page: https://longbench2.github.io
💻 Github Repo: https://github.com/THUDM/LongBench
📚 Arxiv Paper: https://arxiv.org/abs/2412.15204
LongBench v2 is designed to assess the ability of LLMs to handle long-con... |
dataset | wmt/wmt_t2t | wmt | 2022-03-02 | 2024-04-04 | [
"de",
"en"
] | 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... | 1M<n<10M | 35,573 | [
"translation"
] | 4,598,292 | 835,040,545 | [] |
# Dataset Card for "wmt_t2t"
## 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 | nvidia/AceReason-Math | nvidia | 2025-06-02 | 2025-06-18 | [
"en"
] | cc-by-4.0 | [
"text-generation"
] | [
"task_categories:text-generation",
"language:en",
"license:cc-by-4.0",
"size_categories:10K<n<100K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2505.16400",
"arxiv:2506.13284",
"region:us",
"math",
"reasoning"
] | 10K<n<100K | 35,015 | null | 49,585 | 15,374,951 | [
"2505.16400",
"2506.13284"
] |
# AceReason-Math Dataset
<p align="center">
[](https://arxiv.org/abs/2505.16400)
[](https://huggingface.co/datasets/nvidia/AceReason-Math)
[![Models](https://img.shields.io... |
dataset | amirveyseh/acronym_identification | amirveyseh | 2022-03-02 | 2024-01-09 | [
"en"
] | mit | [
"token-classification"
] | [
"task_categories:token-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:mit",
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pan... | 10K<n<100K | 35,151 | [
"monolingual"
] | 17,473 | 2,080,917 | [
"2010.14678"
] |
# Dataset Card for Acronym Identification Dataset
## 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 Instanc... |
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