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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
dataset | ScienceOne-AI/S1-MMAlign | ScienceOne-AI | 2025-12-03 | 2026-03-13 | [
"en"
] | cc-by-nc-4.0 | [
"image-to-text",
"visual-question-answering",
"feature-extraction"
] | [
"task_categories:image-to-text",
"task_categories:visual-question-answering",
"task_categories:feature-extraction",
"language:en",
"license:cc-by-nc-4.0",
"size_categories:10M<n<100M",
"format:webdataset",
"modality:image",
"modality:text",
"library:datasets",
"library:webdataset",
"library:ml... | 10M<n<100M | 26,529 | null | 31,000 | 3,035,182,365,689 | [
"2601.00264"
] |
<h1>S1-MMAlign</h1>
<p><b>A Large-Scale Multi-Disciplinary Scientific Multimodal Dataset</b></p>
**S1-MMAlign** is a large-scale, multi-disciplinary multimodal dataset comprising over **15.5 million** high-quality image-text pairs derived from **2.5 million** open-access scientific papers.
Multimodal learni... |
dataset | MaxLong/gdelt-news-headlines-2022 | MaxLong | 2024-03-31 | 2024-03-31 | [] | mit | [] | [
"license:mit",
"size_categories:10M<n<100M",
"format:json",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | 10M<n<100M | 26,286 | null | 17,007,316 | 3,893,339,217 | [] | |
dataset | FinWorkBench/Finch | FinWorkBench | 2025-11-30 | 2026-04-17 | [] | cc-by-3.0 | [] | [
"license:cc-by-3.0",
"size_categories:n<1K",
"format:json",
"modality:text",
"modality:document",
"modality:image",
"modality:tabular",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"arxiv:2512.13168",
"region:us",
"agent",
"workflow",
"multimodal",
"... | n<1K | 26,298 | null | 172 | 175,072,804 | [
"2512.13168"
] |
<img src="figs/finch_workflow.jpeg" width="1000" />
# Finch (FinWorkBench): Benchmarking Finance & Accounting across Spreadsheet-Centric Enterprise Workflows
This repository contains the dataset for **Finch**, an enterprise-grade benchmark for evaluating an agent’s ability to work like a skilled finance & accounting... |
dataset | open-index/open-markdown | open-index | 2026-03-21 | 2026-05-29 | [
"en"
] | odc-by | [
"text-generation",
"feature-extraction"
] | [
"task_categories:text-generation",
"task_categories:feature-extraction",
"language:en",
"license:odc-by",
"size_categories:10M<n<100M",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"region:us",
"common-c... | 10M<n<100M | 34,238 | null | 83,982,891 | 3,689,183,783,953 | [] |
# **Open Markdown**
> Clean markdown from the web, ready for training and retrieval
## What is it?
**Open Markdown** is a large-scale web text dataset built from [Common Crawl](https://commoncrawl.org). Common Crawl is a non-profit that crawls the web and freely provides its archives and datasets to the public — se... |
dataset | tmnam20/Vietnamese-News | tmnam20 | 2024-01-15 | 2024-01-16 | [] | null | [] | [
"size_categories:1M<n<10M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | 1M<n<10M | 26,173 | null | 4,843,652 | 21,961,960,997 | [] | # Dataset Card for "VietnameseNewsparquet"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
dataset | blanchon/opencs2_dataset | blanchon | 2026-05-08 | 2026-05-04 | [
"en"
] | cc-by-4.0 | [
"video-classification",
"reinforcement-learning",
"other"
] | [
"task_categories:video-classification",
"task_categories:reinforcement-learning",
"task_categories:other",
"language:en",
"license:cc-by-4.0",
"size_categories:100K<n<1M",
"format:parquet",
"modality:tabular",
"modality:text",
"modality:video",
"modality:audio",
"library:datasets",
"library:... | 100K<n<1M | 26,210 | null | 686,145 | null | [] |
# OpenCS2 - POV Renders
> Browse with the [OpenCS2 Viewer](https://huggingface.co/spaces/blanchon/counter-strike-2-dataset-viewer) - every match, map and round, with all 10 player POVs synced on one timeline.
Tick-a... |
dataset | JailbreakBench/JBB-Behaviors | JailbreakBench | 2024-06-12 | 2024-09-26 | [
"en"
] | mit | [] | [
"language:en",
"license:mit",
"size_categories:n<1K",
"format:csv",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2404.01318",
"arxiv:2311.03348",
"arxiv:2307.15043",
"arxiv:2402.04249",
"doi:10.57967/hf/2540",
... | n<1K | 26,314 | null | 500 | 1,016,027 | [
"2404.01318",
"2311.03348",
"2307.15043",
"2402.04249"
] |
<!-- <h1 align="center">
JailbreakBench
</h1>
-->
<div align="center">
<img src="assets/logo.png" alt="Image" />
</div>
<p align="center">
<p align="center">An Open Robustness Benchmark for Jailbreaking Language Models
<br>
</p>
<p align="center">
<p align="center"><b>NeurIPS 2024 Datasets and Benc... |
dataset | jwang-rs/GROC | jwang-rs | 2026-05-21 | 2026-05-25 | [] | null | [
"object-detection"
] | [
"task_categories:object-detection",
"size_categories:10K<n<100K",
"format:text",
"modality:image",
"modality:text",
"modality:geospatial",
"library:datasets",
"library:mlcroissant",
"region:us",
"remote-sensing",
"geospatial",
"aerial-imagery",
"benchmark",
"object-counting",
"object-loc... | 10K<n<100K | 34,162 | null | 13,793 | 42,919,578,537 | [] |
# GROC
GROC is a geospatial remote-sensing benchmark with paired raster tiles, labels, split files, tile extents, and source map vector data. The data are acquired from PDOK.
## Dataset Structure
```text
GROC/
rgb/<group>/ RGB image tiles
cir/<group>/ Color-infrared image tiles
basemap/<grou... |
dataset | tensorshield/reddit_dataset_157 | tensorshield | 2025-03-29 | 2025-04-02 | [] | mit | [
"text-classification",
"token-classification",
"question-answering",
"summarization",
"text-generation"
] | [
"task_categories:text-classification",
"task_categories:token-classification",
"task_categories:question-answering",
"task_categories:summarization",
"task_categories:text-generation",
"task_ids:sentiment-analysis",
"task_ids:topic-classification",
"task_ids:named-entity-recognition",
"task_ids:lang... | 10M<n<100M | 26,439 | [
"multilingual"
] | 43,107,308 | 24,672,844,474 | [] |
# Bittensor Subnet 13 Reddit Dataset
<center>
<img src="https://huggingface.co/datasets/macrocosm-os/images/resolve/main/bittensor.png" alt="Data-universe: The finest collection of social media data the web has to offer">
</center>
<center>
<img src="https://huggingface.co/datasets/macrocosm-os/images/resol... |
dataset | pixparse/cc12m-wds | pixparse | 2023-12-12 | 2023-12-15 | [] | other | [
"image-to-text"
] | [
"task_categories:image-to-text",
"license:other",
"size_categories:10M<n<100M",
"format:webdataset",
"modality:image",
"modality:text",
"library:datasets",
"library:webdataset",
"library:mlcroissant",
"arxiv:2102.08981",
"region:us"
] | 10M<n<100M | 26,284 | null | 49,000 | 1,182,794,707,191 | [
"2102.08981"
] | # Dataset Card for Conceptual Captions 12M (CC12M)
## Dataset Description
- **Repository:** [Conceptual 12M repository](https://github.com/google-research-datasets/conceptual-12m)
- **Paper:** [Conceptual 12M: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Concepts](https://arxiv.org/abs/2102... |
dataset | acl-anonymous/CrowdEval | acl-anonymous | 2025-02-15 | 2025-02-15 | [] | 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 | 26,205 | null | 388,080 | 9,994,150 | [] | |
dataset | MDGA-2/quokka_ckpts | MDGA-2 | 2025-10-29 | 2025-11-04 | [] | null | [] | [
"size_categories:n<1K",
"format:json",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"region:us"
] | n<1K | 28,651 | null | 315 | 5,811,423,467,978 | [] | |
dataset | angeluriot/chess_games | angeluriot | 2024-11-18 | 2024-11-21 | [] | mit | [] | [
"license:mit",
"size_categories:10M<n<100M",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 10M<n<100M | 25,877 | null | 14,188,454 | 7,312,096,044 | [] |
# ♟️ Chess games
The [**Chess games dataset**](https://github.com/angeluriot/Chess_games) is a collection of high level chess games for training machine learning models.
<p align="center">
<img src="resources/misc/thumbnail.png" width="750">
</p>
<br/>
# 📊 Overview
The dataset is composed of 14M chess games fro... |
dataset | espnet/yodas-granary | espnet | 2025-06-09 | 2025-08-08 | [
"bg",
"cs",
"da",
"de",
"el",
"en",
"es",
"et",
"fi",
"fr",
"hr",
"hu",
"it",
"lt",
"lv",
"nl",
"pl",
"pt",
"ro",
"ru",
"sk",
"sv",
"uk"
] | cc-by-3.0 | [
"automatic-speech-recognition",
"translation"
] | [
"task_categories:automatic-speech-recognition",
"task_categories:translation",
"language:bg",
"language:cs",
"language:da",
"language:de",
"language:el",
"language:en",
"language:es",
"language:et",
"language:fi",
"language:fr",
"language:hr",
"language:hu",
"language:it",
"language:lt... | 10M<n<100M | 30,344 | null | 67,622,750 | 21,478,107,569,632 | [
"2505.13404"
] |
## Table of Contents
- [Dataset Description](#dataset-description)
- [Overview](#overview)
- [Data Distribution](#data-distribution)
- [How to Use](#how-to-use)
- [Standard Loading](#standard-loading)
- [Streaming](#streaming)
- [Dataset Structure](#dataset-structu... |
dataset | creative-graphic-design/GenPoster100K | creative-graphic-design | 2026-03-12 | 2026-03-12 | [
"en"
] | cc-by-nc-4.0 | [
"text-to-image",
"image-to-text"
] | [
"task_categories:text-to-image",
"task_categories:image-to-text",
"annotations_creators:machine-generated",
"language_creators:found",
"source_datasets:original",
"language:en",
"license:cc-by-nc-4.0",
"size_categories:100K<n<1M",
"format:parquet",
"modality:image",
"modality:text",
"library:d... | 100K<n<1M | 25,599 | null | 102,703 | 227,886,236,517 | [
"2510.15749"
] |
# Dataset Card for GenPoster100K
[](https://github.com/creative-graphic-design/huggingface-datasets/actions/workflows/ci.yaml)
[![Sync HF](https://github.com/creative-graphic-design/huggingface-datasets/actions/w... |
dataset | OALL/details_Nexusflow__Athene-70B | OALL | 2024-09-18 | 2024-09-18 | [] | 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 | 26,048 | null | 145,930 | 91,608,315 | [] |
# Dataset Card for Evaluation run of Nexusflow/Athene-70B
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [Nexusflow/Athene-70B](https://huggingface.co/Nexusflow/Athene-70B).
The dataset is composed of 136 configuration, each one coresponding to one ... |
dataset | MultiTalk/MultiTalkPT | MultiTalk | 2026-05-05 | 2026-05-12 | [
"zh",
"en"
] | cc-by-nc-4.0 | [
"audio-to-audio"
] | [
"task_categories:audio-to-audio",
"language:zh",
"language:en",
"license:cc-by-nc-4.0",
"size_categories:n<1K",
"format:parquet",
"modality:audio",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us",
"speech",
"dialogue",
"duplex... | n<1K | 25,569 | null | 100 | 5,202,183,911,076 | [] |
# MultiTalkPT
Pre-training corpus for full-duplex spoken-dialogue models.
## Schemas
`data_{zh,en}.jsonl` (one record per line):
| field | type | description |
|------------|--------|------------------------------------------|
| `path` | string | relative path to the dialog... |
dataset | novcor/CADS-dataset | novcor | 2026-04-15 | 2026-04-15 | [] | 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 | 33,193 | null | 22,062 | 803,261,262,377 | [
"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 | hynky/okapi_arc_challenge | hynky | 2024-06-28 | 2024-06-28 | [] | null | [] | [
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 10K<n<100K | 25,388 | null | 23,586 | 10,397,065 | [] | |
dataset | opencompass/NeedleBench | opencompass | 2024-07-21 | 2025-09-01 | [] | mit | [
"question-answering"
] | [
"task_categories:question-answering",
"license:mit",
"size_categories:1K<n<10K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2407.11963",
"region:us"
] | 1K<n<10K | 25,221 | null | 6,804 | 14,106,021,649 | [
"2407.11963"
] |
# Dataset Description
## Dataset Summary
The NeedleBench dataset is a part of the OpenCompass project, designed to evaluate the capabilities of large language models (LLMs) in processing and understanding long documents. It includes a series of test scenarios that assess models' abilities in long text information ex... |
dataset | m-a-p/SuperGPQA | m-a-p | 2025-02-20 | 2025-04-30 | [
"en"
] | odc-by | [
"text2text-generation"
] | [
"language:en",
"license:odc-by",
"size_categories:10K<n<100K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2502.14739",
"region:us"
] | 10K<n<100K | 25,076 | null | 26,529 | 23,964,083 | [
"2502.14739"
] |
This repository contains the data presented in [SuperGPQA: Scaling LLM Evaluation across 285 Graduate Disciplines](https://huggingface.co/papers/2502.14739).
## Tutorials for submitting to the official leadboard
coming soon
## 📜 License
**SuperGPQA** is a composite dataset that includes both original content and... |
dataset | SongLingRan2001/M3DS | SongLingRan2001 | 2025-11-10 | 2026-02-02 | [] | null | [] | [
"size_categories:1K<n<10K",
"format:imagefolder",
"modality:image",
"modality:text",
"library:datasets",
"library:mlcroissant",
"region:us"
] | 1K<n<10K | 26,989 | null | 8,754 | 77,915,968 | [] | |
dataset | mercor/apex-agents | mercor | 2026-01-14 | 2026-03-03 | [
"en"
] | cc-by-4.0 | [] | [
"benchmark:official",
"benchmark:eval-yaml",
"language:en",
"license:cc-by-4.0",
"size_categories:n<1K",
"format:json",
"modality:document",
"modality:image",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"arxiv:2601.14242",
"region:us",
... | n<1K | 24,803 | null | null | 9,028,302,312 | [
"2601.14242"
] |
# APEX–Agents
APEX–Agents is a benchmark from [Mercor](https://www.mercor.com/apex/) for evaluating whether AI agents can execute long-horizon, cross-application professional services tasks. Tasks were created by **investment banking analysts**, **management consultants**, and **corporate lawyers**, and require agents... |
dataset | McGill-NLP/WebLINX-full | McGill-NLP | 2024-02-05 | 2025-09-21 | [
"en"
] | null | [] | [
"language:en",
"size_categories:10K<n<100K",
"modality:text",
"modality:image",
"modality:video",
"arxiv:2402.05930",
"region:us",
"conversational",
"image-to-text",
"vision",
"convAI",
"text",
"image",
"video"
] | 10K<n<100K | 32,826 | null | null | 61,068,841,126 | [
"2402.05930"
] |
# WebLINX: Real-World Website Navigation with Multi-Turn Dialogue
WARNING: This is not the main WebLINX data card! You might want to use the main WebLINX data card instead:
> **[WebLINX: Real-World Website Navigation with Multi-Turn Dialogue](https://huggingface.co/datasets/mcgill-nlp/weblinx)**
---
<div align="... |
dataset | xycoord/deception-probes-activations | xycoord | 2026-03-22 | 2026-05-20 | [
"en"
] | other | [
"text-classification"
] | [
"task_categories:text-classification",
"language:en",
"license:other",
"size_categories:1M<n<10M",
"format:json",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"arxiv:2304.13734",
"arxiv:2407.15285",
"region:us",
"deception",
"mechanistic-i... | 1M<n<10M | 28,455 | null | 1,854,832 | 3,602,690,046,069 | [
"2304.13734",
"2407.15285"
] |
# Deception Probes Activations
Pre-extracted residual-stream activations for training and evaluating deception
detection probes on LLMs. Each example contains per-token hidden states from a
specific transformer layer, saved in bfloat16 safetensors format.
## License
This dataset contains activations derived from mu... |
dataset | x65617379/bailii_260505 | x65617379 | 2026-05-09 | 2026-05-09 | [
"en"
] | unknown | [] | [
"language:en",
"license:unknown",
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"region:us"
] | 100K<n<1M | 24,713 | null | 551,330 | 4,061,754,643 | [] |
<div style="
display: inline-block;
width: fit-content;
max-width: 100%;
background: radial-gradient(ellipse at 70% 20%, #1a1a2e 0%, #0a0a0b 60%);
border-radius: 14px;
padding: 18px 26px 18px 22px;
margin-bottom: 20px;
color: #fff;
font-family: system-ui, -apple-system, BlinkMacSystemFont, 'Segoe UI'... |
dataset | nvidia/Nemotron-CC-v2 | nvidia | 2025-08-14 | 2025-12-23 | [] | other | [
"text-generation"
] | [
"task_categories:text-generation",
"license:other",
"size_categories:1B<n<10B",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"arxiv:2508.14444",
"region:us"
] | 1B<n<10B | 25,734 | null | null | 10,333,000,464,798 | [
"2508.14444"
] |
# Nemotron-Pre-Training-Dataset-v1 Release
## Data Overview
This pretraining dataset, for generative AI model training, preserves high-value math and code while enriching it with diverse multilingual Q&A, fueling the next generation of intelligent, globally-capable models.
This dataset supports [NVIDIA Nemotron Nan... |
dataset | iamtarun/python_code_instructions_18k_alpaca | iamtarun | 2023-07-24 | 2023-07-27 | [] | null | [
"question-answering",
"text2text-generation",
"text-generation"
] | [
"task_categories:question-answering",
"task_categories:text-generation",
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us",
"code"
] | 10K<n<100K | 24,455 | null | 18,612 | 11,360,288 | [] |
# Dataset Card for python_code_instructions_18k_alpaca
The dataset contains problem descriptions and code in python language.
This dataset is taken from [sahil2801/code_instructions_120k](https://huggingface.co/datasets/sahil2801/code_instructions_120k), which adds a prompt column in alpaca style. Refer to the source... |
dataset | yaak-ai/L2D | yaak-ai | 2025-03-05 | 2026-05-26 | [] | apache-2.0 | [
"robotics"
] | [
"task_categories:robotics",
"license:apache-2.0",
"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",
"LeRobot"
] | 10M<n<100M | 25,946 | null | 26,466,954 | 4,759,595,515,133 | [] |
TL;DR of L2D, the world's largest self-driving dataset! Read more about L2D on the official Huggingface blog: [LeRobot goes to driving school](https://huggingface.co/blog/lerobot-goes-to-driving-school)
- 90+ TeraBytes of multimodal data (5000+ hours of driving) from 30 cities in Germany
- 6x surrounding HD cameras a... |
dataset | vsevolodpl/REPID | vsevolodpl | 2025-12-04 | 2026-04-27 | [] | other | [
"image-classification",
"image-to-image",
"other"
] | [
"task_categories:image-classification",
"task_categories:image-to-image",
"task_categories:other",
"license:other",
"size_categories:100K<n<1M",
"format:csv",
"modality:image",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",... | 100K<n<1M | 26,805 | null | 151,666 | 11,602,831,396 | [
"2512.05209"
] |
# REPID: Rendering Evaluation of Photographic Image Dataset
**REPID** (officially introduced as the Rendering Evaluation of Photographic Image Dataset) is a large-scale benchmark designed for **Image Rendering Quality Assessment (IRQA)**.
> Unlike traditional Image Quality Assessment (IQA) which focuses on technica... |
dataset | AssistantBench/AssistantBench | AssistantBench | 2024-07-21 | 2024-07-26 | [
"en"
] | apache-2.0 | [
"question-answering"
] | [
"task_categories:question-answering",
"language:en",
"license:apache-2.0",
"size_categories:n<1K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2407.15711",
"region:us"
] | n<1K | 24,556 | null | 214 | 101,252 | [
"2407.15711"
] |
## Bibtex citation
```bibtex
@misc{yoran2024assistantbenchwebagentssolve,
title={AssistantBench: Can Web Agents Solve Realistic and Time-Consuming Tasks?},
author={Ori Yoran and Samuel Joseph Amouyal and Chaitanya Malaviya and Ben Bogin and Ofir Press and Jonathan Berant},
year={2024},
eprint... |
dataset | open-index/arctic | open-index | 2026-03-14 | 2026-04-29 | [
"en"
] | other | [
"text-generation",
"text-classification",
"feature-extraction"
] | [
"task_categories:text-generation",
"task_categories:text-classification",
"task_categories:feature-extraction",
"language:en",
"license:other",
"size_categories:10B<n<100B",
"modality:tabular",
"modality:text",
"region:us",
"reddit",
"social-media",
"arctic-shift",
"pushshift",
"comments",... | 10B<n<100B | 26,390 | null | 11,850,668,820 | 1,136,012,042,271 | [] |
# Arctic Shift Reddit Archive
> Every Reddit comment and submission since 2005, organized as monthly Parquet shards
## Table of Contents
- [What is it?](#what-is-it)
- [What is being released?](#what-is-being-released)
- [Breakdown by type and year](#breakdown-by-type-and-year)
- [How to download and use this datas... |
dataset | OpenAssistant/oasst1 | OpenAssistant | 2023-04-13 | 2023-05-02 | [
"en",
"es",
"ru",
"de",
"pl",
"th",
"vi",
"sv",
"bn",
"da",
"he",
"it",
"fa",
"sk",
"id",
"nb",
"el",
"nl",
"hu",
"eu",
"zh",
"eo",
"ja",
"ca",
"cs",
"bg",
"fi",
"pt",
"tr",
"ro",
"ar",
"uk",
"gl",
"fr",
"ko"
] | apache-2.0 | [] | [
"language:en",
"language:es",
"language:ru",
"language:de",
"language:pl",
"language:th",
"language:vi",
"language:sv",
"language:bn",
"language:da",
"language:he",
"language:it",
"language:fa",
"language:sk",
"language:id",
"language:nb",
"language:el",
"language:nl",
"language:... | 10K<n<100K | 24,448 | null | 88,838 | 231,507,594 | [
"2304.07327"
] |
# OpenAssistant Conversations Dataset (OASST1)
## Dataset Description
- **Homepage:** https://www.open-assistant.io/
- **Repository:** https://github.com/LAION-AI/Open-Assistant
- **Paper:** https://arxiv.org/abs/2304.07327
### Dataset Summary
In an effort to democratize research on large-scale alignment, we relea... |
dataset | ilee0022/ImageNet100 | ilee0022 | 2024-04-21 | 2024-04-23 | [] | null | [] | [
"size_categories:100K<n<1M",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | 100K<n<1M | 24,454 | null | 135,000 | 17,367,455,195 | [] |
# Dataset Card for Dataset Name
<!-- Provide a quick summary of the dataset. -->
This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
## ... |
dataset | code-search-net/code_search_net | code-search-net | 2022-03-02 | 2026-02-23 | [
"code"
] | other | [
"text-generation",
"fill-mask"
] | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:no-annotation",
"language_creators:machine-generated",
"multilinguality:multilingual",
"source_datasets:original",
"language:code",
"license:othe... | 1M<n<10M | 24,399 | [
"multilingual"
] | 4,141,072 | 3,926,019,983 | [
"1909.09436"
] |
# Dataset Card for CodeSearchNet corpus
## 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-... |
dataset | jammai/chords_and_lyrics | jammai | 2025-02-20 | 2025-02-20 | [] | null | [] | [
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 100K<n<1M | 24,375 | null | 135,783 | 127,569,112 | [] | |
dataset | SmellsLikeAISpirit/plant-disease-train | SmellsLikeAISpirit | 2026-03-24 | 2026-03-25 | [] | null | [] | [
"size_categories:10K<n<100K",
"format:csv",
"modality:image",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us"
] | 10K<n<100K | 28,428 | null | 43,729 | 709,813,997 | [] | |
dataset | laion/majestrino-data | laion | 2026-01-11 | 2026-03-15 | [] | null | [] | [
"size_categories:1M<n<10M",
"format:webdataset",
"modality:audio",
"modality:text",
"library:datasets",
"library:webdataset",
"library:mlcroissant",
"region:us"
] | 1M<n<10M | 24,427 | null | 20,700 | 6,511,973,442,346 | [] | |
dataset | antinomyhq/terminal-bench-2-leaderboard-3 | antinomyhq | 2026-02-26 | 2026-02-26 | [] | apache-2.0 | [] | [
"license:apache-2.0",
"modality:text",
"region:us"
] | null | 26,206 | null | null | 4,111,800,140 | [] |
# 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 | bio-nlp-umass/MedThinkVQA | bio-nlp-umass | 2026-03-03 | 2026-05-20 | [
"en"
] | cc-by-nc-sa-4.0 | [
"question-answering",
"text-generation"
] | [
"task_categories:question-answering",
"task_categories:text-generation",
"language:en",
"license:cc-by-nc-sa-4.0",
"size_categories:1K<n<10K",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us",
"m... | 1K<n<10K | 26,948 | null | 8,067 | 13,628,219,417 | [] |
# MedThinkVQA
MedThinkVQA is an expert-annotated benchmark for **multi-image** diagnostic reasoning in radiology. Unlike prior medical VQA benchmarks that typically contain at most one image per case, MedThinkVQA requires models to **extract evidence from each image**, **integrate cross-view information**, and **perf... |
dataset | simon-donike/OceanVariableReconstruction | simon-donike | 2026-05-21 | 2026-05-27 | [] | other | [] | [
"license:other",
"size_categories:1M<n<10M",
"format:parquet",
"modality:image",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us",
"oceanography",
"argo",
"glorys",
"ostia",
"sea-level",
"sea-surface-salin... | 1M<n<10M | 29,226 | null | 6,637,438 | 5,309,834,124 | [] |
<p align="center">
<img src="assets/figures/banner_depthdif.png" width="85%" alt="Banner Image" />
</p>
<p align="center">
<a href="https://depthdif.donike.net/">
<img src="https://img.shields.io/badge/Project-Website-0b2e4f?style=for-the-badge" alt="Open Documentation" />
</a>
<a href="https://github.com... |
dataset | Fengzhuo/LLM_opt_backup_406 | Fengzhuo | 2026-04-06 | 2026-05-06 | [] | null | [] | [
"modality:text",
"region:us"
] | null | 25,233 | null | null | 3,896,396,799,103 | [] | |
dataset | arcolab-dev/FinDoc-Robust | arcolab-dev | 2026-05-25 | 2026-05-26 | [
"en"
] | apache-2.0 | [
"object-detection"
] | [
"task_categories:object-detection",
"language:en",
"license:apache-2.0",
"size_categories:1K<n<10K",
"format:csv",
"modality:image",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us",
"financial",
"document-ai",
"multimodal"
] | 1K<n<10K | 26,440 | null | 3,000 | 142,224,727,108 | [] |
# Financial Document Extraction & Robustness Dataset (FinDoc-Robust)
## Dataset Description
FinDoc-Robust is a multimodal, benchmark-grade dataset designed for **Document Layout Analysis (DLA)**, **Visual Information Extraction (VIE)**, and evaluating model robustness against real-world degradation.
The dataset con... |
dataset | lmms-lab/ScienceQA | lmms-lab | 2024-01-30 | 2024-03-08 | [] | null | [] | [
"size_categories:10K<n<100K",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 10K<n<100K | 23,941 | null | 12,596 | 1,342,587,366 | [] | <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 | argilla/distilabel-intel-orca-dpo-pairs | argilla | 2024-01-07 | 2025-08-07 | [] | apache-2.0 | [] | [
"license:apache-2.0",
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 10K<n<100K | 24,192 | null | 12,859 | 79,221,432 | [] |
<p align="right">
<a href="https://github.com/argilla-io/distilabel">
<img src="https://raw.githubusercontent.com/argilla-io/distilabel/main/docs/assets/distilabel-badge-light.png" alt="Built with Distilabel" width="200" height="32"/>
</a>
</p>
# distilabel Orca Pairs for DPO
The dataset is a "distilabeled" ... |
dataset | mteb/sts22-crosslingual-sts | mteb | 2022-05-30 | 2026-02-24 | [
"ara",
"cmn",
"deu",
"eng",
"fra",
"ita",
"pol",
"rus",
"spa",
"tur"
] | unknown | [
"sentence-similarity"
] | [
"task_categories:sentence-similarity",
"task_ids:semantic-similarity-scoring",
"annotations_creators:human-annotated",
"multilinguality:multilingual",
"source_datasets:mteb/sts22-crosslingual-sts",
"language:ara",
"language:cmn",
"language:deu",
"language:eng",
"language:fra",
"language:ita",
... | 10K<n<100K | 24,234 | [
"multilingual"
] | 17,160 | 91,570,273 | [
"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 | lmms-lab/LLaVA-Video-178K | lmms-lab | 2024-08-27 | 2024-10-11 | [
"en"
] | null | [
"visual-question-answering",
"video-text-to-text"
] | [
"task_categories:visual-question-answering",
"task_categories:video-text-to-text",
"language:en",
"size_categories:1M<n<10M",
"modality:text",
"modality:video",
"arxiv:2410.02713",
"region:us",
"video"
] | 1M<n<10M | 24,000 | null | 1,627,017 | 1,281,048,393,477 | [
"2410.02713"
] |
# Dataset Card for LLaVA-Video-178K
## Dataset Description
- **Curated by:** Yuanhan Zhang, Jinming Wu, Wei Li
- **Language(s) (NLP):** English, Chinese
- **License:** Apache License 2.0
## Uses
This dataset is used for the training of the LLaVA-Video model. We only allow the use of this dataset for academic resea... |
dataset | codeparrot/codeparrot-clean | codeparrot | 2022-03-02 | 2022-10-10 | [] | null | [] | [
"size_categories:1M<n<10M",
"format:json",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"region:us",
"python",
"code"
] | 1M<n<10M | 23,681 | null | 477,028 | 12,807,700,898 | [] |
# CodeParrot 🦜 Dataset Cleaned
## What is it?
A dataset of Python files from Github. This is the deduplicated version of the [codeparrot](https://huggingface.co/datasets/transformersbook/codeparrot).
## Processing
The original dataset contains a lot of duplicated and noisy data. Therefore, the dataset was cleaned ... |
dataset | Yelp/yelp_review_full | Yelp | 2022-03-02 | 2024-01-04 | [
"en"
] | other | [
"text-classification"
] | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:other",
"size_categories:100K<n<1M",
"format:parquet",
"modality:t... | 100K<n<1M | 24,031 | [
"monolingual"
] | 700,000 | 322,960,267 | [
"1509.01626"
] | ---
# Dataset Card for YelpReviewFull
## 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-in... |
dataset | RidheshBhati/Complete_Data_Source_100K_HOURS | RidheshBhati | 2026-04-01 | 2026-04-28 | [
"as",
"bn",
"en",
"hi",
"kn",
"ta",
"te",
"gu",
"ml",
"mr",
"sd",
"ur",
"fa",
"fil",
"ne",
"brx",
"mai",
"bho",
"or",
"sa",
"pa",
"doi"
] | null | [] | [
"language:as",
"language:bn",
"language:en",
"language:hi",
"language:kn",
"language:ta",
"language:te",
"language:gu",
"language:ml",
"language:mr",
"language:sd",
"language:ur",
"language:fa",
"language:fil",
"language:ne",
"language:brx",
"language:mai",
"language:bho",
"langu... | 1M<n<10M | 25,981 | null | 1,956,280 | 1,378,267,953,021 | [] |
# Multi-Language Audio Collection (100K Hours)
This repository is physically reorganized for **Absolute 100% Data Visibility**.
### 🏗️ Global Consolidator
Select your language subset to listen to high-quality waveform audio. All shards from legacy and modern pipelines are automatically routed here.
|
dataset | maxidl/FineNews-unfiltered | maxidl | 2024-06-14 | 2024-06-16 | [
"en",
"de",
"fr",
"pl",
"es",
"ru",
"it",
"ar",
"pt",
"tr",
"el",
"vi",
"ro",
"zh",
"uk",
"ko",
"hi",
"nl"
] | odc-by | [
"text-generation"
] | [
"task_categories:text-generation",
"language:en",
"language:de",
"language:fr",
"language:pl",
"language:es",
"language:ru",
"language:it",
"language:ar",
"language:pt",
"language:tr",
"language:el",
"language:vi",
"language:ro",
"language:zh",
"language:uk",
"language:ko",
"langua... | 10M<n<100M | 24,255 | null | 31,400,750 | 33,565,576,896 | [] |
# FineNews
WIP. Like FineWeb, but built from [Common Crawl News](https://commoncrawl.org/news-crawl) instead of main web.
For languages not listed as a *split*, check the `data/` directory.
For now, it contains the 2024-05 (May),-04 (April),-03 (March) dumps.
This is the unfiltered version, with only URL filtering a... |
dataset | microsoft/ms_marco | microsoft | 2022-03-02 | 2024-01-04 | [
"en"
] | null | [] | [
"language:en",
"size_categories:1M<n<10M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"arxiv:1611.09268",
"region:us"
] | 1M<n<10M | 23,808 | null | 1,112,939 | 2,323,061,357 | [
"1611.09268"
] |
# Dataset Card for "ms_marco"
## 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 | colaoverdrive/commaCarSegments | colaoverdrive | 2026-04-17 | 2026-04-17 | [] | mit | [] | [
"license:mit",
"size_categories:n<1K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us",
"comma",
"openpilot"
] | n<1K | 23,600 | null | 1 | null | [] |
# commaCarSegments
`commaCarSegments` is a dataset of raw [CAN bus](https://en.wikipedia.org/wiki/CAN_bus) data recorded from our fleet of [openpilot](https://github.com/commaai/openpilot) users driving over [300 different production vehicles](https://comma.ai/vehicles), around the USA and rest of the world.
![](htt... |
dataset | google/wiki40b | google | 2022-03-02 | 2024-03-11 | [
"en"
] | null | [] | [
"language:en",
"size_categories:10M<n<100M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | 10M<n<100M | 23,491 | null | 18,126,525 | 37,603,861,200 | [] |
# Dataset Card for "wiki40b"
## 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 | facebook/Common-O | facebook | 2025-10-24 | 2026-03-10 | [
"en"
] | mit | [] | [
"language:en",
"license:mit",
"size_categories:10K<n<100K",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"arxiv:2511.03768",
"region:us"
] | 10K<n<100K | 23,445 | null | 23,026 | 382,525,837 | [
"2511.03768"
] |
# Common-O
> measuring multimodal reasoning across scenes
Common-O, inspired by cognitive tests for humans, probes multimodal LLMs' ability to reason across scenes by asking "what’s in common?"
![fair conference content copy.001](https://cdn-uploads.huggingface.co/production/uploads/64c17345e82e55936cf971bc/5av7av... |
dataset | InnovatorLab/Innovator-VL-Instruct-46M | InnovatorLab | 2026-01-24 | 2026-02-12 | [
"en"
] | mit | [
"image-text-to-text"
] | [
"task_categories:image-text-to-text",
"language:en",
"license:mit",
"size_categories:10M<n<100M",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"arxiv:2601.19325",
"region:us",
"multimodal",
"vision-langu... | 10M<n<100M | 24,366 | null | 46,077,178 | 11,339,823,956,086 | [
"2601.19325"
] |
# Innovator-VL-Instruct-46M
[Paper](https://huggingface.co/papers/2601.19325) | [Code](https://github.com/InnovatorLM/Innovator-VL)
**🤗🤗 The data is being uploaded continuously**
## Introduction
To further enhance the model’s ability to handle a broad range of visual tasks with **accurate, grounded, and instruct... |
dataset | G4KMU/LEMUR | G4KMU | 2025-07-03 | 2026-03-31 | [
"bg",
"cs",
"de",
"el",
"en",
"es",
"et",
"fi",
"fr",
"ga",
"hr",
"hu",
"it",
"lt",
"lv",
"mt",
"nl",
"pl",
"pt",
"ro",
"sk",
"sl",
"sv"
] | apache-2.0 | [
"text-retrieval"
] | [
"task_categories:text-retrieval",
"annotations_creators:machine-generated",
"language_creators:found",
"multilinguality:multilingual",
"source_datasets:original",
"language:bg",
"language:cs",
"language:de",
"language:el",
"language:en",
"language:es",
"language:et",
"language:fi",
"langua... | 10K<n<100K | 25,127 | [
"multilingual"
] | 44,640 | 25,724,184,770 | [
"2602.09570"
] |
# EU Law Dataset – Category 15.10: Environment
This dataset contains official legal documents from the European Union, collected from the [EUR-Lex website](https://eur-lex.europa.eu/browse/directories/legislation.html), specifically under category 15.10: *"Environment"*. The documents span from the year 1961 to 2025 ... |
dataset | princeton-vl/LayeredFlow-Syn | princeton-vl | 2025-08-28 | 2026-05-15 | [] | other | [] | [
"license:other",
"size_categories:1M<n<10M",
"format:parquet",
"format:optimized-parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"region:us",
"synthetic",
"ground-truth",
"optical-flow",
"layered-scene"
] | 1M<n<10M | 24,286 | null | 1,663,743 | 838,882,650,564 | [] |
# LayeredFlow-Syn Extracted Ground Truth
This repository contains extracted LayeredFlow ground-truth annotations in Parquet format.
## Layout
```text
data/<scene>/<sample>.parquet
```
For example:
```text
data/0/0_0.parquet
```
Each Parquet file stores one extracted sample. Rows correspond to files from the extr... |
dataset | longvideobench/LongVideoBench | longvideobench | 2024-06-12 | 2024-10-14 | [
"en"
] | cc-by-nc-sa-4.0 | [
"multiple-choice",
"visual-question-answering"
] | [
"task_categories:multiple-choice",
"task_categories:visual-question-answering",
"language:en",
"license:cc-by-nc-sa-4.0",
"size_categories:1K<n<10K",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2... | 1K<n<10K | 23,311 | null | null | 161,689,807,231 | [
"2407.15754"
] |
# Dataset Card for LongVideoBench
<!-- Provide a quick summary of the dataset. -->
Large multimodal models (LMMs) are handling increasingly longer and more complex inputs. However, few public benchmarks are available to... |
dataset | LouisM2001/donut | LouisM2001 | 2026-03-26 | 2026-04-28 | [
"en"
] | mit | [
"other"
] | [
"task_categories:other",
"language:en",
"license:mit",
"size_categories:100K<n<1M",
"format:text",
"modality:text",
"library:datasets",
"library:mlcroissant",
"arxiv:2604.22334",
"region:us",
"topology",
"geometric deep-learning",
"point clouds"
] | 100K<n<1M | 26,266 | null | 179,102 | 100,035,926,833 | [
"2604.22334"
] |
# DONUT (Dataset Of MaNifold strUcTures)
This repository contains a dataset of 3D samples made of watertight meshes and corresponding point clouds. Each sample is composed of one or several watertight mesh components and one 8192-point cloud representation.
The dataset contains **29,517 samples** in total.
## Over... |
dataset | GAIA-URJC/Rellis3D-5Labels | GAIA-URJC | 2024-08-25 | 2024-08-26 | [] | cc-by-nc-sa-3.0 | [] | [
"license:cc-by-nc-sa-3.0",
"size_categories:1K<n<10K",
"format:text",
"modality:image",
"modality:text",
"library:datasets",
"library:mlcroissant",
"region:us"
] | 1K<n<10K | 23,379 | null | 5,957 | 5,275,730,411 | [] | |
dataset | gaianet/paris | gaianet | 2024-03-17 | 2025-02-04 | [] | null | [] | [
"size_categories:n<1K",
"format:text",
"modality:text",
"library:datasets",
"library:mlcroissant",
"region:us"
] | n<1K | 22,932 | null | 631 | 277,846,398 | [] | |
dataset | pmchard/3D-ADAM | pmchard | 2025-05-16 | 2025-10-13 | [
"en"
] | cc-by-nc-sa-4.0 | [] | [
"language:en",
"license:cc-by-nc-sa-4.0",
"size_categories:1K<n<10K",
"format:imagefolder",
"modality:image",
"modality:text",
"library:datasets",
"library:mlcroissant",
"arxiv:2507.07838",
"doi:10.57967/hf/5526",
"region:us",
"computer-vision",
"anomaly-detection",
"3D-anomaly-detection",... | 1K<n<10K | 28,222 | null | 9,000 | 385,843,852,258 | [
"2507.07838"
] | Repository for the 3D-ADAM (3D Anomaly Detection in Additive Manufacturing) Dataset. This is the raw data for our complete dataset, separated by part-instance to allow users to utilise the dataset as desired.
We provide a single-camera (using the MechMind-Nano) subset prepared for unsupervised training at anomaly dete... |
dataset | EarthSpeciesProject/BEANS-Next | EarthSpeciesProject | 2026-05-04 | 2026-05-07 | [] | cc-by-nc-sa-4.0 | [] | [
"license:cc-by-nc-sa-4.0",
"size_categories:10K<n<100K",
"format:audiofolder",
"modality:audio",
"modality:text",
"library:datasets",
"library:mlcroissant",
"region:us",
"neurips2026",
"audio",
"bioacoustics"
] | 10K<n<100K | 25,594 | null | 57,637 | 146,652,607,328 | [] |
# BEANS-Next
Audio files live under `audio/`. Metadata is in `metadata.parquet` with a
`file_name` column (repo-relative paths) so the Hugging Face Dataset Viewer can
play clips—**only** `file_name` uses that convention; tier 4 rows instead use
`context_audio_paths` (list) and `query_audio_path` so the viewer is not ... |
dataset | EleutherAI/the_pile_deduplicated | EleutherAI | 2022-12-02 | 2022-12-02 | [] | null | [] | [
"size_categories:100M<n<1B",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | 100M<n<1B | 23,335 | null | 134,318,121 | 451,079,115,272 | [] | |
dataset | Publicus/common_crawl_pointer_indices | Publicus | 2026-02-28 | 2026-02-28 | [] | null | [] | [
"size_categories:1M<n<10M",
"modality:text",
"region:us"
] | 1M<n<10M | 23,028 | null | 5,000,000 | 217,364,530,338 | [] | |
dataset | PleIAs/SYNTH | PleIAs | 2025-11-10 | 2026-05-06 | [
"en",
"fr",
"it",
"es",
"de",
"pl",
"nl",
"la"
] | cc-by-4.0 | [
"text-generation",
"zero-shot-classification",
"summarization"
] | [
"task_categories:text-generation",
"task_categories:zero-shot-classification",
"task_categories:summarization",
"language:en",
"language:fr",
"language:it",
"language:es",
"language:de",
"language:pl",
"language:nl",
"language:la",
"license:cc-by-4.0",
"size_categories:10M<n<100M",
"format... | 10M<n<100M | 23,129 | null | 1,089,584 | 236,335,589,860 | [] |
# SYNTH
<div align="center">
<img src="figures/pleias.png" width="60%" alt="Pleias" />
</div>
<p align="center">
<a href="https://pleias.fr/blog/blogsynth-the-new-data-frontier"><b>Blog announcement</b></a>
</p>
**SYNTH** is the first open generalist synthetic dataset for training small reasoning model end-to-e... |
dataset | open-thoughts/OpenThoughts3-1.2M | open-thoughts | 2025-05-28 | 2025-06-09 | [] | apache-2.0 | [
"text-generation"
] | [
"task_categories:text-generation",
"license:apache-2.0",
"size_categories:1M<n<10M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2506.04178",
"region:us",
"reasoning",
"mathematics",
"code",
"science"
] | 1M<n<10M | 23,251 | null | 1,200,000 | 28,188,906,161 | [
"2506.04178"
] |
<p align="center">
<img src="https://huggingface.co/datasets/open-thoughts/open-thoughts-114k/resolve/main/open_thoughts.png" width="50%">
</p>
<p align="center">
<a href="https://arxiv.org/abs/2506.04178" style="margin-right: 24px;">paper</a> |
<a href="https://huggingface.co/datasets/open-thoughts/OpenThoughts3... |
dataset | ComplexDataLab/OpenFake | ComplexDataLab | 2025-05-15 | 2026-05-07 | [
"en"
] | cc-by-nc-4.0 | [
"image-classification"
] | [
"task_categories:image-classification",
"language:en",
"license:cc-by-nc-4.0",
"size_categories:1M<n<10M",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"arxiv:2509.09495",
"region:us",
"deepfake",
"synth... | 1M<n<10M | 23,329 | null | 2,493,222 | 3,440,728,488,116 | [
"2509.09495"
] |
# Dataset Card for OpenFake
OpenFake is a dataset and benchmark for detecting AI-generated images, with a focus on politically and socially salient content where misinformation risk is highest. It pairs real photographs with synthetic counterparts produced by a wide range of frontier proprietary generators, open-sour... |
dataset | junhyeokrui/AMASS | junhyeokrui | 2026-03-25 | 2026-03-25 | [] | null | [] | [
"size_categories:10K<n<100K",
"format:webdataset",
"modality:text",
"library:datasets",
"library:webdataset",
"library:mlcroissant",
"region:us"
] | 10K<n<100K | 23,889 | null | 96,179 | 68,059,731,990 | [] | |
dataset | nyu-mll/blimp | nyu-mll | 2022-03-02 | 2024-01-23 | [
"en"
] | cc-by-4.0 | [
"text-classification"
] | [
"task_categories:text-classification",
"task_ids:acceptability-classification",
"annotations_creators:crowdsourced",
"language_creators:machine-generated",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"size_categories:10K<n<100K",
"format:parquet",... | 10K<n<100K | 23,057 | [
"monolingual"
] | 67,000 | 3,955,316 | [
"1912.00582"
] |
# Dataset Card for "blimp"
## 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 | Lin-Chen/MMStar | Lin-Chen | 2024-04-02 | 2024-04-07 | [
"en"
] | null | [
"multiple-choice",
"question-answering",
"visual-question-answering"
] | [
"task_categories:multiple-choice",
"task_categories:question-answering",
"task_categories:visual-question-answering",
"language:en",
"size_categories:1K<n<10K",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
... | 1K<n<10K | 22,919 | null | 1,500 | 101,364,404 | [
"2403.20330"
] |
# MMStar (Are We on the Right Way for Evaluating Large Vision-Language Models?)
[**🌐 Homepage**](https://mmstar-benchmark.github.io/) | [**🤗 Dataset**](https://huggingface.co/datasets/Lin-Chen/MMStar) | [**🤗 Paper**](https://huggingface.co/papers/2403.20330) | [**📖 arXiv**](https://arxiv.org/pdf/2403.20330.pdf) |... |
dataset | chcaa/kb-books | chcaa | 2025-06-12 | 2025-11-28 | [] | cc0-1.0 | [] | [
"license:cc0-1.0",
"size_categories:1M<n<10M",
"format:arrow",
"modality:image",
"modality:text",
"library:datasets",
"library:mlcroissant",
"region:us"
] | 1M<n<10M | 22,927 | null | 3,485 | 3,429,889,383,417 | [] |
# open-rdl-books
## Dataset Description
| | |
| ----------- | ----------- |
| **Language** | dan, dansk, Danish |
| **License** | Public Domain, cc0-1.0 |
### Dataset Summary
Documents from the [Royal Danish Library](https://www.kb.dk/en) published between 1750 and 1930.
The dataset has each page of each ... |
dataset | EmmiAI/DrivAerML_subsampled_10x | EmmiAI | 2026-04-13 | 2026-04-16 | [] | cc-by-sa-4.0 | [
"tabular-regression"
] | [
"task_categories:tabular-regression",
"license:cc-by-sa-4.0",
"size_categories:n<1K",
"format:csv",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"arxiv:2408.11969",
"region:us",
"cfd",
"computational-fluid-dynamics",
"a... | n<1K | 23,393 | null | 484 | 357,049,101,599 | [
"2408.11969"
] |
# DrivAerML Subsampled 10x
A subsampled and compressed version of the [DrivAerML](https://huggingface.co/datasets/neashton/drivaerml) dataset by Ashton et al. (2024), prepared for convenient use with ML frameworks for automotive aerodynamics tasks such as drag and lift coefficient prediction.
## Disclaimer
**This d... |
dataset | lmms-lab/VQAv2 | lmms-lab | 2024-01-19 | 2024-01-26 | [] | cc-by-4.0 | [] | [
"license:cc-by-4.0",
"size_categories:100K<n<1M",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | 100K<n<1M | 22,789 | null | 769,541 | 44,780,408,384 | [] | |
dataset | schism-audio/e-gmd | schism-audio | 2026-05-18 | 2026-05-20 | [] | cc-by-4.0 | [
"audio-classification"
] | [
"task_categories:audio-classification",
"license:cc-by-4.0",
"size_categories:10K<n<100K",
"format:audiofolder",
"modality:audio",
"modality:text",
"library:datasets",
"library:mlcroissant",
"arxiv:2004.00188",
"region:us",
"format:audiofolder",
"modality:audio",
"modality:text",
"library:... | 10K<n<100K | 29,995 | null | 45,537 | 141,393,356,074 | [
"2004.00188"
] |
# Expanded Groove MIDI Dataset (E-GMD)
This repository mirrors version 1.0.0 of Google's Expanded Groove MIDI Dataset
(E-GMD) for access through the Hugging Face Hub.
E-GMD is a large dataset of human drum performances with audio recordings
annotated in MIDI. It contains 444.5 hours of audio from 43 drum kits, with ... |
dataset | G4KMU/t2-ragbench | G4KMU | 2025-05-14 | 2026-03-31 | [
"en"
] | cc-by-4.0 | [
"table-question-answering",
"question-answering"
] | [
"task_categories:table-question-answering",
"task_categories:question-answering",
"language:en",
"license:cc-by-4.0",
"size_categories:10K<n<100K",
"format:json",
"modality:document",
"modality:image",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars... | 10K<n<100K | 23,598 | null | 23,088 | 2,983,897,702 | [
"2506.12071"
] |
# Dataset Card for T2-RAGBench
[**Project Page**](https://t2ragbench.demo.hcds.uni-hamburg.de) | [**Paper**](https://huggingface.co/papers/2506.12071) | [**Code**](https://github.com/uhh-hcds/g4kmu-paper)
## Table of Contents
- [Dataset Card for T2-RAGBench](#dataset-card-for-t2-ragbench)
- [Table of Contents](#t... |
dataset | Matthijs/cmu-arctic-xvectors | Matthijs | 2023-02-07 | 2023-02-07 | [] | mit | [
"text-to-speech",
"audio-to-audio"
] | [
"task_categories:text-to-speech",
"task_categories:audio-to-audio",
"license:mit",
"size_categories:1K<n<10K",
"modality:text",
"modality:timeseries",
"library:datasets",
"library:mlcroissant",
"region:us"
] | 1K<n<10K | 22,665 | null | 7,931 | 17,948,151 | [] |
# Speaker embeddings extracted from CMU ARCTIC
There is one `.npy` file for each utterance in the dataset, 7931 files in total. The speaker embeddings are 512-element X-vectors.
The [CMU ARCTIC](http://www.festvox.org/cmu_arctic/) dataset divides the utterances among the following speakers:
- bdl (US male)
- slt (U... |
dataset | m-a-p/CodeFeedback-Filtered-Instruction | m-a-p | 2024-02-26 | 2024-02-26 | [
"en"
] | apache-2.0 | [
"question-answering"
] | [
"task_categories:question-answering",
"language:en",
"license:apache-2.0",
"size_categories:100K<n<1M",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2402.14658",
"region:us",
"code"
] | 100K<n<1M | 22,679 | null | 156,526 | 371,233,631 | [
"2402.14658"
] |
<h1 align="center"> OpenCodeInterpreter: Integrating Code Generation with Execution and Refinement<h1>
<p align="center">
<img width="1000px" alt="OpenCodeInterpreter" src="https://opencodeinterpreter.github.io/static/images/figure1.png">
</p>
<p align="center">
<a href="https://opencodeinterpreter.github.io/">[🏠H... |
dataset | CohereLabs/wikipedia-2023-11-embed-multilingual-v3-int8-binary | CohereLabs | 2024-03-18 | 2026-03-25 | [] | null | [] | [
"size_categories:100M<n<1B",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"region:us"
] | 100M<n<1B | 22,876 | null | 247,154,006 | 344,070,309,645 | [] |
# Multilingual Embeddings for Wikipedia in 300+ Languages (int8 & binary embeddings)
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 embeddings are provided as **int8** and **ubinary** that allo... |
dataset | gmongaras/SlimPajama-627B_Reupload | gmongaras | 2025-04-05 | 2025-04-06 | [] | null | [] | [
"size_categories:100M<n<1B",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | 100M<n<1B | 22,727 | null | 591,399,449 | 1,507,040,725,378 | [] | As datasets puts limits on the number of calls to huggingface, downloading SlimPajama-627B is problematic as it's composed of a ton of small files.
I have reuploaded it here as larger chunks to easily download the dataset without having to do anything hacky.
The original dataset can be found here [https://huggingface... |
dataset | EleutherAI/drop | EleutherAI | 2023-08-30 | 2025-01-10 | [] | cc-by-4.0 | [] | [
"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 | 22,478 | null | 86,945 | 11,639,991 | [] | |
dataset | pixparse/cc3m-wds | pixparse | 2023-12-14 | 2023-12-15 | [] | other | [
"image-to-text"
] | [
"task_categories:image-to-text",
"license:other",
"size_categories:1M<n<10M",
"format:webdataset",
"modality:image",
"modality:text",
"library:datasets",
"library:webdataset",
"library:mlcroissant",
"region:us"
] | 1M<n<10M | 22,430 | null | 68,943 | 280,950,201,682 | [] |
# Dataset Card for Conceptual Captions (CC3M)
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Infor... |
dataset | shaunmarvell/qvhighlights-1fps | shaunmarvell | 2026-05-09 | 2026-05-17 | [] | null | [] | [
"size_categories:1K<n<10K",
"format:json",
"modality:image",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us"
] | 1K<n<10K | 26,868 | null | 8,995 | 2,489,018,772 | [] | # QVHighlights 1fps — Preprocessed Frames
Preprocessed version of the QVHighlights dataset for temporal video grounding.
Videos are extracted at **1fps**, resized to **384×384 JPEG**, ready for training without any video I/O at runtime.
## Contents
| File | Description ... |
dataset | BangumiBase/orewaseikankokkanoakutokuryoushu | 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 | 22,349 | null | null | 11,748,042,318 | [] |
# Bangumi Image Base of Ore Wa Seikan Kokka No Akutoku Ryoushu!
This is the image base of bangumi Ore wa Seikan Kokka no Akutoku Ryoushu!, we detected 74 characters, 4170 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 nois... |
dataset | touhid314/cktformer-dataset | touhid314 | 2026-05-06 | 2026-05-06 | [] | null | [] | [
"size_categories:10K<n<100K",
"format:json",
"modality:image",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"region:us"
] | 10K<n<100K | 25,321 | null | 49,352 | 7,974,026,768 | [] | # CircuitFormer Dataset
This dataset contains a collection of 33,889 analog circuit netlists, images, and metadata collected from 62 textbooks.
## Directory Structure
The dataset is organized into the following flattened directories (no `train/test` subfolders):
- **`images/`**: Circuit diagrams (PNG format).
- **`m... |
dataset | dszohib/graph-pannuke | dszohib | 2026-03-10 | 2026-03-10 | [] | 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 | 22,911 | 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 | turing-motors/Cauldron-JA | turing-motors | 2024-08-05 | 2024-10-24 | [
"ja"
] | cc-by-4.0 | [
"visual-question-answering"
] | [
"task_categories:visual-question-answering",
"language:ja",
"license:cc-by-4.0",
"size_categories:1M<n<10M",
"format:parquet",
"modality:text",
"modality:image",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2405.02246",
"arxiv:1603.07396",
"arxiv:2206... | 1M<n<10M | 23,400 | null | 1,484,539 | 143,448,870,988 | [
"2405.02246",
"1603.07396",
"2206.01718",
"2208.05358",
"1612.06890",
"2310.00367",
"1710.07300",
"2312.12241",
"1912.03098",
"2211.08545",
"2306.05425",
"1709.00103",
"2003.12462",
"1612.00837",
"2205.00363",
"2403.09029"
] | # Dataset Card for The Cauldron-JA
## Dataset description
The **Cauldron-JA** is a Vision Language Model dataset that translates 'The Cauldron' into Japanese using the DeepL API. **[The Cauldron](https://huggingface.co/datasets/HuggingFaceM4/the_cauldron)** is a massive collection of 50 vision-language datasets (trai... |
dataset | CohereLabs/aya_dataset | CohereLabs | 2024-01-31 | 2025-04-15 | [
"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",
"... | apache-2.0 | [
"other"
] | [
"task_categories:other",
"annotations_creators:crowdsourced",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:multilingual",
"source_datasets:original",
"language:amh",
"language:arb",
"language:ary",
"language:ars... | 100K<n<1M | 22,310 | [
"multilingual"
] | 205,568 | 140,160,376 | [
"2402.06619"
] | 
# Dataset Summary
The `Aya Dataset` is a multilingual instruction fine-tuning dataset curated by an open-science community via [Aya Annotation Platform](https://aya.for.ai/) from Cohere Labs. The dataset contains a total o... |
dataset | echo840/OCRBench | echo840 | 2024-03-24 | 2024-12-18 | [] | null | [] | [
"size_categories:1K<n<10K",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2305.07895",
"region:us"
] | 1K<n<10K | 22,231 | null | 1,000 | 67,579,958 | [
"2305.07895"
] | [Github](https://github.com/Yuliang-Liu/MultimodalOCR)|[Paper](https://arxiv.org/abs/2305.07895)
OCRBench has been accepted by [Science China Information Sciences](https://link.springer.com/article/10.1007/s11432-024-4235-6).
|
dataset | google-research-datasets/go_emotions | google-research-datasets | 2022-03-02 | 2024-01-04 | [
"en"
] | apache-2.0 | [
"text-classification"
] | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"task_ids:multi-label-classification",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:apache-2.0",
"size_categories:100K... | 100K<n<1M | 22,172 | [
"monolingual"
] | 265,488 | 28,303,266 | [
"2005.00547"
] |
# Dataset Card for GoEmotions
## 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 | mteb/scifact | mteb | 2024-02-26 | 2025-05-03 | [
"eng"
] | unknown | [
"text-retrieval"
] | [
"task_categories:text-retrieval",
"multilinguality:monolingual",
"language:eng",
"license:unknown",
"size_categories:1K<n<10K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2502.13595",
"arxiv:2210.07316",
"region:us"... | 1K<n<10K | 22,218 | [
"monolingual"
] | 7,550 | 8,257,100 | [
"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 | PKU-Alignment/BeaverTails | PKU-Alignment | 2023-06-07 | 2023-10-17 | [
"en"
] | cc-by-nc-4.0 | [
"text-classification"
] | [
"task_categories:text-classification",
"language:en",
"license:cc-by-nc-4.0",
"size_categories:100K<n<1M",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2307.04657",
"region:us",
"safe",
"safety",
"ai-safety",
"mod... | 100K<n<1M | 21,928 | null | 364,170 | 39,451,314 | [
"2307.04657"
] |
# Dataset Card for BeaverTails
BeaverTails is an AI safety-focused collection comprising a series of datasets.
This repository includes human-labeled data consisting of question-answer (QA) pairs, each identified with their corresponding harm categories.
It should be noted that a single QA pair can be associated with... |
dataset | liamsbhoo/GiftEvalPretrain | liamsbhoo | 2025-04-14 | 2025-04-15 | [] | apache-2.0 | [
"time-series-forecasting"
] | [
"task_categories:time-series-forecasting",
"license:apache-2.0",
"size_categories:1M<n<10M",
"format:arrow",
"modality:text",
"modality:timeseries",
"library:datasets",
"library:mlcroissant",
"arxiv:2410.10393",
"region:us",
"timeseries",
"forecasting",
"benchmark",
"gifteval"
] | 1M<n<10M | 22,382 | null | 1,185,525 | 975,087,445,585 | [
"2410.10393"
] | # GIFT-Eval Pre-training Datasets
Pretraining dataset aligned with [GIFT-Eval](https://huggingface.co/datasets/Salesforce/GiftEval) that has 71 univariate and 17 multivariate datasets, spanning seven domains and 13 frequencies, totaling 4.5 million time series and 230 billion data points. Notably this collection of da... |
dataset | allenai/lila | allenai | 2023-02-08 | 2023-03-15 | [] | cc-by-4.0 | [] | [
"license:cc-by-4.0",
"size_categories:100K<n<1M",
"modality:text",
"library:datasets",
"library:mlcroissant",
"region:us"
] | 100K<n<1M | 22,271 | null | 317,262 | 10,639 | [] |
## Dataset Description
- **Repository:** [allenai/lila](https://github.com/allenai/lila)
- **Paper:** [LILA: A Unified Benchmark for Mathematical Reasoning](https://aclanthology.org/2022.emnlp-main.392.pdf)
- **Point of Contact:** [Matthew Finlayson](https://mattf1n.github.io/), [Sean Welleck](https://wellecks.com/)
... |
dataset | common-canvas/commoncatalog-cc-by | common-canvas | 2024-04-22 | 2024-05-16 | [
"en"
] | cc-by-4.0 | [
"text-to-image"
] | [
"task_categories:text-to-image",
"language:en",
"license:cc-by-4.0",
"size_categories:10M<n<100M",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2310.16825",
"region:us"
] | 10M<n<100M | 22,695 | null | 14,581,672 | 21,465,186,537,816 | [
"2310.16825"
] | # Dataset Card for CommonCatalog CC-BY
This dataset is a large collection of high-resolution Creative Common images (composed of different licenses, see paper Table 1 in the Appendix) collected in 2014 from users of Yahoo Flickr.
The dataset contains images of up to 4k resolution, making this one of the highest resol... |
dataset | LiteFold/PDB | LiteFold | 2026-05-12 | 2026-05-27 | [] | cc0-1.0 | [] | [
"license:cc0-1.0",
"size_categories:10K<n<100K",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us",
"biology",
"proteins",
"protein-structure",
"pdb",
"rcsb",
"mmcif",
"parquet"
] | 10K<n<100K | 26,027 | null | 98,824 | 31,152,535,292 | [] |
# PDB mmCIF Entry Index
The Protein Data Bank is the single global archive of experimentally-determined 3D structures of biological macromolecules, established in 1971 and now holding well over 230,000 entries. It stores atomic coordinates for proteins, nucleic acids, and their complexes determined by X-ray crystallo... |
dataset | nguha/legalbench | nguha | 2023-03-16 | 2026-03-30 | [
"en"
] | cc-by-4.0 | [
"text-classification",
"question-answering"
] | [
"task_categories:text-classification",
"task_categories:question-answering",
"language:en",
"license:cc-by-4.0",
"size_categories:10K<n<100K",
"format:csv",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"arxiv:2308.11462",... | 10K<n<100K | 21,985 | null | 91,750 | 82,475,248 | [
"2308.11462",
"2110.01799",
"2103.06268",
"2301.00876",
"1911.00841",
"2105.07903"
] | # Dataset Card for Dataset Name
- **Homepage: https://hazyresearch.stanford.edu/legalbench/**
- **Repository: https://github.com/HazyResearch/legalbench/**
- **Paper: https://arxiv.org/abs/2308.11462**
## Dataset Description
### Dataset Summary
The LegalBench project is an ongoing open science effort to collabo... |
dataset | DEXHAND-70K/DEXHAND-70K | DEXHAND-70K | 2026-04-29 | 2026-05-06 | [
"en"
] | cc-by-4.0 | [
"robotics",
"reinforcement-learning"
] | [
"task_categories:robotics",
"task_categories:reinforcement-learning",
"language:en",
"license:cc-by-4.0",
"size_categories:1M<n<10M",
"modality:text",
"modality:timeseries",
"library:datasets",
"library:mlcroissant",
"region:us",
"dexterous-manipulation",
"multi-modal",
"tactile-sensing",
... | 1M<n<10M | 25,149 | null | 2,725 | 2,088,395,678,320 | [] |
# DexHand-70K: A Multi-Modal Dataset for Studying Scaling Laws in High-DOF Dexterous Manipulation
**70,000 teleoperated grasping demonstrations** with a 16-DOF LEAP Hand,
synchronized **RGB (4 cameras) + tactile forces (51-dim) + proprioception (16-dim)** at 30 Hz,
collected across **101 objects × 5 environments × 10... |
dataset | yentinglin/aime_2025 | yentinglin | 2025-02-13 | 2025-12-21 | [] | null | [] | [
"size_categories:n<1K",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us"
] | n<1K | 21,926 | null | 60 | 44,907 | [] |
# AIME 2025
This dataset contains 30 problems from the 2025 AIME tests, including:
- [**AIME I**](https://artofproblemsolving.com/wiki/index.php/2025_AIME_I): 15 problems
- [**AIME II**](https://artofproblemsolving.com/wiki/index.php/2025_AIME_II): 15 problems |
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