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 | wendlerc/RenderedText | wendlerc | 2023-06-26 | 2025-10-23 | [
"en"
] | null | [
"text-to-image",
"image-to-text"
] | [
"task_categories:text-to-image",
"task_categories:image-to-text",
"language:en",
"size_categories:10M<n<100M",
"format:webdataset",
"modality:image",
"modality:text",
"library:datasets",
"library:webdataset",
"library:mlcroissant",
"region:us",
"OCR",
"blender",
"LAION",
"Stability"
] | 10M<n<100M | 37,874 | null | 4,700 | 12,870,054,527,688 | [] | *This dataset has been created by Stability AI and LAION.*
This dataset contains 12 million 1024x1024 images of handwritten text written on a digital 3D sheet of paper generated using Blender geometry nodes and rendered using Blender Cycles. The text has varying font size, color, and rotation, and the paper was render... |
dataset | databricks/databricks-dolly-15k | databricks | 2023-04-11 | 2023-06-30 | [
"en"
] | cc-by-sa-3.0 | [
"question-answering",
"summarization"
] | [
"task_categories:question-answering",
"task_categories:summarization",
"language:en",
"license:cc-by-sa-3.0",
"size_categories:10K<n<100K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2203.02155",
"region:us"
] | 10K<n<100K | 34,433 | null | 15,011 | 13,095,866 | [
"2203.02155"
] | # Summary
`databricks-dolly-15k` is an open source dataset of instruction-following records generated by thousands of Databricks employees in several
of the behavioral categories outlined in the [InstructGPT](https://arxiv.org/abs/2203.02155) paper, including brainstorming, classification,
closed QA, generation, info... |
dataset | dair-ai/emotion | dair-ai | 2022-03-02 | 2024-08-08 | [
"en"
] | other | [
"text-classification"
] | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"annotations_creators:machine-generated",
"language_creators:machine-generated",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:other",
"size_categories:100K<n<1M",
"format:parquet",
... | 100K<n<1M | 34,527 | [
"monolingual"
] | 436,809 | 28,185,957 | [] |
# Dataset Card for "emotion"
## 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 | cis-lmu/Glot500 | cis-lmu | 2023-11-01 | 2025-12-10 | [
"abk",
"ace",
"ach",
"acm",
"acr",
"ada",
"afb",
"afr",
"ahk",
"ajp",
"aka",
"aln",
"als",
"alt",
"amh",
"aoj",
"apc",
"ara",
"arb",
"arg",
"arn",
"ary",
"arz",
"asm",
"ast",
"aym",
"ayr",
"azb",
"aze",
"azj",
"bak",
"bam",
"ban",
"bar",
"bcl",
"... | other | [] | [
"multilinguality:multilingual",
"language:abk",
"language:ace",
"language:ach",
"language:acm",
"language:acr",
"language:ada",
"language:afb",
"language:afr",
"language:ahk",
"language:ajp",
"language:aka",
"language:aln",
"language:als",
"language:alt",
"language:amh",
"language:ao... | 1B<n<10B | 35,939 | [
"multilingual"
] | 1,225,300,317 | 308,097,472,603 | [
"2305.12182"
] |
# Glot500 Corpus
A dataset of natural language data collected by putting together more than 150
existing mono-lingual and multilingual datasets together and crawling known multilingual websites.
The focus of this dataset is on 500 extremely low-resource languages.
(More Languages still to be uploaded here)
This ... |
dataset | NuTonic/sat-vl-sft-training-ready-v1 | NuTonic | 2026-04-30 | 2026-04-30 | [
"en"
] | other | [
"text-generation",
"image-text-to-text"
] | [
"task_categories:text-generation",
"task_categories:image-text-to-text",
"language:en",
"license:other",
"size_categories:100K<n<1M",
"format:json",
"modality:image",
"modality:text",
"modality:geospatial",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"reg... | 100K<n<1M | 50,146 | null | 807,678 | 5,737,275,026 | [] |
## Dataset Summary
`NuTonic/sat-bbox-metadata-sft-v1` is a **metadata-first, procedural VLM SFT dataset** built from an existing “sat-bbox” style dataset tree (Sentinel‑2 chips + per-tile JSON metadata sidecars, optionally paired Mapbox stills).
The goal is to create **high-signal, production-shaped supervision** fo... |
dataset | LogeshChandran/newsroom | LogeshChandran | 2024-09-13 | 2024-09-16 | [
"en"
] | null | [] | [
"language:en",
"size_categories:1M<n<10M",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | 1M<n<10M | 33,948 | null | 1,212,740 | 5,966,451,985 | [] | |
dataset | rajpurkar/squad_v2 | rajpurkar | 2022-03-02 | 2024-03-04 | [
"en"
] | cc-by-sa-4.0 | [
"question-answering"
] | [
"task_categories:question-answering",
"task_ids:open-domain-qa",
"task_ids:extractive-qa",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:cc-by-sa-4.0",
"size_categories:100K<n<1M",
"format... | 100K<n<1M | 34,163 | [
"monolingual"
] | 142,192 | 17,730,583 | [
"1806.03822",
"1606.05250"
] |
# Dataset Card for SQuAD 2.0
## Table of Contents
- [Dataset Card for "squad_v2"](#dataset-card-for-squad_v2)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards... |
dataset | epfml/FineWeb2-HQ | 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... | 100M<n<1B | 34,926 | null | 380,138,261 | 6,042,407,029,882 | [
"2502.10361"
] | # FineWeb2-HQ
## Dataset summary
FineWeb2-HQ is a **high-quality, model-filtered pretraining dataset** derived as a subset of [**FineWeb2**](https://huggingface.co/datasets/HuggingFaceFW/fineweb-2), spanning **20 languages**. It enables around 6x faster pretraining compared to the base dataset. FineWeb2-HQ was create... |
dataset | subsurfacegen/field-scale-dataset | subsurfacegen | 2026-05-03 | 2026-05-06 | [] | cc-by-4.0 | [
"image-to-image"
] | [
"task_categories:image-to-image",
"license:cc-by-4.0",
"size_categories:10K<n<100K",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us",
"benchmark",
"geoscience",
"seismology",
"geophysics",
... | 10K<n<100K | 40,262 | null | 94,114 | 11,923,346,031,911 | [] |
# Field-Scale Dataset
A large-scale benchmark dataset of **field-scale 3D subsurface velocity
volumes** (SOS-smoothed, depth-truncated to 619 samples) paired with 2D
velocity slices, their corresponding acoustic wavefields, and multi-source
shot-gather cubes. The dataset spans multiple geological settings and covers
... |
dataset | common-canvas/commoncatalog-cc-by-sa | common-canvas | 2023-10-19 | 2024-05-16 | [
"en"
] | cc-by-sa-4.0 | [
"text-to-image"
] | [
"task_categories:text-to-image",
"language:en",
"license:cc-by-sa-4.0",
"size_categories:1M<n<10M",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2310.16825",
"region:us"
] | 1M<n<10M | 35,085 | null | 7,891,493 | 13,607,487,620,079 | [
"2310.16825"
] | # Dataset Card for CommonCatalog CC-BY-SA
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 re... |
dataset | bop-benchmark/hot3d | bop-benchmark | 2025-02-07 | 2025-09-29 | [] | null | [] | [
"size_categories:100K<n<1M",
"format:webdataset",
"modality:image",
"modality:text",
"library:datasets",
"library:webdataset",
"library:mlcroissant",
"arxiv:2411.19167",
"arxiv:2504.02812",
"region:us"
] | 100K<n<1M | 36,125 | null | 17,000 | 762,348,638,737 | [
"2411.19167",
"2504.02812"
] | # HOT3D-Clips
This Hugging Face repository hosts [HOT3D-Clips](https://github.com/facebookresearch/hot3d/tree/main/hot3d/clips), a set of curated sub-sequences of the [HOT3D dataset](https://facebookresearch.github.io/hot3d/).
Download instructions for HOT3D-Clips and the full HOT3D dataset can be found [here](https:... |
dataset | PromptEval/PromptEval_MMLU_full | PromptEval | 2024-06-04 | 2024-06-07 | [
"en"
] | mit | [
"question-answering"
] | [
"task_categories:question-answering",
"language:en",
"license:mit",
"size_categories:10M<n<100M",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2405.17202",
"region:us"
] | 10M<n<100M | 35,399 | null | 21,063,000 | 15,598,967,978 | [
"2405.17202"
] | # MMLU Multi-Prompt Evaluation Data
## Overview
This dataset contains the results of a comprehensive evaluation of various Large Language Models (LLMs) using multiple prompt templates on the Massive Multitask Language Understanding (MMLU) benchmark. The data is introduced in
[Maia Polo, Felipe, Ronald Xu, Lucas Webe... |
dataset | KiteFishAI/arxiv-tex-corpus-full | KiteFishAI | 2026-02-17 | 2026-02-21 | [
"en"
] | mit | [
"text-generation"
] | [
"task_categories:text-generation",
"language:en",
"license:mit",
"size_categories:100K<n<1M",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"arxiv:2602.17288",
"region:us",
"arxiv",
"maths",
"computer-science",
"physics"
... | 100K<n<1M | 33,807 | null | 91,035 | 79,653,810,586 | [
"2602.17288"
] |
<h1 align="center">arxiv-tex-corpus-full (80GB)</h1>
<p align="center">
Large-scale LaTeX corpus from arXiv (math, CS, physics, statistics)
</p>
📄 Paper: https://arxiv.org/abs/2602.17288
## 📚 Overview
**arxiv-tex-corpus-full (80GB)** is a large-scale dataset of LaTeX source content extracted from papers hosted on... |
dataset | zjunlp/OceanInstruction | zjunlp | 2026-04-08 | 2026-05-06 | [
"zh",
"en"
] | mit | [
"question-answering",
"image-text-to-text"
] | [
"task_categories:question-answering",
"task_categories:image-text-to-text",
"task_ids:visual-question-answering",
"language:zh",
"language:en",
"license:mit",
"size_categories:n<1K",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
... | n<1K | 38,882 | null | 400 | 6,587,655,786 | [] |
# OceanInstruction Dataset
## 1. Dataset Description
OceanInstruction is a specialized instruction-tuning dataset designed for multimodal large language models (MLLMs) in the marine domain. The data has been rigorously curated, deduplicated, and standardized. It encompasses a diverse range of tasks, spanning from te... |
dataset | amphion/Emilia-Dataset | amphion | 2024-08-23 | 2025-02-28 | [
"zh",
"en",
"ja",
"fr",
"de",
"ko"
] | cc-by-4.0 | [
"text-to-speech",
"automatic-speech-recognition"
] | [
"task_categories:text-to-speech",
"task_categories:automatic-speech-recognition",
"language:zh",
"language:en",
"language:ja",
"language:fr",
"language:de",
"language:ko",
"license:cc-by-4.0",
"size_categories:10M<n<100M",
"format:webdataset",
"modality:audio",
"modality:text",
"library:da... | 10M<n<100M | 34,301 | null | null | 4,746,257,588,199 | [
"2407.05361",
"2501.15907"
] |
# Emilia: An Extensive, Multilingual, and Diverse Speech Dataset for Large-Scale Speech Generation
<!-- [](https://arxiv.org/abs/2407.05361) [](https://huggingface.co/datasets/amphi... |
dataset | mteb/nfcorpus | mteb | 2024-03-02 | 2025-05-04 | [
"eng"
] | null | [
"text-retrieval"
] | [
"task_categories:text-retrieval",
"multilinguality:monolingual",
"language:eng",
"size_categories:100K<n<1M",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2502.13595",
"arxiv:2210.07316",
"region:us",
"mteb",
"text"... | 100K<n<1M | 33,734 | [
"monolingual"
] | 141,164 | 18,019,304 | [
"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 | lmqg/qa_squadshifts_synthetic | lmqg | 2022-12-20 | 2023-01-15 | [
"en"
] | cc-by-4.0 | [
"question-answering"
] | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"multilinguality:monolingual",
"source_datasets:extended|wikipedia",
"language:en",
"license:cc-by-4.0",
"size_categories:1M<n<10M",
"modality:text",
"library:datasets",
"library:mlcroissant",
"arxiv:2210.03992",
"region:us"
] | 1M<n<10M | 33,909 | [
"monolingual"
] | 1,427,016 | 1,906,309,757 | [
"2210.03992"
] |
# Dataset Card for "lmqg/qa_squadshifts_synthetic"
## Dataset Description
- **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation)
- **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992)
- **Point of Contact:** [Asahi Ushio](h... |
dataset | AI-MO/aimo-validation-aime | AI-MO | 2024-07-09 | 2025-05-07 | [] | apache-2.0 | [] | [
"license:apache-2.0",
"size_categories:n<1K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | n<1K | 33,624 | null | 90 | 264,424 | [] | # Dataset Card for AIMO Validation AIME
All 90 problems come from AIME 22, AIME 23, and AIME 24, and have been extracted directly from the AOPS wiki page https://artofproblemsolving.com/wiki/index.php/AIME_Problems_and_Solutions
This dataset serves as an internal validation set during our participation in the AIMO pr... |
dataset | the-masses/ReplicaOcc | the-masses | 2026-05-05 | 2026-05-24 | [] | apache-2.0 | [] | [
"license:apache-2.0",
"size_categories:10K<n<100K",
"modality:image",
"modality:text",
"modality:3d",
"arxiv:2604.28115",
"region:us",
"3d",
"robotics",
"slam",
"rgb-d",
"occupancy",
"embodied-ai",
"replica"
] | 10K<n<100K | 40,027 | null | null | 12,350,052,332 | [
"2604.28115"
] |
# Replica_OCC Benchmark
Replica_OCC is a Replica-based occupancy benchmark constructed in the data organization style of [EmbodiedOcc-ScanNet](https://huggingface.co/datasets/YkiWu/EmbodiedOcc-ScanNet) and [OccScanNet](https://huggingface.co/datasets/hongxiaoy/OccScanNet). It provides RGB-D sequences and scene-level ... |
dataset | verstar/MRSAudio | verstar | 2025-05-10 | 2025-10-22 | [
"en",
"zh"
] | cc-by-4.0 | [] | [
"language:en",
"language:zh",
"license:cc-by-4.0",
"size_categories:100K<n<1M",
"format:csv",
"modality:audio",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 100K<n<1M | 45,154 | null | 130,640 | 111,925,167,029 | [] |
# MRSAudio: A Large-Scale Multimodal Recorded Spatial Audio Dataset with Refined Annotations
Humans rely on multisensory integration to perceive spatial environments, where auditory cues enable sound source localization in three-dimensional space.
Despite the critical role of spatial audio in immersive technologies... |
dataset | Salesforce/xlam-function-calling-60k | Salesforce | 2024-06-13 | 2025-01-24 | [
"en"
] | cc-by-4.0 | [
"question-answering",
"text-generation",
"reinforcement-learning"
] | [
"task_categories:question-answering",
"task_categories:text-generation",
"task_categories:reinforcement-learning",
"language:en",
"license:cc-by-4.0",
"size_categories:10K<n<100K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
... | 10K<n<100K | 32,179 | null | null | 97,680,202 | [
"2406.18518"
] |
# APIGen Function-Calling Datasets
[Paper](https://arxiv.org/abs/2406.18518) | [Website](https://apigen-pipeline.github.io/) | [Models](https://huggingface.co/collections/Salesforce/xlam-models-65f00e2a0a63bbcd1c2dade4)
This repo contains 60,000 data collected by [APIGen](https://apigen-pipeline.github.io/), an aut... |
dataset | a2aj/canadian-case-law | a2aj | 2025-06-07 | 2026-05-28 | [
"en",
"fr"
] | mit | [] | [
"language:en",
"language:fr",
"license:mit",
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"region:us"
] | 100K<n<1M | 32,846 | null | 217,071 | 3,941,038,350 | [] |
# A2AJ Canadian Case Law
**Last updated:** 2026-05-26
**Maintainer:** [Access to Algorithmic Justice (A2AJ)](https://a2aj.ca)
---
## Dataset Summary
The **A2AJ Canadian Case Law** dataset provides bulk, open-access full-text decisions from Canadian courts and tribunals.
Each row corresponds to a single case and co... |
dataset | vetonKlinakuRtechko1/plantrtechko1-RBAL | vetonKlinakuRtechko1 | 2026-03-26 | 2026-03-26 | [] | 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 | 36,752 | null | 43,729 | 709,813,997 | [] | |
dataset | seonglae/streamwriter-triplets-10k | seonglae | 2026-03-06 | 2026-04-22 | [] | null | [] | [
"size_categories:1K<n<10K",
"format:parquet",
"format:optimized-parquet",
"modality:audio",
"modality:image",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us"
] | 1K<n<10K | 39,893 | null | 9,987 | 20,630,803,451 | [] | |
dataset | sahil2801/CodeAlpaca-20k | sahil2801 | 2023-03-26 | 2023-10-03 | [
"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",
"region:us",
"code"
] | 10K<n<100K | 31,964 | null | 20,022 | 8,060,308 | [] | |
dataset | stanfordnlp/snli | stanfordnlp | 2022-03-02 | 2024-03-06 | [
"en"
] | cc-by-sa-4.0 | [
"text-classification"
] | [
"task_categories:text-classification",
"task_ids:natural-language-inference",
"task_ids:multi-input-text-classification",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"source_datasets:extended|other-flicker-30k",
"source_datasets:extended|other-... | 100K<n<1M | 31,915 | [
"monolingual"
] | 570,152 | 20,456,444 | [
"1508.05326"
] | # Dataset Card for SNLI
## 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)
- [D... |
dataset | mteb/stsbenchmark-sts | mteb | 2022-04-19 | 2026-02-24 | [
"eng"
] | unknown | [
"sentence-similarity"
] | [
"task_categories:sentence-similarity",
"task_ids:semantic-similarity-scoring",
"annotations_creators:human-annotated",
"multilinguality:translated",
"language:eng",
"license:unknown",
"size_categories:1K<n<10K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:pol... | 1K<n<10K | 31,861 | [
"translated"
] | 8,628 | 435,254 | [
"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 | TNSA/PT-HF500B | TNSA | 2026-03-21 | 2026-03-21 | [
"en"
] | odc-by | [
"text-generation"
] | [
"task_categories:text-generation",
"task_ids:language-modeling",
"annotations_creators:machine-generated",
"language_creators:found",
"source_datasets:fineweb-edu (sample-350BT)",
"language:en",
"license:odc-by",
"size_categories:1B<n<10B",
"modality:tabular",
"modality:text",
"region:us",
"sy... | 1B<n<10B | 35,437 | null | 2,708,089,422 | 5,161,921,384,680 | [] |
# PT-HF500B (FinePhrase)
## Overview
**FinePhrase** is a large-scale synthetic dataset designed for high-quality language modeling, reasoning, and instruction-following tasks. It transforms raw educational web data into structured, instruction-rich formats suitable for training advanced language models.
This datase... |
dataset | google-research-datasets/natural_questions | google-research-datasets | 2022-03-02 | 2024-03-11 | [
"en"
] | cc-by-sa-3.0 | [
"question-answering"
] | [
"task_categories:question-answering",
"task_ids:open-domain-qa",
"annotations_creators:no-annotation",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:cc-by-sa-3.0",
"size_categories:10K<n<100K",
"format:parquet",
"modality:tex... | 10K<n<100K | 32,123 | [
"monolingual"
] | 26,299 | 58,180,768,184 | [] |
# Dataset Card for Natural Questions
## 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-ins... |
dataset | yale-nlp/FOLIO | yale-nlp | 2023-12-21 | 2023-12-21 | [] | mit | [] | [
"license:mit",
"size_categories:1K<n<10K",
"format:json",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 1K<n<10K | 31,779 | null | null | 1,224,119 | [] | |
dataset | vfdanil/licht-speech | vfdanil | 2026-03-06 | 2026-03-28 | [] | null | [] | [
"modality:audio",
"modality:text",
"region:us"
] | null | 36,929 | null | null | 11,398,656,798 | [] | |
dataset | bigscience/xP3all | bigscience | 2022-07-30 | 2023-05-30 | [
"ak",
"ar",
"as",
"bm",
"bn",
"ca",
"code",
"en",
"es",
"eu",
"fon",
"fr",
"gu",
"hi",
"id",
"ig",
"ki",
"kn",
"lg",
"ln",
"ml",
"mr",
"ne",
"nso",
"ny",
"or",
"pa",
"pt",
"rn",
"rw",
"sn",
"st",
"sw",
"ta",
"te",
"tn",
"ts",
"tum",
"tw",
... | apache-2.0 | [
"other"
] | [
"task_categories:other",
"annotations_creators:expert-generated",
"annotations_creators:crowdsourced",
"multilinguality:multilingual",
"language:ak",
"language:ar",
"language:as",
"language:bm",
"language:bn",
"language:ca",
"language:code",
"language:en",
"language:es",
"language:eu",
"... | 10M<n<100M | 34,396 | [
"multilingual"
] | 47,752,575 | 207,497,948,435 | [
"2211.01786"
] |
# Dataset Card for xP3
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
... |
dataset | qiaojin/PubMedQA | qiaojin | 2022-03-02 | 2024-03-06 | [
"en"
] | mit | [
"question-answering"
] | [
"task_categories:question-answering",
"task_ids:multiple-choice-qa",
"annotations_creators:expert-generated",
"annotations_creators:machine-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:mit",
"size_categories:10... | 100K<n<1M | 31,618 | [
"monolingual"
] | 273,518 | 300,503,090 | [
"1909.06146"
] |
# Dataset Card for [Dataset Name]
## 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-instan... |
dataset | open-index/hacker-news | open-index | 2026-03-14 | 2026-05-29 | [
"en"
] | odc-by | [
"text-generation",
"feature-extraction",
"text-classification",
"question-answering"
] | [
"task_categories:text-generation",
"task_categories:feature-extraction",
"task_categories:text-classification",
"task_categories:question-answering",
"language:en",
"license:odc-by",
"size_categories:10M<n<100M",
"modality:text",
"region:us",
"hacker-news",
"forum",
"text",
"parquet",
"com... | 10M<n<100M | 33,530 | null | null | 12,005,242,092 | [] |
# Hacker News - Complete Archive
> Every Hacker News item since 2006, live-updated every 5 minutes
## Table of Contents
- [What is it?](#what-is-it)
- [What is being released?](#what-is-being-released)
- [Breakdown by today](#breakdown-by-today)
- [Breakdown by year](#breakdown-by-year)
- [How to download and use t... |
dataset | joshmiao/gfmc_hyworld1.5_processed_160frames_20fps | joshmiao | 2026-03-21 | 2026-03-21 | [] | null | [] | [
"size_categories:10K<n<100K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us"
] | 10K<n<100K | 40,707 | null | 38,624 | 531,201,299,582 | [] | |
dataset | InternRobotics/InternData-A1 | InternRobotics | 2025-07-24 | 2026-03-27 | [
"en"
] | null | [
"other",
"robotics"
] | [
"task_categories:other",
"task_categories:robotics",
"language:en",
"size_categories:n>1T",
"modality:3d",
"modality:image",
"modality:text",
"arxiv:2511.16651",
"region:us",
"Embodied-AI",
"Robotic manipulation"
] | n>1T | 36,130 | null | null | 32,292,426,186 | [
"2511.16651"
] |
# InternData-A1
<div style="display: flex; justify-content: center; align-items: center; margin: 20px 0;">
<img src="https://huggingface.co/spaces/xushicd/InternData_Media/resolve/main/teaser.png" alt="Teaser Image" style="max-width: 100%; border-radius: 10px; box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1);">
</div>
<... |
dataset | DigitalLearningGmbH/MATH-lighteval | DigitalLearningGmbH | 2025-01-15 | 2025-01-15 | [
"en"
] | mit | [
"text2text-generation"
] | [
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"source_datasets:hendrycks/competition_math",
"language:en",
"license:mit",
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:pola... | 10K<n<100K | 31,486 | null | 25,000 | 9,742,751 | [
"2103.03874"
] |
# Dataset Card for Mathematics Aptitude Test of Heuristics (MATH) dataset in lighteval format
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instan... |
dataset | mhenrichsen/alpaca_2k_test | mhenrichsen | 2023-07-22 | 2023-07-22 | [] | apache-2.0 | [] | [
"license:apache-2.0",
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 1K<n<10K | 31,533 | null | 2,000 | 1,762,909 | [] | |
dataset | SWE-bench/SWE-bench_Lite | SWE-bench | 2025-04-29 | 2025-04-29 | [] | null | [] | [
"size_categories:n<1K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2310.06770",
"region:us"
] | n<1K | 31,181 | null | 323 | 1,224,620 | [
"2310.06770"
] |
### Dataset Summary
SWE-bench *Lite* is _subset_ of [SWE-bench](https://huggingface.co/datasets/princeton-nlp/SWE-bench), a dataset that tests systems’ ability to solve GitHub issues automatically. The dataset collects 300 test Issue-Pull Request pairs from 11 popular Python. Evaluation is performed by unit test verif... |
dataset | openlifescienceai/medmcqa | openlifescienceai | 2022-05-06 | 2024-01-04 | [
"en"
] | apache-2.0 | [
"question-answering",
"multiple-choice"
] | [
"task_categories:question-answering",
"task_categories:multiple-choice",
"task_ids:multiple-choice-qa",
"task_ids:open-domain-qa",
"annotations_creators:no-annotation",
"language_creators:expert-generated",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:apache-2... | 100K<n<1M | 31,256 | [
"monolingual"
] | 193,155 | 88,323,753 | [] |
# Dataset Card for MedMCQA
## 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 Fields](#... |
dataset | lmms-lab/DocVQA | lmms-lab | 2024-01-22 | 2024-04-18 | [] | apache-2.0 | [] | [
"license:apache-2.0",
"size_categories:10K<n<100K",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2007.00398",
"region:us"
] | 10K<n<100K | 31,134 | null | 16,626 | 12,100,689,020 | [
"2007.00398"
] |
<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 | daekeun-ml/naver-news-summarization-ko | daekeun-ml | 2022-08-01 | 2023-01-10 | [
"ko"
] | apache-2.0 | [
"summarization"
] | [
"task_categories:summarization",
"language:ko",
"license:apache-2.0",
"size_categories:10K<n<100K",
"format:csv",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 10K<n<100K | 31,231 | null | 27,400 | 81,918,636 | [] | This dataset is a custom dataset created by the author by crawling Naver News (https://news.naver.com) for the Korean NLP model hands-on.
- Period: July 1, 2022 - July 10, 2022
- Subject: IT, economics
```
DatasetDict({
train: Dataset({
features: ['date', 'category', 'press', 'title', 'document', 'link', ... |
dataset | MathArena/aime_2025 | MathArena | 2025-05-10 | 2026-05-15 | [
"en"
] | cc-by-nc-sa-4.0 | [] | [
"language:en",
"license:cc-by-nc-sa-4.0",
"size_categories:n<1K",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"arxiv:2605.00674",
"region:us"
] | n<1K | 30,784 | null | 30 | 19,047 | [
"2605.00674"
] |
### Homepage and repository
- **Homepage:** [https://matharena.ai/](https://matharena.ai/)
- **Repository:** [https://github.com/eth-sri/matharena](https://github.com/eth-sri/matharena)
### Dataset Summary
This dataset contains the questions from AIME 2025 used for the MathArena Leaderboard
### Data Fields
The d... |
dataset | yahma/alpaca-cleaned | yahma | 2023-03-24 | 2023-04-10 | [
"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",
"region:us",
"instruction-finetuning"
] | 10K<n<100K | 31,067 | null | 51,760 | 44,321,497 | [] |
# Dataset Card for Alpaca-Cleaned
- **Repository:** https://github.com/gururise/AlpacaDataCleaned
## Dataset Description
This is a cleaned version of the original Alpaca Dataset released by Stanford. The following issues have been identified in the original release and fixed in this dataset:
1. **Hallucinations:**... |
dataset | MRSAudio/MRSAudio | MRSAudio | 2025-10-09 | 2026-04-15 | [
"en",
"zh"
] | cc-by-4.0 | [] | [
"language:en",
"language:zh",
"license:cc-by-4.0",
"size_categories:100K<n<1M",
"format:csv",
"modality:audio",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us"
] | 100K<n<1M | 40,954 | null | 246,410 | 122,815,420,610 | [] |
# MRSAudio: A Large-Scale Multimodal Recorded Spatial Audio Dataset with Refined Annotations
Humans rely on multisensory integration to perceive spatial environments, where auditory cues enable sound source localization in three-dimensional space.
Despite the critical role of spatial audio in immersive technologies... |
dataset | AnonymouScientist/SiliciclasticReservoirs | AnonymouScientist | 2026-05-03 | 2026-05-09 | [
"en"
] | cc-by-4.0 | [
"image-segmentation",
"image-classification",
"text-to-image",
"other"
] | [
"task_categories:image-segmentation",
"task_categories:image-classification",
"task_categories:text-to-image",
"task_categories:other",
"language:en",
"license:cc-by-4.0",
"size_categories:1M<n<10M",
"format:parquet",
"modality:text",
"modality:3d",
"library:datasets",
"library:pandas",
"lib... | 1M<n<10M | 32,841 | null | 1,000,000 | 786,815,000,095 | [] |
# SiliciclasticReservoirs
**1,000,000 synthetic 3D siliciclastic-reservoir geology cubes** generated from rule-based sedimentological simulations (turbidite lobes + 6 fluvial-channel architectures + delta-fan distributary). Cubes are voxelized at `(64, 64, 32)` cells. Each sample carries facies, porosity, permeabilit... |
dataset | BramVanroy/CommonCrawl-CreativeCommons-fine | BramVanroy | 2025-08-13 | 2025-08-28 | [
"afr",
"deu",
"eng",
"fra",
"fry",
"ita",
"nld",
"spa",
"af",
"de",
"en",
"fr",
"fy",
"it",
"nl",
"es"
] | cc | [
"text-generation"
] | [
"task_categories:text-generation",
"task_ids:language-modeling",
"language:afr",
"language:deu",
"language:eng",
"language:fra",
"language:fry",
"language:ita",
"language:nld",
"language:spa",
"language:af",
"language:de",
"language:en",
"language:fr",
"language:fy",
"language:it",
"... | 10M<n<100M | 31,058 | null | 75,055,472 | 90,663,586,214 | [] |
# Common Crawl Creative Commons Corpus Fine (C5f)
A filtered version of the [Common Crawl Creative Commons Corpus](https://huggingface.co/datasets/BramVanroy/CommonCrawl-CreativeCommons) (C5), only retaining samples that are also present in the [FineWeb](https://huggingface.co/datasets/HuggingFaceFW/fineweb) or [Fine... |
dataset | tahoebio/Tahoe-100M | tahoebio | 2025-03-12 | 2025-07-23 | [] | cc0-1.0 | [] | [
"license:cc0-1.0",
"size_categories:1B<n<10B",
"format:parquet",
"modality:tabular",
"modality:text",
"modality:timeseries",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us",
"biology",
"single-cell",
"RNA",
"chemistry"
] | 1B<n<10B | 31,264 | null | 4,286,159,337 | 428,806,855,684 | [] |
# Tahoe-100M
Tahoe-100M is a giga-scale single-cell perturbation atlas consisting of over 100 million transcriptomic profiles from
50 cancer cell lines exposed to 1,100 small-molecule perturbations. Generated using Vevo Therapeutics'
Mosaic high-throughput platform, Tahoe-100M enables deep, context-aware exploration... |
dataset | nkp37/OpenVid-1M | nkp37 | 2024-06-11 | 2026-03-31 | [
"en"
] | cc-by-4.0 | [
"text-to-video",
"image-to-video"
] | [
"task_categories:text-to-video",
"task_categories:image-to-video",
"language:en",
"license:cc-by-4.0",
"size_categories:1M<n<10M",
"format:csv",
"modality:tabular",
"modality:text",
"modality:video",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2407.0... | 1M<n<10M | 30,583 | null | 1,453,466 | 12,400,102,028,485 | [
"2407.02371"
] |
<p align="center">
<img src="https://huggingface.co/datasets/nkp37/OpenVid-1M/resolve/main/OpenVid-1M.png">
</p>
# Summary
This is the dataset proposed in our paper [**[ICLR 2025] OpenVid-1M: A Large-Scale High-Quality Dataset for Text-to-video Generation**](https://arxiv.org/abs/2407.02371).
OpenVid-1M is a high-q... |
dataset | Helsinki-NLP/opus-100 | Helsinki-NLP | 2022-03-02 | 2024-02-28 | [
"af",
"am",
"an",
"ar",
"as",
"az",
"be",
"bg",
"bn",
"br",
"bs",
"ca",
"cs",
"cy",
"da",
"de",
"dz",
"el",
"en",
"eo",
"es",
"et",
"eu",
"fa",
"fi",
"fr",
"fy",
"ga",
"gd",
"gl",
"gu",
"ha",
"he",
"hi",
"hr",
"hu",
"hy",
"id",
"ig",
"is"... | unknown | [
"translation"
] | [
"task_categories:translation",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:translation",
"source_datasets:extended",
"language:af",
"language:am",
"language:an",
"language:ar",
"language:as",
"language:az",
"language:be",
"language:bg",
"language:bn",
... | 10M<n<100M | 30,815 | [
"translation"
] | 55,057,504 | 4,770,686,160 | [
"2004.11867"
] |
# Dataset Card for OPUS-100
## 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 | Konst12/1 | Konst12 | 2022-10-03 | 2022-12-05 | [] | null | [] | [
"modality:image",
"modality:text",
"region:us"
] | null | 30,208 | null | null | 85,144,574,765 | [] | |
dataset | EdinburghNLP/xsum | EdinburghNLP | 2022-03-02 | 2026-01-12 | [
"en"
] | unknown | [
"summarization"
] | [
"task_categories:summarization",
"task_ids:news-articles-summarization",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:unknown",
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"librar... | 100K<n<1M | 30,282 | [
"monolingual"
] | 226,711 | 332,798,942 | [
"1808.08745"
] |
# Dataset Card for "xsum"
## 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 | Smith42/sdss_gaia_crossmatched | Smith42 | 2025-07-15 | 2025-07-28 | [] | cc-by-4.0 | [] | [
"license:cc-by-4.0",
"size_categories:10K<n<100K",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2412.02527",
"region:us"
] | 10K<n<100K | 30,481 | null | 41,901 | 2,227,502,647 | [
"2412.02527"
] | <div align="center">
<img src="https://huggingface.co/datasets/Smith42/sdss_gaia_crossmatched/resolve/main/im.png" alt="samples" width="100%"/>
</div>
# Crossmatched samples from the Multimodal Universe for SDSS/Gaia
Mother paper here: [https://arxiv.org/abs/2412.02527](arxiv.org/abs/2412.02527) |
dataset | disco-eth/EuroSpeech | disco-eth | 2025-05-10 | 2026-05-04 | [
"de",
"en",
"mt",
"sl",
"sk",
"da",
"sv",
"no",
"bs",
"fr",
"hr",
"pt",
"it",
"lt",
"el",
"bg",
"lv",
"uk",
"fi",
"et",
"is",
"sr"
] | other | [
"automatic-speech-recognition",
"text-to-speech"
] | [
"task_categories:automatic-speech-recognition",
"task_categories:text-to-speech",
"language:de",
"language:en",
"language:mt",
"language:sl",
"language:sk",
"language:da",
"language:sv",
"language:no",
"language:bs",
"language:fr",
"language:hr",
"language:pt",
"language:it",
"language... | 10M<n<100M | 32,068 | null | 12,263,228 | 5,906,377,769,177 | [
"2510.00514"
] |
# EuroSpeech Dataset
## Dataset Description
EuroSpeech is a large-scale multilingual speech corpus containing high-quality aligned parliamentary speech across 22 European languages. The dataset was constructed by processing parliamentary proceedings using a robust alignment pipeline that handles diverse audio format... |
dataset | singletongue/wikipedia-paragraphs | singletongue | 2025-12-14 | 2026-03-13 | [
"ar",
"de",
"en",
"es",
"fr",
"it",
"ja",
"ko",
"pt",
"ru",
"zh"
] | cc-by-sa-4.0 | [] | [
"language:ar",
"language:de",
"language:en",
"language:es",
"language:fr",
"language:it",
"language:ja",
"language:ko",
"language:pt",
"language:ru",
"language:zh",
"license:cc-by-sa-4.0",
"license:gfdl",
"size_categories:100M<n<1B",
"format:parquet",
"modality:tabular",
"modality:te... | 100M<n<1B | 30,514 | null | 144,886,846 | 408,252,248,500 | [] |
# wikipedia-paragraphs
`wikipedia-paragraphs` is a dataset generated from Wikipedia, designed for natural language processing (NLP) research.
Each entry contains cleaned paragraph text and Wikilink information extracted from a Wikipedia page, along with useful metadata such as categories, templates, and the associat... |
dataset | CodedotAI/code_clippy_github | CodedotAI | 2022-03-02 | 2022-08-05 | [
"code"
] | mit | [
"sequence-modeling"
] | [
"task_ids:language-modeling",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:multilingual",
"language:code",
"license:mit",
"size_categories:1M<n<10M",
"modality:text",
"library:datasets",
"library:mlcroissant",
"arxiv:2107.03374",
"region:us"
] | 1M<n<10M | 35,688 | [
"multilingual"
] | 2,398,000 | 3,100,925,628,087 | [
"2107.03374"
] | # Code Clippy Github Dataset
## Dataset Description
The Code Clippy dataset consists of various public codebases from GitHub in 22 programming languages with 23 extensions totaling about 16 TB of data when uncompressed. The dataset was created from the public GitHub dataset on Google BigQuery.
### How to use it
This da... |
dataset | Chrisneverdie/OnlySports_Dataset | Chrisneverdie | 2024-06-29 | 2024-09-11 | [
"en"
] | cc-by-sa-4.0 | [
"text-generation"
] | [
"task_categories:text-generation",
"language:en",
"license:cc-by-sa-4.0",
"size_categories:1B<n<10B",
"modality:text",
"arxiv:2409.00286",
"doi:10.57967/hf/3054",
"region:us",
"sports"
] | 1B<n<10B | 30,686 | null | 1,727,979,830 | 1,657,916,055,927 | [
"2409.00286"
] |
# 🏀nlySports Dataset
## Overview
OnlySports Dataset is a comprehensive collection of English sports documents, comprising a diverse range of content including news articles, blogs, match reports, interviews, and tutorials. This dataset is part of the larger OnlySports collection, which includes:
1. [OnlySportsLM](... |
dataset | ise-uiuc/Magicoder-Evol-Instruct-110K | ise-uiuc | 2023-12-03 | 2023-12-28 | [] | apache-2.0 | [
"text-generation",
"conversational"
] | [
"task_categories:text-generation",
"license:apache-2.0",
"size_categories:100K<n<1M",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 100K<n<1M | 29,871 | null | 111,183 | 255,189,001 | [] |
A decontaminated version of [evol-codealpaca-v1](https://huggingface.co/datasets/theblackcat102/evol-codealpaca-v1). Decontamination is done in the same way as StarCoder ([bigcode decontamination process](https://github.com/bigcode-project/bigcode-dataset/tree/main/decontamination)). |
dataset | lmms-lab/MMMU | lmms-lab | 2024-01-15 | 2024-03-08 | [] | null | [] | [
"size_categories:10K<n<100K",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2311.16502",
"region:us"
] | 10K<n<100K | 30,089 | null | 11,550 | 3,377,782,626 | [
"2311.16502"
] |
This is a merged version of [MMMU/MMMU](https://huggingface.co/datasets/MMMU/MMMU) with all subsets concatenated.
<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... |
dataset | hf-internal-testing/fixtures_image_utils | hf-internal-testing | 2022-03-02 | 2021-12-07 | [] | null | [] | [
"size_categories:n<1K",
"modality:image",
"modality:text",
"library:datasets",
"library:mlcroissant",
"region:us"
] | n<1K | 29,754 | null | 6 | 1,548,598 | [] | This dataset includes 5 images for testing.
It includes 4 different kinds of images (RGBA, LA, L, Rotated Image) as well as an original cats image of the COCO dataset.
This dataset is used for testing in the HuggingFace Transformers library. You can see [here](https://github.com/huggingface/transformers/search?q=fixt... |
dataset | moondream/seeclick | moondream | 2025-04-05 | 2025-07-27 | [] | 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 | 30,279 | null | 271,121 | 130,416,157,684 | [] | ERROR: type should be large_string, got "\nhttps://github.com/njucckevin/SeeClick" |
dataset | apple/DataCompDR-1B | apple | 2024-06-04 | 2026-04-20 | [
"en"
] | apple-amlr | [
"text-to-image",
"image-to-text"
] | [
"task_categories:text-to-image",
"task_categories:image-to-text",
"language:en",
"license:apple-amlr",
"size_categories:1B<n<10B",
"format:webdataset",
"modality:image",
"modality:text",
"library:datasets",
"library:webdataset",
"library:mlcroissant",
"arxiv:2311.17049",
"region:us"
] | 1B<n<10B | 35,310 | null | 50,300 | 133,681,281,455,592 | [
"2311.17049"
] |
# Dataset Card for DataCompDR-1B
<!-- Provide a quick summary of the dataset. -->
This dataset contains synthetic captions, embeddings, and metadata for DataCompDR-1B.
The metadata has been generated using pretrained image-text models on [DataComp-1B](https://huggingface.co/datasets/mlfoundations/datacomp_1b).
For d... |
dataset | hf-audio/open-asr-leaderboard | hf-audio | 2024-06-21 | 2026-05-27 | [] | null | [] | [
"benchmark:official",
"benchmark:eval-yaml",
"size_categories:10K<n<100K",
"modality:audio",
"modality:text",
"arxiv:2510.06961",
"region:us"
] | 10K<n<100K | 29,622 | null | 99,019 | 20,541,757,882 | [
"2510.06961"
] |
# ESB Test Sets: Parquet & Sorted
This dataset takes the [open-asr-leaderboard/datasets-test-only](hf.co/datasets/open-asr-leaderboard/datasets-test-only) data and sorts each split by audio length.
The format is also changed, from custom loading script (un-safe remote code) to parquet (safe).
Broadly speaking, this... |
dataset | stanfordnlp/sst2 | stanfordnlp | 2022-06-13 | 2024-01-04 | [
"en"
] | unknown | [
"text-classification"
] | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:unknown",
"size_categories:10K<n<100K",
"format:parquet",
"modality:text"... | 10K<n<100K | 29,592 | [
"monolingual"
] | 70,042 | 3,337,898 | [] |
# Dataset Card for [Dataset Name]
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-st... |
dataset | joshmiao/gfmc_hyworld1.5_processed_160latents_16fps | joshmiao | 2026-03-27 | 2026-03-27 | [] | null | [] | [
"size_categories:1K<n<10K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us"
] | 1K<n<10K | 30,821 | null | 9,088 | 204,787,067,791 | [] | |
dataset | lewei123/fineweb-index | lewei123 | 2026-05-04 | 2026-05-04 | [] | null | [] | [
"size_categories:1B<n<10B",
"modality:tabular",
"modality:text",
"region:us"
] | 1B<n<10B | 28,365 | null | 6,591,023,787 | 416,167,462,324 | [] | |
dataset | Skywork/SkyPile-150B | Skywork | 2023-10-23 | 2023-12-07 | [
"zh"
] | null | [
"text-generation"
] | [
"task_categories:text-generation",
"language:zh",
"size_categories:1M<n<10M",
"format:json",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2310.19341",
"region:us",
"llm ",
"casual-lm",
"language-modeling"
] | 1M<n<10M | 28,862 | null | 1,764,518 | 665,312,604,195 | [
"2310.19341"
] |
# SkyPile-150B
## Dataset Summary
SkyPile-150B is a comprehensive, large-scale Chinese dataset specifically designed for the pre-training of large language models. It is derived from a broad array of publicly accessible Chinese Internet web pages. Rigorous filtering, extensive deduplication, and thorough sensitive d... |
dataset | PortPy-Project/PortPy_Dataset | PortPy-Project | 2025-04-11 | 2026-05-03 | [
"en"
] | cc-by-nc-4.0 | [] | [
"language:en",
"license:cc-by-nc-4.0",
"size_categories:n<1K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us"
] | n<1K | 37,336 | null | 331 | 7,961,578,307,355 | [] |
# PortPy: Planning and Optimization for Radiation Therapy
## Data Overview
<img src="./images/PortPy_Data_Curation.png" width="70%" height="50%">
PortPy equips researchers with a robust benchmark patient dataset, sourced from the FDA-approved Eclipse commercial treatment planning system through its API. This datase... |
dataset | Viet-Mistral/CulturaY | Viet-Mistral | 2024-02-08 | 2024-03-30 | [
"af",
"ar",
"az",
"be",
"bg",
"bn",
"ca",
"cs",
"cy",
"da",
"de",
"el",
"en",
"eo",
"es",
"et",
"eu",
"fa",
"fi",
"fr",
"ga",
"gl",
"gu",
"hbs",
"he",
"hi",
"hu",
"hy",
"id",
"is",
"it",
"ja",
"ka",
"kk",
"kn",
"ko",
"ky",
"la",
"lt",
"lv... | cc-by-4.0 | [
"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:found",
"multilinguality:multilingual",
"source_datasets:original",
"language:af",
"language:ar",
"language:... | 1B<n<10B | 29,213 | [
"multilingual"
] | 33,194,559 | 3,134,639,593,633 | [] | ## CulturaY: A Large Cleaned Multilingual Dataset of 75 Languages
### Dataset Summary
From the team that brought you [CulturaX](https://huggingface.co/datasets/uonlp/CulturaX), we present CulturaY, another substantial multilingual dataset of 15TB (uncompressed)/3TB (zstd-compressed) that applies the same dataset clean... |
dataset | gaia3dai/data-kor | gaia3dai | 2024-02-05 | 2024-02-05 | [] | null | [] | [
"size_categories:1K<n<10K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 1K<n<10K | 27,972 | null | 1,537 | 225,526 | [] | |
dataset | azavras/GAIA | azavras | 2025-02-12 | 2026-02-11 | [
"en"
] | mit | [
"image-to-text"
] | [
"task_categories:image-to-text",
"language:en",
"license:mit",
"size_categories:10K<n<100K",
"format:json",
"modality:image",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us",
"Remote Sensing",
"Earth Observat... | 10K<n<100K | 28,027 | null | 41,030 | 76,913,307 | [] |
# 🌍 GAIA: A global, multimodal, multiscale vision–language dataset for remote sensing image analysis
This repository contains the pre-trained model weights, associated code, and complete dataset of the paper [GAIA: A global, multimodal, multiscale visi... |
dataset | uonlp/CulturaX | uonlp | 2023-09-04 | 2024-12-16 | [
"af",
"als",
"am",
"an",
"ar",
"arz",
"as",
"ast",
"av",
"az",
"azb",
"ba",
"bar",
"bcl",
"be",
"bg",
"bh",
"bn",
"bo",
"bpy",
"br",
"bs",
"bxr",
"ca",
"cbk",
"ce",
"ceb",
"ckb",
"cs",
"cv",
"cy",
"da",
"de",
"dsb",
"dv",
"el",
"eml",
"en",
... | null | [
"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:found",
"multilinguality:multilingual",
"source_datasets:original",
"language:af",
"language:als",
"language... | 1B<n<10B | 29,500 | [
"multilingual"
] | null | 17,467,188,201,970 | [
"2309.09400"
] |
<div align="center">
<h1> CulturaX </h1>
<h3> Cleaned, Enormous, and Public: The Multilingual Fuel to Democratize Large Language Models for 167 Languages </h3>
</div>
## Dataset Description
- **Repository:** [https://github.com/nlp-uoregon/CulturaX](https://github.com/nlp-uoregon/CulturaX)
- **Papers:**... |
dataset | agibot-world/AgiBotWorld2026 | agibot-world | 2026-03-11 | 2026-05-27 | [
"en"
] | cc-by-nc-sa-4.0 | [
"robotics"
] | [
"task_categories:robotics",
"language:en",
"license:cc-by-nc-sa-4.0",
"size_categories:1K<n<10K",
"modality:image",
"modality:text",
"region:us",
"agibot",
"imitation-learning",
"embodied-ai",
"lerobot",
"real-world",
"dual-arm"
] | 1K<n<10K | 28,158 | null | null | 9,362,161,628,659 | [] |
<div align="center">
# AgiBot World 2026
**Real-World Embodied Intelligence Dataset**
[](https://creativecommons.org/licenses/by-nc-sa/4.0/)
[](https://githu... |
dataset | mit-han-lab/pile-val-backup | mit-han-lab | 2023-08-21 | 2023-08-21 | [] | null | [] | [
"size_categories:100K<n<1M",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 100K<n<1M | 27,716 | null | 214,670 | 470,909,954 | [] | This is a backup for the pile val dataset downloaded from here: `https://the-eye.eu/public/AI/pile/val.jsonl.zst`
Please respect the original license of the dataset. |
dataset | NeelNanda/pile-10k | NeelNanda | 2022-10-02 | 2022-10-14 | [] | bigscience-bloom-rail-1.0 | [] | [
"license:bigscience-bloom-rail-1.0",
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 10K<n<100K | 27,725 | null | 10,000 | 33,266,321 | [] |
The first 10K elements of [The Pile](https://pile.eleuther.ai/), useful for debugging models trained on it. See the [HuggingFace page for the full Pile](https://huggingface.co/datasets/the_pile) for more info. Inspired by [stas' great resource](https://huggingface.co/datasets/stas/openwebtext-10k) doing the same for O... |
dataset | RadGenome/RadGenome-ChestCT | RadGenome | 2024-05-04 | 2025-05-02 | [] | cc-by-4.0 | [] | [
"license:cc-by-4.0",
"size_categories:100K<n<1M",
"modality:text",
"arxiv:2404.16754",
"arxiv:2403.17834",
"doi:10.57967/hf/5331",
"region:us"
] | 100K<n<1M | 32,536 | null | 665,218 | 1,324,564,015,807 | [
"2404.16754",
"2403.17834"
] |
## [RadGenome Chest CT: A Grounded Vision-Language Dataset for Chest CT Analysis](https://arxiv.org/pdf/2404.16754)
Developing generalist foundation model has recently attracted tremendous attention among researchers in the field of AI for Medicine (AI4Medicine). A pivotal insight in developing these models is the... |
dataset | sentence-transformers/miracl | sentence-transformers | 2024-06-19 | 2024-06-20 | [
"en",
"ar",
"bn",
"es",
"fa",
"fi",
"fr",
"hi",
"id",
"ja",
"ko",
"ru",
"sw",
"te",
"th",
"zh"
] | null | [
"feature-extraction",
"sentence-similarity"
] | [
"task_categories:feature-extraction",
"task_categories:sentence-similarity",
"language:en",
"language:ar",
"language:bn",
"language:es",
"language:fa",
"language:fi",
"language:fr",
"language:hi",
"language:id",
"language:ja",
"language:ko",
"language:ru",
"language:sw",
"language:te",... | 1M<n<10M | 27,994 | null | 8,951,231 | 4,272,763,796 | [] |
# Dataset Card for MIRACL
This is a reformatting of the MIRACL dataset used to train the [BGE-M3 model](https://huggingface.co/BAAI/bge-m3). See the full BGE-M3 dataset in [Shitao/bge-m3-data](https://huggingface.co/datasets/Shitao/bge-m3-data).
## Dataset Subsets
### `...-triplet` subset
* Columns: "anchor", "pos... |
dataset | nvidia/Nemotron-CC-v2.1 | nvidia | 2025-12-12 | 2025-12-22 | [] | 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",
"arxiv:2508.15096",
"arxiv:2412.02595",
"arxiv:2505.02881",
"region:us"
] | 1B<n<10B | 27,901 | null | null | 4,590,657,117,351 | [
"2508.14444",
"2508.15096",
"2412.02595",
"2505.02881"
] | # Nemotron-Pre-Training-Dataset-v2.1
## Dataset Description
The Nemotron-Pre-Training-Dataset-v2.1 extends the previously released Nemotron pretraining datasets with refreshed, higher-quality, and more diverse data across math, code, English Common Crawl, and large-scale synthetic ... |
dataset | max-id/gaianet-qdrant-snapshot | max-id | 2024-04-01 | 2024-12-30 | [] | apache-2.0 | [] | [
"license:apache-2.0",
"size_categories:10K<n<100K",
"format:text",
"modality:text",
"library:datasets",
"library:mlcroissant",
"region:us"
] | 10K<n<100K | 27,683 | null | 38,826 | 109,394,235,718 | [] | |
dataset | gaia3dai/data-text-v3 | gaia3dai | 2024-02-05 | 2024-02-05 | [] | null | [] | [
"size_categories:1K<n<10K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 1K<n<10K | 27,447 | null | 1,612 | 241,043 | [] | |
dataset | Phsntom/WaxalNLP | Phsntom | 2026-02-04 | 2026-02-04 | [
"ach",
"aka",
"amh",
"dag",
"dga",
"ewe",
"fat",
"ful",
"hau",
"ibo",
"kik",
"kpo",
"lin",
"lug",
"luo",
"mas",
"mlg",
"nyn",
"orm",
"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 | 29,100 | [
"multilingual"
] | 2,557,261 | 771,370,797,069 | [] |
# Waxal Datasets
## Table of Contents
- [Dataset Description](#dataset-description)
- [ASR Dataset](#asr-dataset)
- [TTS Dataset](#tts-dataset)
- [How to Use](#how-to-use)
- [Dataset Structure](#dataset-structure)
- [ASR Data Fields](#asr-data-fields)
- [TTS Data Fields](#tts-data-f... |
dataset | lmms-lab/MMBench | lmms-lab | 2024-03-14 | 2024-03-15 | [] | 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 | 27,525 | null | 24,030 | 527,209,438 | [] | |
dataset | HuggingFaceFW/fineweb_edu_100BT-shuffled | HuggingFaceFW | 2026-02-15 | 2026-03-02 | [
"en"
] | odc-by | [] | [
"language:en",
"license:odc-by",
"size_categories:100M<n<1B",
"format:parquet",
"format:optimized-parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"region:us",
"pretraining",
"smol-data"
] | 100M<n<1B | 27,398 | null | 102,063,987 | 302,789,590,792 | [] |
# FineWeb-Edu 100BT (Shuffled)
A globally shuffled version of [HuggingFaceFW/fineweb_edu_100BT](https://huggingface.co/datasets/HuggingFaceFW/fineweb_edu_100BT).
Part of the [Smol-Data](https://huggingface.co/collections/HuggingFaceFW/smol-data) collection — tried and tested mixes for strong pretraining.
## Dataset... |
dataset | locuslab/safeweb | locuslab | 2025-04-22 | 2026-05-14 | [] | odc-by | [] | [
"license:odc-by",
"size_categories:10M<n<100M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | 10M<n<100M | 27,449 | null | 14,778,823 | 73,476,117,984 | [] | |
dataset | bigcode/starcoderdata | bigcode | 2023-03-30 | 2023-05-16 | [
"code"
] | other | [
"text-generation"
] | [
"task_categories:text-generation",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:multilingual",
"language:code",
"license:other",
"size_categories:100M<n<1B",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant"... | 100M<n<1B | 27,358 | [
"multilingual"
] | null | 310,802,038,700 | [] | # StarCoder Training Dataset
## Dataset description
This is the dataset used for training [StarCoder](https://huggingface.co/bigcode/starcoder) and [StarCoderBase](https://huggingface.co/bigcode/starcoderbase). It contains 783GB of code in 86 programming languages, and includes 54GB GitHub Issues + 13GB Jupyter notebo... |
dataset | NousResearch/hermes-function-calling-v1 | NousResearch | 2024-08-14 | 2026-01-03 | [
"en"
] | apache-2.0 | [
"text-generation",
"question-answering",
"feature-extraction"
] | [
"task_categories:text-generation",
"task_categories:question-answering",
"task_categories:feature-extraction",
"language:en",
"license:apache-2.0",
"size_categories:10K<n<100K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"re... | 10K<n<100K | 27,380 | null | 11,578 | 66,776,852 | [] |
# Hermes Function-Calling V1
This dataset is the compilation of structured output and function calling data used in the Hermes 2 Pro series of models.
This repository contains a structured output d... |
dataset | General-Medical-AI/GMAI-VL-5.5M | General-Medical-AI | 2025-11-17 | 2026-04-13 | [
"en",
"zh"
] | other | [
"visual-question-answering",
"image-text-to-text"
] | [
"task_categories:visual-question-answering",
"task_categories:image-text-to-text",
"language:en",
"language:zh",
"license:other",
"size_categories:1M<n<10M",
"format:json",
"modality:image",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"libra... | 1M<n<10M | 27,233 | null | 2,434,902 | 151,593,009,835 | [
"2411.14522"
] |
# GMAI-VL-5.5M Dataset
<p align="center">
<a href="https://github.com/uni-medical/GMAI-VL"><img src="https://img.shields.io/badge/GitHub-Code-blue?logo=github&style=flat-square" alt="GitHub"></a>
<a href="https://huggingface.co/datasets/General-Medical-AI/GMAI-VL-5.5M"><img src="https://img.shields.io/badge... |
dataset | gaia3dai/data-text-v2 | gaia3dai | 2024-02-05 | 2024-02-05 | [] | null | [] | [
"size_categories:1K<n<10K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 1K<n<10K | 26,811 | null | 1,593 | 234,631 | [] | |
dataset | google/xtreme | google | 2022-03-02 | 2024-02-22 | [
"af",
"ar",
"bg",
"bn",
"de",
"el",
"en",
"es",
"et",
"eu",
"fa",
"fi",
"fr",
"he",
"hi",
"hu",
"id",
"it",
"ja",
"jv",
"ka",
"kk",
"ko",
"ml",
"mr",
"ms",
"my",
"nl",
"pt",
"ru",
"sw",
"ta",
"te",
"th",
"tl",
"tr",
"ur",
"vi",
"yo",
"zh"... | apache-2.0 | [
"multiple-choice",
"question-answering",
"token-classification",
"text-classification",
"text-retrieval",
"token-classification"
] | [
"task_categories:multiple-choice",
"task_categories:question-answering",
"task_categories:token-classification",
"task_categories:text-classification",
"task_categories:text-retrieval",
"task_ids:multiple-choice-qa",
"task_ids:extractive-qa",
"task_ids:open-domain-qa",
"task_ids:natural-language-inf... | 1M<n<10M | 27,665 | [
"multilingual",
"translation"
] | 2,768,692 | 363,555,253 | [
"2003.11080"
] |
# Dataset Card for "xtreme"
## 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 | AmazonScience/document-haystack | AmazonScience | 2025-04-28 | 2025-08-04 | [
"en"
] | null | [
"question-answering",
"visual-question-answering",
"document-question-answering"
] | [
"task_categories:question-answering",
"task_categories:visual-question-answering",
"task_categories:document-question-answering",
"language:en",
"modality:text",
"modality:image",
"modality:document",
"arxiv:2507.15882",
"region:us",
"long-context",
"multimodal",
"llm",
"vlm",
"benchmark",... | null | 32,031 | null | null | 18,856,779,506 | [
"2507.15882"
] | # Document Haystack Dataset
This repository contains the dataset for the paper “[Document Haystack: A Long Context Multimodal Image/Document Understanding Vision LLM Benchmark](https://arxiv.org/abs/2507.15882)”.
---
## 📑 Abstract Paper
The proliferation of multimodal Large Language Models has significantly advanc... |
dataset | wannaphong/wikipedia-monthly | wannaphong | 2026-05-04 | 2026-05-04 | [
"ab",
"ace",
"ady",
"af",
"ak",
"als",
"alt",
"am",
"ami",
"an",
"ang",
"ann",
"anp",
"ar",
"arc",
"ary",
"arz",
"as",
"ast",
"atj",
"av",
"avk",
"awa",
"ay",
"az",
"azb",
"ba",
"ban",
"bar",
"bbc",
"bcl",
"bdr",
"be",
"bew",
"bg",
"bh",
"bi",
... | cc-by-sa-4.0 | [
"text-generation"
] | [
"task_categories:text-generation",
"language:ab",
"language:ace",
"language:ady",
"language:af",
"language:ak",
"language:als",
"language:alt",
"language:am",
"language:ami",
"language:an",
"language:ang",
"language:ann",
"language:anp",
"language:ar",
"language:arc",
"language:ary",... | 1M<n<10M | 36,053 | null | 1,591,347 | 822,606,234,299 | [] | # 🚀 Wikipedia Monthly
*Last updated: March 14, 2026, 21:06 UTC*
This repository provides **monthly, multilingual dumps of Wikipedia**, processed and prepared for easy use in NLP projects.
## 📊 Current Statistics
| Metric | Current Export (March 2026) | All Exports (Total) |
|--------|------------|----------|
| **... |
dataset | nguyenvulebinh/asr-alignment | nguyenvulebinh | 2024-01-04 | 2024-01-08 | [
"en"
] | apache-2.0 | [] | [
"language:en",
"license:apache-2.0",
"size_categories:10M<n<100M",
"format:parquet",
"modality:audio",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | 10M<n<100M | 27,093 | null | 10,244,585 | 1,453,381,373,380 | [] |
# Speech Recognition Alignment Dataset
This dataset is a variation of several widely-used ASR datasets, encompassing Librispeech, MuST-C, TED-LIUM, VoxPopuli, Common Voice, and GigaSpeech. The difference is this dataset includes:
- Precise alignment between audio and text.
- Text that has been punctuated and made ca... |
dataset | trentmkelly/police-scanner-audio | trentmkelly | 2025-08-15 | 2025-08-17 | [
"en"
] | cc-by-3.0 | [
"automatic-speech-recognition"
] | [
"task_categories:automatic-speech-recognition",
"language:en",
"license:cc-by-3.0",
"size_categories:1K<n<10K",
"format:audiofolder",
"modality:audio",
"modality:text",
"library:datasets",
"library:mlcroissant",
"region:us"
] | 1K<n<10K | 35,958 | null | 1,500 | 359,940,038,648 | [] | # Police Scanner Audio Dataset
A comprehensive collection of police and emergency services radio communications from multiple US cities, captured from publicly available scanner feeds.
## Dataset Overview
This dataset contains **103,660 audio recordings** totaling **357GB** of police scanner audio from 6 different c... |
dataset | ecastillot/UTDQuake | ecastillot | 2026-01-21 | 2026-05-28 | [] | gpl-3.0 | [] | [
"license:gpl-3.0",
"size_categories:10M<n<100M",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"doi:10.57967/hf/7835",
"region:us",
"seismology",
"earthquake",
"network",
"picks",
"AI"
] | 10M<n<100M | 26,882 | null | 18,349,802 | 6,023,446,147 | [] |
# <span style="background:#E87500; color:white; padding:2px 6px; border-radius:6px;">UTD</span>Quake
University of Texas at Dallas Earthquake Dataset
A global earthquake dataset constructed from high-quality source and receiver metadata, including associated seismic phase picks across diverse station geometries.
<di... |
dataset | chuonghm/OmniRet | chuonghm | 2026-03-01 | 2026-03-04 | [] | null | [] | [
"size_categories:10M<n<100M",
"format:parquet",
"format:optimized-parquet",
"modality:image",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"region:us"
] | 10M<n<100M | 27,392 | null | 17,063,331 | 2,089,264,125,177 | [] | |
dataset | laubonghaudoi/legco-speech | laubonghaudoi | 2026-02-24 | 2026-02-26 | [
"yue"
] | cc0-1.0 | [
"automatic-speech-recognition",
"audio-to-audio",
"audio-classification",
"text-generation"
] | [
"task_categories:automatic-speech-recognition",
"task_categories:audio-to-audio",
"task_categories:audio-classification",
"task_categories:text-generation",
"language:yue",
"license:cc0-1.0",
"size_categories:1M<n<10M",
"format:parquet",
"modality:audio",
"modality:text",
"library:datasets",
"... | 1M<n<10M | 30,111 | null | 9,573,516 | 1,890,810,432,399 | [] |
# 香港立法會會議語音數據集
## Dataset Description
- **License:** [CC0 1.0 Universal](https://creativecommons.org/publicdomain/zero/1.0/)
- **Language:** Cantonese
- **Audio Format:** 16kHz OPUS
- **Total Duration (Raw):** 22,195.55 hours
- **Total Duration (Segmented):** 20,71.21 hours
- **Average Meeting Duration:** 5692.79 se... |
dataset | evalplus/humanevalplus | evalplus | 2024-01-22 | 2024-05-01 | [
"en"
] | apache-2.0 | [
"text2text-generation"
] | [
"language:en",
"license:apache-2.0",
"size_categories:n<1K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us",
"code-generation"
] | n<1K | 26,867 | null | 164 | 14,358,049 | [] | |
dataset | allenai/dolma3_dolmino_mix-100B-1025 | allenai | 2025-10-12 | 2026-01-05 | [
"en"
] | odc-by | [
"text-generation"
] | [
"task_categories:text-generation",
"language:en",
"license:odc-by",
"size_categories:10M<n<100M",
"modality:text",
"arxiv:2512.13961",
"region:us"
] | 10M<n<100M | 34,622 | null | 2,787,460 | 179,570,911,213 | [
"2512.13961"
] |
<img alt="Logo for Dolmino Mix" src="dolmino-mix.png" width="289px" style="margin-left:'auto' margin-right:'auto' display:'block'">
# Dolma 3 Dolmino Mix (100B)
The Dolma 3 Dolmino Mix (100B) is the mixture of high-quality data used for the second stage of training for Olmo 3 7B model.
### Dataset Sources
| So... |
dataset | hotchpotch/wikipedia-ja-20231030 | hotchpotch | 2023-11-07 | 2023-11-13 | [
"ja"
] | cc | [] | [
"language:ja",
"license:cc",
"size_categories:1M<n<10M",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | 1M<n<10M | 26,784 | null | 7,968,185 | 42,780,467,645 | [] |
# Wikipedia Japanese data (20231030)
- Source Date: 2023/10/30
- Source: https://dumps.wikimedia.org/other/cirrussearch/
# License
CC BY-SA 4.0
# Example
WIP |
dataset | agibot-world/AgiBotWorld-Beta | agibot-world | 2025-02-11 | 2025-10-13 | [
"en"
] | null | [
"other",
"robotics"
] | [
"task_categories:other",
"task_categories:robotics",
"language:en",
"size_categories:100M<n<1B",
"format:webdataset",
"modality:text",
"library:datasets",
"library:webdataset",
"library:mlcroissant",
"region:us",
"real-world",
"dual-arm",
"Robotics manipulation"
] | 100M<n<1B | 26,670 | null | null | 48,050,136,152,505 | [] |
<!-- <img src="assets/agibot_world.gif" alt="Image Alt Text" width="70%" style="display: block; margin-left: auto; margin-right: auto;" /> -->
<video controls autoplay loop muted src="https://cdn-uploads.huggingface.co/production/uploads/6763e2cfd3c85f9b6d828f6c/9HkLtnqI_Qx62dNLLsI2I.mp4"></video>
<div align="center... |
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