Datasets

MedDialogue-Audio English Medical Dialogue Corpus for Speech Recognition Research. This repository contains MedDialogue-Audio, an English audio corpus designed for research in Automatic Speech Recognition (ASR) in the healthcare domain. The dataset was published in the proceedings of the 7th SBBD Dataset Showcase Workshop, and is available online at the following link: https://sol.sbc.org.br/index.php/dsw/article/view/37199 Dataset Description MedDialogue-Audio is… See the full description on the dataset page: https://huggingface.co/datasets/aline-gassenn/MedDialog-Audio.

Dataset Card for NuminaMath CoT Dataset Summary Approximately 860k math problems, where each solution is formatted in a Chain of Thought (CoT) manner. The sources of the dataset range from Chinese high school math exercises to US and international mathematics olympiad competition problems. The data were primarily collected from online exam paper PDFs and mathematics discussion forums. The processing steps include (a) OCR from the original PDFs, (b) segmentation… See the full description on the dataset page: https://huggingface.co/datasets/AI-MO/NuminaMath-CoT.

Dataset Card for CIFAR-10 Dataset Summary The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes, with 6000 images per class. There are 50000 training images and 10000 test images. The dataset is divided into five training batches and one test batch, each with 10000 images. The test batch contains exactly 1000 randomly-selected images from each class. The training batches contain the remaining images in random order, but some training batches may contain… See the full description on the dataset page: https://huggingface.co/datasets/uoft-cs/cifar10.

Dataset Card for Boolq Dataset Summary BoolQ is a question answering dataset for yes/no questions containing 15942 examples. These questions are naturally occurring ---they are generated in unprompted and unconstrained settings. Each example is a triplet of (question, passage, answer), with the title of the page as optional additional context. The text-pair classification setup is similar to existing natural language inference tasks. Supported Tasks and… See the full description on the dataset page: https://huggingface.co/datasets/google/boolq.

Dataset Card for "imdb" Dataset Summary Large Movie Review Dataset. This is a dataset for binary sentiment classification containing substantially more data than previous benchmark datasets. We provide a set of 25,000 highly polar movie reviews for training, and 25,000 for testing. There is additional unlabeled data for use as well. Supported Tasks and Leaderboards More Information Needed Languages More Information Needed Dataset Structure… See the full description on the dataset page: https://huggingface.co/datasets/stanfordnlp/imdb.

[ICLR 2026] OmniWorld: A Multi-Domain and Multi-Modal Dataset for 4D World Modeling         🎉NEWS [2026.3.21] 🔥 OmniWorld-Game with Metric Scale is now released! Check out our latest model Pi3X (an enhanced version of Pi3), which leverages this data to achieve better performance! [2026.1.26] 🎉 OmniWorld was accepted by ICLR 2026! [2026.1.7] Update OmniWorld-Game, release RH20T-Robot, RH20T-Human, Ego-Exo4D, EgoDex, Epic-Kitchens. [2025.11.11] The OmniWorld is… See the full description on the dataset page: https://huggingface.co/datasets/InternRobotics/OmniWorld.

Dataset Card for truthful_qa Dataset Summary TruthfulQA is a benchmark to measure whether a language model is truthful in generating answers to questions. The benchmark comprises 817 questions that span 38 categories, including health, law, finance and politics. Questions are crafted so that some humans would answer falsely due to a false belief or misconception. To perform well, models must avoid generating false answers learned from imitating human texts.… See the full description on the dataset page: https://huggingface.co/datasets/truthfulqa/truthful_qa.

Dataset Card for MedMCQA Dataset Summary MedMCQA is a large-scale, Multiple-Choice Question Answering (MCQA) dataset designed to address real-world medical entrance exam questions. MedMCQA has more than 194k high-quality AIIMS & NEET PG entrance exam MCQs covering 2.4k healthcare topics and 21 medical subjects are collected with an average token length of 12.77 and high topical diversity. Each sample contains a question, correct answer(s), and other options which require… See the full description on the dataset page: https://huggingface.co/datasets/openlifescienceai/medmcqa.

Leopard-Instruct Paper | Github | Models-LLaVA | Models-Idefics2 Summaries Leopard-Instruct is a large instruction-tuning dataset, comprising 925K instances, with 739K specifically designed for text-rich, multiimage scenarios. It's been used to train Leopard-LLaVA [checkpoint] and Leopard-Idefics2 [checkpoint]. Loading dataset to load the dataset without automatically downloading and process the images (Please run the following codes with datasets==2.18.0)… See the full description on the dataset page: https://huggingface.co/datasets/wyu1/Leopard-Instruct.

🥞 The Stack v3 What is it? What is being released How to download and use it Dataset statistics Dataset structure Dataset creation Considerations for using the data Additional information What is it? The Stack v3 is the largest, most up-to-date open dataset of source code, crawled directly from GitHub and built to pre-train code LLMs with full-repository context. It is the successor to The Stack v2 and, like its predecessor, is released to make the training… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceCode/stack-v3-train.

ngii-map-full-light Light point/line extract from NGII 1/1000 topographic data for Korea. Not for shipping into GitHub — use this Hugging Face dataset instead. CRS Korea_2000_Central_Belt_2010 projected meters [x, y] Layers (per region under by_region/<region>/) Layer Description C023 poles (전주/통신주) C022 lights (가로등·보안등) A002 roads (도로 중심선) B001_tiny building footprints <25 m² as centroids B002 lines (구분/재질 라인) Also:… See the full description on the dataset page: https://huggingface.co/datasets/SKPark1/ngii-map-full-light.

Dataset Card for GUI Odyssey News⭐️ A new and improved version of the GUIOdyssey dataset has been released! 🎉🎉 👉 Please use the latest version and refer to the updated README for the most up-to-date information. We highly recommend using the new version for all training and evaluation! Repository: https://github.com/OpenGVLab/GUI-Odyssey Latest Version of Dataset: hflqf88888/GUIOdyssey Paper: https://arxiv.org/pdf/2406.08451 Introduction GUI Odyssey is… See the full description on the dataset page: https://huggingface.co/datasets/OpenGVLab/GUI-Odyssey.

Common Corpus Full paper - ICLR 2026 oral Common Corpus is the largest open and permissible licensed text dataset, comprising 2.27 trillion tokens (2,267,302,720,836 tokens). It is a diverse dataset, consisting of books, newspapers, scientific articles, government and legal documents, code, and more. Common Corpus has been created by Pleias in association with several partners. Common Corpus differs from existing open datasets in that it is: Truly Open: contains only data that… See the full description on the dataset page: https://huggingface.co/datasets/PleIAs/common_corpus.

Dataset Card for "anli" Dataset Summary The Adversarial Natural Language Inference (ANLI) is a new large-scale NLI benchmark dataset, The dataset is collected via an iterative, adversarial human-and-model-in-the-loop procedure. ANLI is much more difficult than its predecessors including SNLI and MNLI. It contains three rounds. Each round has train/dev/test splits. Supported Tasks and Leaderboards More Information Needed Languages English… See the full description on the dataset page: https://huggingface.co/datasets/facebook/anli.

Open Australian Legal Corpus ‍⚖️ The Open Australian Legal Corpus by Isaacus, a foundational legal AI research company, is the first and only multijurisdictional open corpus of Australian legislative and judicial documents. Comprised of 229,122 texts totalling over 60 million lines and 1.4 billion tokens, the Corpus includes every in force statute and regulation in the Commonwealth, New South Wales, Queensland, Western Australia, South Australia, Tasmania and Norfolk Island, in… See the full description on the dataset page: https://huggingface.co/datasets/isaacus/open-australian-legal-corpus.

SWE-rebench-V2-PRs Dataset Summary SWE-rebench-V2-PRs is a large-scale dataset of real-world GitHub pull requests collected across multiple programming languages, intended for training and evaluating code-generation and software-engineering agents. The dataset contains 126,300 samples covering Go, Python, JavaScript, TypeScript, Rust, Java, C, C++, Julia, Elixir, Kotlin, PHP, Scala, Clojure, Dart, OCaml, and other languages. For log parser functions, base Dockerfiles, and… See the full description on the dataset page: https://huggingface.co/datasets/nebius/SWE-rebench-V2-PRs.

NB: HPLT2.0 is now superseded by a newer release: HPLT3.0 We recommed switching to v3.0, unless you have a compelling reason to stay on 2.0. This is a large-scale collection of web-crawled documents in 191 world languages, produced by the HPLT project. The source of the data is mostly Internet Archive with some additions from Common Crawl. For a detailed description of the dataset, please refer to our website and our pre-print. The Cleaned variant of HPLT Datasets v2.0 This is… See the full description on the dataset page: https://huggingface.co/datasets/HPLT/HPLT2.0_cleaned.

🍃 MINT-1T:Scaling Open-Source Multimodal Data by 10x: A Multimodal Dataset with One Trillion Tokens 🍃 MINT-1T is an open-source Multimodal INTerleaved dataset with 1 trillion text tokens and 3.4 billion images, a 10x scale-up from existing open-source datasets. Additionally, we include previously untapped sources such as PDFs and ArXiv papers. 🍃 MINT-1T is designed to facilitate research in multimodal pretraining. 🍃 MINT-1T is created by a team from the University of Washington in… See the full description on the dataset page: https://huggingface.co/datasets/mlfoundations/MINT-1T-PDF-CC-2024-18.

MADLAD-400 Dataset and Introduction MADLAD-400 (Multilingual Audited Dataset: Low-resource And Document-level) is a document-level multilingual dataset based on Common Crawl, covering 419 languages in total. This uses all snapshots of CommonCrawl available as of August 1, 2022. The primary advantage of this dataset over similar datasets is that it is more multilingual (419 languages), it is audited and more highly filtered, and it is document-level. The main disadvantage… See the full description on the dataset page: https://huggingface.co/datasets/allenai/MADLAD-400.