Datasets
Training datasets with quantum-safe provenance
MultiEURLEX comprises 65k EU laws in 23 official EU languages (some low-ish resource). Each EU law has been annotated with EUROVOC concepts (labels) by the Publication Office of EU. As with the English EURLEX, the goal is to predict the relevant EUROVOC concepts (labels); this is multi-label classification task (given the text, predict multiple labels).
Papas Nativas Peruanas — 83 Variedades (UNSAAC 2024) Colección de imágenes de 83 variedades de papas nativas peruanas para clasificación visual mediante modelos de visión computacional. Descripción del dataset Dataset recopilado de forma colaborativa por estudiantes de Ingeniería Informática de la Universidad Nacional de San Antonio Abad del Cusco (UNSAAC) en el curso de Aprendizaje Automático (2024). Las imágenes fueron capturadas en condiciones variadas (distintos… See the full description on the dataset page: https://huggingface.co/datasets/ayayon/papas-nativas-peru-83-variedades.
Wikipedia dataset containing cleaned articles of all languages. The datasets are built from the Wikipedia dump (https://dumps.wikimedia.org/) with one split per language. Each example contains the content of one full Wikipedia article with cleaning to strip markdown and unwanted sections (references, etc.).
YODAS2-Sidon Overview This dataset is a cleansed version of YODAS-2 with Sidon speech restoration mode for Speech Synthesis and Spoken Language Modeling. YODAS-2 is a massive, multilingual YouTube-derived dataset. We have applied the Sidon restoration model to remove background noise and enhance audio quality, making it suitable for high-quality generation tasks. We resampled original sidon output to 24kHz due to a storage constraints. The dataset is provided in… See the full description on the dataset page: https://huggingface.co/datasets/sarulab-speech/yodas2_sidon.
MMMU (A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI) 🌐 Homepage | 🏆 Leaderboard | 🤗 Dataset | 🤗 Paper | 📖 arXiv | GitHub 🔔News 🛠️[2026-04-21]: Fixed option issue in test_Psychology_15. ‼️[2026-02-12]: We have released the answers for the test set! You can now evaluate your models on the test set locally! 🎉 🛠️[2024-05-30]: Fixed duplicate option issues in Materials dataset items (validation_Materials_25;… See the full description on the dataset page: https://huggingface.co/datasets/MMMU/MMMU.
Dataset Card for GPQA GPQA is a multiple-choice, Q&A dataset of very hard questions written and validated by experts in biology, physics, and chemistry. When attempting questions out of their own domain (e.g., a physicist answers a chemistry question), these experts get only 34% accuracy, despite spending >30m with full access to Google. We request that you do not reveal examples from this dataset in plain text or images online, to reduce the risk of leakage into foundation model… See the full description on the dataset page: https://huggingface.co/datasets/Idavidrein/gpqa.
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.
MedQA-Darija-MultiLingual The largest open trilingual medical Q&A dataset with directly-playable speech audio for English, French, and Moroccan Darija. A research dataset for the BRAIN HEALTH initiative, designed for multilingual medical NLP, low-resource speech recognition, healthcare chatbots, and clinical education tools targeting Morocco and the broader Maghreb region. Dataset is currently in scientific validation phase. After programmatic validation (Stage 1 LOF outlier… See the full description on the dataset page: https://huggingface.co/datasets/Williamsanderson/MedQA-Darija-MultiLingual.
GroundCUA: Grounding Computer Use Agents on Human Demonstrations 🌐 Website | 📑 Paper | 🤗 Dataset | 🤖 Models GroundCUA Dataset GroundCUA is a large and diverse dataset of real UI screenshots paired with structured annotations for building multimodal computer use agents. It covers 87 software platforms across productivity tools, browsers, creative tools, communication apps, development environments, and system utilities. GroundCUA is designed for research on GUI… See the full description on the dataset page: https://huggingface.co/datasets/ServiceNow/GroundCUA.
GUI-360°: A Comprehensive Dataset And Benchmark For Computer-Using Agents Paper | Code GUI-360° is a large-scale, comprehensive dataset and benchmark suite designed to advance Computer-Using Agents (CUAs). 🎯 Key Features 🔢 1.2M+ executed action steps across thousands of trajectories 💼 Popular Windows office applications (Word, Excel, PowerPoint) 📸 Full-resolution screenshots with accessibility metadata 🎨 Multi-modal trajectories with reasoning traces ✅ Both… See the full description on the dataset page: https://huggingface.co/datasets/vyokky/GUI-360.
⚠️ Important: If you have already submitted an access request but have not completed the required DocuSign agreement, your request will remain pending. Please complete signing and we will grant access once verified. Interactive Intelligence from Human Xperience Xperience-10M Dataset Summary Xperience-10M is a large-scale egocentric multimodal dataset of human experience for embodied AI, robotics, world models, and spatial… See the full description on the dataset page: https://huggingface.co/datasets/ropedia-ai/xperience-10m.
Dataset Card for "ag_news" Dataset Summary AG is a collection of more than 1 million news articles. News articles have been gathered from more than 2000 news sources by ComeToMyHead in more than 1 year of activity. ComeToMyHead is an academic news search engine which has been running since July, 2004. The dataset is provided by the academic comunity for research purposes in data mining (clustering, classification, etc), information retrieval (ranking, search, etc), xml… See the full description on the dataset page: https://huggingface.co/datasets/fancyzhx/ag_news.
TOFU: Task of Fictitious Unlearning 🍢 The TOFU dataset serves as a benchmark for evaluating unlearning performance of large language models on realistic tasks. The dataset comprises question-answer pairs based on autobiographies of 200 different authors that do not exist and are completely fictitiously generated by the GPT-4 model. The goal of the task is to unlearn a fine-tuned model on various fractions of the forget set. Quick Links Website: The landing page for TOFU… See the full description on the dataset page: https://huggingface.co/datasets/locuslab/TOFU.
🍃 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-2023-50.
Dataset containing synthetically generated (by GPT-3.5 and GPT-4) short stories that only use a small vocabulary. Described in the following paper: https://arxiv.org/abs/2305.07759. The models referred to in the paper were trained on TinyStories-train.txt (the file tinystories-valid.txt can be used for validation loss). These models can be found on Huggingface, at roneneldan/TinyStories-1M/3M/8M/28M/33M/1Layer-21M. Additional resources: tinystories_all_data.tar.gz - contains a superset of… See the full description on the dataset page: https://huggingface.co/datasets/roneneldan/TinyStories.