Datasets

The images are resized and then encoded with the DC-AE f32 autoencoder. The resizing is done with a bucketmanager with base resolution 512x512, minimum side length 256, maximum side length 1024, all sides are divisible by 32 ofcourse as they needed to be encoded by the DCAEf32 encoder. The captions are generated with moondream2, encoded with siglip and bert. (Bert embeddings variance is very high, so use a norm layer). The text embeddings are padded to 64 tokens, but i have provided the… See the full description on the dataset page: https://huggingface.co/datasets/SwayStar123/preprocessed_commoncatalog-cc-by_DCAE.

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.

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.

Quick Start · Overview · Tasks · Comparison · Dataset Stats · Data Sources · Pipeline · Schema · License · Citation This is the official MathNet v0. A larger version v1 will be uploaded soon (more countires, problems and richer metadata). Schema is stable but field values may be revised in v1. Quick start from datasets import load_dataset # Default: all problems ds = load_dataset("ShadenA/MathNet", split="train") # Or a specific country / competition-body config… See the full description on the dataset page: https://huggingface.co/datasets/ShadenA/MathNet.

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.

To apply eyeshadow without a brush, should I use a cotton swab or a toothpick? Questions requiring this kind of physical commonsense pose a challenge to state-of-the-art natural language understanding systems. The PIQA dataset introduces the task of physical commonsense reasoning and a corresponding benchmark dataset Physical Interaction: Question Answering or PIQA. Physical commonsense knowledge is a major challenge on the road to true AI-completeness, including robots that interact with the world and understand natural language. PIQA focuses on everyday situations with a preference for atypical solutions. The dataset is inspired by instructables.com, which provides users with instructions on how to build, craft, bake, or manipulate objects using everyday materials. The underlying task is formualted as multiple choice question answering: given a question `q` and two possible solutions `s1`, `s2`, a model or a human must choose the most appropriate solution, of which exactly one is correct. The dataset is further cleaned of basic artifacts using the AFLite algorithm which is an improvement of adversarial filtering. The dataset contains 16,000 examples for training, 2,000 for development and 3,000 for testing.

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.

MegaPairs-Standard (Standardized Version) Dataset Summary This is a standardized, high-efficiency version of the JUNJIE99/MegaPairs dataset. Why use this version? The original dataset is distributed as a massive Tar archive containing millions of images, accompanied by a separate JSONL annotation file. The Problem: Using the original format requires extracting terabytes of small files (which can exhaust disk inodes) or writing complex logic to read from archives. It… See the full description on the dataset page: https://huggingface.co/datasets/86Cao/MegaPairs-Standard.

C-Eval is a comprehensive Chinese evaluation suite for foundation models. It consists of 13948 multi-choice questions spanning 52 diverse disciplines and four difficulty levels. Please visit our website and GitHub or check our paper for more details. Each subject consists of three splits: dev, val, and test. The dev set per subject consists of five exemplars with explanations for few-shot evaluation. The val set is intended to be used for hyperparameter tuning. And the test set is for model… See the full description on the dataset page: https://huggingface.co/datasets/ceval/ceval-exam.

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.

Multilingual Speech Commands Dataset (15 Languages, Augmented) This dataset contains augmented speech command samples in 15 languages, derived from multiple public datasets. Only commands that overlap with the Google Speech Commands (GSC) vocabulary are included, making the dataset suitable for multilingual keyword spotting tasks aligned with GSC-style classification. Audio samples have been augmented using standard audio techniques to improve model robustness (e.g., time-shifting… See the full description on the dataset page: https://huggingface.co/datasets/artur-muratov/multilingual-speech-commands-15lang.

Dataset Card for CML-TTS Dataset Summary CML-TTS is a recursive acronym for CML-Multi-Lingual-TTS, a Text-to-Speech (TTS) dataset developed at the Center of Excellence in Artificial Intelligence (CEIA) of the Federal University of Goias (UFG). CML-TTS is a dataset comprising audiobooks sourced from the public domain books of Project Gutenberg, read by volunteers from the LibriVox project. The dataset includes recordings in Dutch, German, French, Italian, Polish… See the full description on the dataset page: https://huggingface.co/datasets/ylacombe/cml-tts.

UTDQuake: 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. Installation (utdquake) pip install utdquake Documentation Full documentation for UTDQuake is available here: You will see: QuickStart guide to get you up and running Detailed API reference Tutorials… See the full description on the dataset page: https://huggingface.co/datasets/ecastillot/UTDQuake.

Dataset Card for "hotpot_qa" Dataset Summary HotpotQA is a new dataset with 113k Wikipedia-based question-answer pairs with four key features: (1) the questions require finding and reasoning over multiple supporting documents to answer; (2) the questions are diverse and not constrained to any pre-existing knowledge bases or knowledge schemas; (3) we provide sentence-level supporting facts required for reasoning, allowingQA systems to reason… See the full description on the dataset page: https://huggingface.co/datasets/hotpotqa/hotpot_qa.