Datasets

NU-TONIC raw SFT Full Satellite imagery and aligned land-cover outputs packaged as image–text rows for fine-tuning in SFT format. JSONL user prompts name the modality (satellite imagery vs. overhead context) where it matters. Provenance Locations: GeoGuessr-style POIs (source: stochastic/random_streetview_images_pano_v0.0.2) Optical: Sentinel-2 multispectral optical COGs from a public STAC catalog, blue/green/red or visual preview, percentile-stretched to uint8. Labels:… See the full description on the dataset page: https://huggingface.co/datasets/PEBE691445/sat-image-boundingbox-sft-full.

MegaWika is a multi- and crosslingual text dataset containing 30 million Wikipedia passages with their scraped and cleaned web citations. The passages span 50 Wikipedias in 50 languages, and the articles in which the passages were originally embedded are included for convenience. Where a Wikipedia passage is in a non-English language, an automated English translation is provided. Furthermore, nearly 130 million English question/answer pairs were extracted from the passages, and FrameNet events occurring in the passages are detected using the LOME FrameNet parser.

AnimalCLEF26 Kaggle Competition Dataset This is a HuggingFace mirror of the official AnimalCLEF26 competition dataset. Images have been repackaged into one zipfile per split, which include additional metadata that makes the dataset easier to use with HuggingFace. Otherwise, no files have been changed. Loading from datasets import load_dataset dataset = load_dataset("BVRA/animal-clef-2026") print(dataset["train"][0]["image"]) Documentation For… See the full description on the dataset page: https://huggingface.co/datasets/BVRA/animal-clef-2026.

⚠️ 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.

🇮🇩 Bahasa Indonesia · 🇬🇧 English · 🇯🇵 日本語 · 🇨🇳 中文 · 🇰🇷 한국어 Bahasa Indonesia Pustaka adalah korpus teks terbuka berskala besar yang dikembangkan oleh MEA Ecosystem, dimulai dengan fokus penuh pada Bahasa Indonesia. Corpus ini disusun dari beberapa sumber publik berkualitas — web crawl yang telah difilter, ensiklopedia, berita, forum, hingga lexicon bahasa gaul — lalu diproses ulang melalui pipeline pembersihan (deteksi bahasa, filter panjang dokumen… See the full description on the dataset page: https://huggingface.co/datasets/meaecosystem/pustaka.

MedThinkVQA MedThinkVQA is an expert-annotated benchmark for multi-image diagnostic reasoning in radiology. Unlike prior medical VQA benchmarks that typically contain at most one image per case, MedThinkVQA requires models to extract evidence from each image, integrate cross-view information, and perform differential-diagnosis reasoning. Links GitHub: https://github.com/benluwang/MedThinkVQA Leaderboard: https://benluwang.github.io/MedThinkVQA/ Submission Guide:… See the full description on the dataset page: https://huggingface.co/datasets/bio-nlp-umass/MedThinkVQA.

The Belebele Benchmark for Massively Multilingual NLU Evaluation Belebele is a multiple-choice machine reading comprehension (MRC) dataset spanning 122 language variants. This dataset enables the evaluation of mono- and multi-lingual models in high-, medium-, and low-resource languages. Each question has four multiple-choice answers and is linked to a short passage from the FLORES-200 dataset. The human annotation procedure was carefully curated to create questions that discriminate… See the full description on the dataset page: https://huggingface.co/datasets/facebook/belebele.

MacroLens A benchmarking corpus for contextual financial reasoning under macroeconomic scenarios across 4,416 U.S. small- and micro-cap equities (2021-01-04 — 2026-03-31). MacroLens unifies seven tasks over a single point-in-time panel: contextual time-series forecasting, public valuation, financial-statement generation, scenario-conditioned return forecasting, private-company valuation, generator evaluation from natural-language descriptions, and real-estate valuation. Task… See the full description on the dataset page: https://huggingface.co/datasets/DeepAuto-AI/MacroLens.

Dataset Card for SNLI Dataset Summary The SNLI corpus (version 1.0) is a collection of 570k human-written English sentence pairs manually labeled for balanced classification with the labels entailment, contradiction, and neutral, supporting the task of natural language inference (NLI), also known as recognizing textual entailment (RTE). Supported Tasks and Leaderboards Natural Language Inference (NLI), also known as Recognizing Textual Entailment (RTE), is the… See the full description on the dataset page: https://huggingface.co/datasets/stanfordnlp/snli.

Dataset Card for MVTec AD This dataset originates from MVTec but is provided in a different format. You can easily load it using FiftyOne The total number of samples remains the same as the original: 5,354. Installation If you haven't already, install FiftyOne: pip install -U fiftyone Usage import fiftyone as fo import fiftyone.utils.huggingface as fouh # Load the dataset # Note: other available arguments include 'max_samples', etc dataset =… See the full description on the dataset page: https://huggingface.co/datasets/Voxel51/mvtec-ad.

Dataset Card for DASP Dataset Description The DASP (Distributed Analysis of Sentinel-2 Pixels) dataset consists of cloud-free satellite images captured by Sentinel-2 satellites. Each image represents the most recent, non-partial, and cloudless capture from over 30 million Sentinel-2 images in every band. The dataset provides a near-complete cloudless view of Earth's surface, ideal for various geospatial applications. Images were converted from JPEG2000 to JPEG-XL to… See the full description on the dataset page: https://huggingface.co/datasets/RichardErkhov/DASP.

This is a partial copy of CoVoST2 dataset. The main difference is that the audio data is included in the dataset, which makes usage easier and allows browsing the samples using HF Dataset Viewer. The limitation of this method is that all audio samples of the EN_XX subsets are duplicated, as such the size of the dataset is larger. As such, not all the data is included: Only the validation and test subsets are available. From the XX_EN subsets, only fr, es, and zh-CN are included.

Introduction TL;DR: DreamDojo is a generalist robot world model pretrained on 44k hours of human egocentric data, showing unprecedented generalization to diverse objects and environments. Project page: https://dreamdojo-world.github.io/ Paper: https://arxiv.org/abs/2602.06949 Code: https://github.com/NVIDIA/DreamDojo How to Use Check out https://github.com/NVIDIA/DreamDojo Citation @article{gao2026dreamdojo, title={DreamDojo: A Generalist Robot… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/PhysicalAI-Robotics-GR00T-Teleop-GR1.

Dataset Card for Voxpopuli Dataset Summary VoxPopuli is a large-scale multilingual speech corpus for representation learning, semi-supervised learning and interpretation. The raw data is collected from 2009-2020 European Parliament event recordings. We acknowledge the European Parliament for creating and sharing these materials. This implementation contains transcribed speech data for 18 languages. It also contains 29 hours of transcribed speech data of non-native… See the full description on the dataset page: https://huggingface.co/datasets/facebook/voxpopuli.

Android in the Wild (AITW) This is a mirror of Google's Android in the Wild (AITW) dataset, re-hosted on Hugging Face for easier community access. Original Source Paper: Android in the Wild: A Large-Scale Dataset for Android Device Control Original Repository: google-research/google-research/tree/master/android_in_the_wild Dataset Description Android in the Wild (AITW) is a large-scale dataset for Android device control. It contains human demonstrations of… See the full description on the dataset page: https://huggingface.co/datasets/leosltl/Android-in-the-Wild.

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.

This dataset is uploaded in two places: here and additionally here as 'Aya Collection Language Split.' These datasets are identical in content but differ in structure of upload. This dataset is structured by folders split according to dataset name. The version here instead divides the Aya collection into folders split by language. We recommend you use the language split version if you are only interested in downloading data for a single or smaller set of languages, and this version if you… See the full description on the dataset page: https://huggingface.co/datasets/CohereLabs/aya_collection.

Dataset Card for MNIST Dataset Summary The MNIST dataset consists of 70,000 28x28 black-and-white images of handwritten digits extracted from two NIST databases. There are 60,000 images in the training dataset and 10,000 images in the validation dataset, one class per digit so a total of 10 classes, with 7,000 images (6,000 train images and 1,000 test images) per class. Half of the image were drawn by Census Bureau employees and the other half by high school students… See the full description on the dataset page: https://huggingface.co/datasets/ylecun/mnist.

Scientific Summaries 22 million LLM-generated structured summaries of scientific papers, enriched with OpenAlex scholarly metadata. Each paper has an 18-field structured summary covering methodology, key results, claims, limitations, and more. This public dataset includes full paper text for ~5.3 million papers where open-access status has been confirmed -- either through OpenAlex metadata or because the paper originates from a permissively licensed source such as the arXiv preprint… See the full description on the dataset page: https://huggingface.co/datasets/laion/Scientific-Summaries.