Datasets

Dataset Card for HuggingFaceFW/finephrase Dataset Summary Synthetic data generated by DataTrove: Model: HuggingFaceTB/SmolLM2-1.7B-Instruct (main) Source dataset: HuggingFaceFW/fineweb-edu, config sample-350BT, split train Generation config: temperature=1.0, top_p=1.0, top_k=50, max_tokens=2048, model_max_context=8192 Speculative decoding: {"method":"suffix","num_speculative_tokens":32} System prompt: None Input column: text Prompt families: faq prompt Rewrite the… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceFW/finephrase.

nuReasoning Paper Website nuReasoning Website nuReasoning Dev-kit nuReasoning is a reasoning-centric multimodal autonomous driving dataset for evaluating and training end-to-end driving systems in long-tail real-world scenarios. Each sample is built around a driving clip with synchronized multi-camera images, LiDAR data, ego state, object annotations, HD map, routing, and frame-level reasoning annotations. Reasoning annotations are organized into three complementary… See the full description on the dataset page: https://huggingface.co/datasets/nureasoning/nuReasoning.

[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 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.

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.

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.

arXiv Complete Corpus A snapshot of arXiv's metadata, version history, submission files and rendered documents. It covers 3,148,796 papers and includes file contents, paths, sizes and SHA-256 digests. Metadata comes from arXiv's OAI-PMH arXivRaw interface; files come from the GCS mirror, S3 source archives and direct PDF fetches. This release holds a PDF for 99.47% of papers and 99.54% of versions reported with a non-zero submission size. It is a one-off snapshot; coverage gaps… See the full description on the dataset page: https://huggingface.co/datasets/secemp9/arxiv-complete.

JAT Dataset Dataset Description The Jack of All Trades (JAT) dataset combines a wide range of individual datasets. It includes expert demonstrations by expert RL agents, image and caption pairs, textual data and more. The JAT dataset is part of the JAT project, which aims to build a multimodal generalist agent. Paper: https://huggingface.co/papers/2402.09844 Usage >>> from datasets import load_dataset >>> dataset = load_dataset("jat-project/jat-dataset"… See the full description on the dataset page: https://huggingface.co/datasets/jat-project/jat-dataset.

The GrandTour Dataset A project brought to you by RSL - ETH Zurich. References • Contributing • Citation References Official dataset webpage: grand-tour.leggedrobotics.com Getting started & examples: github.com/leggedrobotics/grand_tour_dataset Boxi used to collect the data: github.com/leggedrobotics/grand_tour_box Contributing We warmly welcome contributions to improve and expand this project. Whether it's new examples, enhancements, or… See the full description on the dataset page: https://huggingface.co/datasets/leggedrobotics/grand_tour_dataset.

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.

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.

Dataset Card for OpenBookQA Dataset Summary OpenBookQA aims to promote research in advanced question-answering, probing a deeper understanding of both the topic (with salient facts summarized as an open book, also provided with the dataset) and the language it is expressed in. In particular, it contains questions that require multi-step reasoning, use of additional common and commonsense knowledge, and rich text comprehension. OpenBookQA is a new kind of… See the full description on the dataset page: https://huggingface.co/datasets/allenai/openbookqa.

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.

Dataset Card for Oxford Flowers 102 This is a FiftyOne dataset with 8189 samples. 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 = fouh.load_from_hub("Voxel51/OxfordFlowers102") # Launch the App session = fo.launch_app(dataset) Dataset Details… See the full description on the dataset page: https://huggingface.co/datasets/Voxel51/OxfordFlowers102.

🍃 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.

Dataset Card for IFEval Dataset Summary This dataset contains the prompts used in the Instruction-Following Eval (IFEval) benchmark for large language models. It contains around 500 "verifiable instructions" such as "write in more than 400 words" and "mention the keyword of AI at least 3 times" which can be verified by heuristics. To load the dataset, run: from datasets import load_dataset ifeval = load_dataset("google/IFEval") Supported Tasks and… See the full description on the dataset page: https://huggingface.co/datasets/google/IFEval.

HABIT: Human-Aware Behavior and Interaction Training Dataset for Robot Manipulation TL;DR: HABIT is a large-scale robot demonstration dataset for human-present environments, designed to teach robot policies human-aware behaviors. Keywords: Robot Manipulation Dataset, Human-Robot Interaction, Vision-Language-Action Model Overview Data-driven approaches have emerged as a promising direction for training robotic manipulation policies. Recent robot datasets have… See the full description on the dataset page: https://huggingface.co/datasets/configinc/HABIT.

Dataset Card for Food-101 This is a FiftyOne dataset with 35000 samples. Note: This dataset is subset of the full Food101 dataset. The recipe notebook for creating this dataset can be found here 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/Food101.