Model Hub

Fineweb-Edu-Fortified The composition of fineweb-edu-fortified, produced by automatically clustering a 500k row sample in Airtrain What is it? Fineweb-Edu-Fortified is a dataset derived from Fineweb-Edu by applying exact-match deduplication across the whole dataset and producing an embedding for each row. The number of times the text from each row appears is also included as a count column. The embeddings were produced using TaylorAI/bge-micro Fineweb and… See the full description on the dataset page: https://huggingface.co/datasets/airtrain-ai/fineweb-edu-fortified.

Dataset Card for "qasc" Dataset Summary QASC is a question-answering dataset with a focus on sentence composition. It consists of 9,980 8-way multiple-choice questions about grade school science (8,134 train, 926 dev, 920 test), and comes with a corpus of 17M sentences. Supported Tasks and Leaderboards More Information Needed Languages More Information Needed Dataset Structure Data Instances default Size of… See the full description on the dataset page: https://huggingface.co/datasets/allenai/qasc.

⚠️ WARNING: This dataset is intended ONLY for reproducing Olmo 3 7B ⚠️ For all other training use cases, including training from scratch, please utilize our primary dolma 3 data mix: https://huggingface.co/datasets/allenai/dolma3_mix-6T. Note: Some olmOCR science PDFs in the current dataset have been redacted following the training of Olmo 3 7B. These texts are indicated with [REMOVED] in the text field. This will affect reproducibility of Olmo 3 7B. For this reason, please use our… See the full description on the dataset page: https://huggingface.co/datasets/allenai/dolma3_mix-6T-1025-7B.

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.

MNBVC: Massive Never-ending BT Vast Chinese corpus

21cm Hydrogen Line Sky Survey Continuous 1-second-cadence power spectra of the 21cm neutral hydrogen (HI) line at 1420.405 MHz, recorded 24/7 from a fixed omnidirectional observer on the US East Coast. What this is A radio telescope pointed at the whole sky, recording one spectrum per second, indefinitely. The Earth's rotation scans the beam across the galactic plane daily, producing a natural drift scan. Every row is a self-timestamped power spectrum spanning 2… See the full description on the dataset page: https://huggingface.co/datasets/phanerozoic/hi-21cm-survey.

Arctic Shift Reddit Archive Every Reddit comment and submission since 2005, organized as monthly Parquet shards What is it? The full Reddit archive from Arctic Shift, converted to Parquet and hosted here for easy access. Covers every public subreddit from 2005-12 through 2026-02. Right now the archive has 15.7B items (12.9B comments, 2.8B submissions) in 1.3 TB of compressed Parquet. Comments and submissions are stored as separate datasets, split into monthly… See the full description on the dataset page: https://huggingface.co/datasets/open-index/arctic.

Dataset Card for "uspto_full" More Information needed

Ultra-FineWeb 📜 Technical Report | 📦 UltraData Collection | 🌐 UltraData | 🤗 MiniCPM4 Series | 🤗 MiniCPM5 Series English | 中文 📚 Introduction Ultra-FineWeb is a large-scale, high-quality, and efficiently-filtered dataset. We use the proposed efficient verification-based high-quality filtering pipeline to the FineWeb and Chinese FineWeb datasets (source data from Chinese FineWeb-edu-v2, which includes IndustryCorpus2, MiChao, WuDao, SkyPile… See the full description on the dataset page: https://huggingface.co/datasets/openbmb/Ultra-FineWeb.

Forecast News Deduplicated daily news corpus used by forecast-sim and future-sim. Snapshot 31,696,086 articles 3,401 daily partitions Coverage: 2016-08-26 through 2026-06-30 Snapshot published: 2026-07-13 Stored data size: approximately 168.8 GB The Parquet files are the canonical complete representation. The repository also contains daily JSONL files where available and compact headline JSON files used by article-browsing workflows. Layout Files… See the full description on the dataset page: https://huggingface.co/datasets/shash42/forecast-news.

MICCAI FLARE25 Task 4: Foundation Models for 3D CT and MRI Scans (Homepage) This is the official dataset for CT image foundation model development. We provide 10,000+ CT scans for model pretraining. Downstream tasks include: Abdominal disease classification Abdominal lesion segmentation Abdominal organ segmentation Lung lesion segmentation Dataset Dataset Name Task Metric Source License Abdominal Disease Classification multi-label… See the full description on the dataset page: https://huggingface.co/datasets/FLARE-MedFM/FLARE-Task4-CT-FM.

Audio-FLAN Dataset (Paper) (the FULL audio files and jsonl files are still updating) An Instruction-Tuning Dataset for Unified Audio Understanding and Generation Across Speech, Music, and Sound. 1. Dataset Structure The Audio-FLAN-Dataset has the following directory structure: Audio-FLAN-Dataset/ ├── audio_files/ │ ├── audio/ │ │ └── 177_TAU_Urban_Acoustic_Scenes_2022/ │ │ └── 179_Audioset_for_Audio_Inpainting/ │ │ └── ... │ ├── music/ │ │ └──… See the full description on the dataset page: https://huggingface.co/datasets/HKUSTAudio/Audio-FLAN-Dataset.

LOTSA Data The Large-scale Open Time Series Archive (LOTSA) is a collection of open time series datasets for time series forecasting. It was collected for the purpose of pre-training Large Time Series Models. See the paper and codebase for more information. Citation If you're using LOTSA data in your research or applications, please cite it using this BibTeX: BibTeX: @article{woo2024unified, title={Unified Training of Universal Time Series Forecasting Transformers}… See the full description on the dataset page: https://huggingface.co/datasets/Salesforce/lotsa_data.

OpenMathInstruct-2 OpenMathInstruct-2 is a math instruction tuning dataset with 14M problem-solution pairs generated using the Llama3.1-405B-Instruct model. The training set problems of GSM8K and MATH are used for constructing the dataset in the following ways: Solution augmentation: Generating chain-of-thought solutions for training set problems in GSM8K and MATH. Problem-Solution augmentation: Generating new problems, followed by solutions for these new problems.… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/OpenMathInstruct-2.

The CT-RATE Team organizes the VLM3D Challenge VLM3D 2026 (2nd Edition) → Challenge Finals at MICCAI 2026 VLM3D 2025 (1st Edition) → Challenge Finals at MICCAI 2025 • Workshop at ICCV 2025 The CT-RATE Team is developing the MR-RATE Dataset A large-scale brain MRI dataset with paired radiology reports for training 3D vision-language models. GitHub   |   Dataset   |   Metadata Dashboard Generalist Foundation Models from a Multimodal Dataset for 3D Computed Tomography… See the full description on the dataset page: https://huggingface.co/datasets/ibrahimhamamci/CT-RATE.

🥂 FineWeb2 A sparkling update with 1000s of languages What is it? This is the second iteration of the popular 🍷 FineWeb dataset, bringing high quality pretraining data to over 1000 🗣️ languages. The 🥂 FineWeb2 dataset is fully reproducible, available under the permissive ODC-By 1.0 license and extensively validated through hundreds of ablation experiments. In particular, on the set of 9 diverse languages we used to guide our processing decisions, 🥂 FineWeb2… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceFW/fineweb-2.

PandaBench PandaBench is a comprehensive benchmark for evaluating Large Language Model (LLM) safety, focusing on jailbreak attacks, defense mechanisms, and evaluation methodologies. The PandaGuard framework architecture illustrating the end-to-end pipeline for LLM safety evaluation. The system connects three key components: Attackers, Defenders, and Judges. Dataset Description This repository contains the benchmark results from extensive evaluations of various LLMs… See the full description on the dataset page: https://huggingface.co/datasets/Beijing-AISI/panda-bench.