datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
catalog
Mesh-LLM Catalog
This dataset is the Hugging Face-backed catalog for Mesh-LLM.
The runtime catalog entries live under entries/**/*.json. The Dataset Viewer
uses catalog_rows.jsonl, a flat generated table with one row per model variant.
The catalog deliberately excludes raw blob URLs. Entries should resolve to
Hugging Face repositories and canonical Mesh refs.
xr-motion-dataset-catalogue
XR Motion Dataset Catalogue
Overview
The XR Motion Dataset Catalogue, accompanying our paper "Navigating the Kinematic Maze: A Comprehensive Guide to XR Motion Dataset Standards," standardizes and simplifies access to Extended Reality (XR) motion datasets. The catalogue represents our initiative to streamline the usage of kinematic data in XR research by aligning various datasets to a consistent format and structure.
Dataset Specifications
All datasets in this… See the full description on the dataset page: https://huggingface.co/datasets/cschell/xr-motion-dataset-catalogue.catholic-resources
Vietnamese Catholic resources by v-bible
Data Structure
calendar: Generated Liturgical calendars using
v-bible/js-sdk.
misc/proper-names.json: Name translation from
ktcgkpv.org, generated by
v-bible/bible-scraper.
liturgical: Liturgical data from
The Lectionary for Mass (1998/2002 USA Edition),
compiled by Felix Just, S.J., Ph.D., and generated by
v-bible/bible-scraper.
books/bible: Generated Bible markdown data.
books/catechism-books: Official catechism… See the full description on the dataset page: https://huggingface.co/datasets/v-bible/catholic-resources.montok
MonTok: A Suite of Monolingual Tokenizers
This is a set of monolingual tokenizers for 98 languages. For each language, there are Unigram, BPE, and SuperBPE tokenizers, ranging in vocabulary size from around 6k to over 200k.
Training Details
Training Data
All tokenizers are trained on samples of the data used to the train the Goldfish language models.
The tokenizers were either trained on scaled or unscaled data. This refers to whether the models are trained on… See the full description on the dataset page: https://huggingface.co/datasets/catherinearnett/montok.cats-imagevbvr-latent-cache-832x832x33f-t2v-only
VBVR Latent Cache (832×832 × 33f, Wan2.2-TI2V-5B VAE + UMT5-XXL)
Pre-encoded latent cache for the
Video-Reason/VBVR-Dataset
geometric / logical reasoning video corpus, prepared for Equilibrium Matching
(EqM) post-training of Wan-AI/Wan2.2-TI2V-5B-Diffusers on AWS Trainium2.
This is a working cache, not a primary dataset. It exists to skip the
~5 s/sample VAE+T5 encode cost during training. The original videos +
prompts live in the upstream VBVR-Dataset repo.
Source →… See the full description on the dataset page: https://huggingface.co/datasets/Central-Cat/vbvr-latent-cache-832x832x33f-t2v-only.ine-catalog
INE
Este repositorio contiene todas las tablas¹ del Instituto Nacional de Estadística exportadas a ficheros Parquet.
Puedes encontrar cualquiera de las tablas o sus metadatos en la carpeta tablas.
Cada tabla está identificado un una ID. Puedes encontrar la ID de la tabla tanto en el INE (es el número que aparece en la URL) or en el archivo tablas.jsonl de este repositorio que puedes explorar en el Data Viewer.
Por ejemplo, la tabla de Índices nacionales de clases se corresponde al… See the full description on the dataset page: https://huggingface.co/datasets/datania/ine-catalog.cati-singapore-dataset
CATI Singapore Expressway Traffic Dataset
Real-time vehicle detection data collected from Singapore's 90 LTA traffic cameras using CATI (Context-Aware Traffic Intelligence) — a novel FiLM-conditioned YOLOv11 detector that adapts to environmental conditions in real time.
Dataset Description
This dataset contains per-camera vehicle detection results collected continuously from Singapore's Land Transport Authority (LTA) expressway camera network. Each record captures… See the full description on the dataset page: https://huggingface.co/datasets/SuhxsReddy/cati-singapore-dataset.medieval
Dataset Card for CATMuS Medieval
Join our Discord to ask questions about the dataset:
Dataset Details
Handwritten Text Recognition (HTR) has emerged as a crucial tool for converting manuscripts images into machine-readable formats,
enabling researchers and scholars to analyse vast collections efficiently.
Despite significant technological progress, establishing consistent ground truth across projects for HTR tasks,
particularly for complex and heterogeneous… See the full description on the dataset page: https://huggingface.co/datasets/CATMuS/medieval.Cat-Mangafineweb-2-turkish-categorized
What is this
THis is the categorized version of the Turkish subset of the fineweb-2 dataset.
It is an ongoing effort, and the details will be added soon with the rest of the dataset.
CATalog
Dataset Summary
CATalog is a diverse, open-source Catalan corpus for language modelling. It consists of text documents from 26 different sources, including web crawling, news, forums, digital libraries and public institutions, totaling in 17.45 billion words.
Supported Tasks and Leaderboards
Fill-Mask
Text Generation
other:Language-Modelling: The dataset is suitable for training a model in Language Modelling, predicting the next word in a given context. Success is… See the full description on the dataset page: https://huggingface.co/datasets/projecte-aina/CATalog.cats_vs_dogs_samplecats_vs_dogs
Dataset Card for Cats Vs. Dogs
Dataset Summary
A large set of images of cats and dogs. There are 1738 corrupted images that are dropped. This dataset is part of a now-closed Kaggle competition and represents a subset of the so-called Asirra dataset.
From the competition page:
The Asirra data set
Web services are often protected with a challenge that's supposed to be easy for people to solve, but difficult for computers. Such a challenge is often called a CAPTCHA… See the full description on the dataset page: https://huggingface.co/datasets/microsoft/cats_vs_dogs.stack-v3-train
🥞 The Stack v3
What is it?
What is being released
How to download and use it
Dataset statistics
Dataset structure
Dataset creation
Considerations for using the data
Additional information
What is it?
The Stack v3 is the largest, most up-to-date open dataset of source code, crawled directly from GitHub and built to pre-train code LLMs with full-repository context. It is the successor to The Stack v2 and, like its predecessor, is released to make the training… See the full description on the dataset page: https://huggingface.co/datasets/CathleenTico/stack-v3-train.wikimedia-common-audio-catalanThis is a collection of Catalan-language audio with free licenses extracted from Wikimedia Commons.
License identifiers are normalized to cc-zero, cc-by-4.0,
cc-by-sa-3.0, cc-by-sa-4.0, GFDL, and PD-self.
This provides a richer alternative to Common Voice.
Characteristics of the dataset:
One or multiple speakers
Different accents
Different domain texts
761 audio files
We found this dataset useful for audio tasks such as:
Language detection
Evaluation of STT systems
New candidates are… See the full description on the dataset page: https://huggingface.co/datasets/softcatala/wikimedia-common-audio-catalan.usgs-global-earthquake-catalog
USGS Global Earthquake Catalog
Provides historical data on global seismic events, sourced directly from the U.S. Geological Survey (USGS) Earthquake Hazards Program via its FDSN Event Web Service.
Each record represents a single seismic event (primarily earthquakes) and contains detailed information, including:
Event Time & Location: Precise timestamp, geographic coordinates (latitude, longitude), and depth of the event.
Magnitude: The magnitude of the event (mag) and the method… See the full description on the dataset page: https://huggingface.co/datasets/mnemoraorg/usgs-global-earthquake-catalog.Products-Catalogeuropean-open-data-catalogue
European Open Data Catalogue
This repository publishes independently versioned metadata and licensed source snapshots:
A discovery catalogue with 15565 dataset entries from
ISTAT, Eurostat, OECD, ILO, DoveVannoINostriSoldi (DVNS) and Cruscotto Italia.
3 independently pinned availability indexes with
911,795 joint combinations across 35 datasets, built from complete
source responses within the explicitly declared scope.
Licensed Cruscotto source snapshots, stored separately from… See the full description on the dataset page: https://huggingface.co/datasets/Gramscii-IT/european-open-data-catalogue.catch-a-vlm-embeddingsBRIGHTER-emotion-categories
BRIGHTER Emotion Categories Dataset
This dataset contains the emotion categories data from the BRIGHTER paper: BRIdging the Gap in Human-Annotated Textual Emotion Recognition Datasets for 28 Languages.
Dataset Description
The BRIGHTER Emotion Categories dataset is a comprehensive multi-language, multi-label emotion classification dataset with separate configurations for each language. It represents one of the largest human-annotated emotion datasets across multiple… See the full description on the dataset page: https://huggingface.co/datasets/brighter-dataset/BRIGHTER-emotion-categories.cats-vs-dogs-sample
Dataset Card for Dataset Name
Subset of https://huggingface.co/datasets/microsoft/cats_vs_dogs, converted into FiftyOne dataset format.
This is a FiftyOne dataset with 5000 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 =… See the full description on the dataset page: https://huggingface.co/datasets/Voxel51/cats-vs-dogs-sample.medieval-segmentation
Dataset Card for CATMuS Medieval (Segmentation Version)
Join our Discord to ask questions about the dataset:
Dataset Details
CATMuS Medieval Segmentation (Consistent Approaches to Transcribing Manuscripts) is a specialized dataset designed for layout analysis of medieval manuscripts using the SegmOnto vocabulary for region and line classification. This dataset addresses the challenges associated with establishing consistent ground truth in layout analysis tasks… See the full description on the dataset page: https://huggingface.co/datasets/CATMuS/medieval-segmentation.news-category-datasetDataset from https://www.kaggle.com/datasets/rmisra/news-category-dataset
ssl-checkpoints
ssl-checkpoints
The code to load the checkpoints to follow...
The repository is organised as follows:
Each folder corresponds to the data set used for our experiment.
Each subfolder represents the corresponding SSL technique used.
These subfolders contain the checkpoints for each transformation/pretext task considered. The five checkpoint files correspond to
the transformation Baseline, SimClr, Orthogonality, LoRot and DCL, respectively, described in the blog.
CatVision
CatVision: Human–Cat Vision Frame Pairs
Official dataset for the paper:
Purrturbed but Stable: Human-Cat Invariant Representations Across CNNs, ViTs and Self-Supervised ViTsArya Shah et al. · arXiv:2511.02404
This dataset contains 346,400 paired video frames rendered under human vision and simulated cat vision optics.
It was used to benchmark cross-species representational alignment across CNNs, supervised ViTs, windowed transformers,
and self-supervised ViTs (DINO) using CKA and… See the full description on the dataset page: https://huggingface.co/datasets/aryashah00/CatVision.wiki_cat_sumSummarise the most important facts of a given entity in the Film, Company, and Animal domains from a cluster of related documents.cats-vs-dogs-imbalanced
Dataset Card for cats-vs-dogs-imbalanced
This is a FiftyOne dataset with 2551 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/cats-vs-dogs-imbalanced")
# Launch the App
session = fo.launch_app(dataset)
Dataset… See the full description on the dataset page: https://huggingface.co/datasets/Voxel51/cats-vs-dogs-imbalanced.monolingual-tokenizer-dataTodo:
add language to metadata
cite source and explain sampling
apertus_multiblimp