datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
anli
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.voxpopuli
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.xnli
Dataset Card for "xnli"
Dataset Summary
XNLI is a subset of a few thousand examples from MNLI which has been translated
into a 14 different languages (some low-ish resource). As with MNLI, the goal is
to predict textual entailment (does sentence A imply/contradict/neither sentence
B) and is a classification task (given two sentences, predict one of three
labels).
Supported Tasks and Leaderboards
More Information Needed
Languages
More Information… See the full description on the dataset page: https://huggingface.co/datasets/facebook/xnli.ego-1k
Ego-1K — A Large-Scale Multiview Video Dataset for Egocentric Vision
Jae Yong Lee, Daniel Scharstein, Akash Bapat, Hao Hu, Andrew Fu, Haoru Zhao, Paul Sammut, Xiang Li,
Stephen Jeapes, Anik Gupta, Lior David, Saketh Madhuvarasu, Jay Girish Joshi, and Jason Wither
CVPR 2026
arXiv:2603.13741
We present Ego-1K, a large-scale collection of time-synchronized egocentric multiview videos designed to advance neural 3D video
synthesis and dynamic scene understanding.… See the full description on the dataset page: https://huggingface.co/datasets/facebook/ego-1k.multilingual_librispeech
Dataset Card for MultiLingual LibriSpeech
Dataset Summary
This is a streamable version of the Multilingual LibriSpeech (MLS) dataset.
The data archives were restructured from the original ones from OpenSLR to make it easier to stream.
MLS dataset is a large multilingual corpus suitable for speech research. The dataset is derived from read audiobooks from LibriVox and consists of
8 languages - English, German, Dutch, Spanish, French, Italian, Portuguese, Polish.… See the full description on the dataset page: https://huggingface.co/datasets/facebook/multilingual_librispeech.flores
Dataset Card for Flores 200
Dataset Summary
⚠️ This repository is no longer being updated ⚠️
A newer version of the FLORES dataset managed by the Open Language Data Initiative
is available at https://huggingface.co/datasets/openlanguagedata/flores_plus.
FLORES is a benchmark dataset for machine translation between English and low-resource languages.
The creation of FLORES-200 doubles the existing language coverage of FLORES-101.
Given the nature of the new… See the full description on the dataset page: https://huggingface.co/datasets/facebook/flores.facesyntheticsspigacaptioned
Dataset Card for "face_synthetics_spiga_captioned"
This is a copy of the Microsoft FaceSynthetics dataset with SPIGA-calculated landmark annotations, and additional BLIP-generated captions.
For a copy of the original FaceSynthetics dataset with no extra annotations, please refer to pcuenq/face_synthetics.
Here is the code for parsing the dataset and generating the BLIP captions:
from transformers import pipeline
dataset_name = "pcuenq/face_synthetics_spiga"
faces =… See the full description on the dataset page: https://huggingface.co/datasets/multimodalart/facesyntheticsspigacaptioned.omnilingual-asr-corpus
Meta Omnilingual ASR Corpus
The Omnilingual ASR Corpus is a collection of spontaneous speech recordings and their transcriptions for 348 under-served languages. The corpus was collected as part of Meta FAIR’s Omnilingual ASR project (blog, model, paper) for the purposes of training automatic speech recognition (ASR) and spoken language identification models.
Data schema
{
`language`: "lij_Latn",
`iso_639_3`: "lij",
`iso_15924`: "Latn",
`glottocode`:… See the full description on the dataset page: https://huggingface.co/datasets/facebook/omnilingual-asr-corpus.kilt_tasks
Dataset Card for KILT
Dataset Summary
KILT has been built from 11 datasets representing 5 types of tasks:
Fact-checking
Entity linking
Slot filling
Open domain QA
Dialog generation
All these datasets have been grounded in a single pre-processed Wikipedia dump, allowing for fairer and more consistent evaluation as well as enabling new task setups such as multitask and transfer learning with minimal effort. KILT also provides tools to analyze and understand the… See the full description on the dataset page: https://huggingface.co/datasets/facebook/kilt_tasks.CelebA-faces2M-Belebele
2M-Belebele
Highly-Multilingual Speech and American Sign Language Comprehension Dataset
We introduce 2M-Belebele as the first highly multilingual speech and American Sign Language (ASL) comprehension dataset. Our dataset, which is an extension of the existing Belebele only-text dataset, covers 74 spoken languages at the intersection of Belebele and Fleurs, and one sign language (ASL).
The speech dataset is built from aligning Belebele, Flores200 and Fleurs datasets as… See the full description on the dataset page: https://huggingface.co/datasets/facebook/2M-Belebele.FACET-Terminal-Tasks-6k
FACET-Terminal-Tasks-6k
6,020 execution-grounded tasks for terminal agents, coding agents, and executable workflow research
🌐 FACET Project Website
📄 FACET Paper
💻 FACET-Terminal GitHub Repository
🤗 FACET-Terminal Models & Data
Dataset Overview
FACET-Terminal-Tasks-6k contains 6,020 public-release-ready Harbor tasks produced by FACET. Each task is an executable environment rather than a standalone prompt: it includes a natural-language instruction… See the full description on the dataset page: https://huggingface.co/datasets/FACET-Terminal/FACET-Terminal-Tasks-6k.Face-Gender-Swap
Dataset Card for "Face-Gender-Swap"
More Information needed
face_synthetics_spiga
Dataset Card for "face_synthetics_spiga"
This is a copy of Microsoft FaceSynthetics dataset with SPIGA landmark annotations. For a copy of the original FaceSynthetics dataset with no extra annotations, please refer to pcuenq/face_synthetics.
Please, refer to the original license, which we replicate in this repo. The SPIGA annotations were created by Hugging Face Inc. and are distributed under the MIT license.
This dataset was prepared using the code below. It iterates through the… See the full description on the dataset page: https://huggingface.co/datasets/pcuenq/face_synthetics_spiga.imppres
Dataset Card for IMPPRES
Dataset Summary
Over >25k semiautomatically generated sentence pairs illustrating well-studied pragmatic inference types. IMPPRES is an NLI dataset following the format of SNLI (Bowman et al., 2015), MultiNLI (Williams et al., 2018) and XNLI (Conneau et al., 2018), which was created to evaluate how well trained NLI models recognize several classes of presuppositions and scalar implicatures.
Supported Tasks and Leaderboards
Natural… See the full description on the dataset page: https://huggingface.co/datasets/facebook/imppres.Face-Aging-Dataset
Dataset Card for "Face-Aging-Dataset"
More Information needed
bouquet
BOUQuET 💐: Benchmark and Open initiative for Universal Quality Evaluation in Translation
BOUQuET is a multi-way parallel, multi-centric and multi-register/domain dataset and benchmark for machine translation quality.
The underlying texts have been handcrafted by linguists in 8 diverse languages (Egyptian Arabic, French, German, Hindi, Indonesian, Mandarin Chinese, Russian, and Spanish) and translated to English and 266 other languoids (language + script combinations). The… See the full description on the dataset page: https://huggingface.co/datasets/facebook/bouquet.action100m-preview
Action100M: A Large-scale Video Action Dataset
Paper | GitHub
Action100M is a large-scale dataset constructed from 1.2M Internet instructional videos (14.6 years of duration), yielding ~100 million temporally localized segments with open-vocabulary action supervision and rich captions. It serves as a foundation for scalable research in video understanding and world modeling.
Load Action100M Annotations
Our data can be loaded from the 🤗 huggingface repo at… See the full description on the dataset page: https://huggingface.co/datasets/facebook/action100m-preview.winoground
Dataset Card for Winoground
Dataset Description
Winoground is a novel task and dataset for evaluating the ability of vision and language models to conduct visio-linguistic compositional reasoning. Given two images and two captions, the goal is to match them correctly—but crucially, both captions contain a completely identical set of words/morphemes, only in a different order. The dataset was carefully hand-curated by expert annotators and is labeled with a rich set of… See the full description on the dataset page: https://huggingface.co/datasets/facebook/winoground.asset
Dataset Card for ASSET
Dataset Summary
ASSET (Alva-Manchego et al., 2020) is multi-reference dataset for the evaluation of sentence simplification in English. The dataset uses the same 2,359 sentences from TurkCorpus (Xu et al., 2016) and each sentence is associated with 10 crowdsourced simplifications. Unlike previous simplification datasets, which contain a single transformation (e.g., lexical paraphrasing in TurkCorpus or sentence
splitting in HSplit), the… See the full description on the dataset page: https://huggingface.co/datasets/facebook/asset.vox2-face-landmark
Dataset Card for "voxceleb2-features"
More Information needed
casia_web_facePUG_ImageNet
PUG: ImageNet
The PUG: ImageNet dataset contains 88,328 pre-rendered images based on Unreal Engine using 724 assets representing 151 ImageNet classes with 64 environments, 7 sizes, 9 textures, 18 different camera orientations, 18 different character orientations and 7 light intensities. In contrast to PUG: Animals, PUG: ImageNet was created by varying only a single factor at a time (which explains the lower number of images than PUG: Animals despite using more factors). The main… See the full description on the dataset page: https://huggingface.co/datasets/facebook/PUG_ImageNet.PUG_Animals
PUG Animals
The PUG: Animals dataset contains 215,040 pre-rendered images based on Unreal-Engine using 70 animal assets, 64 environments, 3 sizes, 4 textures, under 4 camera orientations.
It was designed with the intent to create a dataset with variation factors available. Inspired by research on out-of-distribution generalization, PUG: Animals allows one to precisely control distribution shifts between training and testing which can provide better insight on how a deep neural… See the full description on the dataset page: https://huggingface.co/datasets/facebook/PUG_Animals.Common-O
Common-O
measuring multimodal reasoning across scenes
Common-O, inspired by cognitive tests for humans, probes multimodal LLMs' ability to reason across scenes by asking "what’s in common?"
Common-O is comprised of household objects:
We have two subsets: Common-O (3 - 8 objects) and Common-O Complex (8 - 16 objects).
Multimodal LLMs excel at single image perception, but struggle with multi-scene reasoning
Evaluating a Multimodal LLM on Common-O
import… See the full description on the dataset page: https://huggingface.co/datasets/facebook/Common-O.anime-faces-256agent-task-facet-terminal-6k
FACET-Terminal-Tasks-6k for tmax
Images require building: the complete dataset and build contexts are included. Image builds are deferred; run the resumable script below before using these environments.
All 6,020 task directories from FACET-Terminal/FACET-Terminal-Tasks-6k, pinned to b2d02645932e3989c8332a41e57d0f0855de7002, converted to tmax's swerl_vanillux_sandbox format.
The train split uses the same messages, ground_truth, dataset, env_config, and source schema as the other… See the full description on the dataset page: https://huggingface.co/datasets/hamishivi/agent-task-facet-terminal-6k.emu_edit_test_set
Dataset Card for the Emu Edit Test Set
Dataset Summary
To create a benchmark for image editing we first define seven different categories of potential image editing operations: background alteration (background), comprehensive image changes (global), style alteration (style), object removal (remove), object addition (add), localized modifications (local), and color/texture alterations (texture).
Then, we utilize the diverse set of input images from the MagicBrush… See the full description on the dataset page: https://huggingface.co/datasets/facebook/emu_edit_test_set.140k-Real-and-Fake-Faces
140k Real and Fake Faces Dataset
📝 Dataset Description
Dataset Summary
The 140k Real and Fake Faces Dataset is a balanced face image collection designed to benchmark models for detecting StyleGAN-generated faces. The dataset contains 70,000 real human face photographs sourced from the Flickr-Faces-HQ (FFHQ) dataset compiled by NVIDIA, and 70,000 fake faces sampled from the 1 Million Fake Faces dataset generated by StyleGAN and originally provided by Bojan.… See the full description on the dataset page: https://huggingface.co/datasets/TheKernel01/140k-Real-and-Fake-Faces.PLM-VideoBench
Dataset Summary
PLM-VideoBench is a collection of human-annotated resources for evaluating Vision Language models, focused on detailed video understanding.
[📃 Tech Report]
[📂 Github]
Supported Tasks
PLM-VideoBench includes evaluation data for the following tasks:
FGQA
In this task, a model must answer a multiple-choice question (MCQ) that probes fine-grained activity understanding. Given a question and multiple options that differ in a… See the full description on the dataset page: https://huggingface.co/datasets/facebook/PLM-VideoBench.