datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
belebele
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.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.babi_qaThe (20) QA bAbI tasks are a set of proxy tasks that evaluate reading
comprehension via question answering. Our tasks measure understanding
in several ways: whether a system is able to answer questions via chaining facts,
simple induction, deduction and many more. The tasks are designed to be prerequisites
for any system that aims to be capable of conversing with a human.
The aim is to classify these tasks into skill sets,so that researchers
can identify (and then rectify)the failings of their systems.2M-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.ExploreToM
Data sample for ExploreToM: Program-guided adversarial data generation for theory of mind reasoning
ExploreToM is the first framework to allow large-scale generation of diverse and challenging theory of mind data for robust training and evaluation.
Our approach leverages an A* search over a custom domain-specific language to produce complex story structures and novel, diverse, yet plausible scenarios to stress test the limits of LLMs.
Our A* search procedure aims to find… See the full description on the dataset page: https://huggingface.co/datasets/facebook/ExploreToM.multiloko
MultiLoKo: a multilingual local knowledge benchmark for LLMs
MultiLoKo is a multilingual knowledge benchmark, covering 30 languages plus English.
The questions are separately sourced for each language, with an annotation protocol designed to target locally relevant topics for the respective language.
MultiLoKo contains the original data for each language, as well as both human and machine-authored translations of each non-English subset into English and vice versa, facilitating… See the full description on the dataset page: https://huggingface.co/datasets/facebook/multiloko.BigOBench
👋 Overview
🚀 Introduction
📋 Getting Started with the data
🔥 problem_and_human_solutions_list.jsonl
🔥 complexity_labels_light.jsonl
🔥 complexity_labels_full.jsonl
🔥 time_complexity_test_set.jsonl
🔥 space_complexity_test_set.jsonl
License
📝 Citation
🚀 Introduction
BigO(Bench) is a benchmark of ~300 code problems to be solved in Python, along with 3,105 coding problems… See the full description on the dataset page: https://huggingface.co/datasets/facebook/BigOBench.WearableQA
WearableQA
A benchmark for health reasoning over real-world wearable data.
WearableQA comprises 4,084 ten-option multiple-choice questions built from the wearable time series,
blood biomarkers, and demographics of 200 real users, each with up to about 500 days of daily
measurements. Unlike benchmarks built on synthetic or idealized signals, it preserves authentic
wearable distributions — device noise, missing days, and inter-individual variability included.
📄 Paper:… See the full description on the dataset page: https://huggingface.co/datasets/facebook/WearableQA.EgoAVU_data
[CVPR2026 HIGHLIGHT] EgoAVU, [ICASSP2026 Oral] Exploring Audio Hallucination in Egocentric Video Understanding
Official Implementation of EgoAVU: Egocentric Audio-Visual Understanding and Exploring Audio Hallucination in Egocentric Video Understanding
See our github for the code and setup instructions.
Check out our homepage, paper (CVPR) and paper (ICASSP) for more information.
We introduce EgoAVU, a scalable and automated data engine to enable egocentric audio–visual… See the full description on the dataset page: https://huggingface.co/datasets/facebook/EgoAVU_data.linguiniDataset of the paper Linguini 🍝: A benchmark for language-agnostic linguistic reasoning
Code: https://github.com/facebookresearch/linguini
minimal_video_pairs
Minimal Video Pairs
A shortcut-aware benchmark for spatio-temporal and intuitive physics video understanding (VideoQA) using minimally different video pairs.
Github
For legal reasons, we are unable to upload the videos directly to Huggingface. However, we provide scripts in this repository for downloading the videos in our github repository. Our benchmark is built on top of videos source from 9 domains:
Subset
Data sources
Human object interactions
PerceptionTest… See the full description on the dataset page: https://huggingface.co/datasets/facebook/minimal_video_pairs.GAMUT
GAMUT🌈: Two-Level Meta-Rubrics for Evaluating Open-Ended Generation
📄 Paper: https://arxiv.org/abs/2607.19322
GAMUT (Grounded Assessment of Multimodal Factuality) is a multimodal
everyday deep-research benchmark and a two-level meta-rubric framework for
evaluating open-ended, long-form generation.
Two-level meta-rubric framework — resolving a tension in rubric-based evaluation.
Judging open-ended generation requires structure, because a complete answer often does not… See the full description on the dataset page: https://huggingface.co/datasets/facebook/GAMUT.facebook_mlqa_plusSource Dataset
Link: facebook/mlqa
Revision: 397ed406c1a7902140303e7faf60fff35b58d285
MLQAMLQA (MultiLingual Question Answering) is a benchmark dataset for evaluating cross-lingual question answering performance.
MLQA consists of over 5K extractive QA instances (12K in English) in SQuAD format in seven languages - English, Arabic,
German, Spanish, Hindi, Vietnamese and Simplified Chinese. MLQA is highly parallel, with QA instances parallel between
4 different languages on average.
MLQA… See the full description on the dataset page: https://huggingface.co/datasets/AkshitaS/facebook_mlqa_plus.airs-bench
AIRS-Bench: a Suite of Tasks for Frontier AI Research Science Agents
The AI Research Science Benchmark (AIRS-Bench) quantifies the autonomous research abilities of LLM agents in the area of machine learning. AIRS-Bench comprises 20 tasks from state-of-the-art machine learning papers spanning diverse domains: NLP, Code, Math, biochemical modelling, and time series forecasting.
Each task is specified by a ⟨problem, dataset, metric⟩ triplet and a SOTA value. The agent receives the… See the full description on the dataset page: https://huggingface.co/datasets/facebook/airs-bench.live-face-swap-support-qa
LiveFaceSwap AI Public Support Q&A
This dataset contains English question and answer pairs from the public LiveFaceSwap AI browser, desktop, and pricing FAQs, captured on 2026-09-17. Each row records its source page. The official website is the current source for product behavior and pricing; this snapshot can become outdated.
It is suitable for evaluating or prototyping retrieval over LiveFaceSwap AI product support content. It is not a face image dataset, a face swap training… See the full description on the dataset page: https://huggingface.co/datasets/LiveFaceSwapAI/live-face-swap-support-qa.