datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
flickr30ki2tmetaworld_mt50This dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v3.0",
"robot_type": "metaworld",
"total_episodes": 2500,
"total_frames": 204806,
"total_tasks": 50,
"chunks_size": 1000,
"fps": 80,
"splits": {
"train": "0:2500"
},
"data_path": "data/chunk-{chunk_index:03d}/file-{file_index:03d}.parquet",
"video_path": "videos/{video_key}/chunk-{chunk_index:03d}/file-{file_index:03d}.mp4"… See the full description on the dataset page: https://huggingface.co/datasets/lerobot/metaworld_mt50.mosel
Dataset Description, Collection, and Source
The MOSEL corpus is a multilingual dataset collection including up to 950K hours of open-source speech recordings covering the 24 official languages of the European Union. We collect data by surveying labeled and unlabeled speech corpora under open-source compliant licenses.
In particular, MOSEL includes the automatic transcripts of 441k hours of unlabeled speech from VoxPopuli and LibriLight. The data is transcribed using Whisper large… See the full description on the dataset page: https://huggingface.co/datasets/FBK-MT/mosel.MTabVQA-InstructPaper
MTabVQA-Instruct Sub-datasets
This directory contains multiple MTabVQA-Instruct datasets for visual question answering over tables.
Datasets
MTabVQA-Atis-Instruct
MTabVQA-MiMo-Instruct
MTabVQA-Multitab-Instruct
MTabVQA-Spider-Instruct
Each dataset contains a VQA.jsonl file and a table_images directory with the corresponding table images.
Important Note for Multitab-Instruct
You must unzip the table_images.zip file in MTabVQA-Multitab-Instruct/ to access… See the full description on the dataset page: https://huggingface.co/datasets/mtabvqa/MTabVQA-Instruct.MTabVQA-Eval
Dataset Card for MTabVQA
Paper
Dataset Description
Dataset Summary
MTabVQA (Multi-Tabular Visual Question Answering) is a novel benchmark designed to evaluate the ability of Vision-Language Models (VLMs) to perform multi-hop reasoning over multiple tables presented as images. This scenario is common in real-world documents like web pages and PDFs but is critically under-represented in existing benchmarks.
The dataset consists of two main parts:
MTabVQA-Eval:… See the full description on the dataset page: https://huggingface.co/datasets/mtabvqa/MTabVQA-Eval.xm3600
XM3600T2IRetrieval
An MTEB dataset
Massive Text Embedding Benchmark
Retrieve images based on multilingual descriptions.
Task category
Any2AnyMultilingualRetrieval (text-to-image)
Domains
Encyclopaedic, Written
Reference
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
Source datasets:
mteb/xm3600
How to evaluate on this task
You can evaluate an embedding model on this dataset using the following code:
import… See the full description on the dataset page: https://huggingface.co/datasets/mteb/xm3600.mvl-sib-sent2img-mteb
MVL-SIB sentence-to-image for MTEB
Native MTEB retrieval packaging of the official MVL-SIB single-reference
(k=1) sentence-to-image task. Each of 205 language subsets has 3,012
sentence queries, four candidate images per query, and one correct image.
All subsets reference one shared 70-image corpus file.
Source and changes
Derived from the official WueNLP/MVL-SIB
dataset and MVL-SIB paper,
pinned at 1df5974e8fb204e91ee70cef2b3b7196a14b390f. The official builder's… See the full description on the dataset page: https://huggingface.co/datasets/artist/mvl-sib-sent2img-mteb.tatdqa_test_beirBEIR version of vidore/tatdqa_test.
docvqa_test_subsampled_beirBEIR version of vidore/docvqa_test_subsampled.
OmniMedVQA-V2
OmniMedVQA — Granular Subsets (v2)
OmniMedVQA is a large-scale medical visual question answering benchmark covering 12 imaging modalities and 5 clinical question types. This v2 release replaces the coarse modality and question_type configs with 13 granular named configs (mod-* and qt-*) whose train/test boundaries follow Med-R1's partitioning. Images are sourced from the canonical foreverbeliever/OmniMedVQA release; restricted-access images (those not distributed in the open-access… See the full description on the dataset page: https://huggingface.co/datasets/mtybilly/OmniMedVQA-V2.infovqa_test_subsampled_beirBEIR version of vidore/infovqa_test_subsampled.
cifar10
Dataset Card for CIFAR-10
Dataset Summary
The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes, with 6000 images per class. There are 50000 training images and 10000 test images.
The dataset is divided into five training batches and one test batch, each with 10000 images. The test batch contains exactly 1000 randomly-selected images from each class. The training batches contain the remaining images in random order, but some training batches may contain… See the full description on the dataset page: https://huggingface.co/datasets/mteb/cifar10.MTA-Vision-DeepSearchsyntheticDocQA_artificial_intelligence_test_beirBEIR version of vidore/syntheticDocQA_artificial_intelligence_test.
syntheticDocQA_healthcare_industry_test_beirBEIR version of vidore/syntheticDocQA_healthcare_industry_test.
arxivqa_test_subsampled_beirBEIR version of vidore/arxivqa_test_subsampled.
tabfquad_test_subsampled_beirBEIR version of vidore/tabfquad_test_subsampled.
metaworld_mt50This dataset was created using LeRobot.
Dataset Description
This dataset contains 50 demonstrations per task from the Meta-world simulation benchmarks. Demonstrations are generated using expert policies.
Meta-world: https://arxiv.org/abs/1910.10897
We reposition the camera and flip the rendered images as follow:
Homepage: [More Information Needed]
Paper: [More Information Needed]
License: apache-2.0
Dataset Structure
meta/info.json:
{
"codebase_version":… See the full description on the dataset page: https://huggingface.co/datasets/ML-GOD/metaworld_mt50.syntheticDocQA_government_reports_test_beirBEIR version of vidore/syntheticDocQA_government_reports_test.
vidore_v3_finance_en_mteb_format
Vidore3FinanceEnRetrieval
An MTEB dataset
Massive Text Embedding Benchmark
Retrieve associated pages according to questions.
Task category
t2i
Domains
Academic
Reference
https://huggingface.co/blog/QuentinJG/introducing-vidore-v3
Source datasets:
vidore/vidore_v3_finance_en
How to evaluate on this task
You can evaluate an embedding model on this dataset using the following code:
import mteb
task = mteb.get_task("Vidore3FinanceEnRetrieval")
evaluator… See the full description on the dataset page: https://huggingface.co/datasets/vidore/vidore_v3_finance_en_mteb_format.sun397syntheticDocQA_energy_test_beirBEIR version of vidore/syntheticDocQA_energy_test.
shiftproject_test_beirBEIR version of vidore/shiftproject_test.
imagenet-dog-15vidore_v3_computer_science_mteb_format
Vidore3ComputerScienceRetrieval
An MTEB dataset
Massive Text Embedding Benchmark
Retrieve associated pages according to questions.
Task category
t2i
Domains
Academic
Reference
https://huggingface.co/blog/QuentinJG/introducing-vidore-v3
Source datasets:
vidore/vidore_v3_computer_science
How to evaluate on this task
You can evaluate an embedding model on this dataset using the following code:
import mteb
task =… See the full description on the dataset page: https://huggingface.co/datasets/vidore/vidore_v3_computer_science_mteb_format.vidore_v3_industrial_mteb_format
Vidore3IndustrialRetrieval
An MTEB dataset
Massive Text Embedding Benchmark
Retrieve associated pages according to questions.
Task category
t2i
Domains
Academic
Reference
https://huggingface.co/blog/QuentinJG/introducing-vidore-v3
Source datasets:
vidore/vidore_v3_industrial
How to evaluate on this task
You can evaluate an embedding model on this dataset using the following code:
import mteb
task = mteb.get_task("Vidore3IndustrialRetrieval")… See the full description on the dataset page: https://huggingface.co/datasets/vidore/vidore_v3_industrial_mteb_format.vidore_v3_hr_mteb_format
Vidore3HrRetrieval
An MTEB dataset
Massive Text Embedding Benchmark
Retrieve associated pages according to questions.
Task category
t2i
Domains
Academic
Reference
https://huggingface.co/blog/QuentinJG/introducing-vidore-v3
Source datasets:
vidore/vidore_v3_hr
How to evaluate on this task
You can evaluate an embedding model on this dataset using the following code:
import mteb
task = mteb.get_task("Vidore3HrRetrieval")
evaluator = mteb.MTEB([task])… See the full description on the dataset page: https://huggingface.co/datasets/vidore/vidore_v3_hr_mteb_format.forb_retrievalvidore_v3_finance_fr_mteb_format
Vidore3FinanceFrRetrieval
An MTEB dataset
Massive Text Embedding Benchmark
Retrieve associated pages according to questions.
Task category
t2i
Domains
Academic
Reference
https://huggingface.co/blog/QuentinJG/introducing-vidore-v3
Source datasets:
vidore/vidore_v3_finance_fr
How to evaluate on this task
You can evaluate an embedding model on this dataset using the following code:
import mteb
task = mteb.get_task("Vidore3FinanceFrRetrieval")
evaluator… See the full description on the dataset page: https://huggingface.co/datasets/vidore/vidore_v3_finance_fr_mteb_format.gld-v2