datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
arxivqa_test_subsampled_beirBEIR version of vidore/arxivqa_test_subsampled.
docvqa_test_subsampled_beirBEIR version of vidore/docvqa_test_subsampled.
tabfquad_test_subsampled_beirBEIR version of vidore/tabfquad_test_subsampled.
infovqa_test_subsampled_beirBEIR version of vidore/infovqa_test_subsampled.
docvqa_test_subsampled_beirBEIR version of vidore/docvqa_test_subsampled.
infovqa_test_subsampled_beirBEIR version of vidore/infovqa_test_subsampled.
arxivqa_test_subsampled_beirBEIR version of vidore/arxivqa_test_subsampled.
tabfquad_test_subsampled_beirBEIR version of vidore/tabfquad_test_subsampled.
docvqa_test_subsampled
Dataset Description
This is the test set taken from the DocVQA dataset. It includes collected images from the UCSF Industry Documents Library. Questions and answers were manually annotated.
Example of data (see viewer)
Data Curation
To ensure homogeneity across our benchmarked datasets, we subsampled the original test set to 500 pairs and renamed the different columns.
Load the dataset
from datasets import load_dataset
ds =… See the full description on the dataset page: https://huggingface.co/datasets/vidore/docvqa_test_subsampled.tabfquad_test_subsampled
Dataset Description
TabFQuAD (Table French Question Answering Dataset) is designed to evaluate TableQA models in realistic industry settings. Using a vision language model (GPT4V), we create additional queries to augment the existing human-annotated ones.
Data Curation
To ensure homogeneity across our benchmarked datasets, we subsampled the original test set to 280 pairs, leaving the rest for training and renaming the different columns.
Load the dataset
from… See the full description on the dataset page: https://huggingface.co/datasets/vidore/tabfquad_test_subsampled.arxivqa_test_subsampled
Dataset Description
This is a VQA dataset based on figures extracted from arXiv publications taken from ArXiVQA dataset from Multimodal ArXiV. The questions were generated synthetically using GPT-4 Vision.
Data Curation
To ensure homogeneity across our benchmarked datasets, we subsampled the original test set to 500 pairs. Furthermore we renamed the different columns for our purpose.
Load the dataset
from datasets import load_dataset
ds =… See the full description on the dataset page: https://huggingface.co/datasets/vidore/arxivqa_test_subsampled.infovqa_test_subsampled
Dataset Description
This is the test set taken from the InfoVQA dataset. includes infographics collected from the Internet using the search query “infographics”. Questions and answers were manually annotated.
Questions and answers were manually annotated.
Example of data : (see viewer)
Data Curation
To ensure homogeneity across our benchmarked datasets, we subsampled the original test set to 500 pairs and renamed the different columns.
Load the dataset
from… See the full description on the dataset page: https://huggingface.co/datasets/vidore/infovqa_test_subsampled.atomic_subsampled_500kdocvqa_test_subsampled_tesseracttabfquad_test_subsampled_ocr_chunkarxivqa_test_subsampled_tesseractwiki-screenshot-corpus_subsampled-5kinfovqa_test_subsampled_tesseracttabfquad_test_subsampled_tesseractarxivqa_test_subsampled_captioningMMEB-train-subsampleddocvqa_test_subsampled_captioningdocvqa_test_subsampled_ocr_chunkinfovqa_test_subsampled_ocr_chunkarxivqa_test_subsampled_ocr_chunkAMAZON-Products-2023-subsamplearxivqa_test_subsampled
Dataset Description
This is a VQA dataset based on figures extracted from arXiv publications taken from ArXiVQA dataset from Multimodal ArXiV. The questions were generated synthetically using GPT-4 Vision.
Data Curation
To ensure homogeneity across our benchmarked datasets, we subsampled the original test set to 500 pairs. Furthermore we renamed the different columns for our purpose.
Load the dataset
from datasets import load_dataset
ds =… See the full description on the dataset page: https://huggingface.co/datasets/aakash-projects/arxivqa_test_subsampled.tabfquad_test_subsampled_captioninginfovqa_test_subsampled_captioningretriever-vidore-tabfquad_test_subsampled-clean
Description
vidore/tabfquad_test_subsampled dataset that we processed.Although useless, we have created an empty answer column to facilitate the concatenation of this dataset with VQA datasets where only the quesion and image columns would be used to train a Colpali-type model or one of its derivatives.
Citation
@misc{faysse2024colpaliefficientdocumentretrieval,
title={ColPali: Efficient Document Retrieval with Vision Language Models},
author={Manuel Faysse… See the full description on the dataset page: https://huggingface.co/datasets/CATIE-AQ/retriever-vidore-tabfquad_test_subsampled-clean.