datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
document-haystack
Document Haystack Dataset
This repository contains the dataset for the paper “Document Haystack: A Long Context Multimodal Image/Document Understanding Vision LLM Benchmark”.
📑 Abstract Paper
The proliferation of multimodal Large Language Models has significantly advanced the ability to analyze and understand complex data inputs from different modalities. However, the processing of long documents remains under-explored, largely due to a lack of suitable benchmarks. To… See the full description on the dataset page: https://huggingface.co/datasets/AmazonScience/document-haystack.docvqa-single-page-questions
Dataset Card for DocVQA Dataset
Dataset Summary
DocVQA dataset is a document dataset introduced in Mathew et al. (2021) consisting of 50,000 questions defined on 12,000+ document images.
Please visit the challenge page (https://rrc.cvc.uab.es/?ch=17) and paper (https://arxiv.org/abs/2007.00398) for further information.
Usage
This dataset can be used with current releases of Hugging Face datasets library.
Here is an example using a custom collator to bundle… See the full description on the dataset page: https://huggingface.co/datasets/pixparse/docvqa-single-page-questions.DocVQA-2026
DocVQA 2026 | ICDAR2026 Competition on Multimodal Reasoning over Documents in Multiple Domains
Building upon previous DocVQA benchmarks, this evaluation dataset introduces challenging reasoning questions over a diverse collection of documents spanning eight domains, including business reports, scientific papers, slides, posters, maps, comics, infographics, and engineering drawings.
By expanding coverage to new document domains and… See the full description on the dataset page: https://huggingface.co/datasets/VLR-CVC/DocVQA-2026.InSight-doc-SFT-18k
InSight-doc-SFT-18k
Agentic Visual Perception for Long-Document Understanding
📄 Paper |
💻 Code |
🤗 Model |
🎯 RL Data |
🎬 Replay Demo |
🚀 Live Demo
Understand the big picture. Focus on the right details. Answer from the evidence.
InSight-doc-SFT-18k is the supervised fine-tuning corpus used to train the
InSight-doc long-document understanding agent. Each example is a complete
multimodal trajectory: the agent starts from low-resolution document… See the full description on the dataset page: https://huggingface.co/datasets/m-Just/InSight-doc-SFT-18k.DocVQADocVQA-2026
DocVQA 2026 | ICDAR2026 Competition on Multimodal Reasoning over Documents in Multiple Domains
Building upon previous DocVQA benchmarks, this evaluation dataset introduces challenging reasoning questions over a diverse collection of documents spanning eight domains, including business reports, scientific papers, slides, posters, maps, comics, infographics, and engineering drawings.
By expanding coverage to new document domains and… See the full description on the dataset page: https://huggingface.co/datasets/Prabhu3674/DocVQA-2026.adaption-multilingual-doc-qa
This dataset is a remastered version of Reubencf/magazines-multilingual-vqa prepared using Adaption's Adaptive Data platform.
multilingual_doc_qa
This dataset contains multilingual question-answer pairs focused on extracting specific factual details from documents (page numbers, names, ages, dates, counts, titles, etc.). Each entry consists of a prompt asking for a specific detail and a completion providing the precise answer grounded in the source page text. Cross-lingual: the… See the full description on the dataset page: https://huggingface.co/datasets/Reubencf/adaption-multilingual-doc-qa.docvqa-single-page-questions-answer-ocr
DocVQA with Answer Localization
This dataset provides answer-localization annotations produced by our pipeline on top of the DocVQA dataset.
Usage
from datasets import load_dataset
# Load the dataset with answer OCR annotations
ds = load_dataset("indrehus/docvqa-single-page-questions-answer-ocr", split="validation")
# Get a single sample
sample = ds[0]
# Available fields in each sample:
print("Image:", sample["image"]) # PIL.Image
print("Question:"… See the full description on the dataset page: https://huggingface.co/datasets/indrehus/docvqa-single-page-questions-answer-ocr.Adaption-low-resource-doc-qa
Adaption Low-Resource Document Q/A
This dataset is a remastered version of
Reubencf/magazines-multilingual-vqa
prepared using Adaption's Adaptive Data platform,
with a deliberate focus on low-resource source languages — the
languages that are underrepresented in most open multimodal datasets.
What's inside
10,200 rows of multilingual document question-answer pairs grounded in
public-domain magazine / newspaper pages from archive.org.
Every row carries verbatim OCR in… See the full description on the dataset page: https://huggingface.co/datasets/Reubencf/Adaption-low-resource-doc-qa.docvqa-single-page-questions-answer-ocr-colSmol500M-q-priors
DocVQA Evidence Heatmaps (colsmol-500M)
This dataset contains question–evidence aligned heatmaps produced by our pipeline using colsmol-500M. It is intended as an auxiliary artifact to accompany our main dataset:
Main dataset (DocVQA with Answer Localization): https://huggingface.co/datasets/indrehus/docvqa-single-page-questions-answer-ocr
Usage
from datasets import load_dataset
# Load the dataset with ColSmol-500M question priors
ds = load_dataset(… See the full description on the dataset page: https://huggingface.co/datasets/indrehus/docvqa-single-page-questions-answer-ocr-colSmol500M-q-priors.docvqa
Dataset Summary
This dataset contains scanned documents and questions from DocVQA task.
Fields
Name
Type
Description
question
string
The visual question
image
image
Document page image
answers
list[string]
Ground-truth answers