datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
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.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.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.EVisRAG-Test-DocVQADataset Description
This is a VQA dataset on Document Images from DocVQA.
Load the dataset
import pandas as pd
import os
import sys
data_name = sys.argv[1]
df = pd.read_parquet(f"data/{data_name}/images.parquet", engine="pyarrow")
output_dir = f"data/{data_name}"
os.makedirs(f"{output_dir}/imgs", exist_ok=True)
for idx, row in df.iterrows():
img_bytes = row['image']['bytes']
output_path = os.path.join(output_dir, row["path"])
with open(output_path, "wb") as f:… See the full description on the dataset page: https://huggingface.co/datasets/Boggy666/EVisRAG-Test-DocVQA.docvqa-nanochat
DocVQA for Nanochat
Single-page document QA dataset processed for nanochat fine-tuning.
Description
This dataset is derived from pixparse/docvqa-single-page-questions and has been processed for efficient fine-tuning of small language models with limited context windows.
Modifications from Source
OCR truncation: Answer-priority truncation ensures the answer is always present in the truncated context. Lines containing the answer are prioritized, then surrounding… See the full description on the dataset page: https://huggingface.co/datasets/morgan/docvqa-nanochat.EVisRAG-Test-DocVQADataset Description
This is a VQA dataset on Document Images from DocVQA.
Load the dataset
import pandas as pd
import os
import sys
data_name = sys.argv[1]
df = pd.read_parquet(f"data/{data_name}/images.parquet", engine="pyarrow")
output_dir = f"data/{data_name}"
os.makedirs(f"{output_dir}/imgs", exist_ok=True)
for idx, row in df.iterrows():
img_bytes = row['image']['bytes']
output_path = os.path.join(output_dir, row["path"])
with open(output_path, "wb") as f:… See the full description on the dataset page: https://huggingface.co/datasets/openbmb/EVisRAG-Test-DocVQA.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