File size: 2,139 Bytes
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dataset_info:
features:
- name: questionId
dtype: int32
- name: question
dtype: string
- name: question_types
list: string
- name: image
dtype: image
- name: docId
dtype: int32
- name: ucsf_document_id
dtype: string
- name: ucsf_document_page_no
dtype: string
- name: answers
list: string
splits:
- name: train
num_bytes: 5658303093.631
num_examples: 39463
- name: validation
num_bytes: 2532362556.066
num_examples: 5349
- name: test
num_bytes: 2500321215.732
num_examples: 5188
download_size: 9591606021
dataset_size: 10690986865.428999
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
<div align="center">
# Neural Metrics · Asking documents questions, and grading the answers.
<img src="https://img.shields.io/badge/Neural%20Metrics-document%20extraction-4F46E5?style=for-the-badge" alt="Neural Metrics" />
<img src="https://img.shields.io/badge/fork%20of-HuggingFaceM4%2FDocumentVQA-2563EB?style=flat-square" alt="fork" />
</div>
Document visual question answering: given a page image and a natural-language question, produce the answer. It measures whether a model genuinely read the layout or merely pattern-matched the text.
**We use it for:** evaluating question-answering over extracted documents - catching models that read text but misread structure.
> ### Attribution
> This is an **unmodified fork** of [`HuggingFaceM4/DocumentVQA`](https://huggingface.co/datasets/HuggingFaceM4/DocumentVQA), created by the [Qwen team](https://huggingface.co/Qwen).
> All weights, files and behaviour are identical to upstream — we rehost it so our experiments stay
> reproducible and version-pinned. The original license and all credit remain with the Qwen team.
> If you want the canonical dataset, please use [the original](https://huggingface.co/datasets/HuggingFaceM4/DocumentVQA).
---
<details>
<summary><b>Original dataset card from HuggingFaceM4/DocumentVQA</b> (click to expand)</summary>
</details>
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