NeuralMetrics/DocumentVQA
Neural Metrics · Asking documents questions, and grading the answers. 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… See the full description on the dataset page: https://huggingface.co/datasets/NeuralMetrics/DocumentVQA.
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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" />
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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`, created by the Qwen team. 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.
<details> <summary><b>Original dataset card from HuggingFaceM4/DocumentVQA</b> (click to expand)</summary>
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