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---
license: gpl-3.0
tags:
- DocVQA
- Document Question Answering
- Document Visual Question Answering
datasets:
- MP-DocVQA
language:
- en
---
# BERT-BASE fine-tuned on MP-DocVQA
This is BERT trained on [SinglePage DocVQA](https://arxiv.org/abs/2007.00398) and fine-tuned on Multipage DocVQA (MP-DocVQA) dataset.
This model was used as a baseline in [Hierarchical multimodal transformers for Multi-Page DocVQA](https://arxiv.org/pdf/2212.05935.pdf).
- Results on the MP-DocVQA dataset are reported in Table 2.
- Training hyperparameters can be found in Table 8 of Appendix D.
## How to use
Here is how to use this model to get the features of a given text in PyTorch:
```python
from transformers import AutoModelForQuestionAnswering, AutoTokenizer
model = AutoModelForQuestionAnswering.from_pretrained("rubentito/bert-base-mpdocvqa")
question = "Replace me by any text you'd like."
context = "Put some context for answering"
encoded_input = tokenizer(question, context, return_tensors='pt')
output = model(**encoded_input)
```
## BibTeX entry
```tex
@article{tito2022hierarchical,
title={Hierarchical multimodal transformers for Multi-Page DocVQA},
author={Tito, Rub{\`e}n and Karatzas, Dimosthenis and Valveny, Ernest},
journal={arXiv preprint arXiv:2212.05935},
year={2022}
}
``` |