metadata

license: gpl-3.0
tags:
  - DocVQA
  - Document Question Answering
  - Document Visual Question Answering
datasets:
  - rubentito/mp-docvqa
language:
  - en

T5 base fine-tuned on MP-DocVQA

This is pretrained T5 base fine-tuned on Multipage DocVQA (MP-DocVQA) dataset.

This model was used as a baseline in Hierarchical multimodal transformers for Multi-Page DocVQA.

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:

import torch
from transformers import T5Tokenizer, T5ForConditionalGeneration

tokenizer = LongformerTokenizerFast.from_pretrained("rubentito/t5-base-mpdocvqa")
model = LongformerForQuestionAnswering.from_pretrained("rubentito/t5-base-mpdocvqa")

context = "Huggingface has democratized NLP. Huge thanks to Huggingface for this."
question = "What has Huggingface done?"
input_text = "question: {:s}  context: {:s}".format(question, context)

encoding = tokenizer(input_text, return_tensors="pt")
output = self.model.generate(**encoding)
answer = tokenizer.decode(output['sequences'], skip_special_tokens=True)

Model results

Extended experimentation can be found in Table 2 of Hierarchical multimodal transformers for Multi-Page DocVQA. You can also check the live leaderboard at the RRC Portal.

Model	HF name	Parameters	ANLS	APPA
Bert large	rubentito/bert-large-mpdocvqa	334M	0.4183	51.6177
Longformer base	rubentito/longformer-base-mpdocvqa	148M	0.5287	71.1696
BigBird ITC base	rubentito/bigbird-base-itc-mpdocvqa	131M	0.4929	67.5433
LayoutLMv3 base	rubentito/layoutlmv3-base-mpdocvqa	125M	0.4538	51.9426
T5 base	rubentito/t5-base-mpdocvqa	223M	0.5050	0.0000
Hi-VT5	TBA	316M	0.6201	79.23

Citation Information

@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}
}