license	tags	datasets	language
gpl-3.0	DocVQA Document Question Answering Document Visual Question Answering	MP-DocVQA	en

LayoutLMv3 base fine-tuned on MP-DocVQA

This is pretrained LayoutLMv3 from Microsoft hub and 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 LayoutLMv3Processor, LayoutLMv3ForQuestionAnswering

processor = LayoutLMv3Processor.from_pretrained("rubentito/layoutlmv3-base-mpdocvqa", apply_ocr=False)
model = LayoutLMv3ForQuestionAnswering.from_pretrained("rubentito/layoutlmv3-base-mpdocvqa")

image = Image.open("example.jpg").convert("RGB")
question = "Is this a question?"
context = ["Example"]
boxes = [0, 0, 1000, 1000]  # This is an example bounding box covering the whole image.
document_encoding = processor(image, question, context, boxes=boxes, return_tensors="pt")
outputs = model(**document_encoding)

# Get the answer
start_idx = torch.argmax(outputs.start_logits, axis=1)
end_idx = torch.argmax(outputs.end_logits, axis=1)
answers = self.processor.tokenizer.decode(input_tokens[start_idx: end_idx+1]).strip()

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	ANLS	APPA
Bert large	rubentito/bert-large-mpdocvqa	0.4183	51.6177
Longformer base	rubentito/longformer-base-mpdocvqa	0.5287	71.1696
BigBird ITC base	rubentito/bigbird-base-itc-mpdocvqa	0.4929	67.5433
LayoutLMv3 base	rubentito/layoutlmv3-base-mpdocvqa	0.4538	51.9426
T5 base	rubentito/t5-base-mpdocvqa	0.5050	0.0000
Hi-VT5	TBA	0.6201	79.23

BibTeX entry

@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}
}