metadata
license: gpl-3.0
tags:
- DocVQA
- Document Question Answering
- Document Visual Question Answering
datasets:
- MP-DocVQA
language:
- 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}
}