base_model: microsoft/layoutlmv3-base
license: cc-by-nc-sa-4.0
tags:
- DocVQA
- Document Question Answering
- Document Visual Question Answering
datasets:
- rubentito/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()
Metrics
Average Normalized Levenshtein Similarity (ANLS)
The standard metric for text-based VQA tasks (ST-VQA and DocVQA). It evaluates the method's reasoning capabilities while smoothly penalizes OCR recognition errors. Check Scene Text Visual Question Answering for detailed information.
Answer Page Prediction Accuracy (APPA)
In the MP-DocVQA task, the models can provide the index of the page where the information required to answer the question is located. For this subtask accuracy is used to evaluate the predictions: i.e. if the predicted page is correct or not. Check Hierarchical multimodal transformers for Multi-Page DocVQA for detailed information.
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 | rubentito/hivt5-base-mpdocvqa | 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}
}