README.md

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

# BigBird base (ITC) fine-tuned on MP-DocVQA

This is BigBird-base trained on TriviaQA from [Google hub](https://huggingface.co/google/bigbird-base-trivia-itc) and fine-tuned on Multipage DocVQA (MP-DocVQA) dataset.
* Due to Huggingface implementation, the global tokens are defined according to the Internal Transformer Construction (ITC) strategy.

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

How to use this model to perform inference on a sample question and context in PyTorch:

```python
from transformers import BigBirdForQuestionAnswering, BigBirdTokenizerFast

# by default its in `block_sparse` mode with num_random_blocks=3, block_size=64
model = BigBirdForQuestionAnswering.from_pretrained("rubentito/bigbird-base-itc-mpdocvqa")

# you can change `attention_type` to full attention like this:
model = BigBirdForQuestionAnswering.from_pretrained("rubentito/bigbird-base-itc-mpdocvqa", attention_type="original_full")

# you can change `block_size` & `num_random_blocks` like this:
model = BigBirdForQuestionAnswering.from_pretrained("rubentito/bigbird-base-itc-mpdocvqa", block_size=16, num_random_blocks=2)

tokenizer = BigBirdTokenizerFast.from_pretrained("rubentito/bigbird-base-itc-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)

start_pos = torch.argmax(output.start_logits, dim=-1).item()
end_pos = torch.argmax(output.end_logits, dim=-1).item()

context_tokens = tokenizer.convert_ids_to_tokens(encoded_input["input_ids"][0].tolist())
answer_tokens = context_tokens[start_pos: end_pos]
answer = tokenizer.decode(tokenizer.convert_tokens_to_ids(answer_tokens))
```

## Model results

Extended experimentation can be found in Table 2 of [Hierarchical multimodal transformers for Multi-Page DocVQA](https://arxiv.org/pdf/2212.05935.pdf).
You can also check the live leaderboard at the [RRC Portal](https://rrc.cvc.uab.es/?ch=17&com=evaluation&task=4).
| Model 		 																	| HF name								| Parameters 	|	ANLS 		| APPA		|
|-----------------------------------------------------------------------------------|:--------------------------------------|:-------------:|:-------------:|:---------:|
| [Bert large](https://huggingface.co/rubentito/bert-large-mpdocvqa)	            | rubentito/bert-large-mpdocvqa			| 334M 			| 0.4183 		| 51.6177 	|
| [Longformer base](https://huggingface.co/rubentito/longformer-base-mpdocvqa)		| rubentito/longformer-base-mpdocvqa	| 148M			| 0.5287		| 71.1696 	|
| [**BigBird ITC base**](https://huggingface.co/rubentito/bigbird-base-itc-mpdocvqa)| rubentito/bigbird-base-itc-mpdocvqa	| 131M			| 0.4929		| 67.5433 	|
| [LayoutLMv3 base](https://huggingface.co/rubentito/layoutlmv3-base-mpdocvqa)		| rubentito/layoutlmv3-base-mpdocvqa	| 125M 			| 0.4538		| 51.9426 	|
| [T5 base](https://huggingface.co/rubentito/t5-base-mpdocvqa)						| rubentito/t5-base-mpdocvqa			| 223M 			| 0.5050		| 0.0000 	|
| Hi-VT5 																			| TBA 									| 316M 			| 0.6201		| 79.23		|

## Citation Information 

```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}
}
```