
# BERT large fine-tuned on MP-DocVQA

This is BERT trained on [SinglePage DocVQA](https://arxiv.org/abs/2007.00398) and fine-tuned on Multipage DocVQA (MP-DocVQA) dataset.

This model was used as a baseline in [Hierarchical multimodal transformers for Multi-Page DocVQA](https://arxiv.org/pdf/2212.05935.pdf).
- Training hyperparameters can be found in Table 8 of Appendix D.

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

```python
from transformers import AutoModelForQuestionAnswering, AutoTokenizer

model = AutoModelForQuestionAnswering.from_pretrained("rubentito/bert-large-mpdocvqa")
tokenizer = AutoTokenizer.from_pretrained("rubentito/bert-large-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.argmax, dim=-1).item()

pred_answer = context[start_pos:end_pos]
```

## 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](https://arxiv.org/abs/1905.13648) 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](https://arxiv.org/abs/2212.05935) for detailed information.

## 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](https://huggingface.co/rubentito/hivt5-base-mpdocvqa)  					| rubentito/hivt5-base-mpdocvqa 		| 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}
}
```
{"language": ["en"], "license": "gpl-3.0", "tags": ["DocVQA", "Document Question Answering", "Document Visual Question Answering"], "datasets": ["rubentito/mp-docvqa"], "base_model": "bert-large-uncased-whole-word-masking-finetuned-squad"}
transformers pytorch bert question-answering DocVQA Document Question Answering Document Visual Question Answering en dataset:rubentito/mp-docvqa arxiv:2007.00398 arxiv:2212.05935 arxiv:1905.13648 base_model:bert-large-uncased-whole-word-masking-finetuned-squad license:gpl-3.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #question-answering #DocVQA #Document Question Answering #Document Visual Question Answering #en #dataset-rubentito/mp-docvqa #arxiv-2007.00398 #arxiv-2212.05935 #arxiv-1905.13648 #base_model-bert-large-uncased-whole-word-masking-finetuned-squad #license-gpl-3.0 #endpoints_compatible #region-us 

BERT large fine-tuned on MP-DocVQA
==================================


This is BERT trained on SinglePage DocVQA and fine-tuned on Multipage DocVQA (MP-DocVQA) dataset.


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


* Training hyperparameters can be found in Table 8 of Appendix D.


How to use
----------


### Inference


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


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.




### Inference


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


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.
TAGS
#transformers #pytorch #bert #question-answering #DocVQA #Document Question Answering #Document Visual Question Answering #en #dataset-rubentito/mp-docvqa #arxiv-2007.00398 #arxiv-2212.05935 #arxiv-1905.13648 #base_model-bert-large-uncased-whole-word-masking-finetuned-squad #license-gpl-3.0 #endpoints_compatible #region-us 
 ### Inference


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


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.
TAGS
#transformers #pytorch #bert #question-answering #DocVQA #Document Question Answering #Document Visual Question Answering #en #dataset-rubentito/mp-docvqa #arxiv-2007.00398 #arxiv-2212.05935 #arxiv-1905.13648 #base_model-bert-large-uncased-whole-word-masking-finetuned-squad #license-gpl-3.0 #endpoints_compatible #region-us 
### Inference


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


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.