
# T5 base fine-tuned on MP-DocVQA

This is [pretrained](https://huggingface.co/t5-base) T5 base 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).
- 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:

```python
import torch
from transformers import T5Tokenizer, T5ForConditionalGeneration

tokenizer = LongformerTokenizerFast.from_pretrained("rubentito/t5-base-mpdocvqa")
model = LongformerForQuestionAnswering.from_pretrained("rubentito/t5-base-mpdocvqa")

context = "Huggingface has democratized NLP. Huge thanks to Huggingface for this."
question = "What has Huggingface done?"
input_text = "question: {:s}  context: {:s}".format(question, context)

encoding = tokenizer(input_text, return_tensors="pt")
output = self.model.generate(**encoding)
answer = tokenizer.decode(output['sequences'], skip_special_tokens=True)
```

## 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": "t5-base"}
transformers pytorch t5 text2text-generation DocVQA Document Question Answering Document Visual Question Answering en dataset:rubentito/mp-docvqa arxiv:2212.05935 arxiv:1905.13648 base_model:t5-base license:gpl-3.0 autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #t5 #text2text-generation #DocVQA #Document Question Answering #Document Visual Question Answering #en #dataset-rubentito/mp-docvqa #arxiv-2212.05935 #arxiv-1905.13648 #base_model-t5-base #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

T5 base fine-tuned on MP-DocVQA
===============================


This is pretrained T5 base 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:


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 #t5 #text2text-generation #DocVQA #Document Question Answering #Document Visual Question Answering #en #dataset-rubentito/mp-docvqa #arxiv-2212.05935 #arxiv-1905.13648 #base_model-t5-base #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

TAGS
#transformers #pytorch #t5 #text2text-generation #DocVQA #Document Question Answering #Document Visual Question Answering #en #dataset-rubentito/mp-docvqa #arxiv-2212.05935 #arxiv-1905.13648 #base_model-t5-base #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
