Update README.md

README.md

@@ -19,6 +19,7 @@ This model was used as a baseline in [Hierarchical multimodal transformers for M
 - 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:
@@ -45,6 +46,19 @@ answer_tokens = all_tokens[torch.argmax(start_scores) :torch.argmax(end_scores)+
 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								| ANLS 			| APPA		|
+|-----------------------------------------------------------------------------------|:--------------------------------------|:-------------:|:---------:|
+| [Bert-large](https://huggingface.co/rubentito/bert-large-mpdocvqa)	            | rubentito/bert-large-mpdocvqa			| 0.4183 		| 51.6177 	|
+| [Longformer-base](https://huggingface.co/rubentito/longformer-base-mpdocvqa)		| rubentito/longformer-base-mpdocvqa	| 0.5287		| 71.1696 	|
+| [**BigBird ITC base**](https://huggingface.co/rubentito/bigbird-base-itc-mpdocvqa)| rubentito/bigbird-base-itc-mpdocvqa	| 0.4929		| 67.5433 	|
+| [LayoutLMv3 base](https://huggingface.co/rubentito/layoutlmv3-base-mpdocvqa)		| rubentito/layoutlmv3-base-mpdocvqa	| 0.4538		| 51.9426 	|
+| [T5 base](https://huggingface.co/rubentito/t5-base-mpdocvqa)						| rubentito/t5-base-mpdocvqa			| 0.5050		| 0.0000 	|
+| Hi-VT5 																			| TBA 									| 0.6201		| 79.23		|
+
 ## BibTeX entry
 
 ```tex