Update README.md

README.md

@@ -27,10 +27,10 @@ as encoder and the checkpoints of the [DistilBERTimbau](https://huggingface.co/a
 The encoder checkpoints come from Swin Trasnformer version pre-trained on ImageNet-1k at resolution 224x224.
 
 The code used for training and evaluation is available at: https://github.com/laicsiifes/ved-transformer-caption-ptbr. In this work, Swin-DistilBERTimbau
-was trained together with its buddy [Swin-GPorTuguese](https://huggingface.co/laicsiifes/swin-gpt2-flickr30k-pt-br).
+was trained together with its buddy [Swin-GPorTuguese-2](https://huggingface.co/laicsiifes/swin-gpt2-flickr30k-pt-br).
 
-Other models evaluated didn't achieve performance as high as Swin-DistilBERTimbau and Swin-GPorTuguese, namely: DeiT-BERTimbau,
-DeiT-DistilBERTimbau, DeiT-GPorTuguese, Swin-BERTimbau, ViT-BERTimbau, ViT-DistilBERTimbau and ViT-GPorTuguese.
+Other models evaluated didn't achieve performance as high as Swin-DistilBERTimbau and Swin-GPorTuguese-2, namely: DeiT-BERTimbau,
+DeiT-DistilBERTimbau, DeiT-GPorTuguese-2, Swin-BERTimbau, ViT-BERTimbau, ViT-DistilBERTimbau and ViT-GPorTuguese-2.
 
 ## 🧑‍💻 How to Get Started with the Model
 
@@ -65,7 +65,7 @@ The evaluation metrics Cider-D, BLEU@4, ROUGE-L, METEOR and BERTScore are abbrev
 |Model|Training|Evaluation|C|B@4|RL|M|BS|
 |:---:|:------:|:--------:|:-----:|:----:|:-----:|:----:|:-------:|
 |Swin-DistilBERTimbau|Flickr30K Portuguese|Flickr30K Portuguese|66.73|24.65|39.98|44.71|72.30|
-|Swin-GPorTuguese|Flickr30K Portuguese|Flickr30K Portuguese|64.71|23.15|39.39|44.36|71.70|
+|Swin-GPorTuguese-2|Flickr30K Portuguese|Flickr30K Portuguese|64.71|23.15|39.39|44.36|71.70|
 
 ## 📋 BibTeX entry and citation info