Update README.md

README.md

@@ -14,23 +14,23 @@ base_model:
 pipeline_tag: text-generation
 ---
 
-# 🎉 Swin-GPorTuguese for Brazilian Portuguese Image Captioning
+# 🎉 Swin-GPorTuguese-2 for Brazilian Portuguese Image Captioning
 
-Swin-GPorTuguese model trained for image captioning on [Flickr30K Portuguese](https://huggingface.co/datasets/laicsiifes/flickr30k-pt-br) (translated version using Google Translator API)
+Swin-GPorTuguese-2 model trained for image captioning on [Flickr30K Portuguese](https://huggingface.co/datasets/laicsiifes/flickr30k-pt-br) (translated version using Google Translator API)
 at resolution 224x224 and max sequence length of 1024 tokens.
 
 
 ## 🤖 Model Description
 
-The Swin-GPorTuguese is a type of Vision Encoder Decoder which leverage the checkpoints of the [Swin Transformer](https://huggingface.co/microsoft/swin-base-patch4-window7-224)
-as encoder and the checkpoints of the [GPorTuguese](pierreguillou/gpt2-small-portuguese) as decoder.
+The Swin-GPorTuguese-2 is a type of Vision Encoder Decoder which leverage the checkpoints of the [Swin Transformer](https://huggingface.co/microsoft/swin-base-patch4-window7-224)
+as encoder and the checkpoints of the [GPorTuguese-2](pierreguillou/gpt2-small-portuguese) as decoder.
 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-GPorTuguese
+The code used for training and evaluation is available at: https://github.com/laicsiifes/ved-transformer-caption-ptbr. In this work, Swin-GPorTuguese-2
 was trained together with its buddy [Swin-DistilBERTimbau](https://huggingface.co/laicsiifes/swin-distilbert-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