Update README.md

README.md

@@ -10,22 +10,21 @@ license: apache-2.0
 pipeline_tag: token-classification
 ---
 
-![image/png](https://cdn-uploads.huggingface.co/production/uploads/64c9888b3137cc529d0761c4/GqKutRwiGGif69Gjd8Df3.png)
-**Note this is the pretrained model. We recommend using the finetuned model available at https://huggingface.co/adibvafa/CodonTransformer**
-
+![image/png](https://github.com/Adibvafa/CodonTransformer/raw/main/src/banner_final.png)
 
 **CodonTransformer** is the ultimate tool for codon optimization, transforming protein sequences into optimized DNA sequences specific for your target organisms. Whether you are a researcher or a practitioner in genetic engineering, CodonTransformer provides a comprehensive suite of features to facilitate your work. By leveraging the Transformer architecture and a user-friendly Jupyter notebook, it reduces the complexity of codon optimization, saving you time and effort.
 
 
 ## Authors
-Adibvafa Fallahpour<sup>1,2</sup>\*, Vincent Gureghian<sup>3</sup>\*, Guillaume J. Filion<sup>2</sup>, Ariel B. Lindner<sup>3</sup>, Amir Pandi<sup>3</sup>‡
+Adibvafa Fallahpour<sup>1,2</sup>\*, Vincent Gureghian<sup>3</sup>\*, Guillaume J. Filion<sup>2</sup>‡,  Ariel B. Lindner<sup>3</sup>‡,  Amir Pandi<sup>3</sup>‡
 
 <sup>1</sup> Vector Institute for Artificial Intelligence, Toronto ON, Canada  
 <sup>2</sup> University of Toronto Scarborough; Department of Biological Science; Scarborough ON, Canada  
 <sup>3</sup> Université Paris Cité, INSERM U1284, Center for Research and Interdisciplinarity, F-75006 Paris, France  
 \* These authors contributed equally to this work.  
-‡ To whom correspondence should be addressed: **[email protected]** <br>
-
+‡ To whom correspondence should be addressed:
+[email protected], [email protected], [email protected]
+<br>
 
 ## Use Case
 **For a guide on finetuning CodonTransformer, check out our [GitHub.](https://github.com/Adibvafa/CodonTransformer/tree/main?tab=readme-ov-file#finetuning-codontransformer)**
@@ -56,7 +55,7 @@ output = predict_dna_sequence(
     organism=organism,
     device=DEVICE,
     tokenizer_object=tokenizer,
-    model_object=model,
+    model=model,
     attention_type="original_full",
 )
 print(format_model_output(output))