Update app.py

app.py

@@ -270,14 +270,15 @@ igen_pubmed = gr.Interface(keyphrase_generator,
                                                  'sentence-transformers/all-distilroberta-v1',
                                                  'sentence-transformers/gtr-t5-large',
                                                  'pritamdeka/S-Bluebert-snli-multinli-stsb',
-                                                 'pritamdeka/S-Biomed-Roberta-snli-multinli-stsb',                                                 
+                                                 'pritamdeka/S-Biomed-Roberta-snli-multinli-stsb',
+                                                 'pritamdeka/S-BioBert-snli-multinli-stsb',                                                 
                                                  'sentence-transformers/stsb-mpnet-base-v2',
                                                  'sentence-transformers/stsb-roberta-base-v2',
                                                  'sentence-transformers/stsb-distilroberta-base-v2',
                                                  'sentence-transformers/sentence-t5-large',
                                                  'sentence-transformers/sentence-t5-base'], 
                                         type="value", 
-                                        default='pritamdeka/S-Biomed-Roberta-snli-multinli-stsb', 
+                                        default='pritamdeka/S-BioBert-snli-multinli-stsb', 
                                         label="Select any SBERT model for TextRank from the list below"),
                      gr.inputs.Dropdown(choices=['sentence-transformers/paraphrase-mpnet-base-v2',
                                                  'sentence-transformers/all-mpnet-base-v1',
@@ -304,9 +305,10 @@ igen_pubmed = gr.Interface(keyphrase_generator,
                                         label="Select any SapBERT model for clustering from the list below"),
                      gr.inputs.Slider(minimum=5, maximum=15, step=1, default=10, label="PubMed Max Abstracts"),
                      gr.inputs.Dropdown(choices=['pritamdeka/S-Bluebert-snli-multinli-stsb',
+                                                 'pritamdeka/S-BioBert-snli-multinli-stsb',
                                                  'pritamdeka/S-Biomed-Roberta-snli-multinli-stsb'], 
                                         type="value", 
-                                        default='pritamdeka/S-Biomed-Roberta-snli-multinli-stsb', 
+                                        default='pritamdeka/S-BioBert-snli-multinli-stsb', 
                                         label="Select any SBERT model for abstracts from the list below")], 
              #outputs=gr.outputs.Dataframe(type="auto", label="Retrieved Results from PubMed",max_cols=2, overflow_row_behaviour="paginate"), 
              outputs=gr.outputs.JSON(label="Title and Abstracts"),