Update app.py

app.py

@@ -54,8 +54,8 @@ def get_all_predictions(text_sentence, model_name,top_clean=5):
    
   with torch.no_grad():
     predict = bert_model(input_ids)[0]
-  bert = decode(bert_tokenizer, predict[0, mask_idx, :].topk(top_k*5).indices.tolist(), top_clean)
-  cls = decode(bert_tokenizer, predict[0, 0, :].topk(top_k*5).indices.tolist(), top_clean)
+  bert = decode(bert_tokenizer, predict[0, mask_idx, :].topk(top_k*10).indices.tolist(), top_clean)
+  cls = decode(bert_tokenizer, predict[0, 0, :].topk(top_k*10).indices.tolist(), top_clean)
   
   if ("[MASK]" in text_sentence or "<mask>" in text_sentence):
     return {'Input sentence':text_sentence,'Tokenized text': tokenized_text, 'results_count':top_k,'Model':model_name,'Masked position': bert,'[CLS]':cls}
@@ -143,7 +143,7 @@ def main():
   st.write("This app can be used to examine both model prediction for a masked  position as well as the neighborhood of CLS vector")
   st.write("   - To examine model prediction for a position, enter the token [MASK] or <mask>")
   st.write("   - To examine just the [CLS] vector, enter a word/phrase or sentence. Example: eGFR or EGFR or non small cell lung cancer")
-  st.sidebar.slider("Select how many predictions do you need", 1 , 50, 20,key='my_slider',on_change=on_results_count_change) #some times it is possible to have less words
+  st.sidebar.slider("Select how count of predictions to display", 1 , 50, 20,key='my_slider',on_change=on_results_count_change) #some times it is possible to have less words
 
 
   try: