Update app.py

app.py

@@ -28,11 +28,6 @@ input_text = st.text_input("Testo:", value="Lorem ipsum dolor sit amet, [MASK] a
 modelname = "./models/bert-base-latin-uncased"
 
 
-
-#tokenizer_roberta = AutoTokenizer.from_pretrained("pstroe/roberta-base-latin-cased3")
-#model_roberta = AutoModelForMaskedLM.from_pretrained("pstroe/roberta-base-latin-cased3")
-#fill_mask_roberta = pipeline("fill-mask", model=model_roberta, tokenizer=tokenizer_roberta)
-
 tokenizer_robertaclasscat = AutoTokenizer.from_pretrained("ClassCat/roberta-base-latin-v2")
 model_robertaclasscat = AutoModelForMaskedLM.from_pretrained("ClassCat/roberta-base-latin-v2")
 fill_mask_robertaclasscat = pipeline("fill-mask", model=model_robertaclasscat, tokenizer=tokenizer_robertaclasscat)
@@ -41,9 +36,7 @@ tokenizer = AutoTokenizer.from_pretrained(modelname)
 model = AutoModelForMaskedLM.from_pretrained(modelname)
 fill_mask = pipeline("fill-mask", model=model, tokenizer=tokenizer)
 
-#tokenizer_lv = AutoTokenizer.from_pretrained(modelname_lv)
-#model_lv = AutoModelForMaskedLM.from_pretrained(modelname_lv)
-#fill_mask_lv = pipeline("fill-mask", model=model_lv, tokenizer=tokenizer_lv)
+
 
 if input_text:
     predictions = fill_mask(input_text)
@@ -51,10 +44,6 @@ if input_text:
     for pred in predictions:
         st.write(f"**Parola**: {pred['token_str']}, **Probabilità**: {pred['score']:.4f}, **Sequence**: {pred['sequence']}")
     input_text_roberta = input_text.replace("[MASK]", "<mask>")
-    #predictions_roberta = fill_mask_roberta(input_text_roberta)    
-    #st.subheader("Risultati delle previsioni con Roberta Base Latin Cased 3:")
-    #for pred_roberta in predictions_roberta:
-    #    st.write(f"**Parola**: {pred_roberta['token_str']}, **Probabilità**: {pred_roberta['score']:.4f}, **Sequence**: {pred_roberta['sequence']}")
     predictions_robertaclasscat = fill_mask_robertaclasscat(input_text_roberta)    
     st.subheader("Risultati delle previsioni con Roberta:")
     for pred_robertaclasscat in predictions_robertaclasscat: