Aggiunta Pstroe model per compare

app.py

@@ -32,6 +32,13 @@ tokenizer_robertaclasscat = AutoTokenizer.from_pretrained("ClassCat/roberta-base
 model_robertaclasscat = AutoModelForMaskedLM.from_pretrained("ClassCat/roberta-base-latin-v2")
 fill_mask_robertaclasscat = pipeline("fill-mask", model=model_robertaclasscat, tokenizer=tokenizer_robertaclasscat)
 
+
+
+
+tokenizer_robertapstroe = AutoTokenizer.from_pretrained("pstroe/roberta-base-latin-cased")
+model_robertapstroe = AutoModelForMaskedLM.from_pretrained("pstroe/roberta-base-latin-cased")
+fill_mask_robertapstroe = pipeline("fill-mask", model=model_robertapstroe, tokenizer=tokenizer_robertapstroe)
+
 tokenizer = AutoTokenizer.from_pretrained(modelname)
 model = AutoModelForMaskedLM.from_pretrained(modelname)
 fill_mask = pipeline("fill-mask", model=model, tokenizer=tokenizer)
@@ -45,8 +52,12 @@ if input_text:
         st.write(f"**Parola**: {pred['token_str']}, **Probabilità**: {pred['score']:.4f}, **Sequence**: {pred['sequence']}")
     input_text_roberta = input_text.replace("[MASK]", "<mask>")
     predictions_robertaclasscat = fill_mask_robertaclasscat(input_text_roberta)    
-    st.subheader("Risultati delle previsioni con Roberta:")
+    st.subheader("Risultati delle previsioni con Roberta ClassCat:")
     for pred_robertaclasscat in predictions_robertaclasscat:
         st.write(f"**Parola**: {pred_robertaclasscat['token_str']}, **Probabilità**: {pred_robertaclasscat['score']:.4f}, **Sequence**: {pred_robertaclasscat['sequence']}")
+    predictions_robertapstroe = fill_mask_robertapstroe(input_text_roberta)    
+    st.subheader("Risultati delle previsioni con Roberta Pstroe:")
+    for pred_robertapstroe in predictions_robertapstroe:
+        st.write(f"**Parola**: {pred_robertapstroe['token_str']}, **Probabilità**: {pred_robertapstroe['score']:.4f}, **Sequence**: {pred_robertapstroe['sequence']}")