message

app.py

@@ -26,21 +26,21 @@ for i in range(0, 20):
     claims[dataset['patent_number'][i]] = dataset['claims'][i]
 
 
-#def get_score(abstract):
+def get_score(abstract):
 
-   # model = AutoModelForSequenceClassification.from_pretrained("arianasutanto/finetuned-distilbert")
-   # tokenizer = AutoTokenizer.from_pretrained("arianasutanto/finetuned-distilbert", pad_to_max_length=True)
-    #inputs = tokenizer(abstract, max_length=512, padding='max_length', truncation=True, return_tensors='pt')
+    model = AutoModelForSequenceClassification.from_pretrained("arianasutanto/finetuned-distilbert")
+    tokenizer = AutoTokenizer.from_pretrained("arianasutanto/finetuned-distilbert", pad_to_max_length=True)
+    inputs = tokenizer(abstract, max_length=512, padding='max_length', truncation=True, return_tensors='pt')
 
-    #outputs = model(inputs['input_ids'], attention_mask=inputs['attention_mask'])
-    #scores = torch.softmax(outputs.logits, dim=1)[0]
+    outputs = model(inputs['input_ids'], attention_mask=inputs['attention_mask'])
+    scores = torch.softmax(outputs.logits, dim=1)[0]
 
-    #probs = F.softmax(scores, dim=0)
-   # accept_prob = probs[0].item()
+    probs = F.softmax(scores, dim=0)
+    accept_prob = probs[0].item()
 
-    #print(accept_prob)
+    print(accept_prob)
 
-    #return #accept_prob
+    return accept_prob
 
 
 
@@ -49,7 +49,8 @@ patent_num = st.selectbox("Choose a patent number", options=abstracts.keys())
 if st.button("Submit"):
    abstract = st.text_area(label="Abstract",value=abstracts[patent_num])
    claim = st.text_area(label="Claims",value=claims[patent_num])
-   #get_score(abstract)
+   predictability = get_score(abstract)
+   st.write(predictability)
 
 
 #abstract = abstracts[patent_num]