message

app.py

@@ -22,13 +22,7 @@ claims={}
 
 
 dataset = dataset_dict["train"].shuffle(seed=42).select(range(20))
-
-print(dataset[0])
-
-for i in range(0, 20):
-    print(dataset['patent_number'][i])
-    print(dataset['decision'][i])
-    
+  
 
 for i in range(0, 20):
 
@@ -46,15 +40,10 @@ def get_score(abstract):
     with torch.no_grad():
         logits = model(**inputs).logits
 
-    predictions = F.softmax(logits, dim=1)
-    probs = F.softmax(predictions, dim=0)
-    accept_prob = probs[0].item()
-    #idk = torch.argmax(predictions)
-    print(predictions)
-    print(accept_prob)
+    predictions = F.softmax(logits, dim=1) #will get the probabilities of each label 0 and 1
+    score = predictions[1] #get the probability of the label being 1 (patent accepted)
 
-   
-    return logits
+    return score
 
 
 
@@ -63,8 +52,8 @@ 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])
-   predictability = get_score(abstract)
-   st.write(predictability)
+   patentability= get_score(abstract)
+   st.write(f"The patentability score is: {patentability}")