Exponentiation

app.py

@@ -17,7 +17,7 @@ def predict(context, intent, multi_class):
     input_text = "In one word, what is the following describing: " + context
     input_ids = qa_tokenizer(input_text, return_tensors="pt").input_ids.to(device)
     object_output = qa_tokenizer.decode(qa_model.generate(input_ids, max_length=2)[0])
-    batch = ['I think the ' + object_output + ' are long.', 'I think the ' + object_output + ' are ' + opposite_output, 'I think the ' + object_output + ' are the perfect']
+    batch = ['I think the ' + object_output + ' is ' + intent, 'I think the ' + object_output + ' is ' + opposite_output, 'I think the ' + object_output  + ' are neither ' + intent + ' nor ' + opposite_output]
     outputs = []
     for i, hypothesis in enumerate(batch):
         input_ids = te_tokenizer.encode(context, hypothesis, return_tensors='pt').to(device)
@@ -35,10 +35,12 @@ def predict(context, intent, multi_class):
     outputs[2] = outputs[2].flip(dims=[0])
     # -> [entailment, neutral, contradiction]
     outputs[0] = outputs[0].flip(dims=[0])
-    pn_tensor = (outputs[0] + outputs[1]).softmax(dim=0)
+    pn_tensor = (outputs[0] + outputs[1])/2
     pn_tensor[1] = pn_tensor[1] * outputs[2][0]
     pn_tensor[2] = pn_tensor[2] * outputs[2][1]
     pn_tensor[0] = pn_tensor[0] * outputs[2][1]
+    
+    pn_tensor = pn_tensor.exp() - 1
 
     if (multi_class):
         pn_tensor = torch.sigmoid(pn_tensor)