Update README.md

README.md

@@ -20,21 +20,19 @@ metrics:
     # get category probability
     def get_category_proba( text ):
         max_length = 250
-        # prepare our text into tokenized sequence
+        # prepare token sequence
         inputs = tokenizer([text], padding=True, truncation=True, max_length=max_length, return_tensors="pt")
-        # perform inference to our model
+        # perform inference
         outputs = model(**inputs)
         # get output probabilities by doing softmax
         probs = outputs[0].softmax(1)
     
-        # executing argmax function to get the candidate label
-        # probs.argmax()
+        # executing argmax function to get the candidate label index
         label_index = probs.argmax(dim=1)[0].tolist() # convert tensor to int
-        # label_index = np.argmax(probs.detach(), axis=1)
-        
+        # get the label name        
         label = idx2cate[ label_index ]
     
-        # Note that result is numpy format and it should be convert to float
+        # get the label probability
         proba = round(float(probs.tolist()[0][label_index]),2)
     
         response = {'label': label, 'proba': proba}