Update README.md

README.md

@@ -34,9 +34,12 @@ The model leverages the BertForSequenceClassification architecture, It has been
 ## Example 
 
 from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
 import numpy as np
+
 from scipy.special import expit
 
+
 MODEL = "PavanDeepak/Topic_Classification"
 tokenizer = AutoTokenizer.from_pretrained(MODEL)
 model = AutoModelForSequenceClassification.from_pretrained(MODEL)
@@ -51,7 +54,9 @@ scores = expit(scores)
 predictions = (scores >= 0.5) * 1
 
 for i in range(len(predictions)):
+
     if predictions[i]:
+    
         print(class_mapping[i])