Update README.md

README.md

@@ -33,13 +33,11 @@ The model leverages the BertForSequenceClassification architecture, It has been
 
 ## Example 
 
+```python
 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)
@@ -54,10 +52,9 @@ scores = expit(scores)
 predictions = (scores >= 0.5) * 1
 
 for i in range(len(predictions)):
-
     if predictions[i]:
-    
         print(class_mapping[i])
+```python
 
 
 ## Output: