Update README.md

README.md

@@ -33,27 +33,27 @@ The model uses the GELU activation function in its hidden layers and applies dro
 
 ## Example 
 
-```from transformers import AutoModelForSequenceClassification, TFAutoModelForSequenceClassification
-from transformers import AutoTokenizer
-import numpy as np
-from scipy.special import expit
+```from transformers import AutoModelForSequenceClassification, TFAutoModelForSequenceClassification```
+```from transformers import AutoTokenizer
+```import numpy as np
+```from scipy.special import expit
 
     
-MODEL = f"PavanDeepak/Topic_Classification"
-tokenizer = AutoTokenizer.from_pretrained(MODEL)
+```MODEL = f"PavanDeepak/Topic_Classification"
+```tokenizer = AutoTokenizer.from_pretrained(MODEL)
 
-model = AutoModelForSequenceClassification.from_pretrained(MODEL)
-class_mapping = model.config.id2label
+```model = AutoModelForSequenceClassification.from_pretrained(MODEL)
+```class_mapping = model.config.id2label
 
-text = "I love chicken manchuria"
-tokens = tokenizer(text, return_tensors='pt')
-output = model(**tokens)
+```text = "I love chicken manchuria"
+```tokens = tokenizer(text, return_tensors='pt')
+```output = model(**tokens)
 
-scores = output[0][0].detach().numpy()
-scores = expit(scores)
-predictions = (scores >= 0.5) * 1
+```scores = output[0][0].detach().numpy()
+```scores = expit(scores)
+```predictions = (scores >= 0.5) * 1
 
 
-for i in range(len(predictions)):
-  if predictions[i]:
-    print(class_mapping[i])```
+```for i in range(len(predictions)):
+  ```if predictions[i]:
+    ```print(class_mapping[i])```