Update README.md

README.md

@@ -34,28 +34,27 @@ 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
+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)
->>> class_mapping = model.config.id2label
+MODEL = "PavanDeepak/Topic_Classification"
+tokenizer = AutoTokenizer.from_pretrained(MODEL)
+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.logits[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])
-```python
+scores = output.logits[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])
+```
 
 ## Output: