Update README.md

README.md

@@ -34,26 +34,26 @@ 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
+>>> 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])
+>>> for i in range(len(predictions)):
+    >>> if predictions[i]:
+        >>> print(class_mapping[i])
 ```python