Update README.md

README.md

@@ -32,12 +32,12 @@ tokenizer = BertTokenizer.from_pretrained("udit-k/HamSpamBERT")
 model = BertForSequenceClassification.from_pretrained("udit-k/HamSpamBERT")
 
 classifier = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)
-text = "Call this number to win FREE IPL FINAL tickets!!!"
-result = classifier(text)
-print(result)
+print(classifier("Call this number to win FREE IPL FINAL tickets!!!"))
+print(classifier("Call me when you reach home :)"))
 ```
 ```
 [{'label': 'LABEL_1', 'score': 0.9999189376831055}]
+[{'label': 'LABEL_0', 'score': 0.9999370574951172}]
 ```
 
 ## Model description