Update README.md

README.md

@@ -29,19 +29,16 @@ This model is developed to tag Names, Organisations and addresses. I have used a
 
 ### Direct Use
 
+'''python
 from transformers import BertTokenizer, AutoModelForTokenClassification
 from transformers import pipeline
-
 tokenizer = BertTokenizer.from_pretrained('bert-base-cased')
-
 model = AutoModelForTokenClassification.from_pretrained("ctrlbuzz/bert-addresses")
-
 nlp = pipeline("ner", model=model, tokenizer=tokenizer)
-
 example = "While Maria was representing Johnson & Associates at a conference in Spain, she mailed me a letter from her new office at 123 Elm St., Apt. 4B, Springfield, IL.",
 
 print(nlp(example))
-
+'''