Update README.md

README.md

@@ -29,7 +29,7 @@ This model is developed to tag Names, Organisations and addresses. I have used a
 
 ### Direct Use
 
-'''python
+```python
 from transformers import BertTokenizer, AutoModelForTokenClassification
 from transformers import pipeline
 tokenizer = BertTokenizer.from_pretrained('bert-base-cased')
@@ -38,7 +38,7 @@ 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))
-'''
+```