Update README.md

README.md

@@ -32,14 +32,14 @@ The model can be loaded to perform a few-shot classification like so:
 ```py
 from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
 
-AutoModelForSeq2SeqLM.from_pretrained("jpelhaw/t5-word-sense-disambiguation")
-AutoTokenizer.from_pretrained("jpelhaw/t5-word-sense-disambiguation")
+model = AutoModelForSeq2SeqLM.from_pretrained("jpelhaw/t5-word-sense-disambiguation")
+tokenizer = AutoTokenizer.from_pretrained("jpelhaw/t5-word-sense-disambiguation")
 
-input = 'question: which description describes the word " java " best in the following context? \
+input = '''question: which description describes the word " java " best in the following context? \
 descriptions:[  " A drink consisting of an infusion of ground coffee beans " , 
                 " a platform-independent programming lanugage "
                 ,  or " an island in Indonesia to the south of Borneo " ] 
-context: I like to drink " java " in the morning .'
+context: I like to drink " java " in the morning .'''
 
 
 example = tokenizer.tokenize(input, add_special_tokens=True)