Update README.md

README.md

@@ -23,12 +23,12 @@ You can use this model directly using the AutoModelForSeq2SeqLM class.
 >>> input_str = "[TGT] stocks dropped 42% while Samsung rallied."
 >>> input = tokenizer(input_str, return_tensors='pt')
 >>> output = model.generate(**input, max_length=20)
->>> print(output)
+>>> print(tokenizer.decode(output[0]))
 The sentiment for [TGT] in the given sentence is NEGATIVE.
 >>> input_str = "Tesla stocks dropped 42% while [TGT] rallied."
 >>> input = tokenizer(input_str, return_tensors='pt')
 >>> output = model.generate(**input, max_length=20)
->>> print(output)
+>>> print(tokenizer.decode(output[0]))
 The sentiment for [TGT] in the given sentence is POSITIVE.
 ```
 ## Evaluation Results