Showcase the example with the correct prediction for demonstration purposes

README.md

@@ -81,7 +81,7 @@ def target_sentiment_extraction(sentence, target):
 Finally, you can infer model results as follows:
 ```python
 # Input sentence.
-sentence = "I would support him despite his bad behavior."
+sentence = "I would support him."
 # Input target.
 target = "him"
 # output response
@@ -90,7 +90,7 @@ print(f"Author opinion towards `{target}` in `{sentence}` is:\n{flant5_response}
 ```
 
 The response of the model is as follows:
-> Author opinion towards `him` in `I would support him despite his bad behavior.` is: **positive**
+> Author opinion towards `him` in `I would support him.` is: **positive**
  
 ### Downstream Use