Update README.md

README.md

@@ -25,8 +25,21 @@ metrics:
 ## How to Use the model:
 ```python
 from transformers import pipeline
-classifier = pipeline("sentiment-analysis",model='bhadresh-savani/bert-base-uncased-emotion')
-prediction = classifier("I love using transformers. The best part is wide range of support and its easy to use")
+classifier = pipeline("text-classification",model='bhadresh-savani/bert-base-uncased-emotion', return_all_scores=True)
+prediction = classifier("I love using transformers. The best part is wide range of support and its easy to use", )
+print(prediction)
+
+"""
+output:
+[[
+{'label': 'sadness', 'score': 0.0005138228880241513}, 
+{'label': 'joy', 'score': 0.9972520470619202}, 
+{'label': 'love', 'score': 0.0007443308713845909}, 
+{'label': 'anger', 'score': 0.0007404946954920888}, 
+{'label': 'fear', 'score': 0.00032938539516180754}, 
+{'label': 'surprise', 'score': 0.0004197491507511586}
+]]
+"""
 ```
 
 ## Dataset: