Update README.md

README.md

@@ -25,9 +25,21 @@ metrics:
 ## How to Use the model:
 ```python
 from transformers import pipeline
-classifier = pipeline("sentiment-analysis",model='bhadresh-savani/distilbert-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/distilbert-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': 0.0006792712374590337}, 
+{'label': 'joy', 'score': 0.9959300756454468}, 
+{'label': 'love', 'score': 0.0009452480007894337}, 
+{'label': 'anger', 'score': 0.0018055217806249857}, 
+{'label': 'fear', 'score': 0.00041110432357527316}, 
+{'label': 'surprise', 'score': 0.0002288572577526793}
+]]
+"""
 ```
 
 ## Dataset: