Update README.md

README.md

@@ -30,12 +30,8 @@ It achieves the following results on the evaluation set:
 ## Model description
 
 Model takes text as input and outputs an predictions for one of the 6 emotions. 
-          { 0: 'sadness',
-            1:'joy',
-            2: "love",
-            3: "anger",
-            4: "fear",
-            5: "surprise"}
+          
+          [0:'anger', 1: 'fear', 2: 'joy', 3: 'love', 4: 'sadness', 5: 'surprise']
             
 ## Intended uses & limitations
 
@@ -45,8 +41,16 @@ Use to identify an emotion of a user from above mentioned emotions.
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
 
 tokenizer = AutoTokenizer.from_pretrained("Arjun4707/Distilbert-base-uncased_dair-ai_emotion")
+
 model = AutoModelForSequenceClassification.from_pretrained("Arjun4707/Distilbert-base-uncased_dair-ai_emotion", from_tf = True)
 
+example = "I am feeling low"
+
+inputs = tokenizer(example, padding = True, return_tensors = 'pt')
+
+output_logits = model(inputs)['logits']
+
+
 ## Training and evaluation data
 
 Training data size = 16000, validation data = 2000, and test data = 2000