Update README.md

README.md

@@ -19,13 +19,25 @@ It achieves the following results on the evaluation set:
 - Uar: 0.8800
 - Acc: 0.8897
 
+For the test Set: 
+- UAR: 0.805
+- 0.845
+
+FI scores:  
+labels: ['anger', 'happiness', 'sadness', 'neutral']
+Result per class (F1 score): [0.84, 0.364, 1.0, 1.0]
+
 ## Model description
 
-More information needed
+This model is to predict one of four emotion categories: 'anger', 'happiness', 'sadness', 'neutral'
 
 ## Intended uses & limitations
 
-More information needed
+How to use:  
+```
+from transformers import pipeline
+pipe = pipeline("audio-classification", model="Bagus/hubert_large_emodb")
+pipe('file.wav')
 
 ## Training and evaluation data