Update README.md

README.md

@@ -80,6 +80,7 @@ result = emotion_classifier("We are delighted that you will be coming to visit u
 print(result)
 #[{'label': 'joy', 'score': 0.9983291029930115}]
 ```
+This model faces challenges in accurately categorizing negative sentences, as well as those containing elements of sarcasm or irony. These limitations are largely attributable to DistilBERT's constrained capabilities in semantic understanding. Although the model is generally proficient in emotion detection tasks, it may lack the nuance necessary for interpreting complex emotional nuances.
 
 ## Training and evaluation data