Update tokenizer

README.md

@@ -97,19 +97,19 @@ You can use this model for text classification with the `transformers` library:
 from transformers import pipeline
 
 # Load the text classification pipeline
-classifier = pipeline("text-classification", model="MatteoFasulo/mdeberta-v3-base-subjectivity-english")
+classifier = pipeline("text-classification", model="MatteoFasulo/mdeberta-v3-base-subjectivity-english", tokenizer="microsoft/mdeberta-v3-base")
 
 # Example usage for an objective sentence
 text1 = "The company reported a 10% increase in profits in the last quarter."
 result1 = classifier(text1)
 print(f"Text: '{text1}' Classification: {result1}")
-# Expected output: [{'label': 'OBJ', 'score': 0.99...}]
+# Expected output: [{'label': 'OBJ', 'score': ...}]
 
 # Example usage for a subjective sentence
 text2 = "This product is absolutely amazing and everyone should try it!"
 result2 = classifier(text2)
 print(f"Text: '{text2}' Classification: {result2}")
-# Expected output: [{'label': 'SUBJ', 'score': 0.98...}]
+# Expected output: [{'label': 'SUBJ', 'score': ...}]
 ```
 
 ## Training procedure