Update README.md

README.md

@@ -44,7 +44,7 @@ tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)
 model = AutoModelForSequenceClassification.from_pretrained(MODEL_PATH)
 
 text = "CIA Realizes It's Been Using Black Highlighters All These Years."
-tokenized_text = tokenizer([preprocess_data(text)], padding=True, truncation=True, max_length=512, return_tensors="pt")
+tokenized_text = tokenizer([preprocess_data(text)], padding=True, truncation=True, max_length=256, return_tensors="pt")
 output = model(**tokenized_text)
 probs = output.logits.softmax(dim=-1).tolist()[0]
 confidence = max(probs)
@@ -56,7 +56,7 @@ results = {"is_sarcastic": prediction, "confidence": confidence}
 Output: 
 
 ```
-{'is_sarcastic': 1, 'confidence': 0.9997416138648987}
+{'is_sarcastic': 1, 'confidence': 0.9337034225463867}
 ```
 
 ## Performance