Update README.md

README.md

@@ -66,22 +66,20 @@ Note: On the **entire annotated dataset** (in-domain evaluation, no hold-out), t
 ## Usage
 
 ```python
-from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
+from transformers import pipeline
 
-model_name = "bilalzafar/cbdc-sentiment"
+# Load pipeline
+classifier = pipeline("text-classification", model="bilalzafar/CBDC-Sentiment")
 
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForSequenceClassification.from_pretrained(model_name)
+# Example sentences
+sentences = [
+"CBDCs will revolutionize payment systems and improve financial inclusion."
+]
 
-classifier = pipeline(
-    "text-classification",
-    model=model,
-    tokenizer=tokenizer,
-    truncation=True,
-    padding=True,
-    top_k=1  # return only the top prediction
-)
+# Predict
+for s in sentences:
+    result = classifier(s, return_all_scores=False)[0]
+    print(f"{s}\n → {result['label']} (score={result['score']:.4f})\n")
 
-text = "CBDCs will revolutionize payment systems and improve financial inclusion."
-print(classifier(text))
-# Example output: [{'label': 'positive', 'score': 0.9789}]
+# Example output:
+# [{CBDCs will revolutionize payment systems and improve financial inclusion. → positive (score=0.9789)}]