Update README.md

README.md

@@ -152,7 +152,7 @@ model = AutoModelForSequenceClassification.from_pretrained(model_name)
 model.eval()
 
 # 🧪 Example input
-text = "Turn off the fan"
+text = "Turn on the AC"
 
 # ✂️ Tokenize the input
 inputs = tokenizer(text, return_tensors="pt")
@@ -236,7 +236,7 @@ for text, answer in tests:
     top_scores = torch.softmax(topk.values, dim=1)[0]
     guesses = [(tokenizer.decode([i]).strip().lower(), float(score)) for i, score in zip(top_ids, top_scores)]
     results.append({
-        "sentence": title,
+        "sentence": text,
         "expected": answer,
         "predictions": guesses,
         "pass": answer.lower() in [g[0] for g in guesses]