Update README.md

README.md

@@ -95,10 +95,10 @@ id2label = {idx: label for label, idx in label2id.items()}
 text = "Discover the latest trends in AI and wearable technology."
 
 with torch.no_grad():
-  inputs = tokenizer(text, return_tensors="pt", truncation=True)
-  outputs = model(**inputs)
-  probs = torch.sigmoid(outputs.logits).squeeze().cpu().numpy()
-  predicted_labels = [(id2label[i], round(p, 3)) for i, p in enumerate(probs) if p >= 0.5]
+    inputs = tokenizer(text, return_tensors="pt", truncation=True)
+    outputs = model(**inputs)
+    probs = torch.sigmoid(outputs.logits).squeeze().cpu().numpy()
+    predicted_labels = [(id2label[i], round(p, 3)) for i, p in enumerate(probs) if p >= 0.5]
 ```
 
 📖 Citation