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@@ -81,23 +81,28 @@ import torch.nn.functional as F
 
 # Load model and tokenizer
 model_name = "AurelPx/FinRoBERTa-Mendeley"
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
 tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForSequenceClassification.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name).to(device)
 
-# Example text
 text = "Tesla shares rally after strong earnings report"
 
-# Tokenize and predict
-inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
+# Tokenize and move to correct device
+inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(device)
+
+# Predict
 with torch.no_grad():
     logits = model(**inputs).logits
+
+# Compute probabilities and predicted label
 probs = F.softmax(logits, dim=-1)
 pred = probs.argmax().item()
 
 labels = ["Negative", "Neutral", "Positive"]
 print(f"Sentence: {text}")
 print(f"Predicted Sentiment: {labels[pred]}")
-print(f"Probabilities: {probs}")
+print(f"Probabilities: {probs.cpu().numpy()}")
 ```
 
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