# Function to evaluate model performance
def evaluate_model(model, dataloader, device):
    model.eval()  # Set model to evaluation mode
    all_preds = []
    all_labels = []

    # Disable gradient calculations
    with torch.no_grad():
        for batch in dataloader:
            input_ids = batch['input_ids'].to(device)
            attention_mask = batch['attention_mask'].to(device)
            labels = batch['labels'].to(device)

            # Forward pass to get logits
            outputs = model(input_ids, attention_mask=attention_mask)
            logits = outputs.logits

            # Get predictions
            preds = torch.argmax(logits, dim=1).cpu().numpy()
            all_preds.extend(preds)
            all_labels.extend(labels.cpu().numpy())

    # Calculate evaluation metrics
    accuracy = accuracy_score(all_labels, all_preds)
    precision, recall, f1, _ = precision_recall_fscore_support(all_labels, 
                                                              all_preds, 
                                                              average='binary')

    return accuracy, precision, recall, f1