[{'role': 'system', 'content': "You are an expert in machine learning and your task is to classify code snippets into one of the following categories: ['helper_functions', 'load_data', 'data_exploration', 'data_preprocessing', 'evaluation', 'modelling', 'prediction', 'result_visualization', 'save_results', 'comment_only']."}, {'role': 'user', 'content': "Identify the single most appropriate category to which the provided code belongs. Provide your response as a single category label without any additional information, commentary, or personal opinions.\\nPython Code:# Prediction on test set\n\n# Put model in evaluation mode\nmodel.eval()\n\n# Tracking variables \npredictions , true_labels = [], []\n\n# Predict \nfor batch in prediction_dataloader:\n  # Add batch to GPU\n  batch = tuple(t.to(device) for t in batch)\n  # Unpack the inputs from our dataloader\n  b_input_ids, b_input_mask, b_labels = batch\n  # Telling the model not to compute or store gradients, saving memory and speeding up prediction\n  with torch.no_grad():\n    # Forward pass, calculate logit predictions\n    logits = model(b_input_ids, token_type_ids=None, attention_mask=b_input_mask)\n\n  # Move logits and labels to CPU\n  logits = logits.detach().cpu().numpy()\n  label_ids = b_labels.to('cpu').numpy()\n  \n  # Store predictions and true labels\n  predictions.append(logits)\n#   true_labels.append(label_ids)"}, {'role': 'assistant', 'content': 'evaluation'}]