[{'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:# Select a batch size for training. For fine-tuning BERT on a specific task, the authors recommend a batch size of 16 or 32\nbatch_size = 32\n\n# Create an iterator of our data with torch DataLoader. This helps save on memory during training because, unlike a for loop, \n# with an iterator the entire dataset does not need to be loaded into memory\n\ntrain_data = TensorDataset(train_inputs, train_masks, train_labels)\ntrain_sampler = RandomSampler(train_data)\ntrain_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=batch_size)\n\nvalidation_data = TensorDataset(validation_inputs, validation_masks, validation_labels)\nvalidation_sampler = SequentialSampler(validation_data)\nvalidation_dataloader = DataLoader(validation_data, sampler=validation_sampler, batch_size=batch_size)\n'}, {'role': 'assistant', 'content': 'data_preprocessing'}]