[{'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:# There may be a few NaN values, where the offset of a target word is greater than the max_seq_length of BERT.\n# They are very few, so I'm just dropping the rows.\nremove_test = [row for row in range(len(X_test)) if np.sum(np.isnan(X_test[row]))]\nX_train = np.delete(X_test, remove_test, 0)\nY_train = np.delete(Y_test, remove_test, 0)\n\n# We want predictions for all validation rows. So instead of removing rows, make them 0\nremove_validation = [row for row in range(len(X_validation)) if np.sum(np.isnan(X_validation[row]))]\nX_validation[remove_validation] = np.zeros(2*768+19)\n\n# We want predictions for all development rows. So instead of removing rows, make them 0\nremove_development = [row for row in range(len(X_development)) if np.sum(np.isnan(X_development[row]))]\nX_development[remove_development] = np.zeros(2*768+19)"}, {'role': 'assistant', 'content': 'data_preprocessing'}]