[{'role': 'system', 'content': "You are an expert in machine learning and your task is to classify the code snippets into categories from the provided list of categories. List of categories: ['helper_functions', 'load_data', 'data_exploration', 'data_preprocessing', 'evaluation', 'modelling', 'prediction', 'result_visualization', 'save_results', 'comment_only']"}, {'role': 'user', 'content': "Identify to which category the provided code belongs and assign the code snippet to at least 1 but up to 5 categories based on the probabilities, ignore if the probability of the category is negligible. Strictly provide your response as a list. Avoid providing any additional information, commentary, or personal opinions.\\nPython Code:# initialize Tokenizer to encode strings into integers\ntokenizer = Tokenizer()\n\n# calculate number of rows in our dataset\nnum_rows = df_train.shape[0]\n\n# create vocabulary from all words in our dataset for encoding\ntokenizer.fit_on_texts(df_train['Phrase'].values)\n\n# max length of 1 row (number of words)\nrow_max_length = max([len(x.split()) for x in df_train['Phrase'].values])\n\n# count number of unique words\nvocabulary_size = len(tokenizer.word_index) + 1\n\n# convert words into integers\nX_train_tokens = tokenizer.texts_to_sequences(X_train)\nX_test_tokens = tokenizer.texts_to_sequences(X_test)\nX_sub_tokens = tokenizer.texts_to_sequences(df_submission['Phrase'].values)\n\n# ensure every row has same size - pad missing with zeros\nX_train_pad = pad_sequences(X_train_tokens, maxlen=row_max_length, padding='post')\nX_test_pad = pad_sequences(X_test_tokens, maxlen=row_max_length, padding='post')\nX_sub_pad = pad_sequences(X_sub_tokens, maxlen=row_max_length, padding='post')"}, {'role': 'assistant', 'content': "['data_preprocessing']"}]