[{'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:def flesch_reading_ease(text, show_details = False):\ntext = text.lower()\nsentences = nltk.tokenize.sent_tokenize(text)\nwords = nltk.wordpunct_tokenize(re.sub(\'[^a-zA-Z_ ]\', \'\',text))\nsyllables = [count_syllables(word) for word in words]\nnum_sentences = len(sentences)\nnum_unknown_words = syllables.count(-1)\nnum_words = len(words) - num_unknown_words\nnum_syllables = sum([s for s in syllables if s > 0])\nif (num_sentences == 0 or num_words == 0): return None\nfre = readability_ease(num_sentences, num_words, num_syllables)\nif show_details:\nreturn {\n"sentence" : num_sentences,\n"word" : num_words,\n"syllables" : num_syllables,\n"unknown": num_unknown_words\n}\nreturn(fre)'}, {'role': 'assistant', 'content': 'data_preprocessing'}]