[{'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:import nltk\nimport string\nimport os\nfrom nltk.stem.porter import PorterStemmer\nfrom nltk.stem.snowball import SnowballStemmer\npath = \'/opt/datacourse/data/parts\'\ntoken_dict = {}\nstemmer = SnowballStemmer("english", ignore_stopwords=True)\ndef stem_tokens(tokens, stemmer):\nstemmed = []\nfor item in tokens:\nstemmed.append(stemmer.stem(item))\nreturn stemmed\ndef tokenize(text):\ntokens = nltk.word_tokenize(text)\nstems = stem_tokens(tokens, stemmer)\nreturn stems\nvectorizer_process = CountVectorizer(analyzer=\'word\',stop_words=\'english\',preprocessor = empty_preprocessor,tokenizer=tokenize)\nword_matrix_process= vectorizer_process.fit_transform(train_all)\ndev_matrix_process = vectorizer_process.transform(dev_all)\nmodel_LG_L1 = LogisticRegression(penalty =\'l1\',C=520)#C from the above test\nmodel_LG_L1.fit(word_matrix_process, train_labels)\nindex=[]\nfor i,z in enumerate(np.sum(np.abs(model_LG_L1.coef_),axis=0) ):\nif z!=0:\nindex.append(i)\nmodel_LG = LogisticRegression(penalty =\'l2\',C=5)#C from the above test\nmodel_LG.fit(word_matrix_process[:,index], train_labels)\nprint(\'f1_score: %f\' %metrics.f1_score(dev_labels,model_LG.predict(dev_matrix_process[:,index]),average=\'binary\'))\nprint(\'accuracy_score: %f\' %metrics.precision_score(dev_labels,model_LG.predict(dev_matrix_process[:,index])))\nprint(\'recall_score: %f\' %metrics.recall_score(dev_labels,model_LG.predict(dev_matrix_process[:,index])))'}, {'role': 'assistant', 'content': 'modelling'}]