<jupyter_start><jupyter_text>Initialize Data<jupyter_code>ATHEISM = 'alt.atheism'
CHRISTIANITY = 'soc.religion.christian'
categories = ['alt.atheism', 'soc.religion.christian']
newsgroups_train = fetch_20newsgroups(subset='train', categories=categories)
newsgroups_test = fetch_20newsgroups(subset='test', categories=categories)
class_names = ['atheism', 'christian']

vectorizer = sklearn.feature_extraction.text.TfidfVectorizer(lowercase=False)
train_vectors = vectorizer.fit_transform(newsgroups_train.data).toarray()
test_vectors = vectorizer.transform(newsgroups_test.data).toarray()<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.12459546925566344, 'fraction_numerical': 0.006472491909385114, 'mean_word_length': 11.895833333333334, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '415324', 'license': 'MIT', 'n_tokens_mistral': 224, 'n_tokens_neox': 204, 'n_words': 23, 'path': 'examples/text/News_Group.ipynb', 'repo_name': 'msamogh/rrr', 'size': '618'}