<jupyter_start><jupyter_text>Let's wrap it all up in a pipeline<jupyter_code>steps = (('vectorizer', tfidf_vectorizer), ('classifier', lin_class))
from sklearn.pipeline import Pipeline
short_pipeline = Pipeline(steps)
import pickle
with open('./models/linear_classifier.pkl', 'wb') as f:
    pickle.dump(short_pipeline, f)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.1278409090909091, 'fraction_numerical': 0.0, 'mean_word_length': 8.051282051282051, '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': '136228', 'license': 'MIT', 'n_tokens_mistral': 124, 'n_tokens_neox': 124, 'n_words': 27, 'path': 'simple_classifier.ipynb', 'repo_name': 'Herr-Whit/covid_nlp', 'size': '352'}