<jupyter_start><jupyter_text>Pickle models for flask app<jupyter_code>dump(scaler, 'scaler.joblib', compress=True)
dump(model, 'NNmodel.joblib', compress=True)
import joblib
import sklearn
print(f'joblib=={joblib.__version__}')
print(f'scikit-learn=={sklearn.__version__}')<jupyter_output>joblib==0.14.1
scikit-learn==0.22.1
{'fraction_non_alphanumeric': 0.16049382716049382, 'fraction_numerical': 0.024691358024691357, 'mean_word_length': 11.037037037037036, 'pattern_counts': {'":': 0, '<': 4, '<?xml version=': 0, '>': 4, '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': '403392', 'license': 'MIT', 'n_tokens_mistral': 137, 'n_tokens_neox': 132, 'n_words': 17, 'path': 'Notebooks/Models.ipynb', 'repo_name': 'Madinanachan/Data-science', 'size': '324'}