<jupyter_start><jupyter_text>Using the model<jupyter_code>from joblib import dump, load
import numpy as np
model = load('Dragon.joblib')
features = np.array([[-0.44229893,  1.18549186, -1.1230799 , -0.27288841, -1.42263284,
       -0.64852152, -1.73629764,  2.56501119, -0.99239136, -0.572851  ,
       -0.98922541,  0.4386066 , -0.49436066]])
model.predict(features)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.15869017632241814, 'fraction_numerical': 0.28463476070528965, 'mean_word_length': 4.1688311688311686, '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': '159811', 'license': 'MIT', 'n_tokens_mistral': 233, 'n_tokens_neox': 175, 'n_words': 28, 'path': 'ml project/Model Usage.ipynb', 'repo_name': 'Shubhamditya36/Ist-ML-Project', 'size': '397'}