<jupyter_start><jupyter_text>Métricas<jupyter_code>y_real = data_df["rejeicao"].values
pred = data_df["predicao"].values
for metric in METRICS.keys():
    print("{}: {}".format(metric, METRICS[metric](y_real, pred)))<jupyter_output>mse: 0.04706239757861578
mae: 0.18073948484554625
r2: 0.5191742189293784
{'fraction_non_alphanumeric': 0.15789473684210525, 'fraction_numerical': 0.17763157894736842, 'mean_word_length': 9.166666666666666, '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': '402631', 'license': 'MIT', 'n_tokens_mistral': 166, 'n_tokens_neox': 134, 'n_words': 17, 'path': 'model_analysis/evaluation.ipynb', 'repo_name': 'kenzosakiyama/BBBert20', 'size': '304'}