<jupyter_start><jupyter_text>The threshold may be overfitting to individual author texts, not the dataset<jupyter_code># score_data = np.concatenate([results[None, :] for results in score_data])

# results_df = pd.DataFrame(np.array(score_data), index=model_names, columns=["balanced_accuracy"])

# results_df<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.11504424778761062, 'fraction_numerical': 0.0, 'mean_word_length': 9.303030303030303, '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': '651850', 'license': 'Apache-2.0', 'n_tokens_mistral': 116, 'n_tokens_neox': 113, 'n_words': 23, 'path': 'notebooks/deliver/2021-03-03-gc-profile-benchmarking.ipynb', 'repo_name': 'grchristensen/avpd', 'size': '339'}