<jupyter_start><jupyter_text>Evaluate metrics<jupyter_code>baseline_results = model.evaluate(test_features, test_labels,
                                  batch_size=BATCH_SIZE, verbose=0)
for name, value in zip(model.metrics_names, baseline_results):
  print(name, ': ', value)
print()

plot_cm(test_labels, test_predictions_baseline)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.10136986301369863, 'fraction_numerical': 0.0027397260273972603, 'mean_word_length': 5.203389830508475, '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': 1, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '573515', 'license': 'MIT', 'n_tokens_mistral': 125, 'n_tokens_neox': 116, 'n_words': 17, 'path': 'reza_bojnordi.ipynb', 'repo_name': 'rezabojnordi/image_classification', 'size': '365'}