<jupyter_start><jupyter_text>Create list with indices for themes to predict<jupyter_code>theme_indices = []
for theme in themes_to_predict:
  print('Theme #:', theme)
  theme_index = theme_codes[theme]
  theme_indices.append(theme_index)
Y = np.array([[row['themes'][theme_index] for theme_index in theme_indices] for index, row in df.iterrows()])
X_train, X_test, Y_train, Y_test = model_selection.train_test_split(X, Y, test_size=0.1, random_state=42)
epochs = 5
mult_mod.fit(np.array(X_train), np.array(Y_train),
          #batch_size=128,
          validation_data=(np.array(X_test),np.array(Y_test)),
          epochs=epochs)
X_test[15]
Y_test[13:15]
X_test.shape
X_test[15].shape<jupyter_output><empty_output><jupyter_text>This shows that model output only zeros<jupyter_code>np.unique(mult_mod.predict(X_test))<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.12750885478158205, 'fraction_numerical': 0.018890200708382526, 'mean_word_length': 6.709090909090909, 'pattern_counts': {'":': 0, '<': 9, '<?xml version=': 0, '>': 9, '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': '2', 'dir': 'jupyter-structured-clean-dedup', 'id': '497571', 'license': 'BSD-2-Clause', 'n_tokens_mistral': 329, 'n_tokens_neox': 322, 'n_words': 53, 'path': 'train/danylo/Choosing_themes_and_model.ipynb', 'repo_name': 'triantafillu/Bootcamp-Repository', 'size': '847'}