<jupyter_start><jupyter_text>Next step takes very long...<jupyter_code>new_embeddings = mittens_model.fit(
    np.asarray(cooccurrence),
    vocab=vocabLst,
    initial_embedding_dict= original_embedding)


new_embeddings["nutritionist"]
"nutritionist" in original_embedding
original_embedding["nutritionist"]
import pickle<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.09631728045325778, 'fraction_numerical': 0.0, 'mean_word_length': 8.567567567567568, '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': '295444', 'license': 'MIT', 'n_tokens_mistral': 128, 'n_tokens_neox': 123, 'n_words': 17, 'path': 'Hyperband/Embeddings/GloveFineTune.ipynb', 'repo_name': 'tian1327/AutoLDA', 'size': '353'}