<jupyter_start><jupyter_text>Charger le modèle que nous venons de créer<jupyter_code>model = Word2Vec.load("../data/one.txt")<jupyter_output><empty_output><jupyter_text>Comment est représenté le mot "rue" ?<jupyter_code>model["rue"]<jupyter_output><empty_output><jupyter_text>Et fleuriste ?<jupyter_code>model['fleuriste']<jupyter_output><empty_output><jupyter_text>Quel est leur similarité ?<jupyter_code>model.wv.similarity("rue", "fleuriste")<jupyter_output><empty_output><jupyter_text>Quel mot est le plus proche de rue ?<jupyter_code>model.wv.most_similar("rue", topn=10)<jupyter_output><empty_output><jupyter_text>Et de Bruxelles ?<jupyter_code>model.wv.most_similar("bruxelles")<jupyter_output><empty_output><jupyter_text>Comment obtenir une ville de France grâce à notre modèle ?<jupyter_code>model.wv.most_similar(positive=['bruxelles', 'france'], negative=['belgique'], topn=1)
model.wv.most_similar(positive=['bruxelles', 'liege', 'france'], negative=['belgique'], topn=1)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.14906219151036526, 'fraction_numerical': 0.004935834155972359, 'mean_word_length': 13.695652173913043, 'pattern_counts': {'":': 0, '<': 29, '<?xml version=': 0, '>': 29, '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': '7', 'dir': 'jupyter-structured-clean-dedup', 'id': '76287', 'license': 'MIT', 'n_tokens_mistral': 420, 'n_tokens_neox': 404, 'n_words': 52, 'path': 'module4/s5_model_explorer.ipynb', 'repo_name': 'ivi2/tac', 'size': '1013'}