<jupyter_start><jupyter_text>Word2Vec test and benchmark Inizialize Model<jupyter_code>from gensim.test.utils import common_texts, get_tmpfile
from gensim.models import Word2Vec

get_tmpfile("test_word2vec.model")
model = Word2Vec(common_texts, size=100, window=5, min_count=1, workers=4)
model.save("../models/test_word2vec.model")
model = Word2Vec.load("../models/test_word2vec.model")
model.train([["hello", "world"]], total_examples=1, epochs=1)

vector = model.wv['computer']  # numpy vector of a word
vector<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.13627992633517497, 'fraction_numerical': 0.027624309392265192, 'mean_word_length': 7.918032786885246, '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': '210958', 'license': 'FTL', 'n_tokens_mistral': 204, 'n_tokens_neox': 197, 'n_words': 36, 'path': 'notebooks/06_lst_glove_transferlearning.ipynb', 'repo_name': 'lukk60/RETrends', 'size': '543'}