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word2vec python

import gensim

text = df.colomn.apply(gensim.utils.simple_preprocess)

model = gensim.models.Word2Vec(
    window=10,
    min_count=2,
    workers=4,
)

model.build_vocab(text, progress_per=1000)

model.train(text, total_examples=model.corpus_count, epochs=model.epochs)

model.save("./word2vec2.model")

model.wv.most_similar("test")

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