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from gensim.models import Word2Vec |
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import gensim |
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def tokenize_sentence(sentence): |
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return sentence.split() |
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with open('korpus_malti_tok.txt', 'r', encoding='utf-8') as file: |
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sentences = file.read().splitlines() |
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data = [tokenize_sentence(sentence) for sentence in sentences] |
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model = Word2Vec(data, |
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vector_size=300, |
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window=10, |
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min_count=20, |
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workers=16, |
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sample=1e-5, |
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alpha=0.03, |
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min_alpha=0.0007, |
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negative=20) |
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model.build_vocab(data, progress_per=1000) |
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print(model.corpus_count) |
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model.train(data, total_examples=model.corpus_count, epochs=15) |
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model.wv.save_word2vec_format('mt_word2vec_2.txt', binary=False) |
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print('Émbeddings successfully trained!') |
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