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0ee6a96 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 | import gensim
from gensim.models import Word2Vec
import MeCab
def read_training_data(filepath):
english_sentences = []
japanese_sentences = []
with open(filepath, "r", encoding="utf-8") as f:
for line in f:
jp_sentence, en_sentence = line.strip().split("\t")
english_sentences.append(en_sentence.strip().split())
japanese_sentences.append(mecab.parse(jp_sentence).strip().split())
return english_sentences, japanese_sentences
def train_word2vec(sentences, language="en", embedding_size=300, window=5, min_count=1, sg=1):
# sg=1 skip-gram, sg=0 CBOW
model = Word2Vec(sentences, vector_size=embedding_size, window=window, min_count=min_count, sg=sg)
model.save(f"word2vec_{language}.model")
return model
if __name__ == "__main__":
mecab = MeCab.Tagger("-Owakati")
english_sentences, japanese_sentences = read_training_data("./data/train.txt")
en_model = train_word2vec(english_sentences, language="en", embedding_size=300, window=5, min_count=1, sg=0)
print(f"Trained English Word Embedding with {len(en_model.wv)} words.")
jp_model = train_word2vec(japanese_sentences, language="jp", embedding_size=300, window=5, min_count=1, sg=0)
print(f"Trained Japanese Word Embedding with {len(jp_model.wv)} words.")
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