gensim word2vec simple usage

from gensim.models import word2vec

token = [['나는','너를', '사랑해'],['나도','너를','사랑해']]

embedding = word2vec.Word2Vec(token, size=5, window=1, negative=3, min_count=1)

embedding.save('model') #모델 저장

embedding.wv.save_word2vec_format('my.embedding', binary=False) #모델 저장

embedding.wv['너를'] 

embedding.most_similar('너를')

from gensim.models.keyedvectors import KeyedVectors

embedding.wv.save_word2vec_format('my.embedding', binary=False) #모델 저장

model = KeyedVectors.load_word2vec_format('my.embedding', binary=False, encoding='utf-8')