def train_artist2vec_model(fout_path, input_datas=None, data_path=None, min_count=5, sorted_vocab=1, window=10,
size=250,
iter_n=50):
if not input_datas and data_path:
input_datas = pickle.load(open(data_path, 'rb'))
data_process_logger.info('start training')
wv_model = gensim.models.Word2Vec(input_datas, min_count=min_count, sorted_vocab=sorted_vocab, window=window,
size=size, iter=iter_n)
with open(fout_path, 'wb') as fout:
data_process_logger.info('start saving model')
pickle.dump(wv_model, fout)
print 'model saved'
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