def train_word2vec_model(df, columns):
model_param = {
"alpha": config.EMBEDDING_ALPHA,
"learning_rate_decay": config.EMBEDDING_LEARNING_RATE_DECAY,
"n_epoch": config.EMBEDDING_N_EPOCH,
"sg": 1,
"hs": 1,
"min_count": config.EMBEDDING_MIN_COUNT,
"size": config.EMBEDDING_DIM,
"sample": 0.001,
"window": config.EMBEDDING_WINDOW,
"workers": config.EMBEDDING_WORKERS,
}
model_dir = config.WORD2VEC_MODEL_DIR
model_name = "Homedepot-word2vec-D%d-min_count%d.model"%(
model_param["size"], model_param["min_count"])
word2vec = DataFrameWord2Vec(df, columns, model_param)
word2vec.train()
word2vec.save(model_dir, model_name)
#---------------------- Doc2Vec ----------------------
embedding_trainer.py 文件源码
python
阅读 21
收藏 0
点赞 0
评论 0
评论列表
文章目录