use Google Bert model to encode a sentence to vector.
usage
BERT-Base, Chinese:
Chinese Simplified and Traditional, 12-layer, 768-hidden, 12-heads, 110M
parameters
Download the model above, unzip and place in current directory.
How to encode a sentence?
from bert_encoder import BertEncoder
be = BertEncoder()
embedding = be.encode("新年快乐，恭喜发财，万事如意！")
print(embedding)
print(embedding.shape)
update：直接使用bert的CLS位置得到句向量然后计算相似度被证明是不可行的，后来有很多工作研究这一点，如果想得到可用的bert句向量也有很多办法，例如可以参考：Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks