Bert获得词向量，如何建立Bert环境MAC

1. python<=3.6（3.7不行），1.10<=tensorflow<2.0

2. 将目前环境换成3.6版本，conda activate python36（这是我的3.6环境名字）

3. pip安装Bert服务器和客户端

pip install bert-serving-server # 服务端
pip install bert-serving-client # 客户端，与服务端互相独立

 

3. 启动bert服务

bert-serving-start -model_dir /lab/skill/uncased_L-12_H-768_A-12

 

4. 重新打开一个命令行窗口，写入python代码

from bert_serving.client import BertClient
bc = BertClient()
doc_vecs = bc.encode(['First do it', 'then do it right', 'then do it better'])

bc.encode(['First do it ||| then do it right'])

bc = BertClient()
vec = bc.encode(['hey you', 'whats up?'])

'''vec  # [2, 25, 768]
vec[0]  # [1, 25, 768], sentence embeddings for `hey you`
vec[0][0]  # [1, 1, 768], word embedding for `[CLS]`
vec[0][1]  # [1, 1, 768], word embedding for `hey`
vec[0][2]  # [1, 1, 768], word embedding for `you`
vec[0][3]  # [1, 1, 768], word embedding for `[SEP]`
vec[0][4]  # [1, 1, 768], word embedding for padding symbol
vec[0][25]  # error, out of index!
'''

参考

https://blog.csdn.net/qq_34832393/article/details/90414293