BERT 服务化 bert-as-service

bert-as-service 用 BERT 作为句子编码器, 并通过 ZeroMQ 服务托管, 只需两行代码就可以将句子映射成固定长度的向量表示;

准备

windows10 + python3.5 + tensorflow1.2.1

安装流程

安装 tensorflow, 参考
安装 bert-as-service

bert-as-service, 依赖于 python≥3.5 AND tensorflow≥1.10;

pip install bert-serving-server
pip instlal bert-serving-client

下载中文 bert 预训练的模型

BERT-Base, Uncased	12-layer, 768-hidden, 12-heads, 110M parameters
BERT-Large, Uncased	24-layer, 1024-hidden, 16-heads, 340M parameters
BERT-Base, Cased	12-layer, 768-hidden, 12-heads , 110M parameters
BERT-Large, Cased	24-layer, 1024-hidden, 16-heads, 340M parameters
BERT-Base, Multilingual Cased (New)	104 languages, 12-layer, 768-hidden, 12-heads, 110M parameters
BERT-Base, Multilingual Cased (Old)	102 languages, 12-layer, 768-hidden, 12-heads, 110M parameters
BERT-Base, Chinese	Chinese Simplified and Traditional, 12-layer, 768-hidden, 12-heads, 110M parameters

启动 bert-as-serving 服务

bert-serving-start -model_dir /tmp/english_L-12_H-768_A-12/ -num_worker=2  //模型路径自改
usage: xxxx\Anaconda3\envs\py35\Scripts\bert-serving-start -model_dir D:\env\bert\chinese_L-12_H-768_A-12 -num_worker=2
                 ARG   VALUE
__________________________________________________
           ckpt_name = bert_model.ckpt
         config_name = bert_config.json
                cors = *
                 cpu = False
          device_map = []
       do_lower_case = True
  fixed_embed_length = False
                fp16 = False
 gpu_memory_fraction = 0.5
       graph_tmp_dir = None
    http_max_connect = 10
           http_port = None
        mask_cls_sep = False
      max_batch_size = 256
         max_seq_len = 25
           model_dir = D:\env\bert\chinese_L-12_H-768_A-12
no_position_embeddings = False
    no_special_token = False
          num_worker = 2
       pooling_layer = [-2]
    pooling_strategy = REDUCE_MEAN
                port = 5555
            port_out = 5556
       prefetch_size = 10
 priority_batch_size = 16
show_tokens_to_client = False
     tuned_model_dir = None
             verbose = False
                 xla = False

I:[35mVENTILATOR[0m:freeze, optimize and export graph, could take a while...
I:[36mGRAPHOPT[0m:model config: D:\env\bert\chinese_L-12_H-768_A-12\bert_config.json
I:[36mGRAPHOPT[0m:checkpoint: D:\env\bert\chinese_L-12_H-768_A-12\bert_model.ckpt
I:[36mGRAPHOPT[0m:build graph...
I:[36mGRAPHOPT[0m:load parameters from checkpoint...
I:[36mGRAPHOPT[0m:optimize...
I:[36mGRAPHOPT[0m:freeze...
I:[36mGRAPHOPT[0m:write graph to a tmp file: C:\Users\Memento\AppData\Local\Temp\tmpo07002um
I:[35mVENTILATOR[0m:bind all sockets
I:[35mVENTILATOR[0m:open 8 ventilator-worker sockets
I:[35mVENTILATOR[0m:start the sink
I:[32mSINK[0m:ready
I:[35mVENTILATOR[0m:get devices
W:[35mVENTILATOR[0m:no GPU available, fall back to CPU
I:[35mVENTILATOR[0m:device map:
                worker  0 -> cpu
                worker  1 -> cpu
I:[33mWORKER-0[0m:use device cpu, load graph from C:\Users\Memento\AppData\Local\Temp\tmpo07002um
I:[33mWORKER-1[0m:use device cpu, load graph from C:\Users\Memento\AppData\Local\Temp\tmpo07002um
I:[33mWORKER-0[0m:ready and listening!
I:[33mWORKER-1[0m:ready and listening!
I:[35mVENTILATOR[0m:all set, ready to serve request!

用 python 模拟调用 bert-as-service 服务

bc = BertClient(ip="localhost", check_version=False, check_length=False)
vec = bc.encode(['你好', '你好呀', '我很好'])
print(vec)

输出结果:

[[ 0.2894022  -0.13572647  0.07591158 ... -0.14091237  0.54630077
  -0.30118054]
 [ 0.4535432  -0.03180456  0.3459639  ... -0.3121457   0.42606848
  -0.50814617]
 [ 0.6313594  -0.22302179  0.16799903 ... -0.1614125   0.23098437
  -0.5840646 ]]

准备

安装流程

亮点