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

  1. 安装 tensorflow, 参考
  2. 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

  1. 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

  2. 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!
  1. 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 ]]

亮点