bert模型字向量获取---web服务

周末花时间写了个bert子向量获取的web服务，这东西以后完全是要替换word2vec模型，再过些估计会像word2vec那样，每个人都可以玩，下面看看怎么使用把，不难：

 

中文bert模型下载：https://storage.googleapis.com/bert_models/2018_11_03/chinese_L-12_H-768_A-12.zip

下载之后解压 把下面代码中的bert_path改为自己的路径既可以

import  tensorflow as  tf
from bert import modeling
import os
import collections
import  six
from gevent import monkey
monkey.patch_all()
from flask import Flask, request
from gevent import pywsgi
import numpy  as np
import json
flags = tf.flags

FLAGS = flags.FLAGS

bert_path = '/Users/zhoumeixu/Documents/python/BERT-BiLSTM-CRF-NER/data/chinese_L-12_H-768_A-12/'

flags.DEFINE_string(
    "bert_config_file", os.path.join(bert_path, 'bert_config.json'),
    "The config json file corresponding to the pre-trained BERT model."
)

flags.DEFINE_string(
    "bert_vocab_file", os.path.join(bert_path, 'vocab.txt'),
    "The config vocab file"
)

flags.DEFINE_string(
    "init_checkpoint", os.path.join(bert_path, 'bert_model.ckpt'),
    "Initial checkpoint (usually from a pre-trained BERT model)."
)

app = Flask(__name__)

def convert_to_unicode(text):
  """Converts `text` to Unicode (if it's not already), assuming utf-8 input."""
  if six.PY3:
    if isinstance(text, str):
      return text
    elif isinstance(text, bytes):
      return text.decode("utf-8", "ignore")
    else:
      raise ValueError("Unsupported string type: %s" % (type(text)))
  elif six.PY2:
    if isinstance(text, str):
      return text.decode("utf-8", "ignore")
    elif isinstance(text, unicode):
      return text
    else:
      raise ValueError("Unsupported string type: %s" % (type(text)))
  else:
    raise ValueError("Not running on Python2 or Python 3?")


def load_vocab(vocab_file):
  vocab = collections.OrderedDict()
  vocab.setdefault("blank",2)
  index = 0
  with tf.gfile.GFile(vocab_file, "r") as reader:
    while True:
      token = convert_to_unicode(reader.readline())
      if not token:
        break
      token = token.strip()
      vocab[token] = index
      index += 1
  return vocab

di=load_vocab(vocab_file=FLAGS.bert_vocab_file)
init_checkpoint=FLAGS.init_checkpoint
use_tpu=False

sess=tf.Session()

bert_config = modeling.BertConfig.from_json_file(FLAGS.bert_config_file)

print(init_checkpoint)

is_training=False
use_one_hot_embeddings=False

def inputs(vectors,maxlen=10):
    length=len(vectors)
    if length>=maxlen:
        return  vectors[0:maxlen],[1]*maxlen,[0]*maxlen
    else:
        input=vectors+[0]*(maxlen-length)
        mask=[1]*length+[0]*(maxlen-length)
        segment=[0]*maxlen
        return input,mask,segment


input_ids_p=tf.placeholder(shape=[None,None],dtype=tf.int32,name="input_ids_p")
input_mask_p=tf.placeholder(shape=[None,None],dtype=tf.int32,name="input_mask_p")
segment_ids_p=tf.placeholder(shape=[None,None],dtype=tf.int32,name="segment_ids_p")

model = modeling.BertModel(
        config=bert_config,
        is_training=is_training,
        input_ids=input_ids_p,
        input_mask=input_mask_p,
        token_type_ids=segment_ids_p,
        use_one_hot_embeddings=use_one_hot_embeddings
    )

restore_saver = tf.train.Saver()
restore_saver.restore(sess, init_checkpoint)



@app.route('/bertvectors')
def response_request():
    text = request.args.get('text')

    vectors = [di.get("[CLS]")] + [di.get(i) if i in di else di.get("[UNK]") for i in list(text)] + [di.get("[SEP]")]

    input, mask, segment = inputs(vectors)

    input_ids = np.reshape(np.array(input), [1, -1])
    input_mask = np.reshape(np.array(mask), [1, -1])
    segment_ids = np.reshape(np.array(segment), [1, -1])

    embedding = tf.squeeze(model.get_sequence_output())

    ret=sess.run(embedding,feed_dict={"input_ids_p:0":input_ids,"input_mask_p:0":input_mask,"segment_ids_p:0":segment_ids})
    return  json.dumps(ret.tolist(), ensure_ascii=False)

if __name__ == "__main__":
    server = pywsgi.WSGIServer(('0.0.0.0', 19877), app)
    server.serve_forever()

 

最后效果可以看测试：

http://localhost:19877/bertvectors?text=%E7%BE%8E%E6%97%AD%E5%9C%A8%E5%86%99bert%E6%A8%A1%E5%9E%8B

最后祝贺大家都能玩起来。。。