# 参考 https://blog.****.net/luoyexuge/article/details/84939755 小做改动
需要：
github上下载bert的代码：https://github.com/google-research/bert
下载google训练好的中文语料模型：https://storage.googleapis.com/bert_models/2018_11_03/chinese_L-12_H-768_A-12.zip
使用：
使用bert，其实是使用几个checkpoint（ckpt）文件。上面下载的zip是google训练好的bert，我们可以在那个zip内的ckpt文件基础上继续训练，获得更贴近具体任务的ckpt文件。
如果是直接使用训练好的ckpt文件（就是bert模型），只需如下代码，定义model，获得model的值
from bert import modeling
# 使用数据加载BertModel,获取对应的字embedding
model = modeling.BertModel(
config=bert_config,
is_training=is_training,
input_ids=input_ids,
input_mask=input_mask,
token_type_ids=segment_ids,
use_one_hot_embeddings=use_one_hot_embeddings
)
# 获取对应的embedding 输入数据[batch_size, seq_length, embedding_size]
embedding = model.get_sequence_output()
这里的bert_config 是之前定义的bert_config = modeling.BertConfig.from_json_file(FLAGS.bert_config_file)；输入是input_ids, input_mask, segment_ids三个向量；还有两个设置is_training（False）, use_one_hot_embedding（False），这样的设置还有很多，这里只列举这两个。。
关于FLAGS，需要提到TensorFlow的flags，相当于配置运行变量，设置如下：
import tensorflow as tf flags = tf.flags
FLAGS = flags.FLAGS # 预训练的中文model路径和项目路径
bert_path = '/home/xiangbo_wang/xiangbo/NER/chinese_L-12_H-768_A-12/'
root_path = '/home/xiangbo_wang/xiangbo/NER/BERT-BiLSTM-CRF-NER' # 设置bert_config_file
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."
)
关于输入的三个向量，具体内容可以参照之前的博客https://www.cnblogs.com/rucwxb/p/10277217.html
input_ids, segment_ids 分别是 token embedding, segment embedding
position embedding会自动生成
input_mask 是input中需要mask的位置，本来是随机取一部分，这里的做法是把全部输入位置都mask住。
获得输入的这三个向量的方式如下：
# 获得三个向量的函数
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 # 测试的句子
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]")] # 转成1*maxlen的向量
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])
最后是将变量输入模型获得最终的bert向量：
# 定义输入向量形状
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
) # 载入预训练模型
restore_saver = tf.train.Saver()
restore_saver.restore(sess, init_checkpoint) # 一个[batch_size, seq_length, embedding_size]大小的向量
embedding = tf.squeeze(model.get_sequence_output())
# 运行结果
ret=sess.run(embedding,feed_dict={"input_ids_p:0":input_ids,"input_mask_p:0":input_mask,"segment_ids_p:0":segment_ids})
完整可运行代码如下：
import tensorflow as tf
from bert import modeling
import collections
import os
import numpy as np
import json flags = tf.flags
FLAGS = flags.FLAGS
bert_path = '/home/xiangbo_wang/xiangbo/NER/chinese_L-12_H-768_A-12/' flags.DEFINE_string(
'bert_config_file', os.path.join(bert_path, 'bert_config.json'),
'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'),
'from a pre-trained BERT get an initial checkpoint',
)
flags.DEFINE_bool("use_tpu", False, "Whether to use TPU or GPU/CPU.") def convert2Uni(text):
if isinstance(text, str):
return text
elif isinstance(text, bytes):
return text.decode('utf-8','ignore')
else:
print(type(text))
print('####################wrong################') def load_vocab(vocab_file):
vocab = collections.OrderedDict()
vocab.setdefault('blank', 2)
index = 0
with open(vocab_file) as reader:
# with tf.gfile.GFile(vocab_file, 'r') as reader:
while True:
tmp = reader.readline()
if not tmp:
break
token = convert2Uni(tmp)
token = token.strip()
vocab[token] = index
index+=1
return vocab def inputs(vectors, maxlen = 50):
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 def response_request(text):
vectors = [dictionary.get('[CLS]')] + [dictionary.get(i) if i in dictionary else dictionary.get('[UNK]') for i in list(text)] + [dictionary.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())
rst = 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(rst.tolist(), ensure_ascii=False) dictionary = load_vocab(FLAGS.bert_vocab_file)
init_checkpoint = FLAGS.init_checkpoint sess = tf.Session()
bert_config = modeling.BertConfig.from_json_file(FLAGS.bert_config_file) 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 = FLAGS.use_tpu,
input_ids = input_ids_p,
input_mask = input_mask_p,
token_type_ids = segment_ids_p,
use_one_hot_embeddings = FLAGS.use_tpu,
)
print('####################################')
restore_saver = tf.train.Saver()
restore_saver.restore(sess, init_checkpoint) print(response_request('我叫水奈樾。'))