【NLP】使用bert

# 参考 https://blog.csdn.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。'))