通过本文，你将了解如何基于训练好的模型，来编写一个rest风格的命名实体提取接口，传入一个句子，接口会提取出句子中的人名、地址、组织、公司、产品、时间信息并返回。

核心模块entity_extractor.py

关键函数

# 加载实体识别模型
def person_model_init():...# 预测句子中的实体
def predict(sentence, labels_config, model_dir, batch_size, id2label, label_list, graph, input_ids_p, input_mask_p,pred_ids,tokenizer,sess, max_seq_length):...

完整代码

# -*- coding: utf-8 -*-"""
基于模型的地址提取
"""
__author__ = '程序员一一涤生'import codecs
import os
import pickle
from datetime import datetime
from pprint import pprint
import numpy as np
import tensorflow as tf
from bert_base.bert import tokenization, modeling
from bert_base.train.models import create_model, InputFeatures
from bert_base.train.train_helper import get_args_parserargs = get_args_parser()def convert(line, model_dir, label_list, tokenizer, batch_size, max_seq_length):feature = convert_single_example(model_dir, 0, line, label_list, max_seq_length, tokenizer, 'p')input_ids = np.reshape([feature.input_ids], (batch_size, max_seq_length))input_mask = np.reshape([feature.input_mask], (batch_size, max_seq_length))segment_ids = np.reshape([feature.segment_ids], (batch_size, max_seq_length))label_ids = np.reshape([feature.label_ids], (batch_size, max_seq_length))return input_ids, input_mask, segment_ids, label_idsdef predict(sentence, labels_config, model_dir, batch_size, id2label, label_list, graph, input_ids_p, input_mask_p,pred_ids,tokenizer,sess, max_seq_length):with graph.as_default():start = datetime.now()# print(id2label)sentence = tokenizer.tokenize(sentence)# print('your input is:{}'.format(sentence))input_ids, input_mask, segment_ids, label_ids = convert(sentence, model_dir, label_list, tokenizer, batch_size,max_seq_length)feed_dict = {input_ids_p: input_ids,input_mask_p: input_mask}# run session get current feed_dict resultpred_ids_result = sess.run([pred_ids], feed_dict)pred_label_result = convert_id_to_label(pred_ids_result, id2label, batch_size)# print(pred_ids_result)print(pred_label_result)# todo: 组合策略result = strage_combined(sentence, pred_label_result[0], labels_config)print('time used: {} sec'.format((datetime.now() - start).total_seconds()))return result, pred_label_resultdef convert_id_to_label(pred_ids_result, idx2label, batch_size):"""将id形式的结果转化为真实序列结果:param pred_ids_result::param idx2label::return:"""result = []for row in range(batch_size):curr_seq = []for ids in pred_ids_result[row][0]:if ids == 0:breakcurr_label = idx2label[ids]if curr_label in ['[CLS]', '[SEP]']:continuecurr_seq.append(curr_label)result.append(curr_seq)return resultdef strage_combined(tokens, tags, labels_config):"""组合策略:param pred_label_result::param types::return:"""def get_output(rs, data, type):words = []for i in data:words.append(str(i.word).replace("#", ""))# words.append(i.word)rs[type] = wordsreturn rseval = Result(labels_config)if len(tokens) > len(tags):tokens = tokens[:len(tags)]labels_dict = eval.get_result(tokens, tags)arr = []for k, v in labels_dict.items():arr.append((k, v))rs = {}for item in arr:rs = get_output(rs, item[1], item[0])return rsdef convert_single_example(model_dir, ex_index, example, label_list, max_seq_length, tokenizer, mode):"""将一个样本进行分析，然后将字转化为id, 标签转化为id,然后结构化到InputFeatures对象中:param ex_index: index:param example: 一个样本:param label_list: 标签列表:param max_seq_length::param tokenizer::param mode::return:"""label_map = {}# 1表示从1开始对label进行index化for (i, label) in enumerate(label_list, 1):label_map[label] = i# 保存label->index 的mapif not os.path.exists(os.path.join(model_dir, 'label2id.pkl')):with codecs.open(os.path.join(model_dir, 'label2id.pkl'), 'wb') as w:pickle.dump(label_map, w)tokens = example# tokens = tokenizer.tokenize(example.text)# 序列截断if len(tokens) >= max_seq_length - 1:tokens = tokens[0:(max_seq_length - 2)]  # -2 的原因是因为序列需要加一个句首和句尾标志ntokens = []segment_ids = []label_ids = []ntokens.append("[CLS]")  # 句子开始设置CLS 标志segment_ids.append(0)# append("O") or append("[CLS]") not sure!label_ids.append(label_map["[CLS]"])  # O OR CLS 没有任何影响，不过我觉得O 会减少标签个数,不过拒收和句尾使用不同的标志来标注，使用LCS 也没毛病for i, token in enumerate(tokens):ntokens.append(token)segment_ids.append(0)label_ids.append(0)ntokens.append("[SEP]")  # 句尾添加[SEP] 标志segment_ids.append(0)# append("O") or append("[SEP]") not sure!label_ids.append(label_map["[SEP]"])input_ids = tokenizer.convert_tokens_to_ids(ntokens)  # 将序列中的字(ntokens)转化为ID形式input_mask = [1] * len(input_ids)# padding, 使用while len(input_ids) < max_seq_length:input_ids.append(0)input_mask.append(0)segment_ids.append(0)# we don't concerned about it!label_ids.append(0)ntokens.append("**NULL**")# label_mask.append(0)# print(len(input_ids))assert len(input_ids) == max_seq_lengthassert len(input_mask) == max_seq_lengthassert len(segment_ids) == max_seq_lengthassert len(label_ids) == max_seq_length# assert len(label_mask) == max_seq_length# 结构化为一个类feature = InputFeatures(input_ids=input_ids,input_mask=input_mask,segment_ids=segment_ids,label_ids=label_ids,# label_mask = label_mask)return featureclass Pair(object):def __init__(self, word, start, end, type, merge=False):self.__word = wordself.__start = startself.__end = endself.__merge = mergeself.__types = type@propertydef start(self):return self.__start@propertydef end(self):return self.__end@propertydef merge(self):return self.__merge@propertydef word(self):return self.__word@propertydef types(self):return self.__types@word.setterdef word(self, word):self.__word = word@start.setterdef start(self, start):self.__start = start@end.setterdef end(self, end):self.__end = end@merge.setterdef merge(self, merge):self.__merge = merge@types.setterdef types(self, type):self.__types = typedef __str__(self) -> str:line = []line.append('entity:{}'.format(self.__word))line.append('start:{}'.format(self.__start))line.append('end:{}'.format(self.__end))line.append('merge:{}'.format(self.__merge))line.append('types:{}'.format(self.__types))return '\t'.join(line)class Result(object):def __init__(self, labels_config):self.others = []self.labels_config = labels_configself.labels = {}for la in self.labels_config:self.labels[la] = []def get_result(self, tokens, tags):# 先获取标注结果self.result_to_json(tokens, tags)return self.labelsdef result_to_json(self, string, tags):"""将模型标注序列和输入序列结合 转化为结果:param string: 输入序列:param tags: 标注结果:return:"""item = {"entities": []}entity_name = ""entity_start = 0idx = 0last_tag = ''for char, tag in zip(string, tags):if tag[0] == "S":self.append(char, idx, idx + 1, tag[2:])item["entities"].append({"word": char, "start": idx, "end": idx + 1, "type": tag[2:]})elif tag[0] == "B":if entity_name != '':self.append(entity_name, entity_start, idx, last_tag[2:])item["entities"].append({"word": entity_name, "start": entity_start, "end": idx, "type": last_tag[2:]})entity_name = ""entity_name += charentity_start = idxelif tag[0] == "I":entity_name += charelif tag[0] == "O":if entity_name != '':self.append(entity_name, entity_start, idx, last_tag[2:])item["entities"].append({"word": entity_name, "start": entity_start, "end": idx, "type": last_tag[2:]})entity_name = ""else:entity_name = ""entity_start = idxidx += 1last_tag = tagif entity_name != '':self.append(entity_name, entity_start, idx, last_tag[2:])item["entities"].append({"word": entity_name, "start": entity_start, "end": idx, "type": last_tag[2:]})return itemdef append(self, word, start, end, tag):if tag in self.labels_config:self.labels[tag].append(Pair(word, start, end, tag))else:self.others.append(Pair(word, start, end, tag))def person_model_init():return model_init("person")def model_init(model_name):if os.name == 'nt':  # windows path configmodel_dir = 'E:/quickstart/deeplearning/nlp_demo/%s/model' % model_namebert_dir = 'E:/quickstart/deeplearning/nlp_demo/bert_model_info/chinese_L-12_H-768_A-12'else:  # linux path configmodel_dir = '/home/yjy/project/deeplearning/nlp_demo/%s/model' % model_namebert_dir = '/home/yjy/project/deeplearning/nlp_demo/bert_model_info/chinese_L-12_H-768_A-12'batch_size = 1max_seq_length = 500print('checkpoint path:{}'.format(os.path.join(model_dir, "checkpoint")))if not os.path.exists(os.path.join(model_dir, "checkpoint")):raise Exception("failed to get checkpoint. going to return ")# 加载label->id的词典with codecs.open(os.path.join(model_dir, 'label2id.pkl'), 'rb') as rf:label2id = pickle.load(rf)id2label = {value: key for key, value in label2id.items()}with codecs.open(os.path.join(model_dir, 'label_list.pkl'), 'rb') as rf:label_list = pickle.load(rf)num_labels = len(label_list) + 1gpu_config = tf.ConfigProto()gpu_config.gpu_options.allow_growth = Truegraph = tf.Graph()sess = tf.Session(graph=graph, config=gpu_config)with graph.as_default():print("going to restore checkpoint")# sess.run(tf.global_variables_initializer())input_ids_p = tf.placeholder(tf.int32, [batch_size, max_seq_length], name="input_ids")input_mask_p = tf.placeholder(tf.int32, [batch_size, max_seq_length], name="input_mask")bert_config = modeling.BertConfig.from_json_file(os.path.join(bert_dir, 'bert_config.json'))(total_loss, logits, trans, pred_ids) = create_model(bert_config=bert_config, is_training=False, input_ids=input_ids_p, input_mask=input_mask_p,segment_ids=None,labels=None, num_labels=num_labels, use_one_hot_embeddings=False, dropout_rate=1.0)saver = tf.train.Saver()saver.restore(sess, tf.train.latest_checkpoint(model_dir))tokenizer = tokenization.FullTokenizer(vocab_file=os.path.join(bert_dir, 'vocab.txt'), do_lower_case=args.do_lower_case)return model_dir, batch_size, id2label, label_list, graph, input_ids_p, input_mask_p, pred_ids, tokenizer, sess, max_seq_lengthif __name__ == "__main__":_model_dir, _batch_size, _id2label, _label_list, _graph, _input_ids_p, _input_mask_p, _pred_ids, _tokenizer, _sess, _max_seq_length = person_model_init()PERSON_LABELS = ["TIME", "LOCATION", "PERSON_NAME", "ORG_NAME", "COMPANY_NAME", "PRODUCT_NAME"]while True:print('input the test sentence:')_sentence = str(input())pred_rs, pred_label_result = predict(_sentence, ADDRESS_LABELS, _model_dir, _batch_size, _id2label, _label_list,_graph,_input_ids_p,_input_mask_p, _pred_ids, _tokenizer, _sess, _max_seq_length)pprint(pred_rs)

编写rest风格的接口

我们将采用python的flask框架来提供rest接口。

首先，新建一个python项目，项目根路径下放入以下目录和文件：

• bert_base目录及文件、bert_model_info目录及文件在上一篇文章 用深度学习做命名实体识别(四)——模型训练 给出的云盘项目中可以找到；
• person目录下的model就是我们在上一篇文章中训练得到的命名实体识别模型以及一些附属文件，在项目的output目录下可以得到。

然后，创建启动文件nlp_main.py，内容如下:

# -*- coding: utf-8 -*-"""
flask 入口
"""
import os
import nlp_config as nc
from flaskr import create_app, loadProjContext__author__ = '程序员一一涤生'from flask import jsonify, make_response, redirect# 加载flask配置信息
# app = create_app('config.DevelopmentConfig')
app = create_app(nc.config['default'])
# 加载项目上下文信息
loadProjContext()@app.errorhandler(404)
def not_found(error):return make_response(jsonify({'error': 'Not found'}), 404)@app.errorhandler(400)
def not_found(error):return make_response(jsonify({'error': '400 Bad Request,参数或参数内容异常'}), 400)@app.route('/')
def index_sf():# return render_template('index.html')return redirect('index.html')if __name__ == '__main__':app.run('localhost', 5006, app, use_reloader=False)

接着，创建本flask项目的初始化文件flaskr.py，用于启动项目的时候预设置和加载一些信息，内容如下:

# -*- coding: utf-8 -*-
"""
flask初始化
"""
from logging.config import dictConfig
from flask import Flask
from flask_cors import CORS
import address_ner_resource
import person_ner_resource
from address_ner_resource import address
from entity_extractor import address_model_init, person_model_init
from person_ner_resource import person__author__ = '程序员一一涤生'def create_app(config_type):dictConfig({'version': 1,'formatters': {'default': {'format': '[%(asctime)s] %(name)s %(levelname)s in %(module)s %(lineno)d: %(message)s',}},'handlers': {'wsgi': {'class': 'logging.StreamHandler','stream': 'ext://flask.logging.wsgi_errors_stream','formatter': 'default'}},'root': {'level': 'DEBUG',# 'level': 'WARN',# 'level': 'INFO','handlers': ['wsgi']}})# 加载flask配置信息app = Flask(__name__, static_folder='static', static_url_path='')# CORS(app, resources=r'/*',origins=['192.168.1.104'])  # r'/*' 是通配符，允许跨域请求本服务器所有的URL，"origins": '*'表示允许所有ip跨域访问本服务器的urlCORS(app, resources={r"/*": {"origins": '*'}})  # r'/*' 是通配符，允许跨域请求本服务器所有的URL，"origins": '*'表示允许所有ip跨域访问本服务器的urlapp.config.from_object(config_type)app.register_blueprint(person, url_prefix='/person')# 初始化上下文ctx = app.app_context()ctx.push()return appdef loadProjContext():# 加载人名提取模型model_dir, batch_size, id2label, label_list, graph, input_ids_p, input_mask_p, pred_ids, tokenizer, sess, max_seq_length = person_model_init()person_ner_resource.model_dir = model_dirperson_ner_resource.batch_size = batch_sizeperson_ner_resource.id2label = id2labelperson_ner_resource.label_list = label_listperson_ner_resource.graph = graphperson_ner_resource.input_ids_p = input_ids_pperson_ner_resource.input_mask_p = input_mask_pperson_ner_resource.pred_ids = pred_idsperson_ner_resource.tokenizer = tokenizerperson_ner_resource.sess = sessperson_ner_resource.max_seq_length = max_seq_length

然后，创建配置文件nlp_config.py，用于切换生产、开发、测试环境，内容如下：

# -*- coding: utf-8 -*-"""
本模块是Flask的配置模块
"""
import os__author__ = '程序员一一涤生'basedir = os.path.abspath(os.path.dirname(__file__))class BaseConfig:  # 基本配置类SECRET_KEY = b'\xe4r\x04\xb5\xb2\x00\xf1\xadf\xa3\xf3V\x03\xc5\x9f\x82$^\xa25O\xf0R\xda'JSONIFY_MIMETYPE = 'application/json; charset=utf-8'  # 默认JSONIFY_MIMETYPE的配置是不带'; charset=utf-8的'JSON_AS_ASCII = False  # 若不关闭，使用JSONIFY返回json时中文会显示为Unicode字符ENCODING = 'utf-8'# 自定义的配置项ADDRESS_LABELS = ["COUNTY", "STREET", "COMMUNITY", "ROAD", "NUM", "POI", "CITY", "VILLAGE"]PERSON_LABELS = ["TIME", "LOCATION", "PERSON_NAME", "ORG_NAME", "COMPANY_NAME", "PRODUCT_NAME"]class DevelopmentConfig(BaseConfig):ENV = 'development'DEBUG = Trueclass TestingConfig(BaseConfig):TESTING = TrueWTF_CSRF_ENABLED = Falseclass ProductionConfig(BaseConfig):DEBUG = Falseconfig = {'testing': TestingConfig,'default': DevelopmentConfig# 'default': ProductionConfig
}

接着，创建人名识别接口文件person_ner_resource.py，内容如下：

# -*- coding: utf-8 -*-"""
命名实体识别接口
"""
from entity_extractor import predict__author__ = '程序员一一涤生'from flask import Blueprint, make_response, request, current_app
from flask import jsonify
person = Blueprint('person', __name__)model_dir, batch_size, id2label, label_list, graph, input_ids_p, input_mask_p, pred_ids, tokenizer, sess, max_seq_length = None, None, None, None, None, None, None, None, None, None, None
@person.route('/extract', methods=['POST'])def extract():params = request.get_json()if 't' not in params or params['t'] is None or len(params['t']) > 500 or len(params['t']) < 2:return make_response(jsonify({'error': '文本长度不符合要求,长度限制：2~500'}), 400)sentence = params['t']# 成句sentence = sentence + "。" if not sentence.endswith(("，", "。", "！", "？")) else sentence# 利用模型提取pred_rs, pred_label_result = predict(sentence, current_app.config['PERSON_LABELS'], model_dir, batch_size, id2label,label_list, graph, input_ids_p,input_mask_p,pred_ids, tokenizer, sess, max_seq_length)print(sentence)return jsonify(pred_rs)if __name__ == '__main__':pass

接着，将requirements.txt文件放到项目根路径下，文件内容如下：

absl-py==0.7.0
astor==0.7.1
backcall==0.1.0
backports.weakref==1.0rc1
bleach==1.5.0
certifi==2016.2.28
click==6.7
colorama==0.4.1
colorful==0.5.0
decorator==4.3.2
defusedxml==0.5.0
entrypoints==0.3
Flask==1.0.2
Flask-Cors==3.0.3
gast==0.2.2
grpcio==1.18.0
h5py==2.9.0
html5lib==0.9999999
ipykernel==5.1.0
ipython==7.2.0
ipython-genutils==0.2.0
ipywidgets==7.4.2
itsdangerous==0.24
jedi==0.13.2
Jinja2==2.10
jsonschema==2.6.0
jupyter==1.0.0
jupyter-client==5.2.4
jupyter-console==6.0.0
jupyter-core==4.4.0
Keras-Applications==1.0.6
Keras-Preprocessing==1.0.5
Markdown==3.0.1
MarkupSafe==1.1.0
mistune==0.8.4
mock==3.0.5
nbconvert==5.4.0
nbformat==4.4.0
notebook==5.7.4
numpy==1.16.0
pandocfilters==1.4.2
parso==0.3.2
pickleshare==0.7.5
prettyprinter==0.17.0
prometheus-client==0.5.0
prompt-toolkit==2.0.8
protobuf==3.6.1
Pygments==2.3.1
python-dateutil==2.7.5
pywinpty==0.5.5
pyzmq==17.1.2
qtconsole==4.4.3
Send2Trash==1.5.0
six==1.12.0
tensorboard==1.13.1
tensorflow==1.13.1
tensorflow-estimator==1.13.0
termcolor==1.1.0
terminado==0.8.1
testpath==0.4.2
tornado==5.1.1
traitlets==4.3.2
wcwidth==0.1.7
Werkzeug==0.14.1
widgetsnbextension==3.4.2
wincertstore==0.2

然后，执行如下命令，安装requirements.txt中的包：

pip install -i https://pypi.tuna.tsinghua.edu.cn/simple -r requirements.txt

以上步骤完成后，我们就可以尝试启动项目了。

启动项目

运行如下命令，启动该flask项目：

python nlp_main.py

调用接口

本文使用postman来调用命名实体提取接口，接口地址：

http://localhost:5006/person/extract

调用效果展示：

注意，在cpu上使用模型的时间大概在2到3秒，而如果项目部署在搭载了支持深度学习的GPU的电脑上，接口的返回会快很多很多，当然不要忘记将tensorflow改为安装tensorflow-gpu。

本篇就这么多内容，到此，我们已经基于深度学习开发了一个可以从自然语言中提取出人名、地址、组织、公司、产品、时间的项目，从下一篇开始，我们将介绍本项目使用的深度学习算法Bert和crf，通过对算法的了解，我们将更好的理解为什么模型能够准确的从句子中提取出我们想要的实体。

ok，本篇就这么多内容啦~，感谢阅读O(∩_∩)O，88~

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