├── README.md ├── Start.py ├── gStore配置 ├── GstoreConnector.py └── TT_Mission_gStore.py ├── 命名实体识别 ├── BERT-BiLSTM-CRF-NER │ └── README.md ├── NER_Model │ ├── README.md │ └── train.txt ├── NER_test.py ├── README.md ├── bert │ ├── .gitignore │ ├── CONTRIBUTING.md │ ├── LICENSE │ ├── README.md │ ├── __init__.py │ ├── create_pretraining_data.py │ ├── extract_features.py │ ├── modeling.py │ ├── modeling_test.py │ ├── multilingual.md │ ├── optimization.py │ ├── optimization_test.py │ ├── predicting_movie_reviews_with_bert_on_tf_hub.ipynb │ ├── requirements.txt │ ├── run_classifier.py │ ├── run_classifier_with_tfhub.py │ ├── run_pretraining.py │ ├── run_squad.py │ ├── sample_text.txt │ ├── tokenization.py │ └── tokenization_test.py ├── chinese_L-12_H-768_A-12 │ └── README.md └── start_ner ├── 实体消歧 ├── README.md └── mention2ent_file_test.py ├── 数据清洗 ├── README.md ├── stop_words.txt └── 数据清洗.py └── 由已知实体抽取子图等操作 ├── README.md ├── cos_sim_and_delete_get_type.py ├── entity_keywords_chou.py ├── get_type.txt ├── start_bert_vector └── z_inget.py /README.md: -------------------------------------------------------------------------------- 1 | # EA-CKGQA 2 | 3 | 基于知识图谱的中文问答系统（EA-CKGQA） 4 | 5 | CCKS2019 评测任务六 6 | 7 | ## Start.py是程序入口 8 | 9 | 由于是多人合作书写的代码，且比赛时间紧凑， 10 | 11 | 代码可读性不是很强，所以特意将整体思路分割成各个部分， 12 | 13 | 并对各个部分进行了详细的阐述，提供了复现所需要的所有文件。 14 | 15 | ## 项目介绍 16 | 17 | 此程序是CCKS2019评测任务六——基于知识图谱的中文问答系统而做的。 18 | 19 | 总排名第九名，排除百度、平安、华为、网易等企业分数，高校排名为第四名。 20 | 21 | ## 代码详解 22 | 23 | ### 对中文问题进行数据清洗（见目录） 24 | 25 | 由于官方给的问题集包含有很多冗余信息，所以我们对这些信息进行数据清洗， 26 | 27 | 具体清洗所用的停用词为stop_words.txt， 28 | 29 | 我们对stop_words.txt中的内容进行了删改，从而更适合我们的QA场景。 30 | 31 | ### 对中文问题进行命名实体识别（见目录） 32 | 33 | 此文件夹内为全部进行NER实现所需要的文件， 34 | 35 | 部分文件由于大小问题没有放在这里，但均给予了下载链接， 36 | 37 | 同时提供了我们方法的训练集、运行脚本等。 38 | 39 | NER方法来自： 40 | https://github.com/macanv/BERT-BiLSTM-CRF-NER 41 | 42 | 训练集和训练后的模型均已提供， 43 | 44 | 放入必要的依赖后，配置一下start_ner脚本内的模型路径， 45 | 46 | 使用NER_test中的ner_on_work函数， 47 | 48 | 即可在代码中使用。 49 | 50 | ### 对识别后的实体进行实体消歧（见目录） 51 | 52 | pkubase-mention2ent.txt 文件下载地址 53 | 链接:https://pan.baidu.com/s/1MaZGxDg9KQrpZWE9bSAi0Q 密码:1cok 54 | 55 | 实体消歧所需要的文件下载地址 56 | 链接:https://pan.baidu.com/s/1Oa10t3t73zO13ydv1KcBFg 密码:sssa 57 | 58 | ### 使用gStore工具（见目录） 59 | 60 | ### 对实体进行召回所有子图等操作获得准确属性（见目录） 61 | 62 | 通过NER和实体消歧获得的准确实体， 63 | 64 | 抽取该实体下的所有子图， 65 | 66 | 并从子图中抽取所有待选属性， 67 | 68 | 并比较待选属性列表与关键词列表的余弦相似度， 69 | 70 | 从而从待选属性中找到最终的准确属性。 71 | 72 | 73 | z_inget.py用于提取实体所对应的全部属性并存放到get_type.txt中； 74 | 75 | entity_keywords_chou.py用于根据词性筛选出关键词列表； 76 | 77 | start_bert_vector脚本用于开启Bert服务对候选属性列表和关键词列表向量化； 78 | 79 | cos_sim_and_delete_get_type.py用于计算余弦相似度以及清空get_type.txt文件。 80 | 81 | ### Start.py中组建实体、属性查询语句，获得中文问题的最终答案。 82 | 83 | ## 额外的资源下载 84 | 85 | Jieba分词使用的字典是我们通过mention2entity文件进行反向构建的 86 | 87 | 字典dict1.txt下载地址为： 88 | 链接:https://pan.baidu.com/s/1EOi-y7-dbNzKJEAjosQ3FA 密码:uhet 89 | 90 | 91 | 作者邮箱：xutaowk0@163.com 92 | -------------------------------------------------------------------------------- /Start.py: -------------------------------------------------------------------------------- 1 | # coding = UTF-8 2 | __Date__ = '2019/7/19' 3 | __Author__ = 'Xu Tao' 4 | 5 | import os 6 | import signal 7 | import subprocess 8 | import time 9 | import TT_Mission_gStore 10 | import cixing_LTP词性标注 # 词性标注目录信息 11 | from pyltp import Postagger # 词性标注 12 | import cos_sim_and_delete_get_type # 余弦值计算以及清除get_type的信息 13 | import entity_keywords_chou # 抽取关键词列表 14 | import jieba 15 | import process_every_逐个扫描函数 # 需要单独处理复杂问题时才使用 16 | # 以下为队友写的代码 17 | import z_NLP 18 | import z_NLP_result 19 | import z_inget 20 | # 以上为队友写的代码 21 | from bert_serving.client import BertClient 22 | import NER_test # 自动NER 23 | import mention2ent_file_test # 自动消歧 24 | import sys 25 | 26 | sys.path.append('./ltp_function') 27 | sys.path.append('./auto_NER') 28 | sys.path.append('./实体消歧') 29 | 30 | jieba.load_userdict('dict1.txt') # 第一步加载字典 31 | 32 | question_file_number = 0 # question文件默认行数，等会遍历执行的时候直接加到1，这样执行也可以 33 | 34 | question_file = open('question_done.txt', encoding='utf-8-sig') 35 | answer_file = open('finally_result.txt', 'a+') 36 | 37 | alone_to_do_question_number = [] 38 | 39 | content = question_file.read().splitlines() 40 | 41 | for each_question in content: 42 | question_file_number += 1 43 | print('此问题序号 = ', question_file_number, each_question) 44 | answer_file.flush() 45 | 46 | if alone_to_do_question_number.count(question_file_number) == 0: 47 | start_ner_mission = subprocess.Popen('. /home/xt/PycharmProjects/CCKS_END/auto_NER/start_ner', shell=True, 48 | close_fds=True, preexec_fn=os.setsid) 49 | print('ner服务开启成功，pid为：', start_ner_mission.pid) 50 | time.sleep(3) # 等待ner服务完全开启 51 | 52 | wei_true_index = -1 # 记录实体的当前索引，词性标注会跳过此索引 53 | 54 | ready_entity = NER_test.ner_on_work(each_question) 55 | print('待消歧实体为：', ready_entity) 56 | if ready_entity is None: 57 | answer_file.write('\n') 58 | os.killpg(start_ner_mission.pid, signal.SIGUSR1) 59 | cos_sim_and_delete_get_type.delete_get_type() 60 | time.sleep(1) # 等待ner服务完全关闭 61 | continue 62 | true_entity = mention2ent_file_test.mention2ent_on_work(ready_entity) 63 | if true_entity is None: 64 | answer_file.write('\n') 65 | os.killpg(start_ner_mission.pid, signal.SIGUSR1) 66 | cos_sim_and_delete_get_type.delete_get_type() 67 | time.sleep(1) # 等待ner服务完全关闭 68 | continue 69 | print('消歧后的真实体为：', true_entity) 70 | gStore_List = [true_entity] 71 | print('消歧后马上的gStore列表为：', gStore_List) 72 | os.killpg(start_ner_mission.pid, signal.SIGUSR1) 73 | time.sleep(1) # 等待ner服务完全关闭 74 | # 接下来进行常规操作 75 | start_bert_mission = subprocess.Popen('. /home/xt/PycharmProjects/CCKS_END/start_bert_vector', shell=True, 76 | close_fds=True, preexec_fn=os.setsid) 77 | print('bert服务开启成功，pid为：', start_bert_mission.pid) 78 | 79 | seg_list = list(jieba.cut(each_question)) # 分词处理，并且转化为列表形式 80 | 81 | wei_true_index = seg_list.count(ready_entity) 82 | if wei_true_index == 0: 83 | wei_true_index = -1 84 | else: 85 | wei_true_index = seg_list.index(ready_entity) 86 | 87 | print('seg_list为：', seg_list) # 分词后的列表 88 | print('现在的gStore_List为：', gStore_List) # 确定好的三元组 89 | postagger = Postagger() # 初始化实例 90 | postagger.load(cixing_LTP词性标注.pos_model_path) # 加载模型 91 | postags_str = postagger.postag(seg_list) # 对列表进行词性标注,并输出为str 92 | postags = list(postags_str) # 转化为列表 93 | print(postags) # 打印列表 94 | postagger.release() # 释放模型 95 | 96 | Keywords_List = entity_keywords_chou.keyword_chou(postags, 1, wei_true_index, seg_list) 97 | print('Keywords_List为：', Keywords_List) 98 | ls = [] 99 | dict1 = {} 100 | if len(gStore_List) == 0: # 如果得出的三元组为空，则自动跳过 101 | answer_file.write('\n') 102 | os.killpg(start_bert_mission.pid, signal.SIGUSR1) 103 | cos_sim_and_delete_get_type.delete_get_type() 104 | time.sleep(1) # 给bert关闭时间作缓冲 105 | continue 106 | if len(gStore_List) == 2: # 如果得出的三元组已经有了实体和属性，则直接进行查询 107 | triple_flag = 1 # 若三元组已经为1，则不向量化等后续操作，后续的操作函数无效 108 | TT_Mission_gStore.net(z_NLP_result.nlp(gStore_List)) 109 | tiaoguo = z_NLP_result.inget() # inget函数中存在最终检索不到结果的情况，此标志位用于跳过此情况，减少bug 110 | if tiaoguo == 1: 111 | tiaoguo = 0 112 | answer_file.write('\n') 113 | os.killpg(start_bert_mission.pid, signal.SIGUSR1) 114 | cos_sim_and_delete_get_type.delete_get_type() 115 | time.sleep(1) # 给bert关闭时间作缓冲 116 | continue 117 | else: 118 | G = gStore_List 119 | TT_Mission_gStore.net(z_NLP.nlp(G)) 120 | z_inget.getlist() # 提取属性 121 | time.sleep(6) # 给bert开启时间作缓冲 122 | bc = BertClient() 123 | f = open('get_type.txt', 'r') 124 | content = f.read().splitlines() 125 | c = 0 126 | if len(content) == 0: 127 | answer_file.write('\n') 128 | os.killpg(start_bert_mission.pid, signal.SIGUSR1) 129 | cos_sim_and_delete_get_type.delete_get_type() 130 | time.sleep(1) # 给bert关闭时间作缓冲 131 | continue 132 | if len(Keywords_List) == 0: 133 | answer_file.write('\n') 134 | os.killpg(start_bert_mission.pid, signal.SIGUSR1) 135 | cos_sim_and_delete_get_type.delete_get_type() 136 | time.sleep(1) # 给bert关闭时间作缓冲 137 | continue 138 | for a in Keywords_List: 139 | if c - 0.99 >= 0: 140 | break 141 | for b in content: 142 | vector_a = bc.encode([a]) 143 | vector_b = bc.encode([b]) 144 | c = cos_sim_and_delete_get_type.cos_sim(vector_a, vector_b) 145 | print(c, a, b) 146 | dict1[c] = b 147 | keys = list(dict1.keys()) 148 | keys.sort(reverse=True) 149 | print(keys) 150 | print(dict1) 151 | ls.append(dict1[keys[0]]) 152 | 153 | if len(ls) == 0: 154 | answer_file.write('\n') 155 | os.killpg(start_bert_mission.pid, signal.SIGUSR1) 156 | cos_sim_and_delete_get_type.delete_get_type() 157 | time.sleep(1) # 给bert关闭时间作缓冲 158 | continue 159 | 160 | print('抽取得到的属性是：', dict1[keys[0]]) 161 | s = set(ls) 162 | l = list(s) 163 | gStore_List.extend(l) 164 | print(gStore_List) 165 | # aaa(gStore_List) 166 | TT_Mission_gStore.net(z_NLP_result.nlp(gStore_List)) 167 | z_NLP_result.inget() 168 | cos_sim_and_delete_get_type.delete_get_type() 169 | print('此问题序号 = ', question_file_number, each_question) 170 | os.killpg(start_bert_mission.pid, signal.SIGUSR1) 171 | time.sleep(1) # 给bert关闭时间作缓冲 172 | 173 | else: 174 | start_bert_mission = subprocess.Popen('. /home/xt/PycharmProjects/CCKS_END/start_bert_vector', shell=True, 175 | close_fds=True, preexec_fn=os.setsid) 176 | print('bert服务开启成功，pid为：', start_bert_mission.pid) 177 | 178 | wei_index = -1 # 记录手工规则得到的真实体的当前索引，词性标注会跳过此索引 179 | entity_flag = 0 # 若实体已经经过分词扫描被确定了，则 entity_flag = 1，否则为0 180 | triple_flag = 0 # later 181 | 182 | seg_list = list(jieba.cut(each_question)) # 分词处理，并且转化为列表形式 183 | seg_list, gStore_List, entity_flag, wei_index = process_every_逐个扫描函数.process_every(seg_list, entity_flag, 184 | wei_index) 185 | # 分词的时候便将第一个实体确定下来，保存在gStore_List中 186 | print('seg_list为：', seg_list) # 分词后的列表 187 | print('gStore_List为：', gStore_List) # 确定好的三元组 188 | print('entity_flag为：', entity_flag) # 实体是否被确定的标志位，执行到此，此位必定为1 189 | 190 | postagger = Postagger() # 初始化实例 191 | postagger.load(cixing_LTP词性标注.pos_model_path) # 加载模型 192 | postags_str = postagger.postag(seg_list) # 对列表进行词性标注,并输出为str 193 | postags = list(postags_str) # 转化为列表 194 | print(postags) # 打印列表 195 | postagger.release() # 释放模型 196 | 197 | Keywords_List = entity_keywords_chou.keyword_chou(postags, entity_flag, wei_index, seg_list) 198 | print('Keywords_List为：', Keywords_List) 199 | 200 | ls = [] 201 | dict1 = {} 202 | if len(gStore_List) == 0: # 如果得出的三元组为空，则自动跳过 203 | answer_file.write('\n') 204 | os.killpg(start_bert_mission.pid, signal.SIGUSR1) 205 | cos_sim_and_delete_get_type.delete_get_type() 206 | time.sleep(1) # 给bert关闭时间作缓冲 207 | continue 208 | 209 | if len(gStore_List) == 2: # 如果得出的三元组已经有了实体和属性，则直接进行查询 210 | triple_flag = 1 # 若三元组已经为1，则不向量化等后续操作，后续的操作函数无效 211 | TT_Mission_gStore.net(z_NLP_result.nlp(gStore_List)) 212 | tiaoguo = z_NLP_result.inget() # inget函数中存在最终检索不到结果的情况，此标志位用于跳过此情况，减少bug 213 | if tiaoguo == 1: 214 | tiaoguo = 0 215 | answer_file.write('\n') 216 | os.killpg(start_bert_mission.pid, signal.SIGUSR1) 217 | cos_sim_and_delete_get_type.delete_get_type() 218 | time.sleep(1) # 给bert关闭时间作缓冲 219 | continue 220 | else: 221 | G = gStore_List 222 | TT_Mission_gStore.net(z_NLP.nlp(G)) 223 | z_inget.getlist() # 提取属性 224 | time.sleep(6) # 给bert开启时间作缓冲 225 | bc = BertClient() 226 | f = open('get_type.txt', 'r') 227 | content = f.read().splitlines() 228 | c = 0 229 | if len(content) == 0: 230 | answer_file.write('\n') 231 | os.killpg(start_bert_mission.pid, signal.SIGUSR1) 232 | cos_sim_and_delete_get_type.delete_get_type() 233 | time.sleep(1) # 给bert关闭时间作缓冲 234 | continue 235 | if len(Keywords_List) == 0: 236 | answer_file.write('\n') 237 | os.killpg(start_bert_mission.pid, signal.SIGUSR1) 238 | cos_sim_and_delete_get_type.delete_get_type() 239 | time.sleep(1) # 给bert关闭时间作缓冲 240 | continue 241 | 242 | for a in Keywords_List: 243 | if c - 0.99 >= 0: 244 | break 245 | for b in content: 246 | vector_a = bc.encode([a]) 247 | vector_b = bc.encode([b]) 248 | c = cos_sim_and_delete_get_type.cos_sim(vector_a, vector_b) 249 | print(c, a, b) 250 | dict1[c] = b 251 | keys = list(dict1.keys()) 252 | keys.sort(reverse=True) 253 | print(keys) 254 | print(dict1) 255 | ls.append(dict1[keys[0]]) 256 | 257 | if len(ls) == 0: 258 | answer_file.write('\n') 259 | os.killpg(start_bert_mission.pid, signal.SIGUSR1) 260 | cos_sim_and_delete_get_type.delete_get_type() 261 | time.sleep(1) # 给bert关闭时间作缓冲 262 | continue 263 | 264 | print('抽取得到的属性是：', dict1[keys[0]]) 265 | s = set(ls) 266 | l = list(s) 267 | gStore_List.extend(l) 268 | print(gStore_List) 269 | TT_Mission_gStore.net(z_NLP_result.nlp(gStore_List)) 270 | z_NLP_result.inget() 271 | cos_sim_and_delete_get_type.delete_get_type() 272 | print('此问题序号 = ', question_file_number, each_question) 273 | os.killpg(start_bert_mission.pid, signal.SIGUSR1) 274 | time.sleep(1) # 给bert关闭时间作缓冲 275 | -------------------------------------------------------------------------------- /gStore配置/GstoreConnector.py: -------------------------------------------------------------------------------- 1 | """ 2 | # Filename: GStoreConnector.py 3 | # Author: yangchaofan 4 | # Last Modified: 2018-7-18 14:44 5 | # Description: http api for python 6 | """ 7 | 8 | import requests 9 | 10 | defaultServerIP = "127.0.0.1" 11 | defaultServerPort = "3305" 12 | 13 | class GstoreConnector: 14 | def __init__(self, ip, port): 15 | if (ip == "localhost"): 16 | self.serverIP = defaultServerIP 17 | else: 18 | self.serverIP = ip 19 | self.serverPort = port 20 | self.Url = "http://" + self.serverIP + ":" + str(self.serverPort) 21 | 22 | def UrlEncode(self, s): 23 | ret = "" 24 | for i in range(len(s)): 25 | c = s[i] 26 | if ((ord(c)==42) or (ord(c)==45) or (ord(c)==46) or (ord(c)==47) or (ord(c)==58) or (ord(c)==95)): 27 | ret += c 28 | elif ((ord(c)>=48) and (ord(c)<=57)): 29 | ret += c 30 | elif ((ord(c)>=65) and (ord(c)<=90)): 31 | ret += c 32 | elif ((ord(c)>=97) and (ord(c)<=122)): 33 | ret += c 34 | elif (ord(c)>=256): 35 | ret += chr(ord(c)) 36 | elif ((ord(c)!=10) and (ord(c)!=11) and (ord(c)!=13)): 37 | ret += "{}{:X}".format("%", ord(c)) 38 | return ret 39 | 40 | def Get(self, strUrl): 41 | r = requests.get(self.UrlEncode(strUrl)) 42 | return r.text 43 | 44 | def fGet(self, strUrl, filename): 45 | r = requests.get(self.UrlEncode(strUrl), stream=True) 46 | with open(filename, 'wb') as fd: 47 | for chunk in r.iter_content(4096): 48 | fd.write(chunk) 49 | return 50 | 51 | def load(self, db_name, username, password): 52 | cmd = self.Url + "/?operation=load&db_name=" + db_name + "&username=" + username + "&password=" + password 53 | res = self.Get(cmd) 54 | print(res) 55 | if res == "load database done.": 56 | return True 57 | return False 58 | 59 | def unload(self, db_name, username, password): 60 | cmd = self.Url + "/?operation=unload&db_name=" + db_name + "&username=" + username + "&password=" + password 61 | res = self.Get(cmd) 62 | print(res) 63 | if res == "unload database done.": 64 | return True 65 | return False 66 | 67 | def build(self, db_name, rdf_file_path, username, password): 68 | cmd = self.Url + "/?operation=build&db_name=" + db_name + "&ds_path=" + rdf_file_path + "&username=" + username + "&password=" + password 69 | res = self.Get(cmd) 70 | print(res) 71 | if res == "import RDF file to database done.": 72 | return True 73 | return False 74 | 75 | def query(self, username, password, db_name, sparql): 76 | cmd = self.Url + "/?operation=query&username=" + username + "&password=" + password + "&db_name=" + db_name + "&format=json&sparql=" + sparql 77 | return self.Get(cmd) 78 | 79 | def fquery(self, username, password, db_name, sparql, filename): 80 | cmd = self.Url + "/?operation=query&username=" + username + "&password=" + password + "&db_name=" + db_name + "&format=json&sparql=" + sparql 81 | self.fGet(cmd, filename) 82 | return 83 | 84 | def show(self): 85 | cmd = self.Url + "/?operation=show" 86 | return self.Get(cmd) 87 | 88 | def user(self, type, username1, password1, username2, addition): 89 | cmd = self.Url + "/?operation=user&type=" + type + "&username1=" + username1+ "&password1=" + password1 + "&username2=" + username2 + "&addition=" +addition 90 | return self.Get(cmd) 91 | 92 | def showUser(self): 93 | cmd = self.Url + "/?operation=showUser" 94 | return self.Get(cmd) 95 | 96 | def monitor(self, db_name): 97 | cmd = self.Url + "/?operation=monitor&db_name=" + db_name; 98 | return self.Get(cmd) 99 | 100 | def checkpoint(self, db_name): 101 | cmd = self.Url + "/?operation=checkpoint&db_name=" + db_name 102 | return self.Get(cmd) 103 | -------------------------------------------------------------------------------- /gStore配置/TT_Mission_gStore.py: -------------------------------------------------------------------------------- 1 | # coding=utf-8 2 | # Version:python3.6.0 3 | # Tools:Pycharm 2019 4 | __date__ = '2019/4/24 下午12:34' 5 | __author__ = 'TT' 6 | 7 | import GstoreConnector 8 | 9 | # before you run this example, make sure that you have started up ghttp service (using bin/ghttp db_name port) 10 | username = "endpoint" 11 | password = "123" 12 | filename = "res.txt" 13 | 14 | 15 | def net(sparql1): 16 | """ 17 | 实现gStore的API连接，以及进行查询，后续会根据问题的类型进行相应的一些选项修改 18 | :param sparql1: 查询语句1，以后可能会有查询语句2、3、4... 19 | """ 20 | gc = GstoreConnector.GstoreConnector("pkubase.gstore-pku.com", 80) 21 | gc.fquery(username, password, "pkubase", sparql1, filename) 22 | print(gc.query(username, password, "pkubase", sparql1)) 23 | 24 | # unload the database 25 | # ret = gc.unload("test", username, password) 26 | 27 | # load the database 28 | # ret = gc.load("lubm", username, password) 29 | 30 | # query 31 | # print(gc.query(username, password, "pkubase", sparql)) 32 | 33 | # query and save the result in a file 34 | # gc.fquery(username, password, "pkubase", sparql, filename) 35 | -------------------------------------------------------------------------------- /命名实体识别/BERT-BiLSTM-CRF-NER/README.md: -------------------------------------------------------------------------------- 1 | 使用的NER方法来自： 2 | https://github.com/macanv/BERT-BiLSTM-CRF-NER -------------------------------------------------------------------------------- /命名实体识别/NER_Model/README.md: -------------------------------------------------------------------------------- 1 | 训练好的模型参数比较大，没有放在这里， 2 | 这里提供我们的训练集，是利用mention2entity文件进行反向标注训练集 3 | 并且人工方式确保训练集的准确性。 -------------------------------------------------------------------------------- /命名实体识别/NER_test.py: -------------------------------------------------------------------------------- 1 | import time 2 | from bert_base.client import BertClient 3 | 4 | 5 | def ner_on_work(str_input): 6 | with BertClient(show_server_config=False, check_version=False, check_length=False, mode='NER') as bc: 7 | start_t = time.perf_counter() 8 | str_input_list = list(str_input) 9 | rst = bc.encode([str_input]) 10 | result = list(rst[0]) 11 | print('rst:', result) 12 | print(time.perf_counter() - start_t) 13 | 14 | entity_list = [] 15 | entity_list_number = 0 16 | if (result.count('B-LOC') == 1 and result.count('B-ORG') == 0 and result.count('B-PER') == 0) \ 17 | or (result.count('B-LOC') == 0 and result.count('B-ORG') == 1 and result.count('B-PER') == 0) \ 18 | or (result.count('B-LOC') == 0 and result.count('B-ORG') == 0 and result.count('B-PER') == 1): 19 | for every in result: 20 | if every != 'O': 21 | entity_list.append(str_input_list[entity_list_number]) 22 | entity_list_number += 1 23 | entity_str = "".join(entity_list) 24 | return entity_str 25 | else: 26 | print('属于多实体问题，需要单独进行处理') 27 | return None 28 | -------------------------------------------------------------------------------- /命名实体识别/README.md: -------------------------------------------------------------------------------- 1 | 此文件夹内为全部进行NER实现所需要的文件 2 | 部分文件由于大小问题没有放在这里，但均给予了下载链接 3 | 同时提供了我们方法的训练集、运行脚本等。 4 | 5 | NER方法来自： 6 | https://github.com/macanv/BERT-BiLSTM-CRF-NER 7 | 8 | 训练集和训练后的模型均已提供 9 | 放入必要的依赖后，配置一下start_ner脚本内的模型路径 10 | 使用NER_test中的ner_on_work函数 11 | 即可在代码中使用。 -------------------------------------------------------------------------------- /命名实体识别/bert/.gitignore: -------------------------------------------------------------------------------- 1 | # Initially taken from Github's Python gitignore file 2 | 3 | # Byte-compiled / optimized / DLL files 4 | __pycache__/ 5 | *.py[cod] 6 | *$py.class 7 | 8 | # C extensions 9 | *.so 10 | 11 | # Distribution / packaging 12 | .Python 13 | build/ 14 | develop-eggs/ 15 | dist/ 16 | downloads/ 17 | eggs/ 18 | .eggs/ 19 | lib/ 20 | lib64/ 21 | parts/ 22 | sdist/ 23 | var/ 24 | wheels/ 25 | *.egg-info/ 26 | .installed.cfg 27 | *.egg 28 | MANIFEST 29 | 30 | # PyInstaller 31 | # Usually these files are written by a python script from a template 32 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 33 | *.manifest 34 | *.spec 35 | 36 | # Installer logs 37 | pip-log.txt 38 | pip-delete-this-directory.txt 39 | 40 | # Unit test / coverage reports 41 | htmlcov/ 42 | .tox/ 43 | .nox/ 44 | .coverage 45 | .coverage.* 46 | .cache 47 | nosetests.xml 48 | coverage.xml 49 | *.cover 50 | .hypothesis/ 51 | .pytest_cache/ 52 | 53 | # Translations 54 | *.mo 55 | *.pot 56 | 57 | # Django stuff: 58 | *.log 59 | local_settings.py 60 | db.sqlite3 61 | 62 | # 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The text should be enclosed in the appropriate 185 | comment syntax for the file format. We also recommend that a 186 | file or class name and description of purpose be included on the 187 | same "printed page" as the copyright notice for easier 188 | identification within third-party archives. 189 | 190 | Copyright [yyyy] [name of copyright owner] 191 | 192 | Licensed under the Apache License, Version 2.0 (the "License"); 193 | you may not use this file except in compliance with the License. 194 | You may obtain a copy of the License at 195 | 196 | http://www.apache.org/licenses/LICENSE-2.0 197 | 198 | Unless required by applicable law or agreed to in writing, software 199 | distributed under the License is distributed on an "AS IS" BASIS, 200 | WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 201 | See the License for the specific language governing permissions and 202 | limitations under the License. 203 | -------------------------------------------------------------------------------- /命名实体识别/bert/__init__.py: -------------------------------------------------------------------------------- 1 | # coding=utf-8 2 | # Copyright 2018 The Google AI Language Team Authors. 3 | # 4 | # Licensed under the Apache License, Version 2.0 (the "License"); 5 | # you may not use this file except in compliance with the License. 6 | # You may obtain a copy of the License at 7 | # 8 | # http://www.apache.org/licenses/LICENSE-2.0 9 | # 10 | # Unless required by applicable law or agreed to in writing, software 11 | # distributed under the License is distributed on an "AS IS" BASIS, 12 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 13 | # See the License for the specific language governing permissions and 14 | # limitations under the License. 15 | 16 | -------------------------------------------------------------------------------- /命名实体识别/bert/create_pretraining_data.py: -------------------------------------------------------------------------------- 1 | # coding=utf-8 2 | # Copyright 2018 The Google AI Language Team Authors. 3 | # 4 | # Licensed under the Apache License, Version 2.0 (the "License"); 5 | # you may not use this file except in compliance with the License. 6 | # You may obtain a copy of the License at 7 | # 8 | # http://www.apache.org/licenses/LICENSE-2.0 9 | # 10 | # Unless required by applicable law or agreed to in writing, software 11 | # distributed under the License is distributed on an "AS IS" BASIS, 12 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 13 | # See the License for the specific language governing permissions and 14 | # limitations under the License. 15 | """Create masked LM/next sentence masked_lm TF examples for BERT.""" 16 | 17 | from __future__ import absolute_import 18 | from __future__ import division 19 | from __future__ import print_function 20 | 21 | import collections 22 | import random 23 | import tokenization 24 | import tensorflow as tf 25 | 26 | flags = tf.flags 27 | 28 | FLAGS = flags.FLAGS 29 | 30 | flags.DEFINE_string("input_file", None, 31 | "Input raw text file (or comma-separated list of files).") 32 | 33 | flags.DEFINE_string( 34 | "output_file", None, 35 | "Output TF example file (or comma-separated list of files).") 36 | 37 | flags.DEFINE_string("vocab_file", None, 38 | "The vocabulary file that the BERT model was trained on.") 39 | 40 | flags.DEFINE_bool( 41 | "do_lower_case", True, 42 | "Whether to lower case the input text. Should be True for uncased " 43 | "models and False for cased models.") 44 | 45 | flags.DEFINE_bool( 46 | "do_whole_word_mask", False, 47 | "Whether to use whole word masking rather than per-WordPiece masking.") 48 | 49 | flags.DEFINE_integer("max_seq_length", 128, "Maximum sequence length.") 50 | 51 | flags.DEFINE_integer("max_predictions_per_seq", 20, 52 | "Maximum number of masked LM predictions per sequence.") 53 | 54 | flags.DEFINE_integer("random_seed", 12345, "Random seed for data generation.") 55 | 56 | flags.DEFINE_integer( 57 | "dupe_factor", 10, 58 | "Number of times to duplicate the input data (with different masks).") 59 | 60 | flags.DEFINE_float("masked_lm_prob", 0.15, "Masked LM probability.") 61 | 62 | flags.DEFINE_float( 63 | "short_seq_prob", 0.1, 64 | "Probability of creating sequences which are shorter than the " 65 | "maximum length.") 66 | 67 | 68 | class TrainingInstance(object): 69 | """A single training instance (sentence pair).""" 70 | 71 | def __init__(self, tokens, segment_ids, masked_lm_positions, masked_lm_labels, 72 | is_random_next): 73 | self.tokens = tokens 74 | self.segment_ids = segment_ids 75 | self.is_random_next = is_random_next 76 | self.masked_lm_positions = masked_lm_positions 77 | self.masked_lm_labels = masked_lm_labels 78 | 79 | def __str__(self): 80 | s = "" 81 | s += "tokens: %s\n" % (" ".join( 82 | [tokenization.printable_text(x) for x in self.tokens])) 83 | s += "segment_ids: %s\n" % (" ".join([str(x) for x in self.segment_ids])) 84 | s += "is_random_next: %s\n" % self.is_random_next 85 | s += "masked_lm_positions: %s\n" % (" ".join( 86 | [str(x) for x in self.masked_lm_positions])) 87 | s += "masked_lm_labels: %s\n" % (" ".join( 88 | [tokenization.printable_text(x) for x in self.masked_lm_labels])) 89 | s += "\n" 90 | return s 91 | 92 | def __repr__(self): 93 | return self.__str__() 94 | 95 | 96 | def write_instance_to_example_files(instances, tokenizer, max_seq_length, 97 | max_predictions_per_seq, output_files): 98 | """Create TF example files from `TrainingInstance`s.""" 99 | writers = [] 100 | for output_file in output_files: 101 | writers.append(tf.python_io.TFRecordWriter(output_file)) 102 | 103 | writer_index = 0 104 | 105 | total_written = 0 106 | for (inst_index, instance) in enumerate(instances): 107 | input_ids = tokenizer.convert_tokens_to_ids(instance.tokens) 108 | input_mask = [1] * len(input_ids) 109 | segment_ids = list(instance.segment_ids) 110 | assert len(input_ids) <= max_seq_length 111 | 112 | while len(input_ids) < max_seq_length: 113 | input_ids.append(0) 114 | input_mask.append(0) 115 | segment_ids.append(0) 116 | 117 | assert len(input_ids) == max_seq_length 118 | assert len(input_mask) == max_seq_length 119 | assert len(segment_ids) == max_seq_length 120 | 121 | masked_lm_positions = list(instance.masked_lm_positions) 122 | masked_lm_ids = tokenizer.convert_tokens_to_ids(instance.masked_lm_labels) 123 | masked_lm_weights = [1.0] * len(masked_lm_ids) 124 | 125 | while len(masked_lm_positions) < max_predictions_per_seq: 126 | masked_lm_positions.append(0) 127 | masked_lm_ids.append(0) 128 | masked_lm_weights.append(0.0) 129 | 130 | next_sentence_label = 1 if instance.is_random_next else 0 131 | 132 | features = collections.OrderedDict() 133 | features["input_ids"] = create_int_feature(input_ids) 134 | features["input_mask"] = create_int_feature(input_mask) 135 | features["segment_ids"] = create_int_feature(segment_ids) 136 | features["masked_lm_positions"] = create_int_feature(masked_lm_positions) 137 | features["masked_lm_ids"] = create_int_feature(masked_lm_ids) 138 | features["masked_lm_weights"] = create_float_feature(masked_lm_weights) 139 | features["next_sentence_labels"] = create_int_feature([next_sentence_label]) 140 | 141 | tf_example = tf.train.Example(features=tf.train.Features(feature=features)) 142 | 143 | writers[writer_index].write(tf_example.SerializeToString()) 144 | writer_index = (writer_index + 1) % len(writers) 145 | 146 | total_written += 1 147 | 148 | if inst_index < 20: 149 | tf.logging.info("*** Example ***") 150 | tf.logging.info("tokens: %s" % " ".join( 151 | [tokenization.printable_text(x) for x in instance.tokens])) 152 | 153 | for feature_name in features.keys(): 154 | feature = features[feature_name] 155 | values = [] 156 | if feature.int64_list.value: 157 | values = feature.int64_list.value 158 | elif feature.float_list.value: 159 | values = feature.float_list.value 160 | tf.logging.info( 161 | "%s: %s" % (feature_name, " ".join([str(x) for x in values]))) 162 | 163 | for writer in writers: 164 | writer.close() 165 | 166 | tf.logging.info("Wrote %d total instances", total_written) 167 | 168 | 169 | def create_int_feature(values): 170 | feature = tf.train.Feature(int64_list=tf.train.Int64List(value=list(values))) 171 | return feature 172 | 173 | 174 | def create_float_feature(values): 175 | feature = tf.train.Feature(float_list=tf.train.FloatList(value=list(values))) 176 | return feature 177 | 178 | 179 | def create_training_instances(input_files, tokenizer, max_seq_length, 180 | dupe_factor, short_seq_prob, masked_lm_prob, 181 | max_predictions_per_seq, rng): 182 | """Create `TrainingInstance`s from raw text.""" 183 | all_documents = [[]] 184 | 185 | # Input file format: 186 | # (1) One sentence per line. These should ideally be actual sentences, not 187 | # entire paragraphs or arbitrary spans of text. (Because we use the 188 | # sentence boundaries for the "next sentence prediction" task). 189 | # (2) Blank lines between documents. Document boundaries are needed so 190 | # that the "next sentence prediction" task doesn't span between documents. 191 | for input_file in input_files: 192 | with tf.gfile.GFile(input_file, "r") as reader: 193 | while True: 194 | line = tokenization.convert_to_unicode(reader.readline()) 195 | if not line: 196 | break 197 | line = line.strip() 198 | 199 | # Empty lines are used as document delimiters 200 | if not line: 201 | all_documents.append([]) 202 | tokens = tokenizer.tokenize(line) 203 | if tokens: 204 | all_documents[-1].append(tokens) 205 | 206 | # Remove empty documents 207 | all_documents = [x for x in all_documents if x] 208 | rng.shuffle(all_documents) 209 | 210 | vocab_words = list(tokenizer.vocab.keys()) 211 | instances = [] 212 | for _ in range(dupe_factor): 213 | for document_index in range(len(all_documents)): 214 | instances.extend( 215 | create_instances_from_document( 216 | all_documents, document_index, max_seq_length, short_seq_prob, 217 | masked_lm_prob, max_predictions_per_seq, vocab_words, rng)) 218 | 219 | rng.shuffle(instances) 220 | return instances 221 | 222 | 223 | def create_instances_from_document( 224 | all_documents, document_index, max_seq_length, short_seq_prob, 225 | masked_lm_prob, max_predictions_per_seq, vocab_words, rng): 226 | """Creates `TrainingInstance`s for a single document.""" 227 | document = all_documents[document_index] 228 | 229 | # Account for [CLS], [SEP], [SEP] 230 | max_num_tokens = max_seq_length - 3 231 | 232 | # We *usually* want to fill up the entire sequence since we are padding 233 | # to `max_seq_length` anyways, so short sequences are generally wasted 234 | # computation. However, we *sometimes* 235 | # (i.e., short_seq_prob == 0.1 == 10% of the time) want to use shorter 236 | # sequences to minimize the mismatch between pre-training and fine-tuning. 237 | # The `target_seq_length` is just a rough target however, whereas 238 | # `max_seq_length` is a hard limit. 239 | target_seq_length = max_num_tokens 240 | if rng.random() < short_seq_prob: 241 | target_seq_length = rng.randint(2, max_num_tokens) 242 | 243 | # We DON'T just concatenate all of the tokens from a document into a long 244 | # sequence and choose an arbitrary split point because this would make the 245 | # next sentence prediction task too easy. Instead, we split the input into 246 | # segments "A" and "B" based on the actual "sentences" provided by the user 247 | # input. 248 | instances = [] 249 | current_chunk = [] 250 | current_length = 0 251 | i = 0 252 | while i < len(document): 253 | segment = document[i] 254 | current_chunk.append(segment) 255 | current_length += len(segment) 256 | if i == len(document) - 1 or current_length >= target_seq_length: 257 | if current_chunk: 258 | # `a_end` is how many segments from `current_chunk` go into the `A` 259 | # (first) sentence. 260 | a_end = 1 261 | if len(current_chunk) >= 2: 262 | a_end = rng.randint(1, len(current_chunk) - 1) 263 | 264 | tokens_a = [] 265 | for j in range(a_end): 266 | tokens_a.extend(current_chunk[j]) 267 | 268 | tokens_b = [] 269 | # Random next 270 | is_random_next = False 271 | if len(current_chunk) == 1 or rng.random() < 0.5: 272 | is_random_next = True 273 | target_b_length = target_seq_length - len(tokens_a) 274 | 275 | # This should rarely go for more than one iteration for large 276 | # corpora. However, just to be careful, we try to make sure that 277 | # the random document is not the same as the document 278 | # we're processing. 279 | for _ in range(10): 280 | random_document_index = rng.randint(0, len(all_documents) - 1) 281 | if random_document_index != document_index: 282 | break 283 | 284 | random_document = all_documents[random_document_index] 285 | random_start = rng.randint(0, len(random_document) - 1) 286 | for j in range(random_start, len(random_document)): 287 | tokens_b.extend(random_document[j]) 288 | if len(tokens_b) >= target_b_length: 289 | break 290 | # We didn't actually use these segments so we "put them back" so 291 | # they don't go to waste. 292 | num_unused_segments = len(current_chunk) - a_end 293 | i -= num_unused_segments 294 | # Actual next 295 | else: 296 | is_random_next = False 297 | for j in range(a_end, len(current_chunk)): 298 | tokens_b.extend(current_chunk[j]) 299 | truncate_seq_pair(tokens_a, tokens_b, max_num_tokens, rng) 300 | 301 | assert len(tokens_a) >= 1 302 | assert len(tokens_b) >= 1 303 | 304 | tokens = [] 305 | segment_ids = [] 306 | tokens.append("[CLS]") 307 | segment_ids.append(0) 308 | for token in tokens_a: 309 | tokens.append(token) 310 | segment_ids.append(0) 311 | 312 | tokens.append("[SEP]") 313 | segment_ids.append(0) 314 | 315 | for token in tokens_b: 316 | tokens.append(token) 317 | segment_ids.append(1) 318 | tokens.append("[SEP]") 319 | segment_ids.append(1) 320 | 321 | (tokens, masked_lm_positions, 322 | masked_lm_labels) = create_masked_lm_predictions( 323 | tokens, masked_lm_prob, max_predictions_per_seq, vocab_words, rng) 324 | instance = TrainingInstance( 325 | tokens=tokens, 326 | segment_ids=segment_ids, 327 | is_random_next=is_random_next, 328 | masked_lm_positions=masked_lm_positions, 329 | masked_lm_labels=masked_lm_labels) 330 | instances.append(instance) 331 | current_chunk = [] 332 | current_length = 0 333 | i += 1 334 | 335 | return instances 336 | 337 | 338 | MaskedLmInstance = collections.namedtuple("MaskedLmInstance", 339 | ["index", "label"]) 340 | 341 | 342 | def create_masked_lm_predictions(tokens, masked_lm_prob, 343 | max_predictions_per_seq, vocab_words, rng): 344 | """Creates the predictions for the masked LM objective.""" 345 | 346 | cand_indexes = [] 347 | for (i, token) in enumerate(tokens): 348 | if token == "[CLS]" or token == "[SEP]": 349 | continue 350 | # Whole Word Masking means that if we mask all of the wordpieces 351 | # corresponding to an original word. When a word has been split into 352 | # WordPieces, the first token does not have any marker and any subsequence 353 | # tokens are prefixed with ##. So whenever we see the ## token, we 354 | # append it to the previous set of word indexes. 355 | # 356 | # Note that Whole Word Masking does *not* change the training code 357 | # at all -- we still predict each WordPiece independently, softmaxed 358 | # over the entire vocabulary. 359 | if (FLAGS.do_whole_word_mask and len(cand_indexes) >= 1 and 360 | token.startswith("##")): 361 | cand_indexes[-1].append(i) 362 | else: 363 | cand_indexes.append([i]) 364 | 365 | rng.shuffle(cand_indexes) 366 | 367 | output_tokens = list(tokens) 368 | 369 | num_to_predict = min(max_predictions_per_seq, 370 | max(1, int(round(len(tokens) * masked_lm_prob)))) 371 | 372 | masked_lms = [] 373 | covered_indexes = set() 374 | for index_set in cand_indexes: 375 | if len(masked_lms) >= num_to_predict: 376 | break 377 | # If adding a whole-word mask would exceed the maximum number of 378 | # predictions, then just skip this candidate. 379 | if len(masked_lms) + len(index_set) > num_to_predict: 380 | continue 381 | is_any_index_covered = False 382 | for index in index_set: 383 | if index in covered_indexes: 384 | is_any_index_covered = True 385 | break 386 | if is_any_index_covered: 387 | continue 388 | for index in index_set: 389 | covered_indexes.add(index) 390 | 391 | masked_token = None 392 | # 80% of the time, replace with [MASK] 393 | if rng.random() < 0.8: 394 | masked_token = "[MASK]" 395 | else: 396 | # 10% of the time, keep original 397 | if rng.random() < 0.5: 398 | masked_token = tokens[index] 399 | # 10% of the time, replace with random word 400 | else: 401 | masked_token = vocab_words[rng.randint(0, len(vocab_words) - 1)] 402 | 403 | output_tokens[index] = masked_token 404 | 405 | masked_lms.append(MaskedLmInstance(index=index, label=tokens[index])) 406 | assert len(masked_lms) <= num_to_predict 407 | masked_lms = sorted(masked_lms, key=lambda x: x.index) 408 | 409 | masked_lm_positions = [] 410 | masked_lm_labels = [] 411 | for p in masked_lms: 412 | masked_lm_positions.append(p.index) 413 | masked_lm_labels.append(p.label) 414 | 415 | return (output_tokens, masked_lm_positions, masked_lm_labels) 416 | 417 | 418 | def truncate_seq_pair(tokens_a, tokens_b, max_num_tokens, rng): 419 | """Truncates a pair of sequences to a maximum sequence length.""" 420 | while True: 421 | total_length = len(tokens_a) + len(tokens_b) 422 | if total_length <= max_num_tokens: 423 | break 424 | 425 | trunc_tokens = tokens_a if len(tokens_a) > len(tokens_b) else tokens_b 426 | assert len(trunc_tokens) >= 1 427 | 428 | # We want to sometimes truncate from the front and sometimes from the 429 | # back to add more randomness and avoid biases. 430 | if rng.random() < 0.5: 431 | del trunc_tokens[0] 432 | else: 433 | trunc_tokens.pop() 434 | 435 | 436 | def main(_): 437 | tf.logging.set_verbosity(tf.logging.INFO) 438 | 439 | tokenizer = tokenization.FullTokenizer( 440 | vocab_file=FLAGS.vocab_file, do_lower_case=FLAGS.do_lower_case) 441 | 442 | input_files = [] 443 | for input_pattern in FLAGS.input_file.split(","): 444 | input_files.extend(tf.gfile.Glob(input_pattern)) 445 | 446 | tf.logging.info("*** Reading from input files ***") 447 | for input_file in input_files: 448 | tf.logging.info(" %s", input_file) 449 | 450 | rng = random.Random(FLAGS.random_seed) 451 | instances = create_training_instances( 452 | input_files, tokenizer, FLAGS.max_seq_length, FLAGS.dupe_factor, 453 | FLAGS.short_seq_prob, FLAGS.masked_lm_prob, FLAGS.max_predictions_per_seq, 454 | rng) 455 | 456 | output_files = FLAGS.output_file.split(",") 457 | tf.logging.info("*** Writing to output files ***") 458 | for output_file in output_files: 459 | tf.logging.info(" %s", output_file) 460 | 461 | write_instance_to_example_files(instances, tokenizer, FLAGS.max_seq_length, 462 | FLAGS.max_predictions_per_seq, output_files) 463 | 464 | 465 | if __name__ == "__main__": 466 | flags.mark_flag_as_required("input_file") 467 | flags.mark_flag_as_required("output_file") 468 | flags.mark_flag_as_required("vocab_file") 469 | tf.app.run() 470 | -------------------------------------------------------------------------------- /命名实体识别/bert/extract_features.py: -------------------------------------------------------------------------------- 1 | # coding=utf-8 2 | # Copyright 2018 The Google AI Language Team Authors. 3 | # 4 | # Licensed under the Apache License, Version 2.0 (the "License"); 5 | # you may not use this file except in compliance with the License. 6 | # You may obtain a copy of the License at 7 | # 8 | # http://www.apache.org/licenses/LICENSE-2.0 9 | # 10 | # Unless required by applicable law or agreed to in writing, software 11 | # distributed under the License is distributed on an "AS IS" BASIS, 12 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 13 | # See the License for the specific language governing permissions and 14 | # limitations under the License. 15 | """Extract pre-computed feature vectors from BERT.""" 16 | 17 | from __future__ import absolute_import 18 | from __future__ import division 19 | from __future__ import print_function 20 | 21 | import codecs 22 | import collections 23 | import json 24 | import re 25 | 26 | import modeling 27 | import tokenization 28 | import tensorflow as tf 29 | 30 | flags = tf.flags 31 | 32 | FLAGS = flags.FLAGS 33 | 34 | flags.DEFINE_string("input_file", None, "") 35 | 36 | flags.DEFINE_string("output_file", None, "") 37 | 38 | flags.DEFINE_string("layers", "-1,-2,-3,-4", "") 39 | 40 | flags.DEFINE_string( 41 | "bert_config_file", None, 42 | "The config json file corresponding to the pre-trained BERT model. " 43 | "This specifies the model architecture.") 44 | 45 | flags.DEFINE_integer( 46 | "max_seq_length", 128, 47 | "The maximum total input sequence length after WordPiece tokenization. " 48 | "Sequences longer than this will be truncated, and sequences shorter " 49 | "than this will be padded.") 50 | 51 | flags.DEFINE_string( 52 | "init_checkpoint", None, 53 | "Initial checkpoint (usually from a pre-trained BERT model).") 54 | 55 | flags.DEFINE_string("vocab_file", None, 56 | "The vocabulary file that the BERT model was trained on.") 57 | 58 | flags.DEFINE_bool( 59 | "do_lower_case", True, 60 | "Whether to lower case the input text. Should be True for uncased " 61 | "models and False for cased models.") 62 | 63 | flags.DEFINE_integer("batch_size", 32, "Batch size for predictions.") 64 | 65 | flags.DEFINE_bool("use_tpu", False, "Whether to use TPU or GPU/CPU.") 66 | 67 | flags.DEFINE_string("master", None, 68 | "If using a TPU, the address of the master.") 69 | 70 | flags.DEFINE_integer( 71 | "num_tpu_cores", 8, 72 | "Only used if `use_tpu` is True. Total number of TPU cores to use.") 73 | 74 | flags.DEFINE_bool( 75 | "use_one_hot_embeddings", False, 76 | "If True, tf.one_hot will be used for embedding lookups, otherwise " 77 | "tf.nn.embedding_lookup will be used. On TPUs, this should be True " 78 | "since it is much faster.") 79 | 80 | 81 | class InputExample(object): 82 | 83 | def __init__(self, unique_id, text_a, text_b): 84 | self.unique_id = unique_id 85 | self.text_a = text_a 86 | self.text_b = text_b 87 | 88 | 89 | class InputFeatures(object): 90 | """A single set of features of data.""" 91 | 92 | def __init__(self, unique_id, tokens, input_ids, input_mask, input_type_ids): 93 | self.unique_id = unique_id 94 | self.tokens = tokens 95 | self.input_ids = input_ids 96 | self.input_mask = input_mask 97 | self.input_type_ids = input_type_ids 98 | 99 | 100 | def input_fn_builder(features, seq_length): 101 | """Creates an `input_fn` closure to be passed to TPUEstimator.""" 102 | 103 | all_unique_ids = [] 104 | all_input_ids = [] 105 | all_input_mask = [] 106 | all_input_type_ids = [] 107 | 108 | for feature in features: 109 | all_unique_ids.append(feature.unique_id) 110 | all_input_ids.append(feature.input_ids) 111 | all_input_mask.append(feature.input_mask) 112 | all_input_type_ids.append(feature.input_type_ids) 113 | 114 | def input_fn(params): 115 | """The actual input function.""" 116 | batch_size = params["batch_size"] 117 | 118 | num_examples = len(features) 119 | 120 | # This is for demo purposes and does NOT scale to large data sets. We do 121 | # not use Dataset.from_generator() because that uses tf.py_func which is 122 | # not TPU compatible. The right way to load data is with TFRecordReader. 123 | d = tf.data.Dataset.from_tensor_slices({ 124 | "unique_ids": 125 | tf.constant(all_unique_ids, shape=[num_examples], dtype=tf.int32), 126 | "input_ids": 127 | tf.constant( 128 | all_input_ids, shape=[num_examples, seq_length], 129 | dtype=tf.int32), 130 | "input_mask": 131 | tf.constant( 132 | all_input_mask, 133 | shape=[num_examples, seq_length], 134 | dtype=tf.int32), 135 | "input_type_ids": 136 | tf.constant( 137 | all_input_type_ids, 138 | shape=[num_examples, seq_length], 139 | dtype=tf.int32), 140 | }) 141 | 142 | d = d.batch(batch_size=batch_size, drop_remainder=False) 143 | return d 144 | 145 | return input_fn 146 | 147 | 148 | def model_fn_builder(bert_config, init_checkpoint, layer_indexes, use_tpu, 149 | use_one_hot_embeddings): 150 | """Returns `model_fn` closure for TPUEstimator.""" 151 | 152 | def model_fn(features, labels, mode, params): # pylint: disable=unused-argument 153 | """The `model_fn` for TPUEstimator.""" 154 | 155 | unique_ids = features["unique_ids"] 156 | input_ids = features["input_ids"] 157 | input_mask = features["input_mask"] 158 | input_type_ids = features["input_type_ids"] 159 | 160 | model = modeling.BertModel( 161 | config=bert_config, 162 | is_training=False, 163 | input_ids=input_ids, 164 | input_mask=input_mask, 165 | token_type_ids=input_type_ids, 166 | use_one_hot_embeddings=use_one_hot_embeddings) 167 | 168 | if mode != tf.estimator.ModeKeys.PREDICT: 169 | raise ValueError("Only PREDICT modes are supported: %s" % (mode)) 170 | 171 | tvars = tf.trainable_variables() 172 | scaffold_fn = None 173 | (assignment_map, 174 | initialized_variable_names) = modeling.get_assignment_map_from_checkpoint( 175 | tvars, init_checkpoint) 176 | if use_tpu: 177 | 178 | def tpu_scaffold(): 179 | tf.train.init_from_checkpoint(init_checkpoint, assignment_map) 180 | return tf.train.Scaffold() 181 | 182 | scaffold_fn = tpu_scaffold 183 | else: 184 | tf.train.init_from_checkpoint(init_checkpoint, assignment_map) 185 | 186 | tf.logging.info("**** Trainable Variables ****") 187 | for var in tvars: 188 | init_string = "" 189 | if var.name in initialized_variable_names: 190 | init_string = ", *INIT_FROM_CKPT*" 191 | tf.logging.info(" name = %s, shape = %s%s", var.name, var.shape, 192 | init_string) 193 | 194 | all_layers = model.get_all_encoder_layers() 195 | 196 | predictions = { 197 | "unique_id": unique_ids, 198 | } 199 | 200 | for (i, layer_index) in enumerate(layer_indexes): 201 | predictions["layer_output_%d" % i] = all_layers[layer_index] 202 | 203 | output_spec = tf.contrib.tpu.TPUEstimatorSpec( 204 | mode=mode, predictions=predictions, scaffold_fn=scaffold_fn) 205 | return output_spec 206 | 207 | return model_fn 208 | 209 | 210 | def convert_examples_to_features(examples, seq_length, tokenizer): 211 | """Loads a data file into a list of `InputBatch`s.""" 212 | 213 | features = [] 214 | for (ex_index, example) in enumerate(examples): 215 | tokens_a = tokenizer.tokenize(example.text_a) 216 | 217 | tokens_b = None 218 | if example.text_b: 219 | tokens_b = tokenizer.tokenize(example.text_b) 220 | 221 | if tokens_b: 222 | # Modifies `tokens_a` and `tokens_b` in place so that the total 223 | # length is less than the specified length. 224 | # Account for [CLS], [SEP], [SEP] with "- 3" 225 | _truncate_seq_pair(tokens_a, tokens_b, seq_length - 3) 226 | else: 227 | # Account for [CLS] and [SEP] with "- 2" 228 | if len(tokens_a) > seq_length - 2: 229 | tokens_a = tokens_a[0:(seq_length - 2)] 230 | 231 | # The convention in BERT is: 232 | # (a) For sequence pairs: 233 | # tokens: [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP] 234 | # type_ids: 0 0 0 0 0 0 0 0 1 1 1 1 1 1 235 | # (b) For single sequences: 236 | # tokens: [CLS] the dog is hairy . [SEP] 237 | # type_ids: 0 0 0 0 0 0 0 238 | # 239 | # Where "type_ids" are used to indicate whether this is the first 240 | # sequence or the second sequence. The embedding vectors for `type=0` and 241 | # `type=1` were learned during pre-training and are added to the wordpiece 242 | # embedding vector (and position vector). This is not *strictly* necessary 243 | # since the [SEP] token unambiguously separates the sequences, but it makes 244 | # it easier for the model to learn the concept of sequences. 245 | # 246 | # For classification tasks, the first vector (corresponding to [CLS]) is 247 | # used as as the "sentence vector". Note that this only makes sense because 248 | # the entire model is fine-tuned. 249 | tokens = [] 250 | input_type_ids = [] 251 | tokens.append("[CLS]") 252 | input_type_ids.append(0) 253 | for token in tokens_a: 254 | tokens.append(token) 255 | input_type_ids.append(0) 256 | tokens.append("[SEP]") 257 | input_type_ids.append(0) 258 | 259 | if tokens_b: 260 | for token in tokens_b: 261 | tokens.append(token) 262 | input_type_ids.append(1) 263 | tokens.append("[SEP]") 264 | input_type_ids.append(1) 265 | 266 | input_ids = tokenizer.convert_tokens_to_ids(tokens) 267 | 268 | # The mask has 1 for real tokens and 0 for padding tokens. Only real 269 | # tokens are attended to. 270 | input_mask = [1] * len(input_ids) 271 | 272 | # Zero-pad up to the sequence length. 273 | while len(input_ids) < seq_length: 274 | input_ids.append(0) 275 | input_mask.append(0) 276 | input_type_ids.append(0) 277 | 278 | assert len(input_ids) == seq_length 279 | assert len(input_mask) == seq_length 280 | assert len(input_type_ids) == seq_length 281 | 282 | if ex_index < 5: 283 | tf.logging.info("*** Example ***") 284 | tf.logging.info("unique_id: %s" % (example.unique_id)) 285 | tf.logging.info("tokens: %s" % " ".join( 286 | [tokenization.printable_text(x) for x in tokens])) 287 | tf.logging.info("input_ids: %s" % " ".join([str(x) for x in input_ids])) 288 | tf.logging.info("input_mask: %s" % " ".join([str(x) for x in input_mask])) 289 | tf.logging.info( 290 | "input_type_ids: %s" % " ".join([str(x) for x in input_type_ids])) 291 | 292 | features.append( 293 | InputFeatures( 294 | unique_id=example.unique_id, 295 | tokens=tokens, 296 | input_ids=input_ids, 297 | input_mask=input_mask, 298 | input_type_ids=input_type_ids)) 299 | return features 300 | 301 | 302 | def _truncate_seq_pair(tokens_a, tokens_b, max_length): 303 | """Truncates a sequence pair in place to the maximum length.""" 304 | 305 | # This is a simple heuristic which will always truncate the longer sequence 306 | # one token at a time. This makes more sense than truncating an equal percent 307 | # of tokens from each, since if one sequence is very short then each token 308 | # that's truncated likely contains more information than a longer sequence. 309 | while True: 310 | total_length = len(tokens_a) + len(tokens_b) 311 | if total_length <= max_length: 312 | break 313 | if len(tokens_a) > len(tokens_b): 314 | tokens_a.pop() 315 | else: 316 | tokens_b.pop() 317 | 318 | 319 | def read_examples(input_file): 320 | """Read a list of `InputExample`s from an input file.""" 321 | examples = [] 322 | unique_id = 0 323 | with tf.gfile.GFile(input_file, "r") as reader: 324 | while True: 325 | line = tokenization.convert_to_unicode(reader.readline()) 326 | if not line: 327 | break 328 | line = line.strip() 329 | text_a = None 330 | text_b = None 331 | m = re.match(r"^(.*) \|\|\| (.*)$", line) 332 | if m is None: 333 | text_a = line 334 | else: 335 | text_a = m.group(1) 336 | text_b = m.group(2) 337 | examples.append( 338 | InputExample(unique_id=unique_id, text_a=text_a, text_b=text_b)) 339 | unique_id += 1 340 | return examples 341 | 342 | 343 | def main(_): 344 | tf.logging.set_verbosity(tf.logging.INFO) 345 | 346 | layer_indexes = [int(x) for x in FLAGS.layers.split(",")] 347 | 348 | bert_config = modeling.BertConfig.from_json_file(FLAGS.bert_config_file) 349 | 350 | tokenizer = tokenization.FullTokenizer( 351 | vocab_file=FLAGS.vocab_file, do_lower_case=FLAGS.do_lower_case) 352 | 353 | is_per_host = tf.contrib.tpu.InputPipelineConfig.PER_HOST_V2 354 | run_config = tf.contrib.tpu.RunConfig( 355 | master=FLAGS.master, 356 | tpu_config=tf.contrib.tpu.TPUConfig( 357 | num_shards=FLAGS.num_tpu_cores, 358 | per_host_input_for_training=is_per_host)) 359 | 360 | examples = read_examples(FLAGS.input_file) 361 | 362 | features = convert_examples_to_features( 363 | examples=examples, seq_length=FLAGS.max_seq_length, tokenizer=tokenizer) 364 | 365 | unique_id_to_feature = {} 366 | for feature in features: 367 | unique_id_to_feature[feature.unique_id] = feature 368 | 369 | model_fn = model_fn_builder( 370 | bert_config=bert_config, 371 | init_checkpoint=FLAGS.init_checkpoint, 372 | layer_indexes=layer_indexes, 373 | use_tpu=FLAGS.use_tpu, 374 | use_one_hot_embeddings=FLAGS.use_one_hot_embeddings) 375 | 376 | # If TPU is not available, this will fall back to normal Estimator on CPU 377 | # or GPU. 378 | estimator = tf.contrib.tpu.TPUEstimator( 379 | use_tpu=FLAGS.use_tpu, 380 | model_fn=model_fn, 381 | config=run_config, 382 | predict_batch_size=FLAGS.batch_size) 383 | 384 | input_fn = input_fn_builder( 385 | features=features, seq_length=FLAGS.max_seq_length) 386 | 387 | with codecs.getwriter("utf-8")(tf.gfile.Open(FLAGS.output_file, 388 | "w")) as writer: 389 | for result in estimator.predict(input_fn, yield_single_examples=True): 390 | unique_id = int(result["unique_id"]) 391 | feature = unique_id_to_feature[unique_id] 392 | output_json = collections.OrderedDict() 393 | output_json["linex_index"] = unique_id 394 | all_features = [] 395 | for (i, token) in enumerate(feature.tokens): 396 | all_layers = [] 397 | for (j, layer_index) in enumerate(layer_indexes): 398 | layer_output = result["layer_output_%d" % j] 399 | layers = collections.OrderedDict() 400 | layers["index"] = layer_index 401 | layers["values"] = [ 402 | round(float(x), 6) for x in layer_output[i:(i + 1)].flat 403 | ] 404 | all_layers.append(layers) 405 | features = collections.OrderedDict() 406 | features["token"] = token 407 | features["layers"] = all_layers 408 | all_features.append(features) 409 | output_json["features"] = all_features 410 | writer.write(json.dumps(output_json) + "\n") 411 | 412 | 413 | if __name__ == "__main__": 414 | flags.mark_flag_as_required("input_file") 415 | flags.mark_flag_as_required("vocab_file") 416 | flags.mark_flag_as_required("bert_config_file") 417 | flags.mark_flag_as_required("init_checkpoint") 418 | flags.mark_flag_as_required("output_file") 419 | tf.app.run() 420 | -------------------------------------------------------------------------------- /命名实体识别/bert/modeling_test.py: -------------------------------------------------------------------------------- 1 | # coding=utf-8 2 | # Copyright 2018 The Google AI Language Team Authors. 3 | # 4 | # Licensed under the Apache License, Version 2.0 (the "License"); 5 | # you may not use this file except in compliance with the License. 6 | # You may obtain a copy of the License at 7 | # 8 | # http://www.apache.org/licenses/LICENSE-2.0 9 | # 10 | # Unless required by applicable law or agreed to in writing, software 11 | # distributed under the License is distributed on an "AS IS" BASIS, 12 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 13 | # See the License for the specific language governing permissions and 14 | # limitations under the License. 15 | from __future__ import absolute_import 16 | from __future__ import division 17 | from __future__ import print_function 18 | 19 | import collections 20 | import json 21 | import random 22 | import re 23 | 24 | import modeling 25 | import six 26 | import tensorflow as tf 27 | 28 | 29 | class BertModelTest(tf.test.TestCase): 30 | 31 | class BertModelTester(object): 32 | 33 | def __init__(self, 34 | parent, 35 | batch_size=13, 36 | seq_length=7, 37 | is_training=True, 38 | use_input_mask=True, 39 | use_token_type_ids=True, 40 | vocab_size=99, 41 | hidden_size=32, 42 | num_hidden_layers=5, 43 | num_attention_heads=4, 44 | intermediate_size=37, 45 | hidden_act="gelu", 46 | hidden_dropout_prob=0.1, 47 | attention_probs_dropout_prob=0.1, 48 | max_position_embeddings=512, 49 | type_vocab_size=16, 50 | initializer_range=0.02, 51 | scope=None): 52 | self.parent = parent 53 | self.batch_size = batch_size 54 | self.seq_length = seq_length 55 | self.is_training = is_training 56 | self.use_input_mask = use_input_mask 57 | self.use_token_type_ids = use_token_type_ids 58 | self.vocab_size = vocab_size 59 | self.hidden_size = hidden_size 60 | self.num_hidden_layers = num_hidden_layers 61 | self.num_attention_heads = num_attention_heads 62 | self.intermediate_size = intermediate_size 63 | self.hidden_act = hidden_act 64 | self.hidden_dropout_prob = hidden_dropout_prob 65 | self.attention_probs_dropout_prob = attention_probs_dropout_prob 66 | self.max_position_embeddings = max_position_embeddings 67 | self.type_vocab_size = type_vocab_size 68 | self.initializer_range = initializer_range 69 | self.scope = scope 70 | 71 | def create_model(self): 72 | input_ids = BertModelTest.ids_tensor([self.batch_size, self.seq_length], 73 | self.vocab_size) 74 | 75 | input_mask = None 76 | if self.use_input_mask: 77 | input_mask = BertModelTest.ids_tensor( 78 | [self.batch_size, self.seq_length], vocab_size=2) 79 | 80 | token_type_ids = None 81 | if self.use_token_type_ids: 82 | token_type_ids = BertModelTest.ids_tensor( 83 | [self.batch_size, self.seq_length], self.type_vocab_size) 84 | 85 | config = modeling.BertConfig( 86 | vocab_size=self.vocab_size, 87 | hidden_size=self.hidden_size, 88 | num_hidden_layers=self.num_hidden_layers, 89 | num_attention_heads=self.num_attention_heads, 90 | intermediate_size=self.intermediate_size, 91 | hidden_act=self.hidden_act, 92 | hidden_dropout_prob=self.hidden_dropout_prob, 93 | attention_probs_dropout_prob=self.attention_probs_dropout_prob, 94 | max_position_embeddings=self.max_position_embeddings, 95 | type_vocab_size=self.type_vocab_size, 96 | initializer_range=self.initializer_range) 97 | 98 | model = modeling.BertModel( 99 | config=config, 100 | is_training=self.is_training, 101 | input_ids=input_ids, 102 | input_mask=input_mask, 103 | token_type_ids=token_type_ids, 104 | scope=self.scope) 105 | 106 | outputs = { 107 | "embedding_output": model.get_embedding_output(), 108 | "sequence_output": model.get_sequence_output(), 109 | "pooled_output": model.get_pooled_output(), 110 | "all_encoder_layers": model.get_all_encoder_layers(), 111 | } 112 | return outputs 113 | 114 | def check_output(self, result): 115 | self.parent.assertAllEqual( 116 | result["embedding_output"].shape, 117 | [self.batch_size, self.seq_length, self.hidden_size]) 118 | 119 | self.parent.assertAllEqual( 120 | result["sequence_output"].shape, 121 | [self.batch_size, self.seq_length, self.hidden_size]) 122 | 123 | self.parent.assertAllEqual(result["pooled_output"].shape, 124 | [self.batch_size, self.hidden_size]) 125 | 126 | def test_default(self): 127 | self.run_tester(BertModelTest.BertModelTester(self)) 128 | 129 | def test_config_to_json_string(self): 130 | config = modeling.BertConfig(vocab_size=99, hidden_size=37) 131 | obj = json.loads(config.to_json_string()) 132 | self.assertEqual(obj["vocab_size"], 99) 133 | self.assertEqual(obj["hidden_size"], 37) 134 | 135 | def run_tester(self, tester): 136 | with self.test_session() as sess: 137 | ops = tester.create_model() 138 | init_op = tf.group(tf.global_variables_initializer(), 139 | tf.local_variables_initializer()) 140 | sess.run(init_op) 141 | output_result = sess.run(ops) 142 | tester.check_output(output_result) 143 | 144 | self.assert_all_tensors_reachable(sess, [init_op, ops]) 145 | 146 | @classmethod 147 | def ids_tensor(cls, shape, vocab_size, rng=None, name=None): 148 | """Creates a random int32 tensor of the shape within the vocab size.""" 149 | if rng is None: 150 | rng = random.Random() 151 | 152 | total_dims = 1 153 | for dim in shape: 154 | total_dims *= dim 155 | 156 | values = [] 157 | for _ in range(total_dims): 158 | values.append(rng.randint(0, vocab_size - 1)) 159 | 160 | return tf.constant(value=values, dtype=tf.int32, shape=shape, name=name) 161 | 162 | def assert_all_tensors_reachable(self, sess, outputs): 163 | """Checks that all the tensors in the graph are reachable from outputs.""" 164 | graph = sess.graph 165 | 166 | ignore_strings = [ 167 | "^.*/assert_less_equal/.*$", 168 | "^.*/dilation_rate$", 169 | "^.*/Tensordot/concat$", 170 | "^.*/Tensordot/concat/axis$", 171 | "^testing/.*$", 172 | ] 173 | 174 | ignore_regexes = [re.compile(x) for x in ignore_strings] 175 | 176 | unreachable = self.get_unreachable_ops(graph, outputs) 177 | filtered_unreachable = [] 178 | for x in unreachable: 179 | do_ignore = False 180 | for r in ignore_regexes: 181 | m = r.match(x.name) 182 | if m is not None: 183 | do_ignore = True 184 | if do_ignore: 185 | continue 186 | filtered_unreachable.append(x) 187 | unreachable = filtered_unreachable 188 | 189 | self.assertEqual( 190 | len(unreachable), 0, "The following ops are unreachable: %s" % 191 | (" ".join([x.name for x in unreachable]))) 192 | 193 | @classmethod 194 | def get_unreachable_ops(cls, graph, outputs): 195 | """Finds all of the tensors in graph that are unreachable from outputs.""" 196 | outputs = cls.flatten_recursive(outputs) 197 | output_to_op = collections.defaultdict(list) 198 | op_to_all = collections.defaultdict(list) 199 | assign_out_to_in = collections.defaultdict(list) 200 | 201 | for op in graph.get_operations(): 202 | for x in op.inputs: 203 | op_to_all[op.name].append(x.name) 204 | for y in op.outputs: 205 | output_to_op[y.name].append(op.name) 206 | op_to_all[op.name].append(y.name) 207 | if str(op.type) == "Assign": 208 | for y in op.outputs: 209 | for x in op.inputs: 210 | assign_out_to_in[y.name].append(x.name) 211 | 212 | assign_groups = collections.defaultdict(list) 213 | for out_name in assign_out_to_in.keys(): 214 | name_group = assign_out_to_in[out_name] 215 | for n1 in name_group: 216 | assign_groups[n1].append(out_name) 217 | for n2 in name_group: 218 | if n1 != n2: 219 | assign_groups[n1].append(n2) 220 | 221 | seen_tensors = {} 222 | stack = [x.name for x in outputs] 223 | while stack: 224 | name = stack.pop() 225 | if name in seen_tensors: 226 | continue 227 | seen_tensors[name] = True 228 | 229 | if name in output_to_op: 230 | for op_name in output_to_op[name]: 231 | if op_name in op_to_all: 232 | for input_name in op_to_all[op_name]: 233 | if input_name not in stack: 234 | stack.append(input_name) 235 | 236 | expanded_names = [] 237 | if name in assign_groups: 238 | for assign_name in assign_groups[name]: 239 | expanded_names.append(assign_name) 240 | 241 | for expanded_name in expanded_names: 242 | if expanded_name not in stack: 243 | stack.append(expanded_name) 244 | 245 | unreachable_ops = [] 246 | for op in graph.get_operations(): 247 | is_unreachable = False 248 | all_names = [x.name for x in op.inputs] + [x.name for x in op.outputs] 249 | for name in all_names: 250 | if name not in seen_tensors: 251 | is_unreachable = True 252 | if is_unreachable: 253 | unreachable_ops.append(op) 254 | return unreachable_ops 255 | 256 | @classmethod 257 | def flatten_recursive(cls, item): 258 | """Flattens (potentially nested) a tuple/dictionary/list to a list.""" 259 | output = [] 260 | if isinstance(item, list): 261 | output.extend(item) 262 | elif isinstance(item, tuple): 263 | output.extend(list(item)) 264 | elif isinstance(item, dict): 265 | for (_, v) in six.iteritems(item): 266 | output.append(v) 267 | else: 268 | return [item] 269 | 270 | flat_output = [] 271 | for x in output: 272 | flat_output.extend(cls.flatten_recursive(x)) 273 | return flat_output 274 | 275 | 276 | if __name__ == "__main__": 277 | tf.test.main() 278 | -------------------------------------------------------------------------------- /命名实体识别/bert/multilingual.md: -------------------------------------------------------------------------------- 1 | ## Models 2 | 3 | There are two multilingual models currently available. We do not plan to release 4 | more single-language models, but we may release `BERT-Large` versions of these 5 | two in the future: 6 | 7 | * **[`BERT-Base, Multilingual Cased (New, recommended)`](https://storage.googleapis.com/bert_models/2018_11_23/multi_cased_L-12_H-768_A-12.zip)**: 8 | 104 languages, 12-layer, 768-hidden, 12-heads, 110M parameters 9 | * **[`BERT-Base, Multilingual Uncased (Orig, not recommended)`](https://storage.googleapis.com/bert_models/2018_11_03/multilingual_L-12_H-768_A-12.zip)**: 10 | 102 languages, 12-layer, 768-hidden, 12-heads, 110M parameters 11 | * **[`BERT-Base, Chinese`](https://storage.googleapis.com/bert_models/2018_11_03/chinese_L-12_H-768_A-12.zip)**: 12 | Chinese Simplified and Traditional, 12-layer, 768-hidden, 12-heads, 110M 13 | parameters 14 | 15 | **The `Multilingual Cased (New)` model also fixes normalization issues in many 16 | languages, so it is recommended in languages with non-Latin alphabets (and is 17 | often better for most languages with Latin alphabets). When using this model, 18 | make sure to pass `--do_lower_case=false` to `run_pretraining.py` and other 19 | scripts.** 20 | 21 | See the [list of languages](#list-of-languages) that the Multilingual model 22 | supports. The Multilingual model does include Chinese (and English), but if your 23 | fine-tuning data is Chinese-only, then the Chinese model will likely produce 24 | better results. 25 | 26 | ## Results 27 | 28 | To evaluate these systems, we use the 29 | [XNLI dataset](https://github.com/facebookresearch/XNLI) dataset, which is a 30 | version of [MultiNLI](https://www.nyu.edu/projects/bowman/multinli/) where the 31 | dev and test sets have been translated (by humans) into 15 languages. Note that 32 | the training set was *machine* translated (we used the translations provided by 33 | XNLI, not Google NMT). For clarity, we only report on 6 languages below: 34 | 35 | 36 | 37 | | System | English | Chinese | Spanish | German | Arabic | Urdu | 38 | | --------------------------------- | -------- | -------- | -------- | -------- | -------- | -------- | 39 | | XNLI Baseline - Translate Train | 73.7 | 67.0 | 68.8 | 66.5 | 65.8 | 56.6 | 40 | | XNLI Baseline - Translate Test | 73.7 | 68.3 | 70.7 | 68.7 | 66.8 | 59.3 | 41 | | BERT - Translate Train Cased | **81.9** | **76.6** | **77.8** | **75.9** | **70.7** | 61.6 | 42 | | BERT - Translate Train Uncased | 81.4 | 74.2 | 77.3 | 75.2 | 70.5 | 61.7 | 43 | | BERT - Translate Test Uncased | 81.4 | 70.1 | 74.9 | 74.4 | 70.4 | **62.1** | 44 | | BERT - Zero Shot Uncased | 81.4 | 63.8 | 74.3 | 70.5 | 62.1 | 58.3 | 45 | 46 | 47 | 48 | The first two rows are baselines from the XNLI paper and the last three rows are 49 | our results with BERT. 50 | 51 | **Translate Train** means that the MultiNLI training set was machine translated 52 | from English into the foreign language. So training and evaluation were both 53 | done in the foreign language. Unfortunately, training was done on 54 | machine-translated data, so it is impossible to quantify how much of the lower 55 | accuracy (compared to English) is due to the quality of the machine translation 56 | vs. the quality of the pre-trained model. 57 | 58 | **Translate Test** means that the XNLI test set was machine translated from the 59 | foreign language into English. So training and evaluation were both done on 60 | English. However, test evaluation was done on machine-translated English, so the 61 | accuracy depends on the quality of the machine translation system. 62 | 63 | **Zero Shot** means that the Multilingual BERT system was fine-tuned on English 64 | MultiNLI, and then evaluated on the foreign language XNLI test. In this case, 65 | machine translation was not involved at all in either the pre-training or 66 | fine-tuning. 67 | 68 | Note that the English result is worse than the 84.2 MultiNLI baseline because 69 | this training used Multilingual BERT rather than English-only BERT. This implies 70 | that for high-resource languages, the Multilingual model is somewhat worse than 71 | a single-language model. However, it is not feasible for us to train and 72 | maintain dozens of single-language model. Therefore, if your goal is to maximize 73 | performance with a language other than English or Chinese, you might find it 74 | beneficial to run pre-training for additional steps starting from our 75 | Multilingual model on data from your language of interest. 76 | 77 | Here is a comparison of training Chinese models with the Multilingual 78 | `BERT-Base` and Chinese-only `BERT-Base`: 79 | 80 | System | Chinese 81 | ----------------------- | ------- 82 | XNLI Baseline | 67.0 83 | BERT Multilingual Model | 74.2 84 | BERT Chinese-only Model | 77.2 85 | 86 | Similar to English, the single-language model does 3% better than the 87 | Multilingual model. 88 | 89 | ## Fine-tuning Example 90 | 91 | The multilingual model does **not** require any special consideration or API 92 | changes. We did update the implementation of `BasicTokenizer` in 93 | `tokenization.py` to support Chinese character tokenization, so please update if 94 | you forked it. However, we did not change the tokenization API. 95 | 96 | To test the new models, we did modify `run_classifier.py` to add support for the 97 | [XNLI dataset](https://github.com/facebookresearch/XNLI). This is a 15-language 98 | version of MultiNLI where the dev/test sets have been human-translated, and the 99 | training set has been machine-translated. 100 | 101 | To run the fine-tuning code, please download the 102 | [XNLI dev/test set](https://s3.amazonaws.com/xnli/XNLI-1.0.zip) and the 103 | [XNLI machine-translated training set](https://s3.amazonaws.com/xnli/XNLI-MT-1.0.zip) 104 | and then unpack both .zip files into some directory `$XNLI_DIR`. 105 | 106 | To run fine-tuning on XNLI. The language is hard-coded into `run_classifier.py` 107 | (Chinese by default), so please modify `XnliProcessor` if you want to run on 108 | another language. 109 | 110 | This is a large dataset, so this will training will take a few hours on a GPU 111 | (or about 30 minutes on a Cloud TPU). To run an experiment quickly for 112 | debugging, just set `num_train_epochs` to a small value like `0.1`. 113 | 114 | ```shell 115 | export BERT_BASE_DIR=/path/to/bert/chinese_L-12_H-768_A-12 # or multilingual_L-12_H-768_A-12 116 | export XNLI_DIR=/path/to/xnli 117 | 118 | python run_classifier.py \ 119 | --task_name=XNLI \ 120 | --do_train=true \ 121 | --do_eval=true \ 122 | --data_dir=$XNLI_DIR \ 123 | --vocab_file=$BERT_BASE_DIR/vocab.txt \ 124 | --bert_config_file=$BERT_BASE_DIR/bert_config.json \ 125 | --init_checkpoint=$BERT_BASE_DIR/bert_model.ckpt \ 126 | --max_seq_length=128 \ 127 | --train_batch_size=32 \ 128 | --learning_rate=5e-5 \ 129 | --num_train_epochs=2.0 \ 130 | --output_dir=/tmp/xnli_output/ 131 | ``` 132 | 133 | With the Chinese-only model, the results should look something like this: 134 | 135 | ``` 136 | ***** Eval results ***** 137 | eval_accuracy = 0.774116 138 | eval_loss = 0.83554 139 | global_step = 24543 140 | loss = 0.74603 141 | ``` 142 | 143 | ## Details 144 | 145 | ### Data Source and Sampling 146 | 147 | The languages chosen were the 148 | [top 100 languages with the largest Wikipedias](https://meta.wikimedia.org/wiki/List_of_Wikipedias). 149 | The entire Wikipedia dump for each language (excluding user and talk pages) was 150 | taken as the training data for each language 151 | 152 | However, the size of the Wikipedia for a given language varies greatly, and 153 | therefore low-resource languages may be "under-represented" in terms of the 154 | neural network model (under the assumption that languages are "competing" for 155 | limited model capacity to some extent). 156 | 157 | However, the size of a Wikipedia also correlates with the number of speakers of 158 | a language, and we also don't want to overfit the model by performing thousands 159 | of epochs over a tiny Wikipedia for a particular language. 160 | 161 | To balance these two factors, we performed exponentially smoothed weighting of 162 | the data during pre-training data creation (and WordPiece vocab creation). In 163 | other words, let's say that the probability of a language is *P(L)*, e.g., 164 | *P(English) = 0.21* means that after concatenating all of the Wikipedias 165 | together, 21% of our data is English. We exponentiate each probability by some 166 | factor *S* and then re-normalize, and sample from that distribution. In our case 167 | we use *S=0.7*. So, high-resource languages like English will be under-sampled, 168 | and low-resource languages like Icelandic will be over-sampled. E.g., in the 169 | original distribution English would be sampled 1000x more than Icelandic, but 170 | after smoothing it's only sampled 100x more. 171 | 172 | ### Tokenization 173 | 174 | For tokenization, we use a 110k shared WordPiece vocabulary. The word counts are 175 | weighted the same way as the data, so low-resource languages are upweighted by 176 | some factor. We intentionally do *not* use any marker to denote the input 177 | language (so that zero-shot training can work). 178 | 179 | Because Chinese (and Japanese Kanji and Korean Hanja) does not have whitespace 180 | characters, we add spaces around every character in the 181 | [CJK Unicode range](https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_\(Unicode_block\)) 182 | before applying WordPiece. This means that Chinese is effectively 183 | character-tokenized. Note that the CJK Unicode block only includes 184 | Chinese-origin characters and does *not* include Hangul Korean or 185 | Katakana/Hiragana Japanese, which are tokenized with whitespace+WordPiece like 186 | all other languages. 187 | 188 | For all other languages, we apply the 189 | [same recipe as English](https://github.com/google-research/bert#tokenization): 190 | (a) lower casing+accent removal, (b) punctuation splitting, (c) whitespace 191 | tokenization. We understand that accent markers have substantial meaning in some 192 | languages, but felt that the benefits of reducing the effective vocabulary make 193 | up for this. Generally the strong contextual models of BERT should make up for 194 | any ambiguity introduced by stripping accent markers. 195 | 196 | ### List of Languages 197 | 198 | The multilingual model supports the following languages. These languages were 199 | chosen because they are the top 100 languages with the largest Wikipedias: 200 | 201 | * Afrikaans 202 | * Albanian 203 | * Arabic 204 | * Aragonese 205 | * Armenian 206 | * Asturian 207 | * Azerbaijani 208 | * Bashkir 209 | * Basque 210 | * Bavarian 211 | * Belarusian 212 | * Bengali 213 | * Bishnupriya Manipuri 214 | * Bosnian 215 | * Breton 216 | * Bulgarian 217 | * Burmese 218 | * Catalan 219 | * Cebuano 220 | * Chechen 221 | * Chinese (Simplified) 222 | * Chinese (Traditional) 223 | * Chuvash 224 | * Croatian 225 | * Czech 226 | * Danish 227 | * Dutch 228 | * English 229 | * Estonian 230 | * Finnish 231 | * French 232 | * Galician 233 | * Georgian 234 | * German 235 | * Greek 236 | * Gujarati 237 | * Haitian 238 | * Hebrew 239 | * Hindi 240 | * Hungarian 241 | * Icelandic 242 | * Ido 243 | * Indonesian 244 | * Irish 245 | * Italian 246 | * Japanese 247 | * Javanese 248 | * Kannada 249 | * Kazakh 250 | * Kirghiz 251 | * Korean 252 | * Latin 253 | * Latvian 254 | * Lithuanian 255 | * Lombard 256 | * Low Saxon 257 | * Luxembourgish 258 | * Macedonian 259 | * Malagasy 260 | * Malay 261 | * Malayalam 262 | * Marathi 263 | * Minangkabau 264 | * Nepali 265 | * Newar 266 | * Norwegian (Bokmal) 267 | * Norwegian (Nynorsk) 268 | * Occitan 269 | * Persian (Farsi) 270 | * Piedmontese 271 | * Polish 272 | * Portuguese 273 | * Punjabi 274 | * Romanian 275 | * Russian 276 | * Scots 277 | * Serbian 278 | * Serbo-Croatian 279 | * Sicilian 280 | * Slovak 281 | * Slovenian 282 | * South Azerbaijani 283 | * Spanish 284 | * Sundanese 285 | * Swahili 286 | * Swedish 287 | * Tagalog 288 | * Tajik 289 | * Tamil 290 | * Tatar 291 | * Telugu 292 | * Turkish 293 | * Ukrainian 294 | * Urdu 295 | * Uzbek 296 | * Vietnamese 297 | * Volapük 298 | * Waray-Waray 299 | * Welsh 300 | * West Frisian 301 | * Western Punjabi 302 | * Yoruba 303 | 304 | The **Multilingual Cased (New)** release contains additionally **Thai** and 305 | **Mongolian**, which were not included in the original release. 306 | -------------------------------------------------------------------------------- /命名实体识别/bert/optimization.py: -------------------------------------------------------------------------------- 1 | # coding=utf-8 2 | # Copyright 2018 The Google AI Language Team Authors. 3 | # 4 | # Licensed under the Apache License, Version 2.0 (the "License"); 5 | # you may not use this file except in compliance with the License. 6 | # You may obtain a copy of the License at 7 | # 8 | # http://www.apache.org/licenses/LICENSE-2.0 9 | # 10 | # Unless required by applicable law or agreed to in writing, software 11 | # distributed under the License is distributed on an "AS IS" BASIS, 12 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 13 | # See the License for the specific language governing permissions and 14 | # limitations under the License. 15 | """Functions and classes related to optimization (weight updates).""" 16 | 17 | from __future__ import absolute_import 18 | from __future__ import division 19 | from __future__ import print_function 20 | 21 | import re 22 | import tensorflow as tf 23 | 24 | 25 | def create_optimizer(loss, init_lr, num_train_steps, num_warmup_steps, use_tpu): 26 | """Creates an optimizer training op.""" 27 | global_step = tf.train.get_or_create_global_step() 28 | 29 | learning_rate = tf.constant(value=init_lr, shape=[], dtype=tf.float32) 30 | 31 | # Implements linear decay of the learning rate. 32 | learning_rate = tf.train.polynomial_decay( 33 | learning_rate, 34 | global_step, 35 | num_train_steps, 36 | end_learning_rate=0.0, 37 | power=1.0, 38 | cycle=False) 39 | 40 | # Implements linear warmup. I.e., if global_step < num_warmup_steps, the 41 | # learning rate will be `global_step/num_warmup_steps * init_lr`. 42 | if num_warmup_steps: 43 | global_steps_int = tf.cast(global_step, tf.int32) 44 | warmup_steps_int = tf.constant(num_warmup_steps, dtype=tf.int32) 45 | 46 | global_steps_float = tf.cast(global_steps_int, tf.float32) 47 | warmup_steps_float = tf.cast(warmup_steps_int, tf.float32) 48 | 49 | warmup_percent_done = global_steps_float / warmup_steps_float 50 | warmup_learning_rate = init_lr * warmup_percent_done 51 | 52 | is_warmup = tf.cast(global_steps_int < warmup_steps_int, tf.float32) 53 | learning_rate = ( 54 | (1.0 - is_warmup) * learning_rate + is_warmup * warmup_learning_rate) 55 | 56 | # It is recommended that you use this optimizer for fine tuning, since this 57 | # is how the model was trained (note that the Adam m/v variables are NOT 58 | # loaded from init_checkpoint.) 59 | optimizer = AdamWeightDecayOptimizer( 60 | learning_rate=learning_rate, 61 | weight_decay_rate=0.01, 62 | beta_1=0.9, 63 | beta_2=0.999, 64 | epsilon=1e-6, 65 | exclude_from_weight_decay=["LayerNorm", "layer_norm", "bias"]) 66 | 67 | if use_tpu: 68 | optimizer = tf.contrib.tpu.CrossShardOptimizer(optimizer) 69 | 70 | tvars = tf.trainable_variables() 71 | grads = tf.gradients(loss, tvars) 72 | 73 | # This is how the model was pre-trained. 74 | (grads, _) = tf.clip_by_global_norm(grads, clip_norm=1.0) 75 | 76 | train_op = optimizer.apply_gradients( 77 | zip(grads, tvars), global_step=global_step) 78 | 79 | # Normally the global step update is done inside of `apply_gradients`. 80 | # However, `AdamWeightDecayOptimizer` doesn't do this. But if you use 81 | # a different optimizer, you should probably take this line out. 82 | new_global_step = global_step + 1 83 | train_op = tf.group(train_op, [global_step.assign(new_global_step)]) 84 | return train_op 85 | 86 | 87 | class AdamWeightDecayOptimizer(tf.train.Optimizer): 88 | """A basic Adam optimizer that includes "correct" L2 weight decay.""" 89 | 90 | def __init__(self, 91 | learning_rate, 92 | weight_decay_rate=0.0, 93 | beta_1=0.9, 94 | beta_2=0.999, 95 | epsilon=1e-6, 96 | exclude_from_weight_decay=None, 97 | name="AdamWeightDecayOptimizer"): 98 | """Constructs a AdamWeightDecayOptimizer.""" 99 | super(AdamWeightDecayOptimizer, self).__init__(False, name) 100 | 101 | self.learning_rate = learning_rate 102 | self.weight_decay_rate = weight_decay_rate 103 | self.beta_1 = beta_1 104 | self.beta_2 = beta_2 105 | self.epsilon = epsilon 106 | self.exclude_from_weight_decay = exclude_from_weight_decay 107 | 108 | def apply_gradients(self, grads_and_vars, global_step=None, name=None): 109 | """See base class.""" 110 | assignments = [] 111 | for (grad, param) in grads_and_vars: 112 | if grad is None or param is None: 113 | continue 114 | 115 | param_name = self._get_variable_name(param.name) 116 | 117 | m = tf.get_variable( 118 | name=param_name + "/adam_m", 119 | shape=param.shape.as_list(), 120 | dtype=tf.float32, 121 | trainable=False, 122 | initializer=tf.zeros_initializer()) 123 | v = tf.get_variable( 124 | name=param_name + "/adam_v", 125 | shape=param.shape.as_list(), 126 | dtype=tf.float32, 127 | trainable=False, 128 | initializer=tf.zeros_initializer()) 129 | 130 | # Standard Adam update. 131 | next_m = ( 132 | tf.multiply(self.beta_1, m) + tf.multiply(1.0 - self.beta_1, grad)) 133 | next_v = ( 134 | tf.multiply(self.beta_2, v) + tf.multiply(1.0 - self.beta_2, 135 | tf.square(grad))) 136 | 137 | update = next_m / (tf.sqrt(next_v) + self.epsilon) 138 | 139 | # Just adding the square of the weights to the loss function is *not* 140 | # the correct way of using L2 regularization/weight decay with Adam, 141 | # since that will interact with the m and v parameters in strange ways. 142 | # 143 | # Instead we want ot decay the weights in a manner that doesn't interact 144 | # with the m/v parameters. This is equivalent to adding the square 145 | # of the weights to the loss with plain (non-momentum) SGD. 146 | if self._do_use_weight_decay(param_name): 147 | update += self.weight_decay_rate * param 148 | 149 | update_with_lr = self.learning_rate * update 150 | 151 | next_param = param - update_with_lr 152 | 153 | assignments.extend( 154 | [param.assign(next_param), 155 | m.assign(next_m), 156 | v.assign(next_v)]) 157 | return tf.group(*assignments, name=name) 158 | 159 | def _do_use_weight_decay(self, param_name): 160 | """Whether to use L2 weight decay for `param_name`.""" 161 | if not self.weight_decay_rate: 162 | return False 163 | if self.exclude_from_weight_decay: 164 | for r in self.exclude_from_weight_decay: 165 | if re.search(r, param_name) is not None: 166 | return False 167 | return True 168 | 169 | def _get_variable_name(self, param_name): 170 | """Get the variable name from the tensor name.""" 171 | m = re.match("^(.*):\\d+$", param_name) 172 | if m is not None: 173 | param_name = m.group(1) 174 | return param_name 175 | -------------------------------------------------------------------------------- /命名实体识别/bert/optimization_test.py: -------------------------------------------------------------------------------- 1 | # coding=utf-8 2 | # Copyright 2018 The Google AI Language Team Authors. 3 | # 4 | # Licensed under the Apache License, Version 2.0 (the "License"); 5 | # you may not use this file except in compliance with the License. 6 | # You may obtain a copy of the License at 7 | # 8 | # http://www.apache.org/licenses/LICENSE-2.0 9 | # 10 | # Unless required by applicable law or agreed to in writing, software 11 | # distributed under the License is distributed on an "AS IS" BASIS, 12 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 13 | # See the License for the specific language governing permissions and 14 | # limitations under the License. 15 | from __future__ import absolute_import 16 | from __future__ import division 17 | from __future__ import print_function 18 | 19 | import optimization 20 | import tensorflow as tf 21 | 22 | 23 | class OptimizationTest(tf.test.TestCase): 24 | 25 | def test_adam(self): 26 | with self.test_session() as sess: 27 | w = tf.get_variable( 28 | "w", 29 | shape=[3], 30 | initializer=tf.constant_initializer([0.1, -0.2, -0.1])) 31 | x = tf.constant([0.4, 0.2, -0.5]) 32 | loss = tf.reduce_mean(tf.square(x - w)) 33 | tvars = tf.trainable_variables() 34 | grads = tf.gradients(loss, tvars) 35 | global_step = tf.train.get_or_create_global_step() 36 | optimizer = optimization.AdamWeightDecayOptimizer(learning_rate=0.2) 37 | train_op = optimizer.apply_gradients(zip(grads, tvars), global_step) 38 | init_op = tf.group(tf.global_variables_initializer(), 39 | tf.local_variables_initializer()) 40 | sess.run(init_op) 41 | for _ in range(100): 42 | sess.run(train_op) 43 | w_np = sess.run(w) 44 | self.assertAllClose(w_np.flat, [0.4, 0.2, -0.5], rtol=1e-2, atol=1e-2) 45 | 46 | 47 | if __name__ == "__main__": 48 | tf.test.main() 49 | -------------------------------------------------------------------------------- /命名实体识别/bert/requirements.txt: -------------------------------------------------------------------------------- 1 | tensorflow >= 1.11.0 # CPU Version of TensorFlow. 2 | # tensorflow-gpu >= 1.11.0 # GPU version of TensorFlow. 3 | -------------------------------------------------------------------------------- /命名实体识别/bert/run_classifier.py: -------------------------------------------------------------------------------- 1 | # coding=utf-8 2 | # Copyright 2018 The Google AI Language Team Authors. 3 | # 4 | # Licensed under the Apache License, Version 2.0 (the "License"); 5 | # you may not use this file except in compliance with the License. 6 | # You may obtain a copy of the License at 7 | # 8 | # http://www.apache.org/licenses/LICENSE-2.0 9 | # 10 | # Unless required by applicable law or agreed to in writing, software 11 | # distributed under the License is distributed on an "AS IS" BASIS, 12 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 13 | # See the License for the specific language governing permissions and 14 | # limitations under the License. 15 | """BERT finetuning runner.""" 16 | 17 | from __future__ import absolute_import 18 | from __future__ import division 19 | from __future__ import print_function 20 | 21 | import collections 22 | import csv 23 | import os 24 | import modeling 25 | import optimization 26 | import tokenization 27 | import tensorflow as tf 28 | 29 | flags = tf.flags 30 | 31 | FLAGS = flags.FLAGS 32 | 33 | ## Required parameters 34 | flags.DEFINE_string( 35 | "data_dir", None, 36 | "The input data dir. Should contain the .tsv files (or other data files) " 37 | "for the task.") 38 | 39 | flags.DEFINE_string( 40 | "bert_config_file", None, 41 | "The config json file corresponding to the pre-trained BERT model. " 42 | "This specifies the model architecture.") 43 | 44 | flags.DEFINE_string("task_name", None, "The name of the task to train.") 45 | 46 | flags.DEFINE_string("vocab_file", None, 47 | "The vocabulary file that the BERT model was trained on.") 48 | 49 | flags.DEFINE_string( 50 | "output_dir", None, 51 | "The output directory where the model checkpoints will be written.") 52 | 53 | ## Other parameters 54 | 55 | flags.DEFINE_string( 56 | "init_checkpoint", None, 57 | "Initial checkpoint (usually from a pre-trained BERT model).") 58 | 59 | flags.DEFINE_bool( 60 | "do_lower_case", True, 61 | "Whether to lower case the input text. Should be True for uncased " 62 | "models and False for cased models.") 63 | 64 | flags.DEFINE_integer( 65 | "max_seq_length", 128, 66 | "The maximum total input sequence length after WordPiece tokenization. " 67 | "Sequences longer than this will be truncated, and sequences shorter " 68 | "than this will be padded.") 69 | 70 | flags.DEFINE_bool("do_train", False, "Whether to run training.") 71 | 72 | flags.DEFINE_bool("do_eval", False, "Whether to run eval on the dev set.") 73 | 74 | flags.DEFINE_bool( 75 | "do_predict", False, 76 | "Whether to run the model in inference mode on the test set.") 77 | 78 | flags.DEFINE_integer("train_batch_size", 32, "Total batch size for training.") 79 | 80 | flags.DEFINE_integer("eval_batch_size", 8, "Total batch size for eval.") 81 | 82 | flags.DEFINE_integer("predict_batch_size", 8, "Total batch size for predict.") 83 | 84 | flags.DEFINE_float("learning_rate", 5e-5, "The initial learning rate for Adam.") 85 | 86 | flags.DEFINE_float("num_train_epochs", 3.0, 87 | "Total number of training epochs to perform.") 88 | 89 | flags.DEFINE_float( 90 | "warmup_proportion", 0.1, 91 | "Proportion of training to perform linear learning rate warmup for. " 92 | "E.g., 0.1 = 10% of training.") 93 | 94 | flags.DEFINE_integer("save_checkpoints_steps", 1000, 95 | "How often to save the model checkpoint.") 96 | 97 | flags.DEFINE_integer("iterations_per_loop", 1000, 98 | "How many steps to make in each estimator call.") 99 | 100 | flags.DEFINE_bool("use_tpu", False, "Whether to use TPU or GPU/CPU.") 101 | 102 | tf.flags.DEFINE_string( 103 | "tpu_name", None, 104 | "The Cloud TPU to use for training. This should be either the name " 105 | "used when creating the Cloud TPU, or a grpc://ip.address.of.tpu:8470 " 106 | "url.") 107 | 108 | tf.flags.DEFINE_string( 109 | "tpu_zone", None, 110 | "[Optional] GCE zone where the Cloud TPU is located in. If not " 111 | "specified, we will attempt to automatically detect the GCE project from " 112 | "metadata.") 113 | 114 | tf.flags.DEFINE_string( 115 | "gcp_project", None, 116 | "[Optional] Project name for the Cloud TPU-enabled project. If not " 117 | "specified, we will attempt to automatically detect the GCE project from " 118 | "metadata.") 119 | 120 | tf.flags.DEFINE_string("master", None, "[Optional] TensorFlow master URL.") 121 | 122 | flags.DEFINE_integer( 123 | "num_tpu_cores", 8, 124 | "Only used if `use_tpu` is True. Total number of TPU cores to use.") 125 | 126 | 127 | class InputExample(object): 128 | """A single training/test example for simple sequence classification.""" 129 | 130 | def __init__(self, guid, text_a, text_b=None, label=None): 131 | """Constructs a InputExample. 132 | 133 | Args: 134 | guid: Unique id for the example. 135 | text_a: string. The untokenized text of the first sequence. For single 136 | sequence tasks, only this sequence must be specified. 137 | text_b: (Optional) string. The untokenized text of the second sequence. 138 | Only must be specified for sequence pair tasks. 139 | label: (Optional) string. The label of the example. This should be 140 | specified for train and dev examples, but not for test examples. 141 | """ 142 | self.guid = guid 143 | self.text_a = text_a 144 | self.text_b = text_b 145 | self.label = label 146 | 147 | 148 | class PaddingInputExample(object): 149 | """Fake example so the num input examples is a multiple of the batch size. 150 | 151 | When running eval/predict on the TPU, we need to pad the number of examples 152 | to be a multiple of the batch size, because the TPU requires a fixed batch 153 | size. The alternative is to drop the last batch, which is bad because it means 154 | the entire output data won't be generated. 155 | 156 | We use this class instead of `None` because treating `None` as padding 157 | battches could cause silent errors. 158 | """ 159 | 160 | 161 | class InputFeatures(object): 162 | """A single set of features of data.""" 163 | 164 | def __init__(self, 165 | input_ids, 166 | input_mask, 167 | segment_ids, 168 | label_id, 169 | is_real_example=True): 170 | self.input_ids = input_ids 171 | self.input_mask = input_mask 172 | self.segment_ids = segment_ids 173 | self.label_id = label_id 174 | self.is_real_example = is_real_example 175 | 176 | 177 | class DataProcessor(object): 178 | """Base class for data converters for sequence classification data sets.""" 179 | 180 | def get_train_examples(self, data_dir): 181 | """Gets a collection of `InputExample`s for the train set.""" 182 | raise NotImplementedError() 183 | 184 | def get_dev_examples(self, data_dir): 185 | """Gets a collection of `InputExample`s for the dev set.""" 186 | raise NotImplementedError() 187 | 188 | def get_test_examples(self, data_dir): 189 | """Gets a collection of `InputExample`s for prediction.""" 190 | raise NotImplementedError() 191 | 192 | def get_labels(self): 193 | """Gets the list of labels for this data set.""" 194 | raise NotImplementedError() 195 | 196 | @classmethod 197 | def _read_tsv(cls, input_file, quotechar=None): 198 | """Reads a tab separated value file.""" 199 | with tf.gfile.Open(input_file, "r") as f: 200 | reader = csv.reader(f, delimiter="\t", quotechar=quotechar) 201 | lines = [] 202 | for line in reader: 203 | lines.append(line) 204 | return lines 205 | 206 | 207 | class XnliProcessor(DataProcessor): 208 | """Processor for the XNLI data set.""" 209 | 210 | def __init__(self): 211 | self.language = "zh" 212 | 213 | def get_train_examples(self, data_dir): 214 | """See base class.""" 215 | lines = self._read_tsv( 216 | os.path.join(data_dir, "multinli", 217 | "multinli.train.%s.tsv" % self.language)) 218 | examples = [] 219 | for (i, line) in enumerate(lines): 220 | if i == 0: 221 | continue 222 | guid = "train-%d" % (i) 223 | text_a = tokenization.convert_to_unicode(line[0]) 224 | text_b = tokenization.convert_to_unicode(line[1]) 225 | label = tokenization.convert_to_unicode(line[2]) 226 | if label == tokenization.convert_to_unicode("contradictory"): 227 | label = tokenization.convert_to_unicode("contradiction") 228 | examples.append( 229 | InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label)) 230 | return examples 231 | 232 | def get_dev_examples(self, data_dir): 233 | """See base class.""" 234 | lines = self._read_tsv(os.path.join(data_dir, "xnli.dev.tsv")) 235 | examples = [] 236 | for (i, line) in enumerate(lines): 237 | if i == 0: 238 | continue 239 | guid = "dev-%d" % (i) 240 | language = tokenization.convert_to_unicode(line[0]) 241 | if language != tokenization.convert_to_unicode(self.language): 242 | continue 243 | text_a = tokenization.convert_to_unicode(line[6]) 244 | text_b = tokenization.convert_to_unicode(line[7]) 245 | label = tokenization.convert_to_unicode(line[1]) 246 | examples.append( 247 | InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label)) 248 | return examples 249 | 250 | def get_labels(self): 251 | """See base class.""" 252 | return ["contradiction", "entailment", "neutral"] 253 | 254 | 255 | class MnliProcessor(DataProcessor): 256 | """Processor for the MultiNLI data set (GLUE version).""" 257 | 258 | def get_train_examples(self, data_dir): 259 | """See base class.""" 260 | return self._create_examples( 261 | self._read_tsv(os.path.join(data_dir, "train.tsv")), "train") 262 | 263 | def get_dev_examples(self, data_dir): 264 | """See base class.""" 265 | return self._create_examples( 266 | self._read_tsv(os.path.join(data_dir, "dev_matched.tsv")), 267 | "dev_matched") 268 | 269 | def get_test_examples(self, data_dir): 270 | """See base class.""" 271 | return self._create_examples( 272 | self._read_tsv(os.path.join(data_dir, "test_matched.tsv")), "test") 273 | 274 | def get_labels(self): 275 | """See base class.""" 276 | return ["contradiction", "entailment", "neutral"] 277 | 278 | def _create_examples(self, lines, set_type): 279 | """Creates examples for the training and dev sets.""" 280 | examples = [] 281 | for (i, line) in enumerate(lines): 282 | if i == 0: 283 | continue 284 | guid = "%s-%s" % (set_type, tokenization.convert_to_unicode(line[0])) 285 | text_a = tokenization.convert_to_unicode(line[8]) 286 | text_b = tokenization.convert_to_unicode(line[9]) 287 | if set_type == "test": 288 | label = "contradiction" 289 | else: 290 | label = tokenization.convert_to_unicode(line[-1]) 291 | examples.append( 292 | InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label)) 293 | return examples 294 | 295 | 296 | class MrpcProcessor(DataProcessor): 297 | """Processor for the MRPC data set (GLUE version).""" 298 | 299 | def get_train_examples(self, data_dir): 300 | """See base class.""" 301 | return self._create_examples( 302 | self._read_tsv(os.path.join(data_dir, "train.tsv")), "train") 303 | 304 | def get_dev_examples(self, data_dir): 305 | """See base class.""" 306 | return self._create_examples( 307 | self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev") 308 | 309 | def get_test_examples(self, data_dir): 310 | """See base class.""" 311 | return self._create_examples( 312 | self._read_tsv(os.path.join(data_dir, "test.tsv")), "test") 313 | 314 | def get_labels(self): 315 | """See base class.""" 316 | return ["0", "1"] 317 | 318 | def _create_examples(self, lines, set_type): 319 | """Creates examples for the training and dev sets.""" 320 | examples = [] 321 | for (i, line) in enumerate(lines): 322 | if i == 0: 323 | continue 324 | guid = "%s-%s" % (set_type, i) 325 | text_a = tokenization.convert_to_unicode(line[3]) 326 | text_b = tokenization.convert_to_unicode(line[4]) 327 | if set_type == "test": 328 | label = "0" 329 | else: 330 | label = tokenization.convert_to_unicode(line[0]) 331 | examples.append( 332 | InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label)) 333 | return examples 334 | 335 | 336 | class ColaProcessor(DataProcessor): 337 | """Processor for the CoLA data set (GLUE version).""" 338 | 339 | def get_train_examples(self, data_dir): 340 | """See base class.""" 341 | return self._create_examples( 342 | self._read_tsv(os.path.join(data_dir, "train.tsv")), "train") 343 | 344 | def get_dev_examples(self, data_dir): 345 | """See base class.""" 346 | return self._create_examples( 347 | self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev") 348 | 349 | def get_test_examples(self, data_dir): 350 | """See base class.""" 351 | return self._create_examples( 352 | self._read_tsv(os.path.join(data_dir, "test.tsv")), "test") 353 | 354 | def get_labels(self): 355 | """See base class.""" 356 | return ["0", "1"] 357 | 358 | def _create_examples(self, lines, set_type): 359 | """Creates examples for the training and dev sets.""" 360 | examples = [] 361 | for (i, line) in enumerate(lines): 362 | # Only the test set has a header 363 | if set_type == "test" and i == 0: 364 | continue 365 | guid = "%s-%s" % (set_type, i) 366 | if set_type == "test": 367 | text_a = tokenization.convert_to_unicode(line[1]) 368 | label = "0" 369 | else: 370 | text_a = tokenization.convert_to_unicode(line[3]) 371 | label = tokenization.convert_to_unicode(line[1]) 372 | examples.append( 373 | InputExample(guid=guid, text_a=text_a, text_b=None, label=label)) 374 | return examples 375 | 376 | 377 | def convert_single_example(ex_index, example, label_list, max_seq_length, 378 | tokenizer): 379 | """Converts a single `InputExample` into a single `InputFeatures`.""" 380 | 381 | if isinstance(example, PaddingInputExample): 382 | return InputFeatures( 383 | input_ids=[0] * max_seq_length, 384 | input_mask=[0] * max_seq_length, 385 | segment_ids=[0] * max_seq_length, 386 | label_id=0, 387 | is_real_example=False) 388 | 389 | label_map = {} 390 | for (i, label) in enumerate(label_list): 391 | label_map[label] = i 392 | 393 | tokens_a = tokenizer.tokenize(example.text_a) 394 | tokens_b = None 395 | if example.text_b: 396 | tokens_b = tokenizer.tokenize(example.text_b) 397 | 398 | if tokens_b: 399 | # Modifies `tokens_a` and `tokens_b` in place so that the total 400 | # length is less than the specified length. 401 | # Account for [CLS], [SEP], [SEP] with "- 3" 402 | _truncate_seq_pair(tokens_a, tokens_b, max_seq_length - 3) 403 | else: 404 | # Account for [CLS] and [SEP] with "- 2" 405 | if len(tokens_a) > max_seq_length - 2: 406 | tokens_a = tokens_a[0:(max_seq_length - 2)] 407 | 408 | # The convention in BERT is: 409 | # (a) For sequence pairs: 410 | # tokens: [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP] 411 | # type_ids: 0 0 0 0 0 0 0 0 1 1 1 1 1 1 412 | # (b) For single sequences: 413 | # tokens: [CLS] the dog is hairy . [SEP] 414 | # type_ids: 0 0 0 0 0 0 0 415 | # 416 | # Where "type_ids" are used to indicate whether this is the first 417 | # sequence or the second sequence. The embedding vectors for `type=0` and 418 | # `type=1` were learned during pre-training and are added to the wordpiece 419 | # embedding vector (and position vector). This is not *strictly* necessary 420 | # since the [SEP] token unambiguously separates the sequences, but it makes 421 | # it easier for the model to learn the concept of sequences. 422 | # 423 | # For classification tasks, the first vector (corresponding to [CLS]) is 424 | # used as the "sentence vector". Note that this only makes sense because 425 | # the entire model is fine-tuned. 426 | tokens = [] 427 | segment_ids = [] 428 | tokens.append("[CLS]") 429 | segment_ids.append(0) 430 | for token in tokens_a: 431 | tokens.append(token) 432 | segment_ids.append(0) 433 | tokens.append("[SEP]") 434 | segment_ids.append(0) 435 | 436 | if tokens_b: 437 | for token in tokens_b: 438 | tokens.append(token) 439 | segment_ids.append(1) 440 | tokens.append("[SEP]") 441 | segment_ids.append(1) 442 | 443 | input_ids = tokenizer.convert_tokens_to_ids(tokens) 444 | 445 | # The mask has 1 for real tokens and 0 for padding tokens. Only real 446 | # tokens are attended to. 447 | input_mask = [1] * len(input_ids) 448 | 449 | # Zero-pad up to the sequence length. 450 | while len(input_ids) < max_seq_length: 451 | input_ids.append(0) 452 | input_mask.append(0) 453 | segment_ids.append(0) 454 | 455 | assert len(input_ids) == max_seq_length 456 | assert len(input_mask) == max_seq_length 457 | assert len(segment_ids) == max_seq_length 458 | 459 | label_id = label_map[example.label] 460 | if ex_index < 5: 461 | tf.logging.info("*** Example ***") 462 | tf.logging.info("guid: %s" % (example.guid)) 463 | tf.logging.info("tokens: %s" % " ".join( 464 | [tokenization.printable_text(x) for x in tokens])) 465 | tf.logging.info("input_ids: %s" % " ".join([str(x) for x in input_ids])) 466 | tf.logging.info("input_mask: %s" % " ".join([str(x) for x in input_mask])) 467 | tf.logging.info("segment_ids: %s" % " ".join([str(x) for x in segment_ids])) 468 | tf.logging.info("label: %s (id = %d)" % (example.label, label_id)) 469 | 470 | feature = InputFeatures( 471 | input_ids=input_ids, 472 | input_mask=input_mask, 473 | segment_ids=segment_ids, 474 | label_id=label_id, 475 | is_real_example=True) 476 | return feature 477 | 478 | 479 | def file_based_convert_examples_to_features( 480 | examples, label_list, max_seq_length, tokenizer, output_file): 481 | """Convert a set of `InputExample`s to a TFRecord file.""" 482 | 483 | writer = tf.python_io.TFRecordWriter(output_file) 484 | 485 | for (ex_index, example) in enumerate(examples): 486 | if ex_index % 10000 == 0: 487 | tf.logging.info("Writing example %d of %d" % (ex_index, len(examples))) 488 | 489 | feature = convert_single_example(ex_index, example, label_list, 490 | max_seq_length, tokenizer) 491 | 492 | def create_int_feature(values): 493 | f = tf.train.Feature(int64_list=tf.train.Int64List(value=list(values))) 494 | return f 495 | 496 | features = collections.OrderedDict() 497 | features["input_ids"] = create_int_feature(feature.input_ids) 498 | features["input_mask"] = create_int_feature(feature.input_mask) 499 | features["segment_ids"] = create_int_feature(feature.segment_ids) 500 | features["label_ids"] = create_int_feature([feature.label_id]) 501 | features["is_real_example"] = create_int_feature( 502 | [int(feature.is_real_example)]) 503 | 504 | tf_example = tf.train.Example(features=tf.train.Features(feature=features)) 505 | writer.write(tf_example.SerializeToString()) 506 | writer.close() 507 | 508 | 509 | def file_based_input_fn_builder(input_file, seq_length, is_training, 510 | drop_remainder): 511 | """Creates an `input_fn` closure to be passed to TPUEstimator.""" 512 | 513 | name_to_features = { 514 | "input_ids": tf.FixedLenFeature([seq_length], tf.int64), 515 | "input_mask": tf.FixedLenFeature([seq_length], tf.int64), 516 | "segment_ids": tf.FixedLenFeature([seq_length], tf.int64), 517 | "label_ids": tf.FixedLenFeature([], tf.int64), 518 | "is_real_example": tf.FixedLenFeature([], tf.int64), 519 | } 520 | 521 | def _decode_record(record, name_to_features): 522 | """Decodes a record to a TensorFlow example.""" 523 | example = tf.parse_single_example(record, name_to_features) 524 | 525 | # tf.Example only supports tf.int64, but the TPU only supports tf.int32. 526 | # So cast all int64 to int32. 527 | for name in list(example.keys()): 528 | t = example[name] 529 | if t.dtype == tf.int64: 530 | t = tf.to_int32(t) 531 | example[name] = t 532 | 533 | return example 534 | 535 | def input_fn(params): 536 | """The actual input function.""" 537 | batch_size = params["batch_size"] 538 | 539 | # For training, we want a lot of parallel reading and shuffling. 540 | # For eval, we want no shuffling and parallel reading doesn't matter. 541 | d = tf.data.TFRecordDataset(input_file) 542 | if is_training: 543 | d = d.repeat() 544 | d = d.shuffle(buffer_size=100) 545 | 546 | d = d.apply( 547 | tf.contrib.data.map_and_batch( 548 | lambda record: _decode_record(record, name_to_features), 549 | batch_size=batch_size, 550 | drop_remainder=drop_remainder)) 551 | 552 | return d 553 | 554 | return input_fn 555 | 556 | 557 | def _truncate_seq_pair(tokens_a, tokens_b, max_length): 558 | """Truncates a sequence pair in place to the maximum length.""" 559 | 560 | # This is a simple heuristic which will always truncate the longer sequence 561 | # one token at a time. This makes more sense than truncating an equal percent 562 | # of tokens from each, since if one sequence is very short then each token 563 | # that's truncated likely contains more information than a longer sequence. 564 | while True: 565 | total_length = len(tokens_a) + len(tokens_b) 566 | if total_length <= max_length: 567 | break 568 | if len(tokens_a) > len(tokens_b): 569 | tokens_a.pop() 570 | else: 571 | tokens_b.pop() 572 | 573 | 574 | def create_model(bert_config, is_training, input_ids, input_mask, segment_ids, 575 | labels, num_labels, use_one_hot_embeddings): 576 | """Creates a classification model.""" 577 | model = modeling.BertModel( 578 | config=bert_config, 579 | is_training=is_training, 580 | input_ids=input_ids, 581 | input_mask=input_mask, 582 | token_type_ids=segment_ids, 583 | use_one_hot_embeddings=use_one_hot_embeddings) 584 | 585 | # In the demo, we are doing a simple classification task on the entire 586 | # segment. 587 | # 588 | # If you want to use the token-level output, use model.get_sequence_output() 589 | # instead. 590 | output_layer = model.get_pooled_output() 591 | 592 | hidden_size = output_layer.shape[-1].value 593 | 594 | output_weights = tf.get_variable( 595 | "output_weights", [num_labels, hidden_size], 596 | initializer=tf.truncated_normal_initializer(stddev=0.02)) 597 | 598 | output_bias = tf.get_variable( 599 | "output_bias", [num_labels], initializer=tf.zeros_initializer()) 600 | 601 | with tf.variable_scope("loss"): 602 | if is_training: 603 | # I.e., 0.1 dropout 604 | output_layer = tf.nn.dropout(output_layer, keep_prob=0.9) 605 | 606 | logits = tf.matmul(output_layer, output_weights, transpose_b=True) 607 | logits = tf.nn.bias_add(logits, output_bias) 608 | probabilities = tf.nn.softmax(logits, axis=-1) 609 | log_probs = tf.nn.log_softmax(logits, axis=-1) 610 | 611 | one_hot_labels = tf.one_hot(labels, depth=num_labels, dtype=tf.float32) 612 | 613 | per_example_loss = -tf.reduce_sum(one_hot_labels * log_probs, axis=-1) 614 | loss = tf.reduce_mean(per_example_loss) 615 | 616 | return (loss, per_example_loss, logits, probabilities) 617 | 618 | 619 | def model_fn_builder(bert_config, num_labels, init_checkpoint, learning_rate, 620 | num_train_steps, num_warmup_steps, use_tpu, 621 | use_one_hot_embeddings): 622 | """Returns `model_fn` closure for TPUEstimator.""" 623 | 624 | def model_fn(features, labels, mode, params): # pylint: disable=unused-argument 625 | """The `model_fn` for TPUEstimator.""" 626 | 627 | tf.logging.info("*** Features ***") 628 | for name in sorted(features.keys()): 629 | tf.logging.info(" name = %s, shape = %s" % (name, features[name].shape)) 630 | 631 | input_ids = features["input_ids"] 632 | input_mask = features["input_mask"] 633 | segment_ids = features["segment_ids"] 634 | label_ids = features["label_ids"] 635 | is_real_example = None 636 | if "is_real_example" in features: 637 | is_real_example = tf.cast(features["is_real_example"], dtype=tf.float32) 638 | else: 639 | is_real_example = tf.ones(tf.shape(label_ids), dtype=tf.float32) 640 | 641 | is_training = (mode == tf.estimator.ModeKeys.TRAIN) 642 | 643 | (total_loss, per_example_loss, logits, probabilities) = create_model( 644 | bert_config, is_training, input_ids, input_mask, segment_ids, label_ids, 645 | num_labels, use_one_hot_embeddings) 646 | 647 | tvars = tf.trainable_variables() 648 | initialized_variable_names = {} 649 | scaffold_fn = None 650 | if init_checkpoint: 651 | (assignment_map, initialized_variable_names 652 | ) = modeling.get_assignment_map_from_checkpoint(tvars, init_checkpoint) 653 | if use_tpu: 654 | 655 | def tpu_scaffold(): 656 | tf.train.init_from_checkpoint(init_checkpoint, assignment_map) 657 | return tf.train.Scaffold() 658 | 659 | scaffold_fn = tpu_scaffold 660 | else: 661 | tf.train.init_from_checkpoint(init_checkpoint, assignment_map) 662 | 663 | tf.logging.info("**** Trainable Variables ****") 664 | for var in tvars: 665 | init_string = "" 666 | if var.name in initialized_variable_names: 667 | init_string = ", *INIT_FROM_CKPT*" 668 | tf.logging.info(" name = %s, shape = %s%s", var.name, var.shape, 669 | init_string) 670 | 671 | output_spec = None 672 | if mode == tf.estimator.ModeKeys.TRAIN: 673 | 674 | train_op = optimization.create_optimizer( 675 | total_loss, learning_rate, num_train_steps, num_warmup_steps, use_tpu) 676 | 677 | output_spec = tf.contrib.tpu.TPUEstimatorSpec( 678 | mode=mode, 679 | loss=total_loss, 680 | train_op=train_op, 681 | scaffold_fn=scaffold_fn) 682 | elif mode == tf.estimator.ModeKeys.EVAL: 683 | 684 | def metric_fn(per_example_loss, label_ids, logits, is_real_example): 685 | predictions = tf.argmax(logits, axis=-1, output_type=tf.int32) 686 | accuracy = tf.metrics.accuracy( 687 | labels=label_ids, predictions=predictions, weights=is_real_example) 688 | loss = tf.metrics.mean(values=per_example_loss, weights=is_real_example) 689 | return { 690 | "eval_accuracy": accuracy, 691 | "eval_loss": loss, 692 | } 693 | 694 | eval_metrics = (metric_fn, 695 | [per_example_loss, label_ids, logits, is_real_example]) 696 | output_spec = tf.contrib.tpu.TPUEstimatorSpec( 697 | mode=mode, 698 | loss=total_loss, 699 | eval_metrics=eval_metrics, 700 | scaffold_fn=scaffold_fn) 701 | else: 702 | output_spec = tf.contrib.tpu.TPUEstimatorSpec( 703 | mode=mode, 704 | predictions={"probabilities": probabilities}, 705 | scaffold_fn=scaffold_fn) 706 | return output_spec 707 | 708 | return model_fn 709 | 710 | 711 | # This function is not used by this file but is still used by the Colab and 712 | # people who depend on it. 713 | def input_fn_builder(features, seq_length, is_training, drop_remainder): 714 | """Creates an `input_fn` closure to be passed to TPUEstimator.""" 715 | 716 | all_input_ids = [] 717 | all_input_mask = [] 718 | all_segment_ids = [] 719 | all_label_ids = [] 720 | 721 | for feature in features: 722 | all_input_ids.append(feature.input_ids) 723 | all_input_mask.append(feature.input_mask) 724 | all_segment_ids.append(feature.segment_ids) 725 | all_label_ids.append(feature.label_id) 726 | 727 | def input_fn(params): 728 | """The actual input function.""" 729 | batch_size = params["batch_size"] 730 | 731 | num_examples = len(features) 732 | 733 | # This is for demo purposes and does NOT scale to large data sets. We do 734 | # not use Dataset.from_generator() because that uses tf.py_func which is 735 | # not TPU compatible. The right way to load data is with TFRecordReader. 736 | d = tf.data.Dataset.from_tensor_slices({ 737 | "input_ids": 738 | tf.constant( 739 | all_input_ids, shape=[num_examples, seq_length], 740 | dtype=tf.int32), 741 | "input_mask": 742 | tf.constant( 743 | all_input_mask, 744 | shape=[num_examples, seq_length], 745 | dtype=tf.int32), 746 | "segment_ids": 747 | tf.constant( 748 | all_segment_ids, 749 | shape=[num_examples, seq_length], 750 | dtype=tf.int32), 751 | "label_ids": 752 | tf.constant(all_label_ids, shape=[num_examples], dtype=tf.int32), 753 | }) 754 | 755 | if is_training: 756 | d = d.repeat() 757 | d = d.shuffle(buffer_size=100) 758 | 759 | d = d.batch(batch_size=batch_size, drop_remainder=drop_remainder) 760 | return d 761 | 762 | return input_fn 763 | 764 | 765 | # This function is not used by this file but is still used by the Colab and 766 | # people who depend on it. 767 | def convert_examples_to_features(examples, label_list, max_seq_length, 768 | tokenizer): 769 | """Convert a set of `InputExample`s to a list of `InputFeatures`.""" 770 | 771 | features = [] 772 | for (ex_index, example) in enumerate(examples): 773 | if ex_index % 10000 == 0: 774 | tf.logging.info("Writing example %d of %d" % (ex_index, len(examples))) 775 | 776 | feature = convert_single_example(ex_index, example, label_list, 777 | max_seq_length, tokenizer) 778 | 779 | features.append(feature) 780 | return features 781 | 782 | 783 | def main(_): 784 | tf.logging.set_verbosity(tf.logging.INFO) 785 | 786 | processors = { 787 | "cola": ColaProcessor, 788 | "mnli": MnliProcessor, 789 | "mrpc": MrpcProcessor, 790 | "xnli": XnliProcessor, 791 | } 792 | 793 | tokenization.validate_case_matches_checkpoint(FLAGS.do_lower_case, 794 | FLAGS.init_checkpoint) 795 | 796 | if not FLAGS.do_train and not FLAGS.do_eval and not FLAGS.do_predict: 797 | raise ValueError( 798 | "At least one of `do_train`, `do_eval` or `do_predict' must be True.") 799 | 800 | bert_config = modeling.BertConfig.from_json_file(FLAGS.bert_config_file) 801 | 802 | if FLAGS.max_seq_length > bert_config.max_position_embeddings: 803 | raise ValueError( 804 | "Cannot use sequence length %d because the BERT model " 805 | "was only trained up to sequence length %d" % 806 | (FLAGS.max_seq_length, bert_config.max_position_embeddings)) 807 | 808 | tf.gfile.MakeDirs(FLAGS.output_dir) 809 | 810 | task_name = FLAGS.task_name.lower() 811 | 812 | if task_name not in processors: 813 | raise ValueError("Task not found: %s" % (task_name)) 814 | 815 | processor = processors[task_name]() 816 | 817 | label_list = processor.get_labels() 818 | 819 | tokenizer = tokenization.FullTokenizer( 820 | vocab_file=FLAGS.vocab_file, do_lower_case=FLAGS.do_lower_case) 821 | 822 | tpu_cluster_resolver = None 823 | if FLAGS.use_tpu and FLAGS.tpu_name: 824 | tpu_cluster_resolver = tf.contrib.cluster_resolver.TPUClusterResolver( 825 | FLAGS.tpu_name, zone=FLAGS.tpu_zone, project=FLAGS.gcp_project) 826 | 827 | is_per_host = tf.contrib.tpu.InputPipelineConfig.PER_HOST_V2 828 | run_config = tf.contrib.tpu.RunConfig( 829 | cluster=tpu_cluster_resolver, 830 | master=FLAGS.master, 831 | model_dir=FLAGS.output_dir, 832 | save_checkpoints_steps=FLAGS.save_checkpoints_steps, 833 | tpu_config=tf.contrib.tpu.TPUConfig( 834 | iterations_per_loop=FLAGS.iterations_per_loop, 835 | num_shards=FLAGS.num_tpu_cores, 836 | per_host_input_for_training=is_per_host)) 837 | 838 | train_examples = None 839 | num_train_steps = None 840 | num_warmup_steps = None 841 | if FLAGS.do_train: 842 | train_examples = processor.get_train_examples(FLAGS.data_dir) 843 | num_train_steps = int( 844 | len(train_examples) / FLAGS.train_batch_size * FLAGS.num_train_epochs) 845 | num_warmup_steps = int(num_train_steps * FLAGS.warmup_proportion) 846 | 847 | model_fn = model_fn_builder( 848 | bert_config=bert_config, 849 | num_labels=len(label_list), 850 | init_checkpoint=FLAGS.init_checkpoint, 851 | learning_rate=FLAGS.learning_rate, 852 | num_train_steps=num_train_steps, 853 | num_warmup_steps=num_warmup_steps, 854 | use_tpu=FLAGS.use_tpu, 855 | use_one_hot_embeddings=FLAGS.use_tpu) 856 | 857 | # If TPU is not available, this will fall back to normal Estimator on CPU 858 | # or GPU. 859 | estimator = tf.contrib.tpu.TPUEstimator( 860 | use_tpu=FLAGS.use_tpu, 861 | model_fn=model_fn, 862 | config=run_config, 863 | train_batch_size=FLAGS.train_batch_size, 864 | eval_batch_size=FLAGS.eval_batch_size, 865 | predict_batch_size=FLAGS.predict_batch_size) 866 | 867 | if FLAGS.do_train: 868 | train_file = os.path.join(FLAGS.output_dir, "train.tf_record") 869 | file_based_convert_examples_to_features( 870 | train_examples, label_list, FLAGS.max_seq_length, tokenizer, train_file) 871 | tf.logging.info("***** Running training *****") 872 | tf.logging.info(" Num examples = %d", len(train_examples)) 873 | tf.logging.info(" Batch size = %d", FLAGS.train_batch_size) 874 | tf.logging.info(" Num steps = %d", num_train_steps) 875 | train_input_fn = file_based_input_fn_builder( 876 | input_file=train_file, 877 | seq_length=FLAGS.max_seq_length, 878 | is_training=True, 879 | drop_remainder=True) 880 | estimator.train(input_fn=train_input_fn, max_steps=num_train_steps) 881 | 882 | if FLAGS.do_eval: 883 | eval_examples = processor.get_dev_examples(FLAGS.data_dir) 884 | num_actual_eval_examples = len(eval_examples) 885 | if FLAGS.use_tpu: 886 | # TPU requires a fixed batch size for all batches, therefore the number 887 | # of examples must be a multiple of the batch size, or else examples 888 | # will get dropped. So we pad with fake examples which are ignored 889 | # later on. These do NOT count towards the metric (all tf.metrics 890 | # support a per-instance weight, and these get a weight of 0.0). 891 | while len(eval_examples) % FLAGS.eval_batch_size != 0: 892 | eval_examples.append(PaddingInputExample()) 893 | 894 | eval_file = os.path.join(FLAGS.output_dir, "eval.tf_record") 895 | file_based_convert_examples_to_features( 896 | eval_examples, label_list, FLAGS.max_seq_length, tokenizer, eval_file) 897 | 898 | tf.logging.info("***** Running evaluation *****") 899 | tf.logging.info(" Num examples = %d (%d actual, %d padding)", 900 | len(eval_examples), num_actual_eval_examples, 901 | len(eval_examples) - num_actual_eval_examples) 902 | tf.logging.info(" Batch size = %d", FLAGS.eval_batch_size) 903 | 904 | # This tells the estimator to run through the entire set. 905 | eval_steps = None 906 | # However, if running eval on the TPU, you will need to specify the 907 | # number of steps. 908 | if FLAGS.use_tpu: 909 | assert len(eval_examples) % FLAGS.eval_batch_size == 0 910 | eval_steps = int(len(eval_examples) // FLAGS.eval_batch_size) 911 | 912 | eval_drop_remainder = True if FLAGS.use_tpu else False 913 | eval_input_fn = file_based_input_fn_builder( 914 | input_file=eval_file, 915 | seq_length=FLAGS.max_seq_length, 916 | is_training=False, 917 | drop_remainder=eval_drop_remainder) 918 | 919 | result = estimator.evaluate(input_fn=eval_input_fn, steps=eval_steps) 920 | 921 | output_eval_file = os.path.join(FLAGS.output_dir, "eval_results.txt") 922 | with tf.gfile.GFile(output_eval_file, "w") as writer: 923 | tf.logging.info("***** Eval results *****") 924 | for key in sorted(result.keys()): 925 | tf.logging.info(" %s = %s", key, str(result[key])) 926 | writer.write("%s = %s\n" % (key, str(result[key]))) 927 | 928 | if FLAGS.do_predict: 929 | predict_examples = processor.get_test_examples(FLAGS.data_dir) 930 | num_actual_predict_examples = len(predict_examples) 931 | if FLAGS.use_tpu: 932 | # TPU requires a fixed batch size for all batches, therefore the number 933 | # of examples must be a multiple of the batch size, or else examples 934 | # will get dropped. So we pad with fake examples which are ignored 935 | # later on. 936 | while len(predict_examples) % FLAGS.predict_batch_size != 0: 937 | predict_examples.append(PaddingInputExample()) 938 | 939 | predict_file = os.path.join(FLAGS.output_dir, "predict.tf_record") 940 | file_based_convert_examples_to_features(predict_examples, label_list, 941 | FLAGS.max_seq_length, tokenizer, 942 | predict_file) 943 | 944 | tf.logging.info("***** Running prediction*****") 945 | tf.logging.info(" Num examples = %d (%d actual, %d padding)", 946 | len(predict_examples), num_actual_predict_examples, 947 | len(predict_examples) - num_actual_predict_examples) 948 | tf.logging.info(" Batch size = %d", FLAGS.predict_batch_size) 949 | 950 | predict_drop_remainder = True if FLAGS.use_tpu else False 951 | predict_input_fn = file_based_input_fn_builder( 952 | input_file=predict_file, 953 | seq_length=FLAGS.max_seq_length, 954 | is_training=False, 955 | drop_remainder=predict_drop_remainder) 956 | 957 | result = estimator.predict(input_fn=predict_input_fn) 958 | 959 | output_predict_file = os.path.join(FLAGS.output_dir, "test_results.tsv") 960 | with tf.gfile.GFile(output_predict_file, "w") as writer: 961 | num_written_lines = 0 962 | tf.logging.info("***** Predict results *****") 963 | for (i, prediction) in enumerate(result): 964 | probabilities = prediction["probabilities"] 965 | if i >= num_actual_predict_examples: 966 | break 967 | output_line = "\t".join( 968 | str(class_probability) 969 | for class_probability in probabilities) + "\n" 970 | writer.write(output_line) 971 | num_written_lines += 1 972 | assert num_written_lines == num_actual_predict_examples 973 | 974 | 975 | if __name__ == "__main__": 976 | flags.mark_flag_as_required("data_dir") 977 | flags.mark_flag_as_required("task_name") 978 | flags.mark_flag_as_required("vocab_file") 979 | flags.mark_flag_as_required("bert_config_file") 980 | flags.mark_flag_as_required("output_dir") 981 | tf.app.run() 982 | -------------------------------------------------------------------------------- /命名实体识别/bert/run_classifier_with_tfhub.py: -------------------------------------------------------------------------------- 1 | # coding=utf-8 2 | # Copyright 2018 The Google AI Language Team Authors. 3 | # 4 | # Licensed under the Apache License, Version 2.0 (the "License"); 5 | # you may not use this file except in compliance with the License. 6 | # You may obtain a copy of the License at 7 | # 8 | # http://www.apache.org/licenses/LICENSE-2.0 9 | # 10 | # Unless required by applicable law or agreed to in writing, software 11 | # distributed under the License is distributed on an "AS IS" BASIS, 12 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 13 | # See the License for the specific language governing permissions and 14 | # limitations under the License. 15 | """BERT finetuning runner with TF-Hub.""" 16 | 17 | from __future__ import absolute_import 18 | from __future__ import division 19 | from __future__ import print_function 20 | 21 | import os 22 | import optimization 23 | import run_classifier 24 | import tokenization 25 | import tensorflow as tf 26 | import tensorflow_hub as hub 27 | 28 | flags = tf.flags 29 | 30 | FLAGS = flags.FLAGS 31 | 32 | flags.DEFINE_string( 33 | "bert_hub_module_handle", None, 34 | "Handle for the BERT TF-Hub module.") 35 | 36 | 37 | def create_model(is_training, input_ids, input_mask, segment_ids, labels, 38 | num_labels, bert_hub_module_handle): 39 | """Creates a classification model.""" 40 | tags = set() 41 | if is_training: 42 | tags.add("train") 43 | bert_module = hub.Module(bert_hub_module_handle, tags=tags, trainable=True) 44 | bert_inputs = dict( 45 | input_ids=input_ids, 46 | input_mask=input_mask, 47 | segment_ids=segment_ids) 48 | bert_outputs = bert_module( 49 | inputs=bert_inputs, 50 | signature="tokens", 51 | as_dict=True) 52 | 53 | # In the demo, we are doing a simple classification task on the entire 54 | # segment. 55 | # 56 | # If you want to use the token-level output, use 57 | # bert_outputs["sequence_output"] instead. 58 | output_layer = bert_outputs["pooled_output"] 59 | 60 | hidden_size = output_layer.shape[-1].value 61 | 62 | output_weights = tf.get_variable( 63 | "output_weights", [num_labels, hidden_size], 64 | initializer=tf.truncated_normal_initializer(stddev=0.02)) 65 | 66 | output_bias = tf.get_variable( 67 | "output_bias", [num_labels], initializer=tf.zeros_initializer()) 68 | 69 | with tf.variable_scope("loss"): 70 | if is_training: 71 | # I.e., 0.1 dropout 72 | output_layer = tf.nn.dropout(output_layer, keep_prob=0.9) 73 | 74 | logits = tf.matmul(output_layer, output_weights, transpose_b=True) 75 | logits = tf.nn.bias_add(logits, output_bias) 76 | probabilities = tf.nn.softmax(logits, axis=-1) 77 | log_probs = tf.nn.log_softmax(logits, axis=-1) 78 | 79 | one_hot_labels = tf.one_hot(labels, depth=num_labels, dtype=tf.float32) 80 | 81 | per_example_loss = -tf.reduce_sum(one_hot_labels * log_probs, axis=-1) 82 | loss = tf.reduce_mean(per_example_loss) 83 | 84 | return (loss, per_example_loss, logits, probabilities) 85 | 86 | 87 | def model_fn_builder(num_labels, learning_rate, num_train_steps, 88 | num_warmup_steps, use_tpu, bert_hub_module_handle): 89 | """Returns `model_fn` closure for TPUEstimator.""" 90 | 91 | def model_fn(features, labels, mode, params): # pylint: disable=unused-argument 92 | """The `model_fn` for TPUEstimator.""" 93 | 94 | tf.logging.info("*** Features ***") 95 | for name in sorted(features.keys()): 96 | tf.logging.info(" name = %s, shape = %s" % (name, features[name].shape)) 97 | 98 | input_ids = features["input_ids"] 99 | input_mask = features["input_mask"] 100 | segment_ids = features["segment_ids"] 101 | label_ids = features["label_ids"] 102 | 103 | is_training = (mode == tf.estimator.ModeKeys.TRAIN) 104 | 105 | (total_loss, per_example_loss, logits, probabilities) = create_model( 106 | is_training, input_ids, input_mask, segment_ids, label_ids, num_labels, 107 | bert_hub_module_handle) 108 | 109 | output_spec = None 110 | if mode == tf.estimator.ModeKeys.TRAIN: 111 | train_op = optimization.create_optimizer( 112 | total_loss, learning_rate, num_train_steps, num_warmup_steps, use_tpu) 113 | 114 | output_spec = tf.contrib.tpu.TPUEstimatorSpec( 115 | mode=mode, 116 | loss=total_loss, 117 | train_op=train_op) 118 | elif mode == tf.estimator.ModeKeys.EVAL: 119 | 120 | def metric_fn(per_example_loss, label_ids, logits): 121 | predictions = tf.argmax(logits, axis=-1, output_type=tf.int32) 122 | accuracy = tf.metrics.accuracy(label_ids, predictions) 123 | loss = tf.metrics.mean(per_example_loss) 124 | return { 125 | "eval_accuracy": accuracy, 126 | "eval_loss": loss, 127 | } 128 | 129 | eval_metrics = (metric_fn, [per_example_loss, label_ids, logits]) 130 | output_spec = tf.contrib.tpu.TPUEstimatorSpec( 131 | mode=mode, 132 | loss=total_loss, 133 | eval_metrics=eval_metrics) 134 | elif mode == tf.estimator.ModeKeys.PREDICT: 135 | output_spec = tf.contrib.tpu.TPUEstimatorSpec( 136 | mode=mode, predictions={"probabilities": probabilities}) 137 | else: 138 | raise ValueError( 139 | "Only TRAIN, EVAL and PREDICT modes are supported: %s" % (mode)) 140 | 141 | return output_spec 142 | 143 | return model_fn 144 | 145 | 146 | def create_tokenizer_from_hub_module(bert_hub_module_handle): 147 | """Get the vocab file and casing info from the Hub module.""" 148 | with tf.Graph().as_default(): 149 | bert_module = hub.Module(bert_hub_module_handle) 150 | tokenization_info = bert_module(signature="tokenization_info", as_dict=True) 151 | with tf.Session() as sess: 152 | vocab_file, do_lower_case = sess.run([tokenization_info["vocab_file"], 153 | tokenization_info["do_lower_case"]]) 154 | return tokenization.FullTokenizer( 155 | vocab_file=vocab_file, do_lower_case=do_lower_case) 156 | 157 | 158 | def main(_): 159 | tf.logging.set_verbosity(tf.logging.INFO) 160 | 161 | processors = { 162 | "cola": run_classifier.ColaProcessor, 163 | "mnli": run_classifier.MnliProcessor, 164 | "mrpc": run_classifier.MrpcProcessor, 165 | } 166 | 167 | if not FLAGS.do_train and not FLAGS.do_eval: 168 | raise ValueError("At least one of `do_train` or `do_eval` must be True.") 169 | 170 | tf.gfile.MakeDirs(FLAGS.output_dir) 171 | 172 | task_name = FLAGS.task_name.lower() 173 | 174 | if task_name not in processors: 175 | raise ValueError("Task not found: %s" % (task_name)) 176 | 177 | processor = processors[task_name]() 178 | 179 | label_list = processor.get_labels() 180 | 181 | tokenizer = create_tokenizer_from_hub_module(FLAGS.bert_hub_module_handle) 182 | 183 | tpu_cluster_resolver = None 184 | if FLAGS.use_tpu and FLAGS.tpu_name: 185 | tpu_cluster_resolver = tf.contrib.cluster_resolver.TPUClusterResolver( 186 | FLAGS.tpu_name, zone=FLAGS.tpu_zone, project=FLAGS.gcp_project) 187 | 188 | is_per_host = tf.contrib.tpu.InputPipelineConfig.PER_HOST_V2 189 | run_config = tf.contrib.tpu.RunConfig( 190 | cluster=tpu_cluster_resolver, 191 | master=FLAGS.master, 192 | model_dir=FLAGS.output_dir, 193 | save_checkpoints_steps=FLAGS.save_checkpoints_steps, 194 | tpu_config=tf.contrib.tpu.TPUConfig( 195 | iterations_per_loop=FLAGS.iterations_per_loop, 196 | num_shards=FLAGS.num_tpu_cores, 197 | per_host_input_for_training=is_per_host)) 198 | 199 | train_examples = None 200 | num_train_steps = None 201 | num_warmup_steps = None 202 | if FLAGS.do_train: 203 | train_examples = processor.get_train_examples(FLAGS.data_dir) 204 | num_train_steps = int( 205 | len(train_examples) / FLAGS.train_batch_size * FLAGS.num_train_epochs) 206 | num_warmup_steps = int(num_train_steps * FLAGS.warmup_proportion) 207 | 208 | model_fn = model_fn_builder( 209 | num_labels=len(label_list), 210 | learning_rate=FLAGS.learning_rate, 211 | num_train_steps=num_train_steps, 212 | num_warmup_steps=num_warmup_steps, 213 | use_tpu=FLAGS.use_tpu, 214 | bert_hub_module_handle=FLAGS.bert_hub_module_handle) 215 | 216 | # If TPU is not available, this will fall back to normal Estimator on CPU 217 | # or GPU. 218 | estimator = tf.contrib.tpu.TPUEstimator( 219 | use_tpu=FLAGS.use_tpu, 220 | model_fn=model_fn, 221 | config=run_config, 222 | train_batch_size=FLAGS.train_batch_size, 223 | eval_batch_size=FLAGS.eval_batch_size, 224 | predict_batch_size=FLAGS.predict_batch_size) 225 | 226 | if FLAGS.do_train: 227 | train_features = run_classifier.convert_examples_to_features( 228 | train_examples, label_list, FLAGS.max_seq_length, tokenizer) 229 | tf.logging.info("***** Running training *****") 230 | tf.logging.info(" Num examples = %d", len(train_examples)) 231 | tf.logging.info(" Batch size = %d", FLAGS.train_batch_size) 232 | tf.logging.info(" Num steps = %d", num_train_steps) 233 | train_input_fn = run_classifier.input_fn_builder( 234 | features=train_features, 235 | seq_length=FLAGS.max_seq_length, 236 | is_training=True, 237 | drop_remainder=True) 238 | estimator.train(input_fn=train_input_fn, max_steps=num_train_steps) 239 | 240 | if FLAGS.do_eval: 241 | eval_examples = processor.get_dev_examples(FLAGS.data_dir) 242 | eval_features = run_classifier.convert_examples_to_features( 243 | eval_examples, label_list, FLAGS.max_seq_length, tokenizer) 244 | 245 | tf.logging.info("***** Running evaluation *****") 246 | tf.logging.info(" Num examples = %d", len(eval_examples)) 247 | tf.logging.info(" Batch size = %d", FLAGS.eval_batch_size) 248 | 249 | # This tells the estimator to run through the entire set. 250 | eval_steps = None 251 | # However, if running eval on the TPU, you will need to specify the 252 | # number of steps. 253 | if FLAGS.use_tpu: 254 | # Eval will be slightly WRONG on the TPU because it will truncate 255 | # the last batch. 256 | eval_steps = int(len(eval_examples) / FLAGS.eval_batch_size) 257 | 258 | eval_drop_remainder = True if FLAGS.use_tpu else False 259 | eval_input_fn = run_classifier.input_fn_builder( 260 | features=eval_features, 261 | seq_length=FLAGS.max_seq_length, 262 | is_training=False, 263 | drop_remainder=eval_drop_remainder) 264 | 265 | result = estimator.evaluate(input_fn=eval_input_fn, steps=eval_steps) 266 | 267 | output_eval_file = os.path.join(FLAGS.output_dir, "eval_results.txt") 268 | with tf.gfile.GFile(output_eval_file, "w") as writer: 269 | tf.logging.info("***** Eval results *****") 270 | for key in sorted(result.keys()): 271 | tf.logging.info(" %s = %s", key, str(result[key])) 272 | writer.write("%s = %s\n" % (key, str(result[key]))) 273 | 274 | if FLAGS.do_predict: 275 | predict_examples = processor.get_test_examples(FLAGS.data_dir) 276 | if FLAGS.use_tpu: 277 | # Discard batch remainder if running on TPU 278 | n = len(predict_examples) 279 | predict_examples = predict_examples[:(n - n % FLAGS.predict_batch_size)] 280 | 281 | predict_file = os.path.join(FLAGS.output_dir, "predict.tf_record") 282 | run_classifier.file_based_convert_examples_to_features( 283 | predict_examples, label_list, FLAGS.max_seq_length, tokenizer, 284 | predict_file) 285 | 286 | tf.logging.info("***** Running prediction*****") 287 | tf.logging.info(" Num examples = %d", len(predict_examples)) 288 | tf.logging.info(" Batch size = %d", FLAGS.predict_batch_size) 289 | 290 | predict_input_fn = run_classifier.file_based_input_fn_builder( 291 | input_file=predict_file, 292 | seq_length=FLAGS.max_seq_length, 293 | is_training=False, 294 | drop_remainder=FLAGS.use_tpu) 295 | 296 | result = estimator.predict(input_fn=predict_input_fn) 297 | 298 | output_predict_file = os.path.join(FLAGS.output_dir, "test_results.tsv") 299 | with tf.gfile.GFile(output_predict_file, "w") as writer: 300 | tf.logging.info("***** Predict results *****") 301 | for prediction in result: 302 | probabilities = prediction["probabilities"] 303 | output_line = "\t".join( 304 | str(class_probability) 305 | for class_probability in probabilities) + "\n" 306 | writer.write(output_line) 307 | 308 | 309 | if __name__ == "__main__": 310 | flags.mark_flag_as_required("data_dir") 311 | flags.mark_flag_as_required("task_name") 312 | flags.mark_flag_as_required("bert_hub_module_handle") 313 | flags.mark_flag_as_required("output_dir") 314 | tf.app.run() 315 | -------------------------------------------------------------------------------- /命名实体识别/bert/run_pretraining.py: -------------------------------------------------------------------------------- 1 | # coding=utf-8 2 | # Copyright 2018 The Google AI Language Team Authors. 3 | # 4 | # Licensed under the Apache License, Version 2.0 (the "License"); 5 | # you may not use this file except in compliance with the License. 6 | # You may obtain a copy of the License at 7 | # 8 | # http://www.apache.org/licenses/LICENSE-2.0 9 | # 10 | # Unless required by applicable law or agreed to in writing, software 11 | # distributed under the License is distributed on an "AS IS" BASIS, 12 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 13 | # See the License for the specific language governing permissions and 14 | # limitations under the License. 15 | """Run masked LM/next sentence masked_lm pre-training for BERT.""" 16 | 17 | from __future__ import absolute_import 18 | from __future__ import division 19 | from __future__ import print_function 20 | 21 | import os 22 | import modeling 23 | import optimization 24 | import tensorflow as tf 25 | 26 | flags = tf.flags 27 | 28 | FLAGS = flags.FLAGS 29 | 30 | ## Required parameters 31 | flags.DEFINE_string( 32 | "bert_config_file", None, 33 | "The config json file corresponding to the pre-trained BERT model. " 34 | "This specifies the model architecture.") 35 | 36 | flags.DEFINE_string( 37 | "input_file", None, 38 | "Input TF example files (can be a glob or comma separated).") 39 | 40 | flags.DEFINE_string( 41 | "output_dir", None, 42 | "The output directory where the model checkpoints will be written.") 43 | 44 | ## Other parameters 45 | flags.DEFINE_string( 46 | "init_checkpoint", None, 47 | "Initial checkpoint (usually from a pre-trained BERT model).") 48 | 49 | flags.DEFINE_integer( 50 | "max_seq_length", 128, 51 | "The maximum total input sequence length after WordPiece tokenization. " 52 | "Sequences longer than this will be truncated, and sequences shorter " 53 | "than this will be padded. Must match data generation.") 54 | 55 | flags.DEFINE_integer( 56 | "max_predictions_per_seq", 20, 57 | "Maximum number of masked LM predictions per sequence. " 58 | "Must match data generation.") 59 | 60 | flags.DEFINE_bool("do_train", False, "Whether to run training.") 61 | 62 | flags.DEFINE_bool("do_eval", False, "Whether to run eval on the dev set.") 63 | 64 | flags.DEFINE_integer("train_batch_size", 32, "Total batch size for training.") 65 | 66 | flags.DEFINE_integer("eval_batch_size", 8, "Total batch size for eval.") 67 | 68 | flags.DEFINE_float("learning_rate", 5e-5, "The initial learning rate for Adam.") 69 | 70 | flags.DEFINE_integer("num_train_steps", 100000, "Number of training steps.") 71 | 72 | flags.DEFINE_integer("num_warmup_steps", 10000, "Number of warmup steps.") 73 | 74 | flags.DEFINE_integer("save_checkpoints_steps", 1000, 75 | "How often to save the model checkpoint.") 76 | 77 | flags.DEFINE_integer("iterations_per_loop", 1000, 78 | "How many steps to make in each estimator call.") 79 | 80 | flags.DEFINE_integer("max_eval_steps", 100, "Maximum number of eval steps.") 81 | 82 | flags.DEFINE_bool("use_tpu", False, "Whether to use TPU or GPU/CPU.") 83 | 84 | tf.flags.DEFINE_string( 85 | "tpu_name", None, 86 | "The Cloud TPU to use for training. This should be either the name " 87 | "used when creating the Cloud TPU, or a grpc://ip.address.of.tpu:8470 " 88 | "url.") 89 | 90 | tf.flags.DEFINE_string( 91 | "tpu_zone", None, 92 | "[Optional] GCE zone where the Cloud TPU is located in. If not " 93 | "specified, we will attempt to automatically detect the GCE project from " 94 | "metadata.") 95 | 96 | tf.flags.DEFINE_string( 97 | "gcp_project", None, 98 | "[Optional] Project name for the Cloud TPU-enabled project. If not " 99 | "specified, we will attempt to automatically detect the GCE project from " 100 | "metadata.") 101 | 102 | tf.flags.DEFINE_string("master", None, "[Optional] TensorFlow master URL.") 103 | 104 | flags.DEFINE_integer( 105 | "num_tpu_cores", 8, 106 | "Only used if `use_tpu` is True. Total number of TPU cores to use.") 107 | 108 | 109 | def model_fn_builder(bert_config, init_checkpoint, learning_rate, 110 | num_train_steps, num_warmup_steps, use_tpu, 111 | use_one_hot_embeddings): 112 | """Returns `model_fn` closure for TPUEstimator.""" 113 | 114 | def model_fn(features, labels, mode, params): # pylint: disable=unused-argument 115 | """The `model_fn` for TPUEstimator.""" 116 | 117 | tf.logging.info("*** Features ***") 118 | for name in sorted(features.keys()): 119 | tf.logging.info(" name = %s, shape = %s" % (name, features[name].shape)) 120 | 121 | input_ids = features["input_ids"] 122 | input_mask = features["input_mask"] 123 | segment_ids = features["segment_ids"] 124 | masked_lm_positions = features["masked_lm_positions"] 125 | masked_lm_ids = features["masked_lm_ids"] 126 | masked_lm_weights = features["masked_lm_weights"] 127 | next_sentence_labels = features["next_sentence_labels"] 128 | 129 | is_training = (mode == tf.estimator.ModeKeys.TRAIN) 130 | 131 | model = modeling.BertModel( 132 | config=bert_config, 133 | is_training=is_training, 134 | input_ids=input_ids, 135 | input_mask=input_mask, 136 | token_type_ids=segment_ids, 137 | use_one_hot_embeddings=use_one_hot_embeddings) 138 | 139 | (masked_lm_loss, 140 | masked_lm_example_loss, masked_lm_log_probs) = get_masked_lm_output( 141 | bert_config, model.get_sequence_output(), model.get_embedding_table(), 142 | masked_lm_positions, masked_lm_ids, masked_lm_weights) 143 | 144 | (next_sentence_loss, next_sentence_example_loss, 145 | next_sentence_log_probs) = get_next_sentence_output( 146 | bert_config, model.get_pooled_output(), next_sentence_labels) 147 | 148 | total_loss = masked_lm_loss + next_sentence_loss 149 | 150 | tvars = tf.trainable_variables() 151 | 152 | initialized_variable_names = {} 153 | scaffold_fn = None 154 | if init_checkpoint: 155 | (assignment_map, initialized_variable_names 156 | ) = modeling.get_assignment_map_from_checkpoint(tvars, init_checkpoint) 157 | if use_tpu: 158 | 159 | def tpu_scaffold(): 160 | tf.train.init_from_checkpoint(init_checkpoint, assignment_map) 161 | return tf.train.Scaffold() 162 | 163 | scaffold_fn = tpu_scaffold 164 | else: 165 | tf.train.init_from_checkpoint(init_checkpoint, assignment_map) 166 | 167 | tf.logging.info("**** Trainable Variables ****") 168 | for var in tvars: 169 | init_string = "" 170 | if var.name in initialized_variable_names: 171 | init_string = ", *INIT_FROM_CKPT*" 172 | tf.logging.info(" name = %s, shape = %s%s", var.name, var.shape, 173 | init_string) 174 | 175 | output_spec = None 176 | if mode == tf.estimator.ModeKeys.TRAIN: 177 | train_op = optimization.create_optimizer( 178 | total_loss, learning_rate, num_train_steps, num_warmup_steps, use_tpu) 179 | 180 | output_spec = tf.contrib.tpu.TPUEstimatorSpec( 181 | mode=mode, 182 | loss=total_loss, 183 | train_op=train_op, 184 | scaffold_fn=scaffold_fn) 185 | elif mode == tf.estimator.ModeKeys.EVAL: 186 | 187 | def metric_fn(masked_lm_example_loss, masked_lm_log_probs, masked_lm_ids, 188 | masked_lm_weights, next_sentence_example_loss, 189 | next_sentence_log_probs, next_sentence_labels): 190 | """Computes the loss and accuracy of the model.""" 191 | masked_lm_log_probs = tf.reshape(masked_lm_log_probs, 192 | [-1, masked_lm_log_probs.shape[-1]]) 193 | masked_lm_predictions = tf.argmax( 194 | masked_lm_log_probs, axis=-1, output_type=tf.int32) 195 | masked_lm_example_loss = tf.reshape(masked_lm_example_loss, [-1]) 196 | masked_lm_ids = tf.reshape(masked_lm_ids, [-1]) 197 | masked_lm_weights = tf.reshape(masked_lm_weights, [-1]) 198 | masked_lm_accuracy = tf.metrics.accuracy( 199 | labels=masked_lm_ids, 200 | predictions=masked_lm_predictions, 201 | weights=masked_lm_weights) 202 | masked_lm_mean_loss = tf.metrics.mean( 203 | values=masked_lm_example_loss, weights=masked_lm_weights) 204 | 205 | next_sentence_log_probs = tf.reshape( 206 | next_sentence_log_probs, [-1, next_sentence_log_probs.shape[-1]]) 207 | next_sentence_predictions = tf.argmax( 208 | next_sentence_log_probs, axis=-1, output_type=tf.int32) 209 | next_sentence_labels = tf.reshape(next_sentence_labels, [-1]) 210 | next_sentence_accuracy = tf.metrics.accuracy( 211 | labels=next_sentence_labels, predictions=next_sentence_predictions) 212 | next_sentence_mean_loss = tf.metrics.mean( 213 | values=next_sentence_example_loss) 214 | 215 | return { 216 | "masked_lm_accuracy": masked_lm_accuracy, 217 | "masked_lm_loss": masked_lm_mean_loss, 218 | "next_sentence_accuracy": next_sentence_accuracy, 219 | "next_sentence_loss": next_sentence_mean_loss, 220 | } 221 | 222 | eval_metrics = (metric_fn, [ 223 | masked_lm_example_loss, masked_lm_log_probs, masked_lm_ids, 224 | masked_lm_weights, next_sentence_example_loss, 225 | next_sentence_log_probs, next_sentence_labels 226 | ]) 227 | output_spec = tf.contrib.tpu.TPUEstimatorSpec( 228 | mode=mode, 229 | loss=total_loss, 230 | eval_metrics=eval_metrics, 231 | scaffold_fn=scaffold_fn) 232 | else: 233 | raise ValueError("Only TRAIN and EVAL modes are supported: %s" % (mode)) 234 | 235 | return output_spec 236 | 237 | return model_fn 238 | 239 | 240 | def get_masked_lm_output(bert_config, input_tensor, output_weights, positions, 241 | label_ids, label_weights): 242 | """Get loss and log probs for the masked LM.""" 243 | input_tensor = gather_indexes(input_tensor, positions) 244 | 245 | with tf.variable_scope("cls/predictions"): 246 | # We apply one more non-linear transformation before the output layer. 247 | # This matrix is not used after pre-training. 248 | with tf.variable_scope("transform"): 249 | input_tensor = tf.layers.dense( 250 | input_tensor, 251 | units=bert_config.hidden_size, 252 | activation=modeling.get_activation(bert_config.hidden_act), 253 | kernel_initializer=modeling.create_initializer( 254 | bert_config.initializer_range)) 255 | input_tensor = modeling.layer_norm(input_tensor) 256 | 257 | # The output weights are the same as the input embeddings, but there is 258 | # an output-only bias for each token. 259 | output_bias = tf.get_variable( 260 | "output_bias", 261 | shape=[bert_config.vocab_size], 262 | initializer=tf.zeros_initializer()) 263 | logits = tf.matmul(input_tensor, output_weights, transpose_b=True) 264 | logits = tf.nn.bias_add(logits, output_bias) 265 | log_probs = tf.nn.log_softmax(logits, axis=-1) 266 | 267 | label_ids = tf.reshape(label_ids, [-1]) 268 | label_weights = tf.reshape(label_weights, [-1]) 269 | 270 | one_hot_labels = tf.one_hot( 271 | label_ids, depth=bert_config.vocab_size, dtype=tf.float32) 272 | 273 | # The `positions` tensor might be zero-padded (if the sequence is too 274 | # short to have the maximum number of predictions). The `label_weights` 275 | # tensor has a value of 1.0 for every real prediction and 0.0 for the 276 | # padding predictions. 277 | per_example_loss = -tf.reduce_sum(log_probs * one_hot_labels, axis=[-1]) 278 | numerator = tf.reduce_sum(label_weights * per_example_loss) 279 | denominator = tf.reduce_sum(label_weights) + 1e-5 280 | loss = numerator / denominator 281 | 282 | return (loss, per_example_loss, log_probs) 283 | 284 | 285 | def get_next_sentence_output(bert_config, input_tensor, labels): 286 | """Get loss and log probs for the next sentence prediction.""" 287 | 288 | # Simple binary classification. Note that 0 is "next sentence" and 1 is 289 | # "random sentence". This weight matrix is not used after pre-training. 290 | with tf.variable_scope("cls/seq_relationship"): 291 | output_weights = tf.get_variable( 292 | "output_weights", 293 | shape=[2, bert_config.hidden_size], 294 | initializer=modeling.create_initializer(bert_config.initializer_range)) 295 | output_bias = tf.get_variable( 296 | "output_bias", shape=[2], initializer=tf.zeros_initializer()) 297 | 298 | logits = tf.matmul(input_tensor, output_weights, transpose_b=True) 299 | logits = tf.nn.bias_add(logits, output_bias) 300 | log_probs = tf.nn.log_softmax(logits, axis=-1) 301 | labels = tf.reshape(labels, [-1]) 302 | one_hot_labels = tf.one_hot(labels, depth=2, dtype=tf.float32) 303 | per_example_loss = -tf.reduce_sum(one_hot_labels * log_probs, axis=-1) 304 | loss = tf.reduce_mean(per_example_loss) 305 | return (loss, per_example_loss, log_probs) 306 | 307 | 308 | def gather_indexes(sequence_tensor, positions): 309 | """Gathers the vectors at the specific positions over a minibatch.""" 310 | sequence_shape = modeling.get_shape_list(sequence_tensor, expected_rank=3) 311 | batch_size = sequence_shape[0] 312 | seq_length = sequence_shape[1] 313 | width = sequence_shape[2] 314 | 315 | flat_offsets = tf.reshape( 316 | tf.range(0, batch_size, dtype=tf.int32) * seq_length, [-1, 1]) 317 | flat_positions = tf.reshape(positions + flat_offsets, [-1]) 318 | flat_sequence_tensor = tf.reshape(sequence_tensor, 319 | [batch_size * seq_length, width]) 320 | output_tensor = tf.gather(flat_sequence_tensor, flat_positions) 321 | return output_tensor 322 | 323 | 324 | def input_fn_builder(input_files, 325 | max_seq_length, 326 | max_predictions_per_seq, 327 | is_training, 328 | num_cpu_threads=4): 329 | """Creates an `input_fn` closure to be passed to TPUEstimator.""" 330 | 331 | def input_fn(params): 332 | """The actual input function.""" 333 | batch_size = params["batch_size"] 334 | 335 | name_to_features = { 336 | "input_ids": 337 | tf.FixedLenFeature([max_seq_length], tf.int64), 338 | "input_mask": 339 | tf.FixedLenFeature([max_seq_length], tf.int64), 340 | "segment_ids": 341 | tf.FixedLenFeature([max_seq_length], tf.int64), 342 | "masked_lm_positions": 343 | tf.FixedLenFeature([max_predictions_per_seq], tf.int64), 344 | "masked_lm_ids": 345 | tf.FixedLenFeature([max_predictions_per_seq], tf.int64), 346 | "masked_lm_weights": 347 | tf.FixedLenFeature([max_predictions_per_seq], tf.float32), 348 | "next_sentence_labels": 349 | tf.FixedLenFeature([1], tf.int64), 350 | } 351 | 352 | # For training, we want a lot of parallel reading and shuffling. 353 | # For eval, we want no shuffling and parallel reading doesn't matter. 354 | if is_training: 355 | d = tf.data.Dataset.from_tensor_slices(tf.constant(input_files)) 356 | d = d.repeat() 357 | d = d.shuffle(buffer_size=len(input_files)) 358 | 359 | # `cycle_length` is the number of parallel files that get read. 360 | cycle_length = min(num_cpu_threads, len(input_files)) 361 | 362 | # `sloppy` mode means that the interleaving is not exact. This adds 363 | # even more randomness to the training pipeline. 364 | d = d.apply( 365 | tf.contrib.data.parallel_interleave( 366 | tf.data.TFRecordDataset, 367 | sloppy=is_training, 368 | cycle_length=cycle_length)) 369 | d = d.shuffle(buffer_size=100) 370 | else: 371 | d = tf.data.TFRecordDataset(input_files) 372 | # Since we evaluate for a fixed number of steps we don't want to encounter 373 | # out-of-range exceptions. 374 | d = d.repeat() 375 | 376 | # We must `drop_remainder` on training because the TPU requires fixed 377 | # size dimensions. For eval, we assume we are evaluating on the CPU or GPU 378 | # and we *don't* want to drop the remainder, otherwise we wont cover 379 | # every sample. 380 | d = d.apply( 381 | tf.contrib.data.map_and_batch( 382 | lambda record: _decode_record(record, name_to_features), 383 | batch_size=batch_size, 384 | num_parallel_batches=num_cpu_threads, 385 | drop_remainder=True)) 386 | return d 387 | 388 | return input_fn 389 | 390 | 391 | def _decode_record(record, name_to_features): 392 | """Decodes a record to a TensorFlow example.""" 393 | example = tf.parse_single_example(record, name_to_features) 394 | 395 | # tf.Example only supports tf.int64, but the TPU only supports tf.int32. 396 | # So cast all int64 to int32. 397 | for name in list(example.keys()): 398 | t = example[name] 399 | if t.dtype == tf.int64: 400 | t = tf.to_int32(t) 401 | example[name] = t 402 | 403 | return example 404 | 405 | 406 | def main(_): 407 | tf.logging.set_verbosity(tf.logging.INFO) 408 | 409 | if not FLAGS.do_train and not FLAGS.do_eval: 410 | raise ValueError("At least one of `do_train` or `do_eval` must be True.") 411 | 412 | bert_config = modeling.BertConfig.from_json_file(FLAGS.bert_config_file) 413 | 414 | tf.gfile.MakeDirs(FLAGS.output_dir) 415 | 416 | input_files = [] 417 | for input_pattern in FLAGS.input_file.split(","): 418 | input_files.extend(tf.gfile.Glob(input_pattern)) 419 | 420 | tf.logging.info("*** Input Files ***") 421 | for input_file in input_files: 422 | tf.logging.info(" %s" % input_file) 423 | 424 | tpu_cluster_resolver = None 425 | if FLAGS.use_tpu and FLAGS.tpu_name: 426 | tpu_cluster_resolver = tf.contrib.cluster_resolver.TPUClusterResolver( 427 | FLAGS.tpu_name, zone=FLAGS.tpu_zone, project=FLAGS.gcp_project) 428 | 429 | is_per_host = tf.contrib.tpu.InputPipelineConfig.PER_HOST_V2 430 | run_config = tf.contrib.tpu.RunConfig( 431 | cluster=tpu_cluster_resolver, 432 | master=FLAGS.master, 433 | model_dir=FLAGS.output_dir, 434 | save_checkpoints_steps=FLAGS.save_checkpoints_steps, 435 | tpu_config=tf.contrib.tpu.TPUConfig( 436 | iterations_per_loop=FLAGS.iterations_per_loop, 437 | num_shards=FLAGS.num_tpu_cores, 438 | per_host_input_for_training=is_per_host)) 439 | 440 | model_fn = model_fn_builder( 441 | bert_config=bert_config, 442 | init_checkpoint=FLAGS.init_checkpoint, 443 | learning_rate=FLAGS.learning_rate, 444 | num_train_steps=FLAGS.num_train_steps, 445 | num_warmup_steps=FLAGS.num_warmup_steps, 446 | use_tpu=FLAGS.use_tpu, 447 | use_one_hot_embeddings=FLAGS.use_tpu) 448 | 449 | # If TPU is not available, this will fall back to normal Estimator on CPU 450 | # or GPU. 451 | estimator = tf.contrib.tpu.TPUEstimator( 452 | use_tpu=FLAGS.use_tpu, 453 | model_fn=model_fn, 454 | config=run_config, 455 | train_batch_size=FLAGS.train_batch_size, 456 | eval_batch_size=FLAGS.eval_batch_size) 457 | 458 | if FLAGS.do_train: 459 | tf.logging.info("***** Running training *****") 460 | tf.logging.info(" Batch size = %d", FLAGS.train_batch_size) 461 | train_input_fn = input_fn_builder( 462 | input_files=input_files, 463 | max_seq_length=FLAGS.max_seq_length, 464 | max_predictions_per_seq=FLAGS.max_predictions_per_seq, 465 | is_training=True) 466 | estimator.train(input_fn=train_input_fn, max_steps=FLAGS.num_train_steps) 467 | 468 | if FLAGS.do_eval: 469 | tf.logging.info("***** Running evaluation *****") 470 | tf.logging.info(" Batch size = %d", FLAGS.eval_batch_size) 471 | 472 | eval_input_fn = input_fn_builder( 473 | input_files=input_files, 474 | max_seq_length=FLAGS.max_seq_length, 475 | max_predictions_per_seq=FLAGS.max_predictions_per_seq, 476 | is_training=False) 477 | 478 | result = estimator.evaluate( 479 | input_fn=eval_input_fn, steps=FLAGS.max_eval_steps) 480 | 481 | output_eval_file = os.path.join(FLAGS.output_dir, "eval_results.txt") 482 | with tf.gfile.GFile(output_eval_file, "w") as writer: 483 | tf.logging.info("***** Eval results *****") 484 | for key in sorted(result.keys()): 485 | tf.logging.info(" %s = %s", key, str(result[key])) 486 | writer.write("%s = %s\n" % (key, str(result[key]))) 487 | 488 | 489 | if __name__ == "__main__": 490 | flags.mark_flag_as_required("input_file") 491 | flags.mark_flag_as_required("bert_config_file") 492 | flags.mark_flag_as_required("output_dir") 493 | tf.app.run() 494 | -------------------------------------------------------------------------------- /命名实体识别/bert/sample_text.txt: -------------------------------------------------------------------------------- 1 | This text is included to make sure Unicode is handled properly: 力加勝北区ᴵᴺᵀᵃছজটডণত 2 | Text should be one-sentence-per-line, with empty lines between documents. 3 | This sample text is public domain and was randomly selected from Project Guttenberg. 4 | 5 | The rain had only ceased with the gray streaks of morning at Blazing Star, and the settlement awoke to a moral sense of cleanliness, and the finding of forgotten knives, tin cups, and smaller camp utensils, where the heavy showers had washed away the debris and dust heaps before the cabin doors. 6 | Indeed, it was recorded in Blazing Star that a fortunate early riser had once picked up on the highway a solid chunk of gold quartz which the rain had freed from its incumbering soil, and washed into immediate and glittering popularity. 7 | Possibly this may have been the reason why early risers in that locality, during the rainy season, adopted a thoughtful habit of body, and seldom lifted their eyes to the rifted or india-ink washed skies above them. 8 | "Cass" Beard had risen early that morning, but not with a view to discovery. 9 | A leak in his cabin roof,--quite consistent with his careless, improvident habits,--had roused him at 4 A. M., with a flooded "bunk" and wet blankets. 10 | The chips from his wood pile refused to kindle a fire to dry his bed-clothes, and he had recourse to a more provident neighbor's to supply the deficiency. 11 | This was nearly opposite. 12 | Mr. Cassius crossed the highway, and stopped suddenly. 13 | Something glittered in the nearest red pool before him. 14 | Gold, surely! 15 | But, wonderful to relate, not an irregular, shapeless fragment of crude ore, fresh from Nature's crucible, but a bit of jeweler's handicraft in the form of a plain gold ring. 16 | Looking at it more attentively, he saw that it bore the inscription, "May to Cass." 17 | Like most of his fellow gold-seekers, Cass was superstitious. 18 | 19 | The fountain of classic wisdom, Hypatia herself. 20 | As the ancient sage--the name is unimportant to a monk--pumped water nightly that he might study by day, so I, the guardian of cloaks and parasols, at the sacred doors of her lecture-room, imbibe celestial knowledge. 21 | From my youth I felt in me a soul above the matter-entangled herd. 22 | She revealed to me the glorious fact, that I am a spark of Divinity itself. 23 | A fallen star, I am, sir!' continued he, pensively, stroking his lean stomach--'a fallen star!--fallen, if the dignity of philosophy will allow of the simile, among the hogs of the lower world--indeed, even into the hog-bucket itself. Well, after all, I will show you the way to the Archbishop's. 24 | There is a philosophic pleasure in opening one's treasures to the modest young. 25 | Perhaps you will assist me by carrying this basket of fruit?' And the little man jumped up, put his basket on Philammon's head, and trotted off up a neighbouring street. 26 | Philammon followed, half contemptuous, half wondering at what this philosophy might be, which could feed the self-conceit of anything so abject as his ragged little apish guide; 27 | but the novel roar and whirl of the street, the perpetual stream of busy faces, the line of curricles, palanquins, laden asses, camels, elephants, which met and passed him, and squeezed him up steps and into doorways, as they threaded their way through the great Moon-gate into the ample street beyond, drove everything from his mind but wondering curiosity, and a vague, helpless dread of that great living wilderness, more terrible than any dead wilderness of sand which he had left behind. 28 | Already he longed for the repose, the silence of the Laura--for faces which knew him and smiled upon him; but it was too late to turn back now. 29 | His guide held on for more than a mile up the great main street, crossed in the centre of the city, at right angles, by one equally magnificent, at each end of which, miles away, appeared, dim and distant over the heads of the living stream of passengers, the yellow sand-hills of the desert; 30 | while at the end of the vista in front of them gleamed the blue harbour, through a network of countless masts. 31 | At last they reached the quay at the opposite end of the street; 32 | and there burst on Philammon's astonished eyes a vast semicircle of blue sea, ringed with palaces and towers. 33 | He stopped involuntarily; and his little guide stopped also, and looked askance at the young monk, to watch the effect which that grand panorama should produce on him. 34 | -------------------------------------------------------------------------------- /命名实体识别/bert/tokenization.py: -------------------------------------------------------------------------------- 1 | # coding=utf-8 2 | # Copyright 2018 The Google AI Language Team Authors. 3 | # 4 | # Licensed under the Apache License, Version 2.0 (the "License"); 5 | # you may not use this file except in compliance with the License. 6 | # You may obtain a copy of the License at 7 | # 8 | # http://www.apache.org/licenses/LICENSE-2.0 9 | # 10 | # Unless required by applicable law or agreed to in writing, software 11 | # distributed under the License is distributed on an "AS IS" BASIS, 12 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 13 | # See the License for the specific language governing permissions and 14 | # limitations under the License. 15 | """Tokenization classes.""" 16 | 17 | from __future__ import absolute_import 18 | from __future__ import division 19 | from __future__ import print_function 20 | 21 | import collections 22 | import re 23 | import unicodedata 24 | import six 25 | import tensorflow as tf 26 | 27 | 28 | def validate_case_matches_checkpoint(do_lower_case, init_checkpoint): 29 | """Checks whether the casing config is consistent with the checkpoint name.""" 30 | 31 | # The casing has to be passed in by the user and there is no explicit check 32 | # as to whether it matches the checkpoint. The casing information probably 33 | # should have been stored in the bert_config.json file, but it's not, so 34 | # we have to heuristically detect it to validate. 35 | 36 | if not init_checkpoint: 37 | return 38 | 39 | m = re.match("^.*?([A-Za-z0-9_-]+)/bert_model.ckpt", init_checkpoint) 40 | if m is None: 41 | return 42 | 43 | model_name = m.group(1) 44 | 45 | lower_models = [ 46 | "uncased_L-24_H-1024_A-16", "uncased_L-12_H-768_A-12", 47 | "multilingual_L-12_H-768_A-12", "chinese_L-12_H-768_A-12" 48 | ] 49 | 50 | cased_models = [ 51 | "cased_L-12_H-768_A-12", "cased_L-24_H-1024_A-16", 52 | "multi_cased_L-12_H-768_A-12" 53 | ] 54 | 55 | is_bad_config = False 56 | if model_name in lower_models and not do_lower_case: 57 | is_bad_config = True 58 | actual_flag = "False" 59 | case_name = "lowercased" 60 | opposite_flag = "True" 61 | 62 | if model_name in cased_models and do_lower_case: 63 | is_bad_config = True 64 | actual_flag = "True" 65 | case_name = "cased" 66 | opposite_flag = "False" 67 | 68 | if is_bad_config: 69 | raise ValueError( 70 | "You passed in `--do_lower_case=%s` with `--init_checkpoint=%s`. " 71 | "However, `%s` seems to be a %s model, so you " 72 | "should pass in `--do_lower_case=%s` so that the fine-tuning matches " 73 | "how the model was pre-training. If this error is wrong, please " 74 | "just comment out this check." % (actual_flag, init_checkpoint, 75 | model_name, case_name, opposite_flag)) 76 | 77 | 78 | def convert_to_unicode(text): 79 | """Converts `text` to Unicode (if it's not already), assuming utf-8 input.""" 80 | if six.PY3: 81 | if isinstance(text, str): 82 | return text 83 | elif isinstance(text, bytes): 84 | return text.decode("utf-8", "ignore") 85 | else: 86 | raise ValueError("Unsupported string type: %s" % (type(text))) 87 | elif six.PY2: 88 | if isinstance(text, str): 89 | return text.decode("utf-8", "ignore") 90 | elif isinstance(text, unicode): 91 | return text 92 | else: 93 | raise ValueError("Unsupported string type: %s" % (type(text))) 94 | else: 95 | raise ValueError("Not running on Python2 or Python 3?") 96 | 97 | 98 | def printable_text(text): 99 | """Returns text encoded in a way suitable for print or `tf.logging`.""" 100 | 101 | # These functions want `str` for both Python2 and Python3, but in one case 102 | # it's a Unicode string and in the other it's a byte string. 103 | if six.PY3: 104 | if isinstance(text, str): 105 | return text 106 | elif isinstance(text, bytes): 107 | return text.decode("utf-8", "ignore") 108 | else: 109 | raise ValueError("Unsupported string type: %s" % (type(text))) 110 | elif six.PY2: 111 | if isinstance(text, str): 112 | return text 113 | elif isinstance(text, unicode): 114 | return text.encode("utf-8") 115 | else: 116 | raise ValueError("Unsupported string type: %s" % (type(text))) 117 | else: 118 | raise ValueError("Not running on Python2 or Python 3?") 119 | 120 | 121 | def load_vocab(vocab_file): 122 | """Loads a vocabulary file into a dictionary.""" 123 | vocab = collections.OrderedDict() 124 | index = 0 125 | with tf.gfile.GFile(vocab_file, "r") as reader: 126 | while True: 127 | token = convert_to_unicode(reader.readline()) 128 | if not token: 129 | break 130 | token = token.strip() 131 | vocab[token] = index 132 | index += 1 133 | return vocab 134 | 135 | 136 | def convert_by_vocab(vocab, items): 137 | """Converts a sequence of [tokens|ids] using the vocab.""" 138 | output = [] 139 | for item in items: 140 | output.append(vocab[item]) 141 | return output 142 | 143 | 144 | def convert_tokens_to_ids(vocab, tokens): 145 | return convert_by_vocab(vocab, tokens) 146 | 147 | 148 | def convert_ids_to_tokens(inv_vocab, ids): 149 | return convert_by_vocab(inv_vocab, ids) 150 | 151 | 152 | def whitespace_tokenize(text): 153 | """Runs basic whitespace cleaning and splitting on a piece of text.""" 154 | text = text.strip() 155 | if not text: 156 | return [] 157 | tokens = text.split() 158 | return tokens 159 | 160 | 161 | class FullTokenizer(object): 162 | """Runs end-to-end tokenziation.""" 163 | 164 | def __init__(self, vocab_file, do_lower_case=True): 165 | self.vocab = load_vocab(vocab_file) 166 | self.inv_vocab = {v: k for k, v in self.vocab.items()} 167 | self.basic_tokenizer = BasicTokenizer(do_lower_case=do_lower_case) 168 | self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab) 169 | 170 | def tokenize(self, text): 171 | split_tokens = [] 172 | for token in self.basic_tokenizer.tokenize(text): 173 | for sub_token in self.wordpiece_tokenizer.tokenize(token): 174 | split_tokens.append(sub_token) 175 | 176 | return split_tokens 177 | 178 | def convert_tokens_to_ids(self, tokens): 179 | return convert_by_vocab(self.vocab, tokens) 180 | 181 | def convert_ids_to_tokens(self, ids): 182 | return convert_by_vocab(self.inv_vocab, ids) 183 | 184 | 185 | class BasicTokenizer(object): 186 | """Runs basic tokenization (punctuation splitting, lower casing, etc.).""" 187 | 188 | def __init__(self, do_lower_case=True): 189 | """Constructs a BasicTokenizer. 190 | 191 | Args: 192 | do_lower_case: Whether to lower case the input. 193 | """ 194 | self.do_lower_case = do_lower_case 195 | 196 | def tokenize(self, text): 197 | """Tokenizes a piece of text.""" 198 | text = convert_to_unicode(text) 199 | text = self._clean_text(text) 200 | 201 | # This was added on November 1st, 2018 for the multilingual and Chinese 202 | # models. This is also applied to the English models now, but it doesn't 203 | # matter since the English models were not trained on any Chinese data 204 | # and generally don't have any Chinese data in them (there are Chinese 205 | # characters in the vocabulary because Wikipedia does have some Chinese 206 | # words in the English Wikipedia.). 207 | text = self._tokenize_chinese_chars(text) 208 | 209 | orig_tokens = whitespace_tokenize(text) 210 | split_tokens = [] 211 | for token in orig_tokens: 212 | if self.do_lower_case: 213 | token = token.lower() 214 | token = self._run_strip_accents(token) 215 | split_tokens.extend(self._run_split_on_punc(token)) 216 | 217 | output_tokens = whitespace_tokenize(" ".join(split_tokens)) 218 | return output_tokens 219 | 220 | def _run_strip_accents(self, text): 221 | """Strips accents from a piece of text.""" 222 | text = unicodedata.normalize("NFD", text) 223 | output = [] 224 | for char in text: 225 | cat = unicodedata.category(char) 226 | if cat == "Mn": 227 | continue 228 | output.append(char) 229 | return "".join(output) 230 | 231 | def _run_split_on_punc(self, text): 232 | """Splits punctuation on a piece of text.""" 233 | chars = list(text) 234 | i = 0 235 | start_new_word = True 236 | output = [] 237 | while i < len(chars): 238 | char = chars[i] 239 | if _is_punctuation(char): 240 | output.append([char]) 241 | start_new_word = True 242 | else: 243 | if start_new_word: 244 | output.append([]) 245 | start_new_word = False 246 | output[-1].append(char) 247 | i += 1 248 | 249 | return ["".join(x) for x in output] 250 | 251 | def _tokenize_chinese_chars(self, text): 252 | """Adds whitespace around any CJK character.""" 253 | output = [] 254 | for char in text: 255 | cp = ord(char) 256 | if self._is_chinese_char(cp): 257 | output.append(" ") 258 | output.append(char) 259 | output.append(" ") 260 | else: 261 | output.append(char) 262 | return "".join(output) 263 | 264 | def _is_chinese_char(self, cp): 265 | """Checks whether CP is the codepoint of a CJK character.""" 266 | # This defines a "chinese character" as anything in the CJK Unicode block: 267 | # https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block) 268 | # 269 | # Note that the CJK Unicode block is NOT all Japanese and Korean characters, 270 | # despite its name. The modern Korean Hangul alphabet is a different block, 271 | # as is Japanese Hiragana and Katakana. Those alphabets are used to write 272 | # space-separated words, so they are not treated specially and handled 273 | # like the all of the other languages. 274 | if ((cp >= 0x4E00 and cp <= 0x9FFF) or # 275 | (cp >= 0x3400 and cp <= 0x4DBF) or # 276 | (cp >= 0x20000 and cp <= 0x2A6DF) or # 277 | (cp >= 0x2A700 and cp <= 0x2B73F) or # 278 | (cp >= 0x2B740 and cp <= 0x2B81F) or # 279 | (cp >= 0x2B820 and cp <= 0x2CEAF) or 280 | (cp >= 0xF900 and cp <= 0xFAFF) or # 281 | (cp >= 0x2F800 and cp <= 0x2FA1F)): # 282 | return True 283 | 284 | return False 285 | 286 | def _clean_text(self, text): 287 | """Performs invalid character removal and whitespace cleanup on text.""" 288 | output = [] 289 | for char in text: 290 | cp = ord(char) 291 | if cp == 0 or cp == 0xfffd or _is_control(char): 292 | continue 293 | if _is_whitespace(char): 294 | output.append(" ") 295 | else: 296 | output.append(char) 297 | return "".join(output) 298 | 299 | 300 | class WordpieceTokenizer(object): 301 | """Runs WordPiece tokenziation.""" 302 | 303 | def __init__(self, vocab, unk_token="[UNK]", max_input_chars_per_word=200): 304 | self.vocab = vocab 305 | self.unk_token = unk_token 306 | self.max_input_chars_per_word = max_input_chars_per_word 307 | 308 | def tokenize(self, text): 309 | """Tokenizes a piece of text into its word pieces. 310 | 311 | This uses a greedy longest-match-first algorithm to perform tokenization 312 | using the given vocabulary. 313 | 314 | For example: 315 | input = "unaffable" 316 | output = ["un", "##aff", "##able"] 317 | 318 | Args: 319 | text: A single token or whitespace separated tokens. This should have 320 | already been passed through `BasicTokenizer. 321 | 322 | Returns: 323 | A list of wordpiece tokens. 324 | """ 325 | 326 | text = convert_to_unicode(text) 327 | 328 | output_tokens = [] 329 | for token in whitespace_tokenize(text): 330 | chars = list(token) 331 | if len(chars) > self.max_input_chars_per_word: 332 | output_tokens.append(self.unk_token) 333 | continue 334 | 335 | is_bad = False 336 | start = 0 337 | sub_tokens = [] 338 | while start < len(chars): 339 | end = len(chars) 340 | cur_substr = None 341 | while start < end: 342 | substr = "".join(chars[start:end]) 343 | if start > 0: 344 | substr = "##" + substr 345 | if substr in self.vocab: 346 | cur_substr = substr 347 | break 348 | end -= 1 349 | if cur_substr is None: 350 | is_bad = True 351 | break 352 | sub_tokens.append(cur_substr) 353 | start = end 354 | 355 | if is_bad: 356 | output_tokens.append(self.unk_token) 357 | else: 358 | output_tokens.extend(sub_tokens) 359 | return output_tokens 360 | 361 | 362 | def _is_whitespace(char): 363 | """Checks whether `chars` is a whitespace character.""" 364 | # \t, \n, and \r are technically contorl characters but we treat them 365 | # as whitespace since they are generally considered as such. 366 | if char == " " or char == "\t" or char == "\n" or char == "\r": 367 | return True 368 | cat = unicodedata.category(char) 369 | if cat == "Zs": 370 | return True 371 | return False 372 | 373 | 374 | def _is_control(char): 375 | """Checks whether `chars` is a control character.""" 376 | # These are technically control characters but we count them as whitespace 377 | # characters. 378 | if char == "\t" or char == "\n" or char == "\r": 379 | return False 380 | cat = unicodedata.category(char) 381 | if cat in ("Cc", "Cf"): 382 | return True 383 | return False 384 | 385 | 386 | def _is_punctuation(char): 387 | """Checks whether `chars` is a punctuation character.""" 388 | cp = ord(char) 389 | # We treat all non-letter/number ASCII as punctuation. 390 | # Characters such as "^", "$", and "`" are not in the Unicode 391 | # Punctuation class but we treat them as punctuation anyways, for 392 | # consistency. 393 | if ((cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or 394 | (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126)): 395 | return True 396 | cat = unicodedata.category(char) 397 | if cat.startswith("P"): 398 | return True 399 | return False 400 | -------------------------------------------------------------------------------- /命名实体识别/bert/tokenization_test.py: -------------------------------------------------------------------------------- 1 | # coding=utf-8 2 | # Copyright 2018 The Google AI Language Team Authors. 3 | # 4 | # Licensed under the Apache License, Version 2.0 (the "License"); 5 | # you may not use this file except in compliance with the License. 6 | # You may obtain a copy of the License at 7 | # 8 | # http://www.apache.org/licenses/LICENSE-2.0 9 | # 10 | # Unless required by applicable law or agreed to in writing, software 11 | # distributed under the License is distributed on an "AS IS" BASIS, 12 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 13 | # See the License for the specific language governing permissions and 14 | # limitations under the License. 15 | from __future__ import absolute_import 16 | from __future__ import division 17 | from __future__ import print_function 18 | 19 | import os 20 | import tempfile 21 | import tokenization 22 | import six 23 | import tensorflow as tf 24 | 25 | 26 | class TokenizationTest(tf.test.TestCase): 27 | 28 | def test_full_tokenizer(self): 29 | vocab_tokens = [ 30 | "[UNK]", "[CLS]", "[SEP]", "want", "##want", "##ed", "wa", "un", "runn", 31 | "##ing", "," 32 | ] 33 | with tempfile.NamedTemporaryFile(delete=False) as vocab_writer: 34 | if six.PY2: 35 | vocab_writer.write("".join([x + "\n" for x in vocab_tokens])) 36 | else: 37 | vocab_writer.write("".join( 38 | [x + "\n" for x in vocab_tokens]).encode("utf-8")) 39 | 40 | vocab_file = vocab_writer.name 41 | 42 | tokenizer = tokenization.FullTokenizer(vocab_file) 43 | os.unlink(vocab_file) 44 | 45 | tokens = tokenizer.tokenize(u"UNwant\u00E9d,running") 46 | self.assertAllEqual(tokens, ["un", "##want", "##ed", ",", "runn", "##ing"]) 47 | 48 | self.assertAllEqual( 49 | tokenizer.convert_tokens_to_ids(tokens), [7, 4, 5, 10, 8, 9]) 50 | 51 | def test_chinese(self): 52 | tokenizer = tokenization.BasicTokenizer() 53 | 54 | self.assertAllEqual( 55 | tokenizer.tokenize(u"ah\u535A\u63A8zz"), 56 | [u"ah", u"\u535A", u"\u63A8", u"zz"]) 57 | 58 | def test_basic_tokenizer_lower(self): 59 | tokenizer = tokenization.BasicTokenizer(do_lower_case=True) 60 | 61 | self.assertAllEqual( 62 | tokenizer.tokenize(u" \tHeLLo!how \n Are yoU? "), 63 | ["hello", "!", "how", "are", "you", "?"]) 64 | self.assertAllEqual(tokenizer.tokenize(u"H\u00E9llo"), ["hello"]) 65 | 66 | def test_basic_tokenizer_no_lower(self): 67 | tokenizer = tokenization.BasicTokenizer(do_lower_case=False) 68 | 69 | self.assertAllEqual( 70 | tokenizer.tokenize(u" \tHeLLo!how \n Are yoU? "), 71 | ["HeLLo", "!", "how", "Are", "yoU", "?"]) 72 | 73 | def test_wordpiece_tokenizer(self): 74 | vocab_tokens = [ 75 | "[UNK]", "[CLS]", "[SEP]", "want", "##want", "##ed", "wa", "un", "runn", 76 | "##ing" 77 | ] 78 | 79 | vocab = {} 80 | for (i, token) in enumerate(vocab_tokens): 81 | vocab[token] = i 82 | tokenizer = tokenization.WordpieceTokenizer(vocab=vocab) 83 | 84 | self.assertAllEqual(tokenizer.tokenize(""), []) 85 | 86 | self.assertAllEqual( 87 | tokenizer.tokenize("unwanted running"), 88 | ["un", "##want", "##ed", "runn", "##ing"]) 89 | 90 | self.assertAllEqual( 91 | tokenizer.tokenize("unwantedX running"), ["[UNK]", "runn", "##ing"]) 92 | 93 | def test_convert_tokens_to_ids(self): 94 | vocab_tokens = [ 95 | "[UNK]", "[CLS]", "[SEP]", "want", "##want", "##ed", "wa", "un", "runn", 96 | "##ing" 97 | ] 98 | 99 | vocab = {} 100 | for (i, token) in enumerate(vocab_tokens): 101 | vocab[token] = i 102 | 103 | self.assertAllEqual( 104 | tokenization.convert_tokens_to_ids( 105 | vocab, ["un", "##want", "##ed", "runn", "##ing"]), [7, 4, 5, 8, 9]) 106 | 107 | def test_is_whitespace(self): 108 | self.assertTrue(tokenization._is_whitespace(u" ")) 109 | self.assertTrue(tokenization._is_whitespace(u"\t")) 110 | self.assertTrue(tokenization._is_whitespace(u"\r")) 111 | self.assertTrue(tokenization._is_whitespace(u"\n")) 112 | self.assertTrue(tokenization._is_whitespace(u"\u00A0")) 113 | 114 | self.assertFalse(tokenization._is_whitespace(u"A")) 115 | self.assertFalse(tokenization._is_whitespace(u"-")) 116 | 117 | def test_is_control(self): 118 | self.assertTrue(tokenization._is_control(u"\u0005")) 119 | 120 | self.assertFalse(tokenization._is_control(u"A")) 121 | self.assertFalse(tokenization._is_control(u" ")) 122 | self.assertFalse(tokenization._is_control(u"\t")) 123 | self.assertFalse(tokenization._is_control(u"\r")) 124 | self.assertFalse(tokenization._is_control(u"\U0001F4A9")) 125 | 126 | def test_is_punctuation(self): 127 | self.assertTrue(tokenization._is_punctuation(u"-")) 128 | self.assertTrue(tokenization._is_punctuation(u"$")) 129 | self.assertTrue(tokenization._is_punctuation(u"`")) 130 | self.assertTrue(tokenization._is_punctuation(u".")) 131 | 132 | self.assertFalse(tokenization._is_punctuation(u"A")) 133 | self.assertFalse(tokenization._is_punctuation(u" ")) 134 | 135 | 136 | if __name__ == "__main__": 137 | tf.test.main() 138 | -------------------------------------------------------------------------------- /命名实体识别/chinese_L-12_H-768_A-12/README.md: -------------------------------------------------------------------------------- 1 | 从Bert下载中文预训练模型解压到此即可。 -------------------------------------------------------------------------------- /命名实体识别/start_ner: -------------------------------------------------------------------------------- 1 | bert-base-serving-start \ 2 | -model_dir /NER_Model/ner的模型目录 \ 3 | -bert_model_dir /chinese_L-12_H-768_A-12 \ 4 | -model_pb_dir /NER_Model/model_pb_dir \ 5 | -mode NER -------------------------------------------------------------------------------- /实体消歧/README.md: -------------------------------------------------------------------------------- 1 | pkubase-mention2ent.txt 文件下载地址 2 | 链接:https://pan.baidu.com/s/1MaZGxDg9KQrpZWE9bSAi0Q 密码:1cok 3 | 4 | 实体消歧所需要的文件下载地址 5 | 链接:https://pan.baidu.com/s/1Oa10t3t73zO13ydv1KcBFg 密码:sssa -------------------------------------------------------------------------------- /实体消歧/mention2ent_file_test.py: -------------------------------------------------------------------------------- 1 | # coding = UTF-8 2 | __Date__ = '2019/7/4' 3 | __Author__ = 'Xu Tao' 4 | 5 | import re 6 | import linecache 7 | 8 | # process_in_data()函数所需参数 9 | All_One_lie = [] 10 | All_Two_lie = [] 11 | 12 | # 在AllTwolie文本中进行全名称匹配，此文本中均为真实体，所以每个实体都是唯一的 13 | line_number_All_Two_lie = 1 # 在AllTwolie文本中匹配到真实体时的行数 14 | 15 | # 若查询不到真实体，此标志位会进行置位，接下来则会在伪实体中进行匹配 16 | Two_to_One_flag = 0 17 | 18 | # 若伪实体中查询到，则此标志位会进行置0，否则接下来我们构建手工规则，对此类现象单独进行处理 19 | Man_doing_flag = 1 20 | 21 | # 在AllOnelie文本中进行全名称匹配，此文本中均为伪实体，所以同一个content可能对应多个伪实体 22 | line_number_All_One_lie = 1 # 在AllOnelie文本中匹配到伪实体时的行数，一般有多行 23 | 24 | # 当找到多个伪实体时，我们将其伪实体的对应行数进行存储，以便后续消歧使用 25 | ready_to_receive_numbers = [] 26 | 27 | # 将候选实体存放于列表中 28 | ready_true_entities_flag = 0 29 | ready_to_entities = [] 30 | 31 | 32 | # 情景是： 33 | # 目前已经得到了一个具体的“实体” 34 | # 这个实体可能是真实体，也可能是假实体 35 | # 所以首先在txt中的第二列中去进行全名称匹配，如果匹配到，则直接归为真实体 36 | # 如果未匹配到，则在第一列中进行匹配，如果匹配到，则取对应排名第一的真实体 37 | # 否则则输出需要手工规则，等待我们去配置手工规则 38 | 39 | 40 | def wash_in_data(): # 清洗数据 41 | All_line_number_in_wash = 1 42 | with open("pkubase-mention2ent.txt", "r", encoding="utf-8") as f: 43 | lines = f.readlines() 44 | # print(lines) 45 | with open("清洗后的数据.txt", "w", encoding="utf-8") as f_w: 46 | for line in lines: 47 | Every_line = line.split() 48 | if len(Every_line) != 3: 49 | print(All_line_number_in_wash) 50 | continue 51 | f_w.write(line) 52 | All_line_number_in_wash += 1 53 | 54 | 55 | def process_in_data(): 56 | with open('清洗后的数据.txt', 'r', encoding="utf-8") as file_to_read: 57 | while True: 58 | lines = file_to_read.readline() # 整行读取数据 59 | if not lines: 60 | break 61 | Every_line = lines.split() # 以空格来隔开每行的内容，并进行存储于列表中 62 | All_One_lie.append(Every_line[0]) 63 | All_Two_lie.append(Every_line[1]) 64 | 65 | with open('AllOnelie.txt', 'w+', encoding="utf-8") as write_to_All_One_lie: 66 | for One in All_One_lie: 67 | write_to_All_One_lie.write(One) 68 | write_to_All_One_lie.write('\n') 69 | 70 | with open('AllTwolie.txt', 'w+', encoding="utf-8") as write_to_All_Two_lie: 71 | for Two in All_Two_lie: 72 | write_to_All_Two_lie.write(Two) 73 | write_to_All_Two_lie.write('\n') 74 | 75 | 76 | def mention2ent_on_work(ready_entity): 77 | del ready_to_entities[:] 78 | global line_number_All_Two_lie, Two_to_One_flag, line_number_All_One_lie, Man_doing_flag, ready_true_entities_flag 79 | # ready_entity = input('请输入待消歧实体：') 80 | content = r'^%s$' % ready_entity # 全名称匹配的内容 81 | with open('AllTwolie.txt', 'r', encoding="utf-8") as in_All_Two_lie_get_entity: 82 | while True: 83 | lines = in_All_Two_lie_get_entity.readline() # 整行读取数据 84 | if not lines: 85 | line_number_All_Two_lie = 1 # 恢复计数器 86 | Two_to_One_flag = 1 87 | print('Two_to_One_flag = 1, 该实体非真实体, 则在伪实体中查询其存在') 88 | break 89 | com_content = re.compile(content) # 正则表达式的使用 90 | is_content = re.match(com_content, lines) 91 | if is_content: 92 | print(line_number_All_Two_lie) 93 | print('该实体为真实体, 可直接放入三元组第一位中') 94 | line_number_All_Two_lie = 1 # 恢复计数器 95 | return ready_entity 96 | line_number_All_Two_lie += 1 97 | 98 | if Two_to_One_flag == 1: # 该实体为非真实体，所以现在在伪实体库中进行查询其存在 99 | Two_to_One_flag = 0 # 标志位恢复 100 | with open('AllOnelie.txt', 'r', encoding="utf-8") as in_All_One_lie_get_entity: 101 | while True: 102 | lines = in_All_One_lie_get_entity.readline() # 整行读取数据 103 | if not lines: 104 | if Man_doing_flag == 1: 105 | print('Man_doing_flag = 1, 非伪实体, 则需要手工规则进行构建') 106 | return None 107 | break 108 | com_another_content = re.compile(content) 109 | is_another_content = re.findall(com_another_content, lines) 110 | if is_another_content: 111 | Man_doing_flag = 0 # 如果找到，则标志位置0，表明用户不需要单独为此建立手工规则 112 | # 如果找到，说明存在伪实体，即伪实体有潜力成为真实体，此时标志位置位，打开后续处理伪实体程序的开关 113 | ready_true_entities_flag = 1 114 | ready_to_receive_numbers.append(line_number_All_One_lie) # 存储行数 115 | print(line_number_All_One_lie) 116 | line_number_All_One_lie += 1 117 | Man_doing_flag = 1 # 标志位恢复 118 | line_number_All_One_lie = 1 # 行数计数恢复 119 | 120 | if ready_true_entities_flag == 1: 121 | for true_entity in ready_to_receive_numbers: 122 | ready_to_entities.append(linecache.getline(r'AllTwolie.txt', true_entity)) 123 | del ready_to_receive_numbers[:] 124 | ready_true_entities_flag = 0 # 标志位恢复 125 | print('消歧为：', ready_to_entities[0]) 126 | return ready_to_entities[0] 127 | -------------------------------------------------------------------------------- /数据清洗/README.md: -------------------------------------------------------------------------------- 1 | 由于官方给的问题集包含有很多冗余信息，所以我们对这些信息进行数据清洗 2 | 3 | 具体清洗所用的停用词为stop_words.txt 4 | 5 | 我们对stop_words.txt中的内容进行了删改，从而更适合我们的QA场景。 6 | -------------------------------------------------------------------------------- /数据清洗/stop_words.txt: -------------------------------------------------------------------------------- 1 | ! 2 | " 3 | # 4 | $ 5 | % 6 | & 7 | ' 8 | ( 9 | ) 10 | * 11 | + 12 | , 13 | - 14 | -- 15 | . 16 | .. 17 | ... 18 | ...... 19 | ................... 20 | ./ 21 | .一 22 | .数 23 | .日 24 | / 25 | // 26 | 0 27 | 1 28 | 2 29 | 3 30 | 4 31 | 5 32 | 6 33 | 7 34 | 8 35 | 9 36 | : 37 | :// 38 | :: 39 | ; 40 | < 41 | = 42 | > 43 | >> 44 | ? 45 | @ 46 | A 47 | Lex 48 | [ 49 | \ 50 | ] 51 | ^ 52 | _ 53 | ` 54 | exp 55 | sub 56 | sup 57 | | 58 | } 59 | ~ 60 | ~~~~ 61 | · 62 | × 63 | ××× 64 | Δ 65 | Ψ 66 | γ 67 | μ 68 | φ 69 | φ． 70 | В 71 | — 72 | —— 73 | ——— 74 | ‘ 75 | ’ 76 | ’‘ 77 | “ 78 | ” 79 | ”， 80 | … 81 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| 不外 194 | 不外乎 195 | 不够 196 | 不大 197 | 不如 198 | 不妨 199 | 不定 200 | 不对 201 | 不少 202 | 不尽 203 | 不尽然 204 | 不巧 205 | 不已 206 | 不常 207 | 不得 208 | 不得不 209 | 不得了 210 | 不得已 211 | 不必 212 | 不怎么 213 | 不怕 214 | 不惟 215 | 不成 216 | 不拘 217 | 不择手段 218 | 不敢 219 | 不料 220 | 不断 221 | 不日 222 | 不时 223 | 不是 224 | 不曾 225 | 不止 226 | 不止一次 227 | 不比 228 | 不消 229 | 不满 230 | 不然 231 | 不然的话 232 | 不特 233 | 不独 234 | 不由得 235 | 不知不觉 236 | 不管 237 | 不管怎样 238 | 不经意 239 | 不胜 240 | 不能 241 | 不能不 242 | 不至于 243 | 不若 244 | 不要 245 | 不论 246 | 不起 247 | 不足 248 | 不过 249 | 不迭 250 | 不问 251 | 不限 252 | 与 253 | 与其 254 | 与其说 255 | 与否 256 | 与此同时 257 | 专门 258 | 且 259 | 且不说 260 | 且说 261 | 两者 262 | 严格 263 | 严重 264 | 个 265 | 个人 266 | 个别 267 | 中小 268 | 中间 269 | 丰富 270 | 串行 271 | 临 272 | 临到 273 | 为 274 | 为主 275 | 为了 276 | 为什么 277 | 为什麽 278 | 为何 279 | 为止 280 | 为此 281 | 为着 282 | 主张 283 | 主要 284 | 举凡 285 | 举行 286 | 乃 287 | 乃至 288 | 乃至于 289 | 么 290 | 之 291 | 之一 292 | 之前 293 | 之后 294 | 之後 295 | 之所以 296 | 之类 297 | 乌乎 298 | 乎 299 | 乒 300 | 乘 301 | 乘势 302 | 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1075 | 或是 1076 | 或曰 1077 | 或者 1078 | 或许 1079 | 战斗 1080 | 截然 1081 | 截至 1082 | 所 1083 | 所以 1084 | 所在 1085 | 所幸 1086 | 所有 1087 | 所谓 1088 | 才 1089 | 才能 1090 | 扑通 1091 | 打 1092 | 打从 1093 | 打开天窗说亮话 1094 | 扩大 1095 | 把 1096 | 抑或 1097 | 抽冷子 1098 | 拦腰 1099 | 拿 1100 | 按 1101 | 按时 1102 | 按期 1103 | 按照 1104 | 按理 1105 | 按说 1106 | 挨个 1107 | 挨家挨户 1108 | 挨次 1109 | 挨着 1110 | 挨门挨户 1111 | 挨门逐户 1112 | 换句话说 1113 | 换言之 1114 | 据 1115 | 据实 1116 | 据悉 1117 | 据我所知 1118 | 据此 1119 | 据称 1120 | 据说 1121 | 掌握 1122 | 接下来 1123 | 接着 1124 | 接著 1125 | 接连不断 1126 | 放量 1127 | 故 1128 | 故意 1129 | 故此 1130 | 故而 1131 | 敞开儿 1132 | 敢 1133 | 敢于 1134 | 敢情 1135 | 数/ 1136 | 整个 1137 | 断然 1138 | 方 1139 | 方便 1140 | 方才 1141 | 方能 1142 | 方面 1143 | 旁人 1144 | 无 1145 | 无宁 1146 | 无法 1147 | 无论 1148 | 既 1149 | 既...又 1150 | 既往 1151 | 既是 1152 | 既然 1153 | 日复一日 1154 | 日渐 1155 | 日益 1156 | 日臻 1157 | 日见 1158 | 时候 1159 | 昂然 1160 | 明显 1161 | 明确 1162 | 是 1163 | 是不是 1164 | 是以 1165 | 是否 1166 | 是的 1167 | 显然 1168 | 显著 1169 | 普通 1170 | 普遍 1171 | 暗中 1172 | 暗地里 1173 | 暗自 1174 | 更 1175 | 更为 1176 | 更加 1177 | 更进一步 1178 | 曾 1179 | 曾经 1180 | 替 1181 | 替代 1182 | 最 1183 | 最后 1184 | 最大 1185 | 最好 1186 | 最後 1187 | 最近 1188 | 最高 1189 | 有 1190 | 有些 1191 | 有关 1192 | 有利 1193 | 有力 1194 | 有及 1195 | 有所 1196 | 有效 1197 | 有时 1198 | 有点 1199 | 有的 1200 | 有的是 1201 | 有着 1202 | 有著 1203 | 望 1204 | 朝 1205 | 朝着 1206 | 末##末 1207 | 本 1208 | 本人 1209 | 本地 1210 | 本着 1211 | 本身 1212 | 权时 1213 | 来 1214 | 来不及 1215 | 来得及 1216 | 来看 1217 | 来着 1218 | 来自 1219 | 来讲 1220 | 来说 1221 | 极 1222 | 极为 1223 | 极了 1224 | 极其 1225 | 极力 1226 | 极大 1227 | 极度 1228 | 极端 1229 | 构成 1230 | 果然 1231 | 果真 1232 | 某 1233 | 某个 1234 | 某些 1235 | 某某 1236 | 根据 1237 | 根本 1238 | 格外 1239 | 梆 1240 | 概 1241 | 次第 1242 | 欢迎 1243 | 欤 1244 | 正值 1245 | 正在 1246 | 正如 1247 | 正巧 1248 | 正常 1249 | 正是 1250 | 此 1251 | 此中 1252 | 此后 1253 | 此地 1254 | 此处 1255 | 此外 1256 | 此时 1257 | 此次 1258 | 此间 1259 | 殆 1260 | 毋宁 1261 | 每 1262 | 每个 1263 | 每天 1264 | 每年 1265 | 每当 1266 | 每时每刻 1267 | 每每 1268 | 每逢 1269 | 比 1270 | 比及 1271 | 比如 1272 | 比如说 1273 | 比方 1274 | 比照 1275 | 比起 1276 | 比较 1277 | 毕竟 1278 | 毫不 1279 | 毫无 1280 | 毫无例外 1281 | 毫无保留地 1282 | 汝 1283 | 沙沙 1284 | 没 1285 | 没奈何 1286 | 没有 1287 | 沿 1288 | 沿着 1289 | 注意 1290 | 活 1291 | 深入 1292 | 清楚 1293 | 满 1294 | 满足 1295 | 漫说 1296 | 焉 1297 | 然 1298 | 然则 1299 | 然后 1300 | 然後 1301 | 然而 1302 | 照 1303 | 照着 1304 | 牢牢 1305 | 特别是 1306 | 特殊 1307 | 特点 1308 | 犹且 1309 | 犹自 1310 | 独 1311 | 独自 1312 | 猛然 1313 | 猛然间 1314 | 率尔 1315 | 率然 1316 | 现代 1317 | 现在 1318 | 理应 1319 | 理当 1320 | 理该 1321 | 瑟瑟 1322 | 甚且 1323 | 甚么 1324 | 甚或 1325 | 甚而 1326 | 甚至 1327 | 甚至于 1328 | 用 1329 | 用来 1330 | 甫 1331 | 甭 1332 | 由 1333 | 由于 1334 | 由是 1335 | 由此 1336 | 由此可见 1337 | 略 1338 | 略为 1339 | 略加 1340 | 略微 1341 | 白 1342 | 白白 1343 | 的 1344 | 的确 1345 | 的话 1346 | 皆可 1347 | 目前 1348 | 直到 1349 | 直接 1350 | 相似 1351 | 相信 1352 | 相反 1353 | 相同 1354 | 相对 1355 | 相对而言 1356 | 相应 1357 | 相当 1358 | 相等 1359 | 省得 1360 | 看 1361 | 看上去 1362 | 看出 1363 | 看到 1364 | 看来 1365 | 看样子 1366 | 看看 1367 | 看见 1368 | 看起来 1369 | 真是 1370 | 真正 1371 | 眨眼 1372 | 着 1373 | 着呢 1374 | 矣 1375 | 矣乎 1376 | 矣哉 1377 | 知道 1378 | 砰 1379 | 确定 1380 | 碰巧 1381 | 社会主义 1382 | 离 1383 | 种 1384 | 积极 1385 | 移动 1386 | 究竟 1387 | 穷年累月 1388 | 突出 1389 | 突然 1390 | 窃 1391 | 立 1392 | 立刻 1393 | 立即 1394 | 立地 1395 | 立时 1396 | 立马 1397 | 竟 1398 | 竟然 1399 | 竟而 1400 | 第 1401 | 第二 1402 | 等 1403 | 等到 1404 | 等等 1405 | 策略地 1406 | 简直 1407 | 简而言之 1408 | 简言之 1409 | 管 1410 | 类如 1411 | 粗 1412 | 精光 1413 | 紧接着 1414 | 累年 1415 | 累次 1416 | 纯 1417 | 纯粹 1418 | 纵 1419 | 纵令 1420 | 纵使 1421 | 纵然 1422 | 练习 1423 | 组成 1424 | 经 1425 | 经常 1426 | 经过 1427 | 结合 1428 | 结果 1429 | 给 1430 | 绝 1431 | 绝不 1432 | 绝对 1433 | 绝非 1434 | 绝顶 1435 | 继之 1436 | 继后 1437 | 继续 1438 | 继而 1439 | 维持 1440 | 综上所述 1441 | 缕缕 1442 | 罢了 1443 | 老 1444 | 老大 1445 | 老是 1446 | 老老实实 1447 | 考虑 1448 | 者 1449 | 而 1450 | 而且 1451 | 而况 1452 | 而又 1453 | 而后 1454 | 而外 1455 | 而已 1456 | 而是 1457 | 而言 1458 | 而论 1459 | 联系 1460 | 联袂 1461 | 背地里 1462 | 背靠背 1463 | 能 1464 | 能否 1465 | 能够 1466 | 腾 1467 | 自 1468 | 自个儿 1469 | 自从 1470 | 自各儿 1471 | 自后 1472 | 自家 1473 | 自己 1474 | 自打 1475 | 自身 1476 | 臭 1477 | 至 1478 | 至于 1479 | 至今 1480 | 至若 1481 | 致 1482 | 般的 1483 | 良好 1484 | 若 1485 | 若夫 1486 | 若是 1487 | 若果 1488 | 若非 1489 | 范围 1490 | 莫 1491 | 莫不 1492 | 莫不然 1493 | 莫如 1494 | 莫若 1495 | 莫非 1496 | 获得 1497 | 藉以 1498 | 虽 1499 | 虽则 1500 | 虽然 1501 | 虽说 1502 | 蛮 1503 | 行为 1504 | 行动 1505 | 表明 1506 | 表示 1507 | 被 1508 | 要 1509 | 要不 1510 | 要不是 1511 | 要不然 1512 | 要么 1513 | 要是 1514 | 要求 1515 | 见 1516 | 规定 1517 | 觉得 1518 | 譬喻 1519 | 譬如 1520 | 认为 1521 | 认真 1522 | 认识 1523 | 让 1524 | 许多 1525 | 论 1526 | 论说 1527 | 设使 1528 | 设或 1529 | 设若 1530 | 诚如 1531 | 诚然 1532 | 话说 1533 | 该 1534 | 该当 1535 | 说明 1536 | 说来 1537 | 说说 1538 | 请勿 1539 | 诸 1540 | 诸位 1541 | 诸如 1542 | 谁 1543 | 谁人 1544 | 谁料 1545 | 谁知 1546 | 谨 1547 | 豁然 1548 | 贼死 1549 | 赖以 1550 | 赶 1551 | 赶快 1552 | 赶早不赶晚 1553 | 起 1554 | 起先 1555 | 起初 1556 | 起头 1557 | 起来 1558 | 起见 1559 | 起首 1560 | 趁 1561 | 趁便 1562 | 趁势 1563 | 趁早 1564 | 趁机 1565 | 趁热 1566 | 趁着 1567 | 越是 1568 | 距 1569 | 跟 1570 | 路经 1571 | 转动 1572 | 转变 1573 | 转贴 1574 | 轰然 1575 | 较 1576 | 较为 1577 | 较之 1578 | 较比 1579 | 边 1580 | 达到 1581 | 达旦 1582 | 迄 1583 | 迅速 1584 | 过 1585 | 过于 1586 | 过去 1587 | 过来 1588 | 运用 1589 | 近 1590 | 近几年来 1591 | 近年来 1592 | 近来 1593 | 还 1594 | 还是 1595 | 还有 1596 | 还要 1597 | 这 1598 | 这一来 1599 | 这个 1600 | 这么 1601 | 这么些 1602 | 这么样 1603 | 这么点儿 1604 | 这些 1605 | 这会儿 1606 | 这儿 1607 | 这就是说 1608 | 这时 1609 | 这样 1610 | 这次 1611 | 这点 1612 | 这种 1613 | 这般 1614 | 这边 1615 | 这里 1616 | 这麽 1617 | 进入 1618 | 进去 1619 | 进来 1620 | 进步 1621 | 进而 1622 | 进行 1623 | 连 1624 | 连同 1625 | 连声 1626 | 连日 1627 | 连日来 1628 | 连袂 1629 | 连连 1630 | 迟早 1631 | 迫于 1632 | 适应 1633 | 适当 1634 | 适用 1635 | 逐步 1636 | 逐渐 1637 | 通常 1638 | 通过 1639 | 造成 1640 | 逢 1641 | 遇到 1642 | 遭到 1643 | 遵循 1644 | 遵照 1645 | 避免 1646 | 那 1647 | 那个 1648 | 那么 1649 | 那么些 1650 | 那么样 1651 | 那些 1652 | 那会儿 1653 | 那儿 1654 | 那时 1655 | 那末 1656 | 那样 1657 | 那般 1658 | 那边 1659 | 那里 1660 | 那麽 1661 | 部分 1662 | 都 1663 | 鄙人 1664 | 采取 1665 | 里面 1666 | 重大 1667 | 重新 1668 | 重要 1669 | 鉴于 1670 | 针对 1671 | 长期以来 1672 | 长此下去 1673 | 长线 1674 | 长话短说 1675 | 问题 1676 | 间或 1677 | 防止 1678 | 阿 1679 | 附近 1680 | 陈年 1681 | 限制 1682 | 陡然 1683 | 除 1684 | 除了 1685 | 除却 1686 | 除去 1687 | 除外 1688 | 除开 1689 | 除此 1690 | 除此之外 1691 | 除此以外 1692 | 除此而外 1693 | 除非 1694 | 随 1695 | 随后 1696 | 随时 1697 | 随着 1698 | 随著 1699 | 隔夜 1700 | 隔日 1701 | 难得 1702 | 难怪 1703 | 难说 1704 | 难道 1705 | 难道说 1706 | 集中 1707 | 零 1708 | 需要 1709 | 非但 1710 | 非常 1711 | 非徒 1712 | 非得 1713 | 非特 1714 | 非独 1715 | 靠 1716 | 顶多 1717 | 顷 1718 | 顷刻 1719 | 顷刻之间 1720 | 顷刻间 1721 | 顺 1722 | 顺着 1723 | 顿时 1724 | 颇 1725 | 风雨无阻 1726 | 饱 1727 | 首先 1728 | 马上 1729 | 高低 1730 | 高兴 1731 | 默然 1732 | 默默地 1733 | 齐 1734 | ︿ 1735 | ！ 1736 | ＃ 1737 | ＄ 1738 | ％ 1739 | ＆ 1740 | ＇ 1741 | （ 1742 | ） 1743 | ）÷（１－ 1744 | ）、 1745 | ＊ 1746 | ＋ 1747 | ＋ξ 1748 | ＋＋ 1749 | ， 1750 | ，也 1751 | － 1752 | －β 1753 | －－ 1754 | －［＊］－ 1755 | ． 1756 | ／ 1757 | ０ 1758 | ０：２ 1759 | １ 1760 | １． 1761 | １２％ 1762 | ２ 1763 | ２．３％ 1764 | ３ 1765 | ４ 1766 | ５ 1767 | ５：０ 1768 | ６ 1769 | ７ 1770 | ８ 1771 | ９ 1772 | ： 1773 | ； 1774 | ＜ 1775 | ＜± 1776 | ＜Δ 1777 | ＜λ 1778 | ＜φ 1779 | ＜＜ 1780 | ＝ 1781 | ＝″ 1782 | ＝☆ 1783 | ＝（ 1784 | ＝－ 1785 | ＝［ 1786 | ＝｛ 1787 | ＞ 1788 | ＞λ 1789 | ？ 1790 | ＠ 1791 | Ａ 1792 | ＬＩ 1793 | Ｒ．Ｌ． 1794 | ＺＸＦＩＴＬ 1795 | ［ 1796 | ［①①］ 1797 | ［①②］ 1798 | ［①③］ 1799 | ［①④］ 1800 | ［①⑤］ 1801 | ［①⑥］ 1802 | ［①⑦］ 1803 | ［①⑧］ 1804 | ［①⑨］ 1805 | ［①Ａ］ 1806 | ［①Ｂ］ 1807 | ［①Ｃ］ 1808 | ［①Ｄ］ 1809 | ［①Ｅ］ 1810 | ［①］ 1811 | ［①ａ］ 1812 | ［①ｃ］ 1813 | ［①ｄ］ 1814 | ［①ｅ］ 1815 | ［①ｆ］ 1816 | ［①ｇ］ 1817 | ［①ｈ］ 1818 | ［①ｉ］ 1819 | ［①ｏ］ 1820 | ［② 1821 | ［②①］ 1822 | ［②②］ 1823 | ［②③］ 1824 | ［②④ 1825 | ［②⑤］ 1826 | ［②⑥］ 1827 | ［②⑦］ 1828 | ［②⑧］ 1829 | ［②⑩］ 1830 | ［②Ｂ］ 1831 | ［②Ｇ］ 1832 | ［②］ 1833 | ［②ａ］ 1834 | ［②ｂ］ 1835 | ［②ｃ］ 1836 | ［②ｄ］ 1837 | ［②ｅ］ 1838 | ［②ｆ］ 1839 | ［②ｇ］ 1840 | ［②ｈ］ 1841 | ［②ｉ］ 1842 | ［②ｊ］ 1843 | ［③①］ 1844 | ［③⑩］ 1845 | ［③Ｆ］ 1846 | ［③］ 1847 | ［③ａ］ 1848 | ［③ｂ］ 1849 | ［③ｃ］ 1850 | ［③ｄ］ 1851 | ［③ｅ］ 1852 | ［③ｇ］ 1853 | ［③ｈ］ 1854 | ［④］ 1855 | ［④ａ］ 1856 | ［④ｂ］ 1857 | ［④ｃ］ 1858 | ［④ｄ］ 1859 | ［④ｅ］ 1860 | ［⑤］ 1861 | ［⑤］］ 1862 | ［⑤ａ］ 1863 | ［⑤ｂ］ 1864 | ［⑤ｄ］ 1865 | ［⑤ｅ］ 1866 | ［⑤ｆ］ 1867 | ［⑥］ 1868 | ［⑦］ 1869 | ［⑧］ 1870 | ［⑨］ 1871 | ［⑩］ 1872 | ［＊］ 1873 | ［－ 1874 | ［］ 1875 | ］ 1876 | ］∧′＝［ 1877 | ］［ 1878 | ＿ 1879 | ａ］ 1880 | ｂ］ 1881 | ｃ］ 1882 | ｅ］ 1883 | ｆ］ 1884 | ｎｇ昉 1885 | ｛ 1886 | ｛－ 1887 | ｜ 1888 | ｝ 1889 | ｝＞ 1890 | ～ 1891 | ～± 1892 | ～＋ 1893 | ￥ -------------------------------------------------------------------------------- /数据清洗/数据清洗.py: -------------------------------------------------------------------------------- 1 | # coding = UTF-8 2 | __Date__ = '2019/6/30' 3 | __Author__ = 'Xu Tao' 4 | # TODO 注意对空格的清洗！！！ 5 | import jieba 6 | import re # 使用正则表达式 7 | 8 | filename = "./questions1.txt" 9 | stopwords_file = "./stop_words.txt" 10 | pattern = r"^q[1-766]:$" 11 | 12 | stop_f = open(stopwords_file,"r",encoding='utf-8') 13 | stop_words = list() 14 | for line in stop_f.readlines(): 15 | line = line.strip() 16 | if not len(line): 17 | continue 18 | stop_words.append(line) 19 | stop_f.close 20 | 21 | print(len(stop_words)) 22 | 23 | # f = open(filename,"r",encoding='utf-8') 24 | f = open('question1.txt',"r",encoding='utf-8') 25 | 26 | result = list() 27 | for line in f.readlines(): 28 | line = line.strip() 29 | if not len(line): 30 | continue 31 | outstr = '' 32 | seg_list = jieba.cut(line,cut_all=False) 33 | for word in seg_list: 34 | if word not in stop_words: 35 | if word != '\t': 36 | if re.match(pattern, word) is None: 37 | outstr += word 38 | outstr += "" 39 | result.append(outstr.strip()) 40 | f.close 41 | 42 | with open("./question2.txt","w",encoding='utf-8') as fw: 43 | for sentence in result: 44 | sentence.encode('utf-8') 45 | data=sentence.strip() 46 | if len(data)!=0: 47 | fw.write(data) 48 | fw.write("\n") 49 | print("end") 50 | -------------------------------------------------------------------------------- /由已知实体抽取子图等操作/README.md: -------------------------------------------------------------------------------- 1 | 通过NER和实体消歧获得的准确实体 2 | 3 | 抽取该实体下的所有子图 4 | 5 | 并从子图中抽取所有待选属性 6 | 7 | 并比较待选属性列表与关键词列表的余弦相似度 8 | 9 | 从而从待选属性中找到最终的准确属性。 10 | 11 | 12 | z_inget.py用于提取实体所对应的全部属性并存放到get_type.txt中 13 | 14 | entity_keywords_chou.py用于根据词性筛选出关键词列表 15 | 16 | start_bert_vector脚本用于开启Bert服务对候选属性列表和关键词列表向量化 17 | 18 | cos_sim_and_delete_get_type.py用于计算余弦相似度以及清空get_type.txt文件 19 | -------------------------------------------------------------------------------- /由已知实体抽取子图等操作/cos_sim_and_delete_get_type.py: -------------------------------------------------------------------------------- 1 | # coding = UTF-8 2 | __Date__ = '2019/7/19' 3 | __Author__ = 'Xu Tao' 4 | 5 | import numpy as np 6 | 7 | 8 | def cos_sim(vector_a, vector_b): 9 | vector_a = np.mat(vector_a) 10 | vector_b = np.mat(vector_b) 11 | num = float(vector_a * vector_b.T) 12 | denom = np.linalg.norm(vector_a) * np.linalg.norm(vector_b) 13 | cos = num / denom 14 | sim = 0.5 + 0.5 * cos 15 | return sim 16 | 17 | 18 | def delete_get_type(): 19 | f = open('get_type.txt', "a+") 20 | f.seek(0) 21 | f.truncate() # 清空文件 22 | -------------------------------------------------------------------------------- /由已知实体抽取子图等操作/entity_keywords_chou.py: -------------------------------------------------------------------------------- 1 | # coding=utf-8 2 | # Version:python3.6.0 3 | # Tools:Pycharm 2019 4 | __date__ = '2019/5/24 下午10:33' 5 | __author__ = 'TT' 6 | 7 | 8 | def keyword_chou(form_pos_list, form_entity_flag, form_wei_index, form_seg_list): 9 | """ 10 | 实体确认过后，再执行此函数才有效； 11 | 执行效果为 把实体index除去，其余关键词均放入列表中，并进行return输出，供向量化替换使用 12 | :param form_seg_list:分词列表 13 | :param form_pos_list:词性列表 14 | :param form_entity_flag:实体是否锁定的标志位 15 | :param form_wei_index:上面函数return出来的临时保存索引号，此函数中对此索引号自动过滤掉 16 | :return:返回所有除去实体以外的关键词 17 | """ 18 | pos_list_count = 0 # 下方遍历计数使用 19 | Keywords_List = [] # 创建关键词列表 20 | for if_named in form_pos_list: 21 | if form_entity_flag == 1: # 锁定标志位必须被锁定，即 = 1，此函数才有效 22 | if pos_list_count != form_wei_index: # 过滤 23 | if if_named == 'n' or if_named == 'nh' or if_named == 'ni' or if_named == 'nl' \ 24 | or if_named == 'ns' or if_named == 'nt' or if_named == 'nz' or if_named == 'ws' or if_named == 'v': 25 | Keywords_List.append(form_seg_list[pos_list_count]) 26 | 27 | pos_list_count += 1 28 | return Keywords_List 29 | -------------------------------------------------------------------------------- /由已知实体抽取子图等操作/get_type.txt: -------------------------------------------------------------------------------- 1 | # 如下词均为待选属性 2 | 中文名 3 | 外文名 4 | 创办时间 5 | 所属地区 6 | 主要奖项 7 | 主要奖项 8 | 主要奖项 9 | 主要奖项 10 | 主要奖项 11 | 主要奖项 12 | 主要奖项 13 | 主要奖项 14 | 主要奖项 15 | 校园面积 16 | 校园面积 17 | 创办者 18 | 主要院系 19 | 官网 20 | 本科生 21 | 本科生 22 | 所属联盟 23 | 研究生 24 | 研究生 25 | -------------------------------------------------------------------------------- /由已知实体抽取子图等操作/start_bert_vector: -------------------------------------------------------------------------------- 1 | bert-serving-start -model_dir /home/wu/CCKS_END/chinese_L-12_H-768_A-12 -num_worker=2 -------------------------------------------------------------------------------- /由已知实体抽取子图等操作/z_inget.py: -------------------------------------------------------------------------------- 1 | import json 2 | 3 | 4 | # 此程序用于提取实体所对应的全部属性并存放到get_type.txt中 5 | def getlist(): 6 | path = r"./res.txt" # json文件所在路径 7 | 8 | with open(path, "rb") as f: 9 | all_part = json.load(f) # 读取所有文件内容 10 | results = all_part['results'] # 获取results标签下的内容 11 | results_bindings = results['bindings'] # 获取results标签下的bingdings内容 12 | # 定义一个list，将数据全部放到list中 13 | ls = [] 14 | for res in results_bindings: 15 | res1 = res['x'] 16 | res1 = res1['value'] 17 | get_type = open("get_type.txt", "a+") 18 | get_type.write(res1) 19 | get_type.write('\n') 20 | if res1 not in ls: 21 | ls.append(res1) 22 | return ls 23 | --------------------------------------------------------------------------------