#! -*- coding:utf-8 -*-import codecsimport osimport numpy as npimport pandas as pdfrom keras_bert import load_trained_model_from_checkpoint, Tokenizerimport picklefrom tensorflow import kerastf_board_callback = keras.callbacks.TensorBoard(log_dir='./tf_dir', update_freq=1000)max_len = 100config_path = '/input1/BERT/chinese_L-12_H-768_A-12/bert_config.json'checkpoint_path = '/input1/BERT/chinese_L-12_H-768_A-12/bert_model.ckpt'dict_path = '/input1/BERT/chinese_L-12_H-768_A-12/vocab.txt'token_dict = {}def read_message():if not os.path.exists("sets/x_items_without000.pkl"):x_items = []train_y = []user_message = pd.read_csv("/input0/table1_user",sep="\t")jd_message = pd.read_csv("/input0/table2_jd",sep="\t")match_message = pd.read_csv("/input0/table3_action",sep="\t")user_message_index = {}for i in user_message.values.tolist():user_message_index[i[0]] = ijd_message_index = {}for i in jd_message.values.tolist():jd_message_index[i[0]] = ifor i in match_message.values.tolist():x_item = []if i[0] in user_message_index.keys():x_item = list(str(user_message_index[i[0]]))if i[1] in jd_message_index.keys():x_item.extend(list(str(jd_message_index[i[1]])))y_label = str(i[2]) + str(i[3]) + str(i[4])if y_label != "000":x_items.append(x_item)train_y.append(y_label)with open('sets/x_items_without000.pkl', 'wb') as f:pickle.dump(x_items, f)with open('sets/train_y_without000.pkl', 'wb') as f:pickle.dump(train_y, f)else:with open('sets/x_items_without000.pkl', 'rb') as f:x_items = pickle.load(f)with open('sets/train_y_without000.pkl', 'rb') as f:train_y = pickle.load(f)return x_items, train_ywith codecs.open(dict_path, 'r', 'utf8') as reader:for line in reader:token = line.strip()token_dict[token] = len(token_dict)class OurTokenizer(Tokenizer):def _tokenize(self, text):R = []for c in text:if c in self._token_dict:R.append(c)elif self._is_space(c):R.append('[unused1]') # space类用未经训练的[unused1]表示else:R.append('[UNK]') # 剩余的字符是[UNK]return Rtokenizer = OurTokenizer(token_dict)# neg = pd.read_excel('neg.xls', header=None)# pos = pd.read_excel('pos.xls', header=None)neg,pos =read_message()data = []for d in neg[0]:data.append((d, 0))for d in pos[0]:data.append((d, 1))# 按照9:1的比例划分训练集和验证集random_order = list(range(len(data)))np.random.shuffle(random_order)train_data = [data[j] for i, j in enumerate(random_order) if i % 10 != 0]valid_data = [data[j] for i, j in enumerate(random_order) if i % 10 == 0]def seq_padding(X, padding=0):L = [len(x) for x in X]ML = max(L)return np.array([np.concatenate([x, [padding] * (ML - len(x))]) if len(x) < ML else x for x in X])class data_generator:def __init__(self, data, batch_size=32):self.data = dataself.batch_size = batch_sizeself.steps = len(self.data) // self.batch_sizeif len(self.data) % self.batch_size != 0:self.steps += 1def __len__(self):return self.stepsdef __iter__(self):while True:idxs = list(range(len(self.data)))np.random.shuffle(idxs)X1, X2, Y = [], [], []for i in idxs:d = self.data[i]text = d[0][:max_len]x1, x2 = tokenizer.encode(first=text)y = d[1]X1.append(x1)X2.append(x2)Y.append([y])if len(X1) == self.batch_size or i == idxs[-1]:X1 = seq_padding(X1)X2 = seq_padding(X2)Y = seq_padding(Y)yield [X1, X2], Y[X1, X2, Y] = [], [], []from keras.layers import *from keras.models import Modelfrom keras.optimizers import Adambert_model = load_trained_model_from_checkpoint(config_path, checkpoint_path, seq_len=None)for l in bert_model.layers:l.trainable = Truex1_in = Input(shape=(None,))x2_in = Input(shape=(None,))x = bert_model([x1_in, x2_in])x = Lambda(lambda x: x[:, 0])(x)p = Dense(1, activation='sigmoid')(x)model = Model([x1_in, x2_in], p)model.compile(loss='binary_crossentropy',optimizer=Adam(1e-5), # 用足够小的学习率metrics=['accuracy'])model.summary()train_D = data_generator(train_data)valid_D = data_generator(valid_data)model.fit_generator(train_D.__iter__(),steps_per_epoch=len(train_D),epochs=5,validation_data=valid_D.__iter__(),validation_steps=len(valid_D),callbacks=[tf_board_callback])
基于keras-bert和bert中文预训练语言模型的分类任务，如果你有分类任务可以替换83行的方法成自己的方法，代码已经测试可以跑通。
import osimport pickleimport kashgariimport pandas as pd# 读取文件数据 返回 训练数据 以及标签from kashgari.embeddings import BERTEmbeddingfrom kashgari.tasks.classification import CNNModelfrom tensorflow import kerastf_board_callback = keras.callbacks.TensorBoard(log_dir='./tf_dir', update_freq=1000)def read_message():if not os.path.exists("sets/x_items_without000.pkl"):x_items = []train_y = []user_message = pd.read_csv("../data/zhaopin_round1_train_20190716/zhaopin_round1_train_20190716/table1_user",sep="\t")jd_message = pd.read_csv("../data/zhaopin_round1_train_20190716/zhaopin_round1_train_20190716/table2_jd",sep="\t")match_message = pd.read_csv("../data/zhaopin_round1_train_20190716/zhaopin_round1_train_20190716/table3_action",sep="\t")user_message_index = {}for i in user_message.values.tolist():user_message_index[i[0]] = ijd_message_index = {}for i in jd_message.values.tolist():jd_message_index[i[0]] = ifor i in match_message.values.tolist():x_item = []if i[0] in user_message_index.keys():x_item = list(str(user_message_index[i[0]]))if i[1] in jd_message_index.keys():x_item.extend(list(str(jd_message_index[i[1]])))y_label = str(i[2]) + str(i[3]) + str(i[4])if y_label != "000":x_items.append(x_item)train_y.append(y_label)with open('sets/x_items_without000.pkl', 'wb') as f:pickle.dump(x_items, f)with open('sets/train_y_without000.pkl', 'wb') as f:pickle.dump(train_y, f)else:with open('sets/x_items_without000.pkl', 'rb') as f:x_items = pickle.load(f)with open('sets/train_y_without000.pkl', 'rb') as f:train_y = pickle.load(f)return x_items, train_y# 训练模型def train():x_xiyao, xiyao_y = read_message()embed = BERTEmbedding("chinese_L-12_H-768_A-12",task=kashgari.CLASSIFICATION,sequence_length=64)# 获取bert字向量model = CNNModel(embed)# 输入模型训练数据 标签 步数model.fit(x_xiyao,xiyao_y,epochs=8,batch_size=16,callbacks=[tf_board_callback])# 保存模型model.save("/output/CNN_classfition_4-model")if __name__ == '__main__':train()
如果你觉得keras-bert比较麻烦你可以试着学习一下kashgari。今天时间比较紧急所以我就直接写代码了，所有的数据来源都是阿里天池大赛，人岗匹配比赛，不传播数据集只传播比赛。
详细的kashgari文档在下面。
https://kashgari-zh.bmio.net/tutorial/text-classification/#_3
我是北京妙医佳健康科技集团妙云事业部闫广庆，专注医疗nlp。