BERT+TextCNN实现医疗意图识别项目_bert意图识别

BERT+TextCNN实现医疗意图识别项目

一、说明

本项目采用医疗意图识别数据集CMID传送门
数据集示例：

{
 "originalText": "间质性肺炎的症状?", 
 "entities": [{"label_type": "疾病和诊断", "start_pos": 0, "end_pos": 5}], 
 "seg_result": ["间质性肺炎", "的", "症状", "?"], 
 "label_4class": ["病症"], 
 "label_36class": ["临床表现"]
}
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模型使用BERT、TextCNN实现意图分类

二、BERT模型加载

使用苏建林开发的bert4keras深度学习框架加载BERT模型

from bert4keras.backend import keras,set_gelu
from bert4keras.models import build_transformer_model # 加载BERT的方法
from bert4keras.optimizers import Adam # 优化器
set_gelu('tanh')
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1.定义函数加载BERT

def build_bert_model(config_path , checkpoint_path , class_nums) : # config_path配置文件的路径 checkpoint_path预训练路径 class_nums类别的数量
    bert = build_transformer_model(
        config_path = config_path ,
        checkpoint_path = checkpoint_path ,
        model = 'bert' ,
        return_keras_model= False)
    # 在BERT模型输出中抽取[CLS]
    cls_features = keras.layers.Lambda(lambda x:x[:,0],name='cls-token')(bert.model.output) # [:,0]选取输出的第一列，BERT模型的输出中[CLS]在第一个位置 shape = [batch_size ,768]
    all_token_embedding = keras.layers.Lambda(lambda x:x[:,1:-1],name='all-token')(bert.model.output) # 获取第2列至倒数第二列的所有token  shape = [batch_size ,maxlen-2,768] 除去CLS、SEP

    # textcnn抽取特征
    cnn_features = textcnn(all_token_embedding, bert.initializer) # 输入all_token_embedding  shape = [batch_size,cnn_output_dim]
    # 将cls_features 与 cnn_features 进行拼接
    concat_features = keras.layers.concatenate([cls_features,cnn_features] ,axis= -1)

    # 全连接层
    dense = keras.layers.Dense (
        units= 512, # 输出维度
        activation = 'relu' , # 激活函数
        kernel_initializer= bert.initializer # bert权重初始化
    )(concat_features) # 输入

    # 输出
    output = keras.layers.Dense (
        units= class_nums, # 输出类别数量
        activation= 'softmax', # 激活函数 (多分类输出层最常用的激活函数)
        kernel_initializer= bert.initializer # bert权重初始化
    )(dense) # 输入

    model = keras.models.Model(bert.model.input,output) # (bert.model.input输入，output输出)
    print(model.summary())

    return model
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2.实现TextCNN

def textcnn(input,kernel_initializer) :
    # 3,4,5
    cnn1 = keras.layers.Conv1D(
        256, # 卷积核数量
        3, # 卷积核大小
        strides= 1, # 步长
        padding= 'same', # 输出与输入维度一致
        activation='relu',  # 激活函数
        kernel_initializer = kernel_initializer # 初始化器
    )(input) # shape = [batch_size ,maxlen-2,256]
    cnn1 = keras.layers.GlobalAvgPool1D()(cnn1) # 全局最大池化操作 shape = [batch_size ,256]

    cnn2 = keras.layers.Conv1D(
        256,  # 卷积核数量
        4,  # 卷积核大小
        strides=1,  # 步长
        padding='same',  # 输出与输入维度一致
        activation='relu',  # 激活函数
        kernel_initializer=kernel_initializer  # 初始化器
    )(input)
    cnn2 = keras.layers.GlobalAvgPool1D()(cnn2)  # 全局最大池化操作 shape = [batch_size ,256]

    cnn3 = keras.layers.Conv1D(
        256,  # 卷积核数量
        5,  # 卷积核大小
        strides=1,  # 步长
        padding='same',  # 输出与输入维度一致
        kernel_initializer=kernel_initializer  # 初始化器
    )(input)
    cnn3 = keras.layers.GlobalAvgPool1D()(cnn3) # 全局最大池化操作 shape = [batch_size ,256]

    # 将三个卷积结果进行拼接
    output = keras.layers.concatenate([cnn1,cnn2,cnn3],
                                   axis= -1)
    output = keras.layers.Dropout(0.2)(output) # 最后接Dropout

    return output
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3.程序入口

if __name__ == '__main__':
    config_path = '.\chinese_L-12_H-768_A-12\\bert_config.json'
    checkpoint_path = '.\chinese_L-12_H-768_A-12\\bert_model.ckpt'
    class_nums = 13
    build_bert_model(config_path , checkpoint_path , class_nums)
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其中BERT模型文件可以自行在Github中下载，也可私信。

当程序开始加载模型时，表示运行成功。
切记！运行代码前，检查TensorFlow、bert4keras等第三方库的版本是否一致，否则容易报错！
4.本项目第三方库以及对应的版本

pyahocorasick==1.4.2
requests==2.25.1
gevent==1.4.0
jieba==0.42.1
six==1.15.0
gensim==3.8.3
matplotlib==3.1.3
Flask==1.1.1
numpy==1.16.0
bert4keras==0.9.1
tensorflow==1.14.0
Keras==2.3.1
py2neo==2020.1.1
tqdm==4.42.1
pandas==1.0.1
termcolor==1.1.0
itchat==1.3.10
ahocorasick==0.9
flask_compress==1.9.0
flask_cors==3.0.10
flask_json==0.3.4
GPUtil==1.4.0
pyzmq==22.0.3
scikit_learn==0.24.1
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三、数据预处理

抽取CMID.json中的数据，并划分为训练集与测试集
从中选取13个类别作为最终意图分类的标签

定义
病因
预防
临床表现(病症表现)
相关病症
治疗方法
所属科室
传染性
治愈率
禁忌
化验/体检方案
治疗时间
其他

1.抽取数据

def gen_training_data(row_data_path) :
    label_list = [line.strip() for line in open('./dataset/label', 'r' ,encoding='utf8')]
    print(label_list)

    # 映射id，为每一条数据添加id
    label2id = {label : idx for idx, label in enumerate(label_list)}
    data = []
    with open('./dataset/CMID.json','r',encoding='utf8') as f :
        origin_data = f.read()
        origin_data = eval(origin_data)

    label_set = set()
    for item in origin_data :
        text = item['originalText']

        label_class = item['label_4class'][0].strip("'")
        if label_class == '其他' :
            data.append([text , label_class ,label2id[label_class]])
            continue
        label_class = item["label_36class"][0].strip("'") # 所有的意图标签都从label_36class中取出
        label_set.add(label_class)
        if label_class not in label_list:
            continue
        data.append([text, label_class ,label2id[label_class]])
    print(label_set)

    data = pd.DataFrame(data , columns=['text','label_class','label'])
    print(data['label_class'].value_counts())
    data['text_len'] = data['text'].map(lambda x : len(x)) # 序列长度
    print(data['text_len'].describe())
    plt.hist(data['text_len'], bins=30, rwidth= 0.9, density=True)
    plt.show()

    del data['text_len']

    data = data.sample(frac = 1.0)
    # 将数据集拆分为测试集和训练集
    train_num = int(0.9*len(data))
    train , test = data[:train_num],data[train_num:]
    train.to_csv('./dataset/train.csv', index=False)
    test.to_csv('./dataset/test.csv', index = False)
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2.加载训练数据集

# 加载训练数据集
def load_data(filename) :
    df = pd.read_csv(filename , header= 0 )
    return df[['text','label']].values
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3.数据集信息可视化

数据样本长度基本上在100以内，此时在BERT模型中可以设置样本最大长度为128.
4.划分的训练集与测试集示例
训练集

测试集

四、模型训练

1.定义配置文件以及超参数

# 定义超参数和配置文件
class_nums = 13
maxlen = 128
batch_size = 32

config_path = './chinese_rbt3_L-3_H-768_A-12/bert_config_rbt3.json'
checkpoint_path = './chinese_rbt3_L-3_H-768_A-12/bert_model.ckpt'
dict_path = './chinese_rbt3_L-3_H-768_A-12/vocab.txt'
tokenizer = Tokenizer(dict_path)
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2.定义数据生成器，将样本传递到模型中

# 定义数据生成器 将数据传递到模型中
class data_generator(DataGenerator) :
    """
    数据生成器
    """
    def __iter__(self , random = False):
        batch_token_ids , batch_segment_ids , batch_labels = [] , [] , [] # 对于每一个batchsize的训练，包括 token  分隔符segment 标签label三者的序列
        for is_end, (text , label ) in self.sample(random):
            token_ids , segments_ids = tokenizer.encode(text , maxlen=maxlen) # [1,3,2,5,9,12,243,0,0,0]  编码token和分隔符segment序列，按照最大长度进行padding
            batch_token_ids.append(token_ids)
            batch_segment_ids.append(segments_ids)
            batch_labels.append([label])

            if len(batch_token_ids) == self.batch_size or is_end :
                batch_token_ids = sequence_padding(batch_token_ids)
                batch_segment_ids =sequence_padding(batch_segment_ids)
                batch_labels = sequence_padding(batch_labels)
                yield  [batch_token_ids , batch_segment_ids] ,batch_labels
                batch_token_ids,batch_segment_ids,batch_labels = [],[],[]
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3.程序入口

if __name__ == '__main__':
    # 加载数据集
    train_data = load_data('./dataset/train.csv')
    test_data = load_data('./dataset/test.csv')
    # 转换数据集
    train_generator = data_generator(train_data,batch_size)
    test_generator = data_generator(test_data,batch_size)

    model = build_bert_model(config_path, checkpoint_path ,class_nums)
    print(model.summary())

    model.compile(
        loss='sparse_categorical_crossentropy', # 离散值损失函数 交叉熵损失
        optimizer=Adam(5e-6),
        metrics=['accuracy']
    )
    earlystop = keras.callbacks.EarlyStopping(
        monitor='var_loss',
        patience= 3,
        verbose=2,
        mode='min'
    )

    bast_model_filepath = './chinese_L-12_H-768_A-12/best_model.weights'
    checkpoint = keras.callbacks.ModelCheckpoint(
        bast_model_filepath ,
        monitor = 'val_loss',
        verbose= 1,
        save_best_only=True,
        mode='min'
    )
    model.fit_generator(
        train_generator.forfit(),
        steps_per_epoch=len(train_generator),
        epochs=10,
        validation_data=test_generator.forfit(),
        validation_steps=len(test_generator),
        shuffle=True,
        callbacks=[earlystop,checkpoint]
    )

    model.load_weights(bast_model_filepath)
    test_pred = []
    test_true = []
    for x, y in test_generator:
        p = model.predict(x).argmax(axis=1)
        test_pred.extend(p)

    test_true = test_data[:1].tolist()
    print(set(test_true))
    print(set(test_pred))

    target_names = [line.strip() for line in open('label','r',encoding='utf8')]
    print(classification_report(test_true , test_pred ,target_names=target_names))
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五、运行