【深度学习、工程实践】预训练模型进行情感分析(以bert-base-chinese为例)

目录

1.预训练模型下载

2.下载预训练模型

 3.导入需要的库

4.定义数据路径

5.查看数据

 6.定义神经网络

7.使用BertTokenizer 编码成Bert需要的输入格式

8.将数据加载为Tensor格式

9.实例化DataLoader

10.定义验证函数

11.定义训练函数 

12.实例化模型并进行训练与验证

13.定义预测函数

14.使用训练好的模型进行预测

15.获得预测值与预测的概率

16.调用函数计算准确率等指标

1.预训练模型下载

        预训练模型基于transformers库使用，bert-base-chinese预训练模型是通过Models - Hugging Face 下载，将模型下载至服务器。

2.下载预训练模型

 3.导入需要的库

import numpy as np
import pandas as pd
import csv
import torch.nn as nn
from torch.optim.lr_scheduler import ReduceLROnPlateau
from torch.utils.data import TensorDataset, DataLoader
from transformers import BertTokenizer,BertConfig,AdamW
from sklearn.metrics import accuracy_score
from sklearn.metrics import classification_report
from sklearn.model_selection import train_test_split
from tqdm import tqdm

4.定义数据路径

#读取数据
Data_path = "./data.csv"
Totle_data = pd.read_csv(Data_path)
 
train_dataset,temp_data =  train_test_split(Totle_data,test_size=0.2)
validate_dataset, test_dataset = train_test_split(temp_data,test_size=0.5)
print(len(train_dataset))
print(len(validate_dataset))
print(len(test_dataset))
 
#设置保存路径
train_data_path="./Train.csv"
dev_data_path = "./Dev.csv" 
test_data_path="./Test.csv"
 
#index参数设置为False表示不保存行索引,header设置为False表示不保存列索引
train_dataset.to_csv(train_data_path,index=False,header=True)
validate_dataset.to_csv(dev_data_path ,index=False,header=True)
test_dataset.to_csv(test_data_path,index=False,header=True)

5.查看数据

data = pd.read_csv(train_data_path)

 6.定义神经网络

class BertClassificationModel(nn.Module):
    def __init__(self):
        super(BertClassificationModel, self).__init__()   
        #加载预训练模型
        pretrained_weights="/root/Bert/bert-base-chinese/"
        self.bert = transformers.BertModel.from_pretrained(pretrained_weights)
        for param in self.bert.parameters():
            param.requires_grad = True
        #定义线性函数      
        self.dense = nn.Linear(768, 2)  #bert默认的隐藏单元数是768， 输出单元是2，表示二分类
        
    def forward(self, input_ids,token_type_ids,attention_mask):
        #得到bert_output
        bert_output = self.bert(input_ids=input_ids,token_type_ids=token_type_ids, attention_mask=attention_mask)
        #获得预训练模型的输出
        bert_cls_hidden_state = bert_output[1]
        #将768维的向量输入到线性层映射为二维向量
        linear_output = self.dense(bert_cls_hidden_state)
        return  linear_output

7.使用BertTokenizer 编码成Bert需要的输入格式

        数据送入预训练模型之间需要进行预处理，使用BertTokenizer将数据编码为Bert需要的输入格式。预训练模型有三种输入分别是input_ids、token_type_ids 、attention_mask。

def encoder(max_len,vocab_path,text_list):
    #将text_list embedding成bert模型可用的输入形式
    #加载分词模型
    tokenizer = BertTokenizer.from_pretrained(vocab_path)
    tokenizer = tokenizer(
        text_list,
        padding = True,
        truncation = True,
        max_length = max_len,
        return_tensors='pt'  # 返回的类型为pytorch tensor
        )
    input_ids = tokenizer['input_ids']
    token_type_ids = tokenizer['token_type_ids']
    attention_mask = tokenizer['attention_mask']
    return input_ids,token_type_ids,attention_mask

8.将数据加载为Tensor格式

def load_data(path):
    csvFileObj = open(path)
    readerObj = csv.reader(csvFileObj)
    text_list = []
    labels = []
    for row in readerObj:
        #跳过表头
        if readerObj.line_num == 1:
            continue
        #label在什么位置就改成对应的index
        label = int(row[1])
        text = row[0]
        text_list.append(text)
        labels.append(label)
    #调用encoder函数，获得预训练模型的三种输入形式
    input_ids,token_type_ids,attention_mask = encoder(max_len=150,vocab_path="/root/Bert/bert-base-chinese/vocab.txt",text_list=text_list)
    labels = torch.tensor(labels)
    #将encoder的返回值以及label封装为Tensor的形式
    data = TensorDataset(input_ids,token_type_ids,attention_mask,labels)
    return data

9.实例化DataLoader

#设定batch_size
batch_size = 16
#引入数据路径
train_data_path="/root/Data/JD_Bert_Train.csv"
dev_data_path="/root/Data/JD_Bert_Dev.csv"
test_data_path="/root/Data/JD_Bert_Test.csv"
#调用load_data函数，将数据加载为Tensor形式
train_data = load_data(train_data_path)
dev_data = load_data(dev_data_path)
test_data = load_data(test_data_path)
#将训练数据和测试数据进行DataLoader实例化
train_loader = DataLoader(dataset=train_data, batch_size=batch_size, shuffle=True)
dev_loader = DataLoader(dataset=dev_data, batch_size=batch_size, shuffle=True)
test_loader = DataLoader(dataset=test_data, batch_size=batch_size, shuffle=False)  

10.定义验证函数

def dev(model,dev_loader):
    #将模型放到服务器上
    model.to(device)
#设定模式为验证模式
    model.eval()
#设定不会有梯度的改变仅作验证
    with torch.no_grad():
        correct = 0
        total = 0
        for step, (input_ids,token_type_ids,attention_mask,labels) in tqdm(enumerate(dev_loader),desc='Dev Itreation:'):                input_ids,token_type_ids,attention_mask,labels=input_ids.to(device),token_type_ids.to(device),attention_mask.to(device),labels.to(device)
            out_put = model(input_ids,token_type_ids,attention_mask)
            _, predict = torch.max(out_put.data, 1)
            correct += (predict==labels).sum().item()
            total += labels.size(0)
        res = correct / total
        return res

11.定义训练函数 

def train(model,train_loader,dev_loader) :
    #将model放到服务器上
    model.to(device)
    #设定模型的模式为训练模式
    model.train()
    #定义模型的损失函数
    criterion = nn.CrossEntropyLoss()
    param_optimizer = list(model.named_parameters())
    no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
    #设置模型参数的权重衰减
    optimizer_grouped_parameters = [
        {'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)],
         'weight_decay': 0.01},
        {'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay': 0.0}
    ]
    #学习率的设置
    optimizer_params = {'lr': 1e-5, 'eps': 1e-6, 'correct_bias': False}
    #使用AdamW 主流优化器
    optimizer = AdamW(optimizer_grouped_parameters, **optimizer_params)
    #学习率调整器，检测准确率的状态，然后衰减学习率
    scheduler = ReduceLROnPlateau(optimizer,mode='max',factor=0.5,min_lr=1e-7, patience=5,verbose= True, threshold=0.0001, eps=1e-08)
    t_total = len(train_loader)
    #设定训练轮次
    total_epochs = 2
    bestAcc = 0
    correct = 0
    total = 0
    print('Training and verification begin!')
    for epoch in range(total_epochs): 
        for step, (input_ids,token_type_ids,attention_mask,labels) in enumerate(train_loader):
#从实例化的DataLoader中取出数据，并通过 .to(device)将数据部署到服务器上    input_ids,token_type_ids,attention_mask,labels=input_ids.to(device),token_type_ids.to(device),attention_mask.to(device),labels.to(device)
            #梯度清零
            optimizer.zero_grad()
            #将数据输入到模型中获得输出
            out_put =  model(input_ids,token_type_ids,attention_mask)
            #计算损失
            loss = criterion(out_put, labels)
            _, predict = torch.max(out_put.data, 1)
            correct += (predict == labels).sum().item()
            total += labels.size(0)
            loss.backward()
            optimizer.step()
             #每两步进行一次打印
            if (step + 1) % 2 == 0:
                train_acc = correct / total
                print("Train Epoch[{}/{}],step[{}/{}],tra_acc{:.6f} %,loss:{:.6f}".format(epoch + 1, total_epochs, step + 1, len(train_loader),train_acc*100,loss.item()))
            #每五十次进行一次验证
            if (step + 1) % 50 == 0:
                train_acc = correct / total
                #调用验证函数dev对模型进行验证，并将有效果提升的模型进行保存
                acc = dev(model, dev_loader)
                if bestAcc < acc:
                    bestAcc = acc
                    #模型保存路径
                    path = '/root/data/savedmodel/span_bert_hide_model1.pkl'
                    torch.save(model, path)
                print("DEV Epoch[{}/{}],step[{}/{}],tra_acc{:.6f} %,bestAcc{:.6f}%,dev_acc{:.6f} %,loss:{:.6f}".format(epoch + 1, total_epochs, step + 1, len(train_loader),train_acc*100,bestAcc*100,acc*100,loss.item()))
        scheduler.step(bestAcc)

12.实例化模型并进行训练与验证

device = torch.device('cuda:3' if torch.cuda.is_available() else 'cpu')
#实例化模型
model = BertClassificationModel()
#调用训练函数进行训练与验证
train(model,train_loader,dev_loader)

13.定义预测函数

def predict(model,test_loader):
    model.to(device)
    model.eval()
    predicts = []
    predict_probs = []
    with torch.no_grad():
        correct = 0
        total = 0
        for step, (input_ids,token_type_ids,attention_mask,labels) in enumerate(test_loader): 
            input_ids,token_type_ids,attention_mask,labels=input_ids.to(device),token_type_ids.to(device),attention_mask.to(device),labels.to(device)
            out_put = model(input_ids,token_type_ids,attention_mask)
           
            _, predict = torch.max(out_put.data, 1)
 
            pre_numpy = predict.cpu().numpy().tolist()
            predicts.extend(pre_numpy)
            probs = F.softmax(out_put).detach().cpu().numpy().tolist()
            predict_probs.extend(probs)
 
            correct += (predict==labels).sum().item()
            total += labels.size(0)
        res = correct / total
        print('predict_Accuracy : {} %'.format(100 * res))
        #返回预测结果和预测的概率
        return predicts,predict_probs

14.使用训练好的模型进行预测

#引进训练好的模型进行测试
path = '/root/data/savedmodel/span_bert_hide_model.pkl'
Trained_model = torch.load(path)
#predicts是预测的（0   1），predict_probs是概率值
predicts,predict_probs = predict(Trained_model,dev_loader)

15.获得预测值与预测的概率

16.调用函数计算准确率等指标

P = sklearn.metrics.precision_score(y_true, y_pred, average=’binary’,sample_weight=None)
R = sklearn.metrics.recall_score(y_true, y_pred, average=’binary’,sample_weight=None)
F1 = sklearn.metrics.f1_score(y_true, y_pred,average=’binary’,sample_weight=None)

average的选项详解：