中文新闻情感分类 Bert-Pytorch-transformers
中文新闻情感分类 Bert-Pytorch-transformers
使用pytorch框架以及transformers包，中文以及Bert的新闻中文预训练模型
文件目录
data
Train_DataSet.csv
Train_DataSet_Label.csv
main.py
NewsData.py
#main.py
from transformers import BertTokenizer
from transformers import BertForSequenceClassification
from transformers import BertConfig
from transformers import BertPreTrainedModel
import torch
import torch.nn as nn
from transformers import BertModel
import time
import argparse
from NewsData import NewsData
import os
def get_train_args():
parser=argparse.ArgumentParser()
parser.add_argument(--batch_size,type=int,default=10,help = 每批数据的数量)
parser.add_argument(--nepoch,type=int,default=3,help = 训练的轮次)
parser.add_argument(--lr,type=float,default=0.001,help = 学习率)
parser.add_argument(--gpu,type=bool,default=True,help = 是否使用gpu)
parser.add_argument(--num_workers,type=int,default=2,help=dataloader使用的云南idc服务商线程数量)
parser.add_argument(--num_labels,type=int,default=3,help=分类类数)
parser.add_argument(--data_path,type=str,default=./data,help=数据路径)
opt=parser.parse_args()
print(opt)
return opt
def get_model(opt):
#类方法.from_pretrained()获取预训练模型,num_labels是分类的类数
model = BertForSequenceClassification.from_pretrained(bert-base-chinese,num_labels=opt.num_labels)
return model
def get_data(opt):
#NewsData继承于pytorch的Dataset类
trainset = NewsData(opt.data_path,is_train = 1)
trainloader=torch.utils.data.DataLoader(trainset,batch_size=opt.batch_size,shuffle=True,num_workers=opt.num_workers)
testset = NewsData(opt.data_path,is_train = 0)
testloader=torch.utils.data.DataLoader(testset,batch_size=opt.batch_size,shuffle=False,num_workers=opt.num_workers)
return trainloader,testloader
def train(epoch,model,trainloader,testloader,optimizer,opt):
print(\ntrain-Epoch: %d % (epoch+1))
model.train()
start_time = time.time()
print_step = int(len(trainloader)/10)
for batch_idx,(sue,label,posi) in enumerate(trainloader):
if opt.gpu:
sue = sue.cuda()
posi = posi.cuda()
label = label.unsqueeze(1).cuda()
optimizer.zero_grad()
#输入参数为词列表、位置列表、站群服务器情感标签
outputs = model(sue,分类 position_ids=posi,labels = label)
loss, logits = outputs[0],outputs[1]
loss.backward()
optimizer.step()
if batch_idx % print_step == 0:
print("Epoch:%d [%d|%d] loss:%f" %(epoch+1,batch_idx,len(trainloader),loss.mean()))
print("time:%.3f" % (time.time() - start_time))
def test(epoch,model,trainloader,testloader,opt):
print(\ntest-Epoch: %d % (epoch+1))
model.eval()
total=0
correct=0
with torch.no_grad():
for batch_idx,(sue,label,posi) in enumerate(testloader):
if opt.gpu:
sue = sue.cuda()
posi = posi.cuda()
labels = label.unsqueeze(1).cuda()
label = label.cuda()
else:
labels = label.unsqueeze(1)
outputs = model(sue, labels=labels)
loss, logits = outputs[:2]
_,predicted=torch.max(logits.data,1)
total+=sue.size(0)
correct+=predicted.data.eq(label.data).cpu().sum()
s = ("Acc:%.3f" %((1.0*correct.numpy())/total))
print(s)
if __name__==__main__:
opt = get_train_args()
model = get_model(opt)
trainloader,testloader = get_data(opt)
if opt.gpu:
model.cuda()
optimizer=torch.optim.SGD(model.parameters(),lr=opt.lr,momentum=0.9)
if not os.path.exists(./model.pth):
for epoch in range(opt.nepoch):
train(epoch,model,trainloader,testloader,optimizer,opt)
test(epoch,model,trainloader,testloader,opt)
torch.save(model.state_dict(),./model.pth)
else: 郑州治疗妇科哪个医院好 http://www.120kdfk.com/
model.load_state_dict(torch.load(model.pth))
print(模型存在,直接test)
test(0,model,trainloader,testloader,opt)
#NewsData.py
from transformers import BertTokenizer
from transformers import BertForSequenceClassification
from transformers import BertConfig
from transformers import BertPreTrainedModel
import torch
import torch.nn as nn
from transformers import BertModel
import time
import argparse
class NewsData(torch.utils.data.Dataset):
def __init__(self,root,is_train = 1):
self.tokenizer = BertTokenizer.from_pretrained(bert-base-chinese)
self.data_num = 7346
self.x_list = []
self.y_list = []
self.posi = []
with open(root + /Train_DataSet.csv,encoding=UTF-8) as f:
for i in range(self.data_num+1):
line = f.readline()[:-1] + 这是一个中性的数据
data_one_str = line.split(,)[len(line.split(,))-2]
data_two_str = line.split(,)[len(line.split(,))-1]
if len(data_one_str) < 6:
z = len(data_one_str)
data_one_str = data_one_str + ， + data_two_str[0:min(200,中文len(data_two_str))]
else:
data_one_str = data_one_str
if i==0:
continue
word_l = self.tokenizer.encode(data_one_str, add_special_tokens=False)
if len(word_l)<100:
while(len(word_l)!=100):
word_l.append(0)
else:
word_l = word_l[0:100]
word_l.append(102)
l = word_l
word_l = [101]
word_l.extend(l)
self.x_list.append(torch.tensor(word_l))
self.posi.append(torch.tensor([i for i in range(102)]))
with open(root + /Train_DataSet_Label.csv,encoding=UTF-8) as f:
for i in range(self.data_num+1):
#print(i)
label_one = f.readline()[-2]
if i==0:
continue
label_one = int(label_one)
self.y_list.append(torch.tensor(label_one))
#训练集或者是测试集
if is_train == 1:
self.x_list = self.x_list[0:6000]
self.y_list = self.y_list[0:6000]
self.posi = self.posi[0:6000]
else:
self.x_list = self.x_list[6000:]
self.y_list = self.y_list[6000:]
self.posi = self.posi[6000:]
self.len = len(self.x_list)
def __getitem__(self, index):
return self.x_list[index], self.y_list[index],self.posi[index]
def __len__(self):
return self.len
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