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深度学习打卡第N8周：使用Word2vec实现文本分类

news 2025/10/4 11:17:42

🍨 本文为🔗365天深度学习训练营中的学习记录博客
🍖 原作者：K同学啊

一、数据预处理

本次将加入Word2vec使用PyTorch实现中文文本分类，Word2Vec则是其中的一种词嵌入方去，是一种用于生成词向量的浅层神经网络模型，由Tomas Mikolov及其团队于2013年提出。 Word2Vec通过学习大量文本数据，将每个单词表示为一个连续的向量，这些向量可以捕捉单词之间的语义和句法关系。数据示例如下:

import torch
from torch import nn
import torchvision
from torchvision import transforms,datasets
import os,PIL,pathlib,warningswarnings.filterwarnings("ignore")device = torch.device("cuda" if torch.cuda.is_available else "cpu")

import pandas as pd# CSV 格式通常为 无表头（header=None），以制表符（sep='\t'）分隔
train_data = pd.read_csv('./data/train.csv',sep='\t',header=None)# 构造数据集迭代器
def custom_data_iter(texts,labels):for x,y in zip(texts,labels):yield x,ytrain_iter = custom_data_iter(train_data[0].values[:],train_data[1].values[:])x = train_data[0].values[:]
y = train_data[1].values[:]import jieba
input_x = []
for line in x:input_x.append(jieba.lcut(line))# 添加自定义停用词
stopwords_list = ["，","。","\n","\u3000"," ","：","！","？","…"] # \u3000 是 Unicode 编码中的全角空格（也称为 “全角空白符”），是中文排版中常用的空格形式。def remove_stopwords(ls):  # 去除停用词return [word for word in ls if word not in stopwords_list]result_stop=[remove_stopwords(x) for x in input_x if remove_stopwords(x)]

from gensim.models.word2vec import Word2Vec # 与from gensim.models import Word2Vec 等价
import numpy as np# 训练 Word2Vec 浅层神经网络模型
w2v = Word2Vec(vector_size=100, #是指特征向量的维度，默认为100。min_count=3)     #可以对字典做截断. 词频少于min_count次数的单词会被丢弃掉, 默认值为5。w2v.build_vocab(result_stop)
w2v.train(result_stop,                         total_examples=w2v.corpus_count, epochs=20)def average_vec(text):vec = np.zeros(100).reshape((1,100))for word in text:try:vec += w2v.wv[word].reshape((1,100))except KeyError:continuereturn vec# 将词向量保存为 Ndarray
x_vec = np.concatenate([average_vec(z) for z in result_stop])# 保存Word2Vec模型及词向量
w2v.save('./data/w2v_model.pkl')train_iter = custom_data_iter(x_vec,y)label_name = list(set(train_data[1].values[:]))

text_pipeline = lambda x:average_vec(x)
label_pipeline = lambda x:label_name.index(x)def collate_batch(batch):label_list,text_list = [],[]for(text,label) in batch:label_list.append(label_pipeline(label))processed_text = torch.tensor(text_pipeline(text),dtype=torch.float32)text_list.append(processed_text)label_list = torch.tensor(label_list,dtype=torch.int64)text_list = torch.cat(text_list)return text_list.to(device),label_list.to(device)

二、模型构建

from torch import nnclass TextClassificationModel(nn.Module):def __init__(self,num_class):super(TextClassificationModel,self).__init__()self.fc = nn.Linear(100,num_class)def forward(self,text):return self.fc(text)num_class = len(label_name)
model = TextClassificationModel(num_class).to(device)import timedef train(dataloader):model.train()total_acc,train_loss,total_count = 0,0,0log_interval = 50start_time = time.time()for idx,(text,label) in enumerate(dataloader):predicted_label = model(text)optimizer.zero_grad()loss = criterion(predicted_label,label)loss.backward()torch.nn.utils.clip_grad_norm_(model.parameters(),0.1) # 梯度裁剪optimizer.step()total_acc += (predicted_label.argmax(1)==label).sum().item()train_loss += loss.item()*label.size(0)total_count += label.size(0)if idx % log_interval == 0 and idx > 0:elapsed = time.time() - start_timeprint('| epoch {:1d} | {:4d}/{:4d} batches ''| train_acc {:4.3f} train_loss {:4.5f}'.format(epoch, idx, len(dataloader),total_acc/total_count, train_loss/total_count))total_acc, train_loss, total_count = 0, 0, 0start_time = time.time()def evaluate(dataloader):model.eval()total_acc,test_loss,total_count =0,0,0with torch.no_grad():for idx,(text,label) in enumerate(dataloader):predicted_label = model(text)loss = criterion(predicted_label,label)total_acc += (predicted_label.argmax(1)==label).sum().item()test_loss += loss.item()*label.size(0)total_count += label.size(0)return total_acc/total_count,test_loss/total_count

三、训练模型

from torch.utils.data.dataset import random_split
from torchtext.data.functional import to_map_style_dataset
from torch.utils.data import DataLoader# 超参数
EPOCHS = 10
LR = 5
BATCH_SIZE = 64criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(),lr=LR)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer,1.0,gamma=0.1)
total_accu = Nonetrain_iter = custom_data_iter(result_stop,train_data[1].values[:])
train_dataset = to_map_style_dataset(train_iter)num_train = int(len(train_dataset)*0.8)
split_train,split_valid = random_split(train_dataset,[num_train,len(train_dataset)-num_train])train_dataloader = DataLoader(split_train,batch_size=BATCH_SIZE,shuffle=True,collate_fn=collate_batch)
valid_dataloader = DataLoader(split_valid,batch_size=BATCH_SIZE,shuffle=True,collate_fn=collate_batch)for epoch in range(1,EPOCHS+1):epoch_start_time = time.time()train(train_dataloader)val_acc,val_loss = evaluate(valid_dataloader)lr = optimizer.state_dict()['param_groups'][0]['lr']if total_accu is not None and total_accu > val_acc:scheduler.step()else:total_accu = val_accprint('-' * 69)print('| epoch {:1d} | time: {:4.2f}s | ''valid_acc {:4.3f} valid_loss {:4.3f} | lr {:4.6f}'.format(epoch,time.time()-epoch_start_time,val_acc,val_loss,lr))print('-' * 69)

def predict(text):with torch.no_grad():text = torch.tensor(text_pipeline(text),dtype=torch.float32).to(device)print(text.shape)output = model(text)return output.argmax(1).item()ex_text_str = '随便播放一首歌'
print("该文本的类别是：%s"%label_name[predict(jieba.lcut(ex_text_str))])