建立一个传统文本二分类的卷积神经网络模型，首先对数据集进行读取，数据集分为lebal列和reveiw列，lebal列为标签，为0和1，作为分类标签，reveiw列为中文文本数据列，模型首先对数据集进行读取，随机抽取各分类的80%作为训练集，剩余为测试集，对于文本转换为数字数据则采取文本最大长度作为数据长度，其余不足长度的数据按0填充，卷积核尺寸为3，采用平均池化，一个全连接层进行二分类输出结果，模型共训练20轮，输出每轮训练的测试集和训练集的损失率和准确率结果并绘制折线图。基于pytorch对模型进行构建，给出相应代码

好的，以下是基于PyTorch构建传统文本二分类卷积神经网络模型的代码：

import torch
import torch.nn as nn
import torch.optim as optim
import torch.utils.data as data
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import jieba

# 定义模型超参数
MAX_LEN = 128  # 文本最大长度
BATCH_SIZE = 64  # 批处理大小
KERNEL_SIZE = 3  # 卷积核尺寸
NUM_FILTERS = 100  # 卷积核数量
HIDDEN_SIZE = 64  # 全连接层隐藏层大小
NUM_CLASSES = 2  # 类别数
NUM_EPOCHS = 20  # 训练轮数
LEARNING_RATE = 0.001  # 学习率
EMBEDDING_SIZE = 128  # 词向量维度

# 定义数据预处理函数
def preprocess_text(text):
    text = jieba.lcut(text)  # 分词
    text = [word for word in text if len(word) > 1]  # 去掉长度为1的词
    text = " ".join(text)  # 合并为字符串
    return text

# 定义数据读取类
class CustomDataset(data.Dataset):
    def __init__(self, data_path):
        self.df = pd.read_csv(data_path, sep="\t", header=None, names=["label", "review"], error_bad_lines=False)
        self.df["review"] = self.df["review"].apply(preprocess_text)
        self.tokenizer = None

    def __len__(self):
        return len(self.df)

    def __getitem__(self, index):
        label = self.df.iloc[index]["label"]
        review = self.df.iloc[index]["review"]
        if self.tokenizer is None:
            self.tokenizer = torchtext.vocab.FastText(language='zh').get_vecs_by_tokens(list(review))
        review = [self.tokenizer.stoi.get(word, 0) for word in review.split()]  # 转换为数字序列
        review = review[:MAX_LEN] + [0] * (MAX_LEN - len(review))  # 填充到最大长度
        return torch.LongTensor(review), torch.LongTensor([label])

# 定义卷积神经网络模型
class TextCNN(nn.Module):
    def __init__(self):
        super(TextCNN, self).__init__()
        self.embedding = nn.Embedding(len(CustomDataset(data_path)), EMBEDDING_SIZE)
        self.conv = nn.Conv1d(in_channels=EMBEDDING_SIZE, out_channels=NUM_FILTERS, kernel_size=KERNEL_SIZE)
        self.pool = nn.AdaptiveAvgPool1d(1)
        self.fc = nn.Linear(NUM_FILTERS, HIDDEN_SIZE)
        self.relu = nn.ReLU()
        self.dropout = nn.Dropout(p=0.5)
        self.out = nn.Linear(HIDDEN_SIZE, NUM_CLASSES)

    def forward(self, x):
        x = self.embedding(x)
        x = x.permute(0, 2, 1)  # 将维度转换为[batch_size, embedding_size, seq_len]
        x = self.conv(x)
        x = self.pool(x).squeeze()
        x = self.fc(x)
        x = self.relu(x)
        x = self.dropout(x)
        x = self.out(x)
        return x

# 定义训练函数
def train(model, device, train_loader, optimizer, criterion):
    model.train()
    train_loss = 0
    train_acc = 0
    for x, y in train_loader:
        x, y = x.to(device), y.to(device)
        optimizer.zero_grad()
        pred = model(x)
        loss = criterion(pred, y.squeeze())
        loss.backward()
        optimizer.step()
        train_loss += loss.item()
        train_acc += (pred.argmax(dim=1) == y.squeeze()).sum().item()
    return train_loss / len(train_loader), train_acc / len(train_loader.dataset)

# 定义测试函数
def test(model, device, test_loader, criterion):
    model.eval()
    test_loss = 0
    test_acc = 0
    with torch.no_grad():
        for x, y in test_loader:
            x, y = x.to(device), y.to(device)
            pred = model(x)
            loss = criterion(pred, y.squeeze())
            test_loss += loss.item()
            test_acc += (pred.argmax(dim=1) == y.squeeze()).sum().item()
    return test_loss / len(test_loader), test_acc / len(test_loader.dataset)

# 定义主函数
if __name__ == "__main__":
    # 加载数据集
    data_path = "data.csv"
    dataset = CustomDataset(data_path)

    # 划分数据集
    train_size = int(len(dataset) * 0.8)
    test_size = len(dataset) - train_size
    train_dataset, test_dataset = data.random_split(dataset, [train_size, test_size])

    # 创建数据加载器
    train_loader = data.DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True)
    test_loader = data.DataLoader(test_dataset, batch_size=BATCH_SIZE, shuffle=True)

    # 定义设备
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

    # 创建模型
    model = TextCNN().to(device)

    # 定义损失函数和优化器
    criterion = nn.CrossEntropyLoss()
    optimizer = optim.Adam(model.parameters(), lr=LEARNING_RATE)

    # 训练模型
    train_loss_list, train_acc_list, test_loss_list, test_acc_list = [], [], [], []
    for epoch in range(NUM_EPOCHS):
        train_loss, train_acc = train(model, device, train_loader, optimizer, criterion)
        test_loss, test_acc = test(model, device, test_loader, criterion)
        train_loss_list.append(train_loss)
        train_acc_list.append(train_acc)
        test_loss_list.append(test_loss)
        test_acc_list.append(test_acc)
        print(f"Epoch {epoch + 1}: Train Loss={train_loss:.4f}, Train Acc={train_acc:.4f}, Test Loss={test_loss:.4f}, Test Acc={test_acc:.4f}")

    # 绘制训练过程中的损失率和准确率折线图
    x = range(1, NUM_EPOCHS+1)
    plt.plot(x, train_loss_list, label="Train Loss")
    plt.plot(x, train_acc_list, label="Train Acc")
    plt.plot(x, test_loss_list, label="Test Loss")
    plt.plot(x, test_acc_list, label="Test Acc")
    plt.xlabel("Epochs")
    plt.ylabel("Loss/Accuracy")
    plt.legend()
    plt.show()

以上代码中，我们首先定义了模型的超参数，包括文本最大长度、批处理大小、卷积核尺寸等；然后定义了数据预处理函数，用于将中文文本转换为数字序列；接着定义了数据读取类`CustomDataset`，用于读取数据集、进行预处理和转换为数字序列；然后定义了卷积神经网络模型`TextCNN`，包括嵌入层、卷积层、池化层、全连接层等；然后定义了训练函数和测试函数，用于训练和测试模型；最后定义了主函数，用于加载数据集、创建数据加载器、构建模型、定义损失函数和优化器、训练模型、绘制折线图等步骤。