transformer二分类代码

以下是一个使用Transformer模型进行二分类的示例代码：

import torch
import torch.nn as nn
import torch.optim as optim
import torch.utils.data as data
from torchtext.data import Field, TabularDataset, BucketIterator

# 定义Transformer模型
class TransformerClassifier(nn.Module):
    def __init__(self, input_dim, hidden_dim, output_dim, num_layers, num_heads, dropout):
        super(TransformerClassifier, self).__init__()

        self.embedding = nn.Embedding(input_dim, hidden_dim)
        self.encoder = nn.TransformerEncoder(
            nn.TransformerEncoderLayer(hidden_dim, num_heads, hidden_dim, dropout),
            num_layers
        )
        self.fc = nn.Linear(hidden_dim, output_dim)
        self.dropout = nn.Dropout(dropout)

    def forward(self, text):
        embedded = self.embedding(text)
        embedded = embedded.permute(1, 0, 2)
        encoded = self.encoder(embedded)
        pooled = encoded.mean(dim=0)
        pooled = self.dropout(pooled)
        return self.fc(pooled)

# 设置超参数
input_dim = len(TEXT.vocab)
hidden_dim = 256
output_dim = 2
num_layers = 4
num_heads = 8
dropout = 0.5

# 初始化模型和优化器
model = TransformerClassifier(input_dim, hidden_dim, output_dim, num_layers, num_heads, dropout)
optimizer = optim.Adam(model.parameters())
criterion = nn.CrossEntropyLoss()

# 训练模型
for epoch in range(num_epochs):
    for batch in train_iterator:
        text = batch.text
        label = batch.label

        optimizer.zero_grad()
        output = model(text)
        loss = criterion(output, label)
        loss.backward()
        optimizer.step()

# 模型评估
model.eval()
correct = 0
total = 0

with torch.no_grad():
    for batch in test_iterator:
        text = batch.text
        label = batch.label

        output = model(text)
        _, predicted = torch.max(output.data, 1)
        total += label.size(0)
        correct += (predicted == label).sum().item()

accuracy = correct / total
print('Test Accuracy: {:.2f}%'.format(100 * accuracy))

请注意，这只是一个示例代码，并且假设你已经准备好了文本数据集并使用TorchText库进行了预处理。你需要根据自己的数据集和需求对代码进行适当的修改和调整。