尝试使用不同的集成策略或优化方法，以提高BERT集成模型的性能。

具体实现方法可以使用以下代码：

导入相关库并定义模型

import torch from transformers import BertModel

class BERTClassifier(torch.nn.Module): def init(self, num_classes): super().init() self.bert = BertModel.from_pretrained('bert-base-uncased') self.classifier = torch.nn.Linear(self.bert.pooler.dense.out_features, num_classes)

def forward(self, input_ids, attention_mask):
    outputs = self.bert(input_ids, attention_mask=attention_mask)
    pooled_output = outputs.pooler_output
    logits = self.classifier(pooled_output)
    return logits

定义BERT集成模型

class BERTEnsemble(torch.nn.Module): def init(self, num_models, num_classes): super().init() self.models = torch.nn.ModuleList([BERTClassifier(num_classes) for _ in range(num_models)])

def forward(self, input_ids, attention_mask):
    logits = []
    for model in self.models:
        logits.append(model(input_ids, attention_mask))
    logits = torch.stack(logits).mean(dim=0)
    return logits

定义训练代码

def train(model, train_loader, val_loader, num_epochs=10, learning_rate=1e-3): optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate) criterion = torch.nn.CrossEntropyLoss()

for epoch in range(num_epochs):
    train_loss = 0.0
    train_accs = []
    for batch in train_loader:
        input_ids, attention_mask, labels = batch
        optimizer.zero_grad()
        logits = model(input_ids, attention_mask)
        loss = criterion(logits, labels)
        loss.backward()
        optimizer.step()
        
        train_loss += loss.item()
        train_accs.append(accuracy(logits, labels))
    train_acc = torch.stack(train_accs).mean()
    train_loss /= len(train_loader)
    
    val_loss = 0.0
    val_accs = []
    for batch in val_loader:
        input_ids