使用正则化、提前停止和dropout等技术来减少过拟合问题。下面是一个代码示例，展示如何在BERT+BiGRU+Softmax网络中使用dropout技术来减少过拟合。

class BERT_BiGRU_Softmax(nn.Module):
    def __init__(self, bert_config, num_labels, hidden_size):
        super(BERT_BiGRU_Softmax, self).__init__()
        self.bert = BertModel(bert_config)
        self.dropout = nn.Dropout(0.1)
        self.bigru = nn.GRU(bert_config.hidden_size, hidden_size,
                            num_layers=2, batch_first=True, bidirectional=True)
        self.fc = nn.Linear(hidden_size * 2, num_labels)
        self.num_labels = num_labels

    def forward(self, input_ids, token_type_ids=None, attention_mask=None):
        bert_output = self.bert(input_ids=input_ids,
                                token_type_ids=token_type_ids,
                                attention_mask=attention_mask)[0]
        bert_output = self.dropout(bert_output)
        bigru_output, _ = self.bigru(bert_output)
        bigru_output = self.dropout(bigru_output)
        logits = self.fc(bigru_output)
        return F.softmax(logits, dim=-1)
    
model = BERT_BiGRU_Softmax(bert_config, num_labels, hidden_size)
optimizer = AdamW(model.parameters(), lr=2e-5, eps=1e-8, weight_decay=0.01)
scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=0, num_training_steps=len(train_dataloader) * epochs)
loss_fn = nn.CrossEntropyLoss()

# 训练模型
for epoch in range(epochs):
    model.train()
    for batch in train_dataloader:
        input_ids = batch['input_ids'].to(device)
        token_type_ids = batch['token_type_ids'].to(device)
        attention_mask = batch['attention_mask'].to(device)
        labels = batch['labels'].to(device)
        
        optimizer.zero_grad()
        outputs = model(input_ids=input_ids, token_type_ids=token_type_ids, attention_mask=attention_mask)
        loss = loss_fn(outputs.view(-1, num_labels), labels.view(-1))
        loss.backward()
        nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)
        optimizer.step()
        scheduler.step()
        
        # 在验证集上测试并提前停止训练
        model.eval()
        with torch.no_grad():
            total_loss = 0