基于深度学习的自然语言处理模型优化策略

import torch
import torch.nn as nn
from transformers import BertModel, BertTokenizer
class CustomNLPModel(nn.Module):
    def __init__(self, bert_model, num_labels):
        super(CustomNLPModel, self).__init__()
        self.bert = bert_model
        self.classifier = nn.Linear(self.bert.config.hidden_size, num_labels)
        
    def forward(self, input_ids, attention_mask):
        outputs = self.bert(input_ids=input_ids, attention_mask=attention_mask)
        pooled_output = outputs.pooler_output
        logits = self.classifier(pooled_output)
        return logits
# 加载预训练BERT模型
pretrained_model = BertModel.from_pretrained('bert-base-uncased')
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
# 实例化自定义模型
model = CustomNLPModel(pretrained_model, num_labels=2)

import torch.optim as optim
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler
# 定义优化器和学习率调度器
optimizer = optim.Adam(model.parameters(), lr=3e-5)
scheduler = optim.lr_scheduler.WarmupLinearSchedule(optimizer, warmup_steps=0, t_total=num_train_steps)
# 定义损失函数
criterion = nn.CrossEntropyLoss()
# 训练模型
for epoch in range(num_epochs):
    for step, batch in enumerate(train_dataloader):
        input_ids = batch['input_ids'].to(device)
        attention_mask = batch['attention_mask'].to(device)
        labels = batch['labels'].to(device)
        
        model.train()
        optimizer.zero_grad()
        outputs = model(input_ids, attention_mask)
        loss = criterion(outputs, labels)
        
        # 梯度裁剪
        torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)
        
        loss.backward()
        optimizer.step()
        scheduler.step()
        
        # 验证模型性能并保存最佳模型
        if step % validation_steps == 0:
            val_loss, val_acc = evaluate_model(model, val_dataloader, criterion)
            if val_acc > best_acc:
                best_acc = val_acc
                torch.save(model.state_dict(), 'best_model.pth')