现有数据集敏感词的训练集和测试集txt文本，使用python，MGAT-Transformer-CNN算法进行机器学习训练，使得在测试集上表现良好，给出代码

在Python中，使用MGAT（Multi-Transformer-CNN（卷积神经网络）模型对敏感词检测的文本数据进行训练，首先需要安装必要的库如transformers、torch等。以下是简单的步骤和代码示例： ```python # 导入所需库 import torch from torch.utils.data import Dataset, DataLoader from transformers import BertTokenizer, BertModel, nn from mgat_module import MGATLayer # 如果有自定义的MGAT层，记得导入 class TextDataset(Dataset): def __init__(self, data_path, tokenizer, max_len=512): self.texts = [line.strip() for line in open(data_path, 'r', encoding='utf-8')] self.tokenizer = tokenizer self.max_len = max_len def __len__(self): return len(self.texts) def __getitem__(self, idx): text = self.texts[idx] inputs = self.tokenizer.encode_plus( text, truncation=True, padding='max_length', max_length=self.max_len, return_tensors='pt' ) input_ids = inputs['input_ids'] attention_mask = inputs['attention_mask'] label = int('敏感词' in text) # 假设'敏感词'作为标签，1表示是敏感词，0表示不是 return { 'input_ids': input_ids, 'attention_mask': attention_mask, 'labels': torch.tensor(label, dtype=torch.long) } # 初始化tokenizer和模型 tokenizer = BertTokenizer.from_pretrained('bert-base-chinese') model = BertModel.from_pretrained('bert-base-chinese') # 使用预训练的Bert模型作为基础 mgat_layer = MGATLayer() # 自定义的MGAT层 # 定义模型结构 (这里仅作示例，具体细节会更复杂) class SensitiveWordDetector(nn.Module): def __init__(self): super().__init__() self.bert = model self.mgat = mgat_layer self.classifier = nn.Linear(model.config.hidden_size, 1) # 输出一层用于二分类 def forward(self, input_ids, attention_mask): _, pooled_output = self.bert(input_ids=input_ids, attention_mask=attention_mask) output = self.mgat(pooled_output) logits = self.classifier(output) return logits # 加载数据集并创建DataLoader train_data_path = 'train.txt' # 训练集路径 test_data_path = 'test.txt' # 测试集路径 train_dataset = TextDataset(train_data_path, tokenizer) test_dataset = TextDataset(test_data_path, tokenizer) train_loader = DataLoader(train_dataset, batch_size=16, shuffle=True) test_loader = DataLoader(test_dataset, batch_size=16) # 模型训练 device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model.to(device) optimizer = torch.optim.AdamW(model.parameters(), lr=1e-5) criterion = nn.BCEWithLogitsLoss() for epoch in range(num_epochs): # 设置训练轮数 model.train() for inputs in train_loader: input_ids = inputs['input_ids'].to(device) attention_mask = inputs['attention_mask'].to(device) labels = inputs['labels'].to(device) optimizer.zero_grad() outputs = model(input_ids, attention_mask) loss = criterion(outputs, labels.float()) # BCELoss for binary classification loss.backward() optimizer.step() # 评估阶段 model.eval() with torch.no_grad(): total_loss = 0. correct_count = 0 for inputs in test_loader: ... (继续处理并计算准确度) # 训练结束后保存模型 torch.save(model.state_dict(), 'sensitive_word_detector.pth') ``` 请注意，以上代码是一个简化的示例，实际应用中你需要处理更多的细节，例如数据预处理、模型超参数调整、验证集的划分以及性能指标计算等。此外，`MGATLayer` 需要你自己实现或从相应的论文中找到。