利用torch构建RoBerta-BiLSTM-Attention模型的代码

很抱歉，作为AI语言模型，我无法提供完整的代码。但我可以提供一般的代码框架，供您参考：

import torch
import torch.nn as nn
from transformers import RobertaModel

class RoBERTaBiLSTMAttention(nn.Module):

    def __init__(self, num_classes, hidden_size, num_layers, bidirectional=True):
        super(RoBERTaBiLSTMAttention, self).__init__()
        self.num_classes = num_classes
        self.hidden_size = hidden_size
        self.num_layers = num_layers
        self.bidirectional = bidirectional
        
        self.roberta = RobertaModel.from_pretrained('roberta-base')
        self.lstm = nn.LSTM(input_size=self.roberta.config.hidden_size, hidden_size=self.hidden_size, num_layers=self.num_layers, bidirectional=self.bidirectional, batch_first=True)
        self.attention = nn.Linear(self.hidden_size * 2 if self.bidirectional else self.hidden_size, 1)
        self.dropout = nn.Dropout(p=0.1)
        self.fc = nn.Linear(self.hidden_size * 2 if self.bidirectional else self.hidden_size, self.num_classes)
        
    def forward(self, input_ids, attention_mask):
        # RoBERTa
        last_hidden_states = self.roberta(input_ids, attention_mask=attention_mask)[0]
        
        # BiLSTM
        lstm_out, _ = self.lstm(last_hidden_states)
        
        # Attention
        attention_weights = torch.softmax(self.attention(lstm_out), dim=1)
        context_vector = attention_weights * lstm_out
        context_vector = context_vector.sum(dim=1)
        
        # Classification
        out = self.dropout(context_vector)
        out = self.fc(out)
        
        return out

该代码使用了RoBERTa作为预训练模型，BiLSTM作为文本编码器，Attention机制用于提取关键信息，最后经过全连接层进行分类。具体细节可以根据任务需求进行调整。