pytorch编写文本二分类RNN代码

以下是一个使用 PyTorch 编写的文本二分类 RNN 代码示例：

import torch
import torch.nn as nn

class TextRNN(nn.Module):
    def __init__(self, vocab_size, embedding_dim, hidden_dim, output_dim, n_layers, bidirectional, dropout):
        super().__init__()

        self.embedding = nn.Embedding(vocab_size, embedding_dim)
        self.rnn = nn.LSTM(embedding_dim, hidden_dim, num_layers=n_layers, bidirectional=bidirectional, dropout=dropout)
        self.fc = nn.Linear(hidden_dim * 2 if bidirectional else hidden_dim, output_dim)
        self.dropout = nn.Dropout(dropout)

    def forward(self, text, text_lengths):
        embedded = self.dropout(self.embedding(text))
        packed_embedded = nn.utils.rnn.pack_padded_sequence(embedded, text_lengths.to('cpu'), enforce_sorted=False)
        packed_output, (hidden, cell) = self.rnn(packed_embedded)
        output, output_lengths = nn.utils.rnn.pad_packed_sequence(packed_output)
        hidden = self.dropout(torch.cat((hidden[-2, :, :], hidden[-1, :, :]), dim=1)) if self.rnn.bidirectional else self.dropout(hidden[-1, :, :])
        return self.fc(hidden)

该模型使用了 LSTM 层，并使用了 PyTorch 提供的 `pack_padded_sequence` 和 `pad_packed_sequence` 函数，来处理变长的文本序列。模型的输入为一个文本序列和该序列的长度，输出为两个类别的概率。