模型结构

分为三部分，第一部分采用Bi-RNN来获取文本的上下文特征表示，第二部分采用max-pooling进一步提取特征信息，第三部分采用全连接层来获取类别概率。

代码

class Model(nn.Module):
    def __init__(self, config):
        super(Model, self).__init__()
        if config.embedding_pretrained is not None:
            self.embedding = nn.Embedding.from_pretrained(config.embedding_pretrained, freeze=False)
        else:
            self.embedding = nn.Embedding(config.n_vocab, config.embed, padding_idx=config.n_vocab - 1)
        self.lstm = nn.LSTM(config.embed, config.hidden_size, config.num_layers,
                            bidirectional=True, batch_first=True, dropout=config.dropout)
        self.maxpool = nn.MaxPool1d(config.pad_size)
        self.fc = nn.Linear(config.hidden_size * 2 + config.embed, config.num_classes)

    def forward(self, x):
        x, _ = x
        embed = self.embedding(x)  
        out, _ = self.lstm(embed)
        out = torch.cat((embed, out), 2)
        out = F.relu(out)
        out = out.permute(0, 2, 1)
        out = self.maxpool(out).squeeze()
        out = self.fc(out)
        return out