1. Fasttext

1.1 模型架构

Fasttext模型架构和Word2vec的CBOW模型架构非常相似，下面就是FastText模型的架构图：

从上图可以看出来，Fasttext模型包括输入层、隐含层、输出层共三层。其中输入的是词向量，输出的是label，隐含层是对多个词向量的叠加平均

• CBOW的输入是目标单词的上下文，Fasttext的输入是多个单词及其n-gram特征，这些单词用来表示单个文档
• CBOW的输入单词使用one-hot编码，Fasttext的输入特征时使用embedding编码
• CBOW的输出是目标词汇，Fasttext的输出是文档对应的类别

1.2 模型实现

class Model(nn.Module):
    def __init__(self, config):
        super(Model, self).__init__()
        self.embedding = nn.Embedding(config.n_vocab, config.embed, padding_idx=config.n_vocab - 1)
        self.embedding_ngram2 = nn.Embedding(config.n_gram_vocab, config.embed)
        self.embedding_ngram3 = nn.Embedding(config.n_gram_vocab, config.embed)
        self.dropout = nn.Dropout(config.dropout)
        self.fc1 = nn.Linear(config.embed * 3, config.hidden_size)
        # self.dropout2 = nn.Dropout(config.dropout)
        self.fc2 = nn.Linear(config.hidden_size, config.num_classes)
    def forward(self, x):
        out_word = self.embedding(x[0])
        out_bigram = self.embedding_ngram2(x[2])
        out_trigram = self.embedding_ngram3(x[3])
        out = torch.cat((out_word, out_bigram, out_trigram), -1)
        out = out.mean(dim=1)
        out = self.dropout(out)
        out = self.fc1(out)
        out = F.relu(out)
        out = self.fc2(out)
        return out

2. TextCNN

2.1 模型架构

与传统图像的CNN网络相比,TextCNN在网络结构上没有任何变化, 从下图可以看出TextCNN其实只有一层convolution，一层max-pooling, 最后将输出外接softmax来n分类

2.2 模型实现

class Model(nn.Module):
    def __init__(self, config):
        super(Model, self).__init__()
        self.embedding = nn.Embedding(config.n_vocab, config.embed, padding_idx=config.n_vocab - 1)
        self.convs = nn.ModuleList([nn.Conv2d(1, config.num_filters, (k, config.embed)) for k in config.filter_sizes])
        self.dropout = nn.Dropout(config.dropout)
        self.fc = nn.Linear(config.num_filters * len(config.filter_sizes), config.num_classes)
    def conv_and_pool(self, x, conv):
        x = F.relu(conv(x)).squeeze(3)
        x = F.max_pool1d(x, x.size(2)).squeeze(2)
        return x
    def forward(self, x):
        out = self.embedding(x[0])
        out = out.unsqueeze(1)
        out = torch.cat([self.conv_and_pool(out, conv) for conv in self.convs], 1)
        out = self.dropout(out)
        out = self.fc(out)
        return 

3. TextRNN

3.1 模型架构

一般取前向/反向LSTM在最后一个时间步长上隐藏状态，然后进行拼接，在经过一个softmax层进行一个多分类；或者取前向/反向LSTM在每一个时间步长上的隐藏状态，对每一个时间步长上的两个隐藏状态进行拼接concat，然后对所有时间步长上拼接后的隐藏状态取均值，再经过一个softmax层进行一个多分类

3.2 模型实现

class Model(nn.Module):
    def __init__(self, config):
        super(Model, self).__init__()
        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.fc = nn.Linear(config.hidden_size * 2, config.num_classes)
    def forward(self, x):
        x, _ = x
        out = self.embedding(x)       # [batch_size, seq_len, embeding]=[128, 32, 300]
        out, _ = self.lstm(out)
        out = self.fc(out[:, -1, :])  # 句子最后时刻的 hidden state
        return out

4. TextRCNN

4.1 模型架构

与TextCNN比较类似，都是把文本表示为一个嵌入矩阵，再进行卷积操作。不同的是TextCNN中的文本嵌入矩阵每一行只是文本中一个词的向量表示，而在RCNN中，文本嵌入矩阵的每一行是当前词的词向量以及上下文嵌入表示的拼接

4.2 模型实现

class Model(nn.Module):
    def __init__(self, config):
        super(Model, self).__init__()
        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)  # [batch_size, seq_len, embeding]=[64, 32, 64]
        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 

5. BiLSTM_Attention

5.1 模型架构

相对于以前的文本分类中的BiLSTM模型，BiLSTM+Attention模型的主要区别是在BiLSTM层之后，全连接softmax分类层之前接入了一个叫做Attention Layer的结构

5.2 模型实现

class Model(nn.Module):
    def __init__(self, config):
        super(Model, self).__init__()
        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.tanh1 = nn.Tanh()
        self.w = nn.Parameter(torch.zeros(config.hidden_size * 2))
        self.tanh2 = nn.Tanh()
        self.fc1 = nn.Linear(config.hidden_size * 2, config.hidden_size2)
        self.fc = nn.Linear(config.hidden_size2, config.num_classes)
    def forward(self, x):
        x, _ = x
        emb = self.embedding(x)  # [batch_size, seq_len, embeding]=[128, 32, 300]
        H, _ = self.lstm(emb)  # [batch_size, seq_len, hidden_size * num_direction]=[128, 32, 256]
        M = self.tanh1(H)  # [128, 32, 256]
        alpha = F.softmax(torch.matmul(M, self.w), dim=1).unsqueeze(-1)  # [128, 32, 1]
        out = H * alpha  # [128, 32, 256]
        out = torch.sum(out, 1)  # [128, 256]
        out = F.relu(out)
        out = self.fc1(out)
        out = self.fc(out)  # [128, 64]
        return out

6. DPCNN

6.1 模型架构

第一层采用text region embedding，其实就是对一个n-gram文本块进行卷积，得到的feature maps作为该文本块的embedding。然后是convolution blocks的堆叠，就是两个卷积层与shortcut的组合。convolution blocks中间采用max-pooling，设置步长为2以进行负采样。最后一个pooling层将每个文档的数据整合成一个向量

6.2 模型实现

class Model(nn.Module):
    def __init__(self, config):
        super(Model, self).__init__()
        self.embedding = nn.Embedding(config.n_vocab, config.embed, padding_idx=config.n_vocab - 1)
        self.conv_region = nn.Conv2d(1, config.num_filters, (3, config.embed), stride=1)
        self.conv = nn.Conv2d(config.num_filters, config.num_filters, (3, 1), stride=1)
        self.max_pool = nn.MaxPool2d(kernel_size=(3, 1), stride=2)
        self.padding1 = nn.ZeroPad2d((0, 0, 1, 1))  # top bottom
        self.padding2 = nn.ZeroPad2d((0, 0, 0, 1))  # bottom
        self.relu = nn.ReLU()
        self.fc = nn.Linear(config.num_filters, config.num_classes)
    def forward(self, x):
        x = x[0]
        x = self.embedding(x)
        x = x.unsqueeze(1)  # [batch_size, 250, seq_len, 1]
        x = self.conv_region(x)  # [batch_size, 250, seq_len-3+1, 1]

        x = self.padding1(x)  # [batch_size, 250, seq_len, 1]
        x = self.relu(x)
        x = self.conv(x)  # [batch_size, 250, seq_len-3+1, 1]
        x = self.padding1(x)  # [batch_size, 250, seq_len, 1]
        x = self.relu(x)
        x = self.conv(x)  # [batch_size, 250, seq_len-3+1, 1]
        while x.size()[2] > 2:
            x = self._block(x)
        x = x.squeeze()  # [batch_size, num_filters(250)]
        x = self.fc(x)
        return x
    def _block(self, x):
        x = self.padding2(x)
        px = self.max_pool(x)
        x = self.padding1(px)
        x = F.relu(x)
        x = self.conv(x)
        x = self.padding1(x)
        x = F.relu(x)
        x = self.conv(x)
        x = x + px
        return x

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