文本情感分类:

文本情感分类采用LSTM的最后一层输出

比如双层的LSTM,使用正向的最后一层和反向的最后一层进行拼接

    def forward(self,input):
        '''
        :param input: 
        :return: 
        '''
        input_embeded = self.embedding(input)    #[batch_size,seq_len,200]

        output,(h_n,c_n) = self.lstm(input_embeded)
        out = torch.cat(h_n[-1,:,:],h_n[-2,:,:],dim=-1) #拼接正向最后一个输出和反向最后一个输出

        #进行全连接
        out_fc1 = self.fc1(out)
        #进行relu
        out_fc1_relu = F.relu(out_fc1)
        #全连接
        out = self.fc2(out_fc1_relu)
        return  F.log_softmax(out,dim=-1)

  命名实体识别NER:

实体识别需要LSTM的output的全部输出。

如果有CRF层,则将输出全部给CRF层。

 

    def forward(self, words,emdlabel,speechtag, labels, seq_lengths):

        output, _ = self.bilstm(words,emdlabel,speechtag, seq_lengths)
        output = self.logistic(output)
        pre_score = self.crf(output)
        label_score = self.crf._score_sentence(output, labels)
        return (pre_score - label_score).mean(), None

    def predict(self, word, emdlabel,speechtag,seq_lengths):
        lstm_out, _ = self.bilstm(word, emdlabel,speechtag, seq_lengths)
        out = self.logistic(lstm_out)
        return self.crf.viterbi_decode(out)