如何在 bert 模型之上添加 Bi-LSTM 层? [英] How can i add a Bi-LSTM layer on top of bert model?

问题描述

我正在使用 pytorch 并且我正在使用基本的预训练的 bert 对仇恨言论的句子进行分类.我想实现一个 Bi-LSTM 层，它将最新的所有输出作为输入来自 bert 模型的转换器编码器作为一个新模型(实现 nn.Module 的类)，我对 nn.LSTM 参数感到困惑.我使用

I'm using pytorch and I'm using the base pretrained bert to classify sentences for hate speech. I want to implement a Bi-LSTM layer that takes as an input all outputs of the latest transformer encoder from the bert model as a new model (class that implements nn.Module), and i got confused with the nn.LSTM parameters. I tokenized the data using

bert = BertForSequenceClassification.from_pretrained("bert-base-uncased", num_labels=int(data['class'].nunique()),output_attentions=False,output_hidden_states=False)

我的数据集有 2 列:class(label)、sentence.有人可以帮我弄这个吗?提前致谢.

My data-set has 2 columns: class(label), sentence. Can someone help me with this? Thank you in advance.

编辑:此外，在 bi-lstm 中处理输入后，网络将最终隐藏状态发送到使用 softmax 激活函数执行分类的全连接网络.我该怎么做?

Edit: Also, after processing the input in the bi-lstm, the network sends the final hidden state to a fully connected network that performs classication using the softmax activation function. how can I do that ?

推荐答案

您可以这样做:

from transformers import BertModel
class CustomBERTModel(nn.Module):
    def __init__(self):
          super(CustomBERTModel, self).__init__()
          self.bert = BertModel.from_pretrained("bert-base-uncased")
          ### New layers:
          self.lstm = nn.LSTM(768, 256, batch_first=True,bidirectional=True)
          self.linear = nn.Linear(256*2, <number_of_classes>)
          

    def forward(self, ids, mask):
          sequence_output, pooled_output = self.bert(
               ids, 
               attention_mask=mask)

          # sequence_output has the following shape: (batch_size, sequence_length, 768)
          lstm_output, (h,c) = self.lstm(sequence_output) ## extract the 1st token's embeddings
          hidden = torch.cat((lstm_output[:,-1, :256],lstm_output[:,0, 256:]),dim=-1)
          linear_output = self.linear(hidden.view(-1,256*2)) ### assuming that you are only using the output of the last LSTM cell to perform classification

          return linear_output

tokenizer = BertTokenizerFast.from_pretrained("bert-base-uncased")
model = CustomBERTModel()

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