1. deepclassifier.models.TextCNNΒΆ

I will show you that the parameters of TextCNN model.

class TextCNN(self,embedding_dim,dropout_rate,
       num_class,vocab_size=0,seq_length=0,
       num_layers=3,kernel_sizes=[3, 4, 5],
       strides=[1, 1, 1],paddings=[0, 0, 0],
       num_filters=[100, 100, 100],
       embedding_matrix=None,
       requires_grads=False):

Initialize TextCNN model.

Important

We strongly recommand you to use pre-trained embedding such as GloVe.

Parameters:

  • embedding_dim: embedding dim

  • dropout_rate: drouput rate

  • num_class: the number of label

  • vocab_size: vocabulary size

  • seq_length: max length of sequence after padding

  • num_layers: the number of cnn

  • kernel_sizes: list of conv kernel size

  • strides: list of conv strides

  • paddings: list of padding

  • num_filters: list of num filters

  • embedding_matrix: pretrained embedding look-up table,shape is:[vocab_size,embedding_dim]

  • requires_grads: whether to update gradient of embedding in training

forward(self, input_ids)
Parameters:

  • input_ids: [batch_size,seq_length]

Reference

@inproceedings{kim-2014-convolutional,
   title = "Convolutional Neural Networks for Sentence Classification",
   author = "Kim, Yoon",
   booktitle = "Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing ({EMNLP})",
   month = oct,
   year = "2014",
   address = "Doha, Qatar",
   publisher = "Association for Computational Linguistics",
   url = "https://www.aclweb.org/anthology/D14-1181",
   doi = "10.3115/v1/D14-1181",
   pages = "1746--1751",

}