Classifier

A Pytorch implementation of a Deep Averaging Network (DAN) classifier. Uses pre-trained ELMo or Glove embeddings.

Hidden layer dimensions must be specified with the --layers flag, i.e --layers 50 30.

Other optional flags:

Flag	Action
--data_dir	the directory containing the data files
--temp_dir	the directory for temporary files (i.e. saved model state, embeddings)
--file_type	the tabular format (csv / tsv)
--glove_type	the type of pretrained Glove embedding (6B, 42B, 840B, twitter.27B)
--glove_dim	the size of the pretrained Glove embedding (50, 100, 200, 300)
--batch_dims	the batch size of training and test sets
--lr	the learning rate
--num_steps	the number of training steps
--num_processes	the number of parallel training processes (default = no parallelism)
--baseline	compare with naive bayes baseline
--plot	plot loss
--elmo	use pretrained contextual ELMo embeddings

Note that if --num_processes is greater than 1, logging and --plot are not supported.

TO DO

• Add logging for torch.multiprocessing
• Support ELMo contextual embeddings