Implementation of a standard LSTM with regularization using PyTorch for text classification.
To run the model on Reuters dataset on static, just run the following from the project working directory.
python -m models.reg_lstm --dataset Reuters --mode static --batch-size 32 --lr 0.01 --epochs 30 --bidirectional --num-layers 1 --hidden-dim 512 --wdrop 0.1 --embed-droprate 0.2 --dropout 0.5 --beta-ema 0.99 --seed 3435
The best model weights will be saved in
To test the model, you can use the following command.
python -m models.reg_lstm --dataset Reuters --mode static --batch-size 32 --trained-model models/reg_lstm/saves/Reuters/best_model.pt --seed 3435
- rand: All words are randomly initialized and then modified during training.
- static: A model with pre-trained vectors from word2vec. All words, including the unknown ones that are initialized with zero, are kept static and only the other parameters of the model are learned.
- non-static: Same as above but the pretrained vectors are fine-tuned for each task.
Regularization options like embedding dropout, weight drop, temporal activation regularization and temporal averaging are available through command line args.
We experiment the model on the following datasets:
- Reuters (ModApte)
- Yelp 2014
Adam is used for training with an option for temporal averaging.
- The additional modules have been heavily inspired by two open source repositories: