First run python data_utils.py to preprocess the data. Then run python train.py with appropriate command line arguments to train the model.

• -v FOLDER will save checkpoints at checkpoints/FOLDER and the final model at models/FOLDER.
• -p will use the DistilBERT model pretrained with in-domain MLM instead of the default original base model.
• -o will train an ordinal regression model instead of the default classification model.
• -f will freeze the pretrained layers instead of training the entire model end-to-end.
• -e NUM_EPOCHS will set the number of training epochs to NUM_EPOCHS (default 4).
• -lw _ _ _ _ _ will weight the loss of each class according to the five weights provided. Each blank should contain a number representing the weight of points whose true class is 1-star, 2-stars, 3-stars, 4-stars, or 5-stars respectively.
• -dn will disable layer normalization before the final classification output.
• -b BATCHES_PER_GPU will set the batch size per GPU (total batch size is BATCHES_PER_GPU x # of GPUs).