LING380: Topics in Computational Linguistics Final Project

James Cross (j.cross@yale.edu) and Daniel Kim (d.j.kim@yale.edu), December 2021

Two options available to run the code; on Google colab and/or locally on your machine.

Model training notebook

Model evaluation/analysis notebook

Notebooks: You can run the model_training.ipynb or model_evaluation.ipynb notebooks on your local machine. Directory paths will have to be adjusted as needed.

• data: contains medical transcriptions and other medical report data used in model fine-tuning and analysis, clinical stop words, and saved accuracy and entropy metrics during evaluation
• notebooks:

• model_training.ipynb: code to train and fine-tune BERT and BioBERT
• model_evaluation.ipynb: code to run various model evaluations, visualize word importances, perform post-training clinical stopword masking, and other analyses
• scripts: same functionality as in the notebooks, in executable python scripts / functions

• models: where checkpoints of the best performing BERT and BioBERT models after hyperparameter optimization are stored

All packages needed to run the code are available in the default Google Colab environment (see documentation for full list), with the exception of huggingface (transformers), used for loading transformer models, and captum.ai (captum), which provides access for a variety of model interpretation tools.