Toward Model Interpretability in Medical NLP
LING380: Topics in Computational Linguistics Final Project James Cross ([email protected]) and Daniel Kim ([email protected]), December 2021
Code Organization
data: contains medical report data [LINK TO THAT REPO] used in model fine-tuning and analysis, clinical stop words, and saved accuracy and entropy metrics during evaluationmodels: checkpoints of the best performing BERT and BioBERT models after hyperparameter optimizationnotebooks:
model_training.ipynb: code to train and fine-tune BERT and BioBERTmodel_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
Dependencies
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.
How to run code
Two options available to run the code; on Google colab and/or locally on your machine.
Option 1) Google Colab
Model training notebook: [https://colab.research.google.com/drive/1uPIi-OVchs_8A-SNcQtLfwelr0ccsz19?usp=sharing] Model evaluation/analysis notebook: []
Option 2) Local Machine
Notebooks: You can run the model_training.ipynb or model_evaluation.ipynb notebooks as is, changing directory paths when needed.
