|__ text/ --> Directory with all text models and experimental scripts |__ data/ --> includes task data and simulation test data for conducting tests |__ anchor/ --> Anchor code from original authors |__ lime/ --> LIME code from original authors |__ saved_models/ --> training reports for task and prototype models |__ src/ |models/ --> code for neural task model and prototype model, including decision boundary and prototype explanation methods |classes/ --> supporting classes and utility functions for explanations |*/ --> directories for supporting classes including network layers and data loaders |__ figure_examples.py --> script for generating example explanations used in paper figures |__ gather-experiment-data.py --> script for gathering simulation test data |__ nearest-neighbors.py --> script for finding nearerest neighbors to prototypes |__ run_tagger.py --> script for evaluating classifier accuracy |__ requirements.txt --> package requirements for text experiments |__ tabular/ --> |__ data/ --> includes task data and simulation test data for conducting tests |__ anchor/ --> Anchor code from original authors |__ saved_models/ --> training reports for task and prototype models |__ src/ |models/ --> code for neural task model and prototype model, including decision boundary and prototype explanation methods |classes/ --> supporting classes and utility functions for explanations |*/ --> directories for supporting classes including network layers and data loaders |__ gather-experiment-data.py --> script for gathering simulation test data |__ nearest-neighbors.py --> script for finding nearerest neighbors to prototypes |__ run_tagger.py --> script for evaluating classifier accuracy |__ requirements.txt --> package requirements for tab experiments results_analysis.Rmd --> R markdown file that computes all empirical/statistical results in paper 

@inproceedings{hase_evaluating_2020, title={Evaluating Explainable AI: Which Algorithmic Explanations Help Users Predict Model Behavior?}, author={Peter Hase and Mohit Bansal}, booktitle={ACL}, url={https://arxiv.org/abs/2005.01831}, year={2020} }