Awesome

For Lei et al. Encoder Generator Model

1. For training a bert encoder generator model :

dataset_folder=data/movies \
dataset_name=movies \
classifier=bert_encoder_generator \
output_dir=outputs \
exp_name=$EXP_NAME \
batch_size=4 \
rs_weight="use 1 if using rationale supervision else 0" \
bash Rationale_model/commands/model_train_script.sh

2. For making prediction on test set :

dataset_folder=data/movies \
dataset_name=movies \
classifier=bert_encoder_generator \
output_dir=outputs \
exp_name=$EXP_NAME \
batch_size=4 \
bash Rationale_model/commands/model_predict.sh

2. For calculating metrics :

python rationale_benchmark/metrics.py \
--data_dir data/movies \
--split test \
--results outputs/bert_encoder_generator/movies/$EXP_NAME/test_prediction.jsonl
--score_file outputs/bert_encoder_generator/movies/$EXP_NAME/test_scores.json

For Soft Scores BERT-LSTM Model

1. For training a bert soft scores model :

dataset_folder=data/movies \
dataset_name=movies \
classifier=soft_bert \
output_dir=outputs \
exp_name=$EXP_NAME \
batch_size=4 \
threshold=0.0 \
saliency=wrapper \
bash Rationale_model/commands/model_train_script.sh

2. For making prediction using saliency method (For attention, use $saliency below with wrapper and for simple gradient, replace it with simple_gradient). For computing comprehensiveness and sufficiency metrics, use the threshold provided in paper as a number in (0, 1), not percentages.

dataset_folder=data/movies \
dataset_name=movies \
classifier=soft_bert \
output_dir=outputs \
exp_name=$EXP_NAME \
batch_size=4 \
saliency=$saliency \
threshold=$threshold \
bash Rationale_model/commands/model_predict.sh

3. For calculating metrics :

python rationale_benchmark/metrics.py \
--data_dir data/movies \
--split test \
--results outputs/soft_bert/movies/$EXP_NAME/$saliency/test_prediction.jsonl
--score_file outputs/soft_bert/movies/$EXP_NAME/$saliency/test_scores.json

To train GloVe-LSTM models, please replace soft_bert with soft_word_emb in above code.