Pytorch Implementation of EACL paper(findings) Bridging the Gap between Pre-Training and Fine-Tuning for Commonsense Generation
./train.sh
We also provide our trained model here
python test.py --output_dir models/
models/ is the directory that contains the trained model model.bin.
We provide our predicted test file test_pred.txt under data/
Download and unzip the evaluation files here
1. obtain coverage score
cd evaluation/PivotScore
python evaluate.py --pred your_pred_file --ref ../../data/test_trg.txt --cs ../../data/test_src.txt
2. obtain other score
python convert_to_json.py --src data/test_src.txt --trg data/test_trg.txt --pred your_pred_file
cd evaluation/CaptionMetrics/
python main.py --trg ../../temp_trg.json --pred ../../temp_pred.json