Code and data for the paper:
Bridging the Gap between Reality and Ideality of Entity Matching: A Revisiting and Benchmark Re-Construction
Details of the released data can be found in the REAME of the data.
First, install dependencies
# clone project
git clone https://github.com/tshu-w/EMBer
cd EMBer
# [SUGGESTED] use conda environment
conda env create -n ember -f environment.yaml
conda activate ember
# [ALTERNATIVE] install requirements directly
pip install -r requirements.txt
Next, to obtain the main results of the paper:
bash scripts/download_images.sh
python scripts/run_ali.py --gpus 0 1 2 3
python scripts/test_ali.py --gpus 0 1 2 3
python scripts/run_dm_ali.py --gpus 0 1 2 3
python scripts/test_dm_ali.py --gpus 0 1 2 3
python scripts/print_results results/test -k test/f1 test/prc test/rec
You can also run experiments with the run
script.
# fit with the TextMatcher config
./run fit --config configs/ali_tm.yaml
# or specific command line arguments
./run fit --model TextMatcher --data AliDataModule --data.batch_size 32 --trainer.gpus 0,
# evaluate with the checkpoint
./run test --config configs/ali_tm.yaml --ckpt_path ckpt_path
# get the script help
./run --help
./run fit --help
@inproceedings{ijcai2022p552,
title = {Bridging the Gap between Reality and Ideality of Entity Matching: A Revisting and Benchmark Re-Constrcution},
author = {Wang, Tianshu and Lin, Hongyu and Fu, Cheng and Han, Xianpei and Sun, Le and Xiong, Feiyu and Chen, Hui and Lu, Minlong and Zhu, Xiuwen},
booktitle = {Proceedings of the Thirty-First International Joint Conference on
Artificial Intelligence, {IJCAI-22}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
editor = {Lud De Raedt},
pages = {3978--3984},
year = {2022},
month = {7},
note = {Main Track},
doi = {10.24963/ijcai.2022/552},
url = {https://doi.org/10.24963/ijcai.2022/552},
}