purujitgoyal / seal-neurips-2022

🦭 This is the official implementation for the paper [Structured Energy Network As a Loss](https://openreview.net/pdf?id=F0DowhX7_x).

SEAL 🦭

This is the official implementation for the paper Structured Energy Network As a Loss.

Setup

1. Clone the repo

2. Run setup enviroment bash script

$ chmod +x setup_env.sh
$ /bin/bash setup_env.sh

3. Download datasets

chmod +x download_datasets.sh
/bin/bash download_datasets.sh

4. Export environment variables

export CUDA_DEVICE=0 # 0 for GPU, -1 for CPU
export DATA_DIR="./data/"
export TEST=1 # for a dryrun and without uploading results to wandb
export WANDB_IGNORE_GLOBS=*\*\*\*.th,*\*\*\*.tar.gz,*\*\*.th,*\*\*.tar.gz,*\*.th,*\*.tar.gz,*.tar.gz,*.th

5. Training single models

The model configs are stored in best_models_configs. To run, for example, the cross-entropy model on bibtex dataset you would use best_models_configs/bibtex_strat_cross-entropy_ezllp30k/config.json.

allennlp train best_models_configs/bibtex_strat_cross-entropy_ezllp30k/config.json -s run_bibtex_cross-entropy --include-package seal --overrides {\"trainer.cuda_device\":-1}

Cite

@inproceedings{
lee2022structured,
title={Structured Energy Network As a Loss},
author={Jay-Yoon Lee and Dhruvesh Patel and Purujit Goyal and Wenlong Zhao and Zhiyang Xu and Andrew McCallum},
booktitle={Advances in Neural Information Processing Systems},
editor={Alice H. Oh and Alekh Agarwal and Danielle Belgrave and Kyunghyun Cho},
year={2022},
url={https://openreview.net/forum?id=F0DowhX7_x}
}