SwisE
Switch spaces for knowledge graph embeddings.
Requirements:
python3
pytorch
numpy
tqdm
Reproduce the results
To reproduce the reported results, please follow the following steps:
-
Run "source set_env.sh"
-
Run the following commands for WN18RR
CUDA_VISIBLE_DEVICES=YourDeviceID python3 run.py --model=SwisE --max_epochs=100 --optimizer=Adam --regularizer=N3 --multi_c --rank=100 --batch_size 500
--neg_sample_size 50 --init_size 0.001 --learning_rate 0.01 --bias learn --valid 1 --dataset=WN18RR
--k=2 -manifolds Spherical Spherical Spherical Spherical Euclidean
- Run the following commands for FB15K237
CUDA_VISIBLE_DEVICES=YourDeviceID python3 run.py --model=SwisE --max_epochs=100 --optimizer=Adam --regularizer=N3 --multi_c --rank=100 --batch_size 500
--neg_sample_size 50 --init_size 0.001 --learning_rate 0.005 --bias learn --valid 1 --dataset=FB237
--k=4 -manifolds Hyperbolic Hyperbolic Hyperbolic Spherical Spherical
Reference
@article{zhang2021switch,
title={Switch spaces: Learning product spaces with sparse gating},
author={Zhang, Shuai and Tay, Yi and Jiang, Wenqi and Juan, Da-cheng and Zhang, Ce},
journal={arXiv preprint arXiv:2102.08688},
year={2021}
}