Anonymized version of implementation of Disk Embeddings. Full version will be available online in camera-ready version.
This code is derived from Ganea et al. 2018, and there might be comments by them or their collaborators, however, the authors are not aware of it.
We conducted the experiments with python 3.6.6, Ubuntu 18.04.1 LTS in AWS EC2 c5.18xlarge instance.
Evaluated baseline methods are as follows:
- Poincare Embeddings (Nickel et.al., 2017)
- Order Embeddings (Vendrov et.al., 2016)
- Hyperbolic Entailment Cones (Ganea et.al., 2018)
- We used WordNet data already processed by Ganea et al. 2018.
- Preprocess on Hep-Th was made in
notebook/Citation.ipynb
For single worker,
python run.py RunAll --local-scheduler
For parallelization, luigid scheduler is required to be running.
python run.py RunAll --workers=32