A Fully Hyperbolic Neural Model for Hierarchical Multi-class Classification

Code for the paper "A Fully Hyperbolic Neural Model for Hierarchical Multi-class Classification" published at Findings of EMNLP 2020.

Model overview:

Citation

The source code and data in this repository aims at facilitating the integration of hyperbolic components into neural models for NLP. If you use the code/data, please cite it as follows:

@inproceedings{lopez2020hyfi,
    title = "A Fully Hyperbolic Neural Model for Hierarchical Multi-Class Classification",
    author = "L{\'o}pez, Federico  and Strube, Michael",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.42",
    pages = "460--475"
}

Dependencies

python 3.7
PyTorch 1.4
geoopt 0.2.0
tqdm
tensorboardX

Running experiments

1. Download data

Download and uncompress Ultra-Fine dataset, and download Poincare word embeddings:

./scripts/hyfi.sh get_data

Experiments were run with poincare_glove_100D_cosh-dist-sq_init_trick.txt.

2. Preprocess data

The parameter poin-ctx25 can be replaced to store different preprocessing configurations:

./scripts/hyfi.sh preprocess poin-ctx25

3. Train model

The name of the preprocessing used in the previous step must be given as a parameter.

./scripts/hyfi.sh train poin-ctx25

3. Do inference

./scripts/figet.sh inference poin-ctx25 models/poin-ctx25-{N}bstep.pt

N should be the number of the best epoch, according to the validation results.

Acknowledgements

We kindly thank the open-source community for their support with high quality software, and specially to:

Choi et al. for the release of the Ultra-Fine dataset and their model.
Tifrea et al. for the release of the Poincare Glove word embeddings.
Ganea et al. for the release of the Hyperbolic Neural Networks.
Kochurov et al. for the implementation and release of the magnificent Geoopt.

License

MIT

nlpAThits / hyfi