https://github.com/facebookresearch/poincare-embeddings

PyTorch implementation of the NIPS-17 paper "Poincaré Embeddings for Learning Hierarchical Representations"
https://github.com/facebookresearch/poincare-embeddings

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PyTorch implementation of the NIPS-17 paper "Poincaré Embeddings for Learning Hierarchical Representations"

• Host: GitHub
• URL: https://github.com/facebookresearch/poincare-embeddings
• Owner: facebookresearch
• License: other
• Created: 2018-01-19T18:53:04.000Z (over 6 years ago)
• Default Branch: main
• Last Pushed: 2024-05-09T16:51:08.000Z (5 months ago)
• Last Synced: 2024-05-09T18:12:19.938Z (5 months ago)
• Language: Python
• Homepage:
• Size: 395 KB
• Stars: 1,652
• Watchers: 51
• Forks: 229
• Open Issues: 30
• Metadata Files:
  • Readme: README.org
  • Contributing: CONTRIBUTING.md
  • License: LICENSE
  • Code of conduct: CODE_OF_CONDUCT.md

Awesome Lists containing this project

README

        
* Poincaré Embeddings for Learning Hierarchical Representations

PyTorch implementation of [[https://papers.nips.cc/paper/7213-poincare-embeddings-for-learning-hierarchical-representations][Poincaré Embeddings for Learning Hierarchical Representations]]

[[file:wn-nouns.jpg]]

** Installation

Simply clone this repository via

#+BEGIN_SRC sh

  git clone https://github.com/facebookresearch/poincare-embeddings.git

  cd poincare-embeddings

  conda env create -f environment.yml

  source activate poincare

  python setup.py build_ext --inplace

#+END_SRC

** Example: Embedding WordNet Mammals

To embed the transitive closure of the WordNet mammals subtree, first generate the data via

#+BEGIN_SRC sh

  cd wordnet

  python transitive_closure.py

#+END_SRC

This will generate the transitive closure of the full noun hierarchy as well as of the mammals subtree of WordNet.

To embed the mammals subtree in the reconstruction setting (i.e., without missing data), go to the /root directory/ of the project and run

#+BEGIN_SRC sh

  ./train-mammals.sh

#+END_SRC

This shell script includes the appropriate parameter settings for the mammals subtree and saves the trained model as =mammals.pth=.

An identical script to learn embeddings of the entire noun hierarchy is located at =train-nouns.sh=. This script contains the hyperparameter setting to reproduce the results for 10-dimensional embeddings of [[https://papers.nips.cc/paper/7213-poincare-embeddings-for-learning-hierarchical-representations][(Nickel & Kiela, 2017)]]. The hyperparameter setting to reproduce the MAP results are provided as comments in the script.

The embeddings are trained via multithreaded async SGD. In the example above, the number of threads is set to a conservative setting (=NHTREADS=2=) which should run well even on smaller machines. On machines with many cores, increase =NTHREADS= for faster convergence.

** Dependencies

- Python 3 with NumPy

- PyTorch

- Scikit-Learn

- NLTK (to generate the WordNet data)

** References

If you find this code useful for your research, please cite the following paper in your publication:

#+BEGIN_SRC bibtex

@incollection{nickel2017poincare,

  title = {Poincar\'{e} Embeddings for Learning Hierarchical Representations},

  author = {Nickel, Maximilian and Kiela, Douwe},

  booktitle = {Advances in Neural Information Processing Systems 30},

  editor = {I. Guyon and U. V. Luxburg and S. Bengio and H. Wallach and R. Fergus and S. Vishwanathan and R. Garnett},

  pages = {6341--6350},

  year = {2017},

  publisher = {Curran Associates, Inc.},

  url = {http://papers.nips.cc/paper/7213-poincare-embeddings-for-learning-hierarchical-representations.pdf}

}

#+END_SRC

** License

This code is licensed under [[https://creativecommons.org/licenses/by-nc/4.0/][CC-BY-NC 4.0]].

[[https://img.shields.io/badge/License-CC%20BY--NC%204.0-lightgrey.svg]]