Pytorch and Tensorflow implemention of box embedding models
Project description
Open-source library for Box Embeddings and Box Representations, built on PyTorch & TensorFlow.
Status
Installation
Installing via pip
The preferred way to install Box Embeddings is via pip
. Just run pip install box-embeddings
Installing from source
You can also install Box Embeddings by cloning our git repository
git clone https://github.com/iesl/box-embeddings
Create a Python 3.7 or 3.8 virtual environment, and install Box Embeddings in editable mode by running:
pip install --editable . --user
pip install -r core_requirements.txt
Package Overview
Command | Description |
---|---|
box_embeddings |
An open-source library for NLP or graph learning |
box_embeddings.common |
Utility modules that are used across the library |
box_embeddings.initializations |
Initialization modules |
box_embeddings.modules |
A collection of modules to operate on boxes |
box_embeddings.parameterizations |
A collection of modules to parameterize boxes |
Citing
- If you use simple hard boxes with surrogate loss then cite the following paper:
@inproceedings{vilnis2018probabilistic,
title={Probabilistic Embedding of Knowledge Graphs with Box Lattice Measures},
author={Vilnis, Luke and Li, Xiang and Murty, Shikhar and McCallum, Andrew},
booktitle={Proceedings of the 56th Annual Meeting of the Association for
Computational Linguistics (Volume 1: Long Papers)},
pages={263--272},
year={2018}
}
- If you use softboxes without any regularizaton the cite the following paper:
@inproceedings{
li2018smoothing,
title={Smoothing the Geometry of Probabilistic Box Embeddings},
author={Xiang Li and Luke Vilnis and Dongxu Zhang and Michael Boratko and Andrew McCallum},
booktitle={International Conference on Learning Representations},
year={2019},
url={https://openreview.net/forum?id=H1xSNiRcF7},
}
- If you use softboxes with regularizations defined in the
Regularizations
module then cite the following paper:
@inproceedings{
patel2020representing,
title={Representing Joint Hierarchies with Box Embeddings},
author={Dhruvesh Patel and Shib Sankar Dasgupta and Michael Boratko and Xiang Li and Luke Vilnis
and Andrew McCallum},
booktitle={Automated Knowledge Base Construction},
year={2020},
url={https://openreview.net/forum?id=J246NSqR_l}
}
- If you use Gumbel box then cite the following paper:
@article{dasgupta2020improving,
title={Improving Local Identifiability in Probabilistic Box Embeddings},
author={Dasgupta, Shib Sankar and Boratko, Michael and Zhang, Dongxu and Vilnis, Luke
and Li, Xiang Lorraine and McCallum, Andrew},
journal={arXiv preprint arXiv:2010.04831},
year={2020}
}
The code for this library can be found here.
Contributors
-
Dhruvesh Patel @dhruvdcoder
-
Shib Sankar Dasgupta @ssdasgupta
-
Michael Boratko @mboratko
-
Xiang (Lorraine) Li @Lorraine333
-
Trang Tran @trangtran72
-
Purujit Goyal @purujitgoyal
-
Tejas Chheda @tejas4888
Contributions
We welcome all contributions from the community to make Box Embeddings a better package. If you're a first time contributor, we recommend you start by reading our CONTRIBUTING.md guide.
Team
Box Embeddings is an open-source project developed by the research team from the Information Extraction and Synthesis Laboratory at the College of Information and Computer Sciences (UMass Amherst).
Project details
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