Metadata-Version: 2.1
Name: box_embeddings
Version: 0.0.1
Summary: Pytorch and Tensorflow implemention of box embedding models
Home-page: http://www.iesl.cs.umass.edu/box-embeddings/
Author: Dhruvesh Patel, Shib Sankar Dasgupta, Michael Boratko, Purujit Goyal, Tejas Chheda, Trang Tran, Xiang (Lorraine) Li
Author-email: 1793dnp@gmail.com
License: UNKNOWN
Project-URL: Documentation, http://www.iesl.cs.umass.edu/box-embeddings
Project-URL: Source Code, https://github.com/iesl/box-embeddings
Description: 
        Pytorch implementation for box embeddings and box representations.
        
        <p align="center">
          <img src="/images/boxes.png">
        </p>
        
        ## Status
        
        ![Tests](https://github.com/iesl/box-embeddings/workflows/Tests/badge.svg) ![Typing/Doc/Style](https://github.com/iesl/box-embeddings/workflows/Typing/Doc/Style/badge.svg)
        
        
        
        ## 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 NLP research library, built on PyTorch & TensorFlow |
        | `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
        
        1. 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}
        }
        ```
        
        2. 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},
        }
        ```
        
        3. 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}
        }
        ```
        
        The code for this library can be found [here](https://github.com/iesl/box-embeddings).
        
        ## Contributors
        
        * Dhruvesh Patel [@dhruvdcoder](https://github.com/dhruvdcoder)
        
        * Shib Sankar Dasgupta [@ssdasgupta](https://github.com/ssdasgupta)
        
        * Michael Boratko [@mboratko](https://github.com/mboratko)
        
        * Xiang (Lorraine) Li [@Lorraine333](https://github.com/Lorraine333)
        
        * Trang Tran [@trangtran72](https://github.com/trangtran72)
        
        * Purujit Goyal [@purujitgoyal](https://github.com/purujitgoyal)
        
        * Tejas Chheda [@tejas4888](https://github.com/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](https://github.com/iesl/box-embeddings/blob/main/.github/CONTRIBUTING.md) guide.
        
        ## Team
        Box Embeddings is an open-source project developed by the research team from the
        [Information Extraction and Synthesis Laboratory](http://www.iesl.cs.umass.edu/) at the
        [College of Information and Computer Sciences (UMass Amherst)](https://www.cics.umass.edu/).
        
Keywords: pytorch,tensorflow,AI,ML,Machine Learning,Deep Learning
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.5
Description-Content-Type: text/markdown
