Metadata-Version: 2.4
Name: EncExp
Version: 0.1.2
Summary: Set of tools to create and use explainable embeddings.
Author: Eric S. Tellez, Mario Graff, Daniela Moctezuma
Maintainer-email: Mario Graff <mgraffg@ieee.org>
Project-URL: Repository, https://github.com/INGEOTEC/EncExp
Project-URL: Issues, https://github.com/INGEOTEC/EncExp/issues
Classifier: Development Status :: 3 - Alpha
Classifier: Environment :: Console
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Natural Language :: English
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: POSIX :: Linux
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Description-Content-Type: text/x-rst
License-File: LICENSE
Requires-Dist: numpy
Requires-Dist: scikit-learn>=1.3.0
Requires-Dist: microtc
Dynamic: license-file

EncExp (Encaje Explicable)
====================================

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	:target: https://github.com/INGEOTEC/EncExp/actions/workflows/test.yaml

.. image:: https://badge.fury.io/py/EncExp.svg
	:target: https://badge.fury.io/py/EncExp

.. image:: https://coveralls.io/repos/github/INGEOTEC/EncExp/badge.svg?branch=develop
    :target: https://coveralls.io/github/INGEOTEC/EncExp?branch=develop

EncExp is a set of tools for creating and using explainable embeddings. As with any embedding, the aim is to have a set of vectors that can be associated with tokens, and consequently, an utterance can be represented in the vector space span by the vectors. However, the difference concerning the embedding estimated with GloVe or Word2Vec, among others, is that EncExp associates vectors where each component has a meaning. The component's value indicates whether the word associated with the component might be present in the sentence. 

The component's meaning is a direct consequence of the procedure used to estimate the embedding. EncExp estimates the embedding by solving $d$ binary self-supervised classification problems, where the label is the presence of a particular token. The classifier used is a linear Support Vector Machine. 
