Mlconjug

------------------

* Updated Dependencies.

------------------

* Updated Documentation.
* Added support for pipenv.
* Included tests and documentation in the package distribution.

------------------

* Updated `Type annotations`_ to the whole library for PEP-561 compliance.

------------------

* Minor Api enhancement (see `API documentation`_)

------------------

* Updated the conjugation models for Spanish and Portuguese.
* Internal changes to the format of the verbiste data from xml to json for better handling of unicode characters.
* New class ConjugManager to more easily add new languages to mlconjug.
* Minor Api enhancement (see `API documentation`_)

------------------

* Updated all provided pre-trained prediction models:
- Implemented a new vectrorizer extracting more meaningful features.
- As a result the performance of the models has gone through the roof in all languages.
- Recall and Precision are intesimally close to 100 %. English being the anly to achieve a perfect score at both Recall and Precision.

* Major API changes:
- I removed the class EndingCustomVectorizer and refactored it's functionnality in a top level function called extract_verb_features()
- The provided new improved model are now being zip compressed before release because the feature space has so much grown that their size made them impractical to distribute with the package.
- Renamed "Model.model" to "Model.pipeline"
- Renamed "DataSet.liste_verbes" and "DataSet.liste_templates" to "DataSet.verbs_list" and "DataSet.templates_list" respectively. (Pardon my french ;-) )
- Added the attributes "predicted" and "confidence_score" to the class Verb.
- The whole package have been typed check. I will soon add mlconjug's type stubs to typeshed.