A Python library to conjugate French, English, Spanish, Italian, Portuguese and Romanian verbs using Machine Learning techniques.
Project description
MLConjug
a binary feature extractor,
a feature selector using Linear Support Vector Classification,
a classifier using Stochastic Gradient Descent.
Free software: MIT license
Documentation: https://mlconjug.readthedocs.io.
Supported Languages
French
English
Spanish
Italian
Portuguese
Romanian
Features
Easy to use API.
Includes pre-trained models with 99% + accuracy in predicting conjugation class of unknown verbs.
Easily train new models or add new languages.
Easily integrate MLConjug in your own projects.
Can be used as a command line tool.
Credits
This package was created with the help of Verbiste and scikit-learn.
Installation
Stable release
To install MLConjug, run this command in your terminal:
$ pip install mlconjug
This is the preferred method to install MLConjug, as it will always install the most recent stable release.
If you don’t have pip installed, this Python installation guide can guide you through the process.
From sources
The sources for MLConjug can be downloaded from the Github repo.
You can either clone the public repository:
$ git clone git://github.com/SekouD/mlconjug
Or download the tarball:
$ curl -OL https://github.com/SekouD/mlconjug/tarball/master
Once you have a copy of the source, you can install it with:
$ python setup.py install
History
2.1.9 (2018-06-21)
- Now the Conjugator adds additional information to the Verb object returned.
If the verb under consideration is already in Verbiste, the conjugation for the verb is retrieved directly from memory.
If the verb under consideration is unknown in Verbiste, the Conjugator class now sets the boolean attribute ‘predicted’ and the float attribute confidence score to the instance of the Verb object the Conjugator.conjugate(verb) returns.
Added Type annotations to the whole library for robustness and ease of scaling-out
The performance of the Engish and Romanian Models have improved significantly lately. I guess in a few more iteration they will be on par with the French Model which is the best performing at the moment as i have been tuning its parameters for a caouple of year now. Not so much with the other languages, but if you update regularly you will see nice improvents in the 2.2 release.
Enhanced the localization of the program.
Now the user interface of mlconjug is avalaible in French, Spanish, Italian, Portuguese and Romanian, in addition to English.
All the documentation of the project have been translated in the supported languages.
2.1.5 (2018-06-15)
Added localization.
Now the user interface of mlconjug is avalaible in French, Spanish, Italian, Portuguese and Romanian, in addition to English.
2.1.2 (2018-06-15)
Added invalid verb detection.
2.1.0 (2018-06-15)
Updated all language models for compatibility with scikit-learn 0.19.1.
2.0.0 (2018-06-14)
Includes English conjugation model.
Includes Spanish conjugation model.
Includes Italian conjugation model.
Includes Portuguese conjugation model.
Includes Romanian conjugation model.
1.2.0 (2018-06-12)
Refactored the API. Now a Single class Conjugator is needed to interface with the module.
Includes improved french conjugation model.
Added support for multiple languages.
1.1.0 (2018-06-11)
Refactored the API. Now a Single class Conjugator is needed to interface with the module.
Includes improved french conjugation model.
1.0.0 (2018-06-10)
First release on PyPI.
Project details
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