mlconjug

MLConjug

A Python library to conjugate verbs in French, English, Spanish, Italian, Portuguese and Romanian (more soon)
using Machine Learning techniques.
Any verb in one of the supported language can be conjugated, as the module contains a Machine Learning model of how the verbs behave.
Even completely new or made-up verbs can be successfully conjugated in this manner.
The supplied pre-trained models are composed of:

a binary feature extractor,
a feature selector using Linear Support Vector Classification,
a classifier using Stochastic Gradient Descent.

MLConjug uses scikit-learn to implement the Machine Learning algorithms.

Users of the library can use any compatible classifiers from scikit-learn to modify and retrain the models.

The training data for the french model is based on Verbiste https://perso.b2b2c.ca/~sarrazip/dev/verbiste.html .
The training data for English, Spanish, Italian, Portuguese and Romanian was generated using unsupervised learning techniques
using the French model as a model to query during the trainig.

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.

Metadata

Release files for mlconjug 2.1.9

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for mlconjug 2.1.9
File	Size	Uploaded
mlconjug-2.1.9.tar.gz	24.6 kB	Jun 20, 2018	Details

Built distribution (wheel)

Table of built distributions (wheels) for mlconjug 2.1.9
File	Interpreter	ABI	Platform	Reset
mlconjug-2.1.9-py2.py3-none-any.whl 13.4 kB Jun 20, 2018	Python 2, Python 3	none	any	Details

Total release size: 38.1 kB

Release files / mlconjug-2.1.9.tar.gz

Download URL	mlconjug-2.1.9.tar.gz
Size	24.6 kB
Tags	Source
SHA-256 checksum How to use checksums	`556f3b3da82d6a8ddaeb3db62fa06c5e362b660cda9d1fd62c742d9847ce7e61`
BLAKE2b-256 checksum How to use checksums	`aabcbe71abee548b038774f6ff532361cceb0bac26bb9cb8a571ec73247c015a`
Upload date	Jun 20, 2018
Uploaded using Trusted Publishing? What is trusted publishing?	No

Release files / mlconjug-2.1.9-py2.py3-none-any.whl

Download URL	mlconjug-2.1.9-py2.py3-none-any.whl
Size	13.4 kB
Tags	Python 2 Python 3
SHA-256 checksum How to use checksums	`2212b9c6133099ffbd71b13dc28572e2a99468490edb1d90fe37e579871a06b0`
BLAKE2b-256 checksum How to use checksums	`7e7aae2c4e22ae4fe7720aa40ce17054d543ae116e015b6d3bfdaf6a62fcb5c0`
Upload date	Jun 20, 2018
Uploaded using Trusted Publishing? What is trusted publishing?	No