articlequality 0.4.4

A library for performing automatic detection of assessment classes of Wikipedia articles.

Wikipedia article quality classification

This library provides a set of utilities for performing automatic detection of assessment classes of Wikipedia articles. For more information, see the full documentation at https://articlequality.readthedocs.io .

Compatible with Python 3.x only. Sorry.

• Install: pip install articlequality
• Models: https://github.com/wikimedia/articlequality/tree/master/models
• Documentation: https://articlequality.readthedocs.io

Basic usage

>>> import articlequality
>>> from revscoring import Model
>>>
>>> scorer_model = Model.load(open("models/enwiki.nettrom_wp10.gradient_boosting.model", "rb"))
>>>
>>> text = "I am the text of a page.  I have a <ref>word</ref>"
>>> articlequality.score(scorer_model, text)
{'prediction': 'stub',
 'probability': {'stub': 0.27156163795807853,
                 'b': 0.14707452309674252,
                 'fa': 0.16844898943510833,
                 'c': 0.057668704007171959,
                 'ga': 0.21617801281707663,
                 'start': 0.13906813268582238}}

Install

Requirements

• Python 3.5, 3.6 or 3.7
• All the system requirements of revscoring

Installation steps

1. clone this repository
2. install the package itself and its dependencies python setup.py install
3. You can verify that your installation worked by running make enwiki_models to build the English Wikipedia article quality model or make wikidatawiki_models to build the item quality model for Wikidata

Retraining the models

To retrain a model, run make -B MODEL e.g. make -B wikidatawiki_models. This will redownload the labels, re-extract the features from the revisions, and then retrain and rescore the model.

To skip re-downloading the training labels and re-extracting the features, it is enough touch the files in the datasets/ directory and run the make command without the -B flag.

Running tests

Example:

pytest -vv tests/feature_lists/test_wikidatawiki.py

Authors

• Aaron Halfaker -- https://github.com/halfak
• Morten Warncke-Wang -- https://github.com/nettrom