https://github.com/wikimedia/articlequality
Github mirror - our actual code is hosted with Gerrit (please see https://www.mediawiki.org/wiki/Developer_access for contributing)
https://github.com/wikimedia/articlequality
artificial-intelligence
Last synced: about 1 month ago
JSON representation
Github mirror - our actual code is hosted with Gerrit (please see https://www.mediawiki.org/wiki/Developer_access for contributing)
- Host: GitHub
- URL: https://github.com/wikimedia/articlequality
- Owner: wikimedia
- License: mit
- Created: 2014-05-29T14:49:46.000Z (about 11 years ago)
- Default Branch: master
- Last Pushed: 2024-06-11T15:43:31.000Z (about 1 year ago)
- Last Synced: 2025-05-01T23:03:05.347Z (about 1 month ago)
- Topics: artificial-intelligence
- Language: Python
- Homepage: https://articlequality.readthedocs.io/en/latest/
- Size: 8.86 MB
- Stars: 49
- Watchers: 20
- Forks: 30
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE
Awesome Lists containing this project
README
# Wikipedia article quality classification
⚠️ Warning: As of late 2023, the ORES infrastructure is being deprecated by the
WMF Machine Learning team, please check https://wikitech.wikimedia.org/wiki/ORES for more info.
While the code in this repository may still work, it is unmaintained, and as
such may break at any time. Special consideration should also be given to
machine learning models seeing drift in quality of predictions.
The replacement for ORES and associated infrastructure is Lift Wing:
https://wikitech.wikimedia.org/wiki/Machine_Learning/LiftWing
Some Revscoring models from ORES run on the Lift Wing infrastructure, but they
are otherwise unsupported (no new training or code updates).
They can be downloaded from the links documented at:
https://wikitech.wikimedia.org/wiki/Machine_Learning/LiftWing#Revscoring_models_(migrated_from_ORES)
In the long term, some or all these models *may* be replaced by newer models
specifically tailored to be run on modern ML infrastructure like Lift Wing.
If you have any questions, contact the WMF Machine Learning team:
https://wikitech.wikimedia.org/wiki/Machine_Learning
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 word"
>>> 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](https://github.com/wikimedia/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