A generic, machine learning-based revision scoring system designed to be used to automatically differentiate damage from productive contributory behavior on Wikipedia.
Using a scorer_model to score a revision:
>>> import mwapi
>>> from revscoring import ScorerModel
>>> from revscoring.extractors import APIExtractor
>>>
>>> with open("models/enwiki.damaging.linear_svc.model") as f:
... scorer_model = ScorerModel.load(f)
...
>>> extractor = APIExtractor(mwapi.Session(host="https://en.wikipedia.org",
... user_agent="revscoring demo"))
>>>
>>> feature_values = extractor.extract(123456789, scorer_model.features)
>>>
>>> print(scorer_model.score(feature_values))
{'prediction': True, 'probability': {False: 0.4694409344514984, True: 0.5305590655485017}}
The easiest way to install revscoring is via the Python package installer (pip).
pip install revscoring
You may find that some of revscorings dependencies fail to compile (namely scipy, numpy and sklearn). In that case, you'll need to install some dependencies in your operating system.
- Ubuntu & Debian:
Run
sudo apt-get install python3-dev g++ gfortran liblapack-dev libopenblas-dev
- Windows:
'TODO'
- MacOS:
'TODO'
Finally, in order to make use of language features, you'll need to download some NLTK data. The following command will get the necessary corpus.
python -m nltk.downloader stopwords
You'll also need to install enchant compatible dictionaries of the languages you'd like to use. We recommend the following:
languages.arabic
: aspell-arlanguages.dutch
: myspell-nllanguages.english
: myspell-en-us myspell-en-gb myspell-en-aulanguages.estonian
: myspell-etlanguages.french
: myspell-frlanguages.german
: myspell-de-at myspell-de-ch myspell-de-delanguages.indonesian
: aspell-idlanguages.italian
: myspell-itlanguages.hebrew
: myspell-helanguages.persian
: myspell-falanguages.polish
: aspell-pllanguages.portuguese
: myspell-ptlanguages.spanish
: myspell-eslanguages.ukrainian
: myspell-uklanguages.vietnamese
: hunspell-vi
- Aaron Halfaker:
- Helder:
- Adam Roses Wight:
Amir Sarabadani: * https://github.com/Ladsgroup