ORES

The Objective Revision Evaluation Service (ORES) is a web service and API that provides machine learning as a service for Wikimedia Projects. The system is designed to help automate critical wiki-work -- for example: vandalism detection and removal. Currently, the two general types of scores that ORES generates are in the context of "edit quality" and "article quality". ORES is a back-end service and does not directly provide a way to make use of the scores. If you'd like to use ORES scores, check out for a list of tools that use ORES scores. If ORES doesn't support your wiki yet, see our instructions for requesting support.
 * production: https://ores.wikimedia.org
 * experiment: https://ores.wmflabs.org
 * beta: https://ores-beta.wmflabs.org
 * staging: https://ores-staging.wmflabs.org



Edit quality
One of the most critical concerns about Wikimedia's open projects is the review of potentially damaging contributions ("edits"). There's also the need to identify good-faith contributors (who may be inadvertently causing damage) and offer them support. These models intended to make the work of filtering through the Special:RecentChanges feed easier. We offer two levels of support for edit quality prediction models: basic and advanced.

Basic support
Assuming that most damaging edits will be reverted and edits that are not damaging will not be, we can build using the history of edits (and reverted edits) from a wiki. This model is easy to set up, but it suffers from the problem that many edits are reverted for reasons other than damage and vandalism.
 * -- predicts whether an edit will eventually be reverted

Advanced support
Rather than assuming, we can ask editors to train ORES which edits are in-fact  and which edits look like they were saved in. This requires additional work on the part of volunteers in the community, but it affords a more accurate and nuanced prediction with regards to the quality of an edit. Many tools will only function when advanced support is available for a target wiki.
 * -- predicts whether or not an edit causes damage
 * -- predicts whether an edit was saved in good-faith



Article quality
The quality of encyclopedia articles is a core concern for Wikipedians. New pages must be reviewed and curated to ensure that spam, vandalism, and attack articles do not remain in the wiki. For articles that survive the initial curation, some of the Wikipedians periodically evaluate the quality of articles, but this is highly labor intensive and the assessments are often out of date.

Curation support
The faster that seriously problematic types of draft articles are removed, the better. Curating new page creations can be a lot of work. Like the problem of counter-vandalism in edits, machine predictions can help curators focus on the most problematic new pages first. Based on comments left by admins when they delete pages (see the logging table), we can train a model to predict which pages will need quick deletion. See en:WP:CSD for a list of quick deletion reasons for English Wikipedia. For the English model, we used G3 "vandalism", G10 "attack", and G11 "spam".
 * -- predicts if the article will need to be speedy deleted (spam, vandalism, attack, or OK)

Assessment scale support
For articles that survive the initial curation, some of the large Wikipedias periodically evaluate the quality of articles using a scale that roughly corresponds to the Wikipedia 1.0 assessment rating scale ("wp10"). Having these assessments is very useful because it helps us gauge our progress and identify missed opportunities (e.g., popular articles that are low quality). However, keeping these assessments up to date is challenging, so coverage is inconsistent. This is where the  machine learning model comes in handy. By training a model to replicate the article quality assessments that humans perform, we can automatically assess every article and every revision with a computer. This model has been used to help WikiProjects triage re-assessment work and to explore the editing dynamics that lead to article quality improvements.
 * -- predicts the (Wikipedia 1.0-like) assessment class of an article or draft