ORES/fr

ORES (/ɔɹz/, Objective Revision Evaluation Service) is a web service and API that provides machine learning as a service for Wikimedia projects maintained by the Scoring Platform team. 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 est un service sous-jacent, qui ne permet pas d'utiliser directement ses classements. Pour utiliser les classements d'ORES, consultez notre liste d'outils qui les utilisent. Si ORES n'est pas encore accessible sur votre wiki, lisez nos instructions pour demander du support.

Vous cherchez des réponses à vos questions sur ORES? Consultez la Foire Aux Questions sur ORES.

==Qualité des interventions Une des préoccupations majeures quant aux projets ouverts de Wikimédia est d'identifier les contributions qui pourraient causer des dommages. De plus, il faut identifier les contributeurs de bonne foi (qui pourraient, par inadvertance, causer des dommages) et leur offrir du support. Ces modèles devraient faciliter la tâche de scruter la page Spécial:ChangementsRécents. Nous offrons deux modes pour nos modèles d'évaluation de la qualité des contributions: de base et avancé.

Support de base
Prenant pour acquit que la plupart des contributions dommageables vont être révoquées et que les contributions qui ne sont pas dommageables ne seront pas, nous pouvons travailler, à l'aide de l'historique des contributions (révoquées ou pas) sur un wiki. Ce modèle est facile à bâtir, mais son problème est que bien des contributions sont révoquées pour d'autres raisons que pour dangerosité ou vandalisme. Pour cette raison, nous bâtissons un modèle basé sur les gros mots.


 * – 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 English 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.

The wp10 model bases its predictions on structural characteristics of the article. E.g. How many sections are there? Is there an infobox? How many references? And do the references use a cite template? The wp10 model doesn't evaluate the quality of the writing or whether or not there's a tone problem (e.g. a point of view being pushed). However, many of the structural characteristics of articles seem to correlate strongly with good writing and tone, so the models work very well in practice.


 * – predicts the (Wikipedia 1.0-like) assessment class of an article or draft

Support table
The following table reports the status of ORES support by wiki and model available. If you don't see your wiki listed, or support for the model you'd like to use, you can request support.

API usage
ORES offers a Restful API service for dynamically retrieving scoring information about revisions. See https://ores.wikimedia.org for more information on how to use the API.

If you're querying the service about a large number of revisions, it's recommended to batch 50 revisions in each request as described below. It's acceptable to use up to 4 parallel requests. For even larger number of queries, you can run ORES locally

Example query: |wp10&revids=34854345|485104318 http://ores.wmflabs.org/v3/scores/enwiki/?modelsdraftquality|wp10&revids34854345|485104318

Example query: https://ores.wikimedia.org/v3/scores/wikidatawiki/421063984/damaging

Local usage
To run ORES locally you can install ORES by

Then you should be able to run it through

You should see output of