ORES/ja

ORES（/ɔɹz/）はScoring Platform teamがウィキメディアのプロジェクト群の点検用に、サービスの一環として機械学習を提供するというウェブサービスとAPIのことです. このシステムは（荒らしの発見や除去など）重要なウィキ作業の自動化の補助を目指して設計されました. 現状ではORESが生成する採点は大まかに2種類あり、それぞれ「編集の質」と「記事の質」を示します.

ORESは裏方のサービスであり、スコアを直接利用する方法は提供しません. 使用するにはORESスコアを用いるツールの一覧をご確認ください. また、ご利用のウィキがORESに対応していない場合は対応のご要望に関する指示をご覧ください.

ORESについてのご質問への回答をお探しですか？ORES よくある質問（英語）をご確認ください.

編集の質
ウィキメディアがオープンなプロジェクトである以上、もっとも深刻な問題は有害かもしれない投稿（「編集」）の査読です. 善良な投稿者（害を及ぼしたのは偶然）を特定してサポートを提供する必要もあります. これらのモデルには「特別:最近の更新」のフィードの絞り込み作業を楽にする目的があります. 編集の質の予測モデルで提供するサポートには、「基本」と「高度」の2つのレベルが用意されています.

基本的なサポート
前提として、有害度が高い編集は差し戻しの対象であり、害がそれほど深刻でない編集は 対象ではないとすると、ウィキにおける編集履歴（および巻き戻しされた編集）の記録を利用できます. このモデルはセットアップは簡単でありながら、編集の巻き戻しの原因の多くが有害性や荒らし以外の要素である点に振り回されます. その点を補うため、有害な用語に基づくモデルwe create a model based on bad wordsを作成しています.


 * – predicts whether an edit will eventually be reverted

高度なサポート
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

記事の質
百科事典記事の質はウィキペディア編集者にとって重点的指標となります. 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

精選と修正のサポート
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)

評価指標のサポート
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

サポートの一覧表
下記の一覧表にウィキごとの ORES サポートの進捗状況ならびにモデルをまとめてあります. もしご利用のウィキが未掲載の場合、あるいはモデルのサポートが見当たらない場合はサポートを申請してください.

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

クエリのサンプル: |wp10&revids=34854345|485104318 http://ores.wmflabs.org/v3/scores/enwiki/?modelsdraftquality|wp10&revids34854345|485104318

クエリのサンプル: 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