ORES/id

ORES (/ɔɹz/) adalah layanan web dan API yang menyediakan pembelajaran mesin sebagai layanan untuk proyek-proyek Wikimedia yang dikelola oleh tim Scoring Platform. Sistem ini dirancang untuk membantu mengotomatisasi kerja pada wiki – contohnya, deteksi dan penghapusan vandalisme. Saat ini, dua tipe umum skor yang dikeluarkan oleh ORES adalah dalam "kualitas penyuntingan" dan "kualitas artikel."

ORES merupakan sebuah layanan sisi belakang dan tidak secara langsung menyediakan cara untuk menggunakan skor. Jika Anda ingin menggunakan skor ORES, periksa daftar perkakas yang menggunakan skor ORES. Jika ORES belum mendukung wiki Anda, lihatlah instruksi untuk meminta dukungan.

Sedang mencari jawaban untuk pertanyaan Anda mengenai ORES? Lihatlah FAQ ORES.

Kualitas suntingan
Salah satu masalah kritis tentang proyek terbuka Wikimedia adalah peninjauan kontribusi ("suntingan") yang berpotensi merusak. Juga harus ada cara mengenali kontributor yang berniat baik (yang mungkin tidak sengaja membuat kerusakan) dan memberikan mereka dukungan. Model-model ini dimaksudkan agar pekerjaan menyaring umpan Special:RecentChanges lebih mudah. Kami menawarkan dua tingkat dukungan untuk model prediksi kualitas suntingan: dasar dan lanjutan.

Dukungan dasar
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. To help that, we create a model based on bad words.


 * – memprediksi apakah hasil suntingan harus dibalikkan

Dukungan lanjutan
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

Kualitas artikel
The quality of Wikipedia 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 ("articlequality"). 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 articlequality 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 articlequality 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

Topic routing


ORES' article topic model applies an intuitive top-down taxonomy to any article in Wikipedia -- even new article drafts. This topic routing is useful for curating new articles, building work lists, forming new WikiProjects, and analyzing coverage gaps.

ORES topic models are trained using word embeddings of the actual content. For each language, a language-specific embedding is learned and applied natively. Since this modeling strategy depends on the topic of the article, topic predictions may differ between languages depending on the topics present in the text of the article.

Curation support


The biggest difficulty with reviewing new articles is finding someone familiar with the subject matter to judge notability, relevance, and accuracy. Our  model is designed to route newly created articles based on their apparent topical nature to interested reviewers. The model is trained and tested against the first revision of articles and is thus suitable to use on new article drafts.


 * – predicts the topic of an a new article draft

Topic interest mapping


The topical relatedness of articles is an important concept for the organization of work in Wikipedia. Topical working groups have become a common strategy for managing content production and patrolling in Wikipedia. Yet a high-level hierarchy is not available or query-able for many reasons. The result is that anyone looking to organize around a topic or make a work-list has to do substantial manual work to identify the relevant articles. With our  model, these queries can be done automatically.


 * – predicts the topic of an article

Tabel dukungan
Tabel dukungan ORES melaporkan status dukungan ORES menurut wiki dan model yang tersedia. Jika Anda tidak melihat wiki Anda di daftar, atau dukungan untuk model yang Anda ingin gunakan, Anda bisa meminta dukungan.

Penggunaan API
ORES menawarkan sebuah layanan API Restful untuk mengambil secara dinamis informasi skor tentang revisi. Lihat https://ores.wikimedia.org untuk informasi lebih lanjut mengenai cara menggunakan API.

Jika Anda mengkueri layanan tentang banyak revisi, disarankan untuk tidak menumpukkan lebih dari 50 revisi dalam satu permintaan sebagaimana yang dijelaskan di bawah. Diperbolehkan menggunakan maksimal 4 permintaan secara paralel. Tolong jangan lebihi batas ini atau ORES bisa menjadi tidak stabil. Untuk kueri yang lebih banyak lagi, Anda bisa menjalankan ORES secara lokal.

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

Contoh kueri: https://ores.wikimedia.org/v3/scores/wikidatawiki/421063984/damaging

Penggunaan EventStream
Skor ORES juga disediakan sebagai sebuah EventStream di https://stream.wikimedia.org/v2/stream/revision-score

Penggunaan lokal
Untuk menjalankan ORES secara lokal, Anda dapat memasang ORES dengan:

Kemudian Anda dapat menjalankannya dengan:

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