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.mw-parser-output table.ombox img{max-width:none!important}@media screen{html.skin-theme-clientpref-night .mw-parser-output .ombox-speedy{background-color:#310402}}@media screen and (prefers-color-scheme:dark){html.skin-theme-clientpref-os .mw-parser-output .ombox-speedy{background-color:#310402}}</style><table class="plainlinks ombox ombox-notice" role="presentation"><tbody><tr><td class="mbox-image"><span typeof="mw:File"><a href="/wiki/File:Emoji_u1f4dd.svg" class="mw-file-description"><img src="//upload.wikimedia.org/wikipedia/commons/thumb/8/82/Emoji_u1f4dd.svg/80px-Emoji_u1f4dd.svg.png" decoding="async" width="80" height="80" class="mw-file-element" srcset="//upload.wikimedia.org/wikipedia/commons/thumb/8/82/Emoji_u1f4dd.svg/120px-Emoji_u1f4dd.svg.png 1.5x, //upload.wikimedia.org/wikipedia/commons/thumb/8/82/Emoji_u1f4dd.svg/160px-Emoji_u1f4dd.svg.png 2x" data-file-width="128" data-file-height="128"/></a></span></td><td class="mbox-text">This model card page currently has a 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.infobox-image,.mw-parser-output .infobox-full-data,.mw-parser-output .infobox-below{text-align:center}.mw-parser-output .infobox-navbar{text-align:right}.mw-parser-output .infobox .navbar{font-size:100%}@media(min-width:720px){.mw-parser-output .infobox{margin:0.5em 0 0.5em 1em;float:right;clear:right;width:22em}}.mw-parser-output .infobox-subbox{padding:0;border:none;margin:-3px;width:auto;clear:none;float:none;min-width:100%;font-size:100%;background-color:transparent}</style><table class="infobox"><tbody><tr><th colspan="2" class="infobox-above">Model card</th></tr><tr><td colspan="2" class="infobox-subheader">This page is an on-wiki <a href="https://en.wikipedia.org/wiki/Machine_learning" class="extiw" title="en:Machine learning">machine learning</a> <b>model card</b>.</td></tr><tr><td colspan="2" class="infobox-image"><span typeof="mw:File"><a href="/wiki/File:Neural_network.svg" class="mw-file-description"><img alt="A diagram of a neural network" src="//upload.wikimedia.org/wikipedia/commons/thumb/3/3d/Neural_network.svg/200px-Neural_network.svg.png" decoding="async" width="200" height="133" class="mw-file-element" srcset="//upload.wikimedia.org/wikipedia/commons/thumb/3/3d/Neural_network.svg/300px-Neural_network.svg.png 1.5x, //upload.wikimedia.org/wikipedia/commons/thumb/3/3d/Neural_network.svg/400px-Neural_network.svg.png 2x" data-file-width="600" data-file-height="400"/></a></span><div class="infobox-caption">A <a rel="nofollow" class="external text" href="https://arxiv.org/abs/1810.03993">model card</a> is a document about a <a href="https://en.wikipedia.org/wiki/Statistical_model" class="extiw" title="en:Statistical model">machine learning model</a> that seeks to answer basic questions about the model.</div></td></tr><tr><th colspan="2" class="infobox-header">Model Information Hub</th></tr><tr><th scope="row" class="infobox-label" style="background:#e6e8eb;">Model creator(s)</th><td class="infobox-data"><a href="/w/index.php?title=User:Trokhymovych&action=edit&redlink=1" class="new" title="User:Trokhymovych (page does not exist)">Mykola Trokhymovych</a>, Muniza Aslam, Ai-Jou Chou, and <a href="/wiki/User:Diego_(WMF)" title="User:Diego (WMF)">Diego Saez-Trumper</a></td></tr><tr><th scope="row" class="infobox-label" style="background:#e6e8eb;">Model owner(s)</th><td class="infobox-data"><a href="/wiki/User:Diego_(WMF)" title="User:Diego (WMF)">Diego Saez-Trumper</a></td></tr><tr><th scope="row" class="infobox-label" style="background:#e6e8eb;">Code</th><td class="infobox-data"><a rel="nofollow" class="external text" href="https://github.com/trokhymovych/KI_multilingual_training">training</a> and <a class="external text" href="https://gitlab.wikimedia.org/repos/research/knowledge_integrity">inference</a></td></tr><tr><th scope="row" class="infobox-label" style="background:#e6e8eb;">Uses PII</th><td class="infobox-data">No</td></tr><tr><th scope="row" class="infobox-label" style="background:#e6e8eb;">In production?</th><td class="infobox-data">Yes</td></tr><tr><td colspan="2" class="infobox-full-data">This model uses revision content and metadata to predict the risk of being reverted.</td></tr><tr><td colspan="2" class="infobox-navbar"><style data-mw-deduplicate="TemplateStyles:r24720806">.mw-parser-output .hlist dl,.mw-parser-output .hlist ol,.mw-parser-output .hlist ul{margin:0;padding:0}.mw-parser-output .hlist dd,.mw-parser-output .hlist dt,.mw-parser-output .hlist li{margin:0;display:inline}.mw-parser-output .hlist.inline,.mw-parser-output .hlist.inline dl,.mw-parser-output .hlist.inline ol,.mw-parser-output .hlist.inline ul,.mw-parser-output .hlist dl dl,.mw-parser-output .hlist dl ol,.mw-parser-output .hlist dl ul,.mw-parser-output .hlist ol dl,.mw-parser-output .hlist ol ol,.mw-parser-output .hlist ol ul,.mw-parser-output .hlist ul dl,.mw-parser-output .hlist ul ol,.mw-parser-output .hlist ul ul{display:inline}.mw-parser-output .hlist .mw-empty-li,.mw-parser-output .hlist .mw-empty-elt{display:none}.mw-parser-output .hlist dt:after{content:": "}.mw-parser-output .hlist dd:after,.mw-parser-output .hlist li:after{content:" · ";font-weight:bold}.mw-parser-output .hlist dd:last-child:after,.mw-parser-output .hlist dt:last-child:after,.mw-parser-output .hlist li:last-child:after{content:none}.mw-parser-output .hlist dd dd:first-child:before,.mw-parser-output .hlist dd dt:first-child:before,.mw-parser-output .hlist dd li:first-child:before,.mw-parser-output .hlist dt dd:first-child:before,.mw-parser-output .hlist dt dt:first-child:before,.mw-parser-output .hlist dt li:first-child:before,.mw-parser-output .hlist li dd:first-child:before,.mw-parser-output .hlist li dt:first-child:before,.mw-parser-output .hlist li li:first-child:before{content:" (";font-weight:normal}.mw-parser-output .hlist dd dd:last-child:after,.mw-parser-output .hlist dd dt:last-child:after,.mw-parser-output .hlist dd li:last-child:after,.mw-parser-output .hlist dt dd:last-child:after,.mw-parser-output .hlist dt dt:last-child:after,.mw-parser-output .hlist dt li:last-child:after,.mw-parser-output .hlist li dd:last-child:after,.mw-parser-output .hlist li dt:last-child:after,.mw-parser-output .hlist li li:last-child:after{content:")";font-weight:normal}.mw-parser-output .hlist ol{counter-reset:listitem}.mw-parser-output .hlist ol>li{counter-increment:listitem}.mw-parser-output .hlist ol>li:before{content:" "counter(listitem)"\a0 "}.mw-parser-output .hlist dd ol>li:first-child:before,.mw-parser-output .hlist dt ol>li:first-child:before,.mw-parser-output .hlist li ol>li:first-child:before{content:" ("counter(listitem)"\a0 "}</style><style data-mw-deduplicate="TemplateStyles:r24717021">.mw-parser-output .navbar{display:inline;font-size:88%;font-weight:normal}.mw-parser-output .navbar-collapse{float:left;text-align:left}.mw-parser-output .navbar-boxtext{word-spacing:0}.mw-parser-output .navbar ul{display:inline-block;white-space:nowrap;line-height:inherit}.mw-parser-output .navbar-brackets::before{margin-right:-0.125em;content:"[ "}.mw-parser-output .navbar-brackets::after{margin-left:-0.125em;content:" ]"}.mw-parser-output .navbar li{word-spacing:-0.125em}.mw-parser-output .navbar a>span,.mw-parser-output .navbar a>abbr{text-decoration:inherit}.mw-parser-output .navbar-mini abbr{font-variant:small-caps;border-bottom:none;text-decoration:none;cursor:inherit}.mw-parser-output .navbar-ct-full{font-size:114%;margin:0 7em}.mw-parser-output .navbar-ct-mini{font-size:114%;margin:0 4em}</style><div class="navbar plainlinks hlist navbar-mini"><ul><li class="nv-view"><a href="/w/index.php?title=Template:Model_card&action=edit&redlink=1" class="new" title="Template:Model card (page does not exist)"><abbr title="View this template">v</abbr></a></li><li class="nv-talk"><a href="/w/index.php?title=Template_talk:Model_card&action=edit&redlink=1" class="new" title="Template talk:Model card (page does not exist)"><abbr title="Discuss this template">t</abbr></a></li><li class="nv-edit"><a class="external text" href="https://meta.wikimedia.org/w/index.php?title=Template:Model_card&action=edit"><abbr title="Edit this template">e</abbr></a></li></ul></div></td></tr></tbody></table> <p><br/> </p><p>How can we help editors to identify revisions that need to be “<a href="https://en.wikipedia.org/wiki/Wikipedia:Patrolling" class="extiw" title="en:Wikipedia:Patrolling">patrolled</a>”? </p><p>The goal of this model is to detect revisions that might be reverted independently if they were made in good faith or with the intention of creating damage. Wikipedia has a group of dedicated volunteer editors, known as <a href="https://en.wikipedia.org/wiki/Wikipedia:Recent_changes_patrol" class="extiw" title="en:Wikipedia:Recent changes patrol">patrollers</a>, who work to ensure the accuracy and integrity of the information on the site. These patrollers review and edit articles, monitor for vandalism, and enforce community guidelines. However, their work is not easy, as they have to keep up with the fast pace and language diversity of Wikipedia, where on average, around 16 pages are edited per second in 250+ languages <sup id="cite_ref-1" class="reference"><a href="#cite_note-1"><span class="cite-bracket">[</span>1<span class="cite-bracket">]</span></a></sup>. The aim of this model is to help patrollers quickly identify potential problems, prioritize the work, and revert damaging edits when needed. </p><p>This model is deployed on LiftWing. Right now, it is available for internal usage. This model can be used to detect revisions that might need to be reverted. </p> <meta property="mw:PageProp/toc"/> <div class="mw-heading mw-heading2 ext-discussiontools-init-section"><h2 id="Motivation" data-mw-thread-id="h-Motivation"><span data-mw-comment-start="" id="h-Motivation"></span>Motivation<span data-mw-comment-end="h-Motivation"></span></h2><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=Machine_learning_models/Production/Multilingual_revert_risk&action=edit&section=1" title="Edit section: Motivation"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <p><a href="/wiki/Knowledge_Integrity" title="Knowledge Integrity">Knowledge Integrity</a> is one of the strategic programs of Wikimedia Research with the goal of identifying and addressing threats to content on Wikipedia, increasing the capabilities of patrollers, and providing mechanisms for assessing the reliability of sources<sup id="cite_ref-zia2019_2-0" class="reference"><a href="#cite_note-zia2019-2"><span class="cite-bracket">[</span>2<span class="cite-bracket">]</span></a></sup>. The main goal of the project is to create a new generation of patrolling models, improving accuracy, fairness, and maintainability compared to previous state-of-the-art ORES<sup id="cite_ref-3" class="reference"><a href="#cite_note-3"><span class="cite-bracket">[</span>3<span class="cite-bracket">]</span></a></sup>. </p><p>The current model is able to work on almost any Wikipedia article in any of the 47 chosen languages: <code>['ka', 'lv', 'ta', 'ur', 'eo', 'lt', 'sl', 'hy', 'hr', 'sk', 'eu', 'et', 'ms', 'az', 'da', 'bg', 'sr', 'ro', 'el', 'th', 'bn', 'no', 'hi', 'ca', 'hu', 'ko', 'fi', 'vi', 'uz', 'sv', 'cs', 'he', 'id', 'tr', 'uk', 'nl', 'pl', 'ar', 'fa', 'it', 'zh', 'ru', 'es', 'ja', 'de', 'fr', 'en']</code> </p> <div class="mw-heading mw-heading2 ext-discussiontools-init-section"><h2 id="Users_and_uses" data-mw-thread-id="h-Users_and_uses"><span data-mw-comment-start="" id="h-Users_and_uses"></span>Users and uses<span data-mw-comment-end="h-Users_and_uses"></span></h2><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=Machine_learning_models/Production/Multilingual_revert_risk&action=edit&section=2" title="Edit section: Users and uses"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <style data-mw-deduplicate="TemplateStyles:r23010500">.mw-parser-output .wiki-template-contentgrid{padding:10px;margin-bottom:1em;display:grid}</style> <div class="wiki-template-contentgrid" style="margin-right: 1em; grid-template-columns: repeat(auto-fit, minmax(300px, 1fr)); grid-gap: 10px; grid-auto-rows: minmax(100px, auto); width: auto;"><div style="box-shadow: 0 0 0.2em #999999; border-radius: 0.2em; margin: 0.5em 0.5em 1em 0.5em; background: var(--background-color-base, #fff); color:inherit; ;"> <div style="background: var(--background-color-neutral, #eaecf0); border-radius: 0.2em 0.2em 0 0; padding: 0.5em 1em 0.5em 1em;"><span style="color: var(--color-emphasized, #101418); font-weight: bold;">Use this model for</span> </div> <div style="padding: 0.5em 1em 0.5em 1em; border-radius: 0 0 0.2em 0.2em;"> <ul><li>Define the revert risk of Wikipedia article revision<div style="clear:both"></div></li></ul></div> </div> <div style="box-shadow: 0 0 0.2em #999999; border-radius: 0.2em; margin: 0.5em 0.5em 1em 0.5em; background: var(--background-color-base, #fff); color:inherit; ;"> <div style="background: var(--background-color-neutral, #eaecf0); border-radius: 0.2em 0.2em 0 0; padding: 0.5em 1em 0.5em 1em;"><span style="color: var(--color-emphasized, #101418); font-weight: bold;">Don't use this model for</span> </div> <div style="padding: 0.5em 1em 0.5em 1em; border-radius: 0 0 0.2em 0.2em;"> <ul><li>making predictions on language editions of Wikipedia that are not in the listed 47 languages or other Wiki projects (Wiktionary, Wikinews, Wikidata, etc.)</li> <li>making predictions on the revisions that are created by bots</li> <li>making predictions on the revisions that create a new article (the first revision of a page)</li> <li>making predictions on a revision that is the only one for a page</li> <li>As any AI/ML model, we recommend to keep humans in the loop, and not consider model's predictions as training data for other ML models.<div style="clear:both"></div></li></ul></div> </div> <div style="box-shadow: 0 0 0.2em #999999; border-radius: 0.2em; margin: 0.5em 0.5em 1em 0.5em; background: var(--background-color-base, #fff); color:inherit; ;"> <div style="background: var(--background-color-neutral, #eaecf0); border-radius: 0.2em 0.2em 0 0; padding: 0.5em 1em 0.5em 1em;"><span style="color: var(--color-emphasized, #101418); font-weight: bold;">Current uses</span> </div> <div style="padding: 0.5em 1em 0.5em 1em; border-radius: 0 0 0.2em 0.2em;"> <div style="clear:both"></div></div> </div></div> <div class="mw-heading mw-heading2 ext-discussiontools-init-section"><h2 id="Ethical_considerations,_caveats,_and_recommendations" data-mw-thread-id="h-Ethical_considerations,_caveats,_and_recommendations"><span id="Ethical_considerations.2C_caveats.2C_and_recommendations"></span><span data-mw-comment-start="" id="h-Ethical_considerations,_caveats,_and_recommendations"></span>Ethical considerations, caveats, and recommendations<span data-mw-comment-end="h-Ethical_considerations,_caveats,_and_recommendations"></span></h2><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=Machine_learning_models/Production/Multilingual_revert_risk&action=edit&section=3" title="Edit section: Ethical considerations, caveats, and recommendations"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <ul><li>This model was developed to improve the performance of it's <a href="/wiki/Machine_learning_models/Proposed/Language-agnostic_revert_risk" class="mw-redirect" title="Machine learning models/Proposed/Language-agnostic revert risk">Language Agnostic (RRLA)</a> version. The Multilingual version shows a better performance, especially for IP edits. However, it requires more processing power, and might be slower (or given timeouts).</li></ul> <ul><li>This models relies on <a rel="nofollow" class="external text" href="https://github.com/google-research/bert/blob/master/multilingual.md">Multilingual Bert</a>, a <a href="https://en.wikipedia.org/wiki/Large_Language_Model" class="extiw" title="en:Large Language Model">Large Language Model</a>, that might contain <a rel="nofollow" class="external text" href="https://link.springer.com/article/10.1007/s12559-021-09881-2">certain biases</a>.</li></ul> <div class="mw-heading mw-heading2 ext-discussiontools-init-section"><h2 id="Model" data-mw-thread-id="h-Model"><span data-mw-comment-start="" id="h-Model"></span>Model<span data-mw-comment-end="h-Model"></span></h2><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=Machine_learning_models/Production/Multilingual_revert_risk&action=edit&section=4" title="Edit section: Model"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <p>The presented model is based on content features extracted using fine-tuned language model mBERT<sup id="cite_ref-4" class="reference"><a href="#cite_note-4"><span class="cite-bracket">[</span>4<span class="cite-bracket">]</span></a></sup>, mwedittypes<sup id="cite_ref-5" class="reference"><a href="#cite_note-5"><span class="cite-bracket">[</span>5<span class="cite-bracket">]</span></a></sup> based features, along with user and page metadata. It is built in a paradigm of having one generalized model for all covered languages, which is currently the 47 most frequently edited languages in Wikipedia. The system includes the following steps: </p><p>1. Text features preparation: </p> <ul><li>Process wikitext and compare with parent revision</li> <li>Extract mwedittypes-based features</li> <li>Extract texts that were added, removed, and changed</li></ul> <p>2. Masked Language Models (MLMs) features extraction: </p> <ul><li>Pass each of the texts that were added, removed, or changed to the pre-trained classification model</li> <li>Apply mean and max pooling to the list of scores of each signal to extract the final unified feature set</li></ul> <p>3. Final Classification </p> <ul><li>Combine all extracted features with user and revision metadata</li> <li>Pass the features to the final classifier</li></ul> <figure class="mw-default-size mw-halign-left" typeof="mw:File/Thumb"><a href="/wiki/File:System_design_inference.png" class="mw-file-description"><img src="//upload.wikimedia.org/wikipedia/commons/thumb/1/17/System_design_inference.png/220px-System_design_inference.png" decoding="async" width="220" height="233" class="mw-file-element" srcset="//upload.wikimedia.org/wikipedia/commons/thumb/1/17/System_design_inference.png/330px-System_design_inference.png 1.5x, //upload.wikimedia.org/wikipedia/commons/thumb/1/17/System_design_inference.png/440px-System_design_inference.png 2x" data-file-width="1160" data-file-height="1226"/></a><figcaption>System design. Inference</figcaption></figure> <div class="mw-heading mw-heading3"><h3 id="Performance" data-mw-thread-id="h-Performance-Model"><span data-mw-comment-start="" id="h-Performance-Model"></span>Performance<span data-mw-comment-end="h-Performance-Model"></span></h3><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=Machine_learning_models/Production/Multilingual_revert_risk&action=edit&section=5" title="Edit section: Performance"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <div class="mw-heading mw-heading3"><h3 id="Implementation" data-mw-thread-id="h-Implementation-Model"><span data-mw-comment-start="" id="h-Implementation-Model"></span>Implementation<span data-mw-comment-end="h-Implementation-Model"></span></h3><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=Machine_learning_models/Production/Multilingual_revert_risk&action=edit&section=6" title="Edit section: Implementation"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <p>The presented model is a multistage solution that includes the fine-tuned masked language model (mBERT) for feature extraction and the final classifier (CatBoost) for getting the probability of being reverted based on the extracted features. </p><p><br/> <link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r23010500"> </p> <div class="wiki-template-contentgrid" style="margin-right: 1em; grid-template-columns: repeat(auto-fit, minmax(300px, 1fr)); grid-gap: 10px; grid-auto-rows: minmax(100px, auto); width: auto;"><div style="box-shadow: 0 0 0.2em #999999; border-radius: 0.2em; margin: 0.5em 0.5em 1em 0.5em; background: var(--background-color-base, #fff); color:inherit; ;"> <div style="background: var(--background-color-neutral, #eaecf0); border-radius: 0.2em 0.2em 0 0; padding: 0.5em 1em 0.5em 1em;"><span style="color: var(--color-emphasized, #101418); font-weight: bold;">Model architecture</span> </div> <div style="padding: 0.5em 1em 0.5em 1em; border-radius: 0 0 0.2em 0.2em;"> <p>mBERT models tunning (four models for the title, changes, inserts, and removes): </p> <ul><li>Learning rate: 2e-5</li> <li>Weight Decay: 0.01</li> <li>Epochs: 5</li> <li>Maximum input length: 512</li> <li>Number of encoder attention layers: 12</li> <li>Number of decoder attention layers: 12</li> <li>Number of attention heads: 12</li> <li>Length of encoder embedding: 768</li></ul> <p>CatBoost: </p> <ul><li>Iterations: 5000</li> <li>Learning Rate: 0.01</li> <li>Loss: Logloss<div style="clear:both"></div></li></ul></div> </div> <div style="box-shadow: 0 0 0.2em #999999; border-radius: 0.2em; margin: 0.5em 0.5em 1em 0.5em; background: var(--background-color-base, #fff); color:inherit; ;"> <div style="background: var(--background-color-neutral, #eaecf0); border-radius: 0.2em 0.2em 0 0; padding: 0.5em 1em 0.5em 1em;"><span style="color: var(--color-emphasized, #101418); font-weight: bold;">Output schema</span> </div> <div style="padding: 0.5em 1em 0.5em 1em; border-radius: 0 0 0.2em 0.2em;"> <div class="mw-highlight mw-highlight-lang-text mw-content-ltr" dir="ltr"><pre><span></span>{ lang: <language code string>, rev_id: <revision_id string>, score: { prediction: <boolean decision result> probability: { true: <probability of being reverted>, false: <probability of being NOT reverted> } } </pre></div><div style="clear:both"></div></div> </div> <div style="box-shadow: 0 0 0.2em #999999; border-radius: 0.2em; margin: 0.5em 0.5em 1em 0.5em; background: var(--background-color-base, #fff); color:inherit; ;"> <div style="background: var(--background-color-neutral, #eaecf0); border-radius: 0.2em 0.2em 0 0; padding: 0.5em 1em 0.5em 1em;"><span style="color: var(--color-emphasized, #101418); font-weight: bold;">Example input and output</span> </div> <div style="padding: 0.5em 1em 0.5em 1em; border-radius: 0 0 0.2em 0.2em;"> <p>Input </p> <div class="mw-highlight mw-highlight-lang-text mw-content-ltr" dir="ltr"><pre><span></span>curl https://api.wikimedia.org/service/lw/inference/v1/models/revertrisk-multilingual:predict -X POST -d '{"rev_id": 123855516, "lang":"ru"}' </pre></div> <p>Output </p> <div class="mw-highlight mw-highlight-lang-text mw-content-ltr" dir="ltr"><pre><span></span>{ lang: "ru", rev_id: 123855516, score: { prediction: true probability: { true: 0.9392203688621521, false: 0.0607796311378479 } } </pre></div><div style="clear:both"></div></div> </div></div> <div class="mw-heading mw-heading2 ext-discussiontools-init-section"><h2 id="Data" data-mw-thread-id="h-Data"><span data-mw-comment-start="" id="h-Data"></span>Data<span data-mw-comment-end="h-Data"></span></h2><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=Machine_learning_models/Production/Multilingual_revert_risk&action=edit&section=7" title="Edit section: Data"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <p>The model was trained on a dataset collected using the two tables from the <a href="https://wikitech.wikimedia.org/wiki/Analytics/Data_Lake" class="extiw" title="wikitech:Analytics/Data Lake">Wikimedia Data Lake</a>. We used the <a href="https://wikitech.wikimedia.org/wiki/Analytics/Data_Lake/Edits/MediaWiki_history" class="extiw" title="wikitech:Analytics/Data Lake/Edits/MediaWiki history">MediaWiki History</a> table, and the <a href="https://wikitech.wikimedia.org/wiki/Analytics/Data_Lake/Content/Mediawiki_wikitext_history" class="extiw" title="wikitech:Analytics/Data Lake/Content/Mediawiki wikitext history">Wikitext History</a> one. Snapshot dated 2022-07 was used with the observation period from 2022-01-01 to 2022-07-01 (6 months) for training and the following week for testing. We also filtered out revisions related to <a href="https://en.wikipedia.org/wiki/Wikipedia:Edit_war" class="extiw" title="en:Wikipedia:Edit war">edit wars</a> and revisions created by <a href="https://en.wikipedia.org/wiki/Wikipedia:Bot" class="extiw" title="en:Wikipedia:Bot">bots</a>. </p><p><br/> </p> <link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r23010500"> <div class="wiki-template-contentgrid" style="margin-right: 1em; grid-template-columns: repeat(auto-fit, minmax(300px, 1fr)); grid-gap: 10px; grid-auto-rows: minmax(100px, auto); width: auto;"><div style="box-shadow: 0 0 0.2em #999999; border-radius: 0.2em; margin: 0.5em 0.5em 1em 0.5em; background: var(--background-color-base, #fff); color:inherit; ;"> <div style="background: var(--background-color-neutral, #eaecf0); border-radius: 0.2em 0.2em 0 0; padding: 0.5em 1em 0.5em 1em;"><span style="color: var(--color-emphasized, #101418); font-weight: bold;">Data Pipeline</span> </div> <div style="padding: 0.5em 1em 0.5em 1em; border-radius: 0 0 0.2em 0.2em;"> <p>The data was collected using <a href="https://wikitech.wikimedia.org/wiki/Analytics/Data_Lake" class="extiw" title="wikitech:Analytics/Data Lake">Wikimedia Data Lake</a> and <a href="https://wikitech.wikimedia.org/wiki/Analytics/Cluster" class="extiw" title="wikitech:Analytics/Cluster">Wikimedia Analytics cluster</a>. </p> For each language, we collected revisions data. Then we merged the wikitext data and extracted the required features from the content using udf functions. Data collection pipeline for one language can be found in <a rel="nofollow" class="external text" href="https://github.com/trokhymovych/KI_multilingual_training/blob/main/modules/data_collection.py">data collection script</a><div style="clear:both"></div></div> </div> <div style="box-shadow: 0 0 0.2em #999999; border-radius: 0.2em; margin: 0.5em 0.5em 1em 0.5em; background: var(--background-color-base, #fff); color:inherit; ;"> <div style="background: var(--background-color-neutral, #eaecf0); border-radius: 0.2em 0.2em 0 0; padding: 0.5em 1em 0.5em 1em;"><span style="color: var(--color-emphasized, #101418); font-weight: bold;">Training data</span> </div> <div style="padding: 0.5em 1em 0.5em 1em; border-radius: 0 0 0.2em 0.2em;"> <ul><li>Data period: 6 months</li> <li>Number of revisions: 8,586,362</li> <li><a href="https://en.wikipedia.org/wiki/Wikipedia:IP_users" class="extiw" title="en:Wikipedia:IP users">IP users</a> edits rate: 0.17</li> <li>Revert rate: 0.08</li> <li>Random sample of up to 300,000 revisions per language<div style="clear:both"></div></li></ul></div> </div> <div style="box-shadow: 0 0 0.2em #999999; border-radius: 0.2em; margin: 0.5em 0.5em 1em 0.5em; background: var(--background-color-base, #fff); color:inherit; ;"> <div style="background: var(--background-color-neutral, #eaecf0); border-radius: 0.2em 0.2em 0 0; padding: 0.5em 1em 0.5em 1em;"><span style="color: var(--color-emphasized, #101418); font-weight: bold;">Test data</span> </div> <div style="padding: 0.5em 1em 0.5em 1em; border-radius: 0 0 0.2em 0.2em;"> <ul><li>Data period: 1 week</li> <li>Number of revisions: 1,079,265</li> <li><a href="https://en.wikipedia.org/wiki/Wikipedia:IP_users" class="extiw" title="en:Wikipedia:IP users">IP users</a> edits rate: 0.19</li> <li>Revert rate: 0.07<div style="clear:both"></div></li></ul></div> </div></div> <div class="mw-heading mw-heading2 ext-discussiontools-init-section"><h2 id="Licenses" data-mw-thread-id="h-Licenses"><span data-mw-comment-start="" id="h-Licenses"></span>Licenses<span data-mw-comment-end="h-Licenses"></span></h2><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=Machine_learning_models/Production/Multilingual_revert_risk&action=edit&section=8" title="Edit section: Licenses"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <ul><li>Code: <a href="https://en.wikipedia.org/wiki/Apache_License" class="extiw" title="en:Apache License">Apache 2.0 License</a></li> <li>Model: <a href="https://en.wikipedia.org/wiki/Apache_License" class="extiw" title="en:Apache License">Apache 2.0 License</a></li></ul> <div class="mw-heading mw-heading2 ext-discussiontools-init-section"><h2 id="Citation" data-mw-thread-id="h-Citation"><span data-mw-comment-start="" id="h-Citation"></span>Citation<span data-mw-comment-end="h-Citation"></span></h2><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=Machine_learning_models/Production/Multilingual_revert_risk&action=edit&section=9" title="Edit section: Citation"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <p>Cite this model as: </p> <div class="mw-highlight mw-highlight-lang-bibtex mw-content-ltr" dir="ltr"><pre><span></span><span class="nc">@inproceedings</span><span class="p">{</span><span class="nl">trokhymovych2023fair</span><span class="p">,</span> <span class="w"> </span><span class="na">title</span><span class="p">=</span><span class="s">{Fair multilingual vandalism detection system for Wikipedia}</span><span class="p">,</span> <span class="w"> </span><span class="na">author</span><span class="p">=</span><span class="s">{Trokhymovych, Mykola and Aslam, Muniza and Chou, Ai-Jou and Baeza-Yates, Ricardo and Saez-Trumper, Diego}</span><span class="p">,</span> <span class="w"> </span><span class="na">booktitle</span><span class="p">=</span><span class="s">{Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining}</span><span class="p">,</span> <span class="w"> </span><span class="na">pages</span><span class="p">=</span><span class="s">{4981--4990}</span><span class="p">,</span> <span class="w"> </span><span class="na">year</span><span class="p">=</span><span class="s">{2023}</span> <span class="p">}</span> </pre></div> <div class="mw-heading mw-heading2 ext-discussiontools-init-section"><h2 id="References" data-mw-thread-id="h-References"><span data-mw-comment-start="" id="h-References"></span>References<span data-mw-comment-end="h-References"></span></h2><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=Machine_learning_models/Production/Multilingual_revert_risk&action=edit&section=10" title="Edit section: References"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <div class="mw-references-wrap"><ol class="references"> <li id="cite_note-1"><span class="mw-cite-backlink"><a href="#cite_ref-1">↑</a></span> <span class="reference-text"><a class="external free" href="https://stats.wikimedia.org/">https://stats.wikimedia.org/</a></span> </li> <li id="cite_note-zia2019-2"><span class="mw-cite-backlink"><a href="#cite_ref-zia2019_2-0">↑</a></span> <span class="reference-text">Zia, Leila and Johnson, Isaac and Mansurov, Bahodir and Morgan, Jonathan and Redi, Miriam and Saez-Trumper, Diego and Taraborelli, Dario. 2019. Knowledge Integrity. <a rel="nofollow" class="external free" href="https://doi.org/10.6084/m9.figshare.7704626">https://doi.org/10.6084/m9.figshare.7704626</a></span> </li> <li id="cite_note-3"><span class="mw-cite-backlink"><a href="#cite_ref-3">↑</a></span> <span class="reference-text"><a class="external free" href="https://www.mediawiki.org/wiki/ORES">https://www.mediawiki.org/wiki/ORES</a></span> </li> <li id="cite_note-4"><span class="mw-cite-backlink"><a href="#cite_ref-4">↑</a></span> <span class="reference-text"><a rel="nofollow" class="external free" href="https://huggingface.co/bert-base-multilingual-cased">https://huggingface.co/bert-base-multilingual-cased</a></span> </li> <li id="cite_note-5"><span class="mw-cite-backlink"><a href="#cite_ref-5">↑</a></span> <span class="reference-text"><a rel="nofollow" class="external free" href="https://github.com/geohci/edit-types">https://github.com/geohci/edit-types</a></span> </li> </ol></div>    </div><noscript><img src="https://login.wikimedia.org/wiki/Special:CentralAutoLogin/start?type=1x1&useformat=desktop" alt="" width="1" height="1" style="border: none; position: absolute;"></noscript> <div class="printfooter" data-nosnippet="">Retrieved from "<a dir="ltr" href="https://meta.wikimedia.org/w/index.php?title=Machine_learning_models/Production/Multilingual_revert_risk&oldid=27466665">https://meta.wikimedia.org/w/index.php?title=Machine_learning_models/Production/Multilingual_revert_risk&oldid=27466665</a>"</div></div> <div id="catlinks" class="catlinks" data-mw="interface"><div id="mw-normal-catlinks" class="mw-normal-catlinks"><a href="/wiki/Special:Categories" title="Special:Categories">Category</a>: <ul><li><a href="/wiki/Category:Machine_learning_models" title="Category:Machine learning models">Machine learning models</a></li></ul></div></div> </div> </main> </div> <div class="mw-footer-container"> <footer id="footer" class="mw-footer" > <ul id="footer-info"> <li id="footer-info-lastmod"> This page was last edited on 16 September 2024, at 09:51.</li> <li id="footer-info-copyright">Text is available under the <a rel="nofollow" class="external text" href="https://creativecommons.org/licenses/by-sa/4.0/deed.en">Creative Commons Attribution-ShareAlike License</a>; additional terms may apply. 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