ORES/Get support/zh

supports a limited set of Wikis, but support is growing all of the time. This page describes how to get ORES support for your favorite wiki(s) even outside the Wikimedia Foundation. Make sure to double-check the support table as we may already support your wiki. There are several different types of support that we can provide. It really comes down to which class of model and the tier of support we are aiming for. If you need help figuring out how to make a request, post on the talk page.



基本語言支援
In order to set up basic support for your wiki, the first thing we'll need to do is set up the language assets that ORES will need to operate in your wiki. Check if your language is listed in our code or check phabricator for open tasks. If you don't find your language listed, use the link below to request support!

(範例)

At the very basic level, we provide a  model that attempts to predict whether or not an edit will need to be reverted. This model is "trained" using a sample of past reverted edits that happened in a particular wiki. Once we have basic language support ready, we'll set up a  model for your wiki.



進階編輯質素支援
While the  model is useful and can be trained based on the history of article, it is slightly problematic. It's much better if we can train our prediction models on more nuanced judgements of the quality of an edit. Many of the tools that use ORES to support reviewing recent changes require this level of support. The  model predicts whether an edit causes damage and the   model predicts whether an edit was saved with good intentions.

为了收集训练这些模型所需的数据，我们可以设置一个维基标签活动，随机抽样进行评估. 请参阅[$campaigns 正在进行的活动]加入其中一个（最好是您有编辑经验），或在下面请求标注活动. See the ongoing campaigns to join one (preferably if you have editing experience), or request a labeling campaign below.

(範例)



條目質素支援
We train article quality models based on quality assessments (like en:WP:1.0 in English Wikipedia) if they are available. If not, we'll need to set up a Wiki labels campaign and have editors assess a sample of articles (usually ~5000) in order to train ORES how to make assessments.

(範例)



草稿質素支援
We train draft quality models based on deletion reasons that are recorded in the deletion log. In order to start work on the model, we'll need to ask for some help identifying what types comments are present in deletion reasons that we might flag as deeply problematic.