The WMF Research team is conducting a second labeling campaign to evaluate
the Revert Risk model for Wikidata. Please assist by visiting this link
<https://annotool.toolforge.org/projects/7> and labeling each revision as
Keep, Not Sure, or Revert.
The Research team at WMF is currently conducting a second labeling campaign
to evaluate the Revert Risk model for Wikidata. As you might already know,
this is part of the ongoing efforts to develop a new generation of machine
learning models supporting patrolling work on Wikimedia projects.
In pursuit of this goal, the Research team at WMF created the Revert Risk
models to help identify content that "might be reverted". The Revert Risk
models for Wikipedia are already in production
and the focus now is on evaluating the model for Wikidata. Many of you
participated in the first labeling campaign
<https://annotool.toolforge.org/> (thanks to you all!), and now we seek
your assistance once again for this second campaign to gather more data.
Kindly visit this link <https://annotool.toolforge.org/projects/7> and
label each revision in one of these three categories: Keep, Not Sure,
note that "Not Sure" should be used in all cases where the Keep or Revert
labels are unclear to you. you.
If you have any questions, please contact Diego via e-mail: diego(at)
or on Meta (Diego (WMF)
On Thu, Aug 10, 2023 at 9:27 AM Mohammed Sadat Abdulai <
As you're likely aware, the Wikimedia Foundation's Machine Learning and
Research teams have been working on migrating from ORES to Lift Wing — a
new open-source machine learning infrastructure. This shift brings a host
of new capabilities and simplifies the process of retraining models over
time. (For more details, see the previous announcement
Lift Wing has already been trained using a dataset comprising reverted and
patrolled edits. However, it would be extremely helpful to have additional
new training data to help the model get even better at detecting
problematic edits on Wikidata. Therefore, we need your help.
How You Can Contribute
The Research team built a tool to make your involvement easy and
effective. This tool allows you to label new training data quickly and
efficiently. You can find the tool here: Annotation Tool.
<https://annotool.toolforge.org/> It will show you an edit and ask you if
you would keep or revert the edit. You can skip any you are not sure about.
By participating in this process, you're helping enhance the accuracy of
the bad edits detection system on Wikidata, making it more robust and
If you encounter any issues or want to provide general feedback, feel free
to leave us a note on this ticket phab:T341820
Mohammed S. Abdulai
*Community Communications Manager, Wikidata*
Wikimedia Deutschland e. V. | Tempelhofer Ufer 23-24 | 10963 Berlin
Phone: +49 (0) 30 577 116 2466
Grab a spot in my calendar for a chat: calendly.com/masssly
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