Hi everyone,
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 https://lists.wikimedia.org/hyperkitty/list/[email protected]/thread/EK65B7QCQHEG37C2ERPIUSP64OX3ZEUJ/ )
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 reliable.
If you encounter any issues or want to provide general feedback, feel free to leave us a note on this ticket phab:T341820 https://phabricator.wikimedia.org/T341820.
Cheers,
*Awesome* tool! We should make this a banner on Wikidata to get as many people to annotate as possible! The potential for reverting edits like the ones shown in the tool is very exciting!
lectrician1 https://www.wikidata.org/wiki/User:Lectrician1
El jue, 10 ago 2023 a la(s) 02:28, Mohammed Sadat Abdulai ( [email protected]) escribiĂł:
Hi everyone,
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 https://lists.wikimedia.org/hyperkitty/list/[email protected]/thread/EK65B7QCQHEG37C2ERPIUSP64OX3ZEUJ/ )
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 reliable.
If you encounter any issues or want to provide general feedback, feel free to leave us a note on this ticket phab:T341820 https://phabricator.wikimedia.org/T341820.
Cheers,
Mohammed S. Abdulai *Community Communications Manager, Wikidata*
Wikimedia Deutschland e. V. | Tempelhofer Ufer 23-24 | 10963 Berlin Phone: +49 (0) 30 577 116 2466 https://wikimedia.de
Grab a spot in my calendar for a chat: calendly.com/masssly.
A lot is happening around Wikidata - Keep up to date! https://www.wikidata.org/wiki/Wikidata:Status_updates Current news and exciting stories about Wikimedia, Wikipedia and Free Knowledge in our newsletter (in German): Subscribe now https://www.wikimedia.de/newsletter/.
Imagine a world in which every single human being can freely share in the sum of all knowledge. Help us to achieve our vision! https://spenden.wikimedia.de
Wikimedia Deutschland — Gesellschaft zur Förderung Freien Wissens e. V. Eingetragen im Vereinsregister des Amtsgerichts Charlottenburg, VR 23855 B. Als gemeinnützig anerkannt durch das Finanzamt für Körperschaften I Berlin, Steuernummer 27/029/42207. Geschäftsführende Vorstände: Franziska Heine, Dr. Christian Humborg _______________________________________________ Wikidata mailing list -- [email protected] Public archives at https://lists.wikimedia.org/hyperkitty/list/[email protected]/mes... To unsubscribe send an email to [email protected]
I love this tool. Congratulations to everyone who was involved! The more I use it, the more I wonder about some questions that have been raised in the Telegram group: 1) are there some stats on the amount of vandalism that comes from IPs? 2) related to (1) most of the clear spam edits I've found using the tool, come from unregistered users changing descriptions and "instance of" an item to silly claims. If this trend is backed up by the data you get from the annotation tool, would it make sense to at least protect those fields to be edited only by registered users? 3) is there (or will there be) an easier* way to report vandalism from an IP other than writing a report at Administrator's noticeboard https://www.wikidata.org/wiki/Wikidata:Administrators%27_noticeboard (ca. 3 min for each report)? I imagine something like looking at this IP's "contributions" https://www.wikidata.org/wiki/Special:Contributions/80.32.121.151 (all vandalism) & having a button under "User" where I could report this person's contributions as vandalism.
Again, thank you for letting us know about this awesome tool!
On Fri, Aug 11, 2023 at 7:14 PM Seth Deegan [email protected] wrote:
*Awesome* tool! We should make this a banner on Wikidata to get as many people to annotate as possible! The potential for reverting edits like the ones shown in the tool is very exciting!
lectrician1 https://www.wikidata.org/wiki/User:Lectrician1
El jue, 10 ago 2023 a la(s) 02:28, Mohammed Sadat Abdulai ( [email protected]) escribiĂł:
Hi everyone,
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 https://lists.wikimedia.org/hyperkitty/list/[email protected]/thread/EK65B7QCQHEG37C2ERPIUSP64OX3ZEUJ/ )
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 reliable.
If you encounter any issues or want to provide general feedback, feel free to leave us a note on this ticket phab:T341820 https://phabricator.wikimedia.org/T341820.
Cheers,
Mohammed S. Abdulai *Community Communications Manager, Wikidata*
Wikimedia Deutschland e. V. | Tempelhofer Ufer 23-24 | 10963 Berlin Phone: +49 (0) 30 577 116 2466 https://wikimedia.de
Grab a spot in my calendar for a chat: calendly.com/masssly.
A lot is happening around Wikidata - Keep up to date! https://www.wikidata.org/wiki/Wikidata:Status_updates Current news and exciting stories about Wikimedia, Wikipedia and Free Knowledge in our newsletter (in German): Subscribe now https://www.wikimedia.de/newsletter/.
Imagine a world in which every single human being can freely share in the sum of all knowledge. Help us to achieve our vision! https://spenden.wikimedia.de
Wikimedia Deutschland — Gesellschaft zur Förderung Freien Wissens e. V. Eingetragen im Vereinsregister des Amtsgerichts Charlottenburg, VR 23855 B. Als gemeinnützig anerkannt durch das Finanzamt für Körperschaften I Berlin, Steuernummer 27/029/42207. Geschäftsführende Vorstände: Franziska Heine, Dr. Christian Humborg _______________________________________________ Wikidata mailing list -- [email protected] Public archives at https://lists.wikimedia.org/hyperkitty/list/[email protected]/mes... To unsubscribe send an email to [email protected]
Wikidata mailing list -- [email protected] Public archives at https://lists.wikimedia.org/hyperkitty/list/[email protected]/mes... To unsubscribe send an email to [email protected]
tl;dr 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.
Hi folks,
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 https://meta.wikimedia.org/wiki/Research:Develop_a_ML-based_service_to_predict_reverts_on_Wikipedia, 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, Revert. Please 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) wikimedia.org or on Meta (Diego (WMF) https://meta.wikimedia.org/wiki/User_talk:Diego_(WMF).
Thanks,
-Mohammed
On Thu, Aug 10, 2023 at 9:27 AM Mohammed Sadat Abdulai < [email protected]> wrote:
Hi everyone,
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 https://lists.wikimedia.org/hyperkitty/list/[email protected]/thread/EK65B7QCQHEG37C2ERPIUSP64OX3ZEUJ/ )
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 reliable.
If you encounter any issues or want to provide general feedback, feel free to leave us a note on this ticket phab:T341820 https://phabricator.wikimedia.org/T341820.
Cheers,
Mohammed S. Abdulai *Community Communications Manager, Wikidata*
Wikimedia Deutschland e. V. | Tempelhofer Ufer 23-24 | 10963 Berlin Phone: +49 (0) 30 577 116 2466 https://wikimedia.de
Grab a spot in my calendar for a chat: calendly.com/masssly.
A lot is happening around Wikidata - Keep up to date! https://www.wikidata.org/wiki/Wikidata:Status_updates Current news and exciting stories about Wikimedia, Wikipedia and Free Knowledge in our newsletter (in German): Subscribe now https://www.wikimedia.de/newsletter/.
Imagine a world in which every single human being can freely share in the sum of all knowledge. Help us to achieve our vision! https://spenden.wikimedia.de
Wikimedia Deutschland — Gesellschaft zur Förderung Freien Wissens e. V. Eingetragen im Vereinsregister des Amtsgerichts Charlottenburg, VR 23855 B. Als gemeinnützig anerkannt durch das Finanzamt für Körperschaften I Berlin, Steuernummer 27/029/42207. Geschäftsführende Vorstände: Franziska Heine, Dr. Christian Humborg