Hey SJ! > Is there a reason to think that separate models for each wiki are more effective than one general model that sees the name of the wiki as part of its context?
Intuitively one model per wiki has a lot of merit. The training data comes from the community that is impacted by the model, etc. However, there are scale and equity issues we wrestled with. One lesson we have learned training ~300+ models for the Add-A-Link <https://www.mediawiki.org/wiki/Growth/Personalized_first_day/Structured_tasks/Add_a_link> project is that if we continued down that path, Lift Wing would eventually be hosting 3000+ models (i.e. 330 models per new feature) pretty quickly and overwhelm any ability of our small team to maintain, quality control, support, and improve them over their lifespan. Regarding equity, even with a multi-year effort the one model per wiki RevScoring models only covered ~33 out of 330 wikis. The communities we didn't reach didn't get the benefit of those models. But with language agnostic models we can make that model available to all communities. For example, the language agnostic revert risk model will likely be the model selected for the automoderator project <https://www.mediawiki.org/wiki/Moderator_Tools/Automoderator>, which means hundreds more wikis get access to the tool compared to a one model per wiki approach. > I'd love to read more about the cost of training and updating current models, how much material they are trained on, and how others w/ their own GPUs can contribute to updates. The training data information should be available in the model cards <https://meta.wikimedia.org/wiki/Machine_learning_models>. If it isn't, let me know so we can change it. Regarding GPUs and contributions, we are still working on what a good training environment will be. Our initial idea was a kubeflow-based cluster we called Train Wing, but we've had to put them on hold for resource reasons (i.e. we couldn't build Lift Wing, deprecate ORES, and build Train Wing all at the same time). More on that soon after the Research-ML offsite when we'll have those conversations. All this said, one thing we do want to support is hosting community created models. So, if a community has a model they want to host, we can load it into Lift Wing and host it for them at scale. We have a lot of details to work out (e.g. community consensus, human rights review as part of being a Very Large Online Platform etc.) as to what that would look like, but that is the goal. Chris On Fri, Sep 22, 2023 at 5:42 PM Samuel Klein <meta...@gmail.com> wrote: > Luca writes: > > > Managing several hundreds models for goodfaith and damaging is not very > scalable in a modern micro-service architecture like Lift Wing > > (since we have a model for each supported wiki). We (both Research and > ML) are oriented on having fewer models that manage more languages at the > same time, > > Is there a reason to think that separate models for each wiki are more > effective than one general model that sees the name of the wiki as part of > its context? > I'd love to read more about the cost of training and updating current > models, how much material they are trained on, and how others w/ their own > GPUs can contribute to updates. > > Personally I wouldn't mind a single model that can suggest multiple > properties of an edit, including goodfaith, damaging, and likelihood of > reversion. They are different if related concepts -- the first deals with > the intent and predicted further editing history of the editor, the second > with article accuracy and quality, and the latter with the size + > activity + norms of the other editors... > > SJ > > > > > On Fri, Sep 22, 2023 at 5:34 PM Aaron Halfaker <aaron.halfa...@gmail.com> > wrote: > >> All fine points. As you can see, I've filed some phab tasks where I saw >> a clear opportunity to do so. >> >> > as mentioned before all the models that currently run on ORES are >> available in both ores-legacy and Lift Wing. >> >> I thought I read that damaging and goodfaith models are going to be >> replaced. Should I instead read that they are likely to remain available >> for the foreseeable future? When I asked about a community discussion >> about the transition from damaging/goodfaith to revertrisk, I was imagining >> that many people who use those predictions might have an opinion about them >> going away. E.g. people who use the relevant filters in RecentChanges. >> Maybe I missed the discussions about that. >> >> I haven't seen a mention of the article quality or article topic models >> in the docs. Are those also going to remain available? I have some user >> scripts that use these models and are relatively widely used. I didn't >> notice anyone reaching out. ... So I checked and setting a User-Agent on my >> user scripts doesn't actually change the User-Agent. I've read that you >> need to set "Api-User-Agent" instead, but that causes a CORS error when >> querying ORES. I'll file a bug. >> >> On Fri, Sep 22, 2023 at 1:22 PM Luca Toscano <ltosc...@wikimedia.org> >> wrote: >> >>> >>> >>> On Fri, Sep 22, 2023 at 8:59 PM Aaron Halfaker <aaron.halfa...@gmail.com> >>> wrote: >>> >>>> We could definitely file a task. However, it does seem like >>>> highlighting the features that will no longer be available is an >>>> appropriate topic for a discussion about migration in a technical mailing >>>> list. >>>> >>> >>> A specific question related to a functionality is the topic for a task, >>> I don't think that we should discuss every detail that differs from the >>> ORES API (Wikitech-l doesn't seem a good medium for it). We are already >>> following up on Phabricator, let's use tasks if possible to keep the >>> conversation as light and targeted as possible. >>> >>> Is there a good reference for which features have been excluded from >>>> ores-legacy? It looks like https://wikitech.wikimedia.org/wiki/ORES >>>> covers >>>> some of the excluded features/models, but not all of them. >>>> >>> >>> We spent the last months helping the community to migrate away from the >>> ORES API (to use Lift Wing instead), the remaining traffic is only related >>> to few low traffic IPs that we are not able to contact. We didn't add >>> feature injection or threshold optimization to ores-legacy, for example, >>> since there was no indication on our logs that users were relying on it. We >>> have always stated everywhere (including all emails sent in this mailing >>> list) that we are 100% open to add a functionality if it is backed up by a >>> valid use case. >>> >>> >>>> I see now that it looks like the RevertRisk model will be replacing the >>>> *damaging >>>> *and *goodfaith *models that differentiate intentional damage from >>>> unintentional damage. There's a large body of research on why this is >>>> valuable and important to the social functioning of the wikis. This >>>> literature also discusses why being reverted is not a very good signal for >>>> damage/vandalism and can lead to problems when used as a signal for >>>> patrolling. Was there a community discussion about this deprecation that I >>>> missed? I have some preliminary results (in press) that demonstrate that >>>> the RevertRisk model performs significantly worse than the damaging and >>>> goodfaith models in English Wikipedia for patrolling work. Do you have >>>> documentation for how you evaluated this model and compared it to >>>> damaging/goodfaith? >>>> >>> >>> We have model cards related to both Revert Risk models, all of them >>> linked in the API portal docs (more info: >>> https://api.wikimedia.org/wiki/Lift_Wing_API). All the community folks >>> that migrated their bots/tools/etc.. to Revert Risk were very happy about >>> the change, and we haven't had any request to switch back since then. >>> >>> The ML team provides all the models deployed on ORES on Lift Wing, so >>> any damaging and goodfaith variant is available in the new API. We chose to >>> not pursue the development of those models for several reasons: >>> - We haven't had any indication/request from the community about those >>> models in almost two years, except few Phabricator updates that we followed >>> up on. >>> - Managing several hundreds models for goodfaith and damaging is not >>> very scalable in a modern micro-service architecture like Lift Wing (since >>> we have a model for each supported wiki). We (both Research and ML) are >>> oriented on having fewer models that manage more languages at the same >>> time, and this is the direction that we are following at the moment. It may >>> not be the perfect one but so far it seems a good choice. If you want to >>> chime in and provide your inputs we are 100% available in hearing >>> suggestions/concerns/doubts/recommendations/etc.., please follow up in any >>> of our channels (IRC, mailing lists, Phabricator for example). >>> - Last but not the least, most of the damaging/goodfaith models have >>> been trained with data coming from years ago, and never re-trained. The >>> efforts to keep several hundreds models up-to-date with recent data versus >>> doing the same of few models (like revert risk) weights in favor of the >>> latter for a relatively small team of engineers like us. >>> >>> >>>> FWIW, from my reading of these announcement threads, I believed that >>>> generally functionality and models would be preserved in >>>> ores-legacy/LiftWing. This is the first time I've realized the scale of >>>> what will become unavailable. >>>> >>> >>> This is the part that I don't get, since as mentioned before all the >>> models that currently run on ORES are available in both ores-legacy and >>> Lift Wing. What changes is that we don't expose anymore functionality that >>> logs clearly show are not used, and that would need to be maintained and >>> improved over time. We are open to improve and add any requirement that the >>> community needs, the only thing that we ask is to provide a valid use case >>> to support it. >>> >>> I do think that Lift Wing is a great improvement for the community, we >>> have been working with all the folks that reached out to us, without hiding >>> anything (including deprecation plans and path forwards). >>> >>> Thanks for following up! >>> >>> Luca >>> _______________________________________________ >>> Wikitech-l mailing list -- wikitech-l@lists.wikimedia.org >>> To unsubscribe send an email to wikitech-l-le...@lists.wikimedia.org >>> >>> https://lists.wikimedia.org/postorius/lists/wikitech-l.lists.wikimedia.org/ >> >> _______________________________________________ >> Wikitech-l mailing list -- wikitech-l@lists.wikimedia.org >> To unsubscribe send an email to wikitech-l-le...@lists.wikimedia.org >> >> https://lists.wikimedia.org/postorius/lists/wikitech-l.lists.wikimedia.org/ > > > > -- > Samuel Klein @metasj w:user:sj +1 617 529 4266 > _______________________________________________ > Wikitech-l mailing list -- wikitech-l@lists.wikimedia.org > To unsubscribe send an email to wikitech-l-le...@lists.wikimedia.org > https://lists.wikimedia.org/postorius/lists/wikitech-l.lists.wikimedia.org/
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