I'm not sure whether we should pursue this task right now. When I created this big maze of related language ID tasks, I was brainstorming about what was possible and how things are related. Not everything linked to anything else is necessary, but there may be a preferred order of operations for various tasks.

This is a lot of work per language—half a week to a week if you are familiar with the language, maybe double if not, and I was only guessing that 20K examples would be enough to create a decent model.

Now having more experience with different groups of languages searched in on different wikis, I think maybe this isn't worth doing for the really ugly query corpora (e.g., those where almost half the queries are in English but the wiki isn't in English).

Query-based models do get a few more percentage points of accuracy than wiki-text based models (e.g., no one will type pq for porque in an article on eswiki or ptwiki, but users do it all the time in queries, so models accounting for that will perform better). The biggest improvement, though, is from restricting the language set applied to a corpus (i.e., the language evaluations I'm working on for the bigger wikis).

Maybe we should review all these tasks again and think about what's worth doing and what's not, what's the biggest bang for the buck, and clarify our priorities on where to focus effort (bigger wikis with more users, or smaller wikis on the long tail), and whether to explore other less effortful or more automated options.