I'm working on this TextCat task next because I was using these parameters as test cases for tool dev, and noticed that there's seems to be some obvious potential improvements to be made here.

Write up with full details is available.

Summary:

• There's a very strong pressure to optimize the maximum returned languages to 1—no ambiguity allowed!
• I didn't intend to consider model size, but I did, and bigger models do better, possibly to the point where we need to go beyond the 5K we currently support in production 9I have up to 10K in dev, and 9K is the current best).
• The optimal results ratio depends on the model size.

The improvement from optimizing maximum returned languages, results ratio, and model size averages 1.4% to 2.6% in F0.5 score across the nine wikis/corpora (depending on model size), with the poorer performing wikis generally getting bigger improvements. That may seem small, but the "poorly" performing wikis have F0.5 scores in the ~82-83% range, and improve to the ~86-88% range. An improvement from 82% to 88% is a third of the maximum possible improvement (6% improved/18% possible), so it isn't trivial.

Other notes:

• Re-verified that query-based models are helpful.
• Accidentally discovered that adding an additional unknown n-gram penalty may improve results. (T151230)
• Proportional limits don't matter, since it always optimizes downward until it round to 1, so no TextCat code is changed.

Deployment will occur (T149324) after other related tasks are complete and the interplay between them is worked out.