Based on a conversation with @TJones, moving this to the 'later' column to re-consider it in a quarter or two after we’ve done other stuff...and here's why:

Larger training sets make for higher resolution frequency tables, which could give better detection accuracy. The training sets don’t need to be entirely equalized, but the smaller ones could be significantly enlarged.

There are a few options:

• We could do one or more tests to see whether increased model size improves accuracy to make a guess at the impact it would have. We’d have to find a good test case where a smaller model doesn’t perform that well, but that shouldn’t be too hard.
• We could compare accuracy of query-based models built on small training sets to wikitext models and see how much the small models are worth. If they don’t give a big advantage, we could drop them and use the wikitext models.