Thanks TIm. It’s helpful to know what the issues with the latest version of the model are.
Leo has no common sense so it’ll simply be a question of feeding the model training data where highlighted words are transcribed correctly. I don’t think we’ll need many examples for it to learn this but it’s a relatively niche issue.
Fortunately we do have a plan for solving the potentially infinite numbers of small problems, where Leo encounters something that is “out of distribution” (i.e., not reflected in the training data. The idea is to introduce the ability for users to fine-tune the Leo model with your own set of training data. So you’d be able to solve this problem for yourself by correcting a few transcripts, using these to fine-tune the base model, and then using that specialised model to transcribe the rest of these newspapers. We would then be able to use that data to train the base model, so you’d be solving the problem for other users too.