Using active learning on a multiclass problem

[ryanwesslen]

hi @joannvuong!

Thanks for your post and welcome to the Prodigy community

@joannvuong

I've configured my own model in order to use Prodigy's active learning option. My dataset is a multiclass problem with 3 classes. My question is whether or not we can use Prodigy's active learning method to display the 3 options, rather than a binary option of accept/reject?

Here's a similar post that has been asked before:

Active learning and correct directly, instead of binary classification first

Bonjour @didmar! Thanks for your question! I think you're asking why the active learning recipe frames tasks as binary, not like a manual recipe like ner.correct. This has come up before: As Matt mentioned, this is just a design choice to avoid cramming too many things within the built-in recipes. There is nothing stopping you from developing your own custom recipe to do this. It's important to think of the built-in recipes as the floor of what's possible, not the ceiling of what's possibl…

@joannvuong

Along with this, is there a way to avoid showing the user/annotator what label the model has predicted? Curious, because I would look to avoid as much bias as possible from the annotator.

That's a good question! Typically we would've assume the annotator wants it, but I can see certain types of task may want to remove them. Likely the best way would be to modify the css for .prodigy-label to make the label nothing. But let me talk to the team on Monday to see if there are other ideas!

Hope this helps!