I find myself really confused by the accept/reject logic of Prodigy, and would love some help to figure it out.
I’m trying to use
textcat.teach for annotating a single topic of messages (cancellation requests). There can be other topics, but I’m focusing on one topic for annotation right now.
Using patterns I bootstrapped the annotation task and I get in Prodigy texts with “cancellation” or “None” as a category, and need to accept/reject. What does exactly rejecting the “None” category means? AFAIK each category is a neuron in the output layer of the model - does “None” behaves the same?
I also tried to load previous data as a dataset to train on, using the None/cancellation categories as labels with answer=accept for all the samples, and the model couldn’t learn anything. Changing the samples to be all with label=cancellation and answer=accept/reject “fixed” the issue, but I don’t entirely get why.
The last question I have here, is what does accepting “None” means? If I focus on one topic per annotation, accepting “None” means for me that it’s not the category I’m annotating, but it may well be another category - In this case should I merge the datasets, or I cat let Prodigy read all datasets and it will figure this out automagically?
Thanks a lot,