Purchased prodigy day before yesterday and already a big fan. Thank you so much for the product and kudos to the team.
I am trying to come up with an optimal strategy for my NER annotation and training. I need 4-6 named entities to be recognized from the text that is being parsed. But couple of these entities are conceptually very similar and I am unsure if the model would be able to tease out the difference. So my questions here is,
Is it better to do all the tags in one go and train, what I am worried about with this strategy is that since is the evaluation is made by accuracy, the low accuracy is because of the model mis identifying the similar tags. Is there any provision in prodigy to merge two tags in the annotated data? (eg: merge the tags "black dogs" and "white dogs" to a single tag "dogs").
If I am doing multiple tags in one go, while training, is there any way to check the accuracy tag wise? (eg: what is the accuracy is identifying the tags "animals" vs the tags "plants")
Thank you very much.