I'm not entirely sure if this would be a Spacy issue or a Prodigy issue.
I've been testing out accuracy between NER and SPANCAT with a few labels to see which one gives me better results. The first data set I had two labels which I labeled together using Prodigy (both NER and SPANCAT). The next label I did just by itself in a new data set. NER I got roughly 77% but with SPANCAT I get 0%, I was super puzzled by this and thought maybe I didn't have enough examples, it's a super easy label with patterns so I got up to 2,000 examples fairly quickly and I still am getting 0% on the precision, recall, & f scores.
Just to be clear, each data set I've created twice, one as a NER and another as a SPANCAT.
I merged the two SPANCAT data sets together (first one with two labels, second one with one label) and tried to train again, that one I also got 0% across the board as well (before the one with two labels worked fine). Any idea what might be going on here? I also exported datasets to Spacy and had the same results.
Here's output from the training with one label.