Thanks, Ines, that looks tantalizingly close to what’s happening to me. I didn’t get the problem to go away using, e.g., the matcher = PatternMatcher(model.nlp, label_span=False, label_task=True).from_disk(patterns) fix that you suggested. (I realize that this was for textcat.teach not ner.teach, but still, it was worth a shot.)
So I have roughly 700 patterns in my file. Could this be causing the matching to be so extensive that there is no more room for the model to explore around in? I’m referring to the interaction of the combined model-cum-matcher (combine_models(model, matcher)) and the prefer_uncertain(.) behavior. [edit: But that still wouldn’t explain why only two patterns – as Matt suggested – on my larger inputs.json file did not work either, though.]