Which model are you using and how long are your texts? If you're using
ner.teach, Prodigy will not just ask the model for the best analysis, but multiple possible analyses – so the longer your texts, the longer this may take. So if you're not already doing this, try using shorter examples, like single sentences.
In addition to that, you can also experiment with changing the
batch_size setting. When the queue is running low, Prodigy will fetch the next batch of examples in the background, so maybe you can find a good trade-off batch size where it takes you long enough to annotate so that the model already has the next batch ready in the background.
If you get the feeling that you're not seeing enough examples for the given score threshold, then that's definitely an option. It will give your model more positive examples to learn from. If you only want to annotate examples with matches, check out the
match recipe: https://prodi.gy/docs/recipes#match If you're annotating entities, you want to set
--label-span to add the matched label to the span.
Alternatively, you could also start with a fully manual or semi-manual round of annotation using
ner.manual (with patterns) or
ner.correct (if you have a model that already predicts something).