the train-curve command currently uses the default spacy model, can I get it to use a transformers model instead?
Yes, in Prodigy v1.11 (currently available as a nightly pre-release) you can use the
--base-model argument to provide a different spaCy model package, including one that's initialised with transformer weights. This can either be a package provided by spaCy (e.g.
en_core_web_trf) or a custom one using a config that defines a
transformer component with pretrained weights of your choice and no other components. The
spacy assemble command lets you turn a config into a saved pipeline you can use: https://spacy.io/api/cli#assemble
A quick note on using transformers here: keep in mind that the
train-curve command runs the training multiple times with different portions of the data so it likely won't run very efficiently on your local machine if you don't have a GPU available (in general, you should train your transformer-based pipelines on GPU). So if you're using the train curve as a quick diagnostic to see if your model is learning something, it might make sense to run it without a transformer first. It'll still give you similar insights: if your model is converging well, it'll likely do even better once you initialise it with transformer weights. If it's not learning anything, there's likely a problem with your data that you want to fix first. Initialising with a transformer may give you slightly higher accuracy, but if there are deeper issues with your data, you'll still end up with worse results than what you could achieve.
makes perfect sense, thanks!