Very cool, thank you for sharing

The banking77 numbers called my attention. Using a local classifier you can get 94%+ accuracy: https://playground.jeffyclassify.com/#model/banking77

I think Jev-like models are amazing for exploration and finding the right workflows, but the moment you have fixed classification tasks, it’s often more efficient to use an adhoc classifier, which you can quickly and easily train on CPU with not that much data (you can get an email classifier to 95% accuracy/f1 with 50-100 emails)

Edit: Would love to somehow mix both approaches automatically and have a general model which can take novel tasks, but then switch to a classifier after it gets enough data for training an adhoc model