This is very cool. If you are looking for something similar but more lightweight, that you can run (and train) on CPU, try out Jeffy: https://jeffyclassify.com/
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
On GitHub: https://github.com/nicobrenner/jeffy
https://github.com/kouhxp/gutsy
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