Comment by nico
1 day ago
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/
On GitHub: https://github.com/nicobrenner/jeffy
1 day ago
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/
On GitHub: https://github.com/nicobrenner/jeffy
Cool project too. If you are looking for a 500MB instead of gigabytes, with evals on Jevbench that you can run fast on CPU check out gutsy.
https://github.com/kouhxp/gutsy
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
Thank you, that makes perfect sense. I would likely go the clf route once I have enough data for a task.