You are correct, I didn’t train the embeddings model

Here's a gist with code you can use to test the Banking77 dataset: https://gist.github.com/nicobrenner/056a5aaff5d0119c0032ecda...

The gist uses BAAI/bge-large-en-v1.5, which is 1.2GB approx. You can replace it for all-MiniLM-L6-v2 (91 MB @ fp32 or 45 MB quantized fp16) small enough for mobile/edge. With all-MiniLM-L6-v2 it still gets 93.0% on Banking77, only 1.3 points behind bge-large at 15x smaller

I haven’t compared different ways of sending requests to Jev

The data to train the classifiers comes from the datasets used to test them (not from Jev)