Jev is a classifier. The big thing about it is that it has high accuracy on domains it wasn't fine-tuned for, like an LLM, but with speed and cost comparable to traditional classifiers.

BERT need to be fine tuned for your use case, Jev generalizes. It’s a pretty big difference!

FLAN-T5 generated text (Jev does not generate text), and BERT wasn't able to do tasks without fine-tuning.

Jev is basically a kind of FLAN-BERT, if you want, where it has built-in multi-task ability, but doesn't generate text. It only generates 255 floats all at once, making it much faster, and what those floats mean (if anything) depends on the prompt.

Eg, the following query is put in the encoder model:

{"question": "Rank these 5 things by increasing order of how big they are", "choices": ["truck", "cow", "mouse", "ant", "building"] }

The model returns [3., 2., 1., 0., 4.], and 249 other meaningless floats that are hidden from you by the UI.

The UI stitches the first 5 floats with the choices and returns something like:

{"rank": ["ant", "mouse", "cow", "truck", "tower"]}

nothing in utility. we used various bert variants to satisfy our usecases and still in uses. free and they run locally.