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"]}
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"]}