Sebastian Raschka argues that Jev is more than just a classifier, since it generalizes well where earlier encoder-style classification models were usually special-purpose and limited. He suggests the main advantage lies in its data rather than the training algorithm, along with a well-designed API.
It’s easy to dismiss Jev it as “just a classifier”.
But people (me included) who have been training encoder-style models for classification for many years know they were usually special-purpose and limited in some way.
The breakthrough of Jev is that it generalizes well.
And I’d say the secret sauce is probably more in the data than in the training algorithm. (Plus a nice API design on top of it.)
poor guy claim to have built Jev a year ago but no one cared, and now Jev stole all the thunder many people are saying “you gotta tell your story” or “marketing is important”, and they just completely missed what actually made the difference here i just looked into this laya model https://laya.convaiinnovations.com/ and: - it only supports 512-1k context… a lot of use cases won’t fit at all - evaluating the model directly shows its accuracy is as good as a coin flip. in order to get good results, you need to first fine tune it i’m sorry, but that’s not Jev there’s a massive gap between an interesting research and a useful product you can “tell your story” all you like, but you can’t blame Jev for stealing your thunder when Jev did all the work to make a well-packaged solution anyone can just grab and go Jev is not completely new from an academic sense, just like how ChatGPT was not the first LLM don’t underestimate the effort and value in putting together something that’s actually good enough for adoption - it makes all the difference
Source: Sebastian Raschka · x.comPublished · added here
