A classifier requires a ton of labeled data to be processed for it to be good. Jev and likes are zero shot classifiers where none of the data is required. Imo both are useful for different tasks. I am not going to use jev based models for compliance or any tasks where being wrong can bankrupt my business. For something trivial like sentiment analysis it’s a god sent.

I've found that while this technically true, if you're building a classifier of any importance, you're going to need to monitor it and grade the efficacy of any changes, which will require labeled data anyways.

This kind of approach works for one-shot "let's just see if it works" type stuff, but anything that I would stand behind needs at least some level of labeled data and curated dataset.

Can’t you use existing open weights sentiment analysis models that are far more lightweight though?