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The biggest upside to using a zero-shot classification model is that it's actually zero-shot, i.e., you don't need to construct task-specific training sets.

If it needs to be fine-tuned, then why not fine-tune smaller and cheaper models that are only as big as the task requires?

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I hear what you're saying. But this is not such a model. The article says that various internal teams had to fine-tune Clef to suit their specific needs. They're even rolling out a RL training product line for it.

Fair. Though in this case my actual classifier (dino v3 features fed into a very shallow DNN) works surprisingly well and is insanely faster than using an LLM -- so it really was the one shot performance I was hoping for.

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