You can now build a custom text classifier without days of human labeling!
π LLMs work reasonably well as text classifiers. π They are expensive to run at scale and their performance drops in specialized domains.
π Purpose-built classifiers have low latency and can potentially run on CPU. π They require labeled training data.
Combine the best of both worlds: the automatic labeling capabilities of LLMs and the high-quality annotations from human experts to train and deploy a specialized model.