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You can now build a custom text classifier without days of human labeling! ๐ LLMs work reasonably well as text classifiers. ๐ They are expensive to run at scale and their performance drops in specialized domains. ๐ Purpose-built classifiers have low latency and can potentially run on CPU. ๐ 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. Blog: https://huggingface.co/blog/sdiazlor/custom-text-classifier-ai-human-feedback
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