Every time I've written annotation guidelines, I started from scratch. Not anymore.

Eugene Yan breaks down what good labeling guidelines look like, using real examples from Google and Bing Search. The structure: explain why the task matters, define every term, then show step-by-step decision-making with challenging examples. It's deceptively hard to get right.

If your ML pipeline involves human labels, this saves you from learning the hard way.

Check it out here: http://amplt.de/HandsomePeacefulDeal
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