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If you wouldn't mind chatting about your usage, my email is in my profile, and I'd love to share experiences with other HNers using self-hosted models.
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Does spam filtering really need a better model? My impression is that the whole game is based on having the best and freshest user-contributed labels.
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He said it’s a benchmark.
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Better models help on the day the spam mutates, before you have fresh labels for the new scam and before spammers can infer from a few test runs which phrasing still slips through. If you need labels for each pivot you're letting them experiment on your users.
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In my experience the contents of the message are all but totally irrelevant to the classification, and it is the behavior of the mailing peer that gives all the relevant features.
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