mvr 🐹
@mvr
Spam label transitions: null -> 0: 1491 null -> 1: 0 null -> 2: 4138 0 -> null: 0 1 -> null: 1 2 -> null: 0 0 -> 1: 0 0 -> 2: 30231 1 -> 0: 4539 1 -> 2: 133174 2 -> 0: 2789 2 -> 1: 0 * -> null: 1 * -> 0: 8819 * -> 1: 0 * -> 2: 167543
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Cryptogirl🔴
@nataaa
Hooooray! It's a great day for those who was with label 1
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Njal
@cryptonjal
Interesting that a lot of 1s went to 2. I assume that the old LLM outcome was used to make a correlation calculation. In such test you can have positive or negative correlation (spam / no spam), you need enough data (explains why they can't label everyone), and you need a significance test (that was label 1 I assume). It interest me, why they haven't a non significant group anymore. Can't believe all outcomes are significant. The things that I came up with is: - the new LLM polarise the outcome more - or maybe the run a multiple regression analysis now (maybe they already did, but more simplistic) to determine if a caster is a certain type of 'spam' or 'no spam'. In that way all non significant correlations are ruled out, redo the analysis with the significant correlations and if the mayority of outcome correlatates with non spam it's a 2 and visa versa. - or something else
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fhn_gt
@9r1n6c0r3
finally
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