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Re-generate topics and re-train fraud detection #1
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7 tasks
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Instead of predicting all topics at once (the sum of the predictions equal to 1) predict (binary) topic pairs e.g ["hot","cold"]
New technique performs better. A potential drawback is that a higher number of topics might increase the inference time and makes it take to long on CPU only systems. |
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Note: This will be great to reduce false positives, since the model has not yet seen many ham (and spam) data for governance proposals.
Note: consider reducing the ham dataset by filtering some of the rejected proposals with high votes against. To make sure not to train likely spam as ham.
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