Collect non-spam messages in a mailbox, and then use this command:
% sa-learn --progress --mbox --ham <filename>where <filename> is the filename of the mailbox containing non-spam (termed 'ham').
Collect spam messages in another mailbox, and then use this command:
% sa-learn --progress --mbox --spam <filename>The sa-learn command will automatically ignore duplicates, so you can efficiently run it on the same mailbox(es) over and over again.
After sa-learn has seen a few hundred spam messages, and a few hundred
non-spam messages, Bayesian filtering will become active. Then you will
notice that the X-Spam-Status lines in email will includes tokens such
as BAYES_99 indicating the Bayesian probability of the message being
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