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Posted to users@spamassassin.apache.org by sasa <sa...@shoponweb.it> on 2005/10/03 16:58:42 UTC
Re: sa-learn on mailbox or not ?
"mouss" wrote:
> run the message through spamassassin -t (under the same uid as amavisd)
> and look at which rules were hit.
> also, put the message on a web page and post the url, so that we check
> it on our configs. you may be missing some "custom" rules.
..sorry but pheraps I don't understand that you say me, I have execute:
#spamassassin /var/mail/spam-report
..and at the bottom output I have:
Content analysis details: (-2.4 points, 5.0 required)
pts rule name description
---- ---------------------- --------------------------------------------------
-2.6 BAYES_00 BODY: Bayesian spam probability is 0 to 1%
[score: 0.0010]
0.2 AWL AWL: From: address is in the auto white-list
..is this what do you want ??
still thanks.
Salvatore.
Re: sa-learn on mailbox or not ?
Posted by sasa <sa...@shoponweb.it>.
"mouss" wrote:
> you need to run it as the amavisd user, not as root. the same goes for
> training bayes.
..but on what file I must to configure user root and not user amavisd ??
still thank.
Salvatore.
Re: sa-learn on mailbox or not ?
Posted by mouss <us...@free.fr>.
sasa a écrit :
>"mouss" wrote:
>
>
>>run the message through spamassassin -t (under the same uid as amavisd)
>>and look at which rules were hit.
>>also, put the message on a web page and post the url, so that we check
>>it on our configs. you may be missing some "custom" rules.
>>
>>
>
>..sorry but pheraps I don't understand that you say me, I have execute:
>
>#spamassassin /var/mail/spam-report
>
>
>..and at the bottom output I have:
>
>Content analysis details: (-2.4 points, 5.0 required)
>
> pts rule name description
>---- ---------------------- --------------------------------------------------
>-2.6 BAYES_00 BODY: Bayesian spam probability is 0 to 1%
> [score: 0.0010]
>0.2 AWL AWL: From: address is in the auto white-list
>
>..is this what do you want ??
>
>
you need to run it as the amavisd user, not as root. the same goes for
training bayes.