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additional note about the automatic matching algorithm

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Jonas Winkler 2020-11-25 15:06:27 +01:00
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docs/advanced_usage.rst
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@ -147,7 +147,9 @@ America are tagged with the tag "bofa_123" and the matching algorithm of this
tag is set to *Auto*, this neural network will examine your documents and tag is set to *Auto*, this neural network will examine your documents and
automatically learn when to assign this tag. automatically learn when to assign this tag.
There are a couple caveats you need to keep in mind when using this feature: Paperless tries to hide much of the involved complexity with this approach.
However, there are a couple caveats you need to keep in mind when using this
feature:
* Changes to your documents are not immediately reflected by the matching * Changes to your documents are not immediately reflected by the matching
algorithm. The neural network needs to be *trained* on your documents after algorithm. The neural network needs to be *trained* on your documents after
@ -167,6 +169,11 @@ There are a couple caveats you need to keep in mind when using this feature:
has the correspondent "Very obscure web shop I bought something five years has the correspondent "Very obscure web shop I bought something five years
ago", it will probably not assign this correspondent automatically if you buy ago", it will probably not assign this correspondent automatically if you buy
something from them again. The more documents, the better. something from them again. The more documents, the better.
* Paperless also needs a reasonable amount of negative examples to decide when
not to assign a certain tag, correspondent or type. This will usually be the
case as you start filling up paperless with documents. Example: If all your
documents are either from "Webshop" and "Bank", paperless will assign one of
these correspondents to ANY new document, if both are set to automatic matching.
Hooking into the consumption process Hooking into the consumption process
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