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https://issues.apache.org/jira/browse/SOLR-11838?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16321244#comment-16321244
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Jeroen Steggink commented on SOLR-11838:
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That's nice guys! I would like to help on this as well. I have lots of 
experience with Solr and Deeplearning4j. I already played with using 
Deeplearning4j in the Solr streaming API for applying models just like Gus 
mentioned. 

Any ideas on what kind of models should be supported or what kind of data it 
should use as input? As we can have bag of words and or features in simple feed 
forward networks or sequences data with LSTMs which require different formatted 
inputs.
Furthermore, I can imagine users would like to have some influence on what kind 
of data will be used, sparse vectors, word vectors, etc, as these can have a 
huge impact on the performance of the network in both results and the system 
itself.

There's a lot of things to explore here.

Personally I would like to have something which is easy to use which increases 
the adoption of this feature. We could build further on the experiences of 
users.



> explore supporting Deeplearning4j NeuralNetwork models in contrib/ltr
> ---------------------------------------------------------------------
>
>                 Key: SOLR-11838
>                 URL: https://issues.apache.org/jira/browse/SOLR-11838
>             Project: Solr
>          Issue Type: New Feature
>          Components: contrib - LTR
>            Reporter: Christine Poerschke
>         Attachments: SOLR-11838.patch
>
>
> [~yuyano] wrote in SOLR-11597:
> bq. ... If we think to apply this to more complex neural networks in the 
> future, we will need to support layers ...
> [~malcorn_redhat] wrote in SOLR-11597:
> bq. ... In my opinion, if this is a route Solr eventually wants to go, I 
> think a better strategy would be to just add a dependency on 
> [Deeplearning4j|https://deeplearning4j.org/] ...
> Creating this ticket for the idea to be explored further (if anyone is 
> interested in exploring it), complimentary to and independent of the 
> SOLR-11597 RankNet related effort.



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