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Michael Nilsson commented on SOLR-8542: --------------------------------------- Thanks for all of the feedback Alessandro, we're actively working on some of your comments so far! Nice catch on the hash function, and we're looking into adding default values for the external feature information (efi). As a part of this pull request we do not plan on adding training built into Solr, but that would be a very good next enhancement. However, to help people in the Solr community get started with training and testing with machine learned ranking models, we are putting together some scripts and updating our readme to incorporate actual steps to train a model with libsvm instead of using the sample model.json file we provided. This should make it a lot easier for people to pick this up and start using a real ranking model based of their own data. We're keeping track of both JIRA comments and Github pull request comments on our end so they don't get lost. This is working ok so far, but if others have better suggestions we're open to them too. > Integrate Learning to Rank into Solr > ------------------------------------ > > Key: SOLR-8542 > URL: https://issues.apache.org/jira/browse/SOLR-8542 > Project: Solr > Issue Type: New Feature > Reporter: Joshua Pantony > Assignee: Christine Poerschke > Priority: Minor > Attachments: README.md, README.md, SOLR-8542-branch_5x.patch, > SOLR-8542-trunk.patch > > > This is a ticket to integrate learning to rank machine learning models into > Solr. Solr Learning to Rank (LTR) provides a way for you to extract features > directly inside Solr for use in training a machine learned model. You can > then deploy that model to Solr and use it to rerank your top X search > results. This concept was previously presented by the authors at Lucene/Solr > Revolution 2015 ( > http://www.slideshare.net/lucidworks/learning-to-rank-in-solr-presented-by-michael-nilsson-diego-ceccarelli-bloomberg-lp > ). > The attached code was jointly worked on by Joshua Pantony, Michael Nilsson, > David Grohmann and Diego Ceccarelli. > Any chance this could make it into a 5x release? We've also attached > documentation as a github MD file, but are happy to convert to a desired > format. > h3. Test the plugin with solr/example/techproducts in 6 steps > Solr provides some simple example of indices. In order to test the plugin > with > the techproducts example please follow these steps > h4. 1. compile solr and the examples > cd solr > ant dist > ant example > h4. 2. run the example > ./bin/solr -e techproducts > h4. 3. stop it and install the plugin: > > ./bin/solr stop > mkdir example/techproducts/solr/techproducts/lib > cp build/contrib/ltr/lucene-ltr-6.0.0-SNAPSHOT.jar > example/techproducts/solr/techproducts/lib/ > cp contrib/ltr/example/solrconfig.xml > example/techproducts/solr/techproducts/conf/ > h4. 4. run the example again > > ./bin/solr -e techproducts > h4. 5. index some features and a model > curl -XPUT 'http://localhost:8983/solr/techproducts/schema/fstore' > --data-binary "@./contrib/ltr/example/techproducts-features.json" -H > 'Content-type:application/json' > curl -XPUT 'http://localhost:8983/solr/techproducts/schema/mstore' > --data-binary "@./contrib/ltr/example/techproducts-model.json" -H > 'Content-type:application/json' > h4. 6. have fun ! > *access to the default feature store* > http://localhost:8983/solr/techproducts/schema/fstore/_DEFAULT_ > *access to the model store* > http://localhost:8983/solr/techproducts/schema/mstore > *perform a query using the model, and retrieve the features* > http://localhost:8983/solr/techproducts/query?indent=on&q=test&wt=json&rq={!ltr%20model=svm%20reRankDocs=25%20efi.query=%27test%27}&fl=*,[features],price,score,name&fv=true -- This message was sent by Atlassian JIRA (v6.3.4#6332) --------------------------------------------------------------------- To unsubscribe, e-mail: dev-unsubscr...@lucene.apache.org For additional commands, e-mail: dev-h...@lucene.apache.org