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Posted to commits@lucene.apache.org by ct...@apache.org on 2021/01/15 21:00:46 UTC
[lucene-solr] 01/02: SOLR-14560: ref guide: remove references to
XML output when examples are all JSON
This is an automated email from the ASF dual-hosted git repository.
ctargett pushed a commit to branch branch_8x
in repository https://gitbox.apache.org/repos/asf/lucene-solr.git
commit 9bea04b8a4974334e52051f5f58391921c89a56e
Author: Cassandra Targett <ct...@apache.org>
AuthorDate: Thu Jan 14 15:07:22 2021 -0600
SOLR-14560: ref guide: remove references to XML output when examples are all JSON
---
solr/solr-ref-guide/src/learning-to-rank.adoc | 14 +++++++-------
1 file changed, 7 insertions(+), 7 deletions(-)
diff --git a/solr/solr-ref-guide/src/learning-to-rank.adoc b/solr/solr-ref-guide/src/learning-to-rank.adoc
index 6f54b86..4edd554 100644
--- a/solr/solr-ref-guide/src/learning-to-rank.adoc
+++ b/solr/solr-ref-guide/src/learning-to-rank.adoc
@@ -158,7 +158,7 @@ To extract features as part of a query, add `[features]` to the `fl` parameter,
[source,text]
http://localhost:8983/solr/techproducts/query?q=test&fl=id,score,[features]
-The output XML will include feature values as a comma-separated list, resembling the output shown here:
+The output will include feature values as a comma-separated list, resembling the output shown here:
[source,json]
----
@@ -223,14 +223,14 @@ To rerank the results of a query, add the `rq` parameter to your search, for exa
[source,text]
http://localhost:8983/solr/techproducts/query?q=test&rq={!ltr model=myModel reRankDocs=100}&fl=id,score
-The addition of the `rq` parameter will not change the output XML of the search.
+The addition of the `rq` parameter will not change the output of the search.
To obtain the feature values computed during reranking, add `[features]` to the `fl` parameter, for example:
[source,text]
http://localhost:8983/solr/techproducts/query?q=test&rq={!ltr model=myModel reRankDocs=100}&fl=id,score,[features]
-The output XML will include feature values as a comma-separated list, resembling the output shown here:
+The output will include feature values as a comma-separated list, resembling the output shown here:
[source,json]
----
@@ -266,7 +266,7 @@ To obtain the model that interleaving picked for a search result, computed durin
[source,text]
http://localhost:8983/solr/techproducts/query?q=test&rq={!ltr model=myModelA model=myModelB reRankDocs=100}&fl=id,score,[interleaving]
-The output XML will include the model picked for each search result, resembling the output shown here:
+The output will include the model picked for each search result, resembling the output shown here:
[source,json]
----
@@ -291,21 +291,21 @@ The output XML will include the model picked for each search result, resembling
----
=== Running a Rerank Query Interleaving a model with the original ranking
-When approaching Search Quality Evaluation with interleaving it may be useful to compare a model with the original ranking.
+When approaching Search Quality Evaluation with interleaving it may be useful to compare a model with the original ranking.
To rerank the results of a query, interleaving a model with the original ranking, add the `rq` parameter to your search, passing the special inbuilt `_OriginalRanking_` model identifier as one model and your comparison model as the other model, for example:
[source,text]
http://localhost:8983/solr/techproducts/query?q=test&rq={!ltr model=_OriginalRanking_ model=myModel reRankDocs=100}&fl=id,score
-The addition of the `rq` parameter will not change the output XML of the search.
+The addition of the `rq` parameter will not change the output of the search.
To obtain the model that interleaving picked for a search result, computed during reranking, add `[interleaving]` to the `fl` parameter, for example:
[source,text]
http://localhost:8983/solr/techproducts/query?q=test&rq={!ltr model=_OriginalRanking_ model=myModel reRankDocs=100}&fl=id,score,[interleaving]
-The output XML will include the model picked for each search result, resembling the output shown here:
+The output will include the model picked for each search result, resembling the output shown here:
[source,json]
----