Fix interdoc links and formatting of products

Signed-off-by: Eric Pugh <[email protected]>

_search-plugins/ltr/building-features.md

@@ -294,7 +294,7 @@ without creating an error.
 You'll notice we *appended* to the feature set. Feature sets perhaps
 ought to be really called "lists". Each feature has an ordinal (its
 place in the list) in addition to a name. Some LTR training
-applications, such as Ranklib, refer to a feature by ordinal (the
+applications, such as RankLib, refer to a feature by ordinal (the
 "1st" feature, the "2nd" feature). Others more conveniently refer to
 the name. So you may need both/either. You'll see that when features
 are logged, they give you a list of features back to preserve the
@@ -304,8 +304,7 @@ ordinal.
 
 Feature engineering is a complex part of OpenSearch Learning to Rank,
 and additional features (such as features that can be derived from other
-features) are listed in `advanced-functionality`{.interpreted-text
-role="doc"}.
+features) are listed in [advanced functionality]({{site.url}}{{site.baseurl}}/search-plugins/ltr/advanced-functionality/).
 
 Next-up, we'll talk about some specific use cases you\'ll run into when
 [Feature Engineering]({{site.url}}{{site.baseurl}}/search-plugins/ltr/feature-engineering/).

_search-plugins/ltr/core-concepts.md

@@ -185,7 +185,7 @@ In actual systems, you might log these values after the fact, gathering
 them to annotate a judgement list with feature values. In others the
 judgement list might come from user analytics, so it may be logged as the
 user interacts with the search application. More on this when we cover
-it in `logging-features`{.interpreted-text role="doc"}.
+it in [logging features]({{site.url}}{{site.baseurl}}/search-plugins/ltr/logging-features/).
 
 ## Training a ranking function 
 

_search-plugins/ltr/fits-in.md

@@ -30,7 +30,7 @@ development.
 Then other tools take over. With a logged set of features for documents,
 you join data with your Judgement lists you've developed on your own.
 You've now got a training set you can use to test/train ranking models.
-Using of a tool like Ranklib or XGBoost, you'll hopefully arrive at a
+Using of a tool like RankLib or XGBoost, you'll hopefully arrive at a
 satisfactory model.
 
 With a ranking model, you turn back to the plugin. You upload the model
@@ -52,12 +52,12 @@ company or mechanical turk.
 
 The plugin does not train or test models. This also happens offline in
 tools appropriate to the task. Instead the plugin uses models generated
-by XGboost and Ranklib libraries. Training and testing models is CPU
+by XGboost and RankLib libraries. Training and testing models is CPU
 intensive task that, involving data scientist supervision and offline
 testing. Most organizations want some data science supervision on model
 development. And you would not want this running in your production
 Elasticsearch cluster!
 
 The rest of this guide is dedicated to walking you through how the
 plugin works to get you there. Continue on to
-`building-features`{.interpreted-text role="doc"}.
+[building features]({{site.url}}{{site.baseurl}}/search-plugins/ltr/building-features/).

_search-plugins/ltr/index.md

@@ -25,9 +25,9 @@ OpenSearch.
 
 -   Want a quickstart? Check out the demo in
     [hello-ltr](https://github.com/o19s/hello-ltr).
--   Brand new to learning to rank? head to
-    `core-concepts`{.interpreted-text role="doc"}.
--   Otherwise, start with `fits-in`{.interpreted-text role="doc"}
+-   Brand new to learning to rank? Head to
+    [core concepts]({{site.url}}{{site.baseurl}}/search-plugins/ltr/core-concepts/).
+-   Otherwise, start with how the plugin [fits in]({{site.url}}{{site.baseurl}}/search-plugins/ltr/fits-in/).
 
 ## Installing
 

_search-plugins/ltr/training-models.md

@@ -17,9 +17,9 @@ an extensive overview) and then dig into uploading a model.
 
 ## RankLib training
 
-We provide two demos for training a model. A fully-fledged [Ranklib
+We provide two demos for training a model. A fully-fledged [RankLib
 Demo](http://github.com/o19s/elasticsearch-learning-to-rank/tree/master/demo)
-uses Ranklib to train a model from OpenSearch queries. You can see
+uses RankLib to train a model from OpenSearch queries. You can see
 how features are
 [logged](http://github.com/o19s/elasticsearch-learning-to-rank-learning-to-rank/tree/master/demo/collectFeatures.py)
 and how models are
@@ -36,16 +36,16 @@ Here for query id 1 (Rambo) we've logged features 1 (a title `TF*IDF`
 score) and feature 2 (a description `TF*IDF` score) for a set of
 documents. In
 [train.py](http://github.com/o19s/elasticsearch-learning-to-rank/demo/train.py)
-you'll see how we call Ranklib to train one of it's supported models
+you'll see how we call RankLib to train one of it's supported models
 on this line:
 
     cmd = "java -jar RankLib-2.8.jar -ranker %s -train%rs -save %s -frate 1.0" % (whichModel, judgmentsWithFeaturesFile, modelOutput)
 
 Our "judgmentsWithFeatureFile" is the input to RankLib. Other
-parameters are passed, which you can read about in [Ranklib's
+parameters are passed, which you can read about in [RankLib's
 documentation](https://sourceforge.net/p/lemur/wiki/RankLib/).
 
-Ranklib will output a model in it's own serialization format. For
+RankLib will output a model in it's own serialization format. For
 example a LambdaMART model is an ensemble of regression trees. It looks
 like:
 
@@ -65,8 +65,8 @@ like:
 Notice how each tree examines the value of features, makes a decision
 based on the value of a feature, then ultimately outputs the relevance
 score. You'll note features are referred to by ordinal, starting by
-"1" with Ranklib (this corresponds to the 0th feature in your feature
-set). Ranklib does not use feature names when training.
+"1" with RankLib (this corresponds to the 0th feature in your feature
+set). RankLib does not use feature names when training.
 
 ## XGBoost example
 
@@ -130,10 +130,10 @@ to upload it to OpenSearch LTR. Models are uploaded specifying the
 following arguments
 
 -   The feature set that was trained against
--   The type of model (such as ranklib or xgboost)
+-   The type of model (such as RankLib or XGBoost)
 -   The model contents
 
-Uploading a Ranklib model trained against `more_movie_features` looks
+Uploading a RankLib model trained against `more_movie_features` looks
 like:
 
 ```json