peer reiview nits

Author: skrthomas

modules/guardrails-configuring-the-hugging-face-detector-serving-runtime.adoc

@@ -9,8 +9,7 @@ ifdef::context[:parent-context: {context}]
 
 To use link:https://huggingface.co/docs/transformers/model_doc/auto#transformers.AutoModelForSequenceClassification[Hugging Face] `AutoModelsForSequenceClassification` as detectors within the Guardrails Orchestrator, you need to first configure a Hugging Face serving runtime.
 
-The link:https://github.com/opendatahub-io/odh-model-controller/blob/incubating/config/runtimes/hf-detector-template.yaml[guardrails-detector-huggingface-runtime] is a KServe serving runtime for Hugging Face predictive text models. This
-allows models such as the link:https://huggingface.co/ibm-granite/granite-guardian-hap-38m[ibm-granite/granite-guardian-hap-38m] to be used within the TrustyAI Guardrails ecosystem.
+The link:https://github.com/opendatahub-io/odh-model-controller/blob/incubating/config/runtimes/hf-detector-template.yaml[guardrails-detector-huggingface-runtime] is a KServe serving runtime for Hugging Face predictive text models. This allows models such as the link:https://huggingface.co/ibm-granite/granite-guardian-hap-38m[ibm-granite/granite-guardian-hap-38m] to be used within the TrustyAI Guardrails ecosystem.
 
 .Example custom serving runtime
 
@@ -57,10 +56,8 @@ spec:
         - containerPort: 8000
           protocol: TCP
 ----
-+
-The above serving runtime example matches the default template used with {productname-long}, and should suffice
-for the majority of use-cases. The main relevant configuration parameter is the `SAFE_LABELS` environment variable. This specifies which prediction label or labels from the `AutoModelForSequenceClassification`  constitute a "safe" response and therefore should not trigger guardrailing. For example, if `[0, 1]` is specified as `SAFE_LABELS`
-for a four-class model, a predicted label of `0` or `1` is considered "safe", while a predicted label of `2` or `3` triggers guardrailing. The default value is `[0]`.
+
+The above serving runtime example matches the default template used with {productname-long}, and should suffice for the majority of use-cases. The main relevant configuration parameter is the `SAFE_LABELS` environment variable. This specifies which prediction label or labels from the `AutoModelForSequenceClassification`  constitute a "safe" response and therefore should not trigger guardrailing. For example, if `[0, 1]` is specified as `SAFE_LABELS` for a four-class model, a predicted label of `0` or `1` is considered "safe", while a predicted label of `2` or `3` triggers guardrailing. The default value is `[0]`.
 
 == Guardrails Detector Hugging Face serving runtime configuration values
 

modules/guardrails-orchestrator-detectors.adoc

@@ -48,9 +48,9 @@ The Guardrails framework provides a set of “built-in” detectors out-of-the-b
 `custom`::
 _Developer preview_
 * Custom detectors defined via a `custom_detectors.py` file.
-
++
 The detector algorithm can be chosen with `detector_params`, by first choosing the top-level taxonomy (e.g., `regex` or `file_type`) and then providing a list of the desired algorithms from within
-that category, for example:
+that category. In the following example, both the `credit-card` and `email` algorithms are run against the provided message:
 
 [source,json]
 ----
@@ -62,6 +62,4 @@ that category, for example:
     "regex": ["credit-card", "email"]
   }
 }
-----
-+
-In the above example, both the `credit-card` and `email` algorithms are run against the provided message.
+----