#5 📝 Tranche 3 of docs - checkpoint

docs/modules/ROOT/pages/model-lineage.adoc

@@ -40,7 +40,7 @@ We've provided the following custom facets for common use cases:
 * `HardwareDetailsRunFacet`
 * `HyperparameterFacet`
 
-[cols="1,1,2,2"]
+[cols="a,a,b,b"]
 |===
 | Facet Name | Description | Input Parameters | Example
 
@@ -66,7 +66,8 @@ capturing the unique MLflow run ID.
 [NOTE]
 In this context, a `run` refers to the execution instance of an MLflow experiment within the MLflow platform and not
 the associated but distinct lineage run object.
-| `runUUID`: Unique identifier for the MLflow run.
+|
+* `runUUID`: Unique identifier for the MLflow run.
 |
 [source]
 ----
@@ -75,29 +76,44 @@ MLflowRunFacet(
 )
 ----
 
-| HardwareDetailsRunFacet
-| The HardwareDetailsRunFacet is a Run Facet that captures details about the hardware components utilized during model training.
-| hardware: List of hardware components with specifications
+| `HardwareDetailsRunFacet`
+| This Run Facet that captures details about the hardware components utilized during model training.
+|
+* `hardware:` List of hardware components with specifications
 |
+[source]
+----
 hardware_components = [
   HardwareComponent(component="GPU", spec="NVIDIA GeForce RTX 3090"),
   HardwareComponent(component="CPU", spec="Intel Core i9-11900K"),
 ]
 hardware_facet = HardwareDetailsRunFacet(hardware=hardware_components)
-
-| HyperparameterRunFacet
-| The HyperparameterRunFacet is a Run Facet that tracks the hyperparameters used to train the model. 
-| hyperparameters: List of Hyperparameter objects that tracks the parameter's name and the configured value
-| HyperparameterRunFacet(Hyperparameter(parameter="n_estimator", value=100))
-
-| PerformanceMetricRunFacet
-| The PerformanceMetricRunFacet is a Run Facet that tracks the performance of the trained model. 
-| performance: List of PerformanceMetric objects that track the performance metric's name and value.
-| PerformanceMetricRunFacet(PerformanceMetric(metric="accuracy", value="0"))
-
+----
+| `HyperparameterRunFacet`
+| This Run Facet that tracks the hyperparameters used to train the model.
+|
+* `hyperparameters:` List of Hyperparameter objects that tracks the parameter's name and the configured value
+|
+[source]
+----
+HyperparameterRunFacet(Hyperparameter(parameter="n_estimator", value=100))
+---
+| `PerformanceMetricRunFacet`
+| This  Run Facet that tracks the performance of the trained model.
+|
+* `performance`: List of PerformanceMetric objects that track the performance metric's name and value.
+|
+[source]
+----
+PerformanceMetricRunFacet(PerformanceMetric(metric="accuracy", value="0"))
+----
 |===
-TIP: Leverage the `LineageBuilder` class from aiSSEMBLE's `aissemble-foundation-model-lineage` package for a structured approach to populating the facets that aiSSEMBLE captures by default.
+
+TIP: Leverage the `LineageBuilder` class from aiSSEMBLE's `aissemble-foundation-model-lineage` package for a
+structured approach to populating the facets that aiSSEMBLE captures by default.
 
 == Additional Resources
-The full aiSSEMBLE model lineage source code can be reviewed on https://github.com/boozallen/aissemble/tree/dev/foundation/foundation-lineage/aissemble-foundation-model-lineage[GitHub].
-Methods for tracking that generate minimal instances of Run, Job, and RunEvent for emission are described in `src/<pipeline-name>/generated/step/<training-name>_base.py`
+The full aiSSEMBLE model lineage source code can be reviewed on
+https://github.com/boozallen/aissemble/tree/dev/foundation/foundation-lineage/aissemble-foundation-model-lineage[GitHub].
+Methods for tracking that generate minimal instances of Run, Job, and RunEvent for emission are described in
+`src/<pipeline-name>/generated/step/<training-name>_base.py`