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diff --git a/‎docs/dyn/aiplatform_v1.projects.locations.evaluationItems.html‎
Lines changed: 16 additions & 16 deletions b/‎docs/dyn/aiplatform_v1.projects.locations.evaluationItems.html‎
Lines changed: 16 additions & 16 deletions
diff --git a/‎docs/dyn/aiplatform_v1.projects.locations.evaluationRuns.html‎
Lines changed: 48 additions & 0 deletions b/‎docs/dyn/aiplatform_v1.projects.locations.evaluationRuns.html‎
Lines changed: 48 additions & 0 deletions
@@ -282,6 +282,12 @@ <h3>Method Details</h3>
     },
     &quot;metrics&quot;: [ # Required. The metrics to be calculated in the evaluation run.
       { # The metric used for evaluation runs.
+        &quot;computationBasedMetricSpec&quot;: { # Specification for a computation based metric. # Spec for a computation based metric.
+          &quot;parameters&quot;: { # Optional. A map of parameters for the metric, e.g. {&quot;rouge_type&quot;: &quot;rougeL&quot;}.
+            &quot;a_key&quot;: &quot;&quot;, # Properties of the object.
+          },
+          &quot;type&quot;: &quot;A String&quot;, # Required. The type of the computation based metric.
+        },
         &quot;llmBasedMetricSpec&quot;: { # Specification for an LLM based metric. # Spec for an LLM based metric.
           &quot;additionalConfig&quot;: { # Optional. Optional additional configuration for the metric.
             &quot;a_key&quot;: &quot;&quot;, # Properties of the object.
@@ -543,6 +549,12 @@ <h3>Method Details</h3>
           &quot;bleuSpec&quot;: { # Spec for bleu score metric - calculates the precision of n-grams in the prediction as compared to reference - returns a score ranging between 0 to 1. # Spec for bleu metric.
             &quot;useEffectiveOrder&quot;: True or False, # Optional. Whether to use_effective_order to compute bleu score.
           },
+          &quot;computationBasedMetricSpec&quot;: { # Specification for a computation based metric. # Spec for a computation based metric.
+            &quot;parameters&quot;: { # Optional. A map of parameters for the metric, e.g. {&quot;rouge_type&quot;: &quot;rougeL&quot;}.
+              &quot;a_key&quot;: &quot;&quot;, # Properties of the object.
+            },
+            &quot;type&quot;: &quot;A String&quot;, # Required. The type of the computation based metric.
+          },
           &quot;customCodeExecutionSpec&quot;: { # Specificies a metric that is populated by evaluating user-defined Python code. # Spec for Custom Code Execution metric.
             &quot;evaluationFunction&quot;: &quot;A String&quot;, # Required. Python function. Expected user to define the following function, e.g.: def evaluate(instance: dict[str, Any]) -&gt; float: Please include this function signature in the code snippet. Instance is the evaluation instance, any fields populated in the instance are available to the function as instance[field_name]. Example: Example input: ``` instance= EvaluationInstance( response=EvaluationInstance.InstanceData(text=&quot;The answer is 4.&quot;), reference=EvaluationInstance.InstanceData(text=&quot;4&quot;) ) ``` Example converted input: ``` { &#x27;response&#x27;: {&#x27;text&#x27;: &#x27;The answer is 4.&#x27;}, &#x27;reference&#x27;: {&#x27;text&#x27;: &#x27;4&#x27;} } ``` Example python function: ``` def evaluate(instance: dict[str, Any]) -&gt; float: if instance&#x27;response&#x27; == instance&#x27;reference&#x27;: return 1.0 return 0.0 ``` CustomCodeExecutionSpec is also supported in Batch Evaluation (EvalDataset RPC) and Tuning Evaluation. Each line in the input jsonl file will be converted to dict[str, Any] and passed to the evaluation function.
           },
@@ -1535,6 +1547,12 @@ <h3>Method Details</h3>
     },
     &quot;metrics&quot;: [ # Required. The metrics to be calculated in the evaluation run.
       { # The metric used for evaluation runs.
+        &quot;computationBasedMetricSpec&quot;: { # Specification for a computation based metric. # Spec for a computation based metric.
+          &quot;parameters&quot;: { # Optional. A map of parameters for the metric, e.g. {&quot;rouge_type&quot;: &quot;rougeL&quot;}.
+            &quot;a_key&quot;: &quot;&quot;, # Properties of the object.
+          },
+          &quot;type&quot;: &quot;A String&quot;, # Required. The type of the computation based metric.
+        },
         &quot;llmBasedMetricSpec&quot;: { # Specification for an LLM based metric. # Spec for an LLM based metric.
           &quot;additionalConfig&quot;: { # Optional. Optional additional configuration for the metric.
             &quot;a_key&quot;: &quot;&quot;, # Properties of the object.
@@ -1796,6 +1814,12 @@ <h3>Method Details</h3>
           &quot;bleuSpec&quot;: { # Spec for bleu score metric - calculates the precision of n-grams in the prediction as compared to reference - returns a score ranging between 0 to 1. # Spec for bleu metric.
             &quot;useEffectiveOrder&quot;: True or False, # Optional. Whether to use_effective_order to compute bleu score.
           },
+          &quot;computationBasedMetricSpec&quot;: { # Specification for a computation based metric. # Spec for a computation based metric.
+            &quot;parameters&quot;: { # Optional. A map of parameters for the metric, e.g. {&quot;rouge_type&quot;: &quot;rougeL&quot;}.
+              &quot;a_key&quot;: &quot;&quot;, # Properties of the object.
+            },
+            &quot;type&quot;: &quot;A String&quot;, # Required. The type of the computation based metric.
+          },
           &quot;customCodeExecutionSpec&quot;: { # Specificies a metric that is populated by evaluating user-defined Python code. # Spec for Custom Code Execution metric.
             &quot;evaluationFunction&quot;: &quot;A String&quot;, # Required. Python function. Expected user to define the following function, e.g.: def evaluate(instance: dict[str, Any]) -&gt; float: Please include this function signature in the code snippet. Instance is the evaluation instance, any fields populated in the instance are available to the function as instance[field_name]. Example: Example input: ``` instance= EvaluationInstance( response=EvaluationInstance.InstanceData(text=&quot;The answer is 4.&quot;), reference=EvaluationInstance.InstanceData(text=&quot;4&quot;) ) ``` Example converted input: ``` { &#x27;response&#x27;: {&#x27;text&#x27;: &#x27;The answer is 4.&#x27;}, &#x27;reference&#x27;: {&#x27;text&#x27;: &#x27;4&#x27;} } ``` Example python function: ``` def evaluate(instance: dict[str, Any]) -&gt; float: if instance&#x27;response&#x27; == instance&#x27;reference&#x27;: return 1.0 return 0.0 ``` CustomCodeExecutionSpec is also supported in Batch Evaluation (EvalDataset RPC) and Tuning Evaluation. Each line in the input jsonl file will be converted to dict[str, Any] and passed to the evaluation function.
           },
@@ -2830,6 +2854,12 @@ <h3>Method Details</h3>
     },
     &quot;metrics&quot;: [ # Required. The metrics to be calculated in the evaluation run.
       { # The metric used for evaluation runs.
+        &quot;computationBasedMetricSpec&quot;: { # Specification for a computation based metric. # Spec for a computation based metric.
+          &quot;parameters&quot;: { # Optional. A map of parameters for the metric, e.g. {&quot;rouge_type&quot;: &quot;rougeL&quot;}.
+            &quot;a_key&quot;: &quot;&quot;, # Properties of the object.
+          },
+          &quot;type&quot;: &quot;A String&quot;, # Required. The type of the computation based metric.
+        },
         &quot;llmBasedMetricSpec&quot;: { # Specification for an LLM based metric. # Spec for an LLM based metric.
           &quot;additionalConfig&quot;: { # Optional. Optional additional configuration for the metric.
             &quot;a_key&quot;: &quot;&quot;, # Properties of the object.
@@ -3091,6 +3121,12 @@ <h3>Method Details</h3>
           &quot;bleuSpec&quot;: { # Spec for bleu score metric - calculates the precision of n-grams in the prediction as compared to reference - returns a score ranging between 0 to 1. # Spec for bleu metric.
             &quot;useEffectiveOrder&quot;: True or False, # Optional. Whether to use_effective_order to compute bleu score.
           },
+          &quot;computationBasedMetricSpec&quot;: { # Specification for a computation based metric. # Spec for a computation based metric.
+            &quot;parameters&quot;: { # Optional. A map of parameters for the metric, e.g. {&quot;rouge_type&quot;: &quot;rougeL&quot;}.
+              &quot;a_key&quot;: &quot;&quot;, # Properties of the object.
+            },
+            &quot;type&quot;: &quot;A String&quot;, # Required. The type of the computation based metric.
+          },
           &quot;customCodeExecutionSpec&quot;: { # Specificies a metric that is populated by evaluating user-defined Python code. # Spec for Custom Code Execution metric.
             &quot;evaluationFunction&quot;: &quot;A String&quot;, # Required. Python function. Expected user to define the following function, e.g.: def evaluate(instance: dict[str, Any]) -&gt; float: Please include this function signature in the code snippet. Instance is the evaluation instance, any fields populated in the instance are available to the function as instance[field_name]. Example: Example input: ``` instance= EvaluationInstance( response=EvaluationInstance.InstanceData(text=&quot;The answer is 4.&quot;), reference=EvaluationInstance.InstanceData(text=&quot;4&quot;) ) ``` Example converted input: ``` { &#x27;response&#x27;: {&#x27;text&#x27;: &#x27;The answer is 4.&#x27;}, &#x27;reference&#x27;: {&#x27;text&#x27;: &#x27;4&#x27;} } ``` Example python function: ``` def evaluate(instance: dict[str, Any]) -&gt; float: if instance&#x27;response&#x27; == instance&#x27;reference&#x27;: return 1.0 return 0.0 ``` CustomCodeExecutionSpec is also supported in Batch Evaluation (EvalDataset RPC) and Tuning Evaluation. Each line in the input jsonl file will be converted to dict[str, Any] and passed to the evaluation function.
           },
@@ -4096,6 +4132,12 @@ <h3>Method Details</h3>
         },
         &quot;metrics&quot;: [ # Required. The metrics to be calculated in the evaluation run.
           { # The metric used for evaluation runs.
+            &quot;computationBasedMetricSpec&quot;: { # Specification for a computation based metric. # Spec for a computation based metric.
+              &quot;parameters&quot;: { # Optional. A map of parameters for the metric, e.g. {&quot;rouge_type&quot;: &quot;rougeL&quot;}.
+                &quot;a_key&quot;: &quot;&quot;, # Properties of the object.
+              },
+              &quot;type&quot;: &quot;A String&quot;, # Required. The type of the computation based metric.
+            },
             &quot;llmBasedMetricSpec&quot;: { # Specification for an LLM based metric. # Spec for an LLM based metric.
               &quot;additionalConfig&quot;: { # Optional. Optional additional configuration for the metric.
                 &quot;a_key&quot;: &quot;&quot;, # Properties of the object.
@@ -4357,6 +4399,12 @@ <h3>Method Details</h3>
               &quot;bleuSpec&quot;: { # Spec for bleu score metric - calculates the precision of n-grams in the prediction as compared to reference - returns a score ranging between 0 to 1. # Spec for bleu metric.
                 &quot;useEffectiveOrder&quot;: True or False, # Optional. Whether to use_effective_order to compute bleu score.
               },
+              &quot;computationBasedMetricSpec&quot;: { # Specification for a computation based metric. # Spec for a computation based metric.
+                &quot;parameters&quot;: { # Optional. A map of parameters for the metric, e.g. {&quot;rouge_type&quot;: &quot;rougeL&quot;}.
+                  &quot;a_key&quot;: &quot;&quot;, # Properties of the object.
+                },
+                &quot;type&quot;: &quot;A String&quot;, # Required. The type of the computation based metric.
+              },
               &quot;customCodeExecutionSpec&quot;: { # Specificies a metric that is populated by evaluating user-defined Python code. # Spec for Custom Code Execution metric.
                 &quot;evaluationFunction&quot;: &quot;A String&quot;, # Required. Python function. Expected user to define the following function, e.g.: def evaluate(instance: dict[str, Any]) -&gt; float: Please include this function signature in the code snippet. Instance is the evaluation instance, any fields populated in the instance are available to the function as instance[field_name]. Example: Example input: ``` instance= EvaluationInstance( response=EvaluationInstance.InstanceData(text=&quot;The answer is 4.&quot;), reference=EvaluationInstance.InstanceData(text=&quot;4&quot;) ) ``` Example converted input: ``` { &#x27;response&#x27;: {&#x27;text&#x27;: &#x27;The answer is 4.&#x27;}, &#x27;reference&#x27;: {&#x27;text&#x27;: &#x27;4&#x27;} } ``` Example python function: ``` def evaluate(instance: dict[str, Any]) -&gt; float: if instance&#x27;response&#x27; == instance&#x27;reference&#x27;: return 1.0 return 0.0 ``` CustomCodeExecutionSpec is also supported in Batch Evaluation (EvalDataset RPC) and Tuning Evaluation. Each line in the input jsonl file will be converted to dict[str, Any] and passed to the evaluation function.
               },