diff --git a/prepare/metrics/rag_answer_correctness.py b/prepare/metrics/rag_answer_correctness.py
index b9f7cb712..c9c2ccea5 100644
--- a/prepare/metrics/rag_answer_correctness.py
+++ b/prepare/metrics/rag_answer_correctness.py
@@ -1,37 +1,6 @@
 from unitxt import add_to_catalog
 from unitxt.metrics import MetricPipeline
-from unitxt.operators import Copy, RenameFields
-from unitxt.test_utils.metrics import test_evaluate, test_metric
-
-
-def test_answer_correctness(task_data, catalog_name, global_target, instance_targets):
-    # test the evaluate call
-    test_evaluate(
-        global_target,
-        instance_targets=[
-            {"score": instance["score"]} for instance in instance_targets
-        ],
-        task_data=task_data,
-        metric_name=catalog_name,
-    )
-    # test using the usual metric pipeline
-    test_pipeline = MetricPipeline(
-        main_score="score",
-        preprocess_steps=[
-            RenameFields(field_to_field={"task_data/ground_truths": "ground_truths"}),
-            RenameFields(field_to_field={"task_data/answer": "answer"}),
-        ],
-        metric=f"{catalog_name}",
-    )
-    test_metric(
-        metric=test_pipeline,
-        predictions=[None] * len(instance_targets),
-        references=[[]] * len(instance_targets),
-        instance_targets=instance_targets,
-        global_target=global_target,
-        task_data=task_data,
-    )
-
+from unitxt.operators import Copy
 
 base = "metrics.rag.answer_correctness"
 default = "token_recall"
@@ -59,129 +28,3 @@ def test_answer_correctness(task_data, catalog_name, global_target, instance_tar
 
     if new_catalog_name == default:
         add_to_catalog(metric, base, overwrite=True)
-
-if __name__ == "__main__":
-    # don't use "A" as a token because it is considered an article and removed by the token overlap
-    # metric
-    task_data = [
-        {  # recall is 0.5 for the first ground_truth, 0 for the second ground_truth.
-            # so overall its max(0.5, 0) = 0.5
-            "ground_truths": ["B C", "C"],
-            "answer": "B",
-        },
-        {  # recall is 1/3
-            "ground_truths": ["D E F"],
-            "answer": "B C D",
-        },
-    ]
-
-    recall_instance_targets = [
-        {"f1": 0.67, "precision": 1.0, "recall": 0.5, "score": 0.5, "score_name": "f1"},
-        {
-            "f1": 0.33,
-            "precision": 0.33,
-            "recall": 0.33,
-            "score": 0.33,
-            "score_name": "f1",
-        },
-    ]
-
-    recall_global_target = {
-        "f1": 0.5,
-        "f1_ci_high": 0.67,
-        "f1_ci_low": 0.33,
-        "precision": 0.67,
-        "precision_ci_high": 1.0,
-        "precision_ci_low": 0.33,
-        "recall": 0.42,
-        "recall_ci_high": 0.5,
-        "recall_ci_low": 0.33,
-        "score": 0.42,
-        "score_ci_high": 0.67,
-        "score_ci_low": 0.33,
-        "score_name": "f1",
-    }
-
-    for catalog_name, global_target, instance_targets in [
-        (
-            "metrics.rag.answer_correctness",
-            recall_global_target,
-            recall_instance_targets,
-        ),
-        ("metrics.rag.recall", recall_global_target, recall_instance_targets),
-    ]:
-        test_answer_correctness(
-            task_data, catalog_name, global_target, instance_targets
-        )
-
-    test_answer_correctness(
-        task_data,
-        catalog_name="metrics.rag.bert_recall",
-        global_target={
-            "f1": 0.71,
-            "f1_ci_high": 0.71,
-            "f1_ci_low": 0.71,
-            "precision": 0.74,
-            "precision_ci_high": 0.77,
-            "precision_ci_low": 0.71,
-            "recall": 0.71,
-            "recall_ci_high": 0.71,
-            "recall_ci_low": 0.71,
-            "score": 0.71,
-            "score_ci_high": 0.71,
-            "score_ci_low": 0.71,
-            "score_name": "f1",
-        },
-        instance_targets=[
-            {
-                "f1": 0.71,
-                "precision": 0.77,
-                "recall": 0.71,
-                "score": 0.71,
-                "score_name": "f1",
-            },
-            {
-                "f1": 0.71,
-                "precision": 0.71,
-                "recall": 0.71,
-                "score": 0.71,
-                "score_name": "f1",
-            },
-        ],
-    )
-
-    test_answer_correctness(
-        task_data,
-        catalog_name="metrics.rag.bert_recall_ml",
-        global_target={
-            "f1": 0.86,
-            "f1_ci_high": 0.97,
-            "f1_ci_low": 0.74,
-            "precision": 0.86,
-            "precision_ci_high": 0.97,
-            "precision_ci_low": 0.74,
-            "recall": 0.86,
-            "recall_ci_high": 0.97,
-            "recall_ci_low": 0.74,
-            "score": 0.86,
-            "score_ci_high": 0.97,
-            "score_ci_low": 0.74,
-            "score_name": "f1",
-        },
-        instance_targets=[
-            {
-                "f1": 0.97,
-                "precision": 0.97,
-                "recall": 0.97,
-                "score": 0.97,
-                "score_name": "f1",
-            },
-            {
-                "f1": 0.74,
-                "precision": 0.74,
-                "recall": 0.74,
-                "score": 0.74,
-                "score_name": "f1",
-            },
-        ],
-    )
diff --git a/tests/library/test_metrics.py b/tests/library/test_metrics.py
index 2b3eea812..bbcf411af 100644
--- a/tests/library/test_metrics.py
+++ b/tests/library/test_metrics.py
@@ -1942,3 +1942,168 @@ def text_context_correctness(self):
                 global_target=global_target,
                 task_data=task_data,
             )
+
+    @staticmethod
+    def test_answer_correctness(
+        task_data, catalog_name, global_target, instance_targets
+    ):
+        # test the evaluate call
+        test_evaluate(
+            global_target,
+            instance_targets=[
+                {"score": instance["score"]} for instance in instance_targets
+            ],
+            task_data=task_data,
+            metric_name=catalog_name,
+        )
+        # test using the usual metric pipeline
+        test_pipeline = MetricPipeline(
+            main_score="score",
+            preprocess_steps=[
+                RenameFields(
+                    field_to_field={"task_data/ground_truths": "ground_truths"}
+                ),
+                RenameFields(field_to_field={"task_data/answer": "answer"}),
+            ],
+            metric=f"{catalog_name}",
+        )
+        test_metric(
+            metric=test_pipeline,
+            predictions=[None] * len(instance_targets),
+            references=[[]] * len(instance_targets),
+            instance_targets=instance_targets,
+            global_target=global_target,
+            task_data=task_data,
+        )
+
+    def test_answer_correctness_metrics(self):
+        # don't use "A" as a token because it is considered an article and removed by the token overlap
+        # metric
+        task_data = [
+            {  # recall is 0.5 for the first ground_truth, 0 for the second ground_truth.
+                # so overall its max(0.5, 0) = 0.5
+                "ground_truths": ["B C", "C"],
+                "answer": "B",
+            },
+            {  # recall is 1/3
+                "ground_truths": ["D E F"],
+                "answer": "B C D",
+            },
+        ]
+
+        recall_instance_targets = [
+            {
+                "f1": 0.67,
+                "precision": 1.0,
+                "recall": 0.5,
+                "score": 0.5,
+                "score_name": "f1",
+            },
+            {
+                "f1": 0.33,
+                "precision": 0.33,
+                "recall": 0.33,
+                "score": 0.33,
+                "score_name": "f1",
+            },
+        ]
+
+        recall_global_target = {
+            "f1": 0.5,
+            "f1_ci_high": 0.67,
+            "f1_ci_low": 0.33,
+            "precision": 0.67,
+            "precision_ci_high": 1.0,
+            "precision_ci_low": 0.33,
+            "recall": 0.42,
+            "recall_ci_high": 0.5,
+            "recall_ci_low": 0.33,
+            "score": 0.42,
+            "score_ci_high": 0.67,
+            "score_ci_low": 0.33,
+            "score_name": "f1",
+        }
+
+        for catalog_name, global_target, instance_targets in [
+            (
+                "metrics.rag.answer_correctness",
+                recall_global_target,
+                recall_instance_targets,
+            ),
+            ("metrics.rag.recall", recall_global_target, recall_instance_targets),
+        ]:
+            self.test_answer_correctness(
+                task_data, catalog_name, global_target, instance_targets
+            )
+
+        self.test_answer_correctness(
+            task_data,
+            catalog_name="metrics.rag.bert_recall",
+            global_target={
+                "f1": 0.71,
+                "f1_ci_high": 0.71,
+                "f1_ci_low": 0.71,
+                "precision": 0.74,
+                "precision_ci_high": 0.77,
+                "precision_ci_low": 0.71,
+                "recall": 0.71,
+                "recall_ci_high": 0.71,
+                "recall_ci_low": 0.71,
+                "score": 0.71,
+                "score_ci_high": 0.71,
+                "score_ci_low": 0.71,
+                "score_name": "f1",
+            },
+            instance_targets=[
+                {
+                    "f1": 0.71,
+                    "precision": 0.77,
+                    "recall": 0.71,
+                    "score": 0.71,
+                    "score_name": "f1",
+                },
+                {
+                    "f1": 0.71,
+                    "precision": 0.71,
+                    "recall": 0.71,
+                    "score": 0.71,
+                    "score_name": "f1",
+                },
+            ],
+        )
+
+        self.test_answer_correctness(
+            task_data,
+            catalog_name="metrics.rag.bert_recall_ml",
+            global_target={
+                "f1": 0.86,
+                "f1_ci_high": 0.97,
+                "f1_ci_low": 0.74,
+                "precision": 0.86,
+                "precision_ci_high": 0.97,
+                "precision_ci_low": 0.74,
+                "recall": 0.86,
+                "recall_ci_high": 0.97,
+                "recall_ci_low": 0.74,
+                "score": 0.86,
+                "score_ci_high": 0.97,
+                "score_ci_low": 0.74,
+                "score_name": "f1",
+            },
+            instance_targets=[
+                {
+                    "f1": 0.97,
+                    "precision": 0.97,
+                    "recall": 0.97,
+                    "score": 0.97,
+                    "score_name": "f1",
+                },
+                {
+                    "f1": 0.74,
+                    "precision": 0.74,
+                    "recall": 0.74,
+                    "score": 0.74,
+                    "score_name": "f1",
+                },
+            ],
+        )