diff --git a/deepeval/_version.py b/deepeval/_version.py
index 8aec22e1a..15283f469 100644
--- a/deepeval/_version.py
+++ b/deepeval/_version.py
@@ -1 +1 @@
-__version__: str = "0.9.4"
+__version__: str = "0.9.5"
diff --git a/deepeval/dataset.py b/deepeval/dataset.py
index ca552e3d8..794b1b50b 100644
--- a/deepeval/dataset.py
+++ b/deepeval/dataset.py
@@ -168,6 +168,7 @@ def run_evaluation(
         max_retries: int = 3,
         min_success: int = 1,
         raise_error: bool = False,
+        metrics: List[TestCase] = None,
     ):
         table = []
 
@@ -211,6 +212,8 @@ def assert_metric():
                 else:
                     try:
                         assert_metric()
+                    except AssertionError as e:
+                        print(e)
                     except Exception as e:
                         print(e)
         if test_filename is None:
diff --git a/deepeval/metrics/metric.py b/deepeval/metrics/metric.py
index 941128131..da772be94 100644
--- a/deepeval/metrics/metric.py
+++ b/deepeval/metrics/metric.py
@@ -18,6 +18,8 @@
 
 
 class Metric:
+    _success: bool = False
+
     @abstractmethod
     def measure(self, output, expected_output, query: Optional[str] = None):
         pass
@@ -31,6 +33,14 @@ def _get_init_values(self):
         }
         return init_values
 
+    @property
+    def success(self):
+        return self._success
+
+    @success.setter
+    def success(self, value):
+        self._success = value
+
     @abstractmethod
     def is_successful(self) -> bool:
         return False
diff --git a/deepeval/test_case.py b/deepeval/test_case.py
index ca315da74..127dab8a0 100644
--- a/deepeval/test_case.py
+++ b/deepeval/test_case.py
@@ -3,7 +3,7 @@
 from collections import UserList
 from .metrics.metric import Metric
 from .metrics.randomscore import RandomMetric
-from .metrics.entailment_metric import EntailmentScoreMetric
+from .metrics.factual_consistency import FactualConsistencyMetric
 
 
 class TestCase:
@@ -15,7 +15,7 @@ def __init__(
         id: str = None,
     ):
         if metrics is None:
-            self.metrics = [EntailmentScoreMetric(minimum_score=0.3)]
+            self.metrics = [FactualConsistencyMetric(minimum_score=0.3)]
         else:
             self.metrics = metrics
         self.input = input