diff --git a/abl/models/basic_model.py b/abl/models/basic_model.py
index 9f07e2c..f26a11a 100644
--- a/abl/models/basic_model.py
+++ b/abl/models/basic_model.py
@@ -411,7 +411,7 @@ class BasicModel:
             data_loader = self._data_loader(X)
         return self._predict(data_loader).softmax(axis=1).cpu().numpy()
 
-    def _val(self, data_loader):
+    def _score(self, data_loader):
         model = self.model
         criterion = self.criterion
         device = self.device
@@ -441,7 +441,7 @@ class BasicModel:
 
         return mean_loss, accuracy
 
-    def val(
+    def score(
         self,
         data_loader: DataLoader = None,
         X: List[Any] = None,
@@ -454,7 +454,7 @@ class BasicModel:
         Parameters
         ----------
         data_loader : DataLoader, optional
-            The data loader used for validation, by default None
+            The data loader used for scoring, by default None
         X : List[Any], optional
             The input data, by default None
         y : List[int], optional
@@ -468,44 +468,16 @@ class BasicModel:
             The accuracy of the model.
         """
         recorder = self.recorder
-        recorder.print("Start val ", print_prefix)
+        recorder.print("Start validation ", print_prefix)
 
         if data_loader is None:
             data_loader = self._data_loader(X, y)
-        mean_loss, accuracy = self._val(data_loader)
+        mean_loss, accuracy = self._score(data_loader)
         recorder.print(
-            "[%s] Val loss: %f, accuray: %f" % (print_prefix, mean_loss, accuracy)
+            "[%s] mean loss: %f, accuray: %f" % (print_prefix, mean_loss, accuracy)
         )
         return accuracy
 
-    def score(
-        self,
-        data_loader: DataLoader = None,
-        X: List[Any] = None,
-        y: List[int] = None,
-        print_prefix: str = "",
-    ) -> float:
-        """
-        Score the model.
-
-        Parameters
-        ----------
-        data_loader : DataLoader, optional
-            The data loader used for scoring, by default None
-        X : List[Any], optional
-            The input data, by default None
-        y : List[int], optional
-            The target data, by default None
-        print_prefix : str, optional
-            The prefix used for printing, by default ""
-
-        Returns
-        -------
-        float
-            The accuracy of the model.
-        """
-        return self.val(data_loader, X, y, print_prefix)
-
     def _data_loader(
         self,
         X: List[Any],