diff --git a/bemb/utils/run_helper_lightning.py b/bemb/utils/run_helper_lightning.py
index 8df4131..bc4e402 100644
--- a/bemb/utils/run_helper_lightning.py
+++ b/bemb/utils/run_helper_lightning.py
@@ -23,12 +23,11 @@ def run(model: "LitBEMBFlex",
         dataset_train: ChoiceDataset,
         dataset_val: Optional[ChoiceDataset]=None,
         dataset_test: Optional[ChoiceDataset]=None,
-        # model_optimizer: str='Adam',
         batch_size: int=-1,
-        # learning_rate: float=0.01,
         num_epochs: int=10,
         num_workers: int=0,
         device: Optional[str]=None,
+        check_val_every_n_epoch: Optional[int]=None,
         **kwargs) -> "LitBEMBFlex":
     """_summary_
 
@@ -37,13 +36,13 @@ def run(model: "LitBEMBFlex",
         dataset_train (ChoiceDataset): the dataset for training.
         dataset_val (ChoiceDataset): an optional dataset for validation.
         dataset_test (ChoiceDataset): an optional dataset for testing.
-        model_optimizer (str): the optimizer used to estimate the model. Defaults to 'Adam'.
         batch_size (int, optional): batch size for model training. Defaults to -1.
-        learning_rate (float, optional): learning rate for model training. Defaults to 0.01.
         num_epochs (int, optional): number of epochs for the training. Defaults to 10.
         num_workers (int, optional): number of parallel workers for data loading. Defaults to 0.
         device (Optional[str], optional): the device that trains the model, if None is specified, the function will
             use the current device of the provided model. Defaults to None.
+        check_val_every_n_epoch (Optional[int], optional): the frequency of validation, if None is specified,
+            validation will be performed every 10% of total epochs. Defaults to None.
         **kwargs: other keyword arguments for the pytorch lightning trainer, this is for users with experience in
             pytorch lightning and wish to customize the training process.
 
@@ -93,7 +92,7 @@ def run(model: "LitBEMBFlex",
     trainer = pl.Trainer(accelerator="cuda" if "cuda" in device else device,  # note: "cuda:0" is not a accelerator name.
                          devices="auto",
                          max_epochs=num_epochs,
-                         check_val_every_n_epoch=num_epochs // 10,
+                         check_val_every_n_epoch=num_epochs // 10 if check_val_every_n_epoch is None else check_val_every_n_epoch,
                          log_every_n_steps=1,
                          callbacks=callbacks,
                          **kwargs)