diff --git a/docs/examples.rst b/docs/examples.rst
index d7d6a1e2..5101eba8 100644
--- a/docs/examples.rst
+++ b/docs/examples.rst
@@ -29,15 +29,13 @@ You can also find a simple and quick-start tutorial notebook on Google Colab
 
     # Data preprocessing. Tedious, but PyPOTS can help. 🤓
     data = load_specific_dataset('physionet_2012')  # PyPOTS will automatically download and extract it.
-    X = data['X']
-    num_samples = len(X['RecordID'].unique())
-    X = X.drop(['RecordID', 'Time'], axis = 1)
-    X = StandardScaler().fit_transform(X.to_numpy())
-    X = X.reshape(num_samples, 48, -1)
+    X = data['train_X']
+    num_samples = len(X)
+    X = StandardScaler().fit_transform(X.reshape(-1, X.shape[-1])).reshape(X.shape)
     X_ori = X  # keep X_ori for validation
     X = mcar(X, 0.1)  # randomly hold out 10% observed values as ground truth
     dataset = {"X": X}  # X for model input
-    print(X.shape)  # (11988, 48, 37), 11988 samples, 48 time steps, 37 features
+    print(X.shape)  # (7671, 48, 37), 7671 samples, 48 time steps, 37 features
 
     # initialize the model
     saits = SAITS(
@@ -55,7 +53,7 @@ You can also find a simple and quick-start tutorial notebook on Google Colab
         model_saving_strategy="best", # only save the model with the best validation performance
     )
 
-    # train the model. Here I use the whole dataset as the training set, because ground truth is not visible to the model.
+    # train the model. Here I consider the train dataset only, and evaluate on it, because ground truth is not visible to the model.
     saits.fit(dataset)
     # impute the originally-missing values and artificially-missing values
     imputation = saits.impute(dataset)
@@ -64,6 +62,6 @@ You can also find a simple and quick-start tutorial notebook on Google Colab
     mae = calc_mae(imputation, np.nan_to_num(X_ori), indicating_mask)  # calculate mean absolute error on the ground truth (artificially-missing values)
 
     # the best model has been already saved, but you can still manually save it with function save_model() as below
-    saits.save_model(saving_dir="examples/saits",file_name="manually_saved_saits_model")
+    saits.save(saving_path="examples/saits/manually_saved_saits_model")
     # you can load the saved model into a new initialized model
-    saits.load_model("examples/saits/manually_saved_saits_model")
+    saits.load("examples/saits/manually_saved_saits_model.pypots")