diff --git a/.gitignore b/.gitignore
index b6f7329..ac39966 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,5 +1,6 @@
 penv/ 
 env/ 
+venv/
 .vscode/settings.json
 coco_instances_val2017.json
 mypythonlib.egg-info/
@@ -19,4 +20,4 @@ road_sign_data.yaml
 BCCD_Dataset/
 model_training/
 __pycache__
-samples
+samples
\ No newline at end of file
diff --git a/pylabel/splitter.py b/pylabel/splitter.py
index ea016e2..27df0c7 100644
--- a/pylabel/splitter.py
+++ b/pylabel/splitter.py
@@ -22,8 +22,18 @@ def GroupShuffleSplit(
     ):
         """
         This function uses the GroupShuffleSplit command from sklearn. It can split into 3 groups (train,
-        test, and val) by applying the command twice.
+        test, and val) by applying the command twice. If you want to split into only 2 groups (train and test),
+        then set val_pct to 0.
         """
+
+        # Check inputs and raise errors if needed
+        assert 0 < float(train_pct) < 1, "train_pct must be between 0 and 1"
+        assert 0 < float(test_pct) < 1, "test_pct must be between 0 and 1"
+        # check that the sum of train_pct, test_pct, and val_pct is equal to 1
+        assert (
+            round(train_pct + test_pct + val_pct, 1) == 1
+        ), "Sum of train_pct, test_pct, and val_pct must equal 1."
+
         df_main = self.dataset.df
         gss = sklearnGroupShuffleSplit(
             n_splits=1, train_size=train_pct, random_state=random_state
@@ -69,6 +79,22 @@ def StratifiedGroupShuffleSplit(
         train, test, or val. When a split dataset is exported the annotations will be split into
         seperate groups so that can be used used in model training, testing, and validation.
         """
+
+        # Check inputs and raise errors if needed
+        assert (
+            0 <= float(train_pct) <= 1
+        ), "train_pct must be greater than or equal to 0 and less than or equal to 1"
+        assert (
+            0 <= float(test_pct) <= 1
+        ), "test_pct must be greater than or equal to 0 and less than or equal to 1"
+        assert (
+            0 <= float(val_pct) <= 1
+        ), "val_pct must be greater than or equal to 0 and less than or equal to 1"
+        # check that the sum of train_pct, test_pct, and val_pct is equal to 1
+        assert (
+            round(train_pct + test_pct + val_pct, 1) == 1
+        ), "Sum of train_pct, test_pct, and val_pct must equal 1."
+
         df_main = self.dataset.df
         df_main = df_main.reindex(
             np.random.permutation(df_main.index)