diff --git a/ch06/02_bonus_additional-experiments/additional-experiments.py b/ch06/02_bonus_additional-experiments/additional-experiments.py
index ccbe060..ae60d8d 100644
--- a/ch06/02_bonus_additional-experiments/additional-experiments.py
+++ b/ch06/02_bonus_additional-experiments/additional-experiments.py
@@ -46,7 +46,7 @@ class LinearWithLoRA(torch.nn.Module):
 
 
 class SpamDataset(Dataset):
-    def __init__(self, csv_file, tokenizer, max_length=None, pad_token_id=50256, skip_padding=False):
+    def __init__(self, csv_file, tokenizer, max_length=None, pad_token_id=50256, no_padding=False):
         self.data = pd.read_csv(csv_file)
         self.max_length = max_length if max_length is not None else self._longest_encoded_length(tokenizer)
 
@@ -56,7 +56,7 @@ class SpamDataset(Dataset):
             for text in self.data["Text"]
         ]
 
-        if skip_padding:
+        if not no_padding:
             # Pad sequences to the longest sequence
             self.encoded_texts = [
                 et + [pad_token_id] * (self.max_length - len(et))
@@ -438,7 +438,7 @@ if __name__ == "__main__":
         if args.context_length == "model_context_length":
             max_length = model.pos_emb.weight.shape[0]
         elif args.context_length == "longest_training_example":
-            train_dataset = SpamDataset(base_path / "train.csv", max_length=None, tokenizer=tokenizer)
+            train_dataset = SpamDataset(base_path / "train.csv", max_length=None, tokenizer=tokenizer, no_padding=args.no_padding)
             max_length = train_dataset.max_length
         else:
             try:
@@ -447,9 +447,9 @@ if __name__ == "__main__":
                 raise ValueError("Invalid --context_length argument")
 
     if train_dataset is None:
-        train_dataset = SpamDataset(base_path / "train.csv", max_length=max_length, tokenizer=tokenizer, skip_padding=args.no_padding)
-    val_dataset = SpamDataset(base_path / "validation.csv", max_length=max_length, tokenizer=tokenizer, skip_padding=args.no_padding)
-    test_dataset = SpamDataset(base_path / "test.csv", max_length=max_length, tokenizer=tokenizer, skip_padding=args.no_padding)
+        train_dataset = SpamDataset(base_path / "train.csv", max_length=max_length, tokenizer=tokenizer, no_padding=args.no_padding)
+    val_dataset = SpamDataset(base_path / "validation.csv", max_length=max_length, tokenizer=tokenizer, no_padding=args.no_padding)
+    test_dataset = SpamDataset(base_path / "test.csv", max_length=max_length, tokenizer=tokenizer, no_padding=args.no_padding)
 
     tokenizer = tiktoken.get_encoding("gpt2")
 
@@ -510,4 +510,4 @@ if __name__ == "__main__":
 
     print(f"Training accuracy: {train_accuracy*100:.2f}%")
     print(f"Validation accuracy: {val_accuracy*100:.2f}%")
-    print(f"Test accuracy: {test_accuracy*100:.2f}%")
+    print(f"Test accuracy: {test_accuracy*100:.2f}%")
\ No newline at end of file