diff --git a/ch06/03_bonus_imdb-classification/README.md b/ch06/03_bonus_imdb-classification/README.md
index 08ffa08..2828026 100644
--- a/ch06/03_bonus_imdb-classification/README.md
+++ b/ch06/03_bonus_imdb-classification/README.md
@@ -132,3 +132,25 @@ Training accuracy: 93.44%
 Validation accuracy: 93.02%
 Test accuracy: 92.95%
 ```
+
+
+<br>
+
+---
+
+<br>
+
+A scikit-learn logistic regression classifier as a baseline.
+
+```
+Dummy classifier:
+Training Accuracy: 50.01%
+Validation Accuracy: 50.14%
+Test Accuracy: 49.91%
+
+
+Logistic regression classifier:
+Training Accuracy: 99.80%
+Validation Accuracy: 88.62%
+Test Accuracy: 88.85%
+```
\ No newline at end of file
diff --git a/ch06/03_bonus_imdb-classification/train_bert_hf.py b/ch06/03_bonus_imdb-classification/train_bert_hf.py
index e4fb267..66dd0f1 100644
--- a/ch06/03_bonus_imdb-classification/train_bert_hf.py
+++ b/ch06/03_bonus_imdb-classification/train_bert_hf.py
@@ -327,7 +327,7 @@ if __name__ == "__main__":
         max_length=256,
         tokenizer=tokenizer,
         pad_token_id=tokenizer.pad_token_id,
-        se_attention_mask=use_attention_mask
+        use_attention_mask=use_attention_mask
     )
     test_dataset = IMDBDataset(
         base_path / "test.csv",
diff --git a/ch06/03_bonus_imdb-classification/train_gpt.py b/ch06/03_bonus_imdb-classification/train_gpt.py
index d91403f..ca092ea 100644
--- a/ch06/03_bonus_imdb-classification/train_gpt.py
+++ b/ch06/03_bonus_imdb-classification/train_gpt.py
@@ -235,7 +235,14 @@ if __name__ == "__main__":
             "Number of epochs."
         )
     )
-
+    parser.add_argument(
+        "--learning_rate",
+        type=float,
+        default=5e-5,
+        help=(
+            "Learning rate."
+        )
+    )
     args = parser.parse_args()
 
     if args.trainable_token == "first":
@@ -346,7 +353,7 @@ if __name__ == "__main__":
 
     start_time = time.time()
     torch.manual_seed(123)
-    optimizer = torch.optim.AdamW(model.parameters(), lr=5e-5, weight_decay=0.1)
+    optimizer = torch.optim.AdamW(model.parameters(), lr=args.learning_rate, weight_decay=0.1)
 
     train_losses, val_losses, train_accs, val_accs, examples_seen = train_classifier_simple(
         model, train_loader, val_loader, optimizer, device,