diff --git a/ch06/01_main-chapter-code/ch06.ipynb b/ch06/01_main-chapter-code/ch06.ipynb
index dda54d9..d3df2b0 100644
--- a/ch06/01_main-chapter-code/ch06.ipynb
+++ b/ch06/01_main-chapter-code/ch06.ipynb
@@ -1822,7 +1822,7 @@
     "- In this section, we define and use the training function to improve the classification accuracy of the model\n",
     "- The `train_classifier_simple` function below is practically the same as the `train_model_simple` function we used for pretraining the model in chapter 5\n",
     "- The only two differences are that we now \n",
-    "  1. track the number of training examples seen (`examples_seen`) instead of the number of tokens seen\n",
+    "  1. track the number of training examples seen (`examples_seen`) instead of the number of examples seen\n",
     "  2. calculate the accuracy after each epoch instead of printing a sample text after each epoch"
    ]
   },
@@ -1846,7 +1846,7 @@
     "# Overall the same as `train_model_simple` in chapter 5\n",
     "def train_classifier_simple(model, train_loader, val_loader, optimizer, device, num_epochs,\n",
     "                            eval_freq, eval_iter, tokenizer):\n",
-    "    # Initialize lists to track losses and tokens seen\n",
+    "    # Initialize lists to track losses and examples seen\n",
     "    train_losses, val_losses, train_accs, val_accs = [], [], [], []\n",
     "    examples_seen, global_step = 0, -1\n",
     "\n",
@@ -2003,7 +2003,7 @@
     "    ax1.set_ylabel(label.capitalize())\n",
     "    ax1.legend()\n",
     "\n",
-    "    # Create a second x-axis for tokens seen\n",
+    "    # Create a second x-axis for examples seen\n",
     "    ax2 = ax1.twiny()  # Create a second x-axis that shares the same y-axis\n",
     "    ax2.plot(examples_seen, train_values, alpha=0)  # Invisible plot for aligning ticks\n",
     "    ax2.set_xlabel(\"Examples seen\")\n",