diff --git a/08_introduction_to_nlp_in_tensorflow.ipynb b/08_introduction_to_nlp_in_tensorflow.ipynb
index 897d9f6..f2f3000 100644
--- a/08_introduction_to_nlp_in_tensorflow.ipynb
+++ b/08_introduction_to_nlp_in_tensorflow.ipynb
@@ -3121,19 +3121,20 @@
         "# Set random seed and create embedding layer (new embedding layer for each model)\n",
         "tf.random.set_seed(42)\n",
         "from tensorflow.keras import layers\n",
-        "model_3_embedding = layers.Embedding(input_dim=max_vocab_length,\n",
-        "                                     output_dim=128,\n",
-        "                                     embeddings_initializer=\"uniform\",\n",
+        "model_3_embedding = layers.Embedding(,\n",
+        "                                     input_dim=max_vocab_length,\n",
+        "                                     output_dim=128,",\n",
+        "                                     embeddings_initializer="uniform",\n",
         "                                     input_length=max_length,\n",
-        "                                     name=\"embedding_3\")\n",
+        "                                     name="embedding_3"),
         "\n",
         "# Build an RNN using the GRU cell\n",
-        "inputs = layers.Input(shape=(1,), dtype=\"string\")\n",
+        "inputs = layers.Input(shape=(1,), dtype="string")  # Adjust input shape for sequences\n",
         "x = text_vectorizer(inputs)\n",
         "x = model_3_embedding(x)\n",
-        "# x = layers.GRU(64, return_sequences=True) # stacking recurrent cells requires return_sequences=True\n",
-        "x = layers.GRU(64)(x) \n",
-        "# x = layers.Dense(64, activation=\"relu\")(x) # optional dense layer after GRU cell\n",
+        "# x = layers.GRU(64, return_sequences=True)(x)  # Stacking recurrent cells requires return_sequences=True\n",
+        "x = layers.GRU(64)(x)  # Last GRU without return_sequences\n",
+        "# x = layers.Dense(64, activation="relu")(x) # optional dense layer after GRU cell\n",
         "outputs = layers.Dense(1, activation=\"sigmoid\")(x)\n",
         "model_3 = tf.keras.Model(inputs, outputs, name=\"model_3_GRU\")"
       ]
@@ -7154,4 +7155,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 0
-}
\ No newline at end of file
+}