diff --git a/notebooks/huggingface_notebook.ipynb b/notebooks/huggingface_notebook.ipynb
index 9b813f8c7d..c6c6ce6335 100644
--- a/notebooks/huggingface_notebook.ipynb
+++ b/notebooks/huggingface_notebook.ipynb
@@ -134,7 +134,7 @@
     "                                            nb_classes=10,\n",
     "                                            clip_values=(0, 1),\n",
     "                                            processor=upsampler)\n",
-    "    hf_model.fit(x_train, y_train, nb_epochs=2, display_progress_bar=True)\n",
+    "    hf_model.fit(x_train, y_train, nb_epochs=2, verbose=True)\n",
     "    return hf_model"
    ]
   },
@@ -701,7 +701,7 @@
     "                                            clip_values=(0, 1),\n",
     "                                            processor=upsampler)\n",
     "    (x_train, y_train), (x_test, y_test) = get_cifar_data(train_on_subset)\n",
-    "    hf_model.fit(x_train, y_train, nb_epochs=2, display_progress_bar=True)\n",
+    "    hf_model.fit(x_train, y_train, nb_epochs=2, verbose=True)\n",
     "\n",
     "    return hf_model"
    ]
@@ -1010,7 +1010,7 @@
     "                                                         processor=None) # No processor is needed as the data is of the correct size for the model.\n",
     "    \n",
     "    (x_train, y_train), (x_test, y_test) = get_cifar_data()\n",
-    "    simple_hf_classifier.fit(x_train, y_train, nb_epochs=20, display_progress_bar=True)\n",
+    "    simple_hf_classifier.fit(x_train, y_train, nb_epochs=20, verbose=True)\n",
     "\n",
     "    num_samples = 100\n",
     "    outputs = simple_hf_classifier.predict(x_test[:num_samples])\n",
@@ -1363,7 +1363,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.8.10"
   }
  },
  "nbformat": 4,