diff --git a/docs/2notebook/Example_1_sklearn.ipynb b/docs/2notebook/Example_1_sklearn.ipynb
index 7826f18f..5600c52a 100644
--- a/docs/2notebook/Example_1_sklearn.ipynb
+++ b/docs/2notebook/Example_1_sklearn.ipynb
@@ -72,7 +72,7 @@
     }
    ],
    "source": [
-    "!pip install datasets nltk sklearn"
+    "!pip install datasets nltk scikit-learn"
    ]
   },
   {
@@ -295,11 +295,11 @@
     "    vectFit = vect.fit(training[column_name])\n",
     "    BOW_training = vectFit.transform(training[column_name])\n",
     "    BOW_training_df = pd.DataFrame(\n",
-    "        BOW_training.toarray(), columns=vect.get_feature_names()\n",
+    "        BOW_training.toarray(), columns=vect.get_feature_names_out()\n",
     "    )\n",
     "    BOW_testing = vectFit.transform(testing[column_name])\n",
     "    BOW_testing_Df = pd.DataFrame(\n",
-    "        BOW_testing.toarray(), columns=vect.get_feature_names()\n",
+    "        BOW_testing.toarray(), columns=vect.get_feature_names_out()\n",
     "    )\n",
     "    return vectFit, BOW_training_df, BOW_testing_Df\n",
     "\n",
@@ -311,11 +311,11 @@
     "    Tfidf_fit = Tfidf.fit(training[column_name])\n",
     "    Tfidf_training = Tfidf_fit.transform(training[column_name])\n",
     "    Tfidf_training_df = pd.DataFrame(\n",
-    "        Tfidf_training.toarray(), columns=Tfidf.get_feature_names()\n",
+    "        Tfidf_training.toarray(), columns=Tfidf.get_feature_names_out()\n",
     "    )\n",
     "    Tfidf_testing = Tfidf_fit.transform(testing[column_name])\n",
     "    Tfidf_testing_df = pd.DataFrame(\n",
-    "        Tfidf_testing.toarray(), columns=Tfidf.get_feature_names()\n",
+    "        Tfidf_testing.toarray(), columns=Tfidf.get_feature_names_out()\n",
     "    )\n",
     "    return Tfidf_fit, Tfidf_training_df, Tfidf_testing_df\n",
     "\n",
diff --git a/textattack/models/wrappers/sklearn_model_wrapper.py b/textattack/models/wrappers/sklearn_model_wrapper.py
index 9c9d8074..5e99ab58 100644
--- a/textattack/models/wrappers/sklearn_model_wrapper.py
+++ b/textattack/models/wrappers/sklearn_model_wrapper.py
@@ -23,7 +23,7 @@ def __init__(self, model, tokenizer):
     def __call__(self, text_input_list, batch_size=None):
         encoded_text_matrix = self.tokenizer.transform(text_input_list).toarray()
         tokenized_text_df = pd.DataFrame(
-            encoded_text_matrix, columns=self.tokenizer.get_feature_names()
+            encoded_text_matrix, columns=self.tokenizer.get_feature_names_out()
         )
         return self.model.predict_proba(tokenized_text_df)