diff --git a/notebooks/unit2/unit2.ipynb b/notebooks/unit2/unit2.ipynb
index 81f3652..a4f9b74 100644
--- a/notebooks/unit2/unit2.ipynb
+++ b/notebooks/unit2/unit2.ipynb
@@ -71,7 +71,7 @@
     {
       "cell_type": "markdown",
       "source": [
-        "## This notebook is from Deep Reinforcement Learning Course\n",
+        "## This notebook is from the Deep Reinforcement Learning Course\n",
         "<img src=\"https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/notebooks/deep-rl-course-illustration.jpg\" alt=\"Deep RL Course illustration\"/>"
       ],
       "metadata": {
@@ -190,7 +190,7 @@
       },
       "outputs": [],
       "source": [
-        "!pip install -r https://github.com/huggingface/deep-rl-class/tree/main/notebooks/unit2/requirements-unit2.txt"
+        "!pip install -r pip install -r https://raw.githubusercontent.com/huggingface/deep-rl-class/main/notebooks/unit2/requirements-unit2.txt"
       ]
     },
     {
@@ -996,7 +996,7 @@
       },
       "outputs": [],
       "source": [
-        "from huggingface_hub import HfApi, HfFolder, Repository, snapshot_download\n",
+        "from huggingface_hub import HfApi, snapshot_download\n",
         "from huggingface_hub.repocard import metadata_eval_result, metadata_save\n",
         "\n",
         "from pathlib import Path\n",
@@ -1074,8 +1074,6 @@
         "        if env.spec.kwargs.get(\"is_slippery\", \"\") == False:\n",
         "            model[\"slippery\"] = False\n",
         "\n",
-        "    print(model)\n",
-        "\n",
         "    # Pickle the model\n",
         "    with open((repo_local_path) / \"q-learning.pkl\", \"wb\") as f:\n",
         "        pickle.dump(model, f)\n",
@@ -1092,7 +1090,8 @@
         "        \"eval_datetime\": datetime.datetime.now().isoformat()\n",
         "    }\n",
         "\n",
-        "    # Write a JSON file\n",
+        "    # Write a JSON file called \"results.json\" that will contain the\n",
+        "    # evaluation results\n",
         "    with open(repo_local_path / \"results.json\", \"w\") as outfile:\n",
         "        json.dump(evaluate_data, outfile)\n",
         "\n",
@@ -1139,7 +1138,6 @@
         "\n",
         "    evaluate_agent(env, model[\"max_steps\"], model[\"n_eval_episodes\"], model[\"qtable\"], model[\"eval_seed\"])\n",
         "  \n",
-        "\n",
         "    readme_path = repo_local_path / \"README.md\"\n",
         "    readme = \"\"\n",
         "    print(readme_path.exists())\n",
@@ -1148,7 +1146,6 @@
         "            readme = f.read()\n",
         "    else:\n",
         "        readme = model_card\n",
-        "    print(readme)\n",
         "\n",
         "    with readme_path.open(\"w\", encoding=\"utf-8\") as f:\n",
         "        f.write(readme)\n",