diff --git a/ex4_parameters.ipynb b/ex4_parameters.ipynb
index 81d79e3..a030193 100644
--- a/ex4_parameters.ipynb
+++ b/ex4_parameters.ipynb
@@ -3,8 +3,8 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "view-in-github",
-        "colab_type": "text"
+        "colab_type": "text",
+        "id": "view-in-github"
       },
       "source": [
         "<a href=\"https://colab.research.google.com/github/TurkuNLP/Deep_Learning_in_LangTech_course/blob/master/ex4_parameters.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
@@ -12,96 +12,78 @@
     },
     {
       "cell_type": "markdown",
-      "source": [
-        "# Setup"
-      ],
       "metadata": {
         "id": "wo13ZXoZYB6J"
-      }
+      },
+      "source": [
+        "# Setup"
+      ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "!pip3 install -q transformers datasets evaluate accelerate"
-      ],
+      "execution_count": null,
       "metadata": {
         "id": "4pquj9Xoxaza"
       },
-      "execution_count": 6,
-      "outputs": []
+      "outputs": [],
+      "source": [
+        "!pip3 install -q transformers datasets evaluate accelerate"
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "cQ63zw6BY7tn"
+      },
+      "outputs": [],
       "source": [
         "from pprint import pprint\n",
         "import logging\n",
         "\n",
         "logging.disable(logging.INFO)"
-      ],
-      "metadata": {
-        "id": "cQ63zw6BY7tn"
-      },
-      "execution_count": 7,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "markdown",
+      "metadata": {
+        "id": "W4x7GbT2ZKUJ"
+      },
       "source": [
         "---\n",
         "# Download and prepare data"
-      ],
-      "metadata": {
-        "id": "W4x7GbT2ZKUJ"
-      }
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "5DKskTuoyCf-"
+      },
+      "outputs": [],
       "source": [
         "import datasets\n",
         "\n",
         "dataset = datasets.load_dataset('imdb')\n",
         "dataset = dataset.shuffle() #This is never a bad idea, datasets may have ordering to them, which is not what we want\n",
         "del dataset[\"unsupervised\"] # Delete the unlabeled part of the dataset to make things faster"
-      ],
-      "metadata": {
-        "id": "5DKskTuoyCf-"
-      },
-      "execution_count": 8,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "markdown",
+      "metadata": {
+        "id": "8KF9UtzUbrBA"
+      },
       "source": [
         "---\n",
         "\n",
         "# Tokenize and vectorize data"
-      ],
-      "metadata": {
-        "id": "8KF9UtzUbrBA"
-      }
+      ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "import transformers\n",
-        "\n",
-        "model_name = \"bert-base-cased\"\n",
-        "tokenizer = transformers.AutoTokenizer.from_pretrained(model_name)\n",
-        "\n",
-        "# Define a simple function that applies the tokenizer\n",
-        "def tokenize(example):\n",
-        "    return tokenizer(\n",
-        "        example[\"text\"],\n",
-        "        max_length=128,\n",
-        "        truncation=True,\n",
-        "    )\n",
-        "\n",
-        "# Apply the tokenizer to the whole dataset using .map()\n",
-        "dataset = dataset.map(tokenize)"
-      ],
+      "execution_count": null,
       "metadata": {
-        "id": "wjrAGcFtymJF",
-        "outputId": "9a35e1d5-c074-4598-828f-71326bf24f87",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 137,
@@ -129,63 +111,49 @@
             "1bdf39342f314fbba05e0a0d066f2a0f",
             "432f18bd11114415bd5fb91cce8de324"
           ]
-        }
-      },
-      "execution_count": 9,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n",
-            "  warnings.warn(\n"
-          ]
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "Map:   0%|          | 0/25000 [00:00<?, ? examples/s]"
-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "6a6875eea30b4a64b87996f30d6b9b0e"
-            }
-          },
-          "metadata": {}
         },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "Map:   0%|          | 0/25000 [00:00<?, ? examples/s]"
-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "6d68fd610b8c48f08ee6f56101119802"
-            }
-          },
-          "metadata": {}
-        }
+        "id": "wjrAGcFtymJF",
+        "outputId": "9a35e1d5-c074-4598-828f-71326bf24f87"
+      },
+      "outputs": [],
+      "source": [
+        "import transformers\n",
+        "\n",
+        "model_name = \"bert-base-cased\"\n",
+        "tokenizer = transformers.AutoTokenizer.from_pretrained(model_name)\n",
+        "\n",
+        "# Define a simple function that applies the tokenizer\n",
+        "def tokenize(example):\n",
+        "    return tokenizer(\n",
+        "        example[\"text\"],\n",
+        "        max_length=128,\n",
+        "        truncation=True,\n",
+        "    )\n",
+        "\n",
+        "# Apply the tokenizer to the whole dataset using .map()\n",
+        "dataset = dataset.map(tokenize)"
       ]
     },
     {
       "cell_type": "markdown",
+      "metadata": {
+        "id": "AiX7WeW2X5gv"
+      },
       "source": [
         "---\n",
         "\n",
         "# Define model\n",
         "\n",
         "(Note that here we define the model structure and computation without setting any parameters yet!)"
-      ],
-      "metadata": {
-        "id": "AiX7WeW2X5gv"
-      }
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "9kyQQhu0_wep"
+      },
+      "outputs": [],
       "source": [
         "import torch\n",
         "\n",
@@ -252,27 +220,45 @@
         "        else:\n",
         "            # No labels, so just return the output\n",
         "            return (output,)"
-      ],
-      "metadata": {
-        "id": "9kyQQhu0_wep"
-      },
-      "execution_count": 10,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "markdown",
+      "metadata": {
+        "id": "EF5Pau_PjN-J"
+      },
       "source": [
         "---\n",
         "# Define training support\n",
         "\n",
         "(Collator, evaluation, Callbacks)"
-      ],
-      "metadata": {
-        "id": "EF5Pau_PjN-J"
-      }
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 69,
+          "referenced_widgets": [
+            "ee04263c2ef846e5b1ba93ad687e37b1",
+            "bde5f2c9b4ef4e8c82fddbdb83a67a1e",
+            "1c4288674e7245e9b4697b55950731ef",
+            "eb5b4d35582542d1bdd0296817db1daf",
+            "61a2564e98294a3b98f202ecb051e1ce",
+            "ab20b48f72ce4486a08ccea3f4624486",
+            "ca67aa97ba124742a9c3688e03f21f96",
+            "6c8d2ad0ffce4d2081369030a4c4304d",
+            "db761c9a410f4b8bb1b2ec88fb0b847a",
+            "7a10fc4726e6413397d474c3b5f187b9",
+            "82de00af0c6140c49a1ab5868158b93b"
+          ]
+        },
+        "id": "wBn6iLcMjXGr",
+        "outputId": "2602f8eb-89cf-4052-cabb-c0363be850d6"
+      },
+      "outputs": [],
       "source": [
         "import evaluate\n",
         "\n",
@@ -303,58 +289,30 @@
         "            for k, v in logs.items():\n",
         "                if k != \"epoch\" or v not in self.logs[k]:\n",
         "                    self.logs[k].append(v)"
-      ],
-      "metadata": {
-        "id": "wBn6iLcMjXGr",
-        "outputId": "2602f8eb-89cf-4052-cabb-c0363be850d6",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 69,
-          "referenced_widgets": [
-            "ee04263c2ef846e5b1ba93ad687e37b1",
-            "bde5f2c9b4ef4e8c82fddbdb83a67a1e",
-            "1c4288674e7245e9b4697b55950731ef",
-            "eb5b4d35582542d1bdd0296817db1daf",
-            "61a2564e98294a3b98f202ecb051e1ce",
-            "ab20b48f72ce4486a08ccea3f4624486",
-            "ca67aa97ba124742a9c3688e03f21f96",
-            "6c8d2ad0ffce4d2081369030a4c4304d",
-            "db761c9a410f4b8bb1b2ec88fb0b847a",
-            "7a10fc4726e6413397d474c3b5f187b9",
-            "82de00af0c6140c49a1ab5868158b93b"
-          ]
-        }
-      },
-      "execution_count": 11,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "Downloading builder script:   0%|          | 0.00/4.20k [00:00<?, ?B/s]"
-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "ee04263c2ef846e5b1ba93ad687e37b1"
-            }
-          },
-          "metadata": {}
-        }
       ]
     },
     {
       "cell_type": "markdown",
+      "metadata": {
+        "id": "pVTopuNPW26S"
+      },
       "source": [
         "---\n",
         "# Hyperparameter search - First option"
-      ],
-      "metadata": {
-        "id": "pVTopuNPW26S"
-      }
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "nd_usta7WzTX",
+        "outputId": "80a41eb7-ce0f-4e04-ab0c-ce56e2519bc0"
+      },
+      "outputs": [],
       "source": [
         "for lr in [0.000005, 0.00005, 0.0005, 0.005, 0.05, 0.5]:\n",
         "\n",
@@ -372,7 +330,7 @@
         "    # Set training arguments\n",
         "    trainer_args = transformers.TrainingArguments(\n",
         "        \"checkpoints\",\n",
-        "        evaluation_strategy=\"steps\",\n",
+        "        eval_strategy=\"steps\",\n",
         "        logging_strategy=\"steps\",\n",
         "        load_best_model_at_end=True,\n",
         "        eval_steps=500,\n",
@@ -380,6 +338,7 @@
         "        learning_rate=lr, # <--- parameter goes here\n",
         "        per_device_train_batch_size=8,\n",
         "        max_steps=2500,\n",
+        "        report_to=\"none\", # skip wandb login\n",
         "    )\n",
         "\n",
         "    trainer = transformers.Trainer(\n",
@@ -395,733 +354,49 @@
         "    trainer.train()\n",
         "    eval_results = trainer.evaluate(dataset[\"test\"])\n",
         "    print('Learning rate:', lr, 'Accuracy:', eval_results['eval_accuracy'])"
-      ],
-      "metadata": {
-        "id": "nd_usta7WzTX",
-        "outputId": "80a41eb7-ce0f-4e04-ab0c-ce56e2519bc0",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 1000
-        }
-      },
-      "execution_count": 12,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "/usr/local/lib/python3.10/dist-packages/transformers/training_args.py:1525: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n",
-            "  warnings.warn(\n",
-            "max_steps is given, it will override any value given in num_train_epochs\n"
-          ]
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<IPython.core.display.HTML object>"
-            ],
-            "text/html": [
-              "\n",
-              "    <div>\n",
-              "      \n",
-              "      <progress value='2500' max='2500' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
-              "      [2500/2500 03:13, Epoch 0/1]\n",
-              "    </div>\n",
-              "    <table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              " <tr style=\"text-align: left;\">\n",
-              "      <th>Step</th>\n",
-              "      <th>Training Loss</th>\n",
-              "      <th>Validation Loss</th>\n",
-              "      <th>Accuracy</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <td>500</td>\n",
-              "      <td>0.964600</td>\n",
-              "      <td>0.964276</td>\n",
-              "      <td>0.500000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>1000</td>\n",
-              "      <td>0.960000</td>\n",
-              "      <td>0.937627</td>\n",
-              "      <td>0.500000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>1500</td>\n",
-              "      <td>0.949200</td>\n",
-              "      <td>0.919602</td>\n",
-              "      <td>0.500000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>2000</td>\n",
-              "      <td>0.916000</td>\n",
-              "      <td>0.909444</td>\n",
-              "      <td>0.500000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>2500</td>\n",
-              "      <td>0.906600</td>\n",
-              "      <td>0.906230</td>\n",
-              "      <td>0.500000</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table><p>"
-            ]
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<IPython.core.display.HTML object>"
-            ],
-            "text/html": [
-              "\n",
-              "    <div>\n",
-              "      \n",
-              "      <progress value='3125' max='3125' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
-              "      [3125/3125 00:14]\n",
-              "    </div>\n",
-              "    "
-            ]
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "/usr/local/lib/python3.10/dist-packages/transformers/training_args.py:1525: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n",
-            "  warnings.warn(\n",
-            "max_steps is given, it will override any value given in num_train_epochs\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Learning rate: 5e-06 Accuracy: 0.5\n"
-          ]
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<IPython.core.display.HTML object>"
-            ],
-            "text/html": [
-              "\n",
-              "    <div>\n",
-              "      \n",
-              "      <progress value='2500' max='2500' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
-              "      [2500/2500 02:34, Epoch 0/1]\n",
-              "    </div>\n",
-              "    <table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              " <tr style=\"text-align: left;\">\n",
-              "      <th>Step</th>\n",
-              "      <th>Training Loss</th>\n",
-              "      <th>Validation Loss</th>\n",
-              "      <th>Accuracy</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <td>500</td>\n",
-              "      <td>0.696700</td>\n",
-              "      <td>0.695665</td>\n",
-              "      <td>0.509920</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>1000</td>\n",
-              "      <td>0.695600</td>\n",
-              "      <td>0.693803</td>\n",
-              "      <td>0.518040</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>1500</td>\n",
-              "      <td>0.693400</td>\n",
-              "      <td>0.692734</td>\n",
-              "      <td>0.523360</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>2000</td>\n",
-              "      <td>0.691800</td>\n",
-              "      <td>0.691876</td>\n",
-              "      <td>0.525560</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>2500</td>\n",
-              "      <td>0.691800</td>\n",
-              "      <td>0.691665</td>\n",
-              "      <td>0.526120</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table><p>"
-            ]
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<IPython.core.display.HTML object>"
-            ],
-            "text/html": [
-              "\n",
-              "    <div>\n",
-              "      \n",
-              "      <progress value='3125' max='3125' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
-              "      [3125/3125 00:12]\n",
-              "    </div>\n",
-              "    "
-            ]
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "/usr/local/lib/python3.10/dist-packages/transformers/training_args.py:1525: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n",
-            "  warnings.warn(\n",
-            "max_steps is given, it will override any value given in num_train_epochs\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Learning rate: 5e-05 Accuracy: 0.52612\n"
-          ]
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<IPython.core.display.HTML object>"
-            ],
-            "text/html": [
-              "\n",
-              "    <div>\n",
-              "      \n",
-              "      <progress value='2500' max='2500' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
-              "      [2500/2500 02:58, Epoch 0/1]\n",
-              "    </div>\n",
-              "    <table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              " <tr style=\"text-align: left;\">\n",
-              "      <th>Step</th>\n",
-              "      <th>Training Loss</th>\n",
-              "      <th>Validation Loss</th>\n",
-              "      <th>Accuracy</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <td>500</td>\n",
-              "      <td>0.688100</td>\n",
-              "      <td>0.673649</td>\n",
-              "      <td>0.578840</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>1000</td>\n",
-              "      <td>0.664200</td>\n",
-              "      <td>0.652278</td>\n",
-              "      <td>0.599360</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>1500</td>\n",
-              "      <td>0.638000</td>\n",
-              "      <td>0.629875</td>\n",
-              "      <td>0.639240</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>2000</td>\n",
-              "      <td>0.619400</td>\n",
-              "      <td>0.619752</td>\n",
-              "      <td>0.647800</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>2500</td>\n",
-              "      <td>0.616800</td>\n",
-              "      <td>0.616634</td>\n",
-              "      <td>0.653120</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table><p>"
-            ]
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<IPython.core.display.HTML object>"
-            ],
-            "text/html": [
-              "\n",
-              "    <div>\n",
-              "      \n",
-              "      <progress value='3125' max='3125' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
-              "      [3125/3125 00:12]\n",
-              "    </div>\n",
-              "    "
-            ]
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "/usr/local/lib/python3.10/dist-packages/transformers/training_args.py:1525: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n",
-            "  warnings.warn(\n",
-            "max_steps is given, it will override any value given in num_train_epochs\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Learning rate: 0.0005 Accuracy: 0.65312\n"
-          ]
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<IPython.core.display.HTML object>"
-            ],
-            "text/html": [
-              "\n",
-              "    <div>\n",
-              "      \n",
-              "      <progress value='2500' max='2500' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
-              "      [2500/2500 02:25, Epoch 0/1]\n",
-              "    </div>\n",
-              "    <table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              " <tr style=\"text-align: left;\">\n",
-              "      <th>Step</th>\n",
-              "      <th>Training Loss</th>\n",
-              "      <th>Validation Loss</th>\n",
-              "      <th>Accuracy</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <td>500</td>\n",
-              "      <td>0.686500</td>\n",
-              "      <td>0.637228</td>\n",
-              "      <td>0.626240</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>1000</td>\n",
-              "      <td>0.615900</td>\n",
-              "      <td>0.664938</td>\n",
-              "      <td>0.622760</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>1500</td>\n",
-              "      <td>0.582600</td>\n",
-              "      <td>0.574918</td>\n",
-              "      <td>0.701320</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>2000</td>\n",
-              "      <td>0.549100</td>\n",
-              "      <td>0.561285</td>\n",
-              "      <td>0.706720</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>2500</td>\n",
-              "      <td>0.549200</td>\n",
-              "      <td>0.534774</td>\n",
-              "      <td>0.726720</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table><p>"
-            ]
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<IPython.core.display.HTML object>"
-            ],
-            "text/html": [
-              "\n",
-              "    <div>\n",
-              "      \n",
-              "      <progress value='3125' max='3125' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
-              "      [3125/3125 00:12]\n",
-              "    </div>\n",
-              "    "
-            ]
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "/usr/local/lib/python3.10/dist-packages/transformers/training_args.py:1525: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n",
-            "  warnings.warn(\n",
-            "max_steps is given, it will override any value given in num_train_epochs\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Learning rate: 0.005 Accuracy: 0.72672\n"
-          ]
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<IPython.core.display.HTML object>"
-            ],
-            "text/html": [
-              "\n",
-              "    <div>\n",
-              "      \n",
-              "      <progress value='2500' max='2500' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
-              "      [2500/2500 03:04, Epoch 0/1]\n",
-              "    </div>\n",
-              "    <table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              " <tr style=\"text-align: left;\">\n",
-              "      <th>Step</th>\n",
-              "      <th>Training Loss</th>\n",
-              "      <th>Validation Loss</th>\n",
-              "      <th>Accuracy</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <td>500</td>\n",
-              "      <td>0.686500</td>\n",
-              "      <td>0.637228</td>\n",
-              "      <td>0.626240</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>1000</td>\n",
-              "      <td>0.615900</td>\n",
-              "      <td>0.664938</td>\n",
-              "      <td>0.622760</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>1500</td>\n",
-              "      <td>0.582600</td>\n",
-              "      <td>0.574918</td>\n",
-              "      <td>0.701320</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>2000</td>\n",
-              "      <td>0.549100</td>\n",
-              "      <td>0.561285</td>\n",
-              "      <td>0.706720</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>2500</td>\n",
-              "      <td>0.549200</td>\n",
-              "      <td>0.534774</td>\n",
-              "      <td>0.726720</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table><p>"
-            ]
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<IPython.core.display.HTML object>"
-            ],
-            "text/html": [
-              "\n",
-              "    <div>\n",
-              "      \n",
-              "      <progress value='3125' max='3125' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
-              "      [3125/3125 00:12]\n",
-              "    </div>\n",
-              "    "
-            ]
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "/usr/local/lib/python3.10/dist-packages/transformers/training_args.py:1525: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n",
-            "  warnings.warn(\n",
-            "max_steps is given, it will override any value given in num_train_epochs\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Learning rate: 0.005 Accuracy: 0.72672\n"
-          ]
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<IPython.core.display.HTML object>"
-            ],
-            "text/html": [
-              "\n",
-              "    <div>\n",
-              "      \n",
-              "      <progress value='2500' max='2500' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
-              "      [2500/2500 02:27, Epoch 0/1]\n",
-              "    </div>\n",
-              "    <table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              " <tr style=\"text-align: left;\">\n",
-              "      <th>Step</th>\n",
-              "      <th>Training Loss</th>\n",
-              "      <th>Validation Loss</th>\n",
-              "      <th>Accuracy</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <td>500</td>\n",
-              "      <td>1.332000</td>\n",
-              "      <td>1.047678</td>\n",
-              "      <td>0.551920</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>1000</td>\n",
-              "      <td>1.246800</td>\n",
-              "      <td>0.884091</td>\n",
-              "      <td>0.574600</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>1500</td>\n",
-              "      <td>0.988700</td>\n",
-              "      <td>0.730061</td>\n",
-              "      <td>0.610720</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>2000</td>\n",
-              "      <td>0.837300</td>\n",
-              "      <td>0.830893</td>\n",
-              "      <td>0.609360</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>2500</td>\n",
-              "      <td>0.614600</td>\n",
-              "      <td>0.571852</td>\n",
-              "      <td>0.713400</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table><p>"
-            ]
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<IPython.core.display.HTML object>"
-            ],
-            "text/html": [
-              "\n",
-              "    <div>\n",
-              "      \n",
-              "      <progress value='3125' max='3125' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
-              "      [3125/3125 00:12]\n",
-              "    </div>\n",
-              "    "
-            ]
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "/usr/local/lib/python3.10/dist-packages/transformers/training_args.py:1525: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n",
-            "  warnings.warn(\n",
-            "max_steps is given, it will override any value given in num_train_epochs\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Learning rate: 0.05 Accuracy: 0.7134\n"
-          ]
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<IPython.core.display.HTML object>"
-            ],
-            "text/html": [
-              "\n",
-              "    <div>\n",
-              "      \n",
-              "      <progress value='2500' max='2500' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
-              "      [2500/2500 02:33, Epoch 0/1]\n",
-              "    </div>\n",
-              "    <table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              " <tr style=\"text-align: left;\">\n",
-              "      <th>Step</th>\n",
-              "      <th>Training Loss</th>\n",
-              "      <th>Validation Loss</th>\n",
-              "      <th>Accuracy</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <td>500</td>\n",
-              "      <td>373.884900</td>\n",
-              "      <td>464.389679</td>\n",
-              "      <td>0.501080</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>1000</td>\n",
-              "      <td>434.300200</td>\n",
-              "      <td>237.639359</td>\n",
-              "      <td>0.523120</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>1500</td>\n",
-              "      <td>248.097100</td>\n",
-              "      <td>204.130096</td>\n",
-              "      <td>0.504400</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>2000</td>\n",
-              "      <td>118.084200</td>\n",
-              "      <td>35.678982</td>\n",
-              "      <td>0.493760</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>2500</td>\n",
-              "      <td>30.116600</td>\n",
-              "      <td>1.654839</td>\n",
-              "      <td>0.519800</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table><p>"
-            ]
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<IPython.core.display.HTML object>"
-            ],
-            "text/html": [
-              "\n",
-              "    <div>\n",
-              "      \n",
-              "      <progress value='3125' max='3125' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
-              "      [3125/3125 00:12]\n",
-              "    </div>\n",
-              "    "
-            ]
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Learning rate: 0.5 Accuracy: 0.5198\n"
-          ]
-        }
       ]
     },
     {
       "cell_type": "markdown",
+      "metadata": {
+        "id": "KL6N5tz1mddD"
+      },
       "source": [
         "---\n",
         "# Hyperparameter search – Second option\n",
         "\n",
         "* Hyperparameter search using [Optuna](https://optuna.org/)"
-      ],
-      "metadata": {
-        "id": "KL6N5tz1mddD"
-      }
+      ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "!pip install optuna"
-      ],
+      "execution_count": null,
       "metadata": {
-        "id": "Hes_HBvOmrKD",
-        "outputId": "1e5f78e0-116b-4a95-f731-a4efcfd2a353",
         "colab": {
           "base_uri": "https://localhost:8080/"
-        }
+        },
+        "id": "Hes_HBvOmrKD",
+        "outputId": "1e5f78e0-116b-4a95-f731-a4efcfd2a353"
       },
-      "execution_count": 13,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Collecting optuna\n",
-            "  Downloading optuna-4.0.0-py3-none-any.whl.metadata (16 kB)\n",
-            "Collecting alembic>=1.5.0 (from optuna)\n",
-            "  Downloading alembic-1.13.2-py3-none-any.whl.metadata (7.4 kB)\n",
-            "Collecting colorlog (from optuna)\n",
-            "  Downloading colorlog-6.8.2-py3-none-any.whl.metadata (10 kB)\n",
-            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from optuna) (1.26.4)\n",
-            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from optuna) (24.1)\n",
-            "Requirement already satisfied: sqlalchemy>=1.3.0 in /usr/local/lib/python3.10/dist-packages (from optuna) (2.0.34)\n",
-            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from optuna) (4.66.5)\n",
-            "Requirement already satisfied: PyYAML in /usr/local/lib/python3.10/dist-packages (from optuna) (6.0.2)\n",
-            "Collecting Mako (from alembic>=1.5.0->optuna)\n",
-            "  Downloading Mako-1.3.5-py3-none-any.whl.metadata (2.9 kB)\n",
-            "Requirement already satisfied: typing-extensions>=4 in /usr/local/lib/python3.10/dist-packages (from alembic>=1.5.0->optuna) (4.12.2)\n",
-            "Requirement already satisfied: greenlet!=0.4.17 in /usr/local/lib/python3.10/dist-packages (from sqlalchemy>=1.3.0->optuna) (3.1.0)\n",
-            "Requirement already satisfied: MarkupSafe>=0.9.2 in /usr/local/lib/python3.10/dist-packages (from Mako->alembic>=1.5.0->optuna) (2.1.5)\n",
-            "Downloading optuna-4.0.0-py3-none-any.whl (362 kB)\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m362.8/362.8 kB\u001b[0m \u001b[31m6.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[?25hDownloading alembic-1.13.2-py3-none-any.whl (232 kB)\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m233.0/233.0 kB\u001b[0m \u001b[31m16.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[?25hDownloading colorlog-6.8.2-py3-none-any.whl (11 kB)\n",
-            "Downloading Mako-1.3.5-py3-none-any.whl (78 kB)\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m78.6/78.6 kB\u001b[0m \u001b[31m5.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[?25hInstalling collected packages: Mako, colorlog, alembic, optuna\n",
-            "Successfully installed Mako-1.3.5 alembic-1.13.2 colorlog-6.8.2 optuna-4.0.0\n"
-          ]
-        }
+      "outputs": [],
+      "source": [
+        "!pip install optuna"
       ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "import optuna\n",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 962
+        },
+        "id": "Ag66TkGumvSU",
+        "outputId": "1d0ad2ac-4874-41ef-db7f-c873b23468f1"
+      },
+      "outputs": [],
+      "source": [
+        "import optuna\n",
         "\n",
         "def objective(trial):\n",
         "    # Define the search space for hyperparameters\n",
@@ -1142,7 +417,7 @@
         "    # Set training arguments\n",
         "    trainer_args = transformers.TrainingArguments(\n",
         "        \"checkpoints\",\n",
-        "        evaluation_strategy=\"steps\",\n",
+        "        eval_strategy=\"steps\",\n",
         "        logging_strategy=\"steps\",\n",
         "        load_best_model_at_end=True,\n",
         "        eval_steps=500,\n",
@@ -1150,6 +425,7 @@
         "        learning_rate=learning_rate, # <--- parameter goes here\n",
         "        per_device_train_batch_size=8,\n",
         "        max_steps=2500,\n",
+        "        report_to=\"none\", # skip wandb login\n",
         "    )\n",
         "\n",
         "    trainer = transformers.Trainer(\n",
@@ -1171,308 +447,19 @@
         "\n",
         "study = optuna.create_study(direction=\"maximize\")\n",
         "study.optimize(objective, n_trials=3) # <--- How many trials we run, more would be needed in real case!"
-      ],
-      "metadata": {
-        "id": "Ag66TkGumvSU",
-        "outputId": "1d0ad2ac-4874-41ef-db7f-c873b23468f1",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 962
-        }
-      },
-      "execution_count": 18,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "/usr/local/lib/python3.10/dist-packages/transformers/training_args.py:1525: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n",
-            "  warnings.warn(\n",
-            "max_steps is given, it will override any value given in num_train_epochs\n"
-          ]
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<IPython.core.display.HTML object>"
-            ],
-            "text/html": [
-              "\n",
-              "    <div>\n",
-              "      \n",
-              "      <progress value='2500' max='2500' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
-              "      [2500/2500 03:21, Epoch 0/1]\n",
-              "    </div>\n",
-              "    <table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              " <tr style=\"text-align: left;\">\n",
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-            "_model_module_version": "1.5.0",
-            "_model_name": "DescriptionStyleModel",
-            "_view_count": null,
-            "_view_module": "@jupyter-widgets/base",
-            "_view_module_version": "1.2.0",
-            "_view_name": "StyleView",
-            "description_width": ""
-          }
         }
       }
-    },
-    "accelerator": "GPU"
+    }
   },
   "nbformat": 4,
   "nbformat_minor": 0
-}
\ No newline at end of file
+}