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+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "source": [
+        "# !pip install torch\n",
+        "# !pip install pandas\n",
+        "!pip install datasets\n",
+        "# !pip install scikit-learn==1.3.0  # Install scikit-learn for metrics calculation\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "S5L56kdMJSyW",
+        "outputId": "c0aad11f-5182-428a-9ad5-d482c9f552f7"
+      },
+      "execution_count": 6,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Collecting datasets\n",
+            "  Downloading datasets-3.2.0-py3-none-any.whl.metadata (20 kB)\n",
+            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from datasets) (3.16.1)\n",
+            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from datasets) (1.26.4)\n",
+            "Requirement already satisfied: pyarrow>=15.0.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (17.0.0)\n",
+            "Collecting dill<0.3.9,>=0.3.0 (from datasets)\n",
+            "  Downloading dill-0.3.8-py3-none-any.whl.metadata (10 kB)\n",
+            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from datasets) (2.2.2)\n",
+            "Requirement already satisfied: requests>=2.32.2 in /usr/local/lib/python3.10/dist-packages (from datasets) (2.32.3)\n",
+            "Requirement already satisfied: tqdm>=4.66.3 in /usr/local/lib/python3.10/dist-packages (from datasets) (4.67.1)\n",
+            "Collecting xxhash (from datasets)\n",
+            "  Downloading xxhash-3.5.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (12 kB)\n",
+            "Collecting multiprocess<0.70.17 (from datasets)\n",
+            "  Downloading multiprocess-0.70.16-py310-none-any.whl.metadata (7.2 kB)\n",
+            "Collecting fsspec<=2024.9.0,>=2023.1.0 (from fsspec[http]<=2024.9.0,>=2023.1.0->datasets)\n",
+            "  Downloading fsspec-2024.9.0-py3-none-any.whl.metadata (11 kB)\n",
+            "Requirement already satisfied: aiohttp in /usr/local/lib/python3.10/dist-packages (from datasets) (3.11.11)\n",
+            "Requirement already satisfied: huggingface-hub>=0.23.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (0.27.1)\n",
+            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from datasets) (24.2)\n",
+            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from datasets) (6.0.2)\n",
+            "Requirement already satisfied: aiohappyeyeballs>=2.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (2.4.4)\n",
+            "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.3.2)\n",
+            "Requirement already satisfied: async-timeout<6.0,>=4.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (4.0.3)\n",
+            "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (24.3.0)\n",
+            "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.5.0)\n",
+            "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (6.1.0)\n",
+            "Requirement already satisfied: propcache>=0.2.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (0.2.1)\n",
+            "Requirement already satisfied: yarl<2.0,>=1.17.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.18.3)\n",
+            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.23.0->datasets) (4.12.2)\n",
+            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.32.2->datasets) (3.4.1)\n",
+            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.32.2->datasets) (3.10)\n",
+            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.32.2->datasets) (2.3.0)\n",
+            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.32.2->datasets) (2024.12.14)\n",
+            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2.8.2)\n",
+            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2024.2)\n",
+            "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2024.2)\n",
+            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas->datasets) (1.17.0)\n",
+            "Downloading datasets-3.2.0-py3-none-any.whl (480 kB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m480.6/480.6 kB\u001b[0m \u001b[31m12.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hDownloading dill-0.3.8-py3-none-any.whl (116 kB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m10.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hDownloading fsspec-2024.9.0-py3-none-any.whl (179 kB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m179.3/179.3 kB\u001b[0m \u001b[31m15.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hDownloading multiprocess-0.70.16-py310-none-any.whl (134 kB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m13.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hDownloading xxhash-3.5.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (194 kB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m19.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hInstalling collected packages: xxhash, fsspec, dill, multiprocess, datasets\n",
+            "  Attempting uninstall: fsspec\n",
+            "    Found existing installation: fsspec 2024.10.0\n",
+            "    Uninstalling fsspec-2024.10.0:\n",
+            "      Successfully uninstalled fsspec-2024.10.0\n",
+            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
+            "gcsfs 2024.10.0 requires fsspec==2024.10.0, but you have fsspec 2024.9.0 which is incompatible.\u001b[0m\u001b[31m\n",
+            "\u001b[0mSuccessfully installed datasets-3.2.0 dill-0.3.8 fsspec-2024.9.0 multiprocess-0.70.16 xxhash-3.5.0\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "id": "8DBKKDMsIYmE"
+      },
+      "outputs": [],
+      "source": [
+        "from transformers import BertTokenizer, BertForSequenceClassification\n",
+        "import pandas as pd\n",
+        "from datasets import Dataset\n",
+        "from datasets import load_dataset\n",
+        "from torch.utils.data import DataLoader\n",
+        "from transformers import DataCollatorWithPadding\n",
+        "from transformers import AdamW\n",
+        "from transformers import Trainer, TrainingArguments\n",
+        "\n",
+        "\n",
+        "\n"
+      ]
+    },
+    {
+      "source": [
+        "import numpy as np\n",
+        "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
+        "\n",
+        "def compute_metrics(pred):\n",
+        "    \"\"\"\n",
+        "    Computes and returns a dictionary of metrics (accuracy, precision, recall, F1-score).\n",
+        "    \"\"\"\n",
+        "    labels = pred.label_ids\n",
+        "    preds = pred.predictions.argmax(axis=1)\n",
+        "\n",
+        "    accuracy = accuracy_score(labels, preds)\n",
+        "    precision = precision_score(labels, preds)\n",
+        "    recall = recall_score(labels, preds)\n",
+        "    f1 = f1_score(labels, preds)\n",
+        "\n",
+        "    return {\"accuracy\": accuracy, \"precision\": precision, \"recall\": recall, \"f1\": f1}"
+      ],
+      "cell_type": "code",
+      "metadata": {
+        "id": "xAMF75YVSJFo"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from google.colab import drive\n",
+        "drive.mount('/content/drive')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "becVBilbJLXc",
+        "outputId": "c720c029-c497-4792-98f4-88284fe41045"
+      },
+      "execution_count": 1,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Mounted at /content/drive\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "SBk5ob4wIYmJ"
+      },
+      "outputs": [],
+      "source": [
+        "# Load the dataset from the specified CSV file\n",
+        "raw_datasets = pd.read_csv(\"/content/drive/MyDrive/nlp/clickbait_data.csv\")\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "PL7q5PKFIYmK",
+        "outputId": "d8953fc0-f80c-4ce1-aaea-adde68eef8e0"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "                                                headline  clickbait\n",
+            "0                                     Should I Get Bings          1\n",
+            "1          Which TV Female Friend Group Do You Belong In          1\n",
+            "2      The New \"Star Wars: The Force Awakens\" Trailer...          1\n",
+            "3      This Vine Of New York On \"Celebrity Big Brothe...          1\n",
+            "4      A Couple Did A Stunning Photo Shoot With Their...          1\n",
+            "...                                                  ...        ...\n",
+            "31995  To Make Female Hearts Flutter in Iraq, Throw a...          0\n",
+            "31996  British Liberal Democrat Patsy Calton, 56, die...          0\n",
+            "31997  Drone smartphone app to help heart attack vict...          0\n",
+            "31998  Netanyahu Urges Pope Benedict, in Israel, to D...          0\n",
+            "31999  Computer Makers Prepare to Stake Bigger Claim ...          0\n",
+            "\n",
+            "[32000 rows x 2 columns]\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(raw_datasets)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ucNw7B3lIYmK"
+      },
+      "outputs": [],
+      "source": [
+        "df = pd.DataFrame(raw_datasets, columns=[\"headline\", \"clickbait\"])\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Lev9BlWZIYmK",
+        "outputId": "e16cc1cc-f9ee-4b5c-9ddf-42b59569e4a2"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "                                                headline  clickbait\n",
+            "0      Filipino activist arrested for disrupting Mani...          0\n",
+            "1      International Board fixes soccer field size, h...          0\n",
+            "2          24 Rules For Women On A First Date With A Man          1\n",
+            "3      Political fallout from the sacking of Professo...          0\n",
+            "4      Which \"Clueless\" Character Are You Based On Yo...          1\n",
+            "...                                                  ...        ...\n",
+            "31995  Rocket strike near hotel in Afghan capital inj...          0\n",
+            "31996  How Well Do You Remember The First Episode Of ...          1\n",
+            "31997  16 Photos From The Delhi Queer Pride Parade Th...          1\n",
+            "31998             33 Of The Most Canadian Sentences Ever          1\n",
+            "31999  Man killed after shop robbery in West Yorkshir...          0\n",
+            "\n",
+            "[32000 rows x 2 columns]\n"
+          ]
+        }
+      ],
+      "source": [
+        "df = df.sample(frac=1, random_state=42).reset_index(drop=True)\n",
+        "\n",
+        "# Display the first few rows of the shuffled DataFrame\n",
+        "print(df)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "JGLQrK2oIYmL"
+      },
+      "outputs": [],
+      "source": [
+        "# Step 1: Clean text (lowercase, remove special characters, normalize spaces)\n",
+        "df['headline'] = df['headline'].str.lower()  # Convert to lowercase\n",
+        "df['headline'] = df['headline'].str.replace(r'[^a-z0-9\\s]', '', regex=True)  # Remove special characters\n",
+        "df['headline'] = df['headline'].str.replace(r'\\s+', ' ', regex=True)  # Normalize multiple spaces\n",
+        "\n",
+        "# Step 2: Split into DatasetDict format (to be used with Hugging Face's `datasets` library)\n",
+        "# Convert the cleaned dataframe into a Dataset object for easy tokenization with Hugging Face\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 423
+        },
+        "id": "V0PLQ45hIYmL",
+        "outputId": "92045cbe-2f40-4a8a-9141-41b0f3c03a46"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                                                headline  clickbait\n",
+              "0      filipino activist arrested for disrupting mani...          0\n",
+              "1      international board fixes soccer field size ha...          0\n",
+              "2          24 rules for women on a first date with a man          1\n",
+              "3      political fallout from the sacking of professo...          0\n",
+              "4      which clueless character are you based on your...          1\n",
+              "...                                                  ...        ...\n",
+              "31995  rocket strike near hotel in afghan capital inj...          0\n",
+              "31996  how well do you remember the first episode of ...          1\n",
+              "31997  16 photos from the delhi queer pride parade th...          1\n",
+              "31998             33 of the most canadian sentences ever          1\n",
+              "31999  man killed after shop robbery in west yorkshir...          0\n",
+              "\n",
+              "[32000 rows x 2 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-d5c6de19-cc3d-4e4a-b949-90707b35d029\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
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+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>headline</th>\n",
+              "      <th>clickbait</th>\n",
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+              "  </thead>\n",
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+              "      <td>filipino activist arrested for disrupting mani...</td>\n",
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+              "      <td>international board fixes soccer field size ha...</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>24 rules for women on a first date with a man</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>political fallout from the sacking of professo...</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>which clueless character are you based on your...</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
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+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
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+              "      <td>rocket strike near hotel in afghan capital inj...</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>31996</th>\n",
+              "      <td>how well do you remember the first episode of ...</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>31997</th>\n",
+              "      <td>16 photos from the delhi queer pride parade th...</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>31998</th>\n",
+              "      <td>33 of the most canadian sentences ever</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>31999</th>\n",
+              "      <td>man killed after shop robbery in west yorkshir...</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>32000 rows × 2 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d5c6de19-cc3d-4e4a-b949-90707b35d029')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
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+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-d5c6de19-cc3d-4e4a-b949-90707b35d029 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-d5c6de19-cc3d-4e4a-b949-90707b35d029');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-ee00eaa1-2597-4b9f-bfbe-34ee45fe0b29\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ee00eaa1-2597-4b9f-bfbe-34ee45fe0b29')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-ee00eaa1-2597-4b9f-bfbe-34ee45fe0b29 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "  <div id=\"id_dd256d94-7a78-4a73-8f42-8ee61fff3d06\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_dd256d94-7a78-4a73-8f42-8ee61fff3d06 button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('df');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df",
+              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 32000,\n  \"fields\": [\n    {\n      \"column\": \"headline\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 31998,\n        \"samples\": [\n          \"prolific television producer aaron spelling dies at 83\",\n          \"consumer prices remained steady in april\",\n          \"jon hamm playing baseball will soothe the shit outta you\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"clickbait\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 51
+        }
+      ],
+      "source": [
+        "df"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "oB8E9zfkIYmL",
+        "outputId": "0250cb50-7efa-4f87-f7ed-a5fca2cecce4"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Dataset({\n",
+              "    features: ['headline', 'clickbait'],\n",
+              "    num_rows: 32000\n",
+              "})"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 52
+        }
+      ],
+      "source": [
+        "pre_processed_dataset = Dataset.from_pandas(df)\n",
+        "pre_processed_dataset"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "oxKb3Y2jIYmM",
+        "outputId": "83845fae-7dbe-4857-c713-4359212078db"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
+            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
+          ]
+        }
+      ],
+      "source": [
+        "tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') #Downloads the base version of BERT trained on lowercase English text (e.g., \"hello\" and \"Hello\" are treated the same).\n",
+        "model = BertForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=2) #Configures the model to handle a binary classification problem  \"clickbait\" vs. \"non-clickbait\".\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "HRzOKDl_IYmM"
+      },
+      "outputs": [],
+      "source": [
+        "def tokenize_function(batch):\n",
+        "    # Tokenize the 'headline' column\n",
+        "    return tokenizer(batch['headline'], truncation=True, padding=True, max_length=512)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 49,
+          "referenced_widgets": [
+            "dad32a0edd9841c9ab1ce052f0efd994",
+            "41099ccad51745aeb06c52636ff646ba",
+            "00f15ecb12df4d36aed9ef3135a71f08",
+            "a1121eb967374766bc15ed290c2dc09d",
+            "3b3db9c5766a4fc1908f82d5d86ee86d",
+            "86c7d5da9ac94f4aa7019866cb48da30",
+            "12e0c1a1d2e545db8c89accffb1072fb",
+            "cb1f378c68e3464db6bbe1756ee68b46",
+            "272a34ba164e4b2a88aa1d92a42510b8",
+            "810299000553408ca96ed7b45a8049b8",
+            "93d6dfc7a47d4c76bdbeb868dc0a3369"
+          ]
+        },
+        "id": "2AuxGVJJIYmM",
+        "outputId": "4cf193fb-c5ab-4974-aa52-f05beffb4746"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Map:   0%|          | 0/32000 [00:00<?, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "dad32a0edd9841c9ab1ce052f0efd994"
+            }
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "tokenized_datasets = pre_processed_dataset.map(tokenize_function, batched=True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "_mfTpdzeIYmN",
+        "outputId": "f32b8858-a65c-451d-ecc4-b8d9bf2296f1"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Dataset({\n",
+            "    features: ['headline', 'clickbait', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
+            "    num_rows: 32000\n",
+            "})\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(tokenized_datasets)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "tokenized_datasets = tokenized_datasets.rename_column('clickbait', 'labels')"
+      ],
+      "metadata": {
+        "id": "YoXnzpMjNV9N"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "RaVGS93BIYmN"
+      },
+      "outputs": [],
+      "source": [
+        "split_datasets = tokenized_datasets.train_test_split(test_size=0.2)\n",
+        "\n",
+        "# Further split the train data into train and validation (80% train, 20% validation)\n",
+        "train_val_split = split_datasets['train'].train_test_split(test_size=0.2)\n",
+        "\n",
+        "# Access the splits\n",
+        "train_dataset = train_val_split['train']\n",
+        "validation_dataset = train_val_split['test']\n",
+        "test_dataset = split_datasets['test']"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "8aGzBaiwIYmN",
+        "outputId": "bb8e0f0d-caef-4917-ceb0-c2119e387e63"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "DatasetDict({\n",
+              "    train: Dataset({\n",
+              "        features: ['headline', 'labels', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
+              "        num_rows: 25600\n",
+              "    })\n",
+              "    test: Dataset({\n",
+              "        features: ['headline', 'labels', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
+              "        num_rows: 6400\n",
+              "    })\n",
+              "})"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 59
+        }
+      ],
+      "source": [
+        "split_datasets"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "EtWlbJzOIYmN",
+        "outputId": "512de166-e642-4099-f390-dc687be9a44a"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "DatasetDict({\n",
+              "    train: Dataset({\n",
+              "        features: ['headline', 'labels', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
+              "        num_rows: 20480\n",
+              "    })\n",
+              "    test: Dataset({\n",
+              "        features: ['headline', 'labels', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
+              "        num_rows: 5120\n",
+              "    })\n",
+              "})"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 60
+        }
+      ],
+      "source": [
+        "train_val_split"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "eWrFzffIIYmO",
+        "outputId": "3bed5240-9259-4ee5-c063-2a33c1c96920"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Dataset({\n",
+              "    features: ['headline', 'labels', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
+              "    num_rows: 6400\n",
+              "})"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 61
+        }
+      ],
+      "source": [
+        "test_dataset"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "POiHrmeeIYmO"
+      },
+      "outputs": [],
+      "source": [
+        "data_collator = DataCollatorWithPadding(tokenizer=tokenizer)\n",
+        "train_dataloader = DataLoader(\n",
+        "    train_dataset, batch_size=16, shuffle=True, collate_fn=data_collator\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "hOOVVNPwIYmO",
+        "outputId": "b6756add-09fe-4a2c-a0c2-751f8fc11194"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/transformers/optimization.py:591: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
+            "  warnings.warn(\n"
+          ]
+        }
+      ],
+      "source": [
+        "#AdamW: A type of optimizer that updates the model’s weights during training to minimize the loss.\n",
+        "#lr=5e-5: Sets the learning rate to 0.00005, controlling how much the model adjusts weights during training.\n",
+        "\n",
+        "optimizer = AdamW(model.parameters(), lr=5e-5)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
+        "\n",
+        "def compute_metrics(pred):\n",
+        "    \"\"\"\n",
+        "    Computes and returns a dictionary of metrics (accuracy, precision, recall, F1-score).\n",
+        "    \"\"\"\n",
+        "    labels = pred.label_ids\n",
+        "    preds = pred.predictions.argmax(axis=1)\n",
+        "\n",
+        "    accuracy = accuracy_score(labels, preds)\n",
+        "    precision = precision_score(labels, preds)\n",
+        "    recall = recall_score(labels, preds)\n",
+        "    f1 = f1_score(labels, preds)\n",
+        "\n",
+        "    return {\"accuracy\": accuracy, \"precision\": precision, \"recall\": recall, \"f1\": f1}"
+      ],
+      "metadata": {
+        "id": "F9QmrZ6dSRCk"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Nf8E4KTSIYmP",
+        "outputId": "22929b6e-85d7-4f39-8c6a-2971fbafbc6b"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/transformers/training_args.py:1575: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n",
+            "  warnings.warn(\n"
+          ]
+        }
+      ],
+      "source": [
+        "# output_dir: Saves the trained model and logs to a directory named \"results.\"\n",
+        "# evaluation_strategy=\"epoch\": Evaluates the model after every epoch (one pass through the dataset).\n",
+        "# learning_rate: Sets the learning rate to 0.00002.\n",
+        "# num_train_epochs=3: Specifies 3 training iterations through the dataset.\n",
+        "# weight_decay=0.01: Prevents overfitting by slightly penalizing large model weights.\n",
+        "\n",
+        "training_args = TrainingArguments(\n",
+        "    output_dir=\"/content/drive/MyDrive/nlp/results\",\n",
+        "    evaluation_strategy=\"epoch\",\n",
+        "    learning_rate=2e-5,\n",
+        "    per_device_train_batch_size=16,\n",
+        "    per_device_eval_batch_size=16,\n",
+        "    num_train_epochs=3,\n",
+        "    weight_decay=0.01,\n",
+        ")\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "-UvYmvbjIYmP",
+        "outputId": "1c2f2a41-2cdc-45b7-9a38-8c87975973cd"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "<ipython-input-66-8ac8eaf1a160>:5: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n",
+            "  trainer = Trainer(\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Creates a Trainer object that automates:\n",
+        "# Training: Feeds the training dataset into the model.\n",
+        "# Evaluation: Tests the model's performance on the validation dataset.\n",
+        "\n",
+        "trainer = Trainer(\n",
+        "    model=model,\n",
+        "    args=training_args,\n",
+        "    train_dataset=train_dataset,\n",
+        "    eval_dataset=validation_dataset,\n",
+        "    tokenizer=tokenizer,\n",
+        "    data_collator=data_collator,\n",
+        "    compute_metrics=compute_metrics\n",
+        "\n",
+        ")\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "trainer.train()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 239
+        },
+        "id": "qz65S7xdKKJi",
+        "outputId": "5a2c06d6-89e3-47f3-cb0c-3f73132f564c"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "    <div>\n",
+              "      \n",
+              "      <progress value='3840' max='3840' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+              "      [3840/3840 09:55, Epoch 3/3]\n",
+              "    </div>\n",
+              "    <table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              " <tr style=\"text-align: left;\">\n",
+              "      <th>Epoch</th>\n",
+              "      <th>Training Loss</th>\n",
+              "      <th>Validation Loss</th>\n",
+              "      <th>Accuracy</th>\n",
+              "      <th>Precision</th>\n",
+              "      <th>Recall</th>\n",
+              "      <th>F1</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <td>1</td>\n",
+              "      <td>0.062700</td>\n",
+              "      <td>0.051676</td>\n",
+              "      <td>0.986133</td>\n",
+              "      <td>0.989864</td>\n",
+              "      <td>0.982585</td>\n",
+              "      <td>0.986211</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td>2</td>\n",
+              "      <td>0.018100</td>\n",
+              "      <td>0.069427</td>\n",
+              "      <td>0.986328</td>\n",
+              "      <td>0.981979</td>\n",
+              "      <td>0.991099</td>\n",
+              "      <td>0.986518</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td>3</td>\n",
+              "      <td>0.005600</td>\n",
+              "      <td>0.069584</td>\n",
+              "      <td>0.989258</td>\n",
+              "      <td>0.990306</td>\n",
+              "      <td>0.988390</td>\n",
+              "      <td>0.989347</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table><p>"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "TrainOutput(global_step=3840, training_loss=0.03588072238489985, metrics={'train_runtime': 595.5357, 'train_samples_per_second': 103.168, 'train_steps_per_second': 6.448, 'total_flos': 1035958669377600.0, 'train_loss': 0.03588072238489985, 'epoch': 3.0})"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 67
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model.save_pretrained(\"/content/drive/MyDrive/nlp/fine_tuned_bert\")\n",
+        "tokenizer.save_pretrained(\"/content/drive/MyDrive/nlp/fine_tuned_bert\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "d91uceVJKMtJ",
+        "outputId": "f47a45e0-ac5a-4be7-f6d3-5f04c431b9c7"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "('/content/drive/MyDrive/nlp/fine_tuned_bert/tokenizer_config.json',\n",
+              " '/content/drive/MyDrive/nlp/fine_tuned_bert/special_tokens_map.json',\n",
+              " '/content/drive/MyDrive/nlp/fine_tuned_bert/vocab.txt',\n",
+              " '/content/drive/MyDrive/nlp/fine_tuned_bert/added_tokens.json')"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 68
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "trainer.save_model(\"/content/drive/MyDrive/nlp/api_saved_bert\")"
+      ],
+      "metadata": {
+        "id": "zjeFrP65feAA"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "results = trainer.evaluate(eval_dataset=test_dataset)\n",
+        "print(results)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 74
+        },
+        "id": "z-Z_oX4NQy64",
+        "outputId": "9bebaf03-9b09-4e31-b819-48378162cc91"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "    <div>\n",
+              "      \n",
+              "      <progress value='400' max='400' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+              "      [400/400 00:14]\n",
+              "    </div>\n",
+              "    "
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "{'eval_loss': 0.06335476785898209, 'eval_accuracy': 0.99046875, 'eval_precision': 0.9921826141338337, 'eval_recall': 0.9887815518853226, 'eval_f1': 0.9904791634150147, 'eval_runtime': 14.213, 'eval_samples_per_second': 450.292, 'eval_steps_per_second': 28.143, 'epoch': 3.0}\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import torch\n",
+        "\n",
+        "# Assuming you want to use the GPU\n",
+        "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
+        "\n",
+        "# Move your model to the device\n",
+        "model.to(device)\n",
+        "\n",
+        "# Move your input tensors to the device\n",
+        "text = \"How to get 6 pack abs in 5 days?\"\n",
+        "inputs = tokenizer(text, return_tensors=\"pt\", truncation=True, max_length=512).to(device)\n",
+        "\n",
+        "# Now, run the inference\n",
+        "outputs = model(**inputs)\n",
+        "predicted_class = outputs.logits.argmax(dim=1).item()\n",
+        "\n",
+        "# Print the prediction\n",
+        "print(\"Predicted class:\", \"Clickbait\" if predicted_class == 1 else \"Non-Clickbait\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "KqChnsb6RWKG",
+        "outputId": "779ac87d-672a-481d-d4a5-a7eb88ac254e"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Predicted class: Clickbait\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "##Loading trained model and testing"
+      ],
+      "metadata": {
+        "id": "J2ayxrk7SCx4"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from transformers import BertTokenizer, BertForSequenceClassification\n",
+        "import pandas as pd\n",
+        "from datasets import Dataset\n",
+        "from datasets import load_dataset\n",
+        "from torch.utils.data import DataLoader\n",
+        "from transformers import DataCollatorWithPadding\n",
+        "from transformers import AdamW\n",
+        "from transformers import Trainer, TrainingArguments\n",
+        "import torch"
+      ],
+      "metadata": {
+        "id": "ufJ_kSdJTB29"
+      },
+      "execution_count": 8,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from transformers import BertTokenizer, BertForSequenceClassification\n",
+        "\n",
+        "# Load the saved model and tokenizer\n",
+        "model = BertForSequenceClassification.from_pretrained(\"/content/drive/MyDrive/nlp/fine_tuned_bert\")\n",
+        "tokenizer = BertTokenizer.from_pretrained(\"/content/drive/MyDrive/nlp/fine_tuned_bert\")\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 349
+        },
+        "id": "Ve8bgTaoSCM_",
+        "outputId": "8fcfda48-209e-4468-b2f3-883d084ae0c4"
+      },
+      "execution_count": 10,
+      "outputs": [
+        {
+          "output_type": "error",
+          "ename": "OSError",
+          "evalue": "Error no file named pytorch_model.bin, model.safetensors, tf_model.h5, model.ckpt.index or flax_model.msgpack found in directory /content/drive/MyDrive/nlp/fine_tuned_bert.",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mOSError\u001b[0m                                   Traceback (most recent call last)",
+            "\u001b[0;32m<ipython-input-10-4a37b2773130>\u001b[0m in \u001b[0;36m<cell line: 4>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;31m# Load the saved model and tokenizer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBertForSequenceClassification\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_pretrained\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"/content/drive/MyDrive/nlp/fine_tuned_bert\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0mtokenizer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBertTokenizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_pretrained\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"/content/drive/MyDrive/nlp/fine_tuned_bert\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/modeling_utils.py\u001b[0m in \u001b[0;36mfrom_pretrained\u001b[0;34m(cls, pretrained_model_name_or_path, config, cache_dir, ignore_mismatched_sizes, force_download, local_files_only, token, revision, use_safetensors, weights_only, *model_args, **kwargs)\u001b[0m\n\u001b[1;32m   3777\u001b[0m                     )\n\u001b[1;32m   3778\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3779\u001b[0;31m                     raise EnvironmentError(\n\u001b[0m\u001b[1;32m   3780\u001b[0m                         \u001b[0;34mf\"Error no file named {_add_variant(WEIGHTS_NAME, variant)}, {_add_variant(SAFE_WEIGHTS_NAME, variant)},\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3781\u001b[0m                         \u001b[0;34mf\" {TF2_WEIGHTS_NAME}, {TF_WEIGHTS_NAME + '.index'} or {FLAX_WEIGHTS_NAME} found in directory\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;31mOSError\u001b[0m: Error no file named pytorch_model.bin, model.safetensors, tf_model.h5, model.ckpt.index or flax_model.msgpack found in directory /content/drive/MyDrive/nlp/fine_tuned_bert."
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [],
+      "metadata": {
+        "id": "P6PZkbpSanjH"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "text = \"Is this a clickbait headline?\"\n",
+        "inputs = tokenizer(text, return_tensors=\"pt\", truncation=True, max_length=512)\n",
+        "outputs = model(**inputs)\n",
+        "predicted_class = outputs.logits.argmax(dim=1).item()\n",
+        "\n",
+        "# Print the prediction\n",
+        "print(\"Predicted class:\", \"Clickbait\" if predicted_class == 1 else \"Non-Clickbait\")\n"
+      ],
+      "metadata": {
+        "id": "Cayo8SX1Sv2V"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.12.4"
+    },
+    "colab": {
+      "provenance": [],
+      "gpuType": "T4"
+    },
+    "accelerator": "GPU",
+    "widgets": {
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