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            "\u001b[?25h"
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      ],
      "source": [
        "!pip install -q transformers datasets"
      ]
    },
    {
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      "source": [
        "import tensorflow as tf\n",
        "import pandas as pd\n",
        "import numpy as np"
      ],
      "metadata": {
        "id": "Cd_xXxi0agOt"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Task\n",
        "\n",
        "* Given a text, predict the class that the text is about\n"
      ],
      "metadata": {
        "id": "zSZchTlvb-32"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Preprocess Data"
      ],
      "metadata": {
        "id": "yPwUVhR2cKtd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "dataset = load_dataset(\"ag_news\")\n",
        "dataset"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
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        "id": "fFP3Ojw5cJ4G",
        "outputId": "cac97fde-4d29-412d-a402-9c2384985d4d"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
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        {
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            "text/plain": [
              "Downloading data:   0%|          | 0.00/18.6M [00:00<?, ?B/s]"
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              "Downloading data:   0%|          | 0.00/1.23M [00:00<?, ?B/s]"
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            "text/plain": [
              "Generating train split:   0%|          | 0/120000 [00:00<?, ? examples/s]"
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              "Generating test split:   0%|          | 0/7600 [00:00<?, ? examples/s]"
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        {
          "output_type": "execute_result",
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              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['text', 'label'],\n",
              "        num_rows: 120000\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['text', 'label'],\n",
              "        num_rows: 7600\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 4
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      ]
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    {
      "cell_type": "code",
      "source": [
        "train_df = pd.DataFrame(dataset[\"train\"])\n",
        "test_df = pd.DataFrame(dataset[\"test\"])\n",
        "train_df.head()"
      ],
      "metadata": {
        "colab": {
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      "execution_count": 5,
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        {
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              "                                                text  label\n",
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              "3  Iraq Halts Oil Exports from Main Southern Pipe...      2\n",
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              "  @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-ee8414e9-0ee7-48b8-aaf0-7695ca8ec10c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# view a sample text\n",
        "train_df['text'].iloc[2]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "id": "D-uWtYticXai",
        "outputId": "008c93c1-7a8b-4d54-afca-aa9492e15a2f"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\"Oil and Economy Cloud Stocks' Outlook (Reuters) Reuters - Soaring crude prices plus worries\\\\about the economy and the outlook for earnings are expected to\\\\hang over the stock market next week during the depth of the\\\\summer doldrums.\""
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "label_counts = train_df[\"label\"].value_counts()\n",
        "label_counts"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vu3RbDZVcePA",
        "outputId": "b283ba9c-92e7-49a5-888a-a25ed262854e"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2    30000\n",
              "3    30000\n",
              "1    30000\n",
              "0    30000\n",
              "Name: label, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "0 = world,\n",
        "1 = sports,\n",
        "2 = business,\n",
        "3 = sci/tech"
      ],
      "metadata": {
        "id": "OVJXAnf2cg_E"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### One hot encode the labels"
      ],
      "metadata": {
        "id": "fxgVVm8JclU8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_df = pd.get_dummies(train_df, columns=['label'])\n",
        "train_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "II42ZOSCcg1t",
        "outputId": "fe5fbe46-7208-46ae-a081-2a4e3f1893da"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                     text  label_0  label_1  \\\n",
              "0       Wall St. Bears Claw Back Into the Black (Reute...        0        0   \n",
              "1       Carlyle Looks Toward Commercial Aerospace (Reu...        0        0   \n",
              "2       Oil and Economy Cloud Stocks' Outlook (Reuters...        0        0   \n",
              "3       Iraq Halts Oil Exports from Main Southern Pipe...        0        0   \n",
              "4       Oil prices soar to all-time record, posing new...        0        0   \n",
              "...                                                   ...      ...      ...   \n",
              "119995  Pakistan's Musharraf Says Won't Quit as Army C...        1        0   \n",
              "119996  Renteria signing a top-shelf deal Red Sox gene...        0        1   \n",
              "119997  Saban not going to Dolphins yet The Miami Dolp...        0        1   \n",
              "119998  Today's NFL games PITTSBURGH at NY GIANTS Time...        0        1   \n",
              "119999  Nets get Carter from Raptors INDIANAPOLIS -- A...        0        1   \n",
              "\n",
              "        label_2  label_3  \n",
              "0             1        0  \n",
              "1             1        0  \n",
              "2             1        0  \n",
              "3             1        0  \n",
              "4             1        0  \n",
              "...         ...      ...  \n",
              "119995        0        0  \n",
              "119996        0        0  \n",
              "119997        0        0  \n",
              "119998        0        0  \n",
              "119999        0        0  \n",
              "\n",
              "[120000 rows x 5 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-901993a8-5fe4-400d-8021-cca2321b08de\" 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",
              "    }\n",
              "\n",
              "    .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>text</th>\n",
              "      <th>label_0</th>\n",
              "      <th>label_1</th>\n",
              "      <th>label_2</th>\n",
              "      <th>label_3</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Wall St. Bears Claw Back Into the Black (Reute...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Carlyle Looks Toward Commercial Aerospace (Reu...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Oil and Economy Cloud Stocks' Outlook (Reuters...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Iraq Halts Oil Exports from Main Southern Pipe...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Oil prices soar to all-time record, posing new...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>119995</th>\n",
              "      <td>Pakistan's Musharraf Says Won't Quit as Army C...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>119996</th>\n",
              "      <td>Renteria signing a top-shelf deal Red Sox gene...</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>119997</th>\n",
              "      <td>Saban not going to Dolphins yet The Miami Dolp...</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>119998</th>\n",
              "      <td>Today's NFL games PITTSBURGH at NY GIANTS Time...</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>119999</th>\n",
              "      <td>Nets get Carter from Raptors INDIANAPOLIS -- A...</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>120000 rows × 5 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-901993a8-5fe4-400d-8021-cca2321b08de')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </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",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .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-901993a8-5fe4-400d-8021-cca2321b08de 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-901993a8-5fe4-400d-8021-cca2321b08de');\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-ce359054-daf0-452c-8292-da944bddbbc6\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ce359054-daf0-452c-8292-da944bddbbc6')\"\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-ce359054-daf0-452c-8292-da944bddbbc6 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_a0e7fd5a-a2c3-450b-b797-7dcb6d262b91\">\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('train_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",
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              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('train_df');\n",
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              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Clean up the text"
      ],
      "metadata": {
        "id": "9hFS7CSwc3pq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import nltk\n",
        "import string\n",
        "import re\n",
        "from nltk.stem import PorterStemmer\n",
        "\n",
        "nltk.download(\"punkt\")\n",
        "\n",
        "def preprocess_text(text):\n",
        "  text = text.lower()\n",
        "\n",
        "  # remove punctuation\n",
        "  text = text.translate(str.maketrans(\"\", \"\", string.punctuation))\n",
        "\n",
        "  # remove numbers\n",
        "  text = re.sub(r'\\d+', '', text)\n",
        "\n",
        "  # stemming\n",
        "  stemmer = PorterStemmer()\n",
        "  tokens = nltk.word_tokenize(text)\n",
        "  stemmed_text = ' '.join([stemmer.stem(token) for token in tokens])\n",
        "\n",
        "  return stemmed_text"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uRvoTbsUczIm",
        "outputId": "bbe4df80-7360-435d-9698-3553d9199e20"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_df[\"text\"] = train_df[\"text\"].apply(preprocess_text)\n",
        "train_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "aAFvcl15c5m8",
        "outputId": "494a3ffa-fd1c-45ec-e9d5-08ad5aa66241"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "                                                     text  label_0  label_1  \\\n",
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              "119995  pakistan musharraf say wont quit as armi chief...        1        0   \n",
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              "119997  saban not go to dolphin yet the miami dolphin ...        0        1   \n",
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              "<p>120000 rows × 5 columns</p>\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "      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",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "</div>\n",
              "\n",
              "  <div id=\"id_15741397-58cb-481a-8a2e-a18ca3eb1730\">\n",
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              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
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              "\n",
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              "\n",
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              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('train_df')\"\n",
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              "\n",
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              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Train test split"
      ],
      "metadata": {
        "id": "daOEnlLpdMKR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# train test split\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(\n",
        "    train_df[\"text\"],\n",
        "    train_df.drop([\"text\"], axis=1),\n",
        ")"
      ],
      "metadata": {
        "id": "yzPdubx1c7uY"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Tokenizer"
      ],
      "metadata": {
        "id": "ekXQH_Gcdde5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoTokenizer\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"distilbert-base-uncased\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
            "6a4423d9c0774867968f1e542ce0d7f7",
            "426ad31ce58a470e890698b981937803",
            "1f2a5cfbf1cd40afac16699926b0c649",
            "24804f1af6b04204b75d59a34c45bd9e",
            "d655754fcee747b8b9e619dfb638b268",
            "b40c794faef34651ae2f0b66cd479dec",
            "a212a89e4a2b48e3954ea792f306c82d",
            "23b35652b0254d59a053757bb1578852",
            "ec52c032c8f2496a9e3ef045a0202855",
            "646c5f4507a14a54a7ffe80bd08d58a1",
            "1bc5342db9394f8e881da8e45a603c66",
            "f496fc768a7d45da8e18f860cbaedd8b",
            "a9ca3738f8a74c67ad12d76fad8de8ad",
            "88f686f472a04127843c23b3dec07125",
            "2713daea606d4fa6809df4fd8e193e83",
            "cb233584c22b47d9a91273b2641b4177",
            "58144278228243579558d23a3912d403",
            "f6eac9c95f8e4de797f108c6feb90013",
            "043959e478324926a719f7acd9f97fef",
            "a957221010394721b3297d8e783e287a",
            "e292af89e6334316a632c8e16ce585b4",
            "59d0dcb376bf4580b66c2466e7bc9123",
            "2b4f3689491c4bdcb39eb9b15cb21784",
            "2f5fac930a624d22b69b7eb4b1536747",
            "813adf1868534f2ea1c7ef061d6c75bf",
            "98d2ec7efc574b07b47ad6c3541dea19",
            "9d9ee262ea004b7a96241d88d27ebcef",
            "b8423215adf04ca5829d24883baa7039",
            "d77986fe410943c5b9002c0be8935d53",
            "1005707813b740ddadd112382760f117",
            "7df44fcef3344666b40a9ef5523a039f",
            "a30b66d362954fd297dffea8857d5a22",
            "0116f12ed23247e8a54fd9dc9dea3492",
            "c3e2dc3fa49a4e3487aa2631f8b18224",
            "a457546dcacb4f908617f2a685f7f75b",
            "408546a5f1bd4d99b93f5785b76827a2",
            "06d445b735604a7fab2c2d83784b002d",
            "ef6a3d05626147b2aff5116cb2d612e8",
            "840e6fc64123466195ab50d07a4a7c27",
            "d26d3a800d6b421bb2643dcfadb84dab",
            "65687ef861924c9287a205eda35e8ff3",
            "c951478b9d7348b19b762399837ab0ab",
            "777cd2b0572d4710938d9476294a976a",
            "fe4626e5ce2d4cb890d898be3053a5cc"
          ]
        },
        "id": "fxOK9z7jdfMB",
        "outputId": "b16ffacb-5300-455c-bb7f-a862bfbc28e0"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/28.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6a4423d9c0774867968f1e542ce0d7f7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/483 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f496fc768a7d45da8e18f860cbaedd8b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2b4f3689491c4bdcb39eb9b15cb21784"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c3e2dc3fa49a4e3487aa2631f8b18224"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset[\"train\"][0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "29L8fTW8dhlK",
        "outputId": "76a7afcb-3a06-4432-f578-847edb8dd919"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'text': \"Wall St. Bears Claw Back Into the Black (Reuters) Reuters - Short-sellers, Wall Street's dwindling\\\\band of ultra-cynics, are seeing green again.\",\n",
              " 'label': 2}"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def preprocess_function(examples):\n",
        "  return tokenizer(examples[\"text\"], truncation=True)"
      ],
      "metadata": {
        "id": "ytgUwcuxdt5w"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokenized_dataset = dataset.map(preprocess_function, batched=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
            "602b9aa5f93c4bc7a4c9cece3e6c7bda",
            "f2c9813bf40947b584d3a707ca6d0e54",
            "4d2a063dbd5443c1a332f619f69a3685",
            "e02c27924466439bbe7259b2b4a4d745",
            "f3644945928b48b7ad48c7d9441ef8ec",
            "8f0027459acf446ca08fd491065d489b",
            "edb0643ceb584f0e9ff25302cf31ad4f",
            "897d9bd1aa8e48dfbbf24d3e6fd15502",
            "718c1c8d9b9b40748e3fd2a05560a77e",
            "10a7dc5711924f78ba1b3060ff45c10f",
            "09338f239adb4265b2ad46d7c816fdd2",
            "f2c9e22b5113499b904939c5ebad77f8",
            "ea5c8c7b89f440c9a11ef109b44d4385",
            "82c93b15c6c64af19406316e83d26614",
            "419562b44b5e4dd59cfb0232c63924c1",
            "136720980c314070a2a63c414c76913f",
            "02d32488f26f4c5e8b44f45a89552781",
            "d8a219d984114ca29d3c02dbdbf87a73",
            "6be8999365f14caab60de4000e749cdc",
            "667acc841c4b464388ea933dfc74bedf",
            "9e75e443d8b54c72954ddcee5b2f6fb8",
            "c9f316c003f14fdabfb4192c38d54cbd"
          ]
        },
        "id": "M1XRHny6eaYj",
        "outputId": "da024e03-45b4-44a9-eac8-82810b1f2d18"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/120000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "602b9aa5f93c4bc7a4c9cece3e6c7bda"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/7600 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f2c9e22b5113499b904939c5ebad77f8"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import DataCollatorWithPadding\n",
        "\n",
        "data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors=\"tf\")"
      ],
      "metadata": {
        "id": "fQssmrfId62L"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Evaluate"
      ],
      "metadata": {
        "id": "yfZ5j7KteB5y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install evaluate"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Rf7u03LfeGRP",
        "outputId": "de6c4a45-c924-4dee-d991-b32445997c28"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting evaluate\n",
            "  Downloading evaluate-0.4.1-py3-none-any.whl (84 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m84.1/84.1 kB\u001b[0m \u001b[31m2.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: datasets>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from evaluate) (2.16.1)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from evaluate) (1.23.5)\n",
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            "Requirement already satisfied: huggingface-hub>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from evaluate) (0.20.2)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from evaluate) (23.2)\n",
            "Collecting responses<0.19 (from evaluate)\n",
            "  Downloading responses-0.18.0-py3-none-any.whl (38 kB)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from datasets>=2.0.0->evaluate) (3.13.1)\n",
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            "Requirement already satisfied: pyarrow-hotfix in /usr/local/lib/python3.10/dist-packages (from datasets>=2.0.0->evaluate) (0.6)\n",
            "Requirement already satisfied: aiohttp in /usr/local/lib/python3.10/dist-packages (from datasets>=2.0.0->evaluate) (3.9.1)\n",
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            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas->evaluate) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->evaluate) (2023.3.post1)\n",
            "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets>=2.0.0->evaluate) (23.2.0)\n",
            "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets>=2.0.0->evaluate) (6.0.4)\n",
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            "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets>=2.0.0->evaluate) (1.4.1)\n",
            "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets>=2.0.0->evaluate) (1.3.1)\n",
            "Requirement already satisfied: async-timeout<5.0,>=4.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets>=2.0.0->evaluate) (4.0.3)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas->evaluate) (1.16.0)\n",
            "Installing collected packages: responses, evaluate\n",
            "Successfully installed evaluate-0.4.1 responses-0.18.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import evaluate\n",
        "\n",
        "accuracy = evaluate.load(\"accuracy\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "1cce45b6d5ff4590a5e731823bc88c07",
            "d0c0c5c5ce374707ba76385be8bda2e8",
            "38a84b065f4c4483819d147cc37ddd8e",
            "8c272fc749324288b36e75f1845f67f1",
            "55c920409f5a46e0ad2df5ab91c7be51",
            "7cec59c12d5c4b8580c9e8ee1eda9b59",
            "b763e9f2898e41c2a23e98b52b6ffa26",
            "23bf3e6933804ad086b5844cbc6aa7db",
            "048fd969c4da40cf9d158eec35fba10b",
            "903360098c0c4f49ae0f7d8125223b3d",
            "8bb4c99764fa4058ad6215c40504200f"
          ]
        },
        "id": "l0GDHhcdeDpQ",
        "outputId": "101f8da8-470f-46ca-8dfd-64931d7b5891"
      },
      "execution_count": 18,
      "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": "1cce45b6d5ff4590a5e731823bc88c07"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "\n",
        "def compute_metrics(eval_pred):\n",
        "    predictions, labels = eval_pred\n",
        "    predictions = np.argmax(predictions, axis=1)\n",
        "    return accuracy.compute(predictions=predictions, references=labels)"
      ],
      "metadata": {
        "id": "IYG_YxZMeMj4"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Train"
      ],
      "metadata": {
        "id": "E9pcL4bseNhF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import create_optimizer\n",
        "import tensorflow as tf\n",
        "\n",
        "batch_size = 16\n",
        "num_epochs = 5\n",
        "batches_per_epoch = len(tokenized_dataset[\"train\"]) // batch_size\n",
        "total_train_steps = int(batches_per_epoch * num_epochs)\n",
        "optimizer, schedule = create_optimizer(init_lr=2e-5, num_warmup_steps=0, num_train_steps=total_train_steps)"
      ],
      "metadata": {
        "id": "uMW5f9vxeOvw"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import TFAutoModelForSequenceClassification\n",
        "\n",
        "model = TFAutoModelForSequenceClassification.from_pretrained(\n",
        "    \"distilbert-base-uncased\", num_labels=4\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 156,
          "referenced_widgets": [
            "c32b6583065946e6945f4af21195a3f2",
            "13ec6d658dd24a9aa83b9bf8c44f4505",
            "457c892162ab42d18b591f65d24f93a4",
            "6fb213468a8b46d8ba8875f3f95d97f1",
            "f1ec84fd05ac49eda707c98a87cbf0b1",
            "5f5a19c2adba499f9b97f773810391eb",
            "df9bf740382f4e25865d4945a02509cd",
            "6e0a153ad93d42da9e1e2c1f21dd6390",
            "b768683465664e9b815d1de486b7599b",
            "8a3f21543a124d999aef134bbff38fc1",
            "4e480b481e4047acad6a277380c3c84b"
          ]
        },
        "id": "Sd_zNi_iep8-",
        "outputId": "c3fcb226-75f2-466d-ef0d-ff37022c7b61"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/268M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c32b6583065946e6945f4af21195a3f2"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the PyTorch model were not used when initializing the TF 2.0 model TFDistilBertForSequenceClassification: ['vocab_transform.bias', 'vocab_layer_norm.weight', 'vocab_layer_norm.bias', 'vocab_projector.bias', 'vocab_transform.weight']\n",
            "- This IS expected if you are initializing TFDistilBertForSequenceClassification from a PyTorch model trained on another task or with another architecture (e.g. initializing a TFBertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing TFDistilBertForSequenceClassification from a PyTorch model that you expect to be exactly identical (e.g. initializing a TFBertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights or buffers of the TF 2.0 model TFDistilBertForSequenceClassification were not initialized from the PyTorch model and are newly initialized: ['pre_classifier.weight', 'pre_classifier.bias', 'classifier.weight', 'classifier.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tf_train_set = model.prepare_tf_dataset(\n",
        "    tokenized_dataset[\"train\"],\n",
        "    shuffle=True,\n",
        "    batch_size=16,\n",
        "    collate_fn=data_collator,\n",
        ")\n",
        "\n",
        "tf_validation_set = model.prepare_tf_dataset(\n",
        "    tokenized_dataset[\"test\"],\n",
        "    shuffle=False,\n",
        "    batch_size=16,\n",
        "    collate_fn=data_collator,\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i-DY0lYre0Qq",
        "outputId": "8094737f-1205-4427-8395-4b123c43257c"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "You're using a DistilBertTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(optimizer=optimizer)  # No loss argument!"
      ],
      "metadata": {
        "id": "hNtKZy17e9_G"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers.keras_callbacks import KerasMetricCallback\n",
        "\n",
        "metric_callback = KerasMetricCallback(metric_fn=compute_metrics, eval_dataset=tf_validation_set)"
      ],
      "metadata": {
        "id": "wefz-RtNfChd"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(tf_train_set, validation_data=tf_validation_set, epochs=3, callbacks=[metric_callback])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UXNpeKeLfH-L",
        "outputId": "65730c86-1ce3-4d48-bea6-2d92b4f85bc2"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/3\n",
            "7500/7500 [==============================] - 1345s 176ms/step - loss: 0.2153 - val_loss: 0.1637 - accuracy: 0.9442\n",
            "Epoch 2/3\n",
            "7500/7500 [==============================] - 1307s 174ms/step - loss: 0.1255 - val_loss: 0.1542 - accuracy: 0.9475\n",
            "Epoch 3/3\n",
            "7500/7500 [==============================] - 1308s 174ms/step - loss: 0.0793 - val_loss: 0.1730 - accuracy: 0.9478\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x7bd1f002fb50>"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoTokenizer\n",
        "text = \"Australia has the highest percentage of Kangaroos\"\n",
        "\n",
        "inputs = tokenizer(text, return_tensors=\"tf\")\n",
        "\n",
        "logits = model(**inputs).logits\n",
        "\n",
        "predicted_class_id = int(tf.math.argmax(logits, axis=-1)[0])\n",
        "\n",
        "print(logits), print(predicted_class_id)\n",
        "\n",
        "# model.config.id2label[predicted_class_id]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ClwQ5zbCznFl",
        "outputId": "83b0d47e-e73b-4350-fe9b-0bec18f31ed2"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tf.Tensor([[ 2.59918   -2.0999012 -2.4402075  1.2873685]], shape=(1, 4), dtype=float32)\n",
            "0\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(None, None)"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.save_pretrained(\"distilbert_text_classification_model\")"
      ],
      "metadata": {
        "id": "G9dJvP1N0PYG"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Loading a pre trained model"
      ],
      "metadata": {
        "id": "jLCA62ownl3i"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import TFAutoModelForSequenceClassification\n",
        "\n",
        "model = TFAutoModelForSequenceClassification.from_pretrained('/content/drive/MyDrive/distilbert_text_classification_model')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b2DOyE79noKd",
        "outputId": "27143dad-afc5-4360-924d-a6f1ffa35452"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "All model checkpoint layers were used when initializing TFDistilBertForSequenceClassification.\n",
            "\n",
            "All the layers of TFDistilBertForSequenceClassification were initialized from the model checkpoint at /content/drive/MyDrive/distilbert_text_classification_model.\n",
            "If your task is similar to the task the model of the checkpoint was trained on, you can already use TFDistilBertForSequenceClassification for predictions without further training.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoTokenizer\n",
        "import tensorflow as tf\n",
        "text = \"Australia has the highest percentage of Kangaroos\"\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"distilbert-base-uncased\")\n",
        "\n",
        "inputs = tokenizer(text, return_tensors=\"tf\")\n",
        "\n",
        "logits = model(**inputs).logits\n",
        "\n",
        "predicted_class_id = int(tf.math.argmax(logits, axis=-1)[0])\n",
        "\n",
        "print(logits), print(predicted_class_id)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321,
          "referenced_widgets": [
            "fa6f185076dd4103ab20628899bd11c5",
            "b423107df6dc4c85ad2e2025f822ef69",
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            "3e858b78a054406b98cd5027677536cb",
            "27c034f5af9143e38b81743eb2146c3d",
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            "420ef0895bfb485eb93d3bf59da9a875",
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            "75d202d5baad4ab3812a6eca119a381c",
            "4b717fbf19f64d28be845de760afc840",
            "235f235351f344c6b5adea8b7655f825",
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            "8c29649bd6974c8d986621064db79723",
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            "66bd4672b44b41fb81f051052569e1ec",
            "ca3160d125434426af0eb31d4c96e533",
            "3539bda49d684183928bb87f7450682d",
            "17742efc7dfd41d78b662eb2793874c9",
            "9725c767957a4b38bf8e7a017e4fabec",
            "25f4a36b346c426db6b590a0751873d1",
            "f9cd0c6d2777411184d1f3d8c576fb5c",
            "cde5f787d0a440d692b4a2524c4baf48",
            "205dcde298364768a59f7531fb6c8141",
            "14fdac8b5c714c0999eca45357823e80",
            "7165cab015454b4d96fd08f99a09142a",
            "3ca850974db64516a37fe2aa5351b507",
            "aeeaaf3a815143469363147734a1305a",
            "20098ab9eb7e4c0e936bd680d67bd743",
            "88f3c656b32945d89c75bc1aeaa486aa",
            "b0717f7d0e6d45f4805dfd1628fc1da1",
            "b04e360f546a4e359aa4801ac6469580",
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            "7e0fa982f4dc4873af351c1843773be0",
            "7e49dce877834549b22cdb0f11865266",
            "25e94ab593a14c729089e8db6c2554ba",
            "6c772576a5354d27a80469a431c06cb0"
          ]
        },
        "id": "IrDj0hgupx7o",
        "outputId": "38c68be0-a8a1-4265-9201-070f8a4523ca"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/28.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "fa6f185076dd4103ab20628899bd11c5"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/483 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4b717fbf19f64d28be845de760afc840"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ca3160d125434426af0eb31d4c96e533"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "aeeaaf3a815143469363147734a1305a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tf.Tensor([[ 2.59918   -2.0999012 -2.4402075  1.2873685]], shape=(1, 4), dtype=float32)\n",
            "0\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(None, None)"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "inputs"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ssy-_7Gop9pI",
        "outputId": "d4f2d2a6-2503-4d41-876a-65af3ab0cb67"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'input_ids': <tf.Tensor: shape=(1, 10), dtype=int32, numpy=\n",
              "array([[  101,  2660,  2038,  1996,  3284,  7017,  1997, 21652,  2015,\n",
              "          102]], dtype=int32)>, 'attention_mask': <tf.Tensor: shape=(1, 10), dtype=int32, numpy=array([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], dtype=int32)>}"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "0 = world,\n",
        "1 = sports,\n",
        "2 = business,\n",
        "3 = sci/tech"
      ],
      "metadata": {
        "id": "Kdu9YpigCv1t"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoTokenizer\n",
        "import tensorflow as tf\n",
        "\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"distilbert-base-uncased\")\n",
        "\n",
        "def predict_text(text):\n",
        "  inputs = tokenizer(text, return_tensors=\"tf\")\n",
        "\n",
        "  logits = model(**inputs).logits\n",
        "\n",
        "  predicted_class_id = int(tf.math.argmax(logits, axis=-1)[0])\n",
        "\n",
        "  print(logits), print(predicted_class_id)\n",
        "  return predicted_class_id\n",
        "\n",
        "predict_text(\"Belgium soccer team wins the world cup every year\")\n",
        "predict_text(\"Bitcoin will eventually run the world\")\n",
        "predict_text(\"The royal family owns a large country\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f78_50LtCQ1o",
        "outputId": "055847fd-de65-44e0-9279-6ffebb817cfa"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tf.Tensor([[ 1.3730699  4.8763914 -2.8066082 -3.2761738]], shape=(1, 4), dtype=float32)\n",
            "1\n",
            "tf.Tensor([[-0.12236271 -4.801699   -0.6200071   3.260698  ]], shape=(1, 4), dtype=float32)\n",
            "3\n",
            "tf.Tensor([[ 2.3290505 -2.7871583  1.164522  -2.0756993]], shape=(1, 4), dtype=float32)\n",
            "0\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predict_text(\"Maradona is the top scorer in soccer ever\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n3KUTanmTIlK",
        "outputId": "7bda57b5-695c-43d3-85c6-0e76a51fc321"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tf.Tensor([[ 1.8160654  3.7035215 -1.974746  -3.5851183]], shape=(1, 4), dtype=float32)\n",
            "1\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predict_text(\"Spain has a large population\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4pqwJfIuT20M",
        "outputId": "80dc5ee8-9c3e-4fd9-ac4a-19fbcd9e2466"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tf.Tensor([[ 3.0845523  -2.8736486  -0.6422905  -0.63165694]], shape=(1, 4), dtype=float32)\n",
            "0\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "hgeY5Xntiawv"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}