src/tasks/task-2-data-preprocessing/customer-service/generalizing-categories/generalizing_categories.ipynb

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      "source": [
        "import torch, os\n",
        "import pandas as pd\n",
        "from transformers import pipeline, BertForSequenceClassification, BertTokenizerFast\n",
        "from torch.utils.data import Dataset"
      ]
    },
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        "device"
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              "'cuda'"
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      "source": [
        "from google.colab import drive\n",
        "\n",
        "drive.mount('/content/gdrive/', force_remount=True)"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
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        "outputId": "4f4bb394-8f42-433b-a760-c4c671866af1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive/\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset_path = \"/content/gdrive/MyDrive/categories_customer_service\""
      ],
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        "id": "_j4Ei69aNfEc"
      },
      "execution_count": null,
      "outputs": []
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      "cell_type": "code",
      "source": [
        "train  = pd.read_csv(dataset_path + \"/category_train.csv\")"
      ],
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        "id": "lbiEFuryNmhY"
      },
      "execution_count": null,
      "outputs": []
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        "train.head()"
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              "   Unnamed: 0    category                                           question\n",
              "0           0  Icebreaker                             tell me about yourself\n",
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              "array(['Icebreaker', 'Behavioral', 'Situational', 'Open-ended',\n",
              "       'quality and culture', 'empythy and communication',\n",
              "       'problem solving'], dtype=object)"
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              "   Unnamed: 0    category                                           question  \\\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-08a2a997-3182-4ca3-b38e-eef72c0fb439 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-08a2a997-3182-4ca3-b38e-eef72c0fb439');\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-31e9a1a6-5462-4fde-bc47-e240008298bb\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-31e9a1a6-5462-4fde-bc47-e240008298bb')\"\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-31e9a1a6-5462-4fde-bc47-e240008298bb button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train[\"question_word_count\"].max()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Mio2q1l3PDYb",
        "outputId": "a3e8cfcc-7611-45ba-fcdd-28daa45b8fdf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "30"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "max_len_training = train[\"question_word_count\"].max()"
      ],
      "metadata": {
        "id": "7AN2-GGdPIIW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "max_len_training"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "shApHtVdPMwZ",
        "outputId": "dad84b8f-c5e6-4df2-deff-b04ddf643f12"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "30"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train = train.drop(columns = [\"Unnamed: 0\"])\n"
      ],
      "metadata": {
        "id": "_LKn-KR0PN8_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "hbpDBR1APUxr",
        "outputId": "684b37be-180b-452f-f09c-d36c52e091f8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     category                                           question  \\\n",
              "0  Icebreaker                             tell me about yourself   \n",
              "1  Icebreaker  what do you like about working in customer ser...   \n",
              "2  Icebreaker  what things on your resume are you really an e...   \n",
              "3  Icebreaker      what do you know about our company or product   \n",
              "4  Icebreaker                       why do you want to work here   \n",
              "\n",
              "   question_word_count  \n",
              "0                    4  \n",
              "1                   15  \n",
              "2                   11  \n",
              "3                    9  \n",
              "4                    7  "
            ],
            "text/html": [
              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
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              "      <th></th>\n",
              "      <th>category</th>\n",
              "      <th>question</th>\n",
              "      <th>question_word_count</th>\n",
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              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>Icebreaker</td>\n",
              "      <td>tell me about yourself</td>\n",
              "      <td>4</td>\n",
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              "      <td>Icebreaker</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Icebreaker</td>\n",
              "      <td>what things on your resume are you really an e...</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Icebreaker</td>\n",
              "      <td>what do you know about our company or product</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Icebreaker</td>\n",
              "      <td>why do you want to work here</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d5873fca-2f8e-4386-990b-8a5a2db1511d')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\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",
              "      display: none;\n",
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              "      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-d5873fca-2f8e-4386-990b-8a5a2db1511d 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-d5873fca-2f8e-4386-990b-8a5a2db1511d');\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-6c89a33f-4b88-4204-ad15-b49e7a255c6c\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6c89a33f-4b88-4204-ad15-b49e7a255c6c')\"\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",
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              "    </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-6c89a33f-4b88-4204-ad15-b49e7a255c6c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "labels = train[\"category\"].unique().tolist()\n",
        "labels"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "44daePf5PXbz",
        "outputId": "8c0ef7ec-27ce-4de7-c3be-3032d70fc9dc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Icebreaker',\n",
              " 'Behavioral',\n",
              " 'Situational',\n",
              " 'Open-ended',\n",
              " 'quality and culture',\n",
              " 'empythy and communication',\n",
              " 'problem solving']"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "index_to_label = {id:label for id,label in enumerate(labels)}\n",
        "index_to_label"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FxcbsYhsPgCe",
        "outputId": "c84216f9-5125-43bf-9d2d-f35acb0a7b9f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{0: 'Icebreaker',\n",
              " 1: 'Behavioral',\n",
              " 2: 'Situational',\n",
              " 3: 'Open-ended',\n",
              " 4: 'quality and culture',\n",
              " 5: 'empythy and communication',\n",
              " 6: 'problem solving'}"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "label_to_index = {label:id for id,label in enumerate(labels)}\n",
        "label_to_index"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XlT9BXpOQKHX",
        "outputId": "3502bdfb-429f-4de4-c919-b00e7d4e0199"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Icebreaker': 0,\n",
              " 'Behavioral': 1,\n",
              " 'Situational': 2,\n",
              " 'Open-ended': 3,\n",
              " 'quality and culture': 4,\n",
              " 'empythy and communication': 5,\n",
              " 'problem solving': 6}"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train[\"labels\"] = train[\"category\"].apply(lambda x: label_to_index[x])"
      ],
      "metadata": {
        "id": "YdtwxalyQL2o"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "Ymzu4hFNQSFO",
        "outputId": "db1204e2-0cd7-4d8a-e4c2-53b45bbbabbf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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              "     category                                           question  \\\n",
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              "4  Icebreaker                       why do you want to work here   \n",
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              "   question_word_count  labels  \n",
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              "      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",
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            ]
          },
          "metadata": {},
          "execution_count": 22
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        "train.iloc[[40]]"
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          "height": 81
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        "id": "Cj5QhkCmQs2G",
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               category                               question  \\\n",
              "40  quality and culture  how would you define customer delight   \n",
              "\n",
              "    question_word_count  labels  \n",
              "40                    6       4  "
            ],
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              "\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-91e0ad87-2bee-4ef9-a86e-3e4565c12c0b');\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",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 23
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    {
      "cell_type": "code",
      "source": [
        "train.iloc[[20]]"
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      "metadata": {
        "colab": {
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          "height": 81
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        "id": "TUjsXhwqQxki",
        "outputId": "73b0ebed-c403-4da0-8a30-c1e3e3076907"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       category                                           question  \\\n",
              "20  Situational  tell me about a time when you received poor cu...   \n",
              "\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        const element = document.querySelector('#df-579d11c7-81a2-450f-b9ef-0f655059768c');\n",
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              "          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",
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train.category.value_counts().plot(kind=\"pie\", figsize = (8,8))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 670
        },
        "id": "iUgJ7dN5Q4yq",
        "outputId": "5b6de5ca-84c6-473e-cd45-cc8963288e72"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: ylabel='category'>"
            ]
          },
          "metadata": {},
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer = BertTokenizerFast.from_pretrained('bert-large-uncased', max_length = 30)"
      ],
      "metadata": {
        "id": "esY_RQBqRGRJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = BertForSequenceClassification.from_pretrained('bert-large-uncased', num_labels=number_labels, id2label = index_to_label, label2id = label_to_index )"
      ],
      "metadata": {
        "id": "wBDJCoV_RIWD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "11a03042-b71a-476a-a7a5-08883ce359aa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-large-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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.to(device)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iiMUndRvmhn_",
        "outputId": "2e58ed2b-34bb-43c6-bb8b-113b02017eaa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "BertForSequenceClassification(\n",
              "  (bert): BertModel(\n",
              "    (embeddings): BertEmbeddings(\n",
              "      (word_embeddings): Embedding(30522, 1024, padding_idx=0)\n",
              "      (position_embeddings): Embedding(512, 1024)\n",
              "      (token_type_embeddings): Embedding(2, 1024)\n",
              "      (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
              "      (dropout): Dropout(p=0.1, inplace=False)\n",
              "    )\n",
              "    (encoder): BertEncoder(\n",
              "      (layer): ModuleList(\n",
              "        (0-23): 24 x BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "              (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "              (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "              (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
              "            (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "    (pooler): BertPooler(\n",
              "      (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "      (activation): Tanh()\n",
              "    )\n",
              "  )\n",
              "  (dropout): Dropout(p=0.1, inplace=False)\n",
              "  (classifier): Linear(in_features=1024, out_features=7, bias=True)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_text = list(train.question)\n",
        "train_labels = list(train.labels)"
      ],
      "metadata": {
        "id": "i93mUUn2msE9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_text"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T7Nddl7fmt89",
        "outputId": "a409f36b-dac4-44a9-c39a-1990f34170bb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['tell me about yourself',\n",
              " 'what do you like about working in customer service and what do you not like',\n",
              " 'what things on your resume are you really an expert in',\n",
              " 'what do you know about our company or product',\n",
              " 'why do you want to work here',\n",
              " 'what skills do you possess that will help you excel in this role',\n",
              " 'how well do you work under pressure',\n",
              " 'how do you handle getting negative feedback from a customer',\n",
              " 'what time management techniques do you use',\n",
              " 'how do you prioritize your work',\n",
              " 'how do you handle a disagreement with a teammate or manager',\n",
              " 'how do you respond when you do not know the answer to a question',\n",
              " 'how do you handle an angry customer',\n",
              " 'tell me about a time when you had to deliver bad news to a customer',\n",
              " 'describe a time when you worked on a team projectwhat was your role and what would you have done differently',\n",
              " 'how would you handle a situation where you have to reach out to the customer about an issue without having all the information or answers',\n",
              " 'can you give me an example of a time when you turned an angry customer into a happy one',\n",
              " 'describe a time when you could not solve a customers problem',\n",
              " 'describe a time when you have advocated for your customer',\n",
              " 'tell me about a time you took ownership of something for a customer and reduced their effort to reach a resolutionwhat was the result',\n",
              " 'tell me about a time when you received poor customer servicewhat would you have done better',\n",
              " 'what was the biggest mistake youve made on the job and how did you handle it',\n",
              " 'tell me about a time when you went above and beyond for a customer',\n",
              " 'tell me about a time when you had to say no to a customer',\n",
              " 'describe an experience when you had to empathize with a customers situation for you to truly understand their issuehow did you solve it',\n",
              " 'describe your approach and the result of a situation when you needed to make a judgment call',\n",
              " 'define good customer service',\n",
              " 'what are your strengths whats your greatest weakness',\n",
              " 'what do you think makes a good teammate',\n",
              " 'what are your career goals',\n",
              " 'why do you think youd be a great fit',\n",
              " 'if you had a superpower what would it be and why',\n",
              " 'what questions do you have for us',\n",
              " 'where do you see yourself in three years',\n",
              " 'why is customer service important what does it require',\n",
              " 'has there ever been a time in which youve experienced bad customer service what would you have done to improve it',\n",
              " 'can you describe an example of a situation where you provided exceptional customer service',\n",
              " 'how does our organization offer top customer service',\n",
              " 'which skills are required for a customer service role',\n",
              " 'what does empathy mean in the context of customer service',\n",
              " 'how would you define customer delight',\n",
              " 'what are some essential phases of the customer journey',\n",
              " 'can you describe a situation in which you had to serve a difficult customer which approaches did you use to deal with the situation',\n",
              " 'can you describe a time when you delivered excellent customer service what was the impact of your interaction with the customer',\n",
              " 'has there ever been a situation in which you or a past organization received a negative review or feedback from a customer how did you respond',\n",
              " 'have you ever found it challenging to interact with a customer because you did not understand their request',\n",
              " 'have you ever refused to offer a service to a client due to a company policy',\n",
              " 'how would you handle a situation in which a client who has not gotten the support or service they needed from several other businesses comes to you for a solution',\n",
              " 'have you ever had to de-escalate a situation involving a difficult client which techniques did you use',\n",
              " 'can you describe a situation where you had to communicate with others on your team to resolve an issue or a complaint',\n",
              " 'have you ever been in a situation where you were unsure how to assist a customer what steps did you take to find the right solution to their problem',\n",
              " 'can you tell me about a time that your product or service has caused an issue for a client how did you handle the situation and what was the outcome',\n",
              " 'have you ever made an error when trying to solve a problem for a customer what would you do differently to handle the error now',\n",
              " 'are there any steps that you would take to improve customer service at our organization',\n",
              " 'what would you do if a satisfied customer recommended your product or service to another customer who after using it was disappointed',\n",
              " 'have you ever experienced a customer service issue that required a creative solution',\n",
              " 'describe a time when you provided or observed high-quality customer service',\n",
              " 'tell me about a difficult customer you had to deal with and how you handled it',\n",
              " 'tell me about a time you went above and beyond what was expected to please a customer',\n",
              " 'tell me about a difficult day you experienced at work what happened and how did you handle it',\n",
              " 'give me an example of a time when you had to explain something fairly complex to a frustrated client or coworker how did you make sure they understood you',\n",
              " 'tell me about a time you thought you communicated clearly but were misunderstood what happened and how did you handle the situation',\n",
              " 'describe an instance when you had to improvise or think on your feet to solve a problem',\n",
              " 'tell me about a time you were under a lot of stress at work and how you dealt with it',\n",
              " 'tell me about a time you made a mistake at work what happened',\n",
              " 'when youre working with a large number of customers it is tricky to deliver excellent service to all how do you go about prioritizing customers needs',\n",
              " 'describe a time when you had to interact with a difficult client what was the situation and how did you handle it',\n",
              " 'tell me about a time a customer was pleased with your service',\n",
              " 'give me an example where you did not meet a clients expectations what happened and how did you attempt to fix the situation',\n",
              " 'describe a time when it was especially important to make a good impression on a client how did you approach things',\n",
              " 'how would you handle a customer you know is wrong']"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_labels"
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        "id": "V2fFIqQzmyyP",
        "outputId": "f0e047de-5bd8-47bd-e9f8-24cdac355033"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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          "metadata": {},
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    },
    {
      "cell_type": "code",
      "source": [
        "len(train_text), len(train_labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7cjLjblimz0B",
        "outputId": "3c3817bf-775a-429a-a9e0-57e75e1586a8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(71, 71)"
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          },
          "metadata": {},
          "execution_count": 32
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    {
      "cell_type": "code",
      "source": [
        "train_embedding = tokenizer(train_text, truncation = True , padding = True)"
      ],
      "metadata": {
        "id": "giuZQ_0-m5KN"
      },
      "execution_count": null,
      "outputs": []
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        "train_embedding"
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        "id": "_0jpZ0aMm7q7",
        "outputId": "567f64e5-5cf5-4230-9843-1555f99901dc"
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      "execution_count": null,
      "outputs": [
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1, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for key , val in train_embedding.items():\n",
        "  print(f\"{key}======{val}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SjybtKuKm_Vl",
        "outputId": "ed08db97-a5b8-4d2c-b487-773f3894c8e8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "input_ids======[[101, 2425, 2033, 2055, 4426, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [101, 2054, 2079, 2017, 2066, 2055, 2551, 1999, 8013, 2326, 1998, 2054, 2079, 2017, 2025, 2066, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [101, 2054, 2477, 2006, 2115, 13746, 2024, 2017, 2428, 2019, 6739, 1999, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [101, 2054, 2079, 2017, 2113, 2055, 2256, 2194, 2030, 4031, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [101, 2339, 2079, 2017, 2215, 2000, 2147, 2182, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [101, 2054, 4813, 2079, 2017, 10295, 2008, 2097, 2393, 2017, 24970, 1999, 2023, 2535, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [101, 2129, 2092, 2079, 2017, 2147, 2104, 3778, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [101, 2129, 2079, 2017, 5047, 2893, 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            "token_type_ids======[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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            "attention_mask======[[1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class DataLoader(Dataset):\n",
        "\n",
        "\n",
        "  def __init__(self, encodings , labels):\n",
        "\n",
        "    self.encodings = encodings\n",
        "    self.labels = labels\n",
        "\n",
        "  def __getitem__(self, idx):\n",
        "\n",
        "\n",
        "    item = {key: torch.tensor(val[idx])  for key, val in self.encodings.items()}\n",
        "\n",
        "    item[\"labels\"] = self.labels[idx]\n",
        "\n",
        "    return item\n",
        "\n",
        "  def __len__(self):\n",
        "\n",
        "\n",
        "    return len(self.labels)\n",
        "\n"
      ],
      "metadata": {
        "id": "ST_Rq_hinCza"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataloader = DataLoader(train_embedding, train_labels)"
      ],
      "metadata": {
        "id": "oCA5cEJRnF88"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for i in train_dataloader:\n",
        "\n",
        "  print(i)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EL3cznjvnIkf",
        "outputId": "29e319d9-ee72-4fa8-93fa-2486c75fe505"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'input_ids': tensor([ 101, 2425, 2033, 2055, 4426,  102,    0,    0,    0,    0,    0,    0,\n",
            "           0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
            "           0,    0,    0,    0,    0,    0,    0,    0,    0]), 'token_type_ids': tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
            "        0, 0, 0, 0, 0, 0, 0, 0, 0]), 'attention_mask': tensor([1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
            "        0, 0, 0, 0, 0, 0, 0, 0, 0]), 'labels': 0}\n",
            "{'input_ids': tensor([ 101, 2054, 2079, 2017, 2066, 2055, 2551, 1999, 8013, 2326, 1998, 2054,\n",
            "        2079, 2017, 2025, 2066,  102,    0,    0,    0,    0,    0,    0,    0,\n",
            "           0,    0,    0,    0,    0,    0,    0,    0,    0]), 'token_type_ids': tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
            "        0, 0, 0, 0, 0, 0, 0, 0, 0]), 'attention_mask': tensor([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0,\n",
            "        0, 0, 0, 0, 0, 0, 0, 0, 0]), 'labels': 0}\n",
            "{'input_ids': tensor([  101,  2054,  2477,  2006,  2115, 13746,  2024,  2017,  2428,  2019,\n",
            "         6739,  1999,   102,     0,     0,     0,     0,     0,     0,     0,\n",
            "            0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
            "            0,     0,     0]), 'token_type_ids': tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
            "        0, 0, 0, 0, 0, 0, 0, 0, 0]), 'attention_mask': tensor([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
            "        0, 0, 0, 0, 0, 0, 0, 0, 0]), 'labels': 0}\n",
            "{'input_ids': tensor([ 101, 2054, 2079, 2017, 2113, 2055, 2256, 2194, 2030, 4031,  102,    0,\n",
            "           0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
            "           0,    0,    0,    0,    0,    0,    0,    0,    0]), 'token_type_ids': tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
            "        0, 0, 0, 0, 0, 0, 0, 0, 0]), 'attention_mask': tensor([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
            "        0, 0, 0, 0, 0, 0, 0, 0, 0]), 'labels': 0}\n",
            "{'input_ids': tensor([ 101, 2339, 2079, 2017, 2215, 2000, 2147, 2182,  102,    0,    0,    0,\n",
            "           0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
            "           0,    0,    0,    0,    0,    0,    0,    0,    0]), 'token_type_ids': tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
            "        0, 0, 0, 0, 0, 0, 0, 0, 0]), 'attention_mask': tensor([1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
            "        0, 0, 0, 0, 0, 0, 0, 0, 0]), 'labels': 0}\n",
            "{'input_ids': tensor([  101,  2054,  4813,  2079,  2017, 10295,  2008,  2097,  2393,  2017,\n",
            "        24970,  1999,  2023,  2535,   102,     0,     0,     0,     0,     0,\n",
            "            0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
            "            0,     0,     0]), 'token_type_ids': tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
            "        0, 0, 0, 0, 0, 0, 0, 0, 0]), 'attention_mask': tensor([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
            "        0, 0, 0, 0, 0, 0, 0, 0, 0]), 'labels': 1}\n",
            "{'input_ids': tensor([ 101, 2129, 2092, 2079, 2017, 2147, 2104, 3778,  102,    0,    0,    0,\n",
            "           0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
            "           0,    0,    0,    0,    0,    0,    0,    0,    0]), 'token_type_ids': tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
            "        0, 0, 0, 0, 0, 0, 0, 0, 0]), 'attention_mask': tensor([1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
            "        0, 0, 0, 0, 0, 0, 0, 0, 0]), 'labels': 1}\n",
            "{'input_ids': tensor([  101,  2129,  2079,  2017,  5047,  2893,  4997, 12247,  2013,  1037,\n",
            "         8013,   102,     0,     0,     0,     0,     0,     0,     0,     0,\n",
            "            0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
            "            0,     0,     0]), 'token_type_ids': tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
            "        0, 0, 0, 0, 0, 0, 0, 0, 0]), 'attention_mask': tensor([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
            "        0, 0, 0, 0, 0, 0, 0, 0, 0]), 'labels': 1}\n",
            "{'input_ids': tensor([ 101, 2054, 2051, 2968, 5461, 2079, 2017, 2224,  102,    0,    0,    0,\n",
            "           0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
            "           0,    0,    0,    0,    0,    0,    0,    0,    0]), 'token_type_ids': tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
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            "        0, 0, 0, 0, 0, 0, 0, 0, 0]), 'attention_mask': tensor([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0,\n",
            "        0, 0, 0, 0, 0, 0, 0, 0, 0]), 'labels': 1}\n",
            "{'input_ids': tensor([ 101, 2425, 2033, 2055, 1037, 2051, 2017, 2081, 1037, 6707, 2012, 2147,\n",
            "        2054, 3047,  102,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
            "           0,    0,    0,    0,    0,    0,    0,    0,    0]), 'token_type_ids': tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
            "        0, 0, 0, 0, 0, 0, 0, 0, 0]), 'attention_mask': tensor([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
            "        0, 0, 0, 0, 0, 0, 0, 0, 0]), 'labels': 1}\n",
            "{'input_ids': tensor([  101,  2043,  2115,  2063,  2551,  2007,  1037,  2312,  2193,  1997,\n",
            "         6304,  2009,  2003, 24026,  2000,  8116,  6581,  2326,  2000,  2035,\n",
            "         2129,  2079,  2017,  2175,  2055,  3188, 25090,  6774,  6304,  3791,\n",
            "          102,     0,     0]), 'token_type_ids': tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
            "        0, 0, 0, 0, 0, 0, 0, 0, 0]), 'attention_mask': tensor([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
            "        1, 1, 1, 1, 1, 1, 1, 0, 0]), 'labels': 1}\n",
            "{'input_ids': tensor([  101,  6235,  1037,  2051,  2043,  2017,  2018,  2000, 11835,  2007,\n",
            "         1037,  3697,  7396,  2054,  2001,  1996,  3663,  1998,  2129,  2106,\n",
            "         2017,  5047,  2009,   102,     0,     0,     0,     0,     0,     0,\n",
            "            0,     0,     0]), 'token_type_ids': tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
            "        0, 0, 0, 0, 0, 0, 0, 0, 0]), 'attention_mask': tensor([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
            "        0, 0, 0, 0, 0, 0, 0, 0, 0]), 'labels': 1}\n",
            "{'input_ids': tensor([ 101, 2425, 2033, 2055, 1037, 2051, 1037, 8013, 2001, 7537, 2007, 2115,\n",
            "        2326,  102,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
            "           0,    0,    0,    0,    0,    0,    0,    0,    0]), 'token_type_ids': tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
            "        0, 0, 0, 0, 0, 0, 0, 0, 0]), 'attention_mask': tensor([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
            "        0, 0, 0, 0, 0, 0, 0, 0, 0]), 'labels': 1}\n",
            "{'input_ids': tensor([  101,  2507,  2033,  2019,  2742,  2073,  2017,  2106,  2025,  3113,\n",
            "         1037,  7846, 10908,  2054,  3047,  1998,  2129,  2106,  2017,  3535,\n",
            "         2000,  8081,  1996,  3663,   102,     0,     0,     0,     0,     0,\n",
            "            0,     0,     0]), 'token_type_ids': tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
            "        0, 0, 0, 0, 0, 0, 0, 0, 0]), 'attention_mask': tensor([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
            "        1, 0, 0, 0, 0, 0, 0, 0, 0]), 'labels': 1}\n",
            "{'input_ids': tensor([ 101, 6235, 1037, 2051, 2043, 2009, 2001, 2926, 2590, 2000, 2191, 1037,\n",
            "        2204, 8605, 2006, 1037, 7396, 2129, 2106, 2017, 3921, 2477,  102,    0,\n",
            "           0,    0,    0,    0,    0,    0,    0,    0,    0]), 'token_type_ids': tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
            "        0, 0, 0, 0, 0, 0, 0, 0, 0]), 'attention_mask': tensor([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,\n",
            "        0, 0, 0, 0, 0, 0, 0, 0, 0]), 'labels': 1}\n",
            "{'input_ids': tensor([ 101, 2129, 2052, 2017, 5047, 1037, 8013, 2017, 2113, 2003, 3308,  102,\n",
            "           0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
            "           0,    0,    0,    0,    0,    0,    0,    0,    0]), 'token_type_ids': tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
            "        0, 0, 0, 0, 0, 0, 0, 0, 0]), 'attention_mask': tensor([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
            "        0, 0, 0, 0, 0, 0, 0, 0, 0]), 'labels': 1}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import TrainingArguments, Trainer\n"
      ],
      "metadata": {
        "id": "me3YaaRqnLpS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score , precision_recall_fscore_support"
      ],
      "metadata": {
        "id": "SHBlFeiSnOGC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def compute_metrics(pred):\n",
        "\n",
        "\n",
        "  labels = pred.label_ids\n",
        "\n",
        "  preds = pred.predictions.argmax(-1)\n",
        "\n",
        "  precision , recall , F1 , _ = precision_recall_fscore_support(labels, preds , average =\"macro\")\n",
        "\n",
        "  accuracy = accuracy_score(labels, preds)\n",
        "\n",
        "  return{\n",
        "      \"accuracy\" : accuracy,\n",
        "\n",
        "      \"F1\" : F1,\n",
        "\n",
        "      \"precision\" : precision,\n",
        "\n",
        "      \"recall\" : recall\n",
        "\n",
        "\n",
        "  }"
      ],
      "metadata": {
        "id": "NhKwKfDrnPL6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#!pip install transformers[torch]"
      ],
      "metadata": {
        "id": "kkGRVXLvnmVc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#!pip install accelerate -U"
      ],
      "metadata": {
        "id": "4FS7wb9cnpmE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "training_args = TrainingArguments(\n",
        "    # The output directory where the model predictions and checkpoints will be written\n",
        "    output_dir='/content/gdrive/MyDrive/categories_customer_service',\n",
        "    do_train=True,\n",
        "    do_eval=True,\n",
        "    #  The number of epochs, defaults to 3.0\n",
        "    num_train_epochs=100,\n",
        "    per_device_train_batch_size=16,\n",
        "    per_device_eval_batch_size=32,\n",
        "    # Number of steps used for a linear warmup\n",
        "    warmup_steps=100,\n",
        "    weight_decay=0.01,\n",
        "    logging_strategy='steps',\n",
        "   # TensorBoard log directory\n",
        "    logging_dir='./fourth_multi-class-logs',\n",
        "    logging_steps=50,\n",
        "    evaluation_strategy=\"steps\",\n",
        "    eval_steps=50,\n",
        "    save_strategy=\"steps\",\n",
        "    fp16=True,\n",
        "    load_best_model_at_end=True\n",
        ")"
      ],
      "metadata": {
        "id": "YAR8bizQnSlT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainer = Trainer(\n",
        "    model = model,\n",
        "    args = training_args,\n",
        "    train_dataset = train_dataloader,\n",
        "    eval_dataset = train_dataloader,\n",
        "\n",
        "    compute_metrics = compute_metrics\n",
        ")"
      ],
      "metadata": {
        "id": "CtI5eTCqIaZr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 496
        },
        "id": "uTNHYul1IeRK",
        "outputId": "a83b382c-9a8c-412f-dd5f-7e64e3ce779c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='500' max='500' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [500/500 02:46, Epoch 100/100]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>F1</th>\n",
              "      <th>Precision</th>\n",
              "      <th>Recall</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>50</td>\n",
              "      <td>1.674400</td>\n",
              "      <td>0.862219</td>\n",
              "      <td>0.704225</td>\n",
              "      <td>0.571365</td>\n",
              "      <td>0.743590</td>\n",
              "      <td>0.604657</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>100</td>\n",
              "      <td>0.446700</td>\n",
              "      <td>0.163460</td>\n",
              "      <td>0.943662</td>\n",
              "      <td>0.902597</td>\n",
              "      <td>0.956044</td>\n",
              "      <td>0.904762</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>150</td>\n",
              "      <td>0.034300</td>\n",
              "      <td>0.002171</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>200</td>\n",
              "      <td>0.001700</td>\n",
              "      <td>0.000895</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>250</td>\n",
              "      <td>0.001100</td>\n",
              "      <td>0.000647</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>300</td>\n",
              "      <td>0.000800</td>\n",
              "      <td>0.000523</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>350</td>\n",
              "      <td>0.000700</td>\n",
              "      <td>0.000448</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>400</td>\n",
              "      <td>0.000600</td>\n",
              "      <td>0.000404</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>450</td>\n",
              "      <td>0.000600</td>\n",
              "      <td>0.000380</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>500</td>\n",
              "      <td>0.000500</td>\n",
              "      <td>0.000370</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=500, training_loss=0.216141629409045, metrics={'train_runtime': 167.8908, 'train_samples_per_second': 42.289, 'train_steps_per_second': 2.978, 'total_flos': 426475014154200.0, 'train_loss': 0.216141629409045, 'epoch': 100.0})"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def predict(text):\n",
        "\n",
        "\n",
        "  inputs  = tokenizer(text, padding= True , truncation = True , max_length = 30, return_tensors=\"pt\" ).to(\"cuda\")\n",
        "\n",
        "  outputs = model(**inputs)\n",
        "\n",
        "  probs = outputs[0].softmax(1)\n",
        "\n",
        "  pred_label_index = probs.argmax()\n",
        "\n",
        "  pred_label = model.config.id2label[pred_label_index.item()]\n",
        "\n",
        "  return pred_label"
      ],
      "metadata": {
        "id": "qpkivgeeI3qu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train[\"question\"][0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "Rjw0wgFgI7Ux",
        "outputId": "4c06a49c-cd4b-44b8-9619-937f9c255e22"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'tell me about yourself'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train[\"category\"][0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "ODqRr75GJQpX",
        "outputId": "ed1f11b1-fa1f-4685-b367-679c71f1dfff"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Icebreaker'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_text = \"tell me about yourself\"\n",
        "\n",
        "predict(train_text)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "SihJp7zVJE0P",
        "outputId": "27b457d6-639d-4558-8b9a-d0e7dd6fcdeb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Icebreaker'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train[\"question\"][50]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "id": "Ks2zul7cJfnY",
        "outputId": "df94f1e2-5f5d-4887-8f74-cbe38f700e23"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'have you ever been in a situation where you were unsure how to assist a customer what steps did you take to find the right solution to their problem'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train[\"category\"][50]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "OUFChuCiJN-f",
        "outputId": "bb39549c-7b74-4304-a9fa-a82138315c44"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'problem solving'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_text = \"have you ever been in a situation where you were unsure how to assist a customer what steps did you take to find the right solution to their problem\"\n",
        "\n",
        "predict(train_text)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "3r47L5TdJmwV",
        "outputId": "b315ef21-f3ba-4463-f613-116a0db55072"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'problem solving'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "test = pd.read_csv(dataset_path +\"/all_category_data.csv\")"
      ],
      "metadata": {
        "id": "9mCIjXcRJuDb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "tTZmQZy-KHCk",
        "outputId": "082e54fc-2555-47a3-fb96-41187892eb18"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Unnamed: 0    category                                           question\n",
              "0           0  Icebreaker                             tell me about yourself\n",
              "1           1  Icebreaker  what do you like about working in customer ser...\n",
              "2           2  Icebreaker  what things on your resume are you really an e...\n",
              "3           3  Icebreaker      what do you know about our company or product\n",
              "4           4  Icebreaker                       why do you want to work here"
            ],
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              "\n",
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              "                                                    [key], {});\n",
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              "\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",
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              "\n",
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              "  .colab-df-quickchart-complete:disabled:hover {\n",
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              "\n",
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              "    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",
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              "    60% {\n",
              "      border-color: transparent;\n",
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              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      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-f820704f-437d-4f39-b8fb-352992ebc98c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "questionz = test[\"question\"].tolist()"
      ],
      "metadata": {
        "id": "f6BTausBKIpB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(test), len(questionz)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WPINzdWtKQrV",
        "outputId": "5b3fd8cb-d94c-4885-9c1e-0d387dd3509d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(254, 254)"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "categories = []\n",
        "\n",
        "for question in questionz:\n",
        "\n",
        "  pred = predict(question)\n",
        "  categories.append(pred)"
      ],
      "metadata": {
        "id": "dOrfWONaKV5g"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(len(categories))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zaMJgtLkLTcL",
        "outputId": "d871aa3e-bf12-4a5e-c73e-118031a2c6d3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "254\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test.insert(3, \"categories\", categories)"
      ],
      "metadata": {
        "id": "fH0qeEHcKu8K"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "HRIpbjkWLOun",
        "outputId": "498add29-fb5e-4a95-c42e-69935e985ebc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Unnamed: 0    category                                           question  \\\n",
              "0           0  Icebreaker                             tell me about yourself   \n",
              "1           1  Icebreaker  what do you like about working in customer ser...   \n",
              "2           2  Icebreaker  what things on your resume are you really an e...   \n",
              "3           3  Icebreaker      what do you know about our company or product   \n",
              "4           4  Icebreaker                       why do you want to work here   \n",
              "\n",
              "   categories  \n",
              "0  Icebreaker  \n",
              "1  Icebreaker  \n",
              "2  Icebreaker  \n",
              "3  Icebreaker  \n",
              "4  Icebreaker  "
            ],
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              "      background-color: #E2EBFA;\n",
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              "                                                    [key], {});\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
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              "\n",
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              "      --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-6fc16ba4-345f-424e-847d-2bc13f5dbe86 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test.category.value_counts().plot(kind=\"pie\", figsize = (8,8))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 670
        },
        "id": "l8djqUtbLRqg",
        "outputId": "f46183ca-4930-4474-d3a4-3958dc2e1967"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: ylabel='category'>"
            ]
          },
          "metadata": {},
          "execution_count": 62
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test.categories.value_counts().plot(kind=\"pie\", figsize = (8,8))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 670
        },
        "id": "IxfAJ23BLr7c",
        "outputId": "37d536cd-de7d-4092-c79a-20245bb48502"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: ylabel='categories'>"
            ]
          },
          "metadata": {},
          "execution_count": 63
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_path = '/content/gdrive/MyDrive/customer_service_model_output'"
      ],
      "metadata": {
        "id": "fWlFEvpHM3ta"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.save_model(model_path)\n",
        "tokenizer.save_pretrained(model_path)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i0iCwGmmNEU0",
        "outputId": "95e72276-6c0e-465a-88c0-da9301cf072d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('/content/gdrive/MyDrive/customer_service_model_output/tokenizer_config.json',\n",
              " '/content/gdrive/MyDrive/customer_service_model_output/special_tokens_map.json',\n",
              " '/content/gdrive/MyDrive/customer_service_model_output/vocab.txt',\n",
              " '/content/gdrive/MyDrive/customer_service_model_output/added_tokens.json',\n",
              " '/content/gdrive/MyDrive/customer_service_model_output/tokenizer.json')"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test = test.drop(columns = [\"category\", \"Unnamed: 0\"])"
      ],
      "metadata": {
        "id": "x9BUjK3QNQJS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test = test.rename(columns ={\"categories\":\"category\"})"
      ],
      "metadata": {
        "id": "MLWGOizvNa_5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "Ot8LjjBFNnss",
        "outputId": "19025810-53a5-449d-a37f-9a248b4c195e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                            question    category\n",
              "0                             tell me about yourself  Icebreaker\n",
              "1  what do you like about working in customer ser...  Icebreaker\n",
              "2  what things on your resume are you really an e...  Icebreaker\n",
              "3      what do you know about our company or product  Icebreaker\n",
              "4                       why do you want to work here  Icebreaker"
            ],
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              "                                                    [key], {});\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "\n",
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              "      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",
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              "    60% {\n",
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              "\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-921d2fec-cf24-40b0-ba98-cdd3b9c480c6 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test.to_csv(dataset_path+\"/fourth_final_categories.csv\")"
      ],
      "metadata": {
        "id": "Li4IRctON9Hg"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}