Youtube_GPT_3_Finetuning.ipynb

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        "DataSet: ****ATIS Airline Travel Information System****\n",
        "\n",
        "[https://www.kaggle.com/datasets/hassanamin/atis-airlinetravelinformationsystem](https://www.kaggle.com/datasets/hassanamin/atis-airlinetravelinformationsystem)"
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        "import pandas as pd\n",
        "import numpy as np\n",
        "np.random.seed(0)"
      ]
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        "data = pd.read_csv(\"/content/atis_intents.csv\",header=None)\n",
        "data.head()"
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              "                  0                                                  1\n",
              "0       atis_flight   i want to fly from boston at 838 am and arriv...\n",
              "1       atis_flight   what flights are available from pittsburgh to...\n",
              "2  atis_flight_time   what is the arrival time in san francisco for...\n",
              "3      atis_airfare            cheapest airfare from tacoma to orlando\n",
              "4      atis_airfare   round trip fares from pittsburgh to philadelp..."
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        "data.columns = ['intent','text']"
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              "array(['atis_flight', 'atis_flight_time', 'atis_airfare', 'atis_aircraft',\n",
              "       'atis_ground_service', 'atis_airport', 'atis_airline',\n",
              "       'atis_distance', 'atis_abbreviation', 'atis_ground_fare',\n",
              "       'atis_quantity', 'atis_city', 'atis_flight_no', 'atis_capacity',\n",
              "       'atis_flight#atis_airfare', 'atis_meal', 'atis_restriction',\n",
              "       'atis_airline#atis_flight_no',\n",
              "       'atis_ground_service#atis_ground_fare',\n",
              "       'atis_airfare#atis_flight_time', 'atis_cheapest',\n",
              "       'atis_aircraft#atis_flight#atis_flight_no'], dtype=object)"
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          "execution_count": 4
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        "data['intent'].nunique()"
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              "22"
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          "execution_count": 5
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        "data['intent'] = data['intent'].str.replace('#','_')\n",
        "data['intent'].unique()"
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              "array(['atis_flight', 'atis_flight_time', 'atis_airfare', 'atis_aircraft',\n",
              "       'atis_ground_service', 'atis_airport', 'atis_airline',\n",
              "       'atis_distance', 'atis_abbreviation', 'atis_ground_fare',\n",
              "       'atis_quantity', 'atis_city', 'atis_flight_no', 'atis_capacity',\n",
              "       'atis_flight_atis_airfare', 'atis_meal', 'atis_restriction',\n",
              "       'atis_airline_atis_flight_no',\n",
              "       'atis_ground_service_atis_ground_fare',\n",
              "       'atis_airfare_atis_flight_time', 'atis_cheapest',\n",
              "       'atis_aircraft_atis_flight_atis_flight_no'], dtype=object)"
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          "metadata": {},
          "execution_count": 6
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      "source": [
        "data['intent'] = data['intent'].str.replace('atis_','')\n",
        "data['intent'].unique()"
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      "outputs": [
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          "data": {
            "text/plain": [
              "array(['flight', 'flight_time', 'airfare', 'aircraft', 'ground_service',\n",
              "       'airport', 'airline', 'distance', 'abbreviation', 'ground_fare',\n",
              "       'quantity', 'city', 'flight_no', 'capacity', 'flight_airfare',\n",
              "       'meal', 'restriction', 'airline_flight_no',\n",
              "       'ground_service_ground_fare', 'airfare_flight_time', 'cheapest',\n",
              "       'aircraft_flight_flight_no'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 7
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        "data['intent'].value_counts()"
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            "text/plain": [
              "flight                        3666\n",
              "airfare                        423\n",
              "ground_service                 255\n",
              "airline                        157\n",
              "abbreviation                   147\n",
              "aircraft                        81\n",
              "flight_time                     54\n",
              "quantity                        51\n",
              "flight_airfare                  21\n",
              "airport                         20\n",
              "distance                        20\n",
              "city                            19\n",
              "ground_fare                     18\n",
              "capacity                        16\n",
              "flight_no                       12\n",
              "meal                             6\n",
              "restriction                      6\n",
              "airline_flight_no                2\n",
              "ground_service_ground_fare       1\n",
              "airfare_flight_time              1\n",
              "cheapest                         1\n",
              "aircraft_flight_flight_no        1\n",
              "Name: intent, dtype: int64"
            ]
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          "execution_count": 8
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        "labels = ['flight','ground_service','airfare','abbreviation','flight_time']"
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      "execution_count": null,
      "outputs": []
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        "data = data[data[\"intent\"].isin(labels)]\n",
        "data['intent'].value_counts()"
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              "flight            3666\n",
              "airfare            423\n",
              "ground_service     255\n",
              "abbreviation       147\n",
              "flight_time         54\n",
              "Name: intent, dtype: int64"
            ]
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          "metadata": {},
          "execution_count": 10
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      "source": [
        "sample_data = data.groupby('intent').apply(lambda x: x.sample(n=40)).reset_index(drop = True)\n",
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            "text/plain": [
              "abbreviation      40\n",
              "airfare           40\n",
              "flight            40\n",
              "flight_time       40\n",
              "ground_service    40\n",
              "Name: intent, dtype: int64"
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              "                               text        intent\n",
              "0   fare code y what does that mean  abbreviation\n",
              "1           does dl stand for delta  abbreviation\n",
              "2            what does code qw mean  abbreviation\n",
              "3        please explain fare code f  abbreviation\n",
              "4                       what is ewr  abbreviation"
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-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-6d543367-4320-4c51-9324-47bc5af9b651 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-6d543367-4320-4c51-9324-47bc5af9b651');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sample_data['text'] = sample_data['text'].str.strip()\n",
        "sample_data['intent'] = sample_data['intent'].str.strip()"
      ],
      "metadata": {
        "id": "pLTOXkLcjI7Q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sample_data['text'] = sample_data['text'] + \"\\n\\nIntent:\\n\\n\"\n",
        "# sample_data['text'] = \"Classify text into on the intent: flight, ground_service, airline, aircraft, flight_time. Text: \"+sample_data['text'] + \"\\n\\nIntent:\\n\\n\"\n",
        "sample_data['intent'] = \" \"+sample_data['intent'] + \" END\"\n",
        "sample_data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "OR1BJULNh29M",
        "outputId": "49998e49-c377-4247-c0ea-e2c9e4c8d921"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                             text             intent\n",
              "0  fare code y what does that mean\\n\\nIntent:\\n\\n   abbreviation END\n",
              "1          does dl stand for delta\\n\\nIntent:\\n\\n   abbreviation END\n",
              "2           what does code qw mean\\n\\nIntent:\\n\\n   abbreviation END\n",
              "3       please explain fare code f\\n\\nIntent:\\n\\n   abbreviation END\n",
              "4                      what is ewr\\n\\nIntent:\\n\\n   abbreviation END"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ed53e3ba-f468-4a26-98f2-903f35fa4b52\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
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              "      <th>intent</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>fare code y what does that mean\\n\\nIntent:\\n\\n</td>\n",
              "      <td>abbreviation END</td>\n",
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              "      <th>1</th>\n",
              "      <td>does dl stand for delta\\n\\nIntent:\\n\\n</td>\n",
              "      <td>abbreviation END</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>what does code qw mean\\n\\nIntent:\\n\\n</td>\n",
              "      <td>abbreviation END</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>please explain fare code f\\n\\nIntent:\\n\\n</td>\n",
              "      <td>abbreviation END</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>what is ewr\\n\\nIntent:\\n\\n</td>\n",
              "      <td>abbreviation END</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ed53e3ba-f468-4a26-98f2-903f35fa4b52')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [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-ed53e3ba-f468-4a26-98f2-903f35fa4b52 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-ed53e3ba-f468-4a26-98f2-903f35fa4b52');\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",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(sample_data['text'][0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PHfA-51Vi56x",
        "outputId": "520abf81-ded2-4839-9d3c-274b3fc1dbc3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "fare code y what does that mean\n",
            "\n",
            "Intent:\n",
            "\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(sample_data['intent'][0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cYo95VjijZK1",
        "outputId": "46a9e64f-b0cf-4ee9-962e-0268004fab55"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " abbreviation END\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sample_data.columns = ['prompt','completion']"
      ],
      "metadata": {
        "id": "4twzXMkKZwUX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sample_data.to_json(\"intent_sample.jsonl\", orient='records', lines=True)"
      ],
      "metadata": {
        "id": "0JW51Cb7YcZO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# sample_data.to_json(\"intent_.json\", orient='records')"
      ],
      "metadata": {
        "id": "v0Pg58AHYzXF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install --upgrade openai"
      ],
      "metadata": {
        "id": "1jc26bsGY7V8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!openai tools fine_tunes.prepare_data -f intent_sample.jsonl"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0bhyKnRuZYG9",
        "outputId": "7e287cf7-1149-4273-a4ea-1be0b71427d4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Analyzing...\n",
            "\n",
            "- Your file contains 200 prompt-completion pairs\n",
            "- Based on your data it seems like you're trying to fine-tune a model for classification\n",
            "- For classification, we recommend you try one of the faster and cheaper models, such as `ada`\n",
            "- For classification, you can estimate the expected model performance by keeping a held out dataset, which is not used for training\n",
            "- There are 10 duplicated prompt-completion sets. These are rows: [11, 15, 19, 24, 27, 34, 39, 129, 152, 179]\n",
            "- All prompts end with suffix `\\n\\nIntent:\\n\\n`. This suffix seems very long. Consider replacing with a shorter suffix, such as `\\n\\n###\\n\\n`\n",
            "\n",
            "Based on the analysis we will perform the following actions:\n",
            "- [Recommended] Remove 10 duplicate rows [Y/n]: Y\n",
            "- [Recommended] Would you like to split into training and validation set? [Y/n]: Y\n",
            "\n",
            "\n",
            "Your data will be written to a new JSONL file. Proceed [Y/n]: Y\n",
            "\n",
            "Wrote modified files to `intent_sample_prepared_train.jsonl` and `intent_sample_prepared_valid.jsonl`\n",
            "Feel free to take a look!\n",
            "\n",
            "Now use that file when fine-tuning:\n",
            "> openai api fine_tunes.create -t \"intent_sample_prepared_train.jsonl\" -v \"intent_sample_prepared_valid.jsonl\" --compute_classification_metrics --classification_n_classes 5\n",
            "\n",
            "After you’ve fine-tuned a model, remember that your prompt has to end with the indicator string `\\n\\nIntent:\\n\\n` for the model to start generating completions, rather than continuing with the prompt. Make sure to include `stop=[\" END\"]` so that the generated texts ends at the expected place.\n",
            "Once your model starts training, it'll approximately take 6.89 minutes to train a `curie` model, and less for `ada` and `babbage`. Queue will approximately take half an hour per job ahead of you.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "os.environ['OPENAI_API_KEY'] = \"key\""
      ],
      "metadata": {
        "id": "HXH7FcA74j6q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!openai api fine_tunes.create -t \"intent_sample_prepared_train.jsonl\" -v \"intent_sample_prepared_valid.jsonl\" -m 'davinci'"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cR27GSpl4lQ0",
        "outputId": "b48ec37b-a4d1-414e-edca-a7444de6451c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found potentially duplicated files with name 'intent_sample_prepared_train.jsonl', purpose 'fine-tune' and size 17810 bytes\n",
            "file-ujyQ2W8fzEBP5nKviYzjBE6d\n",
            "Enter file ID to reuse an already uploaded file, or an empty string to upload this file anyway: \n",
            "Upload progress: 100% 17.8k/17.8k [00:00<00:00, 29.4Mit/s]\n",
            "Uploaded file from intent_sample_prepared_train.jsonl: file-qFZO4gBx4D1AfG6nXLZRPYRO\n",
            "Found potentially duplicated files with name 'intent_sample_prepared_valid.jsonl', purpose 'fine-tune' and size 4643 bytes\n",
            "file-KMcFoGeWhZwDVKd2rij0fW2f\n",
            "Enter file ID to reuse an already uploaded file, or an empty string to upload this file anyway: \n",
            "Upload progress: 100% 4.64k/4.64k [00:00<00:00, 6.03Mit/s]\n",
            "Uploaded file from intent_sample_prepared_valid.jsonl: file-PoccQPUMQqex4ctcDqC95gF7\n",
            "Created fine-tune: ft-L68gCxl9xH1Cf6JZ7HYGevDV\n",
            "Streaming events until fine-tuning is complete...\n",
            "\n",
            "(Ctrl-C will interrupt the stream, but not cancel the fine-tune)\n",
            "[2022-09-11 18:52:30] Created fine-tune: ft-L68gCxl9xH1Cf6JZ7HYGevDV\n",
            "[2022-09-11 18:52:34] Fine-tune costs $0.40\n",
            "[2022-09-11 18:52:35] Fine-tune enqueued. Queue number: 0\n",
            "[2022-09-11 18:52:36] Fine-tune started\n",
            "\n",
            "Stream interrupted (client disconnected).\n",
            "To resume the stream, run:\n",
            "\n",
            "  openai api fine_tunes.follow -i ft-L68gCxl9xH1Cf6JZ7HYGevDV\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!openai api fine_tunes.follow -i ft-L68gCxl9xH1Cf6JZ7HYGevDV"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r3Qa8OXw759G",
        "outputId": "ec455502-201b-4f79-f1ca-bc1a079d8f4b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[2022-09-11 18:52:30] Created fine-tune: ft-L68gCxl9xH1Cf6JZ7HYGevDV\n",
            "[2022-09-11 18:52:34] Fine-tune costs $0.40\n",
            "[2022-09-11 18:52:35] Fine-tune enqueued. Queue number: 0\n",
            "[2022-09-11 18:52:36] Fine-tune started\n",
            "[2022-09-11 18:59:59] Completed epoch 1/4\n",
            "[2022-09-11 19:00:46] Completed epoch 2/4\n",
            "[2022-09-11 19:01:36] Completed epoch 3/4\n",
            "[2022-09-11 19:02:23] Completed epoch 4/4\n",
            "[2022-09-11 19:05:11] Uploaded model: davinci:ft-personal-2022-09-11-19-05-11\n",
            "[2022-09-11 19:05:13] Uploaded result file: file-VWYf7Mgq4dL8lXEDP0u85f17\n",
            "[2022-09-11 19:05:13] Fine-tune succeeded\n",
            "\n",
            "Job complete! Status: succeeded 🎉\n",
            "Try out your fine-tuned model:\n",
            "\n",
            "openai api completions.create -m davinci:ft-personal-2022-09-11-19-05-11 -p <YOUR_PROMPT>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# prompt = \"Do we have london flight on Monday\\n\\nIntent:\\n\\n\"\n",
        "# prompt = \"what is the ap57 restriction\\n\\nIntent:\\n\\n\"\n",
        "prompt = \"show me ground transportation in baltimore\\n\\nIntent:\\n\\n\""
      ],
      "metadata": {
        "id": "ev9FUSJEgGQA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "lC1qLV9Vwdvi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import openai\n",
        "openai.api_key ='key'\n",
        "response = openai.Completion.create(\n",
        "  model=\"davinci:ft-personal-2022-09-11-19-05-11\",\n",
        "  prompt=prompt,\n",
        "  max_tokens=5,\n",
        "  temperature=0,\n",
        "  top_p=1,\n",
        "  frequency_penalty=0,\n",
        "  presence_penalty=0,\n",
        "  stop=[\" END\"]\n",
        ")\n",
        "print(response['choices'][0]['text'])"
      ],
      "metadata": {
        "id": "nKlaspj-fFbm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d32648a5-5e10-4472-cebe-d5288107bbe7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " ground_service\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Jt22aIK29d_Z"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}