solar_predibase_0818_final.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UMk9l_1H9nyO"
      },
      "source": [
        "from https://docs.predibase.com/user-guide/getting-started/end-to-end"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "A CPU instance would be sufficient!\n",
        "\n",
        "![image.png](data:image/png;base64,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)\n"
      ],
      "metadata": {
        "id": "WOLMAP-e7Pbf"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-IN0Ftfk9i7z",
        "outputId": "78f0bccd-cce7-4a34-dc01-b3c25284a9ef"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/87.3 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━\u001b[0m \u001b[32m81.9/87.3 kB\u001b[0m \u001b[31m14.3 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m87.3/87.3 kB\u001b[0m \u001b[31m1.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m47.1/47.1 kB\u001b[0m \u001b[31m3.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h\u001b[33mWARNING: typer 0.12.3 does not provide the extra 'all'\u001b[0m\u001b[33m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m140.4/140.4 kB\u001b[0m \u001b[31m5.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m527.3/527.3 kB\u001b[0m \u001b[31m7.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m5.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m39.9/39.9 MB\u001b[0m \u001b[31m15.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m130.2/130.2 kB\u001b[0m \u001b[31m8.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m3.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m362.4/362.4 kB\u001b[0m \u001b[31m6.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m12.4/12.4 MB\u001b[0m \u001b[31m17.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m2.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m75.6/75.6 kB\u001b[0m \u001b[31m4.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m77.9/77.9 kB\u001b[0m \u001b[31m4.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m318.9/318.9 kB\u001b[0m \u001b[31m17.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m49.2/49.2 kB\u001b[0m \u001b[31m2.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m98.7/98.7 kB\u001b[0m \u001b[31m5.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.7/1.7 MB\u001b[0m \u001b[31m16.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.3/58.3 kB\u001b[0m \u001b[31m1.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for predibase (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for predibase-api (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for progress-table (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "cudf-cu12 24.4.1 requires pyarrow<15.0.0a0,>=14.0.1, but you have pyarrow 17.0.0 which is incompatible.\n",
            "ibis-framework 8.0.0 requires pyarrow<16,>=2, but you have pyarrow 17.0.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0m"
          ]
        }
      ],
      "source": [
        "!pip install -q predibase datasets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Cv33Gzur-fcv"
      },
      "outputs": [],
      "source": [
        "import warnings\n",
        "\n",
        "warnings.filterwarnings(\"ignore\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "id": "e4cajluH9skw",
        "outputId": "d8ca9d8a-2095-49d2-8703-3b3f0405ebfe"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1;34mConnected to Predibase as \u001b[0m\u001b[1;35mUser\u001b[0m\u001b[1;34m(\u001b[0m\u001b[1;33mid\u001b[0m\u001b[1;34m=\u001b[0m\u001b[93mbeea47ae\u001b[0m\u001b[93m-d33e-4a55-ae90-aee4d6545bf4\u001b[0m\u001b[1;34m, \u001b[0m\u001b[1;33musername\u001b[0m\u001b[1;34m=\u001b[0m\u001b[1;35mlhk4862\u001b[0m\u001b[1;34m@naver.com\u001b[0m\u001b[1;34m)\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Connected to Predibase as </span><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">User</span><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">(</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">id</span><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">=</span><span style=\"color: #ffff00; text-decoration-color: #ffff00\">beea47ae</span><span style=\"color: #ffff00; text-decoration-color: #ffff00\">-d33e-4a55-ae90-aee4d6545bf4</span><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">, </span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">username</span><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">=</span><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">lhk4862</span><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">@naver.com)</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "from predibase import Predibase, FinetuningConfig, DeploymentConfig\n",
        "\n",
        "# Get a KEY from https://app.predibase.com/\n",
        "api_token = 'pb_lPDQJzoPWgyIUW2pVu4HKg'\n",
        "\n",
        "from google.colab import userdata\n",
        "# api_token: str = userdata.get('PREDIBASE_API_KEY')\n",
        "pb = Predibase(api_token=api_token)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## API KEY\n",
        "![image.png](data:image/png;base64,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)"
      ],
      "metadata": {
        "id": "1m1KGhWq2LCp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Overall Process\n",
        "![image.png](data:image/png;base64,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)"
      ],
      "metadata": {
        "id": "rDFH7gMLUJ9w"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aq9mH-WAEX_K"
      },
      "source": [
        "## Process the dataset\n",
        "### Original\n",
        "headline (string), content (strong), category\n",
        "\n",
        "### Tobe\n",
        "```\n",
        "prompt:\n",
        "\n",
        "<s>[INST] The following passage is content from a news report. Please summarize this passage in one sentence or less.\n",
        "\n",
        "Passage: {content}.\n",
        "\n",
        "Summary:\n",
        " [/INST]\n",
        "---\n",
        " completion:\n",
        " {headline}\n",
        "\n",
        " ---\n",
        " split: train/test\n",
        " ```"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Generated response example before fine-tuning solar model\n",
        "import os\n",
        "from openai import OpenAI\n",
        "from pprint import pprint\n",
        "\n",
        "\n",
        "UPSTAGE_API_KEY = \"up_YsiiwBJ4YwMmf70RuQBLMdgLrI3DS\"\n",
        "\n",
        "client = OpenAI(\n",
        "    api_key=UPSTAGE_API_KEY,\n",
        "    base_url=\"https://api.upstage.ai/v1/solar\"\n",
        ")\n",
        "\n",
        "gc_result = client.chat.completions.create(\n",
        "    model=\"solar-1-mini-chat\",\n",
        "    messages=[\n",
        "        {\"role\": \"user\", \"content\": \"안녕!\"},\n",
        "        {\"role\": \"system\", \"content\": \"반가워!\"},\n",
        "        {\"role\": \"user\", \"content\": \"제주도 여행 가고 싶어\"},\n",
        "    ],\n",
        ")\n",
        "pprint(gc_result)\n",
        "print(\"Message only:\")\n",
        "pprint(gc_result.choices[0].message.content)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xvfvJ9XVWBY0",
        "outputId": "9d6e6c80-1a5d-4b52-f2de-811bf9313af4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ChatCompletion(id='ab7711b3-e09e-4061-a497-575136bfed72', choices=[Choice(finish_reason='stop', index=0, logprobs=None, message=ChatCompletionMessage(content='와, 제주도 여행은 정말 좋은 선택이에요! 제주도는 아름다운 자연 경관과 다양한 관광 명소가 많아서 많은 사람들이 찾는 여행지 중 하나입니다. 제주도에 가신다면, 제주도에서만 볼 수 있는 독특한 풍경과 문화를 경험하실 수 있을 거예요. 어떤 관광 명소를 방문하고 싶으신가요?', refusal=None, role='assistant', function_call=None, tool_calls=None))], created=1723361437, model='solar-1-mini-chat-240612', object='chat.completion', service_tier=None, system_fingerprint=None, usage=CompletionUsage(completion_tokens=62, prompt_tokens=34, total_tokens=96))\n",
            "Message only:\n",
            "('와, 제주도 여행은 정말 좋은 선택이에요! 제주도는 아름다운 자연 경관과 다양한 관광 명소가 많아서 많은 사람들이 찾는 여행지 중 '\n",
            " '하나입니다. 제주도에 가신다면, 제주도에서만 볼 수 있는 독특한 풍경과 문화를 경험하실 수 있을 거예요. 어떤 관광 명소를 방문하고 '\n",
            " '싶으신가요?')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pprint(gc_result.choices[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_oIrF9wAWkuf",
        "outputId": "2636b30c-b0fd-4f6a-ffc1-4b5146b8b1fb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Choice(finish_reason='stop', index=0, logprobs=None, message=ChatCompletionMessage(content='와, 제주도 여행은 정말 좋은 선택이에요! 제주도는 아름다운 자연 경관과 다양한 관광 명소가 많아서 많은 사람들이 찾는 여행지 중 하나입니다. 제주도에 가신다면, 제주도에서만 볼 수 있는 독특한 풍경과 문화를 경험하실 수 있을 거예요. 어떤 관광 명소를 방문하고 싶으신가요?', refusal=None, role='assistant', function_call=None, tool_calls=None))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iIHVQgyiJmcB",
        "outputId": "ce95e93a-668b-4362-a461-be193d610f97"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{\"generated_text\":\"와! 제주도는 레알루다가 아름다워! 어디로 갈 거야?\"}\n"
          ]
        }
      ],
      "source": [
        "# CURL test\n",
        "import requests\n",
        "import json\n",
        "\n",
        "url = \"https://serving.app.predibase.com/7ea6d0/deployments/v2/llms/solar-1-mini-chat-240612/generate\"\n",
        "\n",
        "\n",
        "input_prompt=\"\"\"\n",
        "<|im_start|>user\n",
        "다음 달에 제주도로 여행가! 너무 신난다!!<|im_end|><|im_start|>assistant\n",
        "\"\"\"\n",
        "\n",
        "payload = {\n",
        "    \"inputs\": input_prompt,\n",
        "    \"parameters\": {\n",
        "        \"adapter_id\": adapter_id,\n",
        "        \"adapter_source\": \"pbase\",\n",
        "        \"max_new_tokens\": 20,\n",
        "        \"temperature\": 0.1\n",
        "    }\n",
        "}\n",
        "\n",
        "headers = {\n",
        "    \"Content-Type\": \"application/json\",\n",
        "    \"Authorization\": f\"Bearer {api_token}\"\n",
        "}\n",
        "\n",
        "response = requests.post(url, data=json.dumps(payload), headers=headers)\n",
        "\n",
        "print(response.text)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install pyarrow==9.0.0"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 376
        },
        "id": "b02-CFKkZd-9",
        "outputId": "2065ccc9-6b22-40d0-8003-57a53cd51c8c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting pyarrow==9.0.0\n",
            "  Downloading pyarrow-9.0.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (3.0 kB)\n",
            "Requirement already satisfied: numpy>=1.16.6 in /usr/local/lib/python3.10/dist-packages (from pyarrow==9.0.0) (1.26.4)\n",
            "Downloading pyarrow-9.0.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (35.3 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m35.3/35.3 MB\u001b[0m \u001b[31m39.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: pyarrow\n",
            "  Attempting uninstall: pyarrow\n",
            "    Found existing installation: pyarrow 17.0.0\n",
            "    Uninstalling pyarrow-17.0.0:\n",
            "      Successfully uninstalled pyarrow-17.0.0\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "cudf-cu12 24.4.1 requires pyarrow<15.0.0a0,>=14.0.1, but you have pyarrow 9.0.0 which is incompatible.\n",
            "datasets 2.21.0 requires pyarrow>=15.0.0, but you have pyarrow 9.0.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed pyarrow-9.0.0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "pyarrow"
                ]
              },
              "id": "82b92dc75c694f748ba324533a97d1d8"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xHK6tUOVMCxO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 160,
          "referenced_widgets": [
            "aa39adf43e124d4ba800438a65f67e89",
            "f99cc3824cc74b3788be60102d6be654",
            "a60f955e99ed4eb5ab2bb2971c656fca",
            "958d131fa55a45778041455acc56913b",
            "d00f57a806564d9fb31270faf63a15ed",
            "119c2a1a0a18469198f8508b61db5d36",
            "19dbaf816bf34a2fa3cc415cae5855d3",
            "21585d040c7b403c94f3aebc90684da5",
            "fd7fbaaf9461425da59c2a0eab793740",
            "d787daf8f63242cda3a22bf3c0f9dcba",
            "a79cb3d347a44bb18842949e8216fc1a"
          ]
        },
        "outputId": "76be2d66-6c60-4113-e914-7ad8be75c9bf"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/3.31M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "aa39adf43e124d4ba800438a65f67e89"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from tokenizers import Tokenizer\n",
        "tokenizer = Tokenizer.from_pretrained(\"upstage/solar-1-mini-tokenizer\")\n",
        "\n",
        "def compute_cost(csv_file_name, price_per_million_tokens=0.5):\n",
        "  \"\"\" Compute the cost of the dataset \"\"\"\n",
        "\n",
        "  total_num_of_tokens = 0\n",
        "  with open(csv_file_name, 'r') as f:\n",
        "    reader = csv.DictReader(f)\n",
        "    # get all values\n",
        "    values = [row['completion']+ \" \" + row['prompt'] for row in reader]\n",
        "    for value in values:\n",
        "      # tokenize\n",
        "      enc = tokenizer.encode(value)\n",
        "      num_of_tokens = len(enc.tokens)\n",
        "      total_num_of_tokens += num_of_tokens\n",
        "\n",
        "\n",
        "  return total_num_of_tokens / 1000000 * price_per_million_tokens"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TqT_C6LhffCr",
        "outputId": "a317096b-b1f1-4047-b01e-c6098005dee0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!ls"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QBCMy_cKBGIh",
        "outputId": "fbe47606-119e-4ee1-eb54-fe4e8b715a8b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "drive  sample_data\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "os.chdir('/content/drive/MyDrive/global_ai_week_upstage/') # 파일 경로 이동 (에러 방지를 위함)"
      ],
      "metadata": {
        "id": "36FpJiDkH96b"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!ls"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b5dzD7L8JbnZ",
        "outputId": "46919002-2c1e-4830-8794-a993991688bf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1.zip\n",
            "adapter_1\n",
            "finetuning_Training_Validation_jeju_processed.csv\n",
            "finetuning_Training_Validation_kvqa_intent_classifier.csv\n",
            "make_dataset_2nd.ipynb\n",
            "source_dataset_make_1st.ipynb\n",
            "total_jeju_k_db_df.csv\n",
            "Trainig_jeju_dialogues.csv\n",
            "Training_jeju_history_response_pairs.csv\n",
            "Training_jeju_history_response_pairs_fixed.csv\n",
            "Training_qa_set_df.csv\n",
            "Training_qa_set_df_error_revised.csv\n",
            "Training_qa_set_df_v0.csv\n",
            "Validation_jeju_dialogues.csv\n",
            "Validation_jeju_history_response_pairs.csv\n",
            "Validation_jeju_history_response_pairs_fixed.csv\n",
            "Validation_qa_set_df.csv\n",
            "Validation_qa_set_df_v0.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 데이터셋 미리보기\n",
        "import pandas as pd\n",
        "\n",
        "train_df_path = \"Training_jeju_history_response_pairs.csv\"\n",
        "valid_df_path = \"Validation_jeju_history_response_pairs.csv\"\n",
        "\n",
        "train_df = pd.read_csv(train_df_path)\n",
        "valid_df = pd.read_csv(valid_df_path)\n",
        "\n",
        "train_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "uCVUf2glB2Jo",
        "outputId": "9176623a-5898-410d-a531-41a70ecebcc5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      info_id      keyword   speaker  \\\n",
              "0        2515  제주시티투어 야간코스  speakerB   \n",
              "1        2515  제주시티투어 야간코스  speakerB   \n",
              "2        2515  제주시티투어 야간코스  speakerB   \n",
              "3        2515  제주시티투어 야간코스  speakerB   \n",
              "4        2515  제주시티투어 야간코스  speakerB   \n",
              "...       ...          ...       ...   \n",
              "3635   282632  제주시티투어 야간코스  speakerB   \n",
              "3636   282632  제주시티투어 야간코스  speakerB   \n",
              "3637   282632  제주시티투어 야간코스  speakerB   \n",
              "3638   282632  제주시티투어 야간코스  speakerB   \n",
              "3639   282632  제주시티투어 야간코스  speakerB   \n",
              "\n",
              "                                                history  \\\n",
              "0                        <U> 제주도 여행 어땠음? 나도 가려구.ㅋㅋ <S>    \n",
              "1     <U> 제주도 여행 어땠음? 나도 가려구.ㅋㅋ <S> 혼여하는거야? 아님 같이 가는...   \n",
              "2     <U> 제주도 여행 어땠음? 나도 가려구.ㅋㅋ <S> 혼여하는거야? 아님 같이 가는...   \n",
              "3     <U> 제주도 여행 어땠음? 나도 가려구.ㅋㅋ <S> 혼여하는거야? 아님 같이 가는...   \n",
              "4     <U> 제주도 여행 어땠음? 나도 가려구.ㅋㅋ <S> 혼여하는거야? 아님 같이 가는...   \n",
              "...                                                 ...   \n",
              "3635  <U> 요즘 워라벨을 추구하고 싶어. 너무 바쁘게만 사는 것 같아서 말이야.. <S...   \n",
              "3636  <U> 요즘 워라벨을 추구하고 싶어. 너무 바쁘게만 사는 것 같아서 말이야.. <S...   \n",
              "3637  <U> 요즘 워라벨을 추구하고 싶어. 너무 바쁘게만 사는 것 같아서 말이야.. <S...   \n",
              "3638  <U> 요즘 워라벨을 추구하고 싶어. 너무 바쁘게만 사는 것 같아서 말이야.. <S...   \n",
              "3639  <U> 요즘 워라벨을 추구하고 싶어. 너무 바쁘게만 사는 것 같아서 말이야.. <S...   \n",
              "\n",
              "                                                   text  \n",
              "0                                    혼여하는거야? 아님 같이 가는거?  \n",
              "1                게하도 너무 재밌다궁. ㅋㅋ 나 이번에 야간시티투어도 타고 재밌었어.  \n",
              "2                 응응. 7월 14일부터 야밤버스라고 제주시티투어 야간코스 만들어짐.  \n",
              "3                             11월 11일까지 매주 금토 운행하는거. ㅋㅋ  \n",
              "4                        오후 6시부터 9시까지 1시간 간격으로 3회 운행이야.  \n",
              "...                                                 ...  \n",
              "3635                                  저녁 6시부터 9시까지 운행해.  \n",
              "3636                         하루에 세 번 운행하고 한 바퀴는 60분 걸려.  \n",
              "3637  '탐나오'라고 하는 사이트에서 예약해야 해. 미리 예약하는 게 편하니까 미리 해놓고 가.  \n",
              "3638  한 번 타는 건 5천 원이고 하루 종일 탈 수 있는 이용권은 8천 원이야. 성인 가...  \n",
              "3639                                 말만 하지 말고 진짜로 가봐야지!  \n",
              "\n",
              "[3640 rows x 5 columns]"
            ],
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              "      <td>2515</td>\n",
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              "      <td>speakerB</td>\n",
              "      <td>&lt;U&gt; 제주도 여행 어땠음? 나도 가려구.ㅋㅋ &lt;S&gt;</td>\n",
              "      <td>혼여하는거야? 아님 같이 가는거?</td>\n",
              "    </tr>\n",
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              "      <th>1</th>\n",
              "      <td>2515</td>\n",
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              "      <td>speakerB</td>\n",
              "      <td>&lt;U&gt; 제주도 여행 어땠음? 나도 가려구.ㅋㅋ &lt;S&gt; 혼여하는거야? 아님 같이 가는...</td>\n",
              "      <td>게하도 너무 재밌다궁. ㅋㅋ 나 이번에 야간시티투어도 타고 재밌었어.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2515</td>\n",
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              "      <td>speakerB</td>\n",
              "      <td>&lt;U&gt; 제주도 여행 어땠음? 나도 가려구.ㅋㅋ &lt;S&gt; 혼여하는거야? 아님 같이 가는...</td>\n",
              "      <td>응응. 7월 14일부터 야밤버스라고 제주시티투어 야간코스 만들어짐.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2515</td>\n",
              "      <td>제주시티투어 야간코스</td>\n",
              "      <td>speakerB</td>\n",
              "      <td>&lt;U&gt; 제주도 여행 어땠음? 나도 가려구.ㅋㅋ &lt;S&gt; 혼여하는거야? 아님 같이 가는...</td>\n",
              "      <td>11월 11일까지 매주 금토 운행하는거. ㅋㅋ</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2515</td>\n",
              "      <td>제주시티투어 야간코스</td>\n",
              "      <td>speakerB</td>\n",
              "      <td>&lt;U&gt; 제주도 여행 어땠음? 나도 가려구.ㅋㅋ &lt;S&gt; 혼여하는거야? 아님 같이 가는...</td>\n",
              "      <td>오후 6시부터 9시까지 1시간 간격으로 3회 운행이야.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <th>3635</th>\n",
              "      <td>282632</td>\n",
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              "      <td>speakerB</td>\n",
              "      <td>&lt;U&gt; 요즘 워라벨을 추구하고 싶어. 너무 바쁘게만 사는 것 같아서 말이야.. &lt;S...</td>\n",
              "      <td>저녁 6시부터 9시까지 운행해.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3636</th>\n",
              "      <td>282632</td>\n",
              "      <td>제주시티투어 야간코스</td>\n",
              "      <td>speakerB</td>\n",
              "      <td>&lt;U&gt; 요즘 워라벨을 추구하고 싶어. 너무 바쁘게만 사는 것 같아서 말이야.. &lt;S...</td>\n",
              "      <td>하루에 세 번 운행하고 한 바퀴는 60분 걸려.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3637</th>\n",
              "      <td>282632</td>\n",
              "      <td>제주시티투어 야간코스</td>\n",
              "      <td>speakerB</td>\n",
              "      <td>&lt;U&gt; 요즘 워라벨을 추구하고 싶어. 너무 바쁘게만 사는 것 같아서 말이야.. &lt;S...</td>\n",
              "      <td>'탐나오'라고 하는 사이트에서 예약해야 해. 미리 예약하는 게 편하니까 미리 해놓고 가.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3638</th>\n",
              "      <td>282632</td>\n",
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              "      <td>speakerB</td>\n",
              "      <td>&lt;U&gt; 요즘 워라벨을 추구하고 싶어. 너무 바쁘게만 사는 것 같아서 말이야.. &lt;S...</td>\n",
              "      <td>한 번 타는 건 5천 원이고 하루 종일 탈 수 있는 이용권은 8천 원이야. 성인 가...</td>\n",
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              "      <th>3639</th>\n",
              "      <td>282632</td>\n",
              "      <td>제주시티투어 야간코스</td>\n",
              "      <td>speakerB</td>\n",
              "      <td>&lt;U&gt; 요즘 워라벨을 추구하고 싶어. 너무 바쁘게만 사는 것 같아서 말이야.. &lt;S...</td>\n",
              "      <td>말만 하지 말고 진짜로 가봐야지!</td>\n",
              "    </tr>\n",
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              "</table>\n",
              "<p>3640 rows × 5 columns</p>\n",
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              "\n",
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              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-71816ffc-bd1c-421b-9953-9eade4202443 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-71816ffc-bd1c-421b-9953-9eade4202443');\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",
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              "\n",
              "\n",
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              "            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",
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              "      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-82d31f42-2319-48be-a71d-0a1f124a9c4d button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_e2a21fff-fe28-4524-ab38-ba94d4b3f9a9\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('train_df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_e2a21fff-fe28-4524-ab38-ba94d4b3f9a9 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('train_df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "train_df",
              "summary": "{\n  \"name\": \"train_df\",\n  \"rows\": 3640,\n  \"fields\": [\n    {\n      \"column\": \"info_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 92986,\n        \"min\": 2515,\n        \"max\": 282632,\n        \"num_unique_values\": 453,\n        \"samples\": [\n          228778,\n          13609,\n          244617\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"keyword\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 133,\n        \"samples\": [\n          \"\\uc81c\\uc8fc\\ud55c\\uc794, \\uc81c\\uc8fc \\uc804\\ud1b5\\uc8fc, \\uba54\\ubc00 \\ub9c9\\uac78\\ub9ac, \\ubc00\\uc8fc, \\uc81c\\uc8fc \\uba54\\ubc00 \\uccb4\\ud5d8 \\ud504\\ub85c\\uadf8\\ub7a8\",\n          \"\\uc778\\ucc9c, \\uc804\\uc8fc, \\uc11c\\uc6b8, \\uacbd\\uae30, \\uc81c\\uc8fc, \\ucd98\\ucc9c\",\n          \"\\uc81c\\uc8fc\\uc5d4\\uc824\\uce74\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"speaker\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"speakerB\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"history\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3308,\n        \"samples\": [\n          \"<U> \\uc57c\\ud638~! \\uc774\\uc81c \\uba70\\uce60 \\uc788\\uc73c\\uba74 \\uafc8\\uc5d0 \\uadf8\\ub9ac\\ub358 \\ud734\\uac00\\ub2f9. <S> \\uc624 \\ucd94\\uce74\\ucd94\\uce74... \\ub4dc\\ub38c \\uac00\\ub294\\uad6c\\ub098. \\ud734\\uac00 \\uc7a5\\uc18c\\ub294 \\uc815\\ud588\\uc5b4?? <U> \\u3147\\u3147 \\uc81c\\uc8fc\\ub3c4 \\uac08\\uaebc\\uc57c \\u314b\\u314b \\uc774\\ubc88\\uc5d4 \\ub3d9\\uc0dd\\uc774\\ub791 \\uac00\\uae30\\ub85c \\ud588\\uc5b4. <S> \\uc81c\\uc8fc\\ub3c4 \\uc88b\\uc9c0.. \\ub09c \\uc9c0\\ub09c \\ub2ec\\uc5d0 \\uc774\\ubbf8 \\uac14\\ub2e4 \\uc624\\uc168\\ub2f9. <U> \\ub82c\\ub8e8? \\uadf8\\ub7fc, \\ubb50\\uac00 \\uc7ac\\ubc0c\\uc5c8\\ub294\\uc9c0 \\uc815\\ubcf4 \\uc880 \\ud480\\uc5b4\\ubd10. <S> \\uc57c\\ubc24\\ubc84\\uc2a4\\ub77c\\uace0 \\ubc24\\uc5d0 \\uc2dc\\ud2f0\\ud22c\\uc5b4\\ub97c \\ud588\\ub294\\ub370 \\ubc24\\ubc14\\ub2e4\\ub3c4 \\ubcf4\\uace0 \\ub108\\ubb34 \\uc88b\\ub354\\ub77c. <U> \\uc81c\\uc8fc\\ub3c4 \\uc2dc\\ud2f0\\ud22c\\uc5b4\\ub97c \\uc57c\\uac04\\uc5d0 \\ud55c\\ub2e4\\ub2c8 \\uafc0\\uc7bc\\uc774\\uaca0\\ub2f9..\\uc5b8\\uc81c\\ubd80\\ud130 \\ud55c\\uac70\\uc57c? \\uc9c0\\uae08\\ub3c4 \\ud558\\uace0 \\uc788\\ub294 \\uac70 \\ub9de\\uc5b4? <S> \"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3280,\n        \"samples\": [\n          \"\\uc751\\uc751, \\ub098\\ub294 \\uadf8\\ub7fc \\ubd84\\uc870\\uce74 \\ucc3e\\uc544\\ubcfc\\uac8c.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_df['history'][3]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        },
        "id": "Lofihn12QRA9",
        "outputId": "c15e78c7-4184-400f-e2f9-033b30d707e3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'<U> 제주도 여행 어땠음? 나도 가려구.ㅋㅋ <S> 혼여하는거야? 아님 같이 가는거? <U> 응응. 혼자가고 게하간당. 텅장이야. ㅠㅠ <S> 게하도 너무 재밌다궁. ㅋㅋ 나 이번에 야간시티투어도 타고 재밌었어. <U> 제주도에서 밤에 타는 시티투어버스도 있어?? 언제부터 운영했는데? <S> 응응. 7월 14일부터 야밤버스라고 제주시티투어 야간코스 만들어짐. <U> 우앙. ㅋㅋ 언제까지 운행 하는거야? <S> '"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
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    },
    {
      "cell_type": "code",
      "source": [
        "train_df.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 240
        },
        "id": "i4lRXUQ6DzvN",
        "outputId": "88563a45-b826-4b07-e070-b5f625098d24"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "info_id    0\n",
              "keyword    0\n",
              "speaker    0\n",
              "history    0\n",
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            ],
            "text/html": [
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
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              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "valid_df.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 240
        },
        "id": "Bec7_WnSPNgn",
        "outputId": "c2ad8924-4b12-4e29-eb14-4b7225042fb2"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "info_id    0\n",
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              "speaker    0\n",
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "# train_df['prompt'] = train_df['history'].apply(lambda x: x.replace('<U> ', '<|im_start|>user\\n').replace(' <U> ', '<|im_end|><|im_start|>user\\n').replace(' <S> ', '<|im_end|><|im_start|>assistant\\n'))\n",
        "train_df['prompt'] = train_df['history'].apply(lambda x: x.replace(' <U> ', '<|im_end|><|im_start|>user\\n').replace('<U> ', '<|im_start|>user\\n').replace(' <S> ', '<|im_end|><|im_start|>assistant\\n'))\n",
        "train_df['completion'] = train_df['text'].apply(lambda x: x + \"<|im_end|>\")\n",
        "train_df['split'] = \"train\"\n",
        "\n",
        "train_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 597
        },
        "id": "CbzfzDtOMIhT",
        "outputId": "0e7f93b0-7aa5-4abe-fe01-4b67f375b4ee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      info_id      keyword   speaker  \\\n",
              "0        2515  제주시티투어 야간코스  speakerB   \n",
              "1        2515  제주시티투어 야간코스  speakerB   \n",
              "2        2515  제주시티투어 야간코스  speakerB   \n",
              "3        2515  제주시티투어 야간코스  speakerB   \n",
              "4        2515  제주시티투어 야간코스  speakerB   \n",
              "...       ...          ...       ...   \n",
              "3635   282632  제주시티투어 야간코스  speakerB   \n",
              "3636   282632  제주시티투어 야간코스  speakerB   \n",
              "3637   282632  제주시티투어 야간코스  speakerB   \n",
              "3638   282632  제주시티투어 야간코스  speakerB   \n",
              "3639   282632  제주시티투어 야간코스  speakerB   \n",
              "\n",
              "                                                history  \\\n",
              "0                        <U> 제주도 여행 어땠음? 나도 가려구.ㅋㅋ <S>    \n",
              "1     <U> 제주도 여행 어땠음? 나도 가려구.ㅋㅋ <S> 혼여하는거야? 아님 같이 가는...   \n",
              "2     <U> 제주도 여행 어땠음? 나도 가려구.ㅋㅋ <S> 혼여하는거야? 아님 같이 가는...   \n",
              "3     <U> 제주도 여행 어땠음? 나도 가려구.ㅋㅋ <S> 혼여하는거야? 아님 같이 가는...   \n",
              "4     <U> 제주도 여행 어땠음? 나도 가려구.ㅋㅋ <S> 혼여하는거야? 아님 같이 가는...   \n",
              "...                                                 ...   \n",
              "3635  <U> 요즘 워라벨을 추구하고 싶어. 너무 바쁘게만 사는 것 같아서 말이야.. <S...   \n",
              "3636  <U> 요즘 워라벨을 추구하고 싶어. 너무 바쁘게만 사는 것 같아서 말이야.. <S...   \n",
              "3637  <U> 요즘 워라벨을 추구하고 싶어. 너무 바쁘게만 사는 것 같아서 말이야.. <S...   \n",
              "3638  <U> 요즘 워라벨을 추구하고 싶어. 너무 바쁘게만 사는 것 같아서 말이야.. <S...   \n",
              "3639  <U> 요즘 워라벨을 추구하고 싶어. 너무 바쁘게만 사는 것 같아서 말이야.. <S...   \n",
              "\n",
              "                                                   text  \\\n",
              "0                                    혼여하는거야? 아님 같이 가는거?   \n",
              "1                게하도 너무 재밌다궁. ㅋㅋ 나 이번에 야간시티투어도 타고 재밌었어.   \n",
              "2                 응응. 7월 14일부터 야밤버스라고 제주시티투어 야간코스 만들어짐.   \n",
              "3                             11월 11일까지 매주 금토 운행하는거. ㅋㅋ   \n",
              "4                        오후 6시부터 9시까지 1시간 간격으로 3회 운행이야.   \n",
              "...                                                 ...   \n",
              "3635                                  저녁 6시부터 9시까지 운행해.   \n",
              "3636                         하루에 세 번 운행하고 한 바퀴는 60분 걸려.   \n",
              "3637  '탐나오'라고 하는 사이트에서 예약해야 해. 미리 예약하는 게 편하니까 미리 해놓고 가.   \n",
              "3638  한 번 타는 건 5천 원이고 하루 종일 탈 수 있는 이용권은 8천 원이야. 성인 가...   \n",
              "3639                                 말만 하지 말고 진짜로 가봐야지!   \n",
              "\n",
              "                                                 prompt  \\\n",
              "0     <|im_start|>user\\n제주도 여행 어땠음? 나도 가려구.ㅋㅋ<|im_en...   \n",
              "1     <|im_start|>user\\n제주도 여행 어땠음? 나도 가려구.ㅋㅋ<|im_en...   \n",
              "2     <|im_start|>user\\n제주도 여행 어땠음? 나도 가려구.ㅋㅋ<|im_en...   \n",
              "3     <|im_start|>user\\n제주도 여행 어땠음? 나도 가려구.ㅋㅋ<|im_en...   \n",
              "4     <|im_start|>user\\n제주도 여행 어땠음? 나도 가려구.ㅋㅋ<|im_en...   \n",
              "...                                                 ...   \n",
              "3635  <|im_start|>user\\n요즘 워라벨을 추구하고 싶어. 너무 바쁘게만 사는 ...   \n",
              "3636  <|im_start|>user\\n요즘 워라벨을 추구하고 싶어. 너무 바쁘게만 사는 ...   \n",
              "3637  <|im_start|>user\\n요즘 워라벨을 추구하고 싶어. 너무 바쁘게만 사는 ...   \n",
              "3638  <|im_start|>user\\n요즘 워라벨을 추구하고 싶어. 너무 바쁘게만 사는 ...   \n",
              "3639  <|im_start|>user\\n요즘 워라벨을 추구하고 싶어. 너무 바쁘게만 사는 ...   \n",
              "\n",
              "                                             completion  split  \n",
              "0                          혼여하는거야? 아님 같이 가는거?<|im_end|>  train  \n",
              "1      게하도 너무 재밌다궁. ㅋㅋ 나 이번에 야간시티투어도 타고 재밌었어.<|im_end|>  train  \n",
              "2       응응. 7월 14일부터 야밤버스라고 제주시티투어 야간코스 만들어짐.<|im_end|>  train  \n",
              "3                   11월 11일까지 매주 금토 운행하는거. ㅋㅋ<|im_end|>  train  \n",
              "4              오후 6시부터 9시까지 1시간 간격으로 3회 운행이야.<|im_end|>  train  \n",
              "...                                                 ...    ...  \n",
              "3635                        저녁 6시부터 9시까지 운행해.<|im_end|>  train  \n",
              "3636               하루에 세 번 운행하고 한 바퀴는 60분 걸려.<|im_end|>  train  \n",
              "3637  '탐나오'라고 하는 사이트에서 예약해야 해. 미리 예약하는 게 편하니까 미리 해놓고...  train  \n",
              "3638  한 번 타는 건 5천 원이고 하루 종일 탈 수 있는 이용권은 8천 원이야. 성인 가...  train  \n",
              "3639                       말만 하지 말고 진짜로 가봐야지!<|im_end|>  train  \n",
              "\n",
              "[3640 rows x 8 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-97d70543-6af6-4ac2-8e46-06dbd78e3329\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>info_id</th>\n",
              "      <th>keyword</th>\n",
              "      <th>speaker</th>\n",
              "      <th>history</th>\n",
              "      <th>text</th>\n",
              "      <th>prompt</th>\n",
              "      <th>completion</th>\n",
              "      <th>split</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2515</td>\n",
              "      <td>제주시티투어 야간코스</td>\n",
              "      <td>speakerB</td>\n",
              "      <td>&lt;U&gt; 제주도 여행 어땠음? 나도 가려구.ㅋㅋ &lt;S&gt;</td>\n",
              "      <td>혼여하는거야? 아님 같이 가는거?</td>\n",
              "      <td>&lt;|im_start|&gt;user\\n제주도 여행 어땠음? 나도 가려구.ㅋㅋ&lt;|im_en...</td>\n",
              "      <td>혼여하는거야? 아님 같이 가는거?&lt;|im_end|&gt;</td>\n",
              "      <td>train</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2515</td>\n",
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              "      <td>speakerB</td>\n",
              "      <td>&lt;U&gt; 제주도 여행 어땠음? 나도 가려구.ㅋㅋ &lt;S&gt; 혼여하는거야? 아님 같이 가는...</td>\n",
              "      <td>게하도 너무 재밌다궁. ㅋㅋ 나 이번에 야간시티투어도 타고 재밌었어.</td>\n",
              "      <td>&lt;|im_start|&gt;user\\n제주도 여행 어땠음? 나도 가려구.ㅋㅋ&lt;|im_en...</td>\n",
              "      <td>게하도 너무 재밌다궁. ㅋㅋ 나 이번에 야간시티투어도 타고 재밌었어.&lt;|im_end|&gt;</td>\n",
              "      <td>train</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2515</td>\n",
              "      <td>제주시티투어 야간코스</td>\n",
              "      <td>speakerB</td>\n",
              "      <td>&lt;U&gt; 제주도 여행 어땠음? 나도 가려구.ㅋㅋ &lt;S&gt; 혼여하는거야? 아님 같이 가는...</td>\n",
              "      <td>응응. 7월 14일부터 야밤버스라고 제주시티투어 야간코스 만들어짐.</td>\n",
              "      <td>&lt;|im_start|&gt;user\\n제주도 여행 어땠음? 나도 가려구.ㅋㅋ&lt;|im_en...</td>\n",
              "      <td>응응. 7월 14일부터 야밤버스라고 제주시티투어 야간코스 만들어짐.&lt;|im_end|&gt;</td>\n",
              "      <td>train</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2515</td>\n",
              "      <td>제주시티투어 야간코스</td>\n",
              "      <td>speakerB</td>\n",
              "      <td>&lt;U&gt; 제주도 여행 어땠음? 나도 가려구.ㅋㅋ &lt;S&gt; 혼여하는거야? 아님 같이 가는...</td>\n",
              "      <td>11월 11일까지 매주 금토 운행하는거. ㅋㅋ</td>\n",
              "      <td>&lt;|im_start|&gt;user\\n제주도 여행 어땠음? 나도 가려구.ㅋㅋ&lt;|im_en...</td>\n",
              "      <td>11월 11일까지 매주 금토 운행하는거. ㅋㅋ&lt;|im_end|&gt;</td>\n",
              "      <td>train</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2515</td>\n",
              "      <td>제주시티투어 야간코스</td>\n",
              "      <td>speakerB</td>\n",
              "      <td>&lt;U&gt; 제주도 여행 어땠음? 나도 가려구.ㅋㅋ &lt;S&gt; 혼여하는거야? 아님 같이 가는...</td>\n",
              "      <td>오후 6시부터 9시까지 1시간 간격으로 3회 운행이야.</td>\n",
              "      <td>&lt;|im_start|&gt;user\\n제주도 여행 어땠음? 나도 가려구.ㅋㅋ&lt;|im_en...</td>\n",
              "      <td>오후 6시부터 9시까지 1시간 간격으로 3회 운행이야.&lt;|im_end|&gt;</td>\n",
              "      <td>train</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3635</th>\n",
              "      <td>282632</td>\n",
              "      <td>제주시티투어 야간코스</td>\n",
              "      <td>speakerB</td>\n",
              "      <td>&lt;U&gt; 요즘 워라벨을 추구하고 싶어. 너무 바쁘게만 사는 것 같아서 말이야.. &lt;S...</td>\n",
              "      <td>저녁 6시부터 9시까지 운행해.</td>\n",
              "      <td>&lt;|im_start|&gt;user\\n요즘 워라벨을 추구하고 싶어. 너무 바쁘게만 사는 ...</td>\n",
              "      <td>저녁 6시부터 9시까지 운행해.&lt;|im_end|&gt;</td>\n",
              "      <td>train</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3636</th>\n",
              "      <td>282632</td>\n",
              "      <td>제주시티투어 야간코스</td>\n",
              "      <td>speakerB</td>\n",
              "      <td>&lt;U&gt; 요즘 워라벨을 추구하고 싶어. 너무 바쁘게만 사는 것 같아서 말이야.. &lt;S...</td>\n",
              "      <td>하루에 세 번 운행하고 한 바퀴는 60분 걸려.</td>\n",
              "      <td>&lt;|im_start|&gt;user\\n요즘 워라벨을 추구하고 싶어. 너무 바쁘게만 사는 ...</td>\n",
              "      <td>하루에 세 번 운행하고 한 바퀴는 60분 걸려.&lt;|im_end|&gt;</td>\n",
              "      <td>train</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3637</th>\n",
              "      <td>282632</td>\n",
              "      <td>제주시티투어 야간코스</td>\n",
              "      <td>speakerB</td>\n",
              "      <td>&lt;U&gt; 요즘 워라벨을 추구하고 싶어. 너무 바쁘게만 사는 것 같아서 말이야.. &lt;S...</td>\n",
              "      <td>'탐나오'라고 하는 사이트에서 예약해야 해. 미리 예약하는 게 편하니까 미리 해놓고 가.</td>\n",
              "      <td>&lt;|im_start|&gt;user\\n요즘 워라벨을 추구하고 싶어. 너무 바쁘게만 사는 ...</td>\n",
              "      <td>'탐나오'라고 하는 사이트에서 예약해야 해. 미리 예약하는 게 편하니까 미리 해놓고...</td>\n",
              "      <td>train</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3638</th>\n",
              "      <td>282632</td>\n",
              "      <td>제주시티투어 야간코스</td>\n",
              "      <td>speakerB</td>\n",
              "      <td>&lt;U&gt; 요즘 워라벨을 추구하고 싶어. 너무 바쁘게만 사는 것 같아서 말이야.. &lt;S...</td>\n",
              "      <td>한 번 타는 건 5천 원이고 하루 종일 탈 수 있는 이용권은 8천 원이야. 성인 가...</td>\n",
              "      <td>&lt;|im_start|&gt;user\\n요즘 워라벨을 추구하고 싶어. 너무 바쁘게만 사는 ...</td>\n",
              "      <td>한 번 타는 건 5천 원이고 하루 종일 탈 수 있는 이용권은 8천 원이야. 성인 가...</td>\n",
              "      <td>train</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3639</th>\n",
              "      <td>282632</td>\n",
              "      <td>제주시티투어 야간코스</td>\n",
              "      <td>speakerB</td>\n",
              "      <td>&lt;U&gt; 요즘 워라벨을 추구하고 싶어. 너무 바쁘게만 사는 것 같아서 말이야.. &lt;S...</td>\n",
              "      <td>말만 하지 말고 진짜로 가봐야지!</td>\n",
              "      <td>&lt;|im_start|&gt;user\\n요즘 워라벨을 추구하고 싶어. 너무 바쁘게만 사는 ...</td>\n",
              "      <td>말만 하지 말고 진짜로 가봐야지!&lt;|im_end|&gt;</td>\n",
              "      <td>train</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3640 rows × 8 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-97d70543-6af6-4ac2-8e46-06dbd78e3329')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-97d70543-6af6-4ac2-8e46-06dbd78e3329 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-97d70543-6af6-4ac2-8e46-06dbd78e3329');\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-d702297f-c2c8-4907-8e2c-015a1f56dd58\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d702297f-c2c8-4907-8e2c-015a1f56dd58')\"\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-d702297f-c2c8-4907-8e2c-015a1f56dd58 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_61b96e06-83a6-49aa-abf7-acb2bf9f8edd\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('train_df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_61b96e06-83a6-49aa-abf7-acb2bf9f8edd button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('train_df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "train_df",
              "summary": "{\n  \"name\": \"train_df\",\n  \"rows\": 3640,\n  \"fields\": [\n    {\n      \"column\": \"info_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 92986,\n        \"min\": 2515,\n        \"max\": 282632,\n        \"num_unique_values\": 453,\n        \"samples\": [\n          228778,\n          13609,\n          244617\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"keyword\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 133,\n        \"samples\": [\n          \"\\uc81c\\uc8fc\\ud55c\\uc794, \\uc81c\\uc8fc \\uc804\\ud1b5\\uc8fc, \\uba54\\ubc00 \\ub9c9\\uac78\\ub9ac, \\ubc00\\uc8fc, \\uc81c\\uc8fc \\uba54\\ubc00 \\uccb4\\ud5d8 \\ud504\\ub85c\\uadf8\\ub7a8\",\n          \"\\uc778\\ucc9c, \\uc804\\uc8fc, \\uc11c\\uc6b8, \\uacbd\\uae30, \\uc81c\\uc8fc, \\ucd98\\ucc9c\",\n          \"\\uc81c\\uc8fc\\uc5d4\\uc824\\uce74\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"speaker\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"speakerB\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"history\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3308,\n        \"samples\": [\n          \"<U> \\uc57c\\ud638~! \\uc774\\uc81c \\uba70\\uce60 \\uc788\\uc73c\\uba74 \\uafc8\\uc5d0 \\uadf8\\ub9ac\\ub358 \\ud734\\uac00\\ub2f9. <S> \\uc624 \\ucd94\\uce74\\ucd94\\uce74... \\ub4dc\\ub38c \\uac00\\ub294\\uad6c\\ub098. \\ud734\\uac00 \\uc7a5\\uc18c\\ub294 \\uc815\\ud588\\uc5b4?? <U> \\u3147\\u3147 \\uc81c\\uc8fc\\ub3c4 \\uac08\\uaebc\\uc57c \\u314b\\u314b \\uc774\\ubc88\\uc5d4 \\ub3d9\\uc0dd\\uc774\\ub791 \\uac00\\uae30\\ub85c \\ud588\\uc5b4. <S> \\uc81c\\uc8fc\\ub3c4 \\uc88b\\uc9c0.. \\ub09c \\uc9c0\\ub09c \\ub2ec\\uc5d0 \\uc774\\ubbf8 \\uac14\\ub2e4 \\uc624\\uc168\\ub2f9. <U> \\ub82c\\ub8e8? \\uadf8\\ub7fc, \\ubb50\\uac00 \\uc7ac\\ubc0c\\uc5c8\\ub294\\uc9c0 \\uc815\\ubcf4 \\uc880 \\ud480\\uc5b4\\ubd10. <S> \\uc57c\\ubc24\\ubc84\\uc2a4\\ub77c\\uace0 \\ubc24\\uc5d0 \\uc2dc\\ud2f0\\ud22c\\uc5b4\\ub97c \\ud588\\ub294\\ub370 \\ubc24\\ubc14\\ub2e4\\ub3c4 \\ubcf4\\uace0 \\ub108\\ubb34 \\uc88b\\ub354\\ub77c. <U> \\uc81c\\uc8fc\\ub3c4 \\uc2dc\\ud2f0\\ud22c\\uc5b4\\ub97c \\uc57c\\uac04\\uc5d0 \\ud55c\\ub2e4\\ub2c8 \\uafc0\\uc7bc\\uc774\\uaca0\\ub2f9..\\uc5b8\\uc81c\\ubd80\\ud130 \\ud55c\\uac70\\uc57c? \\uc9c0\\uae08\\ub3c4 \\ud558\\uace0 \\uc788\\ub294 \\uac70 \\ub9de\\uc5b4? <S> \"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3280,\n        \"samples\": [\n          \"\\uc751\\uc751, \\ub098\\ub294 \\uadf8\\ub7fc \\ubd84\\uc870\\uce74 \\ucc3e\\uc544\\ubcfc\\uac8c.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"prompt\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3308,\n        \"samples\": [\n          \"<|im_start|>user\\n\\uc57c\\ud638~! \\uc774\\uc81c \\uba70\\uce60 \\uc788\\uc73c\\uba74 \\uafc8\\uc5d0 \\uadf8\\ub9ac\\ub358 \\ud734\\uac00\\ub2f9.<|im_end|><|im_start|>assistant\\n\\uc624 \\ucd94\\uce74\\ucd94\\uce74... \\ub4dc\\ub38c \\uac00\\ub294\\uad6c\\ub098. \\ud734\\uac00 \\uc7a5\\uc18c\\ub294 \\uc815\\ud588\\uc5b4??<|im_end|><|im_start|>user\\n\\u3147\\u3147 \\uc81c\\uc8fc\\ub3c4 \\uac08\\uaebc\\uc57c \\u314b\\u314b \\uc774\\ubc88\\uc5d4 \\ub3d9\\uc0dd\\uc774\\ub791 \\uac00\\uae30\\ub85c \\ud588\\uc5b4.<|im_end|><|im_start|>assistant\\n\\uc81c\\uc8fc\\ub3c4 \\uc88b\\uc9c0.. \\ub09c \\uc9c0\\ub09c \\ub2ec\\uc5d0 \\uc774\\ubbf8 \\uac14\\ub2e4 \\uc624\\uc168\\ub2f9.<|im_end|><|im_start|>user\\n\\ub82c\\ub8e8? \\uadf8\\ub7fc, \\ubb50\\uac00 \\uc7ac\\ubc0c\\uc5c8\\ub294\\uc9c0 \\uc815\\ubcf4 \\uc880 \\ud480\\uc5b4\\ubd10.<|im_end|><|im_start|>assistant\\n\\uc57c\\ubc24\\ubc84\\uc2a4\\ub77c\\uace0 \\ubc24\\uc5d0 \\uc2dc\\ud2f0\\ud22c\\uc5b4\\ub97c \\ud588\\ub294\\ub370 \\ubc24\\ubc14\\ub2e4\\ub3c4 \\ubcf4\\uace0 \\ub108\\ubb34 \\uc88b\\ub354\\ub77c.<|im_end|><|im_start|>user\\n\\uc81c\\uc8fc\\ub3c4 \\uc2dc\\ud2f0\\ud22c\\uc5b4\\ub97c \\uc57c\\uac04\\uc5d0 \\ud55c\\ub2e4\\ub2c8 \\uafc0\\uc7bc\\uc774\\uaca0\\ub2f9..\\uc5b8\\uc81c\\ubd80\\ud130 \\ud55c\\uac70\\uc57c? \\uc9c0\\uae08\\ub3c4 \\ud558\\uace0 \\uc788\\ub294 \\uac70 \\ub9de\\uc5b4?<|im_end|><|im_start|>assistant\\n\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"completion\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3280,\n        \"samples\": [\n          \"\\uc751\\uc751, \\ub098\\ub294 \\uadf8\\ub7fc \\ubd84\\uc870\\uce74 \\ucc3e\\uc544\\ubcfc\\uac8c.<|im_end|>\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"split\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"train\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_df['prompt'][3]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "id": "5f2ih4rCQZ02",
        "outputId": "40a4eb7e-d73e-456d-ff8f-5814b86e16dd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'<|im_start|>user\\n제주도 여행 어땠음? 나도 가려구.ㅋㅋ<|im_end|><|im_start|>assistant\\n혼여하는거야? 아님 같이 가는거?<|im_end|><|im_start|>user\\n응응. 혼자가고 게하간당. 텅장이야. ㅠㅠ<|im_end|><|im_start|>assistant\\n게하도 너무 재밌다궁. ㅋㅋ 나 이번에 야간시티투어도 타고 재밌었어.<|im_end|><|im_start|>user\\n제주도에서 밤에 타는 시티투어버스도 있어?? 언제부터 운영했는데?<|im_end|><|im_start|>assistant\\n응응. 7월 14일부터 야밤버스라고 제주시티투어 야간코스 만들어짐.<|im_end|><|im_start|>user\\n우앙. ㅋㅋ 언제까지 운행 하는거야?<|im_end|><|im_start|>assistant\\n'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "valid_df['prompt'] = valid_df['history'].apply(lambda x: x.replace(' <U> ', '<|im_end|><|im_start|>user\\n').replace('<U> ', '<|im_start|>user\\n').replace(' <S> ', '<|im_end|><|im_start|>assistant\\n'))\n",
        "valid_df['completion'] = valid_df['text'].apply(lambda x: x + \"<|im_end|>\")\n",
        "valid_df['split'] = \"valid\"\n",
        "\n",
        "valid_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 597
        },
        "id": "Xg6f9GUnMtvr",
        "outputId": "cb547c76-7673-44c7-8778-db38ed9ee2c4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     info_id      keyword   speaker  \\\n",
              "0       6303  제주시티투어 야간코스  speakerB   \n",
              "1       6303  제주시티투어 야간코스  speakerB   \n",
              "2       6303  제주시티투어 야간코스  speakerB   \n",
              "3       6303  제주시티투어 야간코스  speakerB   \n",
              "4       6303  제주시티투어 야간코스  speakerB   \n",
              "..       ...          ...       ...   \n",
              "453   282658  제주시티투어 야간코스  speakerB   \n",
              "454   282658  제주시티투어 야간코스  speakerB   \n",
              "455   282658  제주시티투어 야간코스  speakerB   \n",
              "456   282658  제주시티투어 야간코스  speakerB   \n",
              "457   282658  제주시티투어 야간코스  speakerB   \n",
              "\n",
              "                                               history  \\\n",
              "0         <U> 나 워라벨도 생각하면서 살래.. 너무 정신 없이 사는것 같네.. <S>    \n",
              "1    <U> 나 워라벨도 생각하면서 살래.. 너무 정신 없이 사는것 같네.. <S> 생각...   \n",
              "2    <U> 나 워라벨도 생각하면서 살래.. 너무 정신 없이 사는것 같네.. <S> 생각...   \n",
              "3    <U> 나 워라벨도 생각하면서 살래.. 너무 정신 없이 사는것 같네.. <S> 생각...   \n",
              "4    <U> 나 워라벨도 생각하면서 살래.. 너무 정신 없이 사는것 같네.. <S> 생각...   \n",
              "..                                                 ...   \n",
              "453  <U> 이번 주말에 혼자 제주도 한번 다녀올까 생각 중이야 ㅎㅎ. <S> 진짜? 얼...   \n",
              "454  <U> 이번 주말에 혼자 제주도 한번 다녀올까 생각 중이야 ㅎㅎ. <S> 진짜? 얼...   \n",
              "455  <U> 이번 주말에 혼자 제주도 한번 다녀올까 생각 중이야 ㅎㅎ. <S> 진짜? 얼...   \n",
              "456  <U> 이번 주말에 혼자 제주도 한번 다녀올까 생각 중이야 ㅎㅎ. <S> 진짜? 얼...   \n",
              "457  <U> 이번 주말에 혼자 제주도 한번 다녀올까 생각 중이야 ㅎㅎ. <S> 진짜? 얼...   \n",
              "\n",
              "                                                  text  \\\n",
              "0                         생각 그렇게 했을때 실행을 해!! 말만 하지 말고~   \n",
              "1                               그럴땐 무조건 제주도지!! 뭘 고민해!!   \n",
              "2    워낙 유명한 관광지니까 어디라도 좋을거야. 제주시티투어 버스같은건 어때?? 난 밤에...   \n",
              "3                     해안 코스랑 도심 코스 2가지 있으니까 너 좋은거 골라봐~   \n",
              "4                                  저녁 6시부터 9시까지 운행한대요~   \n",
              "..                                                 ...   \n",
              "453                     버스 타고 제주 전역을 돌면서 야경을 감상하는 코스야!   \n",
              "454  우선 제주국제공항에서 출발해서 무지개 다리로 유명한 도두봉, 어영공원, 동문시장 야...   \n",
              "455   버스는 11월 11일까지 금토에 오후 6시부터 9시까지 1시간 간격으로 돌아간대 ㅋㅋ.   \n",
              "456  성인 기준 8천 원이고, '탐나오' 온라인 마켓에서 미리 사거나 현장에서 살 수 있...   \n",
              "457                    고마워서 감동이야. 여행 잘 다녀와, 건강하게 복귀하자~   \n",
              "\n",
              "                                                prompt  \\\n",
              "0    <|im_start|>user\\n나 워라벨도 생각하면서 살래.. 너무 정신 없이 사...   \n",
              "1    <|im_start|>user\\n나 워라벨도 생각하면서 살래.. 너무 정신 없이 사...   \n",
              "2    <|im_start|>user\\n나 워라벨도 생각하면서 살래.. 너무 정신 없이 사...   \n",
              "3    <|im_start|>user\\n나 워라벨도 생각하면서 살래.. 너무 정신 없이 사...   \n",
              "4    <|im_start|>user\\n나 워라벨도 생각하면서 살래.. 너무 정신 없이 사...   \n",
              "..                                                 ...   \n",
              "453  <|im_start|>user\\n이번 주말에 혼자 제주도 한번 다녀올까 생각 중이야...   \n",
              "454  <|im_start|>user\\n이번 주말에 혼자 제주도 한번 다녀올까 생각 중이야...   \n",
              "455  <|im_start|>user\\n이번 주말에 혼자 제주도 한번 다녀올까 생각 중이야...   \n",
              "456  <|im_start|>user\\n이번 주말에 혼자 제주도 한번 다녀올까 생각 중이야...   \n",
              "457  <|im_start|>user\\n이번 주말에 혼자 제주도 한번 다녀올까 생각 중이야...   \n",
              "\n",
              "                                            completion  split  \n",
              "0               생각 그렇게 했을때 실행을 해!! 말만 하지 말고~<|im_end|>  valid  \n",
              "1                     그럴땐 무조건 제주도지!! 뭘 고민해!!<|im_end|>  valid  \n",
              "2    워낙 유명한 관광지니까 어디라도 좋을거야. 제주시티투어 버스같은건 어때?? 난 밤에...  valid  \n",
              "3           해안 코스랑 도심 코스 2가지 있으니까 너 좋은거 골라봐~<|im_end|>  valid  \n",
              "4                        저녁 6시부터 9시까지 운행한대요~<|im_end|>  valid  \n",
              "..                                                 ...    ...  \n",
              "453           버스 타고 제주 전역을 돌면서 야경을 감상하는 코스야!<|im_end|>  valid  \n",
              "454  우선 제주국제공항에서 출발해서 무지개 다리로 유명한 도두봉, 어영공원, 동문시장 야...  valid  \n",
              "455  버스는 11월 11일까지 금토에 오후 6시부터 9시까지 1시간 간격으로 돌아간대 ㅋ...  valid  \n",
              "456  성인 기준 8천 원이고, '탐나오' 온라인 마켓에서 미리 사거나 현장에서 살 수 있...  valid  \n",
              "457          고마워서 감동이야. 여행 잘 다녀와, 건강하게 복귀하자~<|im_end|>  valid  \n",
              "\n",
              "[458 rows x 8 columns]"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>info_id</th>\n",
              "      <th>keyword</th>\n",
              "      <th>speaker</th>\n",
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              "      <th>text</th>\n",
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              "      <th>0</th>\n",
              "      <td>6303</td>\n",
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              "      <td>speakerB</td>\n",
              "      <td>&lt;U&gt; 나 워라벨도 생각하면서 살래.. 너무 정신 없이 사는것 같네.. &lt;S&gt;</td>\n",
              "      <td>생각 그렇게 했을때 실행을 해!! 말만 하지 말고~</td>\n",
              "      <td>&lt;|im_start|&gt;user\\n나 워라벨도 생각하면서 살래.. 너무 정신 없이 사...</td>\n",
              "      <td>생각 그렇게 했을때 실행을 해!! 말만 하지 말고~&lt;|im_end|&gt;</td>\n",
              "      <td>valid</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>6303</td>\n",
              "      <td>제주시티투어 야간코스</td>\n",
              "      <td>speakerB</td>\n",
              "      <td>&lt;U&gt; 나 워라벨도 생각하면서 살래.. 너무 정신 없이 사는것 같네.. &lt;S&gt; 생각...</td>\n",
              "      <td>그럴땐 무조건 제주도지!! 뭘 고민해!!</td>\n",
              "      <td>&lt;|im_start|&gt;user\\n나 워라벨도 생각하면서 살래.. 너무 정신 없이 사...</td>\n",
              "      <td>그럴땐 무조건 제주도지!! 뭘 고민해!!&lt;|im_end|&gt;</td>\n",
              "      <td>valid</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>6303</td>\n",
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              "      <td>speakerB</td>\n",
              "      <td>&lt;U&gt; 나 워라벨도 생각하면서 살래.. 너무 정신 없이 사는것 같네.. &lt;S&gt; 생각...</td>\n",
              "      <td>워낙 유명한 관광지니까 어디라도 좋을거야. 제주시티투어 버스같은건 어때?? 난 밤에...</td>\n",
              "      <td>&lt;|im_start|&gt;user\\n나 워라벨도 생각하면서 살래.. 너무 정신 없이 사...</td>\n",
              "      <td>워낙 유명한 관광지니까 어디라도 좋을거야. 제주시티투어 버스같은건 어때?? 난 밤에...</td>\n",
              "      <td>valid</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>6303</td>\n",
              "      <td>제주시티투어 야간코스</td>\n",
              "      <td>speakerB</td>\n",
              "      <td>&lt;U&gt; 나 워라벨도 생각하면서 살래.. 너무 정신 없이 사는것 같네.. &lt;S&gt; 생각...</td>\n",
              "      <td>해안 코스랑 도심 코스 2가지 있으니까 너 좋은거 골라봐~</td>\n",
              "      <td>&lt;|im_start|&gt;user\\n나 워라벨도 생각하면서 살래.. 너무 정신 없이 사...</td>\n",
              "      <td>해안 코스랑 도심 코스 2가지 있으니까 너 좋은거 골라봐~&lt;|im_end|&gt;</td>\n",
              "      <td>valid</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>6303</td>\n",
              "      <td>제주시티투어 야간코스</td>\n",
              "      <td>speakerB</td>\n",
              "      <td>&lt;U&gt; 나 워라벨도 생각하면서 살래.. 너무 정신 없이 사는것 같네.. &lt;S&gt; 생각...</td>\n",
              "      <td>저녁 6시부터 9시까지 운행한대요~</td>\n",
              "      <td>&lt;|im_start|&gt;user\\n나 워라벨도 생각하면서 살래.. 너무 정신 없이 사...</td>\n",
              "      <td>저녁 6시부터 9시까지 운행한대요~&lt;|im_end|&gt;</td>\n",
              "      <td>valid</td>\n",
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              "      <th>...</th>\n",
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              "      <td>&lt;U&gt; 이번 주말에 혼자 제주도 한번 다녀올까 생각 중이야 ㅎㅎ. &lt;S&gt; 진짜? 얼...</td>\n",
              "      <td>버스 타고 제주 전역을 돌면서 야경을 감상하는 코스야!</td>\n",
              "      <td>&lt;|im_start|&gt;user\\n이번 주말에 혼자 제주도 한번 다녀올까 생각 중이야...</td>\n",
              "      <td>버스 타고 제주 전역을 돌면서 야경을 감상하는 코스야!&lt;|im_end|&gt;</td>\n",
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              "      <th>454</th>\n",
              "      <td>282658</td>\n",
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              "      <td>&lt;U&gt; 이번 주말에 혼자 제주도 한번 다녀올까 생각 중이야 ㅎㅎ. &lt;S&gt; 진짜? 얼...</td>\n",
              "      <td>우선 제주국제공항에서 출발해서 무지개 다리로 유명한 도두봉, 어영공원, 동문시장 야...</td>\n",
              "      <td>&lt;|im_start|&gt;user\\n이번 주말에 혼자 제주도 한번 다녀올까 생각 중이야...</td>\n",
              "      <td>우선 제주국제공항에서 출발해서 무지개 다리로 유명한 도두봉, 어영공원, 동문시장 야...</td>\n",
              "      <td>valid</td>\n",
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              "    <tr>\n",
              "      <th>455</th>\n",
              "      <td>282658</td>\n",
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              "      <td>speakerB</td>\n",
              "      <td>&lt;U&gt; 이번 주말에 혼자 제주도 한번 다녀올까 생각 중이야 ㅎㅎ. &lt;S&gt; 진짜? 얼...</td>\n",
              "      <td>버스는 11월 11일까지 금토에 오후 6시부터 9시까지 1시간 간격으로 돌아간대 ㅋㅋ.</td>\n",
              "      <td>&lt;|im_start|&gt;user\\n이번 주말에 혼자 제주도 한번 다녀올까 생각 중이야...</td>\n",
              "      <td>버스는 11월 11일까지 금토에 오후 6시부터 9시까지 1시간 간격으로 돌아간대 ㅋ...</td>\n",
              "      <td>valid</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>456</th>\n",
              "      <td>282658</td>\n",
              "      <td>제주시티투어 야간코스</td>\n",
              "      <td>speakerB</td>\n",
              "      <td>&lt;U&gt; 이번 주말에 혼자 제주도 한번 다녀올까 생각 중이야 ㅎㅎ. &lt;S&gt; 진짜? 얼...</td>\n",
              "      <td>성인 기준 8천 원이고, '탐나오' 온라인 마켓에서 미리 사거나 현장에서 살 수 있...</td>\n",
              "      <td>&lt;|im_start|&gt;user\\n이번 주말에 혼자 제주도 한번 다녀올까 생각 중이야...</td>\n",
              "      <td>성인 기준 8천 원이고, '탐나오' 온라인 마켓에서 미리 사거나 현장에서 살 수 있...</td>\n",
              "      <td>valid</td>\n",
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              "    <tr>\n",
              "      <th>457</th>\n",
              "      <td>282658</td>\n",
              "      <td>제주시티투어 야간코스</td>\n",
              "      <td>speakerB</td>\n",
              "      <td>&lt;U&gt; 이번 주말에 혼자 제주도 한번 다녀올까 생각 중이야 ㅎㅎ. &lt;S&gt; 진짜? 얼...</td>\n",
              "      <td>고마워서 감동이야. 여행 잘 다녀와, 건강하게 복귀하자~</td>\n",
              "      <td>&lt;|im_start|&gt;user\\n이번 주말에 혼자 제주도 한번 다녀올까 생각 중이야...</td>\n",
              "      <td>고마워서 감동이야. 여행 잘 다녀와, 건강하게 복귀하자~&lt;|im_end|&gt;</td>\n",
              "      <td>valid</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>458 rows × 8 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-96d632ff-b389-4bdc-b0ea-5dd242e9deb4')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-96d632ff-b389-4bdc-b0ea-5dd242e9deb4 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-96d632ff-b389-4bdc-b0ea-5dd242e9deb4');\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-2173a0a6-2531-4777-bc59-4e61bfab55c9\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-2173a0a6-2531-4777-bc59-4e61bfab55c9')\"\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-2173a0a6-2531-4777-bc59-4e61bfab55c9 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_544bfd1a-dc97-42dd-a31d-fb1e41637627\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('valid_df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_544bfd1a-dc97-42dd-a31d-fb1e41637627 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('valid_df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "valid_df",
              "summary": "{\n  \"name\": \"valid_df\",\n  \"rows\": 458,\n  \"fields\": [\n    {\n      \"column\": \"info_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 94433,\n        \"min\": 6303,\n        \"max\": 282658,\n        \"num_unique_values\": 57,\n        \"samples\": [\n          6303,\n          14362,\n          182116\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"keyword\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 27,\n        \"samples\": [\n          \"\\uc81c\\uc8fc\",\n          \"\\uc81c\\uc8fc\\uad00\\uad11\\uacf5\\uc0ac\",\n          \"\\uc81c\\uc8fc \\uc6d4\\uc815\\ub9ac\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"speaker\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"speakerB\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"history\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 449,\n        \"samples\": [\n          \"<U> \\uc560, \\uc624\\ub298 \\uc81c\\uc8fc\\uc2dc \\uc560\\uc6d4\\uc74d\\uc5d0 \\uac00\\ubcfc\\uae4c? <S> \\uc640! \\uadf8\\ub7fc \\ub9db\\uc9d1 \\uc880 \\ucc3e\\uc544\\ubd10\\uc918! \\u314b\\u314b <U> \\uc54c\\uc558\\uc5b4, \\uadf8\\ub7fc \\ud574\\uc548\\ub3c4\\ub85c \\ub530\\ub77c \\uac00\\uba74 \\ub3c5\\ud2b9\\ud55c \\uc18c\\uae08 \\uc0dd\\uc0b0\\uc9c0\\uc778 '\\ub3cc\\uc5fc\\uc804'\\uc774 \\uc788\\uc5b4! <S> \\ub3cc\\uc5fc\\uc804? \\uc5b4\\ub5a4 \\uacf3\\uc774\\uc57c? \\ub9d0 \\uc880 \\ud574\\uc918~ <U> \\ub3cc\\uc5fc\\uc804\\uc740 \\ubc14\\ub2f7\\ubb3c\\uc744 \\uc99d\\ubc1c\\uc2dc\\ucf1c \\uc18c\\uae08\\uc744 \\uc0dd\\uc0b0\\ud558\\ub294 \\uacf3\\uc774\\uc57c! <S> \\uc624, \\uc2e0\\uae30\\ubc29\\uae30\\ubfe1\\ubfe1\\ubc29\\uae30! \\uadf8\\ub798\\uc11c \\uc5bc\\ub9c8\\ub098 \\uc624\\ub798\\ub41c \\uacf3\\uc774\\uc57c? <U> \\uc54c\\uac8c \\ubb50\\uc784? 390\\ub144 \\ub3d9\\uc548 \\uc81c\\uc8fc \\uc11c\\ubd80\\uc9c0\\uc5ed \\uc8fc\\ubbfc\\ub4e4\\uc5d0\\uac8c \\uc18c\\uae08\\uc744 \\uacf5\\uae09\\ud588\\ub300! <S> \\uc640~ \\uadf8\\ub807\\uac8c \\uc624\\ub798\\ub41c \\uac74 \\ucca8 \\ub4e4\\uc5b4\\ubd24\\uc5b4! \\uc81c\\uc8fc\\uc5d0 \\uc774\\ub7f0 \\uacf3\\uc774 \\uc788\\uc5c8\\uc5b4? <U> \\uc751, \\uc81c\\uc8fc\\uc2dc \\uc560\\uc6d4\\uc74d \\uad6c\\uc5c4\\ub9ac \\ud574\\uc548\\ub3c4\\ub85c \\ub530\\ub77c \\uac00\\uba74 \\ubcfc \\uc218 \\uc788\\uc5b4! <S> \\uadf8\\ub7fc \\uaf2d \\uac00\\ubd10\\uc57c\\uaca0\\ub2e4! \\uac10\\uc0ac\\ud574~ <U> \\ub2e4\\uc74c\\uc5d0 \\uc81c\\uc8fc\\ub3c4\\uc5d0 \\uac00\\uba74 \\uac19\\uc774 \\uac00\\uc790! <S> \"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 449,\n        \"samples\": [\n          \"\\uadf8\\ub7fc \\uaf2d \\uac00\\ubd10\\uc57c\\uaca0\\ub2e4! \\uac10\\uc0ac\\ud574~\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"prompt\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 449,\n        \"samples\": [\n          \"<|im_start|>user\\n\\uc560, \\uc624\\ub298 \\uc81c\\uc8fc\\uc2dc \\uc560\\uc6d4\\uc74d\\uc5d0 \\uac00\\ubcfc\\uae4c?<|im_end|><|im_start|>assistant\\n\\uc640! \\uadf8\\ub7fc \\ub9db\\uc9d1 \\uc880 \\ucc3e\\uc544\\ubd10\\uc918! \\u314b\\u314b<|im_end|><|im_start|>user\\n\\uc54c\\uc558\\uc5b4, \\uadf8\\ub7fc \\ud574\\uc548\\ub3c4\\ub85c \\ub530\\ub77c \\uac00\\uba74 \\ub3c5\\ud2b9\\ud55c \\uc18c\\uae08 \\uc0dd\\uc0b0\\uc9c0\\uc778 '\\ub3cc\\uc5fc\\uc804'\\uc774 \\uc788\\uc5b4!<|im_end|><|im_start|>assistant\\n\\ub3cc\\uc5fc\\uc804? \\uc5b4\\ub5a4 \\uacf3\\uc774\\uc57c? \\ub9d0 \\uc880 \\ud574\\uc918~<|im_end|><|im_start|>user\\n\\ub3cc\\uc5fc\\uc804\\uc740 \\ubc14\\ub2f7\\ubb3c\\uc744 \\uc99d\\ubc1c\\uc2dc\\ucf1c \\uc18c\\uae08\\uc744 \\uc0dd\\uc0b0\\ud558\\ub294 \\uacf3\\uc774\\uc57c!<|im_end|><|im_start|>assistant\\n\\uc624, \\uc2e0\\uae30\\ubc29\\uae30\\ubfe1\\ubfe1\\ubc29\\uae30! \\uadf8\\ub798\\uc11c \\uc5bc\\ub9c8\\ub098 \\uc624\\ub798\\ub41c \\uacf3\\uc774\\uc57c?<|im_end|><|im_start|>user\\n\\uc54c\\uac8c \\ubb50\\uc784? 390\\ub144 \\ub3d9\\uc548 \\uc81c\\uc8fc \\uc11c\\ubd80\\uc9c0\\uc5ed \\uc8fc\\ubbfc\\ub4e4\\uc5d0\\uac8c \\uc18c\\uae08\\uc744 \\uacf5\\uae09\\ud588\\ub300!<|im_end|><|im_start|>assistant\\n\\uc640~ \\uadf8\\ub807\\uac8c \\uc624\\ub798\\ub41c \\uac74 \\ucca8 \\ub4e4\\uc5b4\\ubd24\\uc5b4! \\uc81c\\uc8fc\\uc5d0 \\uc774\\ub7f0 \\uacf3\\uc774 \\uc788\\uc5c8\\uc5b4?<|im_end|><|im_start|>user\\n\\uc751, \\uc81c\\uc8fc\\uc2dc \\uc560\\uc6d4\\uc74d \\uad6c\\uc5c4\\ub9ac \\ud574\\uc548\\ub3c4\\ub85c \\ub530\\ub77c \\uac00\\uba74 \\ubcfc \\uc218 \\uc788\\uc5b4!<|im_end|><|im_start|>assistant\\n\\uadf8\\ub7fc \\uaf2d \\uac00\\ubd10\\uc57c\\uaca0\\ub2e4! \\uac10\\uc0ac\\ud574~<|im_end|><|im_start|>user\\n\\ub2e4\\uc74c\\uc5d0 \\uc81c\\uc8fc\\ub3c4\\uc5d0 \\uac00\\uba74 \\uac19\\uc774 \\uac00\\uc790!<|im_end|><|im_start|>assistant\\n\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"completion\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 449,\n        \"samples\": [\n          \"\\uadf8\\ub7fc \\uaf2d \\uac00\\ubd10\\uc57c\\uaca0\\ub2e4! \\uac10\\uc0ac\\ud574~<|im_end|>\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"split\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"valid\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "valid_df['prompt'][3]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "id": "uRkpKfnMQnyW",
        "outputId": "050373ff-ef62-4499-903e-fcc7e1babde1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'<|im_start|>user\\n나 워라벨도 생각하면서 살래.. 너무 정신 없이 사는것 같네..<|im_end|><|im_start|>assistant\\n생각 그렇게 했을때 실행을 해!! 말만 하지 말고~<|im_end|><|im_start|>user\\n여행 가고 싶은데 멀리는 못가겠고... 어디 가면 좋을까??<|im_end|><|im_start|>assistant\\n그럴땐 무조건 제주도지!! 뭘 고민해!!<|im_end|><|im_start|>user\\n그럴까?? 그럼 어디가서 뭐 할지 계획 잡아봐야겠당<|im_end|><|im_start|>assistant\\n워낙 유명한 관광지니까 어디라도 좋을거야. 제주시티투어 버스같은건 어때?? 난 밤에 운행하는 야밤버스 추천함~<|im_end|><|im_start|>user\\n야~!! 너무 좋다!! 코스가 많아??<|im_end|><|im_start|>assistant\\n'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 불필요한 열 제거\n",
        "train_df = train_df[['prompt', 'completion', 'split']]  # 'prompt', 'completion', 'split' 외 열 제거\n",
        "# 수정된 CSV 파일로 저장\n",
        "train_df.to_csv('Training_jeju_history_response_pairs_fixed.csv', index=False)\n"
      ],
      "metadata": {
        "id": "hKnmHVWkLKHH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "valid_df = valid_df[['prompt', 'completion', 'split']]  # 'prompt', 'completion', 'split' 외 열 제거\n",
        "# 수정된 CSV 파일로 저장\n",
        "valid_df.to_csv('Validation_jeju_history_response_pairs_fixed.csv', index=False)"
      ],
      "metadata": {
        "id": "nTSf-nZiO0Gw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "V_xkhUNrN0-y",
        "outputId": "adb5fc16-0458-4d4a-9360-9dcbc121fa17"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                 prompt  \\\n",
              "0     <|im_start|>user\\n제주도 여행 어땠음? 나도 가려구.ㅋㅋ<|im_en...   \n",
              "1     <|im_start|>user\\n제주도 여행 어땠음? 나도 가려구.ㅋㅋ<|im_en...   \n",
              "2     <|im_start|>user\\n제주도 여행 어땠음? 나도 가려구.ㅋㅋ<|im_en...   \n",
              "3     <|im_start|>user\\n제주도 여행 어땠음? 나도 가려구.ㅋㅋ<|im_en...   \n",
              "4     <|im_start|>user\\n제주도 여행 어땠음? 나도 가려구.ㅋㅋ<|im_en...   \n",
              "...                                                 ...   \n",
              "3635  <|im_start|>user\\n요즘 워라벨을 추구하고 싶어. 너무 바쁘게만 사는 ...   \n",
              "3636  <|im_start|>user\\n요즘 워라벨을 추구하고 싶어. 너무 바쁘게만 사는 ...   \n",
              "3637  <|im_start|>user\\n요즘 워라벨을 추구하고 싶어. 너무 바쁘게만 사는 ...   \n",
              "3638  <|im_start|>user\\n요즘 워라벨을 추구하고 싶어. 너무 바쁘게만 사는 ...   \n",
              "3639  <|im_start|>user\\n요즘 워라벨을 추구하고 싶어. 너무 바쁘게만 사는 ...   \n",
              "\n",
              "                                             completion  split  \n",
              "0                          혼여하는거야? 아님 같이 가는거?<|im_end|>  train  \n",
              "1      게하도 너무 재밌다궁. ㅋㅋ 나 이번에 야간시티투어도 타고 재밌었어.<|im_end|>  train  \n",
              "2       응응. 7월 14일부터 야밤버스라고 제주시티투어 야간코스 만들어짐.<|im_end|>  train  \n",
              "3                   11월 11일까지 매주 금토 운행하는거. ㅋㅋ<|im_end|>  train  \n",
              "4              오후 6시부터 9시까지 1시간 간격으로 3회 운행이야.<|im_end|>  train  \n",
              "...                                                 ...    ...  \n",
              "3635                        저녁 6시부터 9시까지 운행해.<|im_end|>  train  \n",
              "3636               하루에 세 번 운행하고 한 바퀴는 60분 걸려.<|im_end|>  train  \n",
              "3637  '탐나오'라고 하는 사이트에서 예약해야 해. 미리 예약하는 게 편하니까 미리 해놓고...  train  \n",
              "3638  한 번 타는 건 5천 원이고 하루 종일 탈 수 있는 이용권은 8천 원이야. 성인 가...  train  \n",
              "3639                       말만 하지 말고 진짜로 가봐야지!<|im_end|>  train  \n",
              "\n",
              "[3640 rows x 3 columns]"
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>prompt</th>\n",
              "      <th>completion</th>\n",
              "      <th>split</th>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>&lt;|im_start|&gt;user\\n제주도 여행 어땠음? 나도 가려구.ㅋㅋ&lt;|im_en...</td>\n",
              "      <td>혼여하는거야? 아님 같이 가는거?&lt;|im_end|&gt;</td>\n",
              "      <td>train</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>&lt;|im_start|&gt;user\\n제주도 여행 어땠음? 나도 가려구.ㅋㅋ&lt;|im_en...</td>\n",
              "      <td>게하도 너무 재밌다궁. ㅋㅋ 나 이번에 야간시티투어도 타고 재밌었어.&lt;|im_end|&gt;</td>\n",
              "      <td>train</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>&lt;|im_start|&gt;user\\n제주도 여행 어땠음? 나도 가려구.ㅋㅋ&lt;|im_en...</td>\n",
              "      <td>응응. 7월 14일부터 야밤버스라고 제주시티투어 야간코스 만들어짐.&lt;|im_end|&gt;</td>\n",
              "      <td>train</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>&lt;|im_start|&gt;user\\n제주도 여행 어땠음? 나도 가려구.ㅋㅋ&lt;|im_en...</td>\n",
              "      <td>11월 11일까지 매주 금토 운행하는거. ㅋㅋ&lt;|im_end|&gt;</td>\n",
              "      <td>train</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>&lt;|im_start|&gt;user\\n제주도 여행 어땠음? 나도 가려구.ㅋㅋ&lt;|im_en...</td>\n",
              "      <td>오후 6시부터 9시까지 1시간 간격으로 3회 운행이야.&lt;|im_end|&gt;</td>\n",
              "      <td>train</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3635</th>\n",
              "      <td>&lt;|im_start|&gt;user\\n요즘 워라벨을 추구하고 싶어. 너무 바쁘게만 사는 ...</td>\n",
              "      <td>저녁 6시부터 9시까지 운행해.&lt;|im_end|&gt;</td>\n",
              "      <td>train</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3636</th>\n",
              "      <td>&lt;|im_start|&gt;user\\n요즘 워라벨을 추구하고 싶어. 너무 바쁘게만 사는 ...</td>\n",
              "      <td>하루에 세 번 운행하고 한 바퀴는 60분 걸려.&lt;|im_end|&gt;</td>\n",
              "      <td>train</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3637</th>\n",
              "      <td>&lt;|im_start|&gt;user\\n요즘 워라벨을 추구하고 싶어. 너무 바쁘게만 사는 ...</td>\n",
              "      <td>'탐나오'라고 하는 사이트에서 예약해야 해. 미리 예약하는 게 편하니까 미리 해놓고...</td>\n",
              "      <td>train</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3638</th>\n",
              "      <td>&lt;|im_start|&gt;user\\n요즘 워라벨을 추구하고 싶어. 너무 바쁘게만 사는 ...</td>\n",
              "      <td>한 번 타는 건 5천 원이고 하루 종일 탈 수 있는 이용권은 8천 원이야. 성인 가...</td>\n",
              "      <td>train</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3639</th>\n",
              "      <td>&lt;|im_start|&gt;user\\n요즘 워라벨을 추구하고 싶어. 너무 바쁘게만 사는 ...</td>\n",
              "      <td>말만 하지 말고 진짜로 가봐야지!&lt;|im_end|&gt;</td>\n",
              "      <td>train</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3640 rows × 3 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-46430068-a2bc-45f5-a231-04e61b8cffd8')\"\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",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
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              "      height: 32px;\n",
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              "    }\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",
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              "\n",
              "    .colab-df-buttons div {\n",
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              "      fill: #D2E3FC;\n",
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              "\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",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-46430068-a2bc-45f5-a231-04e61b8cffd8 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-46430068-a2bc-45f5-a231-04e61b8cffd8');\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",
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              "      --bg-color: #E8F0FE;\n",
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              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
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              "      --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-df38ec84-233f-4de3-9997-cf7ade82ed14 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_51daf7a1-13f8-481d-896a-266a4b2a465d\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('train_df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
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              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('train_df');\n",
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              "summary": "{\n  \"name\": \"train_df\",\n  \"rows\": 3640,\n  \"fields\": [\n    {\n      \"column\": \"prompt\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3308,\n        \"samples\": [\n          \"<|im_start|>user\\n\\uc57c\\ud638~! \\uc774\\uc81c \\uba70\\uce60 \\uc788\\uc73c\\uba74 \\uafc8\\uc5d0 \\uadf8\\ub9ac\\ub358 \\ud734\\uac00\\ub2f9.<|im_end|><|im_start|>assistant\\n\\uc624 \\ucd94\\uce74\\ucd94\\uce74... \\ub4dc\\ub38c \\uac00\\ub294\\uad6c\\ub098. \\ud734\\uac00 \\uc7a5\\uc18c\\ub294 \\uc815\\ud588\\uc5b4??<|im_end|><|im_start|>user\\n\\u3147\\u3147 \\uc81c\\uc8fc\\ub3c4 \\uac08\\uaebc\\uc57c \\u314b\\u314b \\uc774\\ubc88\\uc5d4 \\ub3d9\\uc0dd\\uc774\\ub791 \\uac00\\uae30\\ub85c \\ud588\\uc5b4.<|im_end|><|im_start|>assistant\\n\\uc81c\\uc8fc\\ub3c4 \\uc88b\\uc9c0.. \\ub09c \\uc9c0\\ub09c \\ub2ec\\uc5d0 \\uc774\\ubbf8 \\uac14\\ub2e4 \\uc624\\uc168\\ub2f9.<|im_end|><|im_start|>user\\n\\ub82c\\ub8e8? \\uadf8\\ub7fc, \\ubb50\\uac00 \\uc7ac\\ubc0c\\uc5c8\\ub294\\uc9c0 \\uc815\\ubcf4 \\uc880 \\ud480\\uc5b4\\ubd10.<|im_end|><|im_start|>assistant\\n\\uc57c\\ubc24\\ubc84\\uc2a4\\ub77c\\uace0 \\ubc24\\uc5d0 \\uc2dc\\ud2f0\\ud22c\\uc5b4\\ub97c \\ud588\\ub294\\ub370 \\ubc24\\ubc14\\ub2e4\\ub3c4 \\ubcf4\\uace0 \\ub108\\ubb34 \\uc88b\\ub354\\ub77c.<|im_end|><|im_start|>user\\n\\uc81c\\uc8fc\\ub3c4 \\uc2dc\\ud2f0\\ud22c\\uc5b4\\ub97c \\uc57c\\uac04\\uc5d0 \\ud55c\\ub2e4\\ub2c8 \\uafc0\\uc7bc\\uc774\\uaca0\\ub2f9..\\uc5b8\\uc81c\\ubd80\\ud130 \\ud55c\\uac70\\uc57c? 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            }
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "total_df = pd.concat([train_df, valid_df], ignore_index=True)\n",
        "total_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "eepC4qy-QHZE",
        "outputId": "c53193d9-3884-4b21-d92b-b9869c793c98"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                 prompt  \\\n",
              "0     <|im_start|>user\\n제주도 여행 어땠음? 나도 가려구.ㅋㅋ<|im_en...   \n",
              "1     <|im_start|>user\\n제주도 여행 어땠음? 나도 가려구.ㅋㅋ<|im_en...   \n",
              "2     <|im_start|>user\\n제주도 여행 어땠음? 나도 가려구.ㅋㅋ<|im_en...   \n",
              "3     <|im_start|>user\\n제주도 여행 어땠음? 나도 가려구.ㅋㅋ<|im_en...   \n",
              "4     <|im_start|>user\\n제주도 여행 어땠음? 나도 가려구.ㅋㅋ<|im_en...   \n",
              "...                                                 ...   \n",
              "4093  <|im_start|>user\\n이번 주말에 혼자 제주도 한번 다녀올까 생각 중이야...   \n",
              "4094  <|im_start|>user\\n이번 주말에 혼자 제주도 한번 다녀올까 생각 중이야...   \n",
              "4095  <|im_start|>user\\n이번 주말에 혼자 제주도 한번 다녀올까 생각 중이야...   \n",
              "4096  <|im_start|>user\\n이번 주말에 혼자 제주도 한번 다녀올까 생각 중이야...   \n",
              "4097  <|im_start|>user\\n이번 주말에 혼자 제주도 한번 다녀올까 생각 중이야...   \n",
              "\n",
              "                                             completion  split  \n",
              "0                          혼여하는거야? 아님 같이 가는거?<|im_end|>  train  \n",
              "1      게하도 너무 재밌다궁. ㅋㅋ 나 이번에 야간시티투어도 타고 재밌었어.<|im_end|>  train  \n",
              "2       응응. 7월 14일부터 야밤버스라고 제주시티투어 야간코스 만들어짐.<|im_end|>  train  \n",
              "3                   11월 11일까지 매주 금토 운행하는거. ㅋㅋ<|im_end|>  train  \n",
              "4              오후 6시부터 9시까지 1시간 간격으로 3회 운행이야.<|im_end|>  train  \n",
              "...                                                 ...    ...  \n",
              "4093           버스 타고 제주 전역을 돌면서 야경을 감상하는 코스야!<|im_end|>  valid  \n",
              "4094  우선 제주국제공항에서 출발해서 무지개 다리로 유명한 도두봉, 어영공원, 동문시장 야...  valid  \n",
              "4095  버스는 11월 11일까지 금토에 오후 6시부터 9시까지 1시간 간격으로 돌아간대 ㅋ...  valid  \n",
              "4096  성인 기준 8천 원이고, '탐나오' 온라인 마켓에서 미리 사거나 현장에서 살 수 있...  valid  \n",
              "4097          고마워서 감동이야. 여행 잘 다녀와, 건강하게 복귀하자~<|im_end|>  valid  \n",
              "\n",
              "[4098 rows x 3 columns]"
            ],
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              "      <th></th>\n",
              "      <th>prompt</th>\n",
              "      <th>completion</th>\n",
              "      <th>split</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>&lt;|im_start|&gt;user\\n제주도 여행 어땠음? 나도 가려구.ㅋㅋ&lt;|im_en...</td>\n",
              "      <td>혼여하는거야? 아님 같이 가는거?&lt;|im_end|&gt;</td>\n",
              "      <td>train</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>&lt;|im_start|&gt;user\\n제주도 여행 어땠음? 나도 가려구.ㅋㅋ&lt;|im_en...</td>\n",
              "      <td>게하도 너무 재밌다궁. ㅋㅋ 나 이번에 야간시티투어도 타고 재밌었어.&lt;|im_end|&gt;</td>\n",
              "      <td>train</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>&lt;|im_start|&gt;user\\n제주도 여행 어땠음? 나도 가려구.ㅋㅋ&lt;|im_en...</td>\n",
              "      <td>응응. 7월 14일부터 야밤버스라고 제주시티투어 야간코스 만들어짐.&lt;|im_end|&gt;</td>\n",
              "      <td>train</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>&lt;|im_start|&gt;user\\n제주도 여행 어땠음? 나도 가려구.ㅋㅋ&lt;|im_en...</td>\n",
              "      <td>11월 11일까지 매주 금토 운행하는거. ㅋㅋ&lt;|im_end|&gt;</td>\n",
              "      <td>train</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>&lt;|im_start|&gt;user\\n제주도 여행 어땠음? 나도 가려구.ㅋㅋ&lt;|im_en...</td>\n",
              "      <td>오후 6시부터 9시까지 1시간 간격으로 3회 운행이야.&lt;|im_end|&gt;</td>\n",
              "      <td>train</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4093</th>\n",
              "      <td>&lt;|im_start|&gt;user\\n이번 주말에 혼자 제주도 한번 다녀올까 생각 중이야...</td>\n",
              "      <td>버스 타고 제주 전역을 돌면서 야경을 감상하는 코스야!&lt;|im_end|&gt;</td>\n",
              "      <td>valid</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4094</th>\n",
              "      <td>&lt;|im_start|&gt;user\\n이번 주말에 혼자 제주도 한번 다녀올까 생각 중이야...</td>\n",
              "      <td>우선 제주국제공항에서 출발해서 무지개 다리로 유명한 도두봉, 어영공원, 동문시장 야...</td>\n",
              "      <td>valid</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4095</th>\n",
              "      <td>&lt;|im_start|&gt;user\\n이번 주말에 혼자 제주도 한번 다녀올까 생각 중이야...</td>\n",
              "      <td>버스는 11월 11일까지 금토에 오후 6시부터 9시까지 1시간 간격으로 돌아간대 ㅋ...</td>\n",
              "      <td>valid</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4096</th>\n",
              "      <td>&lt;|im_start|&gt;user\\n이번 주말에 혼자 제주도 한번 다녀올까 생각 중이야...</td>\n",
              "      <td>성인 기준 8천 원이고, '탐나오' 온라인 마켓에서 미리 사거나 현장에서 살 수 있...</td>\n",
              "      <td>valid</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4097</th>\n",
              "      <td>&lt;|im_start|&gt;user\\n이번 주말에 혼자 제주도 한번 다녀올까 생각 중이야...</td>\n",
              "      <td>고마워서 감동이야. 여행 잘 다녀와, 건강하게 복귀하자~&lt;|im_end|&gt;</td>\n",
              "      <td>valid</td>\n",
              "    </tr>\n",
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              "<p>4098 rows × 3 columns</p>\n",
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              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-a9ebc480-8ee7-41e9-a23f-836d7d83c48e 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-a9ebc480-8ee7-41e9-a23f-836d7d83c48e');\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-3a316465-705e-422a-bf7b-f210faa10a0e\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-3a316465-705e-422a-bf7b-f210faa10a0e')\"\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-3a316465-705e-422a-bf7b-f210faa10a0e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_e98c3ab8-2111-4f9c-a8d8-cfef8b6695c2\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('total_df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_e98c3ab8-2111-4f9c-a8d8-cfef8b6695c2 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('total_df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "total_df",
              "summary": "{\n  \"name\": \"total_df\",\n  \"rows\": 4098,\n  \"fields\": [\n    {\n      \"column\": \"prompt\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3694,\n        \"samples\": [\n          \"<|im_start|>user\\n\\uc624\\ub298\\uc740 \\uc5ec\\ud589 \\uc598\\uae30 \\uc880 \\ud574\\ubcfc\\uae4c?<|im_end|><|im_start|>assistant\\n\\uc88b\\uc544! \\uc5b4\\ub514\\ub85c \\uac08\\uae4c?<|im_end|><|im_start|>user\\n\\uc81c\\uc8fc\\ub3c4\\uac00 \\uc88b\\uc744 \\uac83 \\uac19\\uc544<|im_end|><|im_start|>assistant\\n\\uadf8\\ub798! \\uc81c\\uc8fc\\ub3c4\\ub294 \\ud574\\uc678\\uc5ec\\ud589\\uc744 \\ub300\\uccb4\\ud560 \\uc218 \\uc788\\ub294 \\ub300\\ud45c\\uc801\\uc778 \\ud5c8\\ub2c8\\ubb38 \\uc5ec\\ud589\\uc9c0\\uc57c<|im_end|><|im_start|>user\\n\\uadfc\\ub370 \\uc0ac\\ub78c\\ub4e4\\uc774 \\ub9ce\\uc774 \\ubab0\\ub9ac\\uc9c0 \\uc54a\\ub294 \\uc2e0\\ud63c\\uc5ec\\ud589\\uc9c0\\ub294 \\uc5c6\\uc744\\uae4c?<|im_end|><|im_start|>assistant\\n\\ud638\\ud154\\uc2a4\\ucef4\\ubc14\\uc778\\uc5d0\\uc11c \\ucd94\\ucc9c\\ud55c \\uc774\\uc0c9 \\uc2e0\\ud63c\\uc5ec\\ud589\\uc9c0\\uac00 \\uc788\\uc5b4<|im_end|><|im_start|>user\\n\\uc5b4\\ub514\\uc5b4\\ub514?<|im_end|><|im_start|>assistant\\n\\uacbd\\uc0c1\\ub0a8\\ub3c4 \\uac70\\uc81c\\uac00 16\\uc704\\ub97c \\ucc28\\uc9c0\\ud588\\ub300<|im_end|><|im_start|>user\\n\\uac70\\uc81c\\ub3c4\\ub294 \\uc5b4\\ub5b0? \\uc5b4\\ub5a4 \\uacbd\\uad00\\uc774 \\uc788\\ub294 \\uacf3\\uc774\\uc57c?<|im_end|><|im_start|>assistant\\n\",\n          \"<|im_start|>user\\n\\uc5b4\\uc774, \\uc81c\\uc8fc \\ub4dc\\ub9bc\\ud0c0\\uc6cc \\ubcf5\\ud569\\ub9ac\\uc870\\ud2b8 \\ub9e4\\ucd9c\\uc774 \\ubc15\\ubc15\\uc774\\uc57c!<|im_end|><|im_start|>assistant\\n\\ub808\\uc54c? \\uc5bc\\ub9c8\\ub098 \\ub9ce\\uc774 \\ubc8c\\uc5c8\\uc5b4?<|im_end|><|im_start|>user\\n8\\uc6d4\\uc5d0\\ub294 142\\uc5b5 5,200\\ub9cc\\uc6d0\\uc744 \\ubc8c\\uc5c8\\ub300. \\ucd5c\\uace0 \\uc2e4\\uc801\\uc774\\ub798!<|im_end|><|im_start|>assistant\\n\\uc640\\uc6b0\\ub0b4 \\ub82c\\ub8e8 \\ub808\\uac8c\\ub178\\ub2e4. \\uadf8\\ub7fc \\uc911\\uad6d \\ub2e8\\uccb4\\uad00\\uad11\\uc774 \\uc601\\ud5a5\\uc744 \\ub9ce\\uc774 \\uc900 \\uac70\\uc57c?<|im_end|><|im_start|>user\\n\\uadf8\\ub807\\uc9c0! 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\\uc81c\\uc8fc\\ub294 \\ud56b\\ud50c\\uc774 \\ub9ce\\uc544!<|im_end|>\",\n          \"\\uc751\\uc751, \\uc990\\uac70\\uc6b4 \\uc2dc\\uac04 \\ubcf4\\ub0b4\\uace0 \\uc624\\uae38 \\ubc14\\ub798. \\u314e\\u314e<|im_end|>\",\n          \"\\uc640~ \\uadf8\\ub807\\uac8c \\uc624\\ub798\\ub41c \\uac74 \\ucca8 \\ub4e4\\uc5b4\\ubd24\\uc5b4! \\uc81c\\uc8fc\\uc5d0 \\uc774\\ub7f0 \\uacf3\\uc774 \\uc788\\uc5c8\\uc5b4?<|im_end|>\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"split\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"valid\",\n          \"train\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "total_df.to_csv(\"finetuning_Training_Validation_jeju_processed.csv\")"
      ],
      "metadata": {
        "id": "ZLdQLRERQYMm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "BA0dVptYQHa3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import csv\n",
        "\n",
        "csv_file_name = 'Training_jeju_history_response_pairs_fixed.csv'\n",
        "\n",
        "print(f\"One step FT Cost: {compute_cost(csv_file_name)} USD\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FH6HoK6SMEAp",
        "outputId": "94f9f0b2-dcea-4553-840d-099cb6dad39c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "One step FT Cost: 0.308322 USD\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "csv_file_name = 'Validation_jeju_history_response_pairs_fixed.csv'\n",
        "\n",
        "print(f\"One step FT Cost: {compute_cost(csv_file_name)} USD\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V3Z57a-sP48M",
        "outputId": "af990498-e3bc-4e1f-da7d-f19c800dcec4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "One step FT Cost: 0.0398795 USD\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Vyzv8GeAP7xK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create Predibase dataset for fine-tuning\n",
        "csv_file_name = 'finetuning_Training_Validation_jeju_processed.csv'\n",
        "dataset_name = \"finetuning_Training_Validation_jeju_processed_v1\"\n",
        "\n",
        "print(\"Uploading datset...\") # Training data and Validation data\n",
        "pb_dataset = pb.datasets.from_file(csv_file_name, name=dataset_name)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J8uvD102QjHP",
        "outputId": "0fb95421-99d1-48ef-ad75-7988f049412c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Uploading datset...\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CwjxIt8qHX_A",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9fc771c8-dce4-4e44-d748-d1d31d2b45d1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "uuid='a0092b1d-7fa0-4585-91ab-49840f75090c' name='jeju-multiturn-chat-model' description='Multiturn chat on subject related to traveling Jeju Experiments'\n"
          ]
        }
      ],
      "source": [
        "# Create an adapter repository\n",
        "# repo_name = \"news-summarizer-model\"\n",
        "repo_name = \"jeju-multiturn-chat-model\"\n",
        "repo = pb.repos.create(name=repo_name, description=\"Multiturn chat on subject related to traveling Jeju Experiments\", exists_ok=True)\n",
        "print(repo)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iKJFinRi97mc",
        "outputId": "fc70fb8f-96fd-444d-c1d3-d0b6dc77530e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Successfully requested finetuning of solar-1-mini-chat-240612 as `jeju-multiturn-chat-model/3`. (Job UUID: 498e191f-8d8f-4e74-b858-d8fbc00f8c99).\n",
            "\n",
            "Watching progress of finetuning job 498e191f-8d8f-4e74-b858-d8fbc00f8c99. This call will block until the job has finished. Canceling or terminating this call will NOT cancel or terminate the job itself.\n",
            "\n",
            "Job is starting. Total queue time: 0:00:46         \n",
            "Waiting to receive training metrics...\n",
            "\n",
            "┌────────────┬────────────┬─────────────────┐\n",
            "│ checkpoint \u001b[0m│ train_loss \u001b[0m│ validation_loss \u001b[0m│\n",
            "├────────────┼────────────┼─────────────────┤\n",
            "│     1      \u001b[0m│   1.5065   \u001b[0m│        --       \u001b[0m│\n",
            "└────────────┴────────────┴─────────────────┘\n"
          ]
        }
      ],
      "source": [
        "# Start a fine-tuning job, blocks until training is finished\n",
        "adapter = pb.adapters.create(\n",
        "    config=FinetuningConfig(\n",
        "        base_model=\"solar-1-mini-chat-240612\",\n",
        "        epochs=1, # default: 3\n",
        "        rank=64, # default: 16\n",
        "    ),\n",
        "    dataset=pb_dataset, # Also accepts the dataset name as a string\n",
        "    repo=repo,\n",
        "    description=\"initial model with defaults\"\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9rCUvWE9qHO_",
        "outputId": "097db913-8082-4536-9198-82c5f5dbed7f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Adapter(repo='jeju-multiturn-chat-model', tag=3, archived=False, base_model='solar-1-mini-chat-240612', description='initial model with defaults', artifact_path='498e191f-8d8f-4e74-b858-d8fbc00f8c99/a981080047f24c9988f59ff0d4f9b8e9/artifacts/model/model_weights', finetuning_error=None, finetuning_job_uuid='498e191f-8d8f-4e74-b858-d8fbc00f8c99')"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ],
      "source": [
        "adapter"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "9shNogDlk7Vn",
        "outputId": "0038e633-64ad-46bb-eef0-5a562b41041e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'jeju-multiturn-chat-model/3'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 30
        }
      ],
      "source": [
        "adapter_id = adapter.repo + \"/\" + str(adapter.tag)\n",
        "adapter_id"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YdpMxJQ-HoHA",
        "outputId": "87272fac-3be6-4846-de85-53330e4bb6cf"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Adapter(repo='jeju-multiturn-chat-model', tag=3, archived=False, base_model='solar-1-mini-chat-240612', description='initial model with defaults', artifact_path='498e191f-8d8f-4e74-b858-d8fbc00f8c99/a981080047f24c9988f59ff0d4f9b8e9/artifacts/model/model_weights', finetuning_error=None, finetuning_job_uuid='498e191f-8d8f-4e74-b858-d8fbc00f8c99')"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ],
      "source": [
        "# Get adapter, blocking call if training is still in progress\n",
        "adapter = pb.adapters.get(adapter_id)\n",
        "adapter"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_df['prompt'][0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "ysQh00GtVVen",
        "outputId": "c98fcd5b-379b-4bec-cac6-7b1aa8177220"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'<|im_start|>user\\n제주도 여행 어땠음? 나도 가려구.ㅋㅋ<|im_end|><|im_start|>assistant\\n'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_df['prompt'][3]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "id": "G_3GI6LKV18z",
        "outputId": "ca5222d9-0190-4f6c-d282-d8c17fb6e967"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'<|im_start|>user\\n제주도 여행 어땠음? 나도 가려구.ㅋㅋ<|im_end|><|im_start|>assistant\\n혼여하는거야? 아님 같이 가는거?<|im_end|><|im_start|>user\\n응응. 혼자가고 게하간당. 텅장이야. ㅠㅠ<|im_end|><|im_start|>assistant\\n게하도 너무 재밌다궁. ㅋㅋ 나 이번에 야간시티투어도 타고 재밌었어.<|im_end|><|im_start|>user\\n제주도에서 밤에 타는 시티투어버스도 있어?? 언제부터 운영했는데?<|im_end|><|im_start|>assistant\\n응응. 7월 14일부터 야밤버스라고 제주시티투어 야간코스 만들어짐.<|im_end|><|im_start|>user\\n우앙. ㅋㅋ 언제까지 운행 하는거야?<|im_end|><|im_start|>assistant\\n'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Fpt7UwsKMOL2",
        "outputId": "bac6dad6-176b-4f40-d065-8cb9ac7cdc04"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "나도!!! 제주도는 레알루다가 아름다워!\n"
          ]
        }
      ],
      "source": [
        "# inference - example 1\n",
        "input_prompt=\"\"\"\n",
        "<|im_start|>user\n",
        "제주도로 여행 가고 싶어!!!<|im_end|><|im_start|>assistant\n",
        "\"\"\"\n",
        "\n",
        "lorax_client = pb.deployments.client(\"solar-1-mini-chat-240612\")\n",
        "print(lorax_client.generate(input_prompt, adapter_id=adapter_id, max_new_tokens=1000).generated_text)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# inference - example 2\n",
        "input_prompt=\"\"\"\n",
        "<|im_start|>user\n",
        "이번 여름 휴가 제주도로 가는데 가서 뭐하지?<|im_end|><|im_start|>assistant\n",
        "누구랑 가는데 <|im_start|>user\n",
        "친구랑 둘이서 가<|im_end|><|im_start|>assistant\n",
        "\"\"\"\n",
        "\n",
        "lorax_client = pb.deployments.client(\"solar-1-mini-chat-240612\")\n",
        "print(lorax_client.generate(input_prompt, adapter_id=adapter_id, max_new_tokens=1000).generated_text)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hMI8IXoZVqA1",
        "outputId": "c933c893-bfc9-46db-f4a3-8db7ccd8f24d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "그럼 둘이서 같이 할 수 있는 거 해봐\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# inference - example 3\n",
        "input_prompt=\"\"\"\n",
        "<|im_start|>user\n",
        "제주도 가면 꼭 먹고 와야할까??<|im_end|><|im_start|>assistant\n",
        "\"\"\"\n",
        "\n",
        "lorax_client = pb.deployments.client(\"solar-1-mini-chat-240612\")\n",
        "print(lorax_client.generate(input_prompt, adapter_id=adapter_id, max_new_tokens=1000).generated_text)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ai31ZMa4WLNL",
        "outputId": "d58729b0-19bc-4b3c-8233-7f0118e70e1d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "응! 제주도에는 맛있는 음식이 많아!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# inference - example 4\n",
        "input_prompt=\"\"\"\n",
        "<|im_start|>user\n",
        "제주도 가면 꼭 먹고 와야할까??<|im_end|><|im_start|>assistant\n",
        "응! 제주도는 흑돼지가 유명해!<|im_start|>user\n",
        "추천해줄만한 맛집 있어?<|im_end|><|im_start|>assistant\n",
        "\"\"\"\n",
        "\n",
        "lorax_client = pb.deployments.client(\"solar-1-mini-chat-240612\")\n",
        "print(lorax_client.generate(input_prompt, adapter_id=adapter_id, max_new_tokens=1000).generated_text)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X4SB1Vt9Wf7x",
        "outputId": "51ac2544-9679-40fa-95bd-d5ea93c1e21d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "응! 돈사돈이 유명해!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# inference - example 5\n",
        "input_prompt=\"\"\"\n",
        "<|im_start|>user\n",
        "제주도 가면 꼭 먹고 와야할까??<|im_end|><|im_start|>assistant\n",
        "응! 제주도는 흑돼지가 유명해!<|im_start|>user\n",
        "추천해줄만한 맛집 있어?<|im_end|><|im_start|>assistant\n",
        "흑돼지 맛집으로 유명한 곳이 있어!<|im_start|>user\n",
        "어딘데? 알려주라!<|im_end|><|im_start|>assistant\n",
        "\"\"\"\n",
        "\n",
        "lorax_client = pb.deployments.client(\"solar-1-mini-chat-240612\")\n",
        "print(lorax_client.generate(input_prompt, adapter_id=adapter_id, max_new_tokens=1000).generated_text)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xYZymqLAWst1",
        "outputId": "2ebba472-a03f-4396-fc6f-13e1875e79d5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "제주시에 있는 '돈사돈'이라는 곳이야!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# inference - example 6\n",
        "input_prompt=\"\"\"\n",
        "<|im_start|>user\n",
        "제주도 시티투어 어때?<|im_end|><|im_start|>assistant\n",
        "\"\"\"\n",
        "\n",
        "lorax_client = pb.deployments.client(\"solar-1-mini-chat-240612\")\n",
        "print(lorax_client.generate(input_prompt, adapter_id=adapter_id, max_new_tokens=1000).generated_text)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KktuIfxHXZEa",
        "outputId": "f969dc22-738a-4b9d-a210-839729826f92"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "좋아! 어디로 가볼까?\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# inference - example 7\n",
        "input_prompt=\"\"\"\n",
        "<|im_start|>user\n",
        "제주도 시티투어 어때?<|im_end|><|im_start|>assistant\n",
        "제주도 시티투어는 렬루다가 멋져!<|im_start|>user\n",
        "시티투어 버스가 있다고 들었는데<|im_end|><|im_start|>assistant\n",
        "\"\"\"\n",
        "\n",
        "lorax_client = pb.deployments.client(\"solar-1-mini-chat-240612\")\n",
        "print(lorax_client.generate(input_prompt, adapter_id=adapter_id, max_new_tokens=1000).generated_text)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tML623QGXhnS",
        "outputId": "e57c3458-13c4-49c4-df6f-ac11c646160c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "응, 제주 시티투어 버스라고 해!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# inference - example 7\n",
        "input_prompt=\"\"\"\n",
        "<|im_start|>user\n",
        "제주도 시티투어 어때?<|im_end|><|im_start|>assistant\n",
        "제주도 시티투어는 렬루다가 멋져!<|im_start|>user\n",
        "시티투어 버스가 있다고 들었는데<|im_end|><|im_start|>assistant\n",
        "맞아! 제주도는 시티투어 버스가 있어<|im_start|>user\n",
        "운영시간이 어떻게 되는지 알아?<|im_end|><|im_start|>assistant\n",
        "\"\"\"\n",
        "\n",
        "lorax_client = pb.deployments.client(\"solar-1-mini-chat-240612\")\n",
        "print(lorax_client.generate(input_prompt, adapter_id=adapter_id, max_new_tokens=1000).generated_text)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y3XJxB9cXtIV",
        "outputId": "539f6b73-f8b1-42a2-bd2c-3d6f08cc767d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "7월 14일부터 11월 11일까지 매주 금토에 운영돼\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_df[train_df['prompt'].str.contains(\"흑돼지\")] # 학습 데이터셋 살펴보기"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "UZ05neSvXBwk",
        "outputId": "5ded4d3f-becc-4ec0-d067-845781c7e913"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                 prompt  \\\n",
              "432   <|im_start|>user\\n오늘 제주도 여행 계획 있어?<|im_end|><|...   \n",
              "433   <|im_start|>user\\n오늘 제주도 여행 계획 있어?<|im_end|><|...   \n",
              "434   <|im_start|>user\\n오늘 제주도 여행 계획 있어?<|im_end|><|...   \n",
              "435   <|im_start|>user\\n오늘 제주도 여행 계획 있어?<|im_end|><|...   \n",
              "436   <|im_start|>user\\n오늘 제주도 여행 계획 있어?<|im_end|><|...   \n",
              "...                                                 ...   \n",
              "3115  <|im_start|>user\\n제주향토음식점 짱맛임?<|im_end|><|im_s...   \n",
              "3116  <|im_start|>user\\n제주향토음식점 짱맛임?<|im_end|><|im_s...   \n",
              "3117  <|im_start|>user\\n제주향토음식점 짱맛임?<|im_end|><|im_s...   \n",
              "3348  <|im_start|>user\\n다음주 주말에 제주도 간다면서?<|im_end|><...   \n",
              "3349  <|im_start|>user\\n다음주 주말에 제주도 간다면서?<|im_end|><...   \n",
              "\n",
              "                                             completion  split  \n",
              "432                   호텔이나 펜션 중에 고르면 좋을 것 같아!<|im_end|>  train  \n",
              "433              그래야 제주도를 편하게 돌아다닐 수 있을 것 같아!<|im_end|>  train  \n",
              "434                          여행자 보험은 꼭 들어야 해!<|im_end|>  train  \n",
              "435                  28만8천원부터 시작되는 합리적인 가격이야!<|im_end|>  train  \n",
              "436                            응! 제주도에서 힐링하자!<|im_end|>  train  \n",
              "...                                                 ...    ...  \n",
              "3115                           제주도에 가면 꼭 먹어봐!<|im_end|>  train  \n",
              "3116                            제주도는 레알 아름다워!<|im_end|>  train  \n",
              "3117                       그럼 맛있는 음식 많이 먹어야해!<|im_end|>  train  \n",
              "3348  상상만해도 행복하다. ㅎㅎ 야시장에서 간단하게 맥주도 마실수 있고말야. ㅋㅋ<|im...  train  \n",
              "3349   오키. 예약 가능한지 찾아보고 미리 예약부터 해야겠다. 정보 고마워!<|im_end|>  train  \n",
              "\n",
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              "      <td>&lt;|im_start|&gt;user\\n오늘 제주도 여행 계획 있어?&lt;|im_end|&gt;&lt;|...</td>\n",
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              "      <td>&lt;|im_start|&gt;user\\n제주향토음식점 짱맛임?&lt;|im_end|&gt;&lt;|im_s...</td>\n",
              "      <td>제주도에 가면 꼭 먹어봐!&lt;|im_end|&gt;</td>\n",
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              "type": "dataframe",
              "summary": "{\n  \"name\": \"train_df[train_df['prompt']\",\n  \"rows\": 127,\n  \"fields\": [\n    {\n      \"column\": \"prompt\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 118,\n        \"samples\": [\n          \"<|im_start|>user\\n\\uc640 \\uc81c\\uc8fc\\ub3c4 \\uc5ec\\ud589 \\ub108\\ubb34 \\uc88b\\uc558\\uc5b4 \\u3160\\u3160<|im_end|><|im_start|>assistant\\n\\u3139\\u3147? \\uc5b4\\ub514 \\uac14\\ub2e4 \\uc634?<|im_end|><|im_start|>user\\n\\ud55c\\ub77c\\uc0b0 \\ub4f1\\ubc18\\ud558\\uace0 \\uc131\\uc0b0\\uc77c\\ucd9c\\ubd09\\ub3c4 \\uac00\\ubd24\\uc5b4<|im_end|><|im_start|>assistant\\n\\uc640 \\uba38\\ubc15\\uc774\\ub2e4! \\uadf8\\ub9ac\\uace0 \\ubb50 \\uba39\\uc5c8\\uc5b4?<|im_end|><|im_start|>user\\n\\uc81c\\uc8fc \\ud751\\ub3fc\\uc9c0 \\ub9db\\uc9d1\\uc5d0\\uc11c \\ud751\\ub3fc\\uc9c0 \\uba39\\uc5c8\\uc5b4 \\u314e\\u314e<|im_end|><|im_start|>assistant\\n\\ud751\\ub3fc\\uc9c0 \\ub9db\\uc788\\uc9c0? \\ub098\\ub3c4 \\uac00\\ubd10\\uc57c\\uaca0\\ub2e4<|im_end|><|im_start|>user\\n\\ub108\\ub3c4 \\uc81c\\uc8fc\\ub3c4 \\uac00\\uba74 \\uc88b\\uc744 \\uac70 \\uac19\\uc544<|im_end|><|im_start|>assistant\\n\\uadf8\\ub798! \\ub2e4\\uc74c\\uc5d0 \\uaf2d \\uac00\\ubcfc\\uac8c<|im_end|><|im_start|>user\\n\\uadf8\\ub7fc \\ub108\\ub3c4 \\uc5b4\\ub514 \\uac00\\uace0 \\uc2f6\\uc5b4?<|im_end|><|im_start|>assistant\\n\\ub098\\ub294 \\uc77c\\ubcf8\\uc5d0 \\uac00\\ubcf4\\uace0 \\uc2f6\\uc5b4<|im_end|><|im_start|>user\\n\\uc77c\\ubcf8\\ub3c4 \\uc88b\\uc9c0! \\ub3c4\\ucfc4\\uc5d0 \\uac00\\uba74 \\ubb50 \\uba39\\uace0 \\uc2f6\\uc5b4?<|im_end|><|im_start|>assistant\\n\\ub77c\\uba58 \\uba39\\uace0 \\uc2f6\\uc5b4! \\uc720\\uba85\\ud55c \\ub77c\\uba58\\uc9d1 \\uc54c\\uace0 \\uc788\\uc5b4?<|im_end|><|im_start|>user\\n\\uc544! \\uc774\\uce58\\ub780\\uc774\\ub77c\\ub294 \\ub77c\\uba58\\uc9d1\\uc774 \\ud56b\\ud574<|im_end|><|im_start|>assistant\\n\",\n          \"<|im_start|>user\\n\\ud5ec\\ub871! \\ub108 \\uc81c\\uc8fc\\ub3c4 \\uac14\\ub2e4 \\uc634?<|im_end|><|im_start|>assistant\\n\\uc751! \\uc81c\\uc8fc\\ub3c4 \\ub808\\uc54c\\ub8e8\\ub2e4\\uac00 \\uc608\\ubed0!<|im_end|><|im_start|>user\\n\\ub82c\\ub8e8?! \\uc5b4\\ub514 \\uac00\\ubd04?<|im_end|><|im_start|>assistant\\n\\ud55c\\ub77c\\uc0b0 \\ub4f1\\ubc18\\ub3c4 \\ud588\\uace0, \\uc131\\uc0b0\\uc77c\\ucd9c\\ubd09\\ub3c4 \\uac00\\ubd24\\uc5b4!<|im_end|><|im_start|>user\\n\\uc640 \\uba38\\ubc15\\uc774\\ub2e4! \\uadf8\\ub9ac\\uace0 \\uc74c\\uc2dd\\uc740?<|im_end|><|im_start|>assistant\\n\\ub3d4\\ubca0\\uace0\\uae30\\ub791 \\ud751\\ub3fc\\uc9c0 \\ub9db\\uc788\\uc5c8\\uc5b4!<|im_end|><|im_start|>user\\n\\uc74c\\uc2dd\\uc810 \\ucd94\\ucc9c \\uc880 \\ud574\\uc918!<|im_end|><|im_start|>assistant\\n\\ubc14\\ub2e4\\ud5a5\\uae30, \\uc774\\ub9c8\\ud2b8 24, \\ubbf8\\uc601\\uc774\\ub124 \\uc21c\\ub450\\ubd80, \\ub2e4\\uc774\\uc5b4\\ud2b8\\ud560\\ub550 \\ud589\\ubcf5\\ud558\\uc138\\uc694!<|im_end|><|im_start|>user\\n\\ud589\\ubcf5\\ud558\\uc138\\uc694? \\uc774\\ub984\\uc774 \\uc6c3\\uae30\\ub124 \\u314b\\u314b\\u314b<|im_end|><|im_start|>assistant\\n\\u314b\\u314b\\u314b \\ub9db\\uc740 \\ub808\\uc54c \\uad7f\\uad7f!<|im_end|><|im_start|>user\\n\\uc74c\\uc2dd \\uc678\\uc5d0\\ub3c4 \\ubb50 \\uc88b\\uc558\\uc5b4?<|im_end|><|im_start|>assistant\\n\",\n          \"<|im_start|>user\\n\\uc624\\ub298 \\uc81c\\uc8fc\\ub3c4 \\uc5ec\\ud589 \\uacc4\\ud68d \\uc788\\uc5b4?<|im_end|><|im_start|>assistant\\n\\uc751! \\uc81c\\uc8fc\\ub3c4 \\ub108\\ubb34 \\uad7f\\uad7f!<|im_end|><|im_start|>user\\n\\uc81c\\uc8fc\\ub3c4\\uc5d0\\ub294 \\ubb50 \\ubcfc \\uac70\\ub9ac\\uac00 \\ub9ce\\uc544?<|im_end|><|im_start|>assistant\\n\\ud55c\\ub77c\\uc0b0, \\uc6b0\\ub3c4, \\uc131\\uc0b0\\uc77c\\ucd9c\\ubd09 \\ub4f1\\uc774 \\ud56b\\ud574!<|im_end|><|im_start|>user\\n\\uc74c\\uc2dd\\uc740 \\u3147\\u3138? \\ubb50 \\uba39\\uc744\\uae4c?<|im_end|><|im_start|>assistant\\n\\uc81c\\uc8fc\\ub3c4\\uc5d0\\uc11c\\ub294 \\ud751\\ub3fc\\uc9c0, \\uac08\\uce58\\uc870\\ub9bc, \\ud574\\ubb3c\\ucc1c \\ub4f1\\uc774 \\ud56b\\ud574!<|im_end|><|im_start|>user\\n\\uadf8\\ub7fc \\uc219\\uc18c\\ub294 \\uc5b4\\ub514\\uc11c \\uc7a1\\uc744\\uae4c?<|im_end|><|im_start|>assistant\\n\\ud638\\ud154\\uc774\\ub098 \\ud39c\\uc158 \\uc911\\uc5d0 \\uace0\\ub974\\uba74 \\uc88b\\uc744 \\uac83 \\uac19\\uc544!<|im_end|><|im_start|>user\\n\\ub80c\\ud130\\uce74\\ub3c4 \\ud544\\uc694\\ud560\\uae4c?<|im_end|><|im_start|>assistant\\n\\uadf8\\ub798\\uc57c \\uc81c\\uc8fc\\ub3c4\\ub97c \\ud3b8\\ud558\\uac8c \\ub3cc\\uc544\\ub2e4\\ub2d0 \\uc218 \\uc788\\uc744 \\uac83 \\uac19\\uc544!<|im_end|><|im_start|>user\\n\\uadf8\\ub9ac\\uace0 \\ubcf4\\ud5d8\\ub3c4 \\ub4e4\\uc5b4\\uc57c \\ud558\\uc9c0 \\uc54a\\uc744\\uae4c?<|im_end|><|im_start|>assistant\\n\\uc5ec\\ud589\\uc790 \\ubcf4\\ud5d8\\uc740 \\uaf2d \\ub4e4\\uc5b4\\uc57c \\ud574!<|im_end|><|im_start|>user\\n\\uac00\\uaca9\\uc740 \\uc5b4\\ub5bb\\uac8c \\ub418\\ub2c8?<|im_end|><|im_start|>assistant\\n28\\ub9cc8\\ucc9c\\uc6d0\\ubd80\\ud130 \\uc2dc\\uc791\\ub418\\ub294 \\ud569\\ub9ac\\uc801\\uc778 \\uac00\\uaca9\\uc774\\uc57c!<|im_end|><|im_start|>user\\n\\uadf8\\ub798! \\uadf8\\ub7fc \\uc81c\\uc8fc\\ub3c4 \\uc5ec\\ud589 \\uac00\\uc790!<|im_end|><|im_start|>assistant\\n\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"completion\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 118,\n        \"samples\": [\n          \"\\uadf8\\ub7fc \\uadf8\\uacf3\\ub3c4 \\uaf2d \\uac00\\ubd10\\uc57c\\uaca0\\ub2e4<|im_end|>\",\n          \"\\uc81c\\uc8fc\\ub3c4\\uc758 \\ubb38\\ud654\\uc720\\uc801\\uacfc \\ubc15\\ubb3c\\uad00\\ub3c4 \\uc7ac\\ubc0c\\uc5c8\\uc5b4!<|im_end|>\",\n          \"\\uc751! \\uc81c\\uc8fc\\ub3c4\\uc5d0\\uc11c \\ud790\\ub9c1\\ud558\\uc790!<|im_end|>\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"split\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"train\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_df['prompt'][3349] # 학습 데이터셋 살펴보기"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 128
        },
        "id": "IAXO9cuQXPFe",
        "outputId": "7f3c27e3-9176-416e-97e3-44cab47cec2d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'<|im_start|>user\\n다음주 주말에 제주도 간다면서?<|im_end|><|im_start|>assistant\\n엉. 오랜만에 가족들이랑 갔다오려고 해.<|im_end|><|im_start|>user\\n난 지난주에 제주도 갔다왔는데 시티투어버스 강추해!<|im_end|><|im_start|>assistant\\n제주에도 시티투어버스가 있구나! 매번 렌트만 해서 몰랐엉. ㅎㅎ 어디서 출발하는거야?<|im_end|><|im_start|>user\\n제주국제공항에서 오후 6시에 출발해.<|im_end|><|im_start|>assistant\\n운행은 매일 하는거야? 6시말고 다른시간대는 없으려나?<|im_end|><|im_start|>user\\n금토만 운행하고 6시, 7시, 8시 요렇게 출발한댕.<|im_end|><|im_start|>assistant\\n비행기 시간이나 요런거 봐서 출발시간 정해야겠넹.<|im_end|><|im_start|>user\\n2층 버스라서 제주도 야경 보기도 좋고 운전에 집중안해도 되니 넘 좋았엉. ㅎㅎ<|im_end|><|im_start|>assistant\\n시티투어버스 타고 어디어디 구경했어?<|im_end|><|im_start|>user\\n도두봉, 동문재래시장, 쇼핑거리등 여러 곳에 정차하더라고. ㅎㅎ<|im_end|><|im_start|>assistant\\n동문재래시장이 야시장으로 유명한곳 아니야? 여행스타그램에서 많이 봤엉.<|im_end|><|im_start|>user\\n맞아. 흑돼지 요리부터 꼬치, 음료등 다양한 먹거리가 있더라고.<|im_end|><|im_start|>assistant\\n상상만해도 행복하다. ㅎㅎ 야시장에서 간단하게 맥주도 마실수 있고말야. ㅋㅋ<|im_end|><|im_start|>user\\n잠시라도 운전대 놓고 경치구경도 하고 사람구경도 해봐.ㅎㅎ<|im_end|><|im_start|>assistant\\n'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NR41447NKasL",
        "outputId": "3c422d75-37c2-4c25-8f4a-8f99dac1bca0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading adapter jeju-multiturn-chat-model/3 as 3.zip...\n",
            "Done!\n"
          ]
        }
      ],
      "source": [
        "# Download adapter\n",
        "pb.adapters.download(adapter_id, dest=f\"{adapter.tag}.zip\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Uu4ggLtsKmf6",
        "outputId": "a8729798-93c7-4a34-9e16-5e44565357e7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Uploading adapter to repo jeju-multiturn-chat-model...\n",
            "Done!\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Adapter(repo='jeju-multiturn-chat-model', tag=2, archived=False, base_model='solar-1-mini-chat-240612', description=None, artifact_path='5c53b991-e998-4a3b-a601-e424f26cc7e7/00000000000000000000000000000000/artifacts/model/model_weights', finetuning_error=None, finetuning_job_uuid=None)"
            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ],
      "source": [
        "# Unzip\n",
        "import zipfile\n",
        "with zipfile.ZipFile(f\"{adapter.tag}.zip\", 'r') as zip_ref:\n",
        "  zip_ref.extractall(f\"adapter_{adapter.tag}\")\n",
        "\n",
        "# Upload Adapter\n",
        "adapter2 = pb.adapters.upload(f\"adapter_{adapter.tag}\", repo_name, \"solar-1-mini-chat-240612\")\n",
        "adapter2"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 쿼리 추출 기능"
      ],
      "metadata": {
        "id": "hV11bTOAEJEO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "```\n",
        "prompt:\n",
        "\n",
        "<s>[INST] The following passage is content from a news report. Please summarize this passage in one sentence or less.\n",
        "\n",
        "Passage: {content}.\n",
        "\n",
        "Summary:\n",
        " [/INST]\n",
        "---\n",
        " completion:\n",
        " {headline}\n",
        "\n",
        " ---\n",
        " split: train/test\n",
        "\n",
        "```\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "U5-Fk56tEbU2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "from openai import OpenAI\n",
        "from pprint import pprint\n",
        "\n",
        "\n",
        "UPSTAGE_API_KEY = \"up_YsiiwBJ4YwMmf70RuQBLMdgLrI3DS\"\n",
        "\n",
        "client = OpenAI(\n",
        "    api_key=UPSTAGE_API_KEY,\n",
        "    base_url=\"https://api.upstage.ai/v1/solar\"\n",
        ")\n",
        "\n",
        "gc_result = client.chat.completions.create(\n",
        "    model=\"solar-1-mini-chat\",\n",
        "    messages=[\n",
        "        {\"role\": \"user\", \"content\": \"안녕!\"},\n",
        "        {\"role\": \"system\", \"content\": \"반가워!\"},\n",
        "        {\"role\": \"user\", \"content\": \"제주도 여행 가고 싶어\"},\n",
        "    ],\n",
        ")\n",
        "pprint(gc_result)\n",
        "print(\"Message only:\")\n",
        "pprint(gc_result.choices[0].message.content)"
      ],
      "metadata": {
        "id": "t7GEj1A_EJLN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import requests\n",
        "from openai import OpenAI\n",
        "import json\n",
        "from pprint import pprint\n",
        "\n",
        "# 업스테이지 API 키와 엔드포인트 설정\n",
        "UPSTAGE_API_KEY = \"up_YsiiwBJ4YwMmf70RuQBLMdgLrI3DS\"\n",
        "openai_client = OpenAI(api_key=UPSTAGE_API_KEY, base_url=\"https://api.upstage.ai/v1/solar\")\n",
        "SOLAR_MODEL_ENDPOINT = \"https://api.upstage.ai/v1/solar\"\n",
        "\n",
        "# 구글 맵스 API 키와 엔드포인트 설정\n",
        "GOOGLE_MAPS_API_KEY = 'YOUR_GOOGLE_MAPS_API_KEY'\n",
        "GOOGLE_MAPS_ENDPOINT = 'https://maps.googleapis.com/maps/api/place/findplacefromtext/json'\n",
        "\n",
        "# API 키와 클라이언트 설정\n",
        "UPSTAGE_API_KEY = \"up_YsiiwBJ4YwMmf70RuQBLMdgLrI3DS\"\n",
        "client = OpenAI(\n",
        "    api_key=UPSTAGE_API_KEY,\n",
        "    base_url=\"https://api.upstage.ai/v1/solar\"\n",
        ")\n",
        "\n",
        "def generate_query_and_check(user_input):\n",
        "    prompt = f\"\"\"\n",
        "    사용자 입력: \"{user_input}\"\n",
        "    이 문장이 구글 맵스 API 쿼리 검색이 필요한 문장인지 확인하고, 필요하다면 검색 쿼리를 생성해 주세요.\n",
        "    예시:\n",
        "    - 문장: \"돈사돈 주소가 어떻게 돼?\" -> 쿼리: \"돈사돈\"\n",
        "    - 문장: \"오늘 날씨 어때?\" -> 쿼리: \"N/A\" (검색 필요 없음)\n",
        "    \"\"\"\n",
        "\n",
        "    try:\n",
        "        # 업스테이지 모델 호출\n",
        "        response = client.chat.completions.create(\n",
        "            model=\"solar-1-mini-chat\",\n",
        "            messages=[\n",
        "                {\"role\": \"system\", \"content\": \"You are an assistant that helps to determine if a Google Maps API query is needed and generates the query.\"},\n",
        "                {\"role\": \"user\", \"content\": prompt},\n",
        "            ],\n",
        "        )\n",
        "\n",
        "        # 응답 본문 출력\n",
        "        pprint(response)\n",
        "\n",
        "        # 응답에서 쿼리 추출\n",
        "        output = response.choices[0].message.content.strip()\n",
        "        if '검색 쿼리:' in output:\n",
        "            query = output.split('검색 쿼리:')[1].strip()\n",
        "            if query == \"N/A\":\n",
        "                return False, None\n",
        "            return True, query\n",
        "        return False, None\n",
        "\n",
        "    except Exception as e:\n",
        "        print(f\"Error: {e}\")\n",
        "        return False, None\n",
        "\n",
        "\n",
        "\n",
        "# 쿼리 생성 여부 확인 및 쿼리 생성\n",
        "user_input = \"흑돈가 주소가 어디야?\"\n",
        "need_search, query = generate_query_and_check(user_input)\n",
        "print(need_search)\n",
        "print(query)\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "keCxYKilEJPb",
        "outputId": "32575544-cca8-4c5b-82ff-459f8c0a25a3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ChatCompletion(id='d32f4e90-91da-42e1-98df-e8f010207d42', choices=[Choice(finish_reason='eos', index=0, logprobs=None, message=ChatCompletionMessage(content='문장: \"흑돈가 주소가 어디야?\"\\n\\n이 문장은 구글 맵스 API 쿼리 검색이 필요합니다.\\n\\n검색 쿼리: \"흑돈가\"', refusal=None, role='assistant', function_call=None, tool_calls=None))], created=1723961735, model='solar-1-mini-chat-240612', object='chat.completion', service_tier=None, system_fingerprint=None, usage=CompletionUsage(completion_tokens=42, prompt_tokens=144, total_tokens=186))\n",
            "True\n",
            "\"흑돈가\"\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Exercise\n",
        "1. Take Pre-training LLM course at Deeplearing.ai https://www.deeplearning.ai/short-courses/pretraining-llms/\n",
        "\n",
        "2. Design and Make continuous fine-tuning systems.\n",
        "\n",
        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ICAgICAgIDxleGlmOlVzZXJDb21tZW50PlNjcmVlbnNob3Q8L2V4aWY6VXNlckNvbW1lbnQ+CiAgICAgIDwvcmRmOkRlc2NyaXB0aW9uPgogICA8L3JkZjpSREY+CjwveDp4bXBtZXRhPgp9o6BZAAAAHGlET1QAAAACAAAAAAAAAJcAAAAoAAAAlwAAAJcAAFFbJImpVQAAQABJREFUeAHsnQl8VNXZ/58QCGFPghJQIAGBspRNhIIVF1bBUt7XFQX79lXRfytVX/pqF9S+VWhpq1KltCBiawtU3EqtAiK4IiAgawUBCQhIAghhTwiQ+z+/Q8/0zs2dyWQyM5m593c+n8zcc8/+PTeQ333OeU6apYIwkAAJkAAJkAAJkAAJkAAJkAAJkAAJJIRAGoV4QjizERIgARIgARIgARIgARIgARIgARLQBCjE+SCQAAmQAAmQAAmQAAmQAAmQAAmQQAIJUIgnEDabIgESIAESIAESIAESIAESIAESIAEKcT4DJEACJEACJEACJEACJEACJEACJJBAAhTiCYTNpkiABEiABEiABEiABEiABEiABEiAQpzPAAmQAAmQAAmQAAmQAAmQAAmQAAkkkACFeAJhsykSIAESIAESIAESIAESIAESIAESoBDnM0ACJEACJEACJEACJEACJEACJEACCSRAIZ5A2GyKBEiABEiABEiABEiABEiABEiABCjE+QyQAAmQAAmQAAmQAAmQAAmQAAmQQAIJUIgnEDabIgESIAESIAESIAESIAESIAESIAEKcT4DJEACJEACJEACJEACJEACJEACJJBAAhTiCYTNpkiABEiABEiABEiABEiABEiABEiAQpzPAAmQAAmQAAmQAAmQAAmQAAmQAAkkkACFeAJhsykSIAESIAESIAESIAESIAESIAESoBDnM0ACJEACJEACJEACJEACJEACJEACCSRAIZ5A2GyKBEiABEiABEiABEiABEiABEiABCjE+QyQAAmQAAmQAAmQAAmQAAmQAAmQQAIJUIgnEDabIgESIAESIAESIAESIAESIAESIAEKcT4DJEACJEACJEACJEACJEACJEACJJBAAhTiCYTNpkiABEiABEiABEiABEiABEiABEiAQpzPAAmQAAmQAAmQAAmQAAmQAAmQAAkkkACFeAJhsykSIAESIAESIAESIAESIAESIAESoBDnM0ACJEACJEACJEACJEACJEACJEACCSRAIZ5A2GyKBEiABEiABEiABEiABEiABEiABCjE+QyQAAmQAAmQAAmQAAmQAAmQAAmQQAIJUIgnEDabIgESIAESIAESIAESIAESIAESIAEKcT4DJEACJEACJEACJEACJEACJEACJJBAAhTiCYTNpkiABEiABEiABEiABFKPwCeffBKTTseqnmg7U1Pt9+rVK9ouV6tcddqtTtlqdZqFfUOAQtw3U82BkgAJkAAJkAAJkIA/CUCAPvvss1JTQtSf1P016rvvvlsg3ing/TXv1RkthXh16LEsCZAACZAACZAACZBA0hO45557KMKTfpZSv4MQ4TNmzEj9gXAECSFAIZ4QzGyEBEiABEiABEiABEigpghcdtllNdU02/URAQpxH012DIZKIR4DiKyCBEiABEiABEiABEggeQnYhbhZQmzvbSRL1ivLU1m6vT1eJ55ApEvGw+VzS8O8Y9sDAoV44uc1lVukEE/l2WPfSYAESIAESIAESIAEKiVgF+JYOuwmqCqtJIYZqiraq5o/hl1NWFXRzkm05WI1MMwNtj4gUIjHiqo/6qEQ98c8c5QkQAIkQAIkQAIk4FsCySbEfTsRHhw4hbgHJzVBQ6IQTxBoNkMCJEACJEACJEACJFAzBCjEa4a7H1qlEPfDLMdnjBTi8eHKWkmABEiABEiABEiABJKEAIV4kkyEB7tBIe7BSU3QkCjEEwSazZAACZAACZAACZAACdQMAQrxmuHuh1YpxP0wy/EZI4V4fLiyVhIgARIgARIgARIggSQhQCGeJBPhwW5QiHtwUhM0JArxBIFmMyRAAiRAAiRAAiRAAjVDgEK8Zrj7oVUKcT/McnzGSCEeH66slQRIgARIgARIgARIIEkIUIgnyUR4sBsU4h6c1AQNiUI8QaDZDAmQAAmQAAmQAAmQQM0QsAvxu+++W/DDQAKxIEAhHguK/qyDQtyf885RkwAJkAAJkAAJkIBvCFCI+2aqEz5QCvGEI/dMgxTinplKDoQESIAESIAESIAESMCNAIW4GxXeiwUBCvFYUPRnHRTi/px3jpoESIAESIAESIAEfEOAQtw3U53wgVKIJxy5ZxqkEPfMVHIgJEACJEACJEACJEACbgQoxN2o8F4sCFCIx4KiP+ugEPfnvHPUJEACJEACJEACJOAbAhTivpnqhA+UQjzhyD3TIIW4Z6aSAyEBEiABEiABEiABEnAjQCH+byr7j+wIRHKzLglc8yI6AhTi0XFjKREKcT4FJEACJEACJEACJEACnibgFyFuRPb+IwVyoLggMKf7j+wMXEdykZvVxjVbs+y2gfv2+gM3HRf2/LlZbcWLwp9C3DHpjEZMgEI8YlTMSAIkQAIkQAIkQAIkkIoEvC7EIcA37VwiENwQ0RDAEL4ZmelSXPKFtM2+Sk9bcemuoDhuFhS/H0hHHHmyM/NxqYM9jnaKT30h2fXzTHIFcW3qQ168EDABwh3969pmoL7VLX+wSUrpbwrxlJ6+Gu08hXiN4mfjJEACJEACJEACJEAC8SbgVSFuBDj4tcxtLxn10oNEtRHFhq8zbhfZyBPruLO9D7bMkqx6rdVLg6VakHtBjFOIm6eL31UlQCFeVWLMTwIkQAIkQAIkQAIkkFIEvCjEN+56Wy8/79pmkLZ87yz+UC5tcXtgXpwiuKoiu7LySM+ulxewnjvzu6Xb80OU7ynaLoN6jq1gVQ8MIgUuKMRTYJKStIsU4kk6MewWCZAACZAACZAACZBAbAjYhXivXr1kxowZsam4hmqBCIdVefQ1k/XS8l1HlsmANhMCvXGK4spEuDN/ZSIa6Qhmybtbfme6W/zE0bLAOJCeioFCPBVnLTn6TCGeHPPAXpAACZAACZAACZAACcSJgJeEuBHhsCSftPbKkdLdkpXZOqQodopwN9Fst1RXJrJRn33fuTO/M72y+JJ151+KDOp5T5xmP77VUojHl6+Xa6cQ9/LscmwkQAIkQAIkQAIkQALiFSFuF+FwxIbl6E4Rjuk2lupIRLg9f2WiGnntdTrzIx33TPvOuFOUIx33PtnyltozPigll6hTiGMWGaIhQCEeDTWWIQESIAGfETh+/LgsWrRISkpK9MhzcnJk4MCBUq9ePZ+R4HBJgARSkYBXhDisx/CI3qp5e22VxlwY0esUxU5BbERwg7SW2sN6ee3z/54P6fqAnlKTbupzxp3z7paOe3aP684+hIobp3OpaBWnEHc+GYxHSoBCPFJSzEcCJEACPiXw5JNPykMPPSTl5eVBBGrVqiVvvPGGDBs2LOg+IyRAAiSQbAS8IMSNNdzsC7cvJ3eKYghet/SCXZ/pqWndorMcO/2lnDp1UspKz0qvTkODlpsjk1NU2+PO9tzyhxLdugPqw5k+590f6z3vJj1VvinEU2Wmkq+fFOLJNyfsEQmQAAkkDYEuXbrI5s2b5d5775Xhw4dLdna2nDlzRk6cOCGTJ0+WZcuWydChQ2XhwoVJ02d2hARIgAScBLwixDEuHPllF8W4Z49D4CIYyzaukQ4P5Qh2azriEOdZOU3ksrxRiOpgrw837MIeaZV5aLfnR/m1hX+p4NHd/qIA+dGPVFyebhfiGOuaNWvwxUAClRKgEK8UETOQAAmQgP8IQGx369ZNioqK5JlnnpHbb//3kTh2Go888ohMmjRJ7r//fpkyZYo9idckQAIkkDQEvCDEYTHu12NYkKUbgO2iGdd2R2pINyIYFvXMzEw5Ye3S+8qRBrE+f+3DUq92lgzt9r+4FVQf4k5hb+pDGoJTdDvzR5peVnpO9h8p0C8aztecGp8U4qkxT8nYSwrxZJwV9okESIAEapgArOCwhs+aNUvuuOOOsL0ZPXq0zJ07V/bs2SMtW7YMm5eJJEACJFATBFJdiGMP9ZJ1M6VTxy5BlmW7KHYKXnC238P+8hPWHrmq810BsY70z/euk+aNumgBjLjTkm4X9mivTXb/wD5w5EcwZaoat784SNV94hTi+hHgRxQEKMSjgMYiJEACJOB1Ag0aNNCO2Zz7wkONG1aWsWPHytSpU0Nl4X0SIAESqDECqS7EYc0+UrJbrux0Z4AhRK9Z3u0UwMjkvPfutilStP+gfOPrQ7VwRvrhY4WytWB94DxyU59pBHkiFdl2Ue3WvjPd5DH1Ix3e01PNYRuFuHla+F1VAhTiVSXG/CRAAiTgAwJpaWkyceJEmTBhQkSjHTBggHTo0EGmT58eUX5mIgESIIFEEvCCEAcv7A9HsItsXO86skx6thgd0lINS/ZXXx2S0pNnJL95Dyk6/qnkX9hLdhdu1vuycR45ghHFuIYwNh7Q7e2ZNLulHPfCiXZnuqnD1G/iFOIgweAXAhTifplpjpMESIAEqkAAQhze0sePHx9RqSFDhkheXp7MnDkzovzMRAIkQAKJJJDqQhzLyo2l2C6KIZbdzhK3W7aRH5bvI4eP6jrWfPGiihdJ91Yj9LndbpbqcCIc82YX3fb40aMlcqh8lZ5aI+pfebNQLr18W5DId5Y37dnHqStJgQ9axFNgkpK0ixTiSTox7BYJkAAJ1CQBHE3WqFEjOXr0aETdaNq0qYwaNUqmTZsWUX5mIgESIIFEEvCKELeLcPBzxiFo1xXOkQFtzq9mMiJ7xfqFgeXnKGdEsqnDHjei2KRVlt8uqjdt/VJK678jvVv928HnvAWnZciAwiDrur09e/lUPMKMQhxPCEM0BCjEo6HGMiRAAiTgcQKzZ8/WntIXLVqkjycLN1yI73HjxsnixYtl8ODzyybD5WcaCZAACSSagBeEuPOsbwhmBPvy7nd2TgosUTfp9mPL7CIdZe2iG3GIYmNNt18jDcEumk3c5Ef8nY//KV9r2VYuvrg+ojr/qpX9ZNSwjEDcnt9ZH4W4xsQPnxCgEPfJRHOYJEACJFAVAqWlpdKuXTvJysqSefPmaQ/qbuWXLl0qWJY+YsQImT9/vlsW3iMBEiCBGieQ6kIcAnV4v/8XEN1GZNtFOEQtAqzNJt0pwu37yMOJcKRFcla4aQ/f2IdetudmueCCc9Kubf2AtX7Vir4yanjdQNxYw51CH3FjuUd9qRJoEU+VmUq+flKIJ9+csEckQAIkEJbA8ePHBZbqkpISnS8nJ0cGDhwo9erVC1uuqomHDh2Szp07y+HDh7W1u3Xr1pKbmytlZWVSWFgozz//vDz11FPSqVMnWblypTRu3LiqTTA/CZAACSSEgBeE+OhrJmtWdsFth4f7ELkm/cTRMjlQXKD3hZt7RgS7iXDUZUS803KO8k5LtsmPb1P/oR3flJLTR6Rl509xW2od7ygrNjWWb1wevG/c5Df9QfzAkZ2yY9dnegm9LpwiHxTiKTJRSdhNCvEknBR2iQRIgARCEYADtYceekicx4phT/cbb7whw4YNC1U0qvsQ/T169JCCggLJyMiQhg0b6raPHTumv0eOHCmvvvqqpKenR1U/C5EACZBAIgjYhTjaW7NmTSKajVkb9iXbWH5u9oCjAWP9hnUcgvZI6W591veCFdOVCB8rTo/oyGMEsClv94BuX96OdKdoRnv2/Pb0lavPiBIXcmH7FbqNv739lZwuayTtO6yQXu2vRnUh68OLg007l1KIa0r88AMBCnE/zDLHSAIk4AkCXbp0kc2bN8u9994rw4cPl+zsbDlz5oycOHFCJk+eLMuWLdP7uRcuXBjz8S5fvly2bdum24Al/LHHHpMxY8ZIfn5+zNtihSRAAiQQawJeEeIQvU7LtIkjDSI8K7O1FCjLcrPsttKqeXuN0ixhd5YPJ6pR0C6yzZzgnhHy9vKnT5fJ2g21VDZL+vWpo7O/tKhUfadJVv1iGXJl8wr12cvjrHQKcUOZ334gQCHuh1nmGEmABFKaAMR2t27dpKioSJ555hntRM1tQI888ohMmjRJ7r//fpkyZYpblmrf++Mf/yh33nmnbNiwQbp27Vrt+lgBCZAACSSCgBeEOPaII9hFNeJmObk5xgyWZYSGTTIqiHbcNyIa16FENdLsIhlxBHt+p0hfvfaknD17XoBfkHNS2uQ3kVffPitWebmkK33eq+/Hug57+2jDjIdCXOPhh48IUIj7aLI5VBIggdQkACs4rOGzZs2SO+64I+wgRo8eLXPnzpU9e/ZIy5Ytw+aNJpFCPBpqLEMCJFDTBLwgxM0ecbCEgDXO1Ixgxn27CEfciF6Tx8SRZhfVSEcworiy/E4RjrJYlm6pZemwgJfLOdlz8JS6bCBpaWnqniXNmm2Ua3r1RlYd7O3jBoX4eS789A8BCnH/zDVHSgIkkKIEGjRooB2zOfeFhxpOZmamjB07VqZOnRoqS9T3jRBfv369ttJHXRELkgAJkEACCXhJiEMkGxEOhEZEw0M6nLO1ze+ol6hf2uLfZ3k7RS/KGNGNNLO83UwJPKA7y5s8zvZRZvnHJUpw19aiG1q8+FiZrFp/Vjp1ydRVQouXl1tyizrGDMLc2R/E9xZ9LhiD/YWD6U8yfz/77LOCHxNSzf+A6Te/E0+AQjzxzNkiCZAACVSJAP5omThxokyYMCGicgMGDJAOHTrI9OnTI8pflUwU4lWhxbwkQALJQsBLQtwukrcVfSif7Vkux08US6N6TaV/j5sEHs8jPaYMAhjBbim31480Zx6nM7cPP/pKMuo2VMvS0wSOQ8+dS5MF75Spa5GLWp2Tpjl1lWXcklqwlKtl6r0v/zhI+Jv6U9VZG4U4nhKGaAhQiEdDjWVIgARIIIEEIMThLX38+PERtYpzvfPy8mTmzJkR5a9KJgrxqtBiXhIggWQh4BUhDtFqRPPrq3+l8X6j/Y36e2vhe7J531IZcuk9gTz2/MiE+NnTtWR34WY5VLJLztQqljF9//3SFiK8TXb/gLUc1m+7h3Rn+qpPjsm5s/UlrZallqWLHD9ZLkuWqT3qVppk1KmlxXiDxqXq/6R6/1qiDqF+Rm69TpVR/7fZ699/ZIcsWTeTFnE9m/zwAwEKcT/MMsdIAiSQ0gRgYWjUqJEcPXo0onE0bdpURo0aJdOmTYsof1UyUYhXhRbzkgAJJAsBLwhxOGszy8kXb/qtRjuk6wP6G4L2s68Wyu7du+XqzndJbtYlWuSa/MgEEb56yyJpWi9fWrfoLFu+ekMyyy+UFo27S7f8wTrdLD/Xlf6rjBH+KI+A+Nbt++VwcY4S0+qGEuBS+5wsfb9UTpYqZ21qCTpEOZLgpc2yzql8lnTuUldq14bVHOlp0qzxScnrvDHw0uCzfe/IJ1sXU4iDG4MvCFCI+2KaOUgSIIFUJjB79mztKX3RokX6eLJwY4H4HjdunCxevFgGDx4cLmtUaRDicBgHr+nw5M5AAiRAAqlAwAtC3Oydhuh+bvEP5K4hUwPC3IhkLO/eVbReSsoPS+umXaVB3Qv19BQd/1ROnCqWdrlXaNGN5eUX1O8grZr0lk+2vCVZOU0kp3GLgChGIbRjhPzaz9+VWuW5cuxgM7UMvYmcPi1SeKBEDhXXkn37VebyWpKeLnIWottK1xbycrVEPS29XCViSTpWp1tanGdklktWtkhWkzqSWQ9pZdIi95jUavCR7Nm/jkIc8Bl8QYBC3BfTzEGSAAmkMoHS0lJp166dZGVlybx587QHdbfxLF2qliSqZekjRoyQ+fPnu2Wp9B6OR8PxZ9UNb7zxhlx33XXVrYblSYAESKDaBD755BO55557gupJNYdac979cUCgYnn4hk8/lv8e9Ds9pi+OLJfGmRdp0Yzl3Rv2/EN6t/8PKSs9p9OPlu6TXQc/kd1fbpe7rp2mLd/7T26W3AadtfBe88WLsuPLDXLL5b8MMNq+Y7N8/pbImRON5YQ6kuxUWqYUn7PklLJwn1VO2Wopc3eZiqernd+10i213FwVVVZvYyAvV6I7XSlzS+XBTR1H4r9+tMVcWc7TVEVnlYg/XVYu1w46LAfPTAuMM9CZJL/gHvEkn6Ak7h6FeBJPDrtGAiRAAobAoUOHpHPnznL48GFt7W7durXk5uZKWVmZFBYWyvPPPy9PPfWUdOrUSVauXCmNGzc2Rav8jT9QUa9bWLBggT6r/IUXXtAvB9zy1K1bV3r16uWWFPbe8ePHBVb/kpISnS8nJ0cGDhwo9erVC1uOiSRAAiQQjoCXhLjd8q3HXO+oNMy4MGDJdluybvZ4L1k3Qy9JL03bHyiD+j7fu06aN+qiLeWoE/fclqMj7bMVBXJ2VV3JPJ0h5UqQn6yVLu+fPisHRXlHr1WutLjaA362XDttK7fK1VJ0JdzPqftKcMNRW9mZNNmw+ZScOaeONVP30tJOS48ua+XRh9rKmdNqfzn3iAMzg08IUIj7ZKI5TBIggdQnAKHao0cPKSgokIyMDGnYsKH+w+bYsWP6e+TIkfLqq69qK0S8RmuWpuP4su7du8esGTije+ihh/Q47JVifzys68OGDbPf5jUJkAAJREzAK0K8X4/z/w4akQzRXaAs2e1y+2oWB098IU2zmonZN46bdlENy/e+A1/I19v11ULbiPoV6xcGrNC4Z/aJY2m6/Zg01Gdfrv7pb76UulYdgdG7jrJub6ldW1ZbDZTYPqNyqqXq6t/vc0p8lysr+KlTabJuixLetZQ3dckQq9Y5WfinulJUuhzV6v7QWZtGwQ8fEaAQ99Fkc6gkQALeILB8+XLZtm2bLFu2TFvCH3vsMRkzZozk5+fHfYDxEOJdunSRzZs3y7333ivDhw+X7OxsOaP+kDtx4oRMnjxZj3Po0KGycOHCuI+PDZAACXiPgFeEuN1Zm10kb9z1tp60gye3ilVWRwb1PL8M3y7CjcDGOd2bdi6Vthd1E1jGz52sL82y2wactaEiCH17/eaJsNeHe8hTtFqdH/5hU7U8Xd1QDtgsJb5nn1MO25S1/OzZc0qMl8vHm5UQL8FydqXYVWiYcUQWzm2pyxtrPe6jfvtLAdxLhcCl6akwS8nZRwrx5JwX9ooESIAEKiVgPJjDcVrXrl0rzR+LDLEU4hDbcPhWVFQk2Jt+++23u3bxkUce0cvhsXd9ypQprnl4kwRIgARCEfCKEDfO2jBON1EMx2pGlDdskhG0vPxI6W65tMX5f2NXbJ0r2CPeNucq5V29bcDDul0UO48pM0LeOG+DCEdAvGjTYTm64Ky2gJcry7hS4/KC1NP7wz9ae0bKpY6yhKvbKqll82L56+/Oi3BTHt+oH4FCXGPgh08IUIj7ZKI5TBIgAe8RSHUhDis4rOGzZs3SntjDzdDo0aNl7ty5smfPHmnZsmW4rEwjARIggSACXhPiEMlGVGOgEMVGICM+b/lPpGfb4dKheX+dtq5wjgxoMwFJOjhFNsrbRbhTdLvF7cIe5Xc9f1YanVSu0BHUmWan1b7wR3bUkgOHcZyZUuHKGp6ulqO//3KTCv21t293Sne+suT/pEU8+ecoWXtIIZ6sM8N+kQAJkEAlBIwQx37tRB0lFkuLeIMGDbRjNjjwiSRkZmbK2LFjZerUqZFkZx4SIAES0AS8IMThaK1rm0Fy0tob2MONwUEkmz3jJn72dC11Xvibaon6WPmyZLm0ye4fEOpOEY4ydmFvLNOmTrc4RLip0y7St//moCj/bEqHK0/otc7Ky3JKlnycK+XKozr2hP/5N+pos6yNQf03fTbtUYiDCINfCFCI+2WmOU4SIAHPEUh1IY4/1iZOnCgTJvzbUhNukgYMGCAdOnSQ6dOnh8vGNBIgARIIIuAmxGfMmBHV6Q5BFScwAiHeMKuufK3F1QFRbRfB6IpdNGOJ+pZ970jvTtcGhLo93XQ9nAi3W6qRH3E4b8vKbK3rdLa/7o+bpeFBdW65sn43HZ0hORdlyZU3n1InlllKiZ+TPz67TuyWdFOn3ZpPIW5mht9+IEAh7odZ5hhJgAQ8ScALQhze0sePHx/R/OCM9Ly8PJk5c2ZE+ZmJBEiABEDAK0K8ZW576XjRAD2pTlHtFl+9ZZF0Uvm75Q8OEunmqbALaWf5SEQ46jGWbJP/3HPKX8nZs9L+J820pf3B+76llqnXllE3bpXO/d6Sni1GB71IMOVRF+pYsGJ6wIM77qVC4NL0VJil5OwjhXhyzgt7RQIkQAKVEkh1IY6jyRo1aiRHjx6tdKzI0LRpUxk1apRMmzYtovzMRAIkQAIg4BUhjqXpuVmXaMFq39NtRLARtXZRDQvz19r2kEvUEWd2y7M9j7M8mNkt5YjbRbtbfqSj/S2/3i+SbkndO7aimDw3LVs+XHeJPD7pJWmd2zZIuDv7g/K0iGts/PAJAQpxn0w0h0kCJOA9Ama/Nrymp+Ie8dmzZ2tP6YsWLRIcTxYuQHyPGzdOFi9eLIMHDw6XlWkkQAIkEETAa0LciF4zSHsc1whGlOPs8B3qrPFbLv+lyV7BOu4muk15FILwRjDC2d4e7tvj23/9lRxrdECyRx3UfTh6rFRG3FEuf5q5OtAn5DdnlZvyJo4l+Ob4NaSlQqBFPBVmKTn7SCGenPPCXpEACZCAPtILR3ZVN7zxxhty3XXXVbcaXd6IfziI6969e7XqLC0tlXbt2klWVpbMmzdPe1B3q3Dp0qWCZekjRoyQ+fPnu2XhPRIgARIIScBLQjwjMz0giDFguwh2WqoRx57u2qebazZuS9Tt5U19RhQj7hThiBtBbvLbRfumJ/dKvR5l0m5gWyTr8t++/QL58OWGOu4mwpFg6qAQ15j44RMCFOI+mWgOkwRIIDUJrFmzRsrKylw7v2DBAn2+9gsvvKAFrVumunXrxtQhUSyFOPp76NAh6dy5sxw+fFhbu1u3bi25ubl6zIWFhfL888/LU089JZ06dZKVK1dK48aN3YbJeyRAAiQQkoBXhHivTkODRHA4UWxEuDnmDAI3K6dJ0BJ1N1HsFOHOJfBOEW7Pj/pOLWwmWV2zpWXX5gERP3L0BfLBqw0rWOLRR2f9S9bNlJv6TQo5l8mYQIt4Ms5KavSJQjw15om9JAESIIEKBGItiis04HIjHm0eP35cevToIQUFBZKRkSENGzYUHGl27Ngx/T1y5Eh59dVXJT093aVHvEUCJEAC4Ql4RYjbl2y7WbKNVRk03tk5Kcgx2raiD2XFtrnyX1f+QcNyE+FIMHUg3XlMmUlDPqQjmHsmf87WwZJ3VXM5cnqXTodwv/LGg/L32SeDRDcSUcaUN6K8YNdnXJquyfHDDwQoxP0wyxwjCZCAJwnEQxRXBiqebS5fvly2bdsmy5Yt05bwxx57TMaMGSP5+fmVdYvpJEACJBCSgNeEOASs0xJtj2PPtznnG1AgctcVzpEL0vpoRg2bZOhvI4JDiWpTh1t7qMCUN/XDI3r98ovky9Mrgvr3w5+vknvvK9H5N28uUqugmgeJ8C0Fn0vd7C91Opem66nhh08IUIj7ZKI5TBIgAe8RiKcodqO1d+9eadWqlcDbee3ataWoqEiys7PdslbrHsZ15513CpzQde2qjsJhIAESIIFqEPCSEHcTzUBjRLEzHWl26/jrq38lZ8tPSeOMi6RZdlspOv6ptGvZM1Aeohr7yot3Xyxd2vaTfcfWqyXtZUHpWE5e/0xvad6sfkDk52ddofPY21/++hG5/NtZWnQf2NhL+l71733i9v7azxan13TMGINfCFCI+2WmOU4SIAHPEUi0EG/SpIleNv7mm2/qfed9+vSRFStWxJwrhXjMkbJCEvA1Aa8IcewRt++ptoteTLAzjnuwjmdlttYiGSJ79fb50r3VCCTJ0dJ9snnfImmbc7U+axzpqB+hWcbl8tqiw/LNaz6TS3KukmfnHJX/GFpH/rbwtNxze7YUHSiRlau+kjZf3yWbP+osw79VXzZ+sl+ONZ0n13X/ka7jo38ckYb5B6Tb19vLsvcOSv9rmsk7H6+Snt2byUcfNJG8jptl2+Yc6dHxErkk/7yV/uUVE7hHXNPjhx8IUIj7YZY5RhIgAU8SSJQQ37dvn/aQfurUKfnggw+0CH/ttdfkhhtukLvuukvgqCYtLS1mjI0Qh2f2aI9lw75zHItWUlKi+5WTkyMDBw6UevXqxayfrIgESCA1CHhFiNv3iBvRbLcsYzZMHNdOYb5i61xpUPdCLbqRvqTgcZX/SjlxtEydT95W4JEdAfu6Tf0fv99dRo1sIqvXlcgFbVbLu290lztvz5LC/er/g2WHpW7dc3LmSJYMua6OfHzgt9I77zZd3rIsWfXhLrHOXChpdUR6XV5P1m9fJUeKa8ugy3vLm0t2SL1LXpTC9T+QPj0zpb0S4ht3va3bh3f3VAp01pZKs5VcfaUQT675YG9IgAR8SCBa0ZgIIX7w4EEZNGiQbNy4UVu/+/btG5gh/PFxzz33aCE+duzYwP3qXlRXiD/55JPy0EMPaUdv9r5gST2Ochs2bJj9Nq9JgAQ8TsALQty5ZBtC2XgwN6I5nAhHnj1F27Xgzs26JCDCTRksWe/f9ZZAnS+/vl3a5WfLmn/WkbG3NZGZc47J5b3Oybp/WjLmxhz5fO8O+ejdC2XIgNry8btn5cJvvC4XNr5YOuReo5+mXXt2SuuL8/RWpt27jsrZJuulqKCtpFvp0uHrZbJ4yX4ZMihX/vFmY+nbs4HUzl4pm3a9o/a2XxF4UZAqjyWFeKrMVPL1k0I8+eaEPSIBEvARgeqIxngLcXguv/7667V4xTFjWJruDI8//rg8+uij+hzwm2++2ZkcVbw6QrxLly6yefNmuffee2X48OF6D/uZM2fkxIkTMnnyZO0IbujQobJw4cKo+sZCJEACqUfAK0J8UM+xSkhfoq3VRoRjNrD83BxThrhTmMMyDsENR2hd2wyS/afXqVyWdMu9CdkFlnKEfl+7TX/bRT5umPI6UX0gHcH0wWl5d8tvltSjLPagm+XyqMeUh8d07FunRRxUGHxBQC0dYSABEiABEqgBAur8bEv9R2Mp0WipfdeW8hpuvf/++/q6f//+llrubV177bUhe6bO2Nbl1RLukHmiTTh37pylhLWuf9WqVSGrUcvVLeVQTff1ueeeC5mvKgkYF8aunLVFXEydtW517NjRysrKsv785z+HLPfwww/ruh944IGQeZhAAiTgLQJr1qyxevXqFfSDe6kU3l473Soq/tw6XLIzqNuf7Av+927H4feC8tjjG3Yutt7aOMXaUPRSoI7Vu/5q/X3V5EDcnh833eK4ZwKunXF7H3Ft7yOunflNfPY7PzLVptT3jBkzgp6tlOo8O1ujBGgR98XrFg6SBEggmQjAQou9z/A6/swzz8jtt9/u2r1HHnlEJk2aJPfff79MmTKlQp54WsQffPBBeeKJJ+Sll16Sm246bzWp0AHbjU6dOsn+/ftl7dq11T5uzIwLXtMj3SMOKzis4bNmzZI77rjD1rOKl6NHj5a5c+fKnj17pGXLlhUz8A4JkICnCHjBIr7/yA7ZtHNJ0BnbsCTbjy0zlmWz3Nwt/fO966R5oy56frfse0d/X935Lm1pd8uPDPb67B7O3dqz58e13VrvtKQjbizlqbo/HGPk0nRQYIiKQI2+BmDjJEACJOBDAp9++qm2NCvRWOnob7vtNp1XicYKeeNpEYf16IUXXqjQZrgbPXr0sF5++eVwWXTa008/rcek/tOq1rfa7x1oq379+trSHbhRyUXdunWtcePGVZKLySRAAl4g4AWLOKzhsBjjGwFWZKfl2ViWkY40exzXJo46thZ+YL2z9Slk1cGejhvO8ogvLZgYaNMt3dR/vkZLW8KRDwHf5trEUZ8JqWoNR/9pETezyO+qEqBFPKrXFyxEAiRAAtETaNCggfbmjT3YkYTMzEyBM7SpU6cGZTeWY3gX7969e1BadSPqPxNdRVW8ocPSX6eOco8bQVB/GItaTu6ac8GCBXolgHoRIO3atXPNo4S09t5uEtHPiRMnyoQJE8ytsN8DBgyQDh06yPTp08PmYyIJkEDqE/CCRRyzgD3eCC1z20tGvfSApdrsuzb7xO2WZuR3i68rnCMD2pz/99KZjjKwdhtLOOL2s8gRt1u6EXfmt1vLcW233JvybbL7633mqWwNx1hoEQcFhmgIUIhHQ41lSIAESKAaBGIlGuMpxKsxvGoXjWZcYArHd+PHj4+o/SFDhkheXp7MnDkzovzMRAIkkLoEvCLEsTx9ybqZahl5m6Al6k5RDGFtHKlh1pwiGfmNCEa6M78zjvxO52p2ke7M7xTh9uXspj2zJN1tyT3ypFLA6SF4xkzAi2YGEoiEAIV4JJSYhwRIgARiSCBWojEawVrVYUR7tFpV27Hnj2ZcOJqsUaNGcvToUXtVIa+bNm0qo0aNkmnTpoXMUxNjD9kZJpAACURNwCtCHACM9XjTzqXKA/pAadgkI8hy7SaK7aLZaZ125nfG7aIa7VdW3pnfaUlH/V4S4WBCIQ4KDNEQoBCPhhrLkAAJkEA1CMRCNO7du1datWqlz2itXbu2dvyWnZ1djV5VLFqdo9Uq1hb5nWiE+OzZs7XTu0WLFgmOJwsXIL7V/nBZvHixDB482DVrTY3dtTO8SQIuBJSvBe0cUfmKkPT0dJccvGUIeEmIY0xGjJvxmeO+nCLaGY9ERDtFO9ow95zl3eLO/KHixro/+prJyJLSgUI8paevRjtPIV6j+Nk4CZCAHwnEQjTiTO+GDRuKOvZM75Xu06ePrFixImY4a/I87miEeGlpqd5Pro4v02eao/9uYenSpYJl6SNGjJD58+e7ZdHe13kWuSsa3kwSAurYQLnsssvk5MmTsmPHDsHLOIbQBLwmxDFSiPEDxQWy/8hOvVQ9I7O2ZNVrHTiDGyLZCGjkdxPN9n3bbukoZ+pAeri43dKNfM78Jr1BWkvt/R3nhedm4ecSXW8qf1CIp/Ls1WzfKcRrlj9bJwES8CGB6ojGffv2acds+EP8gw8+0CL8tddekxtuuEHuuusu7TQGS9+jDbE6Wi3a9lEuGiGOcocOHRJ1NrscPnxYW7tbt24tubm52ilcYWGhwHL41FNPCY5aW7lypTRu3BjFAiEZxh7oDC9iQgC/LxBhePGSzOH111+X3r17S4sWLSLq5p/+9Cf52c9+ppfE/vSnP42ojJ8zeVGIm/k0ghxxiHIE7CEvLt0tX2txtY4fKdmt0grUvvHWOm7y6UgEH6Y+Ux5FUD/iENQIaAMvAkwwcbwssIeubQZ5Qnzbx0QhbqfB66oQoBCvCi3mJQESIIEYEYhGNB48eFAGDRokGzdu1Nbvvn37BnoDr634YwDf8LAebUiG87ijFeIYM/Z1q2PUpKCgQDIyMvSqAXinP3bsmOB75MiR8uqrr7ou5U2GsUc7b14sh3Pe4Tm/bdu2+nlHHHMIb/s4QaB9+/aVDhvnxf/kJz/RvzNYRZKsAS+QvvOd78iPf/zjSrsIKzhE+6WXXip/+ctfpDov3iptzCMZvCzE7VOE5d7RhEis0tHWjf5EUn80/U6WMhTiyTITqdcPCvHUmzP2mARIwCMEqiIaIUCuv/56UWdna8uvm6h4/PHH5dFHH9VLs2+++eaoKMXqaLWoGv9XoeoIcdPu8uXLZdu2bbJs2TJtCX/sscdkzJgxkp+fb7JU+E6GsVfolI9vPPHEExWO7MMqBzjag7j+xje+USkdCNWHH35YIMjxO4OVJBdeeKG0adOm0rKJzAAhjvHgOS0qKtJLzbt27eq65Py3v/2tTJkyRT766CNp2bJlIruZsm35RYin7ASleMcpxFN8Amuw+xTiNQifTZMACZAACFQmGiHCb731VnnppZdk1apV2hrmRq6kpET/Mf/Pf/5TH8t15513umULew/WtZo+jzsWQtwMEnWBw4YNGwTCJlxIhrGH65/f0vCiaufOnbJ//3697eI3v/mNRPKCCRbjf/zjH4LtCEuWLBH8PtgDxPyHH35ov1Uj1zjiaP369fLFF1/In//85wp9wIuIm266Keg+ltr369dPfvSjH8n3v//9oDRGQhPwuxD/8ssv9bYHOAp1huLiYr16CC8i4xnw/9NVV10lv/zlL2XgwIHxbCrhdVOIJxy5dxq0GEiABEiABJKCgNrDbCkxaKml50H9+d///V9L/a9jKSEedD9UpGPHjpbyoG4pERMqS8j7aEd5DA+Z7kxQXscttTfdebtacXBAP5RIqVY9KByKqVvFyTB2t375/Z4SrJYSz9batWvDoti1a5d15MgRS3nF1/lRxvwo0WopXwqWcmxmKV8AFerB74qymlvqpVhQurKgW+oFgKWWjVtqL7alnCNa6sVYhfKV3VBCyJozZ441b948Swkf3Ybpm/nu2bOn7rtyuqjzuNX5gx/8wFKWc0uJGrdk3gtBAM9Qr169gn5wzw9BbYPS/56qFz6uw1VbHPSz75qobqpjHi31Ukgnb9q0ybrjjjsstdUnVPaQ95WjTN0PtTUoZJ5UTbj77ruDnq1UHQf7nXgCkvgm2SIJkAAJkIAbgVCiEX9Aqr2ybkVC3lP7pK2XX345ZHqoBLwIUE7MQiVXuJ+Tk2NB5MQyxEOIK4t4pV1MhrFX2kkfZoD4hVj96quvgkav/ADol0DKOZ/+/UCeK664wtqyZYultmlYahuHNWvWLF1WWcWDytojzzzzTECwow61BSQgtvGSyQhl5eRPX+MehIg6s9763e9+Zym/A5ay8Fl4YXb27Fl71fr6rbfeskxZ1AUhDYGvVmtYyqeDBUGIfivfDhXK2m/gRQTKgwdD1Qj4WYirLQxWs2bNXIHhOcQLyO3bt7um42ajRo10Hrzk+vWvf62vo/m/RTlM1GXfeeedkG2lagKFeKrOXM33m0K85ueAPSABEiABTcAIcadohAWuqlY45dAqKqpqT63+Y0mdx11peYgQ/BGnzuOuNG9VMtSUEE+GsVeFk1/yTp8+XQtZ53iNMFWe8ANiGUIVljcT1FYOnQYrs1uYOXOmTv/e975nKZ8C1q9+9SsdR7mtW7fq69tvv11bsPE7+P7771vf/e53rYULF2rxjfZgKVdezHVeWAvtAe0iD6zdf//737WQQdxp3YfV8cYbb7QXDbpWJy1Yw4cPD3pJEJSBkbAE/CzEH3roIeu+++5z5YOXVR06dHBNw02s5MC/8fiB+B42bJi+xkukqgR1KkCgHvxueS1QiHttRhM3HgrxxLFmSyRAAiQQlkAoIR62UIwTseT14osvttQ53FY4K6Lae2up/YbaGhjjLujl5PjDL5ZL050vN9z6nAxjd+uX3++pY7pcRapyxKdFLoQtrMzKq7oW7LBwm7B69Wqdxy7OzdJ0LNlFWfzAGg0ruonD+gfxgLg66s5UF/h+4IEHdBqW6iLg25RV+88D+VAv7sN6j4C2lbM1S52AEMiDi9GjR2uhbW7Csn7u3DkTDfTts88+C9zjReQE/CrE8bzBGv7uu++6wsILoJ///Oeuabj54osvagGtnAJqwW5EOVZyRBqUXwPdB7xQQ3nMhdcChbjXZjRx46EQTxxrtkQCJEACYQkkgxBHB7EEGH+81a5d28Iyws8//9xSjrMsCBeI8/Hjx+s/qLDcFstzYx1qyiKeDGOPNUsv1If92XjmnAF7viFy8RyapbXYJmG3SuN5RR5Y/hDwQgb5T5w4YRlrOIQ7rNHIB8v1ggULdF7lQE3fc74QgrhBXnVCgc4H0Q5Bg3v4geXaiH303SxF15lDfECwX3311YFU5fzJevrpp3XcvEyoqhUyUBkvtPhz7hGfMWOG58nguce/5eZ5tA8YL4MgjNWxjfbbQdewgPfv319v8UBeLFPHc+r2+xhU8F8RrMxCeWzfwIoS1BHJS1G3upL5HoV4Ms9OcveNQjy554e9IwES8BGBZBHiQI49sOr8Zv2HkzqP28Je8KysLG0Fxx9T+MPKbT9sdacLVk3UD2s72sX+3+oEI+qr8sdfTY29OuP0YlksDTd7vCGQsXcbe6khdGFlM0La7sTQLGM3wkMdBabFMUQt7sGSDSEOazP2u0I4G+u00wGaadu59QKCAv1BPZMmTdJCG/X89a9/1XvGcY3l6xD7sBwijj3k2O6hPMC7TpU6Yk3nw55dCCOUwVYJxDFmLI+3W8hdK/HQTcwF9vpjaT++d+7cGdKBXSTDdrOI+0GI47nBM+8W8G+jOjbPLUnfw7+Z+LcYv1/43cEe79///vcWnlU4eIsk/PCHP9R1jBs3zrrtttv0NazrcFjopUAh7qXZTOxYKMQTy5utkQAJkEBIAtGIxpCVxSgBjn5giVNHgGmP7li+iz+K4xXgKO6iiy6y1q1bp8V43759wzYFgYU/Fqv7Yyym9sYSPXa0DQGijt3SjPHtFIf2/nn5GmIXQheC1PzAEoe93HgeIVCxf/WRRx4JeiEEz+koZwQvBAREM+ow9c2ePVujMxZveCJHe/aA+iFAUM7t2cBecVMfBIp96fsvfvELXQ4iCAF7w01e1If+YBz2rR8Q8fY+QnxDyKujnnRZvFDwUzBCHHvx8fO3v/1N/+DfhWhEnF+FOATz0KFDrQ8++EBbot977z3rlVde0S841BF4FkQxVoBAdOPfOzyr6ug//TuFveNId/obgfNB/Htb2TwYHyKoB78LU6ZM0Rb1qjoeTYXnnkI8FWYpOfvIc8TVvyYMJEACJJAIAmoJrNx///3VbkoJA7nuuuuqXU9VKlDiJ+LzuKtSr8mL85G7d+8uSgCJ+qNR1DJSUUuP5YYbbtBnSCvLouCcb7eg/sgW9ceiW5KoPzJFWS1F/fEn7dq1c81Tt25d3Z5roroZ77GbdtUSZ1F7gEWJSVGrDUStQJCvf/3rkp+fb7L46lsJVTlw4IDUr19fn4Gcl5cX0fiV5VjS09MDedWRZKKO5BO1wkNuueUWueyyy3QaGN96662inEeJEscyYMAAQVm191zwTOHMY2XZliuvvFLUNo1AfeYC5ZVY1vNk7plvtSdc1JJgadiwob6FucXZ5RiTsvAKnneci452EfD8Tpw4UT799FNRq01EWf/1864slvr5Q9/8GMDN/OD3Atf4vcDvBL7NT2Vs3M4RV+JJ8OPloF5UirKIuw5RrfjQaWrbUYV05QBR8P+M8l0gaqtHUPrJkyf1cz1//nz9rAYl/iuijigTtd1D//urlrcHsqgXraKWtcv//d//Be554YLniHthFmtmDBTiNcOdrZIACfiUQLxFY7ywxlOMqqXBMmjQIFHnp4taiirKCh4YBgQ4/sjBt9pHG7gf6QX6jT8k1T5fLfQjLWfPF8+xox2IC4gM84P4BRdcoEUGhLg6is7eHV7HkICymIuyfItyPijKH4KuGUJdWd9lzJgxkp2dHcPWql6V8sEgTZo0qXpBD5aw/56Y4WVmZmpR3rx5c/37Yu47v/0qxMEBwllZrzWSBg0aYCWs4N9cvJhUK0H0NV4q4YUXXjip7UD63x+I5lABQl2tjJKpU6e6ZlHbOUSt8JCOHTsGpc+ZM0dyc3P1v/dBCSkeoRBP8Qmswe5TiNcgfDZNAiRAAnYCsRCN9vpieW3EKARtt27dYlY1/hCE9Q/WF+UMzlV0qOXwohxjybx58+Tmm2+uUtuxYBqvsasjqUQ5xtNWcFhKITRMMMIClj8IcQgOhvgSgBhRvgn0T3xbYu3VIYDfE/OjluzrqvD7Yazj5tvehp+FuJ0Dr+NDgEI8Plz9UCuFuB9mmWMkARJICQKxEI3xGmg8xChEOJYGwyKJ5cG9e/d27b7aLyrK87Re1qscdOkl8q4ZXW7Ggmksxw7xbRcRe/fuFYgJLHG2ByMmYFGiELeT4XUoAni2jDANlSfR99En/MQrmN8lfKMdI8jxIsv+8kr5E5Cf/exnQd3ww9L0oAEzEjcCFOJxQ+v5iinEPT/FHCAJkECqEIiFaIzXWGMpRk0fH3zwQXniiSe0EL/pppvM7ZDfyuGVKCdcsnbtWr0cNWRGW0IsmMZy7BALyiu2XiqP/c+hRAqWiUJIYHknrOKpEDAWI4bsIijSvmOlBSzSDNERwDJ25cysyoXNvFW5YAQF7HXjmYj0ubCXi6AZnSVUGYhy+IGArwh7oBC30+B1dQhQiFeHnr/LUoj7e/45ehIggSQiEAvRGK/hxFKMmj5iL+59990n6qxlc6vSbzi3mjBhgnYEVGlmlSEWTGM1diMUIMRXrlwp6pi0gBMwLIu2ByNI4fALQiIZgxmP6ZuJV0VwmbL4Vp6Vg5ys2dN4XTmB6ghx1B6pSK68J+45qvJcmGfJ9CvSvqEcrONYGYBvrCzBihKsqlHe8YM6RiEehIORahCgEK8GPJ8XpRD3+QPA4ZMACSQPgViIxniNxvRNHXMTsz3icBqEEMobuttY4FyrTp06bkmu90y/q+usDQ7fqjt2iAMjEPAN0QDnSBAM2CtuPHPbRTms4cYiXhUh4wojxjeNWIpVv5LVIo75gMOrZHeahvnAcxVNiNUcRtN2ZWUi6ZsZOxwe4hoCHOXwjRdZ8EYPsWQPFOJ2GryuDgEK8erQ83dZCnF/zz9HTwIkkEQEYiEaox1OKh+tFm7MkTBN1NghuPHjDMZbOoQ4xAP2ixuhDmsefhAiESTOuhMRT9Z+xWrs6rxxUeeV6xcxOI5s+vTpMmrUKO1ZOlZtsJ6qEzDi2/yumBogvvHyCt8m0FmbIcHveBCgEI8HVX/USSHuj3nmKEmABFKAQCSiMZ7DSNWj1cIxiZRpTY7dCHQIC3MNCzkEhjlKK9wY/ZQG6zTO4YafAJw3jz3R8LaPY5mef/75oKPvYsUFvgzgUPC9997TVeJoM4hx+/nIsWrL1HPq1Cl9nJSJ81u0pdv8fuAbvy8m4GUQLN/4wbUzUIg7iTAeSwIU4rGk6a+6KMT9Nd8cLQmQQBITiFQ01sQQkrlv4XjEot+xqCNcH02a3cIHqzgs5TjrF8LP72HBggUyd+5c+fDDDwMo2rZtK+3bt5cWLVrI7t27ZfDgwXLbbbcF0mN18e1vf1tbw3HEHsK3vvUtLczhyT8e4fPPP9fH9OFcc7tVNx5tpUKdeCGF3wWn+DaW71Di2z42CnE7DV7HmgCFeKyJ+qc+CnH/zDVHSgIkkOQEEiX4osGQzH0LN55Y9DsWdYTrozPNbvUzYsOZx09x+BLo0qWL3qeNFQJYFv7Nb35TLrroooRgyMvL0+3gZQCs73Aq9+677wpeBMQj7Ny5U798mTdvXlws/PHoczzqxO8BBDh+IMZh6caScwhv/F7gx8367dYXCnE3KrwXKwIU4rEi6b96KMT9N+ccMQmQQJISSLTgqwqGZO5buHHEot+xqCNcH5lWOYHJkyfLH/7wBykoKEioZ3U4aevcubPuIM66X758uTzwwAOycePGuDlvgwDt3r27/Pa3v5X//M//rByOB3OY1SEQ4QhGeEdi/dYFHB9+FOL7j+zQFPYfKZADxQUOIpVH9x/ZKblZbSrNWFm+ZtnnX1jlZrVV9V1SaX2pmIFCPBVnLTn6TCGeHPPAXpAACZBATI7aihfGVBWjseh3LOqI17z4pd6nn35annrqKdmxY0fAu3yosWMfOZZ1Q9BeddVVkpubWyErBN6KFSu0hbV3794h6ywuLtbO8po2bSqrV6/Wv6OPP/64ttJWxdt/hQ7868Y///lPgX8CHFM3ZMiQgBd9CPFf/vKXeqk9TgpYvHixtsKPHj1acISf1wOEuNkDDqt3pJbvUFz8IMQhvDftXKIRFJfuluzM1tK1zSAdz8hM199lpecEwhzhSMluKSsNPjZRJ/zrwwho+z1chxP1pgxENwKEN/plXgYY0Y583fIH6zxe+MBKHXvA7zQDCURCgEI8EkrMQwIkQAIJIJDMgi9efTt+/LgsWrRIn/MLxDk5OTJw4ECpV69eTIjHot+xqCMmg/FxJUaIz5o1S+C5fNOmTdpR27Zt2+TcuXNaIGPZMvaK/9d//Ze2nKydVAMAABEbSURBVBtcf/nLX+TKK680UZk6dao88cQTgTj+iH7llVf0MXoHDx6Uv/71r1rIYy/4iBEjpE+fPvqsewjwRx99VF544QX54osv9FJ5nPfufFZxPjzafPvtt7UHfAhnWPTT08+LITSM5faweOPHhJEjRwo8+BuL+F133aWP6oPXdljmIc7HjRunBbspw+/ICHhZiG/c9bYS4Eu19RrCG+K3oPh9aZt9lYZTXLpLiku+CMRxE/eyM/N1eqg4RLtbQP2Vlbe3jzrscfQXLwH2FG1XLwoGekKQU4i7PSm8FxEB9Z8BAwmQAAmQQBIQUF6fLfUPt6XOvE6C3gR3IR59U2LIUkJGjxnjNj+4p/bjBncgitiePXt0nagvIyPDOnz4cBS1WFY8xh5VR3xcSAlWq3Xr1kE/yomd9dhjj1lKRFslJSWWErCWcqBmderUyXr99detzZs3W0oEW2pPeYDczJkzdR3f+973LCXirV/96lc6rpadW/v379f50c7111+vr//0pz/p9GeffVbXMXbs2KA+IO/QoUMt5VVdp+MZUy+SdJ6f/vSnlimvzqEP9AEXv/vd73SeG2+80VL7za3vfve7Oq6s35aywge1gfref//9oPKMVI2AslBavXr1CvqZMWNG1SpJstxFxZ9bs9/5kbVh5+Kgnu04/F5QfGnBxKC4M/1wyc6g9MrizvJucXsdSA8VR9/dxhDUoRSIOJ+tFOgyu5gkBPBWloEESIAESKCGCcRKNMZrGLEWo2rfrRbJ9957r/Xmm29aau+tFhu47t+/v6WW/VrXXntttYbTuHFjSzn0stQRV1rw9+3bN6r6Yj32qDrh80JGiEMIQ3h/+eWXFYjgxQ6EMcQ3BDoEOOJTpkzReZWjtYDART3Kwh2IQ8T/+te/1nEIYwSIYiOqlXVa38M36lTWawuifsmSJRZeCOCeOlbNUvvH9bWy2Ov8+EYafpTHd32vrKxMvyyAgMcLBAT8/v/+97/XbSqLeqAMyiGNoXoEvCbEIWDfXjvdghi3h0/2/dketRB3imC7cLZfo2AkcWd9zri9DlyHi6PsioI/ajGO8aRqoBBP1Zmr+X5TiNf8HLAHJEACJGDFSjTGC2WsxChESMeOHS3lfMlSS25Ddvfhhx/WYhzCpqoBIu2CCy6w6tevb+EPcIRXX31VC38IqfLy8ipVGauxV6lRZg4iYIR4KFGq9oVr8ar2UFtq+bgWuhCxsHxDZCMYa7ha/m3BEo10iHaz+gJl1VJ0nRf1/fznP9d5jNBWR8pZP/jBD/Q95dlc58OHsXrDKo460T4C2jUvA3B/+PDhWmir7Rg6n1pm7vosqjPkdTqEOsp9//vf1+V0pfyIioCXhLixhDtBOEW3M+4mip0iOlwcaeFEtVv94fKj/ybdjMlp3XeOMVnjFOLJOjPJ3y8K8eSfI/aQBEjAwwRiLRrjhSpWYvTTTz/Vgljt9a20q+pMaJ03lPhyq+DAgQNWt27ddDnljCsoC5aiYvm7WWYclBgmEquxh2mCSZUQMEJ8+/btrjkhkiFaYeVGgDUbP/ZgLN5qH7i+bazRJg+s5FjWPmfOHAtLyVEflperfef6+sUXX7TUWeL6GqJa+Q6w1N7vgOhXjuS0sEcdkyZN0svkUYfacx5Yio4l6OgrRD/SlLdlvRoE4twEtQddp/3tb3/T9SAfLPNYzs4QHQEvCXG3pdxuotsuqp0i2hmvTESDuhHNuHaWr2rc1IFvE7Z8uTRll6lTiJtZ5HdVCVCIV5UY85MACZBAjAjEQzTGqGsVqomVGIWVGsvOIw1169a1YDmMJCinXXrJsHKKFbCCOsthyTLEuDqj2ZkUMh6rsYdsgAmVEoA4hiDF74xbOH36tBbByIO94c6gzqEOWK5h1Xauijh16pQWzChvfpSXdgvPFAKs47CuI+BFDizpyAfRjVUba9eu1WnYy417SIN1fenSpfo+Pn7xi1/o++occkt5BLeefPLJQFvID8v7gw8+aM2fP9+64oorLIhzWOaV93Sdz77XPVApLyIi4BUhDsux02oMEW4XybiGMDYB1/Z94kY0m+XtL370Y+utjee3b6CMSTflzT173N6eW/3okwmR1Ic8CFienopL1CnE9fTxIwoC9Jqu/iJjIAESIIFEE1BCQJRDKFEWNlF7V13PJDZempVolJtvvjnRXQxqT1n/RFkJRTmS096bgxKrEMGRTxMnTpQJEyZEVGrAgAHSoUMHmT59etj84HnrrbeKWh4sOO8ZR1K5BWUFFeXQS3BslFqqLHfeeadbtqB7sRp7UKWMVIkAjiTbu3evPm4sVEEcWfbf//3fOvk73/mOtGzZUpQFXXtAx+8YPKMrq7h+PpSQFjxb8Li+bNkyfYQYjjpT+8lFLTuXvLw8ufDCC4OaUpZs7bEdN/G8wXN6q1atKhx9hr4iL86+dgacg46jyuD5HQF1rFy5UnuBV3vM9b3nnntOGjVqFFTv1q1bRW23EByjxlB1Al7xmg6P4wjm6C94I0cwHtKdcXg3X1c4R/Kzrgjkwb0FK6Zrj+VlaYd0+QyrqT6WrFenoZV6WLd7QEfhtYV/kTbZ/QNe2BG/tMXtul605fTYjvLZ9fIC+e314aizJetmyqCeY1PqzHF6TdfTzY8oCFCIRwGNRUiABEigOgTiKRqr069wZWMlRiHElSVQxo8fH665QBrOVoYogmgOF5QlUR9JBSF+0003hcuq05TVUpSXbFGWzLDiDpljNfZKO8UM1SYAMav8Achbb72ljzKDcIXgxlFkyuIsarm6flmjnKxpgY4G8Uc00saMGSPZ2dnV7gMrSD4CXhLikYpwzIJTmCN+4miZnqBWzdtrkYwIhPEnW96S1i06S4fm/XU6PuwiGXHnsWXO+p2i3Fnemd8tvWDXZ2hKifF79HcqfFCIp8IsJWcfKcSTc17YKxIgAQ8TiKdojBe2WIlRnLsMa9/Ro0cj6iqElFqSK9OmTQubH38I3Xffffq857AZbYmwisIyrxx32e5WvIzV2CvWzDvxJADLdO3atUM2gXQ8j/hh8DYBNyGufEaIWlKcUgOf8+6PZfQ1kyv02c3yjHsI5rxwI3qXrFPjVpbvncUfSlZma50HFvU1X7woRw4fDQhg5Hdaru1xZ5tOEW63jKORSEQ48jVIa6nORV8S6AfuJXugEE/2GUre/lGIJ+/csGckQAIeJRBP0RgPZFgSjCW4ECwQNmq/bdSWw9mzZ4vaIyuLFi0S5RU6bHchvtX+cFm8eLEMHjw4bF61NUunw+IeaYB1tE6dOmGzx3LsYRtiIgmQQNwIeF2IG5FtALqJcCOiIcTTG5ySHq3/M7BsHPn3FG3XxWFxdxPNpjwyIb8R+Ig78ztFuVO0I7+9Pnt5LE+HEMfLAnsbaCdZA4V4ss5M8veLQjz554g9JAES8BiBeInGeGFq0qSJ3tOqzvjWFqQ+ffqI8kgeVXPKSZW0a9dO75/F3vcuXbq41qOcXAmWpSuHV6KcV7nmScTNWI49Ef1lGyRAAhUJeEWIQ0RXtmTbTfSCiNlHPn/tw1LrXCO5qFmeXJY3Sovos6drye7CzbruysrbRTPqdctvF9nIY7eOV1Ye9WEPu5vlH3UlY6AQT8ZZSY0+UYinxjyxlyRAAiSQcAL79u3TjtmUR2n54IMPtAh/7bXX5IYbbhB1Hrco79FSFQu0GQAcZ3Xu3FkOHz6srd3KY7Tk5uaKOmNcCgsLRXkpF+WxWrCPG46s1BnrpmjCvuM19oQNgA2RAAkECHhViEO02q3GRhQbIewUvYgj7cP1L2s2JeWHpV6dLKlfO0s5bxskGZnpASs5MriVP1K6O+CczdRn+uDMjzoqE+FYIm+cuyE/hTgoMPiFAIW4X2aa4yQBEiCBKhBQZy3LoEGDZOPGjdr63bdv30BpCHB1/rEW4urs5cD9qlyoo5mkR48eAi/SGRkZ2uIOJ3bHjh3THqlHjhypnW6po8iqUm1M8sZ77DHpJCupNgG85FHnk+t61Bnh1a6PFSQvAS8KcacINiIcswDrt4kbS7gRyXDWdqC4QLJymugJa9Wkd8BDuV00O8sjbvaVo05Tn7N+Ez96tER2nHpF70M3+Vcty5NRI/J1u/hwW8IOUR+J5T9QSRJc0CKeBJOQol2gEE/RiWO3SYAESCBeBCCIE3W02vLly2Xbtm36CClYwtU539p7dX5+fryGF7beRI49bEeYGFcCOKbMiHA0hG0S9pdNcW2clSecgBeFOISx0xINsEYI20W1XTTD4dvwfv8vyPKNcvb8RnTbLdX2OuzXKOuM494nn34mjZoVSocLrwmkr17RV24ZXhfJgXumv/bxUIhrRPzwAQEKcR9MModIAiRAApESqKmj1eCZHGd6b9iwQbp27Rppd2Oar6bGHtNBsLKwBIwV3O7joF+/fkKLeFhsKZ/oRSFuJgUC1nlWuF1U20UyBG6z7LbSsElGQLCjHuQxy9kRt5dHHG0gQPib9ga0maDv2evXN9QH7hUVtJW85k3ldP3V+jYE94sLy2TUsAxXa70R5MiMftJZm8bGD48ToBD3+ARzeCRAAiRQFQI1dbRaMgjxmhp7VeaHeaMn4LSCQ4A/8MADtIRHjzRlSnpJiGMvd27WJQH27+ycJD1bjA5YxyGi22T3D4jm4pIvtOjeuOttXQbnhxtLOm44hXQkIty05yxrr+/A9r7SKLtYTjd8O7AH/MUFp2XogELdD9MH1GF/CYD4ivUL6axNU+KH1wlQiHt9hjk+EiABEqgCgZo6Ws0I8fXr10u3bt2q0OPYZa2pscduBKzJjYCbFRwC/H/+53/csvOeBwl4VYjbRTemzSmMzXLvSEW4UxSjPIS8EcoQ/flZVwT2oMMSbyzjpn04c8OS9tVrzsqxU1/KwCvz9BO1a/d+WbGpsVw7cH/gRYBbf9EehbhGxg8fEKAQ98Ekc4gkQAIkECmBmjpaLRmEeE2NPdK5Yb6qExg1alTQUXu0gledoRdKeFGI25eLY47cRC2szuZc7rb5HSssR0c5syTcWd6IcmNRt6eb5enGMo56zD0Ic1xv3XSxKJEh3+iTgWR5ZXGhnD2TI1dddlpa5DZ27a9pC/vYeXyZxsYPjxOgEPf4BHN4JEACJJAKBJJBiKcCJ/YxMgKwgt9yyy2BzBTgARS+vPCiELdPpF0k4z6EsFn6DVHbr8ewgFUb6c78bnFYwY0wRn3m2q087tmXyH/8xQtSvn+0umtJvz51dHtrPu6j4rUkq36x9O5TGlQfyjv7DIdyZgxIT+ZAr+nJPDvJ3TcK8eSeH/aOBEiABHxBgELcF9OckEHareAU4AlBnvSNeFmIu4loY+WG07Py2iXSrmXPgOUbgtd+drezvBHdZjk64ghGFDvzI81+D9f7d6jtReUNkCQNG38pRbVflMPbfyiWOpEjvZZIr74fB/qDPHYRjjgt4qDA4AcCFOJ+mGWOkQRIgASSnACE+B133KG9ptfUHvEkR8TuVULAaQXnPvBKgPko2StCHAJ1UM+xAWdtRjQb4Q0RbAQ09oWfPH1QcptdHCR67c7Y7ALaPA72OuzXSHe257xn0g98frmc3+qTJuVyTvYcLBFJqy9paWmqiCUd809K9045KF5BhKN/Wz77lEvTNR1+eJ0AhbjXZ5jjIwESIIEkIPDMM8/I/fffX+2evPHGG3LddddVux5W4B0CTmdstIJ7Z25jNRIvCnEjeu0iHLwQj1SEG8dqhrO9TqdId4pylEF+BFjLzfWWDblKcNfWolttEZfiY2Wyav1Z6dQlU+eFFi8vt+QWdYzZziMfVHhJAI/vn2x5S71wuEfnT4UPLk1PhVlKzj7+fwAAAP//axoU6gAAQABJREFU7Z0HfFXFtsZXAiENSEJJiPQmQQxFpCoCCggqRbqgYq8oYkF9eu3lKhbsgoqKHQRBsaFSVar03nsILaElBEjy9jfcdZxsTkiAAOecfPP7cfaesmdm/ye+d7+91qwJynaSMJEACZAACZDAaSYwZ84cOXTokNdRfvrpJ3n++efl008/lRo1anhtExoaKg0bNvRax8LCSWDGjBnSq1cv8/LNmjWT++67T5o2bVo4YfCtcyXwzz//yO23356jfujQoX73f0++mPSIJFa9TOpWaSspB9dLTFgV8064n71qrAQfCTf5tIy90qJ+D089CucmfSZVY1qYMrRflzJNLoi/zrTXn7UpUyQmvLKkpG+Q1IMbPfUoR6oW01Kbil2G/pCWzI2SkGLhcuRIkAQHB0tmZpD8NPGQcy9yTsVMKV0qVCQoW4IlSLKysqTdZds8c8T8osMqmTF+n+esTe3LPXWmcx/+ufDCC3PMDv+/jokE8kMgiEI8P5jYhgRIgARI4HQS+Pjjj+Wmm26S+fPnS7169U7nUOw7QAi8/vrrMmTIEPM2EOADBw4MkDfjaxQ0gUAU4jaj72e/JE1qdpe46OqycP1v8vfyL6V5Qh8j2NHOLZrnJX0hFcIvkdnLfnSeqSqRESUkoXJzI8AhxFF/adXHzBB41hbl7v5Qj2dWL42VI4eLSVBwtsDEt+9Alvz+p/PhNTtIioUEGzEeWfKgVK4cLkFBQU43EOqH5ZorI2Rd6lQzlgp9CPFqVRJyCH/TwEd/KMR9dGH8YFoU4n6wSJwiCZAACQQ6AQrxQF/hgns/WMEhwKdPny60ghcc10DuKZCF+PQVX0pkaFmP6F6YPEoqRjWSTdtWOSK7mhzI3myWVkUuLM+bN2+ViKLRjnW9jalfvHqGhIWFSrvE+2TiuuelQXxfj+Ucolzz6AjWby2D5Xzdmj0SkdXBEddOpSPApWim/DHloBw4GCKSdVSUo0qKBDsCPdNply3n1QmVokVhNUd9kMQU3yXtWsSjlUmjpj8mPZo9r1mfv1KI+/wS+ewEKcR9dmk4MRIgARIoPAQoxAvPWp/Km9IKfir0Cu+zgSrEYY1eu365tGlw1O1++ub3JC7yPGNJhmX8t/nvS2L1FlKuRB2z+Nv2LZH9aSlSI+5iI9zx/P5D2yUqtLysWrdIikSmSf1KV3tcwiHKq0Rf7LFMQ4T/vWysVI/qKdu2BjnivYxkZIgkbU+XXSnBsjXZGSYrWIoUETkC0Z1dxFjIsxwX9aAiWU4lXNLhnZ7t3IkUC8uS6BiR6KgQCQtH3SEpHrXccW9fKe2bXGvm7A8/FOL+sEq+OUcKcd9cF86KBEiABAoVAQrxQrXcJ/WyKsJpBT8pfIX6oUAS4m0a3Gpc0NUl/J9lvxrLdnLGPGeNs6VuXA+z1hMWHd22Ua9iR5PftGe27N67TTZuWSUdm97viOAisnznzxIZUsY8s3LbNJm39ifp1fxF0x79I0XuT5S5P2yTw/tLyv4jIZIWFCYpmdmS5li4jwQVlWBHUR9y8kUcV/PgItmOu7nzkGP1htCGgTzLEd1FHGWe7bRBocmj8n//4MYe5FjOg5yOjjgiPuNQllSsMVr6XHWReU+npc8nCnGfXyKfnSCFuM8uDSdGAiRAAoWHAIV44Vnrk3nT3r17G1d07gU/GXp8JhCFOKzTCNaWnLpGJi/9UCpVqiTNKtxpFhuieuL84XJH+49NHm3hRg73dOy/hks6hPuBwzvMMxDdqzfPM5ZzBIJDQpm6s2seV+wHR6C3/UtKS+mlDST0YFHJcgT5geAiMiXjiOyQMGf7d5ajxZ094EeyTNC2rOwsxxXdEe6ZTrkjuBGo7dDhIFmwNE0OZ0aasqCgg3JR/S3y3KPny5eTH3Ws/Ec/OGBMX08U4r6+Qr47Pwpx310bzowESIAECg0BCvFCs9Qn/KIU4SeMjA+4CASiENdXhMhGxPQdO7eZiOprts6RTSnzpNdFL+awnGuEdQj3mau+lSqVqjt7yS80Ah19wcU9NqaaVCxX03St7ZHBGJog6CHG7frFgzdJWHaowOgd4li3lxUtKrOzIx2xfdh5zHFVRwR1R3xnOVbwtLQgmbfMcVUPdqKpSzHJDs6U94bMkoOyxROlHdHhG9ZqJwnnXKrD+vSVQtynl8enJ0ch7tPLw8mRAAmQQOEgQCFeONb5RN9SRfg333zDY8lOFB7bewgEshBXyzUENtLiLT9L9qEQs29c3cttyzZE9fINf8uCdb9JTMly5pkjGdlGxBePKnaMyEYfKrxVkNsiXMcosqKWpE+Ee7rTpROALdsR359nhjh+50Wd48wyHTGeJTOXOkI8He7sjmJ3UvFiqfLl8COe4G/oF/1Nn/8zLeKGEH8CnQCFeKCvMN+PBEiABPyAwJkW4s7RnbJr1y4pVaqUcZ0saEQpKSkSGRkpxYoVK+iuC01/KsLpjl5olvy0vWigCnEIY1sUIyI6zgZHoDYEZitTprTHygy4ELkQ5SquYd2ODKrgsZyjjS3aVWSjzJsId5fNmPaDxPzdxFjAsxzLuKPG5VMJN/vD/5p7WLIkxLGEO8VOVYVyKfLV2xXM+eZ6frjO66fp71OIYzGYAp4AhXjALzFfkARIgAR8n8CZEOKpqany66+/ytSpU2XSpEly4MABueuuu+Thhx8+BtCcOXNkxYoV0rBhQ0lISPDUp6enyzvvvCOLFi2SChUqyEUXXSQdOnSQw44LpvaL68aNG8156N9//73nWd7kn4AGZqMIzz8ztsydgDchjv/G/S3BZVv3TrtFuIpYCHPUffPX/8nVDZ/1BDzTen1n3TOOPNrrkWQq7N0i3G6vz+Bqt4dgn/fuailxwAmFjuScaZbh7Av/z5pg2b7bsY47wdgQyK2I444+ZVRUDnGP8dan/mnOL7ff82hHvv1L13TfXh9fnh2FuC+vDudGAiRAAoWEwKkK8d9//12ef/55efvtt6VOnaNH9QDd5MmTpWzZslKiRAlp0aKFoQlLdadOnaR8+fLSqlUrSUxM9FCG0L733ntlwoQJnjLkH3jgAZO/9dZbPXXoB2K+TZs2RnivXLnStLnyyivl3HPPldq1a8vll1/u6Yc3+SNAEZ4/TmyVfwKBKMTtt1eRraIYx45VCL9EZi/7Ufq2/q8RvPbebrWcow9vItwuM21c+8Ld46G/qjEtPKJ81eAd4sRnc3R4kBwOPiyfHFovM+bVlSwnojr2hI8YnCml4veYV9APBwgAp5ZxCnGDhj+FgACFeCFYZL4iCZAACfg6gVMV4k888YR8+umnMn78+BzCGuK7cePGcsstt0j79u0Nhl69esnjjz8uJUuWPAbLzTffLBD1Dz74oEBQo99p06bJ8uXLZdOmTdK2bVtp2bKlDB8+3BzJg7oPP/xQ5s2bJ3v37pUGDRrIkCFDpEqVKsf0zYL8EahcubJpuGHDhvw9wFYkkAeBQBbituUaGGxRDBf19Tvmyvk1mnr2eaN9bu7neN4W4SrebWu4ezy3KMfz679Kk+I7yhrr98FOCyUyNkRuuL2Rc2JZtqPEM+XjYThq7agbPNrrOPohgULc4OFPISBAIV4IFpmvSAIkQAK+TuBUhbgK6DVr1jjH5BQ1r/v333/LNddcI4MGDZK7775bli1bJrC2wj0d1uyBAwea+uLFi5v2f/75p/Tt29fcQ6zjiJ1Ro0YJ3A5Hjx4tP/zwg/Tv319GjhwpTZo0yYF0x44dMnToUPnggw9Meb9+/eT22283VvccDZk5LgFaw4+Lh5UnSSBQhbg3UQxEttD+fvZLEhYWKu0S7/PsEVeMKoLt9hDysEwjIUibLcLRHpZr7ENHco+vZehv1eDd4kRpkyJ3LDP9dL8u2nFTLyK9u6+Q5q2XeSzobiGPPIO1Gbz8KQQEKMQLwSLzFUmABEjA1wmcqhCH+/i4ceMEYrpixYqye/du6dy5s3EZf/HFF6VPnz4eBEuWLDFWa7ifQ5A//fTT0qNHD4GYnz59uhHbEOBr1641+7wHDx4stWrVks8++8xY0rHvu169ep7+7Jvk5GQZNmyYsZKjHJb4Rx991PNxwG7L+2MJUIgfy4Qlp04gEIW4W0R7E8VosyZ5hixa9ack1rxYqsc5lnFnDzkS6pA0j3vtQwW4RktHHdpjH/mlVR9D1tPWFvF4XvPLXk4WKZItoTetMGX/99xCmTavujz7/EipFFfNlLnngOcx5j/LfjVnncdFVzdj+foP94j7+gr57vwoxH13bTgzEiABEigUBDZv3mzEc7Bz3A2s2du2bZOYmP8F+8knAewFhxW6UqVKxhUdwdgOHjxonsb/SBoxYoSxhCOaedeuXU00cwjyl19+2ewjh1v7a6+9JpmZmcbyjblkZGRIaGioZwZwR4dohys6XNTttH79emM1v/HGG00kdgjy9957T/CB4c4775RHHnnEbs77XAgwUnouYFh8SgQCSYhjzzeSLXpVQKsIRj1Errp8Q4xv3b5BOjU6GphSBe/xRLjdF/qzXd69jYd621Ke+UEdSSm+SRrf1QCPy569B6XjTVnyyQezPSJc54d52H3+Pm+oVIiryXPEDTn+BDIBCvFAXl2+GwmQAAn4AYGoqCiBe/iPP/5oopRjTzcs0yeafvnlFxOwDRHLEYztoYcekgULFhixjb3ccEm/7bbbJD4+Xtq1aycRERGyePFiswf8hhtuELiXYw5uCzrmgYjrEPsDBgyQd9991+wft+e3fft2adSokbGwX3HFFc6xQWUEYnzMmDFSrVo1E6Xdbs977wR0fzjPDffOh6UnRyBQhDgEapsGt+eAYAtYrdAytWyjfP+eQ6YaZ4Uj2ULbtkzb96ah85OXCMd4aj3X+y3vFJXoRuFS4ZLSphv02+m6MjJtVPEcHwncIhyNKcQNMv4UAgIU4oVgkfmKJEACJOCLBLZu3WpcvNPS0szRXzgqDMK1W7duxqUbLt6IultQCdZuCG1YtCHQkeCa3rp1axOU7dChQ8adHeeLI1Aboqnv3LlTJk6caNzU4WKOaOiXXHKJV1dzBGxD1HZ8REA0daTmzZubPeoI4saUNwEK8bwZscWJEwhkIW5bokFGRTjuVYir6MZ+8ezw3XJJjTs9LunaHm1sK7pay73Va3/2eCjTthg3dXyEhNTKkgqJ5Uy/aNu5bxkZ98VO3Hr2nuuY2ifyv8/7QHo0e96084cfuqb7wyr55hwpxH1zXTgrEiABEghoArA+49ivhQsXGuHatGlTz/tCgCPQGa44Lux0JIjy7OzsYwQ15vX111/Lb7/95hHrmCdc0bt3735M+9zmBlFfrNhRy1NubVh+LAEK8WOZsOTUCQSqED8REQ6K4xY/LLu3HZAb27xtoEI4qyVbBbHm0UCFtYrk3MZDvfv59ZO3SeWW5WRd6r9jXNJ9h+OavtSMrc/Ywd+0j7Xrlx9j+TcP+egPhbiPLowfTItC3A8WiVMkARIggUAigGjk2KeNo8ZgfYZrujs9++yzxkoNF+WePXu6q89I/ogT8VcjsJ+RATmIUIjzj+B0EAhEIW6LaDCDiLXP4kZerdpajz3Z6qJesVxNTwR07cuOkO4W4cirIEd/3upRrm0y0g7Jlozpknpwo9k7jvbvvBkud9+bbtosXbpNtoV/5An+hvmuWp8kjROaGdd0tws++vbVRCHuqyvj+/OiEPf9NeIMSYAESCBgCECE40gxHAE2a9Yss6/a28ulp6ebI8KwhxtHgiGiOVPgE1Ahft9995nj5QL/jfmGZ4JAoAjx3+a+L3WrtZUD2Zs9lmzwc4vwvETzV1P/T7KKONtnDoVL0dAgqVQ6UeJiy3tENPpTUb5oSbLsDv5TzinRUGpWqGKWyxbh27anSWjJ7Z7AcCr+0QeirDeI7yvLJkRLuRYLzJxXzCwjTVsWz7Hv3B4PA+AccQ1KZwb08R8KcR9fIB+eHoW4Dy8Op0YCJEACgUYAAdReeeUVI8RxZFheqXbt2ibo2dy5c6VKlSp5NWe9nxPQqOnNmjUzH2Jmzpxptgr4+Wtx+meZgL8LcWyl2bt3r0xZNEKanH+FhEWE5LB2T1z3vFSJvtgIaYjk47mXJ6eukRFT75POjQeZVdmbsUUOH8ySA2n7jDu4WxQv3TpF5kxPkOu6xpqYHcM+T5VaDSfIin8ul9uujZJVm9fK/LlH5Ny6ybL870Rpf2WoLJ+/X3ZGD5fmtXqZMZZMKCElqu6SuufXlD8n75Dy9ZfJ+pXhcmmTxjJ+QopUTlgqW1YlSM3qJaR6lWIU4mf5vxcOf+YIUIifOdYciQRIgAQKPQFYDnDm9/XXX59vFgh09thjj5k92vl+iA39hsCMGTNEYwS4hfiQIUNkw4YNfvMunKhvEvBnIY5jGHE84pdffikR5fZJq5atpcl5HT2BLI8X0dy2XOvKjJ37uMRFnifNavUx7uX7D22XunE9jDt4YtU2UiysiEfk289/NTZVeneOktGT/pDul7aRjz5LldZXLZCUncGO8K4gZUqUkr1OHLZ2V4bIir3fSpmSlc2Hgd3p62TVbJHsw2UlKESkVO3ZsmdnmGSnVZFGDeNl3G9LpXPb8+SL7/ZK4wZhklnsT/lnxQRaxHXBeA1oAhTiAb28fDkSIAES8C0CCJCGdCLR0A8fPiwhIc7/gmMKSAIqviG4Icp79TpqRcPLwjKO4HlMJHAqBNxCHCc0DB069FS6PCPPYovOX3/9JYiVgf82Ei84Vzr1aSw92z4kwcHBOdy7bdGMycGybQdCQxnaTPlnlCdY2/TN70lCmQ5GeC9c/5ts27dE2iXeh6aePeDz/6omVSuFy5zFIdKw9ffyz8TO0rxhpsxamCb1WkwUOVBZFk2vL+0uLSozJx2RGpfMlWKh2XJuXGszhz07sqVS+cpmvnOWzpTq1eJk3YoycvjwXilbfZUsn1NNrmhbUUaM3i3Vz90kRYrPk/2pGQzWZlaBP4FOgEI80FeY70cCJEACJEACPkxAxTf2hUN420Kce8V9eOH8aGr+JsRx6gJc0bE1Y+zYsTJ79mzZv3+/1DqvqvS+tbX0vfIR2XNoo1kB7Ml2u5OjAqJbA6dp3lydiOTVqiRI8oGlEhlSxljDUT59xZcSGVpW6lZpm2t/aKd9on/bBR519scAzAlJ94zbdSj3Nj8EdiuaUU62p6ylEAckpoAnQCEe8EvMFyQBEiABEiAB3yagVnFY/ijEfXut/HF2/iTE9+zZI0uWLJGff/5Z1qxZI3PmzJGMjAyJiIiQ/gNvlcjKSdKhyR0SF13dsxTeRK0tkm0RPGHREEk7vNMEZ2tW4U7Tx8Lkb2XR0jked3Bv/aGhinC3yEadPQbu7fHd1nlv9RrY7afp70ti1cvMBwH06w8Jx23ib0wT1oyJBPJDgEI8P5TYhgRIgARIgARI4LQRUKs4LOJI06dPN1cIc90/bgr4QwInQcDXhTiOSsSJEikpKfLZZ5/J5MmTZevWrZKamiphYWGC+mLFismHH34oe4rOkyJFgj0WY2+i2RbBtkBWQbxz5y7ZsXObEbzLtk6UI5np0ibxHiPuvfUH5Cciwt3tIbIvrfqYWTl7PqbA+dEyHK22aN0fFOIKhteAJ0AhHvBLzBckARIgARIgAd8n8PrrrwuCs0GMU4j7/nr50wx9VYhDYGMfOOIjJCcny6effmr+9lGOPeAxMTHmQxSCtSFoJU6dSM/a4QRW+8AR4rcec4yZClpbNOsxZFgvCHE7v3LbNEHU9IpRjfIlwtG/LfLRpz2mCv0L4q9DlUnHCyaHBngGCS7sOLbM36zhmDst4qDAdDIEKMRPhhqfIQESIAESIAESKHAC6qKuHdMiriR4PRUCvijEIcA3b94sU6dOlQULFpj94LCIY384Uvny5aVr166CAJdjxowRxEvo1KmTsZBDsCK6ecOEdjks1bbIdotuW/AqS4hoFe1uke1+HvXYw22LbHcbiG673hbh7raYgz0nBItD8rf94ZgzhTgoMJ0MAQrxk6HGZ0iABEiABEiABE4LgcqVK3v6pRD3oODNKRDwFSGOvd44BeLAgQNGeEOAL1y40FjFa9asKYsWLZK1a9dKeHi4XH755dKyZUuZMmWKlCxZUvr16yf4bwOWcpwFDqt4XHTVXF3UIXLtQGluS7Zd700ku0U1zipX93IshfsZd3vbUo72x6tXEY52cdHVjHUe9/6SKMT9ZaV8b54U4r63JpwRCZAACZAACfg9gU27Uz3vsGXXv/ebU/Z4ynFTISYqR37KnLky9L33TNntd94p1asfDUrlble+dLTnuYql/r33FPKGBP5H4GwL8czMTCO2582bJ1u2bJFJkyZJWlqaEeQXXHCB1KlTx5T9/vvvRmi3atVKHnjgAZk2bZq85/y3AGt4586djTVcF1XFOFy5K5ar6RHdqLct3RDM7mPMtA+92u1RZot05G3Lttbb1nc8bwt9twh39488rOtVY1rIpm2r0KUnIWq7vyUKcX9bMd+ZL4W476wFZ0ICJEACJEACfkFARbYK7EWbk8y8V23dbq77DmZIdGSE7DuSKSWKFvG8UxlHMMcU+/dM+JRDh6V0ieJSOrSoabMr44i5zpp41E21Q4cOsnJnao5ndjrHOGnKOHS0vebLxZQ0t4kV4s1VxTqFuhIqnNezLcTVBR3WbUREr1WrljRq1EguvPBCqVKlikyYMEFGjx4ty5Ytkx49ekiXLl0kNDRU3nzzTRPA7f777xcI9pCQf//bwUraYhx5PXpMLeEoc4tgt8h259HeLartPNq7RTjGsV3c3Xn38wjedk5oM8cNP9O4oqN9bEw1v4qUjjlrohBXEryeKAEK8RMlxvYkQAIkQAIkUMgIQHjPXL3BvPUW535v2kEp4giFNEdIh2ZnmfI051IjvqxEhRwV3rBgr96bLjVKhosK4u3pGRIbHipJ+9NN5Ocd6YckOjtTkpz+4iPCPO01v8sR2umOG2/SgXSJjwyXPYczTf+a35R+WCqGh4jOCXPIdNx/Ne07eNDclggLNdcWtY9a18s64r9pzSrajNcAJ3C2hTiOs/rtt99k3bp10qJFC+NijuBrENaLFy8WBCpcunSpJCQkyG233SbNmzeXX3/9VV544QWzL/zmm2+W2NhYCQoK8rpScO1GtHFYxqPDK3kErTdRrYIZHblFurf2aKfP5CXC86pHX78se0b2pWbIoYOZyBr3+sSqbfzOHd1M/n8/FOI2Dd6fCAEK8ROhxbYkQAIkQAIkUIgIzFi1XuBKDqFb3rFmVy4TLfM3bJW0jEOy43C21I+LkSY1ju7pDi4WKuWLh8sWR2Tb1znJKXKh004T8ppQrvn4yDDznDuP/pIOHBXUaJ9XHn2jDVLWoX9F+ehFq6WkI/qXb0mWpJSjrvLxzseChPLl5LzycRTmhlhg/riFOMQu/p2phKPJVqxYITVq1BC4qcPajb3is2bNkpdeeklWr15tRPg999wjF198sakbPHiwiaA+YMAAueyyy8wzx5svRDDcvBHsTFOK4/5dK76VyaambzRXCHVtg/qYsEra3HFJP/G89o9OMAb616R5HU/LcfV38W2/C4W4TYP3J0KAQvxEaLEtCZAACZAACRQCArYFHOJ7YdIu2b9vnwQ7AqJLvQSBZbuhI15VNAPJ8US11quIVtGdWx7ttY2OYee3ORbyq6qdk0OUa/3x+lQhP2nFetm4bbuUc8Q/0uw1GwRW866N6xnrPV3ZDZaA+oEbuKYzLcR1XL0iEjo+DuDM8L/++kuqVq0qjz76qNStW9fsER87dqw89dRT5ii/hx9+2LivI0jbySS4r5+ulJz6r+h3j4Gga3ZClHck223ervfnewpxf169szt3CvGzy5+jkwAJkAAJkIBPEfh21gITQA3u5HBHhzt4u9rVjDt5cGiYRyCr4MXkvYlwu15FMkS13uM5O2+31z5RhgSLuI4BEd4g9qiFXS3ldnuU6Rju5915WPyXbN8toWHhcmjfXsH+89CiRaVJ9cq0kBvygfPjK0IcInznzp3ywQcfCE4FiIuLk6uvvlq6d+8upUuXNseXvfLKKzJy5EiBS/ott9wikZGRfr0QsNYHogDXRaEQVxK8nigBCvETJcb2JEACJEACJBCgBCBMZzrWYbihI1V13LZh+UZSyzQEsZ2H6HULYrse9yqi9R7X8Wu3OhbpcI9o1v61jYp0FeFoHxJcREqHF/P0p2Nr/7ucPefHq9d2esXzSJuSHHd1Z5/6ZscFf4Xjul6vfKzc2rqZqeNPYBDwBSEOt/Q9e/bIsGHD5NNPP5XixYvLTTfdJD179pRSpUqZM8NnzJghL7/8stkP/uCDD5qgbsWKFfO7RVi1apVxvT/33HPNnnjscQ/URCEeqCt7+t+LQvz0M+YIJEACJEACJOAXBF4e/4eZ5yW1qkvRklFG8KJARbJampFX0Wu/mNa7Lc/enl+Vsk9qxpQwY6goR18qkosEB0lmVrYnb7d3i3R7Pvq8W6TrPLUeeZ2v9jd/wxbZun2HzF27SS5LrGX2v9NNXcn599UXhPi2bdtkzJgxMm7cONm+fbuxeEOIR0REGLgQ6rCUv/POO8ZK/tBDD0mJEiUKBDyOTYuPjzeu7+4OU1JSBGK/IC3vkydPluuuu04QMR4J79GmTRu58cYbpWPHju4p+HWeQtyvl++sTp5C/Kzi5+AkQAIkQAIk4DsEIMRTHHdwHDPWu3nDHMHTMEsVsSqKIWTnbU8xlm273i2CVbTr8+68W3SjL90HDpGs4+F5iHZ1TUdQONRr0v7dzyOvohsfCdSCb7fX+sdG/yIxztFrIZJt9oxTiCtd/76ebSF+0IngP3HiRCO0k5OTjRiFO3q1atWcEwSO7p+GaIU1fOrUqeYccdSHh4efMvjdu3cbt/cNGzZIpUr/BlPTjhs2bCiwvl9zzTValOOKcvDD2eYa5X3IkCH5+khQsWJFsxceLvmIBI/gdP3795e33norxxj+nIGHA/5pQpR8JhLIDwEK8fxQYhsSIAESIAESKAQE4Jo+dcUa2blnvwlaFlOqtMTFls3h7q0iGsL153VJ0qFqfI493Cpu9artNX88ka4i2RbhdnsV4equ7q29LhOeQ7JFt7a326A+KwP/MuT3ZWukUrk4ub7x+QIWSDzmzGDw+5+zKcT3O7EH/vzzT3nxxReNJRxHmCFgXP369aWoE5MACZHVccQZoqUjeNvAgQPNeeMq0k9lAf7++29jYccHAHdKT083Fnm4kiOqu7dUsmRJ2ecEa0xNTTWCc9CgQTJq1Cizr91be7sMR64hMB3OQUeaOXOmNG3aVBYtWiTnn3++3dRv7ynE/XbpzvrEKcTP+hJwAiRAAiRAAiTgGwRUfGI2K1P2y9KNjjtr8TATvGznwUMikcWN9Rui2i2K1b3bLZLRF9pDBEO413cCrakoR52KbtyrUFbxjTrsI7fbow6WcCR1aXeLbm1j1+MZzePZoMMZsmTrTomQTM/xbFXPiZOGFcqhmkLcUAicn7MlxOFu/vXXX8vo0aNl7dq1AuvzI488ksMSDsoHDhyQjz76SL799lu5/vrrpU+fPh6X9VNdBUReh0X+jTfeOKarH3/8Ue6//35zvNoxlU7B1q1bpXz58qYK4nv48OHy888/y8cffyw33HCDt0dylEGIIzI8zkZHwpFtTZo0MVZ/fJAIhGQLcazv0KFDA+G1+A5ngACF+BmAzCFIgARIgARIwB8IqBCf67ibN65RxQhgLftyzhKpHlPciHKI9LLRJaTuObFe3dfxrirM9UxxiPCG5UpJg7LRHndyFcxob4tw5O32WodyfUZFdX5EOM4Tn7gx2bF6H5L9aQeM9XvfoSOSUK7MMeegYwwkvDfOUO/uHGnG5P8EzoYQhzs2LM1ww540aZJx70YkdIg1uJxDpNoJIhzni8NiDBfygrCGHzlyxAhpRGhv1aqVPZy5h9t57dq15YknnjimDgV4rnfv3lKhQgXzYWDlypWmHcTnrbfe6vUZuxDv2KNHD/PhYd26dSYaPN5v2rRpHm8Au70/3lOI++Oq+cacKcR9Yx04CxIgARIgARI46wQgPhE9PD4iLIdLti2qR8xabOapluQjRUOkrOO6WiK0qHkOx55td/ooFx2Vw3IN4XtNQiWP4EYnsHQjoX+4sJ9fpqTJY9852l9SoazJ4weWdhXh2t6OkA738qS0DLkgNlomrd4shzLSJf1IphHdmCPSRZXPMS73i3fukQ7nVjZl+IHQVyu75hFJnULcg8jvb860EIeg3rFjh7FCIzhb5cqVpV+/fmZvOKKlu0W4AkZUdQQ2O9lzw7UfvcLijYBwCNambvBah2PUypYtK0uXLjViXMvt6xVXXCFwrYf1Gx8RMDd8SICr+auvvmo39Xqvbu12JQT8+++/X2DvaPd9Nu4pxM8G9cAYk0I8MNaRb0ECJEACJEACp0zAmxC3RTjukVRAq6UaebWcw1quIh1tk/YfNNbzYkWCHSEdLgt3pEpdxypeJDTUCHe03+3sz65cMtLk4QK/OnWfye/LOCKlnOPKDjr7WJMcN3U8v2HvAXQr6A/7utc4z19SvYIpqxATZVzqYa0/v0yUKUs+nG2uOme8g96jwi3C9X0pxA22gPmxhThchyEmT1eCFXrFihXG1RzHkSF/zz33mD3VBRF87UTmDTd3nE/++uuvH/MY3MtxZvmSJUuOqUPBwoULpV69esaKjbPOu3btKh06dDDu6j/99JPZ++31QauwVq1a5ix0BIND1Pjx48eboG8ISnfHHXdYLf33lkLcf9fubM+cQvxsrwDHJwESIAESIAEfIaBiGtNBkDIVpbAWH0+E6/Tt9iiDyF24dbvEBB0VwzOSdkkN58gyCGskFdUQ4Uipjut4dOjRM5O1DlZsTSnZR115IbIRzdw9nnuO3vJqVUefqLdFud0fWNAiruT9/3omhfjq1avl999/ly+//FJgEYYYhlt4mTJlzrgVGB8cYPV+7LHHJCoqSnBUGSzh2Pc9duxY+eKLL0wANuRh+UYdrPEQ3Oedd56kpaWZve0hIUe9SvCXMGHCBLn88stNX9HR0V7/OLA3Hlb/9u3bm6Bsr732mqddr169JC4uTt58801PmT/fUIj78+qd3blTiJ9d/hydBEiABEiABHyGgC3EcY64ila3oMWEsUf7qmr/imR3G4hwJHX5tkUuym1ruuZxVdFvB3FDubv//OTtPry11/dD/+75UIiDSuCkMyXEEV38888/N8HMIMLbtm0r1157ba5neJ9uwgjQdt9993kdBsHhUIc5uxOEM6zXffv2Na7tdj0Cy8G9HkK+c+fOdpXnvl27dgJvAHgA4AME+slwPFgQPR7HuNmR1D0P+ekN3gVniSPhwweDtfnpQp6FaVOInwXoHJIESIAESIAEfJGACnEEa+tcL8GIYghU7Nm2RbdbhLtFrFt0u0Uw3t3uQ5+HMEbSY9FUxGu9Wq/d/bnniLzdh7u9O+/uH3N49695EhsSzGBtgBEA6XQLcRw/hn3hsDCPHDlScFRYz549zb7wcuXKnXFLuL1kEM6whCNFRkYKgshh/zqOK8O8cQ/3+YiICLOPHOeOQzzjQ0JuCUIdwddyOw8cHHAcG/qC0A91tqLExsaaMWERh8t6oCQK8UBZyTP/HhTiZ545RyQBEiABEiABnySgQhyT0/OzbcGMcohY25KsZbmJZLfoRXu7T61HOfr4cNFacza5inCUo01u/aMez9ySWA23JqF/97Fn+rw30W33jw7QhnvEj7IMlF9biOMM74JOOI8be6ARDXz79u2SmJhoApphf3ZBBV4r6Dmzv4IhACGOf0i0iBcM08LSC4V4YVlpvicJkAAJkAAJ5EFg0+5Umbl6gyDomXuPOB5V0ayiVss0j/r5jvW8Q9V4jzVdzxXXodFGhbzea5v89G+7m6NPiO4GztnkKtxtoW3fo607r2X6LPKYA96HrumgETjJFuIF/VawMMMaDjEOyzL2U6t1uaDHYn++TYBC3LfXx9dmRyHuayvC+ZAACZAACZDAWSJgC3F7jzimk5dIhsi13cGRd7u0232oKLZFOcZRUY97tFGRnFd/2h5Xfca2vGu91nnLY346HwpxEAqchD28arUsyLdSEQ73bwQogwjHvmhcczuirCDHZ1++RQDeFqfD48K33pKzKSgCFOIFRZL9kAAJkAAJkICfE1Ahvv1wljSuUcUjim0Bra9oi1aUuV3K3SLY7sMtwjVvi/C8+nfXow8kFdqot/tDvdahnbseeSR9hkLc4AioH0S3RrIFuX1/oi+rIlwt4TinG3uw3ed1n2i/bO97BGDpPl5Cvf47XjvWkYBNgELcpsF7EiABEiABEijEBCDExy5YLo0qlPPsEc+PSHaLWOTVsgyc3upRDtHrFtDa3n7e7X6eV//u9t5EuN2/t/lt2pYsmU6U5+6N62FKTIWEgC3M7Xv368+ePVv27Nlj9oVjbziiiKtbOi3hblonn89LAGvP+WmXV5u86nUsXkmgoAhQiBcUSfZDAiRAAiRAAn5OQC3iukccr3M8yzbqvYlYlKtlGSLYdlFXYQ8hjL3htiDOb39wgdfgbBgf+8Z1n7hbpHsT4e756R51lOv8juzdw3PEAYTpGALYD47ztl944QVzFFdCQoJ07NhROnXqJDExMXm6pB9P4B8zmJeCU33eS5deiwpamBZ0f14nzUIS8CMCFOJ+tFicKgmQAAmQAAmcTgJuIQ5Rq4Ia4+Ylut31eMYt5FXoog7J7l/rtMxb3ts+9NwipOc1f4zvnp8+Q9d00GFyE4DVe/ny5ebYLkRIj46OlkGDBkmHDh3MEV3u9syTAAmQQG4EKMRzI8NyEiABEiABEihkBGwhjmBtKoiBwS2y3SLZXY9nbBdxtEdSK7ht1Ua5t/5OxFruHh95+3l3/xjTnp/OQfeRU4iDCJNNAJbwNWvWmHPC//jjD+OOfvfdd8tll11GEW6D4j0JkEC+CFCI5wsTG5EACZAACZBA4BNQIY5gbZ3rJXiCm52MyLWfwT1EPcQwhK47sBvIahu9x9X+EOAW0tqXt/b22Kh3P+vtGbs/1FOIgwKTEkBgtiVLlsivv/4qY8eONSK8e/fu0r59e4mPj+dZ4QqKVxIggXwToBDPNyo2JAESIAESIIHAJgAhnlewNrfIBRFv7t0oh5BGe1imkSDC3VZolOdHhOvzuEI0a94tst15tDve/FBvj488+tiUlMw94oDBZIKw7d27V95880355ptvpHr16tKmTRvp0aMHRTj/PkiABE6aAIX4SaPjgyRAAiRAAiQQWAQgxMfMWiBNqlc2UdPdotabCHeLWLsN7m0RbtcpOVskYzx7DzjaaB/qMq5z0n69BYKzLel43p1Hv1rm7l/b0yIOSkw4G3zlypXy9ddfy/jx4yUrK0vgjt6nTx+JiIggIBIgARI4aQIU4ieNjg+SAAmQAAmQQOAReOOXKR4h7hbJtujFm6toVQrII0HkqmDWvF1nGjk/KNN93LmJcH0eV+1Dn7Hnh3r0oYIdedRrNHXk7fE0j6s3UU4hDjKFO8EdfcOGDTJq1CgjwkNDQ6V3797StWtXiYqKyjM6euGmx7cnARLIiwCFeF6EWE8CJEACJEAChYiACnE7WBsErjcRroIYeFQk26IW5WiDAG1IWof73NprG7cw1/baj1tku0W4W3S73wF5b8eW6fgU4iBduFNycrJ8++23MmbMGIFlvGfPnuaYsvLly3NPeOH+0+Dbk0CBEKAQLxCM7IQESIAESIAEAoMAhHil+Di52gnWpsktelUUq2j1JnLxrApw3Gtb3Ht73i2KbRd1ba/9aF779CbCtS2uSO4AcejDft49/rgFyyU2JFi6N653tAP+FhoCEN179uyRjz/+WD766CMpUaKEdOnSRbp162b2hxcpUqTQsOCLkgAJnD4CFOKnjy17JgESIAESIAG/I6AW8aY1q5i550eE20eRQRQjQdjCGu7Nko56WwQjb7uU20JbLddqfbfr8Fx+RDjeQc8axzO2CM8tf2TvHgZrA5xClnBO+OrVq2XEiBEmOjrc0a+99lq54447JDIyspDR4OuSAAmcTgIU4qeTLvsmARIgARIgAT8jYAtxCFYVwHgNFcUqolFmW5pVFKvQhQC+qto5aGZSXiIajdxj2HNwP6/jHO39WEt7bv3Z89c5ax+ap2u6Eik8V5wTvnnzZnn11Vdl8eLFUqxYMSPCER09PDy88IDgm5IACZwRAhTiZwQzByEBEiABEiAB/yCgQhx7xJFUtLoFMupsS7OKYhWybhGOcts6bgts9IWENkhqHbfH9CbC7Y8E7nr0Yz+PvHtM1OtYWq/vSyEOIoUnQYSvWLFC/vrrL7MvHO7ot956q9StW1fKlSsndEcvPH8LfFMSOFMEKMTPFGmOQwIkQAIkQAJ+QABCPKRESWlco4pHhGPaELEqUjWPK8pU4CIPYat5FbkQvLb7Ouq3HUg/xlpuC2sV0ShDcot4lOl80NauR517Dsgj6TPI6z3K3XkKcVApHAl7wjdu3Gj2g3/33XdSuXJlE5StU6dOPCe8cPwJ8C1J4KwQoBA/K9g5KAmQAAmQAAn4JgG1iOsecczSLVJtUYt7FcsqwvGMLXLd7ut2IDbt334GwhpJA6jh+VsSq5kyFejaP/K2yEcje3655W3R7+39Nm1LlsyMDAZrM9QD9yfDWeO1a9fKW2+9JRMnTjTCu1+/fgJ3dO4JD9x155uRgC8QoBD3hVXgHEiABEiABEjARwi4hbhbpEL4jvn+B1kydaK07dJVKjdo5NlH7hbAeCW7DM/Ccm0HTnML6affeFvq1zlPyic2kJj9u2VZULjnLHC0VXGuuGyRjzJ3f+5n7PmgPfJuUY5yBmsDhcBO+/btk1mzZsmHH34oCxcuNC7o99xzj3To0EEQpI2JBEiABE4nAQrx00mXfZMACZAACZCAnxGwhThErLqX4zUgWjPS06VPy2ZSu3ZtWbZsmfS7d6A888B9x+zH1va4qvXa3lOOchXNHarGe1zaB/a8Wlpceqk8fN8Asz932A+/yOV1a6O5GV/7Qj6v/tC/7bK+fvce2Xk4yzOf3EQ4xqBrOggHZoIrelpamowaNUq+//572bFjh5x77rly8803S5MmTSQkJCQwX5xvRQIk4FMEKMR9ajk4GRIgARIgARI4uwS+nbVAKsRECYK12aJXLcmlDqRI69at5Y/Zc2Xu0uXy/MD+cpdztFPDLj1ztIcIRlIhj7w3a3b92BjzHOo3puyV3hc3NpGqm3frLXddfZUsXbrUuAijXvtCvzofe44Q5g2c/rSdHTAOz1/a6AKZO2eO6c/9vHt+FOKgHHgJIhyR0f/44w/58ccfZc2aNYK94J07d5aqVatKdHS0BAcHB96L841IgAR8jgCFuM8tCSdEAiRAAiRAAmePAIT4dsdq3LlegkfQ2qJ13rx50qVLFxk9a75x6d63eYNcfvnlMmXKFKlSpYqZOEQtkgpi3OclpDFG5u7t0rN9O+nY5zpp2f4KefD6a2TDhg3HWMLdohn923NE3i3K8UzzOgny008/SXqZo0eqqYhHnW05x/PfLVjOPeIA4acpOztbgoKCPLNHHmeEb9q0yURGHz16tGzZskVatmwpvXv3lsTERLqje2jxhgRI4EwQoBA/E5Q5BgmQAAmQAAn4CQG1iGuwNlukQsx8/8ckuffmG40Qh5BF/W/ffiNdu3aVkiVLyswtO2TXju3S3nEnh2UR9UgQ5emOW/vevXvlSGTJHNZxtIG1PHznVrniiivk5gcellb1z5cnn3xSBn895riWdvStz6uwNqLeEV2xh/ZLbGysLNt/yPRxwQUXSP+nn5e6jZtKnZJhMnPmTKlw3vkyOXmvJxic9rcpKVk2p+yRbo3qCqyoEHU8wgp0fD/B4o0zwCG04+Li5JxzzpFDhw7Jtm3bZO7cufLDDz8IjiuLj4831vDGjRvTHd33l5UzJIGAI0AhHnBLyhciARIgARIggZMnYAtxCFzbUvzx+F/kqbtvN53feeedEhJXXi51BHOdOnWM8JmwaIX899EHZc2ihUbkPPb2ULkg4Vwjwv/55x9zLvOuXbukfsMLZfTIb6Ro0aKyevVqefOTz6RmlUqSULuO3NKnl4lePcdxIX9/+McyYtRoKVVEPO7kENsQVThmavr06ZIWXFQGDLxf6lQ8auWGCE/atFHeefxhs4c9PCJSxn43RhISEqRegwbS+bobJOTAXvnqq6+kdOnS0vruh+T2q9p6rPd4HmPANX3TrhS5qFKcEXD4yBATE2PeE4Icc8c/fJzABwfb+nry9PnkqRLA3xO8HrD3G2tSq1Ytef755yUqKupUu+bzJEACJFCgBCjECxQnOyMBEiABEiAB/yZgC3F7jzUE6pHDh2T8V1/IZ2+/ITUcS/KhtAOycf06I7rf/GSE9O3SSa7qfLX0v+0Wuea666V+6zYy7Pmn5ffffzeBsO74v/9I+cpV5T+33ySfffaZlDinonS5rJU0aH6RlIgIl5BSZeWPkV+ZfeHPDHlLvh72ngdmVEwp6XbjzfLY3XfIDTfcYKydl3XvLYvmzJLt69fKhAkTZMHu/XLQCcJ1b7eOZr9vp5vvkG+HvSsd2rWVztffKB0ubi57UnYbAf7EE09I+aaXGLFmW9I1gjqE+KrNWyVp9p+ycuVKCQsLk1KlSknZsmWNlR1WVljbIcIh8sLDw40wR6AvlOEf05klgI8iw4cPN8L7DiduwaJFi0xQtkGDBpkgbGd2NhyNBEiABI5PgEL8+HxYSwIkQAIkQAKFioAKcTtYm1rGcexY3ZhIqVmzprz73Xi58oJEY53e4LhwP/jYEzL/t58MK4hmCN4PvxoprRs3NMHdNm7cKCg/cijDiNq3xoyXb997U3akpMpnQ9+Tr+ctk6f7dpd0R9w/PuQdWb10sRHiw4YNk7BqCY4F/WsZ54jqp556SoYOHSovfz5SUiRIhtx6vTkH+tZB/yf1O3SUYqsXG8v7iN+nSo34WAlJ22fcjv9KSZfn+nSVpKQkufDCC+XRdz80c7VFOAo0r8HaKmSlG3dmuDTv3LlT9uzZYyzjENoQ57CSQ5CXKVPGuEHXqFHDlBUvXtyMq2dR02JucJ/Wn5SUFIHoxl7wVq1aGRf0/fv3y6uvvmqi/GOLAdaNWwxO6zKwcxIggXwSoBDPJyg2IwESIAESIIHCQABCvIhzhvLVTrA2TbCM69nfEOWIPv6FY9GGoEVC/TPX9pBHH3pQSlWqKhNmzJJGDRtKl6YNZevWrdKsWTO5e9jnUjskUw6n7pao2nUloWJ5ufvWm6V+rXMl/uJL5fNnn5BzysVJ+Wo1ZGdqqgy6+0658sor5bnX35AtYVGy+ffx8sPob+W2226TXyb8Ji1vu1c2/jpWFi9eLPUbNZZpUybLWCcAF6yieK7Pnf3liQH9jaUa86tfLEsuuugiI9Refvll6ditu1zXq6exlLr3mCM/zgnWFhsSLN0b1zPviPeAEMexVwggByv5gQMHZPfu3ZLqzBdBwCD04AoNqzkE+nnnnWf6V9EeERFhrOboEGKQ4tygPekf3bsPjvgHt/RrrrlGMjIyzN/m/PnzTeyChx9+2HwUQQT+JUuWmA9D+HDCRAIkQAJnkwCF+Nmkz7FJgARIgARIwMcIqEVcg7XBJR1JLcXIv/nQAOnRo4d07NjREyjtxbtuMQGwHnvvIyni7J1Ge1gmETirbfv20q1Xb/nv00952mPf9y+OG/pLL71k+r/22mvlxgEDZXXSdrn/mu6ycOFCeWnImzLsrTdMPQJqwd0YQrdD5y6yd+cOad68uQwePFh+3JkuM997Vf745WcZMWaczFu6TIa9+KwRypUTzpOmjRvJVW3bGBH+2bgfZKnjsvzovf0Fwdteev+DHIHjMBiEuAZrUyFuJmH9HDx40AhvuKL/9ddfxmoOUQ7LP47EQjAwiD24sOv+cny4KFeunInOjf3psJaHOh89INTxTwWlNQxvcyEA/vgAgm0PCPAHlvB2+M9//mOu+HsBbxxLho8eWCMIcnwUeuaZZ8wHmly6ZjEJkAAJnBECFOJnBDMHIQESIAESIAH/IGALcW8iHAIb0c8hIJPSMsxLISJ6cnKytHMEd0SJktK2VUsT4OzXX3+VFpd3kDbOvvEn77pNGjZpKuWqVJMtq1fK/H/mCPaMX9DsIkmIipDKlSt7jilD/9hzjfEzHMF1fnSEJ9gWrNuxYcWkTlS4EV9qrW8YGy1zV62V7KhS5iMAXMjf/XGCZG7ZKDu3bDLu6iUqVDaiDPOFiMOHgoywSE+gNrwMRDjq1TU9NyFuryZEN0Q0IsJDIMIyu27dOiMIIQ7BZt++fUZsI8AbrOWwmlerVs0EkUMe+8xLlChh/qnrNMQ5k3cCiIDev39/E5AP59rjw1ADJxgfPuggYjo+2jRt2lTghQARPmrUKLMON910k1x22WUezwTvvbOUBEiABE4/AQrx08+YI5AACZAACZCA3xBQIY494ki2JVzvUa6CFfdIyO91xO/qubMFEc8R2KxaYj2JrVVHmpQva1y3xzou5etWrZba1atKKcc9vXLNc4/bP440s8d0fxjAmHYbe055tcWc0cbu337+RIQ4+vKWIPbhJg2BDhd6uLSvWLHCCHXsXUYd9plXrFjRWM1hoYcVV92mYTWHqFSLOd3Zj1Jev369+cjx+OOPGzb46AJLN86zx9aBZ599VqKjo43HBrYU4CPI5MmTTTnODGciARIgAV8gQCHuC6vAOZAACZAACZCAjxCAEN9+OEsa16jiEakQrBpNHNP0lrcFLdpA1CLBuoxk590iGVbtq6odPX7M3dY8/L/n3aLbztui2t2/5u13sEU3xrCfR74ghDj6sRPEN9zX8W/t2rXGawD7lrG/HIHGIB5hUYcQh0cARHn58uWNBR3u7erWDmGOerWcFxaXdnge4EPGc889J40aNZLRTkyAXr16Sb169WTs2LHmAxDq4K6+fPlyYy1HO3wUgiUcx+yBFRMJkAAJ+AIBCnFfWAXOgQRIgARIgAR8hAAEKNLx9ohvO5DuEc4qcm0hjjK36IVoRhmSfTa5t7Yq3k1j58fdxltex/dWh/lqsDn0iTbaXvP2fFG/aVuyZDoW6/y4pqOPE0lZWVkmqBwCy2EPPSzmOF8d7tYzZswwFly4s0O4w30eLuuwjuNfXFyc+Ve3bl2TxxYBuF/Dsg5hrlZzFeknMi9fbgs2CJYHL4Off/5ZxowZY6aLiOht27aV2bNny7Rp0wTW8tdff9183IBwR0LQPCYSIAES8DUCFOK+tiKcDwmQAAmQAAmcRQLv/jVPLoiNMUIcVmPb6oz8z+uSpEPVeGPp1vwtidU8M4aIRVKhq32o0P1w0VrR9u62yGs77dBddrxnvNVpPzqf/PSHZ47s3SObnWPZTocQ1zl5u0KQQ0DiDGy4+GOPOQQ5orRj7zzqIL5hKYf4RjC42rVrmz3mODINQh2WdexBRzu4tmvyZ2swLNxDhgwxbvwXX3yxIPI99uEjOBsipSPBRR2eAgigh3fFhw5/fmddN15JgAQCkwCFeGCuK9+KBEiABEiABE6KgG0Rh2hVAYvOIKJVhHvLo73bWm5bo+GC3sAR+bB4exPN9rPoHyIe1vOLS0WYCOUHQyPMhwBvQt7dn34AQD/6DmhjC31tY9dr+9Phmo6+85vgog7LOfaRQ4hjjzn2O0OY79ixw7hoo1zFOUQ5jlODQE9ISBBYzLH3HCIdAeLgzg7LOYQ5rOzI4xlcfSHhAwMEtJ3w7viYoOnmm282PK666irj3g/L92uvvSbIU3ArJV5JgAT8hQCFuL+sFGoMyCwAABpMSURBVOdJAiRAAiRAAmeAgArxivHOmd6OYB40aJARe/XatJfECxubwGuYhi2qkVfRrHu9VRijDkIX+T27d0lc5kE5WLY8ij0CGc/alnbUaRlE94svvijbdqdI4vV3eD4EaP/o2z028uoKr27uWqaiG2OgD83b/aHubAtxzMFOODMb7uYIRgarOVzXIcqXLVtmRCkCkqEMe88htCGyIWyxPzo2Nta4tyNKO6LTw8Ubx321d6LcI1r7mRaxcC8/dOiQmRc+NuDIN3xcwJwTExPNa3/66afmQ0JD5zx6zBHu+2+88YZ8/PHHxhUdHxmqV69ugtzh3HgmEiABEvA3AhTi/rZinC8JkAAJkAAJnEYCEKBJaQfl6noJZpR58+bJI08+JcsXzJf4+Hjp27evNLiqq4Q51lUVsWhoB1xTYaz7slXkbvxrkrz/wQfywidfep5FW4jw+o6l3N2fWs8feugh2SLFZPCTj3tc4tVlXp+3LfUY73iWb8wXz9ki3d63jvrvFiw/bXvE0X9BJQhZiHCIbuyjRnRwuGxDbMOdGxZzCG2Ic7hqw8oM6zMs7ghwBuF7JveTY9xZs2bJuHHjpGfPnkaMjxgxQr7//nt59913pUmTJrLe2ef99ttvm48K7733nmmDeeP8b7RDcDZ4B3z22WdSoUKFgkLJfkiABEjgjBKgED+juDkYCZAACZAACfg2AbWIu4O1vXjXLcb6+LcTTCwqppR88N67RhDhbWzrOAQuRDIShLWK8AZlSsqQ94fJmy//Vx588EFjAa1UqZKUu/SKHIHU8JzdH/I9rr9B6jVuKo/3v8MIaBXhqHO7y2N8JBXZuMccbJFv51XIq7s72qNsU1LyWdkjjvFPJanIhts6IrFDnMNlXY9Og2iHdRlC9v333z/jQhzv9uOPPxoRvWXLFnOWOq4PPPCAtGrVyuzxhrUc1m+c/Y3zweGS/s8//8hjjz1m8u3atTOWfljFmUiABEjAXwlQiPvrynHeJEACJEACJHAaCNhCXEU0RCzcmNv37SeNWrSU5X/8LIhW/cgjj0ipi9t49n1jOnhGrdH6/MfPPC7jx4/3zPb888+Xpk2bSpU6iVL7opbHiGQ0VOEMUXx9j+5yf/+7BC7ItuVd+7fb2iId/azevlM2L11srLDbt2+XLncPlKplS3ks6+NXb5YLI4KlQY2qZr80+kR/vuaajnc52QRxDouyWs/h3o4j07CPHK7rZ2OfOLwc8DcBF/nbb7/dnPmN49mQYLlHsDrsAf/zzz+NlVw/IOBvDueFM5EACZCAvxOgEPf3FeT8SYAESIAESKAACagQxx5xW9Re0qatXH3dDTKwX18z2sKFC414evOjj6Vzm0tN2aiffpE/J02UkOwsad+tpxyMq2COOYPldd7mbVI2tKiM+epLwdnZbss19joP/fIb2Zm8Tbpf0V66dOniadO2cUPjtry7XBXpVKOCCT6G5zG/SkGHzVFWs+bNl9IVKsm9AwZIxZKRZj7vfP2tvPzwA+a+Q4cOEhUbJ51uvkMuqhxvyj6btVBGvvCkLJw317jdP/b2UImvWCnghLh5WesHQhf/zoYAxzQgrr/44gv57bffzDyeffZZ6d27t2c+a9asMR8NYLXHkWSw5mMP+W233SZXXHGF9Sa8JQESIAH/JUAh7r9rx5mTAAmQAAmQQIETUCFetGRUDqv01e3byYA77zB7xDEoLMejPhwqW5Ytls8//1yeG/yqfD78I+l/911m//Fbb70lkyZNMq7HaAsredLyJdKtWzf5e8nyHK7jU6dOleuuu06u7NVH2jZrLPfdd5/c98wL0rNbV4kLCzFBuewXbdCosTz18mCJDStmImbXOj9RGra6TMZ9OlwuatZU/vvf/5r5rZ3ym8Dy2qBBA3Pc1d6osp53+mn+Ehl4TXfp4cznxhtvlOv69ZOmbS6X1576jxkqkCziNruzeQ/xjwBz2Av+1VdfSevWrU00fBzThv3qCCQHd/rhw4eb88KffvppY8lHcLeYmBjB8WxMJEACJBAoBCjEA2Ul+R4kQAIkQAIkUAAEVIjrHnF0CSGNPeLY0w13YeQzneBfE0d8aPbxvvfFV9LTEerY+wu38+sG3C9Tx442ruQ3Pfm8mRXcvbHPt2vXrjLy7znS+JwyMnHiRCPG6l/QUHrecrvZA44+7rrrLrMPfdaM6SaQWI0aNeTidu3lPw8MlIPFwuTWa3pJJ+fIKpwbjbO+B78+RJau3yi3dWxvxnrti5HStH5dI/ZhacdHAfSLsdF3RHwF+c+jj8of339n2kc77tmpu3ebAGL169c3lvhxTrC22JDgM36OuJlQgP6oy/mbb74pZcqUkT59+hgXeYhyWOdxTjjENgQ4xPejzhoxGFuA/jHwtUiABIRCnH8EJEACJEACJEACHgKbdqfKll2pokJc92R/9NFHxvLdwdknPm/G37Lg7z9NEDBYrqtXOEfuuekGwZFTY36fJOM++0SuvfZa0/7u/zwtg265wfT//ax5ck+PLkaQb968WTp37iy/zZwtbZs0MqILx1RhD3Bi67aydtbfpr5L//vllf8bJKGZh6X7PffLDsdd+fXHBhkRN23WbCnluJu36nCVfD7EsZA7x3Tt3n9AwkKKyhdOJG7sf0bU97I1asm+zRvMR4Rff/1V4E7/ytNPyv3332/O3J42Z660btpYatasaeaJDw1H9h4V+d0b1/Ow4c3JE8DRaggah73pOH4NH2zOPfdccxzb6NGj5ZNPPjHB2rD/+5133jFR3/H3BMHORAIkQAKBSIBCPBBXle9EAiRAAiRAAidJwBbiEKTDX3xGfvxuTI7e4OoNt+KE1u3kcPEoaV8pVm644Qazn/ecylVk8AvPS1jNOvLzyK/lw1f+K4MHD5aLrugo45atlZe6X2FEGI7Y6tanrzTpc6Os+WGUvPbaawLLdKOuveXpgffKthVLjQW75RVXyYuP/590v6aPbN2wXkqXLm3c0dv0u1VWLFwgz913t5nbNddcIx3vHCAZ6Wny7J23mGO7sBe5xaWXSVypGLO3eLOzr/znb76U+k6Qst2OBRxBzB577yPP2eg44mv19l2ScE5cQAVry7F4ZyGDIHnYD/7tt98a7gga9+WXX5pzwjEdnCOODyQvv/yyiZoeFxcnA5y9/m3atDFni5+FKXNIEiABEjjtBCjETztiDkACJEACJEAC/kNAhTj2iGNfd1rSZlm5cqWElY4Vx19cLk2obs6lRrA0nP+t53dDtKc5R2ZdUr2ice3Wc7lxfBasmp8s22jOCt+7ZJ788ccf0rZtW9lXsaZ0rF7ewIG1dMmedAkuUsTs40Z/kUcypIITeG1vUFEzVqPiRSSxckXnnPMME6gN8ytbrMjRKNupaaYfuMAjQjjc1jdmh0jw3hT5yRHfsJ4fOXxEGtSpbYJ+wfre9vL2gmvrlpeYY74gBmGlh+s094gX3N8stiBAWCPqfYRz/vwvv/xijiTDsWSaEJANYh1BAKOjo02cAHg0MJEACZBAoBKgEA/UleV7kQAJkAAJkMBJEIAQ/2HZOmlco4onsJlGKLePCXOLcAyl9erOrsNDVNv1uHefFW63wXgq5N2CH8+irR6Rhrx7ftoGV8zJ7g9lSOhjv+N+vm/5IkGwsJDiJeXSi5pJ48aNTVR2CvGjnE71F14G8Hb4+++/zRF48ISYPHmyPPnkk3LZZZcZ1mhTrFgx80EFkdK5L/xUqfN5EiABfyBAIe4Pq8Q5kgAJkAAJkMAZIjBrY5JkORZl3SOOYSFaVWQj/+GitR5LuFsEuwW2u177cwtplJcvHm5EM0T+LYnVUGQEe7nIcM/46A8JbZG89W+X4d7+aIBnbNGvfWh/Wr9pW7JkOhy4RxxE8p/gdq7HoxUtWtREQX/ggQdkypQp0qxZM+N5gL34CMiGPf2Iko6I6TjTvGHDhvkfiC1JgARIwM8JUIj7+QJy+iRAAiRAAiRQkATe/WueXBAb4xHibhEOoW0LY9v6jba5CWydozcRjDoVwraQtwU12iCPpG1xb4+PPJI9Z/ujgdbhqh8W3HPW/KakZBORnUIctPKXYNlGtPPvv//eeBbgODKIcez9HjNmjPTq1UuqVq0q3bt3N+XY8gBBDuH+9ttvm+B6QUFB+RuMrUiABEjAzwlQiPv5AnL6JEACJEACJFCQBOzjyyB8bdHrFtG24PVWZ4tyzNFbG5TboljzbtHtFuVol5cId4+HPtTlHc+j3p6j3Z6u6WIEMoQxBDYENRJE8969e00Z3MlLlChhyvGDvfkPP/ywfPfdd9KoUSNp166dCeI3bNgw+eabb+T666+Xq6++2sQMwHaAH374QebPn2/25ffo0SNHX55OeUMCJEACAUqAQjxAF5avRQIkQAIkQAInQ0CFeMX4uFMS4dsOpMtV1c7xTMEWuSh0522h7RbhaIv+GjiWev0wgDJbRGuf3kS9TgLWcXV5P974aF/YhXh6erqJPI/z1yMjI8353vHx8VKyZEkTbA0B2GrXri1PPPGEOQMce78h0GHZhsjWo+BatWpl7nGW+5YtW4w4h2BPS0sTRLWHOB80aJAuEa8kQAIkUGgIUIgXmqXmi5IACZAACZBA3gQgQOduT5G7LmrgaewWrbYIdtdBRNt7vNGJLbI1b+/btoW33TfaIg8RbrvDu8fUdrhCiHurz8vlPenAQY9lHv28NWmGI/TDC+0e8SVLlkj//v3N2d8Q5XXq1JH9TlR8WMQrVaokiHKOM7/vuusucyQZ9oHjXHBEnsde7z59+pgz3BGB/vHHH5ekpCTzDPaK4/g7REbv2rWrtGjRwgh5MGciARIggcJEgEK8MK0235UESIAESIAE8iCgFnEN1uYWtXYeAtp29UbX7j3ZeYlw9KeWbdzblm/t/1RFuHvObtGNcdTSjndAfvrSlbgttEL8q6++kiFDhhhxPXDgQBNUbdeuXTJ79mwjyHH0mAZXg3V7xIgRAqs3RPuNN95ojh/DEXKIkD5p0iR57rnnzJngOFM8ODhYsH88NDTUMOYPCZAACRRGAhTihXHV+c4kQAIkQAIkkAuB7xYsl/iIMBOszRawaO7Ou/dou+v1GXUXR962TNvtIX5tK7m2PRERjj68iWy7DGPa8/GWx4eBwizEYfVev369sVjfcsst0q1bNylXrpxnr/jGjRvNPm+s0SeffCIJCQnGyn3vvffK1KlTpV+/fvLM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