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      }
    },
    "gpuClass": "standard",
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/gabrielwithappy/dacon_aisr/blob/master/huggingface_chap2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "WikiDocs : \n",
        "- https://wikidocs.net/166795\n",
        "\n",
        "| test | test|\n",
        "|------|------|\n",
        "| s    |   1|"
      ],
      "metadata": {
        "id": "i6ZxPoaOX7WT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8TwZlkR8E0RD",
        "outputId": "33acc9bc-4f34-4d23-c762-5add8338df2c"
      },
      "execution_count": 282,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import subprocess\n",
        "import sys\n",
        "import os\n",
        "from pathlib import Path\n",
        "\n",
        "ROOT = Path('/content/drive/MyDrive/20.Projects/huggingface/')\n",
        "%cd '/content/drive/MyDrive/20.Projects/huggingface/'"
      ],
      "metadata": {
        "id": "PsXinqKKHiAN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "892a5c1b-7031-402f-e810-81387e04675b"
      },
      "execution_count": 283,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/MyDrive/20.Projects/huggingface\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import subprocess\n",
        "\n",
        "%cd '/content/drive/MyDrive/20.Projects/huggingface/'\n",
        "cmd = ['python', '-m', 'pip', 'install']\n",
        "libs = [\"transformers\", \"datasets\"]\n",
        "\n",
        "cmd += libs\n",
        "process_install = subprocess.run(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n",
        "print(f'execute {cmd}')\n",
        "if process_install.stdout:\n",
        "  print(process_install.stdout)\n",
        "else:\n",
        "  print(process_install.stderr)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BfFbMBmeu9CB",
        "outputId": "867a2c59-75aa-4838-e0bc-93b53a44a0f6"
      },
      "execution_count": 284,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/MyDrive/20.Projects/huggingface\n",
            "execute ['python', '-m', 'pip', 'install', 'transformers', 'datasets']\n",
            "b'Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\\nRequirement already satisfied: transformers in /usr/local/lib/python3.8/dist-packages (4.26.0)\\nRequirement already satisfied: datasets in /usr/local/lib/python3.8/dist-packages (2.8.0)\\nRequirement already satisfied: huggingface-hub<1.0,>=0.11.0 in /usr/local/lib/python3.8/dist-packages (from transformers) (0.12.0)\\nRequirement already satisfied: tokenizers!=0.11.3,<0.14,>=0.11.1 in /usr/local/lib/python3.8/dist-packages (from transformers) (0.13.2)\\nRequirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.8/dist-packages (from transformers) (1.21.6)\\nRequirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.8/dist-packages (from transformers) (2022.6.2)\\nRequirement already satisfied: requests in /usr/local/lib/python3.8/dist-packages (from transformers) (2.25.1)\\nRequirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.8/dist-packages (from transformers) (4.64.1)\\nRequirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.8/dist-packages (from transformers) (6.0)\\nRequirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.8/dist-packages (from transformers) (21.3)\\nRequirement already satisfied: filelock in /usr/local/lib/python3.8/dist-packages (from transformers) (3.9.0)\\nRequirement already satisfied: responses<0.19 in /usr/local/lib/python3.8/dist-packages (from datasets) (0.18.0)\\nRequirement already satisfied: dill<0.3.7 in /usr/local/lib/python3.8/dist-packages (from datasets) (0.3.6)\\nRequirement already satisfied: pyarrow>=6.0.0 in /usr/local/lib/python3.8/dist-packages (from datasets) (9.0.0)\\nRequirement already satisfied: fsspec[http]>=2021.11.1 in /usr/local/lib/python3.8/dist-packages (from datasets) (2022.11.0)\\nRequirement already satisfied: aiohttp in /usr/local/lib/python3.8/dist-packages (from datasets) (3.8.3)\\nRequirement already satisfied: multiprocess in /usr/local/lib/python3.8/dist-packages (from datasets) (0.70.14)\\nRequirement already satisfied: xxhash in /usr/local/lib/python3.8/dist-packages (from datasets) (3.2.0)\\nRequirement already satisfied: pandas in /usr/local/lib/python3.8/dist-packages (from datasets) (1.3.5)\\nRequirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.8/dist-packages (from aiohttp->datasets) (1.3.3)\\nRequirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.8/dist-packages (from aiohttp->datasets) (6.0.4)\\nRequirement already satisfied: charset-normalizer<3.0,>=2.0 in /usr/local/lib/python3.8/dist-packages (from aiohttp->datasets) (2.1.1)\\nRequirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.8/dist-packages (from aiohttp->datasets) (22.2.0)\\nRequirement already satisfied: async-timeout<5.0,>=4.0.0a3 in /usr/local/lib/python3.8/dist-packages (from aiohttp->datasets) (4.0.2)\\nRequirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.8/dist-packages (from aiohttp->datasets) (1.3.1)\\nRequirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.8/dist-packages (from aiohttp->datasets) (1.8.2)\\nRequirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.8/dist-packages (from huggingface-hub<1.0,>=0.11.0->transformers) (4.4.0)\\nRequirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.8/dist-packages (from packaging>=20.0->transformers) (3.0.9)\\nRequirement already satisfied: chardet<5,>=3.0.2 in /usr/local/lib/python3.8/dist-packages (from requests->transformers) (4.0.0)\\nRequirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.8/dist-packages (from requests->transformers) (2.10)\\nRequirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.8/dist-packages (from requests->transformers) (1.26.14)\\nRequirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.8/dist-packages (from requests->transformers) (2022.12.7)\\nRequirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.8/dist-packages (from pandas->datasets) (2.8.2)\\nRequirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.8/dist-packages (from pandas->datasets) (2022.7)\\nRequirement already satisfied: six>=1.5 in /usr/local/lib/python3.8/dist-packages (from python-dateutil>=2.7.3->pandas->datasets) (1.15.0)\\n'\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Huggingface Dataset"
      ],
      "metadata": {
        "id": "zmF-ZFcKo9UW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import list_datasets\n",
        "print(f'all datasets {len(list_datasets())}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ET-BWzlZ8qB7",
        "outputId": "8029e0e8-52a9-49cf-b9e6-c0554956945b"
      },
      "execution_count": 285,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "all datasets 19507\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "emotions = load_dataset(\"SetFit/emotion\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105,
          "referenced_widgets": [
            "f2c80919ab9d437a87b348b55bad6192",
            "f4a97dc026e24b36adaf84c1fef2f383",
            "7fc8fb039ba348e890dd5f601cfc122d",
            "62684febdfb94ee79453fa38c32d1e63",
            "6ccba0d0f98d4219afc31bcc23d30d48",
            "e8dc63e7f2e14f54a7da4b62cfdbccfc",
            "4cf3ed5adc9c4c4ebda538496a2e46db",
            "312072bfb46b4912851235c876f1d024",
            "9005564b4299445b82604d41c924e9b8",
            "7e1b1909ac4b4d3d93b491e8b7161113",
            "8431741c93db498c9a31938302313fbe"
          ]
        },
        "id": "P84YXrq-sx_I",
        "outputId": "59b54fff-58b1-4946-8576-745512171b5c"
      },
      "execution_count": 286,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:datasets.builder:Using custom data configuration SetFit--emotion-e444b7640ce3116e\n",
            "WARNING:datasets.builder:Found cached dataset json (/root/.cache/huggingface/datasets/SetFit___json/SetFit--emotion-e444b7640ce3116e/0.0.0/0f7e3662623656454fcd2b650f34e886a7db4b9104504885bd462096cc7a9f51)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/3 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f2c80919ab9d437a87b348b55bad6192"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import ClassLabel\n",
        "\n",
        "emotions['train'].features['label'] = ClassLabel(\n",
        "    num_classes=6, \n",
        "    names=['sadness', 'joy', 'love', 'anger', 'fear', 'surprise'])"
      ],
      "metadata": {
        "id": "Ony6TDKQpl6s"
      },
      "execution_count": 287,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "emotions"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EMXJmG0zuwr8",
        "outputId": "bda8abeb-e9b4-43cd-aa19-123bd53eb4d8"
      },
      "execution_count": 288,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['text', 'label', 'label_text'],\n",
              "        num_rows: 16000\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['text', 'label', 'label_text'],\n",
              "        num_rows: 2000\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['text', 'label', 'label_text'],\n",
              "        num_rows: 2000\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 288
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(load_dataset(\"emotion\")['train'].column_names)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 103,
          "referenced_widgets": [
            "7f1d19cc5d2443daa17b65f03e62bd66",
            "67641407e944403bac4edf20d63e9caa",
            "49cb2afc0ba643c286a5b0dfb2ea14ef",
            "40938d40b7364595a617c04f17542fdf",
            "58dc2742ad4343c8aac03d3b4b23019f",
            "34d535c5d6ea4e7688940f7b78d69db5",
            "a3c5daff629948579a53697c5490ebcf",
            "0eeb27f8dcba4125a9fd1946510041f5",
            "bad2772efe1a4fee89ea13f39836e402",
            "099efb679b604678b1ed69a950fc8e86",
            "87f2c354fa8949fd8518587e0a6f1e1f"
          ]
        },
        "id": "1_tjBCiFFgTP",
        "outputId": "66394b56-dae0-4379-8d67-ef94aa3867d1"
      },
      "execution_count": 289,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:datasets.builder:No config specified, defaulting to: emotion/split\n",
            "WARNING:datasets.builder:Found cached dataset emotion (/root/.cache/huggingface/datasets/emotion/split/1.0.0/cca5efe2dfeb58c1d098e0f9eeb200e9927d889b5a03c67097275dfb5fe463bd)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/3 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7f1d19cc5d2443daa17b65f03e62bd66"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['text', 'label']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataset = emotions['train']\n",
        "display(emotions['train']['text'][0])\n",
        "display(emotions['train']['label'][0])\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "id": "4zQ7_2mGsy_s",
        "outputId": "6a874ce9-2410-4a09-f221-62bffe8fb63a"
      },
      "execution_count": 290,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "'i didnt feel humiliated'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# https://github.com/huggingface/datasets/blob/main/src/datasets/load.py\n",
        "from datasets import load_dataset\n",
        "import re\n",
        "\n",
        "class MyDataSet:\n",
        "  def __init__(self, features):\n",
        "    self._dict = {\n",
        "        features[0] : ['a', 'b'],\n",
        "        features[1] : ['1', '0']}\n",
        "\n",
        "  def __getitem__(self, key):\n",
        "    return self._dict[key]\n",
        "\n",
        "  def __repr__(self):\n",
        "      repr = \"\\n\".join([f\"        {k}: {v}\" for k, v in self._dict.items()])\n",
        "      return f\"DatasetDict({{\\n{repr}\\n}})\"\n",
        "  \n",
        "\n",
        "class MyDatasetDict:\n",
        "  def __init__(self):\n",
        "    self._dict = {'train' : MyDataSet(['text', 'label']),\n",
        "                  'validation' : MyDataSet(['text', 'label'])}\n",
        "\n",
        "  def __getitem__(self, key):\n",
        "    return self._dict[key]\n",
        "\n",
        "  def __repr__(self):\n",
        "      repr = \"\\n\".join([f\"    {k}:{v}\" for k, v in self._dict.items()])\n",
        "      return f\"DatasetDict({{\\n{repr}\\n}})\"\n",
        "\n",
        "\n",
        "# =======================================================\n",
        "test = MyDatasetDict()\n",
        "display(test['train'])\n",
        "display(test['train']['text'][0])\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 108
        },
        "id": "pWL4B5vrv2B3",
        "outputId": "64fb4de5-1e26-4c93-8838-f962520ed2c7"
      },
      "execution_count": 291,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "        text: ['a', 'b']\n",
              "        label: ['1', '0']\n",
              "})"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "'a'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "emotions.set_format(type=\"pandas\")\n",
        "df = emotions['train'][:]\n",
        "df.head()\n",
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "di7sUgxzA-D8",
        "outputId": "d4f43fc3-a6f9-46f3-ab45-9be1bcc5f100"
      },
      "execution_count": 292,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 16000 entries, 0 to 15999\n",
            "Data columns (total 3 columns):\n",
            " #   Column      Non-Null Count  Dtype \n",
            "---  ------      --------------  ----- \n",
            " 0   text        16000 non-null  object\n",
            " 1   label       16000 non-null  int64 \n",
            " 2   label_text  16000 non-null  object\n",
            "dtypes: int64(1), object(2)\n",
            "memory usage: 375.1+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def label_int2str(row):\n",
        "    return emotions[\"train\"].features[\"label\"].int2str(row)\n",
        "\n",
        "df[\"label_name\"] = df[\"label\"].apply(label_int2str)\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
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        "id": "gs8ZcY4k9hOv",
        "outputId": "3f28a9a7-0baa-4403-c41c-1565280c2db5"
      },
      "execution_count": 293,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                text  label label_text  \\\n",
              "0                            i didnt feel humiliated      0    sadness   \n",
              "1  i can go from feeling so hopeless to so damned...      0    sadness   \n",
              "2   im grabbing a minute to post i feel greedy wrong      3      anger   \n",
              "3  i am ever feeling nostalgic about the fireplac...      2       love   \n",
              "4                               i am feeling grouchy      3      anger   \n",
              "\n",
              "  label_name  \n",
              "0    sadness  \n",
              "1    sadness  \n",
              "2      anger  \n",
              "3       love  \n",
              "4      anger  "
            ],
            "text/html": [
              "\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ]
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['label'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UMNf3ylHwxaf",
        "outputId": "dd5ab6d7-98b9-4d43-f4e3-6e65dae4cdf7"
      },
      "execution_count": 294,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 3, 2, 5, 4, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 294
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "for label in np.sort(df['label'].unique()):\n",
        "  str_label = emotions['train'].features['label'].int2str(int(label))\n",
        "  print(f\"{label} : {str_label}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B4Z5Y6ufmWxH",
        "outputId": "715e238a-22db-4fea-b28e-3101f2502ff7"
      },
      "execution_count": 295,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0 : sadness\n",
            "1 : joy\n",
            "2 : love\n",
            "3 : anger\n",
            "4 : fear\n",
            "5 : surprise\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trans_map = {\n",
        "    0 : 'sadness',\n",
        "    1 : 'joy',\n",
        "    2 : 'love',\n",
        "    3 : 'anger',\n",
        "    4 : 'fear',\n",
        "    5 : 'surprise'\n",
        "}\n",
        "df['label_name_map'] = df['label'].map(trans_map)\n",
        "df['label_name'] = df['label'].apply(lambda x : emotions['train'].features['label'].int2str(x))\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "qsTvFmiYyLrZ",
        "outputId": "a2d2e57e-4dbb-4de8-8b6a-391486de9f4c"
      },
      "execution_count": 296,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                text  label label_text  \\\n",
              "0                            i didnt feel humiliated      0    sadness   \n",
              "1  i can go from feeling so hopeless to so damned...      0    sadness   \n",
              "2   im grabbing a minute to post i feel greedy wrong      3      anger   \n",
              "3  i am ever feeling nostalgic about the fireplac...      2       love   \n",
              "4                               i am feeling grouchy      3      anger   \n",
              "\n",
              "  label_name label_name_map  \n",
              "0    sadness        sadness  \n",
              "1    sadness        sadness  \n",
              "2      anger          anger  \n",
              "3       love           love  \n",
              "4      anger          anger  "
            ],
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
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              "    }\n",
              "  </style>\n",
              "\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-0e1ac8d5-dc02-4a13-8363-f68f12fb0f05');\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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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 296
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "result = df['label_name'].value_counts(ascending=True).reset_index()\n",
        "result\n",
        "sns.barplot(data=result, x='index', y='label_name')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "-aNVoQR8zHxh",
        "outputId": "6006f274-922e-4027-c09e-86241354743e"
      },
      "execution_count": 297,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc1bd00e970>"
            ]
          },
          "metadata": {},
          "execution_count": 297
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "PUrfG1U4DeWa"
      },
      "execution_count": 297,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['Words Per Tweet'] = df['text'].str.split().apply(len)\n",
        "display(df.head(2))\n",
        "sns.displot(df[['label_name', 'Words Per Tweet']], x='Words Per Tweet', hue='label_name', kind='kde')"
      ],
      "metadata": {
        "id": "7JvjYaLKDDLU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 482
        },
        "outputId": "e5fd4d77-6bb0-44ad-9490-ddd96c63be6f"
      },
      "execution_count": 298,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                                                text  label label_text  \\\n",
              "0                            i didnt feel humiliated      0    sadness   \n",
              "1  i can go from feeling so hopeless to so damned...      0    sadness   \n",
              "\n",
              "  label_name label_name_map  Words Per Tweet  \n",
              "0    sadness        sadness                4  \n",
              "1    sadness        sadness               21  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3287e19a-5672-47e2-8fb4-1dd15d08cd63\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>text</th>\n",
              "      <th>label</th>\n",
              "      <th>label_text</th>\n",
              "      <th>label_name</th>\n",
              "      <th>label_name_map</th>\n",
              "      <th>Words Per Tweet</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>i didnt feel humiliated</td>\n",
              "      <td>0</td>\n",
              "      <td>sadness</td>\n",
              "      <td>sadness</td>\n",
              "      <td>sadness</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>i can go from feeling so hopeless to so damned...</td>\n",
              "      <td>0</td>\n",
              "      <td>sadness</td>\n",
              "      <td>sadness</td>\n",
              "      <td>sadness</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3287e19a-5672-47e2-8fb4-1dd15d08cd63')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-3287e19a-5672-47e2-8fb4-1dd15d08cd63 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-3287e19a-5672-47e2-8fb4-1dd15d08cd63');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7fc1c14cd4f0>"
            ]
          },
          "metadata": {},
          "execution_count": 298
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 436.75x360 with 1 Axes>"
            ],
            "image/png": 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m8vjushG/6xKPh8zcPEtmxpiAsGQWwkprWshOjgGg9txZAJIzpwb1mbcsnsbp8y3sL6sf8b0yZudTefI4nR0dAYjMGDOZWTILUZ1d3ZyrbyUrydczOwsiJKZnBvW5a+dnEOaRgGwNk5mbT3dXJ1WnTow8MGPMpGbJLESdq2+lq1vJ7jHMGD8lFW9ERFCfmxwbwcqcKQFKZnMAbKjRGDNilsxCVFlNC8CFYcaa8rMkBXmI0ef6BRkcqWgc8azG+NQ0ouMTOHf0rQBFZoyZrCyZhahSN5ldnAAyesls3QJnKHPTgZHtSSYiZObmU249M2PMCFkyC1G+ZDYtKYq25iZa6utIyhidZDYtKZqF2YkBGWrMyJ1DdelpOlpbAxCZMWaysmQWospqm0mPjyTSG9ZjJuO0UXv+9Qsy2X26lnN1I0tCmbl5qHZTfuJogCIzxkxGlsxClDMt3x1iLHd6SIkZwZ3J2NP1CzIAeO7gyHpnvkkgNtRojBkJS2Yh6lxdK1Pdafn1lc67q8T0jFF7fl56PDkpMfyluHJE94lNSiYuJdVmNBpjRsSSWQhSVc7Vt5KZEAVAXUU5UbFxRMbEjmocV81JY8vRato6R7ZjdObsPMqPWTIzxgyfJbMQ1NjWSXN7FxkJkYDTM0tIG71emc9V+Wm0dHSx42TNiO6TPiuXmrNnaG9pDlBkxpjJxpJZCCqvbwMgo0fPbDSHGH0uz03B6xFeeatqRPdJz8kFoOLk8UCEZYyZhCyZhaCKemcGYUZCFKpKfWUFCWnpox5HXKSXpTOTefXIyN6bZcxyk9lxm9FojBkeS2Yh6FyPZNZcV0tnR/uY9MwArp6TxoEz9VQ1tg37HrHJU4hJTKLi+LEARmaMmUwsmYUg3zBjenwkdRXutPwgFxjuz5X5qQC8dmT4Q40iQvqsXCqOlwQqLGPMJGPJLASV17cSH+klNtJLXWUFwJgMMwIUTkskOSacV0Y41JieM5vqstN0trcHKDJjzGRiySwElde3kpHoTP6or3DXmI3BbEYAj0dYk5/Gq0eqRrRhZ8asXLq7uqg6fTKA0RljJgtLZiGovL71wrT8hupKouITCI+KGrN4rsxPpbKhjeLyhmHf48KMRitrZYwZBktmIai8vo2MeCd5NVRXET8lZUzjuSLPeW+2uaR62PdIzMgkMibWJoEYY4YlqMlMRNaLSLGIlIjIl/o4Hykij7jn3xSRHPd4ioj8RUQaReQHva5ZJiL73Gu+JyISzO8w3nR3KxUNF4cZG85XE5+SOqYxZSVFk5MSw+slI5sEkpYzy6bnG2OGJWjJTETCgB8CNwAFwF0iUtCr2UeAGlXNA/4H+JZ7vBX4f8AX+rj1j4GPAvnuz/rARz9+1bZ00NGlpMc7w4yN1VXEjXHPDGB1XipvHj9PZ1f3sO+RMSuXylMn6O4aWXksY8zkE8ye2UqgRFWPqWo78DCwoVebDcAv3c+PAteKiKhqk6q+hpPULhCRqUCCqr6hzmyDXwHvDOJ3GHd867lS4yLpbG+npaGe+Clj2zMDuCI3lca2TvaW1Q37Huk5uXS2t3H+TGkAIzPGTAbBTGZZwOkev5e6x/pso6qdQB0wUDcjy73PQPcEQEQ+JiLbRWR7ZeXIpo2PJ75klhIXQeN55x1V3BgPMwKsynX+sY1kqDE9ZzYAFSfsvZkxZmgm7AQQVb1PVZer6vK0tLSxDidgqhqddVhpcZE0nHcSx1i/MwOYEhvB/KkJI5oEMiVrOt7wCFs8bYwZsmAmszJgeo/fs91jfbYRES+QCAz0/4Zl7n0GuueEVtVwcZixoXr8JDOAK3JT2HGqhtaO4b3z8oSFkTozx2Y0GmOGLJjJbBuQLyKzRCQCuBPY2KvNRuBu9/MdwIs6wMpbVT0L1IvI5e4sxg8Cfwp86ONXdVMbYR4hMTr8QjIbDxNAwJmi397ZzfYTw98SJmNWLhUnjo1oAbYxZvIJWjJz34HdC2wCDgG/U9UDIvINEbnFbXY/kCIiJcDngQvT90XkBPDfwD0iUtpjJuQngZ8BJcBR4OlgfYfxqKqhnZTYCDweofF8FZGxsURERY91WACsnDUFr0fYfHQk781yaWtuos6tbGKMMf7wBvPmqvoU8FSvY1/p8bkVeFc/1+b0c3w7UBi4KENLdVMbKXG+6h/V42Imo09spJfF05NGNglk1sVKIEkZY1M82RgTeibsBJCJqrKxndS4CAAaz1eNi5mMPa3OS2VfWR11LR3Duj51+kzE47HF08aYIbFkFmKqGtpIvdAzG/tSVr1dkZtCt8Kbx4Y3q9EbEUFq9gxLZsaYIbFkFkJUleqmNlLjIujq7KC5rpa4cTTMCLBkRjLR4WG8fnT4U/TTZ+VSbsnMGDMElsxCSFN7F60d3aTGRdJ4/jwA8anjK5lFeD2smDWFzSN8b9ZcV0tjzfkARmaMmcgsmYUQ3xqzlJ4LpsdZzwxgdW4KRyoaqahvHbxxHy5UArHemTHGT5bMQkh1k2/BdASN42zBdE9X5DoxDXeo0ZKZMWaoLJmFkMoGp5RVz+of4+2dGUDBtAQSo8OHPdQYER1D8tRplFtZK2OMnyyZhZCaZieZTYmNoOF8FRHR0UTGxIxxVJcK8wirZqfw+tHqYVfyyMyby9kjxVYJxBjjF0tmIeR8k5PMkmMiaKyuHpe9Mp8r8lIoq23h1PnmYV0/NX8uTbU1NFRPnB0PjDHBY8kshNQ0tRMdHkZ0RBgN56vG5fsyn9V5TmzDraI/LX8eAGePFAcsJmPMxGXJLIScb25nSqxb/aN6fCez2amxZCZEDbtOY+qMHLzhEZx563CAIzPGTESWzEJITVM7ybHhdHd30VRbS2zSlLEOqV8iwuq8FLYcraa7e+jvvcK8XjJy8zh7xJKZMWZwlsxCyPnmDpJjImhtaEC1m9ikpLEOaUBX5KZyvqmdw+cahnX91Px5VBw/SmfH8Oo8GmMmD0tmIaSmyRlmbKp19guLTUoe44gGtjrPqRv56pHhTeKYlj+Prs5OKk/YZp3GmIFZMgshF5JZXS0AMYnju2c2NTGagqkJvHCoYnjX588FsKFGY8ygLJmFiPbObhraOpkSE0FziPTMAK4ryGD7yfMXlhUMRdyUFOJT0mwSiDFmUJbMQkStu2A6+a96ZuM/ma2dn0G3wouHh987O1ti0/ONMQOzZBYizveo/tFUW4M3IpKI6OgxjmpwhVkJZCRE8vzB8mFdPzV/LvWVFTSeH/6WMsaYic+SWYjoWf2jubaGmMQkRGSMoxqciHDd/AxeOVJJa0fXkK/Pnl8IwOlD+wMdmjFmArFkFiJqmpzp6b4JION9Wn5P1xVk0NzexZZh7D6dPms2kTGxnN6/JwiRGWMmCktmIeL8hXdm4W7PbPy/L/NZNTuFmIiwYQ01ejxhZBcUcerA3iBEZoyZKCyZhYiaHsOModYziwoP4+o5aTx3sJyuYVQDmVG4kLryc9RVDO+9mzFm4rNkFiLON7UTH+klDKWloT6kemYANxRNpaKhja3Hzw/52hkLFgJw2npnxph+WDILEXUtHSTFhtNcXweqIbHGrKe18zOIjQjjT7vLhnxtyvSZRCck2lCjMaZflsxCRG1zO4nR4RdLWY3z6h+9RUeEcf2CTJ7ad5a2zqHNahQRZixYyOn9e2yzTjOpiUjjIOdzRGRIU39F5AERuWNkkY09S2Yhoq6lg6ToCJp9C6ZDrGcGcMviadS3dvKXw0Ov1TijcBGNNeepOTv0np0xZuKzZBYials6QrpnBrAmL5XUuAg27hl6Qppe6Lw3O7XfhhqNEZE4EXlBRHaKyD4R2dDjtFdEHhSRQyLyqIjEuNcsE5GXRWSHiGwSkal+PuuEiHy9x7PmucdXisgWEdklIq+LyFz3+D0i8kcRec699l4R+bzb7g0RmeK2yxWRZ9x4XvXdd7gsmYWI+pYOEmPCe/TMQi+ZecM83LxwGs8fqqC+dWjbuiRlTCUhLZ0Te3YGKTpjQkorcKuqLgWuAb4jF6sozAV+pKrzgXrgkyISDnwfuENVlwE/B/51CM+rcp/1Y+AL7rHDwJWqugT4CvBvPdoXArcBK9znNLvttgAfdNvcB3zajecLwI+GEM8lLJmFAFWltvlizyw8MoqIqPFfyqovGxZPo72zm2f2nRvSdSLCrCUrOLlvF53tQy9abMwEI8C/iche4HkgC8hwz51W1c3u598Aa3ASXCHwnIjsBr4MZA/heX9w/9wB5LifE4Hfu+/o/gdY0KP9X1S1QVUrgTrgz+7xfUCOiMQBq93rdwM/BfzqKfbHklkIaG7vorNbSYp2emahNpOxp8XTk8hNi+XhbaeGfG3uspV0trXZFH1j4H1AGrBMVRcD5UCUe673LCnFSX4HVHWx+1OkquuG8Lw2988uwOt+/hecpFUIvKPH83u2B+ju8Xu3e70HqO0Rz2K3JzlslsxCQF2LMyTn65mF4uQPHxHhrpUz2HmqlsPn6od07fSCIsIjozi6Y2uQojMmZCQCFaraISLXADN7nJshIqvcz+8FXgOKgTTfcREJF5EFjEwi4HsBfs9QLlTVeuC4iLzLjUdEZNFIgrFkFgJqmy8ms+a62pCc/NHT7UuzifB6+O2bQ+udeSMimLlwMcd2brMp+mayexBYLiL7cN5B9dz0rxj4lIgcApKBH6tqO3AH8C0R2QPsxhnmG4n/BP5dRHZxsbc2FO8DPuLGcwDYMEj7AQ0nADPKLvTMYsI5VltDdkHRGEc0MsmxEdxYmMnjO8v40g3ziInw/1/D2ctWUrLtDSpPHic9Z3YQozRm/FHVOPfPKmBVP836nBWoqruBq/o4fs8gz8zp8Xk78Db38xZgTo+mX3aPPwA80M/1F86p6nFg/UDPHgrrmYWAuhZnwkN8hNDa2BDyPTOA9142k4a2Tp7Ye3ZI181esgKAYzu3BSMsY0yICmoyE5H1IlIsIiUi8qU+zkeKyCPu+TdFJKfHuX90jxeLyPU9jn9ORA6IyH4ReUhEonrfd6Lx9cwiO1sBQnoCiM+KnGTy0uOGPNQYm5RMZt4cjtl7M2MCSkQeF5HdvX6uH/zK8SFoyUxEwoAfAjcABcBdIlLQq9lHgBpVzcOZ2vkt99oC4E6cqZ7rgR+JSJiIZAGfAZa7M2jC3HYTmu+dWVirU8kmZgL0zHwTQXafruXgmaFNBMldupKzR9+isWboRYuNMX1T1Vt7zS5crKqbxjoufwWzZ7YSKFHVY+7Lx4e59AXfBuCX7udHgWvdhX8bgIdVtc0dVy1x7wfOe75oEfECMcCZIH6HcaGupQOvR6C5DpgYPTOA25dmEeH18NDWofXO8lZcDqoc3f5mkCIzxoSaYCazLOB0j99L3WN9tlHVTpzFdSn9XauqZcB/AaeAs0Cdqj7b18NF5GMisl1EtldWDr0W4HhS55ayaq53q39MgJ4ZQFJMBDcXTeWPu8pobu/0+7qU6TNJypzKka2vBzE6Y0woCakJICKSjNNrmwVMA2JF5P19tVXV+1R1uaouT0tLG80wA85Xl7Gl3hmOi0lIHOOIAue9l82goa2TP+/xv4MtIuStWMXpA3tpbRqwiLgxZpIIZjIrA6b3+D2biwvsLmnjDhsmAtUDXHsdcFxVK1W1A6fEykjXSox7vrqMLQ31eMMj8EZGjnVIAbNsZjL56XE8tPX04I17yF+5iu6uLo7brEZjxsxwtpwJlmAms21AvojMEpEInIkaG3u12Qjc7X6+A3hRndWwG4E73dmOs4B8YCvO8OLlIhLjvlu7FjgUxO8wLvjqMrY01BMVH8/FeqKhb7gTQabmzSU2eQpHtm0JYnTGmFDhVzITkT+IyE0i4nfyc9+B3Qtswkk4v1PVAyLyDRG5xW12P5AiIiXA54EvudceAH4HHASeAT6lql2q+ibORJGdOAUrPTiVlyc0Zy8zJ5lFxyeMdTgBd5s7EWQo9RrF4yFv+WUc372Djva2wS8wxvRLRGJF5EkR2eMue3qPiHxFRLa5v9/nq8rvbiWzx63c8ake97jHzRXPiMgREfnPHufWudvF7BSR37uFhhGR/xCRgyKyV0T+yz32LveZe0TkFX+/g7+lF34EfAj4noj8HviFqhYPdpGqPpgmxcIAACAASURBVAU81evYV3p8bgXe1c+1/0ofWxSo6leBr/oZ94Tg22V6oiazpJgIbiqayuM7y/jHG+YTHRHm13V5K1ax57mnObl3N3nLLwtylMaMjpwvPfldYHGAb7v7xH/c9NkBzq8HzqjqTQAikgg8p6rfcH//NXAzTvX7XwD3quorIvLtXvdZDCzBKSxcLCLfB1pwqoNcp6pNIvJF4PMi8kPgVmCeqqqI+Ga2fQW4XlXLehwblF89LVV9XlXfBywFTgDPi7MZ24fcfXJMkHR3Kw1tnSTGRNA6QZMZwF0rZ7gVQfyfCDJ9QRGRMbGUbLWhRmNGaB+wVkS+JSJXqmodcI1bzGIf8HZggZtcklTV12P6da/7vKCqdW5H5SBOAeTLcdYab3a3e7nbPV6Hsy/b/SJyG9Ds3mMz8ICIfBRnLbFf/C6KJyIpwPuBDwC7cApdrnEDe5u/9zFD09DaiSoXZjNGJ0zMZLYiJ9ndGuY071o+ffALgDBvOLOXruDozq10d3XhCfP733tjxq1BelBBoapvichS4EbgmyLyAs4Q4nJVPS0iX+Ovt3jpT88xf992MYLTy7urd2MRWYkz9+EOnNdSb1fVj4vIZcBNwA4RWaaq1YM92N93Zo8Dr+IsUn6Hqt6iqo+o6qeBOH/uYYan1q3LmBAZRmtz04TtmYkIdyybzo6TNZysbvL7uryVq2htqKf00IEgRmfMxCYi03B2g/4N8G2cUTiAKvf91h0AqloL1IrIGvf8+/y4/RvAFSKS5z4rVkTmuPdNdF9HfQ5Y5J7PVdU33VdSlfz1zPZ++Tuh4/9UtUBV/11Vz7oPjHS/3HI/72GGwVeXMcbTCaoTNpkBvHPJNETg8V29V3D0b9aiZXjDIyixWY3GjEQRsNUdBvwq8E3g/4D9OJP4eq6B+RDwQ7ftoFOr3d2m7wEeEmdn7C04lf3jgSfcY6/hTAIE+LaI7HOn/L8O7PHnC/g7zPhNek3kcANa2kdbE0C+uoyRXU4PbSIns6mJ0ayancLju8r4u2vz/VqCEB4VxcxFSynZ9gbX3POxCbVswZjR4tZg7F2HcTvuti692u7A7UW5/sE9/gB/vfXLzT0+vwis6OPRK3sfUNXb/I/8ogF7ZiKSKSLLcGohLhGRpe7P23CGHE2QXaiY3+W8G42awMkM4NYlWZysbmbnqVq/r8lfuYqG6krKj5UEMTJjzHg22DDj9Ti1ELOB/wa+4/58Hvin4IZmwCllBRDe7iSzidwzA7ihaCpR4R4e31Xq9zWzl61EPB4bajRmEhswmanqL1X1GuAeVb2mx88tqvqHUYpxUqt3k5m3rQGY+MksLtLLuoJMnth7lvbObr+uiY6LZ3pBIUdsir4xk9Zgw4y+Ir45IvL53j+jEN+kV9vcTlS4h64mXzKLH+OIgu/WpVnUNnfwUnGF39fMXnoZ58tOU1dRHsTIjDHj1WDDjLHun3E4M096/5gg823/0tJQjzcykvDICb+xNmvyUkmKCefp/ef8viZnsTMX6cSencEKyxgzjg04m1FVf+r++fXRCcf05tRljKC1sWHCDzH6hId5WFeQwdP7ztHW2UWkd/DF0FOmZZOQls7x3TtYtPaGUYjSGDOe+Lto+j9FJEFEwkXkBRGp7G8fMRNYPSvmT5ZkBnBj0VQa2jp57UiVX+1FhFmLl3Fq/x66OjuCHJ0xZrzxd9H0OlWtxyk0eQLIA/4+WEGZi+p8e5nVT65ktjo3lYQoL0/tG8JQ46JldLS2UHZ4wu8KZEzIEkfAtx/z94a+4cibgN+7RSjNKOj5zmwyJbMIr4e1BZk8d/Cc37MaZxQuxBPm5cSeHUGOzpiJR0T+KCI7ROSAiHzMPdYoIv/qbsfyhohkuMdz3d/3icg3RaSxx33+3t06Zq+IfN09liMixSLyK5yqIv4VYB0CfyuAPCEih3FK+X9CRNJwqh2bIJvoe5kN5MaiTB7bWcrmo1VcMzd90PYR0TFkzSvgxO4dXPW+D41ChMYEwdcSg7IFDF+rG6yA8YdV9byIRAPbROQxnEmAb6jqP7v7k30UpyLU/wL/q6oPicjHfTcQkXU4mymvxCl1tVFErsLZWDkfuFtV3wjwdwP83wLmS8BqnArKHUATsCEYAZmL2ju7aW7vIj4yjLYJXGS4P2vyU4mP9PLU3rN+X5OzaCmVp07QcN6/d23GmAs+4264+QZOzykfaAeecM/vAHLcz6uA37uff9vjHuvcn104myjPc+8DcDJYiQyGsAUMTlA5ItLzml8FOB7Tg6+UVWxYF3VM/AXTvUV6w7h2fjrPHSqns6sbb9jgf/eatXgZr/72AU7u2UXhNWtHIUpjAmzwHlTAuSUKrwNWqWqziLyEs+VLh6qq28y3pcuAtwL+3TcTvsf9c3A6QUHj72zGX+OUtVqDUyxyBWDV8oPMl8yi1Plzou5lNpDrF2RS29zB1hPn/WqfOn0m0fEJnD6wN8iRGTOhJAI1biKbh7Oh5kDeAG53P9/Z4/gm4MPu9i6ISJaIDP6OIAD87ZktBwp6ZGgzCurcvcyiup397iZbzwzg6rlpRHo9PHugnNW5qYO2F4+H6QsWcurgPlTVqugb459ngI+LyCGgGCdZDeSzwG9E5J/da+sAVPVZEZkPbHH/22vE2dS5K1iB+/g7m3E/kBnMQMylfD2ziM4WYHIms5gIL1fmp/LcwXL8/bvU9AULaayuorbc/3dtxkxmqtqmqjeo6nxVfaeqvk1VX1LVuB5tHlXVe9xfy4DLVXUhzrux7T3a/a+qFrk/q1T1qKqeUNXCYH4Hf3tmqcBBEdlKj22xVfWWoERlgIt7mYW3O0PNkzGZAaxbkMnzhyo4cKaewqzEQdtPX1AEwOn9e0nOnBbs8IyZjJYBPxCn+1ULfHiM4/E7mX0tmEGYvvl6Zt5Wp8jwRN/LrD/XzkvHI7DpwDm/ktmUadnEJk/h1IG9LLxu/ShEaMzkoqqv8tcbdI45f6fmv4xT+SPc/bwNp2tpgsjXM/O01BMeFY03PHyMIxobKXGRrMiZwrMH/KuILyJMLyji9IG9fg9NGmNCm7+zGT8KPAr4pltmAX8MVlDGUdfSQXyUl7bGybdgurd1CzIpLm/gRJV/s3unL1hIc10t58tOBzkyY8x44O8EkE8BVwD1AKp6BBiV6ZaTWf0kLWXVl3UFGQA8e9C/Wo0zCp0RkFM2Rd+YScHfZNamqu2+X9yF0zZ+E2S1vmRWXz8p15j1NH1KDAVTE9jk51BjYnoG8alpnN5vycyYycDfZPayiPwTEC0ia3HKmPw5eGEZcOsyxoTTasOMgLOAeuepGioaBi8L6ntvVnpov703M8YPPYsFhyJ/k9mXgEpgH/C3wFPAl4MVlHHUNrfbMGMP6xZkoArPH6zwq312QSEtDfWcLysNcmTGmLHm72zGbpwJH59U1TtU9f+sGkjw1bV0khDppb2lxZIZMC8znhlTYvx+b5Y931mjWXpoXzDDMmZCcfcb+7aI7He3eHmPe/xhEbmpR7sHROQOEQlz2/u2ffnbsYh7wHVm7oK4rwL34iY+EekCvq+q3wh+eJOXqlLX0k5smLOXV3R8/BhHNPZEhHUFGfxqy0kaWjuIjxp4qUJSxlRik6dQeugAi9beOEpRGjMyRb8sCsoWMPvu3udvAePb3OcvwimYsU1EXgEeAd4NPCkiEcC1wCeAjwB1qrpCRCKBzSLyrKoeD/B3GNBgPbPP4cxiXKGqU1R1CnAZcIWIfC7o0U1iLR1ddHQpMdIJTN7qH71dX5hJe1c3LxVXDtpWRMiet8DemxkzNGuAh1S1S1XLgZdxiss/DVzjJqwbgFdUtQVny5cPishu4E0ghYvbvoyawSqAfABYq6oXNodS1WMi8n7gWeB/ghncZHaxYn47rVgy81k6I5mU2Ag2HTjHOxYNXqoqu6CI4i2vUldRTlKGlRc1498QelCjSlVb3a1hrgfeAzzsnhLg06q6aaxig8F7ZuE9E5mPqlYCk7McxSjxVf+InMQV8/sS5hHWFmTwUnElbZ2DF+LOnr8AgNJD+4MdmjETxavAe9x3YWnAVcBW99wjwIeAK3Gq5YOz7csnRCQcQETmiEjsKMc8aDJrH+Y5M0IXKuZ3uBXzEwavSThZrFuQQWNbJ68frR60bUrWdKLiEyyZGeO/x4G9wB7gReAfVNU36+pZ4Grg+R5rj38GHAR2ish+nEpRQ9n4OSAGe+AiEanv47jg7EJqgsTXM/O2O0s/ouJsAojP6txUYiPCePZAOdfMHbgQjXg8ZM8rsGRmzCB82724M9X/3v3p3aYDmNLrWDfwT+7PmBmwZ6aqYaqa0MdPvKoOOswoIutFpFhESkTkS32cjxSRR9zzb7pba/vO/aN7vFhEru9xPElEHhWRwyJySERWDe0rh4Z6X8X8tgYiY2IJ8476X3TGrajwMN42N53nDpbT3T34xI7s+YXUlZ+jofqSEXNjzATh76LpIRORMOCHOLNeCoC7RKSgV7OP4GzVnYczmeRb7rUFOFtxLwDWAz9y7wfwv8AzqjoPZ+rooWB9h7FU6+4y7Wmqs/dlfVi3IIOqxja2nTg/aNsL680OHwh2WMaYMRK0ZAasBEpU9Zg7tvowsKFXmw3AL93PjwLXumvbNgAPu7ufHgdKgJUikojzMvJ+AFVtV9XaIH6HMVPX0kGYR9DGWqJsjdklrpufQVS4h417zgzaNi1nFhHR0ZTZUKMxE1Ywk1kW0HP/jVL3WJ9tVLUTqMNZo9DftbNwymr9QkR2icjP+ps1IyIfE5HtIrK9snLwNUnjTZ1bZNjqMvYtNtLL2oJMntp3lo6u7gHbejxhZM0toPSQ9cyMmaiCmcyCwQssBX6sqkuAJpy6kZdQ1ftUdbmqLk9LSxvNGAOittm2fxnMLYumUdPcwWtHBn8XljW/kOrSUzTX141CZMaY0RbMZFYGTO/xe7Z7rM827rYyiUD1ANeWAqWq+qZ7/FGc5Dbh1NleZoO6ek4aidHhfg01+t6blVnvzJgJKZjJbBuQLyKz3DpedwIbe7XZCNztfr4DeNGdFroRuNOd7TgLpzTKVnetw2kRmetecy3O+oYJp66lg4TIMDrb2iyZ9SPC6+GGwkw2HThHS/vAC6gzc/PwRkTaFH1jxpCIPCUiScG4d9CSmfsO7F6c1eGHgN+p6gER+YaI3OI2ux9IEZES4PO4Q4aqegD4HU6iegb4lKr6/t/q08CDIrIXpxjmvwXrO4ylupYO4tzFD5N9Y86B3LJ4Gs3tXbxweOBNO8O84UybM9femxkTQO6Imj/tREQ8qnpjsCbtBfWdmao+papzVDVXVf/VPfYVVd3ofm5V1Xepap6qrlTVYz2u/Vf3urmq+nSP47vdd2ELVfWdqloTzO8wVmqbO4j1+CrmWzLrz2WzUshIiOSPu3qPYF8qa14hFSeP0dbcNAqRGRM6RCRWRJ4UkT3u1i/vEZETIpLqnl/u1mVERL4mIr8Wkc3Ar0XkHhH5k4i8JCJHROSrbrscd53wr4D9wHTfPft6nnvNMhF5WUR2iMgmEZnq73ewlbjjUHe3Ut/aQbRVzB9UmEfYsDiLn792nOrGNlLiIvttmz2/EFQpKz7I7CUrRjFKY/x3aN78oGwBM//woYEKGK8HzqjqTQDuMqhvDdC+AFijqi0icg/OUqxCoBlny5gngSqcV0R3q+ob7n37fZ5b2/H7wAZVrXQT3L8CH/bnC4babMZJoaGtE1WIsiLDfrl9aTad3cqfdg88EWRq/hw8YV5KD9p7M2N62QesFZFviciVqjrYtN+N7vYvPs+parV77A8428gAnPQlMj+eNxcnIT7nbifzZZzJf36xntk4VHehYn4rYMlsMHMz4ynMSuCxnaV8eM2sftuFR0aRmTfHJoGYcW2QHlRQqOpbIrIUuBH4poi8AHRyscPTuxZv77H63nXltJ92Az3vceCAqg6rRKH1zMahixXzmwErMuyP25dmc+BMPYfP9VUX+6Ls+QsoP1ZCR2vrKEVmzPgnItOAZlX9DfBtnCVPJ4BlbpPbB7nFWhGZIiLRwDuBzcN4XjGQ5qu3KyLhIrLA3+9gyWwc8tVl9LY1ERUbhycsbJArzC2LpuH1CI/tKB2wXfb8Qrq7ujhz5PAoRWZMSCgCtrrDe18Fvgl8HfhfEdkODLZ54FbgMZytYx5T1e1DfZ5b9vAO4FsisgfYDaz29wvYMOM45OuZeVvqCbNp+X5JiYvkmnnpPL7rDF9cPw9vWN9/T5s2Zz4iHkoPHWBmUaDfsRsTmtxdovvaKXpOH22/1ke7UlV9Z692J3DegfU8luN+7PN5qrobp/7ukFnPbBzy7WUmLXVE2fsyv92+NJuqxjZeHaC8VWRMDOmzZlvRYWMmGEtm45CvZyZNNTb5YwjePi+d5JhwHt05+FDj2SPFdHZ0jFJkxkxcqvqAqt471nFYMhuH6lo6iPR66GyotWQ2BBFeD7csmsZzB8svzAjtS/b8Qjo72jl39K1RjM4YE0yWzMahuuYOkmLCaa23IsNDdfuybNo7u3liX/9rzrLmOXvEWtFhYyYOS2bjUG1LOwmRXjo72i2ZDVFRViL56XEDzmqMjk8gdfpMW29mzARiyWwcqm3uICHS+UdjyWxoRITbl2Wz81Qtxyob+22XNb+QsuJDdHcNNuPYGBMKLJmNQ3UtHcR5nQX0I0pm3d1QexqqSqB14MXEE8mtS7LwCPxhZ//Fh2csKKKjtYWzR4pHMTJjxi8R+YyIHBKRB8c6luGwdWbjUG1zB7OjnYKcw0pmzefh5f+EPQ9Bq2+3BYGMQpi5CvLXwayrwRsRuKDHkYyEKNbkp/H4rjI+v3YOHo9c0mZG0WLE4+H47u0X3qEZM8l9ErhOVQeeDjwAEfG623+NOuuZjUM1ze3E+CrmD3XR9Jnd8JM1sO3/IO9auPm7cOt9cM0/QWwq7PoNPHgHfDsXHvsoHNwI7RNvS5Tbl2ZRVtvCG8eq+zwfFRtH1twCju/aMcqRGTP+iMhPgNnA0yLyzyLycxHZKiK7RGSD2yZHRF4VkZ3uz2r3+Nvc4xsZw82SrWc2zrR2dNHW2U2UDmP7l+qj8JvbITwa/uZ5mLbk0jYdrXD8ZTi0EQ4/Bft+B95omL4SZqyCrGWQNgcSZ4AndP+uc/2CTOIjvTy6s5TVeal9tslZvIzXHvoljTXniUueMsoRGtO3H378xaBsAfOpn7y93wLGqvpxEVkPXIOzUfKLqvphd1forSLyPFABrFXVVhHJBx4Clru3WAoUqurxAMftN0tm44yv+kdkVwsiHiJjY/27sKsDfvdB0G74wB8hNa/vduFRMOd65+fmTji5GYqfcv585T+d6wG8UZCS79wndQ6kzXOSXYLfe+WNqajwMG5aOJWNe87wLxs6iY289F/12UuW89pDv+TE7h0UXrN2DKI0ZlxaB9wiIl9wf48CZgBngB+IyGKcWo09S11tHctEBpbMxp2aZqfIcHh7M1FxcXg8fhYZ3vIDKN8Pd/62/0TWW5gXZl/t/IAzSaT8AFS9dfHnzG44+KeLSW7KbMi/Hpa8DzKLhvjtRtfty7J5eNtpnt5/jjuWXbotUuqMHOKmpHB813ZLZmbcGKgHNUoEuF1V/2p2lIh8DSgHFuG8ouq59cSYv6uwZDbO+Hpm4W0N/tdlbKyEl74F826GeTcN+9ntGkNpTR5njqVSc24xbc0dhEeGMSUvjNlZVUwNP4jn5Kuw/X5488eQ+3a47uswdeGwnxlMy2cmM2NKDI/tKO0zmYkIsxYv4603NtPV2UmY1/5zMAanAPCnReTTqqoiskRVdwGJOAWFu0XkbmBcbedh//WOM3Xu9i9hLXX+vy9740fQ2QrXfW3Iz6uvauH4niqO763i7JFaursVb7iHpMwYomLD6Wjr4tDWeva2QVzyElbdegf574xEdj4Am78HP70KVt8L13zZGcIcR0SE25Zm8d3nj1Ba00x2cswlbWYtXs6+F5/l7FuHyS4o7OMuxkw6/wJ8F9grIh7gOHAz8CPgMRH5IPAM46A31pMls3HG1zPzNNUSnZ40+AWtdbDtZ1CwAVLzB2za3dVNzblmKk42UHmynrIjtZw/4/z7OGVaLIvXTmfGghQyZycS5vX81XUn91ez7ckTPPfzgxxbmsbbP/BpIpbdA89/DV7/PpzYDO/5DSRmDferB8VtS7L57vNHeHrfOT561exLzs8oWownLIyjO7daMjOTWo/tWQD+to/zR4CewzBfdI+/BLwUxND8YslsnKnxbf/SUE107ozBL9j3e2irhyv+7pJTqkr58XqO76ni3LE6Kk7W09nuvPsKjwwjPSeBK+6YyqxFqSSmXdpr8fGEeZi1KI2colR2PXeKN/50jLrKnbzj04uJecf/Qt5aePxv4f61cPefISV3eF8+CGakxFCYlcCT+872mcwiY2KYuXAJxVte5ar3fQiRS9ekGWPGP0tm40xtSzsRXg+dDTX+rTHb9RvIKLpkGn7FyXpeefgtyo/X4/EIqdPjmH/FNDJyEkifGU9SegzSx2LigYhHWHr9TFKy43jmp/v403d3cdsXlhI5/2ZIfgZ+tQF+caOT0NIu2dNvzNxQOJVvbyqmrLaFrKToS87Pu+Jqnv7Bdzjz1mGy5s4fgwiNMSMVuguJJqi65g6Sorx0d3YO/s7s3H44s8uZWdijR/HW1nM89u0d1Fe3cvV75/KR71zJu/5xBVe9Zw5zL8skOTN2yImsp5kLUrjxkwupLW/m6Z/so6uj25nZePcToF3wwE3Omrdx4sYiZznBM/vP9Xk+d9lleMMjKH79ldEMyxgTQJbMxpna5g7i/S0yvO/34PFC0bsvHDq+p5LnfnGQzFmJvPerl1F4VRYR0YHvgE+fN4Vr755P2Vu1PP/Lg6gqZBTAPU9Cdyf89t1OWa1xYFZqLPOnJvD0vrN9no+MiWHW0uUUb3mV7m4rPGxMKLJkNs7UNLcTH+58HjSZHX4SctZAbAoAteXNPPfzg6TPiOcdn15EVGx4UGOdszKTVbfmUrK9gj0vnHYOps2FOx+EmpPOIu7O9qDG4K8bCzPZfrKGc3WtfZ6ft/oqmutqKT1o28IYE4osmY0zdS0dxIX5KubH99+wshiqjzhry3Ame7z8UDHiEW74eBHeiNFZArJk3QxmL05jyx+Ocu5YnXNw5mrY8AM48So8/fejEsdgbnCHGjcd6HuocdbSFYRHRXPYhhqNCUmWzMaZ2uaOi0WGB+qZHX7C+XPujQAc21VJ6eEaLt8wm7jk0VvvJSK8/YPziE2OZNPP9tPa5MzGZNGdsOZzsOMB2PPIqMXTn7z0OOZkxPFkP0ON4RGR5K24nCNvbKajvW2UozNm7InI62Mdw0hYMhtnalvaiVJnaC46PrH/hm9tgqmLITELVWXbkydIzoxhwVWjv84rMiac6z9aSHNdOy//tkcFnGu+DDNWwxOfg8q3Rj2u3m4smsq2E+epaOh7qLHomrW0NjVS/PqroxyZMWNPVVePdQwjYclsHGnt6KK1o5uo7lbE4yEypp+1X611ULrd2eIFOLm/muqyRpaun9nn3l2jISMngRU3z6JkRwVHtpU7B8O8cMf9ThX/398N7c1jEpvPjUVTUYVNB8r7PJ9dUERK9gx2PfNnZ0KLMZOIiDSK49sisl9E9onIe9xzvxKRd/Zo+6Bva5jxwtaZjSO+6h8RHc1Exycg/W3BcuI1Zwr87GsA2P38aeKSI8lfkTFaofZp6boZnNhbxcsPFTMtP4nYpEhImAa3/dTZmuaFb8AN/zFm8eWnx5GbFsvT+87ygctnXnJeRFiy/mae/9mPOHukmGlz5o1BlGay+857bg7KFjD/3yNP+FPA+Db32YuAVGCbiLwC3A98DvijiCQCq4G7AxzjiFjPbBypdesyelsbiUkYYIjx2EsQHgPTV1Jf1UJZcQ0Fa6YRFja2/zg9YR6uu6eAro5uXvz14Yu9m7zrYOXHnOLEx8dugoWIcFPRVN44Vk1VY9/vxeZfeQ0R0THs3vTEKEdnzLiwBnhIVbtUtRx4GVihqi8D+SKSBtwFPDZWO0r3x3pm40iNO3nC21JH9EDJ7OhfnBmD3kgObzkGAvNWjY99xpIyYlh1Wy6vPnKEg6+dYcGV7ju8674OJS/AHz8Fn9gMUUPcQTtAbiiayvdeLGHTgXO877JLe2cRUdEUvu06dj/7FFd/4CPEJiWPQZRmMvOzBzUWfgW8H7gT+NAYx3IJ65mNI76K+Z7mmv57Zo0VzpT8nCtRVQ6/cY7p85KJnzJ+KtYXXZ1N1txkNj9aQn1Vi3MwIgZu/QnUl8Kz/zxmsc3LjGd2aixP9TOrEWDRupvo7u5i1zN/HsXIjBkXXgXeIyJhbi/sKmCre+4B4LMAqnpwbMLrX1CTmYisF5FiESkRkS/1cT5SRB5xz78pIjk9zv2je7xYRK7vdV2YiOwSkQk1FuR7Z0ZDdf89s1NbnD9nrqbqdCMN1a1j/q6sN/E40/URnOHGbne4cfpKpyDyzl85szHHIjYRblo4lS1Hq6nuZ6hxyrQs5l6+hp1P/5nm+rpRjtCYMaPA48BeYA/wIvAPqnoOwB12PAT8YswiHEDQkpmIhAE/BG4ACoC7RKSgV7OPADWqmgf8D/At99oCnK7sAmA98CP3fj5/h/M/6oRS2+Ju/9I4QM/s1BvgjYKpizm6qwLxCDkLU0cxSv8kpERzxe15lBXXcODVsosn3vaPkL4ANn7GmZU5Bm4smkq3wjP9LKAGWHXHe+loa2X7n/8wipEZMzZEJAU4r46/V9VCVS1S1Ud6tIkB8oGHxizQAQSzZ7YSKFHVY6raDjwM9J7KuQH4pfv5UeBacfbg2AA8rKptqnocKHHvh4hkAzcBPwti7GOiprmd8DDBq50D98yyloM3gmO7KpmWn0R0XMTooNZoYQAAIABJREFUBuqngjXTmD4/mc1/OHpxuNEbCe/8ITRVwPNfH5O4/BlqTMmezvwrrmbXpidoqq0ZxeiMGV0iMg3YAvzXAG2uw+lAfF9Vx+VwRTCTWRZwusfvpe6xPtu4M2PqgJRBrv0u8A9A90APF5GPich2EdleWVk53O8wquqaO0iMDEOAmL62f2lrhLN7YeYqaiuaqTnXzOzF469X5iMiXPOB+YjAi78+dHG4cdoSuOwTsP1+OPXmmMR1Y9HAQ40Al99+F13tHWzb+OgoRmfM6FLVM6o6R1W/P0Cb51V1pqp+dzRjG4qQmgAiIjcDFaq6Y7C2qnqfqi5X1eVpaWmjEN3I1TZ3XCwy3FfP7MwuZ33Z9Ms4fdCpSD9jQcooRjh0/z975x0eVZX+8c+ZmWQmvfcCAULovVeRjgUBFVQUEcG2imsvy+rq2n6uuq5lXVSKBRAVFelIFRBC7wFCCyG910kyM+f3x51A6AFmJgHP53nuM3Pv3HPPe2cm8815z3ve1yfQRK/b4zl5oIA9a2u4G/u9BH4x8OvkOklGXO1qvNACatDmzlr0vZEdSxdSkHHhUZxCoah7nClmJ4GYGvvR9mPnPUcIYQD8gNyLtO0J3CqEOIbmtrxRCPGNM4yvCwrKK/HSawPO886ZpW3THiM7kLIvD99gE/6hF64QXV9o3jOC2BaBbJiXTGG23d1o9IZh/4Ls/bDhQ9fbFOFD3CVcjQC9Rt+LTm9g9dfXnVdbobiucKaYbUZbZBcnhHBHC+iYf9Y58zm9ivx2YKXUVtrOB8bYox3j0CYdE6WUL0opo6WUDe3XWymlHOvEe3ApWpJhrZ7WeUdmJ7eBfyxWYwAnD+QT26J+j8qq0dyNzdDpBCu/quFuTBgCLYbDmnddXsxTczWGs+FwzkVdjd6BQXQbNYbDWzZxbOc2F1qoUCguB6eJmX0O7C/AUrSJw7lSyr1CiNeEELfaT/sSCBJCJANPAS/Y2+4F5gL7gCXAY1LK675qYkFZFR6yEiF0mLy9zz0hbRtEdiDjSCFVFVZiWgS63sgrxDvARM874kk7VMDuNamnXxj6f1p05q+TwcX5EGvjagToMGw4/uERrJoxFaulXiU9UCgUdpw6ZyalXGSfWGwspXzDfuzvUsr59udmKeUdUsomUsouUsojNdq+YW+XIKVcfJ5rr5ZS3uxM+11NQXklRms5Jh8fdLqz6pGV5kBBCkR1ID25AARExvvXjaFXSPMeEcS2DOKPnw5TmG1POuwTDgNe0Wqf7XRtxG+LCF/igr1YuDvtoucZ3Ny44b6J5KWlsmXBTy6yTqFQXA7XVADI9Ux1xnx3S/kF5st2aI+R7UlLLiQo0svplaQdjRCCfmObodPrWPlVjcXUHcdDTFdY+hKU5rrUnlvsC6gvVIG6msYduxDfpQcbf5hNfvrZU78KhaKuUWJWT6jO/uFWUYLH+cLy07YBAltYGzIOFxLR+NoalVXjHWCkl93duGu13d2o08HN/4aKYlg+xaX2jOgQjU3CzzsuLVA3jn8IvZsby6d+rErEKBT1DCVm9YT8Mi08XV9eiOf5inKe3AbB8eTm6KmqsBLR5CKJiOs5zbqH06B1EBt/OkxBpt3dGNYCejwBO76Fo64rjhkX7EXHBgH8uDX1kgLlHRhEn7HjObFvN3tWLXeRhQqFojYoMasn5JXaxaws/9xIRilPBX+kHy4AIKLJtTkyA7u78Z5m6N10Zy6m7vMs+DeABU+C5cIRho5mZIcoDmWVsOdk0SXPbd1vENEtWrHmmy9VZhCFoh6hxKyecErMSvLOFbPidCjJhMj2pCcX4h1grFdZ8q8EL38jve6MJz25kF2r7O5Gd0+46X3ITYZ1rks0cHPrSNwNOn7clnrJc4VOx8CJj2OprGTltM9cYJ1CoagNSszqCdVi5mEpPzeV1UltfZOMbE9acsE1PSqrSULXcBq2CeaPn2u4G+MHQKtR8Pu/ICfZJXb4eboxsHkYv+w4SaXlolnSAC0zSPdRd3Fw03qSN290gYUKheJSKDGrJ+SWViIAk82Mp99ZYpW2DXQGitziKSusJPIani+riRCCG+5JwOCmY8XM/diq3Y2D3wKDByz8q8vWno3qGEV+WRUrk7JqdX6nW0YSEtuQFV9+SkVZqZOtUygUl0KJWT0hr7QCX6MOHRKPswNA0ndCSDPSj2vzSNfLyAzAy89I79FNyThSyK6V9tzSPmHa2rOja2HXdxe/gIPoEx9CuK+Jbzcdr9X5eoOBQQ89QWlBAb/PmnnpBgqFwqkoMasn5JVW4mtfNnaOmzFjD4S3Jj25EHcPA4ERXq430Ik07RJGXNtgNv5yhPwM+yin43iI7gJLXoTii2focAQGvY67u8by+6EcjubUbqQV3qQpHYbdws7li0hN2utkCxUKxcVQYlZPyC2pxFt/nryMJdlQkgFhrcg8VkRYQx+ETtSRlc5BCEHfuxMwuOtY+ZXd3ajTwfBPoKpMi250gbtxTJcYDDrBNxtrNzoD6HnnvfiGhLH8fx9hqXR99n+FQqGhxKyekFdaibfQFk57+NQYmWXuBsAa3JK8tFJCG5xnQXUdUVJZwt7cvWzO2MzenL0UVlx5zT4vPyN9Rjcl40gRO3+zuxtDmkL/v8OBRS5JdRXqY2JIq3C+33KC8srapQJ1M5kY+OCj5KWlsunnuU62UKFQXAhDXRug0MgrrSRCX4HJ2wedvkZexow9AORY45C2I3UqZjnlOaw7uY51J9exLXMb2eXnFj1tGtCUmxvdzMj4kfgZLy9QJb5zGIe3ZbNp/hEatgkiINxLK+KZtBAWPw9xfcAv2lG3c17u7daABbvS+XVnGnd2jrl0A6Bhu4606N2PxJ+/p2m3XoTENnSqjQqF4lzUyKweYLNJ8ssq8bCWn7vGLHMP+ESSlan93xHSwMeltpVUlvBL8i9MXDaR/t/3Z8r6KWzL3EbXiK5M7jCZD274gC8HfcmH/T7kifZP4OXmxftb32fYvGHMTpqNTV461L2aanejm1F/Orqx2t1os8Ivjznd3dglLpCEMB9m/nHsslJW9b3vQYyeXiyf+hHSVvt7VigUjkGNzOoBheVV2CQYK0vOTTKcsRvCW5N1vAgPHze8A4xOt8dqs7I+bT3zD89n9YnVVFgriPKO4sHWDzKwwUASAhIQ4tx5uxtjb2Rim4kcyDvAv7b8izc3vcmaE2t4p887tR6lefq602dMU5Z9uZfty47TcUhDCIyDwf+EBX+FxM+h6yQH3/FphBCM79mQF+btZn1yLr3ig2tptx99753Akk8/YO/albS6YYDTbFQoFOeiRmb1gFz7gmlDeeGZYmapgJyDEN6KrOPFhMT6nldEHEVOeQ6f7/qcYfOG8diKx9iUvonbmtzG10O/ZvHIxTze/nGaBTa7pA0JgQlMHTiVv3X9G4kZiYxdNJbU4ktn16imSadQmnQMZdP8o2Qcsc/DdRwP8YNg2cvaUgUnMqJDFKE+Rv675vIWbbfo3Y+Ips34fdYMtfZMoXAxSszqAdXZPwxlBWdmzM9OApsFS1AL8tNLCXWSizG1OJWX173MwB8G8p/t/yHGJ4b3+r7HyjtW8rduf6NdaLvLFlEhBKObjeaLQV+QZ87jwWUPkl6SXuu2N4xthk+gkaVf7MFcWgVCwG2fgVcIzB0H5isPNrkURoOeB3vHsT45l50nCmrdTuh09B//MGVFhWz4fpbT7FMoFOeixKwekFeqLYbWleTg6Rdw+oUMLZIxTzZGShwuZkWVRfxz4z+55edbWHpsKaMTRjP/tvl8MfgLBjUchJv+6uuldQjrwNSBUymsKGTCsglkldUuw4bRw8Dgia0oK6xkxcz92vyVVxDcPk0rUjr/cafOn93dtQG+JgP/XX34stqFNWpC6xsHsX3Jr+SmpjjJOoVCcTZKzOoBuTXyMnoH1BSzPeDmSXqediwk1nGRjInpiYyaP4ofDv7AyCYjWThiIS90eYE4vziH9VFNy+CWfDbwM3LLc5m0bBLFlcW1ahfawJceI5twbFcOu1ba3ZSx3WDAq7DvF1j3vsNtrcbbaGBcj4Ys3ZdBclbJZbXtNeY+3Iwm1s6a4RzjFArFOSgxqwfkltjFzFqOp38NMcvcA6EtyE4pw9PXHS9/96vuyyZt/Gfbf5iwbAImvYmvh37NlO5TCPMKu+prX4y2IW356MaPOF50nGfWPIPFZqlVuzY3RhPXNpgN85JJT7a7/Ho8Dq3vgBWvaWH7TuL+Hg0xGfR88NvBy2rn6etHl+G3c2RrIqn79zjJOoVCURMlZvWA7OIKfNwFemx4VbsZpYSMXVokY0oxoQ18rjr4o8pWxQtrX+Dz3Z8zMn4kc2+ZS+uQ1g64g9rRJaILU7pPYUPaBv5v8//Vqo0Qghvva45PoInF/9tNUU65Nn9260cQ1RF+nHhqLZ6jCfI2MrF3HAt3pV/W3BlAh2G34h0QyNpvp6uq1AqFC1BiVg/IKanA3037wfOqHpkVpoK5UAv+yCgl5CoXS1tsFp5f+zyLjy3myQ5P8mr3V/EweFyt6ZfNyPiRjGsxjtlJs/kuqXZJhE1ebtz0WBusFsnCT3dRabaAmweMmQUmX5h9l5b2ywlM6tuYIC933lq8/7JEyc1oovsd95B+6ADJiX84xTaFQnEaJWb1gOziCnzteRk9/e0Z8TO10UY+jeAqgz+klLy56U2WH1/Os52eZULrCU4N8b8Uf+34V3pH9ebtxLfZkrGlVm0Cwr0YMqkV+RllLJm6B2uVDXzCNUErzYI5d0NVucNt9TYaeKJ/PBuP5LH64OUJZqsbBhAYFcPvs2ditdTOrapQKK4MJWb1gOySCrxlBUZPL9zc7YuiM/YAgvSiKABCYq9czGYlzeL7g98zodUE7mt5nwMsvjr0Oj3v9HmHaJ9onl7zdK1D9mOaB9JvbAIn9uWxfNpebFYbRHWAkVMhdTP8MAGsjheNu7rE0iDIk7cXJVFlrX12D51eT++7xpGffpI9q5Y53C6FQnEaJWb1gJziCjysZWcGf2TsgsA4MtNsePkb8fK7sswf+3L38a8t/+KG6Bt4osMTDrL46vFx9+HDGz+k0lrJ5FWTKbfUblTVvEckve6I5/D2bFZ9naSlvGoxHIb+HxxYCIuecXjIvrtBx0vDmnMgs5ipa49cVtvGnboSmdCCDd/PospsdqhdCoXiNErM6pjSCgullVZMFUV4nx3JGKZl/rhSF2NpVSnPrX2OQFMgr/d8HZ2oXx93I79GvNPnHZLyknhl/Su1npNq2z+GLrfEkbQxgxUz9mkjtK6ToNdfYet0WPuuw20d3DKcm1pH8OFvhziUWbulBaAFsPS5ZzxlhQVsWzzf4XYpFAqN+vXr9ickp0RbMG0sLzg9MqsohryjWIJaUpBZdsVi9uamNzlRfIJ3er+Dv6l+VqfuE92HJzo8weJji5m+d3qt23W+KY5utzXiYGLm6Tm0/q9A27tg1Ruw1fHVn1+9tSVeRj3P/bgLq632o7+ohOY06tCZzfN/pLyk9kKoUChqjxKzOqZazHQluacjGTP3AZJCXWPgyhZL/5H2B/MPz+fB1g/SKbyTo8x1ChNaTWBIwyH8e+u/WZWyqtbtOg5pSO/R8RzdmcOvH++k0mzVQvabDNAKeh5Y7FA7Q3yMvHJLS7anFPD575fnbuw15j4qysvYPP9Hh9qkUCg0lJjVMdnF1SOz/BpipqWxSi+LBS4/+KPKWsWbm94kxieGSW2cl2HeUQgh+EePf9AyqCXPrn2W7Vnba922Tb8YBtzfnPRDBfz0/jbKSiXcMRMi2sL398Ox9Q61dXi7SG5qHcG7Sw+w6UhurduFNIijec++bF80n5K82rdTKBS1Q4lZHVMtZp7WstNilr4LPAJIS/fEO9CIp+/lZf6YuW8mx4qO8VLXlzDqnV8yxhF4unnyyYBPiPCK4LEVj3Eo/1Ct2yZ0i2DYo20oyCzjx3e3Ulikg3t+BP9YmD0G0nY4zE4hBG+Pak2DIE8e/mYryVm1dxv2uHMsNpuVjfPmOMwehUKhocSsjskuqUQnwMNqxjsgSDtYXcMspeSyK0unl6QzdddU+sf2p1dULydY7DwCTYF8NvAzPPQeTFo+iYP5tU8j1aBVEMP/2p7KMgs/vruN7Fx3uPdnMPnDNyMh+/JSUl0MH5MbM+7vgkGvY+wXiaTml9WqnX9YOK37D2H3ymXkZ6Q5zB6FQqHErM7JLq7A1w10SLyDgrR1Uln7sAS3ojC7/LJdjB/v+BibtPFc5+ecZLFzifKO4vNBn6NDx/gl49mVvavWbcPj/Bj5bAf0esFP728jNd0D7vsZhB6+vk3Ltu8gYoM8+eqBLpRVWrjvy0SyimoXdt9t5Gh0BgMb5n7rMFsUCoUSszonu7gCX4O2ENcnMAhyD4HFTJG+CXB5mT+OFBxhwZEFjEkYQ6R3pFPsdQWN/Bsxc+hM/Ix+PLjsQVamrKx124BwL0Y91xGfQBO/fryT5GM+cO88qCyBr26DktqVoKkNzSN8mXZ/ZzKKzIz4dEOtsut7BwTSYeitJK1fQ9axywsiUSgUF0aJWR2TWWTGT1Tg7uGJu4fnqRpmGeUNAAi9jEjGj3d8jElvYkLrCU6x1ZVE+0Qzc8hM4vzimLxqMu9ufpcqW1Wt2noHmBjxdAfCGviy9Is97E4KgLu/h+J0+HoklF9e0uCL0alhIN9N6k6Fxcrtn21g6/G8S7bpfOsoTF7e/D7b8csHFIo/K0rM6pj0QjM+1jK8A6vny3aB3siJrCB8g02YvGtXIHN/7n6WH1/OvS3uJcAUcOkG1wAhniF8PfRrxiSM4at9XzF20Vh2Z++uVVuTlxu3Tm5Hw9bBrJ1zkMSdITD6a61696w7tbV8DqJ1tB/zHulJgKc7d03dxPdbTlzCNm+6jriTYzu2cmTbZofZoVD8mXGqmAkhhgghDgghkoUQL5zndaMQ4jv765uEEA1rvPai/fgBIcRg+7EYIcQqIcQ+IcReIcRkZ9rvbCotNnJKKvCoKMQnKFg7mL4LwlqQebz8soI/Pt7xMb7uvoxrOc5J1tYN7np3Xu72Mu/1fY+ssizuXnQ3U9ZPIa3k0gEUBnc9Qx9qRbPu4WxeeIztR5vC7V9C6hb4ZhSYixxmZ2yQJz892oMucYE8+8Mu/vHrXiwXyePYfugtBERGs2rmVCxVtRtxKhSKC+M0MRNC6IFPgKFAC+AuIUSLs06bAORLKZsAHwDv2Nu2AMYALYEhwKf261mAp6WULYBuwGPnueY1Q6Y9aMBYmqtFMkoJGbuxBLWkOM9cazHbn7uftalrGd9qPD7uV56QuD4zqOEgFoxYwPhW41lwZAHD5g3j+bXPsz93/0Xb6fQ6+t3bnMYdQtjwYzL7C7rAHdPh5FYtytFc6DAb/T3dmTG+Mw/0jGP6+mOMm55Ivr2K+NnoDW70GzeRgox0ti36xWE2KBR/Vpw5MusCJEspj0gpK4E5wPCzzhkOVE8c/AD0F1ptkuHAHCllhZTyKJAMdJFSpksptwFIKYuB/UCUE+/BqWTYxcytOAufoCAoOgnleRTq4wEIbVg7YZq2Zxrebt6MThjtNFvrA15uXjzV8SkWj1zMPc3vYfWJ1dy54E4eXPYgf6T9ccHcjjqdYOD4lkQ3C2DVN0mkyF5w+3RI267NoTlQ0Ax6HX+/pQXv3t6GzUfzGf7Jeg5knN+lGdeuI407dWXjj3Mozs1xmA0KxZ8RZ4pZFFBz8iCVc4Xn1DlSSgtQCATVpq3dJdke2HS+zoUQk4QQW4QQW7KznVO48WrJKNTEzKuqGO/A4FPBH5nmBiBql/kjpSiFZceXMTph9HU7KjubcK9wnu38LMvvWM6THZ7kSMERJi2fxOgFo1lybAlWm/WcNno3HUMfbk1ghBdLv9hLfmB/uGMGpO+AGTdDcaZDbbyjUwzfPdQNc5WVEZ+uZ8mejPOed8N9E5FSsvzzj1VFaoXiKrgmA0CEEN7Aj8CTUsrzTnxIKadKKTtJKTuFhIS41sBaUi1m3pZSLQAkYzcgOJ4TTkCYJ+4mwyWvMWPvDAzCwNgWY51sbf3D192XCa0nsGTUEl7t/iplljKeXfMsI+aP4I+0c6s7u5sMDHukNTq9YNF/d2OOHQJjZkNuMnw5EHKSHWpf+9gAfn28F03DfHj4m638+7eDWsmaGviHhdP7rvs4un0L+9bWfgmCQqE4E2eK2UkgpsZ+tP3Yec8RQhgAPyD3Ym2FEG5oQvatlHKeUyx3EemFZjwM4C4rtQCQ9J3IoMakH7cQ2vDS82XZZdn8nPwzw5sMJ9gj2AUW10/c9e6MajqKX4b/wnt938NiszBp+SSeXv00OeVnuu98gz0Y+lBrinLKWfblXmyNB8C4Bdo6tGmDtOAQBxLma2LOpG6M6hDNv387xCPfbqWk4swCou2H3EJUs5asmjGV4jzlblQorgRnitlmIF4IESeEcEcL6Di7oNN8oDr87nZgpdR8LfOBMfZoxzggHki0z6d9CeyXUr7vRNtdQkZROYFuEgF2MduFJbAF5cVVtQr+mJU0C6u0cn/L+51u67WAXqdnUMNB/DT8Jx5r9xhrUtdwx693kJieeMZ5kfH+9L1Lq1i9Yd5hiO4IE5aD0Qdm3gL7HBuQYXLT86872vD3m1vw2/4sRn26gZTc0ymwhE7H4EcmY7VYWPLpv7Gdx02qUCgujtPEzD4H9hdgKVqgxlwp5V4hxGtCiFvtp30JBAkhkoGngBfsbfcCc4F9wBLgMSmlFegJ3AvcKITYYd+GOesenE16oRlfUYG7hwcmyqEwhUL3lsClgz/MFjM/HPyBG6JvINY31hXmXjMY9UYebvsws2+ajY+7DxOXT+R/O/93xpxUi16RtO4Xzc4VJziYmAFBjTVBC20Bc++DFa+BA0VFCMEDveL46oEuZBabufWTdaxPPj0KCwiPpN/9k0jZvYM/vp/lsH4Vij8LTp0zk1IuklI2lVI2llK+YT/2dynlfPtzs5TyDillEyllFynlkRpt37C3S5BSLrYfWyelFFLKNlLKdvZtkTPvwZlkFprxtpbiGxKGSNsGQIa5CTqdIDja+6JtFx5ZSEFFwZ9yrqy2xAfEM+emOQyNG8rHOz7mb+v/dkYWkZ63NyGiiR+rvk4iJ7UYvENh/CJofy/8/h7MGg3l+Q61qWeTYOY/1oswHxP3TUtk2rqjp0S2Tf/BtOo3kI3zvuPw1sRLXEmhUNTkmgwAuR6ostrILK7Aszwf35BQbd2T0HMsJ4rAKC8MbvoLtpVS8s3+b0gISKBTWP0uvFnXeLp58lavt3i07aPMPzyfx357jJJKLYeiXq9j8MRWGD0NLP5sN+bSKjAYtQKfN70PR1bD1H6QvtOhNsUGeTLv0R4MaB7Kawv28fT3OzFXaaPAGx94mNCGjVn88Xvkpl48k4hCoTiNErM6Ir3AjNUmMRVn4BcaBqlbkGEtSD9eSdglgj8SMxJJLkjmnub3oE0jKi6GEIJH2j3Caz1eIzEjkYnLJlJg1vIzevkZGfJQa0ryK1g+ba8WbSgEdJ4A9y8ASwV8MQASP9cWtTsIL6OB/97TkacGNuWn7ScZ+ekGTuSV4eZu5NanX0Lv5saPb/5dBYQoFLVEiVkdkZKnBQB4luXiFxwCadsw+7eh0mwloon/Rdt+s+8bAk2BDGt0zU4X1gkj4kfw737/5mD+QcYvHX8q0jG8kR+9RzclZW8emxccPd0gths8vA4a3QCLnoG59zo0SbFOJ3iifzzT7u9Man4ZN3+0jjUHs/ELDWPki/+goqyEeW++grn00tn4FYo/O0rM6ogT9oKOflVFBHlZwFxIrkwAIKKx34XbFZ1gTeoabm96+zVTRbo+cUPMDXwy4BNOlpxk3OJxp3I8tuwdSfOeEWxZdIwjO2ossvcKgru+g4Gvw4HF8L/eDg/f75cQyq+P9yLCz8T90xP5aMUhQho04tanXiYv7STz3nyFirJSh/apUFxvKDGrI1LyyjAI8LKWEmjTskOkFMTh5eeOT5Dpgu1mJc1CL/TXfeoqZ9ItohtTB04l35zPuCXjOFZ4DCEEfcY0JbSBD7/N2Ed+Rg3x0Omg5xMwfglIYNpg2PCRQ92ODYK8+OnRngxvG8l7yw8y6eutBDRtyc1/fZ7Mo4f5/vW/YS5RIzSF4kIoMasjUvLKCDba0CHxLDkM7t4kp/gT0cT/gvNgJZUl/JT8E4MaDiLUM9TFFl9ftAttx7Qh06i0VjJuyTgO5B3A4KZnyEOt0Rt0WoaQ0rOy2cd0hofXQtMhsOxvMHsMlF26fllt8XDX88Hodrx6SwtWH8hi+MfrIbYVtz79IjkpR/n+9ZcpL3Zcpn+F4npCiVkdkZpXRpCuAqOnF4bMXVhC2lCcbyGiyYVdjL8c/oXSqlLGNlfh+I6gWWAzpg+ZjkFnYPzS8ezK3oVPoEnLEJJbzuLPdmOtOquMi0cAjP4Ghv4fHF4Jn/WC4+emzrpShBDc3zOOWRO7UWy2aImK3WMZ/uwUck+m8P1rL1FW6Lh5O4XiekGJWR2RkleGT2UhQRGhkLGbIqNWySai8fmDP2zSxqz9s2gb0pbWIa2dapuUEllZibWoCEt+PrbSUmRV1XWZCLeRXyO+GvoVfu5+PLjsQRLTE4mM92fAuBakHSpgxcx9yLPyKSIEdH0IJiwDvTvMuAnW/suhi6y7xAWy4PFeJIT78Oi325ib4cUtz0whPyOdOa++QFFO/UyerVDUFZfOZKtwOMXmKvLLqvAwZxHbwACFVaSXN8HNqCcoyuu8bX5P/Z2U4hQeb//4VfcvpaTqxAnM+/ZTcfAAVSfTqMrMxJKejiU3F1t5OVjP88MsBHp/fwyhodoWFoqxSRNMzZpjapaA3v/iUZj1lSjvKGYOncmkZZN4dMWjvH/D+/Tp3Iei3HI2/nz+Kk5pAAAgAElEQVQEo6cbfe5qeq77N7I9PLQWfp0MK1+H5BUw4jMIaOAQu8L9tLyO//h1H/9dfZjtjQJ5bvIU1n/8Jt+9+jy3/+2fBIRHOqQvheJaR1yP/22fTadOneSWLY6NQLsa9qYVctN/1jEkcykvdi2lQcYP/Kybjc4vglsntz9vmweXPsixomMsHrUYN53bZfdpKyujeOUqSn9fS8m69Vhzc7UXdDoMYWG4hYVhiAjHEByCztMTnYcJnYcHCB2yqhJZVYWtogJrfj6WrGwsWVlUpaefvg7gFhWFZ9eueHXvhle3bhjqabWCC5Fvzufh3x7mYN5B3urzFoMbDOaPeYfZvjyF1v2i6X1n/PnnM6WEnXNg0bPaqG3Yu9BmtPbcQXy/5QSvzN+Lm17Hi71CyJ39Lnq9nttffp3g2IYO60fhENTizzpAjczqgCPZWqRcQFUBgdYMbP5xnDxgokvH849skvKS2JSxib92/OtlC5n5wAHyZ82maMECbKWl6P398erZE88uXTC1bIkxvgk645WH+FtycjAnHaAiaT/lO3dRvGIFhfO0YgbG+Hi8b+iLd58+eLRvjzDU769bgCmALwZ9wV9W/IXn1z5PTqcc7h5xNzabZOeKE0ibpPfopuh0Z/1WCQHt7oIG3WHeQ/DTQ3BwiZZFxDPQIbbd0SmGTg0DmTxnOy8sT2Nk38dptH4q3/3jRUa++CoRTRIc0o9Cca1Sv39drlMOZZWgA/wtBXgV7qc0cjBICL/A+rKv932Nh8GDUfGjat1HxeHDZH/0McVLliBMJnyHDMF/1Eg8OnRA6C+cKutyMQQH490rGO9ePQGQVivm/UmUbfyDkt/XkTt9Brmff4HO1xfvXj3x7tsXr969MQQ65kfe0fi4+/DZwM94fu3zvLP5Hfbn7WfKiCkInWDH8hRKCyoYOKElbu7neQ8DGmq5Hdd9AKvf0gJDhr0LzW9xyCgtLtiLHx7uwfvLD/K/tYdp2Ggs/U8u5PvX/8aI56YQ07LNVfehUFyrKDdjHfDot1vZdvAkjx77gPvitnEw8lVW7mrPg+/3xnDWj2R2WTaDfhzEnU3v5MWuL17y2rbycrI/+pi8GTPQmUwEjLuPoPvvR+934ShJZ2ItKaF0/QZK1qyhZO1arDk5IASmNq3x7tsX7z59MbVojtDVr1gkm7Tx2c7P+O/O/9IqqBUf9PuArMQqfp97iNBYHwZPbIVvsMeFL5C2HX55HDJ3Q8IwTdT8oh1m3/rkHP763Q7yyyq5sXIPzdM3cutTL9KoQ2eH9aG4YpSbsQ5QYlYHDHx/DR6FabxQ+l96eO/kF/E1+MUw/Mlz58v+s+0/fLH7CxaOWEiMb8x5rnaasi1bSHvpZapSUvC/805Cnpxcr0ZA0mbDvG8/JWtWU7J2LeZdu0FK9CHBePfpg3efvnj17IHe++IVA1zJipQVvPT7S5gMJt7s9SaROU35bfo+AG64pxnxncMu3NhaBRs/hVVvgU4PN06BLhO15w4gr7SS537YxW/7M2lKDjekLmTUY4+T0L23Q66vuGKUmNUBSsxcTJXVRvMpS+huPcw7ntMI97Hw34Mf0n1EYzoMPjMKrtxSzsAfBtIxtCMf3vjhBa8ppSRv2jSy3nsft6goIl5/Ha9uXZ19K1eNJTeXkt9/p3StFpRiKyoCNzc8O3bUxO2GvrjHxdV5MuXDBYd5Zs0zJBckM7b5WB5o8BBrZyaTcaSIxu1D6HlHPD6BF87aQv4xWPg0JP+mRUDe9B5EdXSIbVJKvtl4nNcW7MPHWsbQk79yz8RxtOhzo0Our7gilJjVAUrMXExyVjED3l/LkJwVfBzzDSXRt/Ht5jHc+VJnQmLPLMg598BcXt/4OtMHT6dT+PlLvdhKS0l78SWKly3DZ/BgIt54A733+cP76zPSYqF8+3ZK1q6lZPUaKg4dAsAtJkZzR/bti2eXzlcVrHI1mC1mPtj6AbOSZtHEvwlv93yb4kQjWxcfA6D9oFja9o/B6HmBAB0pYc+PsPQlKMmCjuOg/ysOCxDZlpLPw19toaCknAFZy3n4nptp03+wQ66tuGyUmNUBSsxczOLd6Tzy7TaeyJ3GU1G/sSPoTbaltGH8O70QNaLkbNLG8J+H4+nmyZyb5px3dGLJzeXEQw9j3reP0GeeIXD8/XU+inEUVSdPnhK20k2bkGYzOm9vfAYNwu/WW/Ds3NmhgSy15ffU35myfgqFlYU80OoBxkTdy9afT3B4WzbuHgba9IumVZ8ovPwvILrmIljzDmz8L5h8YcCr0P4+Lf/jVZJZZGbizM3sPllIj7yNPD2yGx2G3HzV11VcNtfHH+E1hhIzF/PB8oP8Z8UhZpT8jT4hx5lZ8C0x7RvS/77mZ5y3MmUlk1dN5u3eb3NTo5vOuU7l8eOkTJyEJSuLqA/ex6dfP1fdgsuxmc2UbdpE0dJlFC9diq20FENYGH633UbAXWNwCw93qT155jze3fwuC44soIFvA57r/BzNbO3Ytvg4h7dnI3SChq2DaNknipjmgeeG8gNk7tPKyhxfr7kch/0LojpctW3mKitPfbedRXsyaVG8nymDGtF9+Mirvq7islBiVgcoMXMx46cncvBYGj9V/gW/qAS+2PMyQx9uTaN2pxcYSykZvWA0JVUlzL9tPgbdmSsoKpKTOX7/eLBYiPnfZ3i0bevq26gzbGYzJatWUfjzL5SsXQs6HT4DBhBwz93aaM2FI9M/0v7gjU1vcLzoOF3Cu/BUp6eIssax7/c09v+RjrmkCu8AI407hhLfMYzQhj5n2icl7P5eS1pckgUd74f+f79q16PNJnlvWRKfrD5CdPlJXu3px4A7VZUFF6LErA5QYuZCpJR0/OdvNC1PZo77CxwNnczSfTcy4b3euBlPu8zWnFjDX1b+hdd6vMaI+BFnXMN84CAp48eDXkeDGTMwNm7s6tuoN1SmniR/9iwKfvgRW2EhxhbNCbr/fnyHDEG4u7vEhipbFd8f+J7Pdn5GfkU+PSN7MqH1BNoHduDorhwObsogZV8eNqvEN9hEk45hNOkUSnC092lhMxfC6rdh0//A5Gd3Pd571a7HH7ek8PwPO/GuLOLFlhbuGD8WnYMiKRUXRYlZHaDEzIWcyCuj9/+tYnzlAl7xncW8ii8xRsdz02OnR1ZSSu5eeDf5Ffn8OuLXMzJ+mPfvJ2X8AwijkdgZ0zHGxdXFbdQ7bOXlFP76K3kzv6Ly8GEMYWEEjL2HgDvvdNn6uuLKYr478B3f7PuGXHMubYLbMK7lOG6MvRFLueTIjmySt2aRmpSPtEn8wzxp0jGUJp1CCYq0L0XI3AsLn4GUDZrr8ab3tOjHqyDxSA4PfLGBCouN4Z6pvP7MODy8fS7dUHE1KDGrA5SYuZD5O9N4YvZ2ptneoGtACTOO/5uBD7SgaZfTcz7rTq7jkd8e4dXurzKq6emMH+V79pIyYQI6T08azJyBe2xsXdxCvUbabJSuW0fu9OmU/bER4emJ/8iRBN53r8veL7PFzPzD85m+ZzqpJamEe4UzJmEMo+JH4W/yp7y4ksPbNWFLO5iPlBAY6UWTjqHEdwrDP9QDds3VXI+l2dBpvLY+7Spcj2kF5TwydSU786CxNZPXb+9Aj67tHHjXirNQYlYHKDFzIa8v2MfX64+wx+0+TgTcx29HbmP8u71wN2lzYjZp466Fd5FvzmfhiIW46bVRWfnuPaRMmIDe25vYr2biHu24TBLXK+akJPKmz6Bw0SKwWPAZ0J/A8eO1HJEumFez2qysSV3DrP2z2JSxCaPeyM2NbuauZneREKjlUSwtrODI9mwObckkPbkQgOAYb23E1soDvz3/dpjr0WaTfPzLRj7amIUFHR19ynnolm7c2DoG/fkCVBRXg3pD6wAlZi7ktk/WU5GWxGK3p5hb8jl+CS0YPLHVqdd/Tv6ZKeun8Fbvt7i5kRZSfUrIfHxo8NVM3KKi6sr8a5KqzCzyZ80if84cbIWFmNq0IWj8/fgMHOiyxMeH8g8xK2kWCw4vwGw10zm8M/c0u4e+MX1PBfeU5FdweFsWh7ZkknlUqyYd2sCHVs0LaJr1Lvq0TRDVSUuLdRVRj+k5hfxj2hJWZ+sx6z3wNVjp3jiEns0i6BAbQNMwH9wN9Su12DWIErM6QImZi8gvraTjP5czkfk8aExkbsZ73Dq5HTHNNfdRaVUpN/90M5HekXwz9BuEEJTv3k3KAxPQ+/nRYOYMJWRXga2sjIKffyZv5kyqjqegDw7Gd9hQ/G65BVOrVi4ZrRVWFDLv0DxmJ80mvTSdSK9IxjQbw8j4kfgZT8/tFeWWk7w1i+QtWWSnFIOQdGu4hbbWqRgqcyDhJuj77FXNp6UdPcK0uctYm2omzRhBiUGbt3PXC5pH+tEmyo/W0X50bxRETKDnVd/7nwwlZnWAEjMXUT1f9pP7FMoZwmF5C2P/0e3UQukPt33IF7u/4Nth39ImpA3lu3aRMuFBTci+molbpCrC6Aik1UrJmjUU/vQTJavXIKuqcG/YEJ+BA/HudwMebds6fTG2xWZhzYk1fJv0LZszNmPSm7i58c3c2+JeGvk1OuPcgqwyDiZmcmBTBuacHNr7LKSt1wLcZDGyyQBEr6egQY8rzspfmJXB7lW/kbhxKwcLbWS5h5DnF0uGPgCzTRuhNQn1pn/zUEa2jyYhXAWP1AIlZnWAEjMX8fTcnfy2/RAb9I/wbe4MOo1oRYdBWi7GlKIURvwygkENB/FW77co37mTlAcnovf310ZkSsicgrWwkKJlyyhauIiyLVvAYtHqvfXujVe3bnh16+r00fCBvAOnXJCVtkr6RvdlXMtxdArrdMZoUUpJxpEiDmzK4PiWozQVv9LOez4eoghLUEsMvR6FVreD20VyRF4EKSW5J45zdMdWju7YSmrSPnJ13pz0aURacHMOW3yxSmgT7cc9XWO5rX0URoMK878ASszqACVmLsBmk3T551K6m3/nAeshtlT+hfve7IHRw4BN2hi/ZDyH8g/x0/Cf8Np2kNQnJmMIDlZC5kKsRUWUrl9PyerVlKz9HWt+PgBu0dF4du2CV9eueHbtilvYRbLkXwW55bl8d+A75iTNIb8inxZBLRjbfCwDGwzEZDhToKxVNo7vyeXQxmMYk+fR2rSAILcULG4B0OZODB3vgoh2V1VDrbK8jJS9uzm2YwtHd2wlM7eIA97xHAhoQ5bOlyAPHRN6N2Zcz0Z4GVVZxLNQYlYHKDFzAb8fyubeLxP5UP8RefkP0mJIB7reqrmTZu6dyb+2/IvXe75Ov/160l58CWN8PLFT/4chJOQSV1Y4A2mzUXEombJNmyjdtImyzZu1jP5oiY892rfDs0MHPNp3wNiksUPdktWh/V/v+5pjRcfwdvNmSNwQBjUYRKewTqciXKspL6nkUGIGuRuWElvyAw2NW9ALC5XejTF0HI0uYRCEt72qBdhSSvLTT3J0uzZqW38kjy3erTjhEYOv3sqkLmFMuqkT7mqkVo0SszpAiZkL+Mu0Vaw7mMlnVXPZbZvMva/3wOTtxo6sHYxfMp6+0X14cVdD8j6bimeXLkR/8jF6HzU3UV+QVivmpCTKNiVSvn0bZdu2Y83NBUDn7Y1Hu3Z4dGiPZ/v2eLRpg87r6qsW2KSNLRlb+OXwLyw/vpxySzlebl50Du9M25C2tAluQ5xfHMEewafckbknSzi07gCWHfOIEyuJctfqrtmMAeia9IOGvSC8DYQ2B+OV14yrqjCTsmcXi1YlMuekByeN4QTaShjbWHD/LT0IjPzTLx1RYlYHKDFzMvklZrq+sYzRYiUN8vvR5c6OtOkXTXpJOncvuhujcOeD1bGwYh1+o0YS/sor6FyUiklxZUgpqTpxgrJt2yjftp3y7dupSE7Wci3qdLg3bIipWQLGhGbaY7NmGEJDrzhistxSTmJ6IqtTV7M5YzPHi46fes2oNxLpHUmkdyTBpmCCPIIIcA+AXCNVSYUEH9tHPMk0Mu7HU+Rp9iMQgXEQ2Ah8IsA3CnzCtfVsRl8w+py1+V5wZGcuLWX2ovX8b1sBGdKLcHMGw71SGdKnIwnde+PpWzcVzusYJWZ1gBIzJ/N/02bx6UE/XqvYhM67P3e/2pv8ynwmLJ1AZnEa//zJROTBfMJeepGAu+66bkq4/NmwFhVRvmMH5Tt2Yk5KoiIpiaq0tFOvC5MJ95ho3GJitcfoGAxhobiFhmIICUEfElLrf2IKzAXszd1LSnEKJ4tPklqSSlpJGnnmPHLNuVhslnPaCBs0qHCnVbmgeaWVBFlBpKwiSJrxqCxGcInfAXcfrWSN0Vd79IkA/xjwi4WABlhDWvDtzmI+WHGE/CpBk9LD9CxMpF3LeBp37ErDdh3xDf7TuM3VH3EdoMTMiaTtXUe/r7PpzUHaF7Tj7ld7UeFTzENLJ3Ky8AQvzK6kva4Bke+8/afKfP9nwVpcTMWBA5gPHqQq5QSVJ05QdUJ7lOXl55yv9/fHEBKCwS5w2haMPigIQ1AwhuAg9EFB6P39L/hPj5SS4qpi8so1Ycsz5516nlOaw8mcTDKLssmvzKNAl41NZ8MgJaGVehpWhtJIhNPEFEZjrzCivDzxdK/EpC9DbynRarFVFGqJkYvSoOAEWCtOd+4RQFloez6vHMxnKdFUWqFDZTKtMv7A21pKcEwDGrbrSFy7jkQ0bYabe90UWnUBSszqACVmTsJ6YisT/vcbGywJTCqU9L25AbY2Zp5d8VfMVWU894OVXn3GEvrUX9F5eLjUNkXdIqXEmpeHJStL27KzsWRnU1X9PCv71DEs546ycHPDEBiIISgIfXC10FWLnfaovRaM3s8PcQEXYXmFmW2H9rArdS8H8pM4XH6Ik+IoVbpKrRuLkeDSGEJLY4iyxNHI2JRYvxh8gzzwCTLhE+COr2cpPiIN9/z9kLEbMnZB5l6yLJ68b7mDuda+6AX098uijfko5oN7kFYLOr2B8CZNiWnRiuhmLYlMaI67x3WzOFuJWR2gxMzRSIltxxz+OW8j06oGcXN5GT0DT7InfD3zTUlE5En+frQtnR6dgqlZM9fYpLgmkTYb1sJCrLm5WHJyseTmaM+zc7Dk2vdzcu3Pc6Gq6tyLGAwYgoJqjPRqbKE1ngcFIdzcsNqsJOcfZkfqLnZl7GZ/wT6OmpOxoImqyepJSEksAaXh+JlD8CsPwc8cQqA+GN9AT3yCTPgFQJjbIYKqdpKfd4gZGZHMrepFBe4kGDIZ7J9OI0MplqxcUo6mI202APzDIwht0IiQho0IjIrGPywCv9BwjJ7XnMgpMasDnCpmQoghwIeAHvhCSvn2Wa8bga+AjkAuMFpKecz+2ovABMAKPCGlXFqba54Pl4iZzQpH15Cy9FPeSG3JUlsXOpYlEeUxmw1NzFQZ4JbMaJ7s8xLBPfo41xbFnw4pJbbCQk3YcnKx5uZgycnRRDAn59RIz5KdjTUvTwtWqYkQ6AMCzuPmDEEGB5DiU0GSTGd/RQr7Sg5xrOg4FbbTLkYDbgTaQvGtCMKjxA+Pcl+8KvzxrvTHp9KHCH0B6R7l/GHzY5dNWzvpQymt3dKIM5UTbqjEUFVBeW4BZRkZmGyVpxTBw8cXv7Bwu7iF4RMUgm9IKL7BofgGh+BmurKF4k5EiVkd4DQxE0LogYPAQCAV2AzcJaXcV+OcR4E2UsqHhRBjgBFSytFCiBbAbKALEAn8BjS1N7voNc/HlYqZlBIsFqTFgq2iFGtpMVWlxZQX5VCUfoyizBTK80+SVVRAls3AdhnPClt7rOjwCliCLWwd7jZBf5nAxF5P0zSh22XboFA4GllVhSUv7wx35qmthtvTkpMDVut5r2EzGCgM9yIzwkRGoCDdT5LuYyHTs4pcYyVFbue6R92sOjyq3DGW+RNYEoOsiKbAGs5JGY6ZM+fP9FjxphwPUYGHqMCdCgy2SrBVobNZEdKCXlrQ2ay46XWYjEa8PD3w9vTC19cXP18/fHy88fb0wNvTE18vL/y8vfA0mfA0uWNy12M06NALgU4IhMCRwVdKzOoAZy7d7wIkSymPAAgh5gDDgZrCMxx41f78B+BjoX2jhgNzpJQVwFEhRLL9etTimldFVVoah4cOQ1osGNwraXxzJjo96NDerD7mj8kkEAizb51PtTXpC9H7bSbGtIl2gaEMbv8PejUfgqfbNecmUVzHCDc33MLCLpnNRNpsWPPz7eKWg62kGGtRMbbiIqzFJQQVF9GgqBibuRyZXok8asZWUYGsqMBcVUa2wUyOm5kcYyV5xipKTDZKTBZKPMooMaVT6puI3gDheoHe5kOw1RNviw96iy86ixd6qwmLzQOz9KBUelIiPKjQ+1Khc8OMOxVoj1b0YANK7FtW9R1UH6gtEiHAoNMhhEAnQCcEiS8PwFtlOan3OPMTigJO1NhPBbpe6BwppUUIUQgE2Y9vPKttdZK8S10TACHEJGCSfbdCCLHnCu4Bdpx94L5LNkkCVgLvM6fm4WAg54pscBzKBmWDsuEybfB5/bKvt0RKOeRqDFJcPtftvxtSyqnAVAAhxBYpZae6tEfZoGxQNigbFM7DmVX4TgIxNfaj7cfOe44QwgD4oQWCXKhtba6pUCgUij8ZzhSzzUC8ECJOCOEOjAHmn3XOfGCc/fntwEqpRaTMB8YIIYxCiDggHkis5TUVCoVC8SfDaW5G+xzYX4ClaGH006SUe4UQrwFbpJTzgS+Br+0BHnlo4oT9vLlogR0W4DEppRXgfNeshTlTHXx7V4KyQUPZoKFs0FA2KBzCn2LRtEKhUCiub5zpZlQoFAqFwiUoMVMoFArFNc91LWZCiDuEEHuFEDYhRKezXntRCJEshDgghBjsZDuG2PtJFkK84My+avQ5TQiRVXN9nRAiUAixXAhxyP4Y4GQbYoQQq4QQ++yfw2RX2yGEMAkhEoUQO+02/MN+PE4Iscn+mXxnDyhyKkIIvRBiuxBiQV3YIIQ4JoTYLYTYIYTYYj/m6u+EvxDiByFEkhBivxCiu4u/Dwn2+6/eioQQT7r6fVA4nutazIA9wEhgbc2D9nRZY4CWwBDgU3v6LYdjv+4nwFCgBXCXvX9nMwPt3mryArBCShkPrLDvOxML8LSUsgXQDXjMfu+utKMCuFFK2RZoBwwRQnQD3gE+kFI2AfLR8oA6m8nA/hr7dWFDPylluxrrqlz9nfgQbVFxM6At2vvhMhuklAfs998OLSdsGfCTK21QOAkp5XW/AauBTjX2XwRerLG/FOjupL67A0sv1LeT77shsKfG/gEgwv48Ajjg4s/hF7S8mnViB+AJbEPLGpMDGM73GTmp72i0H8kbgQVo+ftcbcMxIPisYy77LNDWkR7FHnhWFzac1e8gYH1d2qA2x23X+8jsQpwv1VbUBc69lvq6FGFSynT78wy05JIuQQjREGgPbHK1HXb33g60rH3LgcNAgZSyOhuuKz6TfwPPoWURBC1tm6ttkMAyIcRWe7o3cO1nEQdkA9Pt7tYvhBBeLrahJmPQEppThzYoHMQ1L2ZCiN+EEHvOsw2va9vqM1L7F9Ql6zKEEN7Aj8CTUsoiV9shpbRKza0UjZaw2qWF5IQQNwNZUsqtruz3PPSSUnZAc3k/JoQ4oxaRCz4LA9AB+K+Usj1QylnuPFd9L+3zk7cC35/9miv/NhSO45rPzSilHHAFzVyZFqs+peDKFEJESCnThRAR1Mgv7iyEEG5oQvatlHJeXdkBIKUsEEKsQnPp+QshDPaRkbM/k57ArUKIYYAJ8EWbO3KlDUgpT9ofs4QQP6EJuys/i1QgVUq5yb7/A5qY1cX3YSiwTUqZad+vk++kwnFc8yOzK+RC6bKcQX1KwVUzfdg4tDkspyGEEGhZXvZLKd+vCzuEECFCCH/7cw+0Obv9wCq0FGpOt0FK+aKUMlpK2RDt818ppbzHlTYIIbyEED7Vz9Hmi/bgws9CSpkBnBBCJNgP9UfL8uPS76WduzjtYqSObFA4krqetHPmBoxA+2+wAsjkzECMl9HmTg4AQ51sxzC0oqKHgZdddO+zgXSgyv4eTECbp1kBHEIreBroZBt6oblrdqEV09lhfy9cZgfQBthut2EP8Hf78UZo/8Ako7majC76XG4AFrjaBntfO+3b3urvYR18J9oBW+yfx89AQB3Y4IWW0NyvxjGX2qA2x28qnZVCoVAornn+rG5GhUKhUFxHKDFTKBQKxTWPEjOFQqFQXPMoMVMoFArFNY8SM4VCoVBc8ygxU9QJQogPhBBP1thfKoT4osb+e0KIp67w2jdUZ6av5flWewb1PUKI74UQnlfQZ1CNTOwZQoiTNfYdlg1fCNHOvvhaoVDUQImZoq5YD/QAEELogGC0KgbV9AA21OZCDqh4UC61TOqtgErg4Vr2eyqDjpQyV57Oxv4ZWjb8dvat8irtq0k7tLV6CoWiBkrMFHXFBrS0UqCJ2B6gWAgRIIQwAs2BbUKI/vaktLuFVqPNCKdqc70jhNgG3CG0mnFJ9v2R1Z0IIfrWGCFtr86CcRF+B5rYM2ZME1ottO3VuT6FEPcLIeYLIVaiLbK9IEKIUCHEVvvztkIIKYSIte8fFkJ42jOU/CiE2GzfetpfP6d/+wjvNWC0/X5GX8b7rVBc11zzuRkV1yZSyjQhhMX+494D+AMta3x3oBDYjfbP1gygv5TyoBDiK+ARtAz0ALlSyg5CCBNa5oYb0bJpfFejq2eAx6SU6+0Jj80Xssk+0hoKLEHLELNSSvmAPR1WohDiN/upHYA2Usq8S9xjltCKg/oCvdEyX/QWQqxDSzxcZnetfiClXGd/L5aiCfk5/aNlpvg7Wjmjv1ysb4Xiz4YSM0VdsgFNyHoA76OJWQ80MVsPJABHpZQH7efPBB7jtJhVi1Yz+3mHAIQQ3wDVJU7WA+8LIb4F5kkpU89jh4fQSsSANk5QX/IAAAHGSURBVDL70m7brUKIZ+zHTUCs/fnySwnZWffYE+gDvIlWMFXY+wEYALTQ0lgC4GsX3UEX6V+hUJyFEjNFXVI9b9Yazc14AngaKAKm16J96aVOkFK+LYRYiDbPtF4IMVhKmXTWaeX2ua5T2JMkj5JSHjjreNfa9FuDtWijsgZoyWufR8tXudD+ug7oJqU8Y8R4if4VCsVZqDkzRV3y/+3dMUoDQRSH8e8V9h5BiBcQPIYXELSwsYt4Bytv4B2sRbQRNQTrBEWw8QASEXufxUx0CQaTSga/Xze7w+5s9WdnH/uGwBYwydJzbAKsUrYah5SfQK9FxHqdvwtc/3CdxzqvV8fb0xMR0cvMcWYeUzoYLNrL7ALo11AhIjaWe7Qvt8AO8JSZH8CEEqyDev4S6HfWOw3Vefd/B3777if9O4aZ/tKYUsV4N3PsLTNf6tvKHnAaEWNKl+aT2YvUefvAWS0A6faiOqwl9yNKB4HzBdd2BKwAo4i4r+OlZeYzZVvxph4aUDpMv9bxAbAZEaOIeOC7knLe/a8o25IWgEgd/jVfktQ838wkSc0zzCRJzTPMJEnNM8wkSc0zzCRJzTPMJEnNM8wkSc37BPIRGlZX7rUvAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "text = \"Tockenizing text is a core task of NLP.\"\n",
        "tockenized_text = list(text)\n",
        "print(tockenized_text)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m9ErTGT-F8Ox",
        "outputId": "2c8666e7-f7ef-4d08-c652-645c60d0e1e0"
      },
      "execution_count": 299,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['T', 'o', 'c', 'k', 'e', 'n', 'i', 'z', 'i', 'n', 'g', ' ', 't', 'e', 'x', 't', ' ', 'i', 's', ' ', 'a', ' ', 'c', 'o', 'r', 'e', ' ', 't', 'a', 's', 'k', ' ', 'o', 'f', ' ', 'N', 'L', 'P', '.']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "token2idx = {ch : idx for idx, ch in enumerate(sorted(set(tockenized_text)))}\n",
        "token2idx"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DENfLF5aQ14y",
        "outputId": "f4e7a1ae-b44a-4fa9-c4ed-e53788afde4d"
      },
      "execution_count": 300,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{' ': 0,\n",
              " '.': 1,\n",
              " 'L': 2,\n",
              " 'N': 3,\n",
              " 'P': 4,\n",
              " 'T': 5,\n",
              " 'a': 6,\n",
              " 'c': 7,\n",
              " 'e': 8,\n",
              " 'f': 9,\n",
              " 'g': 10,\n",
              " 'i': 11,\n",
              " 'k': 12,\n",
              " 'n': 13,\n",
              " 'o': 14,\n",
              " 'r': 15,\n",
              " 's': 16,\n",
              " 't': 17,\n",
              " 'x': 18,\n",
              " 'z': 19}"
            ]
          },
          "metadata": {},
          "execution_count": 300
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "input_ids = [token2idx[tocken] for tocken in tockenized_text]\n",
        "print(tockenized_text)\n",
        "print(input_ids)\n",
        "print(len(input_ids))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M-_hVgd4lPLa",
        "outputId": "8bec9af7-752a-4590-d0cd-71a0918798b6"
      },
      "execution_count": 301,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['T', 'o', 'c', 'k', 'e', 'n', 'i', 'z', 'i', 'n', 'g', ' ', 't', 'e', 'x', 't', ' ', 'i', 's', ' ', 'a', ' ', 'c', 'o', 'r', 'e', ' ', 't', 'a', 's', 'k', ' ', 'o', 'f', ' ', 'N', 'L', 'P', '.']\n",
            "[5, 14, 7, 12, 8, 13, 11, 19, 11, 13, 10, 0, 17, 8, 18, 17, 0, 11, 16, 0, 6, 0, 7, 14, 15, 8, 0, 17, 6, 16, 12, 0, 14, 9, 0, 3, 2, 4, 1]\n",
            "39\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "categorical_df = pd.DataFrame(\n",
        "    {'Name' : ['seoul', 'busan', 'suwon'],\n",
        "     'Label' : [0,1,2]}\n",
        ")\n",
        "categorical_df"
      ],
      "metadata": {
        "id": "nwk1F-Vfnkkz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "outputId": "5d55253c-eb1a-4de5-ba61-d14b54fd4e50"
      },
      "execution_count": 302,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Name  Label\n",
              "0  seoul      0\n",
              "1  busan      1\n",
              "2  suwon      2"
            ],
            "text/html": [
              "\n",
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              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\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>Name</th>\n",
              "      <th>Label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>seoul</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>busan</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>suwon</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3b84730f-1fa1-4036-97c7-da0e33fa7a87')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
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              "      background-color: #3B4455;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "    }\n",
              "  </style>\n",
              "\n",
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              "        const buttonEl =\n",
              "          document.querySelector('#df-3b84730f-1fa1-4036-97c7-da0e33fa7a87 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-3b84730f-1fa1-4036-97c7-da0e33fa7a87');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 302
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.get_dummies(categorical_df['Name'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "ACxqVdfmTnv7",
        "outputId": "6f34d45a-0611-49ee-a88b-73f5a41062fb"
      },
      "execution_count": 303,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   busan  seoul  suwon\n",
              "0      0      1      0\n",
              "1      1      0      0\n",
              "2      0      0      1"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1c6db389-d0f3-42e1-ad4b-3843b57fbd72\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
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              "        vertical-align: top;\n",
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              "\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>busan</th>\n",
              "      <th>seoul</th>\n",
              "      <th>suwon</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1c6db389-d0f3-42e1-ad4b-3843b57fbd72')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-1c6db389-d0f3-42e1-ad4b-3843b57fbd72 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-1c6db389-d0f3-42e1-ad4b-3843b57fbd72');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 303
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torch.nn.functional as F\n",
        "input_ids = torch.tensor(input_ids)\n",
        "one_hot_encodings = F.one_hot(input_ids, num_classes=len(token2idx))\n",
        "one_hot_encodings.shape\n",
        "print(one_hot_encodings)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m3ccxcbsT-o1",
        "outputId": "f1bec7ef-a5be-4ab7-c616-c0f40fe0d7ea"
      },
      "execution_count": 304,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([[0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
            "        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],\n",
            "        [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
            "        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n",
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            "        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n",
            "        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n",
            "        [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
            "        [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
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            "        [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
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            "        [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
            "        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n",
            "        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n",
            "        [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
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            "        [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
            "        [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
            "        [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
            "        [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
            "        [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "E86ODqHsVg_d"
      },
      "execution_count": 304,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "idx = 15\n",
        "print(f\"token : {tockenized_text[idx]}\")\n",
        "print(f\"token : {input_ids[idx]}\")\n",
        "print(f\"token : {one_hot_encodings[idx]}\")\n",
        "print(f\"get_dummies : {pd.get_dummies(tockenized_text).iloc[idx]}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BO8Xl1GvUiJn",
        "outputId": "0c09cf6d-8533-4113-ddfc-c84e6b7924be"
      },
      "execution_count": 305,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "token : t\n",
            "token : 17\n",
            "token : tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0])\n",
            "get_dummies :      0\n",
            ".    0\n",
            "L    0\n",
            "N    0\n",
            "P    0\n",
            "T    0\n",
            "a    0\n",
            "c    0\n",
            "e    0\n",
            "f    0\n",
            "g    0\n",
            "i    0\n",
            "k    0\n",
            "n    0\n",
            "o    0\n",
            "r    0\n",
            "s    0\n",
            "t    1\n",
            "x    0\n",
            "z    0\n",
            "Name: 15, dtype: uint8\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tockenized_text = text.split()\n",
        "print(tockenized_text)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CVBIG1xmYk6D",
        "outputId": "e3dfe676-4d1a-40f5-fee9-0ca8b15cea4a"
      },
      "execution_count": 306,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Tockenizing', 'text', 'is', 'a', 'core', 'task', 'of', 'NLP.']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Subword tockenization\n"
      ],
      "metadata": {
        "id": "ItdRPyQKY7I1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoTokenizer\n",
        "\n",
        "model_ckpt = 'distilbert-base-uncased'\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_ckpt)"
      ],
      "metadata": {
        "id": "ZNJymSHsY-FI"
      },
      "execution_count": 307,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import DistilBertTokenizer\n",
        "\n",
        "distilbert_tokenizer = DistilBertTokenizer.from_pretrained(model_ckpt)"
      ],
      "metadata": {
        "id": "-s1wYNcI0SHY"
      },
      "execution_count": 308,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torch.nn.functional as F\n",
        "\n",
        "\n",
        "text = \"Tockenizing text is a core task of NLP.\"\n",
        "print(f'Text : {text}')\n",
        "print(\"=============================\")\n",
        "print(\"\")\n",
        "#####################################\n",
        "\n",
        "tockenized_text = list(text)\n",
        "# set 중복제거 > sort > dict\n",
        "token2idx = {ch : idx for idx, ch in enumerate(sorted(set(tockenized_text)))}\n",
        "print(f'Token to IDX {token2idx}')\n",
        "input_ids = [token2idx[tocken] for tocken in tockenized_text]\n",
        "# one-hot encoding\n",
        "input_ids = torch.tensor(input_ids)\n",
        "one_hot_encodings = F.one_hot(input_ids, num_classes=len(token2idx))\n",
        "print(f'input_ids {input_ids}')\n",
        "print(f'one-hot-encoding : {tockenized_text[0]} -> {input_ids[0]} -> {one_hot_encodings[0]}')\n",
        "print(f'one-hot-encoding.shape : {one_hot_encodings.shape}')\n",
        "print(f'len(input_ids) : {len(input_ids)}')\n",
        "\n",
        "print(\"--------------\")\n",
        "encoded_text = tokenizer(text)\n",
        "print(f'encoded_text : {encoded_text}')\n",
        "print(f'input_ids : {encoded_text.input_ids}')\n",
        "print(f'len(input_ids) : {len(encoded_text.input_ids)}')\n",
        "\n",
        "print(\"\")\n",
        "print(\"Subword tokenization : 39 --> 14\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JUER4N2p0gV_",
        "outputId": "20cb13cd-8a15-4fc5-881a-eddb66405ba7"
      },
      "execution_count": 309,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Text : Tockenizing text is a core task of NLP.\n",
            "=============================\n",
            "\n",
            "Token to IDX {' ': 0, '.': 1, 'L': 2, 'N': 3, 'P': 4, 'T': 5, 'a': 6, 'c': 7, 'e': 8, 'f': 9, 'g': 10, 'i': 11, 'k': 12, 'n': 13, 'o': 14, 'r': 15, 's': 16, 't': 17, 'x': 18, 'z': 19}\n",
            "input_ids tensor([ 5, 14,  7, 12,  8, 13, 11, 19, 11, 13, 10,  0, 17,  8, 18, 17,  0, 11,\n",
            "        16,  0,  6,  0,  7, 14, 15,  8,  0, 17,  6, 16, 12,  0, 14,  9,  0,  3,\n",
            "         2,  4,  1])\n",
            "one-hot-encoding : T -> 5 -> tensor([0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])\n",
            "one-hot-encoding.shape : torch.Size([39, 20])\n",
            "len(input_ids) : 39\n",
            "--------------\n",
            "encoded_text : {'input_ids': [101, 2000, 19766, 6026, 3793, 2003, 1037, 4563, 4708, 1997, 17953, 2361, 1012, 102], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}\n",
            "input_ids : [101, 2000, 19766, 6026, 3793, 2003, 1037, 4563, 4708, 1997, 17953, 2361, 1012, 102]\n",
            "len(input_ids) : 14\n",
            "\n",
            "Subword tokenization : 39 --> 14\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_ckpt = 'distilbert-base-uncased'\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_ckpt)\n",
        "\n",
        "text = \"Tockenizing text is a core task of NLP.\"\n",
        "encoded_text = tokenizer(text)\n",
        "print(encoded_text)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DtMKpHr3Pfs2",
        "outputId": "16385e7e-2823-468b-f58d-5847c9f220b6"
      },
      "execution_count": 310,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'input_ids': [101, 2000, 19766, 6026, 3793, 2003, 1037, 4563, 4708, 1997, 17953, 2361, 1012, 102], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokens = tokenizer.convert_ids_to_tokens([101, 2000, 19766, 6026, 3793, 2003, 1037, 4563, 4708, 1997, 17953, 2361, 1012, 102])\n",
        "print(tokens)\n",
        "print(len(tokens))\n",
        "print(tokenizer.convert_tokens_to_string(tokens))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "axWkPBPX0tTe",
        "outputId": "4f1456da-975d-4a9f-c449-2eb3f4b14df2"
      },
      "execution_count": 311,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['[CLS]', 'to', '##cken', '##izing', 'text', 'is', 'a', 'core', 'task', 'of', 'nl', '##p', '.', '[SEP]']\n",
            "14\n",
            "[CLS] tockenizing text is a core task of nlp. [SEP]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(tokenizer.convert_tokens_to_string(tokens))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oJWiX3lH0R_a",
        "outputId": "06851a20-3b66-4698-ac3d-bd73e5b6b662"
      },
      "execution_count": 312,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[CLS] tockenizing text is a core task of nlp. [SEP]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(tokenizer.vocab_size)\n",
        "print(tokenizer.model_max_length)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2oP4WdvjRiVa",
        "outputId": "56cfd0f9-cc37-4e48-db73-9256ec38e988"
      },
      "execution_count": 313,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "30522\n",
            "512\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 전체 데이터셋 토큰화하기"
      ],
      "metadata": {
        "id": "7Gou28VzShoY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def tokenize(batch):\n",
        "  print(type(batch['text'][1]))\n",
        "  return tokenizer(batch[\"text\"], padding=True, truncation=True)"
      ],
      "metadata": {
        "id": "aGn3N3v4SesE"
      },
      "execution_count": 314,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "emotions.set_format(type = 'pandas')\n",
        "df = emotions['train'][:].astype('string')\n",
        "df = emotions[\"train\"][:2].astype('string')\n",
        "display(df)\n",
        "# print(emotions[\"train\"][:2])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "w-c7AsljS5Bm",
        "outputId": "0aca8ea6-c142-43ae-8f9f-97177c71ccd7"
      },
      "execution_count": 315,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                                                text label label_text\n",
              "0                            i didnt feel humiliated     0    sadness\n",
              "1  i can go from feeling so hopeless to so damned...     0    sadness"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2cf26647-9769-4ddf-97d7-99463aa13911\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>text</th>\n",
              "      <th>label</th>\n",
              "      <th>label_text</th>\n",
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              "      <th>0</th>\n",
              "      <td>i didnt feel humiliated</td>\n",
              "      <td>0</td>\n",
              "      <td>sadness</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>i can go from feeling so hopeless to so damned...</td>\n",
              "      <td>0</td>\n",
              "      <td>sadness</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2cf26647-9769-4ddf-97d7-99463aa13911')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-2cf26647-9769-4ddf-97d7-99463aa13911 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-2cf26647-9769-4ddf-97d7-99463aa13911');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "emotions.reset_format()"
      ],
      "metadata": {
        "id": "vusrgJgPDwFL"
      },
      "execution_count": 316,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokens2ids = list(zip(tokenizer.all_special_tokens, tokenizer.all_special_ids))\n",
        "data = sorted(tokens2ids, key=lambda x : x[-1])\n",
        "df = pd.DataFrame(data, columns=[\"Special Token\", \"Special Token ID\"])\n",
        "df.T"
      ],
      "metadata": {
        "id": "T27eIH-Hhmf4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "outputId": "6242b249-cc51-4623-9a59-05089e69a6c9"
      },
      "execution_count": 317,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                      0      1      2      3       4\n",
              "Special Token     [PAD]  [UNK]  [CLS]  [SEP]  [MASK]\n",
              "Special Token ID      0    100    101    102     103"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c3c95669-8595-497c-9474-5d26de640137\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\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>0</th>\n",
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              "      <th>4</th>\n",
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              "  <tbody>\n",
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              "      <th>Special Token</th>\n",
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              "      <td>[SEP]</td>\n",
              "      <td>[MASK]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Special Token ID</th>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>101</td>\n",
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              "      <td>103</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c3c95669-8595-497c-9474-5d26de640137')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c3c95669-8595-497c-9474-5d26de640137 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-c3c95669-8595-497c-9474-5d26de640137');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 317
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "display(emotions)\n",
        "display(emotions['train']['text'][0])\n",
        "\n",
        "model_ckpt = 'distilbert-base-uncased'\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_ckpt)\n",
        "emotions_encoded = emotions.map(tokenize, batched=True, batch_size = None)\n",
        "display(emotions_encoded)\n",
        "display(emotions_encoded['train'])"
      ],
      "metadata": {
        "id": "Z9ggiYrafwxm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 691
        },
        "outputId": "396e7d9f-2959-4bbc-c613-753204b05bd3"
      },
      "execution_count": 318,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['text', 'label', 'label_text'],\n",
              "        num_rows: 16000\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['text', 'label', 'label_text'],\n",
              "        num_rows: 2000\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['text', 'label', 'label_text'],\n",
              "        num_rows: 2000\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "'i didnt feel humiliated'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:datasets.arrow_dataset:Loading cached processed dataset at /root/.cache/huggingface/datasets/SetFit___json/SetFit--emotion-e444b7640ce3116e/0.0.0/0f7e3662623656454fcd2b650f34e886a7db4b9104504885bd462096cc7a9f51/cache-7ead156def337ed6.arrow\n",
            "WARNING:datasets.arrow_dataset:Loading cached processed dataset at /root/.cache/huggingface/datasets/SetFit___json/SetFit--emotion-e444b7640ce3116e/0.0.0/0f7e3662623656454fcd2b650f34e886a7db4b9104504885bd462096cc7a9f51/cache-05fda3fe0f8dec4b.arrow\n",
            "WARNING:datasets.arrow_dataset:Loading cached processed dataset at /root/.cache/huggingface/datasets/SetFit___json/SetFit--emotion-e444b7640ce3116e/0.0.0/0f7e3662623656454fcd2b650f34e886a7db4b9104504885bd462096cc7a9f51/cache-9d30f3a4a7fecc63.arrow\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['text', 'label', 'label_text', 'input_ids', 'attention_mask'],\n",
              "        num_rows: 16000\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['text', 'label', 'label_text', 'input_ids', 'attention_mask'],\n",
              "        num_rows: 2000\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['text', 'label', 'label_text', 'input_ids', 'attention_mask'],\n",
              "        num_rows: 2000\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['text', 'label', 'label_text', 'input_ids', 'attention_mask'],\n",
              "    num_rows: 16000\n",
              "})"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "emotions_encoded['train'].column_names"
      ],
      "metadata": {
        "id": "XGVJGabqmPX4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "751e5afa-7ace-4e94-b54c-c2d5ceba2f6e"
      },
      "execution_count": 319,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['text', 'label', 'label_text', 'input_ids', 'attention_mask']"
            ]
          },
          "metadata": {},
          "execution_count": 319
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "  "
      ],
      "metadata": {
        "id": "WsNNMHmRGawu"
      },
      "execution_count": 319,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoModel\n",
        "\n",
        "model_ckpt = 'distilbert-base-uncased'\n",
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "print(device)\n",
        "model = AutoModel.from_pretrained(model_ckpt).to(device)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2xLPuf1HI-tc",
        "outputId": "e0b14bea-762d-4c49-b40a-a99561e07ceb"
      },
      "execution_count": 320,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "cuda\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "text = \"this is a test\"\n",
        "inputs = tokenizer(text, return_tensors='pt')\n",
        "print(f\"input tensor size : {inputs['input_ids'].size()}\")\n",
        "print(inputs)\n",
        "print(f\"{tokenizer.convert_ids_to_tokens([ 101, 2023, 2003, 1037, 3231, 102])}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VkWbepnDJoJx",
        "outputId": "e8f14683-24ad-47f6-a835-9a0034041a01"
      },
      "execution_count": 321,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "input tensor size : torch.Size([1, 6])\n",
            "{'input_ids': tensor([[ 101, 2023, 2003, 1037, 3231,  102]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1]])}\n",
            "['[CLS]', 'this', 'is', 'a', 'test', '[SEP]']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# .to(device)\n",
        "# https://pytorch.org/docs/stable/generated/torch.Tensor.to.html\n",
        "inputs = {k: v.to(device) for k,v in inputs.items()}\n",
        "display(inputs)\n",
        "with torch.no_grad():\n",
        "  outputs = model(**inputs)\n",
        "print(outputs)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 180
        },
        "id": "2BxOrWrzLsWu",
        "outputId": "9e154be8-384a-4337-afc1-3b559fe73fac"
      },
      "execution_count": 322,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "{'input_ids': tensor([[ 101, 2023, 2003, 1037, 3231,  102]], device='cuda:0'),\n",
              " 'attention_mask': tensor([[1, 1, 1, 1, 1, 1]], device='cuda:0')}"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "BaseModelOutput(last_hidden_state=tensor([[[-0.1565, -0.1862,  0.0528,  ..., -0.1188,  0.0662,  0.5470],\n",
            "         [-0.3575, -0.6484, -0.0618,  ..., -0.3040,  0.3508,  0.5221],\n",
            "         [-0.2772, -0.4459,  0.1818,  ..., -0.0948, -0.0076,  0.9958],\n",
            "         [-0.2841, -0.3917,  0.3753,  ..., -0.2151, -0.1173,  1.0526],\n",
            "         [ 0.2661, -0.5094, -0.3180,  ..., -0.4203,  0.0144, -0.2149],\n",
            "         [ 0.9441,  0.0112, -0.4714,  ...,  0.1439, -0.7288, -0.1619]]],\n",
            "       device='cuda:0'), hidden_states=None, attentions=None)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "display(outputs.last_hidden_state.size())\n",
        "display(outputs.last_hidden_state[:,0].size())\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "id": "JnGSExCTlTab",
        "outputId": "089df4cc-4973-4736-9171-467b7b87ff52"
      },
      "execution_count": 323,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "torch.Size([1, 6, 768])"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "torch.Size([1, 768])"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def extract_hidden_states(batch):\n",
        "    # 모델 입력을 GPU로 옮깁니다.\n",
        "    inputs = {k:v.to(device) for k,v in batch.items() \n",
        "              if k in tokenizer.model_input_names}\n",
        "    # 마지막 은닉 상태를 추출합니다.\n",
        "    with torch.no_grad():\n",
        "        last_hidden_state = model(**inputs).last_hidden_state\n",
        "    # [CLS] 토큰에 대한 벡터를 반환합니다.\n",
        "    return {\"hidden_state\": last_hidden_state[:,0].cpu().numpy()}"
      ],
      "metadata": {
        "id": "nfbzFJjcqEra"
      },
      "execution_count": 324,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "emotions_encoded.set_format(\"torch\", \n",
        "                            columns=[\"input_ids\", \"attention_mask\", \"label\"])"
      ],
      "metadata": {
        "id": "ZWga7E_i9O73"
      },
      "execution_count": 325,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle\n",
        "\n",
        "emotions_encoded_pickle = ROOT/'emotions_encoded.pickle'\n",
        "emotions_hidden_pickle = ROOT/'emotions_hidden.pickle'\n",
        "emotions_hidden = emotions_encoded.map(extract_hidden_states, batched=True)\n",
        "\n",
        "# if not emotions_encoded_pickle.exists():\n",
        "#   emotions_hidden = emotions_encoded.map(extract_hidden_states, batched=True)\n",
        "#   with open( ROOT/'emotions_encoded.pickle', 'wb') as f:\n",
        "#     pickle.dump(emotions_encoded, f , pickle.HIGHEST_PROTOCOL)\n",
        "#   with open( ROOT/'emotions_hidden.pickle', 'wb') as f:\n",
        "#     pickle.dump(emotions_hidden, f , pickle.HIGHEST_PROTOCOL)\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EJUf1ps492Bo",
        "outputId": "5464d4ce-98cd-4924-ad4c-d84253b9470e"
      },
      "execution_count": 326,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:datasets.arrow_dataset:Loading cached processed dataset at /root/.cache/huggingface/datasets/SetFit___json/SetFit--emotion-e444b7640ce3116e/0.0.0/0f7e3662623656454fcd2b650f34e886a7db4b9104504885bd462096cc7a9f51/cache-7a6c840ea4c97ce3.arrow\n",
            "WARNING:datasets.arrow_dataset:Loading cached processed dataset at /root/.cache/huggingface/datasets/SetFit___json/SetFit--emotion-e444b7640ce3116e/0.0.0/0f7e3662623656454fcd2b650f34e886a7db4b9104504885bd462096cc7a9f51/cache-990f70c8d83e12bb.arrow\n",
            "WARNING:datasets.arrow_dataset:Loading cached processed dataset at /root/.cache/huggingface/datasets/SetFit___json/SetFit--emotion-e444b7640ce3116e/0.0.0/0f7e3662623656454fcd2b650f34e886a7db4b9104504885bd462096cc7a9f51/cache-5b489ab76575409b.arrow\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# import pickle\n",
        "# if emotions_encoded_pickle.exists():\n",
        "#   with open( ROOT/'emotions_encoded.pickle', 'rb') as f:\n",
        "#     emotions_encoded = pickle.load(f)\n",
        "#   with open( ROOT/'emotions_hidden.pickle', 'rb') as f:\n",
        "#     emotions_hidden = pickle.load(f)\n",
        "emotions_hidden"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zO0ZeojxE3Yd",
        "outputId": "b739ee0d-469e-40f0-fb8e-298ab7ef2cff"
      },
      "execution_count": 327,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['text', 'label', 'label_text', 'input_ids', 'attention_mask', 'hidden_state'],\n",
              "        num_rows: 16000\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['text', 'label', 'label_text', 'input_ids', 'attention_mask', 'hidden_state'],\n",
              "        num_rows: 2000\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['text', 'label', 'label_text', 'input_ids', 'attention_mask', 'hidden_state'],\n",
              "        num_rows: 2000\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 327
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "X_train = np.array(emotions_hidden['train']['hidden_state'])\n",
        "X_valid = np.array(emotions_hidden['validation']['hidden_state'])\n",
        "y_train = np.array(emotions_hidden['train']['label'])\n",
        "y_valid = np.array(emotions_hidden['validation']['label'])\n",
        "X_train.shape, X_valid.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UIC5qOLvHU9n",
        "outputId": "8f2ba7cf-5a38-4b9b-a58e-667196b19b58"
      },
      "execution_count": 328,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((16000, 768), (2000, 768))"
            ]
          },
          "metadata": {},
          "execution_count": 328
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install umap-learn\n",
        "# import umap as UMAP\n",
        "import umap.umap_ as UMAP\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "# 특성 스케일을 [0,1] 범위로 조정합니다.\n",
        "X_scaled = MinMaxScaler().fit_transform(X_train)\n",
        "# UMAP 객체를 생성하고 훈련시킵니다.\n",
        "mapper = UMAP.UMAP(n_components=2, metric=\"cosine\").fit(X_scaled)\n",
        "# 2D 임베딩의 데이터프레임을 만듭니다.\n",
        "df_emb = pd.DataFrame(mapper.embedding_, columns=[\"X\", \"Y\"])\n",
        "df_emb[\"label\"] = y_train\n",
        "df_emb.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 460
        },
        "id": "uNqPazHyHuFs",
        "outputId": "fd4b85e0-19c2-45dd-ccb8-8464ad483c73"
      },
      "execution_count": 329,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: umap-learn in /usr/local/lib/python3.8/dist-packages (0.5.3)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.8/dist-packages (from umap-learn) (1.21.6)\n",
            "Requirement already satisfied: scikit-learn>=0.22 in /usr/local/lib/python3.8/dist-packages (from umap-learn) (1.0.2)\n",
            "Requirement already satisfied: pynndescent>=0.5 in /usr/local/lib/python3.8/dist-packages (from umap-learn) (0.5.8)\n",
            "Requirement already satisfied: scipy>=1.0 in /usr/local/lib/python3.8/dist-packages (from umap-learn) (1.7.3)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.8/dist-packages (from umap-learn) (4.64.1)\n",
            "Requirement already satisfied: numba>=0.49 in /usr/local/lib/python3.8/dist-packages (from umap-learn) (0.56.4)\n",
            "Requirement already satisfied: llvmlite<0.40,>=0.39.0dev0 in /usr/local/lib/python3.8/dist-packages (from numba>=0.49->umap-learn) (0.39.1)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.8/dist-packages (from numba>=0.49->umap-learn) (57.4.0)\n",
            "Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.8/dist-packages (from numba>=0.49->umap-learn) (6.0.0)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.8/dist-packages (from pynndescent>=0.5->umap-learn) (1.2.0)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.8/dist-packages (from scikit-learn>=0.22->umap-learn) (3.1.0)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.8/dist-packages (from importlib-metadata->numba>=0.49->umap-learn) (3.11.0)\n"
          ]
        },
        {
          "output_type": "execute_result",
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              "          X         Y  label\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-9f1a515c-60a7-425e-bfdc-71f23ac65074 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-9f1a515c-60a7-425e-bfdc-71f23ac65074');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 329
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_emb.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0PCycVvAMvFO",
        "outputId": "3e9cf34c-94ea-40d4-e9f4-ca4bdd1fcd55"
      },
      "execution_count": 330,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(16000, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 330
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, axes = plt.subplots(2, 3, figsize=(7,5))\n",
        "axes = axes.flatten()\n",
        "cmaps = [\"Greys\", \"Blues\", \"Oranges\", \"Reds\", \"Purples\", \"Greens\"]\n",
        "labels = emotions[\"train\"].features[\"label\"].names\n",
        "\n",
        "for i, (label, cmap) in enumerate(zip(labels, cmaps)):\n",
        "    df_emb_sub = df_emb.query(f\"label == {i}\")\n",
        "    df_emb_sub.plot.hexbin(x= \"X\", y=\"Y\", cmap=cmap,\n",
        "                   gridsize=20, linewidths=(0,), ax=axes[i])\n",
        "    axes[i].set_title(label)\n",
        "    axes[i].set_xticks([]), axes[i].set_yticks([])\n",
        "# df_emb.plot.hexbin(x=\"X\", y=\"Y\", gridsize=20, linewidths=(0,))\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369
        },
        "id": "wAZstS73Li8E",
        "outputId": "34751bf8-640e-4748-81b4-8170a2ea4966"
      },
      "execution_count": 331,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 504x360 with 12 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "# lr_clf = LogisticRegression(max_iter=3000)\n",
        "# lr_clf.fit(X_train, y_train)"
      ],
      "metadata": {
        "id": "6Z5LzyqzPnNE"
      },
      "execution_count": 332,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# from sklearn.dummy import DummyClassifier\n",
        "\n",
        "# dummy_clf = DummyClassifier(strategy=\"most_frequent\")\n",
        "# dummy_clf.fit(X_train, y_train)\n",
        "# dummy_clf.score(X_valid, y_valid)"
      ],
      "metadata": {
        "id": "mRufMRZ2RQUU"
      },
      "execution_count": 333,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import ConfusionMatrixDisplay, confusion_matrix\n",
        "\n",
        "def plot_confusion_matrix(y_preds, y_true, labels):\n",
        "    cm = confusion_matrix(y_true, y_preds, normalize=\"true\")\n",
        "    fig, ax = plt.subplots(figsize=(6, 6))\n",
        "    disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=labels)\n",
        "    disp.plot(cmap=\"Blues\", values_format=\".2f\", ax=ax, colorbar=False)\n",
        "    plt.title(\"Normalized confusion matrix\")\n",
        "    plt.show()\n",
        "    \n",
        "y_preds = lr_clf.predict(X_valid)\n",
        "plot_confusion_matrix(y_preds, y_valid, labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        },
        "id": "YZqzRWIfRzNa",
        "outputId": "c3990da8-248f-44bd-9607-ad2a0cca86dd"
      },
      "execution_count": 334,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 사전 훈련된 모델 로드하기"
      ],
      "metadata": {
        "id": "RT4WGRNtTUVM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoModelForSequenceClassification\n",
        "\n",
        "num_labels = 6\n",
        "model = (AutoModelForSequenceClassification\n",
        "         .from_pretrained(model_ckpt, num_labels=num_labels)\n",
        "         .to(device))"
      ],
      "metadata": {
        "id": "6UvxXJMETXBR"
      },
      "execution_count": 335,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score, f1_score\n",
        "\n",
        "def compute_metrics(pred):\n",
        "  labels = pred.label_ids\n",
        "  preds = pred.predictions.argmax(-1)\n",
        "  f1 = f1_score(labels, preds, average=\"weighted\")\n",
        "  acc = accuracy_score(labels, preds)\n",
        "  return {\"accuracy\":acc, \"f1\":f1}"
      ],
      "metadata": {
        "id": "T21SrCmMTRtc"
      },
      "execution_count": 336,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from huggingface_hub import notebook_login\n",
        "notebook_login()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 331,
          "referenced_widgets": [
            "af2e72dc1a7646de8187b391f1ae4fd1",
            "d3588d0a12e0440ea308939e3caa4d41",
            "29944b53216d43c09aa6cc5375f9066a",
            "23a6f035dd5d4f929ae6d8cd2fedb455",
            "16c0b4749bbd4f188fd95cc998767524",
            "eaf3259f3629403f8f05ce3747de698b",
            "a9be60bc3c9646fda1a16d2e5b8d4235",
            "dfa38278b79647639d771fff944a43f1",
            "c79c040dadaa4e889fa6431e1c4bba4b",
            "49f1ee28d6a24a9297771392100a649c",
            "0d99f61770aa4228858dc5c98717f6cc",
            "40217e5266d948b19bbaddc4df89cd79",
            "0c64e4fffe894747a906c1d61fb499b0",
            "ca50383681644c2c9c465a2b5c69742f",
            "0d77735c4ade4251ac630e09440d1aad",
            "cbd8ae9a3a6344ebb442661956294717",
            "f0340a87af72403d9b05cb2a50ea32fc"
          ]
        },
        "id": "tahCwX3LZXng",
        "outputId": "bcd6edbd-496c-4181-bd2c-37a44519b88f"
      },
      "execution_count": 337,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "af2e72dc1a7646de8187b391f1ae4fd1"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import Trainer, TrainingArguments\n",
        "\n",
        "batch_size = 64\n",
        "logging_steps = len(emotions_encoded[\"train\"]) // batch_size\n",
        "model_name = f\"{model_ckpt}-finetuned-emotion\"\n",
        "training_args = TrainingArguments(output_dir=ROOT/model_name,\n",
        "                                  num_train_epochs=2,\n",
        "                                  learning_rate=2e-5,\n",
        "                                  per_device_train_batch_size=batch_size,\n",
        "                                  per_device_eval_batch_size=batch_size,\n",
        "                                  weight_decay=0.01,\n",
        "                                  evaluation_strategy=\"epoch\",\n",
        "                                  disable_tqdm=False,\n",
        "                                  logging_steps=logging_steps,\n",
        "                                  push_to_hub=True, \n",
        "                                  save_strategy=\"epoch\",\n",
        "                                  load_best_model_at_end=True,\n",
        "                                  log_level=\"error\")"
      ],
      "metadata": {
        "id": "1tdYk9kAh7ZI"
      },
      "execution_count": 338,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import Trainer\n",
        "\n",
        "trainer = Trainer(model=model, args=training_args, \n",
        "                  compute_metrics=compute_metrics,\n",
        "                  train_dataset=emotions_encoded[\"train\"],\n",
        "                  eval_dataset=emotions_encoded[\"validation\"],\n",
        "                  tokenizer=tokenizer)\n",
        "trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "id": "lYrlLFwYiL6C",
        "outputId": "eaaae5d0-2a27-44c5-f740-65e2404e5be4"
      },
      "execution_count": 339,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Cloning https://huggingface.co/gabrielyang/distilbert-base-uncased-finetuned-emotion into local empty directory.\n",
            "WARNING:huggingface_hub.repository:Cloning https://huggingface.co/gabrielyang/distilbert-base-uncased-finetuned-emotion into local empty directory.\n",
            "/usr/local/lib/python3.8/dist-packages/transformers/optimization.py:306: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='500' max='500' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [500/500 04:09, Epoch 2/2]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>F1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.832600</td>\n",
              "      <td>0.318546</td>\n",
              "      <td>0.902000</td>\n",
              "      <td>0.898285</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.249900</td>\n",
              "      <td>0.220105</td>\n",
              "      <td>0.927500</td>\n",
              "      <td>0.927501</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=500, training_loss=0.5412229843139649, metrics={'train_runtime': 249.7157, 'train_samples_per_second': 128.146, 'train_steps_per_second': 2.002, 'total_flos': 720342861696000.0, 'train_loss': 0.5412229843139649, 'epoch': 2.0})"
            ]
          },
          "metadata": {},
          "execution_count": 339
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "preds_output = trainer.predict(emotions_encoded['validation'])\n",
        "preds_output"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 271
        },
        "id": "52qMCkglkG0E",
        "outputId": "e14186e4-f300-4864-ee81-86ee960d4291"
      },
      "execution_count": 340,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PredictionOutput(predictions=array([[ 4.448224  , -0.5421714 , -0.95556104, -0.7031998 , -1.3263093 ,\n",
              "        -1.7833843 ],\n",
              "       [ 4.3368325 , -0.6607239 , -1.7321436 , -0.8183804 , -0.6608242 ,\n",
              "        -1.5535629 ],\n",
              "       [-1.1505464 ,  2.0866067 ,  2.6553707 , -1.2738903 , -1.7225661 ,\n",
              "        -1.2434086 ],\n",
              "       ...,\n",
              "       [-1.2438401 ,  4.3892975 , -0.08157057, -1.6264218 , -1.5252111 ,\n",
              "        -1.0602263 ],\n",
              "       [-1.7092644 ,  2.657804  ,  2.2270076 , -1.4586372 , -1.7283901 ,\n",
              "        -1.0463082 ],\n",
              "       [-1.3919168 ,  4.1366    , -0.14957356, -1.652004  , -1.5556011 ,\n",
              "        -0.56987625]], dtype=float32), label_ids=array([0, 0, 2, ..., 1, 1, 1]), metrics={'test_loss': 0.22010467946529388, 'test_accuracy': 0.9275, 'test_f1': 0.9275012469136824, 'test_runtime': 4.3622, 'test_samples_per_second': 458.486, 'test_steps_per_second': 7.336})"
            ]
          },
          "metadata": {},
          "execution_count": 340
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "display(preds_output.metrics)\n",
        "display(preds_output)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 380
        },
        "id": "kp4gEEGTkNXI",
        "outputId": "b79cced0-f35a-4d59-f949-e5773c7341c4"
      },
      "execution_count": 341,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "{'test_loss': 0.22010467946529388,\n",
              " 'test_accuracy': 0.9275,\n",
              " 'test_f1': 0.9275012469136824,\n",
              " 'test_runtime': 4.3622,\n",
              " 'test_samples_per_second': 458.486,\n",
              " 'test_steps_per_second': 7.336}"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "PredictionOutput(predictions=array([[ 4.448224  , -0.5421714 , -0.95556104, -0.7031998 , -1.3263093 ,\n",
              "        -1.7833843 ],\n",
              "       [ 4.3368325 , -0.6607239 , -1.7321436 , -0.8183804 , -0.6608242 ,\n",
              "        -1.5535629 ],\n",
              "       [-1.1505464 ,  2.0866067 ,  2.6553707 , -1.2738903 , -1.7225661 ,\n",
              "        -1.2434086 ],\n",
              "       ...,\n",
              "       [-1.2438401 ,  4.3892975 , -0.08157057, -1.6264218 , -1.5252111 ,\n",
              "        -1.0602263 ],\n",
              "       [-1.7092644 ,  2.657804  ,  2.2270076 , -1.4586372 , -1.7283901 ,\n",
              "        -1.0463082 ],\n",
              "       [-1.3919168 ,  4.1366    , -0.14957356, -1.652004  , -1.5556011 ,\n",
              "        -0.56987625]], dtype=float32), label_ids=array([0, 0, 2, ..., 1, 1, 1]), metrics={'test_loss': 0.22010467946529388, 'test_accuracy': 0.9275, 'test_f1': 0.9275012469136824, 'test_runtime': 4.3622, 'test_samples_per_second': 458.486, 'test_steps_per_second': 7.336})"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_preds = np.argmax(preds_output.predictions, axis = 1)\n",
        "y_preds"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p9ixurczkjbF",
        "outputId": "e6ae219d-853d-4831-f02c-aaa8a293b7d4"
      },
      "execution_count": 342,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 2, ..., 1, 1, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 342
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "display(preds_output.predictions.shape)\n",
        "display(y_preds.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "id": "cQ_3r1UW1syG",
        "outputId": "02ba7fe1-f24b-45ab-f908-c4c44ae4edb4"
      },
      "execution_count": 343,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "(2000, 6)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "(2000,)"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_confusion_matrix(y_preds, y_valid, labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        },
        "id": "6Vw9bCTH8BjO",
        "outputId": "2168614d-16e5-448a-868a-8725db422eb1"
      },
      "execution_count": 344,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from torch.nn.functional import cross_entropy\n",
        "\n",
        "def forward_pass_with_label(batch):\n",
        "    # 모든 입력 텐서를 모델과 같은 장치로 이동합니다.\n",
        "    inputs = {k:v.to(device) for k,v in batch.items() \n",
        "              if k in tokenizer.model_input_names}\n",
        "\n",
        "    with torch.no_grad():\n",
        "        output = model(**inputs)\n",
        "        pred_label = torch.argmax(output.logits, axis=-1)\n",
        "        loss = cross_entropy(output.logits, batch[\"label\"].to(device), \n",
        "                             reduction=\"none\")\n",
        "\n",
        "    # 다른 데이터셋 열과 호환되도록 출력을 CPU로 옮깁니다.\n",
        "    return {\"loss\": loss.cpu().numpy(), \n",
        "            \"predicted_label\": pred_label.cpu().numpy()}"
      ],
      "metadata": {
        "id": "bs1tfKqP8Jvy"
      },
      "execution_count": 345,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 데이터셋을 다시 파이토치 텐서로 변환합니다.\n",
        "emotions_encoded.set_format(\"torch\", \n",
        "                            columns=[\"input_ids\", \"attention_mask\", \"label\"])\n",
        "# 손실 값을 계산합니다.\n",
        "emotions_encoded[\"validation\"] = emotions_encoded[\"validation\"].map(\n",
        "    forward_pass_with_label, batched=True, batch_size=16)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "97871413296344dfbdbae0ae8ade0c77",
            "6d39c791bfc64b0f92f67ecbe35cee9e",
            "a80f7af047f140de8f37570cd6909579",
            "fabbfa089ad44b6da5fe5aa8d95cdd00",
            "0d0c2a627a734e55b1dc4c94f7cfe8e8",
            "acf46110901a44b597778290088f9ad4",
            "fee8d336ce1c470cb6fed0cd85a26d58",
            "bcfb3f6b34ad493db5d455ffe44f94b8",
            "69e292ce8ff2491eb5d11295a3cbeb0d",
            "c5808542fed5420f87d28cc40275114b",
            "dee92767fa5942719b754dabf81ed50f"
          ]
        },
        "id": "6lmM7tPe8PTi",
        "outputId": "94e2d0eb-a4a2-4e40-ac1b-aa05f49d5031"
      },
      "execution_count": 346,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/125 [00:00<?, ?ba/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "97871413296344dfbdbae0ae8ade0c77"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "156A8dS29ZIN"
      },
      "execution_count": 346,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "emotions_encoded.set_format(\"pandas\")\n",
        "cols = [\"text\", \"label\", \"predicted_label\", \"loss\"]\n",
        "df_test = emotions_encoded[\"validation\"][:][cols]\n",
        "df_test[\"label\"] = df_test[\"label\"].apply(label_int2str)\n",
        "df_test[\"predicted_label\"] = (df_test[\"predicted_label\"]\n",
        "                              .apply(label_int2str))"
      ],
      "metadata": {
        "id": "GxW7c5UA8To1"
      },
      "execution_count": 347,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_sorted = df_test.sort_values(\"loss\", ascending=False).head(10)\n",
        "display(df_sorted.columns)\n",
        "display(df_sorted.iloc[0]['text'])\n",
        "display(f\"label : {df_sorted.iloc[0]['label']}\")\n",
        "display(f\"predicted : {df_sorted.iloc[0]['predicted_label']}\")\n",
        "display(f\"loss : {df_sorted.iloc[0]['loss']}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 109
        },
        "id": "4nccrZhX-YM3",
        "outputId": "0cb30f3a-41c5-4f7f-e3ee-c2308b0ce8fe"
      },
      "execution_count": 348,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Index(['text', 'label', 'predicted_label', 'loss'], dtype='object')"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "'i feel badly about reneging on my commitment to bring donuts to the faithful at holy family catholic church in columbus ohio'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "'label : love'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "'predicted : sadness'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "'loss : 5.659745216369629'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from huggingface_hub import login\n",
        "login()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 331,
          "referenced_widgets": [
            "394be4803e0a43f6b6c62f1f97972542",
            "99577c068cbf41b8a06010ef1af12d50",
            "1c5b53fc53244326b4132acf9ed3aaef",
            "5f684537f22847c6ba95e185b6c6ac54",
            "0936df1f49814c18bb5ff14f71fbfce5",
            "da9ae245f6f04ae3818941f77c82d867",
            "bb82a1332aeb474495c3399a22d5ecfd",
            "edd751baf05948adab26516eb8d2193e",
            "3e42669e8cc944c7a35a0e5485b0145b",
            "873cf77db6264c3f9c0676f722e888c8",
            "330bc63a19144588982d0ae142cdd064",
            "9c40ffa881314caf9e7488debf03d3e2",
            "2c4b521d098a428f9cf0e39cb13160e9",
            "a2eaad187d734494adc4846d98534601",
            "5afed0909027420a97b2aa74f9cf79ef",
            "3629fb9ada5c45b8afa4e2e28f3c7d8b",
            "d0af0226273b4d468aaaeb3078700a39"
          ]
        },
        "id": "tg_LVa-gksFu",
        "outputId": "016c9010-c9f4-46f2-b316-f8d5cdecc6c3"
      },
      "execution_count": 349,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "394be4803e0a43f6b6c62f1f97972542"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 모델 저장 및 공유"
      ],
      "metadata": {
        "id": "NiG4KnZa-t6g"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install git-lfs\n",
        "!pip install huggingface_hub"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YvViXkerktVv",
        "outputId": "182783ad-73d7-4e51-c9f0-e26782e41c6c"
      },
      "execution_count": 350,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: git-lfs in /usr/local/lib/python3.8/dist-packages (1.6)\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: huggingface_hub in /usr/local/lib/python3.8/dist-packages (0.12.0)\n",
            "Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.8/dist-packages (from huggingface_hub) (21.3)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.8/dist-packages (from huggingface_hub) (4.4.0)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.8/dist-packages (from huggingface_hub) (6.0)\n",
            "Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.8/dist-packages (from huggingface_hub) (4.64.1)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.8/dist-packages (from huggingface_hub) (3.9.0)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.8/dist-packages (from huggingface_hub) (2.25.1)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.8/dist-packages (from packaging>=20.9->huggingface_hub) (3.0.9)\n",
            "Requirement already satisfied: chardet<5,>=3.0.2 in /usr/local/lib/python3.8/dist-packages (from requests->huggingface_hub) (4.0.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.8/dist-packages (from requests->huggingface_hub) (2022.12.7)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.8/dist-packages (from requests->huggingface_hub) (2.10)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.8/dist-packages (from requests->huggingface_hub) (1.26.14)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer"
      ],
      "metadata": {
        "id": "UmVjlh0YtCtM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.push_to_hub(\n",
        "      commit_message='Training Completed')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 414
        },
        "id": "WhNaSvJk-wKb",
        "outputId": "1c4dd9e4-cf9c-463b-ac62-f42f209106c9"
      },
      "execution_count": 352,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Several commits (2) will be pushed upstream.\n",
            "WARNING:huggingface_hub.repository:Several commits (2) will be pushed upstream.\n",
            "The progress bars may be unreliable.\n",
            "WARNING:huggingface_hub.repository:The progress bars may be unreliable.\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-352-4b3b77aa5931>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m trainer.push_to_hub(\n\u001b[0m\u001b[1;32m      2\u001b[0m       commit_message='Training Completed')\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36mpush_to_hub\u001b[0;34m(self, commit_message, blocking, **kwargs)\u001b[0m\n\u001b[1;32m   3490\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpush_in_progress\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3491\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3492\u001b[0;31m         git_head_commit_url = self.repo.push_to_hub(\n\u001b[0m\u001b[1;32m   3493\u001b[0m             \u001b[0mcommit_message\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcommit_message\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mblocking\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mblocking\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mauto_lfs_prune\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3494\u001b[0m         )\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/huggingface_hub/repository.py\u001b[0m in \u001b[0;36mpush_to_hub\u001b[0;34m(self, commit_message, blocking, clean_ok, auto_lfs_prune)\u001b[0m\n\u001b[1;32m   1366\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgit_add\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mauto_lfs_track\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1367\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgit_commit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcommit_message\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1368\u001b[0;31m         return self.git_push(\n\u001b[0m\u001b[1;32m   1369\u001b[0m             \u001b[0mupstream\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34mf\"origin {self.current_branch}\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1370\u001b[0m             \u001b[0mblocking\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mblocking\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/huggingface_hub/repository.py\u001b[0m in \u001b[0;36mgit_push\u001b[0;34m(self, upstream, blocking, auto_lfs_prune)\u001b[0m\n\u001b[1;32m   1133\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1134\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mblocking\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1135\u001b[0;31m                     \u001b[0mstdout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstderr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mprocess\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcommunicate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1136\u001b[0m                     \u001b[0mreturn_code\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mprocess\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpoll\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1137\u001b[0m                     \u001b[0mprocess\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkill\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.8/subprocess.py\u001b[0m in \u001b[0;36mcommunicate\u001b[0;34m(self, input, timeout)\u001b[0m\n\u001b[1;32m   1026\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1027\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1028\u001b[0;31m                 \u001b[0mstdout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstderr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_communicate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mendtime\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1029\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyboardInterrupt\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1030\u001b[0m                 \u001b[0;31m# https://bugs.python.org/issue25942\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.8/subprocess.py\u001b[0m in \u001b[0;36m_communicate\u001b[0;34m(self, input, endtime, orig_timeout)\u001b[0m\n\u001b[1;32m   1866\u001b[0m                             'failed to raise TimeoutExpired.')\n\u001b[1;32m   1867\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1868\u001b[0;31m                     \u001b[0mready\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mselector\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mselect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1869\u001b[0m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_timeout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mendtime\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morig_timeout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstdout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstderr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1870\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.8/selectors.py\u001b[0m in \u001b[0;36mselect\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    413\u001b[0m         \u001b[0mready\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    414\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 415\u001b[0;31m             \u001b[0mfd_event_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_selector\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpoll\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    416\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mInterruptedError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    417\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mready\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import pipeline\n",
        "\n",
        "# `haesun`를 자신의 허브 사용자 이름으로 바꾸세요.\n",
        "model_id = \"distilbert-base-uncased-finetuned-emotion\"\n",
        "classifier = pipeline(\"text-classification\", model=model_id)"
      ],
      "metadata": {
        "id": "mOGAqrAx-yaK"
      },
      "execution_count": 355,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "custom_tweet = \"I saw a movie today and it was really good.\"\n",
        "preds = classifier(custom_tweet, return_all_scores=True)\n",
        "preds"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6rUoPIed27gk",
        "outputId": "59a461b8-9b09-4c9d-fdf4-d80e55d57006"
      },
      "execution_count": 358,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[[{'label': 'LABEL_0', 'score': 0.010434594936668873},\n",
              "  {'label': 'LABEL_1', 'score': 0.9549815058708191},\n",
              "  {'label': 'LABEL_2', 'score': 0.012639087624847889},\n",
              "  {'label': 'LABEL_3', 'score': 0.00574359530583024},\n",
              "  {'label': 'LABEL_4', 'score': 0.007135992404073477},\n",
              "  {'label': 'LABEL_5', 'score': 0.009065313264727592}]]"
            ]
          },
          "metadata": {},
          "execution_count": 358
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "preds_df = pd.DataFrame(preds[0])\n",
        "plt.bar(labels, 100 * preds_df[\"score\"], color='C0')\n",
        "plt.title(f'\"{custom_tweet}\"')\n",
        "plt.ylabel(\"Class probability (%)\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "H775kx656e_1",
        "outputId": "9125236d-0a56-4150-a723-3e6d486dce66"
      },
      "execution_count": 357,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}