koelectra-for-classlabeling/KoELECTRA_classification_basecode(PyTorch).ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G8ZFbswF4JkS"
      },
      "source": [
        "Pytorch를 이용해서 허깅페이스의 \"monologg/koelectra-base-v3-discriminator\" 모델을 불러와서 학습시켰습니다.\n",
        " - 박장원님의 KoELECTRA 깃헙과 개발과정은 다음과 같습니다.<br>\n",
        "https://github.com/monologg/KoELECTRA\n",
        "<br>https://monologg.kr/2020/05/02/koelectra-part1/"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Un7fLeURCnuC",
        "outputId": "cb2ad39a-15fc-4ca0-b4d4-77de1019ef3c"
      },
      "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"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TP-HjhhfVGZk",
        "outputId": "5ee5aea3-0d96-491d-e757-c09245c1b521"
      },
      "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: transformers in /usr/local/lib/python3.7/dist-packages (4.19.2)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.7/dist-packages (from transformers) (6.0)\n",
            "Requirement already satisfied: tokenizers!=0.11.3,<0.13,>=0.11.1 in /usr/local/lib/python3.7/dist-packages (from transformers) (0.12.1)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.7/dist-packages (from transformers) (4.64.0)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from transformers) (3.7.0)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.7/dist-packages (from transformers) (21.3)\n",
            "Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from transformers) (4.11.4)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from transformers) (2.23.0)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (1.21.6)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (2019.12.20)\n",
            "Requirement already satisfied: huggingface-hub<1.0,>=0.1.0 in /usr/local/lib/python3.7/dist-packages (from transformers) (0.7.0)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.7/dist-packages (from huggingface-hub<1.0,>=0.1.0->transformers) (4.2.0)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=20.0->transformers) (3.0.9)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->transformers) (3.8.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2022.5.18.1)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (3.0.4)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2.10)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (1.24.3)\n"
          ]
        }
      ],
      "source": [
        "# HuggingFace transformers 설치 \n",
        "!pip install transformers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "Kuy0f7DdTtSj"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "import torch\n",
        "from torch.nn import functional as F\n",
        "import torch.nn as nn\n",
        "from torch.utils.data import DataLoader, Dataset\n",
        "from transformers import AutoTokenizer, AutoModelForSequenceClassification, ElectraModel, AdamW\n",
        "from transformers import Trainer, TrainingArguments\n",
        "from tqdm.notebook import tqdm\n",
        "from torchsummary import summary\n",
        "\n",
        "MODEL_NAME = \"monologg/koelectra-base-v3-discriminator\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "cSsqYxqMcfvO"
      },
      "outputs": [],
      "source": [
        "# cuda error 정보 가져오기\n",
        "import os\n",
        "os.environ['CUDA_LAUNCH_BLOCKING'] = \"1\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "zJu8deqzVmcI"
      },
      "outputs": [],
      "source": [
        "# GPU 사용\n",
        "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device('cpu')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aYIXFMfa5Tqm"
      },
      "source": [
        "# 1. 데이터 불러오기"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "eBtLjIYBb8gJ",
        "outputId": "1eb03aea-14d9-4763-a2ec-57dec7d8a5b2"
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      "outputs": [
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              "   index  class                                            problem  \\\n",
              "0      0      8  한 변의 길이가 24cm인 정육각형과 둘레가 같은 정팔각형이 있습니다. 이 정팔각형...   \n",
              "1      1      1  윤아는 부추전을 똑같이 8조각으로 나누어 한 조각을 먹었습니다. 윤미는 같은 크기의...   \n",
              "2      2      8  화단 주변에 한 변이 12m인 정팔각형 모양의 울타리를 두른다면, 울타리는 모두 몇...   \n",
              "3      3      3  6장의 숫자 카드 0, 9, 8, 7, 2, 1가 있습니다. 이를, 한 번씩 사용하...   \n",
              "4      4      1                               0.26 x 0.8을 계산해 주세요.   \n",
              "\n",
              "                                                code  answer Unnamed: 5  \n",
              "0     a = 24\\nb = 6\\nc = 8\\ny = a * b // c\\nprint(y)      18        NaN  \n",
              "1                a = 8\\nb = 16\\ny = b // a\\nprint(y)       2        NaN  \n",
              "2                 a = 12\\nb = 8\\ny = a * b\\nprint(y)      96        NaN  \n",
              "3  from itertools import permutations\\n\\na = ['0'...  987210        NaN  \n",
              "4  a = 0.26\\nb = 0.8\\ny = a * b\\nprint(\"{:.2f}\".f...    0.21        NaN  "
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              "      <td>한 변의 길이가 24cm인 정육각형과 둘레가 같은 정팔각형이 있습니다. 이 정팔각형...</td>\n",
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              "      <td>윤아는 부추전을 똑같이 8조각으로 나누어 한 조각을 먹었습니다. 윤미는 같은 크기의...</td>\n",
              "      <td>a = 8\\nb = 16\\ny = b // a\\nprint(y)</td>\n",
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              "      <td>화단 주변에 한 변이 12m인 정팔각형 모양의 울타리를 두른다면, 울타리는 모두 몇...</td>\n",
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              "      <td>from itertools import permutations\\n\\na = ['0'...</td>\n",
              "      <td>987210</td>\n",
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              "      <td>0.26 x 0.8을 계산해 주세요.</td>\n",
              "      <td>a = 0.26\\nb = 0.8\\ny = a * b\\nprint(\"{:.2f}\".f...</td>\n",
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          },
          "metadata": {},
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      ],
      "source": [
        "data_dir = '/content/drive/MyDrive/KMWP/code/data'\n",
        "data = pd.read_csv(data_dir + '/new_train.csv')\n",
        "data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "class\n",
            "1    1000\n",
            "2      30\n",
            "3     200\n",
            "4     240\n",
            "5     200\n",
            "6     150\n",
            "7     200\n",
            "8     800\n",
            "dtype: int64\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fed0dfdb3d0>"
            ]
          },
          "metadata": {},
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Data Visualization \n",
        "class_count = data.groupby('class').size()\n",
        "print(class_count)\n",
        "class_count.plot(kind='bar')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gCYXdLtd3nEQ"
      },
      "source": [
        "라벨이 0이 아닌 1부터 시작할 시 학습시킬 때 에러가 발생하여 라벨 인코더를 통해 라벨을 0-7로 생성시켜 컬럼 추가를 하였습니다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 476
        },
        "id": "l7mAD8a9V2JG",
        "outputId": "58704cab-fee0-485a-9de3-d8a331a9f825"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   index  class                                            problem  \\\n",
              "0      0      8  한 변의 길이가 24cm인 정육각형과 둘레가 같은 정팔각형이 있습니다. 이 정팔각형...   \n",
              "1      1      1  윤아는 부추전을 똑같이 8조각으로 나누어 한 조각을 먹었습니다. 윤미는 같은 크기의...   \n",
              "2      2      8  화단 주변에 한 변이 12m인 정팔각형 모양의 울타리를 두른다면, 울타리는 모두 몇...   \n",
              "3      3      3  6장의 숫자 카드 0, 9, 8, 7, 2, 1가 있습니다. 이를, 한 번씩 사용하...   \n",
              "4      4      1                               0.26 x 0.8을 계산해 주세요.   \n",
              "\n",
              "                                                code  answer Unnamed: 5  label  \n",
              "0     a = 24\\nb = 6\\nc = 8\\ny = a * b // c\\nprint(y)      18        NaN      7  \n",
              "1                a = 8\\nb = 16\\ny = b // a\\nprint(y)       2        NaN      0  \n",
              "2                 a = 12\\nb = 8\\ny = a * b\\nprint(y)      96        NaN      7  \n",
              "3  from itertools import permutations\\n\\na = ['0'...  987210        NaN      2  \n",
              "4  a = 0.26\\nb = 0.8\\ny = a * b\\nprint(\"{:.2f}\".f...    0.21        NaN      0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c1db74e7-bc97-4962-ba69-431c71cf4208\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>index</th>\n",
              "      <th>class</th>\n",
              "      <th>problem</th>\n",
              "      <th>code</th>\n",
              "      <th>answer</th>\n",
              "      <th>Unnamed: 5</th>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>한 변의 길이가 24cm인 정육각형과 둘레가 같은 정팔각형이 있습니다. 이 정팔각형...</td>\n",
              "      <td>a = 24\\nb = 6\\nc = 8\\ny = a * b // c\\nprint(y)</td>\n",
              "      <td>18</td>\n",
              "      <td>NaN</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>윤아는 부추전을 똑같이 8조각으로 나누어 한 조각을 먹었습니다. 윤미는 같은 크기의...</td>\n",
              "      <td>a = 8\\nb = 16\\ny = b // a\\nprint(y)</td>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>8</td>\n",
              "      <td>화단 주변에 한 변이 12m인 정팔각형 모양의 울타리를 두른다면, 울타리는 모두 몇...</td>\n",
              "      <td>a = 12\\nb = 8\\ny = a * b\\nprint(y)</td>\n",
              "      <td>96</td>\n",
              "      <td>NaN</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>6장의 숫자 카드 0, 9, 8, 7, 2, 1가 있습니다. 이를, 한 번씩 사용하...</td>\n",
              "      <td>from itertools import permutations\\n\\na = ['0'...</td>\n",
              "      <td>987210</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0.26 x 0.8을 계산해 주세요.</td>\n",
              "      <td>a = 0.26\\nb = 0.8\\ny = a * b\\nprint(\"{:.2f}\".f...</td>\n",
              "      <td>0.21</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
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              "  </tbody>\n",
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              "          const element = document.querySelector('#df-c1db74e7-bc97-4962-ba69-431c71cf4208');\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": 8
        }
      ],
      "source": [
        "# label encoding\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "label_encoder = LabelEncoder()\n",
        "label_encoder.fit(data['class'])\n",
        "num_labels = len(label_encoder.classes_)\n",
        "\n",
        "data['label'] = np.asarray(label_encoder.transform(data['class']), dtype=np.int32)\n",
        "data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3cbvaNyl2D2V",
        "outputId": "b575ff3b-4d83-4c14-9b06-be62f476c67d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([8, 1, 3, 7, 6, 5, 4, 2])"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "data['class'].unique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uv82L2MShdNs",
        "outputId": "cb41908f-f358-48aa-cf6d-e3edde7b5da4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([7, 0, 2, 6, 5, 4, 3, 1], dtype=int32)"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "data['label'].unique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "ZRUy3NAHehjo"
      },
      "outputs": [],
      "source": [
        "X = data['problem']\n",
        "y = data['label']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o-0jridAevEq",
        "outputId": "c31e94da-c0d2-4a12-9cd5-7e00efee5ca2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0       한 변의 길이가 24cm인 정육각형과 둘레가 같은 정팔각형이 있습니다. 이 정팔각형...\n",
            "1       윤아는 부추전을 똑같이 8조각으로 나누어 한 조각을 먹었습니다. 윤미는 같은 크기의...\n",
            "2       화단 주변에 한 변이 12m인 정팔각형 모양의 울타리를 두른다면, 울타리는 모두 몇...\n",
            "3       6장의 숫자 카드 0, 9, 8, 7, 2, 1가 있습니다. 이를, 한 번씩 사용하...\n",
            "4                                    0.26 x 0.8을 계산해 주세요.\n",
            "                              ...                        \n",
            "2815    10분 동안 6.53k미터를 가는 자동차와 20분 동안 15.12k미터를 가는 오토...\n",
            "2816    한 면의 넓이가 81cm² 인 정육면체가 있습니다. 이 정육면체의 부피는 몇 cm³...\n",
            "2817           4880보다 ○ 큰 수 는 9000입니다. ○안에 알맞은 수를 써 넣으시오.\n",
            "2818    어느 문구점에서 연필 한 자루는 400원이고 공책 한 권은 1000원입니다. 현승이...\n",
            "2819    초콜릿 44개를 상자 11개에 똑같이 나누어 담으려고 합니다. 한 상자에는 몇 개에...\n",
            "Name: problem, Length: 2820, dtype: object\n",
            "0       7\n",
            "1       0\n",
            "2       7\n",
            "3       2\n",
            "4       0\n",
            "       ..\n",
            "2815    0\n",
            "2816    7\n",
            "2817    0\n",
            "2818    0\n",
            "2819    0\n",
            "Name: label, Length: 2820, dtype: int32\n"
          ]
        }
      ],
      "source": [
        "print(X)\n",
        "print(y)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wVKCdf_a5nEt"
      },
      "source": [
        "# 2. 학습용/테스트용 데이터 분리"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "bN8ixpzzeGn0"
      },
      "outputs": [],
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X,\n",
        "                                                     y,\n",
        "                                                     test_size=0.1,\n",
        "                                                     shuffle=True,\n",
        "                                                     random_state=1004)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0uzIaB9uQsal",
        "outputId": "3a47cd17-e82d-4661-a93e-e67d0fb1fc88"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X_train shape: (2538,)\n",
            "X_test shape: (282,)\n",
            "y_train shape: (2538,)\n",
            "y_test shape: (282,)\n"
          ]
        }
      ],
      "source": [
        "print('X_train shape:', X_train.shape)\n",
        "print('X_test shape:', X_test.shape)\n",
        "print('y_train shape:', y_train.shape)\n",
        "print('y_test shape:', y_test.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "aQzUXrctjAxA",
        "outputId": "078bfc34-2ae1-4ca6-92cd-f192ed6b74a9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "                                                problem  label\n",
              "0     한 변의 길이가 24cm인 정육각형과 둘레가 같은 정팔각형이 있습니다. 이 정팔각형...      7\n",
              "1     윤아는 부추전을 똑같이 8조각으로 나누어 한 조각을 먹었습니다. 윤미는 같은 크기의...      0\n",
              "2     화단 주변에 한 변이 12m인 정팔각형 모양의 울타리를 두른다면, 울타리는 모두 몇...      7\n",
              "3     6장의 숫자 카드 0, 9, 8, 7, 2, 1가 있습니다. 이를, 한 번씩 사용하...      2\n",
              "4                                  0.26 x 0.8을 계산해 주세요.      0\n",
              "...                                                 ...    ...\n",
              "2815  10분 동안 6.53k미터를 가는 자동차와 20분 동안 15.12k미터를 가는 오토...      0\n",
              "2816  한 면의 넓이가 81cm² 인 정육면체가 있습니다. 이 정육면체의 부피는 몇 cm³...      7\n",
              "2817         4880보다 ○ 큰 수 는 9000입니다. ○안에 알맞은 수를 써 넣으시오.      0\n",
              "2818  어느 문구점에서 연필 한 자루는 400원이고 공책 한 권은 1000원입니다. 현승이...      0\n",
              "2819  초콜릿 44개를 상자 11개에 똑같이 나누어 담으려고 합니다. 한 상자에는 몇 개에...      0\n",
              "\n",
              "[2538 rows x 2 columns]"
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              "      <td>한 변의 길이가 24cm인 정육각형과 둘레가 같은 정팔각형이 있습니다. 이 정팔각형...</td>\n",
              "      <td>7</td>\n",
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              "      <th>1</th>\n",
              "      <td>윤아는 부추전을 똑같이 8조각으로 나누어 한 조각을 먹었습니다. 윤미는 같은 크기의...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>화단 주변에 한 변이 12m인 정팔각형 모양의 울타리를 두른다면, 울타리는 모두 몇...</td>\n",
              "      <td>7</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>6장의 숫자 카드 0, 9, 8, 7, 2, 1가 있습니다. 이를, 한 번씩 사용하...</td>\n",
              "      <td>2</td>\n",
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              "      <td>0</td>\n",
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              "      <th>...</th>\n",
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              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>2815</th>\n",
              "      <td>10분 동안 6.53k미터를 가는 자동차와 20분 동안 15.12k미터를 가는 오토...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2816</th>\n",
              "      <td>한 면의 넓이가 81cm² 인 정육면체가 있습니다. 이 정육면체의 부피는 몇 cm³...</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2817</th>\n",
              "      <td>4880보다 ○ 큰 수 는 9000입니다. ○안에 알맞은 수를 써 넣으시오.</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2818</th>\n",
              "      <td>어느 문구점에서 연필 한 자루는 400원이고 공책 한 권은 1000원입니다. 현승이...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2819</th>\n",
              "      <td>초콜릿 44개를 상자 11개에 똑같이 나누어 담으려고 합니다. 한 상자에는 몇 개에...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2538 rows × 2 columns</p>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-90c08e92-8d12-4d5c-8bad-7deb5b9630e9')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "          const element = document.querySelector('#df-90c08e92-8d12-4d5c-8bad-7deb5b9630e9');\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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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "train = pd.concat([X_train, y_train], axis=1, sort=True)\n",
        "train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "4Jfq5_QSaWge"
      },
      "outputs": [],
      "source": [
        "# 오버샘플링을 위해 데이터를 따로 저장해야했습니다.\n",
        "# sep_train = train.copy()\n",
        "# sep_train = sep_train.rename(columns={'label':'class'})\n",
        "# sep_train.head()\n",
        "# sep_train.to_csv(data_dir + \"/sep_train.csv\", index=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "QwPctruZkwFX",
        "outputId": "a7aa59e0-3c3f-4e1e-c95b-223a427481e8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                problem  label\n",
              "10    진호가 가진 줄자의 길이는 6m 15 cm 이고 두은이가 가진 줄자의 길이는 0.2...      0\n",
              "36    농구공 18개를 보관함 3개에 똑같이 나누어 담으려고 합니다. 보관함 1개에 농구공...      0\n",
              "41    승준이는 우유를 매일 0.7L씩 마십니다. 승준이가 5일 동안 마실 우유를 준비하려...      0\n",
              "49              원 안에 마름모의 넓이가 90cm²일 때 원의 반지름은 몇 cm일까요?      7\n",
              "71    가로가 45센티미터, 세로가 50센티미터인 직사각형 모양의 종이를 크기가 같은 정사...      7\n",
              "...                                                 ...    ...\n",
              "2777  냉장고에 우유가 3L 있었습니다. 은채는 오늘 우유 전체의 1/3를 마셨습니다. 은...      0\n",
              "2780  가로가 6센티미터, 세로가 3센티미터인 직육면체가 있습니다. 이 직육면체의 부피가 ...      7\n",
              "2786  고리에 걸릴 때마다 3점씩 얻고 걸리지 않으 면 2점씩 잃는다고 합니다. 윤지는 고...      0\n",
              "2791  냉장고에 우유가 4리터 있었습니다. 세은이는 우유 전체의 1/4을 마셨다면, 세은이...      0\n",
              "2796  넓이가 20센티미터² 인 정육각형을 10등분하였다면, 한 등분의 넓이는 몇 센티미터...      7\n",
              "\n",
              "[282 rows x 2 columns]"
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              "    }\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>problem</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>진호가 가진 줄자의 길이는 6m 15 cm 이고 두은이가 가진 줄자의 길이는 0.2...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>농구공 18개를 보관함 3개에 똑같이 나누어 담으려고 합니다. 보관함 1개에 농구공...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>승준이는 우유를 매일 0.7L씩 마십니다. 승준이가 5일 동안 마실 우유를 준비하려...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>원 안에 마름모의 넓이가 90cm²일 때 원의 반지름은 몇 cm일까요?</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>71</th>\n",
              "      <td>가로가 45센티미터, 세로가 50센티미터인 직사각형 모양의 종이를 크기가 같은 정사...</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2777</th>\n",
              "      <td>냉장고에 우유가 3L 있었습니다. 은채는 오늘 우유 전체의 1/3를 마셨습니다. 은...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2780</th>\n",
              "      <td>가로가 6센티미터, 세로가 3센티미터인 직육면체가 있습니다. 이 직육면체의 부피가 ...</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2786</th>\n",
              "      <td>고리에 걸릴 때마다 3점씩 얻고 걸리지 않으 면 2점씩 잃는다고 합니다. 윤지는 고...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2791</th>\n",
              "      <td>냉장고에 우유가 4리터 있었습니다. 세은이는 우유 전체의 1/4을 마셨다면, 세은이...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2796</th>\n",
              "      <td>넓이가 20센티미터² 인 정육각형을 10등분하였다면, 한 등분의 넓이는 몇 센티미터...</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>282 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-da68a130-e1a7-4f5c-95b9-db16a4e4d534')\"\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",
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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-da68a130-e1a7-4f5c-95b9-db16a4e4d534 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-da68a130-e1a7-4f5c-95b9-db16a4e4d534');\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",
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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "test = pd.concat([X_test, y_test], axis=1, sort=True)\n",
        "test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "jO-Sr32niPQl"
      },
      "outputs": [],
      "source": [
        "#test.to_csv(data_dir + \"/sep_test.csv\", index=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OLXUA2y-HzT7",
        "outputId": "a2fdfe15-4974-40bb-f3b9-7fe6fb2b83d5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 2538 entries, 0 to 2819\n",
            "Data columns (total 2 columns):\n",
            " #   Column   Non-Null Count  Dtype \n",
            "---  ------   --------------  ----- \n",
            " 0   problem  2538 non-null   object\n",
            " 1   label    2538 non-null   int32 \n",
            "dtypes: int32(1), object(1)\n",
            "memory usage: 49.6+ KB\n"
          ]
        }
      ],
      "source": [
        "train.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TZ-Sf0-KlbJ-",
        "outputId": "34508b4b-f645-410a-8fec-2132fa1e7f20"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 282 entries, 10 to 2796\n",
            "Data columns (total 2 columns):\n",
            " #   Column   Non-Null Count  Dtype \n",
            "---  ------   --------------  ----- \n",
            " 0   problem  282 non-null    object\n",
            " 1   label    282 non-null    int32 \n",
            "dtypes: int32(1), object(1)\n",
            "memory usage: 5.5+ KB\n"
          ]
        }
      ],
      "source": [
        "test.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DK5iqjBwlo7G",
        "outputId": "646954f3-2290-4db2-f28c-a1793b51b1fc"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['윤아는 부추전을 똑같이 8조각으로 나누어 한 조각을 먹었습니다. 윤미는 같은 크기의 부추전을 똑같이 16 조각으로 나누었습니다. 윤아와 같은 양을 먹으려면 윤미는 몇 조각을 먹어야 할까요?',\n",
              "       0], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "train.iloc[1,0:2].values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "id": "BaA3TNbTEfi6",
        "outputId": "0cbc37c7-fa7e-4ed8-c8c3-15655e66bf28"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'윤아는 부추전을 똑같이 8조각으로 나누어 한 조각을 먹었습니다. 윤미는 같은 크기의 부추전을 똑같이 16 조각으로 나누었습니다. 윤아와 같은 양을 먹으려면 윤미는 몇 조각을 먹어야 할까요?'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "# text 위치 확인\n",
        "train.iloc[1,0:2].values[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FULi8p2lEdvc",
        "outputId": "13f47ea0-1cd3-49d7-d9fd-32abcdf8e966"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "# label 위치 확인\n",
        "train.iloc[1,0:2].values[1]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CyU9izLFtzll",
        "outputId": "bf2e37a1-bb42-4c18-ea90-c78722fe4db0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "8"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "# 라벨 고유값 갯수 확인\n",
        "len(train['label'].unique())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XUVIV83jnKfc",
        "outputId": "6720dece-c096-44db-defe-70e4c0be37a9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "8"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "# 라벨 인코더로 라벨 갯수 확인\n",
        "len(label_encoder.classes_)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Lz-nQkwM1G06"
      },
      "source": [
        "# 3. 데이터셋 불러오는 클래스 작성"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "z_luQ9HSHlwW"
      },
      "outputs": [],
      "source": [
        "class mpDataset(Dataset):\n",
        "  \n",
        "  def __init__(self, dataset):\n",
        "    # 일부 값중에 NaN이 있다면 drop\n",
        "    self.dataset = dataset\n",
        "    self.num_labels = len(label_encoder.classes_)\n",
        "    self.tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, num_labels=self.num_labels)\n",
        "\n",
        "\n",
        "    print(self.dataset.describe())\n",
        "  \n",
        "  def __len__(self):\n",
        "    return len(self.dataset)\n",
        "  \n",
        "  def __getitem__(self, idx):\n",
        "    row = self.dataset.iloc[idx, 0:2].values\n",
        "    #print(row)\n",
        "    text = row[0]\n",
        "    #print(text)\n",
        "    y = row[1]\n",
        "    #print(y)\n",
        "\n",
        "    inputs = self.tokenizer(\n",
        "        text, \n",
        "        return_tensors='pt',\n",
        "        truncation=True,\n",
        "        max_length=256,\n",
        "        pad_to_max_length=True,\n",
        "        add_special_tokens=True\n",
        "        )\n",
        "    \n",
        "    input_ids = inputs['input_ids'][0]\n",
        "    #print(input_ids)\n",
        "    attention_mask = inputs['attention_mask'][0]\n",
        "    #print(attention_mask)\n",
        "    #print(input_ids.size())\n",
        "    #print(attention_mask.size())\n",
        "\n",
        "    return input_ids, attention_mask, y"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 435,
          "referenced_widgets": [
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          ]
        },
        "id": "wnSsshJqHiA9",
        "outputId": "8bd302ac-01c2-4969-9c79-13402601ba7e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/61.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6f26c3e1565e4c45b2632040818592dc"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/467 [00:00<?, ?B/s]"
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        {
          "output_type": "display_data",
          "data": {
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              "Downloading:   0%|          | 0.00/257k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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              "version_minor": 0,
              "model_id": "75864056dcd24a198f05dfb79bc7e100"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "             label\n",
            "count  2538.000000\n",
            "mean      3.390071\n",
            "std       2.931072\n",
            "min       0.000000\n",
            "25%       0.000000\n",
            "50%       3.000000\n",
            "75%       7.000000\n",
            "max       7.000000\n",
            "            label\n",
            "count  282.000000\n",
            "mean     3.177305\n",
            "std      3.000080\n",
            "min      0.000000\n",
            "25%      0.000000\n",
            "50%      3.000000\n",
            "75%      7.000000\n",
            "max      7.000000\n"
          ]
        }
      ],
      "source": [
        "train_dataset = mpDataset(train)\n",
        "test_dataset = mpDataset(test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wihfh4lTEDeQ",
        "outputId": "9c4f8f63-cbb0-436f-81c4-96fbf7bdd899"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2291: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
            "  FutureWarning,\n"
          ]
        },
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              " 7)"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ],
      "source": [
        "#  input_ids, attention_mask, y 값 제대로 출력되는지를 확인합니다.\n",
        "train_dataset[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "fElZx_rA7VOP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3df481ea-ac85-4d41-83b1-70e60f55e890"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2291: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
            "  FutureWarning,\n"
          ]
        },
        {
          "output_type": "execute_result",
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              " tensor([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
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              " 0)"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ],
      "source": [
        "test_dataset[0]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WvD8l8kvSEOk"
      },
      "source": [
        "# 4. 모델 만들기"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 276,
          "referenced_widgets": [
            "c8efda0a4722432cb98033e8914835a9",
            "195a31904d1847839ce9b7b8db826817",
            "57e185fe57d749209082e8c771ba9952",
            "b68818b37f5c4fca87ee6588e687ee84",
            "259ef471e38c45d682502bbc3d5184c9",
            "d5eeed4746a94e1ea241e2a85858f1e2",
            "2714e6bac2174a60b2e5d7898f91291d",
            "7ffc1aec68234001bc7182ecd443d22e",
            "9336d517a138411abd6e331c2f354cd3",
            "67f406525e234dd8b53e382f1044d385",
            "745289902332425d9dbf657d4976cab8"
          ]
        },
        "id": "Shkc1-JIRsSa",
        "outputId": "d289d43d-c264-40fa-82e6-cc1cf165fad5"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/431M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c8efda0a4722432cb98033e8914835a9"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at monologg/koelectra-base-v3-discriminator were not used when initializing ElectraForSequenceClassification: ['discriminator_predictions.dense_prediction.weight', 'discriminator_predictions.dense.weight', 'discriminator_predictions.dense.bias', 'discriminator_predictions.dense_prediction.bias']\n",
            "- This IS expected if you are initializing ElectraForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing ElectraForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights of ElectraForSequenceClassification were not initialized from the model checkpoint at monologg/koelectra-base-v3-discriminator and are newly initialized: ['classifier.out_proj.weight', 'classifier.out_proj.bias', 'classifier.dense.bias', 'classifier.dense.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2291: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
            "  FutureWarning,\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SequenceClassifierOutput([('logits',\n",
              "                           tensor([[-0.0644, -0.0943,  0.0127, -0.0324, -0.0499,  0.1427,  0.0515, -0.0309]],\n",
              "                                  device='cuda:0', grad_fn=<AddmmBackward0>))])"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ],
      "source": [
        "# ※주의※ 기본 KoELECTRA 모델은 이진분류에 해당합니다. 다중분류시 num_labels를 꼭 추가해주세요! \n",
        "model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME,num_labels=8).to(device)\n",
        "\n",
        "# 한번 실행해보기\n",
        "text, attention_mask, y = train_dataset[0]\n",
        "model(text.unsqueeze(0).to(device), attention_mask=attention_mask.unsqueeze(0).to(device))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "IzH8XJQtcj6f"
      },
      "outputs": [],
      "source": [
        "torch.save(model.state_dict(),\"model.pt\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9be62uCBSP9K",
        "outputId": "61669a13-f453-46d3-c91c-5d4604cde612"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<All keys matched successfully>"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ],
      "source": [
        "model.load_state_dict(torch.load(\"model.pt\"))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YXbEHI3hTmTE",
        "outputId": "4fb7bfc1-7d92-47ad-a1ba-2433a86a3b97"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ElectraForSequenceClassification(\n",
              "  (electra): ElectraModel(\n",
              "    (embeddings): ElectraEmbeddings(\n",
              "      (word_embeddings): Embedding(35000, 768, padding_idx=0)\n",
              "      (position_embeddings): Embedding(512, 768)\n",
              "      (token_type_embeddings): Embedding(2, 768)\n",
              "      (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "      (dropout): Dropout(p=0.1, inplace=False)\n",
              "    )\n",
              "    (encoder): ElectraEncoder(\n",
              "      (layer): ModuleList(\n",
              "        (0): ElectraLayer(\n",
              "          (attention): ElectraAttention(\n",
              "            (self): ElectraSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): ElectraSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): ElectraIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): ElectraOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (1): ElectraLayer(\n",
              "          (attention): ElectraAttention(\n",
              "            (self): ElectraSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): ElectraSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): ElectraIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): ElectraOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (2): ElectraLayer(\n",
              "          (attention): ElectraAttention(\n",
              "            (self): ElectraSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): ElectraSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): ElectraIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): ElectraOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (3): ElectraLayer(\n",
              "          (attention): ElectraAttention(\n",
              "            (self): ElectraSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): ElectraSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): ElectraIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): ElectraOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (4): ElectraLayer(\n",
              "          (attention): ElectraAttention(\n",
              "            (self): ElectraSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): ElectraSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): ElectraIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): ElectraOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (5): ElectraLayer(\n",
              "          (attention): ElectraAttention(\n",
              "            (self): ElectraSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): ElectraSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): ElectraIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): ElectraOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (6): ElectraLayer(\n",
              "          (attention): ElectraAttention(\n",
              "            (self): ElectraSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): ElectraSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): ElectraIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): ElectraOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (7): ElectraLayer(\n",
              "          (attention): ElectraAttention(\n",
              "            (self): ElectraSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): ElectraSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): ElectraIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): ElectraOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (8): ElectraLayer(\n",
              "          (attention): ElectraAttention(\n",
              "            (self): ElectraSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): ElectraSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): ElectraIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): ElectraOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (9): ElectraLayer(\n",
              "          (attention): ElectraAttention(\n",
              "            (self): ElectraSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): ElectraSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): ElectraIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): ElectraOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (10): ElectraLayer(\n",
              "          (attention): ElectraAttention(\n",
              "            (self): ElectraSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): ElectraSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): ElectraIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): ElectraOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (11): ElectraLayer(\n",
              "          (attention): ElectraAttention(\n",
              "            (self): ElectraSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): ElectraSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): ElectraIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): ElectraOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (classifier): ElectraClassificationHead(\n",
              "    (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "    (dropout): Dropout(p=0.1, inplace=False)\n",
              "    (out_proj): Linear(in_features=768, out_features=8, bias=True)\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ],
      "source": [
        "# 모델 레이어 보기\n",
        "model.eval()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gKbXFtrVdPUB"
      },
      "source": [
        "# 4. 학습하기"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "7Tgxdl7tdVjJ"
      },
      "outputs": [],
      "source": [
        "epochs = 5\n",
        "batch_size = 16"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "id": "SWL866JvdY9C"
      },
      "outputs": [],
      "source": [
        "train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n",
        "test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RllydqCf8VUY"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UQI0XGHuYo7s",
        "outputId": "71bb1240-d5c9-4560-ee41-62b807914df9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<torch.utils.data.dataloader.DataLoader at 0x7fed06550390>"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ],
      "source": [
        "train_loader"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448,
          "referenced_widgets": [
            "fa0a872272204d0e829962a615198a3a",
            "eead371bfde14581b72b1cfb6a16d7d5",
            "6fc2affc6370482baa8991454cfbe712",
            "41323e7a97e6447e9ff7d949dd98c478",
            "d24ba25f979a4afaae9ca3dde69f49fb",
            "4b846d396c454f4488fc98aad0517743",
            "1ee4c3697a014febb81b91f4b6b37d06",
            "ef6add62bad04fd18e06edb4f47f39e9",
            "5525a9b953904b75a25fc29c4a7eedd0",
            "8679893a7bec4761bafa22efcb376883",
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            "a9f5e01a3cf242fa8dfbdf7087ddee6c",
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            "b901dc143bc9410d8f539a510fb2b99b",
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            "4b8bd9c47cb14a52b195204163f995f1",
            "25a5dc5281a041c0aa74c8f169b4036f"
          ]
        },
        "id": "7_UMJx3wouJC",
        "outputId": "a432e26b-7577-4fb5-e166-a4bd245f5160"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/transformers/optimization.py:309: 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",
            "  FutureWarning,\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/159 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "fa0a872272204d0e829962a615198a3a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2291: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
            "  FutureWarning,\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Batch Loss: 189.6439733505249 Accuracy: tensor(0.3837, device='cuda:0')\n",
            "Train Loss: 281.83394050598145 Accuracy: tensor(0.4641, device='cuda:0')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/159 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "09e175b73c544017a4c9ba9aa8caea3b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Batch Loss: 125.07675039768219 Accuracy: tensor(0.6881, device='cuda:0')\n",
            "Train Loss: 188.4247146844864 Accuracy: tensor(0.6895, device='cuda:0')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/159 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "de3f072a29e2442ba86d554c03eb48a1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Batch Loss: 90.8029842376709 Accuracy: tensor(0.7631, device='cuda:0')\n",
            "Train Loss: 137.50830858945847 Accuracy: tensor(0.7801, device='cuda:0')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/159 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "96e17a4b28e247c1b297087bfc303f3f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Batch Loss: 70.47312653064728 Accuracy: tensor(0.8606, device='cuda:0')\n",
            "Train Loss: 106.37468129396439 Accuracy: tensor(0.8676, device='cuda:0')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/159 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "cfe02c49f1c84292bfabd09cdf5e118e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Batch Loss: 54.89521487802267 Accuracy: tensor(0.8969, device='cuda:0')\n",
            "Train Loss: 85.20717943459749 Accuracy: tensor(0.8972, device='cuda:0')\n"
          ]
        }
      ],
      "source": [
        "losses = []\n",
        "accuracies = []\n",
        "\n",
        "\n",
        "# Optimizer\n",
        "optimizer = AdamW(model.parameters(), lr=5e-6)\n",
        "\n",
        "for i in range(epochs):\n",
        "  total_loss = 0.0\n",
        "  correct = 0\n",
        "  total = 0\n",
        "  batches = 0\n",
        "\n",
        "\n",
        "  model.to(device)\n",
        "  model.train()\n",
        "\n",
        "  for input_ids_batch, attention_masks_batch, y_batch in tqdm(train_loader):\n",
        "    optimizer.zero_grad()\n",
        "    \n",
        "    y_batch = y_batch.type(torch.LongTensor)\n",
        "    y_batch = y_batch.to(device)\n",
        "    #print(y_batch)\n",
        "    #print(\"y_batch의 데이터 타입:\")\n",
        "    #print(y_batch[0].dtype,y_batch.shape)\n",
        "    \n",
        "    #print(\"------------\")\n",
        "    y_pred = model(input_ids_batch.to(device), attention_mask=attention_masks_batch.to(device))[0]\n",
        "    #print(y_pred)\n",
        "    #print(\"y_predict의 데이터 타입:\")\n",
        "    #print(y_pred[0].dtype)\n",
        "    #print(\"Input_ids_batch:\", input_ids_batch)\n",
        "    #print(\"Attention_masks_batch size:\", len(attention_masks_batch))\n",
        "\n",
        "    #loss = F.cross_entropy(y_pred, y_batch)\n",
        "    criterion = nn.CrossEntropyLoss()\n",
        "    loss = criterion(y_pred, y_batch)\n",
        "    #print(loss)\n",
        "    \n",
        "    loss.backward()\n",
        "    optimizer.step()\n",
        "\n",
        "    total_loss += loss.item()\n",
        "\n",
        "    _, predicted = torch.max(y_pred, 1)\n",
        "    correct += (predicted == y_batch).sum()\n",
        "    total += len(y_batch)\n",
        "\n",
        "\n",
        "    batches += 1\n",
        "    if batches % 100 == 0:\n",
        "      print(\"Batch Loss:\", total_loss, \"Accuracy:\", correct.float() / total)\n",
        "  \n",
        "  losses.append(total_loss)\n",
        "  accuracies.append(correct.float() / total)\n",
        "  print(\"Train Loss:\", total_loss, \"Accuracy:\", correct.float() / total)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "973fCjZatrsm"
      },
      "source": [
        "### 에러 발생 1\n",
        "\bPyTorch RuntimeError: unique_by_key: failed to synchronize: cudaErrorAssert: device-side assert triggered\n",
        "- 해결방법\n",
        "[https://stackoverflow.com/questions/68277801/extracting-meaningful-error-message-from-runtimeerror-cuda-error-device-side]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l_FIoFMJo-Tm"
      },
      "source": [
        "### 에러 발생 2\n",
        "RuntimeError: stack expects each tensor to be equal size, but got [43] at entry 0 and [61] at entry 1\n",
        "- input이 이상하게 들어간거 같음"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FEi9qv6IIrn3"
      },
      "source": [
        "### 에러 해결한 방법\n",
        "1. y_batch의 타입 변경 <br>\n",
        "``y_batch = y_batch.type(torch.LongTensor)``\n",
        "2. KoELECTRA 모델 사용 시 label이 2개 이상일 경우 인자로 num_labels 넣어줘야함!!!! <br>\n",
        "``model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME,num_labels=8).to(device)``"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "colab": {
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        },
        "id": "GdzhMbT35YSb",
        "outputId": "b938e459-d608-4235-fc78-efc3e4f9ca40"
      },
      "outputs": [
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          "output_type": "execute_result",
          "data": {
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              "([281.83394050598145,\n",
              "  188.4247146844864,\n",
              "  137.50830858945847,\n",
              "  106.37468129396439,\n",
              "  85.20717943459749],\n",
              " [tensor(0.4641, device='cuda:0'),\n",
              "  tensor(0.6895, device='cuda:0'),\n",
              "  tensor(0.7801, device='cuda:0'),\n",
              "  tensor(0.8676, device='cuda:0'),\n",
              "  tensor(0.8972, device='cuda:0')])"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ],
      "source": [
        "losses, accuracies"
      ]
    },
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      "cell_type": "markdown",
      "source": [
        "# 5. 모델 평가"
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          "text": [
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2291: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
            "  FutureWarning,\n"
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            "Accuracy: tensor(0.9113, device='cuda:0')\n"
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        "model.eval()\n",
        "\n",
        "test_correct = 0\n",
        "test_total = 0\n",
        "\n",
        "for input_ids_batch, attention_masks_batch, y_batch in tqdm(test_loader):\n",
        "  y_batch = y_batch.to(device)\n",
        "  y_pred = model(input_ids_batch.to(device), attention_mask=attention_masks_batch.to(device))[0]\n",
        "  _, predicted = torch.max(y_pred, 1)\n",
        "  test_correct += (predicted == y_batch).sum()\n",
        "  test_total += len(y_batch)\n",
        "\n",
        "print(\"Accuracy:\", test_correct.float() / test_total)"
      ]
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      "execution_count": 40,
      "metadata": {
        "id": "8wxjES6CHMGx"
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        "# 모델 저장하기\n",
        "save_model_path = data_dir + '/_weights'\n",
        "if os.path.exists(save_model_path) == False:\n",
        "    os.mkdir(save_model_path)  \t\n",
        "torch.save(model.state_dict(), os.path.join(save_model_path, \"koelectra-base-finetuned-mathProblem_Final.bin\"))"
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