08_sentences_type_classification.ipynb

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  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Library"
      ],
      "metadata": {
        "id": "Wsbyjiq3cThz"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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        },
        "id": "gaNQXrSLaVaZ",
        "outputId": "1cc40260-d182-4694-a25c-bb95b3d74575"
      },
      "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",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting koreanize_matplotlib\n",
            "  Downloading koreanize_matplotlib-0.0.1-py3-none-any.whl (2.5 MB)\n",
            "\u001b[K     |████████████████████████████████| 2.5 MB 5.2 MB/s \n",
            "\u001b[?25hRequirement already satisfied: matplotlib in /usr/local/lib/python3.8/dist-packages (from koreanize_matplotlib) (3.2.2)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.8/dist-packages (from matplotlib->koreanize_matplotlib) (0.11.0)\n",
            "Requirement already satisfied: numpy>=1.11 in /usr/local/lib/python3.8/dist-packages (from matplotlib->koreanize_matplotlib) (1.21.6)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.8/dist-packages (from matplotlib->koreanize_matplotlib) (2.8.2)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.8/dist-packages (from matplotlib->koreanize_matplotlib) (1.4.4)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.8/dist-packages (from matplotlib->koreanize_matplotlib) (3.0.9)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.8/dist-packages (from python-dateutil>=2.1->matplotlib->koreanize_matplotlib) (1.15.0)\n",
            "Installing collected packages: koreanize-matplotlib\n",
            "Successfully installed koreanize-matplotlib-0.0.1\n"
          ]
        }
      ],
      "source": [
        "# 구글 드라이브 연동\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import random\n",
        "import os\n",
        "\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn import preprocessing\n",
        "from sklearn.metrics import f1_score\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "from tensorflow.keras import Sequential\n",
        "from tensorflow.keras.layers import Conv2D, MaxPool2D, Dropout, Flatten, Dense\n",
        "from tensorflow.keras.layers import Dense, Embedding, SimpleRNN, GRU, Bidirectional, LSTM, Dropout\n",
        "from tensorflow.keras.callbacks import EarlyStopping\n",
        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "import torch.nn.functional as F\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "\n",
        "from tqdm.auto import tqdm\n",
        "\n",
        "# 한글 폰트 설정\n",
        "!pip install koreanize_matplotlib\n",
        "import koreanize_matplotlib\n",
        "%config InlineBackend.figure_format = 'retina'\n",
        "\n",
        "# warning 무시\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')"
      ],
      "metadata": {
        "id": "pYmn9tixD3T_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Hyperparameter Setting"
      ],
      "metadata": {
        "id": "UwIZi-VXD6X7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "CFG = {\n",
        "    'EPOCHS':10,\n",
        "    'LEARNING_RATE':1e-4,\n",
        "    'BATCH_SIZE':256,\n",
        "    'SEED':41\n",
        "}"
      ],
      "metadata": {
        "id": "flum5tyMD--o"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Fixed RandomSEED"
      ],
      "metadata": {
        "id": "_CEP_kUO8dr8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def seed_everything(seed):\n",
        "    random.seed(seed)\n",
        "    os.environ['PYTHONHASHSEED'] = str(seed)\n",
        "    np.random.seed(seed)\n",
        "    torch.manual_seed(seed)\n",
        "    torch.cuda.manual_seed(seed)\n",
        "    torch.backends.cudnn.deterministic = True\n",
        "    torch.backends.cudnn.benchmark = True\n",
        "\n",
        "seed_everything(CFG['SEED']) # Seed 고정"
      ],
      "metadata": {
        "id": "DmP9q4GR8iRU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data Load"
      ],
      "metadata": {
        "id": "kMw6NODwcVd0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def data_load(base_path, label):\n",
        "  data = pd.read_csv(f'{base_path}/{label}.csv')\n",
        "  return data"
      ],
      "metadata": {
        "id": "oIT-7m_acQjR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "base_path = '/content/drive/My Drive/sentence'\n",
        "\n",
        "train = data_load(base_path, 'train')\n",
        "test = data_load(base_path, 'test')"
      ],
      "metadata": {
        "id": "HYhw7hridSbw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train.shape, test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pPvER_k_deb1",
        "outputId": "2e55e09b-99fd-49e9-c000-97d00561dcbb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((16541, 7), (7090, 2))"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "display(train.sample(5))\n",
        "display(test.sample(5))"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 395
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        "id": "M8SHF7iEdnsG",
        "outputId": "d780b6e7-aa7e-42df-f553-e11688d6a5ea"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                ID                                                 문장   유형  \\\n",
              "11726  TRAIN_11726  사회참여와 봉사영역은 개인적으로 다양한 연령층과 함께 봉사 및 기부활동 참여, 사회...  사실형   \n",
              "9458   TRAIN_09458          현재는 안드로이드어서 사용이 가능하지만 추후 PC로도 업데이트될 예정이다.  사실형   \n",
              "1446   TRAIN_01446  신병주 건국대 교수는 이런 조경을 ＂왕과 권력에 굴하지 않고 직언을 한 원칙과 소신...  사실형   \n",
              "11038  TRAIN_11038  앞서 지난 1월 23일(현지 시간) 장줘얼은 생태계 발전을 위한 ＇채굴세 도입＇을 ...  사실형   \n",
              "10597  TRAIN_10597  10일 만에서 일본 애플 앱스토어 매출 1위를 차지했으며 이후 2019년 7월 ＇호...  사실형   \n",
              "\n",
              "       극성  시제  확실성          label  \n",
              "11726  긍정  현재   확실   사실형-긍정-현재-확실  \n",
              "9458   긍정  미래  불확실  사실형-긍정-미래-불확실  \n",
              "1446   긍정  과거   확실   사실형-긍정-과거-확실  \n",
              "11038  긍정  과거   확실   사실형-긍정-과거-확실  \n",
              "10597  긍정  과거   확실   사실형-긍정-과거-확실  "
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              "      <th>11726</th>\n",
              "      <td>TRAIN_11726</td>\n",
              "      <td>사회참여와 봉사영역은 개인적으로 다양한 연령층과 함께 봉사 및 기부활동 참여, 사회...</td>\n",
              "      <td>사실형</td>\n",
              "      <td>긍정</td>\n",
              "      <td>현재</td>\n",
              "      <td>확실</td>\n",
              "      <td>사실형-긍정-현재-확실</td>\n",
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              "    <tr>\n",
              "      <th>9458</th>\n",
              "      <td>TRAIN_09458</td>\n",
              "      <td>현재는 안드로이드어서 사용이 가능하지만 추후 PC로도 업데이트될 예정이다.</td>\n",
              "      <td>사실형</td>\n",
              "      <td>긍정</td>\n",
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              "    <tr>\n",
              "      <th>1446</th>\n",
              "      <td>TRAIN_01446</td>\n",
              "      <td>신병주 건국대 교수는 이런 조경을 ＂왕과 권력에 굴하지 않고 직언을 한 원칙과 소신...</td>\n",
              "      <td>사실형</td>\n",
              "      <td>긍정</td>\n",
              "      <td>과거</td>\n",
              "      <td>확실</td>\n",
              "      <td>사실형-긍정-과거-확실</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11038</th>\n",
              "      <td>TRAIN_11038</td>\n",
              "      <td>앞서 지난 1월 23일(현지 시간) 장줘얼은 생태계 발전을 위한 ＇채굴세 도입＇을 ...</td>\n",
              "      <td>사실형</td>\n",
              "      <td>긍정</td>\n",
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              "    <tr>\n",
              "      <th>10597</th>\n",
              "      <td>TRAIN_10597</td>\n",
              "      <td>10일 만에서 일본 애플 앱스토어 매출 1위를 차지했으며 이후 2019년 7월 ＇호...</td>\n",
              "      <td>사실형</td>\n",
              "      <td>긍정</td>\n",
              "      <td>과거</td>\n",
              "      <td>확실</td>\n",
              "      <td>사실형-긍정-과거-확실</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-11753ed5-50b4-4e22-91f3-475454e785d8')\"\n",
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            "text/plain": [
              "             ID                                                 문장\n",
              "3359  TEST_3359  지금은 대표 스타트업으로 성장한 마켓컬리와 프레시지, 제주맥주, 팜에이트, 엔씽, ...\n",
              "4127  TEST_4127  통풍은 체내 혈액 내 요산이 원활하게 배출되지 않아 요산염 결정체를 형성하고 관절이...\n",
              "4036  TEST_4036  개인적으로 마카다미아는 견과류 중에서도 가장 선호하는 종류이기도 한데, 다소 가격이...\n",
              "224   TEST_0224  전 세계에 160여 점밖에 존재하지 않는 고려불화는 청자와 더불어 고려 문화의 독보...\n",
              "1434  TEST_1434  오페르트는 조선인들에게서 임진왜란 당시 수많은 왜군 탈영병들이 한반도 남쪽으로 달아..."
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              "      <th>3359</th>\n",
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              "      <td>지금은 대표 스타트업으로 성장한 마켓컬리와 프레시지, 제주맥주, 팜에이트, 엔씽, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4127</th>\n",
              "      <td>TEST_4127</td>\n",
              "      <td>통풍은 체내 혈액 내 요산이 원활하게 배출되지 않아 요산염 결정체를 형성하고 관절이...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4036</th>\n",
              "      <td>TEST_4036</td>\n",
              "      <td>개인적으로 마카다미아는 견과류 중에서도 가장 선호하는 종류이기도 한데, 다소 가격이...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>224</th>\n",
              "      <td>TEST_0224</td>\n",
              "      <td>전 세계에 160여 점밖에 존재하지 않는 고려불화는 청자와 더불어 고려 문화의 독보...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1434</th>\n",
              "      <td>TEST_1434</td>\n",
              "      <td>오페르트는 조선인들에게서 임진왜란 당시 수많은 왜군 탈영병들이 한반도 남쪽으로 달아...</td>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7a46a8c4-efa9-40c3-9bcd-5e3f2ea928f1')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
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              "\n",
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              "\n",
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              "\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "          const element = document.querySelector('#df-7a46a8c4-efa9-40c3-9bcd-5e3f2ea928f1');\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": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# EDA & Visualization"
      ],
      "metadata": {
        "id": "xW_JH0mtf15C"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train.describe(include='object')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "ymqhT9tseR24",
        "outputId": "187778c4-9db3-4a08-f59b-32e79f04e0d7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 ID                                       문장     유형     극성  \\\n",
              "count         16541                                    16541  16541  16541   \n",
              "unique        16541                                    16506      4      3   \n",
              "top     TRAIN_00000  이들 게임은 국내 구글 플레이 매출 톱10 진입이 예상되는 기대작이다.    사실형     긍정   \n",
              "freq              1                                        2  13558  15793   \n",
              "\n",
              "           시제    확실성         label  \n",
              "count   16541  16541         16541  \n",
              "unique      3      2            64  \n",
              "top        과거     확실  사실형-긍정-과거-확실  \n",
              "freq     8032  15192          7113  "
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ID</th>\n",
              "      <th>문장</th>\n",
              "      <th>유형</th>\n",
              "      <th>극성</th>\n",
              "      <th>시제</th>\n",
              "      <th>확실성</th>\n",
              "      <th>label</th>\n",
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              "  <tbody>\n",
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              "      <th>count</th>\n",
              "      <td>16541</td>\n",
              "      <td>16541</td>\n",
              "      <td>16541</td>\n",
              "      <td>16541</td>\n",
              "      <td>16541</td>\n",
              "      <td>16541</td>\n",
              "      <td>16541</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unique</th>\n",
              "      <td>16541</td>\n",
              "      <td>16506</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>TRAIN_00000</td>\n",
              "      <td>이들 게임은 국내 구글 플레이 매출 톱10 진입이 예상되는 기대작이다.</td>\n",
              "      <td>사실형</td>\n",
              "      <td>긍정</td>\n",
              "      <td>과거</td>\n",
              "      <td>확실</td>\n",
              "      <td>사실형-긍정-과거-확실</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>13558</td>\n",
              "      <td>15793</td>\n",
              "      <td>8032</td>\n",
              "      <td>15192</td>\n",
              "      <td>7113</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "          const element = document.querySelector('#df-ed8fc840-b3f3-435e-ac5d-18bc270fb2ad');\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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "_=sns.countplot(data=train, x='유형')               \n",
        "plt.title('유형별 count')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "id": "7EE85ZlHfvdU",
        "outputId": "3b08c44d-66b9-4975-850f-9dce9307c380"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, '유형별 count')"
            ]
          },
          "metadata": {},
          "execution_count": 29
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "image/png": {
              "width": 400,
              "height": 277
            },
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "_=sns.countplot(data=train, x='극성')\n",
        "plt.title('극성별 count')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "id": "VIQkNy87fynO",
        "outputId": "755cbf83-b56c-4ce4-9b8a-6e6592934cfb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, '극성별 count')"
            ]
          },
          "metadata": {},
          "execution_count": 30
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "image/png": {
              "width": 400,
              "height": 277
            },
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "_=sns.countplot(data=train, x='시제')\n",
        "plt.title('시제 count')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 311
        },
        "id": "v_3u0ucXgiRD",
        "outputId": "ddd5f992-4870-4ce6-ec4f-01ec9a07e33f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, '시제 count')"
            ]
          },
          "metadata": {},
          "execution_count": 31
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "image/png": {
              "width": 393,
              "height": 276
            },
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "_=sns.countplot(data=train, x='확실성')\n",
        "plt.title('확실성 count')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "id": "aoKVyfGUiDnY",
        "outputId": "575cce61-d66c-4e09-de04-8b88d195c62f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, '확실성 count')"
            ]
          },
          "metadata": {},
          "execution_count": 32
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "image/png": {
              "width": 400,
              "height": 277
            },
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train['label'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DPVMYsxaiUyZ",
        "outputId": "930f813d-b150-4e27-c1f5-4313e21d8b27"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "사실형-긍정-과거-확실     7113\n",
              "사실형-긍정-현재-확실     4743\n",
              "추론형-긍정-현재-확실     1101\n",
              "사실형-긍정-미래-확실      649\n",
              "추론형-긍정-과거-확실      335\n",
              "                 ... \n",
              "대화형-미정-과거-확실        1\n",
              "예측형-미정-현재-확실        1\n",
              "예측형-부정-현재-불확실       1\n",
              "예측형-부정-과거-확실        1\n",
              "예측형-미정-과거-확실        1\n",
              "Name: label, Length: 64, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# ID column 삭제"
      ],
      "metadata": {
        "id": "Bcj6gqRbYQUc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train = train.drop(columns='ID')\n",
        "test = test.drop(columns='ID')"
      ],
      "metadata": {
        "id": "tU0jj1BbYS84"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Resampling\n",
        "- label에 class가 200개 이하인 데이터에서 resampling 과정 진행\n",
        "- train에 concat"
      ],
      "metadata": {
        "id": "rPk22Nq03HDU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# label의 class가 100개 이하인 데이터는 48개\n",
        "train['label'].value_counts()[train['label'].value_counts()<=100].count()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oGVb51dP3d6q",
        "outputId": "ac192578-d1be-4836-879d-89b4cc731fea"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "48"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "label_index = train['label'].value_counts()[train['label'].value_counts()<=200].index\n",
        "label_index"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c1SKs11F3wM3",
        "outputId": "ba2f8204-10d2-4ed3-fa2d-8540e74b0965"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['사실형-긍정-현재-불확실', '사실형-부정-과거-확실', '사실형-부정-현재-확실', '추론형-긍정-미래-불확실',\n",
              "       '사실형-긍정-과거-불확실', '예측형-긍정-미래-불확실', '추론형-긍정-현재-불확실', '대화형-긍정-과거-확실',\n",
              "       '추론형-부정-현재-확실', '예측형-긍정-미래-확실', '추론형-긍정-과거-불확실', '대화형-긍정-현재-불확실',\n",
              "       '대화형-미정-현재-불확실', '대화형-긍정-미래-확실', '사실형-미정-미래-확실', '사실형-미정-미래-불확실',\n",
              "       '대화형-긍정-미래-불확실', '대화형-미정-미래-불확실', '추론형-미정-미래-불확실', '사실형-미정-현재-확실',\n",
              "       '대화형-긍정-과거-불확실', '추론형-부정-과거-확실', '대화형-부정-현재-확실', '사실형-부정-미래-확실',\n",
              "       '예측형-긍정-현재-확실', '사실형-부정-현재-불확실', '예측형-미정-미래-불확실', '사실형-부정-과거-불확실',\n",
              "       '예측형-긍정-과거-확실', '대화형-미정-과거-불확실', '추론형-부정-현재-불확실', '예측형-긍정-현재-불확실',\n",
              "       '예측형-긍정-과거-불확실', '사실형-미정-현재-불확실', '추론형-부정-미래-확실', '사실형-부정-미래-불확실',\n",
              "       '추론형-부정-과거-불확실', '추론형-부정-미래-불확실', '대화형-부정-현재-불확실', '대화형-부정-과거-확실',\n",
              "       '추론형-미정-현재-불확실', '예측형-부정-미래-불확실', '추론형-미정-현재-확실', '추론형-미정-미래-확실',\n",
              "       '사실형-미정-과거-확실', '예측형-미정-현재-불확실', '대화형-미정-미래-확실', '예측형-미정-미래-확실',\n",
              "       '추론형-미정-과거-불확실', '대화형-부정-과거-불확실', '대화형-부정-미래-확실', '대화형-미정-과거-확실',\n",
              "       '예측형-미정-현재-확실', '예측형-부정-현재-불확실', '예측형-부정-과거-확실', '예측형-미정-과거-확실'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 각 class에 골고루 resampling \n",
        "train_sample = train[train['label'].isin(label_index)]\n",
        "resampling = train_sample.sample(n=len(train_sample), replace=False)\n",
        "resampling"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "7GIBpNZg4TND",
        "outputId": "ea0a48ed-f841-4a1d-f2de-65c8af50187c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                      문장   유형  극성  시제  확실성  \\\n",
              "12262  하지만 유머 대신 쫄깃한 첩보 액션물의 바이브를 끝까지 유지했으면 어떨까 하는 아쉬...  사실형  긍정  현재  불확실   \n",
              "14823     CJ, GS 등 6개 투자사로부터 시리즈A 단계에서 330억원의 투자금도 유치했다.  사실형  긍정  과거  불확실   \n",
              "9412   임진왜란 때 승병을 이끌며 공을 세웠던 서산대사가 문집을 냈는데, 솜씨는 별로였던 ...  추론형  긍정  과거  불확실   \n",
              "16017  과학자들은 사망한 소년이 유목민이라는 점을 고려했을 때 따뜻한 날씨에 동토층이 녹으...  대화형  긍정  과거   확실   \n",
              "9154   반도체 컨설팅업체 테크인사이트의 칩 이코노미스트인 댄 허치슨은 ＂하이 NA EUV는...  대화형  긍정  과거   확실   \n",
              "...                                                  ...  ...  ..  ..  ...   \n",
              "4912              장애인주차구역에서 차가 나올 때는 장애인이 꼭 탑승하지 않아도 된다.  사실형  부정  현재   확실   \n",
              "8595                               대신증권은 투자자 피해가 거의 없었다.  사실형  부정  과거   확실   \n",
              "15728              향후 개발이 완료되면 상주인구만 20만명을 넘어설 것으로 예상된다.  예측형  긍정  미래  불확실   \n",
              "9013   올해 새로운 디바이스와 소프트웨어 출시를 준비하고 있는 구글만 하더라도 그런 신제품...  대화형  긍정  과거   확실   \n",
              "9549   세계 1, 2위인 고진영(24)과 박성현(26)을 따라잡기에는 너무 멀리 있고, 세...  추론형  부정  현재   확실   \n",
              "\n",
              "               label  \n",
              "12262  사실형-긍정-현재-불확실  \n",
              "14823  사실형-긍정-과거-불확실  \n",
              "9412   추론형-긍정-과거-불확실  \n",
              "16017   대화형-긍정-과거-확실  \n",
              "9154    대화형-긍정-과거-확실  \n",
              "...              ...  \n",
              "4912    사실형-부정-현재-확실  \n",
              "8595    사실형-부정-과거-확실  \n",
              "15728  예측형-긍정-미래-불확실  \n",
              "9013    대화형-긍정-과거-확실  \n",
              "9549    추론형-부정-현재-확실  \n",
              "\n",
              "[1903 rows x 6 columns]"
            ],
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              "\n",
              "  <div id=\"df-785be0f6-1bff-4165-a7b5-6088f7530789\">\n",
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              "      <div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "    }\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>문장</th>\n",
              "      <th>유형</th>\n",
              "      <th>극성</th>\n",
              "      <th>시제</th>\n",
              "      <th>확실성</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>12262</th>\n",
              "      <td>하지만 유머 대신 쫄깃한 첩보 액션물의 바이브를 끝까지 유지했으면 어떨까 하는 아쉬...</td>\n",
              "      <td>사실형</td>\n",
              "      <td>긍정</td>\n",
              "      <td>현재</td>\n",
              "      <td>불확실</td>\n",
              "      <td>사실형-긍정-현재-불확실</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14823</th>\n",
              "      <td>CJ, GS 등 6개 투자사로부터 시리즈A 단계에서 330억원의 투자금도 유치했다.</td>\n",
              "      <td>사실형</td>\n",
              "      <td>긍정</td>\n",
              "      <td>과거</td>\n",
              "      <td>불확실</td>\n",
              "      <td>사실형-긍정-과거-불확실</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9412</th>\n",
              "      <td>임진왜란 때 승병을 이끌며 공을 세웠던 서산대사가 문집을 냈는데, 솜씨는 별로였던 ...</td>\n",
              "      <td>추론형</td>\n",
              "      <td>긍정</td>\n",
              "      <td>과거</td>\n",
              "      <td>불확실</td>\n",
              "      <td>추론형-긍정-과거-불확실</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16017</th>\n",
              "      <td>과학자들은 사망한 소년이 유목민이라는 점을 고려했을 때 따뜻한 날씨에 동토층이 녹으...</td>\n",
              "      <td>대화형</td>\n",
              "      <td>긍정</td>\n",
              "      <td>과거</td>\n",
              "      <td>확실</td>\n",
              "      <td>대화형-긍정-과거-확실</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9154</th>\n",
              "      <td>반도체 컨설팅업체 테크인사이트의 칩 이코노미스트인 댄 허치슨은 ＂하이 NA EUV는...</td>\n",
              "      <td>대화형</td>\n",
              "      <td>긍정</td>\n",
              "      <td>과거</td>\n",
              "      <td>확실</td>\n",
              "      <td>대화형-긍정-과거-확실</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4912</th>\n",
              "      <td>장애인주차구역에서 차가 나올 때는 장애인이 꼭 탑승하지 않아도 된다.</td>\n",
              "      <td>사실형</td>\n",
              "      <td>부정</td>\n",
              "      <td>현재</td>\n",
              "      <td>확실</td>\n",
              "      <td>사실형-부정-현재-확실</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8595</th>\n",
              "      <td>대신증권은 투자자 피해가 거의 없었다.</td>\n",
              "      <td>사실형</td>\n",
              "      <td>부정</td>\n",
              "      <td>과거</td>\n",
              "      <td>확실</td>\n",
              "      <td>사실형-부정-과거-확실</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15728</th>\n",
              "      <td>향후 개발이 완료되면 상주인구만 20만명을 넘어설 것으로 예상된다.</td>\n",
              "      <td>예측형</td>\n",
              "      <td>긍정</td>\n",
              "      <td>미래</td>\n",
              "      <td>불확실</td>\n",
              "      <td>예측형-긍정-미래-불확실</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9013</th>\n",
              "      <td>올해 새로운 디바이스와 소프트웨어 출시를 준비하고 있는 구글만 하더라도 그런 신제품...</td>\n",
              "      <td>대화형</td>\n",
              "      <td>긍정</td>\n",
              "      <td>과거</td>\n",
              "      <td>확실</td>\n",
              "      <td>대화형-긍정-과거-확실</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9549</th>\n",
              "      <td>세계 1, 2위인 고진영(24)과 박성현(26)을 따라잡기에는 너무 멀리 있고, 세...</td>\n",
              "      <td>추론형</td>\n",
              "      <td>부정</td>\n",
              "      <td>현재</td>\n",
              "      <td>확실</td>\n",
              "      <td>추론형-부정-현재-확실</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1903 rows × 6 columns</p>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-785be0f6-1bff-4165-a7b5-6088f7530789')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\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",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-785be0f6-1bff-4165-a7b5-6088f7530789 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-785be0f6-1bff-4165-a7b5-6088f7530789');\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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              "          element.appendChild(docLink);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "resampling['label'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u1PePAbF89cV",
        "outputId": "ba4cae7c-e17d-494c-fc6d-36f508de20bb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "사실형-긍정-현재-불확실    200\n",
              "사실형-부정-과거-확실     183\n",
              "사실형-부정-현재-확실     174\n",
              "추론형-긍정-미래-불확실    167\n",
              "사실형-긍정-과거-불확실    144\n",
              "예측형-긍정-미래-불확실    141\n",
              "추론형-긍정-현재-불확실    130\n",
              "대화형-긍정-과거-확실     120\n",
              "추론형-부정-현재-확실      93\n",
              "예측형-긍정-미래-확실      57\n",
              "추론형-긍정-과거-불확실     49\n",
              "대화형-긍정-현재-불확실     44\n",
              "대화형-미정-현재-불확실     29\n",
              "사실형-미정-미래-확실      26\n",
              "대화형-긍정-미래-확실      26\n",
              "대화형-미정-미래-불확실     22\n",
              "대화형-긍정-미래-불확실     22\n",
              "사실형-미정-미래-불확실     22\n",
              "추론형-미정-미래-불확실     20\n",
              "사실형-미정-현재-확실      18\n",
              "대화형-긍정-과거-불확실     16\n",
              "추론형-부정-과거-확실      16\n",
              "대화형-부정-현재-확실      14\n",
              "사실형-부정-미래-확실      14\n",
              "예측형-긍정-현재-확실      13\n",
              "사실형-부정-현재-불확실     11\n",
              "예측형-미정-미래-불확실     10\n",
              "사실형-부정-과거-불확실     10\n",
              "대화형-미정-과거-불확실     10\n",
              "예측형-긍정-과거-확실      10\n",
              "추론형-부정-현재-불확실      9\n",
              "예측형-긍정-과거-불확실      8\n",
              "예측형-긍정-현재-불확실      8\n",
              "추론형-부정-미래-확실       7\n",
              "사실형-미정-현재-불확실      7\n",
              "추론형-부정-과거-불확실      6\n",
              "사실형-부정-미래-불확실      6\n",
              "대화형-부정-과거-확실       5\n",
              "대화형-부정-현재-불확실      5\n",
              "추론형-부정-미래-불확실      5\n",
              "추론형-미정-현재-불확실      4\n",
              "예측형-부정-미래-불확실      3\n",
              "추론형-미정-현재-확실       2\n",
              "예측형-미정-현재-불확실      2\n",
              "추론형-미정-미래-확실       2\n",
              "사실형-미정-과거-확실       2\n",
              "대화형-미정-미래-확실       2\n",
              "추론형-미정-과거-불확실      1\n",
              "대화형-부정-미래-확실       1\n",
              "예측형-부정-현재-불확실      1\n",
              "예측형-미정-현재-확실       1\n",
              "예측형-미정-미래-확실       1\n",
              "대화형-미정-과거-확실       1\n",
              "예측형-미정-과거-확실       1\n",
              "예측형-부정-과거-확실       1\n",
              "대화형-부정-과거-불확실      1\n",
              "Name: label, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# concat\n",
        "train_resample = pd.concat([train, resampling], axis=0)\n",
        "train_resample['label'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f296EI5o8iYW",
        "outputId": "f859d1f1-8bdd-4104-bea3-5d6959845c41"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "사실형-긍정-과거-확실     7113\n",
              "사실형-긍정-현재-확실     4743\n",
              "추론형-긍정-현재-확실     1101\n",
              "사실형-긍정-미래-확실      649\n",
              "사실형-긍정-현재-불확실     400\n",
              "                 ... \n",
              "대화형-미정-과거-확실        2\n",
              "예측형-미정-현재-확실        2\n",
              "예측형-부정-현재-불확실       2\n",
              "예측형-부정-과거-확실        2\n",
              "예측형-미정-과거-확실        2\n",
              "Name: label, Length: 64, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Train/Test Split"
      ],
      "metadata": {
        "id": "yKrGrVOvmuLT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_val, _, _ = train_test_split(train, train['label'], test_size=0.2, random_state=CFG['SEED'])\n",
        "X_train.shape, X_val.shape"
      ],
      "metadata": {
        "id": "aAvn_RtWmoa8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "36d398d5-e8ac-47e2-cc7f-fad5674d8b82"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((13232, 6), (3309, 6))"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Preprocessing\n",
        "- '유형', '극세', '시제', '확실성' columns 각각 Encoding\n",
        "- TfidfVectorizer를 이용하여 벡터화"
      ],
      "metadata": {
        "id": "WGDtnX_v9oBF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "vectorizer = TfidfVectorizer(min_df = 4, analyzer = 'word', ngram_range=(1,2))\n",
        "vectorizer.fit(np.array(X_train[\"문장\"]))\n",
        "\n",
        "train_vec = vectorizer.transform(X_train[\"문장\"])\n",
        "val_vec = vectorizer.transform(X_val[\"문장\"])\n",
        "test_vec = vectorizer.transform(test[\"문장\"])\n",
        "\n",
        "print(train_vec.shape, val_vec.shape, test_vec.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AXJKhFG81qBb",
        "outputId": "b5f13b4f-2061-4e5a-cc64-0ef2bb60e9ef"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(13232, 9351) (3309, 9351) (7090, 9351)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "type_le = preprocessing.LabelEncoder()\n",
        "X_train[\"유형\"] = type_le.fit_transform(X_train[\"유형\"].values)\n",
        "X_val[\"유형\"] = type_le.transform(X_val[\"유형\"].values)\n",
        "\n",
        "polarity_le = preprocessing.LabelEncoder()\n",
        "X_train[\"극성\"] = polarity_le.fit_transform(X_train[\"극성\"].values)\n",
        "X_val[\"극성\"] = polarity_le.transform(X_val[\"극성\"].values)\n",
        "\n",
        "tense_le = preprocessing.LabelEncoder()\n",
        "X_train[\"시제\"] = tense_le.fit_transform(X_train[\"시제\"].values)\n",
        "X_val[\"시제\"] = tense_le.transform(X_val[\"시제\"].values)\n",
        "\n",
        "certainty_le = preprocessing.LabelEncoder()\n",
        "X_train[\"확실성\"] = certainty_le.fit_transform(X_train[\"확실성\"].values)\n",
        "X_val[\"확실성\"] = certainty_le.transform(X_val[\"확실성\"].values)"
      ],
      "metadata": {
        "id": "XbchXAPFAm64"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_type = X_train[\"유형\"].values # sentence type\n",
        "train_polarity = X_train[\"극성\"].values # sentence polarity\n",
        "train_tense = X_train[\"시제\"].values # sentence tense\n",
        "train_certainty = X_train[\"확실성\"].values # sentence certainty\n",
        "\n",
        "train_labels = {\n",
        "    'type' : train_type,\n",
        "    'polarity' : train_polarity,\n",
        "    'tense' : train_tense,\n",
        "    'certainty' : train_certainty\n",
        "}"
      ],
      "metadata": {
        "id": "6GdAdy-DBJ6-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_labels"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f_3veSDZG2ng",
        "outputId": "bda9337e-4149-488c-ba09-bab275f1a1f3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'type': array([1, 1, 1, ..., 0, 1, 1]),\n",
              " 'polarity': array([0, 0, 0, ..., 0, 0, 0]),\n",
              " 'tense': array([0, 0, 2, ..., 2, 0, 2]),\n",
              " 'certainty': array([1, 1, 1, ..., 1, 1, 1])}"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "val_type = X_val[\"유형\"].values # sentence type\n",
        "val_polarity = X_val[\"극성\"].values # sentence polarity\n",
        "val_tense = X_val[\"시제\"].values # sentence tense\n",
        "val_certainty = X_val[\"확실성\"].values # sentence certainty\n",
        "\n",
        "val_labels = {\n",
        "    'type' : val_type,\n",
        "    'polarity' : val_polarity,\n",
        "    'tense' : val_tense,\n",
        "    'certainty' : val_certainty\n",
        "}"
      ],
      "metadata": {
        "id": "TyJ3fTNJBfgT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "val_labels"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RefkvVrOG4aF",
        "outputId": "86c8faa1-d60c-4975-e2c5-48777c103faf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'type': array([1, 1, 1, ..., 1, 1, 1]),\n",
              " 'polarity': array([0, 0, 0, ..., 0, 0, 0]),\n",
              " 'tense': array([0, 0, 2, ..., 0, 0, 0]),\n",
              " 'certainty': array([1, 1, 1, ..., 1, 1, 1])}"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# CustomDataset"
      ],
      "metadata": {
        "id": "LN00JOWyEXut"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class CustomDataset(Dataset):\n",
        "    def __init__(self, st_vec, st_labels):\n",
        "        self.st_vec = st_vec\n",
        "        self.st_labels = st_labels\n",
        "\n",
        "    def __getitem__(self, index):\n",
        "        st_vector = torch.FloatTensor(self.st_vec[index].toarray()).squeeze(0)\n",
        "        if self.st_labels is not None:\n",
        "            st_type = self.st_labels['type'][index]\n",
        "            st_polarity = self.st_labels['polarity'][index]\n",
        "            st_tense = self.st_labels['tense'][index]\n",
        "            st_certainty = self.st_labels['certainty'][index]\n",
        "            return st_vector, st_type, st_polarity, st_tense, st_certainty\n",
        "        else:\n",
        "            return st_vector\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.st_vec.toarray())"
      ],
      "metadata": {
        "id": "Xma9baDOEZ27"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataset = CustomDataset(train_vec, train_labels)\n",
        "train_loader = DataLoader(train_dataset, batch_size = CFG['BATCH_SIZE'], shuffle=True, num_workers=0)\n",
        "\n",
        "val_dataset = CustomDataset(val_vec, val_labels)\n",
        "val_loader = DataLoader(val_dataset, batch_size = CFG['BATCH_SIZE'], shuffle=False, num_workers=0)"
      ],
      "metadata": {
        "id": "jrl70SiCEbt4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Modeling\n",
        "- Modeling Define"
      ],
      "metadata": {
        "id": "dY33klZZS34a"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class BaseModel(nn.Module):\n",
        "    def __init__(self, input_dim=9351):\n",
        "        super(BaseModel, self).__init__()\n",
        "        self.feature_extract = nn.Sequential(\n",
        "            nn.Linear(in_features=input_dim, out_features=1024),\n",
        "            nn.BatchNorm1d(1024),\n",
        "            nn.LeakyReLU(),\n",
        "            nn.Linear(in_features=1024, out_features=1024),\n",
        "            nn.BatchNorm1d(1024),\n",
        "            nn.LeakyReLU(),\n",
        "            nn.Linear(in_features=1024, out_features=512),\n",
        "            nn.BatchNorm1d(512),\n",
        "            nn.LeakyReLU(),\n",
        "        )\n",
        "        self.type_classifier = nn.Sequential(\n",
        "            nn.Dropout(p=0.3),\n",
        "            nn.Linear(in_features=512, out_features=4),\n",
        "        )\n",
        "        self.polarity_classifier = nn.Sequential(\n",
        "            nn.Dropout(p=0.3),\n",
        "            nn.Linear(in_features=512, out_features=3),\n",
        "        )\n",
        "        self.tense_classifier = nn.Sequential(\n",
        "            nn.Dropout(p=0.3),\n",
        "            nn.Linear(in_features=512, out_features=3),\n",
        "        )\n",
        "        self.certainty_classifier = nn.Sequential(\n",
        "            nn.Dropout(p=0.3),\n",
        "            nn.Linear(in_features=512, out_features=2),\n",
        "        )\n",
        "            \n",
        "    def forward(self, x):\n",
        "        x = self.feature_extract(x)\n",
        "        # 문장 유형, 극성, 시제, 확실성을 각각 분류\n",
        "        type_output = self.type_classifier(x)\n",
        "        polarity_output = self.polarity_classifier(x)\n",
        "        tense_output = self.tense_classifier(x)\n",
        "        certainty_output = self.certainty_classifier(x)\n",
        "        return type_output, polarity_output, tense_output, certainty_output"
      ],
      "metadata": {
        "id": "OgTtpB6eT9PE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def train(model, optimizer, train_loader, val_loader, scheduler, device):\n",
        "    model.to(device)\n",
        "    \n",
        "    criterion = {\n",
        "        'type' : nn.CrossEntropyLoss().to(device),\n",
        "        'polarity' : nn.CrossEntropyLoss().to(device),\n",
        "        'tense' : nn.CrossEntropyLoss().to(device),\n",
        "        'certainty' : nn.CrossEntropyLoss().to(device)\n",
        "    }\n",
        "    \n",
        "    best_loss = 999999\n",
        "    best_model = None\n",
        "    \n",
        "    for epoch in range(1, CFG['EPOCHS']+1):\n",
        "        model.train()\n",
        "        train_loss = []\n",
        "        for sentence, type_label, polarity_label, tense_label, certainty_label in tqdm(iter(train_loader)):\n",
        "            sentence = sentence.to(device)\n",
        "            type_label = type_label.to(device)\n",
        "            polarity_label = polarity_label.to(device)\n",
        "            tense_label = tense_label.to(device)\n",
        "            certainty_label = certainty_label.to(device)\n",
        "            \n",
        "            optimizer.zero_grad()\n",
        "            \n",
        "            type_logit, polarity_logit, tense_logit, certainty_logit = model(sentence)\n",
        "            \n",
        "            loss = 0.25 * criterion['type'](type_logit, type_label) + \\\n",
        "                    0.25 * criterion['polarity'](polarity_logit, polarity_label) + \\\n",
        "                    0.25 * criterion['tense'](tense_logit, tense_label) + \\\n",
        "                    0.25 * criterion['certainty'](certainty_logit, certainty_label)\n",
        "            \n",
        "            loss.backward()\n",
        "            optimizer.step()\n",
        "            \n",
        "            train_loss.append(loss.item())\n",
        "        \n",
        "        val_loss, val_type_f1, val_polarity_f1, val_tense_f1, val_certainty_f1 = validation(model, val_loader, criterion, device)\n",
        "        print(f'Epoch : [{epoch}] Train Loss : [{np.mean(train_loss):.5f}] Val Loss : [{val_loss:.5f}] 유형 F1 : [{val_type_f1:.5f}] 극성 F1 : [{val_polarity_f1:.5f}] 시제 F1 : [{val_tense_f1:.5f}] 확실성 F1 : [{val_certainty_f1:.5f}]')\n",
        "        \n",
        "        if scheduler is not None:\n",
        "            scheduler.step(val_loss)\n",
        "            \n",
        "        if best_loss > val_loss:\n",
        "            best_loss = val_loss\n",
        "            best_model = model\n",
        "            \n",
        "    return best_model"
      ],
      "metadata": {
        "id": "ohwRvVoetaah"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def validation(model, val_loader, criterion, device):\n",
        "    model.eval()\n",
        "    val_loss = []\n",
        "    \n",
        "    type_preds, polarity_preds, tense_preds, certainty_preds = [], [], [], []\n",
        "    type_labels, polarity_labels, tense_labels, certainty_labels = [], [], [], []\n",
        "    \n",
        "    \n",
        "    with torch.no_grad():\n",
        "        for sentence, type_label, polarity_label, tense_label, certainty_label in tqdm(iter(val_loader)):\n",
        "            sentence = sentence.to(device)\n",
        "            type_label = type_label.to(device)\n",
        "            polarity_label = polarity_label.to(device)\n",
        "            tense_label = tense_label.to(device)\n",
        "            certainty_label = certainty_label.to(device)\n",
        "            \n",
        "            type_logit, polarity_logit, tense_logit, certainty_logit = model(sentence)\n",
        "            \n",
        "            loss = 0.25 * criterion['type'](type_logit, type_label) + \\\n",
        "                    0.25 * criterion['polarity'](polarity_logit, polarity_label) + \\\n",
        "                    0.25 * criterion['tense'](tense_logit, tense_label) + \\\n",
        "                    0.25 * criterion['certainty'](certainty_logit, certainty_label)\n",
        "            \n",
        "            val_loss.append(loss.item())\n",
        "            \n",
        "            type_preds += type_logit.argmax(1).detach().cpu().numpy().tolist()\n",
        "            type_labels += type_label.detach().cpu().numpy().tolist()\n",
        "            \n",
        "            polarity_preds += polarity_logit.argmax(1).detach().cpu().numpy().tolist()\n",
        "            polarity_labels += polarity_label.detach().cpu().numpy().tolist()\n",
        "            \n",
        "            tense_preds += tense_logit.argmax(1).detach().cpu().numpy().tolist()\n",
        "            tense_labels += tense_label.detach().cpu().numpy().tolist()\n",
        "            \n",
        "            certainty_preds += certainty_logit.argmax(1).detach().cpu().numpy().tolist()\n",
        "            certainty_labels += certainty_label.detach().cpu().numpy().tolist()\n",
        "    \n",
        "    type_f1 = f1_score(type_labels, type_preds, average='weighted')\n",
        "    polarity_f1 = f1_score(polarity_labels, polarity_preds, average='weighted')\n",
        "    tense_f1 = f1_score(tense_labels, tense_preds, average='weighted')\n",
        "    certainty_f1 = f1_score(certainty_labels, certainty_preds, average='weighted')\n",
        "    \n",
        "    return np.mean(val_loss), type_f1, polarity_f1, tense_f1, certainty_f1"
      ],
      "metadata": {
        "id": "x4iQETbwtdBn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = BaseModel()\n",
        "model.eval()\n",
        "optimizer = torch.optim.Adam(params = model.parameters(), lr = CFG[\"LEARNING_RATE\"])\n",
        "scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', factor=0.5, patience=2,threshold_mode='abs',min_lr=1e-8, verbose=True)\n",
        "\n",
        "infer_model = train(model, optimizer, train_loader, val_loader, scheduler, device)"
      ],
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            "Epoch : [1] Train Loss : [0.81540] Val Loss : [0.62703] 유형 F1 : [0.73452] 극성 F1 : [0.93030] 시제 F1 : [0.41883] 확실성 F1 : [0.87274]\n"
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            "Epoch : [2] Train Loss : [0.31587] Val Loss : [0.46972] 유형 F1 : [0.77412] 극성 F1 : [0.94001] 시제 F1 : [0.69163] 확실성 F1 : [0.88032]\n"
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            "Epoch : [3] Train Loss : [0.16030] Val Loss : [0.43233] 유형 F1 : [0.78496] 극성 F1 : [0.94493] 시제 F1 : [0.71013] 확실성 F1 : [0.88243]\n"
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            "Epoch : [5] Train Loss : [0.06165] Val Loss : [0.44294] 유형 F1 : [0.79140] 극성 F1 : [0.94874] 시제 F1 : [0.70189] 확실성 F1 : [0.89115]\n"
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch : [6] Train Loss : [0.04456] Val Loss : [0.44963] 유형 F1 : [0.79607] 극성 F1 : [0.95016] 시제 F1 : [0.70750] 확실성 F1 : [0.88887]\n",
            "Epoch 00006: reducing learning rate of group 0 to 5.0000e-05.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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              "  0%|          | 0/52 [00:00<?, ?it/s]"
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          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch : [7] Train Loss : [0.03567] Val Loss : [0.45155] 유형 F1 : [0.79568] 극성 F1 : [0.95012] 시제 F1 : [0.69853] 확실성 F1 : [0.88993]\n"
          ]
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        {
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          "data": {
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          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch : [8] Train Loss : [0.03164] Val Loss : [0.45663] 유형 F1 : [0.79530] 극성 F1 : [0.95008] 시제 F1 : [0.70243] 확실성 F1 : [0.88927]\n"
          ]
        },
        {
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          "data": {
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              "  0%|          | 0/52 [00:00<?, ?it/s]"
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          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch : [9] Train Loss : [0.02834] Val Loss : [0.45999] 유형 F1 : [0.79551] 극성 F1 : [0.94987] 시제 F1 : [0.70443] 확실성 F1 : [0.89098]\n",
            "Epoch 00009: reducing learning rate of group 0 to 2.5000e-05.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/52 [00:00<?, ?it/s]"
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              "version_major": 2,
              "version_minor": 0,
              "model_id": "7b2664a112a14d1d91a33b1d26a62f3a"
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          "metadata": {}
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        {
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              "  0%|          | 0/13 [00:00<?, ?it/s]"
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              "version_minor": 0,
              "model_id": "4e5557b9bcd545f4bf8816070f3511bd"
            }
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          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch : [10] Train Loss : [0.02584] Val Loss : [0.46104] 유형 F1 : [0.79755] 극성 F1 : [0.95261] 시제 F1 : [0.70509] 확실성 F1 : [0.88960]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Inference"
      ],
      "metadata": {
        "id": "UJOnT6fP13l0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "test_dataset = CustomDataset(test_vec, None)\n",
        "test_loader = DataLoader(test_dataset, batch_size=CFG['BATCH_SIZE'], shuffle=False, num_workers=0)"
      ],
      "metadata": {
        "id": "n1hEQiqGthI1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def inference(model, test_loader, device):\n",
        "    model.to(device)\n",
        "    model.eval()\n",
        "    \n",
        "    type_preds, polarity_preds, tense_preds, certainty_preds = [], [], [], []\n",
        "    \n",
        "    with torch.no_grad():\n",
        "        for sentence in tqdm(test_loader):\n",
        "            sentence = sentence.to(device)\n",
        "            \n",
        "            type_logit, polarity_logit, tense_logit, certainty_logit = model(sentence)\n",
        "            \n",
        "            type_preds += type_logit.argmax(1).detach().cpu().numpy().tolist()\n",
        "            polarity_preds += polarity_logit.argmax(1).detach().cpu().numpy().tolist()\n",
        "            tense_preds += tense_logit.argmax(1).detach().cpu().numpy().tolist()\n",
        "            certainty_preds += certainty_logit.argmax(1).detach().cpu().numpy().tolist()\n",
        "            \n",
        "    return type_preds, polarity_preds, tense_preds, certainty_preds"
      ],
      "metadata": {
        "id": "5XRD6D_S15Yi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "type_preds, polarity_preds, tense_preds, certainty_preds = inference(model, test_loader, device)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
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            "e57340c40e974c87bc06e02b4fe38834",
            "7ab95522c94f4c04b183b6b273d6d303",
            "23c742d490a04659b6e53ec00fc063c8"
          ]
        },
        "id": "yPFJa51R1697",
        "outputId": "bba61b3c-b6e9-477f-d77c-781cd82e9184"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/28 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4a1948973731461b8ef5e9fdd5d6bbf4"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "type_preds = type_le.inverse_transform(type_preds)\n",
        "polarity_preds = polarity_le.inverse_transform(polarity_preds)\n",
        "tense_preds = tense_le.inverse_transform(tense_preds)\n",
        "certainty_preds = certainty_le.inverse_transform(certainty_preds)"
      ],
      "metadata": {
        "id": "17TUBfNV18VS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predictions = []\n",
        "for type_pred, polarity_pred, tense_pred, certainty_pred in zip(type_preds, polarity_preds, tense_preds, certainty_preds):\n",
        "    predictions.append(type_pred+'-'+polarity_pred+'-'+tense_pred+'-'+certainty_pred)"
      ],
      "metadata": {
        "id": "QV10nJ0O1-Xg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Submission"
      ],
      "metadata": {
        "id": "P6ZC-o422CY5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "submit = pd.read_csv('/content/drive/My Drive/sentence/sample_submission.csv')\n",
        "submit['label'] = predictions"
      ],
      "metadata": {
        "id": "sNfLBI4U2AUt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "submit.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "mRxb3rZR2J_S",
        "outputId": "3a482587-1f46-427b-ff87-f9446ab8533c"
      },
      "execution_count": null,
      "outputs": [
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              "          ID         label\n",
              "0  TEST_0000  사실형-긍정-과거-확실\n",
              "1  TEST_0001  사실형-긍정-현재-확실\n",
              "2  TEST_0002  사실형-긍정-과거-확실\n",
              "3  TEST_0003  사실형-긍정-과거-확실\n",
              "4  TEST_0004  사실형-긍정-과거-확실"
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "            '<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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        "submit.to_csv(\"baseline_resampling_200.csv\", index=False)"
      ],
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          "height": 424
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        "id": "6Yfih-X82_-R",
        "outputId": "7ce81002-2fbb-4d0e-c6f4-ce039c1d1e2b"
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              "             ID         label\n",
              "0     TEST_0000  사실형-긍정-과거-확실\n",
              "1     TEST_0001  사실형-긍정-현재-확실\n",
              "2     TEST_0002  사실형-긍정-과거-확실\n",
              "3     TEST_0003  사실형-긍정-과거-확실\n",
              "4     TEST_0004  사실형-긍정-과거-확실\n",
              "...         ...           ...\n",
              "7085  TEST_7085  사실형-긍정-현재-확실\n",
              "7086  TEST_7086  추론형-긍정-현재-확실\n",
              "7087  TEST_7087  사실형-긍정-현재-확실\n",
              "7088  TEST_7088  사실형-긍정-미래-확실\n",
              "7089  TEST_7089  사실형-긍정-과거-확실\n",
              "\n",
              "[7090 rows x 2 columns]"
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              "      <th>2</th>\n",
              "      <td>TEST_0002</td>\n",
              "      <td>사실형-긍정-과거-확실</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>TEST_0003</td>\n",
              "      <td>사실형-긍정-과거-확실</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>TEST_0004</td>\n",
              "      <td>사실형-긍정-과거-확실</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7085</th>\n",
              "      <td>TEST_7085</td>\n",
              "      <td>사실형-긍정-현재-확실</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7086</th>\n",
              "      <td>TEST_7086</td>\n",
              "      <td>추론형-긍정-현재-확실</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7087</th>\n",
              "      <td>TEST_7087</td>\n",
              "      <td>사실형-긍정-현재-확실</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7088</th>\n",
              "      <td>TEST_7088</td>\n",
              "      <td>사실형-긍정-미래-확실</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7089</th>\n",
              "      <td>TEST_7089</td>\n",
              "      <td>사실형-긍정-과거-확실</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>7090 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-810c79b9-0a2f-4603-9c3a-164152de95bd')\"\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",
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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-810c79b9-0a2f-4603-9c3a-164152de95bd 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-810c79b9-0a2f-4603-9c3a-164152de95bd');\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": 64
        }
      ]
    }
  ]
}