Petition_classfication.ipynb

{
  "cells": [
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "문제 : https://dacon.io/competitions/open/235597/overview/description"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "sKt9btbF0_7J"
      },
      "source": [
        "# import"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ssQMeTd2o9gH",
        "outputId": "31fd142e-23d7-4ba4-c2ed-29fa056f1a0b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mounted at /content/gdrive/\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive \n",
        "\n",
        "drive.mount('/content/gdrive/')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kcaHL-nLqs9W",
        "outputId": "dea24681-f55d-4606-95e9-c24fdd365dac"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting mxnet\n",
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            "\u001b[?25hCollecting graphviz<0.9.0,>=0.8.1\n",
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            "Installing collected packages: graphviz, mxnet\n",
            "  Attempting uninstall: graphviz\n",
            "    Found existing installation: graphviz 0.10.1\n",
            "    Uninstalling graphviz-0.10.1:\n",
            "      Successfully uninstalled graphviz-0.10.1\n",
            "Successfully installed graphviz-0.8.4 mxnet-1.9.1\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting gluonnlp\n",
            "  Downloading gluonnlp-0.10.0.tar.gz (344 kB)\n",
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            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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            "Building wheels for collected packages: gluonnlp\n",
            "  Building wheel for gluonnlp (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for gluonnlp: filename=gluonnlp-0.10.0-cp38-cp38-linux_x86_64.whl size=688999 sha256=ae8eade7e722561fc424aa64e03ad5d875c6715b97f5470ac0b4ef135e6072c1\n",
            "  Stored in directory: /root/.cache/pip/wheels/b6/93/9d/2237550c409eb3ed725d6302b7897ddd9a037b40cef66dcd9c\n",
            "Successfully built gluonnlp\n",
            "Installing collected packages: gluonnlp\n",
            "Successfully installed gluonnlp-0.10.0\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting sentencepiece\n",
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            "\u001b[?25hInstalling collected packages: sentencepiece\n",
            "Successfully installed sentencepiece-0.1.97\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting transformers==3.0.2\n",
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            "Collecting tokenizers==0.8.1.rc1\n",
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            "Building wheels for collected packages: sacremoses\n",
            "  Building wheel for sacremoses (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for sacremoses: filename=sacremoses-0.0.53-py3-none-any.whl size=895260 sha256=7caa966620e4deea4ce4be448582c96bd8a8baaff4469745154d8758546f82d7\n",
            "  Stored in directory: /root/.cache/pip/wheels/82/ab/9b/c15899bf659ba74f623ac776e861cf2eb8608c1825ddec66a4\n",
            "Successfully built sacremoses\n",
            "Installing collected packages: tokenizers, sacremoses, transformers\n",
            "Successfully installed sacremoses-0.0.53 tokenizers-0.8.1rc1 transformers-3.0.2\n"
          ]
        }
      ],
      "source": [
        "!pip install mxnet\n",
        "!pip install gluonnlp\n",
        "!pip install sentencepiece\n",
        "!pip install transformers==3.0.2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iRavG1chrNw0",
        "outputId": "161f566d-9b9b-4fb5-f1ea-2d3e24fd56f3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting git+https://****@github.com/SKTBrain/KoBERT.git@master\n",
            "  Cloning https://****@github.com/SKTBrain/KoBERT.git (to revision master) to /tmp/pip-req-build-c5k_jkdh\n",
            "  Running command git clone --filter=blob:none --quiet 'https://****@github.com/SKTBrain/KoBERT.git' /tmp/pip-req-build-c5k_jkdh\n",
            "  Resolved https://****@github.com/SKTBrain/KoBERT.git to commit 47a69af87928fc24e20f571fe10c3cc9dd9af9a3\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting boto3<=1.15.18\n",
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            "Collecting mxnet<=1.7.0.post2,>=1.4.0\n",
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            "\u001b[?25hCollecting onnxruntime<=1.8.0,==1.8.0\n",
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            "Collecting tokenizers<0.11,>=0.10.1\n",
            "  Downloading tokenizers-0.10.3-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (3.3 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.3/3.3 MB\u001b[0m \u001b[31m103.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: pyyaml in /usr/local/lib/python3.8/dist-packages (from transformers<=4.8.1,>=4.8.1->kobert==0.2.3) (6.0)\n",
            "Requirement already satisfied: sacremoses in /usr/local/lib/python3.8/dist-packages (from transformers<=4.8.1,>=4.8.1->kobert==0.2.3) (0.0.53)\n",
            "Requirement already satisfied: python-dateutil<3.0.0,>=2.1 in /usr/local/lib/python3.8/dist-packages (from botocore<1.19.0,>=1.18.18->boto3<=1.15.18->kobert==0.2.3) (2.8.2)\n",
            "Requirement already satisfied: urllib3<1.26,>=1.20 in /usr/local/lib/python3.8/dist-packages (from botocore<1.19.0,>=1.18.18->boto3<=1.15.18->kobert==0.2.3) (1.24.3)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.8/dist-packages (from packaging->gluonnlp<=0.10.0,>=0.6.0->kobert==0.2.3) (3.0.9)\n",
            "Requirement already satisfied: chardet<5,>=3.0.2 in /usr/local/lib/python3.8/dist-packages (from requests<3,>=2.20.0->mxnet<=1.7.0.post2,>=1.4.0->kobert==0.2.3) (4.0.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.8/dist-packages (from requests<3,>=2.20.0->mxnet<=1.7.0.post2,>=1.4.0->kobert==0.2.3) (2022.12.7)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.8/dist-packages (from requests<3,>=2.20.0->mxnet<=1.7.0.post2,>=1.4.0->kobert==0.2.3) (2.10)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.8/dist-packages (from sacremoses->transformers<=4.8.1,>=4.8.1->kobert==0.2.3) (1.2.0)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.8/dist-packages (from sacremoses->transformers<=4.8.1,>=4.8.1->kobert==0.2.3) (7.1.2)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.8/dist-packages (from sacremoses->transformers<=4.8.1,>=4.8.1->kobert==0.2.3) (1.15.0)\n",
            "Building wheels for collected packages: kobert\n",
            "  Building wheel for kobert (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for kobert: filename=kobert-0.2.3-py3-none-any.whl size=15708 sha256=acfb307b40cbb17fd2595591d2b190be0bfd38361372093cec9eb9f067d71e53\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-0kxnowar/wheels/bf/5f/74/81bf3a1332130eb6629ecf58876a8746b77021e7d7b0638e91\n",
            "Successfully built kobert\n",
            "Installing collected packages: tokenizers, sentencepiece, torch, onnxruntime, jmespath, mxnet, huggingface-hub, botocore, transformers, s3transfer, boto3, kobert\n",
            "  Attempting uninstall: tokenizers\n",
            "    Found existing installation: tokenizers 0.8.1rc1\n",
            "    Uninstalling tokenizers-0.8.1rc1:\n",
            "      Successfully uninstalled tokenizers-0.8.1rc1\n",
            "  Attempting uninstall: sentencepiece\n",
            "    Found existing installation: sentencepiece 0.1.97\n",
            "    Uninstalling sentencepiece-0.1.97:\n",
            "      Successfully uninstalled sentencepiece-0.1.97\n",
            "  Attempting uninstall: torch\n",
            "    Found existing installation: torch 1.13.1+cu116\n",
            "    Uninstalling torch-1.13.1+cu116:\n",
            "      Successfully uninstalled torch-1.13.1+cu116\n",
            "  Attempting uninstall: mxnet\n",
            "    Found existing installation: mxnet 1.9.1\n",
            "    Uninstalling mxnet-1.9.1:\n",
            "      Successfully uninstalled mxnet-1.9.1\n",
            "  Attempting uninstall: transformers\n",
            "    Found existing installation: transformers 3.0.2\n",
            "    Uninstalling transformers-3.0.2:\n",
            "      Successfully uninstalled transformers-3.0.2\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "torchvision 0.14.1+cu116 requires torch==1.13.1, but you have torch 1.10.1 which is incompatible.\n",
            "torchtext 0.14.1 requires torch==1.13.1, but you have torch 1.10.1 which is incompatible.\n",
            "torchaudio 0.13.1+cu116 requires torch==1.13.1, but you have torch 1.10.1 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed boto3-1.15.18 botocore-1.18.18 huggingface-hub-0.0.12 jmespath-0.10.0 kobert-0.2.3 mxnet-1.7.0.post2 onnxruntime-1.8.0 s3transfer-0.3.7 sentencepiece-0.1.96 tokenizers-0.10.3 torch-1.10.1 transformers-4.8.1\n"
          ]
        }
      ],
      "source": [
        "!pip install git+https://git@github.com/SKTBrain/KoBERT.git@master\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TNzw2YtjpCAX"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import os"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "svssfn2eqsq3"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "from torch import nn\n",
        "import torch.nn.functional as F\n",
        "import torch.optim as optim\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "import gluonnlp as nlp\n",
        "import numpy as np\n",
        "from tqdm import tqdm, tqdm_notebook\n",
        "from kobert.utils import get_tokenizer\n",
        "from kobert.pytorch_kobert import get_pytorch_kobert_model\n",
        "from transformers import AdamW\n",
        "from transformers.optimization import get_cosine_schedule_with_warmup"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gCsc_du71EPW"
      },
      "source": [
        "# main"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "B9kBAWggpfgo"
      },
      "outputs": [],
      "source": [
        "train = pd.read_csv('/content/gdrive/MyDrive/personal/bluehouse_classfication/train.csv', encoding = 'utf-8')\n",
        "test = pd.read_csv('/content/gdrive/MyDrive/personal/bluehouse_classfication/test.csv', encoding = 'utf-8')\n",
        "sample_submission = pd.read_csv('/content/gdrive/MyDrive/personal/bluehouse_classfication/sample_submission.csv', encoding = 'utf-8')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "KS2ulOF1wyRi",
        "outputId": "92b4ac08-420f-41de-9fc7-52bedcac526d"
      },
      "outputs": [
        {
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              "      <td>신혼부부위한 주택정책 보다 보육시설 늘려주세요.. 국민세금으로 일부를 위한 정책펴지...</td>\n",
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            "text/plain": [
              "       index  category                                               data\n",
              "0          0         2  신혼부부위한 주택정책 보다 보육시설 늘려주세요.. 국민세금으로 일부를 위한 정책펴지...\n",
              "1          1         0  학교이름에 '남자'도 붙여주세요. 울산여자중학교에 재학중인 학생입니다 최근 양성평등...\n",
              "2          2         1  빙상연맹, 대한축구협회등 각종 체육협회의 비리를 철저하게 밝혀주세요.. 최근 동계올...\n",
              "3          3         1  티비 12세,15세 관람가도 연령확인 의무화 하자.. 제기 에전에 티비를 보다가 잠...\n",
              "4          4         1  무더운 여름철엔 남성들도 시원한 자율복장을 해야. 무더운 여름철에는 남성들도 노넥타...\n",
              "...      ...       ...                                                ...\n",
              "39995  39995         2  시간유연근무제. 저는 국립대에서 일하고 있는 비정규직 근로자입니다.\\n동시에  두 ...\n",
              "39996  39996         0  소년법을 폐지해 주시고 부산 여중생 가해자 학생들의 강력한 처벌을 요구합니다. 소년...\n",
              "39997  39997         2  무서운데 지켜야 할게 있어요 도와주세요. 안녕하세요 . 한부모엄마 입니다.\\n양육비...\n",
              "39998  39998         2  교복에  고정식 이름표를 달게 하는 것을 금지해 주세요.. 교복에 이름표를 박아놓아...\n",
              "39999  39999         0  합의성관계 가능 나이연령을 올리십시오. 제가 형법공부를하다가 문제를풀고있는데\\n금지...\n",
              "\n",
              "[40000 rows x 3 columns]"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_4v-78szsDYj",
        "outputId": "d1675c79-70b5-4b6b-c987-7063f44cdb7e"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<ipython-input-8-f7151069fc3d>:2: FutureWarning: The default value of regex will change from True to False in a future version.\n",
            "  train['data'] = train['data'].str.replace(\"[^ㄱ-ㅎㅏ-ㅣ가-힣 ]\",\"\")\n",
            "<ipython-input-8-f7151069fc3d>:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  train['data'] = train['data'].str.replace(\"[^ㄱ-ㅎㅏ-ㅣ가-힣 ]\",\"\")\n",
            "<ipython-input-8-f7151069fc3d>:3: FutureWarning: The default value of regex will change from True to False in a future version.\n",
            "  test['data'] = test['data'].str.replace(\"[^ㄱ-ㅎㅏ-ㅣ가-힣 ]\",\"\")\n"
          ]
        }
      ],
      "source": [
        "train = train.dropna(how = 'any')\n",
        "train['data'] = train['data'].str.replace(\"[^ㄱ-ㅎㅏ-ㅣ가-힣 ]\",\"\")\n",
        "test['data'] = test['data'].str.replace(\"[^ㄱ-ㅎㅏ-ㅣ가-힣 ]\",\"\")\n",
        "stopwords = ['의','가','이','은','들','는','좀','잘','걍','과','도','를','으로','자','에','와','한','하다','을']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
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          "height": 424
        },
        "id": "Q5XXaE37t1ZV",
        "outputId": "ac21ef80-2f22-4a2d-8769-af5228cdedcc"
      },
      "outputs": [
        {
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>신혼부부위한 주택정책 보다 보육시설 늘려주세요 국민세금으로 일부를 위한 정책펴지 마...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>학교이름에 남자도 붙여주세요 울산여자중학교에 재학중인 학생입니다 최근 양성평등 글짓...</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>빙상연맹 대한축구협회등 각종 체육협회의 비리를 철저하게 밝혀주세요 최근 동계올림픽에...</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>티비 세세 관람가도 연령확인 의무화 하자 제기 에전에 티비를 보다가 잠시 딴일이 생...</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>무더운 여름철엔 남성들도 시원한 자율복장을 해야 무더운 여름철에는 남성들도 노넥타이...</td>\n",
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              "    <tr>\n",
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              "    <tr>\n",
              "      <th>39995</th>\n",
              "      <td>39995</td>\n",
              "      <td>2</td>\n",
              "      <td>시간유연근무제 저는 국립대에서 일하고 있는 비정규직 근로자입니다동시에  두 자녀의 ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39996</th>\n",
              "      <td>39996</td>\n",
              "      <td>0</td>\n",
              "      <td>소년법을 폐지해 주시고 부산 여중생 가해자 학생들의 강력한 처벌을 요구합니다 소년법...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39997</th>\n",
              "      <td>39997</td>\n",
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              "      <td>무서운데 지켜야 할게 있어요 도와주세요 안녕하세요  한부모엄마 입니다양육비를 받고 ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39998</th>\n",
              "      <td>39998</td>\n",
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              "      <th>39999</th>\n",
              "      <td>39999</td>\n",
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            ],
            "text/plain": [
              "       index  category                                               data\n",
              "0          0         2  신혼부부위한 주택정책 보다 보육시설 늘려주세요 국민세금으로 일부를 위한 정책펴지 마...\n",
              "1          1         0  학교이름에 남자도 붙여주세요 울산여자중학교에 재학중인 학생입니다 최근 양성평등 글짓...\n",
              "2          2         1  빙상연맹 대한축구협회등 각종 체육협회의 비리를 철저하게 밝혀주세요 최근 동계올림픽에...\n",
              "3          3         1  티비 세세 관람가도 연령확인 의무화 하자 제기 에전에 티비를 보다가 잠시 딴일이 생...\n",
              "4          4         1  무더운 여름철엔 남성들도 시원한 자율복장을 해야 무더운 여름철에는 남성들도 노넥타이...\n",
              "...      ...       ...                                                ...\n",
              "39995  39995         2  시간유연근무제 저는 국립대에서 일하고 있는 비정규직 근로자입니다동시에  두 자녀의 ...\n",
              "39996  39996         0  소년법을 폐지해 주시고 부산 여중생 가해자 학생들의 강력한 처벌을 요구합니다 소년법...\n",
              "39997  39997         2  무서운데 지켜야 할게 있어요 도와주세요 안녕하세요  한부모엄마 입니다양육비를 받고 ...\n",
              "39998  39998         2  교복에  고정식 이름표를 달게 하는 것을 금지해 주세요 교복에 이름표를 박아놓아 탈...\n",
              "39999  39999         0  합의성관계 가능 나이연령을 올리십시오 제가 형법공부를하다가 문제를풀고있는데금지착오부...\n",
              "\n",
              "[39992 rows x 3 columns]"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "q6BsB566rqAY"
      },
      "outputs": [],
      "source": [
        "device = torch.device(\"cuda:0\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RXULhjjjrtKu",
        "outputId": "e65f59b7-64c8-4610-8604-3c3bfcf95a34"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/content/.cache/kobert_v1.zip[██████████████████████████████████████████████████]\n",
            "/content/.cache/kobert_news_wiki_ko_cased-1087f8699e.spiece[██████████████████████████████████████████████████]\n"
          ]
        }
      ],
      "source": [
        "bertmodel, vocab = get_pytorch_kobert_model()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MOMAjIayr1nC"
      },
      "outputs": [],
      "source": [
        "class BERTDataset(Dataset):\n",
        "  # dataframe 형태로 넣어주기 위해 sent_idx 를 sent_col로 바꿈.\n",
        "    def __init__(self, dataset, sent_col, label_col, bert_tokenizer, max_len,\n",
        "                 pad, pair):\n",
        "        transform = nlp.data.BERTSentenceTransform(\n",
        "            bert_tokenizer, max_seq_length=max_len, pad=pad, pair=pair)\n",
        "\n",
        "        self.sentences = [transform([i[sent_col]]) for idx, i in dataset.iterrows()]\n",
        "        self.labels = [np.int32(i[label_col]) for idx, i in dataset.iterrows()]\n",
        "\n",
        "    def __getitem__(self, i):\n",
        "        return (self.sentences[i] + (self.labels[i], ))\n",
        "\n",
        "    def __len__(self):\n",
        "        return (len(self.labels))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "M6PmB87Zr8UO"
      },
      "outputs": [],
      "source": [
        "max_len = 64\n",
        "batch_size = 64\n",
        "warmup_ratio = 0.1\n",
        "num_epochs = 10\n",
        "max_grad_norm = 1\n",
        "log_interval = 200\n",
        "learning_rate =  5e-5"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zD0lgifizCv8"
      },
      "outputs": [],
      "source": [
        "train_dataset = train[:3*(len(train)//4)]\n",
        "valid_dataset = train[3*(len(train)//4):]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h-h-fnZZr_GF",
        "outputId": "86399cf4-bdce-4b45-d979-3ec6de6444af"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "using cached model. /content/.cache/kobert_news_wiki_ko_cased-1087f8699e.spiece\n"
          ]
        }
      ],
      "source": [
        "#토큰화\n",
        "tokenizer = get_tokenizer()\n",
        "tok = nlp.data.BERTSPTokenizer(tokenizer, vocab, lower=False)\n",
        "#BERTDataset 클래스 이용, TensorDataset으로 만들어주기\n",
        "data_train = BERTDataset(train_dataset, \"data\", 'category', tok, max_len, True, False)\n",
        "data_valid = BERTDataset(train_dataset, \"data\", 'category', tok, max_len, True, False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "L9YJzsC1uSoD"
      },
      "outputs": [],
      "source": [
        "# data loader\n",
        "train_dataloader = torch.utils.data.DataLoader(data_train, batch_size=batch_size, num_workers=2)\n",
        "valid_dataloader = torch.utils.data.DataLoader(data_valid, batch_size=batch_size, num_workers=2)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EfyCnMJ80WVc",
        "outputId": "487e384d-f6b4-42b1-ec08-21917a900b3f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([2, 0, 1])"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train_dataset['category'].unique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "L9KyvR9s0Mc3"
      },
      "outputs": [],
      "source": [
        "class BERTClassifier(nn.Module):\n",
        "    def __init__(self,\n",
        "                 bert,\n",
        "                 hidden_size = 768,\n",
        "                 num_classes=3, \n",
        "                 dr_rate=None,\n",
        "                 params=None):\n",
        "        super(BERTClassifier, self).__init__()\n",
        "        self.bert = bert\n",
        "        self.dr_rate = dr_rate\n",
        "                 \n",
        "        self.classifier = nn.Linear(hidden_size , num_classes)\n",
        "        if dr_rate:\n",
        "            self.dropout = nn.Dropout(p=dr_rate)\n",
        "    \n",
        "    def gen_attention_mask(self, token_ids, valid_length):\n",
        "        attention_mask = torch.zeros_like(token_ids)\n",
        "        for i, v in enumerate(valid_length):\n",
        "            attention_mask[i][:v] = 1\n",
        "        return attention_mask.float()\n",
        "\n",
        "    def forward(self, token_ids, valid_length, segment_ids):\n",
        "        attention_mask = self.gen_attention_mask(token_ids, valid_length)\n",
        "        \n",
        "        _, pooler = self.bert(input_ids = token_ids, token_type_ids = segment_ids.long(), attention_mask = attention_mask.float().to(token_ids.device))\n",
        "        if self.dr_rate:\n",
        "            out = self.dropout(pooler)\n",
        "        return self.classifier(out)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IwgjyVts0bJx"
      },
      "outputs": [],
      "source": [
        "model = BERTClassifier(bertmodel, dr_rate=0.5).to(device) #gpu\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kIGl2EUq0kiR"
      },
      "outputs": [],
      "source": [
        "# Prepare optimizer and schedule (linear warmup and decay)\n",
        "no_decay = ['bias', 'LayerNorm.weight']\n",
        "optimizer_grouped_parameters = [\n",
        "    {'params': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)], 'weight_decay': 0.01},\n",
        "    {'params': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], 'weight_decay': 0.0}\n",
        "]\n",
        "\n",
        "optimizer = AdamW(optimizer_grouped_parameters, lr=learning_rate)\n",
        "loss_fn = nn.CrossEntropyLoss()\n",
        "\n",
        "t_total = len(train_dataloader) * num_epochs\n",
        "warmup_step = int(t_total * warmup_ratio)\n",
        "\n",
        "scheduler = get_cosine_schedule_with_warmup(optimizer, num_warmup_steps=warmup_step, num_training_steps=t_total)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rz4QhqeE0mnF"
      },
      "outputs": [],
      "source": [
        "def calc_accuracy(X,Y):\n",
        "    max_vals, max_indices = torch.max(X, 1)\n",
        "    train_acc = (max_indices == Y).sum().data.cpu().numpy()/max_indices.size()[0]\n",
        "    return train_acc"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
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        "id": "4uxTpoZC0sQR",
        "outputId": "4882dc52-ee85-4315-cc45-2f100ec157d3"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<ipython-input-22-9af2ab371229>:5: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
            "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n",
            "  for batch_id, (token_ids, valid_length, segment_ids, label) in enumerate(tqdm_notebook(train_dataloader)):\n"
          ]
        },
        {
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "epoch 1 batch id 1 loss 1.1091963052749634 train acc 0.421875\n",
            "epoch 1 batch id 201 loss 0.37522608041763306 train acc 0.6454446517412935\n",
            "epoch 1 batch id 401 loss 0.3167113959789276 train acc 0.7525327306733167\n",
            "epoch 1 train acc 0.7693166818966393\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<ipython-input-22-9af2ab371229>:22: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
            "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n",
            "  for batch_id, (token_ids, valid_length, segment_ids, label) in enumerate(tqdm_notebook(valid_dataloader)):\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "8f72442371ac478882e6771a576f36a0",
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "epoch 1 validation acc 0.8806588866890039\n"
          ]
        },
        {
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              "model_id": "8e0d17fe749e46e8a9a104e8d18283ba",
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "epoch 2 batch id 1 loss 0.4604390263557434 train acc 0.765625\n",
            "epoch 2 batch id 201 loss 0.3116484582424164 train acc 0.8696361940298507\n",
            "epoch 2 batch id 401 loss 0.23824989795684814 train acc 0.8765975685785536\n",
            "epoch 2 train acc 0.8798085465529495\n"
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              "model_id": "95423138923446aa85e0cdc4334eb679",
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "epoch 2 validation acc 0.9190907706366129\n"
          ]
        },
        {
          "data": {
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              "model_id": "3fa83be8ddbe4c00a43d3a8a6583a88c",
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "epoch 3 batch id 1 loss 0.317147433757782 train acc 0.890625\n",
            "epoch 3 batch id 201 loss 0.35734543204307556 train acc 0.9032182835820896\n",
            "epoch 3 batch id 401 loss 0.19695425033569336 train acc 0.9090554862842892\n",
            "epoch 3 train acc 0.9116122195146715\n"
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "epoch 3 validation acc 0.939814638541984\n"
          ]
        },
        {
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              "model_id": "c588bc3bb33148d79bf955935b897685",
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "epoch 4 batch id 1 loss 0.2696664035320282 train acc 0.921875\n",
            "epoch 4 batch id 201 loss 0.22002992033958435 train acc 0.9239738805970149\n",
            "epoch 4 batch id 401 loss 0.12623177468776703 train acc 0.9310707605985037\n",
            "epoch 4 train acc 0.9326993857244391\n"
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "epoch 4 validation acc 0.9514417707381461\n"
          ]
        },
        {
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "epoch 5 batch id 1 loss 0.2053474336862564 train acc 0.9375\n",
            "epoch 5 batch id 201 loss 0.15110564231872559 train acc 0.943407960199005\n",
            "epoch 5 batch id 401 loss 0.12807665765285492 train acc 0.9484102244389028\n",
            "epoch 5 train acc 0.949858488171388\n"
          ]
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          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "epoch 5 validation acc 0.9625358538937964\n"
          ]
        },
        {
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          "metadata": {},
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        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "epoch 6 batch id 1 loss 0.18893396854400635 train acc 0.9375\n",
            "epoch 6 batch id 201 loss 0.07481198012828827 train acc 0.9524253731343284\n",
            "epoch 6 batch id 401 loss 0.10383739322423935 train acc 0.9587749376558603\n",
            "epoch 6 train acc 0.9599531043760788\n"
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          "text": [
            "epoch 6 validation acc 0.9769773581074221\n"
          ]
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          "text": [
            "epoch 7 batch id 1 loss 0.10634562373161316 train acc 0.984375\n",
            "epoch 7 batch id 201 loss 0.1468926966190338 train acc 0.9671175373134329\n",
            "epoch 7 batch id 401 loss 0.22779980301856995 train acc 0.9711268703241895\n",
            "epoch 7 train acc 0.9720133389176566\n"
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        {
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          "text": [
            "epoch 7 validation acc 0.9825077419027313\n"
          ]
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        {
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          "output_type": "stream",
          "text": [
            "epoch 8 batch id 1 loss 0.05148899927735329 train acc 0.984375\n",
            "epoch 8 batch id 201 loss 0.0955594852566719 train acc 0.976445895522388\n",
            "epoch 8 batch id 401 loss 0.08783096075057983 train acc 0.9796212593516209\n",
            "epoch 8 train acc 0.9803422301756524\n"
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          "output_type": "stream",
          "text": [
            "epoch 8 validation acc 0.988804383693776\n"
          ]
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              "  0%|          | 0/469 [00:00<?, ?it/s]"
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          "metadata": {},
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        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "epoch 9 batch id 1 loss 0.025304295122623444 train acc 1.0\n",
            "epoch 9 batch id 201 loss 0.03249470517039299 train acc 0.9846859452736318\n",
            "epoch 9 batch id 401 loss 0.0748484805226326 train acc 0.9861284289276808\n",
            "epoch 9 train acc 0.9867055030967611\n"
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          "metadata": {},
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        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "epoch 9 validation acc 0.9910539775611737\n"
          ]
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        {
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              "  0%|          | 0/469 [00:00<?, ?it/s]"
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          "metadata": {},
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        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "epoch 10 batch id 1 loss 0.016411853954195976 train acc 1.0\n",
            "epoch 10 batch id 201 loss 0.01920325681567192 train acc 0.9875621890547264\n",
            "epoch 10 batch id 401 loss 0.05316860228776932 train acc 0.9886611596009975\n",
            "epoch 10 train acc 0.9890217280942227\n"
          ]
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              "  0%|          | 0/469 [00:00<?, ?it/s]"
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          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "epoch 10 validation acc 0.9923866001624531\n"
          ]
        }
      ],
      "source": [
        "for e in range(num_epochs):\n",
        "    train_acc = 0.0\n",
        "    valid_acc = 0.0\n",
        "    model.train()\n",
        "    for batch_id, (token_ids, valid_length, segment_ids, label) in enumerate(tqdm_notebook(train_dataloader)):\n",
        "        optimizer.zero_grad()\n",
        "        token_ids = token_ids.long().to(device)\n",
        "        segment_ids = segment_ids.long().to(device)\n",
        "        valid_length= valid_length\n",
        "        label = label.long().to(device)\n",
        "        out = model(token_ids, valid_length, segment_ids)\n",
        "        loss = loss_fn(out, label)\n",
        "        loss.backward()\n",
        "        torch.nn.utils.clip_grad_norm_(model.parameters(), max_grad_norm)\n",
        "        optimizer.step()\n",
        "        scheduler.step()  # Update learning rate schedule\n",
        "        train_acc += calc_accuracy(out, label)\n",
        "        if batch_id % log_interval == 0:\n",
        "            print(\"epoch {} batch id {} loss {} train acc {}\".format(e+1, batch_id+1, loss.data.cpu().numpy(), train_acc / (batch_id+1)))\n",
        "    print(\"epoch {} train acc {}\".format(e+1, train_acc / (batch_id+1)))\n",
        "    model.eval()\n",
        "    for batch_id, (token_ids, valid_length, segment_ids, label) in enumerate(tqdm_notebook(valid_dataloader)):\n",
        "        token_ids = token_ids.long().to(device)\n",
        "        segment_ids = segment_ids.long().to(device)\n",
        "        valid_length= valid_length\n",
        "        label = label.long().to(device)\n",
        "        out = model(token_ids, valid_length, segment_ids)\n",
        "        valid_acc += calc_accuracy(out, label)\n",
        "    print(\"epoch {} validation acc {}\".format(e+1, valid_acc / (batch_id+1)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "S7QR-FgW0uaj"
      },
      "outputs": [],
      "source": [
        "class BERTDataset_test(Dataset):\n",
        "  # dataframe 형태로 넣어주기 위해 sent_idx 를 sent_col로 바꿈.\n",
        "    def __init__(self, dataset, sent_col,  bert_tokenizer, max_len,\n",
        "                 pad, pair):\n",
        "        transform = nlp.data.BERTSentenceTransform(\n",
        "            bert_tokenizer, max_seq_length=max_len, pad=pad, pair=pair)\n",
        "        self.len = len(dataset)\n",
        "        self.sentences = [transform([i[sent_col]]) for idx, i in dataset.iterrows()]\n",
        "\n",
        "    def __getitem__(self, i):\n",
        "        return self.sentences[i]\n",
        "\n",
        "    def __len__(self):\n",
        "        return self.len"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0BqgfW9R0v53"
      },
      "outputs": [],
      "source": [
        "data_test = BERTDataset_test(test, 'data', tok, max_len, True, False)\n",
        "test_dataloader = torch.utils.data.DataLoader(data_test, batch_size=1, num_workers=2)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 123,
          "referenced_widgets": [
            "c3643f300c2048469a32ad2fa823f208",
            "bcf4a446273844deb20fa21f81cf3a7f",
            "398506faece345dea10fcbc1d1649b70",
            "2b5187a766fd40b9acc1f389df9434a7",
            "587cd6f84d4d4d38975f15505cdef1f2",
            "3998054dcfab40ab83382f61cfd2ef09",
            "695f34720e6e49b4902c2a89b8a7d201",
            "5baee9659cc04918ab6b7de8f13376a4",
            "8e5fb170488f4dbe817a37003889e222",
            "95fe8c00232d48f0926fcf252ef0377e",
            "7cdd695aaeb44b5cb8cf8e283aa908d2"
          ]
        },
        "id": "LDV-eTz_0zNn",
        "outputId": "208785b3-3223-4944-e97b-7ee6ea93184f"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<ipython-input-25-5573a5b0d0ae>:2: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
            "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n",
            "  for batch_id, (token_ids, valid_length, segment_ids) in enumerate(tqdm_notebook(test_dataloader)):\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c3643f300c2048469a32ad2fa823f208",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/5000 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "output = []\n",
        "for batch_id, (token_ids, valid_length, segment_ids) in enumerate(tqdm_notebook(test_dataloader)): \n",
        "  token_ids = token_ids.long().to(device) \n",
        "  segment_ids = segment_ids.long().to(device) \n",
        "  valid_length= valid_length \n",
        "  out = model(token_ids, valid_length, segment_ids)\n",
        "  prediction = out.cpu().detach().numpy().argmax()\n",
        "  output.append(prediction)\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eRSfrXjf01nP"
      },
      "outputs": [],
      "source": [
        "submit = pd.read_csv(\"/content/gdrive/MyDrive/personal/bluehouse_classfication/sample_submission.csv\", index_col=False)\n",
        "submit['topic_idx'] = output\n",
        "submit.to_csv(\"/content/gdrive/MyDrive/personal/bluehouse_classfication/submission_10epoch.csv\", index=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "JOb3ZQum0-kf",
        "outputId": "f8445791-6a68-4a13-b565-654eeeb53854"
      },
      "outputs": [
        {
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              "  <div id=\"df-39746f68-9031-49e0-991a-bb39f0f7eb1a\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>index</th>\n",
              "      <th>category</th>\n",
              "      <th>topic_idx</th>\n",
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              "      <th>4997</th>\n",
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              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5000 rows × 3 columns</p>\n",
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              "    .colab-df-container {\n",
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              "    .colab-df-convert {\n",
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              "\n",
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              "          const element = document.querySelector('#df-39746f68-9031-49e0-991a-bb39f0f7eb1a');\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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              "  "
            ],
            "text/plain": [
              "      index  category  topic_idx\n",
              "0         0         0          0\n",
              "1         1         0          2\n",
              "2         2         0          1\n",
              "3         3         0          0\n",
              "4         4         0          2\n",
              "...     ...       ...        ...\n",
              "4995   4995         0          0\n",
              "4996   4996         0          1\n",
              "4997   4997         0          1\n",
              "4998   4998         0          0\n",
              "4999   4999         0          2\n",
              "\n",
              "[5000 rows x 3 columns]"
            ]
          },
          "execution_count": 27,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "submit"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TXYfWYY3rlDa"
      },
      "outputs": [],
      "source": [
        "submit = submit[['index', 'topic_idx']]"
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    {
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      "execution_count": null,
      "metadata": {
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          "text": [
            "/usr/local/lib/python3.8/dist-packages/pandas/core/frame.py:5039: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  return super().rename(\n"
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        "submit.rename(columns={\"topic_idx\":\"category\"}, inplace=True)"
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    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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      "source": [
        "submit.to_csv(\"/content/gdrive/MyDrive/personal/bluehouse_classfication/submission_10epoch.csv\", index=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "submit5 = pd.read_csv(\"/content/gdrive/MyDrive/personal/bluehouse_classfication/submission_5epoch.csv\", index_col=False)\n"
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    {
      "cell_type": "code",
      "execution_count": null,
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        "submit5 = submit5[['index', 'topic_idx']]"
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      "execution_count": null,
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        "submit5.rename(columns={\"topic_idx\":\"category\"}, inplace=True)"
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      "execution_count": null,
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