spell_check_일부_증분_데이터로_대분류_예측하기.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/KorStats/classification-project/blob/main/spell_check_%EC%9D%BC%EB%B6%80_%EC%A6%9D%EB%B6%84_%EB%8D%B0%EC%9D%B4%ED%84%B0%EB%A1%9C_%EB%8C%80%EB%B6%84%EB%A5%98_%EC%98%88%EC%B8%A1%ED%95%98%EA%B8%B0.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-imvwIZmPVwp",
        "outputId": "8298337d-49b2-4991-ac6a-767980da20b5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Installing collected packages: graphviz, mxnet\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-cp37-cp37m-linux_x86_64.whl size=595734 sha256=e3d47356c080cc2f90e696bf77346804f1fe746e6913017bf1aa34c60a91ed7f\n",
            "  Stored in directory: /root/.cache/pip/wheels/be/b4/06/7f3fdfaf707e6b5e98b79c041e023acffbe395d78a527eae00\n",
            "Successfully built gluonnlp\n",
            "Installing collected packages: gluonnlp\n",
            "Successfully installed gluonnlp-0.10.0\n",
            "Collecting sentencepiece\n",
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            "Installing collected packages: tokenizers, sacremoses, transformers\n",
            "Successfully installed sacremoses-0.0.49 tokenizers-0.8.1rc1 transformers-3.0.2\n",
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            "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-ue6wz1rg\n",
            "  Running command git clone -q 'https://****@github.com/SKTBrain/KoBERT.git' /tmp/pip-req-build-ue6wz1rg\n",
            "Collecting boto3\n",
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            "Collecting pyyaml>=5.1\n",
            "  Downloading PyYAML-6.0-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (596 kB)\n",
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            "\u001b[?25hCollecting huggingface-hub<1.0,>=0.1.0\n",
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            "  Downloading tokenizers-0.11.6-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (6.5 MB)\n",
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            "\u001b[?25hCollecting s3transfer<0.6.0,>=0.5.0\n",
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            "  Downloading urllib3-1.25.11-py2.py3-none-any.whl (127 kB)\n",
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            "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=15674 sha256=03798b6b9ec7c809331a7e3b4352b78e1a6913ddbbde92927b99dd11913b05ed\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-x185brtf/wheels/d3/68/ca/334747dfb038313b49cf71f84832a33372f3470d9ddfd051c0\n",
            "Successfully built kobert\n",
            "Installing collected packages: urllib3, jmespath, pyyaml, botocore, tokenizers, s3transfer, huggingface-hub, transformers, onnxruntime, boto3, kobert\n",
            "  Attempting uninstall: urllib3\n",
            "    Found existing installation: urllib3 1.24.3\n",
            "    Uninstalling urllib3-1.24.3:\n",
            "      Successfully uninstalled urllib3-1.24.3\n",
            "  Attempting uninstall: pyyaml\n",
            "    Found existing installation: PyYAML 3.13\n",
            "    Uninstalling PyYAML-3.13:\n",
            "      Successfully uninstalled PyYAML-3.13\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: 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",
            "datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.\u001b[0m\n",
            "Successfully installed boto3-1.21.29 botocore-1.24.29 huggingface-hub-0.4.0 jmespath-1.0.0 kobert-0.2.3 onnxruntime-1.8.0 pyyaml-6.0 s3transfer-0.5.2 tokenizers-0.11.6 transformers-4.17.0 urllib3-1.25.11\n",
            "/content/.cache/kobert_v1.zip[██████████████████████████████████████████████████]\n",
            "/content/.cache/kobert_news_wiki_ko_cased-1087f8699e.spiece[██████████████████████████████████████████████████]\n"
          ]
        }
      ],
      "source": [
        "!pip install mxnet\n",
        "!pip install gluonnlp pandas tqdm\n",
        "!pip install sentencepiece\n",
        "!pip install transformers==3.0.2\n",
        "!pip install torch\n",
        "\n",
        "#깃허브에서 KoBERT 파일 로드\n",
        "!pip install git+https://git@github.com/SKTBrain/KoBERT.git@master\n",
        "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",
        "#kobert\n",
        "from kobert.utils import get_tokenizer\n",
        "from kobert.pytorch_kobert import get_pytorch_kobert_model\n",
        "\n",
        "#transformers\n",
        "from transformers import AdamW\n",
        "from transformers.optimization import get_cosine_schedule_with_warmup\n",
        "#GPU 사용\n",
        "device = torch.device(\"cuda:0\")\n",
        "#BERT 모델, Vocabulary 불러오기\n",
        "bertmodel, vocab = get_pytorch_kobert_model()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LjIKhkDtPbvA",
        "outputId": "e52016be-361c-4468-fa87-a6338a08fc9b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J5hcrkWdPbxW",
        "outputId": "f85c8da7-8bd7-48ab-9f11-565c24afec21"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive\n",
            " 산업분류자동화\t\t df_A_inc.csv   df_M_inc.csv\t labels.zip\n",
            " A_inc_model.pt\t\t df_B_inc.csv   df_N_inc.csv\t'My Drive'\n",
            " B_inc_model.pt\t\t df_D_inc.csv   df_O_inc.csv\t spell_check\n",
            " classes.txt\t\t df_E_inc.csv   df_Q_inc.csv\t val.txt\n",
            "'Colab Notebooks'\t df_J_inc.csv   df_R_inc.csv\n",
            "'답안 작성용 파일.csv'\t df_K_inc.csv   E_inc_model.pt\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')\n",
        "!ln -s /content/gdrive/My\\ Drive/ /mydrive\n",
        "!ls /mydrive"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YnCVJyzDPbzg",
        "outputId": "6018c619-942a-4185-9606-d360eea94ea0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  /content/gdrive/My Drive/산업분류자동화/모델 개발용 자료.zip\n",
            "2. 모델개발용자료.txt:  mismatching \"local\" filename (2. ыкиыН╕ъ░Ьы░ЬьЪйьЮРыгМ.txt),\n",
            "         continuing with \"central\" filename version\n",
            "  inflating: /content/dataset/2. 모델개발용자료.txt  \n",
            "Archive:  /content/gdrive/My Drive/산업분류자동화/실습용 자료.zip\n",
            "1. 실습용자료.txt:  mismatching \"local\" filename (1. ьЛдьК╡ьЪйьЮРыгМ.txt),\n",
            "         continuing with \"central\" filename version\n",
            "  inflating: /content/dataset/1. 실습용자료.txt  \n"
          ]
        }
      ],
      "source": [
        "!unzip /content/gdrive/'My Drive'/산업분류자동화/'모델 개발용 자료'.zip -d /content/dataset\n",
        "!unzip /content/gdrive/'My Drive'/산업분류자동화/'실습용 자료'.zip -d /content/dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "clR298QvPb1V"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "df_A=pd.read_csv('/content/gdrive/MyDrive/My Drive/df_A_inc.csv') #증분데이터 사용\n",
        "df_B=pd.read_csv('/content/gdrive/MyDrive/My Drive/df_B_inc.csv') #증분데이터 사용\n",
        "df_C=pd.read_csv('/content/gdrive/MyDrive/spell_check/C_spellcheck.csv')\n",
        "df_D=pd.read_csv('/content/gdrive/MyDrive/My Drive/df_D_inc.csv') #증분데이터 사용\n",
        "df_E=pd.read_csv('/content/gdrive/MyDrive/My Drive/df_E_inc.csv') #증분데이터 사용\n",
        "df_F=pd.read_csv('/content/gdrive/MyDrive/spell_check/F_spell_check.csv')\n",
        "df_G=pd.read_csv('/content/gdrive/MyDrive/spell_check/G_spell_check.csv')\n",
        "df_H=pd.read_csv('/content/gdrive/MyDrive/spell_check/H_spellcheck.csv')\n",
        "df_I=pd.read_csv('/content/gdrive/MyDrive/spell_check/I_spell_check.csv')\n",
        "df_J=pd.read_csv('/content/gdrive/MyDrive/spell_check/J_spellcheck.csv')\n",
        "df_K=pd.read_csv('/content/gdrive/MyDrive/spell_check/K_spellcheck.csv')\n",
        "df_L=pd.read_csv('/content/gdrive/MyDrive/spell_check/L_spell_check.csv')\n",
        "df_M=pd.read_csv('/content/gdrive/MyDrive/spell_check/M_spell_check.csv')\n",
        "df_N=pd.read_csv('/content/gdrive/MyDrive/spell_check/N_spell_check.csv')\n",
        "df_O=pd.read_csv('/content/gdrive/MyDrive/My Drive/df_O_inc.csv') #증분데이터 사용\n",
        "df_P=pd.read_csv('/content/gdrive/MyDrive/spell_check/P_spell_check.csv')\n",
        "df_Q=pd.read_csv('/content/gdrive/MyDrive/spell_check/Q_spellcheck.csv')\n",
        "df_R=pd.read_csv('/content/gdrive/MyDrive/spell_check/R_spellcheck.csv')\n",
        "df_S=pd.read_csv('/content/gdrive/MyDrive/spell_check/S_spellcheck.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "HNU07nOcQ0YS"
      },
      "outputs": [],
      "source": [
        "df=pd.concat([df_A, df_B, df_C, df_D, df_E, df_F, df_G, df_H, df_I, df_J, df_K, df_L, df_M, df_N, df_O, df_P, df_Q, df_R, df_S])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-vgT6CZ2ROow",
        "outputId": "0fb117ef-91da-4c9b-9059-01c9f7f3966e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "G    246472\n",
              "I    187425\n",
              "C    105192\n",
              "S    100396\n",
              "H     98038\n",
              "P     46610\n",
              "L     40140\n",
              "Q     36087\n",
              "F     35050\n",
              "R     29751\n",
              "M     28434\n",
              "N     17701\n",
              "A     13275\n",
              "J     10862\n",
              "E     10428\n",
              "K     10378\n",
              "O      6380\n",
              "B      5847\n",
              "D      1968\n",
              "Name: digit_1, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "df['digit_1'].value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X9BNjZPDQAcL"
      },
      "source": [
        "**데이터 전처리**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "IqjVKJomPb5f"
      },
      "outputs": [],
      "source": [
        "df.loc[(df['digit_1'] == \"A\"), 'digit_1'] = 0  \n",
        "df.loc[(df['digit_1'] == \"B\"), 'digit_1'] = 1  \n",
        "df.loc[(df['digit_1'] == \"C\"), 'digit_1'] = 2  \n",
        "df.loc[(df['digit_1'] == \"D\"), 'digit_1'] = 3  \n",
        "df.loc[(df['digit_1'] == \"E\"), 'digit_1'] = 4  \n",
        "df.loc[(df['digit_1'] == \"F\"), 'digit_1'] = 5  \n",
        "df.loc[(df['digit_1'] == \"G\"), 'digit_1'] = 6  \n",
        "df.loc[(df['digit_1'] == \"H\"), 'digit_1'] = 7  \n",
        "df.loc[(df['digit_1'] == \"I\"), 'digit_1'] = 8  \n",
        "df.loc[(df['digit_1'] == \"J\"), 'digit_1'] = 9  \n",
        "df.loc[(df['digit_1'] == \"K\"), 'digit_1'] = 10  \n",
        "df.loc[(df['digit_1'] == \"L\"), 'digit_1'] = 11 \n",
        "df.loc[(df['digit_1'] == \"M\"), 'digit_1'] = 12  \n",
        "df.loc[(df['digit_1'] == \"N\"), 'digit_1'] = 13  \n",
        "df.loc[(df['digit_1'] == \"O\"), 'digit_1'] = 14  \n",
        "df.loc[(df['digit_1'] == \"P\"), 'digit_1'] = 15  \n",
        "df.loc[(df['digit_1'] == \"Q\"), 'digit_1'] = 16 \n",
        "df.loc[(df['digit_1'] == \"R\"), 'digit_1'] = 17  \n",
        "df.loc[(df['digit_1'] == \"S\"), 'digit_1'] = 18  "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NeACK7jmPb7v",
        "outputId": "3d7d07c4-5cdf-4e2f-87ff-23d255e8d0d9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[['벼 맥류 콩류 잡곡류 종자 생산 보급', '0'], ['벼 모판 생산 농가 출하', '0'], ['사업장에서 오이. 고추를 재배 오이. 고추를 판매', '0'], ['볍씨 발아 모종', '0'], ['  벼 재배', '0']]\n"
          ]
        }
      ],
      "source": [
        "data_list = []\n",
        "for q, label in zip(df['text'], df['digit_1'])  :\n",
        "    data = []\n",
        "    data.append(q)\n",
        "    data.append(str(label))\n",
        "\n",
        "    data_list.append(data)\n",
        "\n",
        "print(data_list[:5])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "UUhz2evRPb94"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split             \n",
        "dataset_train, dataset_test = train_test_split(data_list, test_size=0.1, random_state=0) #추후에 test_size 수정 필요"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d7wPK8GRRaqx"
      },
      "source": [
        "Kobert 입력데이터 만들기"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "BETvSxMCPb_m"
      },
      "outputs": [],
      "source": [
        "class BERTDataset(Dataset):\n",
        "    def __init__(self, dataset, sent_idx, label_idx, 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_idx]]) for i in dataset]\n",
        "        self.labels = [np.int32(i[label_idx]) for i in dataset]\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": 12,
      "metadata": {
        "id": "bhhdNTK7Rc-Y"
      },
      "outputs": [],
      "source": [
        "# Setting parameters\n",
        "max_len = 64\n",
        "batch_size = 64\n",
        "warmup_ratio = 0.1\n",
        "num_epochs = 10 #추후 num_epochs 증가할 필요 있음\n",
        "max_grad_norm = 1\n",
        "log_interval = 200\n",
        "learning_rate =  5e-5"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A7rRowSFRdAn",
        "outputId": "8a596501-1a8b-4dc3-b027-fb349a00fcf7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "WySX5NlpRdEv"
      },
      "outputs": [],
      "source": [
        "data_train = BERTDataset(dataset_train, 0, 1, tok, max_len, True, False)\n",
        "data_test = BERTDataset(dataset_test, 0, 1, tok, max_len, True, False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "en97ZxXARdHY"
      },
      "outputs": [],
      "source": [
        "train_dataloader = torch.utils.data.DataLoader(data_train, batch_size=batch_size, num_workers=5)\n",
        "test_dataloader = torch.utils.data.DataLoader(data_test, batch_size=batch_size, num_workers=5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "18XwcKI4RdJq"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "676TTfuiRmCI"
      },
      "source": [
        "Kobert 학습 모델 만들기"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "7v7PG8iGRk5Q"
      },
      "outputs": [],
      "source": [
        "class BERTClassifier(nn.Module):\n",
        "    def __init__(self,\n",
        "                 bert,\n",
        "                 hidden_size = 768,\n",
        "                 num_classes=19,   ##클래스 수 조정##\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": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ifOA11RbRk7m",
        "outputId": "7955ab93-782a-4b11-fcac-c11bc99ba6c4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/transformers/optimization.py:309: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
            "  FutureWarning,\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<torch.utils.data.dataloader.DataLoader at 0x7f33cd617550>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "#BERT 모델 불러오기\n",
        "model = BERTClassifier(bertmodel,  dr_rate=0.5).to(device)\n",
        "\n",
        "#optimizer와 schedule 설정\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)\n",
        "\n",
        "#정확도 측정을 위한 함수 정의\n",
        "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\n",
        "    \n",
        "train_dataloader"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
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      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:5: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
            "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n",
            "  \"\"\"\n"
          ]
        },
        {
          "output_type": "display_data",
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            "epoch 1 train acc 0.9260822090722978\n"
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          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:23: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
            "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            "epoch 1 test acc 0.9732891061452514\n"
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          "data": {
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            "epoch 10 batch id 8401 loss 0.08958733826875687 train acc 0.9970334632781812\n",
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            "epoch 10 batch id 9001 loss 0.00031380620202980936 train acc 0.9970020692145317\n",
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            "epoch 10 batch id 10001 loss 0.00046071401447989047 train acc 0.9970065493450655\n",
            "epoch 10 batch id 10201 loss 0.0004033945151604712 train acc 0.9970131604744633\n",
            "epoch 10 batch id 10401 loss 0.012329410761594772 train acc 0.9970029925007211\n",
            "epoch 10 batch id 10601 loss 0.005370725877583027 train acc 0.9969961560230167\n",
            "epoch 10 batch id 10801 loss 0.014497168362140656 train acc 0.99700259235256\n",
            "epoch 10 batch id 11001 loss 0.001014307956211269 train acc 0.9970045336787565\n",
            "epoch 10 batch id 11201 loss 0.0017394149908795953 train acc 0.9970064056780644\n",
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            "epoch 10 batch id 11601 loss 0.00027325726114213467 train acc 0.9970072623049737\n",
            "epoch 10 batch id 11801 loss 0.00055134721333161 train acc 0.9970209092449792\n",
            "epoch 10 batch id 12001 loss 0.05652541294693947 train acc 0.9970236855262061\n",
            "epoch 10 batch id 12201 loss 0.000668282387778163 train acc 0.9970353352184247\n",
            "epoch 10 batch id 12401 loss 0.0002023306005867198 train acc 0.9970466091444239\n",
            "epoch 10 batch id 12601 loss 0.0005537378019653261 train acc 0.9970476053487819\n",
            "epoch 10 batch id 12801 loss 0.00112714187707752 train acc 0.9970571146785407\n",
            "epoch 10 batch id 13001 loss 0.060108017176389694 train acc 0.9970687350973002\n",
            "epoch 10 batch id 13201 loss 0.0008878126973286271 train acc 0.9970740852965685\n",
            "epoch 10 batch id 13401 loss 0.0007214887300506234 train acc 0.9970711140959629\n",
            "epoch 10 batch id 13601 loss 0.0007676912937313318 train acc 0.9970682302771855\n",
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            "epoch 10 train acc 0.9970628321026844\n"
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            "epoch 10 test acc 0.9816495965238982\n"
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      ],
      "source": [
        "for e in range(num_epochs):\n",
        "    train_acc = 0.0\n",
        "    test_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",
        "    \n",
        "    model.eval()\n",
        "    for batch_id, (token_ids, valid_length, segment_ids, label) 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",
        "        label = label.long().to(device)\n",
        "        out = model(token_ids, valid_length, segment_ids)\n",
        "        test_acc += calc_accuracy(out, label)\n",
        "    print(\"epoch {} test acc {}\".format(e+1, test_acc / (batch_id+1)))"
      ]
    },
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      "execution_count": 19,
      "metadata": {
        "id": "TMQgbwX-RlAN"
      },
      "outputs": [],
      "source": [
        "#학습 모델 저장\n",
        "torch.save(model, 'dabunryu4_model.pt')"
      ]
    },
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