Fcode 연습.ipynb

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            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        }
      }
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/KorStats/BIGDATAGISA/blob/main/Fcode%20%EC%97%B0%EC%8A%B5.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U3wrPXQ_ORLb"
      },
      "source": [
        "## 구글드라이브에서 파일 읽어오기"
      ]
    },
    {
      "cell_type": "code",
      "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()"
      ],
      "metadata": {
        "id": "gWN17JM7BHC7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "656e05b8-0941-4257-8910-707e63d95f29"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting mxnet\n",
            "  Downloading mxnet-1.9.0-py3-none-manylinux2014_x86_64.whl (47.3 MB)\n",
            "\u001b[K     |████████████████████████████████| 47.3 MB 1.1 MB/s \n",
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            "  Downloading graphviz-0.8.4-py2.py3-none-any.whl (16 kB)\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-1.9.0\n",
            "Collecting gluonnlp\n",
            "  Downloading gluonnlp-0.10.0.tar.gz (344 kB)\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=595739 sha256=a8a3c8003bba39daa90b7eef9b4bfaba260469dc57c3e151ebec6fadb04f2df2\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",
            "  Downloading sentencepiece-0.1.96-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.2 MB)\n",
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            "\u001b[?25hInstalling collected packages: sentencepiece\n",
            "Successfully installed sentencepiece-0.1.96\n",
            "Collecting transformers==3.0.2\n",
            "  Downloading transformers-3.0.2-py3-none-any.whl (769 kB)\n",
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            "Collecting tokenizers==0.8.1.rc1\n",
            "  Downloading tokenizers-0.8.1rc1-cp37-cp37m-manylinux1_x86_64.whl (3.0 MB)\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",
            "Requirement already satisfied: torch in /usr/local/lib/python3.7/dist-packages (1.10.0+cu111)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch) (3.10.0.2)\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-4k4b6zda\n",
            "  Running command git clone -q 'https://****@github.com/SKTBrain/KoBERT.git' /tmp/pip-req-build-4k4b6zda\n",
            "Collecting boto3\n",
            "  Downloading boto3-1.21.22-py3-none-any.whl (132 kB)\n",
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            "Collecting onnxruntime==1.8.0\n",
            "  Downloading onnxruntime-1.8.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.5 MB)\n",
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            "  Downloading transformers-4.17.0-py3-none-any.whl (3.8 MB)\n",
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            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.20.0->mxnet>=1.4.0->kobert==0.2.3) (2021.10.8)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch>=1.7.0->kobert==0.2.3) (3.10.0.2)\n",
            "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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            "Collecting huggingface-hub<1.0,>=0.1.0\n",
            "  Downloading huggingface_hub-0.4.0-py3-none-any.whl (67 kB)\n",
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            "Collecting tokenizers!=0.11.3,>=0.11.1\n",
            "  Downloading tokenizers-0.11.6-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (6.5 MB)\n",
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            "Collecting botocore<1.25.0,>=1.24.22\n",
            "  Downloading botocore-1.24.22-py3-none-any.whl (8.6 MB)\n",
            "\u001b[K     |████████████████████████████████| 8.6 MB 56.2 MB/s \n",
            "\u001b[?25hCollecting s3transfer<0.6.0,>=0.5.0\n",
            "  Downloading s3transfer-0.5.2-py3-none-any.whl (79 kB)\n",
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            "\u001b[?25hCollecting jmespath<2.0.0,>=0.7.1\n",
            "  Downloading jmespath-1.0.0-py3-none-any.whl (23 kB)\n",
            "Collecting urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1\n",
            "  Downloading urllib3-1.25.11-py2.py3-none-any.whl (127 kB)\n",
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            "\u001b[?25hRequirement already satisfied: python-dateutil<3.0.0,>=2.1 in /usr/local/lib/python3.7/dist-packages (from botocore<1.25.0,>=1.24.22->boto3->kobert==0.2.3) (2.8.2)\n",
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            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->transformers>=4.8.1->kobert==0.2.3) (3.7.0)\n",
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            "Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers>=4.8.1->kobert==0.2.3) (7.1.2)\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=15674 sha256=c953c83cad7f0241f423f13c0e69f95b5a9095a7e68add6ade46dbd55d585fed\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-5d5c6cuy/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 boto3-1.21.22 botocore-1.24.22 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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dqJbzoq_OPxK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d1f193d9-0af9-4dcc-bcc6-579f0cb14a79"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wmtIZqMGOdVX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9de7958f-d6f2-4236-be29-10bd99c26680"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')\n",
        "!ln -s /content/gdrive/My\\ Drive/ /mydrive\n",
        "!ls /mydrive"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive\n",
            " 산업분류자동화    '답안 작성용 파일.csv'  'My Drive'\n",
            "'Colab Notebooks'   df_F_inc.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "df=pd.read_csv('/content/gdrive/MyDrive/df_F_inc.csv')"
      ],
      "metadata": {
        "id": "XiIG-qyG7jaT"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 데이터 전처리"
      ],
      "metadata": {
        "id": "geFjTXHh5-dy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['digit_3'].value_counts()"
      ],
      "metadata": {
        "id": "FpA7i6nJ9wgV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8d85371a-f1eb-4d13-e14f-c4e9294cfae3"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    26904\n",
              "7    12528\n",
              "5    11152\n",
              "3     5744\n",
              "2     4724\n",
              "6     4472\n",
              "4     4360\n",
              "0     3582\n",
              "Name: digit_3, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "_-yXDehY9wmo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "#### **추후 과제: 분류가 현재 매우 불균형함 / 정규화를 이용해서 균형을 맞추면 정확도가 올라가는지 확인 필요**\n"
      ],
      "metadata": {
        "id": "a1OFqS_390dM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data_list = []\n",
        "for q, label in zip(df['text'], df['digit_3'])  :\n",
        "    data = []\n",
        "    data.append(q)\n",
        "    data.append(str(label))\n",
        "\n",
        "    data_list.append(data)\n",
        "\n",
        "print(data_list[:5])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TMCFMLE66gpQ",
        "outputId": "f5481be5-2e19-43f0-d963-6b68bddb90e2"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[['영업장에서 고객의뢰를 받아 내부전기공사', '4'], ['사업장에서 의뢰.요청받아 상수도배관공사', '3'], ['현장에서 고개의 요청에따라 주택건설', '0'], ['건축물내 기계,장비설치공사 공조기 설치', '3'], ['건물내 고객요구에 따라 인테리어공사', '5']]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Train data & test data"
      ],
      "metadata": {
        "id": "cmIeL8xg_UC1"
      }
    },
    {
      "cell_type": "code",
      "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"
      ],
      "metadata": {
        "id": "gJzLkJY6_dDm"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "xdv6VPGk_dIX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aFjncd4CnGZR"
      },
      "source": [
        "# KoBERT 입력 데이터로 만들기"
      ]
    },
    {
      "cell_type": "code",
      "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))"
      ],
      "metadata": {
        "id": "19YLwHwC_3y2"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Setting parameters\n",
        "max_len = 64\n",
        "batch_size = 64\n",
        "warmup_ratio = 0.1\n",
        "num_epochs = 30 #추후 num_epochs 증가할 필요 있음\n",
        "max_grad_norm = 1\n",
        "log_interval = 200\n",
        "learning_rate =  5e-5"
      ],
      "metadata": {
        "id": "sO26V97-_31Z"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        " ### 추후 과제 : 쓰이는 parameters 최적화 필요"
      ],
      "metadata": {
        "id": "uCkta63aAI9f"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#토큰화\n",
        "tokenizer = get_tokenizer()\n",
        "tok = nlp.data.BERTSPTokenizer(tokenizer, vocab, lower=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x9cRyQFZ_33r",
        "outputId": "175f5c25-851e-4ae0-934a-fee47e2bf82f"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "using cached model. /content/.cache/kobert_news_wiki_ko_cased-1087f8699e.spiece\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "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)"
      ],
      "metadata": {
        "id": "qdchyUi8MHuZ"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_train[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x9NbB8ut_36O",
        "outputId": "bb1f9705-f8c9-4233-9d2c-d53bd66c9c01"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([   2,  885, 6873, 1306,  885, 4737, 6217, 6712, 6241, 2658, 7782,\n",
              "        2874, 5474, 1043,    3,    1,    1,    1,    1,    1,    1,    1,\n",
              "           1,    1,    1,    1,    1,    1,    1,    1,    1,    1,    1,\n",
              "           1,    1,    1,    1,    1,    1,    1,    1,    1,    1,    1,\n",
              "           1,    1,    1,    1,    1,    1,    1,    1,    1,    1,    1,\n",
              "           1,    1,    1,    1,    1,    1,    1,    1,    1], dtype=int32),\n",
              " array(15, dtype=int32),\n",
              " array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
              "       dtype=int32),\n",
              " 1)"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "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)"
      ],
      "metadata": {
        "id": "-UgKG-K6Qg5n",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fb497d77-e1d3-4c47-de4c-5d02d2ea8a7a"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:481: UserWarning: This DataLoader will create 5 worker processes in total. Our suggested max number of worker in current system is 4, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  cpuset_checked))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# KoBERT 학습 모델 만들기"
      ],
      "metadata": {
        "id": "txXyxDxdPE5w"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class BERTClassifier(nn.Module):\n",
        "    def __init__(self,\n",
        "                 bert,\n",
        "                 hidden_size = 768,\n",
        "                 num_classes=8,   ##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)"
      ],
      "metadata": {
        "id": "xMb9tpMkQg-Z"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "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"
      ],
      "metadata": {
        "id": "wX1tgnXZQhA5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a4e7af1b-a9f8-4e92-8767-a260093268f2"
      },
      "execution_count": 29,
      "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 0x7f08473607d0>"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# KoBERT 모델 학습시키기"
      ],
      "metadata": {
        "id": "wok_Ds2QPYGG"
      }
    },
    {
      "cell_type": "code",
      "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)))"
      ],
      "metadata": {
        "id": "GzT0FWndXYvw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
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            "/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"
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            "/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"
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          "text": [
            "epoch 28 test acc 0.9612425404944587\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 29 batch id 1 loss 0.022082477807998657 train acc 0.984375\n",
            "epoch 29 batch id 201 loss 0.00038889775169081986 train acc 0.9977456467661692\n",
            "epoch 29 batch id 401 loss 0.0031647919677197933 train acc 0.997350374064838\n",
            "epoch 29 batch id 601 loss 0.0014324404764920473 train acc 0.997660149750416\n",
            "epoch 29 batch id 801 loss 0.00022124216775409877 train acc 0.9975031210986267\n",
            "epoch 29 batch id 1001 loss 0.00015501670714002103 train acc 0.9976585914085914\n",
            "epoch 29 train acc 0.9976275386847195\n"
          ]
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        {
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          "data": {
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              "  0%|          | 0/115 [00:00<?, ?it/s]"
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 29 test acc 0.9616501491901108\n"
          ]
        },
        {
          "output_type": "display_data",
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          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 30 batch id 1 loss 0.019400443881750107 train acc 0.984375\n",
            "epoch 30 batch id 201 loss 0.0016431224066764116 train acc 0.9977456467661692\n",
            "epoch 30 batch id 401 loss 0.000558879692107439 train acc 0.9974283042394015\n",
            "epoch 30 batch id 601 loss 0.0036130594089627266 train acc 0.9976861480865225\n",
            "epoch 30 batch id 801 loss 0.00020611716900020838 train acc 0.9975226279650437\n",
            "epoch 30 batch id 1001 loss 0.00015661827637813985 train acc 0.9976429820179821\n",
            "epoch 30 train acc 0.9976426499032882\n"
          ]
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          "data": {
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              "  0%|          | 0/115 [00:00<?, ?it/s]"
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 30 test acc 0.9615142796248934\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#학습 모델 저장\n",
        "torch.save(model, 'F_model_2.pt')"
      ],
      "metadata": {
        "id": "nUvgntFpXY0X"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "IXtGwrWrFa8n"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 문장 test 해보기"
      ],
      "metadata": {
        "id": "l9sDYuTPPhQE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "path='/content/'\n",
        "model = torch.load('dabunryu_model.pt')\n",
        "\n",
        "model.eval()"
      ],
      "metadata": {
        "id": "puEIYgU4I-OH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#토큰화\n",
        "tokenizer = get_tokenizer()\n",
        "tok = nlp.data.BERTSPTokenizer(tokenizer, vocab, lower=False)\n",
        "\n",
        "def predict(predict_sentence):\n",
        "\n",
        "    data = [predict_sentence, '0']\n",
        "    dataset_another = [data]\n",
        "\n",
        "    another_test = BERTDataset(dataset_another, 0, 1, tok, max_len, True, False)\n",
        "    test_dataloader = torch.utils.data.DataLoader(another_test, batch_size=batch_size, num_workers=5)  \n",
        "    model.eval()\n",
        "    for batch_id, (token_ids, valid_length, segment_ids, label) in enumerate(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_eval=[]\n",
        "        for i in out:\n",
        "            logits=i\n",
        "            logits = logits.detach().cpu().numpy()\n",
        "        return np.argmax(logits)"
      ],
      "metadata": {
        "id": "iFIdmbZ3Ql1w",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f3cd7805-1277-4cb6-a9d9-9d4c36be9594"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "using cached model. /content/.cache/kobert_news_wiki_ko_cased-1087f8699e.spiece\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 제출하기"
      ],
      "metadata": {
        "id": "p67LGw4M6n2d"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ts['text']=ts['text_obj'].map(str)+\" \"+ts['text_mthd'].map(str)+\" \"+ts['text_deal'].map(str)\n",
        "\n",
        "i=[]\n",
        "for sentence in ts['text']:\n",
        "    a=sentence.replace('nan', '')\n",
        "    i.append(a)\n",
        "ts['text']=pd.DataFrame(i)\n",
        "\n",
        "ts=ts.drop(['text_obj','text_mthd', 'text_deal'], axis=1) # 기존에 있던 세개의 text변수 삭제\n",
        "\n",
        "ts.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "kheYXl3WqBeT",
        "outputId": "b4d4db2c-e1f0-4b0e-9e75-416076de535a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       AI_id  digit_1  digit_2  digit_3                        text\n",
              "0  id_000001      NaN      NaN      NaN       치킨전문점에서 고객의주문에의해 치킨판매\n",
              "1  id_000002      NaN      NaN      NaN       산업공구 다른 소매업자에게 철물 수공구\n",
              "2  id_000003      NaN      NaN      NaN         절에서 신도을 대상으로 불교단체운영\n",
              "3  id_000004      NaN      NaN      NaN           영업장에서 고객요구로 자동차튜닝\n",
              "4  id_000005      NaN      NaN      NaN  실내포장마차에서 접객시설을 갖추고 소주,맥주제공\n",
              "5  id_000006      NaN      NaN      NaN          철,아크릴,포맥스 스크린인쇄 명판\n",
              "6  id_000007      NaN      NaN      NaN          음식점 접객시설가지고 조개구이판매\n",
              "7  id_000008      NaN      NaN      NaN       스테인레스를 프레스가공하여제조 주방용품\n",
              "8  id_000009      NaN      NaN      NaN         수리 서비스센터에서 전문수리 수입차\n",
              "9  id_000010      NaN      NaN      NaN       약품(화공), 미싱 완성품입고, 수선 "
            ],
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              "<style scoped>\n",
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              "      <th></th>\n",
              "      <th>AI_id</th>\n",
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              "      <td>NaN</td>\n",
              "      <td>치킨전문점에서 고객의주문에의해 치킨판매</td>\n",
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              "      <th>1</th>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>산업공구 다른 소매업자에게 철물 수공구</td>\n",
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              "      <th>2</th>\n",
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              "      <td>NaN</td>\n",
              "      <td>절에서 신도을 대상으로 불교단체운영</td>\n",
              "    </tr>\n",
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              "      <th>3</th>\n",
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              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>실내포장마차에서 접객시설을 갖추고 소주,맥주제공</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>id_000006</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>철,아크릴,포맥스 스크린인쇄 명판</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>id_000007</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>음식점 접객시설가지고 조개구이판매</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>id_000008</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>스테인레스를 프레스가공하여제조 주방용품</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>id_000009</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>수리 서비스센터에서 전문수리 수입차</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>id_000010</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>약품(화공), 미싱 완성품입고, 수선</td>\n",
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              "  </tbody>\n",
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              "                                                     [key], {});\n",
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          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ts1=ts[:]\n",
        "result=[]\n",
        "for sentence in ts1['text']:\n",
        "  temp=predict(sentence)\n",
        "  result.append(temp)\n"
      ],
      "metadata": {
        "id": "eq6JjCO7CcZN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "a=pd.DataFrame(result)\n",
        "ts1=ts1.reset_index()\n",
        "result0=pd.concat([ts1, a], axis=1)\n",
        "result0.to_csv('result.csv', index=False, encoding='utf-8-sig')"
      ],
      "metadata": {
        "id": "S-XuWlx16jS4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "DvplvVddk7kT"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}