KoBERT.ipynb

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            "grid_template_columns": null,
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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/dlagur/ProjectHanium/blob/main/KoBERT.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "https://velog.io/@dev-junku/KoBERT-모델에-대해"
      ],
      "metadata": {
        "id": "bA06EZNhq-yU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.drive import mount\n",
        "drive.mount('content/drive/')"
      ],
      "metadata": {
        "id": "IsRYWTF3CGMk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "data = pd.read_csv('/content/drive/MyDrive/keyword.csv')"
      ],
      "metadata": {
        "id": "Yg-rAB4RGjn5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install --upgrade pip"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SlH8L3PTfrhp",
        "outputId": "b2f5b822-f6d8-4a73-ed1d-ca64ec14f740"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: pip in /usr/local/lib/python3.7/dist-packages (22.2)\n",
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0m"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KTeNGWZXd5Gu",
        "outputId": "a952977c-9520-4f34-e931-29f194cdaa5a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: mxnet in /usr/local/lib/python3.7/dist-packages (1.9.1)\n",
            "Requirement already satisfied: graphviz<0.9.0,>=0.8.1 in /usr/local/lib/python3.7/dist-packages (from mxnet) (0.8.4)\n",
            "Requirement already satisfied: requests<3,>=2.20.0 in /usr/local/lib/python3.7/dist-packages (from mxnet) (2.23.0)\n",
            "Requirement already satisfied: numpy<2.0.0,>1.16.0 in /usr/local/lib/python3.7/dist-packages (from mxnet) (1.21.6)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.20.0->mxnet) (1.25.11)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.20.0->mxnet) (2022.6.15)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.20.0->mxnet) (3.0.4)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.20.0->mxnet) (2.10)\n",
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0mLooking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: gluonnlp in /usr/local/lib/python3.7/dist-packages (0.10.0)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (1.3.5)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (4.64.0)\n",
            "Requirement already satisfied: numpy>=1.16.0 in /usr/local/lib/python3.7/dist-packages (from gluonnlp) (1.21.6)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from gluonnlp) (21.3)\n",
            "Requirement already satisfied: cython in /usr/local/lib/python3.7/dist-packages (from gluonnlp) (0.29.30)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas) (2022.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas) (1.15.0)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging->gluonnlp) (3.0.9)\n",
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0mLooking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: sentencepiece in /usr/local/lib/python3.7/dist-packages (0.1.96)\n",
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0mLooking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: transformers in /usr/local/lib/python3.7/dist-packages (4.20.1)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.7/dist-packages (from transformers) (6.0)\n",
            "Requirement already satisfied: tokenizers!=0.11.3,<0.13,>=0.11.1 in /usr/local/lib/python3.7/dist-packages (from transformers) (0.12.1)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (1.21.6)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from transformers) (2.23.0)\n",
            "Requirement already satisfied: huggingface-hub<1.0,>=0.1.0 in /usr/local/lib/python3.7/dist-packages (from transformers) (0.8.1)\n",
            "Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from transformers) (4.12.0)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (2022.6.2)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from transformers) (3.7.1)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.7/dist-packages (from transformers) (4.64.0)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.7/dist-packages (from transformers) (21.3)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.7/dist-packages (from huggingface-hub<1.0,>=0.1.0->transformers) (4.1.1)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=20.0->transformers) (3.0.9)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->transformers) (3.8.1)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (3.0.4)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2.10)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2022.6.15)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (1.25.11)\n",
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0mLooking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: torch in /usr/local/lib/python3.7/dist-packages (1.12.0+cu113)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch) (4.1.1)\n",
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0m"
          ]
        }
      ],
      "source": [
        "!pip install mxnet\n",
        "!pip install gluonnlp pandas tqdm\n",
        "!pip install sentencepiece\n",
        "!pip install transformers\n",
        "!pip install torch"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ★\n",
        "!pip install 'git+https://github.com/SKTBrain/KoBERT.git#egg=kobert_tokenizer&subdirectory=kobert_hf'"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xsM-TGfHecpX",
        "outputId": "6718281c-9eb4-493b-db5e-c12b5c920214"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting kobert_tokenizer\n",
            "  Cloning https://github.com/SKTBrain/KoBERT.git to /tmp/pip-install-j41z1leb/kobert-tokenizer_2c820079e15d4190b5d82d462b0194e3\n",
            "  Running command git clone --filter=blob:none --quiet https://github.com/SKTBrain/KoBERT.git /tmp/pip-install-j41z1leb/kobert-tokenizer_2c820079e15d4190b5d82d462b0194e3\n",
            "  Resolved https://github.com/SKTBrain/KoBERT.git to commit e1f2f37055e7460d8427f6912579c0162cb69831\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0m"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "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"
      ],
      "metadata": {
        "id": "6Bw54pe7ejSp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# ★ Hugging Face를 통한 모델 및 토크나이저 Import\n",
        "from kobert_tokenizer import KoBERTTokenizer\n",
        "from transformers import BertModel\n",
        "\n",
        "from transformers import AdamW\n",
        "from transformers.optimization import get_cosine_schedule_with_warmup"
      ],
      "metadata": {
        "id": "MgIkQGXRekNb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# GPU 사용 시\n",
        "#device = torch.device(\"cuda:0\")"
      ],
      "metadata": {
        "id": "mNAHHQeFenAa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# ★\n",
        "tokenizer = KoBERTTokenizer.from_pretrained('skt/kobert-base-v1')\n",
        "bertmodel = BertModel.from_pretrained('skt/kobert-base-v1', return_dict=False)\n",
        "vocab = nlp.vocab.BERTVocab.from_sentencepiece(tokenizer.vocab_file, padding_token='[PAD]')"
      ],
      "metadata": {
        "id": "KXnEpq8RfqmK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d5545486-80b3-47ff-86c4-6452ccf8ef75"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The tokenizer class you load from this checkpoint is not the same type as the class this function is called from. It may result in unexpected tokenization. \n",
            "The tokenizer class you load from this checkpoint is 'XLNetTokenizer'. \n",
            "The class this function is called from is 'KoBERTTokenizer'.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 전처리"
      ],
      "metadata": {
        "id": "vpbARBgRi33D"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data_list = []\n",
        "for q, label in zip(data['content'], data['label'])  :\n",
        "    pro_data = []\n",
        "    pro_data.append(str(q))\n",
        "    pro_data.append(str(label))\n",
        "\n",
        "    data_list.append(pro_data)"
      ],
      "metadata": {
        "id": "YnVlKotai04u"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#train & test 데이터로 나누기\n",
        "from sklearn.model_selection import train_test_split\n",
        "                                                         \n",
        "dataset_train, dataset_test = train_test_split(data_list, test_size=0.25, random_state=0)"
      ],
      "metadata": {
        "id": "0ge-1HuYi9pT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(len(dataset_train)) # 1344 (28945) \n",
        "print(len(dataset_test)) # 448 (9649)"
      ],
      "metadata": {
        "id": "Fn5lwxQZjBy8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f1a31ad2-8844-4fe2-e754-9e0eafc37795"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1620\n",
            "541\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# KoBERT 입력 데이터로 만들기"
      ],
      "metadata": {
        "id": "2_J1ScpsjEeN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# ★\n",
        "class BERTDataset(Dataset):\n",
        "    def __init__(self, dataset, sent_idx, label_idx, bert_tokenizer, vocab, max_len,\n",
        "                 pad, pair):\n",
        "        transform = nlp.data.BERTSentenceTransform(\n",
        "            bert_tokenizer, max_seq_length=max_len, vocab=vocab, 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": "49SoMyiWjGTZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Setting parameters\n",
        "max_len = 64 # 해당 길이를 초과하는 단어에 대해선 bert가 학습하지 않음\n",
        "batch_size = 64\n",
        "warmup_ratio = 0.1\n",
        "num_epochs = 5\n",
        "max_grad_norm = 1\n",
        "log_interval = 200\n",
        "learning_rate =  5e-5"
      ],
      "metadata": {
        "id": "2o6gbWUXjItD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# ★\n",
        "tok = tokenizer.tokenize\n",
        "\n",
        "data_train = BERTDataset(dataset_train, 0, 1, tok, vocab, max_len, True, False)\n",
        "data_test = BERTDataset(dataset_test, 0, 1, tok, vocab, max_len, True, False)"
      ],
      "metadata": {
        "id": "y2KwaA2RjMGA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "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) #num_workers : 데이터를 묶을 때 사용할 프로세스의 수"
      ],
      "metadata": {
        "id": "tQRh5I2pjORl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7c7c1e27-c151-46e5-c62d-a42ba6cba51a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:560: UserWarning: This DataLoader will create 5 worker processes in total. Our suggested max number of worker in current system is 2, 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 학습모델 만들기"
      ],
      "metadata": {
        "id": "SO9NrCZmjRfg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class BERTClassifier(nn.Module):\n",
        "    def __init__(self,\n",
        "                 bert,\n",
        "                 hidden_size = 768,\n",
        "                 num_classes=10,   ##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": "uDQ0XZeJjRDu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "torch.cuda.is_available() # GPU 사용여부 확인"
      ],
      "metadata": {
        "id": "gjxDwX6WjlEj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1e36367a-5659-470a-f639-1e6f32496ac6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "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": "yVpKbMHxjpGQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "73ba726a-37e6-4691-8aa1-60c1bcc8b8e9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/transformers/optimization.py:310: 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 0x7f3c84018910>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "KoBERT 모델 학습시키기"
      ],
      "metadata": {
        "id": "-gkBs2SAj2NW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "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": "fOkyO1TWj3Db",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 768,
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        },
        "outputId": "f0988286-e96f-45e0-a26d-f2202e7d44df"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: 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",
          "data": {
            "text/plain": [
              "  0%|          | 0/26 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6cf45d53bea14e1eb56441a0ca9d4aaf"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:560: UserWarning: This DataLoader will create 5 worker processes in total. Our suggested max number of worker in current system is 2, 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"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 1 batch id 1 loss 2.3033039569854736 train acc 0.15625\n",
            "epoch 1 train acc 0.11358173076923077\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:24: 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": [
              "  0%|          | 0/9 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "98b5807cec7e44119ee4b6fa5aeb0c30"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 1 test acc 0.1191331417624521\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/26 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0c2046a1a9804e23b3e7098b3a91e363"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 2 batch id 1 loss 2.24607515335083 train acc 0.15625\n",
            "epoch 2 train acc 0.17295673076923077\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/9 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0582148d1cff4807b3f1593bc1e1ef9d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 2 test acc 0.2274904214559387\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/26 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "bf935192ff154bfb9b4565cd51711a65"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 3 batch id 1 loss 2.0982565879821777 train acc 0.25\n",
            "epoch 3 train acc 0.2985576923076923\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d725867cc897414b9d2308d67a5e6320"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 3 test acc 0.3793103448275862\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/26 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a44f225b3d444179ba3c7685253f3dd5"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 4 batch id 1 loss 1.8074687719345093 train acc 0.515625\n",
            "epoch 4 train acc 0.47704326923076923\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/9 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "50ebb72354274a35a4e0482bea4f3712"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 4 test acc 0.4400742337164751\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/26 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "403ba5c39bf04f74a902ba2b3a569288"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 5 batch id 1 loss 1.6148658990859985 train acc 0.546875\n",
            "epoch 5 train acc 0.5324519230769231\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/9 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ba5449631f9a4f698ce6a28dcf0d625b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 5 test acc 0.4807231800766283\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## 학습 모델 저장\n",
        "PATH = '/content/drive/MyDrive/Kobert/' # google 드라이브 연동 해야함. 관련코드는 뺐음\n",
        "torch.save(model, PATH + 'koert_train.pkl')  # 전체 모델 저장\n",
        "torch.save(model.state_dict(), PATH + 'model_state_dict.pkl')  # 모델 객체의 state_dict 저장\n",
        "torch.save({\n",
        "    'model': model.state_dict(),\n",
        "    'optimizer': optimizer.state_dict()\n",
        "}, PATH + 'all.tar')  # 여러 가지 값 저장, 학습 중 진행 상황 저장을 위해 epoch, loss 값 등 일반 scalar값 저장 가능"
      ],
      "metadata": {
        "id": "NUOCcqcIkDFR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "모델을 저장한 이후 학습한 모델을 불러와 사용해야할 코드는 다음과 같다."
      ],
      "metadata": {
        "id": "-0izX2qwktAr"
      }
    },
    {
      "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",
        "# !pip install git+https://git@github.com/SKTBrain/KoBERT.git@master\n",
        "\n",
        "# torch\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",
        "\n",
        "#kobert <- 이거아님\n",
        "from kobert.utils import get_tokenizer\n",
        "from kobert.pytorch_kobert import get_pytorch_kobert_model\n",
        "\n",
        "#GPU 사용\n",
        "#device = torch.device(\"cuda:0\")\n",
        "\n",
        "#BERT 모델, Vocabulary 불러오기 필수 \n",
        "bertmodel, vocab = get_pytorch_kobert_model()\n",
        "\n",
        "\n",
        "# KoBERT에 입력될 데이터셋 정리\n",
        "# ★\n",
        "class BERTDataset(Dataset):\n",
        "    def __init__(self, dataset, sent_idx, label_idx, bert_tokenizer, vocab, max_len,\n",
        "                 pad, pair):\n",
        "        transform = nlp.data.BERTSentenceTransform(\n",
        "            bert_tokenizer, max_seq_length=max_len, vocab=vocab, 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))        \n",
        "\n",
        "# 모델 정의\n",
        "class BERTClassifier(nn.Module): ## 클래스를 상속\n",
        "    def __init__(self,\n",
        "                 bert,\n",
        "                 hidden_size = 768,\n",
        "                 num_classes=10,   ##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)\n",
        "\n",
        "# Setting parameters\n",
        "max_len = 64\n",
        "batch_size = 32\n",
        "warmup_ratio = 0.1\n",
        "num_epochs = 20\n",
        "max_grad_norm = 1\n",
        "log_interval = 100\n",
        "learning_rate =  5e-5\n",
        "\n",
        "## 학습 모델 로드\n",
        "PATH = '/content/drive/MyDrive/Kobert/'\n",
        "model = torch.load(PATH + 'koert_train.pkl')  # 전체 모델을 통째로 불러옴, 클래스 선언 필수\n",
        "model.load_state_dict(torch.load(PATH + 'model_state_dict.pkl'))  # state_dict를 불러 온 후, 모델에 저장\n",
        "\n",
        "#토큰화\n",
        "tok = tokenizer.tokenize\n",
        "# tokenizer = get_tokenizer()\n",
        "# tok = nlp.data.BERTSPTokenizer(tokenizer, vocab, lower=False)\n",
        "\n",
        "def new_softmax(a) : \n",
        "    c = np.max(a) # 최댓값\n",
        "    exp_a = np.exp(a-c) # 각각의 원소에 최댓값을 뺀 값에 exp를 취한다. (이를 통해 overflow 방지)\n",
        "    sum_exp_a = np.sum(exp_a)\n",
        "    y = (exp_a / sum_exp_a) * 100\n",
        "    return np.round(y, 3)\n",
        "\n",
        "\n",
        "# 예측 모델 설정\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, vocab, max_len, True, False)\n",
        "    test_dataloader = torch.utils.data.DataLoader(another_test, batch_size=batch_size, num_workers=5)\n",
        "    \n",
        "    model.eval()\n",
        "\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",
        "\n",
        "        valid_length= valid_length\n",
        "        label = label.long()#.to(device)\n",
        "\n",
        "        out = model(token_ids, valid_length, segment_ids)\n",
        "\n",
        "        test_eval=[]\n",
        "        for i in out:\n",
        "            logits=i\n",
        "            logits = logits.detach().cpu().numpy()\n",
        "            min_v = min(logits)\n",
        "            total = 0\n",
        "            ability = ['대인관계', '분석적 사고', '성실', '열정', '의사소통', '전략적 사고', '전문성', '주인의식', '자기개발', '리더십']\n",
        "            probability = []\n",
        "            logits = np.round(new_softmax(logits), 3).tolist()\n",
        "            for logit in logits:\n",
        "                probability.append(np.round(logit, 3))\n",
        "                sort_prob = sorted(probability, reverse=True)\n",
        "            \n",
        "            \n",
        "            answer = []\n",
        "            \n",
        "            \n",
        "             \n",
        "            for i in range(len(probability)):\n",
        "                for j in range(3):\n",
        "                    if probability[i] == sort_prob[j]:\n",
        "                        answer.append(ability[i])\n",
        "        \n",
        "\n",
        "    return print('자소서에 드러난 당신의 직무역량은', answer, '(으)로 드러났습니다') , sort_prob, probability"
      ],
      "metadata": {
        "id": "Qjrc1X1okt6Z",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "20d35be6-9c7a-4bdd-b3ef-46a95fcef7c5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "using cached model. /content/.cache/kobert_v1.zip\n",
            "using cached model. /content/.cache/kobert_news_wiki_ko_cased-1087f8699e.spiece\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predict(data['content'][0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "usBLKRkCHhCE",
        "outputId": "9508471c-5280-4543-bf31-fb0688db4540"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:560: UserWarning: This DataLoader will create 5 worker processes in total. Our suggested max number of worker in current system is 2, 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"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "자소서에 드러난 당신의 직무역량은 ['대인관계', '분석적 사고', '주인의식'] (으)로 드러났습니다\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(None,\n",
              " [45.9, 10.005, 8.158, 6.595, 6.27, 5.883, 5.392, 4.06, 3.976, 3.761],\n",
              " [45.9, 10.005, 3.761, 6.27, 4.06, 5.883, 6.595, 8.158, 5.392, 3.976])"
            ]
          },
          "metadata": {},
          "execution_count": 94
        }
      ]
    }
  ]
}