KoBERT_job_ability.ipynb

{
  "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_job_ability.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bA06EZNhq-yU"
      },
      "source": [
        "https://velog.io/@dev-junku/KoBERT-모델에-대해"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JVeNAz_5Uobh",
        "outputId": "f6d0fac3-1cbd-4477-ebcd-6768a1af881b"
      },
      "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": null,
      "metadata": {
        "id": "Yg-rAB4RGjn5"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "\n",
        "data = pd.read_csv('/content/drive/MyDrive/keyword추가.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SlH8L3PTfrhp",
        "outputId": "7ea1b79f-2acc-45d8-8df6-b35160b794b5"
      },
      "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 (21.1.3)\n",
            "Collecting pip\n",
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            "\u001b[K     |████████████████████████████████| 2.0 MB 12.0 MB/s \n",
            "\u001b[?25hInstalling collected packages: pip\n",
            "  Attempting uninstall: pip\n",
            "    Found existing installation: pip 21.1.3\n",
            "    Uninstalling pip-21.1.3:\n",
            "      Successfully uninstalled pip-21.1.3\n",
            "Successfully installed pip-22.2.2\n"
          ]
        }
      ],
      "source": [
        "!pip install --upgrade pip"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KTeNGWZXd5Gu",
        "outputId": "b290604f-86fb-48c9-f139-c6b897d2fecb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Building wheels for collected packages: gluonnlp\n",
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            "Installing collected packages: gluonnlp\n",
            "Successfully installed gluonnlp-0.10.0\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",
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            "Installing collected packages: tokenizers, pyyaml, huggingface-hub, transformers\n",
            "  Attempting uninstall: pyyaml\n",
            "    Found existing installation: PyYAML 3.13\n",
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            "Successfully installed huggingface-hub-0.8.1 pyyaml-6.0 tokenizers-0.12.1 transformers-4.21.1\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[0mLooking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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            "\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",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xsM-TGfHecpX",
        "outputId": "bee39ba7-1173-4227-fd2b-62cf91830599"
      },
      "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-8k5ubzcg/kobert-tokenizer_929653e993044fbcbbe3aa659fcfdc6f\n",
            "  Running command git clone --filter=blob:none --quiet https://github.com/SKTBrain/KoBERT.git /tmp/pip-install-8k5ubzcg/kobert-tokenizer_929653e993044fbcbbe3aa659fcfdc6f\n",
            "  Resolved https://github.com/SKTBrain/KoBERT.git to commit 47a69af87928fc24e20f571fe10c3cc9dd9af9a3\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Building wheels for collected packages: kobert_tokenizer\n",
            "  Building wheel for kobert_tokenizer (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for kobert_tokenizer: filename=kobert_tokenizer-0.1-py3-none-any.whl size=4649 sha256=e825a1e55ffbfcb9e2be0930fcd1f648a346cdeeea5d0a7ccb0ad5a568b5d100\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-pybxwgv5/wheels/10/b4/d9/cb627bbfaefa266657b0b4e8127f7bf96d27376fa1a23897b4\n",
            "Successfully built kobert_tokenizer\n",
            "Installing collected packages: kobert_tokenizer\n",
            "Successfully installed kobert_tokenizer-0.1\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": [
        "# ★\n",
        "!pip install 'git+https://github.com/SKTBrain/KoBERT.git#egg=kobert_tokenizer&subdirectory=kobert_hf'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7Br-_PJ3xlUg"
      },
      "outputs": [],
      "source": [
        "#kobert\n",
        "import kobert_tokenizer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6Bw54pe7ejSp"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "from torch import nn\n",
        "import torch.nn.functional as F\n",
        "import torch.optim as optim\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "import gluonnlp as nlp\n",
        "import numpy as np\n",
        "from tqdm import tqdm, tqdm_notebook"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MgIkQGXRekNb"
      },
      "outputs": [],
      "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"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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            "6732dc832baf4ed69de81d6414680930",
            "c13a480b3cf94128abf96d3c1b3f9719",
            "b55fd01a605e478fbca689e4027b4855",
            "611a95fe43784e9caea5e0138469df80",
            "43e8af8ee6794b8eb7d1cbe44af6fcfb",
            "bdeefee57f2c4a20871e15100f4877e9"
          ]
        },
        "id": "KXnEpq8RfqmK",
        "outputId": "564fc63e-8fd2-40ff-f143-b07b611dbb3a"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading spiece.model:   0%|          | 0.00/363k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "16356e857ff54af5b8bd83669e64736b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading special_tokens_map.json:   0%|          | 0.00/244 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d3cbe51975dd43ccab89c6a81da08a15"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "Downloading tokenizer_config.json:   0%|          | 0.00/432 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3715fe946ae6429c9f4d73cefae0d46b"
            }
          },
          "metadata": {}
        },
        {
          "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"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading config.json:   0%|          | 0.00/535 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4606e45ce8264f34a226ad78b0ef9f6e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/352M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a3b21d537b5c49018e3cf9c85f2c812f"
            }
          },
          "metadata": {}
        }
      ],
      "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]')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vpbARBgRi33D"
      },
      "source": [
        "# 전처리"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YnVlKotai04u"
      },
      "outputs": [],
      "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)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0ge-1HuYi9pT"
      },
      "outputs": [],
      "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)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Fn5lwxQZjBy8",
        "outputId": "c6d61c8e-d402-4bdf-818d-65f6aa259273"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3918\n",
            "1306\n"
          ]
        }
      ],
      "source": [
        "print(len(dataset_train)) # 1344 (28945) \n",
        "print(len(dataset_test)) # 448 (9649)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2_J1ScpsjEeN"
      },
      "source": [
        "# KoBERT 입력 데이터로 만들기"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "49SoMyiWjGTZ"
      },
      "outputs": [],
      "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))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2o6gbWUXjItD"
      },
      "outputs": [],
      "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"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "y2KwaA2RjMGA"
      },
      "outputs": [],
      "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)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tQRh5I2pjORl",
        "outputId": "86ee7088-ff9d-4721-eb0e-d4a78af87662"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:566: 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"
          ]
        }
      ],
      "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"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SO9NrCZmjRfg"
      },
      "source": [
        "# KoBERT 학습모델 만들기"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uDQ0XZeJjRDu"
      },
      "outputs": [],
      "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)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gjxDwX6WjlEj",
        "outputId": "8b07fbc7-4ccd-499d-d554-c223413421d0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "torch.cuda.is_available() # GPU 사용여부 확인"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yVpKbMHxjpGQ",
        "outputId": "0cd8913b-48a0-4e00-df78-12e7457d7d1a"
      },
      "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 0x7fa20c0358d0>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "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": "markdown",
      "metadata": {
        "id": "-gkBs2SAj2NW"
      },
      "source": [
        "KoBERT 모델 학습시키기"
      ]
    },
    {
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          "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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          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 1 batch id 1 loss 2.3656363487243652 train acc 0.125\n",
            "epoch 1 train acc 0.1372767857142857\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "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": {
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          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 1 test acc 0.22682005494505497\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 2 batch id 1 loss 2.204380512237549 train acc 0.15625\n",
            "epoch 2 train acc 0.3518505184331797\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 2 test acc 0.5227793040293041\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 3 batch id 1 loss 1.5654000043869019 train acc 0.5625\n",
            "epoch 3 train acc 0.6392209101382489\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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          "text": [
            "epoch 3 test acc 0.6744505494505495\n"
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            "text/plain": [
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          "text": [
            "epoch 4 batch id 1 loss 1.0687885284423828 train acc 0.703125\n",
            "epoch 4 train acc 0.7730414746543779\n"
          ]
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          "data": {
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              "  0%|          | 0/21 [00:00<?, ?it/s]"
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          "text": [
            "epoch 4 test acc 0.7052426739926739\n"
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          "data": {
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          "name": "stdout",
          "text": [
            "epoch 5 batch id 1 loss 0.7932150363922119 train acc 0.796875\n",
            "epoch 5 train acc 0.830753168202765\n"
          ]
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        {
          "output_type": "display_data",
          "data": {
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              "  0%|          | 0/21 [00:00<?, ?it/s]"
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          "text": [
            "epoch 5 test acc 0.7041552197802198\n"
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      ],
      "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)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NUOCcqcIkDFR"
      },
      "outputs": [],
      "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"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cSihptCCr5ub"
      },
      "outputs": [],
      "source": [
        "\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', '자기계발', '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",
        "# 자기소개서 id와 유저 id가 동일  -> update, 아니면 insert\n",
        "    return answer, sort_prob, probability"
      ]
    }
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