(활용)모델불러와서_제출용파일_작성.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "(활용) 모델불러와서 제출용파일 작성",
      "provenance": [],
      "collapsed_sections": [],
      "machine_shape": "hm",
      "background_execution": "on",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/KorStats/ocr_receipt/blob/main/(%ED%99%9C%EC%9A%A9)%EB%AA%A8%EB%8D%B8%EB%B6%88%EB%9F%AC%EC%99%80%EC%84%9C_%EC%A0%9C%EC%B6%9C%EC%9A%A9%ED%8C%8C%EC%9D%BC_%EC%9E%91%EC%84%B1.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": "bc40b5e1-e359-4c3b-93ec-f7518725c020"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Successfully installed sacremoses-0.0.49 tokenizers-0.8.1rc1 transformers-3.0.2\n",
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            "  Cloning https://****@github.com/SKTBrain/KoBERT.git (to revision master) to /tmp/pip-req-build-uux2pnqr\n",
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            "Building wheels for collected packages: kobert\n",
            "  Building wheel for kobert (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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            "  Stored in directory: /tmp/pip-ephem-wheel-cache-5f3zkg_k/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",
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            "\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",
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            "/content/.cache/kobert_v1.zip[██████████████████████████████████████████████████]\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dqJbzoq_OPxK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a3cd623d-9b28-402d-de75-867d74d2fdd9"
      },
      "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": "b6f8c01b-cc9a-4a4d-896e-9fecb5e04ce9"
      },
      "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'   L_inc_model.pt   O_inc_model.pt\n",
            "'Colab Notebooks'   D_inc_model.pt\t   'My Drive'\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xV_iSLl7QFgx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "96d78926-165e-45e0-d40b-2c54b07e90b2"
      },
      "source": [
        "!unzip /content/gdrive/'My Drive'/산업분류자동화/'모델 개발용 자료'.zip -d /content/dataset\n",
        "!unzip /content/gdrive/'My Drive'/산업분류자동화/'실습용 자료'.zip -d /content/dataset"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  /content/gdrive/My Drive/산업분류자동화/모델 개발용 자료.zip\n",
            "  inflating: /content/dataset/2. ╕Ё╡и┐ы░│╣▀┐ы└┌╖с.csv  \n",
            "Archive:  /content/gdrive/My Drive/산업분류자동화/실습용 자료.zip\n",
            "1. 실습용자료.txt:  mismatching \"local\" filename (1. ьЛдьК╡ьЪйьЮРыгМ.txt),\n",
            "         continuing with \"central\" filename version\n",
            "  inflating: /content/dataset/1. 실습용자료.txt  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "ts=pd.read_csv('/content/gdrive/MyDrive/답안 작성용 파일.csv' ,encoding = \"euc-kr\")"
      ],
      "metadata": {
        "id": "-lGZy4fGpXlo"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test=ts[ts['digit_1']=='D']\n",
        "test=test.reset_index(drop=True)"
      ],
      "metadata": {
        "id": "YxbNybjyIrb0"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 제출용 파일 만들기"
      ],
      "metadata": {
        "id": "l9sDYuTPPhQE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class BERTClassifier(nn.Module):\n",
        "    def __init__(self,\n",
        "                 bert,\n",
        "                 hidden_size = 768,\n",
        "                 num_classes=3,   ##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",
        "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))\n",
        "\n",
        "# Setting parameters\n",
        "max_len = 64\n",
        "batch_size = 64\n",
        "warmup_ratio = 0.1\n",
        "num_epochs = 5 #추후 num_epochs 증가할 필요 있음\n",
        "max_grad_norm = 1\n",
        "log_interval = 200\n",
        "learning_rate =  5e-5"
      ],
      "metadata": {
        "id": "PJTDOJ7PIOwS"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = torch.load('/content/gdrive/MyDrive/D_inc_model.pt')\n",
        "\n",
        "model.eval()"
      ],
      "metadata": {
        "id": "puEIYgU4I-OH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "12ac39e1-bd95-40a4-b9fc-39e9cdeccbf8"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "BERTClassifier(\n",
              "  (bert): BertModel(\n",
              "    (embeddings): BertEmbeddings(\n",
              "      (word_embeddings): Embedding(8002, 768, padding_idx=1)\n",
              "      (position_embeddings): Embedding(512, 768)\n",
              "      (token_type_embeddings): Embedding(2, 768)\n",
              "      (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "      (dropout): Dropout(p=0.1, inplace=False)\n",
              "    )\n",
              "    (encoder): BertEncoder(\n",
              "      (layer): ModuleList(\n",
              "        (0): BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (1): BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (2): BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (3): BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (4): BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (5): BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (6): BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (7): BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (8): BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (9): BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (10): BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (11): BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "    (pooler): BertPooler(\n",
              "      (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "      (activation): Tanh()\n",
              "    )\n",
              "  )\n",
              "  (classifier): Linear(in_features=768, out_features=3, bias=True)\n",
              "  (dropout): Dropout(p=0.5, inplace=False)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "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": "0d773453-7ef7-4a5e-d28c-ec7089e3ccaf"
      },
      "execution_count": 32,
      "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": [
        "test['text']=test['text_obj'].map(str)+\" \"+test['text_mthd'].map(str)+\" \"+test['text_deal'].map(str)\n",
        "\n",
        "i=[]\n",
        "for sentence in test['text']:\n",
        "    a=sentence.replace('nan', '')\n",
        "    i.append(a)\n",
        "test['text']=pd.DataFrame(i)\n",
        "\n",
        "test=test.drop(['text_obj','text_mthd', 'text_deal'], axis=1) # 기존에 있던 세개의 text변수 삭제\n",
        "\n",
        "test.head(3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "kheYXl3WqBeT",
        "outputId": "4ae2092c-eabb-4252-d5bf-42f97502f4ab"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       AI_id digit_1  digit_2  digit_3                text\n",
              "0  id_000352       D      NaN      NaN  사업장에서 태양광발전시설 개발공급\n",
              "1  id_000499       D      NaN      NaN  면전 태양광발전소에서 태양광 전기\n",
              "2  id_000896       D      NaN      NaN    사업장에서 태양광 발전 전기업"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-621297d7-a43e-4218-b635-c8a491fdf9ed\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>AI_id</th>\n",
              "      <th>digit_1</th>\n",
              "      <th>digit_2</th>\n",
              "      <th>digit_3</th>\n",
              "      <th>text</th>\n",
              "    </tr>\n",
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              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>id_000352</td>\n",
              "      <td>D</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>사업장에서 태양광발전시설 개발공급</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>id_000499</td>\n",
              "      <td>D</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>면전 태양광발전소에서 태양광 전기</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>id_000896</td>\n",
              "      <td>D</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>사업장에서 태양광 발전 전기업</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-621297d7-a43e-4218-b635-c8a491fdf9ed')\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-621297d7-a43e-4218-b635-c8a491fdf9ed button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-621297d7-a43e-4218-b635-c8a491fdf9ed');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "result=[]\n",
        "for sentence in test['text']:\n",
        "  temp=predict(sentence)\n",
        "  result.append(temp)"
      ],
      "metadata": {
        "id": "eq6JjCO7CcZN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c19a4632-2f9b-45e9-992d-1c702b25fde8"
      },
      "execution_count": 33,
      "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": "code",
      "source": [
        "a=pd.DataFrame(result, columns=['digit_3'])\n",
        "a.loc[(a['digit_3'] == 0), 'digit_3'] = 351  \n",
        "a.loc[(a['digit_3'] == 1), 'digit_3'] = 352  \n",
        "a.loc[(a['digit_3'] == 2), 'digit_3'] = 353  "
      ],
      "metadata": {
        "id": "S-XuWlx16jS4"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test['digit_3']=a"
      ],
      "metadata": {
        "id": "Ts01zGPNLUB0"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test.to_csv('result_D.csv', index=False, encoding='utf-8-sig')"
      ],
      "metadata": {
        "id": "DvplvVddk7kT"
      },
      "execution_count": 46,
      "outputs": []
    }
  ]
}