BERT_sentiment_analysis_eng.ipynb

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  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "!pip install transformers"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xRV0h43Jn69n",
        "outputId": "6907945a-ca69-4c27-922e-9b0309dc7423"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting transformers\n",
            "  Downloading transformers-4.15.0-py3-none-any.whl (3.4 MB)\n",
            "\u001b[K     |████████████████████████████████| 3.4 MB 5.4 MB/s \n",
            "\u001b[?25hCollecting pyyaml>=5.1\n",
            "  Downloading PyYAML-6.0-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (596 kB)\n",
            "\u001b[K     |████████████████████████████████| 596 kB 79.1 MB/s \n",
            "\u001b[?25hRequirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.7/dist-packages (from transformers) (21.3)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from transformers) (3.4.0)\n",
            "Collecting tokenizers<0.11,>=0.10.1\n",
            "  Downloading tokenizers-0.10.3-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (3.3 MB)\n",
            "\u001b[K     |████████████████████████████████| 3.3 MB 67.8 MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (1.19.5)\n",
            "Collecting sacremoses\n",
            "  Downloading sacremoses-0.0.47-py2.py3-none-any.whl (895 kB)\n",
            "\u001b[K     |████████████████████████████████| 895 kB 71.9 MB/s \n",
            "\u001b[?25hCollecting huggingface-hub<1.0,>=0.1.0\n",
            "  Downloading huggingface_hub-0.4.0-py3-none-any.whl (67 kB)\n",
            "\u001b[K     |████████████████████████████████| 67 kB 4.4 MB/s \n",
            "\u001b[?25hRequirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (2019.12.20)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.7/dist-packages (from transformers) (4.62.3)\n",
            "Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from transformers) (4.8.2)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from transformers) (2.23.0)\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) (3.10.0.2)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=20.0->transformers) (3.0.6)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->transformers) (3.6.0)\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) (1.24.3)\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) (2021.10.8)\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: joblib in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (1.1.0)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (1.15.0)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (7.1.2)\n",
            "Installing collected packages: pyyaml, tokenizers, sacremoses, huggingface-hub, transformers\n",
            "  Attempting uninstall: pyyaml\n",
            "    Found existing installation: PyYAML 3.13\n",
            "    Uninstalling PyYAML-3.13:\n",
            "      Successfully uninstalled PyYAML-3.13\n",
            "Successfully installed huggingface-hub-0.4.0 pyyaml-6.0 sacremoses-0.0.47 tokenizers-0.10.3 transformers-4.15.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import train_test_split\n",
        "from tensorflow.keras.callbacks import EarlyStopping\n",
        "import re\n",
        "import os\n",
        "import urllib.request\n",
        "from tqdm import tqdm\n",
        "from transformers import BertTokenizer, TFBertForSequenceClassification"
      ],
      "metadata": {
        "id": "Qww_V4GzhgoS"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "urllib.request.urlretrieve(\"https://raw.githubusercontent.com/ukairia777/finance_sentiment_corpus/main/finance_data.csv\", filename=\"finance_data.csv\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8wC6Rnt6iCxB",
        "outputId": "46096ec2-749d-4374-9ab4-1775c4fe8d68"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('finance_data.csv', <http.client.HTTPMessage at 0x7f3c7df07a50>)"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv('finance_data.csv')\n",
        "print('총 샘플의 수 :',len(data))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1aP870z3iRc_",
        "outputId": "116c453f-517f-49cd-e220-c93a5aa0a9e3"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "총 샘플의 수 : 4846\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "9eEIdsG4iZYf",
        "outputId": "a2478e59-e507-4cbc-e2d9-e7d1a75e5e61"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>labels</th>\n",
              "      <th>sentence</th>\n",
              "      <th>kor_sentence</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>neutral</td>\n",
              "      <td>According to Gran, the company has no plans to...</td>\n",
              "      <td>Gran에 따르면, 그 회사는 회사가 성장하고 있는 곳이지만, 모든 생산을 러시아로...</td>\n",
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              "      <th>1</th>\n",
              "      <td>neutral</td>\n",
              "      <td>Technopolis plans to develop in stages an area...</td>\n",
              "      <td>테크노폴리스는 컴퓨터 기술과 통신 분야에서 일하는 회사들을 유치하기 위해 10만 평...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>negative</td>\n",
              "      <td>The international electronic industry company ...</td>\n",
              "      <td>국제 전자산업 회사인 엘코텍은 탈린 공장에서 수십 명의 직원을 해고했으며, 이전의 ...</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>positive</td>\n",
              "      <td>With the new production plant the company woul...</td>\n",
              "      <td>새로운 생산공장으로 인해 회사는 예상되는 수요 증가를 충족시킬 수 있는 능력을 증가...</td>\n",
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              "      <td>positive</td>\n",
              "      <td>According to the company's updated strategy fo...</td>\n",
              "      <td>2009-2012년 회사의 업데이트된 전략에 따르면, Basware는 20% - 4...</td>\n",
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              "      <th>4842</th>\n",
              "      <td>neutral</td>\n",
              "      <td>Rinkuskiai's beer sales fell by 6.5 per cent t...</td>\n",
              "      <td>린쿠스키아의 맥주 판매량은 416만 리터로 6.5% 감소했으며 카우노 알루스의 맥주...</td>\n",
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              "      <td>negative</td>\n",
              "      <td>Operating profit fell to EUR 35.4 mn from EUR ...</td>\n",
              "      <td>영업이익은 2007년 68.8 mn에서 35.4 mn으로 떨어졌으며, 선박 판매 이...</td>\n",
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              "      <td>negative</td>\n",
              "      <td>Net sales of the Paper segment decreased to EU...</td>\n",
              "      <td>페이퍼 부문 순매출은 2008년 2분기 241.1 mn에서 2009년 2분기 221...</td>\n",
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              "      <td>핀란드에서의 판매는 1월에 10.5% 감소한 반면, 국외에서의 판매는 17% 감소했다.</td>\n",
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            "text/plain": [
              "        labels  ...                                       kor_sentence\n",
              "0      neutral  ...  Gran에 따르면, 그 회사는 회사가 성장하고 있는 곳이지만, 모든 생산을 러시아로...\n",
              "1      neutral  ...  테크노폴리스는 컴퓨터 기술과 통신 분야에서 일하는 회사들을 유치하기 위해 10만 평...\n",
              "2     negative  ...  국제 전자산업 회사인 엘코텍은 탈린 공장에서 수십 명의 직원을 해고했으며, 이전의 ...\n",
              "3     positive  ...  새로운 생산공장으로 인해 회사는 예상되는 수요 증가를 충족시킬 수 있는 능력을 증가...\n",
              "4     positive  ...  2009-2012년 회사의 업데이트된 전략에 따르면, Basware는 20% - 4...\n",
              "...        ...  ...                                                ...\n",
              "4841  negative  ...  런던 마켓워치 -- 은행주의 반등이 FTSE 100지수의 약세를 상쇄하지 못하면서 ...\n",
              "4842   neutral  ...  린쿠스키아의 맥주 판매량은 416만 리터로 6.5% 감소했으며 카우노 알루스의 맥주...\n",
              "4843  negative  ...  영업이익은 2007년 68.8 mn에서 35.4 mn으로 떨어졌으며, 선박 판매 이...\n",
              "4844  negative  ...  페이퍼 부문 순매출은 2008년 2분기 241.1 mn에서 2009년 2분기 221...\n",
              "4845  negative  ...   핀란드에서의 판매는 1월에 10.5% 감소한 반면, 국외에서의 판매는 17% 감소했다.\n",
              "\n",
              "[4846 rows x 3 columns]"
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          },
          "metadata": {},
          "execution_count": 5
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['labels'] = data['labels'].replace(['neutral', 'positive', 'negative'],[0, 1, 2])\n",
        "data[:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
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              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>According to Gran, the company has no plans to...</td>\n",
              "      <td>Gran에 따르면, 그 회사는 회사가 성장하고 있는 곳이지만, 모든 생산을 러시아로...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>Technopolis plans to develop in stages an area...</td>\n",
              "      <td>테크노폴리스는 컴퓨터 기술과 통신 분야에서 일하는 회사들을 유치하기 위해 10만 평...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>The international electronic industry company ...</td>\n",
              "      <td>국제 전자산업 회사인 엘코텍은 탈린 공장에서 수십 명의 직원을 해고했으며, 이전의 ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>With the new production plant the company woul...</td>\n",
              "      <td>새로운 생산공장으로 인해 회사는 예상되는 수요 증가를 충족시킬 수 있는 능력을 증가...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>According to the company's updated strategy fo...</td>\n",
              "      <td>2009-2012년 회사의 업데이트된 전략에 따르면, Basware는 20% - 4...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b41c85e7-779f-43e4-9566-0a1e681cda24')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
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              "\n",
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              "      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",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b41c85e7-779f-43e4-9566-0a1e681cda24 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-b41c85e7-779f-43e4-9566-0a1e681cda24');\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",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   labels  ...                                       kor_sentence\n",
              "0       0  ...  Gran에 따르면, 그 회사는 회사가 성장하고 있는 곳이지만, 모든 생산을 러시아로...\n",
              "1       0  ...  테크노폴리스는 컴퓨터 기술과 통신 분야에서 일하는 회사들을 유치하기 위해 10만 평...\n",
              "2       2  ...  국제 전자산업 회사인 엘코텍은 탈린 공장에서 수십 명의 직원을 해고했으며, 이전의 ...\n",
              "3       1  ...  새로운 생산공장으로 인해 회사는 예상되는 수요 증가를 충족시킬 수 있는 능력을 증가...\n",
              "4       1  ...  2009-2012년 회사의 업데이트된 전략에 따르면, Basware는 20% - 4...\n",
              "\n",
              "[5 rows x 3 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "del data['kor_sentence']"
      ],
      "metadata": {
        "id": "RNyrW0ynix3O"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data[:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "5Pz9t4U2i35Q",
        "outputId": "6e08d869-c443-4852-e716-de82dd89d79b"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-b9a9edba-a510-4d9f-9a03-6090b4665474\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "      <th></th>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>According to Gran, the company has no plans to...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
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              "      <td>Technopolis plans to develop in stages an area...</td>\n",
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              "    <tr>\n",
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              "      <td>2</td>\n",
              "      <td>The international electronic industry company ...</td>\n",
              "    </tr>\n",
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              "      <td>1</td>\n",
              "      <td>With the new production plant the company woul...</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>According to the company's updated strategy fo...</td>\n",
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              "  </tbody>\n",
              "</table>\n",
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              "\n",
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              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b9a9edba-a510-4d9f-9a03-6090b4665474 button.colab-df-convert');\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b9a9edba-a510-4d9f-9a03-6090b4665474');\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",
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              "        }\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   labels                                           sentence\n",
              "0       0  According to Gran, the company has no plans to...\n",
              "1       0  Technopolis plans to develop in stages an area...\n",
              "2       2  The international electronic industry company ...\n",
              "3       1  With the new production plant the company woul...\n",
              "4       1  According to the company's updated strategy fo..."
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b7EW631ui4OA",
        "outputId": "405773f2-398f-40d4-9268-18ee9a42cfde"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 4846 entries, 0 to 4845\n",
            "Data columns (total 2 columns):\n",
            " #   Column    Non-Null Count  Dtype \n",
            "---  ------    --------------  ----- \n",
            " 0   labels    4846 non-null   int64 \n",
            " 1   sentence  4846 non-null   object\n",
            "dtypes: int64(1), object(1)\n",
            "memory usage: 75.8+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('결측값 여부 :',data.isnull().values.any())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JkvjJa1Ji7Eo",
        "outputId": "746dca80-c891-4b66-9cc1-1a52ce1e8285"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "결측값 여부 : False\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('kor_sentence 열의 유니크한 값 :',data['sentence'].nunique())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UoAyW4Lii8xX",
        "outputId": "13f3a4dc-b16d-4d12-b5c9-5e83f17433a2"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "kor_sentence 열의 유니크한 값 : 4838\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "duplicate = data[data.duplicated()]"
      ],
      "metadata": {
        "id": "cks_QRBfjKBU"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "duplicate"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "ZwkZ7T0BjL7d",
        "outputId": "11531591-3b61-48f3-cf1d-6379c6126c16"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    .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",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-6e1d2e78-6037-4464-a89d-f004ab93bc28 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-6e1d2e78-6037-4464-a89d-f004ab93bc28');\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",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "      labels                                           sentence\n",
              "1099       0  The issuer is solely responsible for the conte...\n",
              "1416       0  The report profiles 614 companies including ma...\n",
              "2396       0  Ahlstrom's share is quoted on the NASDAQ OMX H...\n",
              "2567       0  SSH Communications Security Corporation is hea...\n",
              "3094       0  Proha Plc ( Euronext :7327 ) announced today (...\n",
              "3206       0  The company serves customers in various indust..."
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 중복 제거\n",
        "data.drop_duplicates(subset=['sentence'], inplace=True)\n",
        "print('총 샘플의 수 :',len(data))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "prnB1Yg5jBt2",
        "outputId": "363bcc24-83d2-4a2a-a974-76e30c05ed35"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "총 샘플의 수 : 4838\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['labels'].value_counts().plot(kind='bar')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "StnulPDqjuKX",
        "outputId": "66783d85-69c2-42de-b4c4-edeabfbf5ab8"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f3c7d66cf90>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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yNwvT1cLx7p0RSfKfwOVV9b/n1C8BjlTV2oXpTBcqyX9X1aqF7kNTS/Iw8K6qej7JauBrwJer6u+S/LCq3r6gDY6Y0zuj8yvgDcBT59SXtW16CUvy0HSbgKWj7EWz9qqzUzpVdTTJlcDXkryRydevK4b+6HwEOJDkCX79o3OrgDcDtyxYVxrWUuAa4Nlz6gH+bfTtaBaeTrK+qh4EaFf8fwrsBP5oYVsbPUN/RKrqO0newuTPSw9+kHuwql5cuM40pG8Brz0bHIOS/Ovo29Es3AicGSxU1RngxiR/vzAtLRzn9CWpI969I0kdMfQlqSOGviR1xNCXpI4Y+pLUkf8DFLnXLhexNawAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('레이블의 분포')\n",
        "print(data.groupby('labels').size().reset_index(name='count'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dP_BFHlojwiN",
        "outputId": "eec1d8c5-18bb-497c-eeef-0b2388e6b4f9"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "레이블의 분포\n",
            "   labels  count\n",
            "0       0   2872\n",
            "1       1   1362\n",
            "2       2    604\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f'중립의 비율 = {round(data[\"labels\"].value_counts()[0]/len(data) * 100,3)}%')\n",
        "print(f'긍정의 비율 = {round(data[\"labels\"].value_counts()[1]/len(data) * 100,3)}%')\n",
        "print(f'부정의 비율 = {round(data[\"labels\"].value_counts()[2]/len(data) * 100,3)}%')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CfQdxYqMj3o8",
        "outputId": "17f3d6aa-c22d-49c8-d9a2-c0e8b1ded3e6"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "중립의 비율 = 59.363%\n",
            "긍정의 비율 = 28.152%\n",
            "부정의 비율 = 12.484%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "jKKmVrDRk46e",
        "outputId": "f3cb104a-891f-49af-aa4d-ac2fdc5a948b"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-8ebb8c26-ef8f-482c-ab41-935c9634bcfd\">\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",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>labels</th>\n",
              "      <th>sentence</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>According to Gran, the company has no plans to...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>Technopolis plans to develop in stages an area...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>The international electronic industry company ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>With the new production plant the company woul...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>According to the company's updated strategy fo...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4841</th>\n",
              "      <td>2</td>\n",
              "      <td>LONDON MarketWatch -- Share prices ended lower...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4842</th>\n",
              "      <td>0</td>\n",
              "      <td>Rinkuskiai's beer sales fell by 6.5 per cent t...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4843</th>\n",
              "      <td>2</td>\n",
              "      <td>Operating profit fell to EUR 35.4 mn from EUR ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4844</th>\n",
              "      <td>2</td>\n",
              "      <td>Net sales of the Paper segment decreased to EU...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4845</th>\n",
              "      <td>2</td>\n",
              "      <td>Sales in Finland decreased by 10.5 % in Januar...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>4838 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8ebb8c26-ef8f-482c-ab41-935c9634bcfd')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\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",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\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",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-8ebb8c26-ef8f-482c-ab41-935c9634bcfd 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-8ebb8c26-ef8f-482c-ab41-935c9634bcfd');\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",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "      labels                                           sentence\n",
              "0          0  According to Gran, the company has no plans to...\n",
              "1          0  Technopolis plans to develop in stages an area...\n",
              "2          2  The international electronic industry company ...\n",
              "3          1  With the new production plant the company woul...\n",
              "4          1  According to the company's updated strategy fo...\n",
              "...      ...                                                ...\n",
              "4841       2  LONDON MarketWatch -- Share prices ended lower...\n",
              "4842       0  Rinkuskiai's beer sales fell by 6.5 per cent t...\n",
              "4843       2  Operating profit fell to EUR 35.4 mn from EUR ...\n",
              "4844       2  Net sales of the Paper segment decreased to EU...\n",
              "4845       2  Sales in Finland decreased by 10.5 % in Januar...\n",
              "\n",
              "[4838 rows x 2 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_data = data['sentence']\n",
        "y_data = data['labels']\n",
        "print('본문의 개수: {}'.format(len(X_data)))\n",
        "print('레이블의 개수: {}'.format(len(y_data)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l8foVC2rkJtF",
        "outputId": "9c1b3112-0577-4de2-f4fc-8b94810fa4a5"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "본문의 개수: 4838\n",
            "레이블의 개수: 4838\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X_data, y_data, test_size=0.2, random_state=0, stratify=y_data)"
      ],
      "metadata": {
        "id": "qmFecWmSkHiu"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('훈련 샘플의 개수 :', len(X_train))\n",
        "print('테스트 샘플의 개수 :', len(X_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HiPJQoBql2T_",
        "outputId": "40f91f9f-1719-4a8b-f30e-90d8b0918862"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "훈련 샘플의 개수 : 3870\n",
            "테스트 샘플의 개수 : 968\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('--------훈련 데이터의 비율-----------')\n",
        "print(f'중립 = {round(y_train.value_counts()[0]/len(y_train) * 100,3)}%')\n",
        "print(f'긍정 = {round(y_train.value_counts()[1]/len(y_train) * 100,3)}%')\n",
        "print(f'부정 = {round(y_train.value_counts()[2]/len(y_train) * 100,3)}%')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m-gTg1Z2kYK2",
        "outputId": "6d7b0f40-7067-4a54-9a0b-2cd284f5df83"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--------훈련 데이터의 비율-----------\n",
            "중립 = 59.354%\n",
            "긍정 = 28.165%\n",
            "부정 = 12.481%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('--------테스트 데이터의 비율-----------')\n",
        "print(f'중립 = {round(y_test.value_counts()[0]/len(y_test) * 100,3)}%')\n",
        "print(f'긍정 = {round(y_test.value_counts()[1]/len(y_test) * 100,3)}%')\n",
        "print(f'부정 = {round(y_test.value_counts()[2]/len(y_test) * 100,3)}%')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6E_XYZIVkcP3",
        "outputId": "cde88dcf-6335-42ac-de86-dece02881807"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--------테스트 데이터의 비율-----------\n",
            "중립 = 59.401%\n",
            "긍정 = 28.099%\n",
            "부정 = 12.5%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "max_seq_len = 128"
      ],
      "metadata": {
        "id": "WqLBfWmypQ6Y"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
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    {
      "cell_type": "code",
      "source": [
        "def convert_examples_to_features(examples, labels, max_seq_len, tokenizer):\n",
        "\n",
        "    input_ids, attention_masks, token_type_ids, data_labels = [], [], [], []\n",
        "\n",
        "    for example, label in tqdm(zip(examples, labels), total=len(examples)):\n",
        "        # input_id는 워드 임베딩을 위한 문장의 정수 인코딩\n",
        "        input_id = tokenizer.encode(example, max_length=max_seq_len, pad_to_max_length=True)\n",
        "\n",
        "        # attention_mask는 실제 단어가 위치하면 1, 패딩의 위치에는 0인 시퀀스.\n",
        "        padding_count = input_id.count(tokenizer.pad_token_id)\n",
        "        attention_mask = [1] * (max_seq_len - padding_count) + [0] * padding_count\n",
        "\n",
        "        # token_type_id은 세그먼트 인코딩\n",
        "        token_type_id = [0] * max_seq_len\n",
        "\n",
        "        assert len(input_id) == max_seq_len, \"Error with input length {} vs {}\".format(len(input_id), max_seq_len)\n",
        "        assert len(attention_mask) == max_seq_len, \"Error with attention mask length {} vs {}\".format(len(attention_mask), max_seq_len)\n",
        "        assert len(token_type_id) == max_seq_len, \"Error with token type length {} vs {}\".format(len(token_type_id), max_seq_len)\n",
        "\n",
        "        input_ids.append(input_id)\n",
        "        attention_masks.append(attention_mask)\n",
        "        token_type_ids.append(token_type_id)\n",
        "        data_labels.append(label)\n",
        "\n",
        "    input_ids = np.array(input_ids, dtype=int)\n",
        "    attention_masks = np.array(attention_masks, dtype=int)\n",
        "    token_type_ids = np.array(token_type_ids, dtype=int)\n",
        "\n",
        "    data_labels = np.asarray(data_labels, dtype=np.int32)\n",
        "\n",
        "    return (input_ids, attention_masks, token_type_ids), data_labels"
      ],
      "metadata": {
        "id": "6Lx0JeclpCd3"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_X, train_y = convert_examples_to_features(X_train, y_train, max_seq_len=max_seq_len, tokenizer=tokenizer)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K6GwA8ropGXJ",
        "outputId": "031fc02b-66f1-42c5-83a7-b94b3f8390d5"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "  0%|          | 0/3870 [00:00<?, ?it/s]Truncation was not explicitly activated but `max_length` is provided a specific value, please use `truncation=True` to explicitly truncate examples to max length. Defaulting to 'longest_first' truncation strategy. If you encode pairs of sequences (GLUE-style) with the tokenizer you can select this strategy more precisely by providing a specific strategy to `truncation`.\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2232: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
            "  FutureWarning,\n",
            "100%|██████████| 3870/3870 [00:03<00:00, 1247.93it/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_X, test_y = convert_examples_to_features(X_test, y_test, max_seq_len=max_seq_len, tokenizer=tokenizer)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FSUWW7XFphCU",
        "outputId": "b64bd2c3-fdf7-4135-c844-33139273bed6"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r  0%|          | 0/968 [00:00<?, ?it/s]/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2232: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
            "  FutureWarning,\n",
            "100%|██████████| 968/968 [00:00<00:00, 1329.88it/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "input_id = train_X[0][0]\n",
        "attention_mask = train_X[1][0]\n",
        "token_type_id = train_X[2][0]\n",
        "label = train_y[0]\n",
        "\n",
        "print('단어에 대한 정수 인코딩 :',input_id)\n",
        "print('어텐션 마스크 :',attention_mask)\n",
        "print('세그먼트 인코딩 :',token_type_id)\n",
        "print('각 인코딩의 길이 :', len(input_id))\n",
        "print('정수 인코딩 복원 :',tokenizer.decode(input_id))\n",
        "print('레이블 :',label)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M9H0TQaUpl6q",
        "outputId": "b7b76eef-823d-4abf-c70d-84f1f3486a86"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "단어에 대한 정수 인코딩 : [  101 12849  2638  2003  3205  2006  1996 13649  3863  1999 12331  1012\n",
            "   102     0     0     0     0     0     0     0     0     0     0     0\n",
            "     0     0     0     0     0     0     0     0     0     0     0     0\n",
            "     0     0     0     0     0     0     0     0     0     0     0     0\n",
            "     0     0     0     0     0     0     0     0     0     0     0     0\n",
            "     0     0     0     0     0     0     0     0     0     0     0     0\n",
            "     0     0     0     0     0     0     0     0     0     0     0     0\n",
            "     0     0     0     0     0     0     0     0     0     0     0     0\n",
            "     0     0     0     0     0     0     0     0     0     0     0     0\n",
            "     0     0     0     0     0     0     0     0     0     0     0     0\n",
            "     0     0     0     0     0     0     0     0]\n",
            "어텐션 마스크 : [1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
            "세그먼트 인코딩 : [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
            "각 인코딩의 길이 : 128\n",
            "정수 인코딩 복원 : [CLS] kone is listed on the nordic exchange in helsinki. [SEP] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD]\n",
            "레이블 : 0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# TPU 작동을 위한 코드\n",
        "resolver = tf.distribute.cluster_resolver.TPUClusterResolver(tpu='grpc://' + os.environ['COLAB_TPU_ADDR'])\n",
        "tf.config.experimental_connect_to_cluster(resolver)\n",
        "tf.tpu.experimental.initialize_tpu_system(resolver)\n",
        "\n",
        "strategy = tf.distribute.experimental.TPUStrategy(resolver)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ltdAJTeIp1_b",
        "outputId": "1637c9e4-6efc-41d3-8814-905e6b6f5984"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:Deallocate tpu buffers before initializing tpu system.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:Deallocate tpu buffers before initializing tpu system.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "WARNING:tensorflow:TPU system grpc://10.125.98.34:8470 has already been initialized. Reinitializing the TPU can cause previously created variables on TPU to be lost.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:TPU system grpc://10.125.98.34:8470 has already been initialized. Reinitializing the TPU can cause previously created variables on TPU to be lost.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:Initializing the TPU system: grpc://10.125.98.34:8470\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:Initializing the TPU system: grpc://10.125.98.34:8470\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:Finished initializing TPU system.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:Finished initializing TPU system.\n",
            "WARNING:absl:`tf.distribute.experimental.TPUStrategy` is deprecated, please use  the non experimental symbol `tf.distribute.TPUStrategy` instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:Found TPU system:\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:Found TPU system:\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:*** Num TPU Cores: 8\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:*** Num TPU Cores: 8\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:*** Num TPU Workers: 1\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:*** Num TPU Workers: 1\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:*** Num TPU Cores Per Worker: 8\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:*** Num TPU Cores Per Worker: 8\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:localhost/replica:0/task:0/device:CPU:0, CPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:localhost/replica:0/task:0/device:CPU:0, CPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:CPU:0, CPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:CPU:0, CPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:0, TPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:0, TPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:1, TPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:1, TPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:2, TPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:2, TPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:3, TPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:3, TPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:4, TPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:4, TPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:5, TPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:5, TPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:6, TPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:6, TPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:7, TPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:7, TPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU_SYSTEM:0, TPU_SYSTEM, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU_SYSTEM:0, TPU_SYSTEM, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:XLA_CPU:0, XLA_CPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:XLA_CPU:0, XLA_CPU, 0, 0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "with strategy.scope():\n",
        "  model = TFBertForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=3)\n",
        "  optimizer = tf.keras.optimizers.Adam(learning_rate=5e-5)\n",
        "  loss = tf.keras.losses.SparseCategoricalCrossentropy()\n",
        "  model.compile(optimizer=optimizer, loss=model.compute_loss, metrics = ['accuracy'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Bl2kHoBup9Di",
        "outputId": "ab14e144-a559-4994-c489-5a1d932fb7fc"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "All model checkpoint layers were used when initializing TFBertForSequenceClassification.\n",
            "\n",
            "Some layers of TFBertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "early_stopping = EarlyStopping(\n",
        "    monitor=\"val_accuracy\", \n",
        "    min_delta=0.001,\n",
        "    patience=2)\n",
        "\n",
        "model.fit(\n",
        "    train_X, train_y, epochs=3, batch_size=32, validation_split=0.2,\n",
        "    callbacks = [early_stopping]\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "huY4p9xPCSIN",
        "outputId": "06c53c64-0889-40b9-9cab-1fe9ecea47d5"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/3\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:absl:TPU has inputs with dynamic shapes: [<tf.Tensor 'Const:0' shape=() dtype=int32>, <tf.Tensor 'cond/Identity:0' shape=(None, 128) dtype=int64>, <tf.Tensor 'cond/Identity_8:0' shape=(None, 128) dtype=int64>, <tf.Tensor 'cond/Identity_16:0' shape=(None, 128) dtype=int64>, <tf.Tensor 'cond/Identity_24:0' shape=(None,) dtype=int32>]\n",
            "INFO:absl:TPU has inputs with dynamic shapes: [<tf.Tensor 'Const:0' shape=() dtype=int32>, <tf.Tensor 'cond/Identity:0' shape=(None, 128) dtype=int64>, <tf.Tensor 'cond/Identity_8:0' shape=(None, 128) dtype=int64>, <tf.Tensor 'cond/Identity_16:0' shape=(None, 128) dtype=int64>, <tf.Tensor 'cond/Identity_24:0' shape=(None,) dtype=int32>]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "97/97 [==============================] - ETA: 0s - loss: 0.5788 - accuracy: 0.7529"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:absl:TPU has inputs with dynamic shapes: [<tf.Tensor 'Const:0' shape=() dtype=int32>, <tf.Tensor 'cond/Identity:0' shape=(None, 128) dtype=int64>, <tf.Tensor 'cond/Identity_8:0' shape=(None, 128) dtype=int64>, <tf.Tensor 'cond/Identity_16:0' shape=(None, 128) dtype=int64>, <tf.Tensor 'cond/Identity_24:0' shape=(None,) dtype=int32>]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "97/97 [==============================] - 147s 725ms/step - loss: 0.5788 - accuracy: 0.7529 - val_loss: 0.4064 - val_accuracy: 0.8333\n",
            "Epoch 2/3\n",
            "97/97 [==============================] - 8s 84ms/step - loss: 0.2787 - accuracy: 0.8976 - val_loss: 0.3676 - val_accuracy: 0.8450\n",
            "Epoch 3/3\n",
            "97/97 [==============================] - 8s 84ms/step - loss: 0.1448 - accuracy: 0.9490 - val_loss: 0.4550 - val_accuracy: 0.8605\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f3bf82ff510>"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.evaluate(test_X, test_y, batch_size=1024)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "56o_VE9vCTpF",
        "outputId": "0c8b2ccf-1f56-458b-a7cb-e9f48a650646"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 8s 8s/step - loss: 0.4848 - accuracy: 0.8585\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.4848063886165619, 0.8584710359573364]"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    }
  ]
}