아이코 KoBERT 모델.ipynb

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  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# **KoBERT 모델**"
      ],
      "metadata": {
        "id": "LXcx-2ob7z73"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "참고 코드: https://velog.io/@seolini43/KOBERT%EB%A1%9C-%EB%8B%A4%EC%A4%91-%EB%B6%84%EB%A5%98-%EB%AA%A8%EB%8D%B8-%EB%A7%8C%EB%93%A4%EA%B8%B0-%ED%8C%8C%EC%9D%B4%EC%8D%ACColab"
      ],
      "metadata": {
        "id": "_gxBzY9e9JlN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iGQL-0QO0gS1",
        "outputId": "f81a3729-b6ee-4fa2-841f-c061b5c00202"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 데이터 전처리"
      ],
      "metadata": {
        "id": "pfqXOaII-kRo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#train data 불러오기\n",
        "import pandas as pd\n",
        "filename = '/content/drive/MyDrive/인공지능프로그래밍/project/data/train5.csv'\n",
        "data = pd.read_csv(filename)\n",
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "-0Ro2l4U02sz",
        "outputId": "efc859e1-e12e-48f3-f506-26c7eb19d297"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
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              "   talker  unethical text  intensity  chat          type  \\\n",
              "0       2            True        2.0     2        SEXUAL   \n",
              "1       3            True        2.0     3        SEXUAL   \n",
              "2       3           False        0.0     2  IMMORAL_NONE   \n",
              "3       1            True        2.0     1        SEXUAL   \n",
              "4       2            True        2.0     2        SEXUAL   \n",
              "\n",
              "                     text  \n",
              "0  특히 벗방보는 애들은 진짜 거세 시켜야함  \n",
              "1      볼 게 없어서 벗방을 보냐 ㅋㅋㅋ  \n",
              "2     쉴드가 아니라 국가가 면제해준거야.  \n",
              "3    에로배우들을 데려다가 아이돌을 만드네  \n",
              "4       쟤네들은 표정이 딱 야동인데..  "
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              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-248e2e3f-3654-4f97-a72f-d0a7d920427a');\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",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "####이진 분류의 경우의 라벨인코딩(욕설을 1, 나머지는 0)\n",
        "data.loc[(data['type'] == \"IMMORAL_NONE\"), 'type'] = 0 #도덕, 무도덕 => 0\n",
        "data.loc[(data['type'] == \"DISCRIMINATION\"), 'type'] = 0 #차별 => 0\n",
        "data.loc[(data['type'] == \"SEXUAL\"), 'type'] = 0 #선정 => 0\n",
        "data.loc[(data['type'] == \"VIOLENCE\"), 'type'] = 0 #폭력 => 0\n",
        "data.loc[(data['type'] == \"ABUSE\"), 'type'] = 1 #욕설 => 1"
      ],
      "metadata": {
        "id": "ejTlAWvb1vOC"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "####다중 분류의 경우의 라벨인코딩\n",
        "data.loc[(data['type'] == \"IMMORAL_NONE\"), 'type'] = 0 #도덕, 무도덕 => 0\n",
        "data.loc[(data['type'] == \"DISCRIMINATION\"), 'type'] = 1 #차별 => 1\n",
        "data.loc[(data['type'] == \"SEXUAL\"), 'type'] = 2 #선정 => 2\n",
        "data.loc[(data['type'] == \"VIOLENCE\"), 'type'] = 3 #폭력 => 3\n",
        "data.loc[(data['type'] == \"ABUSE\"), 'type'] = 4 #욕설 => 4"
      ],
      "metadata": {
        "id": "vKi6QI_27Y2x"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_data = data[['text','type']].to_numpy()\n",
        "train_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0_NwFH0t1mwZ",
        "outputId": "89344999-fc59-49b1-e078-a52eaef2a278"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([['특히 벗방보는 애들은 진짜 거세 시켜야함', 0],\n",
              "       ['볼 게 없어서 벗방을 보냐 ㅋㅋㅋ', 0],\n",
              "       ['쉴드가 아니라 국가가 면제해준거야.', 0],\n",
              "       ...,\n",
              "       ['누가 안 뺏어먹으니까 추접스럽게 후루룩거리지 말라고 걸뱅아', 1],\n",
              "       ['진짜 운전 개좉같이 하네', 1],\n",
              "       ['아 진짜 뚝배기 개 깨고 싶네', 1]], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#val data 불러오기\n",
        "filename2 = '/content/drive/MyDrive/인공지능프로그래밍/project/data/val5.csv'\n",
        "data2 = pd.read_csv(filename2)\n",
        "data2.head()"
      ],
      "metadata": {
        "id": "9yQt_aW974J0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "e340dcf6-fea5-40c3-8a33-04bde6b4ea66"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   talker  unethical text  intensity  chat          type  \\\n",
              "0       1           False        0.0     1  IMMORAL_NONE   \n",
              "1       2           False        0.0     1  IMMORAL_NONE   \n",
              "2       1           False        0.0     1  IMMORAL_NONE   \n",
              "3       4           False        0.0     1  IMMORAL_NONE   \n",
              "4       9           False        0.0     2  IMMORAL_NONE   \n",
              "\n",
              "                             text  \n",
              "0             내 남동생은 키가 왜이렇게 안크지?  \n",
              "1             지금이 딱 성장기라 클 땐데 ...  \n",
              "2  근데 학교 폭력은 피해자 한테도 잘못이 있는 것 같아.  \n",
              "3              부모님 치매 걸리면 어떻게 살아?  \n",
              "4               그거 너무 차별적인 것 같은데.  "
            ],
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              "\n",
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              "      <div>\n",
              "<style scoped>\n",
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              "      <td>지금이 딱 성장기라 클 땐데 ...</td>\n",
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              "      <td>2</td>\n",
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              "      <td>그거 너무 차별적인 것 같은데.</td>\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "    .colab-df-convert:hover {\n",
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              "      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-35917c57-355f-4eec-afc8-196fe9d81fa7 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-35917c57-355f-4eec-afc8-196fe9d81fa7');\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",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "####이진 분류일 경우의 라벨인코딩(욕설을 1, 나머지는 0)\n",
        "data2.loc[(data2['type'] == \"IMMORAL_NONE\"), 'type'] = 0 #도덕, 무도덕 => 0\n",
        "data2.loc[(data2['type'] == \"DISCRIMINATION\"), 'type'] = 0 #차별 => 0\n",
        "data2.loc[(data2['type'] == \"SEXUAL\"), 'type'] = 0 #선정 => 0\n",
        "data2.loc[(data2['type'] == \"VIOLENCE\"), 'type'] = 0 #폭력 => 0\n",
        "data2.loc[(data2['type'] == \"ABUSE\"), 'type'] = 1 #욕설 => 1"
      ],
      "metadata": {
        "id": "e9Iig-0G7qux"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "####다중 분류일 경우의 라벨인코딩\n",
        "data2.loc[(data2['type'] == \"IMMORAL_NONE\"), 'type'] = 0 #도덕, 무도덕 => 0\n",
        "data2.loc[(data2['type'] == \"DISCRIMINATION\"), 'type'] = 1 #차별 => 1\n",
        "data2.loc[(data2['type'] == \"SEXUAL\"), 'type'] = 2 #선정 => 2\n",
        "data2.loc[(data2['type'] == \"VIOLENCE\"), 'type'] = 3 #폭력 => 3\n",
        "data2.loc[(data2['type'] == \"ABUSE\"), 'type'] = 4 #욕설 => 4"
      ],
      "metadata": {
        "id": "uNrUmtym8Urv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "val_data = data2[['text','type']].to_numpy()\n",
        "val_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kQcHHC567uFX",
        "outputId": "856d2ebd-3571-4905-8f42-6f8f94ec4256"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([['내 남동생은 키가 왜이렇게 안크지?', 0],\n",
              "       ['지금이 딱 성장기라 클 땐데 ...', 0],\n",
              "       ['근데 학교 폭력은 피해자 한테도 잘못이 있는 것 같아.', 0],\n",
              "       ...,\n",
              "       ['남자 아래에 깔려서 당하고 싶은건가?', 0],\n",
              "       ['저정도면 놀아달라는 거 아닌가? 저 라인봐봐 만지면 으스러지겠어', 0],\n",
              "       ['저 언덕 사이에 손을 넣고 주물러주면 그럴걸 ㅋㅋ 본인도 바라고 있지 않을까?', 0]], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 학습 모델"
      ],
      "metadata": {
        "id": "Li4KV_QL39WL"
      }
    },
    {
      "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"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NZQo2i2Q4OpK",
        "outputId": "1db6de1a-1880-4e16-9748-d4ec7d73e79e"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting mxnet\n",
            "  Downloading mxnet-1.9.1-py3-none-manylinux2014_x86_64.whl (49.1 MB)\n",
            "\u001b[K     |████████████████████████████████| 49.1 MB 1.2 MB/s \n",
            "\u001b[?25hCollecting graphviz<0.9.0,>=0.8.1\n",
            "  Downloading graphviz-0.8.4-py2.py3-none-any.whl (16 kB)\n",
            "Requirement already satisfied: requests<3,>=2.20.0 in /usr/local/lib/python3.8/dist-packages (from mxnet) (2.23.0)\n",
            "Requirement already satisfied: numpy<2.0.0,>1.16.0 in /usr/local/lib/python3.8/dist-packages (from mxnet) (1.21.6)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.8/dist-packages (from requests<3,>=2.20.0->mxnet) (2022.9.24)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.8/dist-packages (from requests<3,>=2.20.0->mxnet) (1.24.3)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.8/dist-packages (from requests<3,>=2.20.0->mxnet) (3.0.4)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.8/dist-packages (from requests<3,>=2.20.0->mxnet) (2.10)\n",
            "Installing collected packages: graphviz, mxnet\n",
            "  Attempting uninstall: graphviz\n",
            "    Found existing installation: graphviz 0.10.1\n",
            "    Uninstalling graphviz-0.10.1:\n",
            "      Successfully uninstalled graphviz-0.10.1\n",
            "Successfully installed graphviz-0.8.4 mxnet-1.9.1\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting gluonnlp\n",
            "  Downloading gluonnlp-0.10.0.tar.gz (344 kB)\n",
            "\u001b[K     |████████████████████████████████| 344 kB 31.2 MB/s \n",
            "\u001b[?25hRequirement already satisfied: pandas in /usr/local/lib/python3.8/dist-packages (1.3.5)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.8/dist-packages (4.64.1)\n",
            "Requirement already satisfied: numpy>=1.16.0 in /usr/local/lib/python3.8/dist-packages (from gluonnlp) (1.21.6)\n",
            "Requirement already satisfied: cython in /usr/local/lib/python3.8/dist-packages (from gluonnlp) (0.29.32)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.8/dist-packages (from gluonnlp) (21.3)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.8/dist-packages (from pandas) (2022.6)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.8/dist-packages (from pandas) (2.8.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.8/dist-packages (from python-dateutil>=2.7.3->pandas) (1.15.0)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.8/dist-packages (from packaging->gluonnlp) (3.0.9)\n",
            "Building wheels for collected packages: gluonnlp\n",
            "  Building wheel for gluonnlp (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for gluonnlp: filename=gluonnlp-0.10.0-cp38-cp38-linux_x86_64.whl size=619635 sha256=d55015ecaa074c14bfbba5d133a2e56de921edd7f67f8da19be07e8c5cf9b438\n",
            "  Stored in directory: /root/.cache/pip/wheels/b6/93/9d/2237550c409eb3ed725d6302b7897ddd9a037b40cef66dcd9c\n",
            "Successfully built gluonnlp\n",
            "Installing collected packages: gluonnlp\n",
            "Successfully installed gluonnlp-0.10.0\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting sentencepiece\n",
            "  Downloading sentencepiece-0.1.97-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\n",
            "\u001b[K     |████████████████████████████████| 1.3 MB 28.0 MB/s \n",
            "\u001b[?25hInstalling collected packages: sentencepiece\n",
            "Successfully installed sentencepiece-0.1.97\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting transformers==3.0.2\n",
            "  Downloading transformers-3.0.2-py3-none-any.whl (769 kB)\n",
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            "\u001b[?25hRequirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.8/dist-packages (from transformers==3.0.2) (4.64.1)\n",
            "Collecting sacremoses\n",
            "  Downloading sacremoses-0.0.53.tar.gz (880 kB)\n",
            "\u001b[K     |████████████████████████████████| 880 kB 61.5 MB/s \n",
            "\u001b[?25hRequirement already satisfied: sentencepiece!=0.1.92 in /usr/local/lib/python3.8/dist-packages (from transformers==3.0.2) (0.1.97)\n",
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            "Collecting tokenizers==0.8.1.rc1\n",
            "  Downloading tokenizers-0.8.1rc1-cp38-cp38-manylinux1_x86_64.whl (3.0 MB)\n",
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            "Requirement already satisfied: six in /usr/local/lib/python3.8/dist-packages (from sacremoses->transformers==3.0.2) (1.15.0)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.8/dist-packages (from sacremoses->transformers==3.0.2) (7.1.2)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.8/dist-packages (from sacremoses->transformers==3.0.2) (1.2.0)\n",
            "Building wheels for collected packages: sacremoses\n",
            "  Building wheel for sacremoses (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for sacremoses: filename=sacremoses-0.0.53-py3-none-any.whl size=895260 sha256=6a4661931dcce2e43f170cc6e27340e7995ff19d8f1aa2e2863999756aca81f7\n",
            "  Stored in directory: /root/.cache/pip/wheels/82/ab/9b/c15899bf659ba74f623ac776e861cf2eb8608c1825ddec66a4\n",
            "Successfully built sacremoses\n",
            "Installing collected packages: tokenizers, sacremoses, transformers\n",
            "Successfully installed sacremoses-0.0.53 tokenizers-0.8.1rc1 transformers-3.0.2\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: torch in /usr/local/lib/python3.8/dist-packages (1.12.1+cu113)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.8/dist-packages (from torch) (4.1.1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install git+https://git@github.com/SKTBrain/KoBERT.git@master"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jwp4zt3i4K-5",
        "outputId": "2a21f749-e3e1-42a4-d326-a7924a6ca47a"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting git+https://****@github.com/SKTBrain/KoBERT.git@master\n",
            "  Cloning https://****@github.com/SKTBrain/KoBERT.git (to revision master) to /tmp/pip-req-build-0q3vd4zc\n",
            "  Running command git clone -q 'https://****@github.com/SKTBrain/KoBERT.git' /tmp/pip-req-build-0q3vd4zc\n",
            "Collecting boto3<=1.15.18\n",
            "  Downloading boto3-1.15.18-py2.py3-none-any.whl (129 kB)\n",
            "\u001b[K     |████████████████████████████████| 129 kB 22.0 MB/s \n",
            "\u001b[?25hRequirement already satisfied: gluonnlp<=0.10.0,>=0.6.0 in /usr/local/lib/python3.8/dist-packages (from kobert==0.2.3) (0.10.0)\n",
            "Collecting mxnet<=1.7.0.post2,>=1.4.0\n",
            "  Downloading mxnet-1.7.0.post2-py2.py3-none-manylinux2014_x86_64.whl (54.7 MB)\n",
            "\u001b[K     |████████████████████████████████| 54.7 MB 37 kB/s \n",
            "\u001b[?25hCollecting onnxruntime<=1.8.0,==1.8.0\n",
            "  Downloading onnxruntime-1.8.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.5 MB)\n",
            "\u001b[K     |████████████████████████████████| 4.5 MB 16.5 MB/s \n",
            "\u001b[?25hCollecting sentencepiece<=0.1.96,>=0.1.6\n",
            "  Downloading sentencepiece-0.1.96-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.2 MB)\n",
            "\u001b[K     |████████████████████████████████| 1.2 MB 44.1 MB/s \n",
            "\u001b[?25hCollecting torch<=1.10.1,>=1.7.0\n",
            "  Downloading torch-1.10.1-cp38-cp38-manylinux1_x86_64.whl (881.9 MB)\n",
            "\u001b[K     |██████████████████████████████▎ | 834.1 MB 11.8 MB/s eta 0:00:05tcmalloc: large alloc 1147494400 bytes == 0x39dae000 @  0x7f466f89c615 0x5d631c 0x51e4f1 0x51e67b 0x4f7585 0x49ca7c 0x4fdff5 0x49caa1 0x4fdff5 0x49ced5 0x4f60a9 0x55f926 0x4f60a9 0x55f926 0x4f60a9 0x55f926 0x5d7c18 0x5d9412 0x586636 0x5d813c 0x55f3fd 0x55e571 0x5d7cf1 0x49ced5 0x55e571 0x5d7cf1 0x49ec69 0x5d7c18 0x49ca7c 0x4fdff5 0x49ced5\n",
            "\u001b[K     |████████████████████████████████| 881.9 MB 8.3 kB/s \n",
            "\u001b[?25hCollecting transformers<=4.8.1,>=4.8.1\n",
            "  Downloading transformers-4.8.1-py3-none-any.whl (2.5 MB)\n",
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            "\u001b[?25hRequirement already satisfied: flatbuffers in /usr/local/lib/python3.8/dist-packages (from onnxruntime<=1.8.0,==1.8.0->kobert==0.2.3) (1.12)\n",
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            "Requirement already satisfied: numpy>=1.16.6 in /usr/local/lib/python3.8/dist-packages (from onnxruntime<=1.8.0,==1.8.0->kobert==0.2.3) (1.21.6)\n",
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            "  Downloading jmespath-0.10.0-py2.py3-none-any.whl (24 kB)\n",
            "Collecting s3transfer<0.4.0,>=0.3.0\n",
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            "\u001b[?25hCollecting botocore<1.19.0,>=1.18.18\n",
            "  Downloading botocore-1.18.18-py2.py3-none-any.whl (6.7 MB)\n",
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            "Requirement already satisfied: python-dateutil<3.0.0,>=2.1 in /usr/local/lib/python3.8/dist-packages (from botocore<1.19.0,>=1.18.18->boto3<=1.15.18->kobert==0.2.3) (2.8.2)\n",
            "Requirement already satisfied: cython in /usr/local/lib/python3.8/dist-packages (from gluonnlp<=0.10.0,>=0.6.0->kobert==0.2.3) (0.29.32)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.8/dist-packages (from gluonnlp<=0.10.0,>=0.6.0->kobert==0.2.3) (21.3)\n",
            "Requirement already satisfied: graphviz<0.9.0,>=0.8.1 in /usr/local/lib/python3.8/dist-packages (from mxnet<=1.7.0.post2,>=1.4.0->kobert==0.2.3) (0.8.4)\n",
            "Requirement already satisfied: requests<3,>=2.20.0 in /usr/local/lib/python3.8/dist-packages (from mxnet<=1.7.0.post2,>=1.4.0->kobert==0.2.3) (2.23.0)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.8/dist-packages (from python-dateutil<3.0.0,>=2.1->botocore<1.19.0,>=1.18.18->boto3<=1.15.18->kobert==0.2.3) (1.15.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.8/dist-packages (from requests<3,>=2.20.0->mxnet<=1.7.0.post2,>=1.4.0->kobert==0.2.3) (2022.9.24)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.8/dist-packages (from requests<3,>=2.20.0->mxnet<=1.7.0.post2,>=1.4.0->kobert==0.2.3) (2.10)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.8/dist-packages (from requests<3,>=2.20.0->mxnet<=1.7.0.post2,>=1.4.0->kobert==0.2.3) (3.0.4)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.8/dist-packages (from torch<=1.10.1,>=1.7.0->kobert==0.2.3) (4.1.1)\n",
            "Requirement already satisfied: sacremoses in /usr/local/lib/python3.8/dist-packages (from transformers<=4.8.1,>=4.8.1->kobert==0.2.3) (0.0.53)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.8/dist-packages (from transformers<=4.8.1,>=4.8.1->kobert==0.2.3) (4.64.1)\n",
            "Collecting tokenizers<0.11,>=0.10.1\n",
            "  Downloading tokenizers-0.10.3-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (3.3 MB)\n",
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            "\u001b[?25hRequirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.8/dist-packages (from transformers<=4.8.1,>=4.8.1->kobert==0.2.3) (2022.6.2)\n",
            "Collecting huggingface-hub==0.0.12\n",
            "  Downloading huggingface_hub-0.0.12-py3-none-any.whl (37 kB)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.8/dist-packages (from transformers<=4.8.1,>=4.8.1->kobert==0.2.3) (3.8.0)\n",
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.8/dist-packages (from transformers<=4.8.1,>=4.8.1->kobert==0.2.3) (6.0)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.8/dist-packages (from packaging->gluonnlp<=0.10.0,>=0.6.0->kobert==0.2.3) (3.0.9)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.8/dist-packages (from sacremoses->transformers<=4.8.1,>=4.8.1->kobert==0.2.3) (1.2.0)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.8/dist-packages (from sacremoses->transformers<=4.8.1,>=4.8.1->kobert==0.2.3) (7.1.2)\n",
            "Building wheels for collected packages: kobert\n",
            "  Building wheel for kobert (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for kobert: filename=kobert-0.2.3-py3-none-any.whl size=15708 sha256=644ca45175ee4d8eeb434fe7a15d0a56932c15d2d2f9f02625e064d75b0d4a62\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-l75ocyg2/wheels/bf/5f/74/81bf3a1332130eb6629ecf58876a8746b77021e7d7b0638e91\n",
            "Successfully built kobert\n",
            "Installing collected packages: jmespath, botocore, tokenizers, s3transfer, huggingface-hub, transformers, torch, sentencepiece, onnxruntime, mxnet, boto3, kobert\n",
            "  Attempting uninstall: tokenizers\n",
            "    Found existing installation: tokenizers 0.8.1rc1\n",
            "    Uninstalling tokenizers-0.8.1rc1:\n",
            "      Successfully uninstalled tokenizers-0.8.1rc1\n",
            "  Attempting uninstall: transformers\n",
            "    Found existing installation: transformers 3.0.2\n",
            "    Uninstalling transformers-3.0.2:\n",
            "      Successfully uninstalled transformers-3.0.2\n",
            "  Attempting uninstall: torch\n",
            "    Found existing installation: torch 1.12.1+cu113\n",
            "    Uninstalling torch-1.12.1+cu113:\n",
            "      Successfully uninstalled torch-1.12.1+cu113\n",
            "  Attempting uninstall: sentencepiece\n",
            "    Found existing installation: sentencepiece 0.1.97\n",
            "    Uninstalling sentencepiece-0.1.97:\n",
            "      Successfully uninstalled sentencepiece-0.1.97\n",
            "  Attempting uninstall: mxnet\n",
            "    Found existing installation: mxnet 1.9.1\n",
            "    Uninstalling mxnet-1.9.1:\n",
            "      Successfully uninstalled mxnet-1.9.1\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "torchvision 0.13.1+cu113 requires torch==1.12.1, but you have torch 1.10.1 which is incompatible.\n",
            "torchtext 0.13.1 requires torch==1.12.1, but you have torch 1.10.1 which is incompatible.\n",
            "torchaudio 0.12.1+cu113 requires torch==1.12.1, but you have torch 1.10.1 which is incompatible.\u001b[0m\n",
            "Successfully installed boto3-1.15.18 botocore-1.18.18 huggingface-hub-0.0.12 jmespath-0.10.0 kobert-0.2.3 mxnet-1.7.0.post2 onnxruntime-1.8.0 s3transfer-0.3.7 sentencepiece-0.1.96 tokenizers-0.10.3 torch-1.10.1 transformers-4.8.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from torch import nn\n",
        "import torch.nn.functional as F\n",
        "import torch.optim as optim\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "import gluonnlp as nlp\n",
        "import numpy as np\n",
        "from tqdm import tqdm, tqdm_notebook"
      ],
      "metadata": {
        "id": "9xW_0U8B4Hbc"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from kobert.utils import get_tokenizer\n",
        "from kobert.pytorch_kobert import get_pytorch_kobert_model"
      ],
      "metadata": {
        "id": "qwYt0gwc3wLa"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AdamW\n",
        "from transformers.optimization import get_cosine_schedule_with_warmup"
      ],
      "metadata": {
        "id": "n6Tlugdr4KQz"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "##GPU 사용 시\n",
        "device = torch.device(\"cuda:0\")"
      ],
      "metadata": {
        "id": "RhlewWH14b6r"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "bertmodel, vocab = get_pytorch_kobert_model()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-gEDYhCa4bDe",
        "outputId": "d01fdb0d-65e2-476c-d7c4-cc6bfe3d099c"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/.cache/kobert_v1.zip[██████████████████████████████████████████████████]\n",
            "/content/.cache/kobert_news_wiki_ko_cased-1087f8699e.spiece[██████████████████████████████████████████████████]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class BERTDataset(Dataset):\n",
        "    def __init__(self, dataset, sent_idx, label_idx, bert_tokenizer, max_len,\n",
        "                 pad, pair):\n",
        "        transform = nlp.data.BERTSentenceTransform(\n",
        "            bert_tokenizer, max_seq_length=max_len, pad=pad, pair=pair)\n",
        "\n",
        "        self.sentences = [transform([i[sent_idx]]) for i in dataset]\n",
        "        self.labels = [np.int32(i[label_idx]) for i in dataset]\n",
        "\n",
        "    def __getitem__(self, i):\n",
        "        return (self.sentences[i] + (self.labels[i], ))\n",
        "\n",
        "    def __len__(self):\n",
        "        return (len(self.labels))"
      ],
      "metadata": {
        "id": "8SzYbMJG7qZo"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "## Setting parameters\n",
        "max_len = 64\n",
        "batch_size = 64\n",
        "warmup_ratio = 0.1\n",
        "num_epochs = 7\n",
        "max_grad_norm = 1\n",
        "log_interval = 200\n",
        "learning_rate =  5e-5"
      ],
      "metadata": {
        "id": "MmYiO0BA7t70"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#토큰화\n",
        "tokenizer = get_tokenizer()\n",
        "tok = nlp.data.BERTSPTokenizer(tokenizer, vocab, lower=False)\n",
        "\n",
        "data_train = BERTDataset(train_data, 0, 1, tok, max_len, True, False)\n",
        "data_val = BERTDataset(val_data, 0, 1, tok, max_len, True, False)\n",
        "\n",
        "train_dataloader = torch.utils.data.DataLoader(data_train, batch_size=batch_size, num_workers=5)\n",
        "test_dataloader = torch.utils.data.DataLoader(data_val, batch_size=batch_size, num_workers=5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HslGveRN4ePE",
        "outputId": "84d604f3-9ade-4362-c28c-511c618ee043"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "using cached model. /content/.cache/kobert_news_wiki_ko_cased-1087f8699e.spiece\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py:478: UserWarning: This DataLoader will create 5 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  warnings.warn(_create_warning_msg(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class BERTClassifier(nn.Module):\n",
        "    def __init__(self,\n",
        "                 bert,\n",
        "                 hidden_size = 768,\n",
        "                 num_classes=8,\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"
      ],
      "metadata": {
        "id": "CkEdAaTZwXxc"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = BERTClassifier(bertmodel,  dr_rate=0.5).to(device)"
      ],
      "metadata": {
        "id": "uinZ77fywj7F"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Prepare optimizer and schedule (linear warmup and decay)\n",
        "no_decay = ['bias', 'LayerNorm.weight']\n",
        "optimizer_grouped_parameters = [\n",
        "    {'params': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)], 'weight_decay': 0.01},\n",
        "    {'params': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], 'weight_decay': 0.0}\n",
        "]"
      ],
      "metadata": {
        "id": "6mEd14niwl_7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "optimizer = AdamW(optimizer_grouped_parameters, lr=learning_rate)\n",
        "loss_fn = nn.CrossEntropyLoss()"
      ],
      "metadata": {
        "id": "riXbMmkhwnm-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "t_total = len(train_dataloader) * num_epochs\n",
        "warmup_step = int(t_total * warmup_ratio)"
      ],
      "metadata": {
        "id": "L3uuG6SWwp99"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scheduler = get_cosine_schedule_with_warmup(optimizer, num_warmup_steps=warmup_step, num_training_steps=t_total)"
      ],
      "metadata": {
        "id": "n-bw9JeC0lzH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def calc_accuracy(X,Y):\n",
        "    max_vals, max_indices = torch.max(X, 1)\n",
        "    train_acc = (max_indices == Y).sum().data.cpu().numpy()/max_indices.size()[0]\n",
        "    return train_acc"
      ],
      "metadata": {
        "id": "iOxqktfawsWP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for e in range(num_epochs):\n",
        "    train_acc = 0.0\n",
        "    test_acc = 0.0\n",
        "    model.train()\n",
        "    for batch_id, (token_ids, valid_length, segment_ids, label) in enumerate(tqdm_notebook(train_dataloader)):\n",
        "        optimizer.zero_grad()\n",
        "        token_ids = token_ids.long().to(device)\n",
        "        segment_ids = segment_ids.long().to(device)\n",
        "        valid_length= valid_length\n",
        "        label = label.long().to(device)\n",
        "        out = model(token_ids, valid_length, segment_ids)\n",
        "        loss = loss_fn(out, label)\n",
        "        loss.backward()\n",
        "        torch.nn.utils.clip_grad_norm_(model.parameters(), max_grad_norm)\n",
        "        optimizer.step()\n",
        "        scheduler.step()  # Update learning rate schedule\n",
        "        train_acc += calc_accuracy(out, label)\n",
        "        if batch_id % log_interval == 0:\n",
        "            print(\"epoch {} batch id {} loss {} train acc {}\".format(e+1, batch_id+1, loss.data.cpu().numpy(), train_acc / (batch_id+1)))\n",
        "    print(\"epoch {} train acc {}\".format(e+1, train_acc / (batch_id+1)))\n",
        "    \n",
        "    model.eval()\n",
        "    for batch_id, (token_ids, valid_length, segment_ids, label) in enumerate(tqdm_notebook(test_dataloader)):\n",
        "        token_ids = token_ids.long().to(device)\n",
        "        segment_ids = segment_ids.long().to(device)\n",
        "        valid_length= valid_length\n",
        "        label = label.long().to(device)\n",
        "        out = model(token_ids, valid_length, segment_ids)\n",
        "        test_acc += calc_accuracy(out, label)\n",
        "    print(\"epoch {} test acc {}\".format(e+1, test_acc / (batch_id+1)))"
      ],
      "metadata": {
        "id": "AYIYRxaLwt7O",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 828,
          "referenced_widgets": [
            "8a1312ca2a6d4172921e43636cc27b2d",
            "632ef8eadf8c45d88a3938ab65a91895",
            "bf841a84b8344dc7bff2cc2302eb418b",
            "48fa92599aa2420ba8542054eb425eab",
            "c61a1765d99d415e908423104d77db8a",
            "4a58b0702c14492da3c89d5023bc246f",
            "febfb1adf6a34003bdf10257960091fb",
            "06e44f5da790407bb2d8701839d556eb",
            "8606f5ff7f2b4a38a692e6d5590b4962",
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            "d8cca6dcc6b64fc2af1468a180312132",
            "de9a3093f3104d81817abb4922452f95",
            "808367feaebb4cb687ff7012d993fb24",
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            "9624542277204e97a8f631908a4e281e",
            "95dd3e86887441b7ba658ca2f847376c",
            "9bbf9145c93d40c09df7e7ceeb43b5f8",
            "fe30ca6381cd48d899312580cf6a2f57",
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            "0c1231f734cc47caa9fde562c8b5f068",
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            "36edced96a5e47c081f201f620564064",
            "8f83976c3a5c45b9958fce463fc5b23d",
            "535f8cd5d4a542f7bdb01ceb977e9369",
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            "30c72c57c0df45e8bcbddba618ac0377",
            "510bf05e16f74595aa3d5c5e39e58473",
            "d3d940d05930426f8af2fe2f5485326d",
            "befbf382043a43f098fd65b917203feb"
          ]
        },
        "outputId": "efd7d513-3071-4174-8135-ae9485e951a9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-24-480b6a139979>:5: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
            "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n",
            "  for batch_id, (token_ids, valid_length, segment_ids, label) in enumerate(tqdm_notebook(train_dataloader)):\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1781 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8a1312ca2a6d4172921e43636cc27b2d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 1 batch id 1 loss 2.070470094680786 train acc 0.09375\n",
            "epoch 1 batch id 201 loss 0.19065360724925995 train acc 0.7503109452736318\n",
            "epoch 1 batch id 401 loss 0.06795904785394669 train acc 0.855556421446384\n",
            "epoch 1 batch id 601 loss 0.30238839983940125 train acc 0.8830334858569051\n",
            "epoch 1 batch id 801 loss 0.3009798526763916 train acc 0.8861579275905118\n",
            "epoch 1 batch id 1001 loss 0.47892361879348755 train acc 0.8778721278721279\n",
            "epoch 1 batch id 1201 loss 0.25452178716659546 train acc 0.8724890716069942\n",
            "epoch 1 batch id 1401 loss 0.5315055847167969 train acc 0.8700816381156317\n",
            "epoch 1 batch id 1601 loss 0.41306906938552856 train acc 0.86933947532792\n",
            "epoch 1 train acc 0.8672708450308815\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-24-480b6a139979>:23: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
            "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n",
            "  for batch_id, (token_ids, valid_length, segment_ids, label) in enumerate(tqdm_notebook(test_dataloader)):\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/198 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "de9a3093f3104d81817abb4922452f95"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 1 test acc 0.8696255316321105\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1781 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c94a20fa4a5b4c5ca089e2db40d3ad5e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 2 batch id 1 loss 0.3268911838531494 train acc 0.890625\n",
            "epoch 2 batch id 201 loss 0.12406887114048004 train acc 0.962764303482587\n",
            "epoch 2 batch id 401 loss 0.05481500178575516 train acc 0.9642300498753117\n",
            "epoch 2 batch id 601 loss 0.11435074359178543 train acc 0.9601705490848585\n",
            "epoch 2 batch id 801 loss 0.30202579498291016 train acc 0.9484043383270911\n",
            "epoch 2 batch id 1001 loss 0.4257228970527649 train acc 0.9339410589410589\n",
            "epoch 2 batch id 1201 loss 0.16579824686050415 train acc 0.9245680682764363\n",
            "epoch 2 batch id 1401 loss 0.4135432541370392 train acc 0.9188637580299786\n",
            "epoch 2 batch id 1601 loss 0.3810463547706604 train acc 0.915336118051218\n",
            "epoch 2 train acc 0.9121593506858106\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/198 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6f4780981d5b413a8620901337643146"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 2 test acc 0.8786314349636718\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1781 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6868336bd165452980020535d9adda5a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 3 batch id 1 loss 0.19850486516952515 train acc 0.96875\n",
            "epoch 3 batch id 201 loss 0.045686084777116776 train acc 0.9735696517412935\n",
            "epoch 3 batch id 401 loss 0.05880575627088547 train acc 0.97330891521197\n",
            "epoch 3 batch id 601 loss 0.11828985810279846 train acc 0.9702059068219634\n",
            "epoch 3 batch id 801 loss 0.2926866412162781 train acc 0.9607326779026217\n",
            "epoch 3 batch id 1001 loss 0.2812272906303406 train acc 0.9490977772227772\n",
            "epoch 3 batch id 1201 loss 0.1226050853729248 train acc 0.9410126977518735\n",
            "epoch 3 batch id 1401 loss 0.3320595622062683 train acc 0.9358382405424697\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 사용자가 입력하는 문장의 클래스 예측"
      ],
      "metadata": {
        "id": "TOeMtu1g-yW7"
      }
    },
    {
      "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",
        "    \n",
        "    model.eval()\n",
        "\n",
        "    for batch_id, (token_ids, valid_length, segment_ids, label) in enumerate(test_dataloader):\n",
        "        token_ids = token_ids.long().to(device)\n",
        "        segment_ids = segment_ids.long().to(device)\n",
        "\n",
        "        valid_length= valid_length\n",
        "        label = label.long().to(device)\n",
        "\n",
        "        out = model(token_ids, valid_length, segment_ids)\n",
        "\n",
        "\n",
        "        test_eval=[]\n",
        "        for i in out:\n",
        "            logits=i\n",
        "            logits = logits.detach().cpu().numpy()\n",
        "\n",
        "            if np.argmax(logits) == 0:\n",
        "                test_eval.append(\"욕설이 포함되지 않은\")\n",
        "            elif np.argmax(logits) == 1:\n",
        "                test_eval.append(\"욕설이 포함된\")\n",
        "\n",
        "        print(\">> 입력하신 내용은 \" + test_eval[0] + \" 내용입니다.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SzHnJDdjAnP-",
        "outputId": "9c02c75f-b45c-44bd-e0ee-d028de174ce6"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "using cached model. /content/.cache/kobert_news_wiki_ko_cased-1087f8699e.spiece\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#질문 무한반복하기! (0일때 멈춤)\n",
        "end = 1\n",
        "while end == 1 :\n",
        "    sentence = input(\"말을 입력해주세요 : \")\n",
        "    if sentence == '0' :\n",
        "        break\n",
        "    predict(sentence)\n",
        "    print(\"\\n\")"
      ],
      "metadata": {
        "id": "NtsOKkIhAr6_"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}