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    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/da-head0/youreditor/blob/main/4_simpletransfomer_mT5_aihub.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xaPztbwvXrca",
        "outputId": "4d1f4ad8-dc4f-4fb9-a6f9-eecc5b897005"
      },
      "source": [
        "!nvidia-smi"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Tue Apr 27 08:15:51 2021       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 465.19.01    Driver Version: 460.32.03    CUDA Version: 11.2     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla P100-PCIE...  Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   37C    P0    26W / 250W |      0MiB / 16280MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O8u1_IvS7cKW",
        "outputId": "1d787fa5-661d-4792-d714-442a5d80a9e8"
      },
      "source": [
        "!pip install torch==1.8.1+cu102 torchvision==0.9.1+cu102 torchaudio===0.8.1 -f https://download.pytorch.org/whl/torch_stable.html"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Looking in links: https://download.pytorch.org/whl/torch_stable.html\n",
            "Requirement already satisfied: torch==1.8.1+cu102 in /usr/local/lib/python3.7/dist-packages (1.8.1+cu102)\n",
            "Requirement already satisfied: torchvision==0.9.1+cu102 in /usr/local/lib/python3.7/dist-packages (0.9.1+cu102)\n",
            "Requirement already satisfied: torchaudio===0.8.1 in /usr/local/lib/python3.7/dist-packages (0.8.1)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch==1.8.1+cu102) (3.7.4.3)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from torch==1.8.1+cu102) (1.19.5)\n",
            "Requirement already satisfied: pillow>=4.1.1 in /usr/local/lib/python3.7/dist-packages (from torchvision==0.9.1+cu102) (7.1.2)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "um_UULzzYMh5"
      },
      "source": [
        "!pip install simpletransformers"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yMJDzumDDRSG"
      },
      "source": [
        "### Konlpy 토크나이저 사용"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cal2W7BbGVOD"
      },
      "source": [
        "!git clone https://github.com/lovit/huggingface_konlpy.git"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_6eBzy9stVi2",
        "outputId": "a695504d-64fa-46b2-ceb7-fdcbddb40c53"
      },
      "source": [
        "%cd huggingface_konlpy"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/huggingface_konlpy\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y3O1-jwLAIF5",
        "outputId": "08262b4f-f353-475e-d499-ea262aecebb7"
      },
      "source": [
        "!pip install konlpy\n",
        "!pip install tokenizers\n",
        "!pip install tqdm\n",
        "!pip install wandb"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting konlpy\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/85/0e/f385566fec837c0b83f216b2da65db9997b35dd675e107752005b7d392b1/konlpy-0.5.2-py2.py3-none-any.whl (19.4MB)\n",
            "\u001b[K     |████████████████████████████████| 19.4MB 1.2MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.6 in /usr/local/lib/python3.7/dist-packages (from konlpy) (1.19.5)\n",
            "Collecting colorama\n",
            "  Downloading https://files.pythonhosted.org/packages/44/98/5b86278fbbf250d239ae0ecb724f8572af1c91f4a11edf4d36a206189440/colorama-0.4.4-py2.py3-none-any.whl\n",
            "Requirement already satisfied: tweepy>=3.7.0 in /usr/local/lib/python3.7/dist-packages (from konlpy) (3.10.0)\n",
            "Requirement already satisfied: lxml>=4.1.0 in /usr/local/lib/python3.7/dist-packages (from konlpy) (4.2.6)\n",
            "Collecting beautifulsoup4==4.6.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/9e/d4/10f46e5cfac773e22707237bfcd51bbffeaf0a576b0a847ec7ab15bd7ace/beautifulsoup4-4.6.0-py3-none-any.whl (86kB)\n",
            "\u001b[K     |████████████████████████████████| 92kB 11.6MB/s \n",
            "\u001b[?25hCollecting JPype1>=0.7.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/cd/a5/9781e2ef4ca92d09912c4794642c1653aea7607f473e156cf4d423a881a1/JPype1-1.2.1-cp37-cp37m-manylinux2010_x86_64.whl (457kB)\n",
            "\u001b[K     |████████████████████████████████| 460kB 59.9MB/s \n",
            "\u001b[?25hRequirement already satisfied: requests[socks]>=2.11.1 in /usr/local/lib/python3.7/dist-packages (from tweepy>=3.7.0->konlpy) (2.23.0)\n",
            "Requirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.7/dist-packages (from tweepy>=3.7.0->konlpy) (1.15.0)\n",
            "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.7/dist-packages (from tweepy>=3.7.0->konlpy) (1.3.0)\n",
            "Requirement already satisfied: typing-extensions; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from JPype1>=0.7.0->konlpy) (3.7.4.3)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests[socks]>=2.11.1->tweepy>=3.7.0->konlpy) (3.0.4)\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[socks]>=2.11.1->tweepy>=3.7.0->konlpy) (1.24.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests[socks]>=2.11.1->tweepy>=3.7.0->konlpy) (2.10)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests[socks]>=2.11.1->tweepy>=3.7.0->konlpy) (2020.12.5)\n",
            "Requirement already satisfied: PySocks!=1.5.7,>=1.5.6; extra == \"socks\" in /usr/local/lib/python3.7/dist-packages (from requests[socks]>=2.11.1->tweepy>=3.7.0->konlpy) (1.7.1)\n",
            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from requests-oauthlib>=0.7.0->tweepy>=3.7.0->konlpy) (3.1.0)\n",
            "Installing collected packages: colorama, beautifulsoup4, JPype1, konlpy\n",
            "  Found existing installation: beautifulsoup4 4.6.3\n",
            "    Uninstalling beautifulsoup4-4.6.3:\n",
            "      Successfully uninstalled beautifulsoup4-4.6.3\n",
            "Successfully installed JPype1-1.2.1 beautifulsoup4-4.6.0 colorama-0.4.4 konlpy-0.5.2\n",
            "Requirement already satisfied: tokenizers in /usr/local/lib/python3.7/dist-packages (0.10.2)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (4.60.0)\n",
            "Requirement already satisfied: wandb in /usr/local/lib/python3.7/dist-packages (0.10.27)\n",
            "Requirement already satisfied: subprocess32>=3.5.3 in /usr/local/lib/python3.7/dist-packages (from wandb) (3.5.4)\n",
            "Requirement already satisfied: protobuf>=3.12.0 in /usr/local/lib/python3.7/dist-packages (from wandb) (3.12.4)\n",
            "Requirement already satisfied: python-dateutil>=2.6.1 in /usr/local/lib/python3.7/dist-packages (from wandb) (2.8.1)\n",
            "Requirement already satisfied: Click>=7.0 in /usr/local/lib/python3.7/dist-packages (from wandb) (7.1.2)\n",
            "Requirement already satisfied: six>=1.13.0 in /usr/local/lib/python3.7/dist-packages (from wandb) (1.15.0)\n",
            "Requirement already satisfied: docker-pycreds>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from wandb) (0.4.0)\n",
            "Requirement already satisfied: psutil>=5.0.0 in /usr/local/lib/python3.7/dist-packages (from wandb) (5.4.8)\n",
            "Requirement already satisfied: GitPython>=1.0.0 in /usr/local/lib/python3.7/dist-packages (from wandb) (3.1.14)\n",
            "Requirement already satisfied: shortuuid>=0.5.0 in /usr/local/lib/python3.7/dist-packages (from wandb) (1.0.1)\n",
            "Requirement already satisfied: configparser>=3.8.1 in /usr/local/lib/python3.7/dist-packages (from wandb) (5.0.2)\n",
            "Requirement already satisfied: requests<3,>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from wandb) (2.23.0)\n",
            "Requirement already satisfied: sentry-sdk>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from wandb) (1.0.0)\n",
            "Requirement already satisfied: pathtools in /usr/local/lib/python3.7/dist-packages (from wandb) (0.1.2)\n",
            "Requirement already satisfied: PyYAML in /usr/local/lib/python3.7/dist-packages (from wandb) (3.13)\n",
            "Requirement already satisfied: promise<3,>=2.0 in /usr/local/lib/python3.7/dist-packages (from wandb) (2.3)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from protobuf>=3.12.0->wandb) (56.0.0)\n",
            "Requirement already satisfied: gitdb<5,>=4.0.1 in /usr/local/lib/python3.7/dist-packages (from GitPython>=1.0.0->wandb) (4.0.7)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.0.0->wandb) (3.0.4)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.0.0->wandb) (2.10)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.0.0->wandb) (1.24.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.0.0->wandb) (2020.12.5)\n",
            "Requirement already satisfied: smmap<5,>=3.0.1 in /usr/local/lib/python3.7/dist-packages (from gitdb<5,>=4.0.1->GitPython>=1.0.0->wandb) (4.0.0)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3k7KrFbUw6F9",
        "outputId": "d5ff6400-01cc-40ef-f0de-0e077bd0b21e"
      },
      "source": [
        "!git clone https://github.com/SOMJANG/Mecab-ko-for-Google-Colab.git"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'Mecab-ko-for-Google-Colab'...\n",
            "remote: Enumerating objects: 91, done.\u001b[K\n",
            "remote: Counting objects: 100% (91/91), done.\u001b[K\n",
            "remote: Compressing objects: 100% (85/85), done.\u001b[K\n",
            "remote: Total 91 (delta 43), reused 22 (delta 6), pack-reused 0\u001b[K\n",
            "Unpacking objects: 100% (91/91), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eLYWWpeCxIuQ",
        "outputId": "7fc120a4-af95-4fdf-b373-f29e392c42db"
      },
      "source": [
        "!bash /content/huggingface_konlpy/Mecab-ko-for-Google-Colab/install_mecab-ko_on_colab_light_210108.sh"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Installing konlpy.....\n",
            "Requirement already satisfied: konlpy in /usr/local/lib/python3.7/dist-packages (0.5.2)\n",
            "Requirement already satisfied: beautifulsoup4==4.6.0 in /usr/local/lib/python3.7/dist-packages (from konlpy) (4.6.0)\n",
            "Requirement already satisfied: numpy>=1.6 in /usr/local/lib/python3.7/dist-packages (from konlpy) (1.19.5)\n",
            "Requirement already satisfied: lxml>=4.1.0 in /usr/local/lib/python3.7/dist-packages (from konlpy) (4.2.6)\n",
            "Requirement already satisfied: colorama in /usr/local/lib/python3.7/dist-packages (from konlpy) (0.4.4)\n",
            "Requirement already satisfied: JPype1>=0.7.0 in /usr/local/lib/python3.7/dist-packages (from konlpy) (1.2.1)\n",
            "Requirement already satisfied: tweepy>=3.7.0 in /usr/local/lib/python3.7/dist-packages (from konlpy) (3.10.0)\n",
            "Requirement already satisfied: typing-extensions; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from JPype1>=0.7.0->konlpy) (3.7.4.3)\n",
            "Requirement already satisfied: requests[socks]>=2.11.1 in /usr/local/lib/python3.7/dist-packages (from tweepy>=3.7.0->konlpy) (2.23.0)\n",
            "Requirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.7/dist-packages (from tweepy>=3.7.0->konlpy) (1.15.0)\n",
            "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.7/dist-packages (from tweepy>=3.7.0->konlpy) (1.3.0)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests[socks]>=2.11.1->tweepy>=3.7.0->konlpy) (2.10)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests[socks]>=2.11.1->tweepy>=3.7.0->konlpy) (2020.12.5)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests[socks]>=2.11.1->tweepy>=3.7.0->konlpy) (3.0.4)\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[socks]>=2.11.1->tweepy>=3.7.0->konlpy) (1.24.3)\n",
            "Requirement already satisfied: PySocks!=1.5.7,>=1.5.6; extra == \"socks\" in /usr/local/lib/python3.7/dist-packages (from requests[socks]>=2.11.1->tweepy>=3.7.0->konlpy) (1.7.1)\n",
            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from requests-oauthlib>=0.7.0->tweepy>=3.7.0->konlpy) (3.1.0)\n",
            "Done\n",
            "Installing mecab-0.996-ko-0.9.2.tar.gz.....\n",
            "Downloading mecab-0.996-ko-0.9.2.tar.gz.......\n",
            "from https://bitbucket.org/eunjeon/mecab-ko/downloads/mecab-0.996-ko-0.9.2.tar.gz\n",
            "--2021-04-26 06:13:09--  https://bitbucket.org/eunjeon/mecab-ko/downloads/mecab-0.996-ko-0.9.2.tar.gz\n",
            "Resolving bitbucket.org (bitbucket.org)... 104.192.141.1, 2406:da00:ff00::22c2:513, 2406:da00:ff00::22cd:e0db, ...\n",
            "Connecting to bitbucket.org (bitbucket.org)|104.192.141.1|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://bbuseruploads.s3.amazonaws.com/eunjeon/mecab-ko/downloads/mecab-0.996-ko-0.9.2.tar.gz?Signature=P4Fhx8EZQ7GIOluguYyfgxUHJz8%3D&Expires=1619418993&AWSAccessKeyId=AKIA6KOSE3BNJRRFUUX6&versionId=null&response-content-disposition=attachment%3B%20filename%3D%22mecab-0.996-ko-0.9.2.tar.gz%22&response-content-encoding=None [following]\n",
            "--2021-04-26 06:13:09--  https://bbuseruploads.s3.amazonaws.com/eunjeon/mecab-ko/downloads/mecab-0.996-ko-0.9.2.tar.gz?Signature=P4Fhx8EZQ7GIOluguYyfgxUHJz8%3D&Expires=1619418993&AWSAccessKeyId=AKIA6KOSE3BNJRRFUUX6&versionId=null&response-content-disposition=attachment%3B%20filename%3D%22mecab-0.996-ko-0.9.2.tar.gz%22&response-content-encoding=None\n",
            "Resolving bbuseruploads.s3.amazonaws.com (bbuseruploads.s3.amazonaws.com)... 52.216.242.188\n",
            "Connecting to bbuseruploads.s3.amazonaws.com (bbuseruploads.s3.amazonaws.com)|52.216.242.188|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 1414979 (1.3M) [application/x-tar]\n",
            "Saving to: ‘mecab-0.996-ko-0.9.2.tar.gz’\n",
            "\n",
            "mecab-0.996-ko-0.9. 100%[===================>]   1.35M  2.70MB/s    in 0.5s    \n",
            "\n",
            "2021-04-26 06:13:10 (2.70 MB/s) - ‘mecab-0.996-ko-0.9.2.tar.gz’ saved [1414979/1414979]\n",
            "\n",
            "Done\n",
            "Unpacking mecab-0.996-ko-0.9.2.tar.gz.......\n",
            "Done\n",
            "Change Directory to mecab-0.996-ko-0.9.2.......\n",
            "installing mecab-0.996-ko-0.9.2.tar.gz........\n",
            "configure\n",
            "make\n",
            "make check\n",
            "make install\n",
            "ldconfig\n",
            "Done\n",
            "Change Directory to /content\n",
            "Downloading mecab-ko-dic-2.1.1-20180720.tar.gz.......\n",
            "from https://bitbucket.org/eunjeon/mecab-ko-dic/downloads/mecab-ko-dic-2.1.1-20180720.tar.gz\n",
            "--2021-04-26 06:14:40--  https://bitbucket.org/eunjeon/mecab-ko-dic/downloads/mecab-ko-dic-2.1.1-20180720.tar.gz\n",
            "Resolving bitbucket.org (bitbucket.org)... 104.192.141.1, 2406:da00:ff00::6b17:d1f5, 2406:da00:ff00::22c2:513, ...\n",
            "Connecting to bitbucket.org (bitbucket.org)|104.192.141.1|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://bbuseruploads.s3.amazonaws.com/a4fcd83e-34f1-454e-a6ac-c242c7d434d3/downloads/b5a0c703-7b64-45ed-a2d7-180e962710b6/mecab-ko-dic-2.1.1-20180720.tar.gz?Signature=gf6LZgPnSaT%2BE%2FN%2Fyq%2BfuIE5lvs%3D&Expires=1619419480&AWSAccessKeyId=AKIA6KOSE3BNJRRFUUX6&versionId=tzyxc1TtnZU_zEuaaQDGN4F76hPDpyFq&response-content-disposition=attachment%3B%20filename%3D%22mecab-ko-dic-2.1.1-20180720.tar.gz%22&response-content-encoding=None [following]\n",
            "--2021-04-26 06:14:40--  https://bbuseruploads.s3.amazonaws.com/a4fcd83e-34f1-454e-a6ac-c242c7d434d3/downloads/b5a0c703-7b64-45ed-a2d7-180e962710b6/mecab-ko-dic-2.1.1-20180720.tar.gz?Signature=gf6LZgPnSaT%2BE%2FN%2Fyq%2BfuIE5lvs%3D&Expires=1619419480&AWSAccessKeyId=AKIA6KOSE3BNJRRFUUX6&versionId=tzyxc1TtnZU_zEuaaQDGN4F76hPDpyFq&response-content-disposition=attachment%3B%20filename%3D%22mecab-ko-dic-2.1.1-20180720.tar.gz%22&response-content-encoding=None\n",
            "Resolving bbuseruploads.s3.amazonaws.com (bbuseruploads.s3.amazonaws.com)... 52.217.85.188\n",
            "Connecting to bbuseruploads.s3.amazonaws.com (bbuseruploads.s3.amazonaws.com)|52.217.85.188|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 49775061 (47M) [application/x-tar]\n",
            "Saving to: ‘mecab-ko-dic-2.1.1-20180720.tar.gz’\n",
            "\n",
            "mecab-ko-dic-2.1.1- 100%[===================>]  47.47M  21.9MB/s    in 2.2s    \n",
            "\n",
            "2021-04-26 06:14:43 (21.9 MB/s) - ‘mecab-ko-dic-2.1.1-20180720.tar.gz’ saved [49775061/49775061]\n",
            "\n",
            "Done\n",
            "Unpacking  mecab-ko-dic-2.1.1-20180720.tar.gz.......\n",
            "Done\n",
            "Change Directory to mecab-ko-dic-2.1.1-20180720\n",
            "Done\n",
            "installing........\n",
            "configure\n",
            "make\n",
            "make install\n",
            "bash <(curl -s https://raw.githubusercontent.com/konlpy/konlpy/master/scripts/mecab.sh)\n",
            "Done\n",
            "Successfully Installed\n",
            "Now you can use Mecab\n",
            "from konlpy.tag import Mecab\n",
            "mecab = Mecab()\n",
            "사용자 사전 추가 방법 : https://bit.ly/3k0ZH53\n",
            "NameError: name 'Tagger' is not defined 오류 발생 시 런타임을 재실행 해주세요\n",
            "블로그에 해결 방법을 남겨주신 tana님 감사합니다.\n",
            "light 버전 작성 : Dogdriip님 ( https://github.com/Dogdriip )\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vLgCS0L15ytp"
      },
      "source": [
        "from transformers import AutoTokenizer, AutoModelWithLMHead, AutoModel, BertTokenizer\n",
        "from huggingface_konlpy.tokenizers_konlpy import KoNLPyBertWordPieceTrainer, KoNLPyWordPieceTokenizer\n",
        "from huggingface_konlpy.transformers_konlpy import KoNLPyBertTokenizer\n",
        "from konlpy.tag import Komoran, Mecab, Okt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xzs9ZPtSj5X0",
        "outputId": "c9c9a9a9-21cd-4066-a993-78235d5de746"
      },
      "source": [
        "!ls"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "data\t\t\t       Mecab-ko-for-Google-Colab  spoken1.csv\n",
            "examples\t\t       README.md\t\t  spoken2.csv\n",
            "huggingface_konlpy\t       requirements.txt\t\t  tokenizers\n",
            "konlpy_berttokenizer_helsinki  setup.py\t\t\t  tutorials\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d0HXdItU7lQw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1d0f55bb-3959-4b6c-b221-b81721ba69fa"
      },
      "source": [
        "# Hugging Face + KoNLPy\n",
        "sent_ko = '나는 오늘 넷플릭스 자막 8만건을 모으고 열심히 심플트랜스포머를 돌렸지만 이제 버릴 것이다'\n",
        "\n",
        "mecab_wordpiece_notag_trainer = KoNLPyBertWordPieceTrainer(Mecab(), use_tag=False)\n",
        "mecab_wordpiece_notag_trainer.train(\n",
        "    files = '/content/drive/MyDrive/dataset/corpus/spoken_aihub_preprocessed.csv')\n",
        "mecab_wordpiece_notag_trainer.save_model('./tokenizers/BertStyleMecab/', 'aihub')\n",
        "\n",
        "mecab_bert_notag = KoNLPyBertTokenizer(\n",
        "    konlpy_wordpiece = KoNLPyWordPieceTokenizer(Mecab(), use_tag=False),\n",
        "    vocab_file = './tokenizers/BertStyleMecab/aihub-vocab.txt'\n",
        ")\n",
        "print(mecab_bert_notag.tokenize(sent_ko))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Initialize alphabet 1/1: 100%|██████████| 400001/400001 [00:05<00:00, 68069.07it/s]\n",
            "Train vocab 1/1: 100%|██████████| 400001/400001 [01:21<00:00, 4893.10it/s]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[/content/huggingface_konlpy/tokenizers/BertStyleMecab/aihub-vocab.txt]\n",
            "['나', '##는', '오늘', '넷', '플', '릭', '스', '자막', '8', '##만', '##건', '##을', '모으', '##고', '열심히', '심플', '트', '랜', '스', '포', '머', '##를', '돌렸', '##지만', '이제', '버릴', '것', '##이', '##다']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YwGa09d-_wd7"
      },
      "source": [
        "- 엥... T5는 이미 토큰이 됨??"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MiaUDyEzG12Q"
      },
      "source": [
        "그리고 커스텀 토큰을 쓸 수 없는 듯... 심플트랜스포머 기준 증말 심플하다..."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q1tExNz5BQIP"
      },
      "source": [
        "### Hugging Face에 모델 업로드하기"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d4-sdJCI7yhO",
        "outputId": "14830455-a466-4d81-bc41-e622a377c4ea"
      },
      "source": [
        "!sudo apt-get install git-lfs"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Reading package lists... Done\n",
            "Building dependency tree       \n",
            "Reading state information... Done\n",
            "git-lfs is already the newest version (2.3.4-1).\n",
            "The following package was automatically installed and is no longer required:\n",
            "  libnvidia-common-460\n",
            "Use 'sudo apt autoremove' to remove it.\n",
            "0 upgraded, 0 newly installed, 0 to remove and 34 not upgraded.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kQCkY3Ga_E1l"
      },
      "source": [
        "!git config --global user.email \"andyourturntome@gmail.com\"\n",
        "!git config --global user.name \"AimB\""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oPzNFVus71Wm",
        "outputId": "3cac6fc7-42f9-4d5b-c1a6-afce5ac230b0"
      },
      "source": [
        "!transformers-cli login"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2021-04-27 14:40:16.602589: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0\n",
            "\n",
            "        _|    _|  _|    _|    _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|_|_|_|    _|_|      _|_|_|  _|_|_|_|\n",
            "        _|    _|  _|    _|  _|        _|          _|    _|_|    _|  _|            _|        _|    _|  _|        _|\n",
            "        _|_|_|_|  _|    _|  _|  _|_|  _|  _|_|    _|    _|  _|  _|  _|  _|_|      _|_|_|    _|_|_|_|  _|        _|_|_|\n",
            "        _|    _|  _|    _|  _|    _|  _|    _|    _|    _|    _|_|  _|    _|      _|        _|    _|  _|        _|\n",
            "        _|    _|    _|_|      _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|        _|    _|    _|_|_|  _|_|_|_|\n",
            "\n",
            "        \n",
            "Username: AimB\n",
            "Password: \n",
            "Login successful\n",
            "Your token: dHRjBfkfXUoVANdgHMNwxTXuaeAIJGAsisqoeyrMfqiDmFAPZViiwjDggoZAXzZVvVBfienNFnyZDpEXlsfhiPVWvHQNYHPdizeWiSvHnjwDRzwvXScLOveqlnRxSNXI \n",
            "\n",
            "Your token has been saved to /root/.huggingface/token\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f93z2sbArhb0",
        "outputId": "5940492b-3c1a-4a55-8a48-7eabc1fe542f"
      },
      "source": [
        "!pwd"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gurAUypqtwbF",
        "outputId": "bac99a08-608e-44aa-8167-c37cb13a02eb"
      },
      "source": [
        "!transformers-cli repo create mT5-en-kr-natural"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2021-04-27 14:40:51.016928: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0\n",
            "\u001b[90mgit version 2.17.1\u001b[0m\n",
            "Error: unknown flag: --version\n",
            "\n",
            "\u001b[90mSorry, no usage text found for \"git-lfs\"\u001b[0m\n",
            "\n",
            "You are about to create \u001b[1mAimB/mT5-en-kr-natural\u001b[0m\n",
            "Proceed? [Y/n] Y\n",
            "\n",
            "Your repo now lives at:\n",
            "  \u001b[1mhttps://huggingface.co/AimB/mT5-en-kr-natural\u001b[0m\n",
            "\n",
            "You can clone it locally with the command below, and commit/push as usual.\n",
            "\n",
            "  git clone https://huggingface.co/AimB/mT5-en-kr-natural\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X1dYejxT8Qq7",
        "outputId": "9e30e366-24ee-4f78-c4a4-8197af62be18"
      },
      "source": [
        "!git clone https://AimB:dHRjBfkfXUoVANdgHMNwxTXuaeAIJGAsisqoeyrMfqiDmFAPZViiwjDggoZAXzZVvVBfienNFnyZDpEXlsfhiPVWvHQNYHPdizeWiSvHnjwDRzwvXScLOveqlnRxSNXI@huggingface.co/AimB/mT5-en-kr-natural"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'mT5-en-kr-natural'...\n",
            "remote: Enumerating objects: 3, done.\u001b[K\n",
            "remote: Counting objects: 100% (3/3), done.\u001b[K\n",
            "remote: Compressing objects: 100% (2/2), done.\u001b[K\n",
            "remote: Total 3 (delta 0), reused 0 (delta 0)\u001b[K\n",
            "Unpacking objects: 100% (3/3), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "73QVhQlrz3bv"
      },
      "source": [
        "git clone https://huggingface.co/AimB/mT5-en-kr-natural"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zcGjziSArnN4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "35bd6ce9-5822-4c9b-e0ed-6980c973fcd7"
      },
      "source": [
        "!ls -al"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total 32\n",
            "drwxr-xr-x 1 root root 4096 Apr 27 14:41 .\n",
            "drwxr-xr-x 1 root root 4096 Apr 27 07:42 ..\n",
            "drwxr-xr-x 4 root root 4096 Apr 21 13:38 .config\n",
            "drwx------ 5 root root 4096 Apr 27 08:12 drive\n",
            "drwxr-xr-x 2 root root 4096 Apr 27 14:38 .ipynb_checkpoints\n",
            "drwxr-xr-x 3 root root 4096 Apr 27 14:41 mT5-en-kr-natural\n",
            "drwxr-xr-x 2 root root 4096 Apr 27 14:38 outputs\n",
            "drwxr-xr-x 1 root root 4096 Apr 21 13:39 sample_data\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GOITsNCc8ser",
        "outputId": "475977ea-fa7c-4d4c-8a88-16cdde4d0cf5"
      },
      "source": [
        "%cd mT5-en-kr-natural"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/drive/My Drive/dataset/corpus/huggingfacemodel/outputs/mT5-en-kr-natural\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W2Lfh83g74e4",
        "outputId": "25eb3b29-97fd-4d38-920f-b6604c9a8d77"
      },
      "source": [
        "!git lfs install"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Updated git hooks.\n",
            "Git LFS initialized.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vDHw0bpr_Fe3"
      },
      "source": [
        "- 여기로 위의 모델이랑 txt 옮기기"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qoDD6hHdskBB",
        "outputId": "c703f919-6253-4654-8288-a4fe0e538f21"
      },
      "source": [
        "!wget https://huggingface.co/mt5-based/resolve/main/config.json\n",
        "#!wget https://huggingface.co/distilbert-base-uncased/resolve/main/pytorch_model.bin"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2021-04-27 14:35:34--  https://huggingface.co/mt5-based/resolve/main/config.json\n",
            "Resolving huggingface.co (huggingface.co)... 34.201.172.85\n",
            "Connecting to huggingface.co (huggingface.co)|34.201.172.85|:443... connected.\n",
            "HTTP request sent, awaiting response... 404 Not Found\n",
            "2021-04-27 14:35:34 ERROR 404: Not Found.\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WitYcfgl86HS"
      },
      "source": [
        "!git add ."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AXbao6mlsSuK",
        "outputId": "10d5e50c-e07d-472f-cc10-74a090de502a"
      },
      "source": [
        "!git commit -m \"epoch 1\""
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "fatal: cannot exec '.git/hooks/post-commit': Permission denied\n",
            "[main 638a6ef] epoch 1\n",
            " 29 files changed, 137 insertions(+)\n",
            " create mode 100644 best_model/config.json\n",
            " create mode 100644 best_model/eval_results.txt\n",
            " create mode 100644 best_model/model_args.json\n",
            " create mode 100644 best_model/optimizer.pt\n",
            " create mode 100644 best_model/pytorch_model.bin\n",
            " create mode 100644 best_model/scheduler.pt\n",
            " create mode 100644 best_model/special_tokens_map.json\n",
            " create mode 100644 best_model/spiece.model\n",
            " create mode 100644 best_model/tokenizer_config.json\n",
            " create mode 100644 best_model/training_args.bin\n",
            " create mode 100644 checkpoint-50796-epoch-1/config.json\n",
            " create mode 100644 checkpoint-50796-epoch-1/model_args.json\n",
            " create mode 100644 checkpoint-50796-epoch-1/optimizer.pt\n",
            " create mode 100644 checkpoint-50796-epoch-1/pytorch_model.bin\n",
            " create mode 100644 checkpoint-50796-epoch-1/scheduler.pt\n",
            " create mode 100644 checkpoint-50796-epoch-1/special_tokens_map.json\n",
            " create mode 100644 checkpoint-50796-epoch-1/spiece.model\n",
            " create mode 100644 checkpoint-50796-epoch-1/tokenizer_config.json\n",
            " create mode 100644 checkpoint-50796-epoch-1/training_args.bin\n",
            " create mode 100644 config.json\n",
            " create mode 100644 eval.py\n",
            " create mode 100644 eval_results.txt\n",
            " create mode 100644 model_args.json\n",
            " create mode 100644 pytorch_model.bin\n",
            " create mode 100644 special_tokens_map.json\n",
            " create mode 100644 spiece.model\n",
            " create mode 100644 tokenizer_config.json\n",
            " create mode 100644 training_args.bin\n",
            " create mode 100644 training_progress_scores.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0J3riCBCuyGk",
        "outputId": "cc6fa675-cc4e-4ae9-ae56-cdd8a06b6ec4"
      },
      "source": [
        "!git log"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[33mcommit 638a6ef6b73cab78b632e40f7b4103702b1ee2c2\u001b[m\u001b[33m (\u001b[m\u001b[1;36mHEAD -> \u001b[m\u001b[1;32mmain\u001b[m\u001b[33m)\u001b[m\n",
            "Author: AimB <andyourturntome@gmail.com>\n",
            "Date:   Tue Apr 27 14:31:00 2021 +0000\n",
            "\n",
            "    epoch 1\n",
            "\n",
            "\u001b[33mcommit 0c32157dcea6a36cd546c8b9ccdf5883c58b263f\u001b[m\u001b[33m (\u001b[m\u001b[1;31morigin/main\u001b[m\u001b[33m, \u001b[m\u001b[1;31morigin/HEAD\u001b[m\u001b[33m)\u001b[m\n",
            "Author: system <system@huggingface.co>\n",
            "Date:   Tue Apr 27 14:24:56 2021 +0000\n",
            "\n",
            "    initial commit\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mqWbvzmIsVlr",
        "outputId": "815ee1fd-5b40-4392-ce82-8f928d257440"
      },
      "source": [
        "!git push"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "fatal: cannot exec '.git/hooks/pre-push': Permission denied\n",
            "^C\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S9_JsXeZ0WEo"
      },
      "source": [
        "- 너무 샅샅히 나누는데.. .트레인이 덜 되어서 그런가"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H63PqC_rBUxv"
      },
      "source": [
        "### Hugging Face 모델 사용하기"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 273,
          "referenced_widgets": [
            "b402aa778c954c7fa7f807b41b584ed1",
            "c82ecfee21ec4a8c892095a83d772c4a",
            "0a8183b3f820425ea5ae7ca502aed20f",
            "1cb64e32deac4a19b72bef3acec3d056",
            "4b646d05f525481e8f8475f7f7bf831b",
            "79e203f9742e43879265b08ef78b0489",
            "eddb2144f50e4bee91e6213a9180d6c5",
            "1651c2fa7a7a40b8a0b51096b26d87bb",
            "c1d522bc675d4c5184b0e19bb06b601c",
            "ed46de28c5614ca1ae4c6716efcbd935",
            "c8d33165b5704cb69d6f19a58ed5e477",
            "8a7ffecd72d64d8b918f8771afd5d004",
            "493bd2a316bc4df1819c434cef8eb466",
            "1ce0e078ac1b477381a0c05a7ed27aa7",
            "fe1ae51f5b194a44b8ab58a2a01cf122",
            "96a9fef07c9e48788bffa9ac75096e69",
            "30169dd5d55c4b25bdef183a868dc77a",
            "e8e4869265074f338432c6fe4fd064ea",
            "97493e21f42e469eb62aa94812bfd722",
            "b299599393414b9590763f5ab7b037d7",
            "ddf441770e5f480d8e1e3b32f99a39e0",
            "fbb19dc3aa984be2afba769c863e6bb3",
            "bb567e5233e6441a9aea1a89bc61e81d",
            "99f27fd664754216b8011cfc9bb11e86",
            "efe0669fddf740edb18b6939e49c4b42",
            "c44daf695d11446999cff6f320179416",
            "f222ef0d30894afbb3aeba11c77121ff",
            "e5049e6f3a024f07935da4cc1f70b0a6",
            "ff515f0ceb0d4c98b99f6086bbfda540",
            "ca3cd92762d34ba7ba3b9800eb720e3f",
            "47c4022627da4e6abf9191399457525c",
            "d1db97bc586043d0854162d53da70486"
          ]
        },
        "id": "h4PW_p4l8PgU",
        "outputId": "41a44c94-fd0a-4d71-f495-1e83134b7120"
      },
      "source": [
        "# t5-base 토크나이저 사용\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"t5-base\")\n",
        "\n",
        "# konlpy 연계 토크나이저 사용\n",
        "#tokenizer = mecab_bert_notag.tokenize()\n",
        "model = AutoModelWithLMHead.from_pretrained(\"t5-base\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b402aa778c954c7fa7f807b41b584ed1",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=1199.0, style=ProgressStyle(description…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c1d522bc675d4c5184b0e19bb06b601c",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=791656.0, style=ProgressStyle(descripti…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "30169dd5d55c4b25bdef183a868dc77a",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=1389353.0, style=ProgressStyle(descript…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/transformers/models/auto/modeling_auto.py:762: FutureWarning: The class `AutoModelWithLMHead` is deprecated and will be removed in a future version. Please use `AutoModelForCausalLM` for causal language models, `AutoModelForMaskedLM` for masked language models and `AutoModelForSeq2SeqLM` for encoder-decoder models.\n",
            "  FutureWarning,\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "efe0669fddf740edb18b6939e49c4b42",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=891691430.0, style=ProgressStyle(descri…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zMZmCDj5BfaS"
      },
      "source": [
        "### 전처리"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 415
        },
        "id": "P8IrVsXK46bu",
        "outputId": "b729b944-e031-4701-eecc-117fc51d2d0e"
      },
      "source": [
        "df_aihub = pd.read_csv('/content/drive/MyDrive/dataset/corpus/spoken_aihub.csv')\n",
        "df_aihub"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>원문</th>\n",
              "      <th>번역문</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>'Bible Coloring'은 성경의 아름다운 이야기를 체험 할 수 있는 컬러링 ...</td>\n",
              "      <td>Bible Coloring' is a coloring application that...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>씨티은행에서 일하세요?</td>\n",
              "      <td>Do you work at a City bank?</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>푸리토의 베스트셀러는 해외에서 입소문만으로 4차 완판을 기록하였다.</td>\n",
              "      <td>PURITO's bestseller, which recorded 4th rough ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>11장에서는 예수님이 이번엔 나사로를 무덤에서 불러내어 죽은 자 가운데서 살리셨습니다.</td>\n",
              "      <td>In Chapter 11 Jesus called Lazarus from the to...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>6.5, 7, 8 사이즈가 몇 개나 더 재입고 될지 제게 알려주시면 감사하겠습니다.</td>\n",
              "      <td>I would feel grateful to know how many stocks ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>399995</th>\n",
              "      <td>나는 먼저 청소기로 바닥을 밀었어요.</td>\n",
              "      <td>First of all, I vacuumed the floor.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>399996</th>\n",
              "      <td>나는 먼저 팀 과제를 하고 놀러 갔어요.</td>\n",
              "      <td>I did the team assignment first and went out t...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>399997</th>\n",
              "      <td>나는 비 같은 멋진 연예인을 좋아해요.</td>\n",
              "      <td>I like cool entertainer like Rain.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>399998</th>\n",
              "      <td>나는 멋진 자연 경치를 보고 눈물을 흘렸어.</td>\n",
              "      <td>I cried seeing the amazing scenery.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>399999</th>\n",
              "      <td>나는 멋진 중학교 생활을 기대합니다.</td>\n",
              "      <td>I look forward to a great middle school experi...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>400000 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                       원문                                                번역문\n",
              "0       'Bible Coloring'은 성경의 아름다운 이야기를 체험 할 수 있는 컬러링 ...  Bible Coloring' is a coloring application that...\n",
              "1                                            씨티은행에서 일하세요?                        Do you work at a City bank?\n",
              "2                   푸리토의 베스트셀러는 해외에서 입소문만으로 4차 완판을 기록하였다.  PURITO's bestseller, which recorded 4th rough ...\n",
              "3        11장에서는 예수님이 이번엔 나사로를 무덤에서 불러내어 죽은 자 가운데서 살리셨습니다.  In Chapter 11 Jesus called Lazarus from the to...\n",
              "4          6.5, 7, 8 사이즈가 몇 개나 더 재입고 될지 제게 알려주시면 감사하겠습니다.  I would feel grateful to know how many stocks ...\n",
              "...                                                   ...                                                ...\n",
              "399995                               나는 먼저 청소기로 바닥을 밀었어요.                First of all, I vacuumed the floor.\n",
              "399996                             나는 먼저 팀 과제를 하고 놀러 갔어요.  I did the team assignment first and went out t...\n",
              "399997                              나는 비 같은 멋진 연예인을 좋아해요.                 I like cool entertainer like Rain.\n",
              "399998                           나는 멋진 자연 경치를 보고 눈물을 흘렸어.                I cried seeing the amazing scenery.\n",
              "399999                               나는 멋진 중학교 생활을 기대합니다.  I look forward to a great middle school experi...\n",
              "\n",
              "[400000 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EaKW393L5Nzn"
      },
      "source": [
        "- AI 허브에서 다운받은 한-영 말뭉치 페어 40만 문장"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6FXWpYRXfAm3"
      },
      "source": [
        "df_aihub = df_aihub.rename(columns={\"원문\": \"kr\", \"번역문\": \"en\"})"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2eEHqX4drABH",
        "outputId": "17664f62-3ce7-4f5d-e241-541e788fecfd"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(400000, 2)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c1NY_qLirBwm"
      },
      "source": [
        "df3 = pd.concat([df, df_subs], ignore_index=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 502
        },
        "id": "X4Z7HFU55EjH",
        "outputId": "6aaa3456-757e-4ae4-cecb-b202ee6fb30d"
      },
      "source": [
        "df_subs = pd.read_csv('/content/drive/MyDrive/dataset/corpus/netsubs_11.csv')\n",
        "df_subs"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>en</th>\n",
              "      <th>kr</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>that would be four , actually . oh , god . com...</td>\n",
              "      <td>세 번 연속으로 이기셨네요 정확히는 네 번 연속이에요</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>what do you say , we go five outta nine ?</td>\n",
              "      <td>이제 9판 5승제로 갈까요 ?</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>so you can keep abusing me ? no , thank you . ...</td>\n",
              "      <td>계속 갖고 노시려고요 ? 됐어요 , 그만할래요</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>oh , come on . i'll spot you three .</td>\n",
              "      <td>세 판 핸디캡 드릴게요</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>game time's over , wyatt . they're here . they...</td>\n",
              "      <td>와이엇 씨 , 그만 놀고 가시죠 왔나요 ?</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>110503</th>\n",
              "      <td>or you can try to make the best of it .</td>\n",
              "      <td>그 변화를 최대한 이용할 수도 있습니다</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>110504</th>\n",
              "      <td>and that's all a lot easier</td>\n",
              "      <td>사랑하는 사람들이</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>110505</th>\n",
              "      <td>if you've got people who love you</td>\n",
              "      <td>옆에 같이 있다면</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>110506</th>\n",
              "      <td>helping you face whatever life throws at you .</td>\n",
              "      <td>그 어떤 삶의 변화도 꿋꿋하게 이겨낼 것입니다</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>110507</th>\n",
              "      <td>at least , that's what helps me sleep at night .</td>\n",
              "      <td>제가 밤에 잠을 잘 자는 이유도 그것 때문이니까요</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>110508 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                       en                             kr\n",
              "0       that would be four , actually . oh , god . com...  세 번 연속으로 이기셨네요 정확히는 네 번 연속이에요\n",
              "1               what do you say , we go five outta nine ?               이제 9판 5승제로 갈까요 ?\n",
              "2       so you can keep abusing me ? no , thank you . ...      계속 갖고 노시려고요 ? 됐어요 , 그만할래요\n",
              "3                    oh , come on . i'll spot you three .                   세 판 핸디캡 드릴게요\n",
              "4       game time's over , wyatt . they're here . they...        와이엇 씨 , 그만 놀고 가시죠 왔나요 ?\n",
              "...                                                   ...                            ...\n",
              "110503            or you can try to make the best of it .          그 변화를 최대한 이용할 수도 있습니다\n",
              "110504                        and that's all a lot easier                      사랑하는 사람들이\n",
              "110505                  if you've got people who love you                      옆에 같이 있다면\n",
              "110506     helping you face whatever life throws at you .      그 어떤 삶의 변화도 꿋꿋하게 이겨낼 것입니다\n",
              "110507   at least , that's what helps me sleep at night .    제가 밤에 잠을 잘 자는 이유도 그것 때문이니까요\n",
              "\n",
              "[110508 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OdlZ4Br1hnYn"
      },
      "source": [
        "- 이미 전처리를 한 버전."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FsbRqCIt5VdX"
      },
      "source": [
        "df_subs = df_subs[['kr', 'en']]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6SSMf2s15QwF"
      },
      "source": [
        "- 넷플릭스 자막에서 얻은 한영자막 페어 11만 쌍."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O3J-lLv658Nm"
      },
      "source": [
        "- 40만 문장으로 늘려 수집해야 하나?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "da_l7BHoETda",
        "outputId": "fd88f8fc-8bfc-4e42-d6f1-d2e7abbbb867"
      },
      "source": [
        "!pip install sentencepiece"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: sentencepiece in /usr/local/lib/python3.7/dist-packages (0.1.95)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YSNbtxODhESH"
      },
      "source": [
        "### 전처리 2"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "id": "cCgbjjt9hdY-",
        "outputId": "b971dc5f-322c-42ad-9db8-4224699b881f"
      },
      "source": [
        "df_aihub.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>kr</th>\n",
              "      <th>en</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>'Bible Coloring'은 성경의 아름다운 이야기를 체험 할 수 있는 컬러링 ...</td>\n",
              "      <td>Bible Coloring' is a coloring application that...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>씨티은행에서 일하세요?</td>\n",
              "      <td>Do you work at a City bank?</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>푸리토의 베스트셀러는 해외에서 입소문만으로 4차 완판을 기록하였다.</td>\n",
              "      <td>PURITO's bestseller, which recorded 4th rough ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>11장에서는 예수님이 이번엔 나사로를 무덤에서 불러내어 죽은 자 가운데서 살리셨습니다.</td>\n",
              "      <td>In Chapter 11 Jesus called Lazarus from the to...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>6.5, 7, 8 사이즈가 몇 개나 더 재입고 될지 제게 알려주시면 감사하겠습니다.</td>\n",
              "      <td>I would feel grateful to know how many stocks ...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                  kr                                                 en\n",
              "0  'Bible Coloring'은 성경의 아름다운 이야기를 체험 할 수 있는 컬러링 ...  Bible Coloring' is a coloring application that...\n",
              "1                                       씨티은행에서 일하세요?                        Do you work at a City bank?\n",
              "2              푸리토의 베스트셀러는 해외에서 입소문만으로 4차 완판을 기록하였다.  PURITO's bestseller, which recorded 4th rough ...\n",
              "3   11장에서는 예수님이 이번엔 나사로를 무덤에서 불러내어 죽은 자 가운데서 살리셨습니다.  In Chapter 11 Jesus called Lazarus from the to...\n",
              "4     6.5, 7, 8 사이즈가 몇 개나 더 재입고 될지 제게 알려주시면 감사하겠습니다.  I would feel grateful to know how many stocks ..."
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EdGlqfvBg-9-"
      },
      "source": [
        "import unicodedata\n",
        "import re\n",
        "\n",
        "# 유니코드 파일을 아스키 코드 파일로 변환합니다.\n",
        "def unicode_to_ascii(s):\n",
        "  return ''.join(c for c in unicodedata.normalize('NFD', s)\n",
        "      if unicodedata.category(c) != 'Mn')\n",
        "\n",
        "\n",
        "def preprocess_sentence_en(w):\n",
        "  w = unicode_to_ascii(w.lower().strip())\n",
        "\n",
        "  # 단어와 단어 뒤에 오는 구두점(.)사이에 공백을 생성합니다.\n",
        "  # 예시: \"he is a boy.\" => \"he is a boy .\"\n",
        "  # 참고:- https://stackoverflow.com/questions/3645931/python-padding-punctuation-with-white-spaces-keeping-punctuation\n",
        "  w = re.sub(r\"\\[.+\\]\",'',w).strip() # 영어 문장 속의 [smile] 같은 지시문 없애기\n",
        "  w = re.sub(r'\\([^)]*\\)','',w).strip() # (sigh) 같은 지시문 없애기\n",
        "  w = re.sub(r\"([?.!,¿])\", r\" \\1 \", w)\n",
        "  w = re.sub(r'[\" \"]+', \" \", w)\n",
        "\n",
        "  # (a-z, A-Z, \".\", \"?\", \"!\", \",\")을 제외한 모든 것을 공백으로 대체합니다.\n",
        "  w = re.sub(r\"[^0-9a-zA-Z?.!',¿$]+\", \" \", w) # ' 포함 - 숫자 빼면 안 된다!!\n",
        "\n",
        "  w = w.strip()\n",
        "\n",
        "  # 모델이 예측을 시작하거나 중단할 때를 알게 하기 위해서\n",
        "  # 문장에 start와 end 토큰을 추가합니다.\n",
        "  #w = '<start> ' + w + ' <end>'\n",
        "  return w\n",
        "\n",
        "\n",
        "def preprocess_sentence_kr(w):\n",
        "  w = w.strip()\n",
        "\n",
        "  # 단어와 단어 뒤에 오는 구두점(.)사이에 공백을 생성합니다.\n",
        "  # 예시: \"he is a boy.\" => \"he is a boy .\"\n",
        "  # 참고:- https://stackoverflow.com/questions/3645931/python-padding-punctuation-with-white-spaces-keeping-punctuation\n",
        "  w = re.sub(r\"\\\".+\\\"\",'',w).strip() # 큰따옴표 제거 - 화면자막\n",
        "  w = re.sub(r\"([?.!,¿])\", r\" \\1 \", w)\n",
        "  w = re.sub(r'[\" \"]+', \" \", w)\n",
        "\n",
        "  # (a-z, A-Z, \".\", \"?\", \"!\", \",\")을 제외한 모든 것을 공백으로 대체합니다.\n",
        "  w = re.sub(r\"[^0-9A-Za-z가-힣?.!,¿]+\", \" \", w) # \\n도 공백으로 대체해줌\n",
        "\n",
        "  w = w.strip()\n",
        "\n",
        "  # 모델이 예측을 시작하거나 중단할 때를 알게 하기 위해서\n",
        "  # 문장에 start와 end 토큰을 추가합니다.\n",
        "  #w = '<start> ' + w + ' <end>'\n",
        "  return w"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E6IFFdmfhrAa"
      },
      "source": [
        "df = pd.DataFrame()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lnk8uPQwhTBz"
      },
      "source": [
        "df['en'] = [preprocess_sentence_en(l) for l in df_aihub['en']]\n",
        "df['kr'] = [preprocess_sentence_kr(l) for l in df_aihub['kr']]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "id": "ZtvFZMNkhzbb",
        "outputId": "5a611d5a-4fc8-4042-f595-c8471ffc8578"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\n",
              "    .dataframe thead th {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>en</th>\n",
              "      <th>kr</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>bible coloring' is a coloring application that...</td>\n",
              "      <td>Bible Coloring 은 성경의 아름다운 이야기를 체험 할 수 있는 컬러링 앱...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>do you work at a city bank ?</td>\n",
              "      <td>씨티은행에서 일하세요 ?</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>purito's bestseller , which recorded 4th rough...</td>\n",
              "      <td>푸리토의 베스트셀러는 해외에서 입소문만으로 4차 완판을 기록하였다 .</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>in chapter 11 jesus called lazarus from the to...</td>\n",
              "      <td>11장에서는 예수님이 이번엔 나사로를 무덤에서 불러내어 죽은 자 가운데서 살리셨습니다 .</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>i would feel grateful to know how many stocks ...</td>\n",
              "      <td>6 . 5 , 7 , 8 사이즈가 몇 개나 더 재입고 될지 제게 알려주시면 감사하겠...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                  en                                                 kr\n",
              "0  bible coloring' is a coloring application that...  Bible Coloring 은 성경의 아름다운 이야기를 체험 할 수 있는 컬러링 앱...\n",
              "1                       do you work at a city bank ?                                      씨티은행에서 일하세요 ?\n",
              "2  purito's bestseller , which recorded 4th rough...             푸리토의 베스트셀러는 해외에서 입소문만으로 4차 완판을 기록하였다 .\n",
              "3  in chapter 11 jesus called lazarus from the to...  11장에서는 예수님이 이번엔 나사로를 무덤에서 불러내어 죽은 자 가운데서 살리셨습니다 .\n",
              "4  i would feel grateful to know how many stocks ...  6 . 5 , 7 , 8 사이즈가 몇 개나 더 재입고 될지 제게 알려주시면 감사하겠..."
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QZymcxrAiOY2"
      },
      "source": [
        "df3.to_csv('/content/drive/MyDrive/dataset/corpus/spoken_aihub_netflix.csv', index=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PMTFCCEUi1TR",
        "outputId": "d648b3da-3dd8-4dc6-9ae5-9fc8627a8c68"
      },
      "source": [
        "df = pd.read_csv('/content/drive/MyDrive/dataset/corpus/spoken_aihub_netflix.csv')\n",
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th></th>\n",
              "      <th>en</th>\n",
              "      <th>kr</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>bible coloring' is a coloring application that...</td>\n",
              "      <td>Bible Coloring 은 성경의 아름다운 이야기를 체험 할 수 있는 컬러링 앱...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>do you work at a city bank ?</td>\n",
              "      <td>씨티은행에서 일하세요 ?</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>purito's bestseller , which recorded 4th rough...</td>\n",
              "      <td>푸리토의 베스트셀러는 해외에서 입소문만으로 4차 완판을 기록하였다 .</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>in chapter 11 jesus called lazarus from the to...</td>\n",
              "      <td>11장에서는 예수님이 이번엔 나사로를 무덤에서 불러내어 죽은 자 가운데서 살리셨습니다 .</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>i would feel grateful to know how many stocks ...</td>\n",
              "      <td>6 . 5 , 7 , 8 사이즈가 몇 개나 더 재입고 될지 제게 알려주시면 감사하겠...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>510503</th>\n",
              "      <td>or you can try to make the best of it .</td>\n",
              "      <td>그 변화를 최대한 이용할 수도 있습니다</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>510504</th>\n",
              "      <td>and that's all a lot easier</td>\n",
              "      <td>사랑하는 사람들이</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>510505</th>\n",
              "      <td>if you've got people who love you</td>\n",
              "      <td>옆에 같이 있다면</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>510506</th>\n",
              "      <td>helping you face whatever life throws at you .</td>\n",
              "      <td>그 어떤 삶의 변화도 꿋꿋하게 이겨낼 것입니다</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>510507</th>\n",
              "      <td>at least , that's what helps me sleep at night .</td>\n",
              "      <td>제가 밤에 잠을 잘 자는 이유도 그것 때문이니까요</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>510508 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                       en                                                 kr\n",
              "0       bible coloring' is a coloring application that...  Bible Coloring 은 성경의 아름다운 이야기를 체험 할 수 있는 컬러링 앱...\n",
              "1                            do you work at a city bank ?                                      씨티은행에서 일하세요 ?\n",
              "2       purito's bestseller , which recorded 4th rough...             푸리토의 베스트셀러는 해외에서 입소문만으로 4차 완판을 기록하였다 .\n",
              "3       in chapter 11 jesus called lazarus from the to...  11장에서는 예수님이 이번엔 나사로를 무덤에서 불러내어 죽은 자 가운데서 살리셨습니다 .\n",
              "4       i would feel grateful to know how many stocks ...  6 . 5 , 7 , 8 사이즈가 몇 개나 더 재입고 될지 제게 알려주시면 감사하겠...\n",
              "...                                                   ...                                                ...\n",
              "510503            or you can try to make the best of it .                              그 변화를 최대한 이용할 수도 있습니다\n",
              "510504                        and that's all a lot easier                                          사랑하는 사람들이\n",
              "510505                  if you've got people who love you                                          옆에 같이 있다면\n",
              "510506     helping you face whatever life throws at you .                          그 어떤 삶의 변화도 꿋꿋하게 이겨낼 것입니다\n",
              "510507   at least , that's what helps me sleep at night .                        제가 밤에 잠을 잘 자는 이유도 그것 때문이니까요\n",
              "\n",
              "[510508 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hLTxz3jBflK-"
      },
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vjl6q-oJLdKt",
        "outputId": "21d1fadb-ecd0-4445-a31b-441295c6611d"
      },
      "source": [
        "train_df, eval_df = train_test_split(df, test_size=0.005, random_state=22)\n",
        "train_df.shape, eval_df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((507955, 2), (2553, 2))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ey3XWcQdgQny"
      },
      "source": [
        "- 테스트 셋이 이렇게 적어도 되나 싶지만.. 헬싱키 opus 데이터셋도 train data 390만개에 eval_data가 2000개이므로... 최대한 학습데이터를 늘려보겠습니다."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UAOTeb4irpav"
      },
      "source": [
        "# -*- coding:utf-8 -*-\n",
        "\n",
        "korean_text = [text for text in train_df['kr']]\n",
        "english_text = [text for text in train_df['en']]\n",
        "\n",
        "data = []\n",
        "for korean, english in zip(korean_text, english_text):\n",
        "    data.append([\"kor to eng\", korean, english])\n",
        "    data.append([\"eng to kor\", english, korean])\n",
        "\n",
        "train_df2 = pd.DataFrame(data, columns=[\"prefix\", \"input_text\", \"target_text\"])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Uu52hRW-sxH-"
      },
      "source": [
        "korean_text = [text for text in eval_df['kr']]\n",
        "english_text = [text for text in eval_df['en']]\n",
        "\n",
        "data = []\n",
        "for korean, english in zip(korean_text, english_text):\n",
        "    data.append([\"kor to eng\", korean, english])\n",
        "    data.append([\"eng to kor\", english, korean])\n",
        "\n",
        "eval_df2 = pd.DataFrame(data, columns=[\"prefix\", \"input_text\", \"target_text\"])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UBaM5BOxs-c2",
        "outputId": "e127a700-3514-474a-f2f7-2b19110446d7"
      },
      "source": [
        "train_df2.shape, eval_df2.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((1015910, 3), (5106, 3))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KWcymyvutBX6"
      },
      "source": [
        "- mT5 모델의 학습을 위해 각 문장을 교차시켜 2배로 만들고 입력어, 출력어 정보를 추가합니다."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z58CzeMuz0cx"
      },
      "source": [
        "# Todo\n",
        "- 번역기 쌓기\n",
        "- AI허브말뭉치/넷플릭스자막/합친 버전 성능 비교\n",
        "- T5 / 트랜스포머 성능 비교\n",
        "- 토크나이저 별 성능 비교"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mMMNiTQJBYky"
      },
      "source": [
        "### Simpletransfomers"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UiU0GbcDZXxN"
      },
      "source": [
        "import os\n",
        "import pandas as pd"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "obVdRPMt53Hq",
        "outputId": "343762e5-300e-4a0d-a7d5-a83aaf182c6b"
      },
      "source": [
        "!wandb login"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: You can find your API key in your browser here: https://wandb.ai/authorize\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Paste an API key from your profile and hit enter: \n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "exwdN_pjr8RQ"
      },
      "source": [
        "### 모델 훈련"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_hKo4l4TE06d"
      },
      "source": [
        "import logging\n",
        "import pandas as pd\n",
        "from simpletransformers.t5 import T5Model, T5Args\n",
        "\n",
        "\n",
        "logging.basicConfig(level=logging.INFO)\n",
        "transformers_logger = logging.getLogger(\"transformers\")\n",
        "transformers_logger.setLevel(logging.WARNING)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2ZjDEw-FuSHr"
      },
      "source": [
        "- GPU 메모리 사용에 maximum sequence 길이가 중요한 영향을 미친다."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X0b09us9u2HN"
      },
      "source": [
        "- input 문장의 길이에 따라 O(n²)의 공간복잡도를 가짐\n",
        "- 문장 길이가 2배가 되면, 메모리는 4배로 더 많이 필요해진다."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W1BgMSRMvPzd"
      },
      "source": [
        "- 또한, mT5는 T5보다 훨씬 더 큰 어휘를 가지고 있어, 필요한 GPU 메모리 측면에서 mT5가 상당히 불리하게 되었다.\n",
        "\n",
        "- 따라서 토큰 수를 줄여주는 게 좋다? (The takeaway from all this is that the number of tokens we can input to the model (the maximum sequence length) comes at a hefty premium. Based on this, it’s wasteful to use even a small number of tokens on the prefix if the model can do without.)\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8bsof9JOE6dl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 379,
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          ]
        },
        "outputId": "fe282cb1-e8bd-4333-bd76-f78a0286c1a6"
      },
      "source": [
        "model_args = T5Args()\n",
        "model_args.max_seq_length = 60\n",
        "model_args.train_batch_size = 40\n",
        "model_args.eval_batch_size = 40\n",
        "model_args.num_train_epochs = 3\n",
        "model_args.evaluate_during_training = True\n",
        "model_args.evaluate_during_training_steps = 30000\n",
        "model_args.use_multiprocessing = False\n",
        "model_args.fp16 = False\n",
        "model_args.save_steps = -1\n",
        "model_args.save_eval_checkpoints = False\n",
        "model_args.no_cache = True\n",
        "model_args.reprocess_input_data = True\n",
        "model_args.overwrite_output_dir = True\n",
        "model_args.preprocess_inputs = False\n",
        "model_args.num_return_sequences = 1\n",
        "model_args.wandb_project = \"MT5 Kor-Eng Translation(aihub+netflix)\"\n",
        "\n",
        "# model type, model_name\n",
        "model = T5Model(\"mt5\", \"google/mt5-base\", use_cuda=True, args=model_args)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:filelock:Lock 140323826990800 acquired on /root/.cache/huggingface/transformers/5ebfd830555547194403d6803baa127970de59b443c04b7a1a60b16a97ed3958.3950cd4aaa701cb6f55a976ff996001a5fb09bbbe7ba9084619949d9016f519e.lock\n"
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            "INFO:filelock:Lock 140323826990800 released on /root/.cache/huggingface/transformers/5ebfd830555547194403d6803baa127970de59b443c04b7a1a60b16a97ed3958.3950cd4aaa701cb6f55a976ff996001a5fb09bbbe7ba9084619949d9016f519e.lock\n",
            "INFO:filelock:Lock 140321243845008 acquired on /root/.cache/huggingface/transformers/3b7e8056d4ed71d8d7ac2dea78627c4be77ed136399c05b563d4116abfcd9418.1afec9001b62cd5a347e7fd4b664e503ca2377606e11b9ddb8ec1d7b79bc3952.lock\n"
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            "INFO:filelock:Lock 140321096818640 released on /root/.cache/huggingface/transformers/0d7d5b3fc19bf58d4b274990c8bcf5e307726bc18d95f40a1436dfb6a0892f85.294ebaa4cd17bb284635004c92d2c4d522ec488c828dcce0c2471b6f28e3fe82.lock\n",
            "INFO:filelock:Lock 140321096818448 acquired on /root/.cache/huggingface/transformers/afba33be693521ccefbde6d03b93b5c517d7108ba31f6c08000ed52c2cea45c9.28bbf90ae7962b1b7211c0ce8b2006f968c82439ec9c47e0847ba63642f9435a.lock\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e7b9cc446bd14eb8b551501f5652f0bb",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/376 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:filelock:Lock 140321096818448 released on /root/.cache/huggingface/transformers/afba33be693521ccefbde6d03b93b5c517d7108ba31f6c08000ed52c2cea45c9.28bbf90ae7962b1b7211c0ce8b2006f968c82439ec9c47e0847ba63642f9435a.lock\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qxVyZmiDwH7n"
      },
      "source": [
        "- model_args로 pre-trained된 mT5 모델을 어떻게 셋업할 건지 명시한다."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AF2Hs7WQwaEC"
      },
      "source": [
        "- 배치 사이즈를 높이면 GPU 사용이 많아지지만, 학습 시간은 빨라진다.\n",
        "- input 문장의 길이(max_seq_length)가 길어지면 GPU 메모리가 더 필요하고, 따라서 배치 사이즈를 작게 해주기 때문에 학습 시간이 더 걸린다.\n",
        "- 96으로 최대 문장길이를 지정해주었으므로 모델이 긴 문장도 학습할 수 있으면서도 학습 시간이 괜찮은 편이다."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jv9VtdPDw3Wo"
      },
      "source": [
        "- 만약 CUDA 메모리 에러가 일어난다면, 매치 사이즈와 최대 문장 길이를 줄여야 한다."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 415
        },
        "id": "8b5DPG8Uts35",
        "outputId": "6e33cbe6-c4d3-4bbd-acfb-cee69fe15a2d"
      },
      "source": [
        "train_df2"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>prefix</th>\n",
              "      <th>input_text</th>\n",
              "      <th>target_text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>kor to eng</td>\n",
              "      <td>다 먹고 나는 내장산에서 빌려주는 자전거를 탔습니다 .</td>\n",
              "      <td>after eating , i rode a bike which is borrowed...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>eng to kor</td>\n",
              "      <td>after eating , i rode a bike which is borrowed...</td>\n",
              "      <td>다 먹고 나는 내장산에서 빌려주는 자전거를 탔습니다 .</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>kor to eng</td>\n",
              "      <td>기자가 협상이 어떻게 진행되고 있는지 말했습니다 .</td>\n",
              "      <td>the reporter talked about the status of the ne...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>eng to kor</td>\n",
              "      <td>the reporter talked about the status of the ne...</td>\n",
              "      <td>기자가 협상이 어떻게 진행되고 있는지 말했습니다 .</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>kor to eng</td>\n",
              "      <td>우리는 아직 15Z960 설치 가이드의 데이터 출시를 하지 않았습니다 .</td>\n",
              "      <td>we haven't done 15z960 install guide data rele...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1015905</th>\n",
              "      <td>eng to kor</td>\n",
              "      <td>i didn't bring it up to attack you . i just wo...</td>\n",
              "      <td>공격하는 게 아니라 궁금해서 그래</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1015906</th>\n",
              "      <td>kor to eng</td>\n",
              "      <td>나는 무슨 일이 있든 항상 너의 편이 되어 줄게 .</td>\n",
              "      <td>i will be on your side whatever happens .</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1015907</th>\n",
              "      <td>eng to kor</td>\n",
              "      <td>i will be on your side whatever happens .</td>\n",
              "      <td>나는 무슨 일이 있든 항상 너의 편이 되어 줄게 .</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1015908</th>\n",
              "      <td>kor to eng</td>\n",
              "      <td>이 노래를 초콜릿처럼 꺼내먹어요 .</td>\n",
              "      <td>take the song out and eat it like you do with ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1015909</th>\n",
              "      <td>eng to kor</td>\n",
              "      <td>take the song out and eat it like you do with ...</td>\n",
              "      <td>이 노래를 초콜릿처럼 꺼내먹어요 .</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1015910 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "             prefix  ...                                        target_text\n",
              "0        kor to eng  ...  after eating , i rode a bike which is borrowed...\n",
              "1        eng to kor  ...                     다 먹고 나는 내장산에서 빌려주는 자전거를 탔습니다 .\n",
              "2        kor to eng  ...  the reporter talked about the status of the ne...\n",
              "3        eng to kor  ...                       기자가 협상이 어떻게 진행되고 있는지 말했습니다 .\n",
              "4        kor to eng  ...  we haven't done 15z960 install guide data rele...\n",
              "...             ...  ...                                                ...\n",
              "1015905  eng to kor  ...                                 공격하는 게 아니라 궁금해서 그래\n",
              "1015906  kor to eng  ...          i will be on your side whatever happens .\n",
              "1015907  eng to kor  ...                       나는 무슨 일이 있든 항상 너의 편이 되어 줄게 .\n",
              "1015908  kor to eng  ...  take the song out and eat it like you do with ...\n",
              "1015909  eng to kor  ...                                이 노래를 초콜릿처럼 꺼내먹어요 .\n",
              "\n",
              "[1015910 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fv74Oq4PxheN"
      },
      "source": [
        "train_df2.to_csv('/content/drive/MyDrive/dataset/corpus/spoken_aihub_netflix_train.csv', index=False)\n",
        "eval_df2.to_csv('/content/drive/MyDrive/dataset/corpus/spoken_aihub_netflix_eval.csv', index=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZK9pli4D6JRM"
      },
      "source": [
        "train_df2 = pd.read_csv('/content/drive/MyDrive/dataset/corpus/spoken_aihub_netflix_train.csv')\n",
        "eval_df2 = pd.read_csv('/content/drive/MyDrive/dataset/corpus/spoken_aihub_netflix_eval.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J-6Fxa1RtcUM"
      },
      "source": [
        "train_df2['input_text'] = train_df2['input_text'].astype(str)\n",
        "train_df2['target_text'] = train_df2['target_text'].astype(str)\n",
        "train_df2['prefix'] = train_df2['prefix'].astype(str)\n",
        "eval_df2['input_text'] = eval_df2['input_text'].astype(str)\n",
        "eval_df2['target_text'] = eval_df2['target_text'].astype(str)\n",
        "eval_df2['prefix'] = eval_df2['prefix'].astype(str)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-BF_cjLAt5TP"
      },
      "source": [
        "train_df3 = train_df2\n",
        "eval_df3 = eval_df2"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aR-wgFH94pD-"
      },
      "source": [
        "- 이렇게 하면 eval_df2, train_df2의 prefix도 지워짐. 이렇게 하면 안 된다...!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lbcRw2S-uFSi"
      },
      "source": [
        "# 마스킹 - 다국어 번역기의 입력어, 출력어 추론에 유용하다.\n",
        "train_df3[\"prefix\"] = \"\"\n",
        "eval_df3[\"prefix\"] = \"\""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 400
        },
        "id": "2PKDjk998IHR",
        "outputId": "154ff724-735a-4e48-e586-b79ca9468a80"
      },
      "source": [
        "train_df2"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <td>kor to eng</td>\n",
              "      <td>다 먹고 나는 내장산에서 빌려주는 자전거를 탔습니다 .</td>\n",
              "      <td>after eating , i rode a bike which is borrowed...</td>\n",
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              "      <td>eng to kor</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>kor to eng</td>\n",
              "      <td>기자가 협상이 어떻게 진행되고 있는지 말했습니다 .</td>\n",
              "      <td>the reporter talked about the status of the ne...</td>\n",
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              "      <th>3</th>\n",
              "      <td>eng to kor</td>\n",
              "      <td>the reporter talked about the status of the ne...</td>\n",
              "      <td>기자가 협상이 어떻게 진행되고 있는지 말했습니다 .</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>kor to eng</td>\n",
              "      <td>우리는 아직 15Z960 설치 가이드의 데이터 출시를 하지 않았습니다 .</td>\n",
              "      <td>we haven't done 15z960 install guide data rele...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
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              "      <th>1015905</th>\n",
              "      <td>eng to kor</td>\n",
              "      <td>i didn't bring it up to attack you . i just wo...</td>\n",
              "      <td>공격하는 게 아니라 궁금해서 그래</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1015906</th>\n",
              "      <td>kor to eng</td>\n",
              "      <td>나는 무슨 일이 있든 항상 너의 편이 되어 줄게 .</td>\n",
              "      <td>i will be on your side whatever happens .</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1015907</th>\n",
              "      <td>eng to kor</td>\n",
              "      <td>i will be on your side whatever happens .</td>\n",
              "      <td>나는 무슨 일이 있든 항상 너의 편이 되어 줄게 .</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1015908</th>\n",
              "      <td>kor to eng</td>\n",
              "      <td>이 노래를 초콜릿처럼 꺼내먹어요 .</td>\n",
              "      <td>take the song out and eat it like you do with ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1015909</th>\n",
              "      <td>eng to kor</td>\n",
              "      <td>take the song out and eat it like you do with ...</td>\n",
              "      <td>이 노래를 초콜릿처럼 꺼내먹어요 .</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1015910 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "             prefix  ...                                        target_text\n",
              "0        kor to eng  ...  after eating , i rode a bike which is borrowed...\n",
              "1        eng to kor  ...                     다 먹고 나는 내장산에서 빌려주는 자전거를 탔습니다 .\n",
              "2        kor to eng  ...  the reporter talked about the status of the ne...\n",
              "3        eng to kor  ...                       기자가 협상이 어떻게 진행되고 있는지 말했습니다 .\n",
              "4        kor to eng  ...  we haven't done 15z960 install guide data rele...\n",
              "...             ...  ...                                                ...\n",
              "1015905  eng to kor  ...                                 공격하는 게 아니라 궁금해서 그래\n",
              "1015906  kor to eng  ...          i will be on your side whatever happens .\n",
              "1015907  eng to kor  ...                       나는 무슨 일이 있든 항상 너의 편이 되어 줄게 .\n",
              "1015908  kor to eng  ...  take the song out and eat it like you do with ...\n",
              "1015909  eng to kor  ...                                이 노래를 초콜릿처럼 꺼내먹어요 .\n",
              "\n",
              "[1015910 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
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          "height": 847,
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            "e4b1ce7827504154a16778252ccda67d"
          ]
        },
        "id": "EgVP24cyE8J5",
        "outputId": "1e755a6b-e889-4b64-b0ee-94728e4e547b"
      },
      "source": [
        "# Train the model\n",
        "model.train_model(train_df3, eval_data=eval_df3)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:simpletransformers.t5.t5_utils: Creating features from dataset file at cache_dir/\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e43fc089558f43f38fe6909161492b4a",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/1015910 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:3226: FutureWarning: `prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` context manager to prepare your targets. See the documentation of your specific tokenizer for more details\n",
            "  FutureWarning,\n",
            "INFO:simpletransformers.t5.t5_model: Training started\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Adafactor for T5\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "66730f654c184c10af8e715f50b66e15",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Epoch:   0%|          | 0/1 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33maimb\u001b[0m (use `wandb login --relogin` to force relogin)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Tracking run with wandb version 0.10.27<br/>\n",
              "                Syncing run <strong style=\"color:#cdcd00\">apricot-plasma-3</strong> to <a href=\"https://wandb.ai\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://wandb.ai/aimb/MT5%20Kor-Eng%20Translation%28aihub%2Bnetflix%29\" target=\"_blank\">https://wandb.ai/aimb/MT5%20Kor-Eng%20Translation%28aihub%2Bnetflix%29</a><br/>\n",
              "                Run page: <a href=\"https://wandb.ai/aimb/MT5%20Kor-Eng%20Translation%28aihub%2Bnetflix%29/runs/1doqgz3n\" target=\"_blank\">https://wandb.ai/aimb/MT5%20Kor-Eng%20Translation%28aihub%2Bnetflix%29/runs/1doqgz3n</a><br/>\n",
              "                Run data is saved locally in <code>/content/wandb/run-20210426_162119-1doqgz3n</code><br/><br/>\n",
              "            "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
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        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "abfe154619114fa2a09c57a649b23985",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Running Epoch 0 of 1:   0%|          | 0/50796 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py:795: UserWarning: Using a non-full backward hook when the forward contains multiple autograd Nodes is deprecated and will be removed in future versions. This hook will be missing some grad_input. Please use register_full_backward_hook to get the documented behavior.\n",
            "  warnings.warn(\"Using a non-full backward hook when the forward contains multiple autograd Nodes \"\n",
            "/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py:760: UserWarning: Using non-full backward hooks on a Module that does not return a single Tensor or a tuple of Tensors is deprecated and will be removed in future versions. This hook will be missing some of the grad_output. Please use register_full_backward_hook to get the documented behavior.\n",
            "  warnings.warn(\"Using non-full backward hooks on a Module that does not return a \"\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/optimization.py:562: UserWarning: This overload of add_ is deprecated:\n",
            "\tadd_(Number alpha, Tensor other)\n",
            "Consider using one of the following signatures instead:\n",
            "\tadd_(Tensor other, *, Number alpha) (Triggered internally at  /pytorch/torch/csrc/utils/python_arg_parser.cpp:1005.)\n",
            "  exp_avg_sq_row.mul_(beta2t).add_(1.0 - beta2t, update.mean(dim=-1))\n",
            "INFO:simpletransformers.t5.t5_utils: Creating features from dataset file at cache_dir/\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {
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          }
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:3226: FutureWarning: `prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` context manager to prepare your targets. See the documentation of your specific tokenizer for more details\n",
            "  FutureWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:3226: FutureWarning: `prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` context manager to prepare your targets. See the documentation of your specific tokenizer for more details\n",
            "  FutureWarning,\n",
            "INFO:simpletransformers.t5.t5_utils: Creating features from dataset file at cache_dir/\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "74a5d6ed84cd448fa029eb08d2f92265",
              "version_minor": 0,
              "version_major": 2
            },
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:3226: FutureWarning: `prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` context manager to prepare your targets. See the documentation of your specific tokenizer for more details\n",
            "  FutureWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:3226: FutureWarning: `prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` context manager to prepare your targets. See the documentation of your specific tokenizer for more details\n",
            "  FutureWarning,\n",
            "INFO:simpletransformers.t5.t5_model: Training of google/mt5-base model complete. Saved to outputs/.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(50796,\n",
              " {'eval_loss': [1.50125509663485, 1.361645799363032],\n",
              "  'global_step': [30000, 50796],\n",
              "  'train_loss': [1.653444766998291, 1.0702375173568726]})"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c9lUBzf1xI7v"
      },
      "source": [
        "- 이 한 문장으로 모델을 간편하게 학습시킬 수 있다."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dyofSrjkxQC8"
      },
      "source": [
        "- 학습이 끝나면 모델은 outputs 디렉토리에 저장된다."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N_8BQLG4ygG9"
      },
      "source": [
        "- 배치를 더 늘려도 괜찮겠는걸?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R1PT76ii3TAb",
        "outputId": "6c1b309a-53f5-4b0c-eb7f-6aedcd2de946"
      },
      "source": [
        "!pip install sacrebleu"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting sacrebleu\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/7e/57/0c7ca4e31a126189dab99c19951910bd081dea5bbd25f24b77107750eae7/sacrebleu-1.5.1-py3-none-any.whl (54kB)\n",
            "\r\u001b[K     |██████                          | 10kB 24.4MB/s eta 0:00:01\r\u001b[K     |████████████                    | 20kB 13.8MB/s eta 0:00:01\r\u001b[K     |██████████████████              | 30kB 8.9MB/s eta 0:00:01\r\u001b[K     |████████████████████████        | 40kB 7.4MB/s eta 0:00:01\r\u001b[K     |██████████████████████████████  | 51kB 5.3MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 61kB 4.0MB/s \n",
            "\u001b[?25hCollecting portalocker==2.0.0\n",
            "  Downloading https://files.pythonhosted.org/packages/89/a6/3814b7107e0788040870e8825eebf214d72166adf656ba7d4bf14759a06a/portalocker-2.0.0-py2.py3-none-any.whl\n",
            "Installing collected packages: portalocker, sacrebleu\n",
            "Successfully installed portalocker-2.0.0 sacrebleu-1.5.1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eJKlNOJWB5J-"
      },
      "source": [
        "!simple-viewer"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8YRtHyu4FCRU"
      },
      "source": [
        "import logging\n",
        "import sacrebleu\n",
        "import pandas as pd\n",
        "from simpletransformers.t5 import T5Model, T5Args\n",
        "\n",
        "\n",
        "logging.basicConfig(level=logging.INFO)\n",
        "transformers_logger = logging.getLogger(\"transformers\")\n",
        "transformers_logger.setLevel(logging.WARNING)\n",
        "\n",
        "model_args = T5Args()\n",
        "model_args.max_length = 512 # 원래 512, 램 모자라면 다시 하기\n",
        "model_args.length_penalty = 1\n",
        "model_args.num_beams = 10\n",
        "\n",
        "# initialize model and load the fine-tuned model\n",
        "model = T5Model(\"mt5\", \"/content/drive/MyDrive/dataset/corpus/huggingfacemodel/outputs\", \n",
        "                args=model_args, use_cuda=True, from_tf=False, force_download=True)\n",
        "\n",
        "#args={\"overwrite_output_dir\": True}\n",
        "# # Create a ClassificationModel\n",
        "# model = ClassificationModel(\n",
        "#     \"bert\", \"bert-base-chinese\", use_cuda=False, args={\"overwrite_output_dir\": True}, force_download=True\n",
        "# )  # You can set class weights by using the optional weight argument"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yLQi2NDXZ7_h"
      },
      "source": [
        "- HuggingFace로 작업한 fine-tuned 모델이 불러올 때 파일이 크면 제대로 다운로드 되지 않을 수 있습니다.\n",
        "- 구글 드라이브에 모델 파일을 넣어주면 정상 작동합니다."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 568
        },
        "id": "NtGW_ato4B_R",
        "outputId": "23a8e58a-5ada-4502-90e8-07fb24749a18"
      },
      "source": [
        "eval_df2"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>prefix</th>\n",
              "      <th>input_text</th>\n",
              "      <th>target_text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>kor to eng</td>\n",
              "      <td>난 너와 같이 있었을 때가 더 행복했어 .</td>\n",
              "      <td>i was happier when i was with you .</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>eng to kor</td>\n",
              "      <td>i was happier when i was with you .</td>\n",
              "      <td>난 너와 같이 있었을 때가 더 행복했어 .</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>kor to eng</td>\n",
              "      <td>부디 제가 한국어 실력을 쌓을 수 있도록 도와주세요 .</td>\n",
              "      <td>please help me improve my korean .</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>eng to kor</td>\n",
              "      <td>please help me improve my korean .</td>\n",
              "      <td>부디 제가 한국어 실력을 쌓을 수 있도록 도와주세요 .</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>kor to eng</td>\n",
              "      <td>그녀는 화요일에 항상 미술 학원에 갑니다 .</td>\n",
              "      <td>she always goes to the art school on tuesday .</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5101</th>\n",
              "      <td>eng to kor</td>\n",
              "      <td>we authorized the refund and we are processing...</td>\n",
              "      <td>우리는 상품의 반환을 승인했고 , 그 절차를 진행하고 있습니다 .</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5102</th>\n",
              "      <td>kor to eng</td>\n",
              "      <td>너도 알다시피 CFA는 그랜드 챔피언이 출전할 경우 대부분 그랜드 챔피언이 파이널에...</td>\n",
              "      <td>as you know , when a grand champion goes into ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5103</th>\n",
              "      <td>eng to kor</td>\n",
              "      <td>as you know , when a grand champion goes into ...</td>\n",
              "      <td>너도 알다시피 CFA는 그랜드 챔피언이 출전할 경우 대부분 그랜드 챔피언이 파이널에...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5104</th>\n",
              "      <td>kor to eng</td>\n",
              "      <td>마침 그때가 방학이라서 나한테 시간이 많아 .</td>\n",
              "      <td>coincidentally , i will be on vacation at the ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5105</th>\n",
              "      <td>eng to kor</td>\n",
              "      <td>coincidentally , i will be on vacation at the ...</td>\n",
              "      <td>마침 그때가 방학이라서 나한테 시간이 많아 .</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5106 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "          prefix  ...                                        target_text\n",
              "0     kor to eng  ...                i was happier when i was with you .\n",
              "1     eng to kor  ...                            난 너와 같이 있었을 때가 더 행복했어 .\n",
              "2     kor to eng  ...                 please help me improve my korean .\n",
              "3     eng to kor  ...                     부디 제가 한국어 실력을 쌓을 수 있도록 도와주세요 .\n",
              "4     kor to eng  ...     she always goes to the art school on tuesday .\n",
              "...          ...  ...                                                ...\n",
              "5101  eng to kor  ...               우리는 상품의 반환을 승인했고 , 그 절차를 진행하고 있습니다 .\n",
              "5102  kor to eng  ...  as you know , when a grand champion goes into ...\n",
              "5103  eng to kor  ...  너도 알다시피 CFA는 그랜드 챔피언이 출전할 경우 대부분 그랜드 챔피언이 파이널에...\n",
              "5104  kor to eng  ...  coincidentally , i will be on vacation at the ...\n",
              "5105  eng to kor  ...                          마침 그때가 방학이라서 나한테 시간이 많아 .\n",
              "\n",
              "[5106 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ma4gh9AFFDzX"
      },
      "source": [
        "korean_truth = [eval_df2.loc[eval_df2[\"prefix\"] == \"eng to kor\"][\"target_text\"].tolist()]\n",
        "to_korean = eval_df2.loc[eval_df2[\"prefix\"] == \"eng to kor\"][\"input_text\"].tolist()\n",
        "\n",
        "english_truth = [eval_df2.loc[eval_df2[\"prefix\"] == \"kor to eng\"][\"target_text\"].tolist()]\n",
        "to_english = eval_df2.loc[eval_df2[\"prefix\"] == \"kor to eng\"][\"input_text\"].tolist()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R_qoyJkjpirc"
      },
      "source": [
        "원래 데이터셋에 있던 입력어, 출력어 정보를 가지고도 모델 평가. 다국어 번역기에서 입력어 인식을 제대로 할 수 있나 확인하는 목적."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ExuKJcJfFF54"
      },
      "source": [
        "# Predict\n",
        "korean_preds = model.predict(to_korean)\n",
        "\n",
        "eng_kor_bleu = sacrebleu.corpus_bleu(korean_preds, korean_truth)\n",
        "print(\"--------------------------\")\n",
        "print(\"English to Korean: \", eng_kor_bleu.score)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D2AlDGk-eXQS"
      },
      "source": [
        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)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 155,
          "referenced_widgets": [
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            "4e490142892b4b769dfe10523a625974",
            "20c80e2a1e544c37817cbd0c314d5fb8",
            "7b26ac5f64064af7b6d6ae70437713e0"
          ]
        },
        "id": "_ACnMTsMet9g",
        "outputId": "ef3aa261-1fdd-46f0-8914-aad7dc695745"
      },
      "source": [
        "english_preds = model.predict(to_english)\n",
        "\n",
        "kor_eng_bleu = sacrebleu.corpus_bleu(english_preds, english_truth)\n",
        "print(\"Korean to English: \", kor_eng_bleu.score)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "3f4bb9fc42bf43e0beb82ebaf912828b",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Generating outputs:   0%|          | 0/320 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:3226: FutureWarning: `prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` context manager to prepare your targets. See the documentation of your specific tokenizer for more details\n",
            "  FutureWarning,\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1d2cf48d41b84c85883745ca2576fcf4",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Decoding outputs:   0%|          | 0/2553 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Korean to English:  18.031132027467585\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jRvsWB07fEEL"
      },
      "source": [
        "- 와 왜 GPU 안썼지 ^^"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d0qipYFKipza"
      },
      "source": [
        "|Model|DataSet|Lang|Date|BLEU|chr-F|#sent|#words|BP|\n",
        "|---|---|----|---|---|---|---|---|---|\n",
        "|transformer|tatoeba opus|kor-eng|21-02-18|40.8|0.586|2414|17711|0.956|\n",
        "|mT5-base|Aihub+subs|kor to eng|21-04-27|18.03|\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 106
        },
        "id": "qvaWmbKEjBkm",
        "outputId": "cbd9fb83-720a-41ef-f86e-fa67901406a4"
      },
      "source": [
        "import pandas as pd\n",
        "models = pd.DataFrame({'Model': ['transformer', 'mT5-base'], 'Dataset' : ['tatoeba opus', 'aihub+subs'], 'size': ['390만', '51만'],'BLEU':[40.8,18.03]})\n",
        "models"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Model</th>\n",
              "      <th>Dataset</th>\n",
              "      <th>size</th>\n",
              "      <th>BLEU</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>transformer</td>\n",
              "      <td>tatoeba opus</td>\n",
              "      <td>390만</td>\n",
              "      <td>40.80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>mT5-base</td>\n",
              "      <td>aihub+subs</td>\n",
              "      <td>51만</td>\n",
              "      <td>18.03</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         Model       Dataset  size   BLEU\n",
              "0  transformer  tatoeba opus  390만  40.80\n",
              "1     mT5-base    aihub+subs   51만  18.03"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 135
        },
        "id": "JCIXLqjY2rOG",
        "outputId": "4f87cd3b-46fb-4ecc-e12a-9e54536edbab"
      },
      "source": [
        "import pandas as pd\n",
        "\n",
        "models2 = pd.DataFrame({'Model': ['transformer', 'mT5-base', 'mT5-base'], 'Dataset' : ['aihub', 'tatoeba opus','aihub+subs'],'size': ['11만','51만(사이즈 조정)', '51만'], '학습 시간':['6시간 반', '10시간', '10시간'], 'eval loss':[3.925, 1.558,1.361]})\n",
        "models2"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Model</th>\n",
              "      <th>Dataset</th>\n",
              "      <th>size</th>\n",
              "      <th>학습 시간</th>\n",
              "      <th>eval loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>transformer</td>\n",
              "      <td>aihub</td>\n",
              "      <td>11만</td>\n",
              "      <td>6시간 반</td>\n",
              "      <td>3.925</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>mT5-base</td>\n",
              "      <td>tatoeba opus</td>\n",
              "      <td>51만(사이즈 조정)</td>\n",
              "      <td>10시간</td>\n",
              "      <td>1.558</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>mT5-base</td>\n",
              "      <td>aihub+subs</td>\n",
              "      <td>51만</td>\n",
              "      <td>10시간</td>\n",
              "      <td>1.361</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         Model       Dataset         size  학습 시간  eval loss\n",
              "0  transformer         aihub          11만  6시간 반      3.925\n",
              "1     mT5-base  tatoeba opus  51만(사이즈 조정)   10시간      1.558\n",
              "2     mT5-base    aihub+subs          51만   10시간      1.361"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "GEPyJWOFj3-Z",
        "outputId": "8531f2d8-8c98-4726-ea75-82ca8ad9af4e"
      },
      "source": [
        "import seaborn as sns\n",
        "sns.lineplot(data=models2, x=\"Dataset\", y=\"eval loss\");"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kdRQc4xRMy8g"
      },
      "source": [
        "- ㅜ.ㅜ bleu 값이 높을수록 좋다."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r2ppE3Spn7Cg"
      },
      "source": [
        "print(model.predict([\"우리집 고양이는 세상에서 제일 귀엽다!\"]))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4RRhXTq3nxEI"
      },
      "source": [
        "print(model.predict([\"우리집 고양이가 세상에서 제일 귀엽습니다\"]))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NHPNY-B-oDJm"
      },
      "source": [
        "print(model.predict([\"뭐야 이게?\"]))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZuvmBaYXoH_y"
      },
      "source": [
        "print(model.predict([\"너 참 예쁘다\"]))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JuGZ7pFuoOKP"
      },
      "source": [
        "헐 성능 ㅠㅠㅠㅠ 괜찮아 ㅠㅠㅠㅠㅠㅠ"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qu8WXFgRoYNq"
      },
      "source": [
        "os.chdir('/content')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q9QsoX0mevfu"
      },
      "source": [
        "!pip install simpletransformers"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "umOJaB7EojBy"
      },
      "source": [
        "!pip install simpletransformers\n",
        "from simpletransformers.t5 import T5Model\n",
        "\n",
        "model = T5Model(\"mt5\", \"AimB/mT5-en-kr-natural\")\n",
        "print(model.predict([\"나 너 좋아하니?\"]))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rd8TTJ3FfdTc"
      },
      "source": [
        "print(model.predict([\"우리 엄만 매일 내게 말했어\"]))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zF91PZl7e9t5"
      },
      "source": [
        "print(model.predict([\"눈이 뻑뻑해요\"]))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "auBtMtpvgeCn"
      },
      "source": [
        "print(model.predict([\"너 오늘 뭐해?\"]))"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}