Machine Translation/Korean to English (Korean Parallel Corpora)/Korean_Parallel_Corpora_OPUS_Translation_Project.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d5pMr4QZuBnO"
      },
      "source": [
        "## Korean Parallel Corpora: Machine Translation\n",
        "\n",
        "Dataset Source: https://huggingface.co/datasets/Moo/korean-parallel-corpora"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EzjhTmndtTZT"
      },
      "source": [
        "#### Install Necessary Libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BOicHhN5tTPM",
        "outputId": "c160140b-00f6-4e12-99dd-1fcacdfb36a3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
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            "Requirement already satisfied: tabulate>=0.8.9 in /usr/local/lib/python3.10/dist-packages (from sacrebleu) (0.9.0)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from sacrebleu) (1.23.5)\n",
            "Collecting colorama (from sacrebleu)\n",
            "  Downloading colorama-0.4.6-py2.py3-none-any.whl (25 kB)\n",
            "Requirement already satisfied: lxml in /usr/local/lib/python3.10/dist-packages (from sacrebleu) (4.9.3)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from rouge) (1.16.0)\n",
            "Requirement already satisfied: absl-py in /usr/local/lib/python3.10/dist-packages (from rouge-score) (1.4.0)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.10/dist-packages (from nltk) (8.1.6)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.10/dist-packages (from nltk) (1.3.2)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from nltk) (4.66.1)\n",
            "Building wheels for collected packages: rouge-score\n",
            "  Building wheel for rouge-score (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for rouge-score: filename=rouge_score-0.1.2-py3-none-any.whl size=24933 sha256=713cf52f117dd6582d09bac715a060d71d98575e7d5405c01425cf9be5034713\n",
            "  Stored in directory: /root/.cache/pip/wheels/5f/dd/89/461065a73be61a532ff8599a28e9beef17985c9e9c31e541b4\n",
            "Successfully built rouge-score\n",
            "Installing collected packages: rouge, portalocker, colorama, sacrebleu, rouge-score\n",
            "Successfully installed colorama-0.4.6 portalocker-2.7.0 rouge-1.0.1 rouge-score-0.1.2 sacrebleu-2.3.1\n",
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0mCollecting sentencepiece\n",
            "  Downloading sentencepiece-0.1.99-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m11.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: sentencepiece\n",
            "Successfully installed sentencepiece-0.1.99\n",
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0m"
          ]
        }
      ],
      "source": [
        "%pip install pip -U\n",
        "%pip install torch transformers datasets evaluate tensorboard\n",
        "%pip install accelerate -U\n",
        "%pip install sacrebleu rouge rouge-score nltk\n",
        "%pip install sentencepiece"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X0fkKnCStTHp"
      },
      "source": [
        "#### Enter HuggingFace Access Token"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OXMzhoYVtS_g",
        "outputId": "0371317f-1ab0-4dc9-88ff-fff1272b2064"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "    _|    _|  _|    _|    _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|_|_|_|    _|_|      _|_|_|  _|_|_|_|\n",
            "    _|    _|  _|    _|  _|        _|          _|    _|_|    _|  _|            _|        _|    _|  _|        _|\n",
            "    _|_|_|_|  _|    _|  _|  _|_|  _|  _|_|    _|    _|  _|  _|  _|  _|_|      _|_|_|    _|_|_|_|  _|        _|_|_|\n",
            "    _|    _|  _|    _|  _|    _|  _|    _|    _|    _|    _|_|  _|    _|      _|        _|    _|  _|        _|\n",
            "    _|    _|    _|_|      _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|        _|    _|    _|_|_|  _|_|_|_|\n",
            "    \n",
            "    To login, `huggingface_hub` requires a token generated from https://huggingface.co/settings/tokens .\n",
            "Token: \n",
            "Add token as git credential? (Y/n) n\n",
            "Token is valid (permission: write).\n",
            "Your token has been saved to /root/.cache/huggingface/token\n",
            "Login successful\n"
          ]
        }
      ],
      "source": [
        "!huggingface-cli login"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NlMduyosuBnQ"
      },
      "source": [
        "#### Import Necessary Libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qX697--EuBnQ",
        "outputId": "2aed0348-3601-4c87-b2dc-6140ecbc2e93"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Git LFS initialized.\n"
          ]
        }
      ],
      "source": [
        "import os, sys\n",
        "os.environ['TOKENIZERS_PARALLELISM']='false'\n",
        "\n",
        "from tqdm import tqdm\n",
        "\n",
        "import torch\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import nltk\n",
        "\n",
        "import transformers\n",
        "from transformers import DataCollatorForSeq2Seq, AutoModelForSeq2SeqLM, set_seed\n",
        "from transformers import AutoTokenizer, Seq2SeqTrainingArguments, Seq2SeqTrainer\n",
        "\n",
        "import datasets\n",
        "from datasets import Dataset, DatasetDict, load_dataset\n",
        "\n",
        "import evaluate\n",
        "\n",
        "!git lfs install"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gvjo50A8uBnR"
      },
      "source": [
        "#### Versions of Software & Libraries Used"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Duo5DU7yuBnS",
        "outputId": "cfae6d66-1b44-4ad7-8da6-b5477ee75528"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "+-------------------------------+\n",
            "|        Library | Version      |\n",
            "|*******************************|\n",
            "|         Python | 3.10.1       |\n",
            "|          NumPy | 1.23.5       |\n",
            "|         Pandas | 1.5.3        |\n",
            "|          Torch | 2.0.1+cu118  |\n",
            "|       Datasets | 2.14.4       |\n",
            "|    Transformer | 4.31.0       |\n",
            "+-------------------------------+\n"
          ]
        }
      ],
      "source": [
        "library_len = 14\n",
        "version_len = 12\n",
        "\n",
        "print(f\"+{'-' * (library_len + version_len + 5)}+\")\n",
        "print(\"|\",\n",
        "      \"Library\".rjust(library_len),\n",
        "      \"|\",\n",
        "      \"Version\".ljust(version_len),\n",
        "      \"|\")\n",
        "\n",
        "print(f\"|{'*' * (library_len + version_len + 5)}|\")\n",
        "\n",
        "print(\"|\",\n",
        "      \"Python\".rjust(library_len),\n",
        "      \"|\",\n",
        "      sys.version[0:6].ljust(version_len),\n",
        "      \"|\")\n",
        "\n",
        "print(\"|\",\n",
        "      \"NumPy\".rjust(library_len),\n",
        "      \"|\",\n",
        "      np.__version__.ljust(version_len),\n",
        "      \"|\")\n",
        "\n",
        "print(\"|\",\n",
        "      \"Pandas\".rjust(library_len),\n",
        "      \"|\",\n",
        "      pd.__version__.ljust(version_len),\n",
        "      \"|\")\n",
        "\n",
        "print(\"|\",\n",
        "      \"Torch\".rjust(library_len),\n",
        "      \"|\",\n",
        "      torch.__version__.ljust(version_len),\n",
        "      \"|\")\n",
        "\n",
        "print(\"|\",\n",
        "      \"Datasets\".rjust(library_len),\n",
        "      \"|\",\n",
        "      datasets.__version__.ljust(version_len),\n",
        "      \"|\")\n",
        "\n",
        "print(\"|\",\n",
        "      \"Transformer\".rjust(library_len),\n",
        "      \"|\",\n",
        "      transformers.__version__.ljust(version_len),\n",
        "      \"|\")\n",
        "\n",
        "print(f\"+{'-' * (library_len + version_len + 5)}+\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xk5OUlpZuBnS"
      },
      "source": [
        "#### Ingest & Start Preprocessing Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 688,
          "referenced_widgets": [
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        },
        "id": "yYglFxa8uBnS",
        "outputId": "4ec1ba06-5037-4b6b-bd20-6610bd86c7a3"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6357aac167d74b988fbb7f9890fdd227",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/297 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "db2299ca6fe3452080bdbfe88000642f",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading data files:   0%|          | 0/3 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "7cd48a98ed964b7fb8bbcd1c3b06f981",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading data:   0%|          | 0.00/26.2M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ae893a3cc84a4d34b224e5b51aa3310c",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading data:   0%|          | 0.00/297k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2ef1a2ff67434daa8f0d018ebe50e61c",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading data:   0%|          | 0.00/605k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d8a20c5521fe497697c43dc5a4b0232f",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Extracting data files:   0%|          | 0/3 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "eedb80ec96d6438e89a1b19ab505c0ef",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "679de102e6944c16af05e05f61a6a428",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Generating validation split: 0 examples [00:00, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2d7ec081bd7e4a1995cedfb0128b0c2f",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Generating test split: 0 examples [00:00, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['ko', 'en'],\n",
              "        num_rows: 96215\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['ko', 'en'],\n",
              "        num_rows: 1000\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['ko', 'en'],\n",
              "        num_rows: 2000\n",
              "    })\n",
              "})"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data = load_dataset(\"Moo/korean-parallel-corpora\")\n",
        "\n",
        "data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OQK3uo3-uBnS"
      },
      "source": [
        "#### Convert Dataset to Pandas DataFrame"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n5pK2jX1uBnS",
        "outputId": "6527cd5b-91aa-469e-9acb-fa6bfb9b9fd4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 80949 entries, 0 to 80948\n",
            "Data columns (total 2 columns):\n",
            " #   Column  Non-Null Count  Dtype \n",
            "---  ------  --------------  ----- \n",
            " 0   ko      80949 non-null  object\n",
            " 1   en      80949 non-null  object\n",
            "dtypes: object(2)\n",
            "memory usage: 1.2+ MB\n"
          ]
        }
      ],
      "source": [
        "data.set_format(type=\"pandas\")\n",
        "\n",
        "df = data['train'][:]\n",
        "\n",
        "df = df.drop_duplicates()\n",
        "\n",
        "df = df.reset_index(drop=True)\n",
        "\n",
        "df.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "13175Z3DuBnT"
      },
      "source": [
        "#### Visual: Length of English Entries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 477
        },
        "id": "o94TwkWiuBnT",
        "outputId": "39129a24-1579-407f-9ed2-da743d8703d5"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The Maximum length of English text is 90 words.\n"
          ]
        }
      ],
      "source": [
        "df['english_length'] = df['en'].str.split().str.len()\n",
        "\n",
        "max_english_count = max(df['english_length'])\n",
        "\n",
        "n_bin = max_english_count + 1\n",
        "\n",
        "df.hist('english_length',\n",
        "        grid=False,\n",
        "        figsize=(12,8),\n",
        "        range=(0,n_bin),\n",
        "        bins=n_bin)\n",
        "plt.title('Histogram: Length of English Statements')\n",
        "plt.xlabel('# of Words in English Statements')\n",
        "plt.show()\n",
        "\n",
        "print(f\"The Maximum length of English text is {max_english_count} words.\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jj043P8MuBnT"
      },
      "source": [
        "#### Visual: Length of Korean Entries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 477
        },
        "id": "ilVNJyKguBnT",
        "outputId": "24df4395-20ec-48ac-c793-594c79d593b9"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The Maximum length of Korean text is 82 words.\n"
          ]
        }
      ],
      "source": [
        "df['korean_length'] = df['ko'].str.split().str.len()\n",
        "\n",
        "max_korean_count = max(df['korean_length'])\n",
        "\n",
        "n_bin = max_korean_count + 1\n",
        "\n",
        "df.hist('korean_length',\n",
        "        grid=False,\n",
        "        range=(0,n_bin),\n",
        "        figsize=(12,8),\n",
        "        bins=n_bin)\n",
        "plt.title('Histogram: Length of Korean Statements')\n",
        "plt.xlabel('# of Words in Korean Statements')\n",
        "plt.show()\n",
        "\n",
        "print(f\"The Maximum length of Korean text is {max_korean_count} words.\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2HEN9cNKuBnT"
      },
      "source": [
        "#### Trim Dataset Size & Drop Unnecessary Features"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9ShRDvGUuBnU",
        "outputId": "72b62b40-b588-4802-f36a-9f4fc5925610"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 79733 entries, 0 to 79732\n",
            "Data columns (total 2 columns):\n",
            " #   Column  Non-Null Count  Dtype \n",
            "---  ------  --------------  ----- \n",
            " 0   ko      79733 non-null  object\n",
            " 1   en      79733 non-null  object\n",
            "dtypes: object(2)\n",
            "memory usage: 1.2+ MB\n"
          ]
        }
      ],
      "source": [
        "most_input_words_en = 55\n",
        "most_input_words_ko = 41\n",
        "least_input_words = 2\n",
        "\n",
        "# make sure that English text is less than 55 & more than 2 words\n",
        "df = df[df['korean_length'] < most_input_words_ko]\n",
        "df = df[df['korean_length'] > least_input_words]\n",
        "\n",
        "# make sure that Korean text is less than 41 & more than 2 words\n",
        "df = df[df['english_length'] < most_input_words_en]\n",
        "df = df[df['english_length'] > least_input_words]\n",
        "\n",
        "# reset the index\n",
        "df = df.reset_index(drop=True)\n",
        "\n",
        "# Remove columns that are no longer necessary\n",
        "df = df.drop(columns=['korean_length',\n",
        "                      'english_length'\n",
        "                      ]\n",
        "             )\n",
        "\n",
        "df.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VnaWIDnAuBnU"
      },
      "source": [
        "#### Final Preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "ibizKcp8uBnU",
        "outputId": "b4d6d4af-9d2f-4dfb-b432-372e43d6bbb3"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-c196b486-82ac-49c7-9cfc-b83ce3e16b41\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>translation</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>{'ko': '개인용 컴퓨터 사용의 상당 부분은 \"이것보다 뛰어날 수 있느냐?\"',...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>{'ko': '모든 광마우스와 마찬가지 로 이 광마우스도 책상 위에 놓는 마우스 패...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>{'ko': '그러나 이것은 또한 책상도 필요로 하지 않는다.', 'en': 'Li...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>{'ko': '79.95달러하는 이 최첨단 무선 광마우스는 허공에서 팔목, 팔, 그...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>{'ko': '정보 관리들은 동남 아시아에서의 선박들에 대한 많은 (테러) 계획들이...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c196b486-82ac-49c7-9cfc-b83ce3e16b41')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-a79aec4e-f808-4a8b-89e1-187ff5ae646e\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a79aec4e-f808-4a8b-89e1-187ff5ae646e')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-a79aec4e-f808-4a8b-89e1-187ff5ae646e button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c196b486-82ac-49c7-9cfc-b83ce3e16b41 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c196b486-82ac-49c7-9cfc-b83ce3e16b41');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                                         translation\n",
              "0  {'ko': '개인용 컴퓨터 사용의 상당 부분은 \"이것보다 뛰어날 수 있느냐?\"',...\n",
              "1  {'ko': '모든 광마우스와 마찬가지 로 이 광마우스도 책상 위에 놓는 마우스 패...\n",
              "2  {'ko': '그러나 이것은 또한 책상도 필요로 하지 않는다.', 'en': 'Li...\n",
              "3  {'ko': '79.95달러하는 이 최첨단 무선 광마우스는 허공에서 팔목, 팔, 그...\n",
              "4  {'ko': '정보 관리들은 동남 아시아에서의 선박들에 대한 많은 (테러) 계획들이..."
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "all_data = df.to_dict(orient='records')\n",
        "df = pd.Series(all_data)\n",
        "df = pd.DataFrame(df)\n",
        "df.rename(columns={ 0 : 'translation'}, inplace=True)\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jvPTVUEsuBnU"
      },
      "source": [
        "#### Convert to Dataset Format & Split into Train/Test/Eval Datasets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_91B7LDquBnU",
        "outputId": "77fab0f8-b6a5-49d4-c8e2-057b5e212688"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Training Dataset Shape: (51826, 1)\n",
            "Testing Dataset Shape: (13953, 1)\n",
            "Evaluation Dataset Shape: (13954, 1)\n",
            "\n",
            "Training Dataset Example:\n",
            "{'translation': {'en': \"Under her tenure, the company's revenue grew from $285 million to more than $1.5 billion.\", 'ko': '그가 역임하는 동안 오도데스크은 2억8500억원에서 15억달러 이상 수익을 올렸다.'}}\n"
          ]
        }
      ],
      "source": [
        "dataset = Dataset.from_pandas(df)\n",
        "\n",
        "train_testEval = dataset.train_test_split(train_size=0.65)\n",
        "test_eval = train_testEval['test'].train_test_split(train_size=0.50)\n",
        "\n",
        "ds = DatasetDict({\n",
        "    'train' : train_testEval['train'],\n",
        "    'test' : test_eval['train'],\n",
        "    'eval' : test_eval['test'],\n",
        "})\n",
        "\n",
        "print(\"Training Dataset Shape:\", ds['train'].shape)\n",
        "print(\"Testing Dataset Shape:\", ds['test'].shape)\n",
        "print(\"Evaluation Dataset Shape:\", ds['eval'].shape)\n",
        "\n",
        "print(f\"\\nTraining Dataset Example:\\n{ds['train'][25]}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YhMFVT18uBnU"
      },
      "source": [
        "#### Basic Values/Constants"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "SydVZ7A-uBnV"
      },
      "outputs": [],
      "source": [
        "set_seed = 42\n",
        "NUM_OF_EPOCHS = 6\n",
        "\n",
        "BATCH_SIZE = 32\n",
        "LEARNING_RATE = 2e-5\n",
        "\n",
        "SOURCE_LANGUAGE = \"ko\"\n",
        "TARGET_LANGUAGE = \"en\"\n",
        "\n",
        "MODEL_CKPT = \"Helsinki-NLP/opus-mt-ko-en\"\n",
        "\n",
        "MODEL_NAME = f\"{MODEL_CKPT.split(f'/')[-1]}-Korean_Parallel_Corpora\"\n",
        "\n",
        "REPORTS_TO = \"tensorboard\"\n",
        "STRATEGY = \"epoch\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "InWrQTePuBnV"
      },
      "source": [
        "#### Create Tokenizer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 372,
          "referenced_widgets": [
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            "a386e7fb8d524fe49f7fe9dc4cf2d70e",
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        },
        "id": "NfLLW06yuBnV",
        "outputId": "b6b04aa9-1572-4d66-ab65-0534572c81b8"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e6508ae9fa094d818a88189d88e9c462",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/44.0 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c768c196da7f44298c5031a175d799a9",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/1.39k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "422dd60c2b8940b095ff3fcdcea6bdef",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)olve/main/source.spm:   0%|          | 0.00/842k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "cad38013800d444a993164ccef783503",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)olve/main/target.spm:   0%|          | 0.00/813k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b76f1f99281e439d92c10940eddf217e",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)olve/main/vocab.json:   0%|          | 0.00/1.72M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/models/marian/tokenization_marian.py:194: UserWarning: Recommended: pip install sacremoses.\n",
            "  warnings.warn(\"Recommended: pip install sacremoses.\")\n"
          ]
        }
      ],
      "source": [
        "tokenizer = AutoTokenizer.from_pretrained(MODEL_CKPT)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TtIcC4C8uBnV"
      },
      "source": [
        "#### Create Preprocessing Function"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "Pv_h-A_uuBnV"
      },
      "outputs": [],
      "source": [
        "def tokenizing_function(examples):\n",
        "    inputs = [ex[SOURCE_LANGUAGE] for ex in examples['translation']]\n",
        "    targets = [ex[TARGET_LANGUAGE] for ex in examples['translation']]\n",
        "    model_inputs = tokenizer(inputs,\n",
        "                             text_target=targets,\n",
        "                             truncation=True)\n",
        "    return model_inputs"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mCzrfk_ruBnV"
      },
      "source": [
        "#### Apply Preprocessing Function to Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 249,
          "referenced_widgets": [
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            "76ba397cbe66404b9fca1944d06bbcd0",
            "914c9fd9d5734b0aa38c78d786febeca",
            "ad388d13c9904cd897547b1d60e87f5f",
            "2269808a7d3444f8acbd464c29c33590",
            "1f508ad2639e4ee89d23441f48d4b205",
            "907bc2e1efdf486a976fdf4ab2596a8c",
            "3c2ac6e57fc545b8896f6943efbb0146",
            "695b650caad5427b9a0ca9541dd0dcc0",
            "20ca5b5d353c4e7f893c3cf5966a0a40",
            "65f66996070e48b5852f1e7662fd70f1",
            "6984552b3ffc4271bf8a3e2bda89ed3d",
            "76235a8c51ae46609432b8895b9e4f23",
            "5ee6cb84a1c2499da00d3328eede2ab1",
            "aaed4a034fe3455486115ab1a29b830a",
            "ad75fc8c9b434bbc919305a783f44438"
          ]
        },
        "id": "1KiOkUdRuBnV",
        "outputId": "9935053d-81d5-4a9c-93d1-ab5ee7710474"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "62a467e458144d9587f514c05ee20fb2",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/51826 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b8b7b7ee7ec14d3db6a8bc42e04bd261",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/13953 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1f508ad2639e4ee89d23441f48d4b205",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/13954 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(51826, 4)\n",
            "(13953, 4)\n",
            "(13954, 4)\n"
          ]
        }
      ],
      "source": [
        "encoded_ds = ds.map(tokenizing_function,\n",
        "                    batched=True)\n",
        "\n",
        "print(encoded_ds['train'].shape)\n",
        "print(encoded_ds['test'].shape)\n",
        "print(encoded_ds['eval'].shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5dXPvuDTuBnV"
      },
      "source": [
        "#### Instantiate Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 137,
          "referenced_widgets": [
            "4dbe6f574f5740a0a664170ec26460dc",
            "a59885b622e74f038391212a450dea79",
            "178d548d05864cbd8e58cb7ade6b9a4d",
            "debe21260fc649a9a51a7433ecc01a01",
            "a64c4915b61c4ac0a9aefa69c1885853",
            "32369c71db864b28a9d24dd9d3669723",
            "114797ac8ce44754bed794314317fb23",
            "35e89fb8a7a4424682f0debc02f1d5df",
            "e2f2876a64144c5fb73062415e3925d5",
            "14334dc651d944b9a01fbe23367cfb84",
            "d4e009a0f1904fe8aecaf910911847b8",
            "2c5583505880437f86f020046610967e",
            "53a315ed27bd427c98c5c4e7b01b5ec7",
            "9c8a7571484246faa01fda56e087afb9",
            "5bdf19898973460e9874003185ac7d58",
            "e4e6f5e54c8043d086c6ef51ce8d19bd",
            "5d1ec53a60f14310975a2dba818a64d6",
            "46d6e33d080a42e3afd7ea1858b9c2c6",
            "caeded88d46040a49e9f64399fddeccb",
            "abadb6a491c3450eb9df5fd3ec61bb82",
            "451f15732f134ff0bb0b997a81ed1835",
            "e551ea349ff74a4991ef1e0cd02aff9c"
          ]
        },
        "id": "2u32y2CcuBnV",
        "outputId": "90603459-25a0-41e3-dcb8-ebbb27d59081"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4dbe6f574f5740a0a664170ec26460dc",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/312M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2c5583505880437f86f020046610967e",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)neration_config.json:   0%|          | 0.00/293 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "model = AutoModelForSeq2SeqLM.from_pretrained(MODEL_CKPT)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MaSPUokcuBnW"
      },
      "source": [
        "#### Instantiate Data Collator"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "b5BXi0pauBnW"
      },
      "outputs": [],
      "source": [
        "data_collator = DataCollatorForSeq2Seq(tokenizer=tokenizer,\n",
        "                                       model=MODEL_CKPT)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LHhMQdhquBnW"
      },
      "source": [
        "#### Create Function to Compute Metrics"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 137,
          "referenced_widgets": [
            "89c6b11778b14623882c7a5cebe7bd17",
            "aea663cdd9484e66af19118fc8d1b95d",
            "ff44fbafe44141e8b2ea5953cb1389e6",
            "c50f3501508e4ee7ac8b316f5ca032d9",
            "8e3e923099214e49909fd7876dc86475",
            "9f766b21f0e44240954707670eedaadd",
            "5cd26ee1828440a68288a8fe0b467e6e",
            "689b40979fc244a1a09be64e4e621a24",
            "b56e28779acb4e4aa7b81d8eb35396dc",
            "0e6fbfa7b0274efb940209c4f9052637",
            "3b27022bc3bd4129aa751df25a6ad751",
            "53ae534faec24c45acff172c51e9f4a5",
            "5ce13f8e15bb4b86a7b4932181130ed0",
            "6059c2b26c254936af0a5a4f87ef9155",
            "8ba8aa4d10a94e87a7ce73ac43332d24",
            "ff0be90c515146eaa699ae8ce7b8772b",
            "489537e1c4284626bb374e6240088295",
            "48ad7005e9d04275b25d4c666dce4d7b",
            "952524e4df794dc5a066e303bd5276ba",
            "3d02b3fb70b1474980664c521130da68",
            "73272a6696174da6bd4209c59ecd1e84",
            "5276e95e234f4e4ea97037c404b4ed00"
          ]
        },
        "id": "zw0DTLQuuBnW",
        "outputId": "ab4186c0-2b68-46e6-b140-d51a04390cb9"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "89c6b11778b14623882c7a5cebe7bd17",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/8.15k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "53ae534faec24c45acff172c51e9f4a5",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/6.27k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "bleu_metric = evaluate.load(\"sacrebleu\")\n",
        "rouge_metric = evaluate.load(\"rouge\")\n",
        "\n",
        "def postprocess_text(preds, labels):\n",
        "    preds = [pred.strip() for pred in preds]\n",
        "    labels = [[label.strip()] for label in labels]\n",
        "\n",
        "    return preds, labels\n",
        "\n",
        "def compute_metrics(eval_preds):\n",
        "    preds, labels = eval_preds\n",
        "\n",
        "    if isinstance(preds, tuple):\n",
        "        preds = preds[0]\n",
        "\n",
        "    decoded_preds = tokenizer.batch_decode(preds,\n",
        "                                           skip_special_tokens=True)\n",
        "\n",
        "    labels = np.where(labels != -100,\n",
        "                      labels,\n",
        "                      tokenizer.pad_token_id)\n",
        "    decoded_labels = tokenizer.batch_decode(labels,\n",
        "                                            skip_special_tokens=True)\n",
        "\n",
        "    decoded_preds, decoded_labels = postprocess_text(decoded_preds,\n",
        "                                                     decoded_labels)\n",
        "\n",
        "    bleu_results = bleu_metric.compute(predictions=decoded_preds,\n",
        "                                       references=decoded_labels)\n",
        "    rouge_results = rouge_metric.compute(predictions=decoded_preds,\n",
        "                                         references=decoded_labels)\n",
        "\n",
        "    prediction_lens = [np.count_nonzero(pred != tokenizer.pad_token_id)\n",
        "                       for pred in preds]\n",
        "\n",
        "    return {\"bleu\" : bleu_results[\"score\"],\n",
        "            \"rouge\" : rouge_results,\n",
        "            \"Prediction Lengths\": prediction_lens}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HP-SuB1wuBnW"
      },
      "source": [
        "#### Instantiate Training Arguments"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "51B4J9PouBnW"
      },
      "outputs": [],
      "source": [
        "args = Seq2SeqTrainingArguments(output_dir=MODEL_NAME,\n",
        "                                auto_find_batch_size=True,\n",
        "                                gradient_accumulation_steps=4,\n",
        "                                eval_accumulation_steps=4,\n",
        "                                evaluation_strategy=\"no\",\n",
        "                                save_strategy=STRATEGY,\n",
        "                                logging_strategy=\"steps\",\n",
        "                                logging_steps=250,\n",
        "                                logging_first_step=True,\n",
        "                                learning_rate=LEARNING_RATE,\n",
        "                                report_to=REPORTS_TO,\n",
        "                                weight_decay=0.01,\n",
        "                                disable_tqdm=False,\n",
        "                                num_train_epochs=NUM_OF_EPOCHS,\n",
        "                                predict_with_generate=True,\n",
        "                                fp16=True,\n",
        "                                group_by_length=True,\n",
        "                                hub_private_repo=True,\n",
        "                                push_to_hub=True\n",
        "                                )"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S33CVS_fuBnW"
      },
      "source": [
        "#### Instantiate Trainer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "086zT4pVuBnW",
        "outputId": "9ec9b19d-1fe0-4161-83c2-757d04effcc0"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Cloning https://huggingface.co/DunnBC22/opus-mt-ko-en-Korean_Parallel_Corpora into local empty directory.\n",
            "WARNING:huggingface_hub.repository:Cloning https://huggingface.co/DunnBC22/opus-mt-ko-en-Korean_Parallel_Corpora into local empty directory.\n"
          ]
        }
      ],
      "source": [
        "trainer = Seq2SeqTrainer(model=model,\n",
        "                         args=args,\n",
        "                         train_dataset=encoded_ds['train'],\n",
        "                         eval_dataset=encoded_ds['eval'],\n",
        "                         tokenizer=tokenizer,\n",
        "                         data_collator=data_collator,\n",
        "                         compute_metrics=compute_metrics)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WxDeRJfeuBnW"
      },
      "source": [
        "#### Train Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "5LXdILJPuBnX",
        "outputId": "7c19c41a-a277-4cf2-c951-1f5bf88c689b"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='9714' max='9714' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [9714/9714 48:28, Epoch 5/6]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>3.626300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>250</td>\n",
              "      <td>3.266100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>500</td>\n",
              "      <td>3.187800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>750</td>\n",
              "      <td>3.111600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1000</td>\n",
              "      <td>3.065200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1250</td>\n",
              "      <td>3.050700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1500</td>\n",
              "      <td>3.001200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1750</td>\n",
              "      <td>2.921700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2000</td>\n",
              "      <td>2.817900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2250</td>\n",
              "      <td>2.810300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2500</td>\n",
              "      <td>2.817400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2750</td>\n",
              "      <td>2.780400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3000</td>\n",
              "      <td>2.791800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3250</td>\n",
              "      <td>2.751300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3500</td>\n",
              "      <td>2.629600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3750</td>\n",
              "      <td>2.639700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4000</td>\n",
              "      <td>2.642300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4250</td>\n",
              "      <td>2.640000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4500</td>\n",
              "      <td>2.668400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4750</td>\n",
              "      <td>2.627900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5000</td>\n",
              "      <td>2.570500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5250</td>\n",
              "      <td>2.542100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5500</td>\n",
              "      <td>2.518700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5750</td>\n",
              "      <td>2.539900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6000</td>\n",
              "      <td>2.520400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6250</td>\n",
              "      <td>2.549100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6500</td>\n",
              "      <td>2.531600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6750</td>\n",
              "      <td>2.488200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7000</td>\n",
              "      <td>2.458600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7250</td>\n",
              "      <td>2.466500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7500</td>\n",
              "      <td>2.442800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7750</td>\n",
              "      <td>2.451800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8000</td>\n",
              "      <td>2.448200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8250</td>\n",
              "      <td>2.435000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8500</td>\n",
              "      <td>2.435400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8750</td>\n",
              "      <td>2.408200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9000</td>\n",
              "      <td>2.408200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9250</td>\n",
              "      <td>2.434500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9500</td>\n",
              "      <td>2.428500</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Adding files tracked by Git LFS: ['source.spm', 'target.spm']. This may take a bit of time if the files are large.\n",
            "WARNING:huggingface_hub.repository:Adding files tracked by Git LFS: ['source.spm', 'target.spm']. This may take a bit of time if the files are large.\n"
          ]
        }
      ],
      "source": [
        "train_results = trainer.train()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6vDnEZ1puBnX"
      },
      "source": [
        "#### Push Model to HuggingFace Hub (My Profile!)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 498,
          "referenced_widgets": [
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            "337a8eac30944e1ca992468d5f37a83a",
            "fb17b315f4bb42178c49a9c2c7b82174",
            "f5dc628a18534320a155dc17327d7f54",
            "5e16ad9cb3d642d2827d8c5c633f6e87",
            "f75519603fb04469b269efaa5b37c310",
            "75f326cd5a9d44b797dc7ddac9c3a415",
            "706f00169d52423ca8a5fab70fc68e8d",
            "ef42f709002b453db19e94478de947fc",
            "0cd1895259044561b7742695bb434058"
          ]
        },
        "id": "4I0PV7H9uBnX",
        "outputId": "558ebdd1-7777-4410-d375-351c8a062a7c"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Several commits (2) will be pushed upstream.\n",
            "WARNING:huggingface_hub.repository:Several commits (2) will be pushed upstream.\n",
            "The progress bars may be unreliable.\n",
            "WARNING:huggingface_hub.repository:The progress bars may be unreliable.\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "99c6f4fff08a433cb136a9d155d79aed",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Upload file pytorch_model.bin:   0%|          | 1.00/296M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c7b466449fc842c2bf30d213737e76a9",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Upload file runs/Aug15_19-29-28_7b940da3f0d6/events.out.tfevents.1692127787.7b940da3f0d6.1267.0:   0%|        …"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "To https://huggingface.co/DunnBC22/opus-mt-ko-en-Korean_Parallel_Corpora\n",
            "   61e4661..4dda213  main -> main\n",
            "\n",
            "WARNING:huggingface_hub.repository:To https://huggingface.co/DunnBC22/opus-mt-ko-en-Korean_Parallel_Corpora\n",
            "   61e4661..4dda213  main -> main\n",
            "\n",
            "To https://huggingface.co/DunnBC22/opus-mt-ko-en-Korean_Parallel_Corpora\n",
            "   4dda213..98651f5  main -> main\n",
            "\n",
            "WARNING:huggingface_hub.repository:To https://huggingface.co/DunnBC22/opus-mt-ko-en-Korean_Parallel_Corpora\n",
            "   4dda213..98651f5  main -> main\n",
            "\n"
          ]
        },
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'https://huggingface.co/DunnBC22/opus-mt-ko-en-Korean_Parallel_Corpora/commit/4dda213e33bfdad07b32f3c7f3a0620618e8844b'"
            ]
          },
          "execution_count": 24,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "trainer.push_to_hub()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rxOfEaVhuBnX"
      },
      "source": [
        "#### Save & Log Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "Iwah7sMruBnX"
      },
      "outputs": [],
      "source": [
        "trainer.save_model()\n",
        "trainer.save_state()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m_M0sjogIqOL"
      },
      "source": [
        "#### Evaluate Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 92
        },
        "id": "Z4Bz0FqoIqGF",
        "outputId": "f0f9cb11-d068-49f9-d25f-fd74ce2ee787"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='1745' max='1745' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [1745/1745 31:01]\n",
              "    </div>\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Trainer is attempting to log a value of \"{'rouge1': 0.43909974211672487, 'rouge2': 0.20218611803466308, 'rougeL': 0.36711995473573833, 'rougeLsum': 0.36713843158213144}\" of type <class 'dict'> for key \"eval/rouge\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
            "Trainer is attempting to log a value of \"[13, 30, 34, 32, 24, 16, 53, 38, 17, 10, 35, 20, 33, 21, 26, 21, 13, 14, 36, 34, 18, 20, 23, 19, 22, 35, 27, 46, 33, 61, 22, 29, 36, 32, 47, 17, 23, 43, 28, 23, 23, 7, 22, 48, 31, 40, 20, 18, 29, 34, 8, 29, 19, 28, 50, 30, 27, 20, 15, 54, 33, 19, 26, 26, 34, 38, 46, 16, 33, 29, 27, 33, 29, 26, 23, 14, 135, 14, 15, 9, 30, 31, 22, 10, 19, 17, 15, 36, 21, 34, 27, 38, 24, 36, 23, 29, 23, 37, 41, 16, 35, 40, 24, 17, 41, 26, 97, 18, 7, 46, 42, 18, 13, 35, 24, 35, 21, 8, 31, 12, 51, 58, 18, 40, 43, 18, 19, 34, 17, 18, 27, 22, 24, 18, 38, 46, 18, 15, 33, 49, 31, 32, 16, 43, 17, 24, 23, 15, 15, 36, 31, 26, 47, 42, 24, 51, 31, 36, 23, 31, 26, 35, 29, 16, 10, 14, 9, 29, 15, 31, 18, 35, 33, 32, 22, 32, 26, 33, 47, 14, 21, 19, 13, 26, 13, 48, 21, 13, 26, 32, 19, 40, 40, 26, 14, 29, 29, 19, 49, 28, 12, 15, 36, 47, 7, 31, 27, 9, 19, 37, 18, 24, 28, 9, 16, 9, 12, 33, 22, 22, 21, 15, 36, 34, 12, 8, 26, 43, 26, 16, 22, 44, 16, 23, 57, 18, 32, 30, 31, 35, 13, 15, 29, 21, 30, 31, 25, 40, 10, 29, 6, 51, 38, 44, 47, 43, 31, 37, 25, 11, 49, 26, 22, 33, 31, 38, 12, 39, 29, 23, 19, 12, 26, 27, 28, 16, 24, 48, 17, 39, 34, 15, 33, 39, 29, 25, 15, 24, 13, 13, 36, 40, 25, 25, 30, 26, 27, 28, 22, 17, 20, 17, 25, 31, 13, 47, 28, 33, 13, 23, 34, 21, 29, 28, 34, 20, 37, 28, 50, 30, 42, 63, 33, 32, 25, 34, 21, 21, 16, 36, 23, 51, 21, 36, 36, 24, 23, 30, 35, 40, 13, 22, 13, 35, 16, 107, 48, 20, 37, 23, 24, 25, 46, 15, 30, 15, 24, 24, 42, 41, 22, 15, 24, 16, 34, 33, 19, 11, 41, 25, 49, 16, 18, 36, 43, 25, 10, 11, 19, 14, 18, 16, 17, 17, 25, 29, 22, 50, 16, 30, 44, 13, 35, 34, 23, 34, 19, 24, 39, 23, 25, 14, 12, 25, 31, 25, 25, 14, 31, 24, 29, 32, 20, 32, 34, 12, 34, 32, 14, 10, 16, 17, 34, 20, 38, 29, 24, 24, 34, 28, 168, 36, 19, 28, 36, 32, 190, 29, 46, 48, 12, 12, 42, 16, 23, 19, 9, 34, 18, 30, 22, 23, 29, 14, 53, 15, 30, 40, 67, 20, 29, 16, 19, 24, 13, 13, 20, 30, 19, 43, 44, 20, 31, 15, 29, 25, 28, 25, 21, 25, 19, 32, 22, 44, 35, 28, 17, 48, 20, 21, 36, 15, 25, 25, 19, 17, 12, 14, 21, 35, 29, 21, 25, 28, 36, 20, 13, 19, 30, 10, 29, 21, 27, 13, 27, 33, 32, 37, 34, 15, 31, 28, 16, 30, 29, 29, 22, 20, 53, 31, 23, 36, 36, 22, 23, 27, 15, 40, 16, 44, 49, 13, 33, 22, 36, 38, 27, 26, 14, 25, 26, 11, 31, 31, 26, 29, 21, 21, 14, 23, 38, 35, 26, 28, 13, 8, 28, 27, 20, 51, 39, 22, 44, 8, 31, 34, 38, 22, 35, 16, 31, 18, 22, 19, 43, 16, 38, 33, 34, 32, 30, 16, 26, 25, 12, 30, 13, 21, 22, 22, 8, 38, 37, 36, 39, 35, 30, 31, 47, 53, 16, 38, 32, 13, 24, 40, 7, 28, 14, 16, 10, 21, 30, 30, 18, 18, 17, 24, 33, 25, 34, 23, 31, 44, 38, 15, 16, 38, 39, 16, 15, 15, 28, 14, 16, 40, 28, 29, 67, 9, 21, 15, 12, 16, 12, 16, 32, 39, 26, 25, 38, 20, 16, 23, 21, 31, 37, 22, 31, 21, 14, 17, 24, 32, 28, 37, 29, 26, 30, 19, 46, 12, 27, 21, 12, 24, 40, 33, 24, 24, 23, 29, 25, 14, 22, 34, 28, 47, 20, 34, 56, 34, 18, 38, 21, 29, 31, 19, 35, 11, 9, 14, 9, 28, 50, 40, 27, 10, 13, 24, 34, 53, 30, 25, 10, 40, 8, 29, 38, 16, 23, 22, 53, 33, 9, 24, 10, 22, 26, 36, 23, 31, 51, 19, 26, 45, 36, 35, 21, 18, 9, 32, 18, 43, 34, 29, 33, 18, 21, 19, 142, 13, 44, 21, 14, 36, 30, 14, 23, 25, 18, 29, 127, 7, 12, 10, 30, 51, 32, 26, 26, 25, 13, 26, 23, 34, 28, 31, 31, 41, 35, 15, 21, 38, 9, 20, 33, 33, 19, 35, 28, 37, 8, 28, 26, 20, 23, 23, 16, 12, 16, 21, 58, 26, 44, 16, 38, 20, 20, 7, 36, 11, 54, 29, 16, 15, 20, 51, 18, 57, 18, 18, 27, 31, 44, 28, 16, 28, 18, 23, 30, 21, 18, 21, 20, 42, 38, 22, 40, 40, 33, 13, 80, 17, 29, 32, 14, 30, 8, 42, 51, 25, 35, 36, 27, 21, 17, 30, 26, 24, 43, 66, 31, 26, 22, 38, 38, 15, 45, 11, 29, 26, 20, 10, 18, 21, 67, 26, 55, 23, 36, 15, 32, 43, 10, 14, 43, 43, 35, 20, 22, 34, 20, 13, 14, 31, 7, 36, 45, 12, 20, 29, 57, 20, 22, 15, 16, 37, 15, 23, 20, 14, 29, 35, 6, 27, 15, 37, 38, 35, 21, 17, 16, 37, 43, 20, 24, 10, 32, 46, 31, 39, 31, 43, 28, 13, 47, 27, 17, 29, 24, 31, 42, 30, 24, 26, 27, 26, 31, 26, 44, 35, 9, 16, 31, 31, 23, 44, 31, 32, 25, 25, 22, 30, 30, 24, 26, 29, 18, 18, 31, 9, 50, 24, 21, 39, 30, 12, 22, 25, 20, 12, 20, 39, 26, 36, 24, 29, 23, 27, 18, 21, 33, 10, 8, 35, 14, 30, 25, 33, 37, 15, 19, 30, 13, 13, 8, 33, 34, 21, 38, 28, 36, 47, 21, 13, 10, 28, 37, 10, 13, 27, 33, 24, 43, 20, 35, 22, 18, 31, 32, 29, 14, 32, 26, 6, 24, 35, 42, 31, 47, 23, 28, 27, 43, 36, 24, 12, 8, 28, 17, 35, 17, 31, 18, 26, 8, 45, 54, 33, 38, 11, 16, 59, 10, 29, 26, 26, 41, 9, 11, 16, 33, 27, 24, 49, 26, 17, 14, 17, 31, 42, 17, 22, 20, 35, 33, 23, 24, 38, 44, 30, 17, 37, 18, 36, 22, 18, 32, 47, 73, 33, 35, 190, 19, 12, 29, 22, 31, 39, 36, 26, 9, 18, 23, 27, 37, 24, 22, 15, 19, 9, 24, 33, 29, 6, 35, 18, 25, 12, 45, 21, 49, 43, 15, 17, 22, 42, 9, 21, 28, 33, 32, 50, 21, 34, 21, 20, 24, 39, 18, 34, 44, 21, 50, 168, 28, 16, 18, 9, 40, 31, 25, 27, 11, 23, 23, 26, 37, 29, 17, 23, 26, 27, 34, 34, 28, 25, 23, 21, 17, 16, 42, 24, 23, 17, 40, 23, 42, 24, 38, 25, 6, 38, 12, 23, 29, 42, 19, 47, 10, 37, 25, 45, 47, 167, 7, 30, 48, 15, 27, 32, 34, 26, 32, 22, 16, 22, 12, 26, 52, 23, 7, 511, 10, 12, 22, 29, 33, 23, 44, 45, 19, 23, 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20, 16, 38, 41, 19, 41, 9, 8, 34, 25, 34, 21, 49, 25, 28, 49, 30, 25, 22, 24, 41, 35, 36, 13, 16, 23, 34, 32, 32, 20, 21, 66, 10, 33, 40, 34, 73, 14, 34, 25, 24, 35, 16, 28, 28, 36, 45, 34, 46, 33, 41, 34, 30, 32, 45, 31, 8, 15, 19, 19, 16, 15, 26, 22, 47, 23, 29, 26, 25, 22, 17, 46, 28, 18, 23, 33, 19, 31, 15, 11, 25, 30, 21, 24, 25, 23, 42, 23, 27, 34, 37, 24, 53, 40, 26, 14, 9, 30, 15, 21, 28, 46, 43, 51, 29, 20, 15, 21, 21, 30, 26, 25, 20, 23, 22, 15, 17, 50, 22, 16, 37, 20, 54, 28, 14, 26, 36, 24, 31, 12, 11, 22, 26, 34, 24, 36, 32, 40, 32, 41, 24, 27, 21, 29, 34, 38, 13, 15, 15, 31, 35, 49, 20, 9, 26, 33, 27, 15, 17, 36, 31, 19, 23, 8, 29, 22, 32, 19, 16, 24, 27, 21, 14, 32, 53, 24, 50, 21, 24, 19, 26, 29, 49, 12, 10, 8, 22, 23, 32, 48, 11, 19, 41, 16, 19, 15, 38, 28, 20, 25, 16, 47, 37, 17, 37, 16, 39, 24, 21, 17, 49, 44, 29, 43, 30, 29, 6, 18, 33, 8, 23, 21, 30, 11, 37, 30, 18, 35, 26, 34, 21, 25, 20, 35, 30, 30, 24, 36, 22, 21, 16, 18, 33, 17, 24, 25, 20, 28, 34, 22, 56, 13, 30, 21, 35, 30, 29, 51, 41, 27, 8, 29, 35, 22, 15, 18, 24, 26, 48, 13, 20, 26, 20, 38, 33, 23, 37, 9, 8, 24, 18, 28, 19, 25, 31, 31, 11, 15, 36, 12, 11, 21, 24, 23, 23, 27, 18, 18, 15, 24, 26, 25, 22, 39, 30, 51, 42, 31, 33, 13, 12, 27, 36, 14, 27, 20, 22, 39, 33, 21, 21, 57, 18, 35, 9, 28, 37, 20, 37, 17, 44, 35, 17, 22, 12, 21, 43, 8, 18, 10, 20, 18, 179, 41, 27, 28, 40, 14, 30, 24, 35, 22, 16, 28, 26, 25, 40, 42, 18, 9, 18, 10, 21, 22, 10, 23, 36, 38, 20, 14, 27, 27, 31, 36, 11, 48, 27, 34, 25, 52, 39, 28, 19, 20, 18, 42, 24, 28, 32]\" of type <class 'list'> for key \"eval/Prediction Lengths\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
          ]
        }
      ],
      "source": [
        "metrics = trainer.evaluate()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VXYWY9ryrRcB"
      },
      "source": [
        "#### Print Metric Values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5YIcuDqfrQ9f",
        "outputId": "2619fa22-3a17-4279-b323-f616c616c1dc"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{'eval_loss': 2.662041664123535, 'eval_bleu': 14.3394821840289, 'eval_rouge': {'rouge1': 0.43909974211672487, 'rouge2': 0.20218611803466308, 'rougeL': 0.36711995473573833, 'rougeLsum': 0.36713843158213144}, 'eval_Prediction Lengths': [13, 30, 34, 32, 24, 16, 53, 38, 17, 10, 35, 20, 33, 21, 26, 21, 13, 14, 36, 34, 18, 20, 23, 19, 22, 35, 27, 46, 33, 61, 22, 29, 36, 32, 47, 17, 23, 43, 28, 23, 23, 7, 22, 48, 31, 40, 20, 18, 29, 34, 8, 29, 19, 28, 50, 30, 27, 20, 15, 54, 33, 19, 26, 26, 34, 38, 46, 16, 33, 29, 27, 33, 29, 26, 23, 14, 135, 14, 15, 9, 30, 31, 22, 10, 19, 17, 15, 36, 21, 34, 27, 38, 24, 36, 23, 29, 23, 37, 41, 16, 35, 40, 24, 17, 41, 26, 97, 18, 7, 46, 42, 18, 13, 35, 24, 35, 21, 8, 31, 12, 51, 58, 18, 40, 43, 18, 19, 34, 17, 18, 27, 22, 24, 18, 38, 46, 18, 15, 33, 49, 31, 32, 16, 43, 17, 24, 23, 15, 15, 36, 31, 26, 47, 42, 24, 51, 31, 36, 23, 31, 26, 35, 29, 16, 10, 14, 9, 29, 15, 31, 18, 35, 33, 32, 22, 32, 26, 33, 47, 14, 21, 19, 13, 26, 13, 48, 21, 13, 26, 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        "print(metrics)"
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        "### Notes & Other Takeaways From This Project\n",
        "****\n",
        "- These are not the results I was expecting, but I will learn from this project and incorporate that wisdom in projects going forward.\n",
        "\n",
        "****"
      ]
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        "#### Citations\n",
        "\n",
        "- Model Checkpoint\n",
        "    > https://huggingface.co/Helsinki-NLP/opus-mt-ko-en\n",
        "\n",
        "- Metrics\n",
        "    - Bleu\n",
        "        > @INPROCEEDINGS{Papineni02bleu:a, author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu}, title = {BLEU: a Method for Automatic Evaluation of Machine Translation}, booktitle = {}, year = {2002}, pages = {311--318}}\n",
        "        \n",
        "        > @inproceedings{lin-och-2004-orange, title = \"{ORANGE}: a Method for Evaluating Automatic Evaluation Metrics for Machine Translation\", author = \"Lin, Chin-Yew and Och, Franz Josef\", booktitle = \"{COLING} 2004: Proceedings of the 20th International Conference on Computational Linguistics\", month = \"aug 23{--}aug 27\", year = \"2004\", address = \"Geneva, Switzerland\", publisher = \"COLING\", url = \"https://www.aclweb.org/anthology/C04-1072\", pages = \"501--507\",}\n",
        "    \n",
        "    - Rouge\n",
        "        > @inproceedings{lin-2004-rouge, title = '{ROUGE}: A Package for Automatic Evaluation of Summaries', author = 'Lin, Chin-Yew', booktitle = 'Text Summarization Branches Out', month = jul, year = '2004', address = 'Barcelona, Spain', publisher = 'Association for Computational Linguistics', url = 'https://www.aclweb.org/anthology/W04-1013', pages = '74--81',}"
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