Machine Translation/Medical - German to English/OPUS_Medical_German_to_English_OPUS_Translation_Project.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v9_iuBdztBEU"
      },
      "source": [
        "## OPUS Medical German to English: Machine Translation\n",
        "\n",
        "Dataset Source: https://huggingface.co/datasets/ahazeemi/opus-medical-en-de"
      ]
    },
    {
      "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": "9745ff16-d176-4a71-92e7-d105bf244c13"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m118.9/118.9 kB\u001b[0m \u001b[31m2.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting rouge\n",
            "  Downloading rouge-1.0.1-py3-none-any.whl (13 kB)\n",
            "Collecting rouge-score\n",
            "  Downloading rouge_score-0.1.2.tar.gz (17 kB)\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: nltk in /usr/local/lib/python3.10/dist-packages (3.8.1)\n",
            "Collecting portalocker (from sacrebleu)\n",
            "  Downloading portalocker-2.7.0-py2.py3-none-any.whl (15 kB)\n",
            "Requirement already satisfied: regex in /usr/local/lib/python3.10/dist-packages (from sacrebleu) (2023.6.3)\n",
            "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) (4.66.0)\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=55e42dd9893a1a6c24f0e0cc58d246988bf96f4930bceb23e0fd051a4f3b22bf\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[31m18.1 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": "30ad56cd-e6ac-4762-dfba-4c3035f51fc6"
      },
      "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": "HAmqgd3htBEV"
      },
      "source": [
        "#### Import Necessary Libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B8MShMxMtBEW",
        "outputId": "9528342a-cfc4-40c6-92eb-f6130d12933e"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Git LFS initialized.\n"
          ]
        }
      ],
      "source": [
        "import os, sys\n",
        "os.environ['TOKENIZERS_PARALLELISM']='false'\n",
        "\n",
        "from importlib.metadata import distribution\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",
        "nltk.download('punkt')\n",
        "\n",
        "import transformers\n",
        "from transformers import DataCollatorForSeq2Seq, AutoModelForSeq2SeqLM, set_seed\n",
        "from transformers import Seq2SeqTrainer, Seq2SeqTrainingArguments, AutoTokenizer\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": "TKrT51_ktBEX"
      },
      "source": [
        "#### Versions of Software & Libraries Used"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SUCf4LKutBEX",
        "outputId": "b8217f05-b290-43d0-e135-201b9074e015"
      },
      "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": "txJkNerwtBEY"
      },
      "source": [
        "#### Ingest & Start Preprocessing Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
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        },
        "id": "72sK5jCttBEY",
        "outputId": "fd5ce9c4-e7b1-424c-8898-5c61fd7088e4"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "5885154e4a9f40cc8796afe1a983553a",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/943 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
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              "version_major": 2,
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            },
            "text/plain": [
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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              "version_major": 2,
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            ]
          },
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          "output_type": "display_data"
        },
        {
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              "model_id": "985811e5ade14f21a32dc181f6122a3c",
              "version_major": 2,
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          },
          "metadata": {},
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        },
        {
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              "model_id": "e9658649107345ac8ec091e346f6c5b7",
              "version_major": 2,
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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              "model_id": "a6cf9ba139cb4ccfae0190ec5645dcf8",
              "version_major": 2,
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            "text/plain": [
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          },
          "metadata": {},
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        },
        {
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            "text/plain": [
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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              "model_id": "f2ea382aa1d84bcabd246d5a04ef18f6",
              "version_major": 2,
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            "text/plain": [
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['de', 'en'],\n",
              "        num_rows: 248099\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['de', 'en'],\n",
              "        num_rows: 2000\n",
              "    })\n",
              "    dev: Dataset({\n",
              "        features: ['de', 'en'],\n",
              "        num_rows: 2000\n",
              "    })\n",
              "})"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data = load_dataset(\"ahazeemi/opus-medical-en-de\")\n",
        "\n",
        "data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6fdVppMqtBEY"
      },
      "source": [
        "#### Convert Dataset to Pandas DataFrame"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GqPIXNxctBEY",
        "outputId": "7f9cd1ea-3569-42cd-c0f2-e28a141af491"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 252099 entries, 0 to 252098\n",
            "Data columns (total 2 columns):\n",
            " #   Column  Non-Null Count   Dtype \n",
            "---  ------  --------------   ----- \n",
            " 0   de      252099 non-null  object\n",
            " 1   en      252099 non-null  object\n",
            "dtypes: object(2)\n",
            "memory usage: 3.8+ MB\n"
          ]
        }
      ],
      "source": [
        "data.set_format(type=\"pandas\")\n",
        "\n",
        "df = pd.concat(\n",
        "    [\n",
        "        data['train'][:],\n",
        "        data['test'][:],\n",
        "        data['dev'][:]\n",
        "    ]\n",
        ")\n",
        "\n",
        "df = df.drop_duplicates()\n",
        "\n",
        "df = df.reset_index(drop=True)\n",
        "\n",
        "df.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zf4fWk6wtBEY"
      },
      "source": [
        "#### Visual: Length of English Entries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 650
        },
        "id": "Sh02CwEJtBEY",
        "outputId": "5c6c5dec-93c6-4cc6-c6bb-ddd666ec020f"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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79qlatWpm3R9//JHnHk+dOqUVK1Zo2LBhGjJkiDl+I5cr3GpVqlTRr7/+qjZt2lz1s8wPX19feXh45Pr55TbmiHVeKftvu3v37hw3pdu9e3eud6p3huw+8vpZ5VWTJk3UpEkTffDBB5ozZ466du2quXPnFugPZtlnQhw9etQcu9rf9ttvv1Xr1q31f/7P/7EbP336tN2R+KvNn31avZeX13V/aLhZjv5+SNfe511cXNSmTRu1adNGH330kUaMGKHBgwdr1apVBb6tAFBQOHcJAHBdV57uXqJECVWtWtXuEVvFixeX9G9wuFz79u116dIlffLJJ3bj48ePl81mM68/DwsLkyR9+umndnUff/xxnvvMPiJ35dG9CRMm5HkZ+eHIR/09/fTTOnz4cK7Xa587d07p6en5Wl6RIkUUGhqqhQsX6siRI+b4H3/8ket18cWLF8/xt7tZDRs2lK+vr6ZOnWq3ryxZskQ7d+685pMcbqWAgADVqVNHX3zxhdLS0szxNWvWaPv27fle3qlTp3Lsg9lHxq98LN2N+vnnn3O9n8DixYsl2V9qcbW/bZEiRXL0OX/+fLvHMGbPL+X8bgcHB6tKlSr68MMP7T63bH///XeetiUvHP39kK6+XSdPnsxR6+i/HwA4A0f+AQDXVatWLbVq1UrBwcEqXbq0Nm/erG+//Va9evUya4KDgyVJvXv3VlhYmIoUKaIuXbqoQ4cOat26tQYPHqz9+/erfv36Wr58ub7//nv17dvXPHoYHBysTp06acKECTpx4oT5qL/ff/9dUt6OTHt5ealFixYaM2aMMjMzddddd2n58uXat29fAXwq+X/U34oVK3T+/Pkc4x07dlS3bt30zTff6JVXXtGqVavUtGlTXbp0Sbt27dI333yjZcuW5en69stFR0dr+fLlatq0qV599VXzR5g6deooMTHRrjY4OFg//fSTPvroIwUEBCgoKEiNGzfO1/quVLRoUY0ePVo9evRQy5Yt9cwzz5iP+gsMDNQbb7xxU8tfsmRJrj++PPDAA7r77rvztawRI0boscceU9OmTdWjRw+dOnXK/KxyC7bXMmvWLH366ad6/PHHVaVKFZ05c0aff/65vLy81L59+3wt62pGjx6thIQEPfHEE+a9N7Zs2aIvvvhCpUuXVt++fc3a4OBgTZkyRe+//76qVq0qX19fPfjgg3rkkUc0fPhw9ejRQw888IC2b9+u2bNn5/jsqlSpIh8fH02dOlUlS5ZU8eLF1bhxYwUFBWn69Ol6+OGHVbt2bfXo0UN33XWXDh8+rFWrVsnLy0s//vijQ7a3IL4f2f/NGjx4sLp06aKiRYuqQ4cOGj58uNauXavw8HBVrlxZx48f16effqoKFSqoWbNmDtkeAHAGwj8A4Lp69+6tH374QcuXL1dGRoYqV66s999/XwMGDDBrnnjiCb3++uuaO3euvvrqKxmGoS5dusjFxUU//PCDhgwZonnz5mnGjBkKDAzU2LFj1b9/f7v1fPHFF/L399fXX3+tBQsWKDQ0VPPmzVP16tXt7hZ/LXPmzNHrr7+uyZMnyzAMtW3bVkuWLLG7472zLF261HyqweUCAwNVp04dLVy4UOPHj9cXX3yhBQsWqFixYrr77rvVp08f3XPPPfleX3BwsJYsWaI333xT7777ripWrKjhw4dr586dOULzRx99pMjISL3zzjs6d+6cunfvftPhX5JeeOEFFStWTKNGjdJbb72l4sWL6/HHH9fo0aPNu6rfqMsv7bjcjBkz8h3+O3TooK+//lrR0dGKiopStWrVNHPmTM2aNUtJSUn5WlbLli21adMmzZ07V8eOHZO3t7fuv/9+zZ49O9ebVN6It99+W3PmzNGaNWs0e/ZsnT17VuXLl1eXLl307rvv2q1nyJAhOnDggMaMGaMzZ86oZcuWevDBB/X2228rPT1dc+bM0bx583TfffcpJiZGUVFRdusqWrSoZs2apUGDBumVV17RxYsXNWPGDAUFBalVq1aKi4vTe++9p08++URpaWny9/dX48aN9Z///Mch2yr9exq+o78fjRo10nvvvaepU6dq6dKl5mVHjz76qPbv36///ve/+ueff1S2bFm1bNlSw4YNk7e3t8O2CQBuNZtxO9ypBwBwx0pMTNS9996rr776Sl27dnV2O5bQsWPHG350452mQYMGKleunGJjY53dCgAAN4Vr/gEAhca5c+dyjE2YMEEuLi5q0aKFEzq6/V35me7Zs0eLFy9Wq1atnNNQIZWZmamLFy/aja1evVq//vornxUAwBI48g8AKDSGDRumhIQEtW7dWq6urlqyZImWLFmiyMhIffbZZ85u77ZUvnx5vfDCC7r77rt14MABTZkyRRkZGdq6davdUxXudPv371doaKiee+45BQQEaNeuXZo6daq8vb21Y8eOPD33HgCAwozwDwAoNGJjYzVs2DD99ttvSktLU6VKldStWzcNHjxYrq7cpuZG9OjRQ6tWrVJycrLc3d0VEhKiESNG6L777nN2a4VKSkqKIiMjtW7dOv39998qXry42rRpo1GjRpk3pQQA4HZG+AcAAAAAwOK45h8AAAAAAIsj/AMAAAAAYHFcQOkgWVlZOnLkiEqWLCmbzebsdgAAAAAAFmcYhs6cOaOAgAC5uFz72D7h30GOHDmiihUrOrsNAAAAAMAd5tChQ6pQocI1awj/DlKyZElJ/37oXl5eTu4GAAAAAGB1qampqlixoplHr4Xw7yDZp/p7eXkR/gEAAAAAt0xeLj3nhn8AAAAAAFgc4R8AAAAAAItzavhfu3atOnTooICAANlsNi1cuPCqta+88opsNpsmTJhgN37y5El17dpVXl5e8vHxUUREhNLS0uxqtm3bpubNm8vDw0MVK1bUmDFjcix//vz5qlGjhjw8PFS3bl0tXrzYEZsIAAAAAIDTOTX8p6enq379+po8efI16xYsWKANGzYoICAgx7SuXbsqKSlJsbGxWrRokdauXavIyEhzempqqtq2bavKlSsrISFBY8eOVXR0tKZNm2bWrF+/Xs8884wiIiK0detWdezYUR07dtSOHTsct7EAAAAAADiJzTAMw9lNSP/eoGDBggXq2LGj3fjhw4fVuHFjLVu2TOHh4erbt6/69u0rSdq5c6dq1aql+Ph4NWzYUJK0dOlStW/fXn/99ZcCAgI0ZcoUDR48WMnJyXJzc5MkRUVFaeHChdq1a5ckqXPnzkpPT9eiRYvM9TZp0kQNGjTQ1KlTc+03IyNDGRkZ5vvsuyympKRwwz8AAAAAQIFLTU2Vt7d3nnJoob7mPysrS926ddOAAQNUu3btHNPj4uLk4+NjBn9JCg0NlYuLizZu3GjWtGjRwgz+khQWFqbdu3fr1KlTZk1oaKjdssPCwhQXF3fV3kaOHClvb2/zVbFixZvaVgAAAAAACkqhDv+jR4+Wq6urevfunev05ORk+fr62o25urqqdOnSSk5ONmv8/PzsarLfX68me3puBg0apJSUFPN16NCh/G0cAAAAAAC3iKuzG7iahIQETZw4UVu2bMnTMwtvNXd3d7m7uzu7DQAAAAAArqvQHvn/+eefdfz4cVWqVEmurq5ydXXVgQMH1L9/fwUGBkqS/P39dfz4cbv5Ll68qJMnT8rf39+sOXbsmF1N9vvr1WRPBwAAAADgdlZow3+3bt20bds2JSYmmq+AgAANGDBAy5YtkySFhITo9OnTSkhIMOdbuXKlsrKy1LhxY7Nm7dq1yszMNGtiY2NVvXp1lSpVyqxZsWKF3fpjY2MVEhJS0JsJAAAAAECBc+pp/2lpafrjjz/M9/v27VNiYqJKly6tSpUqqUyZMnb1RYsWlb+/v6pXry5Jqlmzptq1a6eXX35ZU6dOVWZmpnr16qUuXbqYjwV89tlnNWzYMEVEROitt97Sjh07NHHiRI0fP95cbp8+fdSyZUuNGzdO4eHhmjt3rjZv3mz3OEAAAAAAAG5XTj3yv3nzZt1777269957JUn9+vXTvffeqyFDhuR5GbNnz1aNGjXUpk0btW/fXs2aNbML7d7e3lq+fLn27dun4OBg9e/fX0OGDFFkZKRZ88ADD2jOnDmaNm2a6tevr2+//VYLFy5UnTp1HLexAAAAAAA4ic0wDMPZTVhBfp6vCAAAAADAzcpPDi201/wDAAAAAADHIPwDAAAAAGBxhH8AAAAAACyO8A8AAAAAgMUR/gEAAAAAsDjCPwAAAAAAFkf4BwAAAADA4gj/AAAAAABYHOEfAAAAAACLI/wDAAAAAGBxhH8AAAAAACyO8A8AAAAAgMUR/gEAAAAAsDjCP64qMCpGgVExzm4DAAAAAHCTCP8AAAAAAFgc4R8AAAAAAIsj/AMAAAAAYHGEfwAAAAAALI7wDwAAAACAxRH+AQAAAACwOMI/AAAAAAAWR/gHAAAAAMDiCP8AAAAAAFgc4R8AAAAAAIsj/AMAAAAAYHGEfwAAAAAALI7wDwAAAACAxRH+AQAAAACwOMI/AAAAAAAWR/gHAAAAAMDiCP8AAAAAAFgc4R8AAAAAAIsj/AMAAAAAYHGEfwAAAAAALI7wDwAAAACAxRH+AQAAAACwOMI/AAAAAAAWR/gHAAAAAMDiCP8AAAAAAFgc4R8AAAAAAIsj/AMAAAAAYHGEfwAAAAAALI7wDwAAAACAxRH+AQAAAACwOMI/AAAAAAAWR/gHAAAAAMDiCP8AAAAAAFgc4R8AAAAAAIsj/AMAAAAAYHGEfwAAAAAALI7wDwAAAACAxRH+AQAAAACwOMI/AAAAAAAWR/gHAAAAAMDiCP8AAAAAAFgc4R8AAAAAAIsj/AMAAAAAYHGEfwAAAAAALI7wDwAAAACAxRH+AQAAAACwOMI/AAAAAAAWR/gHAAAAAMDiCP8AAAAAAFgc4R8AAAAAAIsj/AMAAAAAYHGEfwAAAAAALI7wDwAAAACAxRH+AQAAAACwOMI/AAAAAAAWR/gHAAAAAMDiCP8AAAAAAFgc4R8AAAAAAItzdXYDKHwCo2Kc3QIAAAAAwIE48g8AAAAAgMUR/gEAAAAAsDinhv+1a9eqQ4cOCggIkM1m08KFC81pmZmZeuutt1S3bl0VL15cAQEBev7553XkyBG7ZZw8eVJdu3aVl5eXfHx8FBERobS0NLuabdu2qXnz5vLw8FDFihU1ZsyYHL3Mnz9fNWrUkIeHh+rWravFixcXyDYDAAAAAHCrOTX8p6enq379+po8eXKOaWfPntWWLVv07rvvasuWLfruu++0e/duPfroo3Z1Xbt2VVJSkmJjY7Vo0SKtXbtWkZGR5vTU1FS1bdtWlStXVkJCgsaOHavo6GhNmzbNrFm/fr2eeeYZRUREaOvWrerYsaM6duyoHTt2FNzGAwAAAABwi9gMwzCc3YQk2Ww2LViwQB07drxqTXx8vO6//34dOHBAlSpV0s6dO1WrVi3Fx8erYcOGkqSlS5eqffv2+uuvvxQQEKApU6Zo8ODBSk5OlpubmyQpKipKCxcu1K5duyRJnTt3Vnp6uhYtWmSuq0mTJmrQoIGmTp2ap/5TU1Pl7e2tlJQUeXl53eCnUDhcecO//aPCndQJAAAAAOBq8pNDb6tr/lNSUmSz2eTj4yNJiouLk4+Pjxn8JSk0NFQuLi7auHGjWdOiRQsz+EtSWFiYdu/erVOnTpk1oaGhdusKCwtTXFzcVXvJyMhQamqq3QsAAAAAgMLotgn/58+f11tvvaVnnnnG/EUjOTlZvr6+dnWurq4qXbq0kpOTzRo/Pz+7muz316vJnp6bkSNHytvb23xVrFjx5jYQAAAAAIACcluE/8zMTD399NMyDENTpkxxdjuSpEGDBiklJcV8HTp0yNktAQAAAACQK1dnN3A92cH/wIEDWrlypd11DP7+/jp+/Lhd/cWLF3Xy5En5+/ubNceOHbOryX5/vZrs6blxd3eXu7v7jW8YAAAAAAC3SKE+8p8d/Pfs2aOffvpJZcqUsZseEhKi06dPKyEhwRxbuXKlsrKy1LhxY7Nm7dq1yszMNGtiY2NVvXp1lSpVyqxZsWKF3bJjY2MVEhJSUJsGAAAAAMAt49Twn5aWpsTERCUmJkqS9u3bp8TERB08eFCZmZl68skntXnzZs2ePVuXLl1ScnKykpOTdeHCBUlSzZo11a5dO7388svatGmT1q1bp169eqlLly4KCAiQJD377LNyc3NTRESEkpKSNG/ePE2cOFH9+vUz++jTp4+WLl2qcePGadeuXYqOjtbmzZvVq1evW/6ZAAAAAADgaE591N/q1avVunXrHOPdu3dXdHS0goKCcp1v1apVatWqlSTp5MmT6tWrl3788Ue5uLioU6dOmjRpkkqUKGHWb9u2TT179lR8fLzKli2r119/XW+99ZbdMufPn6933nlH+/fvV7Vq1TRmzBi1b98+z9vCo/4AAAAAALdSfnKoU8O/lRD+AQAAAAC3Un5yaKG+5h8AAAAAANw8wj8AAAAAABZH+AcAAAAAwOII/wAAAAAAWBzhHwAAAAAAiyP8AwAAAABgcYR/AAAAAAAsjvAPAAAAAIDFEf4BAAAAALA4wj8AAAAAABZH+AcAAAAAwOII/wAAAAAAWBzhHwAAAAAAiyP8AwAAAABgcYR/AAAAAAAsjvAPAAAAAIDFEf4BAAAAALA4wj8AAAAAABZH+AcAAAAAwOII/wAAAAAAWBzhHwAAAAAAiyP8AwAAAABgcYR/AAAAAAAsjvAPAAAAAIDFEf4BAAAAALA4wj8AAAAAABZH+AcAAAAAwOII/wAAAAAAWBzhHwAAAAAAiyP8AwAAAABgcYR/AAAAAAAsztXZDaDwCIyKcXYLAAAAAIACwJF/AAAAAAAsjvAPAAAAAIDFEf4BAAAAALA4wj8AAAAAABZH+AcAAAAAwOII/wAAAAAAWBzhHwAAAAAAi3N1dgNwvsCoGGe3AAAAAAAoQBz5BwAAAADA4gj/AAAAAABYHOEfAAAAAACLI/wDAAAAAGBxhH8AAAAAACyO8A8AAAAAgMUR/gEAAAAAsDjCPwAAAAAAFkf4BwAAAADA4gj/AAAAAABYHOEfAAAAAACLI/wDAAAAAGBxhH8AAAAAACyO8I+bEhgVo8CoGGe3AQAAAAC4BsI/AAAAAAAWR/gHAAAAAMDiCP8AAAAAAFgc4R8AAAAAAItzdXYDuD1xkz8AAAAAuH1w5B8AAAAAAIsj/AMAAAAAYHGEfwAAAAAALI7wDwAAAACAxRH+AQAAAACwOMI/AAAAAAAWR/gHAAAAAMDiCP8AAAAAAFgc4R8AAAAAAIsj/AMAAAAAYHGEfwAAAAAALI7wDwAAAACAxRH+AQAAAACwOMI/AAAAAAAWR/gHAAAAAMDiCP8AAAAAAFgc4R8AAAAAAItzavhfu3atOnTooICAANlsNi1cuNBuumEYGjJkiMqXLy9PT0+FhoZqz549djUnT55U165d5eXlJR8fH0VERCgtLc2uZtu2bWrevLk8PDxUsWJFjRkzJkcv8+fPV40aNeTh4aG6detq8eLFDt9eAAAAAACcwanhPz09XfXr19fkyZNznT5mzBhNmjRJU6dO1caNG1W8eHGFhYXp/PnzZk3Xrl2VlJSk2NhYLVq0SGvXrlVkZKQ5PTU1VW3btlXlypWVkJCgsWPHKjo6WtOmTTNr1q9fr2eeeUYRERHaunWrOnbsqI4dO2rHjh0Ft/G3qcCoGAVGxTi7DQAAAABAPtgMwzCc3YQk2Ww2LViwQB07dpT071H/gIAA9e/fX2+++aYkKSUlRX5+fpo5c6a6dOminTt3qlatWoqPj1fDhg0lSUuXLlX79u31119/KSAgQFOmTNHgwYOVnJwsNzc3SVJUVJQWLlyoXbt2SZI6d+6s9PR0LVq0yOynSZMmatCggaZOnZqn/lNTU+Xt7a2UlBR5eXk56mO5Ja4X5vePCr9u7eU1AAAAAICCl58cWmiv+d+3b5+Sk5MVGhpqjnl7e6tx48aKi4uTJMXFxcnHx8cM/pIUGhoqFxcXbdy40axp0aKFGfwlKSwsTLt379apU6fMmsvXk12TvZ7cZGRkKDU11e4FAAAAAEBhVGjDf3JysiTJz8/PbtzPz8+clpycLF9fX7vprq6uKl26tF1Nbsu4fB1Xq8menpuRI0fK29vbfFWsWDG/mwgAAAAAwC1RaMN/YTdo0CClpKSYr0OHDjm7JQAAAAAAclVow7+/v78k6dixY3bjx44dM6f5+/vr+PHjdtMvXryokydP2tXktozL13G1muzpuXF3d5eXl5fdCwAAAACAwqjQhv+goCD5+/trxYoV5lhqaqo2btyokJAQSVJISIhOnz6thIQEs2blypXKyspS48aNzZq1a9cqMzPTrImNjVX16tVVqlQps+by9WTXZK8HAAAAAIDbmVPDf1pamhITE5WYmCjp35v8JSYm6uDBg7LZbOrbt6/ef/99/fDDD9q+fbuef/55BQQEmE8EqFmzptq1a6eXX35ZmzZt0rp169SrVy916dJFAQEBkqRnn31Wbm5uioiIUFJSkubNm6eJEyeqX79+Zh99+vTR0qVLNW7cOO3atUvR0dHavHmzevXqdas/EgAAAAAAHM7VmSvfvHmzWrdubb7PDuTdu3fXzJkzNXDgQKWnpysyMlKnT59Ws2bNtHTpUnl4eJjzzJ49W7169VKbNm3k4uKiTp06adKkSeZ0b29vLV++XD179lRwcLDKli2rIUOGKDIy0qx54IEHNGfOHL3zzjt6++23Va1aNS1cuFB16tS5BZ8CAAAAAAAFy2YYhuHsJqwgP89XLGwCo2KuOX3/qPDr1l5eAwAAAAAoePnJoYX2mn8AAAAAAOAYhH8AAAAAACyO8A8AAAAAgMUR/gEAAAAAsDjCPwAAAAAAFkf4BwAAAADA4gj/AAAAAABYHOEfAAAAAACLI/wDAAAAAGBxhH8AAAAAACyO8A8AAAAAgMUR/nFdgVExzm4BAAAAAHATCP8AAAAAAFgc4R8AAAAAAIsj/AMAAAAAYHGEfwAAAAAALI7wDwAAAACAxRH+AQAAAACwOMI/AAAAAAAWR/gHAAAAAMDiXJ3dAG4PgVExzm4BAAAAAHCDOPIPAAAAAIDFEf4BAAAAALA4wj8AAAAAABZH+AcAAAAAwOII/3CowKgYbg4IAAAAAIUM4R8AAAAAAIvjUX9wCI72AwAAAEDhxZF/AAAAAAAsjvAPAAAAAIDFEf4BAAAAALA4wj8AAAAAABZH+AcAAAAAwOII/wAAAAAAWBzhHwAAAAAAiyP8AwAAAABgcYR/AAAAAAAsjvAPAAAAAIDFEf4BAAAAALA4wj8AAAAAABbn6uwGYE2BUTHmv/ePCndiJwAAAAAAjvwDAAAAAGBxhH8AAAAAACyO8A8AAAAAgMUR/gEAAAAAsDjCPwAAAAAAFkf4R4ELjIqxu/s/AAAAAODWIvwDAAAAAGBxhH8AAAAAACyO8A8AAAAAgMUR/gEAAAAAsDjCPwAAAAAAFkf4BwAAAADA4gj/AAAAAABYHOEfAAAAAACLI/wDAAAAAGBxhH8AAAAAACyO8A8AAAAAgMUR/gEAAAAAsDjCPwAAAAAAFkf4BwAAAADA4gj/AAAAAABYHOEfAAAAAACLI/wDAAAAAGBxhH8AAAAAACyO8A8AAAAAgMUR/gEAAAAAsDjCPwAAAAAAFkf4BwAAAADA4gj/AAAAAABYHOEfAAAAAACLI/wDAAAAAGBxhH8AAAAAACyO8A8AAAAAgMXlO/wfOnRIf/31l/l+06ZN6tu3r6ZNm+bQxgAAAAAAgGPkO/w/++yzWrVqlSQpOTlZDz30kDZt2qTBgwdr+PDhDm8QAAAAAADcnHyH/x07duj++++XJH3zzTeqU6eO1q9fr9mzZ2vmzJkObe7SpUt69913FRQUJE9PT1WpUkXvvfeeDMMwawzD0JAhQ1S+fHl5enoqNDRUe/bssVvOyZMn1bVrV3l5ecnHx0cRERFKS0uzq9m2bZuaN28uDw8PVaxYUWPGjHHotgAAAAAA4Cz5Dv+ZmZlyd3eXJP3000969NFHJUk1atTQ0aNHHdrc6NGjNWXKFH3yySfauXOnRo8erTFjxujjjz82a8aMGaNJkyZp6tSp2rhxo4oXL66wsDCdP3/erOnatauSkpIUGxurRYsWae3atYqMjDSnp6amqm3btqpcubISEhI0duxYRUdHcymDgwVGxSgwKsbZbQAAAADAHcc1vzPUrl1bU6dOVXh4uGJjY/Xee+9Jko4cOaIyZco4tLn169frscceU3h4uCQpMDBQX3/9tTZt2iTp36P+EyZM0DvvvKPHHntMkvTFF1/Iz89PCxcuVJcuXbRz504tXbpU8fHxatiwoSTp448/Vvv27fXhhx8qICBAs2fP1oULF/Tf//5Xbm5uql27thITE/XRRx/Z/UgAAAAAAMDtKN9H/kePHq3PPvtMrVq10jPPPKP69etLkn744QfzcgBHeeCBB7RixQr9/vvvkqRff/1Vv/zyix5++GFJ0r59+5ScnKzQ0FBzHm9vbzVu3FhxcXGSpLi4OPn4+JjBX5JCQ0Pl4uKijRs3mjUtWrSQm5ubWRMWFqbdu3fr1KlTufaWkZGh1NRUuxcAAAAAAIVRvo/8t2rVSv/8849SU1NVqlQpczwyMlLFihVzaHNRUVFKTU1VjRo1VKRIEV26dEkffPCBunbtKunfGw5Kkp+fn918fn5+5rTk5GT5+vraTXd1dVXp0qXtaoKCgnIsI3va5duZbeTIkRo2bJgDthIAAAAAgIKV7yP/0r+n2yckJOizzz7TmTNnJElubm4OD//ffPONZs+erTlz5mjLli2aNWuWPvzwQ82aNcuh67kRgwYNUkpKivk6dOiQs1sCAAAAACBX+T7yf+DAAbVr104HDx5URkaGHnroIZUsWVKjR49WRkaGpk6d6rDmBgwYoKioKHXp0kWSVLduXR04cEAjR45U9+7d5e/vL0k6duyYypcvb8537NgxNWjQQJLk7++v48eP2y334sWLOnnypDm/v7+/jh07ZleT/T675kru7u7mjQ8BAAAAACjM8n3kv0+fPmrYsKFOnTolT09Pc/zxxx/XihUrHNrc2bNn5eJi32KRIkWUlZUlSQoKCpK/v7/delNTU7Vx40aFhIRIkkJCQnT69GklJCSYNStXrlRWVpYaN25s1qxdu1aZmZlmTWxsrKpXr57rKf8AAAAAANxO8h3+f/75Z73zzjt2N8eT/r0T/+HDhx3WmCR16NBBH3zwgWJiYrR//34tWLBAH330kR5//HFJks1mU9++ffX+++/rhx9+0Pbt2/X8888rICBAHTt2lCTVrFlT7dq108svv6xNmzZp3bp16tWrl7p06aKAgABJ0rPPPis3NzdFREQoKSlJ8+bN08SJE9WvXz+Hbg8AAAAAAM6Q79P+s7KydOnSpRzjf/31l0qWLOmQprJ9/PHHevfdd/Xaa6/p+PHjCggI0H/+8x8NGTLErBk4cKDS09MVGRmp06dPq1mzZlq6dKk8PDzMmtmzZ6tXr15q06aNXFxc1KlTJ02aNMmc7u3treXLl6tnz54KDg5W2bJlNWTIEB7zBwAAAACwBJthGEZ+ZujcubO8vb01bdo0lSxZUtu2bVO5cuX02GOPqVKlSpoxY0ZB9VqopaamytvbWykpKfLy8nJ2O/kSGBVzS9e3f1T4LV0fAAAAAFhRfnJovo/8jxs3TmFhYapVq5bOnz+vZ599Vnv27FHZsmX19ddf33DTAAAAAACgYOQ7/FeoUEG//vqr5s6dq23btiktLU0RERHq2rWr3Q0AAQAAAABA4ZDv8C9Jrq6ueu655xzdCwAAAAAAKAB5Cv8//PBDnhf46KOP3nAzAAAAAADA8fIU/rMfm3c9Npst1ycBAJfLvsEgN/4DAAAAgFsjT+E/KyuroPsAAAAAAAAFxMXZDQAAAAAAgIJ1Q+F/xYoVeuSRR1SlShVVqVJFjzzyiH766SdH9wYAAAAAABwg3+H/008/Vbt27VSyZEn16dNHffr0kZeXl9q3b6/JkycXRI8AAAAAAOAm5PtRfyNGjND48ePVq1cvc6x3795q2rSpRowYoZ49ezq0QQAAAAAAcHPyfeT/9OnTateuXY7xtm3bKiUlxSFNAQAAAAAAx8l3+H/00Ue1YMGCHOPff/+9HnnkEYc0BQAAAAAAHCffp/3XqlVLH3zwgVavXq2QkBBJ0oYNG7Ru3Tr1799fkyZNMmt79+7tuE4BAAAAAMANsRmGYeRnhqCgoLwt2GbT3r17b6ip21Fqaqq8vb2VkpIiLy8vZ7eTL4FRMU5Z7/5R4U5ZLwAAAABYQX5yaL6P/O/bt++GGwMAAAAAALdevsM/rMNZR/wBAAAAALdWvsO/YRj69ttvtWrVKh0/flxZWVl207/77juHNQcAAAAAAG5evsN/37599dlnn6l169by8/OTzWYriL4AAAAAAICD5Dv8f/nll/ruu+/Uvn37gugHAAAAAAA4mEt+Z/D29tbdd99dEL0AAAAAAIACkO/wHx0drWHDhuncuXMF0Q8AAAAAAHCwfJ/2//TTT+vrr7+Wr6+vAgMDVbRoUbvpW7ZscVhzAAAAAADg5uU7/Hfv3l0JCQl67rnnuOEfAAAAAAC3gXyH/5iYGC1btkzNmjUriH4AAAAAAICD5fua/4oVK8rLy6sgegEAAAAAAAUg3+F/3LhxGjhwoPbv318A7QAAAAAAAEfL92n/zz33nM6ePasqVaqoWLFiOW74d/LkSYc1BwAAAAAAbl6+w/+ECRMKoA0AAAAAAFBQbuhu/4AjBEbFSJL2jwp3cicAAAAAYG35Dv+XO3/+vC5cuGA3xs0AAQAAAAAoXPJ9w7/09HT16tVLvr6+Kl68uEqVKmX3AgAAAAAAhUu+w//AgQO1cuVKTZkyRe7u7po+fbqGDRumgIAAffHFFwXRIwAAAAAAuAn5Pu3/xx9/1BdffKFWrVqpR48eat68uapWrarKlStr9uzZ6tq1a0H0CQAAAAAAblC+j/yfPHlSd999t6R/r+/PfrRfs2bNtHbtWsd2BwAAAAAAblq+w//dd9+tffv2SZJq1Kihb775RtK/ZwT4+Pg4tDkAAAAAAHDz8h3+e/TooV9//VWSFBUVpcmTJ8vDw0NvvPGGBgwY4PAGAQAAAADAzcn3Nf9vvPGG+e/Q0FDt3LlTW7ZsUdWqVVWvXj2HNgcAAAAAAG5evsP/lQIDAxUYGOiAVgAAAAAAQEHI82n/cXFxWrRokd3YF198oaCgIPn6+ioyMlIZGRkObxAAAAAAANycPIf/4cOHKykpyXy/fft2RUREKDQ0VFFRUfrxxx81cuTIAmkSAAAAAADcuDyH/8TERLVp08Z8P3fuXDVu3Fiff/65+vXrp0mTJpl3/gcAAAAAAIVHnsP/qVOn5OfnZ75fs2aNHn74YfN9o0aNdOjQIcd2BwAAAAAAblqew7+fn5/27dsnSbpw4YK2bNmiJk2amNPPnDmjokWLOr5DAAAAAABwU/Ic/tu3b6+oqCj9/PPPGjRokIoVK6bmzZub07dt26YqVaoUSJMAAAAAAODG5flRf++9956eeOIJtWzZUiVKlNCsWbPk5uZmTv/vf/+rtm3bFkiTAAAAAADgxuU5/JctW1Zr165VSkqKSpQooSJFithNnz9/vkqUKOHwBgEAAAAAwM3Jc/jP5u3tnet46dKlb7oZAAAAAADgeHm+5h8AAAAAANyeCP8AAAAAAFgc4R8AAAAAAIvLU/i/7777dOrUKUnS8OHDdfbs2QJtCgAAAAAAOE6ewv/OnTuVnp4uSRo2bJjS0tIKtCkAAAAAAOA4ebrbf4MGDdSjRw81a9ZMhmHoww8/vOpj/YYMGeLQBgEAAAAAwM3JU/ifOXOmhg4dqkWLFslms2nJkiVydc05q81mI/wDAAAAAFDI5Cn8V69eXXPnzpUkubi4aMWKFfL19S3QxgAAAAAAgGPkKfxfLisrqyD6AAAAAAAABSTf4V+S/vzzT02YMEE7d+6UJNWqVUt9+vRRlSpVHNocAAAAAAC4eXm62//lli1bplq1amnTpk2qV6+e6tWrp40bN6p27dqKjY0tiB4BAAAAAMBNyPeR/6ioKL3xxhsaNWpUjvG33npLDz30kMOaAwAAAAAANy/fR/537typiIiIHOMvvviifvvtN4c0BQAAAAAAHCff4b9cuXJKTEzMMZ6YmMgTAAAAAAAAKITyfdr/yy+/rMjISO3du1cPPPCAJGndunUaPXq0+vXr5/AGYX2BUTGSpP2jwp3cCQAAAABYU77D/7vvvquSJUtq3LhxGjRokCQpICBA0dHR6t27t8MbBAAAAAAANyff4d9ms+mNN97QG2+8oTNnzkiSSpYs6fDGAAAAAACAY+Q7/F+O0A8AAAAAQOGX7xv+AQUlMCrGvP4fAAAAAOA4hH8AAAAAACyO8A8AAAAAgMUR/gEAAAAAsLgbCv+9evXSyZMnHd0LAAAAAAAoAHkO/3/99Zf57zlz5igtLU2SVLduXR06dMjxnQEAAAAAAIfI86P+atSooTJlyqhp06Y6f/68Dh06pEqVKmn//v3KzMwsyB4BAAAAAMBNyPOR/9OnT2v+/PkKDg5WVlaW2rdvr3vuuUcZGRlatmyZjh07VpB9AgAAAACAG5Tn8J+Zman7779f/fv3l6enp7Zu3aoZM2aoSJEi+u9//6ugoCBVr169IHsFAAAAAAA3IM+n/fv4+KhBgwZq2rSpLly4oHPnzqlp06ZydXXVvHnzdNdddyk+Pr4gewUAAAAAADcgz0f+Dx8+rHfeeUfu7u66ePGigoOD1bx5c124cEFbtmyRzWZTs2bNCrJXAAAAAABwA/Ic/suWLasOHTpo5MiRKlasmOLj4/X666/LZrPpzTfflLe3t1q2bOnwBg8fPqznnntOZcqUkaenp+rWravNmzeb0w3D0JAhQ1S+fHl5enoqNDRUe/bssVvGyZMn1bVrV3l5ecnHx0cRERHm0wqybdu2Tc2bN5eHh4cqVqyoMWPGOHxbAAAAAABwhjyH/yt5e3vr6aefVtGiRbVy5Urt27dPr732miN706lTp9S0aVMVLVpUS5Ys0W+//aZx48apVKlSZs2YMWM0adIkTZ06VRs3blTx4sUVFham8+fPmzVdu3ZVUlKSYmNjtWjRIq1du1aRkZHm9NTUVLVt21aVK1dWQkKCxo4dq+joaE2bNs2h2wMAAAAAgDPYDMMw8jvToUOHdNddd8nFxUV16tTRkiVLVLFiRYc3FxUVpXXr1unnn3/OdbphGAoICFD//v315ptvSpJSUlLk5+enmTNnqkuXLtq5c6dq1aql+Ph4NWzYUJK0dOlStW/fXn/99ZcCAgI0ZcoUDR48WMnJyXJzczPXvXDhQu3atSvXdWdkZCgjI8N8n5qaqooVKyolJUVeXl6O/BgKTGBUjLNbyNX+UeHObgEAAAAACr3U1FR5e3vnKYfe0JH/ihUrysXl31l37NhRIMFfkn744Qc1bNhQTz31lHx9fXXvvffq888/N6fv27dPycnJCg0NNce8vb3VuHFjxcXFSZLi4uLk4+NjBn9JCg0NlYuLizZu3GjWtGjRwgz+khQWFqbdu3fr1KlTufY2cuRIeXt7m6+C+gwAAAAAALhZN3za/62wd+9eTZkyRdWqVdOyZcv06quvqnfv3po1a5YkKTk5WZLk5+dnN5+fn585LTk5Wb6+vnbTXV1dVbp0abua3JZx+TquNGjQIKWkpJivQ4cO3eTWAgAAAABQMPL8qD9nyMrKUsOGDTVixAhJ0r333qsdO3Zo6tSp6t69u1N7c3d3l7u7u1N7AAAAAAAgLwr1kf/y5curVq1admM1a9bUwYMHJUn+/v6SpGPHjtnVHDt2zJzm7++v48eP202/ePGiTp48aVeT2zIuXwcAAAAAALerQh3+mzZtqt27d9uN/f7776pcubIkKSgoSP7+/lqxYoU5PTU1VRs3blRISIgkKSQkRKdPn1ZCQoJZs3LlSmVlZalx48Zmzdq1a5WZmWnWxMbGqnr16nZPFgAAAAAA4HZUqMP/G2+8oQ0bNmjEiBH6448/NGfOHE2bNk09e/aUJNlsNvXt21fvv/++fvjhB23fvl3PP/+8AgIC1LFjR0n/ninQrl07vfzyy9q0aZPWrVunXr16qUuXLgoICJAkPfvss3Jzc1NERISSkpI0b948TZw4Uf369XPWpgMAAAAA4DCF+pr/Ro0aacGCBRo0aJCGDx+uoKAgTZgwQV27djVrBg4cqPT0dEVGRur06dNq1qyZli5dKg8PD7Nm9uzZ6tWrl9q0aSMXFxd16tRJkyZNMqd7e3tr+fLl6tmzp4KDg1W2bFkNGTJEkZGRt3R7AQAAAAAoCDbDMAxnN2EF+Xm+YmERGBXj7BZytX9UuLNbAAAAAIBCLz85tFCf9g8AAAAAAG4e4R8AAAAAAIsj/KPQKayXIwAAAADA7YrwDwAAAACAxRH+AQAAAACwOMI/AAAAAAAWR/gHAAAAAMDiCP8AAAAAAFgc4R8AAAAAAIsj/AMAAAAAYHGEfwAAAAAALI7wDwAAAACAxRH+AQAAAACwOMI/AAAAAAAWR/gHAAAAAMDiCP8AAAAAAFgc4R8AAAAAAIsj/AMAAAAAYHGEfwAAAAAALI7wj0IpMCpGgVExzm4DAAAAACyB8A8AAAAAgMUR/gEAAAAAsDjCPwAAAAAAFkf4BwAAAADA4gj/AAAAAABYHOEfAAAAAACLI/wDAAAAAGBxhH8AAAAAACyO8A8AAAAAgMUR/gEAAAAAsDjCPwAAAAAAFkf4BwAAAADA4gj/AAAAAABYHOEfAAAAAACLI/wDAAAAAGBxhH8AAAAAACyO8A8AAAAAgMUR/gEAAAAAsDjCPwAAAAAAFkf4BwAAAADA4gj/AAAAAABYHOEfAAAAAACLI/yjUAuMilFgVIyz2wAAAACA2xrhHwAAAAAAiyP8AwAAAABgcYR/AAAAAAAsjvAPAAAAAIDFEf4BAAAAALA4wj8AAAAAABZH+AcAAAAAwOII/wAAAAAAWBzhHwAAAAAAiyP8AwAAAABgcYR/AAAAAAAsjvAPAAAAAIDFEf4BAAAAALA4wj8AAAAAABZH+AcAAAAAwOII/wAAAAAAWBzhHwAAAAAAiyP8AwAAAABgcYR/AAAAAAAsjvCP20JgVIwCo2Kc3QYAAAAA3JYI/wAAAAAAWBzhHwAAAAAAiyP8AwAAAABgcYR/AAAAAAAsjvAPAAAAAIDFEf4BAAAAALA4wj8AAAAAABZH+AcAAAAAwOII/wAAAAAAWBzhH7eVwKgYBUbFOLsNAAAAALitEP4BAAAAALA4wj8AAAAAABZ3W4X/UaNGyWazqW/fvubY+fPn1bNnT5UpU0YlSpRQp06ddOzYMbv5Dh48qPDwcBUrVky+vr4aMGCALl68aFezevVq3XfffXJ3d1fVqlU1c+bMW7BFAAAAAAAUvNsm/MfHx+uzzz5TvXr17MbfeOMN/fjjj5o/f77WrFmjI0eO6IknnjCnX7p0SeHh4bpw4YLWr1+vWbNmaebMmRoyZIhZs2/fPoWHh6t169ZKTExU37599dJLL2nZsmW3bPsAAAAAACgot0X4T0tLU9euXfX555+rVKlS5nhKSor+z//5P/roo4/04IMPKjg4WDNmzND69eu1YcMGSdLy5cv122+/6auvvlKDBg308MMP67333tPkyZN14cIFSdLUqVMVFBSkcePGqWbNmurVq5eefPJJjR8/3inbCwAAAACAI90W4b9nz54KDw9XaGio3XhCQoIyMzPtxmvUqKFKlSopLi5OkhQXF6e6devKz8/PrAkLC1NqaqqSkpLMmiuXHRYWZi4jNxkZGUpNTbV7AQAAAABQGLk6u4HrmTt3rrZs2aL4+Pgc05KTk+Xm5iYfHx+7cT8/PyUnJ5s1lwf/7OnZ065Vk5qaqnPnzsnT0zPHukeOHKlhw4bd8HYBAAAAAHCrFOoj/4cOHVKfPn00e/ZseXh4OLsdO4MGDVJKSor5OnTokLNbAgAAAAAgV4U6/CckJOj48eO677775OrqKldXV61Zs0aTJk2Sq6ur/Pz8dOHCBZ0+fdpuvmPHjsnf31+S5O/vn+Pu/9nvr1fj5eWV61F/SXJ3d5eXl5fdCwAAAACAwqhQh/82bdpo+/btSkxMNF8NGzZU165dzX8XLVpUK1asMOfZvXu3Dh48qJCQEElSSEiItm/fruPHj5s1sbGx8vLyUq1atcyay5eRXZO9DAAAAAAAbmeF+pr/kiVLqk6dOnZjxYsXV5kyZczxiIgI9evXT6VLl5aXl5def/11hYSEqEmTJpKktm3bqlatWurWrZvGjBmj5ORkvfPOO+rZs6fc3d0lSa+88oo++eQTDRw4UC+++KJWrlypb775RjExMbd2gwEAAAAAKACFOvznxfjx4+Xi4qJOnTopIyNDYWFh+vTTT83pRYoU0aJFi/Tqq68qJCRExYsXV/fu3TV8+HCzJigoSDExMXrjjTc0ceJEVahQQdOnT1dYWJgzNgkAAAAAAIeyGYZhOLsJK0hNTZW3t7dSUlJum+v/A6Nu/zMb9o8Kd3YLAAAAAOAU+cmhhfqafwAAAAAAcPMI/wAAAAAAWBzhHwAAAAAAiyP8AwAAAABgcYR/AAAAAAAsjvAPAAAAAIDFEf4BAAAAALA4wj8AAAAAABZH+AcAAAAAwOII/wAAAAAAWBzhHwAAAAAAiyP8AwAAAABgcYR/AAAAAAAsjvAPAAAAAIDFEf4BAAAAALA4wj8AAAAAABZH+AcAAAAAwOII/wAAAAAAWBzhHwAAAAAAiyP847YWGBWjwKgYZ7cBAAAAAIUa4R8AAAAAAIsj/AMAAAAAYHGEfwAAAAAALI7wDwAAAACAxRH+AQAAAACwOMI/AAAAAAAWR/gHAAAAAMDiCP8AAAAAAFgc4R8AAAAAAIsj/AMAAAAAYHGEfwAAAAAALI7wDwAAAACAxRH+AQAAAACwOMI/AAAAAAAWR/gHAAAAAMDiCP8AAAAAAFgc4R+WEBgVo8CoGGe3AQAAAACFEuEfAAAAAACLI/wDAAAAAGBxhH8AAAAAACyO8A8AAAAAgMUR/gEAAAAAsDjCPwAAAAAAFkf4BwAAAADA4gj/AAAAAABYHOEfAAAAAACLI/wDAAAAAGBxhH9YSmBUjLNbAAAAAIBCh/APAAAAAIDFEf4BAAAAALA4wj8AAAAAABZH+AcAAAAAwOII/7CcwKgYbvwHAAAAAJch/AMAAAAAYHGEfwAAAAAALI7wDwAAAACAxRH+AQAAAACwOMI/AAAAAAAWR/gHAAAAAMDiCP8AAAAAAFgc4R8AAAAAAIsj/AMAAAAAYHGEfwAAAAAALI7wDwAAAACAxRH+AQAAAACwOMI/AAAAAAAWR/gHAAAAAMDiCP+wrMCoGAVGxTi7DQAAAABwOsI/AAAAAAAWR/gHAAAAAMDiCP8AAAAAAFgc4R8AAAAAAIsj/AMAAAAAYHGEfwAAAAAALI7wDwAAAACAxRH+AQAAAACwuEId/keOHKlGjRqpZMmS8vX1VceOHbV79267mvPnz6tnz54qU6aMSpQooU6dOunYsWN2NQcPHlR4eLiKFSsmX19fDRgwQBcvXrSrWb16te677z65u7uratWqmjlzZkFvHm6RwKgYBUbFOLsNAAAAAHCaQh3+16xZo549e2rDhg2KjY1VZmam2rZtq/T0dLPmjTfe0I8//qj58+drzZo1OnLkiJ544glz+qVLlxQeHq4LFy5o/fr1mjVrlmbOnKkhQ4aYNfv27VN4eLhat26txMRE9e3bVy+99JKWLVt2S7cXAAAAAICCYDMMw3B2E3n1999/y9fXV2vWrFGLFi2UkpKicuXKac6cOXryySclSbt27VLNmjUVFxenJk2aaMmSJXrkkUd05MgR+fn5SZKmTp2qt956S3///bfc3Nz01ltvKSYmRjt27DDX1aVLF50+fVpLly7NU2+pqany9vZWSkqKvLy8HL/xBeBOOxq+f1S4s1sAAAAAAIfJTw4t1Ef+r5SSkiJJKl26tCQpISFBmZmZCg0NNWtq1KihSpUqKS4uTpIUFxenunXrmsFfksLCwpSamqqkpCSz5vJlZNdkLyM3GRkZSk1NtXsBAAAAAFAY3TbhPysrS3379lXTpk1Vp04dSVJycrLc3Nzk4+NjV+vn56fk5GSz5vLgnz09e9q1alJTU3Xu3Llc+xk5cqS8vb3NV8WKFW96GwEAAAAAKAi3Tfjv2bOnduzYoblz5zq7FUnSoEGDlJKSYr4OHTrk7JYAAAAAAMiVq7MbyItevXpp0aJFWrt2rSpUqGCO+/v768KFCzp9+rTd0f9jx47J39/frNm0aZPd8rKfBnB5zZVPCDh27Ji8vLzk6emZa0/u7u5yd3e/6W0DAAAAAKCgFeoj/4ZhqFevXlqwYIFWrlypoKAgu+nBwcEqWrSoVqxYYY7t3r1bBw8eVEhIiCQpJCRE27dv1/Hjx82a2NhYeXl5qVatWmbN5cvIrsleBgAAAAAAt7NCfeS/Z8+emjNnjr7//nuVLFnSvEbf29tbnp6e8vb2VkREhPr166fSpUvLy8tLr7/+ukJCQtSkSRNJUtu2bVWrVi1169ZNY8aMUXJyst555x317NnTPHL/yiuv6JNPPtHAgQP14osvauXKlfrmm28UE3Nn3Q0fAAAAAGBNhfrI/5QpU5SSkqJWrVqpfPny5mvevHlmzfjx4/XII4+oU6dOatGihfz9/fXdd9+Z04sUKaJFixapSJEiCgkJ0XPPPafnn39ew4cPN2uCgoIUExOj2NhY1a9fX+PGjdP06dMVFhZ2S7cXAAAAAICCYDMMw3B2E1aQn+crFhaBUXfmmQ37R4U7uwUAAAAAuGn5yaGF+sg/AAAAAAC4eYR/AAAAAAAsjvAPAAAAAIDFEf4BAAAAALA4wj8AAAAAABZH+AcAAAAAwOII/wAAAAAAWBzhH3ecwKgYBUbFOLsNAAAAALhlCP8AAAAAAFgc4R93LM4AAAAAAHCnIPwDAAAAAGBxhH8AAAAAACyO8A8AAAAAgMUR/gEAAAAAsDjCPwAAAAAAFkf4BwAAAADA4gj/AAAAAABYHOEfAAAAAACLI/zjjhcYFePsFgAAAACgQBH+AQAAAACwOMI/AAAAAAAWR/gHAAAAAMDiCP8AAAAAAFgc4R8AAAAAAIsj/AMAAAAAYHGEf0D/Pu6PR/4BAAAAsCrCPwAAAAAAFkf4By7DGQAAAAAArIjwDwAAAACAxRH+AQAAAACwOMI/AAAAAAAWR/gHAAAAAMDiCP8AAAAAAFgc4R8AAAAAAIsj/AMAAAAAYHGEfwAAAAAALI7wD+QiMCpGgVExzm4DAAAAAByC8A9cAz8CAAAAALACwj8AAAAAABZH+AcAAAAAwOII/wAAAAAAWBzhHwAAAAAAiyP8AwAAAABgcYR/AAAAAAAsjvAP5AGP/AMAAABwOyP8AwAAAABgcYR/AAAAAAAsjvAPAAAAAIDFEf6BfODafwAAAAC3I8I/cAP4EQAAAADA7YTwDwAAAACAxRH+AQAAAACwOMI/cBM49R8AAADA7YDwDwAAAACAxRH+AQAAAACwOMI/cJO48z8AAACAwo7wDwAAAACAxRH+AQfjTAAAAAAAhQ3hHwAAAAAAi3N1dgOAVXC0HwAAAEBhxZF/AAAAAAAsjvAPAAAAAIDFEf6BAnLljf+4ESAAAAAAZ+Gaf6CAEfgBAAAAOBtH/gEAAAAAsDjCPwAAAAAAFkf4BwAAAADA4rjmH7jFLr8HwP5R4U7sBAAAAMCdgiP/gBPxBAAAAAAAtwLhHwAAAAAAi+O0f6AQuPLoP5cDAAAAAHAkjvwDhRCXAwAAAABwJMI/cBu4/McAfhgAAAAAkF+c9g8UYleGfEI/AAAAgBvBkX/gNsUZAAAAAADyiiP/wG3uajcLzB7n5oEAAAAAOPJ/hcmTJyswMFAeHh5q3LixNm3a5OyWgHy58oyAK/99rfcAAAAArMlmGIbh7CYKi3nz5un555/X1KlT1bhxY02YMEHz58/X7t275evre815U1NT5e3trZSUFHl5ed2ijm8OoQ9Xc/nZA5w5AAAAABRO+cmhhP/LNG7cWI0aNdInn3wiScrKylLFihX1+uuvKyoq6przEv5xp7ny8gJHLIMfGgAAAIC8y08O5Zr//+fChQtKSEjQoEGDzDEXFxeFhoYqLi4uR31GRoYyMjLM9ykpKZL+/fBvF1kZZ53dAm5jld6Y7/BlOGKZt9KOYWGSpDpDl+W5/mq1V1tW9viNut76cqu/0XVea97sPm52ewAAAPD/ZefPvBzT58j//3PkyBHdddddWr9+vUJCQszxgQMHas2aNdq4caNdfXR0tIYNG3ar2wQAAAAAwM6hQ4dUoUKFa9Zw5P8GDRo0SP369TPfZ2Vl6eTJkypTpoxsNpsTO7u21NRUVaxYUYcOHbptLk8AbgT7Ou4U7Ou4E7Cf407Bvo78MgxDZ86cUUBAwHVrCf//T9myZVWkSBEdO3bMbvzYsWPy9/fPUe/u7i53d3e7MR8fn4Js0aG8vLz4DwruCOzruFOwr+NOwH6OOwX7OvLD29s7T3U86u//cXNzU3BwsFasWGGOZWVlacWKFXaXAQAAAAAAcLvhyP9l+vXrp+7du6thw4a6//77NWHCBKWnp6tHjx7Obg0AAAAAgBtG+L9M586d9ffff2vIkCFKTk5WgwYNtHTpUvn5+Tm7NYdxd3fX0KFDc1yyAFgN+zruFOzruBOwn+NOwb6OgsTd/gEAAAAAsDiu+QcAAAAAwOII/wAAAAAAWBzhHwAAAAAAiyP8AwAAAABgcYT/O8zkyZMVGBgoDw8PNW7cWJs2bXJ2S0CerV27Vh06dFBAQIBsNpsWLlxoN90wDA0ZMkTly5eXp6enQkNDtWfPHruakydPqmvXrvLy8pKPj48iIiKUlpZ2C7cCuL6RI0eqUaNGKlmypHx9fdWxY0ft3r3brub8+fPq2bOnypQpoxIlSqhTp046duyYXc3BgwcVHh6uYsWKydfXVwMGDNDFixdv5aYAVzVlyhTVq1dPXl5e8vLyUkhIiJYsWWJOZx+HVY0aNUo2m019+/Y1x9jfcSsQ/u8g8+bNU79+/TR06FBt2bJF9evXV1hYmI4fP+7s1oA8SU9PV/369TV58uRcp48ZM0aTJk3S1KlTtXHjRhUvXlxhYWE6f/68WdO1a1clJSUpNjZWixYt0tq1axUZGXmrNgHIkzVr1qhnz57asGGDYmNjlZmZqbZt2yo9Pd2seeONN/Tjjz9q/vz5WrNmjY4cOaInnnjCnH7p0iWFh4frwoULWr9+vWbNmqWZM2dqyJAhztgkIIcKFSpo1KhRSkhI0ObNm/Xggw/qscceU1JSkiT2cVhTfHy8PvvsM9WrV89unP0dt4SBO8b9999v9OzZ03x/6dIlIyAgwBg5cqQTuwJujCRjwYIF5vusrCzD39/fGDt2rDl2+vRpw93d3fj6668NwzCM3377zZBkxMfHmzVLliwxbDabcfjw4VvWO5Bfx48fNyQZa9asMQzj3327aNGixvz5882anTt3GpKMuLg4wzAMY/HixYaLi4uRnJxs1kyZMsXw8vIyMjIybu0GAHlUqlQpY/r06ezjsKQzZ84Y1apVM2JjY42WLVsaffr0MQyD/6bj1uHI/x3iwoULSkhIUGhoqDnm4uKi0NBQxcXFObEzwDH27dun5ORku33c29tbjRs3NvfxuLg4+fj4qGHDhmZNaGioXFxctHHjxlveM5BXKSkpkqTSpUtLkhISEpSZmWm3v9eoUUOVKlWy29/r1q0rPz8/syYsLEypqanmkVWgsLh06ZLmzp2r9PR0hYSEsI/Dknr27Knw8HC7/Vriv+m4dVyd3QBujX/++UeXLl2y+w+GJPn5+WnXrl1O6gpwnOTkZEnKdR/PnpacnCxfX1+76a6uripdurRZAxQ2WVlZ6tu3r5o2bao6depI+ndfdnNzk4+Pj13tlft7bt+H7GlAYbB9+3aFhITo/PnzKlGihBYsWKBatWopMTGRfRyWMnfuXG3ZskXx8fE5pvHfdNwqhH8AAAqxnj17aseOHfrll1+c3QrgcNWrV1diYqJSUlL07bffqnv37lqzZo2z2wIc6tChQ+rTp49iY2Pl4eHh7HZwB+O0/ztE2bJlVaRIkRx3DT127Jj8/f2d1BXgONn78bX2cX9//xw3uLx48aJOnjzJ9wCFUq9evbRo0SKtWrVKFSpUMMf9/f114cIFnT592q7+yv09t+9D9jSgMHBzc1PVqlUVHByskSNHqn79+po4cSL7OCwlISFBx48f13333SdXV1e5urpqzZo1mjRpklxdXeXn58f+jluC8H+HcHNzU3BwsFasWGGOZWVlacWKFQoJCXFiZ4BjBAUFyd/f324fT01N1caNG819PCQkRKdPn1ZCQoJZs3LlSmVlZalx48a3vGfgagzDUK9evbRgwQKtXLlSQUFBdtODg4NVtGhRu/199+7dOnjwoN3+vn37drsfvGJjY+Xl5aVatWrdmg0B8ikrK0sZGRns47CUNm3aaPv27UpMTDRfDRs2VNeuXc1/s7/jlnD2HQdx68ydO9dwd3c3Zs6cafz2229GZGSk4ePjY3fXUKAwO3PmjLF161Zj69athiTjo48+MrZu3WocOHDAMAzDGDVqlOHj42N8//33xrZt24zHHnvMCAoKMs6dO2cuo127dsa9995rbNy40fjll1+MatWqGc8884yzNgnI1auvvmp4e3sbq1evNo4ePWq+zp49a9a88sorRqVKlYyVK1camzdvNkJCQoyQkBBz+sWLF406deoYbdu2NRITE42lS5ca5cqVMwYNGuSMTQJyiIqKMtasWWPs27fP2LZtmxEVFWXYbDZj+fLlhmGwj8PaLr/bv2Gwv+PWIPzfYT7++GOjUqVKhpubm3H//fcbGzZscHZLQJ6tWrXKkJTj1b17d8Mw/n3c37vvvmv4+fkZ7u7uRps2bYzdu3fbLePEiRPGM888Y5QoUcLw8vIyevToYZw5c8YJWwNcXW77uSRjxowZZs25c+eM1157zShVqpRRrFgx4/HHHzeOHj1qt5z9+/cbDz/8sOHp6WmULVvW6N+/v5GZmXmLtwbI3YsvvmhUrlzZcHNzM8qVK2e0adPGDP6GwT4Oa7sy/LO/41awGYZhOOecAwAAAAAAcCtwzT8AAAAAABZH+AcAAAAAwOII/wAAAAAAWBzhHwAAAAAAiyP8AwAAAABgcYR/AAAAAAAsjvAPAAAAAIDFEf4BAAAAALA4wj8AANewa9cuNWnSRB4eHmrQoIGz28lVq1at1Ldv3xuad+bMmfLx8XFoPwXJZrNp4cKFkqT9+/fLZrMpMTHxuvPlpxYAACsi/AMALOHvv/+Wm5ub0tPTlZmZqeLFi+vgwYM3vdyhQ4eqePHi2r17t1asWJFj+tSpU1WyZEldvHjRHEtLS1PRokXVqlUru9rVq1fLZrPpzz//vOm+HKVz5876/fffb2oZ2duV2ys5OdlBneZUsWJFHT16VHXq1Cmwdfz666969NFH5evrKw8PDwUGBqpz5846fvy4pP+/7adPn87Xcm/HHyNutx+KAAD2XJ3dAAAAjhAXF6f69eurePHi2rhxo0qXLq1KlSrd9HL//PNPhYeHq3LlyrlOb926tdLS0rR582Y1adJEkvTzzz/L399fGzdu1Pnz5+Xh4SFJWrVqlSpVqqQqVarkuw/DMHTp0iW5ujr2f7o9PT3l6enpkGXt3r1bXl5edmO+vr4OWXZuihQpIn9//wJb/t9//602bdrokUce0bJly+Tj46P9+/frhx9+UHp6eoGtFwCAgsCRfwCAJaxfv15NmzaVJP3yyy/mv68lKytLw4cPV4UKFeTu7q4GDRpo6dKl5nSbzaaEhAQNHz5cNptN0dHROZZRvXp1lS9fXqtXrzbHVq9erccee0xBQUHasGGD3Xjr1q0lSRkZGerdu7d5RLlZs2aKj4+3q7XZbFqyZImCg4Pl7u6uX375Renp6Xr++edVokQJlS9fXuPGjcvR06effqpq1arJw8NDfn5+evLJJ6/6GVx5NDc6OloNGjTQl19+qcDAQHl7e6tLly46c+bMdT9PX19f+fv7271cXP79fzVeeOEFdezYUR9++KHKly+vMmXKqGfPnsrMzDTnP3r0qMLDw+Xp6amgoCDNmTNHgYGBmjBhQq7ru/Lo+alTp9S1a1eVK1dOnp6eqlatmmbMmGE3z969e9W6dWsVK1ZM9evXV1xc3FW3Z926dUpJSdH06dN17733KigoSK1bt9b48eMVFBSk/fv3m3/PUqVKyWaz6YUXXpAkLV26VM2aNZOPj4/KlCmjRx55xO6Mj6CgIEnSvffeK5vNZneWyPTp01WzZk15eHioRo0a+vTTT3Ns8zfffKPmzZvL09NTjRo10u+//674+Hg1bNhQJUqU0MMPP6y///7bbnvystzvvvsu189n9erV6tGjh1JSUsyzOrK/D/nZ3wAATmQAAHCbOnDggOHt7W14e3sbRYsWNTw8PAxvb2/Dzc3NcHd3N7y9vY1XX331qvN/9NFHhpeXl/H1118bu3btMgYOHGgULVrU+P333w3DMIyjR48atWvXNvr3728cPXrUOHPmTK7LefbZZ422bdua7xs1amTMnz/feOWVV4whQ4YYhmEYZ8+eNdzd3Y2ZM2cahmEYvXv3NgICAozFixcbSUlJRvfu3Y1SpUoZJ06cMAzDMFatWmVIMurVq2csX77c+OOPP4wTJ04Yr776qlGpUiXjp59+MrZt22Y88sgjRsmSJY0+ffoYhmEY8fHxRpEiRYw5c+YY+/fvN7Zs2WJMnDjxqp/BjBkzDG9vb/P90KFDjRIlShhPPPGEsX37dmPt2rWGv7+/8fbbb191Gdm9njp16qo13bt3N7y8vIxXXnnF2Llzp/Hjjz8axYoVM6ZNm2bWhIaGGg0aNDA2bNhgJCQkGC1btjQ8PT2N8ePHmzWSjAULFhiGYRj79u0zJBlbt241DMMwevbsaTRo0MCIj4839u3bZ8TGxho//PCDXW2NGjWMRYsWGbt37zaefPJJo3LlykZmZmauPcfFxRmSjG+++cbIysrKMf3ixYvG//73P0OSsXv3buPo0aPG6dOnDcMwjG+//db43//+Z+zZs8fYunWr0aFDB6Nu3brGpUuXDMMwjE2bNhmSjJ9++sk4evSo+Xf/6quvjPLlyxv/+9//jL179xr/+9//jNKlS5v7zeXbsXTpUuO3334zmjRpYgQHBxutWrUyfvnlF2PLli1G1apVjVdeecXsNT/Lze3zycjIMCZMmGB4eXkZR48eNb8P+d3fAADOQ/gHANy2MjMzjX379hm//vqrUbRoUePXX381/vjjD6NEiRLGmjVrjH379hl///33VecPCAgwPvjgA7uxRo0aGa+99pr5vn79+sbQoUOv2cfnn39uFC9e3MjMzDRSU1MNV1dX4/jx48acOXOMFi1aGIZhGCtWrDAkGQcOHDDS0tKMokWLGrNnzzaXceHCBSMgIMAYM2aMYRj/P1AvXLjQrDlz5ozh5uZmfPPNN+bYiRMnDE9PTzP8/+9//zO8vLyM1NTUa394/09u4b9YsWJ28w8YMMBo3LjxVZeR3Wvx4sXtXrVq1TJrunfvblSuXNm4ePGiOfbUU08ZnTt3NgzDMHbu3GlIMuLj483pe/bsMSTlOfx36NDB6NGjR649ZtdOnz7dHEtKSjIkGTt37rzqtr399tuGq6urUbp0aaNdu3bGmDFjjOTk5Bzbfq0fPgzDMP7++29DkrF9+/Zce89WpUoVY86cOXZj7733nhESEnLV7fj6668NScaKFSvMsZEjRxrVq1e/qeVe+flcua8YRv73NwCA83DaPwDgtuXq6qrAwEDt2rVLjRo1Ur169ZScnCw/Pz+1aNFCgYGBKlu2bK7zpqam6siRIzkuD2jatKl27tyZrz5atWql9PR0xcfH6+eff9Y999yjcuXKqWXLluZ1/6tXr9bdd9+tSpUq6c8//1RmZqbduosWLar7778/x7obNmxo/vvPP//UhQsX1LhxY3OsdOnSql69uvn+oYceUuXKlXX33XerW7dumj17ts6ePZuv7QkMDFTJkiXN9+XLlzdvcHctP//8sxITE83X4sWL7abXrl1bRYoUyXW5u3fvlqurq+677z5zetWqVVWqVKk89/3qq69q7ty5atCggQYOHKj169fnqKlXr57d+iVdc9s++OADJScna+rUqapdu7amTp2qGjVqaPv27dfsZc+ePXrmmWd09913y8vLS4GBgZJ0zZtQpqen688//1RERIRKlChhvt5///0cN4m8fDv8/PwkSXXr1rUby96uG11uXj4fR+xvAIBbgxv+AQBuW7Vr19aBAweUmZmprKwslShRQhcvXtTFixdVokQJVa5cWUlJSQXeR9WqVVWhQgWtWrVKp06dUsuWLSVJAQEBqlixotavX69Vq1bpwQcfzPeyixcvnq/6kiVLasuWLVq9erWWL1+uIUOGKDo6WvHx8Xm+U3vRokXt3ttsNmVlZV13vqCgoGuu40aXm1cPP/ywDhw4oMWLFys2NlZt2rRRz5499eGHH+bag81mk6Tr9lCmTBk99dRTeuqppzRixAjde++9+vDDDzVr1qyrztOhQwdVrlxZn3/+uQICApSVlaU6derowoULV50nLS1NkvT555/b/cAjye5Hk6ttx5Vj2dt1s8u91ufjiP0NAHBrcOQfAHDbWrx4sRITE+Xv76+vvvpKiYmJqlOnjiZMmJDrkefLeXl5KSAgQOvWrbMbX7dunWrVqpXvXlq3bq3Vq1dr9erVdjdva9GihZYsWaJNmzaZN4erUqWK3Nzc7NadmZmp+Pj4a667SpUqKlq0qDZu3GiOnTp1Ksej+lxdXRUaGqoxY8Zo27Zt2r9/v1auXJnvbbqVqlevrosXL2rr1q3m2B9//KFTp07laznlypVT9+7d9dVXX2nChAmaNm2aQ/t0c3NTlSpVzLv9u7m5SZIuXbpk1pw4cUK7d+/WO++8ozZt2qhmzZo5tiO3+fz8/BQQEKC9e/eqatWqdq/sGwTeCEct183Nza7fbLfj/gYAdyKO/AMAbluVK1dWcnKyjh07pscee0w2m01JSUnq1KmTecrytQwYMEBDhw5VlSpV1KBBA82YMUOJiYmaPXt2vntp3bq1eff67CP/ktSyZUv16tVLFy5cMMN/8eLF9eqrr2rAgAHmIwnHjBmjs2fPKiIi4qrrKFGihCIiIjRgwACVKVNGvr6+Gjx4sHlHfUlatGiR9u7dqxYtWqhUqVJavHixsrKy7C4NKCjHjx/X+fPn7cbKlCmT44h/bmrUqKHQ0FBFRkZqypQpKlq0qPr37y9PT0/zCPT1DBkyRMHBwapdu7YyMjK0aNEi1axZ84a2Rfr3s5w7d666dOmie+65R4Zh6Mcff9TixYvNpwhUrlxZNptNixYtUvv27eXp6alSpUqpTJkymjZtmsqXL6+DBw8qKirKbtm+vr7y9PTU0qVLVaFCBXl4eMjb21vDhg1T79695e3trXbt2ikjI0ObN2/WqVOn1K9fvxveFkcsNzAwUGlpaVqxYoXq16+vYsWKaeXKlU7b3wAA+UP4BwDc1lavXq1GjRrJw8NDP//8sypUqJCn4C9JvXv3VkpKivr376/jx4+rVq1a+uGHH1StWrV899G6dWudO3dONWrUMK/Blv4N/2fOnDEfCZht1KhRysrKUrdu3XTmzBk1bNhQy5Ytu+417mPHjlVaWpo6dOigkiVLqn///kpJSTGn+/j46LvvvlN0dLTOnz+vatWq6euvv1bt2rXzvU35lVvgi4uLU5MmTfI0/xdffKGIiAi1aNFC/v7+GjlypJKSkuTh4ZGn+d3c3DRo0CDt379fnp6eat68uebOnZuvbbhcrVq1VKxYMfXv31+HDh2Su7u7qlWrpunTp6tbt26SpLvuukvDhg1TVFSUevTooeeff14zZ87U3Llz1bt3b9WpU0fVq1fXpEmT7M4IcXV11aRJkzR8+HANGTJEzZs31+rVq/XSSy+pWLFiGjt2rAYMGKDixYurbt266tu37w1vhySHLPeBBx7QK6+8os6dO+vEiRMaOnSoQkNDnba/AQDyx2YYhuHsJgAAAK70119/qWLFivrpp5/Upk0bZ7cDAMBtjfAPAAAKhZUrVyotLU1169bV0aNHNXDgQB0+fFi///57ni4dAAAAV8dp/wAAoFDIzMzU22+/rb1796pkyZJ64IEHNHv2bII/AAAOwJF/AAAAAAAsjkf9AQAAAABgcYR/AAAAAAAsjvAPAAAAAIDFEf4BAAAAALA4wj8AAAAAABZH+AcAAAAAwOII/wAAAAAAWBzhHwAAAAAAi/u/jkgOsR4S1McAAAAASUVORK5CYII=",
            "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 452 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",
        "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.ylabel(\"# of Samples\")\n",
        "plt.show()\n",
        "\n",
        "print(f\"The Maximum length of English text is {max_english_count} words.\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KdRqo6SotBEZ"
      },
      "source": [
        "#### Visual: Length of German Entries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 650
        },
        "id": "oH2tcviVtBEZ",
        "outputId": "2d354e7d-ec8c-4cf0-d6b8-82fb3a060026"
      },
      "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 German text is 469 words.\n"
          ]
        }
      ],
      "source": [
        "df['german_length'] = df['de'].str.split().str.len()\n",
        "\n",
        "max_german_count = max(df['german_length'])\n",
        "\n",
        "n_bin = max_german_count + 1\n",
        "df.hist('german_length',\n",
        "        grid=False,\n",
        "        range=(0,n_bin),\n",
        "        figsize=(12,8),\n",
        "        bins=n_bin)\n",
        "plt.title('Histogram: Length of German Statements')\n",
        "plt.xlabel('# of Words in German Statements')\n",
        "plt.ylabel(\"# of Samples\")\n",
        "plt.show()\n",
        "\n",
        "print(f\"The Maximum length of German text is {max_german_count} words.\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aHSEC0XRtBEZ"
      },
      "source": [
        "#### Trim Dataset Size & Drop Unnecessary Features"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TtwzdSkttBEZ",
        "outputId": "0e40a8fd-bc01-4e25-bb0d-997c396cdffb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 188017 entries, 0 to 188016\n",
            "Data columns (total 2 columns):\n",
            " #   Column  Non-Null Count   Dtype \n",
            "---  ------  --------------   ----- \n",
            " 0   de      188017 non-null  object\n",
            " 1   en      188017 non-null  object\n",
            "dtypes: object(2)\n",
            "memory usage: 2.9+ MB\n"
          ]
        }
      ],
      "source": [
        "least_input_words = 3\n",
        "most_input_words = 35\n",
        "\n",
        "# make sure that German text is less than 35 & more than 3 words\n",
        "df = df[df['german_length'] < most_input_words]\n",
        "df = df[df['german_length'] > least_input_words]\n",
        "\n",
        "# make sure that English text is less than 35 & more than 3 words\n",
        "df = df[df['english_length'] < most_input_words]\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=['german_length',\n",
        "                      'english_length'\n",
        "                      ]\n",
        "             )\n",
        "\n",
        "df.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TjRQ-VobtBEZ"
      },
      "source": [
        "#### Final Preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "vhPaqUZOtBEa",
        "outputId": "2e1bb142-d344-42aa-9ee1-73a7fd6dca18"
      },
      "outputs": [
        {
          "data": {
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              "      <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",
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              "</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>{'de': 'Ein vorübergehender Anstieg der Körper...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>{'de': 'Im Januar 2001 begann eine neue dreijä...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>{'de': 'Während der ersten Woche beträgt die ü...</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>{'de': 'Sie dürfen ZYPREXA nach dem auf dem Um...</td>\n",
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              "</table>\n",
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              "\n",
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              "\n",
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              "\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "          const element = document.querySelector('#df-2e781e4f-ab1c-494f-93a8-e4193a263b92');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
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              "  </div>\n"
            ],
            "text/plain": [
              "                                         translation\n",
              "0  {'de': 'Ein vorübergehender Anstieg der Körper...\n",
              "1  {'de': 'Im Januar 2001 begann eine neue dreijä...\n",
              "2  {'de': 'Während der ersten Woche beträgt die ü...\n",
              "3  {'de': 'Eine Packung enthält eine bzw. vier Fe...\n",
              "4  {'de': 'Sie dürfen ZYPREXA nach dem auf dem Um..."
            ]
          },
          "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": "utIYEm_jtBEa"
      },
      "source": [
        "#### Convert to Dataset & Split into Train/Test/Eval Datasets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vEJFny5ytBEa",
        "outputId": "4938ff3b-215c-453e-fc5e-54e2483a7d23"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Training Dataset Shape: (141012, 1)\n",
            "Testing Dataset Shape: (23502, 1)\n",
            "Evaluation Dataset Shape: (23503, 1)\n",
            "\n",
            "Training Dataset Example:\n",
            "{'translation': {'de': '8g 10 g 12 g 14 g 16 g 18 g 21 g 25 g', 'en': '12 g 14 g 16 g 18 g 21 g 25 g'}}\n"
          ]
        }
      ],
      "source": [
        "dataset = Dataset.from_pandas(df)\n",
        "\n",
        "train_testEval = dataset.train_test_split(train_size=0.75)\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": "R_6dp-vatBEa"
      },
      "source": [
        "#### Basic Values/Constants"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "X-6V8e4jtBEa"
      },
      "outputs": [],
      "source": [
        "set_seed = 42\n",
        "NUM_OF_EPOCHS = 5\n",
        "\n",
        "BATCH_SIZE = 16\n",
        "LEARNING_RATE = 2e-5\n",
        "\n",
        "SOURCE_LANGUAGE = \"de\"\n",
        "TARGET_LANGUAGE = \"en\"\n",
        "\n",
        "MODEL_CKPT = \"Helsinki-NLP/opus-mt-de-en\"\n",
        "MODEL_NAME = f\"{MODEL_CKPT.split(f'/')[-1]}-OPUS_Medical_German_to_English\"\n",
        "\n",
        "REPORTS_TO = \"tensorboard\"\n",
        "STRATEGY = \"epoch\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oJCsmViZtBEa"
      },
      "source": [
        "#### Create Tokenizer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 232,
          "referenced_widgets": [
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            "a3f7a09c539f442db1329546ad95755a",
            "9a121ff587e748a38044f92901dd4202",
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        },
        "id": "oN_ZNRkMtBEa",
        "outputId": "eef05314-93b8-4677-fcce-f42b39a342c3"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "870abd129bd646a3b71f4b7700fcd6ac",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/42.0 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b244ff57a21046099b85b6b12264104d",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/1.38k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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              "model_id": "628b39498f2f4726a1c15488d9e5ef9f",
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              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)olve/main/source.spm:   0%|          | 0.00/797k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "08a8b585c8b247a0a5815117188e8af0",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)olve/main/target.spm:   0%|          | 0.00/768k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "0f6678cc348946c499e4f6936b648a8a",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)olve/main/vocab.json:   0%|          | 0.00/1.27M [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": "Puao0rfqtBEa"
      },
      "source": [
        "#### Create Preprocessing Function"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "fxfcef5NtBEb"
      },
      "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": "y-VDj-s7tBEb"
      },
      "source": [
        "#### Apply Preprocessing Function to Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 165,
          "referenced_widgets": [
            "6ce80190d5c24c55b900060f6d6a25d0",
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            "7f628d69b092439cb7de386460b4f8cd",
            "d0245d6ac73e47efbead1e3072bda180",
            "36be1082135b4247bf76072e1c85f39f",
            "c762d60010354d7091c1b02bdf9f846a",
            "038c57536d2b40ebb87dcb39742fcbe9",
            "5134fc6adfb44ff7942a74c0e1e31c99",
            "2af57122d9dc4fed922ff70b721229c0",
            "83650e9276084fbeb3d8b8d46f2800a5",
            "4b63d233c8e24f14a698ebdc50ed0411"
          ]
        },
        "id": "P0FfTcWGtBEb",
        "outputId": "d3452787-dd28-4762-d066-5db5b632c644"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6ce80190d5c24c55b900060f6d6a25d0",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/141012 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6ce22dcf15604ed3acd196388477fe14",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/23502 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e145e46f386b4f5ba94fcd2fd8c802c1",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/23503 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(141012, 4)\n",
            "(23502, 4)\n",
            "(23503, 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": "sjz-Jd4etBEb"
      },
      "source": [
        "#### Instantiate Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
            "db02ffaa753b4029872bb7a6a3ad75eb",
            "56756465085b4e7883fcb1a17f930a87",
            "7ee971b178f443058887d441ace600fa",
            "d8a6f9262e4e451d8ceee9ed5c3fcb13",
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            "bcf66aebe5d14e36aa49cb0adfe11181",
            "e4db6ed81858473ca551c5f04e60f6db",
            "a7cb32df86584c03a441eeffdb16c3db",
            "4e1f5d6df8994e39858ef7ed78463ca9",
            "f2bf3c2f1d2246df8f065e33c9c2b202",
            "224b9b1a83d84f32ad45fc42e3f09559",
            "11150dce4b6d408ca9d32729af72aead",
            "9453a38d9ef047bf9b40ed3588476ece",
            "722f64c161e54207802ae7645b0fbf60",
            "b4dd96d73bbf44aa97fa0b7513ac6afa",
            "747a2d27cefd452cb3c02fd0b67dfe43",
            "b52bc0988418487d80fd590ec0144da4",
            "4af8d1e8cd60421a9d4deb6b24a2d672",
            "229e1912878946dfaa70ec6421d0bd54",
            "7800dc58cbcd404b917d04b21f943e95",
            "d5a8dc3f757b456f9e2b8b264bfc2174",
            "7caa44a2a58941e399dda6b9739f52e4"
          ]
        },
        "id": "v6bsEJ2vtBEb",
        "outputId": "94ca3966-83cd-4d46-ecef-ba7aa3cfd9d1"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "db02ffaa753b4029872bb7a6a3ad75eb",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/298M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "11150dce4b6d408ca9d32729af72aead",
              "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": "cEds-u6itBEb"
      },
      "source": [
        "#### Instantiate Data Collator"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "BThlAzsCtBEb"
      },
      "outputs": [],
      "source": [
        "data_collator = DataCollatorForSeq2Seq(tokenizer=tokenizer,\n",
        "                                       model=MODEL_CKPT)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BmXOq1BwtBEb"
      },
      "source": [
        "#### Create Function to Compute Metrics"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
            "f3e0547391124ad7a35c024fa13baf98",
            "cb35020dea084191836c247f7368b392",
            "c1a79dcd76f245498031b2b418c8e49e",
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            "33fdfda652f04e96ae3080c9c0f13d01",
            "826db821592140b9a7139e69e943fbf2",
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            "29f5b2c674a543519be6f755d6df3a66",
            "8c69dab70b434d79997a9978dec968d7",
            "095f030e3b044328a848eef6a5b913d2",
            "a156e7c5b524470f90112f24b323ee1c",
            "92c8789ef5d94a0ca63cb26a3a41cb11"
          ]
        },
        "id": "6ekShIbGtBEb",
        "outputId": "b8ec73af-7ca5-4968-a474-97247748872b"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f3e0547391124ad7a35c024fa13baf98",
              "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": "8bff282c8dbb4857a622375442be80f9",
              "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": "3Ki9N-ittBEc"
      },
      "source": [
        "#### Instantiate Training Arguments"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "TdyqVAFRtBEc"
      },
      "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": "56MYTlXbtBEc"
      },
      "source": [
        "#### Instantiate Trainer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A625XhwmtBEc",
        "outputId": "202f7e33-c5c7-4345-eff5-5bee59939be9"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Cloning https://huggingface.co/DunnBC22/opus-mt-de-en-OPUS_Medical_German_to_English into local empty directory.\n",
            "WARNING:huggingface_hub.repository:Cloning https://huggingface.co/DunnBC22/opus-mt-de-en-OPUS_Medical_German_to_English 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": "_nNBMO9AtBEc"
      },
      "source": [
        "#### Train Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "FHFu1lGMtBEc",
        "outputId": "348e64c2-52d7-4ba1-bb1b-f20e4b2de5dd"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/optimization.py:411: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='22030' max='22030' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [22030/22030 1:41:13, Epoch 4/5]\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>1.087600</td>\n",
              "    </tr>\n",
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              "      <td>1.207600</td>\n",
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              "      <td>1.138400</td>\n",
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              "      <td>1.062600</td>\n",
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              "      <td>3750</td>\n",
              "      <td>1.085400</td>\n",
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              "      <td>4000</td>\n",
              "      <td>1.068000</td>\n",
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              "    <tr>\n",
              "      <td>4250</td>\n",
              "      <td>1.041900</td>\n",
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              "      <td>4500</td>\n",
              "      <td>1.011200</td>\n",
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              "      <td>4750</td>\n",
              "      <td>0.988000</td>\n",
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              "      <td>0.990700</td>\n",
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              "      <td>0.983500</td>\n",
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              "      <td>0.962400</td>\n",
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              "      <td>0.982000</td>\n",
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              "      <td>0.970700</td>\n",
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              "    <tr>\n",
              "      <td>6250</td>\n",
              "      <td>0.987500</td>\n",
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              "    <tr>\n",
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              "      <td>0.986900</td>\n",
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              "      <td>0.970800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9000</td>\n",
              "      <td>0.899100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9250</td>\n",
              "      <td>0.880900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9500</td>\n",
              "      <td>0.891800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9750</td>\n",
              "      <td>0.877600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10000</td>\n",
              "      <td>0.928800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10250</td>\n",
              "      <td>0.898800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10500</td>\n",
              "      <td>0.906100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10750</td>\n",
              "      <td>0.895700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>11000</td>\n",
              "      <td>0.912800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>11250</td>\n",
              "      <td>0.902000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>11500</td>\n",
              "      <td>0.896900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>11750</td>\n",
              "      <td>0.927400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>12000</td>\n",
              "      <td>0.906000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>12250</td>\n",
              "      <td>0.897600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>12500</td>\n",
              "      <td>0.899600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>12750</td>\n",
              "      <td>0.880400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>13000</td>\n",
              "      <td>0.885900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>13250</td>\n",
              "      <td>0.882200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>13500</td>\n",
              "      <td>0.857400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>13750</td>\n",
              "      <td>0.839000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>14000</td>\n",
              "      <td>0.826700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>14250</td>\n",
              "      <td>0.856400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>14500</td>\n",
              "      <td>0.857300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>14750</td>\n",
              "      <td>0.847500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>15000</td>\n",
              "      <td>0.856100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>15250</td>\n",
              "      <td>0.842300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>15500</td>\n",
              "      <td>0.864000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>15750</td>\n",
              "      <td>0.848500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>16000</td>\n",
              "      <td>0.854400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>16250</td>\n",
              "      <td>0.846300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>16500</td>\n",
              "      <td>0.884800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>16750</td>\n",
              "      <td>0.859300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>17000</td>\n",
              "      <td>0.840500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>17250</td>\n",
              "      <td>0.859600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>17500</td>\n",
              "      <td>0.845400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>17750</td>\n",
              "      <td>0.849900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>18000</td>\n",
              "      <td>0.818100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>18250</td>\n",
              "      <td>0.830000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>18500</td>\n",
              "      <td>0.833800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>18750</td>\n",
              "      <td>0.827100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>19000</td>\n",
              "      <td>0.822000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>19250</td>\n",
              "      <td>0.823500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>19500</td>\n",
              "      <td>0.825200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>19750</td>\n",
              "      <td>0.833700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>20000</td>\n",
              "      <td>0.829800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>20250</td>\n",
              "      <td>0.835400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>20500</td>\n",
              "      <td>0.804400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>20750</td>\n",
              "      <td>0.828700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>21000</td>\n",
              "      <td>0.823500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>21250</td>\n",
              "      <td>0.828500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>21500</td>\n",
              "      <td>0.818600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>21750</td>\n",
              "      <td>0.821900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>22000</td>\n",
              "      <td>0.816800</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": "g5mDK_artBEm"
      },
      "source": [
        "#### Push Model to HuggingFace Hub (My Profile!)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442,
          "referenced_widgets": [
            "6ff40324f6a5430f840365dd58fb255d",
            "d323fd279f484b188d9a901ec9db4efe",
            "4ef14d7a7b294fbeaabc302e6b0ce38c",
            "6ef10e8f5f9f4f62aa680a1faa424e15",
            "535c496669a842a5b226821743a6257d",
            "d0428a9a4345468cb33d37ae3ee5745c",
            "afad28f9a07e4c428ca1a0084d20052b",
            "736b37c02f8f4131ae538ec39e1d3e4d",
            "fc242966e5b748048c30267cc914e96c",
            "14005e7d6bde41919f8263aec8d627fd",
            "5caae3fb7a924ca1a89a575b2f26fc6c",
            "fa9aba0ec86243bf8b1953ab634a332d",
            "ada4b98533d440e4afca3253b1bed121",
            "d405c77c07504214a6a731af622faaef",
            "5fd7a92481e7439a89ba556861136dda",
            "40bfbb2cece742f782fc694525f8d432",
            "e29c46af86634aa9af6469f31be1f137",
            "06b8190b0b224df593cb4b69fe8ce139",
            "2e3aebc3186e4d05af49aec64c50104d",
            "0d0318fd48e64f2dacca296e45d7568e",
            "1fa2aaf431124945b3dfd4a02da51183",
            "8d99c2f150cf438ebfd01e1826cefdf9"
          ]
        },
        "id": "DXkiYTR9tBEm",
        "outputId": "0874ec05-4ba6-423a-da49-3fe143d65a36"
      },
      "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": "6ff40324f6a5430f840365dd58fb255d",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Upload file pytorch_model.bin:   0%|          | 1.00/282M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "fa9aba0ec86243bf8b1953ab634a332d",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Upload file runs/Aug15_16-48-49_22b6108997f2/events.out.tfevents.1692118158.22b6108997f2.741.0:   0%|         …"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "To https://huggingface.co/DunnBC22/opus-mt-de-en-OPUS_Medical_German_to_English\n",
            "   47fbb7a..8613c07  main -> main\n",
            "\n",
            "WARNING:huggingface_hub.repository:To https://huggingface.co/DunnBC22/opus-mt-de-en-OPUS_Medical_German_to_English\n",
            "   47fbb7a..8613c07  main -> main\n",
            "\n",
            "To https://huggingface.co/DunnBC22/opus-mt-de-en-OPUS_Medical_German_to_English\n",
            "   8613c07..9ce42bb  main -> main\n",
            "\n",
            "WARNING:huggingface_hub.repository:To https://huggingface.co/DunnBC22/opus-mt-de-en-OPUS_Medical_German_to_English\n",
            "   8613c07..9ce42bb  main -> main\n",
            "\n"
          ]
        },
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'https://huggingface.co/DunnBC22/opus-mt-de-en-OPUS_Medical_German_to_English/commit/8613c07956fff8939d64bfde0855a2fed45b31a8'"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "trainer.push_to_hub()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fCiAyIVJtBEm"
      },
      "source": [
        "#### Save & Log Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "OA9YkII9tBEn"
      },
      "outputs": [],
      "source": [
        "trainer.save_model()\n",
        "trainer.save_state()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HTfsMiaBFBez"
      },
      "source": [
        "#### Evaluate Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 37
        },
        "id": "ttXyxmKBFBVa",
        "outputId": "7dcb0448-c631-4a92-c454-eece010f615d"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='3003' max='2938' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [2938/2938 49:12]\n",
              "    </div>\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "metrics = trainer.evaluate()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sxFRA5BopSgQ"
      },
      "source": [
        "#### Print, Log, & Save Metric Values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R29VIw-XpSR7",
        "outputId": "edfbaf3e-d55e-4c65-a9bf-370f065bc1d0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{'eval_loss': 0.8722850680351257, 'eval_bleu': 53.88195581001609, 'eval_rouge': {'rouge1': 0.7664036885530349, 'rouge2': 0.6283763158877561, 'rougeL': 0.7370112957604222, 'rougeLsum': 0.7370291617503089}, 'eval_Prediction Lengths': [23, 22, 7, 20, 14, 14, 13, 11, 45, 38, 34, 28, 29, 18, 13, 7, 16, 26, 22, 44, 33, 21, 28, 7, 11, 23, 22, 7, 17, 26, 15, 22, 25, 17, 30, 15, 21, 18, 23, 19, 20, 47, 29, 13, 10, 33, 24, 10, 14, 55, 19, 19, 18, 19, 15, 19, 21, 7, 21, 19, 19, 15, 22, 16, 13, 8, 16, 11, 23, 14, 12, 12, 22, 16, 7, 20, 31, 22, 16, 17, 7, 37, 19, 23, 29, 26, 27, 19, 18, 26, 36, 47, 14, 34, 31, 8, 20, 24, 18, 23, 7, 24, 14, 36, 20, 18, 29, 34, 22, 23, 13, 25, 5, 17, 55, 30, 29, 11, 14, 17, 20, 25, 27, 18, 16, 33, 32, 19, 23, 7, 23, 15, 23, 27, 24, 17, 6, 43, 10, 10, 23, 23, 12, 27, 9, 43, 12, 15, 8, 17, 30, 22, 12, 27, 36, 22, 11, 22, 14, 18, 28, 24, 39, 11, 12, 22, 22, 11, 26, 19, 13, 7, 13, 13, 12, 16, 27, 11, 10, 8, 28, 21, 42, 18, 31, 22, 19, 23, 22, 23, 28, 10, 17, 20, 28, 18, 34, 31, 18, 70, 30, 18, 28, 29, 14, 15, 9, 23, 35, 44, 12, 18, 18, 18, 27, 18, 10, 18, 23, 31, 23, 11, 17, 12, 26, 18, 14, 23, 40, 58, 29, 14, 15, 27, 9, 10, 19, 26, 16, 8, 16, 27, 27, 13, 14, 27, 18, 13, 18, 35, 25, 48, 21, 42, 23, 17, 35, 17, 8, 11, 11, 33, 22, 24, 31, 50, 18, 21, 12, 31, 50, 25, 21, 9, 19, 29, 18, 16, 29, 15, 26, 21, 29, 13, 19, 48, 22, 29, 10, 18, 25, 28, 19, 17, 22, 23, 23, 19, 45, 23, 9, 14, 18, 13, 16, 11, 35, 5, 17, 13, 11, 27, 15, 13, 13, 13, 15, 33, 16, 21, 60, 15, 13, 27, 38, 7, 30, 9, 15, 30, 6, 16, 16, 21, 20, 25, 11, 27, 31, 11, 26, 18, 14, 34, 14, 34, 67, 45, 15, 18, 32, 26, 51, 32, 39, 39, 6, 25, 33, 7, 9, 14, 19, 29, 8, 6, 24, 23, 31, 33, 8, 19, 24, 11, 15, 43, 26, 12, 9, 24, 45, 38, 22, 12, 13, 28, 35, 23, 12, 18, 13, 9, 14, 12, 8, 16, 13, 5, 27, 45, 12, 14, 20, 42, 29, 12, 18, 25, 22, 11, 20, 13, 17, 24, 20, 19, 24, 10, 10, 31, 15, 12, 13, 17, 28, 31, 13, 36, 14, 16, 31, 27, 35, 15, 14, 71, 7, 21, 21, 27, 13, 15, 27, 60, 21, 14, 29, 16, 21, 36, 19, 18, 12, 29, 13, 16, 19, 27, 13, 20, 17, 17, 13, 37, 9, 28, 22, 23, 17, 21, 29, 13, 20, 11, 12, 33, 61, 41, 14, 25, 22, 29, 18, 23, 23, 23, 11, 36, 11, 11, 15, 22, 11, 32, 11, 13, 14, 16, 20, 25, 25, 28, 18, 11, 44, 19, 7, 21, 24, 19, 21, 30, 34, 13, 16, 15, 47, 22, 14, 20, 13, 57, 32, 13, 12, 12, 17, 36, 31, 23, 23, 30, 17, 19, 21, 17, 22, 17, 14, 15, 10, 11, 13, 20, 25, 28, 21, 17, 21, 27, 18, 9, 53, 20, 13, 43, 25, 33, 7, 13, 32, 15, 17, 8, 29, 39, 21, 23, 12, 22, 36, 20, 30, 10, 34, 17, 19, 14, 24, 28, 15, 17, 73, 31, 16, 34, 17, 19, 17, 17, 16, 22, 15, 14, 30, 29, 18, 28, 20, 5, 10, 30, 17, 13, 25, 26, 27, 13, 11, 15, 8, 21, 13, 9, 19, 19, 30, 24, 29, 16, 27, 15, 20, 14, 29, 37, 15, 13, 18, 34, 20, 34, 8, 38, 17, 7, 18, 41, 19, 23, 22, 41, 39, 13, 17, 21, 9, 23, 18, 28, 36, 48, 21, 7, 18, 7, 35, 9, 9, 24, 11, 20, 35, 28, 9, 44, 11, 11, 16, 20, 28, 31, 11, 39, 14, 41, 42, 24, 13, 20, 14, 20, 19, 10, 51, 5, 26, 19, 10, 15, 44, 30, 64, 20, 13, 8, 18, 29, 21, 37, 30, 46, 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        "print(metrics)"
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        "### Notes & Other Takeaways From This Project\n",
        "****\n",
        "- I am ecstatic about the results!\n",
        "- Yes, I could have fine-tuned the model for longer, but given the inputs, I am pleased with the output!\n",
        "\n",
        "****"
      ]
    },
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      "source": [
        "#### Citations\n",
        "\n",
        "- Model Checkpoint\n",
        "    >\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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