notebooks/PTT5_Fine_Tuning.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/EdmilsonSantana/tcc-2022-2/blob/main/notebooks/PTT5_Fine_Tuning.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ysIoHIBkHMOV",
        "outputId": "5d11a8bf-efdb-47e8-ae99-262364b0f5fa"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Instalação de pacotes"
      ],
      "metadata": {
        "id": "qKDUlDju4Jv1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%bash\n",
        "pip install nltk\n",
        "pip install datasets\n",
        "pip install transformers[torch]\n",
        "pip install tokenizers\n",
        "pip install evaluate\n",
        "pip install rouge_score\n",
        "pip install sentencepiece\n",
        "pip install huggingface_hub"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Qk16slE6wrFX",
        "outputId": "a6af6cea-b3b6-4e2a-ec8a-465181831764"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
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            "Installing collected packages: evaluate\n",
            "Successfully installed evaluate-0.4.2\n",
            "Collecting rouge_score\n",
            "  Downloading rouge_score-0.1.2.tar.gz (17 kB)\n",
            "  Preparing metadata (setup.py): started\n",
            "  Preparing metadata (setup.py): finished with status 'done'\n",
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            "Building wheels for collected packages: rouge_score\n",
            "  Building wheel for rouge_score (setup.py): started\n",
            "  Building wheel for rouge_score (setup.py): finished with status 'done'\n",
            "  Created wheel for rouge_score: filename=rouge_score-0.1.2-py3-none-any.whl size=24933 sha256=1ef437702913979763313e2d90c73de7349e6c92ad48fe64130d56e82c4d50d5\n",
            "  Stored in directory: /root/.cache/pip/wheels/5f/dd/89/461065a73be61a532ff8599a28e9beef17985c9e9c31e541b4\n",
            "Successfully built rouge_score\n",
            "Installing collected packages: rouge_score\n",
            "Successfully installed rouge_score-0.1.2\n",
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    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "LXJRoJaBVKiG"
      },
      "outputs": [],
      "source": [
        "import evaluate\n",
        "import nltk\n",
        "import json\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from datasets import Dataset, DatasetDict, load_from_disk\n",
        "from transformers import T5Tokenizer\n",
        "from transformers import T5ForConditionalGeneration\n",
        "from transformers import Seq2SeqTrainingArguments\n",
        "from transformers import Seq2SeqTrainer\n",
        "from transformers import EvalPrediction\n",
        "from transformers import DataCollatorForSeq2Seq\n",
        "from sklearn.model_selection import train_test_split\n",
        "import gc\n",
        "import torch"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nltk.download('punkt')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V3Paaw5BiImX",
        "outputId": "a5cc3b30-4f18-4a10-d4ae-afe2fdb63ac7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UPcPtmDiHBDf"
      },
      "source": [
        "## Preparação dos dados"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "hZhwfR7jqs6s"
      },
      "outputs": [],
      "source": [
        "DATA_DIR = '/content/drive/MyDrive/tcc'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "92vYdifrqs6t"
      },
      "outputs": [],
      "source": [
        "with open(f\"{DATA_DIR}/vehicle_repair_and_maintenance_qa.json\", 'r', encoding='utf-8') as fp:\n",
        "    data = json.load(fp)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "amzmvCp1qs6t"
      },
      "outputs": [],
      "source": [
        "questions = [entry['data']['question'] for entry in data]\n",
        "answers = [entry['data']['answer'] for entry in data]\n",
        "sections = [entry['metadata']['section'] for entry in data]"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_qa = pd.DataFrame({'question': questions, 'answer': answers, 'section': sections})"
      ],
      "metadata": {
        "id": "H5xCLeyCqs6u"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_qa.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "44b12cd3-9024-4587-f8c7-eacb78d8ef75",
        "id": "JcU4V3soqs6u"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 13014 entries, 0 to 13013\n",
            "Data columns (total 3 columns):\n",
            " #   Column    Non-Null Count  Dtype \n",
            "---  ------    --------------  ----- \n",
            " 0   question  13014 non-null  object\n",
            " 1   answer    13014 non-null  object\n",
            " 2   section   13014 non-null  object\n",
            "dtypes: object(3)\n",
            "memory usage: 305.1+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_qa.drop_duplicates(subset=['question'], inplace=True)"
      ],
      "metadata": {
        "id": "Kx9gQ79rqs6u"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_qa.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iB8qZY1rTzV8",
        "outputId": "fb7e9ee4-52ab-4dd5-fa65-2e91ad1f93ce"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 10047 entries, 0 to 13013\n",
            "Data columns (total 3 columns):\n",
            " #   Column    Non-Null Count  Dtype \n",
            "---  ------    --------------  ----- \n",
            " 0   question  10047 non-null  object\n",
            " 1   answer    10047 non-null  object\n",
            " 2   section   10047 non-null  object\n",
            "dtypes: object(3)\n",
            "memory usage: 314.0+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_qa.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "HozVX2ouHmS-",
        "outputId": "76abfdee-b7e0-4531-d2a7-607fc81dceea"
      },
      "execution_count": null,
      "outputs": [
        {
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              "                                            question  \\\n",
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              "                             question  answer\n",
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              "                                               question  \\\n",
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              "                                               question  \\\n",
              "2055           O que é representado na figura a seguir?   \n",
              "2600  Onde o cilindro mestre é encontrado na configu...   \n",
              "\n",
              "                                                 answer  \\\n",
              "2055           A figura representa um triciclo a vapor.   \n",
              "2600  O cilindro mestre é comumente encontrado junto...   \n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-dd9f3ea4-c1fb-451d-a7c2-6f4d3f759b19 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-dd9f3ea4-c1fb-451d-a7c2-6f4d3f759b19');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-c18dfcc8-6bb1-438e-be09-46325fed8c5a\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c18dfcc8-6bb1-438e-be09-46325fed8c5a')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-c18dfcc8-6bb1-438e-be09-46325fed8c5a button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df_qa[df_qa['question']\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"question\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Onde o cilindro mestre \\u00e9 encontrado na configura\\u00e7\\u00e3o de freios?\",\n          \"O que \\u00e9 representado na figura a seguir?\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"answer\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"O cilindro mestre \\u00e9 comumente encontrado junto ao servo freio.\",\n          \"A figura representa um triciclo a vapor.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"section\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Cilindro mestre\",\n          \"MOTOR DE COMBUST\\u00c3O EXTERNA\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_qa[df_qa['answer'].str.contains('figura')].info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4ut7Cyn4vG9n",
        "outputId": "4e1f223d-d607-47de-9b96-bbb2038274a5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 56 entries, 292 to 12404\n",
            "Data columns (total 3 columns):\n",
            " #   Column    Non-Null Count  Dtype \n",
            "---  ------    --------------  ----- \n",
            " 0   question  56 non-null     object\n",
            " 1   answer    56 non-null     object\n",
            " 2   section   56 non-null     object\n",
            "dtypes: object(3)\n",
            "memory usage: 1.8+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_qa[df_qa['answer'].str.contains('figura')].sample(1)['answer'].values"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lcIQR_RCvPqW",
        "outputId": "2754d6da-3b58-4cb2-ab5b-f2e7a32aa090"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Na figura mostrada, a catraca é um componente do micrômetro que serve para assegurar uma pressão de medição constante.'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_qa.drop(index=df_qa[df_qa['question'].str.contains('figura')].index, inplace=True)\n",
        "df_qa.drop(index=df_qa[df_qa['answer'].str.contains('figura')].index, inplace=True)"
      ],
      "metadata": {
        "id": "bHgvSe0Bwspu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "assert(df_qa[df_qa['answer'].str.contains('figura')].shape[0] == 0)"
      ],
      "metadata": {
        "id": "uAPM6JeamP4h"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_qa.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jTjFzPZJU6Ye",
        "outputId": "2ebca5ca-b3eb-4be8-8cb6-751974aa07c8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 9990 entries, 0 to 13013\n",
            "Data columns (total 3 columns):\n",
            " #   Column    Non-Null Count  Dtype \n",
            "---  ------    --------------  ----- \n",
            " 0   question  9990 non-null   object\n",
            " 1   answer    9990 non-null   object\n",
            " 2   section   9990 non-null   object\n",
            "dtypes: object(3)\n",
            "memory usage: 312.2+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(df_qa['question'], df_qa['answer'], test_size=0.1, stratify=df_qa['section'], random_state=42)"
      ],
      "metadata": {
        "id": "pKG-GAssUaUW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataset = Dataset.from_dict({'question': X_train, 'answer': y_train})\n",
        "test_dataset = Dataset.from_dict({'question': X_test, 'answer': y_test})"
      ],
      "metadata": {
        "id": "fWaAeWC8nTUD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = DatasetDict({'train': train_dataset, 'test': test_dataset})\n",
        "dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cc4831d3-9127-49f2-cb6f-01fdb7ada607",
        "id": "tJK8dbtHqs6w"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['question', 'answer'],\n",
              "        num_rows: 8991\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['question', 'answer'],\n",
              "        num_rows: 999\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset_dir = f\"{DATA_DIR}/vehicle_repair_and_maintenance_qa\""
      ],
      "metadata": {
        "id": "aWAaqEO4W7I4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.save_to_disk(dataset_dir)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
            "9d34cd5494374ec6b86ae16c4e391d30",
            "fba24734087f4174ad301279e300d237",
            "ca653da5cbc3492f96b312947130c25f",
            "97cb60f9dea440b5917738970ee319de",
            "21367a6697154bc2a4a7e67bb52e85ec",
            "bb8682954133439fa955609cfbaae0da",
            "432462f2164c4fa2ba32bf2375a8ef00",
            "86064f10b46b48d0b193ddb0fc38487d",
            "2aba5b306490496f960a25f7ba1f03a8",
            "a682867197264f90b135aa2b68e7449f",
            "96f7f8aa816b4a76a637d8b45ff69987",
            "2bfe16bec5664f47ae0aeb093cfcc207",
            "a1f1a502a6aa456596504eda2f2a89fb",
            "ce2ad8916cc34bbfba18a779c8b4ae4c",
            "9572961dca964ee5939062091eb52bab",
            "a0312fbf72874147a644837ef3151cab",
            "661067b3afa8482189334baff36a8bfa",
            "d7ce35336e7642bcab187d119db65b47",
            "e8e6e81bee4745319623a01ce380c1b0",
            "20f8645f9c6d44b48f9b66d15afdb9c5",
            "db9228c1d6444483853e20ab292f2b8a",
            "63f5fa14d16a4b87baafbcbbd759c81c"
          ]
        },
        "outputId": "4b285124-a0cc-4a92-bea1-726463b216c3",
        "id": "w6-x8aljqs6w"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Saving the dataset (0/1 shards):   0%|          | 0/8991 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9d34cd5494374ec6b86ae16c4e391d30"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Saving the dataset (0/1 shards):   0%|          | 0/999 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2bfe16bec5664f47ae0aeb093cfcc207"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataset = load_from_disk(f'{dataset_dir}/train')\n",
        "test_dataset = load_from_disk(f'{dataset_dir}/test')\n",
        "dataset = DatasetDict({'train': train_dataset, 'test': test_dataset})\n",
        "dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zx-PLB0tWsRE",
        "outputId": "82499bda-20b6-4d12-d3e1-dee90dc3f20f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['question', 'answer'],\n",
              "        num_rows: 8991\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['question', 'answer'],\n",
              "        num_rows: 999\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Definição de métrica"
      ],
      "metadata": {
        "id": "iB59VPZ-4Hp7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "metric = evaluate.load(\"rouge\")\n",
        "def calculate_rogue(predictions: list, labels: list) -> dict:\n",
        "  decoded_preds = [\"\\n\".join(nltk.sent_tokenize(pred.strip(), language='portuguese')) for pred in predictions]\n",
        "  decoded_labels = [\"\\n\".join(nltk.sent_tokenize(label.strip(), language='portuguese')) for label in labels]\n",
        "  return metric.compute(predictions=decoded_preds, references=decoded_labels, use_stemmer=True)"
      ],
      "metadata": {
        "id": "ZWw-4bTA3Tee",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "b49f69e20bb841919a25bab1c4fef8a8",
            "392c8c1f93254a848123f38781e68ed8",
            "6d6576d2fff642859d5bef0498d1a80a",
            "402e9485d83f4be18a2524c20fbff078",
            "cf6f4067c6974ccba600f9ac5f378a59",
            "2eaa0e6f8a5b4b09a8f5470cd00c4402",
            "3c9ab63ee33a4834bafa3c6cd933c898",
            "89dd07c2464d44658b56f522bd6707cf",
            "1f1f3adcdc374d08965d891868b021a7",
            "ea05cd616b984feca102ef6b82506c64",
            "f85b7a88ea574aa4bb2adb130d31efa3"
          ]
        },
        "outputId": "ee94c13d-7a9c-4df5-9b05-b927030867f1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/6.27k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b49f69e20bb841919a25bab1c4fef8a8"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def compute_metrics(eval_preds: EvalPrediction):\n",
        "   preds, labels = eval_preds\n",
        "\n",
        "   labels = np.where(labels != -100, labels, tokenizer.pad_token_id)\n",
        "   preds = np.where(preds != -100, preds, tokenizer.pad_token_id)\n",
        "\n",
        "   tokenized_preds = tokenizer.batch_decode(preds, skip_special_tokens=True)\n",
        "   tokenized_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)\n",
        "\n",
        "   return calculate_rogue(tokenized_preds, tokenized_labels)"
      ],
      "metadata": {
        "id": "MOqLIWGZq78T"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Treinamento do modelo"
      ],
      "metadata": {
        "id": "rcU0m2vsUNFo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_name = 'unicamp-dl/ptt5-base-portuguese-vocab'\n",
        "max_length = 512\n",
        "learning_rate = 5e-4\n",
        "weight_decay = 0.01\n",
        "n_epochs = 20\n",
        "train_batch_size = 8\n",
        "test_batch_size = 4"
      ],
      "metadata": {
        "id": "vwOId3al9dt4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def tokenize_data(examples):\n",
        "    model_inputs = tokenizer(examples['question'], max_length=max_length, truncation=True)\n",
        "    labels = tokenizer(text_target=examples['answer'], max_length=max_length, truncation=True)\n",
        "    model_inputs['labels'] = labels['input_ids']\n",
        "    return model_inputs"
      ],
      "metadata": {
        "id": "KTd_2y1i65tF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer = T5Tokenizer.from_pretrained(model_name)\n",
        "model = T5ForConditionalGeneration.from_pretrained(model_name).to('cuda')\n",
        "data_collator = DataCollatorForSeq2Seq(tokenizer=tokenizer, model=model)\n",
        "train_dataset_tokenized = dataset['train'].map(tokenize_data, batched=True)\n",
        "test_dataset_tokenized = dataset['test'].map(tokenize_data, batched=True)\n",
        "\n",
        "training_args = Seq2SeqTrainingArguments(\n",
        "    output_dir=DATA_DIR,\n",
        "    num_train_epochs=n_epochs,\n",
        "    per_device_train_batch_size=train_batch_size,\n",
        "    per_device_eval_batch_size=test_batch_size,\n",
        "    weight_decay=weight_decay,\n",
        "    save_strategy=\"epoch\",\n",
        "    evaluation_strategy=\"epoch\",\n",
        "    logging_strategy=\"epoch\",\n",
        "    learning_rate=learning_rate,\n",
        "    predict_with_generate=True,\n",
        "    generation_max_length=100\n",
        ")\n",
        "\n",
        "trainer = Seq2SeqTrainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    train_dataset=train_dataset_tokenized,\n",
        "    eval_dataset=test_dataset_tokenized,\n",
        "    tokenizer=tokenizer,\n",
        "    data_collator=data_collator,\n",
        "    compute_metrics=compute_metrics\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EZxhoSsP7_PE",
        "outputId": "0264bed3-2c63-4e3c-a5b2-f54888d4af0e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n",
            "You are using the default legacy behaviour of the <class 'transformers.models.t5.tokenization_t5.T5Tokenizer'>. This is expected, and simply means that the `legacy` (previous) behavior will be used so nothing changes for you. If you want to use the new behaviour, set `legacy=False`. This should only be set if you understand what it means, and thoroughly read the reason why this was added as explained in https://github.com/huggingface/transformers/pull/24565\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/training_args.py:1474: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 514
        },
        "id": "JmMiJftoq-98",
        "outputId": "5e97c025-86b8-441a-f6d9-a4978f9f92e9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='15808' max='22480' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [15808/22480 3:16:09 < 1:22:48, 1.34 it/s, Epoch 14.06/20]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Rouge1</th>\n",
              "      <th>Rouge2</th>\n",
              "      <th>Rougel</th>\n",
              "      <th>Rougelsum</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>1.883800</td>\n",
              "      <td>0.911703</td>\n",
              "      <td>0.385126</td>\n",
              "      <td>0.238665</td>\n",
              "      <td>0.340636</td>\n",
              "      <td>0.347974</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.868700</td>\n",
              "      <td>0.628922</td>\n",
              "      <td>0.487583</td>\n",
              "      <td>0.358881</td>\n",
              "      <td>0.443715</td>\n",
              "      <td>0.451123</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.581300</td>\n",
              "      <td>0.554870</td>\n",
              "      <td>0.538234</td>\n",
              "      <td>0.422450</td>\n",
              "      <td>0.494779</td>\n",
              "      <td>0.503855</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.437700</td>\n",
              "      <td>0.528685</td>\n",
              "      <td>0.544968</td>\n",
              "      <td>0.429653</td>\n",
              "      <td>0.500942</td>\n",
              "      <td>0.510388</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>0.345300</td>\n",
              "      <td>0.506659</td>\n",
              "      <td>0.570713</td>\n",
              "      <td>0.468838</td>\n",
              "      <td>0.532037</td>\n",
              "      <td>0.539167</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>0.278500</td>\n",
              "      <td>0.523544</td>\n",
              "      <td>0.587311</td>\n",
              "      <td>0.484838</td>\n",
              "      <td>0.545712</td>\n",
              "      <td>0.554389</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>0.230800</td>\n",
              "      <td>0.535356</td>\n",
              "      <td>0.594236</td>\n",
              "      <td>0.493332</td>\n",
              "      <td>0.555832</td>\n",
              "      <td>0.563165</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>0.191300</td>\n",
              "      <td>0.556149</td>\n",
              "      <td>0.607407</td>\n",
              "      <td>0.509752</td>\n",
              "      <td>0.566818</td>\n",
              "      <td>0.576456</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>0.158600</td>\n",
              "      <td>0.575376</td>\n",
              "      <td>0.610233</td>\n",
              "      <td>0.513667</td>\n",
              "      <td>0.572005</td>\n",
              "      <td>0.581155</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>0.136000</td>\n",
              "      <td>0.593316</td>\n",
              "      <td>0.609515</td>\n",
              "      <td>0.512217</td>\n",
              "      <td>0.570272</td>\n",
              "      <td>0.578592</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>11</td>\n",
              "      <td>0.115600</td>\n",
              "      <td>0.616877</td>\n",
              "      <td>0.621149</td>\n",
              "      <td>0.525154</td>\n",
              "      <td>0.580986</td>\n",
              "      <td>0.588743</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>12</td>\n",
              "      <td>0.100500</td>\n",
              "      <td>0.638220</td>\n",
              "      <td>0.617850</td>\n",
              "      <td>0.521666</td>\n",
              "      <td>0.578544</td>\n",
              "      <td>0.586215</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>13</td>\n",
              "      <td>0.086000</td>\n",
              "      <td>0.659352</td>\n",
              "      <td>0.615786</td>\n",
              "      <td>0.517650</td>\n",
              "      <td>0.575147</td>\n",
              "      <td>0.584154</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>14</td>\n",
              "      <td>0.072500</td>\n",
              "      <td>0.682925</td>\n",
              "      <td>0.626550</td>\n",
              "      <td>0.531459</td>\n",
              "      <td>0.586482</td>\n",
              "      <td>0.595764</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.train(resume_from_checkpoint=f'{DATA_DIR}/checkpoint-15736')"
      ],
      "metadata": {
        "id": "1JoPJtD4ZYaQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 339
        },
        "outputId": "8c75f9b6-cc5b-4814-e9b9-18f3d62dbeaa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "There were missing keys in the checkpoint model loaded: ['encoder.embed_tokens.weight', 'decoder.embed_tokens.weight', 'lm_head.weight'].\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='22480' max='22480' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [22480/22480 1:19:46, Epoch 20/20]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Rouge1</th>\n",
              "      <th>Rouge2</th>\n",
              "      <th>Rougel</th>\n",
              "      <th>Rougelsum</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>15</td>\n",
              "      <td>0.063500</td>\n",
              "      <td>0.721499</td>\n",
              "      <td>0.621776</td>\n",
              "      <td>0.526392</td>\n",
              "      <td>0.583014</td>\n",
              "      <td>0.591777</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>16</td>\n",
              "      <td>0.054900</td>\n",
              "      <td>0.734052</td>\n",
              "      <td>0.625036</td>\n",
              "      <td>0.530529</td>\n",
              "      <td>0.587235</td>\n",
              "      <td>0.596462</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>17</td>\n",
              "      <td>0.048600</td>\n",
              "      <td>0.749139</td>\n",
              "      <td>0.625945</td>\n",
              "      <td>0.531596</td>\n",
              "      <td>0.588745</td>\n",
              "      <td>0.597689</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>18</td>\n",
              "      <td>0.041500</td>\n",
              "      <td>0.780283</td>\n",
              "      <td>0.625569</td>\n",
              "      <td>0.530107</td>\n",
              "      <td>0.587309</td>\n",
              "      <td>0.596811</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>19</td>\n",
              "      <td>0.037500</td>\n",
              "      <td>0.789886</td>\n",
              "      <td>0.628372</td>\n",
              "      <td>0.534027</td>\n",
              "      <td>0.590580</td>\n",
              "      <td>0.600067</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>20</td>\n",
              "      <td>0.033600</td>\n",
              "      <td>0.797307</td>\n",
              "      <td>0.627335</td>\n",
              "      <td>0.531797</td>\n",
              "      <td>0.588519</td>\n",
              "      <td>0.599136</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=22480, training_loss=0.013979988505407584, metrics={'train_runtime': 4787.8889, 'train_samples_per_second': 37.557, 'train_steps_per_second': 4.695, 'total_flos': 4902916223093760.0, 'train_loss': 0.013979988505407584, 'epoch': 20.0})"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "final_model_dir = f'{DATA_DIR}/final_model'"
      ],
      "metadata": {
        "id": "cSGf07M12pZj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.save_model(final_model_dir)"
      ],
      "metadata": {
        "id": "FPCycXdS1Owj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Avaliação do modelo"
      ],
      "metadata": {
        "id": "54FjsnLc4Sak"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "del model, tokenizer"
      ],
      "metadata": {
        "id": "KbW4A2LBxiOF"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "torch.cuda.empty_cache()\n",
        "gc.collect()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-PbIe7R34Pxf",
        "outputId": "997fc020-cf4b-448b-d3c2-799ce771f539"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "31"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer = T5Tokenizer.from_pretrained(final_model_dir)\n",
        "model = T5ForConditionalGeneration.from_pretrained(final_model_dir).to('cuda')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VwzIZ-P22h1x",
        "outputId": "55673594-c0ce-4963-bae2-2635ee291914"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install more-itertools\n",
        "import more_itertools as mit\n",
        "import pandas as pd"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ElaUrH-l4umS",
        "outputId": "4642a0dc-a3f8-4b81-ae1a-03a77049a9fd"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: more-itertools in /usr/local/lib/python3.10/dist-packages (10.1.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def inference(model, tokenizer, questions):\n",
        "  model.eval()\n",
        "\n",
        "  inputs = tokenizer(questions, return_tensors=\"pt\", padding=True).to('cuda')\n",
        "  outputs = model.generate(**inputs, max_new_tokens=512)\n",
        "  return tokenizer.batch_decode(outputs, skip_special_tokens=True)\n",
        "\n",
        "def get_preds(questions, answers):\n",
        "  preds = []\n",
        "  batch_size = 100\n",
        "  for chunk in mit.chunked(questions, batch_size):\n",
        "    preds.extend(inference(model, tokenizer, chunk))\n",
        "  return preds\n",
        "\n",
        "def save_preds(inputs, preds, labels, filename):\n",
        "  df = pd.DataFrame({'inputs': inputs, 'preds': preds, 'labels': labels})\n",
        "  df.to_csv(f'{DATA_DIR}/{filename}.csv')\n",
        "  df = pd.read_csv(f'{DATA_DIR}/{filename}.csv')\n",
        "  return df\n",
        "\n",
        "def evaluate_preds(df):\n",
        "  not_exact_matches = df[df['preds'] != df['labels']]\n",
        "  labels = not_exact_matches['labels'].astype(str).values\n",
        "  preds = not_exact_matches['preds'].astype(str).values\n",
        "  metrics = calculate_rogue(preds, labels)\n",
        "  metrics['exactMatches'] = df[df['preds'] == df['labels']].shape[0]\n",
        "  return metrics"
      ],
      "metadata": {
        "id": "MlrsMFfaPRpp"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_questions = test_dataset['question']\n",
        "test_answers = test_dataset['answer']"
      ],
      "metadata": {
        "id": "cVl33OYxYBQu"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_preds = get_preds(test_questions, test_answers)\n",
        "assert(len(test_preds) == len(test_questions))"
      ],
      "metadata": {
        "id": "wSJHSsl80crQ"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_test = save_preds(test_questions, test_preds, test_answers, 'test_result')"
      ],
      "metadata": {
        "id": "WyiSNK2yUKZg"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_test.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "uKPnejIoxayi",
        "outputId": "aead664b-7135-4408-bfde-fb7a18f350f4"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Unnamed: 0                                             inputs  \\\n",
              "0           0  Qual é o procedimento recomendado antes de usa...   \n",
              "1           1  O que acontece se o tamanho do pinhão for muit...   \n",
              "2           2                O que significa torque em um motor?   \n",
              "3           3  Qual é a fórmula para calcular a precisão em p...   \n",
              "4           4  Qual é o significado de cada divisão em uma es...   \n",
              "\n",
              "                                               preds  \\\n",
              "0  Antes de iniciar a medição de uma peça devemos...   \n",
              "1  Se o tamanho do pinhão for muito pequeno, have...   \n",
              "2  O torque depende não só da força (F) que é apl...   \n",
              "3                 Precisão = 1mm ÷ divisões do nônio   \n",
              "4  Cada divisão em uma escala métrica equivale a 5’.   \n",
              "\n",
              "                                              labels  \n",
              "0  Antes de iniciar a medição de uma peça devemos...  \n",
              "1  Se o tamanho do pinhão for muito pequeno, ele ...  \n",
              "2  O torque significa torção. O torque depende nã...  \n",
              "3  A precisão é a menor medida que o instrumento ...  \n",
              "4  Cada centímetro gravado na Escala encontra-se ...  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f3f76dd6-b691-4065-868c-33a752dd803b\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>inputs</th>\n",
              "      <th>preds</th>\n",
              "      <th>labels</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>Qual é o procedimento recomendado antes de usa...</td>\n",
              "      <td>Antes de iniciar a medição de uma peça devemos...</td>\n",
              "      <td>Antes de iniciar a medição de uma peça devemos...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>O que acontece se o tamanho do pinhão for muit...</td>\n",
              "      <td>Se o tamanho do pinhão for muito pequeno, have...</td>\n",
              "      <td>Se o tamanho do pinhão for muito pequeno, ele ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>O que significa torque em um motor?</td>\n",
              "      <td>O torque depende não só da força (F) que é apl...</td>\n",
              "      <td>O torque significa torção. O torque depende nã...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>Qual é a fórmula para calcular a precisão em p...</td>\n",
              "      <td>Precisão = 1mm ÷ divisões do nônio</td>\n",
              "      <td>A precisão é a menor medida que o instrumento ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>Qual é o significado de cada divisão em uma es...</td>\n",
              "      <td>Cada divisão em uma escala métrica equivale a 5’.</td>\n",
              "      <td>Cada centímetro gravado na Escala encontra-se ...</td>\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_test",
              "summary": "{\n  \"name\": \"df_test\",\n  \"rows\": 999,\n  \"fields\": [\n    {\n      \"column\": \"Unnamed: 0\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 288,\n        \"min\": 0,\n        \"max\": 998,\n        \"num_unique_values\": 999,\n        \"samples\": [\n          453,\n          793,\n          209\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"inputs\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 999,\n        \"samples\": [\n          \"O pino semi-flutuante tem quais tipos de \\u00eambolo?\",\n          \"Qual \\u00e9 o sistema de montagem semiflutuante?\",\n          \"O que \\u00e9 a torque e para que \\u00e9 utilizada?\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"preds\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 869,\n        \"samples\": [\n          \"As canaletas que alojam os an\\u00e9is est\\u00e3o localizadas no cabe\\u00e7ote do motor. Elas t\\u00eam, geralmente, perfil oval para que, ao girarem, acionem os dispositivos que estejam em contato com sua superf\\u00edcie, que podem ser: tuchos ou balancins.\",\n          \"Quando o desgaste \\u00e9 muito acentuado e pode danificar o tambor ou o disco. Tamb\\u00e9m deve-se substituir quando o limite de seguran\\u00e7a for atingido.\",\n          \"A jarda foi definida em fun\\u00e7\\u00e3o do metro, valendo 0,91440m. Isso significa que 1 jarda \\u00e9 igual a 0,91440m.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"labels\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 912,\n        \"samples\": [\n          \"\\u00c9 importante colocar a alavanca de mudan\\u00e7as em ponto morto quando o ve\\u00edculo est\\u00e1 parado com o motor em funcionamento para desligar a \\u00e1rvore secund\\u00e1ria das demais e evitar poss\\u00edveis danos.\",\n          \"Os desequil\\u00edbrios em uma roda completa reduzem a vida \\u00fatil dos rolamentos, dos amortecedores e elementos da suspens\\u00e3o e dire\\u00e7\\u00e3o do ve\\u00edculo.\",\n          \"Pode ser instalada no assoalho do ve\\u00edculo, \\u00e0 direita do motorista, ou na coluna de dire\\u00e7\\u00e3o.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "evaluate_preds(df_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u6YcnGFD-v3A",
        "outputId": "f33570fc-9799-4fe6-addd-da74ffdd5d6e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'rouge1': 0.5385830105555676,\n",
              " 'rouge2': 0.4226439063512055,\n",
              " 'rougeL': 0.49095520775615153,\n",
              " 'rougeLsum': 0.5031979105529967,\n",
              " 'exactMatches': 194}"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_questions = train_dataset['question']\n",
        "train_answers = train_dataset['answer']"
      ],
      "metadata": {
        "id": "mMPGcDilZkc7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_preds = get_preds(train_questions, train_answers)\n",
        "assert(len(train_preds) == len(train_questions))"
      ],
      "metadata": {
        "id": "0VGCxNBuSaEb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_train = save_preds(train_questions, train_preds, train_answers, 'train_result')"
      ],
      "metadata": {
        "id": "rhCS08RaSgQU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_train.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 310
        },
        "id": "teNH--lqS-T7",
        "outputId": "b816d330-f0c4-45cf-be2c-e99803b21a1e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Unnamed: 0                                             inputs  \\\n",
              "0           0  Qual é o papel da bomba de óleo no sistema de ...   \n",
              "1           1  O que é o controle periódico do óleo para tran...   \n",
              "2           2                     Como se mide a KPI em direção?   \n",
              "3           3        Qual é o propósito da alavança de mudanças?   \n",
              "4           4  Como funciona a varetas na distribuição mecânica?   \n",
              "\n",
              "                                               preds  \\\n",
              "0  A bomba de óleo tem como finalidade manter o ó...   \n",
              "1  O controle periódico do óleo para transmissões...   \n",
              "2  Na geometria de direção, a KPI é medida em com...   \n",
              "3  A alavança de mudanças é usada para selecionar...   \n",
              "4  As varetas recebem o movimento dos tuchos e tr...   \n",
              "\n",
              "                                              labels  \n",
              "0  A bomba de óleo tem como finalidade manter o ó...  \n",
              "1  O controle periódico do óleo para transmissões...  \n",
              "2  Na geometria de direção, a KPI é medida em com...  \n",
              "3  A alavança de mudanças é usada para selecionar...  \n",
              "4  As varetas recebem o movimento dos tuchos e tr...  "
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              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-269b13df-e92a-4d8f-88dd-0554cd0f9654 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-269b13df-e92a-4d8f-88dd-0554cd0f9654');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-316c2830-fbd0-4d3b-9c45-632f430d7248\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-316c2830-fbd0-4d3b-9c45-632f430d7248')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-316c2830-fbd0-4d3b-9c45-632f430d7248 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_train",
              "summary": "{\n  \"name\": \"df_train\",\n  \"rows\": 8991,\n  \"fields\": [\n    {\n      \"column\": \"Unnamed: 0\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2595,\n        \"min\": 0,\n        \"max\": 8990,\n        \"num_unique_values\": 8991,\n        \"samples\": [\n          1153,\n          8659,\n          5212\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"inputs\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 8991,\n        \"samples\": [\n          \"O que significa \\u00e2ngulo de inclina\\u00e7\\u00e3o em ve\\u00edculos?\",\n          \"O que significa que um \\u00eambolo tenha alta resist\\u00eancia?\",\n          \"Quais pe\\u00e7as do motor s\\u00e3o lubrificadas por salpicos de \\u00f3leo no sistema de lubrifica\\u00e7\\u00e3o convencional?\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"preds\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5367,\n        \"samples\": [\n          \"A polegada, o palmo, o p\\u00e9, a jarda, o passo e a bra\\u00e7a.\",\n          \"Calibrar o instrumento de acordo com a sua capacidade.\",\n          \"a = aproxima\\u00e7\\u00e3o e = menor valor do disco graduado.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"labels\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5638,\n        \"samples\": [\n          \"Atrav\\u00e9s da caixa de mudan\\u00e7as, at\\u00e9 o eixo motriz.\",\n          \"Os micr\\u00f4metros caracterizam-se pela: capacidade; precis\\u00e3o; aplica\\u00e7\\u00e3o.\",\n          \"Para a express\\u00e3o do torque, o sistema m\\u00e9trico utiliza a unidade Newton-metro (Nm) e suas subdivis\\u00f5es, como Ncm, Ndm, Nmm, etc.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "evaluate_preds(df_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qs5km2F6Swk2",
        "outputId": "1b02d294-a5a0-4ce9-8fde-31ed3b2b63f5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'rouge1': 0.786980085807462,\n",
              " 'rouge2': 0.7375047215242283,\n",
              " 'rougeL': 0.7736338696942494,\n",
              " 'rougeLsum': 0.7771416217970222,\n",
              " 'exactMatches': 8086}"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Publicação do modelo"
      ],
      "metadata": {
        "id": "rrc3ZrmT7qum"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from huggingface_hub import login\n",
        "login()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
            "53ed22cbef0440c18f893912c8d4df18",
            "1ea52033ea27480c9434253ca63bfba8",
            "1f4517bc8ea9436e831d24ed57242024",
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            "bfde61cc2e254db58dd47ed398754e2e",
            "4c7cb6abca5a44108f93a245208c36fd",
            "a3b4530eb7c04fdfb72fe3c572c3b161",
            "3d031521072747e58925de846e1f821b",
            "b80b38e7d52644ed86022b979d4915c8",
            "dca640d4d96241e4bf2ea559862db7be",
            "5985c2f52bc14a7aa5b908e5a50b5961",
            "f79300856bab4697887405fe685108e0",
            "b1c9ea51b8d64463a4b8bc30d1736736",
            "2a7cc287457f4f7c93f5484722182f6a",
            "e71af3f83cd74200a8022233d33329b5",
            "cd2e8db3628e4a68be19e5c80f1f4e5f",
            "498da48144f649f6aaeb3d6c5b9d2300",
            "4b2c07cebbaf4737aa7345a6f8d9a171",
            "93dbe67246b44adb9b6816f5888a0e1e",
            "4ba9b0d2a7764e369bb9d5f0c700b9e8"
          ]
        },
        "id": "nOs4KG0a8iIq",
        "outputId": "cb57cc4b-17b6-4479-e42d-9a452e55ac67"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "53ed22cbef0440c18f893912c8d4df18"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.save_pretrained(f\"{DATA_DIR}/emgs/ptt5-qa\")"
      ],
      "metadata": {
        "id": "u0EywNVu7tTn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.push_to_hub(\"emgs/ptt5-qa\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 267,
          "referenced_widgets": [
            "68da737517dd4868a41fb51b5fed0a1e",
            "9584a02ee5ab4803b2892445de2994cc",
            "3a2c3039ce694ac08da292997c449559",
            "6e66171339ca4b1faeafa31be925834c",
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            "e1006bf08a24411ba9ff7c0c5503b7f5"
          ]
        },
        "id": "P_YLHpSR_tDd",
        "outputId": "6bec8433-3f7f-4b66-a14d-21b8c463a649"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "README.md:   0%|          | 0.00/5.17k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "68da737517dd4868a41fb51b5fed0a1e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/892M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "01810979e8ce4eba8d8e09a136c6adb5"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CommitInfo(commit_url='https://huggingface.co/emgs/ptt5-qa/commit/8c6013844bc25715ec6ec940ec4119ac66c9d1f9', commit_message='Upload T5ForConditionalGeneration', commit_description='', oid='8c6013844bc25715ec6ec940ec4119ac66c9d1f9', pr_url=None, pr_revision=None, pr_num=None)"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer.save_pretrained(f\"{DATA_DIR}/emgs/ptt5-qa\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qASwTZpeAkm9",
        "outputId": "4dd438bd-deea-4d31-f902-cfeb7128935b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('/content/drive/MyDrive/tcc/emgs/ptt5-qa/tokenizer_config.json',\n",
              " '/content/drive/MyDrive/tcc/emgs/ptt5-qa/special_tokens_map.json',\n",
              " '/content/drive/MyDrive/tcc/emgs/ptt5-qa/spiece.model',\n",
              " '/content/drive/MyDrive/tcc/emgs/ptt5-qa/added_tokens.json')"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer.push_to_hub(\"emgs/ptt5-qa\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "id": "_nhrJ4uB9s0B",
        "outputId": "2c103712-2217-4ba5-d74f-4e88fd0bfbf6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CommitInfo(commit_url='https://huggingface.co/emgs/ptt5-qa/commit/7719be22354c941ea113bf7595a9eea36de077c8', commit_message='Upload tokenizer', commit_description='', oid='7719be22354c941ea113bf7595a9eea36de077c8', pr_url=None, pr_revision=None, pr_num=None)"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Explorando modelo"
      ],
      "metadata": {
        "id": "fg5fGPQrMJcg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from huggingface_hub import login\n",
        "login()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
            "af14ded055a0439b9a95cc750d41ced1",
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            "bfaed2552d424f9f8c115aaf2be718ec",
            "19895bc47c8346b7985df0f4ae04a6c7",
            "a1a7ce2df98247babbd4ee21b4542a49",
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      "execution_count": 4,
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        "from transformers import T5ForConditionalGeneration, T5Tokenizer\n",
        "\n",
        "model = T5ForConditionalGeneration.from_pretrained('emgs/ptt5-qa').to(\"cuda\")\n",
        "tokenizer = T5Tokenizer.from_pretrained('emgs/ptt5-qa')"
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "DATA_DIR = '/content/drive/MyDrive/tcc'\n",
        "df_train = pd.read_csv(f'{DATA_DIR}/train_result.csv')\n",
        "df_test = pd.read_csv(f'{DATA_DIR}/test_result.csv')"
      ],
      "metadata": {
        "id": "OCidpDOms6gG"
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      "execution_count": 10,
      "outputs": []
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        "questions = [\"Que tipo de combustível é utilizado na locomotiva a vapor?\", \"Qual o combustível utilizado na locomotiva a vapor?\"]\n",
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              "summary": "{\n  \"name\": \"df_train[df_train['inputs']\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"Unnamed: 0\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1950,\n        \"min\": 1987,\n        \"max\": 6127,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          1995,\n          6127,\n          1987\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"inputs\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Qual \\u00e9 o combust\\u00edvel utilizado na locomotiva a vapor?\",\n          \"Qual tipo de combust\\u00edvel \\u00e9 utilizado na locomotiva a vapor?\",\n          \"Como o calor \\u00e9 utilizado na locomotiva a vapor?\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"preds\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"O calor produzido \\u00e9 utilizado para aquecer \\u00e1gua em uma caldeira, transformando-a em vapor.\",\n          \"O combust\\u00edvel \\u00e9 o carv\\u00e3o ou a lenha.\",\n          \"A locomotiva a vapor \\u00e9 movida por um motor de combust\\u00e3o externa, pois a queima do combust\\u00edvel ocorre fora dos compartimentos que produzem o movimento (cilindros).\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"labels\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"O calor produzido \\u00e9 utilizado para aquecer \\u00e1gua em uma caldeira, transformando-a em vapor.\",\n          \"O combust\\u00edvel \\u00e9 o carv\\u00e3o ou a lenha.\",\n          \"A locomotiva a vapor \\u00e9 movida por um motor de combust\\u00e3o externa, pois a queima do combust\\u00edvel ocorre fora dos compartimentos que produzem o movimento (cilindros).\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
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      "cell_type": "code",
      "source": [
        "inference(model, tokenizer, [\"Que tipo de combustível é utilizado na locomotiva a vapor?\", \"Qual o combustível utilizado na locomotiva a vapor?\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VBBycJczs0bs",
        "outputId": "4ddcd5bb-9a23-4ff9-fb53-507cdd7c828d"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['O combustível é o carvão ou a lenha.',\n",
              " 'O combustível é o carvão ou a lenha.']"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
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        "def sample_question(df):\n",
        "  df_test = df.sample()\n",
        "  question = df_test['inputs'].values[0]\n",
        "  answer = df_test['labels'].values[0]\n",
        "  print(f'Question: {question}')\n",
        "  print(f'Answer: {answer}')\n",
        "  print(f'Label: {inference(model, tokenizer, [question])[0]}')"
      ],
      "metadata": {
        "id": "xMGhIN1Yt5lt"
      },
      "execution_count": 21,
      "outputs": []
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      "source": [
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        "outputId": "a782399c-0321-4a59-8e95-c99df6b5f441"
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      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Question: O que é a vantagem de ter o ABS?\n",
            "Answer: A vantagem de ter o ABS é que evita o bloqueio de uma ou mais rodas durante uma frenagem brusca em piso de pouca aderência, como água, neve, cascalho etc. Também atua quando existem condições de aderência diferentes entre as rodas do veículo. Isso garante uma frenagem segura, já que o travamento pode levar à perda de controle do veículo.\n",
            "Label: O ABS é o sistema Anti-lock Breaking System, que evita o bloqueio de uma ou mais rodas durante uma frenagem brusca em piso de pouca aderência, como água, neve, cascalho etc. Também atua quando existem condições de aderência diferentes entre as rodas do veículo. Embora existam várias versões do ABS, seu princípio de funcionamento é sempre o mesmo. Sensores informam uma central eletrônica sobre a velocidade de cada uma das rodas. Ela as compara entre si, calcula a desaceleração de cada uma e controla uma possível tendência ao travamento.\n"
          ]
        }
      ]
    },
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        "df_test[df_test['inputs'].str.contains(question) == True]['labels'].values[0]"
      ],
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'A vantagem de ter o ABS é que evita o bloqueio de uma ou mais rodas durante uma frenagem brusca em piso de pouca aderência, como água, neve, cascalho etc. Também atua quando existem condições de aderência diferentes entre as rodas do veículo. Isso garante uma frenagem segura, já que o travamento pode levar à perda de controle do veículo.'"
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      "cell_type": "code",
      "source": [
        "inference(model, tokenizer, ['Quais as vantagens do ABS?'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
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        "outputId": "90caa713-7d72-4893-a474-6015afd3ad5b"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['O ABS (Anti-lock Breaking System) é um sistema de segurança que evita o bloqueio de uma ou mais rodas durante uma frenagem brusca em piso de pouca aderência, como água, neve, cascalho etc. Também atua quando existem condições de aderência diferentes entre as rodas do veículo. Embora existam várias versões do ABS, seu princípio de funcionamento é sempre o mesmo. Sensores informam uma central eletrônica sobre a velocidade de cada uma das rodas. Ela as compara entre si, calcula a desaceleração de cada uma e controla uma possível tendência ao travamento.']"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "not_exact_matches = df_test[df_test['preds'] != df_test['labels']]"
      ],
      "metadata": {
        "id": "1i5Jg-5I7CCv"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sample_question(not_exact_matches)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X5lvoZBi8RnT",
        "outputId": "4501988f-3eca-4584-b3e2-8dc9d07455a2"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Question: Como são realizados os movimentos das válvulas?\n",
            "Answer: Esses movimentos das válvulas são feitos por meio da árvore de comando de válvulas que é acionada pela árvore de manivelas. Essas árvores têm, cada uma, uma engrenagem.\n",
            "Label: Esses movimentos das válvulas são realizados por meio da árvore de comando de válvulas que é acionada pela árvore de manivelas. Essas árvores têm, cada uma, uma engrenagem.\n"
          ]
        }
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