{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Finetuning of the language model BERTimbau on LeNER-Br text files"
      ],
      "metadata": {
        "id": "9s6mFOorYEo6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "- **Credit**: this notebook is copied/pasted with small changes from [PyTorch Examples](https://huggingface.co/docs/transformers/notebooks#pytorch-examples) of Hugging Face (notebook [language_modeling.ipynb](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/language_modeling.ipynb)).\n",
        "- **Author**: [Pierre GUILLOU](https://www.linkedin.com/in/pierreguillou/)\n",
        "- **Date**: 12/20/2021\n",
        "- **Blog post**: [NLP | Modelos e Web App para Reconhecimento de Entidade Nomeada (NER) no domínio jurídico brasileiro](https://medium.com/@pierre_guillou/nlp-modelos-e-web-app-para-reconhecimento-de-entidade-nomeada-ner-no-dom%C3%ADnio-jur%C3%ADdico-b658db55edfb)"
      ],
      "metadata": {
        "id": "AJ328rUQYXxK"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a3KD3WXU3l-O"
      },
      "source": [
        "## Overview"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JAscNNUD3l-P"
      },
      "source": [
        "In this notebook, we'll see how to fine-tune one of the [🤗 Transformers](https://github.com/huggingface/transformers) model on a masked language modeling tasks. \n",
        "\n",
        "Note: a Masked language modeling is a model that has to predict some tokens that are masked in the input. It still has access to the whole sentence, so it can use the tokens before and after the tokens masked to predict their value.\n",
        "\n",
        "![Widget inference representing the masked language modeling task](https://github.com/huggingface/notebooks/blob/master/examples/images/masked_language_modeling.png?raw=1)\n",
        "\n",
        "We will see how to easily load and preprocess the dataset for each one of those tasks, and how to use the `Trainer` API to fine-tune a model on it.\n",
        "\n",
        "A script version of this notebook you can directly run on a distributed environment or on TPU is available in our [examples folder](https://github.com/huggingface/transformers/tree/master/examples)."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Configuration"
      ],
      "metadata": {
        "id": "zH_MY4GuZSw0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_checkpoint = \"neuralmind/bert-base-portuguese-cased\""
      ],
      "metadata": {
        "id": "0FROJ7_qQSqn"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "If you're opening this Notebook on colab, you will need to connect to your Google Drive and to install 🤗 Transformers and 🤗 Datasets."
      ],
      "metadata": {
        "id": "f36m8O2hYVsd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive \n",
        "drive.mount('/content/drive', force_remount=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k4zV5HnuP7wS",
        "outputId": "825c8a0d-abe4-468f-eef3-525c8d77eadf"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "MOsHUjgdIrIW"
      },
      "outputs": [],
      "source": [
        "%%capture\n",
        "! pip install datasets transformers"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6Q_Qyc0YQGxE"
      },
      "source": [
        "If you're opening this notebook locally, make sure your environment has an install from the last version of those libraries.\n",
        "\n",
        "To be able to share your model with the community and generate results like the one shown in the picture below via the inference API, there are a few more steps to follow.\n",
        "\n",
        "First you have to store your authentication token from the Hugging Face website (sign up [here](https://huggingface.co/join) if you haven't already!) then execute the following cell and input your username and password:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "ID_bdI5EQGxF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 387,
          "referenced_widgets": [
            "346b2c7413e24b6e9a4fec62df9c77bf",
            "b053aa0217fe481f86e1f4ff04609318",
            "25f71c960336451cb9607fcb083b30c6",
            "ac5823324cb04c46a53b8ac4067b7657",
            "20eb45883cca47d1bcca7240e4ca4d7f",
            "3e464cd6e07142f3b6fd40bb5270b80f",
            "f742ecc1f85f426bbe93dc27ba6e8254",
            "cd937c4062514f11b69b2b7035165a09",
            "1fb8b901801f4a9fba8924b004f7d947",
            "9ec6f0dc51eb46c399682c408ab17272",
            "58f7098a299648e988d634226e10bf13",
            "3a2a15911c51435db77c7cc9abcdb2c1",
            "d98cea9e5e414deca52167ae351c0016",
            "28fac573b08e4ec681458d9c67336989",
            "9febad88718d4e90a63721ecd6f4eb09",
            "dcb00bab415247e9895ac8521241935f",
            "b39e6c23eb2246c19fb6a5786814040a"
          ]
        },
        "outputId": "f5311537-3714-45c3-c773-aae8835061df"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Login successful\n",
            "Your token has been saved to /root/.huggingface/token\n",
            "\u001b[1m\u001b[31mAuthenticated through git-credential store but this isn't the helper defined on your machine.\n",
            "You might have to re-authenticate when pushing to the Hugging Face Hub. Run the following command in your terminal in case you want to set this credential helper as the default\n",
            "\n",
            "git config --global credential.helper store\u001b[0m\n"
          ]
        }
      ],
      "source": [
        "from huggingface_hub import notebook_login\n",
        "\n",
        "notebook_login()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a-CTNRqpQGxG"
      },
      "source": [
        "Then you need to install Git-LFS. Uncomment the following instructions:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "gLleehJiQGxG"
      },
      "outputs": [],
      "source": [
        "%%capture\n",
        "!apt install git-lfs"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t2MKwGLYQGxH"
      },
      "source": [
        "Make sure your version of Transformers is at least 4.11.0 since the functionality was introduced in that version:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "p6eZ9_L6QGxI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3487ab5c-7b61-4414-965c-ec15d962af59"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4.15.0\n"
          ]
        }
      ],
      "source": [
        "import transformers\n",
        "\n",
        "print(transformers.__version__)\n",
        "# 4.14.1"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import datasets\n",
        "\n",
        "print(datasets.__version__)\n",
        "# 1.17.0"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IlsM5bJLAFrK",
        "outputId": "2045f791-ee5a-40f9-c88b-9416a8ff4734"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1.17.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pathlib\n",
        "from pathlib import Path\n",
        "\n",
        "import pandas as pd"
      ],
      "metadata": {
        "id": "XPVQ3eFVZ_Zq"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import Dataset, DatasetDict"
      ],
      "metadata": {
        "id": "jEQGmKlybp0Y"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1r_n9OWV3l-Q"
      },
      "source": [
        "## Preparing the dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 1. Load LeNER-Br text files"
      ],
      "metadata": {
        "id": "fMoO8X5MaHDX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "path_to_text_files = \"https://cic.unb.br/~teodecampos/LeNER-Br/LeNER-Br.zip\""
      ],
      "metadata": {
        "id": "aUw_WEN4RF-A"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!wget {path_to_text_files}"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1jfvIodRTncT",
        "outputId": "0ec9ef08-f816-467a-d35a-9178176688fb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2021-12-22 08:43:02--  https://cic.unb.br/~teodecampos/LeNER-Br/LeNER-Br.zip\n",
            "Resolving cic.unb.br (cic.unb.br)... 164.41.110.66\n",
            "Connecting to cic.unb.br (cic.unb.br)|164.41.110.66|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 93637203 (89M) [application/zip]\n",
            "Saving to: ‘LeNER-Br.zip’\n",
            "\n",
            "LeNER-Br.zip        100%[===================>]  89.30M  4.12MB/s    in 18s     \n",
            "\n",
            "2021-12-22 08:43:22 (4.86 MB/s) - ‘LeNER-Br.zip’ saved [93637203/93637203]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!ls -al"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OZomr0EFTxTb",
        "outputId": "7e04c30d-2205-4c89-9368-c1813ce012ca"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "total 91460\n",
            "drwxr-xr-x 1 root root     4096 Dec 22 08:43 .\n",
            "drwxr-xr-x 1 root root     4096 Dec 22 08:26 ..\n",
            "drwxr-xr-x 4 root root     4096 Dec  3 14:33 .config\n",
            "-rw-r--r-- 1 root root 93637203 Aug 31  2018 LeNER-Br.zip\n",
            "drwxr-xr-x 1 root root     4096 Dec  3 14:33 sample_data\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!unzip LeNER-Br.zip"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cb-9TmviT1LG",
        "outputId": "45939bad-76dd-43f1-c6ca-a1a9dff3c0e8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  LeNER-Br.zip\n",
            "   creating: LeNER-Br/\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "path_to_text_files = '/content/LeNER-Br/leNER-Br/raw_text'\n",
        "\n",
        "p = Path(path_to_text_files).glob('**/*')\n",
        "files = [x for x in p if x.is_file() and x.suffix == '.txt']\n",
        "files"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7vpPzC7rRb2n",
        "outputId": "2db60058-767a-4128-87ab-1e59ca294786"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              " PosixPath('/content/LeNER-Br/leNER-Br/raw_text/AIRR10006691020135020322.txt'),\n",
              " PosixPath('/content/LeNER-Br/leNER-Br/raw_text/ED1STM.txt'),\n",
              " PosixPath('/content/LeNER-Br/leNER-Br/raw_text/Pet128TSE5.txt'),\n",
              " PosixPath('/content/LeNER-Br/leNER-Br/raw_text/HC04798525420128130000.txt'),\n",
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              " PosixPath('/content/LeNER-Br/leNER-Br/raw_text/ADI1TJDFT.txt'),\n",
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              " PosixPath('/content/LeNER-Br/leNER-Br/raw_text/AC10024133855890001.txt'),\n",
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              " PosixPath('/content/LeNER-Br/leNER-Br/raw_text/CP32320177080008PA.txt'),\n",
              " PosixPath('/content/LeNER-Br/leNER-Br/raw_text/APO1TJDFT.txt'),\n",
              " PosixPath('/content/LeNER-Br/leNER-Br/raw_text/INSTRUCAOON06043378120186000000.txt'),\n",
              " PosixPath('/content/LeNER-Br/leNER-Br/raw_text/AIRR3999520145020086.txt'),\n",
              " PosixPath('/content/LeNER-Br/leNER-Br/raw_text/AIRR285001420095060020.txt'),\n",
              " PosixPath('/content/LeNER-Br/leNER-Br/raw_text/HC151914AgRES.txt')]"
            ]
          },
          "metadata": {},
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(files)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "soaLT3m7WbQM",
        "outputId": "b2c3a578-8ea6-47f3-c922-e6c1f1432b81"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "70"
            ]
          },
          "metadata": {},
          "execution_count": 112
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "paragraphs_list = list()\n",
        "\n",
        "for file in files:\n",
        "  paragraphs_by_file_list = list()\n",
        "  with open(file, 'r') as f:\n",
        "    data = f.read()\n",
        "    paragraphs = data.split(\"\\n\\n\")\n",
        "    num = 0\n",
        "    for paragraph in paragraphs:\n",
        "      p = paragraph.strip()\n",
        "      if p != '':\n",
        "        paragraphs_by_file_list.append(p.replace('\\n', ' '))\n",
        "        num += 1\n",
        "  paragraphs_list.extend(paragraphs_by_file_list)"
      ],
      "metadata": {
        "id": "ySs_pWImRQ0r"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(paragraphs_list)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C4pHFsBPhOAP",
        "outputId": "f8ea7a48-144b-496e-9c3f-6085fb691e7c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3324"
            ]
          },
          "metadata": {},
          "execution_count": 116
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.DataFrame(paragraphs_list)\n",
        "df.rename(columns={0: 'text'}, inplace=True)\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "i5ur-5tHXUnA",
        "outputId": "338a55ea-ebe6-43bb-86b0-8edca6f88043"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-81ae2c98-9422-4242-a8ca-a3b52baccc61\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>A C Ó R D Ã O</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>(7ª Turma)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>GMDAR/NB/LPLM</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>AGRAVO. AGRAVO DE INSTRUMENTO EM RECURSO DE RE...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Vistos, relatados e discutidos estes autos de ...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-81ae2c98-9422-4242-a8ca-a3b52baccc61')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-81ae2c98-9422-4242-a8ca-a3b52baccc61 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-81ae2c98-9422-4242-a8ca-a3b52baccc61');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                                                text\n",
              "0                                      A C Ó R D Ã O\n",
              "1                                         (7ª Turma)\n",
              "2                                      GMDAR/NB/LPLM\n",
              "3  AGRAVO. AGRAVO DE INSTRUMENTO EM RECURSO DE RE...\n",
              "4  Vistos, relatados e discutidos estes autos de ..."
            ]
          },
          "metadata": {},
          "execution_count": 118
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['text'].str.split().apply(len).value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1hCFbrJo4bL_",
        "outputId": "68d07ab2-71f4-4dba-bf11-8a2930787758"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1       178\n",
              "2       158\n",
              "3       133\n",
              "4       101\n",
              "6       100\n",
              "       ... \n",
              "980       1\n",
              "934       1\n",
              "2957      1\n",
              "860       1\n",
              "1968      1\n",
              "Name: text, Length: 260, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 119
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "There are files that have not been broken into small paragraphs, but the code that will be applied to the tokenized datasets (train and validation) will concatenate them all and group them into chunks. Therefore, we don't need to deal with this problem."
      ],
      "metadata": {
        "id": "ryeu7moc6-6R"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 2. Create train and validation datasets"
      ],
      "metadata": {
        "id": "6t01zjoxacPa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "train, validation = train_test_split(df, test_size=0.2)\n",
        "train.reset_index(drop=True, inplace=True)\n",
        "validation.reset_index(drop=True, inplace=True)"
      ],
      "metadata": {
        "id": "TvJOBsJMagzO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "YReJiEy-aj1p",
        "outputId": "7c3dfde6-a4ab-42cc-e77a-e0a33a63f5d8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-e0fec7e6-ce00-4a43-a616-b7b51b9b221b\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Os honorários periciais inserem-se em um conte...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Com efeito, nos termos do art. 61, §1º, da Lei...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Tendo, com suporte nas razões já demonstradas,...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Neste sentido é o entendimento do Superior Tri...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>E continua: \"por isso é que se diz que a decis...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e0fec7e6-ce00-4a43-a616-b7b51b9b221b')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e0fec7e6-ce00-4a43-a616-b7b51b9b221b 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-e0fec7e6-ce00-4a43-a616-b7b51b9b221b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                                                text\n",
              "0  Os honorários periciais inserem-se em um conte...\n",
              "1  Com efeito, nos termos do art. 61, §1º, da Lei...\n",
              "2  Tendo, com suporte nas razões já demonstradas,...\n",
              "3  Neste sentido é o entendimento do Superior Tri...\n",
              "4  E continua: \"por isso é que se diz que a decis..."
            ]
          },
          "metadata": {},
          "execution_count": 121
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataset = Dataset.from_pandas(train)\n",
        "validation_dataset = Dataset.from_pandas(validation)"
      ],
      "metadata": {
        "id": "OaXaiMvobk_3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "datasets = DatasetDict()\n",
        "datasets['train'] = train_dataset\n",
        "datasets['validation'] = validation_dataset\n",
        "\n",
        "datasets"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vG3F86I1b1Yc",
        "outputId": "1ff6bf58-63c3-46b4-dfb6-84c0cdcb36c5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['text'],\n",
              "        num_rows: 15252\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['text'],\n",
              "        num_rows: 3813\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 122
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 3. Push to the HF dataset hub"
      ],
      "metadata": {
        "id": "IMZEIPoP8x5x"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "repo_id = \"pierreguillou/lener_br_finetuning_language_model\"\n",
        "datasets.push_to_hub(repo_id)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 134,
          "referenced_widgets": [
            "f439a958357a42599a95731edafb3a81",
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            "f2190613dadf4e38a0224cf58f04a5f4",
            "9d990bfc0d9b4935bdd827256baca46c",
            "babea81cdcb94fd5af051439283b9b03",
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            "0a64bf6e8159403db8e2c07a0547e4b6"
          ]
        },
        "id": "rP7656_i82j6",
        "outputId": "40d29c73-4bee-4ebf-ddbb-b83fbd2ed805"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Pushing split train to the Hub.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f439a958357a42599a95731edafb3a81",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Pushing dataset shards to the dataset hub:   0%|          | 0/1 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Pushing split validation to the Hub.\n",
            "The repository already exists: the `private` keyword argument will be ignored.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "580521d9157c4c408605ba804b1eec51",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Pushing dataset shards to the dataset hub:   0%|          | 0/1 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 4. Load dataset from dataset HF"
      ],
      "metadata": {
        "id": "_cdm0JLASZkz"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kswRMhPc3l-Q"
      },
      "source": [
        "For each of the following tasks, we will use this dataset that we pushed to the HF library. You can load it very easily with the 🤗 Datasets library."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "n2ZRs1cL3l-R",
        "outputId": "70f58d06-21e3-4cac-b43a-f62c6162e018",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281,
          "referenced_widgets": [
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            "b0b1fcf6851745cf8487c5a654823fc4",
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            "6be15030dfaf4a2a9ffc250c6562c48a",
            "fcd52074285f45b0872e5b6d097682df",
            "70185f713c2848f7b066bf5f93c7e985",
            "ec5595ffdf764d4da0f5fe729cc165f2",
            "816e525357b54ecf9ac0a9fad459d7e1",
            "30374b889a7c422a8e32560a55981f62",
            "3ead20d74bdc4e4d8c109e969da3d90f",
            "d7d25b6ca08f493087d5d2579b24c2f0",
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            "1cbfa06185e544129e095fd4bae8151b",
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            "62bd8e4d113147d4b3b1418c73e6fd0f",
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            "ce8b12b58beb443ab8899f2fed5987a9",
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            "7ab71e613b7c42eba3e3a1307e9f404e",
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            "16160687fc5f4b17bad0a1136538f6b1",
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            "ab819d5c5c6e4ce2b7c9a7386e36671e",
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            "a2aba3ccbcee4766a21d4d2ad55155a4",
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            "a6e7f6b2ac3a40b4afe0f94974fee4aa",
            "d087107576c54ba68e062f256eac5631",
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            "d946534b5db443988c4a6e250a99cc68",
            "49b077926f5b4145a65343075d53d2c1",
            "3e7a1ed826f547ec9fb76a57264d6e41",
            "2d4195e80ff1463a97678decb138df78",
            "dae471ebb0be46dbbff8fb69099fefd4",
            "632b7e8397a94ce5818e651112884f55",
            "504a9cf2dd4343e3ba9635ff27b2b1f7",
            "b3d21a855ee84a26bb7858cfb54cec3e",
            "4d014648da7e475588f51c0147b6a01c",
            "77f36c11561e4287895d34b6d1e709c7",
            "a8d642040b8740b18bbe1c2c3c89714f",
            "36984dbb1df045a897f6b9401b5a35b4",
            "2222c6fc1f1d4a52a4e77abe35811175",
            "29f047c7197a4cbfbc174ab526e050ec",
            "8e350b8c117d4326b52f1e6e4d1761d8",
            "419f16e436714af380d8b47b1706e2af",
            "06f87e207c504946a20ae61213441ceb",
            "a8d3a03b6dc8455e998c61875aa4bf5e",
            "229b74f4acd24fda886fef7d20f427c7",
            "17abfba35d7d4d9da747d93420860468",
            "f2f0ea5852c1406ab35cee2499486d4b",
            "dbe41b751bd243ffb87b9fb2bc0a03e2",
            "272c7f8e8ae64026b3b1532bf2b41044",
            "187d2ecf1ff34d2bb4a3c81c59ad312d",
            "8a16286a769b48fca716536e407413df",
            "bb5f5d80eb554f23900354a6b8676cb1",
            "c229215247104322bd8a53e03e03c7be",
            "2cd6cdc631ed423c823739254bd8041a",
            "62afdb943c14427d9430279124120a3d",
            "a877227e8a35483281d53922e8217ed2"
          ]
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d1e641482a464922a778ed8115ac4f1b",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/736 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Using custom data configuration pierreguillou--lener_br_finetuning_language_model-d5d35d543fec7e31\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading and preparing dataset None/None (download: 1.11 MiB, generated: 1.79 MiB, post-processed: Unknown size, total: 2.90 MiB) to /root/.cache/huggingface/datasets/parquet/pierreguillou--lener_br_finetuning_language_model-d5d35d543fec7e31/0.0.0/1638526fd0e8d960534e2155dc54fdff8dce73851f21f031d2fb9c2cf757c121...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "443bb06723b6412db643ebf0bc6d0cb5",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/2 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "499e4a474c654bd6bd3b7aaa2d7ce295",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/243k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d087107576c54ba68e062f256eac5631",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/925k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4d014648da7e475588f51c0147b6a01c",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/2 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset parquet downloaded and prepared to /root/.cache/huggingface/datasets/parquet/pierreguillou--lener_br_finetuning_language_model-d5d35d543fec7e31/0.0.0/1638526fd0e8d960534e2155dc54fdff8dce73851f21f031d2fb9c2cf757c121. Subsequent calls will reuse this data.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "17abfba35d7d4d9da747d93420860468",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/2 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "repo_id = \"pierreguillou/lener_br_finetuning_language_model\"\n",
        "datasets = load_dataset(repo_id)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "datasets"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AjrC2Fu1-lDl",
        "outputId": "6f5a8fe3-0bb3-4005-9fcd-8b02f0cde4ca"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    validation: Dataset({\n",
              "        features: ['text'],\n",
              "        num_rows: 3813\n",
              "    })\n",
              "    train: Dataset({\n",
              "        features: ['text'],\n",
              "        num_rows: 15252\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f1-9jepM3l-W"
      },
      "source": [
        "You can replace the dataset above with any dataset hosted on [the hub](https://huggingface.co/datasets) or use your own files. Just uncomment the following cell and replace the paths with values that will lead to your files:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uxSaGa_l3l-W"
      },
      "outputs": [],
      "source": [
        "# datasets = load_dataset(\"text\", data_files={\"train\": path_to_train.txt, \"validation\": path_to_validation.txt}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jY1SwIrY3l-a"
      },
      "source": [
        "You can also load datasets from a csv or a JSON file, see the [full documentation](https://huggingface.co/docs/datasets/loading_datasets.html#from-local-files) for more information."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u3EtYfeHIrIz"
      },
      "source": [
        "To access an actual element, you need to select a split first, then give an index:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "X6HrpprwIrIz",
        "outputId": "dab02e6d-e35b-4dce-b9aa-433e6385bbea",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'text': 'Branco-AC - Mod. 500258 - Autos n.º 1002199-81.2017.8.01.0000/50000'}"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "datasets[\"train\"][10]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WHUmphG3IrI3"
      },
      "source": [
        "To get a sense of what the data looks like, the following function will show some examples picked randomly in the dataset."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "ur5sNUcZ3l-g"
      },
      "outputs": [],
      "source": [
        "from datasets import ClassLabel\n",
        "import random\n",
        "import pandas as pd\n",
        "from IPython.display import display, HTML\n",
        "\n",
        "def show_random_elements(dataset, num_examples=10):\n",
        "    assert num_examples <= len(dataset), \"Can't pick more elements than there are in the dataset.\"\n",
        "    picks = []\n",
        "    for _ in range(num_examples):\n",
        "        pick = random.randint(0, len(dataset)-1)\n",
        "        while pick in picks:\n",
        "            pick = random.randint(0, len(dataset)-1)\n",
        "        picks.append(pick)\n",
        "    \n",
        "    df = pd.DataFrame(dataset[picks])\n",
        "    for column, typ in dataset.features.items():\n",
        "        if isinstance(typ, ClassLabel):\n",
        "            df[column] = df[column].transform(lambda i: typ.names[i])\n",
        "    display(HTML(df.to_html()))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "1Uk8NROQ3l-k",
        "outputId": "29a4998d-1928-424a-e043-3d9c8f43c078",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 362
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>documento pode ser acessado no endereço eletrônico http://www.stf.jus.br/portal/autenticacao/ sob o número 3373814.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>por ele constituída, \"(...) no caso em tela, esta Impetrante NÃO FOI INTIMADA, E DECLARA QUE NÃO</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>\"eventual questão atinente à existência de coligação exige instrução, o que não</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>BELMIRO CONSELHO ESPECIAL, Data de Julgamento:</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Documento assinado digitalmente conforme MP n° 2.200-2/2001 de 24/08/2001, que institui a Infraestrutura de Chaves Públicas Brasileira - ICP-Brasil. O</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>R$ 1.243.372,38 (9,09%) para R$ 161.340,49 (1,18%) o total</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Endereço: Rua Tribunal de Justiça, s/n, Via Verde, CEP 69.915-631, Tel. 68 3302-0444/0445, Rio BrancoAC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>[ACÓRDÃO]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Não procede o inconformismo, uma vez que não se pode fazer distinção, para aplicação do direito, entre servidor público estatutário e servidor com contrato regido pela CLT.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Social teriam extrapolado a previsão legal, criando novos</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "show_random_elements(datasets[\"train\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CKerdF353l-o"
      },
      "source": [
        "As we can see, some of the texts are a full paragraph of a Wikipedia article while others are just titles or empty lines."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q-EIELH43l_T"
      },
      "source": [
        "## Masked language modeling"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LWk97-Ny3l_T"
      },
      "source": [
        "For masked language modeling (MLM) we are going to use the same preprocessing as before for our dataset with one additional step: we will randomly mask some tokens (by replacing them by `[MASK]`) and the labels will be adjusted to only include the masked tokens (we don't have to predict the non-masked tokens).\n",
        "\n",
        "We will use the [`neuralmind/bert-base-portuguese-cased`](https://huggingface.co/neuralmind/bert-base-portuguese-cased) model for this example. You can pick any of the checkpoints listed [here](https://huggingface.co/models?filter=masked-lm) instead:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tSLSNNRwBaiC"
      },
      "source": [
        "To tokenize all our texts with the same vocabulary that was used when training the model, we have to download a pretrained tokenizer. This is all done by the `AutoTokenizer` class:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 177,
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            "49524c3a50ab4bafa2aba29921b45dbb",
            "f67a133e1cf14b949b8eb49589edefa5",
            "3d6734c99e6a48da9f452f0d6bc43d50",
            "77de2bf6f6754ade895f1657d7541bdf",
            "76c9c66e5f74422ca36fc4990d3e947e"
          ]
        },
        "id": "Y4Kaa_3ZBaiC",
        "outputId": "407b7ef1-8684-49fb-fc3b-256323f9299f"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "3fecdd00e01844bb9c5b10898f2c6989",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/43.0 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "91a92d0239c9490d85c817a2817e94fb",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/647 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ec4cc75cfa0648328db7ddb52c58d16a",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/205k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "7af5044f6d3c443486a5707036aa3fd8",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/2.00 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2f4800d141e147278c2ce6e6ba279133",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/112 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "from transformers import AutoTokenizer\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint, use_fast=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IpRjQ7rlBaiD"
      },
      "source": [
        "We can now call the tokenizer on all our texts. This is very simple, using the [`map`](https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.map) method from the Datasets library. First we define a function that call the tokenizer on our texts:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "8K98HDIfBaiD"
      },
      "outputs": [],
      "source": [
        "def tokenize_function(examples):\n",
        "    return tokenizer(examples[\"text\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m2DNlIboBaiD"
      },
      "source": [
        "Then we apply it to all the splits in our `datasets` object, using `batched=True` and 4 processes to speed up the preprocessing. We won't need the `text` column afterward, so we discard it."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "3IQkPf6qBaiD"
      },
      "outputs": [],
      "source": [
        "tokenized_datasets = datasets.map(tokenize_function, batched=True, num_proc=4, remove_columns=[\"text\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GE6vS3MZBaiE"
      },
      "source": [
        "If we now look at an element of our datasets, we will see the text have been replaced by the `input_ids` the model will need:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "outputId": "6dbc56b0-14e7-4c4c-8f5e-bbd59a4f47f1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QK6xfCWrBaiE"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
              " 'input_ids': [101,\n",
              "  3305,\n",
              "  293,\n",
              "  5576,\n",
              "  179,\n",
              "  117,\n",
              "  5776,\n",
              "  285,\n",
              "  123,\n",
              "  20165,\n",
              "  182,\n",
              "  125,\n",
              "  102],\n",
              " 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "tokenized_datasets[\"train\"][1]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y03VOOXcBaiF"
      },
      "source": [
        "Now for the harder part: we need to concatenate all our texts together then split the result in small chunks of a certain `block_size`. To do this, we will use the `map` method again, with the option `batched=True`. This option actually lets us change the number of examples in the datasets by returning a different number of examples than we got. This way, we can create our new samples from a batch of examples.\n",
        "\n",
        "First, we grab the maximum length our model was pretrained with. This might be a big too big to fit in your GPU RAM, so here we take a bit less at just 128."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "F0-ApCdUBaiF"
      },
      "outputs": [],
      "source": [
        "# block_size = tokenizer.model_max_length\n",
        "block_size = 128"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zPE9-auaBaiG"
      },
      "source": [
        "Then we write the preprocessing function that will group our texts:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "LqP-xQFFBaiG"
      },
      "outputs": [],
      "source": [
        "def group_texts(examples):\n",
        "    # Concatenate all texts.\n",
        "    concatenated_examples = {k: sum(examples[k], []) for k in examples.keys()}\n",
        "    total_length = len(concatenated_examples[list(examples.keys())[0]])\n",
        "    # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can\n",
        "        # customize this part to your needs.\n",
        "    total_length = (total_length // block_size) * block_size\n",
        "    # Split by chunks of max_len.\n",
        "    result = {\n",
        "        k: [t[i : i + block_size] for i in range(0, total_length, block_size)]\n",
        "        for k, t in concatenated_examples.items()\n",
        "    }\n",
        "    result[\"labels\"] = result[\"input_ids\"].copy()\n",
        "    return result"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kbGygrDdBaiH"
      },
      "source": [
        "First note that we duplicate the inputs for our labels. This is because the model of the 🤗 Transformers library apply the shifting to the right, so we don't need to do it manually.\n",
        "\n",
        "Also note that by default, the `map` method will send a batch of 1,000 examples to be treated by the preprocessing function. So here, we will drop the remainder to make the concatenated tokenized texts a multiple of `block_size` every 1,000 examples. You can adjust this behavior by passing a higher batch size (which will also be processed slower). You can also speed-up the preprocessing by using multiprocessing:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "J6FKBm5EBaiH"
      },
      "outputs": [],
      "source": [
        "lm_datasets = tokenized_datasets.map(\n",
        "    group_texts,\n",
        "    batched=True,\n",
        "    batch_size=1000,\n",
        "    num_proc=4,\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WBp_jkbGBaiI"
      },
      "source": [
        "And we can check our datasets have changed: now the samples contain chunks of `block_size` contiguous tokens, potentially spanning over several of our original texts."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "outputId": "26d99a30-d315-4000-861b-fc590b212c3b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "id": "Yk0NjF54BaiI"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'o eminente Relator, pedindo respeitosas vênias à divergência. [SEP] [CLS] 5. Instruído o feito, a Unidade Técnica apresentou proposta final de encaminhamento acorde, que, nos termos do inciso I, § [UNK] do art. [UNK] da Lei [UNK] 8. 443 / 92 transcrevo ( Peças 15 / 16 ) : [SEP] [CLS] qualquer outro cadastro de inadimplentes pelo mesmo motivo [SEP] [CLS] Presidência da República [SEP] [CLS] Branco - AC - Mod. 500258 - Autos n. [UNK] 1002199 - 81. 2017. 8. 01. 0000 / 50000 [SEP] [CLS]'"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "tokenizer.decode(lm_datasets[\"train\"][1][\"input_ids\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6ohaK7GcBaiJ"
      },
      "source": [
        "Now that the data has been cleaned, we're ready to instantiate our `Trainer`. irst we use a model suitable for masked LM:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "PM10A9Za3l_Z",
        "outputId": "23437dff-d9d6-4066-867d-92e0cccd3e60",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 121,
          "referenced_widgets": [
            "e37a7eba1c22461a852708824efd855f",
            "731079479e9d40b28f3688dd5a3cd9d7",
            "e34fa275bc6844ceb27cc46aa49f6cf7",
            "b6d585d3bb7345509bf636f385fa482b",
            "1ab4971a73974734a5af17766716a01e",
            "5df5740537c841d3af8b5766eac12d5e",
            "f7b11fe83d264494a9fd4de982d9ba46",
            "f9560e17863747408a6191efd55f997f",
            "363529e3db0b4bb39fe75fae19fa0877",
            "2cc6826828ed49acbe2be54230be6127",
            "00c4fc42223340b089f69c4da85baaf5"
          ]
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e37a7eba1c22461a852708824efd855f",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/418M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at neuralmind/bert-base-portuguese-cased were not used when initializing BertForMaskedLM: ['cls.seq_relationship.weight', 'cls.seq_relationship.bias']\n",
            "- This IS expected if you are initializing BertForMaskedLM from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing BertForMaskedLM from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
          ]
        }
      ],
      "source": [
        "from transformers import AutoModelForMaskedLM\n",
        "model = AutoModelForMaskedLM.from_pretrained(model_checkpoint)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ijhRRM4yHvpM"
      },
      "source": [
        "And some `TrainingArguments`:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "SQM3-rvoHvpN"
      },
      "outputs": [],
      "source": [
        "from transformers import Trainer, TrainingArguments"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# hyperparameters, which are passed into the training job\n",
        "\n",
        "per_device_batch_size = 8\n",
        "gradient_accumulation_steps = 1\n",
        "\n",
        "learning_rate = 2e-5 # (AdamW) we started with 3e-4, then 1e-4, then 5e-5 but the model overfits fastly\n",
        "num_train_epochs = 5 # we started with 10 epochs but the model overfits fastly\n",
        "weight_decay = 0.01\n",
        "\n",
        "save_total_limit = 2\n",
        "logging_steps = 100 # melhor evaluate frequently (5000 seems too high)\n",
        "eval_steps = logging_steps\n",
        "evaluation_strategy = 'steps'\n",
        "logging_strategy = 'steps'\n",
        "save_strategy = 'steps'\n",
        "save_steps = logging_steps\n",
        "load_best_model_at_end = True\n",
        "\n",
        "fp16 = True\n",
        "\n",
        "# folders\n",
        "model_name = model_checkpoint.split(\"/\")[-1]\n",
        "folder_model = 'e' + str(num_train_epochs) + '_lr' + str(learning_rate)\n",
        "output_dir = '/content/drive/MyDrive/' + 'lm-lenerbr-' + str(model_name) + '/checkpoints/' + folder_model\n",
        "logging_dir = '/content/drive/MyDrive/' + 'lm-lenerbr-' + str(model_name) + '/logs/' + folder_model\n",
        "\n",
        "# get best model through a metric\n",
        "metric_for_best_model = 'eval_loss'\n",
        "if metric_for_best_model == 'eval_f1':\n",
        "    greater_is_better = True\n",
        "elif metric_for_best_model == 'eval_loss':\n",
        "    greater_is_better = False  \n",
        "\n",
        "training_args = TrainingArguments(\n",
        "    output_dir=output_dir,\n",
        "    learning_rate=learning_rate,\n",
        "    per_device_train_batch_size=per_device_batch_size,\n",
        "    per_device_eval_batch_size=per_device_batch_size*2,\n",
        "    gradient_accumulation_steps=gradient_accumulation_steps,\n",
        "    num_train_epochs=num_train_epochs,\n",
        "    weight_decay=weight_decay,\n",
        "    save_total_limit=save_total_limit,\n",
        "    logging_steps = logging_steps,\n",
        "    eval_steps = logging_steps,\n",
        "    load_best_model_at_end = load_best_model_at_end,\n",
        "    metric_for_best_model = metric_for_best_model,\n",
        "    greater_is_better = greater_is_better,\n",
        "    gradient_checkpointing = False,\n",
        "    do_train = True,\n",
        "    do_eval = True,\n",
        "    do_predict = True,\n",
        "    evaluation_strategy = evaluation_strategy,\n",
        "    logging_dir=logging_dir, \n",
        "    logging_strategy = logging_strategy,\n",
        "    save_strategy = save_strategy,\n",
        "    save_steps = save_steps,\n",
        "    fp16 = fp16,\n",
        "    push_to_hub=False,\n",
        ")"
      ],
      "metadata": {
        "id": "R5fbf2beG2XO"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z6uuUnvz3l_b"
      },
      "source": [
        "Finally, we use a special `data_collator`. The `data_collator` is a function that is responsible of taking the samples and batching them in tensors. In the previous example, we had nothing special to do, so we just used the default for this argument. Here we want to do the random-masking. We could do it as a pre-processing step (like the tokenization) but then the tokens would always be masked the same way at each epoch. By doing this step inside the `data_collator`, we ensure this random masking is done in a new way each time we go over the data.\n",
        "\n",
        "To do this masking for us, the library provides a `DataCollatorForLanguageModeling`. We can adjust the probability of the masking:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "nRZ-5v_P3l_b"
      },
      "outputs": [],
      "source": [
        "from transformers import DataCollatorForLanguageModeling\n",
        "data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm_probability=0.15)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bqHnWcYC3l_d"
      },
      "source": [
        "Then we just have to pass everything to `Trainer` and begin training:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "V-Y3gNqV3l_d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "53c1c248-8a4a-4dbe-a071-a6e70b7c64f0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Using amp half precision backend\n"
          ]
        }
      ],
      "source": [
        "from transformers.trainer_callback import EarlyStoppingCallback\n",
        "\n",
        "# wait early_stopping_patience x eval_steps before to stp the training in order to get a better model\n",
        "early_stopping_patience = save_total_limit\n",
        "\n",
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    train_dataset=lm_datasets[\"train\"],\n",
        "    eval_dataset=lm_datasets[\"validation\"],\n",
        "    data_collator=data_collator,\n",
        "    callbacks=[EarlyStoppingCallback(early_stopping_patience=early_stopping_patience)],\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "Y9TFqDG_3l_e",
        "outputId": "fe05b164-4a93-4b4a-bb90-e3dda21d3730",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "***** Running training *****\n",
            "  Num examples = 3227\n",
            "  Num Epochs = 5\n",
            "  Instantaneous batch size per device = 8\n",
            "  Total train batch size (w. parallel, distributed & accumulation) = 8\n",
            "  Gradient Accumulation steps = 1\n",
            "  Total optimization steps = 2020\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='1100' max='2020' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [1100/2020 25:01 < 20:58, 0.73 it/s, Epoch 2/5]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>100</td>\n",
              "      <td>1.988700</td>\n",
              "      <td>1.616412</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>200</td>\n",
              "      <td>1.724900</td>\n",
              "      <td>1.561100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>300</td>\n",
              "      <td>1.713400</td>\n",
              "      <td>1.499991</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>400</td>\n",
              "      <td>1.687400</td>\n",
              "      <td>1.451414</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>500</td>\n",
              "      <td>1.579700</td>\n",
              "      <td>1.433665</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>600</td>\n",
              "      <td>1.556900</td>\n",
              "      <td>1.407338</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>700</td>\n",
              "      <td>1.591400</td>\n",
              "      <td>1.421942</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>800</td>\n",
              "      <td>1.546000</td>\n",
              "      <td>1.406395</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>900</td>\n",
              "      <td>1.510100</td>\n",
              "      <td>1.352389</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1000</td>\n",
              "      <td>1.507100</td>\n",
              "      <td>1.394799</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1100</td>\n",
              "      <td>1.462200</td>\n",
              "      <td>1.368093</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "***** Running Evaluation *****\n",
            "  Num examples = 826\n",
            "  Batch size = 16\n",
            "Saving model checkpoint to /content/drive/MyDrive/lm-lenerbr-bert-base-portuguese-cased/checkpoints/e5_lr2e-05/checkpoint-100\n",
            "Configuration saved in /content/drive/MyDrive/lm-lenerbr-bert-base-portuguese-cased/checkpoints/e5_lr2e-05/checkpoint-100/config.json\n",
            "Model weights saved in /content/drive/MyDrive/lm-lenerbr-bert-base-portuguese-cased/checkpoints/e5_lr2e-05/checkpoint-100/pytorch_model.bin\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 826\n",
            "  Batch size = 16\n",
            "Saving model checkpoint to /content/drive/MyDrive/lm-lenerbr-bert-base-portuguese-cased/checkpoints/e5_lr2e-05/checkpoint-200\n",
            "Configuration saved in /content/drive/MyDrive/lm-lenerbr-bert-base-portuguese-cased/checkpoints/e5_lr2e-05/checkpoint-200/config.json\n",
            "Model weights saved in /content/drive/MyDrive/lm-lenerbr-bert-base-portuguese-cased/checkpoints/e5_lr2e-05/checkpoint-200/pytorch_model.bin\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 826\n",
            "  Batch size = 16\n",
            "Saving model checkpoint to /content/drive/MyDrive/lm-lenerbr-bert-base-portuguese-cased/checkpoints/e5_lr2e-05/checkpoint-300\n",
            "Configuration saved in /content/drive/MyDrive/lm-lenerbr-bert-base-portuguese-cased/checkpoints/e5_lr2e-05/checkpoint-300/config.json\n",
            "Model weights saved in /content/drive/MyDrive/lm-lenerbr-bert-base-portuguese-cased/checkpoints/e5_lr2e-05/checkpoint-300/pytorch_model.bin\n",
            "Deleting older checkpoint [/content/drive/MyDrive/lm-lenerbr-bert-base-portuguese-cased/checkpoints/e5_lr2e-05/checkpoint-100] due to args.save_total_limit\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 826\n",
            "  Batch size = 16\n",
            "Saving model checkpoint to /content/drive/MyDrive/lm-lenerbr-bert-base-portuguese-cased/checkpoints/e5_lr2e-05/checkpoint-400\n",
            "Configuration saved in /content/drive/MyDrive/lm-lenerbr-bert-base-portuguese-cased/checkpoints/e5_lr2e-05/checkpoint-400/config.json\n",
            "Model weights saved in /content/drive/MyDrive/lm-lenerbr-bert-base-portuguese-cased/checkpoints/e5_lr2e-05/checkpoint-400/pytorch_model.bin\n",
            "Deleting older checkpoint [/content/drive/MyDrive/lm-lenerbr-bert-base-portuguese-cased/checkpoints/e5_lr2e-05/checkpoint-200] due to args.save_total_limit\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 826\n",
            "  Batch size = 16\n",
            "Saving model checkpoint to /content/drive/MyDrive/lm-lenerbr-bert-base-portuguese-cased/checkpoints/e5_lr2e-05/checkpoint-500\n",
            "Configuration saved in /content/drive/MyDrive/lm-lenerbr-bert-base-portuguese-cased/checkpoints/e5_lr2e-05/checkpoint-500/config.json\n",
            "Model weights saved in /content/drive/MyDrive/lm-lenerbr-bert-base-portuguese-cased/checkpoints/e5_lr2e-05/checkpoint-500/pytorch_model.bin\n",
            "Deleting older checkpoint [/content/drive/MyDrive/lm-lenerbr-bert-base-portuguese-cased/checkpoints/e5_lr2e-05/checkpoint-300] due to args.save_total_limit\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 826\n",
            "  Batch size = 16\n",
            "Saving model checkpoint to /content/drive/MyDrive/lm-lenerbr-bert-base-portuguese-cased/checkpoints/e5_lr2e-05/checkpoint-600\n",
            "Configuration saved in /content/drive/MyDrive/lm-lenerbr-bert-base-portuguese-cased/checkpoints/e5_lr2e-05/checkpoint-600/config.json\n",
            "Model weights saved in /content/drive/MyDrive/lm-lenerbr-bert-base-portuguese-cased/checkpoints/e5_lr2e-05/checkpoint-600/pytorch_model.bin\n",
            "Deleting older checkpoint [/content/drive/MyDrive/lm-lenerbr-bert-base-portuguese-cased/checkpoints/e5_lr2e-05/checkpoint-400] due to args.save_total_limit\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 826\n",
            "  Batch size = 16\n",
            "Saving model checkpoint to /content/drive/MyDrive/lm-lenerbr-bert-base-portuguese-cased/checkpoints/e5_lr2e-05/checkpoint-700\n",
            "Configuration saved in /content/drive/MyDrive/lm-lenerbr-bert-base-portuguese-cased/checkpoints/e5_lr2e-05/checkpoint-700/config.json\n",
            "Model weights saved in /content/drive/MyDrive/lm-lenerbr-bert-base-portuguese-cased/checkpoints/e5_lr2e-05/checkpoint-700/pytorch_model.bin\n",
            "Deleting older checkpoint [/content/drive/MyDrive/lm-lenerbr-bert-base-portuguese-cased/checkpoints/e5_lr2e-05/checkpoint-500] due to args.save_total_limit\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 826\n",
            "  Batch size = 16\n",
            "Saving model checkpoint to /content/drive/MyDrive/lm-lenerbr-bert-base-portuguese-cased/checkpoints/e5_lr2e-05/checkpoint-800\n",
            "Configuration saved in /content/drive/MyDrive/lm-lenerbr-bert-base-portuguese-cased/checkpoints/e5_lr2e-05/checkpoint-800/config.json\n",
            "Model weights saved in /content/drive/MyDrive/lm-lenerbr-bert-base-portuguese-cased/checkpoints/e5_lr2e-05/checkpoint-800/pytorch_model.bin\n",
            "Deleting older checkpoint [/content/drive/MyDrive/lm-lenerbr-bert-base-portuguese-cased/checkpoints/e5_lr2e-05/checkpoint-600] due to args.save_total_limit\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 826\n",
            "  Batch size = 16\n",
            "Saving model checkpoint to /content/drive/MyDrive/lm-lenerbr-bert-base-portuguese-cased/checkpoints/e5_lr2e-05/checkpoint-900\n",
            "Configuration saved in /content/drive/MyDrive/lm-lenerbr-bert-base-portuguese-cased/checkpoints/e5_lr2e-05/checkpoint-900/config.json\n",
            "Model weights saved in /content/drive/MyDrive/lm-lenerbr-bert-base-portuguese-cased/checkpoints/e5_lr2e-05/checkpoint-900/pytorch_model.bin\n",
            "Deleting older checkpoint [/content/drive/MyDrive/lm-lenerbr-bert-base-portuguese-cased/checkpoints/e5_lr2e-05/checkpoint-700] due to args.save_total_limit\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 826\n",
            "  Batch size = 16\n",
            "Saving model checkpoint to /content/drive/MyDrive/lm-lenerbr-bert-base-portuguese-cased/checkpoints/e5_lr2e-05/checkpoint-1000\n",
            "Configuration saved in /content/drive/MyDrive/lm-lenerbr-bert-base-portuguese-cased/checkpoints/e5_lr2e-05/checkpoint-1000/config.json\n",
            "Model weights saved in /content/drive/MyDrive/lm-lenerbr-bert-base-portuguese-cased/checkpoints/e5_lr2e-05/checkpoint-1000/pytorch_model.bin\n",
            "Deleting older checkpoint [/content/drive/MyDrive/lm-lenerbr-bert-base-portuguese-cased/checkpoints/e5_lr2e-05/checkpoint-800] due to args.save_total_limit\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 826\n",
            "  Batch size = 16\n",
            "Saving model checkpoint to /content/drive/MyDrive/lm-lenerbr-bert-base-portuguese-cased/checkpoints/e5_lr2e-05/checkpoint-1100\n",
            "Configuration saved in /content/drive/MyDrive/lm-lenerbr-bert-base-portuguese-cased/checkpoints/e5_lr2e-05/checkpoint-1100/config.json\n",
            "Model weights saved in /content/drive/MyDrive/lm-lenerbr-bert-base-portuguese-cased/checkpoints/e5_lr2e-05/checkpoint-1100/pytorch_model.bin\n",
            "Deleting older checkpoint [/content/drive/MyDrive/lm-lenerbr-bert-base-portuguese-cased/checkpoints/e5_lr2e-05/checkpoint-1000] due to args.save_total_limit\n",
            "\n",
            "\n",
            "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
            "\n",
            "\n",
            "Loading best model from /content/drive/MyDrive/lm-lenerbr-bert-base-portuguese-cased/checkpoints/e5_lr2e-05/checkpoint-900 (score: 1.3523892164230347).\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=1100, training_loss=1.6243395579944957, metrics={'train_runtime': 1502.8827, 'train_samples_per_second': 10.736, 'train_steps_per_second': 1.344, 'total_flos': 578387661603840.0, 'train_loss': 1.6243395579944957, 'epoch': 2.72})"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ],
      "source": [
        "trainer.train()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KDBi0reX3l_g"
      },
      "source": [
        "Like before, we can evaluate our model on the validation set. The perplexity is much lower than for the CLM objective because for the MLM objective, we only have to make predictions for the masked tokens (which represent 15% of the total here) while having access to the rest of the tokens. It's thus an easier task for the model."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "4hSaANqj3l_g",
        "outputId": "1573d08f-baf5-4c85-a31d-1fd3755f871f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 106
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "***** Running Evaluation *****\n",
            "  Num examples = 826\n",
            "  Batch size = 16\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='52' max='52' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [52/52 00:30]\n",
              "    </div>\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Perplexity: 4.11\n"
          ]
        }
      ],
      "source": [
        "import math\n",
        "eval_results = trainer.evaluate()\n",
        "print(f\"Perplexity: {math.exp(eval_results['eval_loss']):.2f}\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# save best model\n",
        "model_dir = '/content/drive/MyDrive/' + 'lm-lenerbr-' + str(model_name) + '/model/'\n",
        "trainer.save_model(model_dir)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r0L1AqpvSt7m",
        "outputId": "33e98246-0651-4c9e-f573-56ff53a1a476"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Saving model checkpoint to /content/drive/MyDrive/lm-lenerbr-bert-base-portuguese-cased/model/\n",
            "Configuration saved in /content/drive/MyDrive/lm-lenerbr-bert-base-portuguese-cased/model/config.json\n",
            "Model weights saved in /content/drive/MyDrive/lm-lenerbr-bert-base-portuguese-cased/model/pytorch_model.bin\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# save tokenizer\n",
        "tokenizer.save_pretrained(model_dir)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CDxm3Sq0vmWc",
        "outputId": "fe972e41-146b-4832-ca18-24a322a8e495"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "tokenizer config file saved in /content/drive/MyDrive/lm-lenerbr-bert-base-portuguese-cased/model/tokenizer_config.json\n",
            "Special tokens file saved in /content/drive/MyDrive/lm-lenerbr-bert-base-portuguese-cased/model/special_tokens_map.json\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('/content/drive/MyDrive/lm-lenerbr-bert-base-portuguese-cased/model/tokenizer_config.json',\n",
              " '/content/drive/MyDrive/lm-lenerbr-bert-base-portuguese-cased/model/special_tokens_map.json',\n",
              " '/content/drive/MyDrive/lm-lenerbr-bert-base-portuguese-cased/model/vocab.txt',\n",
              " '/content/drive/MyDrive/lm-lenerbr-bert-base-portuguese-cased/model/added_tokens.json',\n",
              " '/content/drive/MyDrive/lm-lenerbr-bert-base-portuguese-cased/model/tokenizer.json')"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Push model to HF model hub"
      ],
      "metadata": {
        "id": "S2kjtrGpwi0i"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "id": "o3so0BOsQGxd"
      },
      "outputs": [],
      "source": [
        "# trainer.push_to_hub()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# load best model\n",
        "from transformers import AutoModelForMaskedLM\n",
        "model = AutoModelForMaskedLM.from_pretrained(model_dir)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zg8ZFavBwBel",
        "outputId": "2585a65e-c22c-43c8-943a-687eadee491e"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "loading configuration file /content/drive/MyDrive/lm-lenerbr-bert-base-portuguese-cased/model/config.json\n",
            "Model config BertConfig {\n",
            "  \"_name_or_path\": \"/content/drive/MyDrive/lm-lenerbr-bert-base-portuguese-cased/model/\",\n",
            "  \"architectures\": [\n",
            "    \"BertForMaskedLM\"\n",
            "  ],\n",
            "  \"attention_probs_dropout_prob\": 0.1,\n",
            "  \"classifier_dropout\": null,\n",
            "  \"directionality\": \"bidi\",\n",
            "  \"hidden_act\": \"gelu\",\n",
            "  \"hidden_dropout_prob\": 0.1,\n",
            "  \"hidden_size\": 768,\n",
            "  \"initializer_range\": 0.02,\n",
            "  \"intermediate_size\": 3072,\n",
            "  \"layer_norm_eps\": 1e-12,\n",
            "  \"max_position_embeddings\": 512,\n",
            "  \"model_type\": \"bert\",\n",
            "  \"num_attention_heads\": 12,\n",
            "  \"num_hidden_layers\": 12,\n",
            "  \"output_past\": true,\n",
            "  \"pad_token_id\": 0,\n",
            "  \"pooler_fc_size\": 768,\n",
            "  \"pooler_num_attention_heads\": 12,\n",
            "  \"pooler_num_fc_layers\": 3,\n",
            "  \"pooler_size_per_head\": 128,\n",
            "  \"pooler_type\": \"first_token_transform\",\n",
            "  \"position_embedding_type\": \"absolute\",\n",
            "  \"torch_dtype\": \"float32\",\n",
            "  \"transformers_version\": \"4.15.0\",\n",
            "  \"type_vocab_size\": 2,\n",
            "  \"use_cache\": true,\n",
            "  \"vocab_size\": 29794\n",
            "}\n",
            "\n",
            "loading weights file /content/drive/MyDrive/lm-lenerbr-bert-base-portuguese-cased/model/pytorch_model.bin\n",
            "All model checkpoint weights were used when initializing BertForMaskedLM.\n",
            "\n",
            "All the weights of BertForMaskedLM were initialized from the model checkpoint at /content/drive/MyDrive/lm-lenerbr-bert-base-portuguese-cased/model/.\n",
            "If your task is similar to the task the model of the checkpoint was trained on, you can already use BertForMaskedLM for predictions without further training.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# push model and tokenizer to HF model hub\n",
        "if model_checkpoint == \"neuralmind/bert-base-portuguese-cased\":\n",
        "  model.push_to_hub('pierreguillou/bert-base-cased-pt-lenerbr')\n",
        "  tokenizer.push_to_hub('pierreguillou/bert-base-cased-pt-lenerbr')\n",
        "else:\n",
        "  model.push_to_hub('pierreguillou/bert-large-cased-pt-lenerbr')\n",
        "  tokenizer.push_to_hub('pierreguillou/bert-large-cased-pt-lenerbr')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 274,
          "referenced_widgets": [
            "06f4a1e636254773b57d1aada679ccf9",
            "fec32d21df2b47d38dda6c294a7cda50",
            "543472090b00438c87b31b31b21b55b6",
            "50fcc449f3e74bd4a15d5ce19eef364a",
            "002e2503a16b4e87bc3d5e6bcbe01a2f",
            "36fea090b6704ce0adaf00a882c298e0",
            "95fa2f2d92074b22a9592d1fa4e2446e",
            "11cef1fda8e24986a164a41de13d21d8",
            "d360f32f045047b08640c54fdc394f3e",
            "c921d6b324a748999682acf00812f660",
            "6343a86b3da94c78a2f4a4fc270a7676"
          ]
        },
        "id": "PQsFYv93u7o9",
        "outputId": "2866e195-2ac7-4854-89a5-4072c101fb44"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/huggingface_hub/hf_api.py:726: FutureWarning: `create_repo` now takes `token` as an optional positional argument. Be sure to adapt your code!\n",
            "  FutureWarning,\n",
            "Cloning https://huggingface.co/pierreguillou/bert-base-cased-pt-lenerbr into local empty directory.\n",
            "Configuration saved in pierreguillou/bert-base-cased-pt-lenerbr/config.json\n",
            "Model weights saved in pierreguillou/bert-base-cased-pt-lenerbr/pytorch_model.bin\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "06f4a1e636254773b57d1aada679ccf9",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Upload file pytorch_model.bin:   0%|          | 3.37k/416M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "To https://huggingface.co/pierreguillou/bert-base-cased-pt-lenerbr\n",
            "   58f9e9a..d906a41  main -> main\n",
            "\n",
            "tokenizer config file saved in pierreguillou/bert-base-cased-pt-lenerbr/tokenizer_config.json\n",
            "Special tokens file saved in pierreguillou/bert-base-cased-pt-lenerbr/special_tokens_map.json\n",
            "To https://huggingface.co/pierreguillou/bert-base-cased-pt-lenerbr\n",
            "   d906a41..786c3cf  main -> main\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CRABNSNEQGxe"
      },
      "source": [
        "You can now share this model with all your friends, family, favorite pets: they can all load it with the identifier `\"your-username/the-name-you-picked\"` so for instance:\n",
        "\n",
        "```python\n",
        "from transformers import AutoModelForMaskedLM\n",
        "\n",
        "model = AutoModelForMaskedLM.from_pretrained(\"sgugger/my-awesome-model\")\n",
        "```"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# END"
      ],
      "metadata": {
        "id": "NsKsltmUIVRV"
      }
    }
  ],
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      "name": "Finetuning_language_model_BERtimbau_LeNER_Br",
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      "collapsed_sections": []
    },
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    "language_info": {
      "name": "python"
    },
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      "display_name": "Python 3"
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