{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ikoghoemmanuell/Bizstech-Msc.-Cybersecurity-/blob/main/Copy_of_model_2_with_pytorch.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k2exvBPRM5Fr"
      },
      "source": [
        "# Fake News Detection with Hugging Face"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KyqMa46fM5GR"
      },
      "source": [
        "Hugging Face is an open-source and platform provider of machine learning technologies. You can use install their package to access some interesting pre-built models to use them directly or to fine-tune (retrain it on your dataset leveraging the prior knowledge coming with the first training), then host your trained models on the platform, so that you may use them later on other devices and apps.\n",
        "\n",
        "Please, [go to the website and sign-in](https://huggingface.co/) to access all the features of the platform.\n",
        "\n",
        "[Read more about Text classification with Hugging Face](https://huggingface.co/tasks/text-classification)\n",
        "\n",
        "The Hugging face models are Deep Learning based, so will need a lot of computational GPU power to train them. Please use [Colab](https://colab.research.google.com/) to do it, or your other GPU cloud provider, or a local machine having NVIDIA GPU."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hOmGEPGfM5GV"
      },
      "source": [
        "Find below a simple example, with just 10 epochs of fine-tuning`.\n",
        "\n",
        "Read more about the fine-tuning concept : [here](https://deeplizard.com/learn/video/5T-iXNNiwIs#:~:text=Fine%2Dtuning%20is%20a%20way,perform%20a%20second%20similar%20task.)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Installation"
      ],
      "metadata": {
        "id": "15acY9BWW8sh"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IrxIHtodqxAQ",
        "outputId": "a11c3b44-223e-45cd-e888-f7ad9941643f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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          ]
        }
      ],
      "source": [
        "# !pip install zipfile\n",
        "\n",
        "!pip install transformers\n",
        "!pip install datasets\n",
        "!pip install --upgrade accelerate\n",
        "!pip install sentencepiece"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GzVa1HgauFp9"
      },
      "source": [
        "## Importing Libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "8Hkg-TfCM5GX"
      },
      "outputs": [],
      "source": [
        "import huggingface_hub # Importing the huggingface_hub library for model sharing and versioning\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import transformers\n",
        "from datasets import load_dataset\n",
        "from sklearn.model_selection import train_test_split\n",
        "import os\n",
        "\n",
        "from datasets import DatasetDict, Dataset\n",
        "from sklearn.metrics import mean_squared_error, classification_report\n",
        "\n",
        "from transformers import AutoModelForSequenceClassification\n",
        "from transformers import AutoTokenizer, AutoConfig\n",
        "from transformers import TrainingArguments, Trainer\n",
        "from google.colab import drive\n",
        "import zipfile\n",
        "import torch"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Load Dataset and Delete Null Values"
      ],
      "metadata": {
        "id": "O_hiA9f6Y8H8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Mount your Google Drive\n",
        "drive.mount('/content/drive')\n",
        "\n",
        "# Get the file path from Google Drive\n",
        "file_path = '/content/drive/MyDrive/fake news/archive (2).zip'\n",
        "\n",
        "# Unzip the file\n",
        "with zipfile.ZipFile(file_path, 'r') as zip_ref:\n",
        "    # Find the CSV files in the zip folder\n",
        "    fake_path = zip_ref.extract('Fake.csv', '/content/')\n",
        "    real_path = zip_ref.extract('True.csv', '/content/')\n",
        "\n",
        "# Read the csv file from the url\n",
        "fake = pd.read_csv(fake_path)\n",
        "real = pd.read_csv(real_path)\n",
        "\n",
        "# A way to delete rows with empty or null values\n",
        "fake = fake[~fake.isna().any(axis=1)]\n",
        "real = real[~real.isna().any(axis=1)]"
      ],
      "metadata": {
        "id": "hs4TZrFMEbbZ",
        "outputId": "bea17e91-94df-4e99-ac27-ab86363a9c81",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fake[\"label\"] = 1\n",
        "real[\"label\"] = 0"
      ],
      "metadata": {
        "id": "_YmHe_brTc23"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.concat([fake, real], axis =0 )\n",
        "df.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 581
        },
        "id": "ZbOLR5zdTefJ",
        "outputId": "1f3f4d9a-2027-4b69-9a53-b0b04cf77289"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                               title  \\\n",
              "0   Donald Trump Sends Out Embarrassing New Year’...   \n",
              "1   Drunk Bragging Trump Staffer Started Russian ...   \n",
              "2   Sheriff David Clarke Becomes An Internet Joke...   \n",
              "3   Trump Is So Obsessed He Even Has Obama’s Name...   \n",
              "4   Pope Francis Just Called Out Donald Trump Dur...   \n",
              "5   Racist Alabama Cops Brutalize Black Boy While...   \n",
              "6   Fresh Off The Golf Course, Trump Lashes Out A...   \n",
              "7   Trump Said Some INSANELY Racist Stuff Inside ...   \n",
              "8   Former CIA Director Slams Trump Over UN Bully...   \n",
              "9   WATCH: Brand-New Pro-Trump Ad Features So Muc...   \n",
              "\n",
              "                                                text subject  \\\n",
              "0  Donald Trump just couldn t wish all Americans ...    News   \n",
              "1  House Intelligence Committee Chairman Devin Nu...    News   \n",
              "2  On Friday, it was revealed that former Milwauk...    News   \n",
              "3  On Christmas day, Donald Trump announced that ...    News   \n",
              "4  Pope Francis used his annual Christmas Day mes...    News   \n",
              "5  The number of cases of cops brutalizing and ki...    News   \n",
              "6  Donald Trump spent a good portion of his day a...    News   \n",
              "7  In the wake of yet another court decision that...    News   \n",
              "8  Many people have raised the alarm regarding th...    News   \n",
              "9  Just when you might have thought we d get a br...    News   \n",
              "\n",
              "                date  label  \n",
              "0  December 31, 2017      1  \n",
              "1  December 31, 2017      1  \n",
              "2  December 30, 2017      1  \n",
              "3  December 29, 2017      1  \n",
              "4  December 25, 2017      1  \n",
              "5  December 25, 2017      1  \n",
              "6  December 23, 2017      1  \n",
              "7  December 23, 2017      1  \n",
              "8  December 22, 2017      1  \n",
              "9  December 21, 2017      1  "
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              "      <td>December 31, 2017</td>\n",
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              "      <td>Trump Is So Obsessed He Even Has Obama’s Name...</td>\n",
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              "      <td>1</td>\n",
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              "      <td>News</td>\n",
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              "      <td>1</td>\n",
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              "      <td>Racist Alabama Cops Brutalize Black Boy While...</td>\n",
              "      <td>The number of cases of cops brutalizing and ki...</td>\n",
              "      <td>News</td>\n",
              "      <td>December 25, 2017</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Fresh Off The Golf Course, Trump Lashes Out A...</td>\n",
              "      <td>Donald Trump spent a good portion of his day a...</td>\n",
              "      <td>News</td>\n",
              "      <td>December 23, 2017</td>\n",
              "      <td>1</td>\n",
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              "      <th>7</th>\n",
              "      <td>Trump Said Some INSANELY Racist Stuff Inside ...</td>\n",
              "      <td>In the wake of yet another court decision that...</td>\n",
              "      <td>News</td>\n",
              "      <td>December 23, 2017</td>\n",
              "      <td>1</td>\n",
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              "      <td>Former CIA Director Slams Trump Over UN Bully...</td>\n",
              "      <td>Many people have raised the alarm regarding th...</td>\n",
              "      <td>News</td>\n",
              "      <td>December 22, 2017</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>WATCH: Brand-New Pro-Trump Ad Features So Muc...</td>\n",
              "      <td>Just when you might have thought we d get a br...</td>\n",
              "      <td>News</td>\n",
              "      <td>December 21, 2017</td>\n",
              "      <td>1</td>\n",
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      ]
    },
    {
      "cell_type": "markdown",
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        "id": "ebVVDR2pM5Gc"
      },
      "source": [
        "## Splitting the dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "4F8L25--M5Ge"
      },
      "outputs": [],
      "source": [
        "# Split the train data => {train, eval}  train 80%, test 20%\n",
        "train, eval = train_test_split(df, test_size=0.2, random_state=42, stratify=df['label'])"
      ]
    },
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      "cell_type": "code",
      "execution_count": 7,
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              "                                                     [key], {});\n",
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      "source": [
        "# get the first 5 rows of the train set to make sure it looks right\n",
        "train.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
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        {
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 35918 entries, 13447 to 1255\n",
            "Data columns (total 5 columns):\n",
            " #   Column   Non-Null Count  Dtype \n",
            "---  ------   --------------  ----- \n",
            " 0   title    35918 non-null  object\n",
            " 1   text     35918 non-null  object\n",
            " 2   subject  35918 non-null  object\n",
            " 3   date     35918 non-null  object\n",
            " 4   label    35918 non-null  int64 \n",
            "dtypes: int64(1), object(4)\n",
            "memory usage: 1.6+ MB\n"
          ]
        }
      ],
      "source": [
        "# check datatypes of the train set, object can mean text or string\n",
        "train.info()"
      ]
    },
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-2f54e616-b764-439a-b2f3-6a5f10079830');\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",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "# get the first 5 rows of the eval or test set\n",
        "eval.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "USbmZj8rM5Go",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c388438a-5e5e-4230-da86-83f0704b3889"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "eval.label.unique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "XFuOwIrCM5Gp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b9589fc3-e7de-4742-f6cd-402468bb1a7d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "new dataframe shapes: train is (35918, 5), eval is (8980, 5)\n"
          ]
        }
      ],
      "source": [
        "print(f\"new dataframe shapes: train is {train.shape}, eval is {eval.shape}\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 90 true, 10 fake, 70, 30\n",
        "# 40, 60 good, 55, 45 is good\n",
        "# Checking if our df is well balanced\n",
        "label_size = [df['label'].sum(),len(df['label'])-df['label'].sum()]\n",
        "plt.pie(label_size,explode=[0.1,0.1],colors=['firebrick','navy'],startangle=90,shadow=True,labels=['Fake','True'],autopct='%1.1f%%')"
      ],
      "metadata": {
        "id": "2MMjiFe_rMEM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 510
        },
        "outputId": "a33775cb-0e12-47a5-d750-db629f535538"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "([<matplotlib.patches.Wedge at 0x7f39478c9cc0>,\n",
              "  <matplotlib.patches.Wedge at 0x7f39478c9c00>],\n",
              " [Text(-1.1968727067385088, -0.0865778485782335, 'Fake'),\n",
              "  Text(1.1968726986325005, 0.08657796063754254, 'True')],\n",
              " [Text(-0.6981757455974634, -0.05050374500396954, '52.3%'),\n",
              "  Text(0.6981757408689586, 0.05050381037189981, '47.7%')])"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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9HyhdRsxikJ3AR5s2oa6qCjmpqbAd4H5BRKSc55/fy3UYR8AgG0FteTk+ef99ONPTkbxnD1fwICJFhUIyJ0mPgEE2DEmSsOXNN+Hq6kKawQAL2+2JKAY8+eQu1NV1K11GzGGQDaNs714c2LkTmfn5SNq5E4IsK10SERF8vhB+9auPlS4j5jDIBgkGg9j69tuQgkEkezwwV1crXRIRUZ8//WkHWlvdSpcRUxhkgxz4/HOU7d+PzPx8JHA9RSKKMS5XAA8/vF3pMmIKg6wfn9eLj95+G6JOhwSvF+baWqVLIiIa4ve//wxdXVzv9TgGWT+7t21DVWkpMvPykMANM4koRnV0ePGHP3ymdBkxg0HWy93Tg483bYLZaoXF54O5slLpkoiIRvTf/70dHk9A6TJiAoOs175PP0V9dTUycnNh27ePnYpEFNOamlz4y192Kl1GTGCQIXxv7JP334fJZoMxEOA2LUSkCr/85ccIBLhYA4MMwIEdO3Csqgrp2dmwHTjAVTyISBVqa7vw3HN7lS5DcXEfZMFAAJ++/z70ej2Mej0spaVKl0RENGpPPLFL6RIUF/dBdnj3blSXliI9NxeWykrovF6lSyIiGrUtW6rjfjHhuA6yUCiET99/HxAEmC0WWA8dUrokIqIxkWXg6afje7pQXAdZ5cGDKD9wAOk5OdC3tsLY1KR0SUREY/bXv8b3KkRxHWR7PvkEAb8f1oQEjsaISLXKy9vx0Uc1SpehmLgNstbGRhzYsQPO9HQIfj8s5eVKl0REdMqeeip+R2VxG2QHdu5EV3s77CkpsJSXQwwGlS6JiOiUvfjifni98fk+FpdB5vf5sHPLFlgTEiCKIjfOJCLV6+z04bXX4vMWSVwGWdm+fWg8ehSpWVnQdXbC2NKidElERKftr3+Nz+7FuAsyWZaxe9s2yJIEo8nEe2NEpBmbNpWhsbFH6TKiLu6CrOnYMRzZuxcpmZkAwCAjIs0IhWQ8+2z8LVkVd0FWuncverq6kJicDENzM/Td3UqXREQ0buKxezGugiwYDGLP9u2w2GwQBIF7jhGR5uzZ04j9++NrcYe4CrLasjI01NTAmZ4OyDIsDDIi0qBNm+LrlklcBdmRffvg93phsdmgb22FzuVSuiQionH33nsVSpcQVXETZH6fD/s/+ww2ux0AYK6tVbgiIqLI2Ly5Gn5//OyrGDdBVl1aiub6ejjS0gAAJgYZEWmUyxXAtm3x8x4XN0FWtn8/QoEATGYzRI8HBk6CJiINi6fLi3ERZMFAAIe++AIJyckAwqMxQdmSiIgi6t13GWSacqyqCm1NTbA7nQAA09GjCldERBRZn39eh87O+NjxPi6CrKasDD6vFyaLBZAkmI4dU7okIqKICoVkfPBBldJlRIXmg0yWZRzetQsmiwWCIMDQ1AQxEFC6LCKiiIuX+2SaD7K2pibUV1d/eVmxoUHhioiIoiNe7pNpPshqysrQ09mJhN75Y8bGRoUrIiKKjtLSVtTWdipdRsRpPsgqDh6EqNNBFEVAkmBoiq81yIgovsXD5UVNB5nf50P5gQNI7G27N7S18f4YEcWV997T/pqymg6yxqNH0dPR8eVlRd4fI6I4s3NnvdIlRJymg6yhtvbLtnswyIgo/pSVtWl+3UVNB9nRigoIoghBCK/jwftjRBRvgkEJhw5pe0k+zQZZMBhE1eHDSEhKAgCILhd03viY5U5E1J/WN9rUbJA119Who7W17/6Yoa1N4YqIiJSxf3+z0iVElGaDrKG2Fl6XCxabDQBgaG1VuCIiImUwyFSqvroa6Hd/TM8RGRHFKV5aVKmj5eWwWK19n/PSIhHFq/Lydni9QaXLiBhNBpm7pwetTU19lxWFQAC67m6FqyIiUoYkyZruXNRkkLU1NcHd0wNLQgIAQN/eDkGWFa6KiEg5Wr68qMkga21shN/rhclsBgDoOzqULYiISGFabvjQbJAB6Gv04GVFIop3DDKVOVZVBUPvaAwA9AwyIopzvLSoIsFAAA01NbD2NnoAHJEREVVWdsDn02bnouaCrLOtDR6XC+Z+rfcMMiKKd5Iko7HRdcrfLwjCCT9+8pOfjF+xY6RX7MwR0t3RAZ/XC2dGBoDe1nuusUhEhKYmF/Lz7af0vfX1X24H87e//Q0//vGPcfjw4b5jCb1d4gAgyzJCoRD0+uhEjOZGZF0dHQgGAjAYjQAAXVeXwhUREcWGxsaeU/7ezMzMvg+73Q5BEPo+P3ToEBITE/HWW29h3rx5MJlM2Lp1K9avX4/LLrtswPN8+9vfxrJly/o+lyQJGzZswIQJE2CxWDBr1iz8/e9/H1Nt2huRtbf3DXUBQOc69aE0EZGWNDVF9v3wnnvuwa9+9SsUFRXB4XCM6ns2bNiAZ555Bo8++igmTZqEzZs347rrrkNaWhqWLl06qufQXJB1DlqKSvR4FKqEiCi2nM49stF44IEHsGrVqlE/3ufz4aGHHsJ7772HxYsXAwCKioqwdetWPPbYY/EbZM0NDTCYTH2fi7w/RqRhWwD8E8AiABcCaAfw2xEeeyWAGSN87ScjHF8FYAmAIIB/ADgEIAHAWgAT+z3uIwCdANaMvnQFRHpENn/+/DE9vqysDG63e0j4+f1+zJkzZ9TPo6kgk2UZrQ0NfSt6AAwyIu06BmAHgIx+x+wAvjfocTsAfAyg+ATPNfh7ygC8BmBav+eoA3AzgCMAXgbwfQACwuG5A8CtY/4Joi3SQWbrN+0JAERRhDxoecBAIND37z094Xt2GzduRE5OzoDHmfoNSE5GU0Hm7umBx+1mkBFpng/hMLkYwOZ+x0UAiYMeewjhkdiJ3hiH+54JAJy9nzcDmAIgHYADwLsA3ABsAN5AeORmRqzr7PRF9XxpaWnYt2/fgGO7du2CwWAAAEyfPh0mkwk1NTWjvow4HE11LXp6ehD0+6Hv7VgEGGRE2vQmgMkYeHlvOHUAGgCM/jIV0IPwqKv/92QCqAEQQHi0lgDACmAPwuOBaVCDzs7ovh+uWLECn3/+Of7617/iyJEjuP/++wcEW2JiIu6++2585zvfwVNPPYXy8nLs3LkTjzzyCJ566qlRn0dTIzKvx4NgIAB9b9oD4BwyIs3ZC6AewC2jeOxOAKkA8sfw/LsAGDEwnOYAaATwPwgH2JUAPAA+ALAe4ft0+xAewV0KIGkM54uerq7ojsguuOAC3HffffjBD34Ar9eLm266CTfccAP27t3b95if/exnSEtLw4YNG1BRUYHk5GTMnTsXP/rRj0Z9Hk0FmcflGhJkHJERaUkngLcBXA/AcJLHBhAOvbFesvoCwBmDnl+HcINHf68i3GRSj/ClyDsQbvp4C8BVYzxndIzXpcX169dj/fr1fZ8vW7ZsyL2w437605/ipz/96YjPJQgC7rrrLtx1112nXI+mgszr8UCSJOh0ur5jgt+vYEVENL7qALgAPNbvmAygGsCnAO7Dl3dMDiAcZrPG8PzVAFoRHnGdSCWAJgCXAHgHwCSER3EzeuuITdEekUWLpoLM43IBvROhjxMkSaFqiGj8FSE88unvNYQvHy7BwNv+OxFu0LBh9HYCyEL4nthIAgA2Avi33vPJAI6/z4T6/Xvs0WqQaarZw+fxYECMyTKEUEipcoho3JkQbrfv/2EAYMHANvxWhEdXc0d4nkcAHBx0zIvwKG6k7zluM8IjsKzez/N6n6sB4dHYWO7HRVcwKGlyBXxNBZnH5Rp4nZYhRhSnvkC44WKkrsZWhIOrv30Ij65mnuB5GwHsB7C837HpCAfb//Z+ffUp1Bs9er2m3vYBaOzSotfthih++T+JozGiePC1YY6t7P0YyU+GOTa/9+NEMgB8a9AxEcBFvR+xTRQF6HTaCzJN/UR+nw8Cg4yIaFgGg6be8vto6qcK+P0DR2Rs9CANMJmEkz+IaBSMRt3JH6RCmguy/iMy3iMjLXA6tfnmQ9FnMGjztaSpIAsGAgNGZINb8YlOR0ihyfVOp6Z+TUlBWh2RaarZY/CITBZj9w3gubY2vNDRMeBYjsGAP+bloTsUwnPt7djl8aA5GESSKOJMmw3XOp2wneBneq6tDVtcLrQEg9ALAopNJlzncGBK7yLKAVnGI83N+MTlgkOvx+0pKZhttfZ9//91dKA5GMRtqakR+ZnVzlVdrch5U1K0+eZD0ccgU4EhI7IYDjIAyDcY8LOsrL7Pdb0jyLZQCG3BIL7mdCLPaERTMIg/trSgrbkZ92RkjPR0yDEacZvZjEyDAX5Zxmudnbi/vh6P5efDrtNhU1cXyn0+/DInBzvcbvy6qQl/LSiAIAhoCATwTnc3fjNoKwX6kqe+HiGfD7oxbC8xVnq9HuEW8C9xREbjhc0eKhAMBiH0u5wo62L7rw+dIMCh1/d9JPXWW2A04t7MTCy02ZBlMGCWxYLrHA586nIhNMJ6ZgCwNCEBs61WZBoMyDca8fWUFLhlGVW9y3TVBgJYaLUi32jEmqQkdEoSunobYv7Y0oIbnU5YYzz8FSVJcEd4VOZ02iHLGDAfkvfIaLxodUSmqXctYfA9sRh/U64LBLC+uhq31NTg101NaA6OPOPeLUmwimLfqO1kArKMTV1dsIkiJvRuazPBaMQBrxc+ScIXHg+cOh2SRBEfdnfDKAhYbBvLUj7xqaeqKqLP73TaYTIZ4fN9uUaowSAgKSm2X8ukDloNMk1dWtTr9ZD7tdzLMdzsMcVsxl1GI3IMBrSHQnihvR331NXhkdzcIaOirlAIf+vowAVJJ98a4jOXC79saoJPluHQ6fBAZmbfSG9lYiKq/H584+hRJOl0+EFGBnokCc+1t+PB7Gw809aGzT09yDIY8K20NKToNfXyGBeuCAdZSkoyLBYTPB4fzGZTv+Miuro4nYROD7sWT8OTTz6J5OTkiJ9Hp9cPXKIqhi8tzrNacXZCAiaYTJhrteLHmZlwhULY2rv193FuScIDDQ3IMxhwtcNx0uedabHg4dxc/CI7G3OtVvyiqQkdvdMQ9IKA21NT8Zf8fPwmJwfTzWY80dqKi+x2VPh82O5y4Xe5uZhiMuFPLS0R+bnVLtJBlpycBJvNCo/HM+A4Ly/SeNDqiGxMQbZ+/XoIgjDko6ysLFL1jYlOpxsYZIIQ8/fJjkvQ6ZBtNKK+3+VFtyThJ/X1sIgifpSRAf0oRphmUUS2wYCpZjO+lZYGHYB3u7qGfewejwc1gQDWJiVhn9eLeVYrzKKIsxMSsI/7uA0r0kGm0+mQlZUKt3vgf/+UFF5apNPHIOu1evVq1NfXD/iYMGFCJGobM51eD2lQM4RkONnme7HBI0loCATg7A1etyTh/vp66AUB/5GRAeMp3u+TEb5fNphfkvBYSwvuTE2FThAgyXJfI0lQlmN4IwplRTrIACA3N3PAPTKAIzIaHzabOt4Px2rM744mkwmZmZkDPn77299i5syZsNlsyMvLw5133omeQZfI+mtubsb8+fOxbt06+Hw+SJKEDRs2YMKECbBYLJg1axb+/ve/j/mH0en1wKA3bbm30SHWPNHain0eDxoDARz0evFQYyNEAOcmJMAtSfhxfT28soxvpqXBLUloDwbRHgwO6Fq8o7YW21wuAIBXkvDXtjYc8nrRFAigzOfDb5ua0BoK4eyEhCHn/1tHB+ZZrZjY20o+zWzGNrcblT4fNnZ1YVoEW8zVzNvYiKDbHdFzpKU5h+y2y7lkNB5yc09+n12NxuVuviiK+N3vfocJEyagoqICd955J37wgx/gD3/4w5DH1tbWYtWqVTjzzDPx+OOPQ6fT4cEHH8QzzzyDRx99FJMmTcLmzZtx3XXXIS0tDUuXjn6bct2gZg8AkGL0Dbk1GMSvmprQFQrBrtNhutmMX+bkwK7TYa/Hg1JfeAO822prB3zfn/PykNE7yjwWCMDd+/OKAI76/Xi/uxtdoRCSdDoUm0z4eVYW8geFebXfj609Pfhtbm7fsbNsNuz1enFvXR1yjEZ8Lz09gj+9urmqq2GfNi1iz+90JgMIt+Af78R1OEQIwpC/04jGJD/frnQJETHmIHvjjTeQ0O8v/AsvvBAvvfRS3+eFhYX4z//8T9x+++1Dguzw4cNYtWoV1q1bh4cffhiCIMDn8+Ghhx7Ce++9h8WLFwMAioqKsHXrVjz22GNjCjKD0QhJJUH2/RNMbJ5pseAfRUUnfY7+jzGKIn6UeaJdbb9UYDTisfyBm/+JgoA7UlNxB1f1OClXVVVEgywlJRkmkxFerw8WS3hVFp1OQHKyiPZ2XvSlU8cg67V8+XL88Y9/7PvcZrPhvffew4YNG3Do0CF0dXUhGAzC6/XC7XbD2rsEksfjwTnnnINrrrkGDz/8cN/3l5WVwe12Y9WqVQPO4/f7MWfOnDHVZk1IQGjQXCypd3kmovHiqqyM6PM7nXZYrRZ4PN6+IAsf1zHI6LTk5fHSIoBwcBUXF/d9XlVVhYsuugh33HEHHnzwQTidTmzduhVf//rX4ff7+4LMZDJh5cqVeOONN/D9738fOb1LIR2/l7Zx48a+Y8eZxjiasthsGHzlhUFG4y3SDR92eyJsNgtcrsEt+CLKyyN6atI4jshGsGPHDkiShF//+td96xy++OKLQx4niiKefvppXHPNNVi+fDk+/PBDZGdnY/r06TCZTKipqRnTZcThGM1mCIO7FhlkNM4ivbqHKIrIycnArl2HBhxnwwedDlEUNNvscdqTU4qLixEIBPDII4+goqICTz/9NB599NFhH6vT6fDss89i1qxZWLFiBRoaGpCYmIi7774b3/nOd/DUU0+hvLwcO3fuxCOPPIKnnnpqTLWYzOYhW7eEuOwSjbNotOBnZ2fA19vwcxwXD6bTkZmZwJU9RjJr1iz85je/wS9+8QuUlJTg2WefxYYNG0Z8vF6vx/PPP48ZM2ZgxYoVaGpqws9+9jPcd9992LBhA6ZNm4bVq1dj48aNY56fZrZah7Qth4ZpPSc6Hf7WVgS6uyN6jrQ0B4CBf5RxREanQ6v3xwBAkAe/86tY6Z49eOK//guFkydD7J1YLLrdyHjhBYUrI60555VXkHzGGRF7/s8/34tf/OLPmDZtYt8le0mS8eCD7ZDY70Gn4Morp+PFF69UuoyI0NS1CovNBr3BgGAg0HdMslhUs0wVqUc0Fg8+3oJ/nCgKcDg09StLUaTVRg9AY0FmTUiA0WiE399veR9B4H0yGneR3i06JcUBq9UMj2fgmotcqopOFYNMJRKTk2E0m+EftOAt75PReIt052Jiog1JSQlcPJjGDYNMJYwmExLtdgYZRVykJ0ULgoDs7Ax4PIM7Fzkio1Oj5WYPTQUZADgzhrYtB0exISXRWESjBT8nJ2PgZXKwc5FOjSgKmDw5RekyIkZzQZaSnj6g2QMAgqPYkJJoLAKdnfC3t0f0HCkpyUOOcS4ZnYpp01KRmBib686OB839ViQOsxN1gEFGERCNzkVBEAYshG23i9CPy54VFE8WLco5+YNUTJtBJssDJkZLCQmQYnRfMlKvnigsHmyxDOxcFAQBDgcvL9LYLFzIIFOVxORkGIxGBAbfJ+OojMZZNEZkFotpSMMHOxdprBhkKuNITYXFZoNn0C6+vLxI4y3SQWazWWG3Jw1pwWfnIo2FxaLHzJkj73+oBZoLssTkZCQ5HPC4XAOOB51OhSoirYp0kAmCgNzc9CGTojkio7GYOzcLer22XzOa++kEQUBWQcGQIOOIjMZbpFf3AMKr4AcCAzeL5YiMxkLrjR6ABoMMADJyciCFQgOOBVJSIIua/HFJIcGeHnibmyN6DqfTDgzaLpZzyWgstH5/DNBokDnT04e0LUOvR4CXF2mcRb7hwwFBEBDq94dZYqIIgyGipyUNWbQoV+kSIk6zQWYym+HzDNwq3p+h7RueFH3R6Fzk4sF0qtLTbSgsTFa6jIjTZJA50tLCnYuD75MxyGicRTrIwnPJLMMsHswgo5NbsCBb6RKiQpNBZrZYkJGbC9egXXz96ekKVURaFelJ0VarBQ5H0jCLB2vyV5fGWTw0egAaDTIAKJw6dcgq+JLVimBiokIVkRZFY/HgvLzMYVrwOSKjk1u8OE/pEqJCs0GWkZsLQRQH3CQHOCqj8eWqrh6wHFokZGamIRgc3IKv2V9dGieJiUace26B0mVEhWZ/GzJzc2FLTIR78OXFrCyFKiItkrxeeBsaInqO8Cr4wqBjHJHRiV144SQYjfHxOtFskDnS0uBIS0NPZ+eA475c7beiUnRFepNNp9MOnU4cMCqz2USYTMIJvovi3WWXTVG6hKjRbJAJgoCiqVPhHtS5KFmtnE9G46onaosHc6kqGh2jUYe1aycrXUbUaPo3IaugAJCkIfcwOCqj8RSNSdEWi5mLB9OoLVtWiKQk7W6kOZimgywzLw+WhAS4e3oGHGeQ0XiK9JqLJpMRqakOjsho1OLpsiKg8SBLz8lBSkYGutraBhz3p6dzo00aN5G+RwYAubmZw8wl44iMhhIE4NJLpypdRlRpOsh0Oh2mzJ49pHMRoghfdnzMeKfIc9fWQh40zWO8ZWSkDplKws5FGs6CBTnIzo6v+bKaDjIAyJ84EaJOh2AgMOC4Ly8+JgpS5El+Pzx1dRE9R0pKMmRZHnC/l3PJaDiXXhpflxWBOAiy3IkTkeRwoKu9fcBxb34+t3WhcRONzkWDwTCgBd9iEWGxsAWfBrrssvi6rAjEQZAlJCWhYPLkIUEmm0zw5cTHOmQUedFZPHho5yIvL1J/kyenYPr0NKXLiDrNBxkATJw+HcFAYEgbvnfCBIUqIq2JxqTo8FyygQ0f7Fyk/uLxsiIQJ0GWV1wMi802pOnDW1AAWce/aOn0RXpEZjQakZ6ewn3J6ITWrYu/y4pAnARZZl4esvLz0d7SMuC4bDDAyzllNA6isQp+bm7GMJOi4+JXmEahpCQ9bla7HywufgtEUcTMhQvhdbt5eZEiwn3sGKRBK9SPt4yM1CGvX94jo+PuvHO+0iUoJi6CDACKpk+HNSEBrq6uAce9+fmQ9HqFqiKtkINBuGtrI3oOp9MOYGALPoOMgPCWLddfP0vpMhQTN0GWmZeH3KIitDc3D/yCXg9vUZEyRZGmRH7NxWQYDHoE+s2JNBoFJCSwBT/e3XDDLCQkxO9qRXETZIIgYMa8efB5vUMuz7inxucNUhpfXDyYlHLnnQuULkFRcRNkAFA0YwZsSUlD9igLpKYikJKiUFWkFZEOMocjCTabZZjFgxlk8Wz58sK4nDvWX1wFWXp2NvInTkRbU9OQr7mnxOf8Cxo/PRGeS6bX65GenjrM4sFx9WtMg3zjG/E9GgPiLMgEQcAZZ56JYCCA0KAOM8/EiWz6oNMSjRb8vLxMjsioT05OYtytdD+cuAoyAJgyaxac6elDmj5kg4FNH3RaPPX1CPl8J3/gaUhLc2LQLV6OyOLYrbfOg17P//9x918gwW7HzEWL0NHaOuRrLjZ90OmQJLhraiJ6iuFXweeILB4ZDCJuvXWe0mXEhLgLMgCYMW8ezBYLXIOWrAqmpsKXmalQVaQFkb5P5nTaYTIZ4fP5+44ZDAKSkuLyVzmuXX75NGRmJihdRkyIy1d/XnEx8idNQmtDw5CvuWbOVKAi0opozCXj4sEEsMmjv7h89YuiiNlnnYWA3z+k6cOXl4eAw6FQZaR2kQ6y5OQk2GxWeDyeAcd5eTG+zJ2bhXPOKVC6jJgRl0EGAFNnz4YzPR1tg1f6AOAqKVGgItKCSAeZTqdDVlYqFw+Ocw8+uELpEmJK3L76E+x2zF6yBJ0tLUNW+vBMnIiQzaZQZaRmrurqiJ8jNzdzwD0ygC348WTZskKsXl2sdBkxJW6DDABmn3UWEh2OoR2MogjX9OnKFEWq5m1sRNDtjug5UlMdXAU/jm3YcJ7SJcScuA6yjJwczFy0CG0NDcOuvygZ43cRTjpFshzxUVlKiqP3VF++Zh0OEQLXDta8yy6bijPP5B6Kg8V1kAHA3LPPhtlmQ8+g7V1kgwE97GCkUxCNzkWTyQiv98vORZ1OgN0e97/OmqbTCbw3NoK4f+XnTZyIKbNmobmubsjX3NOnI2Q2K1AVqVmkg8zptMNq5eLB8eb662fF/eLAI4n7IBMEAfOXLoVer4fH5RrwNdlg4LwyGjNXhCdF2+2Jvavgc/HgeGEy6fDTny5TuoyYxVc+gIkzZqBw6lQ0Hj065GuuadPYwUhj0hPhEZkoisjJyRjSgs8RmXbdeecC5OfblS4jZjHIEJ6bs3jlSgCAd3DHmV6P7jlzFKiK1Coaq+BnZ2fA5+OILB4kJZnwox+do3QZMY2v/F5TZs9G8YwZaKitHfI1T3ExAsnJ0S+KVMnf2orAoHU8x1tamgPAwDZFjsi06XvfW4zUVKvSZcQ0BlkvvV6PxeefD0EQ4O7pGfhFUUT3/PnKFEaqFOn7ZE6nHYIASJLUdyw5WYTI32hNSU+34bvfXax0GTGPL/t+Jp9xBqacccawozJffj68uZy/QaOjRAu+KApwOPgrrSU//vG5SEjgfNaT4au+H51OhyWrV8NoNKK7s3PI17sWLYLMP3lpFKIxKdpqNQ9pwefiwdqxeHEu7riDK9yPBt+VBymaPh0zFixAY23tkNU+QnY7FxSmUYl052Jiog1JSQlcPFijTCYdnnjiUogil2sZDb7qBxEEAWevXo0Eux0dLS1Dvt4zaxZCVt54pROL9KVFQRCQnZ0xzL5kHJFpwU9+sgxTp6YqXYZqMMiGkTNhAhYsW4aWhgaEQqEBX5MNBnQtXKhQZaQWkW72AIDs7HT4/VwFX2tmzUrH979/ltJlqAqDbARLLrgAWfn5aBym8cNbVARfZqYCVZFaBDo74W9vj+g5UlOHbgDLS4vqptcLeOaZf4NOx/+PY8H/WiNIcjhw7tq18Ho88A7ajRcAOpcsgazjX780smh0LgqCMKAF324XoddH9LQUQffeezZKStKVLkN1GGQnMPusszD5jDNQN8wbUshuR/fcudEvilQj0g0fTqcdFsvAzkVBEOBw8A8sNZo2zYn77luqdBmqxCA7Ab3BgGUXXwyzxYLOwZtvAnCVlMCfzr+eaHiRvk+WkpIMi8U0TMMHf63VRqcT8OyzV8Bg4B8hp4Kv+JOYMHUq5p1zDprq6iANavyAIKDj7LN5iZGGFelLizabFXZ70jAt+Hw9qs3ddy/GnDlZSpehWgyykxAEAeesWYOs/HzU1dQM+XooOZmLCtOwotGCn5OTPsykaP5aq8mkScl44AFumHk6+IofheTUVJy3bh1CgQBcg3aSBnovMaZxwzsaKNKrewBATk4GAoHggGNswVcPUQSeeeYKGI38f3Y6GGSjNHPRIsxesgT11dUDusQAAKKIjnPPhcR2Meon2NMDb3NzRM/hdNoBDFyBhkGmHj/84VlYuDBH6TJUj0E2SqIoYuW6dcjIy0P9MH9ph+x2dJ3FSYw0UORb8B0QBGHAxP3ERBEGQ0RPS+Pg7LOz8J//uVLpMjSBQTYGjrQ0rLjsMgT8friG2W/KU1wMd3GxApVRrIrGXDIuHqw+aWlGvPba9VxLcZwwyMZo1uLFmH3WWairqhp6iRFA1+LFCNq5JTmFRTrIwnPJLEM6F9mCH7t0OuDll6+C02lRuhTN4Kt9jERRxMrLL0dWfj6ODTNPSDYY0L58OVvyCUDkg8xqtcDhSBoyl4wjsth1330Lcc45RUqXoSkMslPgTE/H6quugiAIaB9mhfyg04muRYsUqIxiTU8UFg/Ozc0YcmmRDR+xadWqDNx//4VKl6E5DLJTNG3uXCxZvRqtDQ3web1Dvu6eOhXuiRMVqIxiiau6esi+duMtKyt9SAs+55LFnqIiM155Zb3SZWgSX+2nSBAELLv4YkybOxdHy8qGvV/WuWQJ/KncUyieSV4vvA0NET3H8cWDBx7jiCyWJCUJ2LRpPWw2s9KlaBKD7DSYzGasueYapGZloW64ya96PdpXruRGnHEuGg0fOp2IYPDLUZnNJsJkYkdcLNDrZTz33CUoLs5QuhTNYpCdpoycHFzwla9ACgbR2dY25OuS1Yr2885j80cci/R9si8XD2bnYuyR8dOfzsfatbOVLkTT+EofBzMXLcLi889Hc13dsHuXBdLS0HH22QpURrEg8iOyZFgsZi4eHIO+8pUc3HvvWqXL0DwG2TgQBAHnrVuHkoULUVtWhlAwOOQx3okT0TNzpgLVkdIiveai2WxCSoqDiwfHmLPOSsTTT9845P4ljT++0seJ2WLBJddfj4LJk1F95MiwnWrd8+fDU1gY/eJIUZEekQFAXl7mMPuScUSmlNmzjdi48WYYjUalS4kLDLJxlJyaiktvvBHJKSnD7ioNQUDH0qXwZXHfoXjirqmBPExX63jKyEgdsN4iwCBTypQpAl599UYkJycpXUrcYJCNs7yJE7HmmmsgyzJam5qGPkCnQ/vKlWzLjyOS3w/PsWMRPUdKSjJkWR5wJYCXFqOvsDCE5567AgUF2UqXElf4So+AmQsXYsWll6KzpWXY/ctkgwHtq1YhmMS/2OJFTxQWDzYYDANa8C0WERYL789ES05OAI8+ej7mzp2udClxh0EWAYIg4Ow1a7Bw+XLU19TA63YPeYxksaDtggs4xyxORGfx4KGdi2zBj46srAB+97tlOP/8JUqXEpf4Ko8QvV6PNddei1mLF6O2rAx+n2/IY0KJiWi74AJIJpMCFVI0RSfITGz4UEBGRgAPP3wu1q07jx2KCmGQRZDZYsGl69dj2ty5qCktRTAQGPKYoMOB1tWrGWYa54rwpGij0Yj0dCf3JYuytLQAfv3rs3DFFSsZYgpikEVYQlISLv/61zFh2jRUHT4MaVBnGQAEU1LQuno1Qmauw6ZV0WjBz83NHGZSNH/FIyUlJYBf/nIRrr56NUSR/52VxP/6UZCcmorLb74ZOYWFqCotHXaB4WBKCtoYZprlPnYM0jAT5cdTRkbqkPmLvLQYGQ5HED//+Xxcd90ahlgM4P+BKMnIycHlN9+MlPR01JaVDTthOuh0ou3CCxlmGiQHg3DX1kb0HE6nHcDAFnwG2fhLT/fjoYdmY/36i6DjGqoxgUEWRXkTJ+Lym2+G3elEdWnp8GHmcITDzMJt0LUm0pcXwy34egT63Ys1GgUkJPDezXiZMMGDBx+cg5tvvhR6vV7pcqgXgyzKJk6fjituvRXJKSknDLPWNWsQTExUoEKKlMgHmYOLB0eIIAAzZ7rwox/NxY03MsRiDYNMAUXTpuHK226DIzUV1SPcMwvZ7Wi96CL4U1IUqJAiIdJB5nAkwWq1cPHgcWYwAAsXduIb35iHG264DAaDQemSaBC+whUyYepUXHnbbXCmpaFmhDCTLBa0rVkDb06OAhXSeIv06h56vR4ZGamcSzaObDbg7LPbceON87F+/TouAhyjGGQKKpwyJRxmGRmoPnx42DA7vpyVu7hYgQppPEV6LhkA5OZmDLPBJoPsVDidEs47rxO33roSN998JUwmhlisYpAprGDyZFx5221Iz8lB5cGDA9bK6yOK6Dz3XPSccUb0C6Rx46mvR2iYFV7GU3p6ypD7rry0OHY5OQGsWePHN795Ba66ai0vJ8Y4vsJjQMGkSbj63/8dBZMno/LgwWGXswLC+5l1nH02ZM5bUSdJgrumJqKnCK+Cj0Gr4HNENhbFxR5ceqkB3/3u9Vi58iyu2KECfEeMEZl5ebj6G9/AtLlzUX34MDwu17CP80yejFa256tWNNZcNJmM8Pn8fccMBgFJSfxVPxlRBGbN6sYVV6Th7rtvwpw5XMVeLfjqjiGOtDRcdccdmHvOOThWWYnuzs5hHxfIyEDLxRezo1GFojGXbPjFg/mrfiJJScCSJe24+uop+N73voYJE/KULonGgK/uGGNLTMTlN9+MJatXo/nYMbQ3Nw/7OCkhAa1r18I9aVKUK6TT0RPhho/k5CTYbFZ4PJ4Bx3l5cWSTJ8tYvrwN1167GN/85nVITXUqXRKNEWf1xSCT2Yy1114LW1ISPvzHP+Bxu5GVnz/0Wr1ej85zzkEgLQ1J27dDGKbrkWJLpEdkOp0OmZmpOHCgbMBxNnwMZTYLWLTIh5wcL9atuxCXXbaSE51Viq/uGKXX63HeZZfh326+GUajEVWHDyM0zMr5AOCeOhUtF1+MoN0e5SpprFzV1RE/R15e5oB7ZABb8AebMEGP88/vQHExcOutV+Hf/u0ChpiKMchimCAImLNkCa791reQlZ+PigMH4B10yei4YEoKWi65hJcaY5y3sRHBEf4fjpfUVAdXwR+BTgcsXarDGWfUobg4HXfddQOWLl3IzkSVY5CpQMHkybj+29/GzEWLcLSiAp2trcM+TjYY0HnOOWhftgwSVyCITbIclTUXw6f6MswcDhHx/l6dkaHDRRd5kZHRjJUrF+Pee2/FzJlTlC6LxgHH0ipxvKMxLSsLH739Nlzd3cgqKBj2L0lvUREC6elI/vBDGJuaFKiWTsRVVQX7tGkRe/6UlGSYTEZ4vT5YLOEtgXQ6AXa7iI6O+LyPumCBHtnZDcjKSsGVV16CpUsXcgsWDWGQqYjJbMbqq65CZl4e3n3pJZTv34+84mKYhtm/LJSQgNY1a5CwZw8Sdu+GMML9NYq+aMwls1jM8Hi8fUEGhFvw4y3IkpIEnHtuEEZjMxYsmIlrrrkY+fnZSpdF44xBpjLH75tlFxTgzeefx8EvvoAzLQ3O9PShDxZF9MyeDU9hIZK3buXoLEZEOsjs9kQkJFjhcg1twS8vj+wu1bFCpwMWLjQiM7MBdrsVl1xyKdasWcr1EjWK98hUKiM3F9d885s4/4or4HG5UF1aOmJXYyg5Ga1r16LzzDMhsTNLcZGeSyaKIrKz04fsSxYvDR/Tphlw7bUiMjKOYtq0Anz/+1/HunWrGGIaxnc1FTOZzThv3TrkFRXh7RdfRMWBA8guLIRtuA05BQHu6dPhzc+H/aOPYD52LPoFE4DIj8gAICcnE59+umfAMa3PJcvM1GHVKjNCoQYEAjIuuWQFrrjiAiQmJihdGkUYg0zlBEHAlNmzkZGXh00vvojd27ahw2hEVkEBxGEWF5YSEtB+wQUwl5cj6dNPoYtwKzgN5W9tRaC7G4YI7gCeluYAMLARSKsjMptNwIoVVhQV+VBdXYnCwhx89atrsXDhGWyrjxMMMo1ITknBFbfeikklJfjgtddQvn8/MvPzkTjCJGnvxInw5ecjYfdu2PbvZzNIlLmqqpA8c2bEnt/ptEMQAEmS+v6gSU4WIYqAVhaA0emAM880Y8ECEXV1R9HcrMN5552Jr3xlDdLTuQ5pPGGQaYhOp8Pcc87BhKlT8cFrr2Hn1q3oaG5GdmEhdMPcG5MNBnTPnw/3lClI/PRTWKKw6gSFRTrI+rfgW63hnRJEUYDDIaK1Vf1JNm2aAStWmNHd3YjaWjdKSibj0kvPw+zZ0zgKi0MMMg1ypKXhsptuwuRZs/DPV15BxYEDSM/JgX2E1fJDiYnoOO88uOvrkbR9Owzt7VGuOP5EvgU/GVZruAX/eJCFj+tUHWRZWTqsWmWBxdKFY8eOoaAgGzfccBnOPnsemzniGINMo0RRRMmCBSiYNAkfvv46Pv/Xv9DW1ISswkKYR9jLzJ+VhZZLL4WlrAwJu3ZB39MT5arjR0+EgywpKQGJiQno6OhC/79f1NrwMXGiHosXW5Ca6kVNTSUMBju++tW1OP/8JXA4uMZovGOQaVxicjIuuu46TJ87F/964w0c2bsXBrMZWXl5w15uhCjCM3kyPMXFsJaWImH3buhG2OSTTl2kR2SCICAnJwP19QO3AVJTw4coAjNnGrF4sRl2ewiVlTVoatJh2bJFuOSSFSgoyFG6RIoRDLI4IAgCJs6YgYLJk7F72zZsefNNVB46hOTUVKRkZAx/T0EU4Z46Fe5Jk2A9fDgcaOxwHDfRaMHPzk6H36++VfBNJgHz5pmwaJEZNpuMmpp6NDd7UFIyiffBaFgMsjiiNxgw79xzMWXWLGx//3188t57KNu374TdjdDp4J4+He7Jk2E7dAi2ffugc7ujW7gGBTo64G9vh9HhiNg5UlOHPncsX1q020UsWmTC3Llm6HQSGhqaUFnZgcLCHN4HoxNikMWhBLsdK9etQ8n8+di8cSP2f/YZmo8dQ0ZuLmxJScN/k14PV0kJXNOnw1JeDtv+/TC0tUW3cI1xVVVFNMhSUpIhCMKAFny7XYROB8TSbIvMTB0WLzajpMSIQCCAY8eOwuVyIysrHddddwlWrDiT98HohBhkcSwzLw9X3nYb5i9diu3vvYdDX3yBxuOBNtJkXVGEZ9IkeCZNgrGuDrZ9+2A6ehS80DN2PVVVcMyZE7HndzrtMJtN8Hi8sNmsAMKXmZ1OHZqblU0yvR6YPNmAefPMKCoywOVyo7S0EpIkYcKEXJx33mIsWjQLdnvkJo2TdjDI4pwgCCiaNg2FU6ag4sABfPzuuyjdswdNR48iIy8P1oSRl/fxZ2fDn50NXUcHbPv3w1JeDjEYH4vSjofI70t2vAXf1xdk4eOiIkEmisDEiQaUlBgxZYoRRiPQ0dGN/furYDDoMWNGMc47bzHmzp0Bs9kU9fpIvRhkBCDcrl9cUoKi6dNRtm8fPn7nHZTt24dQMIjUrCwk2O0j3mAPJSeja8kSdC9cCHNFBaylpTA2Nw/7WPqSK8KLB9tsVtjtiWhp6Rhw3OnUAQhE9NzHCQJQWKjHjBlGTJ9uhMUiQpIkNDe3orGxFYmJNixZMhfLl5+JkpJJ3COMTgmDjAYQRRGTzzgDE2fMQNnevfh882aU7tmDxqNH4UxPR3Jq6rBrOALhlUI8U6bAM2UK9O3tsJSWwlJeDp3XO+zj4120WvBraxsGHI9Gw0durg4lJSbMmGFEQkL4fMFgCLW19Whr60RqqgMXX7wc5567AEVFeexCpNPCIKNh6XQ6TJk9G5NnzcLRigrs+vhj7Nm+HeX79yMxORmpWVnQn2BLmKDDge5Fi9C9YAHMNTUwl5fDfPQo13TsxxWFJcFycjIQCAwcfUWqBT8jQ4eSEiNKSoxITg6fQ5IktLS0o6mpDZIkISsrDWvXLsNZZ81BZmZaROqg+MMgoxMSBAF5Eycib+JEnHX++dj7ySfYsXkzqg8fhsFoREpmJqwJCSP/RS2K8BYWwltYCCEQgKm2FuaqKpiOHo37+2nBnh74WlpgSk2N2DmczqHdfuFLi+Px3CIKCgwoKNCjsFAPuz38vLIso62tE83NbfD7/XA47DjrrNmYN68Es2ZNRVISt1Wh8cUgo1FLycjAsksuwYLly3Fw507s+vhj1JaVob66GkkOB5zp6dAbDCN+v2wwwFtUBG9RERAMwnz0KMyVleFQC0Tnnk2s6amqimiQpaQ4IAgCQqFQ3/2npCQRBgMw1v/k6ek65OeHQ6ugwNB3yRAIh1dnZzcaG1vh9fqQnJyI2bOnYsGCmSgpmczV6CmiGGQ0ZrbERMxfuhRzzzkHRysqcGjXLuzZvh01R45AFEU4MzKQmJx84vseen3fSA2SBGNjI0zHjsF09Cj0bW1x087vqqxEyvz5EXt+p9Pet3hwQoKt33EdGhtHvswrCOH5XQUF4dDKz9fDah14b02WZfT0uNDU1AaXy4OkJBumTSvCokWzUFIyCVlZ6bz3RVHBIKNTJooi8ouLkV9cjHMuvBBH9u3Dnu3bUXHgAJqOHYPRbIYjNfWEHY+9TwR/Vhb8WVnonj8foscDY309THV1MNbXQ9/dHb0fKsqi0YJvsZjhdg8MspQUsS/ITCYBqaki0tJ0SE3VISNDh7w8A0ymof/PQqEQOjt70NHRhZ4eN2w2CwoLc3DmmbNRUjIJ+fnZDC+KOgYZjQuLzYYzFi3CzIUL0VRXh4oDB3Bgxw4craxE49GjMJpMSE5NPflIDYBksXx5CRKA6PHA0NwMY1MTDM3NMDQ3a+b+WqSDzGq1wOGwo6GhZcDxxYstmDfPjLQ0HRITR+5iDASC6OzsRmdnN9xuL0RRQFJSAvLzszB//kzMnDkZEybkjtjJShQNDDIaV4IgICMnBxk5OThz5Uo019ej8uBBHNixA7UVFWiuq4NOr4fd6URicvIJ76kdJ1ks8OXnw5efHz4gy9C3t4dDrbUV+o4O6Ds6VNnmH43Fg3NzM1BZeXTQseF/9f3+ADo7u9HR0Q2v1wudTge7PRGTJhVg+vRiFBbmoKAgB6mpDo68KGYwyChiBEFAenY20rOzsXDFCrQ0NKDy4EEc2bcPNUeOoLasDJIkwWKzIdHhgC0xcXR/2QsCgk4ngk4n+q/HL3i9MPSGmr6jA/rOTuh6eiC6XDE3gjOlpcGan4+kKVMifq6srHQEAkN/fkmS4PH40NPjQmdnN7xePwwGPZKTEzFjRnG/4MpGcnISg4tiliDLsqx0ERR/erq6cLS8HDXl5Tiydy9aGxrg6u4GBAFWmw22pCRYExNPOFdtLASfDzqX68uPnh6IXi8Evx+i3z/wn4HAqOe7yQBkvX7Ih2Q2Q7JYIFksCFmtkCwWdAUC8Igibv35z2HPyBiXn2s0/vWvT/G73/0V6ekpcLu9cLu9kOXwLtEWixk2mxUTJ+Zh2rSJKCjIRkFBDlvkSVU4IiNFJCQlYeqcOZg6Zw5WXn45WhsbUVdVhdqKClSXlqK9uRmtDQ2QJAk6vR62pCTYEhNhtlpP6X6MbDIhaDIh6HSO7htCIUCWIcgy0O+j73NB6AstjHKkIrnd6K6vR4/Hg2iu5Z6Tk4GsrHSEQqG+EVZmZhrS0hxIS3MiPT0FVuvwu4YTqQFHZBRzZFlGZ1sbmo4dQ1NdHY5WVOBoeTm6Ozvh9XjCgSKKMFutsFitMFutMFksMd9wIEkSKg8exI13343pc+dG9dzt7Z2wWMxcjJc0iSMyijmCICA5JQXJKSmYfMYZAIBgIIDm+nq0NzejvbkZLY2NqK+pQXtzM9qamuDzeHD8LzKdTgejyQSjyQRDv3/qdLqI3ueRZRnBQADBQAABvx+BQABBvx9+nw9+rxeCIMBgNMKvQFMK9/MiLeOIjFTN63ajvaUF7S0t6Gxthau7Gx3NzWhraUFXWxt8Xi/8Ph8CPh9CoRAgCH2TreXeS4R6vR7i8ZDr9/X+n8uyDEmSIIVCCIVCkCQpPDIUhL4AhSxDbzBAbzDAYDTCYDTCYrPBkZqK1KwsJDudSHQ4MHX27FF1axLR6DDISLMkSYK7uxuu7m70dHbC5/WGg83rhd/vh9/rhdfthrunB163G5IkQZakcGBJEmRZDn8uy1+O8sxmmHo/jCYT9EYj9AYDrDYbLAkJsFitsCYkwGKzwZqQwMAiigIGGRERqVps3x0nIiI6CQYZERGpGoOMiIhUjUFGRESqxiAjIiJVY5AREZGqMciIiEjVGGRERKRqDDIiIlI1BhkREakag4yIiFSNQUZERKrGICMiIlVjkBERkaoxyIiISNUYZEREpGoMMiIiUjUGGRERqRqDjIiIVI1BRkREqsYgIyIiVWOQERGRqjHIiIhI1RhkRESkagwyIiJSNQYZERGpGoOMiIhUjUFGRESqxiAjIiJVY5AREZGqMciIiEjVGGRERKRqDDIiIlI1BhkREakag4yIiFSNQUZERKrGICMiIlVjkBERkaoxyIiISNUYZEREpGr/H2b2DMwiAeXyAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PmjIiAoDuFqE"
      },
      "source": [
        "## Creating a pytorch dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "id": "J_FpiKwMM5Gr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c02ab03d-5b3d-4c2b-cfba-c909dafaa294"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['text', 'title', 'label'],\n",
              "        num_rows: 35918\n",
              "    })\n",
              "    eval: Dataset({\n",
              "        features: ['text', 'title', 'label'],\n",
              "        num_rows: 8980\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ],
      "source": [
        "# transformers library allows you to use pytorch or tensorflow to save your dataset\n",
        "# pytorch dataset looks like a dictoinary\n",
        "# using this rep works well with the transformers library\n",
        "\n",
        "# Create a pytorch dataset to ensure consistency in our data handling\n",
        "\n",
        "# Create a train and eval datasets using the specified columns from the DataFrame\n",
        "train_dataset = Dataset.from_pandas(train[['text', 'title', 'label']])\n",
        "eval_dataset = Dataset.from_pandas(eval[['text', 'title', 'label']])\n",
        "\n",
        "# Combine the train and eval datasets into a DatasetDict\n",
        "dataset = DatasetDict({'train': train_dataset, 'eval': eval_dataset})\n",
        "\n",
        "# Remove the '__index_level_0__' column from the dataset\n",
        "dataset = dataset.remove_columns('__index_level_0__')\n",
        "dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sIjfqHODuFqF"
      },
      "source": [
        "## Preprocessing and tokenization"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "id": "-6vIJTopM5Gs"
      },
      "outputs": [],
      "source": [
        "# define helper functions\n",
        "\n",
        "# funtion to replace usernames and links with placeholders.\n",
        "def preprocess(text):\n",
        "  # \"@user my name is john\"\n",
        "  # \"http my name is john\"\n",
        "    new_text = []\n",
        "    for t in text.split(\" \"):\n",
        "        t = '@user' if t.startswith('@') and len(t) > 1 else t\n",
        "        t = 'http' if t.startswith('http') else t\n",
        "        new_text.append(t)\n",
        "    return \" \".join(new_text)\n",
        "\n",
        "# no need for encoding: Fake=1, True=0 bcuz the target variable called label is already encoded"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the apply_preprocess function\n",
        "def apply_preprocess(dataset, column='title'):\n",
        "    return dataset.map(lambda example: {column: preprocess(example[column])},\n",
        "                       remove_columns=[column])\n",
        "\n",
        "# Apply the preprocess function to the 'title' column in both 'train' and 'eval' datasets\n",
        "dataset['train'] = apply_preprocess(dataset['train'])\n",
        "dataset['eval'] = apply_preprocess(dataset['eval'])"
      ],
      "metadata": {
        "id": "VamYk7VooFNF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17,
          "referenced_widgets": [
            "d710bc8e374d4f6fb3b17b8c5fca26d1",
            "c1b32041029348bb9bf614570c0f0bcb",
            "f0c256cc311147b2a20c4c9edf60d664",
            "31f4c4237f944b60b19d4992568893a2",
            "e52dfeef936b4e66b35ec0cd32525db5",
            "761fb61de7124e1fbbee9f2f5d03fbb9",
            "a6ea78ac7e2140248d453b10fe102a8b",
            "0ebfa3f377694d43a0256e56f9123b92",
            "86b0fe371b7243b280727ae33e68d845",
            "50608ae7f5334b859ae6ce27f8cee28f",
            "fbf6025d083544e58d4e35bc7a351228",
            "ed069fc922b04a5d81e371bcfb2c0f90",
            "f8edc5fa15f049af8db7630074622682",
            "b349319d361e49c480be92bc6f491e15",
            "cb7686a72f374abeb9e22956d67717b5",
            "bc67fecdb03842d1a2bed385f9ca1ad7",
            "7df8a39680d142699162753ebb9ea43f",
            "62e2ff7359904a2ca4c473d82d220983",
            "84455110b3674c78a4bde905fea9fdd4",
            "85746961001741999ed8f9d250bfb458",
            "10457cf89771418699d5eb2bbf187829",
            "e394941859a54524a58432281dfc7225"
          ]
        },
        "outputId": "574218c4-f7a2-430e-e11e-7652325f1c38"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/35918 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d710bc8e374d4f6fb3b17b8c5fca26d1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/8980 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ed069fc922b04a5d81e371bcfb2c0f90"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Tokenization"
      ],
      "metadata": {
        "id": "QUkAkgKljly9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot histogram of the number of words in train data 'text'\n",
        "seq_len = [len(text.split()) for text in df['title']]\n",
        "\n",
        "pd.Series(seq_len).hist(bins = 40,color='firebrick')\n",
        "plt.xlabel('Number of Words')\n",
        "plt.ylabel('Number of texts')"
      ],
      "metadata": {
        "id": "IvADqbHZrUC0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "outputId": "ddd15b39-69b2-44f0-f578-08f2acd8bde9"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Number of texts')"
            ]
          },
          "metadata": {},
          "execution_count": 46
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
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        "outputId": "7bd38900-18af-4780-b4fc-76361d6d155f"
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      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/321 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)solve/main/vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
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            "application/vnd.jupyter.widget-view+json": {
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
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          "data": {
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              "Map:   0%|          | 0/35918 [00:00<?, ? examples/s]"
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          "metadata": {}
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        {
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            "application/vnd.jupyter.widget-view+json": {
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          "output_type": "display_data",
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c22e33eae20e486589905e28d183cfa5"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['label', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
              "        num_rows: 35918\n",
              "    })\n",
              "    eval: Dataset({\n",
              "        features: ['label', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
              "        num_rows: 8980\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ],
      "source": [
        "# define the tokenizer\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"elozano/bert-base-cased-fake-news\")\n",
        "\n",
        "def tokenize_data(example):\n",
        "    return tokenizer(example['title'], padding='max_length', # compress all sentences to maximum of 30 words which is the max_length\n",
        "                     truncation=True, # cut the sentenced to 30_words\n",
        "                     max_length=20 # increasing the max length doesn't guarantee a better score\n",
        "                     )\n",
        "\n",
        "# Change the tweets to tokens that the models can exploit\n",
        "dataset = dataset.map(tokenize_data, batched=True)\n",
        "\n",
        "# Transform\tlabels and remove the useless columns or columns that are not tokenized\n",
        "remove_columns = ['text', 'title']\n",
        "dataset = dataset.map(remove_columns=remove_columns)\n",
        "dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Trianing"
      ],
      "metadata": {
        "id": "VQmRhd5mui5A"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "id": "a643fStBM5G3",
        "outputId": "a13abb01-8a9c-461c-e8b1-7f5296aef5f5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
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            "a5eba92a1e8e445fba97751954232ea4"
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      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/831 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "31183f7c6c2742d49381a622123b822c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/438M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f01eae8baa364eb7a0be364cb8760800"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Loading a pretrain model for fine-tuning\n",
        "model = AutoModelForSequenceClassification.from_pretrained(\"elozano/bert-base-cased-fake-news\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {
        "id": "eV_vmBAGM5G1"
      },
      "outputs": [],
      "source": [
        "# Configure the trianing parameters like `num_train_epochs`:\n",
        "# the number of time the model will repeat the training loop over the dataset\n",
        "training_args = TrainingArguments(\"test_trainer\",\n",
        "                                  num_train_epochs=2, # epoch is ow many times you repeat training\n",
        "                                  load_best_model_at_end=True,\n",
        "                                  save_strategy='epoch',\n",
        "                                  evaluation_strategy='epoch',\n",
        "                                  logging_strategy='epoch',\n",
        "                                  per_device_train_batch_size=32, # smaller batches take longer to train\n",
        "                                  )"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# set up the optimizer with the PyTorch implementation of AdamW\n",
        "optimizer = torch.optim.AdamW(model.parameters(), lr=1e-5) # I specified the optimizer to avoid a warning message"
      ],
      "metadata": {
        "id": "euUiexe-zBd5"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "id": "2cUkWOFpM5G4"
      },
      "outputs": [],
      "source": [
        "train_dataset = dataset['train'].shuffle(seed=24)\n",
        "eval_dataset = dataset['eval'].shuffle(seed=24) # scatter the dataset 24 times randomly"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {
        "id": "cixKFZeOM5G5"
      },
      "outputs": [],
      "source": [
        "def compute_metrics(eval_pred):      # specify the evaluation metric\n",
        "    logits, labels = eval_pred\n",
        "    predictions = np.argmax(logits, axis=-1)\n",
        "    return {\n",
        "        \"rmse\": mean_squared_error(labels, predictions, squared=False),\n",
        "        \"classification_report\": classification_report(labels, predictions)\n",
        "    }"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "metadata": {
        "id": "qoNNWPz_M5G6"
      },
      "outputs": [],
      "source": [
        "trainer = Trainer(\n",
        "    model,\n",
        "    training_args,\n",
        "    train_dataset=train_dataset,\n",
        "    eval_dataset=eval_dataset,\n",
        "    tokenizer=tokenizer,\n",
        "    compute_metrics=compute_metrics,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "metadata": {
        "id": "xtsQqZJGM5G7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 627
        },
        "outputId": "f5e57d5c-f2bd-412e-e6b6-da6aa6715847"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/optimization.py:411: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
            "  warnings.warn(\n",
            "You're using a BertTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='2246' max='2246' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [2246/2246 06:23, Epoch 2/2]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Rmse</th>\n",
              "      <th>Classification Report</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.112800</td>\n",
              "      <td>0.051050</td>\n",
              "      <td>0.115599</td>\n",
              "      <td>              precision    recall  f1-score   support\n",
              "\n",
              "           0       0.98      0.99      0.99      4284\n",
              "           1       0.99      0.98      0.99      4696\n",
              "\n",
              "    accuracy                           0.99      8980\n",
              "   macro avg       0.99      0.99      0.99      8980\n",
              "weighted avg       0.99      0.99      0.99      8980\n",
              "</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.024900</td>\n",
              "      <td>0.047632</td>\n",
              "      <td>0.109666</td>\n",
              "      <td>              precision    recall  f1-score   support\n",
              "\n",
              "           0       0.98      0.99      0.99      4284\n",
              "           1       0.99      0.98      0.99      4696\n",
              "\n",
              "    accuracy                           0.99      8980\n",
              "   macro avg       0.99      0.99      0.99      8980\n",
              "weighted avg       0.99      0.99      0.99      8980\n",
              "</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Trainer is attempting to log a value of \"              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.98      0.99      0.99      4284\n",
            "           1       0.99      0.98      0.99      4696\n",
            "\n",
            "    accuracy                           0.99      8980\n",
            "   macro avg       0.99      0.99      0.99      8980\n",
            "weighted avg       0.99      0.99      0.99      8980\n",
            "\" of type <class 'str'> for key \"eval/classification_report\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
            "Trainer is attempting to log a value of \"              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.98      0.99      0.99      4284\n",
            "           1       0.99      0.98      0.99      4696\n",
            "\n",
            "    accuracy                           0.99      8980\n",
            "   macro avg       0.99      0.99      0.99      8980\n",
            "weighted avg       0.99      0.99      0.99      8980\n",
            "\" of type <class 'str'> for key \"eval/classification_report\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=2246, training_loss=0.06883141151625241, metrics={'train_runtime': 383.6577, 'train_samples_per_second': 187.24, 'train_steps_per_second': 5.854, 'total_flos': 738314288001600.0, 'train_loss': 0.06883141151625241, 'epoch': 2.0})"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ],
      "source": [
        "trainer.train()  # rmse 0 to 1 closer to 0 means better performance."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cjVR-ilCM5G8"
      },
      "source": [
        "Don't worry the above issue, it is a `KeyboardInterrupt` that means I stopped the training to avoid taking a long time to finish."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "metadata": {
        "id": "vZ8w2aNBM5G9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369
        },
        "outputId": "fa20e73d-19db-4070-a5a8-85bcdd6594c2"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='1123' max='1123' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [1123/1123 00:17]\n",
              "    </div>\n",
              "    "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Trainer is attempting to log a value of \"              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.98      0.99      0.99      4284\n",
            "           1       0.99      0.98      0.99      4696\n",
            "\n",
            "    accuracy                           0.99      8980\n",
            "   macro avg       0.99      0.99      0.99      8980\n",
            "weighted avg       0.99      0.99      0.99      8980\n",
            "\" of type <class 'str'> for key \"eval/classification_report\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'eval_loss': 0.04763183742761612,\n",
              " 'eval_rmse': 0.10966643086152872,\n",
              " 'eval_classification_report': '              precision    recall  f1-score   support\\n\\n           0       0.98      0.99      0.99      4284\\n           1       0.99      0.98      0.99      4696\\n\\n    accuracy                           0.99      8980\\n   macro avg       0.99      0.99      0.99      8980\\nweighted avg       0.99      0.99      0.99      8980\\n',\n",
              " 'eval_runtime': 17.1893,\n",
              " 'eval_samples_per_second': 522.417,\n",
              " 'eval_steps_per_second': 65.331,\n",
              " 'epoch': 2.0}"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ],
      "source": [
        "# Launch the final evaluation\n",
        "trainer.evaluate()  # eval loss is the performance cost of finetuning (0 to 1) 0.5 and above is not suitable."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yHY9rVqfM5G-"
      },
      "source": [
        "## Pushing to HuggingFace\n",
        "Some checkpoints of the model are automatically saved locally in `test_trainer/` during the training."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YnGHAwdLM5HA"
      },
      "source": [
        "You may also upload the model on the Hugging Face Platform... [Read more](https://huggingface.co/docs/hub/models-uploading)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "huggingface_hub.notebook_login()\n",
        "# login to the Hugging Face Hub with your token"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359,
          "referenced_widgets": [
            "27d8ee273ed24cd49b6d75b61b2cec32",
            "d5b1d69ec3e24406a7507eba39af8cf3",
            "a4b5dffd39884508a7539a2588f29a35",
            "ff2d3b27c1aa411cbb3406ba6afde299",
            "92587207275b478bb6d385e1a1bcd959",
            "b7effdc3a53b46049e123b02dde8360c",
            "15a35708f9dd4d97bcfd78589579a0ec",
            "8e77d22073784130b342e8c460f67a6b",
            "e23188bbc480478ebde1bd4849c0fd4c",
            "b61dbb429b9043829888fdbfb7ef40c5",
            "4b772ada897a4471b45448350412327e",
            "dead4ef78cbe4b7593893d1bafac2106",
            "7c25d1d1d314463c805c2b174ae94131",
            "6b5af80beb4240ceacf4c8e3318ad476",
            "c2a89f07e2574eeb847bb0bf92179824",
            "8f7fddcbcaad4312b64074d2cfd836b0",
            "31cf9922ac224bf89028ae0667d5e40c"
          ]
        },
        "id": "axPYP2VAK8kG",
        "outputId": "660b8b20-8a7a-4a37-dfe8-f457a6bb66a0"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "27d8ee273ed24cd49b6d75b61b2cec32"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "metadata": {
        "id": "XcdYCVYCM5HA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "71703caa-95a8-41b4-df93-882ab6424e25"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[31m╭─\u001b[0m\u001b[31m──────────────────────────────\u001b[0m\u001b[31m \u001b[0m\u001b[1;31mTraceback \u001b[0m\u001b[1;2;31m(most recent call last)\u001b[0m\u001b[31m \u001b[0m\u001b[31m───────────────────────────────\u001b[0m\u001b[31m─╮\u001b[0m\n",
              "\u001b[31m│\u001b[0m in \u001b[92m<cell line: 2>\u001b[0m:\u001b[94m2\u001b[0m                                                                              \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.10/dist-packages/transformers/utils/\u001b[0m\u001b[1;33mhub.py\u001b[0m:\u001b[94m803\u001b[0m in \u001b[92mpush_to_hub\u001b[0m             \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m 800 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[94melse\u001b[0m:                                                                             \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m 801 \u001b[0m\u001b[2m│   │   │   \u001b[0mworking_dir = repo_id.split(\u001b[33m\"\u001b[0m\u001b[33m/\u001b[0m\u001b[33m\"\u001b[0m)[-\u001b[94m1\u001b[0m]                                          \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m 802 \u001b[0m\u001b[2m│   │   \u001b[0m                                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m 803 \u001b[2m│   │   \u001b[0mrepo_id = \u001b[96mself\u001b[0m._create_repo(                                                      \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m 804 \u001b[0m\u001b[2m│   │   │   \u001b[0mrepo_id, private=private, use_auth_token=use_auth_token, repo_url=repo_url,   \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m 805 \u001b[0m\u001b[2m│   │   \u001b[0m)                                                                                 \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m 806 \u001b[0m                                                                                          \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.10/dist-packages/transformers/utils/\u001b[0m\u001b[1;33mhub.py\u001b[0m:\u001b[94m661\u001b[0m in \u001b[92m_create_repo\u001b[0m            \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m 658 \u001b[0m\u001b[2m│   │   │   │   │   \u001b[0mrepo_id = repo_id.split(\u001b[33m\"\u001b[0m\u001b[33m/\u001b[0m\u001b[33m\"\u001b[0m)[-\u001b[94m1\u001b[0m]                                      \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m 659 \u001b[0m\u001b[2m│   │   │   │   \u001b[0mrepo_id = \u001b[33mf\u001b[0m\u001b[33m\"\u001b[0m\u001b[33m{\u001b[0morganization\u001b[33m}\u001b[0m\u001b[33m/\u001b[0m\u001b[33m{\u001b[0mrepo_id\u001b[33m}\u001b[0m\u001b[33m\"\u001b[0m                                     \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m 660 \u001b[0m\u001b[2m│   │   \u001b[0m                                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m 661 \u001b[2m│   │   \u001b[0murl = create_repo(repo_id=repo_id, token=use_auth_token, private=private, exist_  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m 662 \u001b[0m\u001b[2m│   │   \u001b[0m                                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m 663 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[2m# If the namespace is not there, add it or `upload_file` will complain\u001b[0m            \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m 664 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[94mif\u001b[0m \u001b[33m\"\u001b[0m\u001b[33m/\u001b[0m\u001b[33m\"\u001b[0m \u001b[95mnot\u001b[0m \u001b[95min\u001b[0m repo_id \u001b[95mand\u001b[0m url != \u001b[33mf\u001b[0m\u001b[33m\"\u001b[0m\u001b[33m{\u001b[0mHUGGINGFACE_CO_RESOLVE_ENDPOINT\u001b[33m}\u001b[0m\u001b[33m/\u001b[0m\u001b[33m{\u001b[0mrepo_id\u001b[33m}\u001b[0m\u001b[33m\"\u001b[0m:  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/\u001b[0m\u001b[1;33m_validators.py\u001b[0m:\u001b[94m118\u001b[0m in \u001b[92m_inner_fn\u001b[0m    \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m115 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[94mif\u001b[0m check_use_auth_token:                                                           \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m116 \u001b[0m\u001b[2m│   │   │   \u001b[0mkwargs = smoothly_deprecate_use_auth_token(fn_name=fn.\u001b[91m__name__\u001b[0m, has_token=ha   \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m117 \u001b[0m\u001b[2m│   │   \u001b[0m                                                                                   \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m118 \u001b[2m│   │   \u001b[0m\u001b[94mreturn\u001b[0m fn(*args, **kwargs)                                                         \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m119 \u001b[0m\u001b[2m│   \u001b[0m                                                                                       \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m120 \u001b[0m\u001b[2m│   \u001b[0m\u001b[94mreturn\u001b[0m _inner_fn  \u001b[2m# type: ignore\u001b[0m                                                       \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m121 \u001b[0m                                                                                           \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.10/dist-packages/huggingface_hub/\u001b[0m\u001b[1;33mhf_api.py\u001b[0m:\u001b[94m2304\u001b[0m in \u001b[92mcreate_repo\u001b[0m            \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m2301 \u001b[0m\u001b[2m│   │   │   \u001b[0m\u001b[2m# Testing purposes only.\u001b[0m                                                      \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m2302 \u001b[0m\u001b[2m│   │   │   \u001b[0m\u001b[2m# See https://github.com/huggingface/huggingface_hub/pull/733/files#r8206044\u001b[0m  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m2303 \u001b[0m\u001b[2m│   │   │   \u001b[0mjson[\u001b[33m\"\u001b[0m\u001b[33mlfsmultipartthresh\u001b[0m\u001b[33m\"\u001b[0m] = \u001b[96mself\u001b[0m._lfsmultipartthresh  \u001b[2m# type: ignore\u001b[0m         \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m2304 \u001b[2m│   │   \u001b[0mheaders = \u001b[96mself\u001b[0m._build_hf_headers(token=token, is_write_action=\u001b[94mTrue\u001b[0m)               \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m2305 \u001b[0m\u001b[2m│   │   \u001b[0mr = get_session().post(path, headers=headers, json=json)                          \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m2306 \u001b[0m\u001b[2m│   │   \u001b[0m                                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m2307 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[94mtry\u001b[0m:                                                                              \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.10/dist-packages/huggingface_hub/\u001b[0m\u001b[1;33mhf_api.py\u001b[0m:\u001b[94m5008\u001b[0m in \u001b[92m_build_hf_headers\u001b[0m      \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m5005 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[94mif\u001b[0m token \u001b[95mis\u001b[0m \u001b[94mNone\u001b[0m:                                                                 \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m5006 \u001b[0m\u001b[2m│   │   │   \u001b[0m\u001b[2m# Cannot do `token = token or self.token` as token can be `False`.\u001b[0m            \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m5007 \u001b[0m\u001b[2m│   │   │   \u001b[0mtoken = \u001b[96mself\u001b[0m.token                                                            \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m5008 \u001b[2m│   │   \u001b[0m\u001b[94mreturn\u001b[0m build_hf_headers(                                                          \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m5009 \u001b[0m\u001b[2m│   │   │   \u001b[0mtoken=token,                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m5010 \u001b[0m\u001b[2m│   │   │   \u001b[0mis_write_action=is_write_action,                                              \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m5011 \u001b[0m\u001b[2m│   │   │   \u001b[0mlibrary_name=library_name \u001b[95mor\u001b[0m \u001b[96mself\u001b[0m.library_name,                               \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/\u001b[0m\u001b[1;33m_validators.py\u001b[0m:\u001b[94m118\u001b[0m in \u001b[92m_inner_fn\u001b[0m    \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m115 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[94mif\u001b[0m check_use_auth_token:                                                           \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m116 \u001b[0m\u001b[2m│   │   │   \u001b[0mkwargs = smoothly_deprecate_use_auth_token(fn_name=fn.\u001b[91m__name__\u001b[0m, has_token=ha   \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m117 \u001b[0m\u001b[2m│   │   \u001b[0m                                                                                   \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m118 \u001b[2m│   │   \u001b[0m\u001b[94mreturn\u001b[0m fn(*args, **kwargs)                                                         \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m119 \u001b[0m\u001b[2m│   \u001b[0m                                                                                       \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m120 \u001b[0m\u001b[2m│   \u001b[0m\u001b[94mreturn\u001b[0m _inner_fn  \u001b[2m# type: ignore\u001b[0m                                                       \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m121 \u001b[0m                                                                                           \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/\u001b[0m\u001b[1;33m_headers.py\u001b[0m:\u001b[94m122\u001b[0m in                 \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m \u001b[92mbuild_hf_headers\u001b[0m                                                                                 \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m119 \u001b[0m\u001b[2;33m│   \u001b[0m\u001b[33m\"\"\"\u001b[0m                                                                                    \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m120 \u001b[0m\u001b[2m│   \u001b[0m\u001b[2m# Get auth token to send\u001b[0m                                                               \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m121 \u001b[0m\u001b[2m│   \u001b[0mtoken_to_send = get_token_to_send(token)                                               \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m122 \u001b[2m│   \u001b[0m_validate_token_to_send(token_to_send, is_write_action=is_write_action)                \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m123 \u001b[0m\u001b[2m│   \u001b[0m                                                                                       \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m124 \u001b[0m\u001b[2m│   \u001b[0m\u001b[2m# Combine headers\u001b[0m                                                                      \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m125 \u001b[0m\u001b[2m│   \u001b[0mheaders = {                                                                            \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/\u001b[0m\u001b[1;33m_headers.py\u001b[0m:\u001b[94m172\u001b[0m in                 \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m \u001b[92m_validate_token_to_send\u001b[0m                                                                          \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m169 \u001b[0m\u001b[94mdef\u001b[0m \u001b[92m_validate_token_to_send\u001b[0m(token: Optional[\u001b[96mstr\u001b[0m], is_write_action: \u001b[96mbool\u001b[0m) -> \u001b[94mNone\u001b[0m:          \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m170 \u001b[0m\u001b[2m│   \u001b[0m\u001b[94mif\u001b[0m is_write_action:                                                                    \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m171 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[94mif\u001b[0m token \u001b[95mis\u001b[0m \u001b[94mNone\u001b[0m:                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m172 \u001b[2m│   │   │   \u001b[0m\u001b[94mraise\u001b[0m \u001b[96mValueError\u001b[0m(                                                              \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m173 \u001b[0m\u001b[2m│   │   │   │   \u001b[0m\u001b[33m\"\u001b[0m\u001b[33mToken is required (write-access action) but no token found. You need\u001b[0m\u001b[33m\"\u001b[0m     \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m174 \u001b[0m\u001b[2m│   │   │   │   \u001b[0m\u001b[33m\"\u001b[0m\u001b[33m to provide a token or be logged in to Hugging Face with\u001b[0m\u001b[33m\"\u001b[0m                 \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m175 \u001b[0m\u001b[2m│   │   │   │   \u001b[0m\u001b[33m\"\u001b[0m\u001b[33m `huggingface-cli login` or `huggingface_hub.login`. See\u001b[0m\u001b[33m\"\u001b[0m                 \u001b[31m│\u001b[0m\n",
              "\u001b[31m╰──────────────────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n",
              "\u001b[1;91mValueError: \u001b[0mToken is required \u001b[1m(\u001b[0mwrite-access action\u001b[1m)\u001b[0m but no token found. You need to provide a token or be logged in\n",
              "to Hugging Face with `huggingface-cli login` or `huggingface_hub.login`. See \n",
              "\u001b[4;94mhttps://huggingface.co/settings/tokens.\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #800000; text-decoration-color: #800000\">╭─────────────────────────────── </span><span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">Traceback </span><span style=\"color: #bf7f7f; text-decoration-color: #bf7f7f; font-weight: bold\">(most recent call last)</span><span style=\"color: #800000; text-decoration-color: #800000\"> ────────────────────────────────╮</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> in <span style=\"color: #00ff00; text-decoration-color: #00ff00\">&lt;cell line: 2&gt;</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">2</span>                                                                              <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/usr/local/lib/python3.10/dist-packages/transformers/utils/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">hub.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">803</span> in <span style=\"color: #00ff00; text-decoration-color: #00ff00\">push_to_hub</span>             <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 800 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">else</span>:                                                                             <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 801 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span>working_dir = repo_id.split(<span style=\"color: #808000; text-decoration-color: #808000\">\"/\"</span>)[-<span style=\"color: #0000ff; text-decoration-color: #0000ff\">1</span>]                                          <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 802 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span>                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span> 803 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span>repo_id = <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>._create_repo(                                                      <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 804 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span>repo_id, private=private, use_auth_token=use_auth_token, repo_url=repo_url,   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 805 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span>)                                                                                 <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 806 </span>                                                                                          <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/usr/local/lib/python3.10/dist-packages/transformers/utils/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">hub.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">661</span> in <span style=\"color: #00ff00; text-decoration-color: #00ff00\">_create_repo</span>            <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 658 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   │   │   </span>repo_id = repo_id.split(<span style=\"color: #808000; text-decoration-color: #808000\">\"/\"</span>)[-<span style=\"color: #0000ff; text-decoration-color: #0000ff\">1</span>]                                      <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 659 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   │   </span>repo_id = <span style=\"color: #808000; text-decoration-color: #808000\">f\"{</span>organization<span style=\"color: #808000; text-decoration-color: #808000\">}/{</span>repo_id<span style=\"color: #808000; text-decoration-color: #808000\">}\"</span>                                     <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 660 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span>                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span> 661 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span>url = create_repo(repo_id=repo_id, token=use_auth_token, private=private, exist_  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 662 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span>                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 663 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"># If the namespace is not there, add it or `upload_file` will complain</span>            <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 664 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">if</span> <span style=\"color: #808000; text-decoration-color: #808000\">\"/\"</span> <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">not</span> <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">in</span> repo_id <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">and</span> url != <span style=\"color: #808000; text-decoration-color: #808000\">f\"{</span>HUGGINGFACE_CO_RESOLVE_ENDPOINT<span style=\"color: #808000; text-decoration-color: #808000\">}/{</span>repo_id<span style=\"color: #808000; text-decoration-color: #808000\">}\"</span>:  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">_validators.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">118</span> in <span style=\"color: #00ff00; text-decoration-color: #00ff00\">_inner_fn</span>    <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">115 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">if</span> check_use_auth_token:                                                           <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">116 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span>kwargs = smoothly_deprecate_use_auth_token(fn_name=fn.<span style=\"color: #ff0000; text-decoration-color: #ff0000\">__name__</span>, has_token=ha   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">117 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span>                                                                                   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span>118 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">return</span> fn(*args, **kwargs)                                                         <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">119 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   </span>                                                                                       <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">120 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">return</span> _inner_fn  <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"># type: ignore</span>                                                       <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">121 </span>                                                                                           <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/usr/local/lib/python3.10/dist-packages/huggingface_hub/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">hf_api.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">2304</span> in <span style=\"color: #00ff00; text-decoration-color: #00ff00\">create_repo</span>            <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">2301 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"># Testing purposes only.</span>                                                      <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">2302 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"># See https://github.com/huggingface/huggingface_hub/pull/733/files#r8206044</span>  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">2303 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span>json[<span style=\"color: #808000; text-decoration-color: #808000\">\"lfsmultipartthresh\"</span>] = <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>._lfsmultipartthresh  <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"># type: ignore</span>         <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span>2304 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span>headers = <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>._build_hf_headers(token=token, is_write_action=<span style=\"color: #0000ff; text-decoration-color: #0000ff\">True</span>)               <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">2305 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span>r = get_session().post(path, headers=headers, json=json)                          <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">2306 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span>                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">2307 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">try</span>:                                                                              <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/usr/local/lib/python3.10/dist-packages/huggingface_hub/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">hf_api.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">5008</span> in <span style=\"color: #00ff00; text-decoration-color: #00ff00\">_build_hf_headers</span>      <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">5005 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">if</span> token <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">is</span> <span style=\"color: #0000ff; text-decoration-color: #0000ff\">None</span>:                                                                 <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">5006 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"># Cannot do `token = token or self.token` as token can be `False`.</span>            <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">5007 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span>token = <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.token                                                            <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span>5008 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">return</span> build_hf_headers(                                                          <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">5009 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span>token=token,                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">5010 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span>is_write_action=is_write_action,                                              <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">5011 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span>library_name=library_name <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">or</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.library_name,                               <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">_validators.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">118</span> in <span style=\"color: #00ff00; text-decoration-color: #00ff00\">_inner_fn</span>    <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">115 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">if</span> check_use_auth_token:                                                           <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">116 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span>kwargs = smoothly_deprecate_use_auth_token(fn_name=fn.<span style=\"color: #ff0000; text-decoration-color: #ff0000\">__name__</span>, has_token=ha   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">117 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span>                                                                                   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span>118 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">return</span> fn(*args, **kwargs)                                                         <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">119 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   </span>                                                                                       <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">120 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">return</span> _inner_fn  <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"># type: ignore</span>                                                       <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">121 </span>                                                                                           <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">_headers.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">122</span> in                 <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #00ff00; text-decoration-color: #00ff00\">build_hf_headers</span>                                                                                 <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">119 </span><span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">│   </span><span style=\"color: #808000; text-decoration-color: #808000\">\"\"\"</span>                                                                                    <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">120 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"># Get auth token to send</span>                                                               <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">121 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   </span>token_to_send = get_token_to_send(token)                                               <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span>122 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   </span>_validate_token_to_send(token_to_send, is_write_action=is_write_action)                <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">123 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   </span>                                                                                       <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">124 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"># Combine headers</span>                                                                      <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">125 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   </span>headers = {                                                                            <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">_headers.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">172</span> in                 <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #00ff00; text-decoration-color: #00ff00\">_validate_token_to_send</span>                                                                          <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">169 </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">def</span> <span style=\"color: #00ff00; text-decoration-color: #00ff00\">_validate_token_to_send</span>(token: Optional[<span style=\"color: #00ffff; text-decoration-color: #00ffff\">str</span>], is_write_action: <span style=\"color: #00ffff; text-decoration-color: #00ffff\">bool</span>) -&gt; <span style=\"color: #0000ff; text-decoration-color: #0000ff\">None</span>:          <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">170 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">if</span> is_write_action:                                                                    <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">171 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">if</span> token <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">is</span> <span style=\"color: #0000ff; text-decoration-color: #0000ff\">None</span>:                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span>172 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">raise</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff\">ValueError</span>(                                                              <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">173 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   │   </span><span style=\"color: #808000; text-decoration-color: #808000\">\"Token is required (write-access action) but no token found. You need\"</span>     <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">174 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   │   </span><span style=\"color: #808000; text-decoration-color: #808000\">\" to provide a token or be logged in to Hugging Face with\"</span>                 <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">175 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   │   </span><span style=\"color: #808000; text-decoration-color: #808000\">\" `huggingface-cli login` or `huggingface_hub.login`. See\"</span>                 <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">╰──────────────────────────────────────────────────────────────────────────────────────────────────╯</span>\n",
              "<span style=\"color: #ff0000; text-decoration-color: #ff0000; font-weight: bold\">ValueError: </span>Token is required <span style=\"font-weight: bold\">(</span>write-access action<span style=\"font-weight: bold\">)</span> but no token found. You need to provide a token or be logged in\n",
              "to Hugging Face with `huggingface-cli login` or `huggingface_hub.login`. See \n",
              "<span style=\"color: #0000ff; text-decoration-color: #0000ff; text-decoration: underline\">https://huggingface.co/settings/tokens.</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# # Push model and tokenizer to HugginFace\n",
        "model.push_to_hub(\"ikoghoemmanuell/finetuned_fake_news_bert\") # (username/model_name)\n",
        "tokenizer.push_to_hub(\"ikoghoemmanuell/finetuned_fake_news_bert\")"
      ]
    }
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