Fine_tuning_Hugging_face_text_classification_model.ipynb

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        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/EnyonamAgbenorhevi/Sentiment-Analysis-NLP/blob/main/Fine_tuning_Hugging_face_text_classification_model.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r9CUbOzbvAB4"
      },
      "source": [
        "# Sentiment Analysis with Hugging Face"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "suMueZtCvACO"
      },
      "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": "2q9pJ1b-vACR"
      },
      "source": [
        "## Application of Hugging Face Text classification model Fune-tuning"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%capture\n",
        "\n",
        "!pip install transformers\n",
        "!pip install accelerate -U\n",
        "!pip install datasets\n",
        "!pip install huggingface_hub"
      ],
      "metadata": {
        "id": "48ZICieRvIyR"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%%capture\n",
        "\n",
        "import torch\n",
        "from sklearn.metrics import f1_score\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "from transformers import AutoTokenizer, AutoModelForSequenceClassification\n",
        "from sklearn.model_selection import train_test_split\n",
        "from transformers import pipeline\n",
        "from datasets import load_dataset\n",
        "import nltk\n",
        "nltk.download('punkt')\n",
        "from torch import nn\n",
        "from transformers import TrainingArguments\n",
        "from transformers import Trainer\n",
        "##others\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "import os\n",
        "os.environ[\"WANDB_DISABLED\"] = \"true\"\n",
        "\n",
        "pd.set_option(\"display.max_rows\", 50)\n",
        "pd.set_option(\"display.max_columns\", 50)\n"
      ],
      "metadata": {
        "id": "Wykg6PDIxXls"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uObNmTI1xmWx",
        "outputId": "57adadd5-e52d-4168-8dcc-53e60eebadb2"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ow77kdSfxx8t"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Importing Dataset from Google Drive**"
      ],
      "metadata": {
        "id": "e9apUhkcx2EO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data_path= \"/content/drive/MyDrive/Colab Notebooks/NLP/clean_copy.csv\""
      ],
      "metadata": {
        "id": "5hNfJ98oyAck"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "##reading data\n",
        "data= pd.read_csv(data_path)"
      ],
      "metadata": {
        "id": "twC10O_YygOw"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.head()"
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          "height": 206
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        "id": "kfy14pGeyttk",
        "outputId": "4725e4b1-3a30-4e54-f279-7039182da7e3"
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              "   Unnamed: 0                                        clean_tweet  label  \\\n",
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        "#Check for null values\n",
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        "data.isna().sum()"
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        "id": "wOxeWgTuy343",
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              "      <td>1.000000</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c2ad3901-c9e8-4308-a8ec-298ace2ba8a2')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-c2ad3901-c9e8-4308-a8ec-298ace2ba8a2 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-c2ad3901-c9e8-4308-a8ec-298ace2ba8a2');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-113e7610-c97b-47ac-8203-fd980190d0df\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-113e7610-c97b-47ac-8203-fd980190d0df')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const charts = await google.colab.kernel.invokeFunction(\n",
              "          'suggestCharts', [key], {});\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-113e7610-c97b-47ac-8203-fd980190d0df button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "##drop Unnamed column and missing values to facilitate analysis\n",
        "data= data.drop(\"Unnamed: 0\", axis=1)\n",
        "\n",
        "data= data.dropna()\n"
      ],
      "metadata": {
        "id": "LAlOk22T0Dd0"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "D3p3gfhf1C8H",
        "outputId": "2fda6c71-a3df-47bf-fc86-9ef7aa0e3189"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                            clean_tweet  label  agreement\n",
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              "...                                                 ...    ...        ...\n",
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              "9996  interesting trends child immunization oklahoma...    0.0   1.000000\n",
              "9997  cdc says measles highest levels decades return...    0.0   1.000000\n",
              "9998  pneumonia vaccine women w risk pulmonary disea...    1.0   0.666667\n",
              "\n",
              "[9970 rows x 3 columns]"
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              "      <th>9996</th>\n",
              "      <td>interesting trends child immunization oklahoma...</td>\n",
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              "      display: none;\n",
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              "      height: 32px;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-3506d089-6f5f-4aa4-b206-4306dfab6706 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-3506d089-6f5f-4aa4-b206-4306dfab6706');\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",
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              "\n",
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              "\n",
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              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const charts = await google.colab.kernel.invokeFunction(\n",
              "          'suggestCharts', [key], {});\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-85a03782-4de0-4674-987d-848bf896800c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Ensuring there are no null values\n",
        "\n",
        "data.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mgGfwhWe1RDn",
        "outputId": "4b6a2ca1-abb1-472f-c577-cfd7ffc6de7d"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "clean_tweet    0\n",
              "label          0\n",
              "agreement      0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Change tweet rows to tuples  to conform to the standard\n",
        "\n",
        "data['clean_tweet'] = data['clean_tweet'].apply(lambda tweet: tuple(tweet.split(),))"
      ],
      "metadata": {
        "id": "hdmI0Wzi1ZtR"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        " **Data Splitting**"
      ],
      "metadata": {
        "id": "yoYBwod02HzU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train, eval= train_test_split(data, test_size= 0.2, stratify= data[\"label\"])"
      ],
      "metadata": {
        "id": "g-9XzY6p2LU4"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "NfBBRi-O2t1l",
        "outputId": "c0871b81-b042-4c3e-c028-d3f5a6edd678"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                            clean_tweet  label  agreement\n",
              "1560  (average, people, complain, live, longer, rele...    0.0   1.000000\n",
              "470        (hangover, disease, u, build, immunity, lol)    0.0   1.000000\n",
              "9638  (illinois, measles, outbreak, cost, health, de...    0.0   1.000000\n",
              "1161         (mmr, dance, party, reed, college, normal)    0.0   1.000000\n",
              "5068  (outbreak, fears, rise, childhood, immunizatio...   -1.0   0.333333"
            ],
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              "      <td>1.000000</td>\n",
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              "    <tr>\n",
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              "      <td>1.000000</td>\n",
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              "      display:flex;\n",
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              "\n",
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              "      background-color: #E8F0FE;\n",
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              "\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
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              "        document.querySelector('#df-ad5ee768-e5b6-4061-b123-e931adcc45e4 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-ad5ee768-e5b6-4061-b123-e931adcc45e4');\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",
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              "\n",
              "\n",
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              "\n",
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              "    width: 32px;\n",
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              "\n",
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              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const charts = await google.colab.kernel.invokeFunction(\n",
              "          'suggestCharts', [key], {});\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-e28bb252-02d3-413b-97b0-35ec28bb6022 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2IxAVSGOvACT"
      },
      "source": [
        "Find below a simple example, with just `3 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": "code",
      "source": [
        "eval.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "ith99nkM20yo",
        "outputId": "55938887-277e-4f2d-c3b2-8878c771c4ad"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                            clean_tweet  label  agreement\n",
              "8226  (watching, science, olympiad, trivia, game, an...    0.0   1.000000\n",
              "3973  (childrens, museum, houston, offer, free, vacc...    0.0   0.666667\n",
              "6377  (disneyland, measles, outbreak, highlights, im...    1.0   1.000000\n",
              "7613  (flu, vaccine, clinic, tomorrow, sponsored, ch...    1.0   0.666667\n",
              "27    (im, obsessed, w, ebola, following, outbreak, ...    1.0   0.666667"
            ],
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              "\n",
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              "\n",
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              "      <td>0.0</td>\n",
              "      <td>1.000000</td>\n",
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              "    <tr>\n",
              "      <th>3973</th>\n",
              "      <td>(childrens, museum, houston, offer, free, vacc...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.666667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6377</th>\n",
              "      <td>(disneyland, measles, outbreak, highlights, im...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7613</th>\n",
              "      <td>(flu, vaccine, clinic, tomorrow, sponsored, ch...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.666667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>(im, obsessed, w, ebola, following, outbreak, ...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.666667</td>\n",
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              "    <div class=\"colab-df-buttons\">\n",
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              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "  </style>\n",
              "\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-6b87c11c-e433-4857-8e28-c65ce220da2f');\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",
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              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8b35ff08-8708-4c86-b458-f6c2973ba3f6')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
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              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
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              "    fill: #1967D2;\n",
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              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const charts = await google.colab.kernel.invokeFunction(\n",
              "          'suggestCharts', [key], {});\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-8b35ff08-8708-4c86-b458-f6c2973ba3f6 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#saving the train and eval data to csv\n",
        "\n",
        "train.to_csv(\"/content/train.csv\")\n",
        "eval.to_csv(\"/content/eval.csv\")"
      ],
      "metadata": {
        "id": "89UophkW29Eo"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        " **Loading Datasets**"
      ],
      "metadata": {
        "id": "nnbmzCle3doB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset= load_dataset(\"csv\", data_files={\"train\":\"train.csv\", \"eval\":\"eval.csv\" }, encoding= \"ISO-8859-1\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
            "86c5e21256eb4213a7fbcc7057b10f93",
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            "e0c86dc185914149bf884bfc0ef0d5c8"
          ]
        },
        "id": "2R6PcXO-3mEj",
        "outputId": "3de11fbb-986b-4477-f239-0e5be6d1ce6a"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data files:   0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "86c5e21256eb4213a7fbcc7057b10f93"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Extracting data files:   0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a0ba4fe28c4747c887657d0dd582faa8"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7b2862b330a24665b1bcf2e7f8687e7d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating eval split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "85ea8e4112364150987b4f2c1ea2f33a"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KPw1xotr29Uw",
        "outputId": "c16c9622-d7e2-4c1e-b97a-87c2ecccb4f2"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['Unnamed: 0', 'clean_tweet', 'label', 'agreement'],\n",
              "        num_rows: 7976\n",
              "    })\n",
              "    eval: Dataset({\n",
              "        features: ['Unnamed: 0', 'clean_tweet', 'label', 'agreement'],\n",
              "        num_rows: 1994\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "zmQSEWtE4ys3"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Tokenization**"
      ],
      "metadata": {
        "id": "wg20QeJ_454p"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#create an instance for tokenizer\n",
        "tokenizer= AutoTokenizer.from_pretrained(\"roberta-base\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
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        "id": "IFwqmkKT5Jro",
        "outputId": "ea49ca0f-ef2e-450d-aeb8-27ee06781665"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/481 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "11ee02f166e34ed4849136ac74255770"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)olve/main/vocab.json:   0%|          | 0.00/899k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4462262d835a4db5bdf04c03fed9dc39"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)olve/main/merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6b5dc55f0af3492084c9cb04dade13dd"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/1.36M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4ffc2da51e9c41ca9ea8c13966894dff"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## changing labels to 0,1,2  from the initial labels -1, 0, 1\n",
        "def transform_labels(input):\n",
        "  label= input[\"label\"]\n",
        "  num =0\n",
        "\n",
        "  if label== -1:\n",
        "    num= 0\n",
        "  elif label== 0:\n",
        "    num =1\n",
        "  elif label == 1:\n",
        "    num = 2\n",
        "  return {\"labels\": num}\n",
        "\n",
        "def tokenize(example):\n",
        "  return tokenizer(example[\"clean_tweet\"], padding= \"max_length\", truncation=True, return_tensors= \"pt\")\n"
      ],
      "metadata": {
        "id": "j0wMW_d_51Jq"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "## Converting tweets to tokens for the model to work with\n",
        "dataset= dataset.map(tokenize, batched= True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
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        },
        "id": "HuqgQrZ66LSF",
        "outputId": "994d4185-236f-48da-f9c2-87e49465e5ee"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/7976 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2813892fea4843e990bac471d4819fa7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/1994 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "308a1349d5f742a6b83442818cc2e865"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## eliminating features that are not needed for the analysis\n",
        "remove_columns= ['Unnamed: 0', 'clean_tweet', 'label', 'agreement']\n",
        "dataset = dataset.map(transform_labels, remove_columns=remove_columns)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
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        },
        "id": "N9GVycoF6UxE",
        "outputId": "d7da32ef-c5ce-4c8c-aa44-9d1b642f3a6d"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/7976 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4c299e72a7144af1a8c862040adb931d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/1994 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4c723ba8c9d64a5bb5906f1110e38c60"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dSMIzbgW6n0G",
        "outputId": "e148651b-683e-4eef-b2d6-61d3e104f12c"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['input_ids', 'attention_mask', 'labels'],\n",
              "        num_rows: 7976\n",
              "    })\n",
              "    eval: Dataset({\n",
              "        features: ['input_ids', 'attention_mask', 'labels'],\n",
              "        num_rows: 1994\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "QVr30Zre6q6R"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Training**"
      ],
      "metadata": {
        "id": "F1jhaw2o7vj3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Configure the trianing parameters like `num_train_epochs`,the number of times the model will repeat the training loop over the dataset\n",
        "training_args = TrainingArguments(\"test_trainer\",\n",
        "                                  num_train_epochs=3,\n",
        "                                  load_best_model_at_end=True,\n",
        "                                  save_strategy='epoch',\n",
        "                                  evaluation_strategy='epoch',\n",
        "                                  logging_strategy='epoch',\n",
        "                                  logging_steps=100,\n",
        "                                  per_device_train_batch_size=16,\n",
        "                                  )"
      ],
      "metadata": {
        "id": "JJnQgqUL73ol"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Modelling**"
      ],
      "metadata": {
        "id": "QFZo9TXp7E2e"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#loading  model and creating an instance for the classes\n",
        "model= AutoModelForSequenceClassification.from_pretrained(\"roberta-base\", num_labels= 3)"
      ],
      "metadata": {
        "id": "DiMOj_Dv7NBM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
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        "outputId": "1b207e02-f83f-4c11-bd80-bfc4c29306e2"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading model.safetensors:   0%|          | 0.00/499M [00:00<?, ?B/s]"
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    },
    {
      "cell_type": "code",
      "source": [
        "##setting a shuffle seed to avoid randomization at each rerun\n",
        "train_dataset= dataset['train'].shuffle(seed=10)\n",
        "eval_dataset= dataset['eval'].shuffle(seed=10)"
      ],
      "metadata": {
        "id": "PLUEOk8c-ThQ"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from huggingface_hub import notebook_login  ##connecting to my hugginface profile\n",
        "\n",
        "notebook_login()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
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        },
        "id": "9VdNUtPrAIVD",
        "outputId": "fa758678-3191-4999-ff38-7db5bbc933b7"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_metric\n",
        "metric = load_metric(\"accuracy\")\n",
        "\n",
        "def compute_metrics(eval_pred):\n",
        "    logits, labels = eval_pred\n",
        "    predictions = np.argmax(logits, axis=-1)\n",
        "    return metric.compute(predictions=predictions, references=labels)"
      ],
      "metadata": {
        "id": "BAAqGccT_LNK"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#load training arguments\n",
        "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",
        ")"
      ],
      "metadata": {
        "id": "wGV7bBos_HEc"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#training my model\n",
        "\n",
        "trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "81HrkwzRAZm8",
        "outputId": "0d38a885-a575-4c5d-df6e-9becdb671fe0"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='1497' max='1497' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [1497/1497 37:13, Epoch 3/3]\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>Accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.770100</td>\n",
              "      <td>0.777772</td>\n",
              "      <td>0.688566</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.688300</td>\n",
              "      <td>0.657972</td>\n",
              "      <td>0.723671</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.608800</td>\n",
              "      <td>0.641090</td>\n",
              "      <td>0.738716</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=1497, training_loss=0.6890467132181028, metrics={'train_runtime': 2235.1612, 'train_samples_per_second': 10.705, 'train_steps_per_second': 0.67, 'total_flos': 6295777859395584.0, 'train_loss': 0.6890467132181028, 'epoch': 3.0})"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Launch the final evaluation\n",
        "trainer.evaluate()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 147
        },
        "id": "di8p1JD4O1TB",
        "outputId": "69067ef9-f191-450d-dc35-f8008ce372ba"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='250' max='250' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [250/250 00:55]\n",
              "    </div>\n",
              "    "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'eval_loss': 0.6410900950431824,\n",
              " 'eval_accuracy': 0.7387161484453361,\n",
              " 'eval_runtime': 55.6213,\n",
              " 'eval_samples_per_second': 35.85,\n",
              " 'eval_steps_per_second': 4.495,\n",
              " 'epoch': 3.0}"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "  ##pushing my trained model together with the results to hugginface\n",
        "\n",
        "trainer.push_to_hub()"
      ],
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        "id": "3Cok9m4pNgc5",
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              "pytorch_model.bin:   0%|          | 0.00/499M [00:00<?, ?B/s]"
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        {
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          "data": {
            "text/plain": [
              "Upload 2 LFS files:   0%|          | 0/2 [00:00<?, ?it/s]"
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              "'https://huggingface.co/Enyonam/test_trainer/tree/main/'"
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    },
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      "cell_type": "markdown",
      "source": [],
      "metadata": {
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    {
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    },
    {
      "cell_type": "markdown",
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        "I manually split the training set to have a training subset ( a dataset the model will learn on), and an evaluation subset ( a dataset the model with use to compute metric scores to help use to avoid some training problems like [the overfitting](https://www.ibm.com/cloud/learn/overfitting) one ).\n",
        "\n",
        "There are multiple ways to do split the dataset. You'll see two commented line showing you another one."
      ]
    },
    {
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      "metadata": {
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