mBERT/Amharic_Hate_Speech_detection_using_mBERT_(Trainer_API).ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/amengemeda/ISproject-2/blob/main/Amharic_Hate_Speech_detection_using_mBERT_(Trainer_API).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## *Amharic Hate Speech Detection using fine-tuned mBERT <br> (multilingual Bidirectional Encoder Representations from Transformer)*\n",
        "\n"
      ],
      "metadata": {
        "id": "sVlziLqR0SEP"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aTzlLyQwy6bg"
      },
      "source": [
        "* The original mBERT made available by Google doesn't support the Amharic language hence for the downstream task of detecting Amharic hate speech, a finetuned mBERT model for the Amharic language was used. \n",
        "* The model was made available by Davlan on Huggingface. You can find the model [here](https://huggingface.co/Davlan/bert-base-multilingual-cased-finetuned-amharic)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "![transformers.png](data:image/png;base64,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)"
      ],
      "metadata": {
        "id": "zJavgDjJ1RSJ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### *Step 1: Install the necessary packages*"
      ],
      "metadata": {
        "id": "adtDHQuH8iQG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We need two packages:\n",
        "* Transformers package made available by Huggingface\n",
        "* Dataset package made availale by Huggingface"
      ],
      "metadata": {
        "id": "gkrHxUcr8sQ4"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BNsNbBRTsfRi",
        "outputId": "0254916c-b623-435e-c5b2-9c590d5ce30f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting transformers\n",
            "  Downloading transformers-4.25.1-py3-none-any.whl (5.8 MB)\n",
            "\u001b[K     |████████████████████████████████| 5.8 MB 25.3 MB/s \n",
            "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.8/dist-packages (from transformers) (3.8.0)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.8/dist-packages (from transformers) (4.64.1)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.8/dist-packages (from transformers) (1.21.6)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.8/dist-packages (from transformers) (21.3)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.8/dist-packages (from transformers) (2.23.0)\n",
            "Collecting tokenizers!=0.11.3,<0.14,>=0.11.1\n",
            "  Downloading tokenizers-0.13.2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.6 MB)\n",
            "\u001b[K     |████████████████████████████████| 7.6 MB 43.5 MB/s \n",
            "\u001b[?25hCollecting huggingface-hub<1.0,>=0.10.0\n",
            "  Downloading huggingface_hub-0.11.1-py3-none-any.whl (182 kB)\n",
            "\u001b[K     |████████████████████████████████| 182 kB 69.7 MB/s \n",
            "\u001b[?25hRequirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.8/dist-packages (from transformers) (6.0)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.8/dist-packages (from transformers) (2022.6.2)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.8/dist-packages (from huggingface-hub<1.0,>=0.10.0->transformers) (4.1.1)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.8/dist-packages (from packaging>=20.0->transformers) (3.0.9)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.8/dist-packages (from requests->transformers) (2.10)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.8/dist-packages (from requests->transformers) (1.24.3)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.8/dist-packages (from requests->transformers) (3.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.8/dist-packages (from requests->transformers) (2022.9.24)\n",
            "Installing collected packages: tokenizers, huggingface-hub, transformers\n",
            "Successfully installed huggingface-hub-0.11.1 tokenizers-0.13.2 transformers-4.25.1\n"
          ]
        }
      ],
      "source": [
        "#installing the transformers package\n",
        "!pip install transformers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f2Kr3hxFH-Y2",
        "outputId": "9198dd04-ce79-49c5-cf80-bcd8ac892e87"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting datasets\n",
            "  Downloading datasets-2.7.1-py3-none-any.whl (451 kB)\n",
            "\u001b[K     |████████████████████████████████| 451 kB 35.7 MB/s \n",
            "\u001b[?25hCollecting responses<0.19\n",
            "  Downloading responses-0.18.0-py3-none-any.whl (38 kB)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.8/dist-packages (from datasets) (1.21.6)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.8/dist-packages (from datasets) (21.3)\n",
            "Collecting xxhash\n",
            "  Downloading xxhash-3.1.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (212 kB)\n",
            "\u001b[K     |████████████████████████████████| 212 kB 76.6 MB/s \n",
            "\u001b[?25hRequirement already satisfied: pyarrow>=6.0.0 in /usr/local/lib/python3.8/dist-packages (from datasets) (9.0.0)\n",
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            "Installing collected packages: urllib3, xxhash, responses, multiprocess, datasets\n",
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          ]
        }
      ],
      "source": [
        "#installing the datasets package\n",
        "!pip install datasets"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### *Step 2: Import the necessay libraries from the installed packages*"
      ],
      "metadata": {
        "id": "R2U8OEAX9I9E"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Bnfla8DsyBhM"
      },
      "outputs": [],
      "source": [
        "#importing the datasets package\n",
        "from datasets import Dataset\n",
        "import datasets\n",
        "#import load metric for model evaluation\n",
        "from datasets import load_metric"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sfB-ohyp3pA6"
      },
      "outputs": [],
      "source": [
        "#import numpy and pandas for mathematical computation and data manipulation respectively \n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "#import drive package to connect this colab file with the drive where the data will be retrived from\n",
        "from google.colab import drive\n",
        "#import the pipeline of transformers\n",
        "from transformers import pipeline\n",
        "#import AutoTokenizer for tokenization purposes\n",
        "from transformers import AutoTokenizer\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "E9HwjYvqBz7S"
      },
      "outputs": [],
      "source": [
        "#import torch\n",
        "import torch\n",
        "#import Data loader from torch\n",
        "from torch.utils.data import DataLoader\n",
        "#import an optimizer\n",
        "from torch.optim import AdamW\n",
        "#import tqdm for a progress bar\n",
        "from tqdm.auto import tqdm"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aECkq4Fguzfb"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### *Step 3: Import the dataset to be used for Training the model*\n"
      ],
      "metadata": {
        "id": "tkWBZpS69sBV"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The dataset used for this project is an Amharic dataset that was made available by Data Mendeley. It contains Amharic posts and comments retrieved from Facebook. It has 30,000 rows. The dataset can be accessed from [here](https://data.mendeley.com/datasets/ymtmxx385m)"
      ],
      "metadata": {
        "id": "xeo3UvY392od"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X-Yp1xAuWgXQ",
        "outputId": "67379133-e46e-4ef5-e831-6c99aea00afe"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "#mount google drive to access the dataset directly from the drive\n",
        "drive.mount('/content/drive') "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eXBLcBgSWl7A"
      },
      "outputs": [],
      "source": [
        "#fetch the dataset from the drive\n",
        "Labels=pd.read_csv('/content/drive/MyDrive/Machine Learning/Data/Amharic-Hate-Speech-Dataset/Labels.txt',header=None)\n",
        "Posts=pd.read_csv('/content/drive/MyDrive/Machine Learning/Data/Amharic-Hate-Speech-Dataset/Posts.txt',header=None)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### *Step 4: Preprocess the Dataset*"
      ],
      "metadata": {
        "id": "uGabpHmlyLa-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "When the dataset was retrived, the labels and the post were in different files. \n",
        "\n",
        "\n",
        "*   Hence, the first step in this phase is merging the files into one panda's dataframe.\n",
        "*   Second step is Label encoding. Lable encoding is the process of converting the labels(classes) into numeric format to make it easier for the machine to understand it\n",
        "*   Third step is dividing the dataset into training, validation and testing categories. The division ratio is 7:1:2 respectively.\n",
        "*   Last step is to remove an unncessary columns from the main dataset and merging the all the categories into one main dataset\n",
        "\n"
      ],
      "metadata": {
        "id": "W919HMiryXg9"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ti6Xtr1y47zj"
      },
      "outputs": [],
      "source": [
        "#naming the columns\n",
        "Labels.columns = [\"labels\"]\n",
        "Posts.columns = [\"post\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "k4GGAdMxjxoQ"
      },
      "outputs": [],
      "source": [
        "#encoding the classes into numerical data\n",
        "Labels = Labels.replace(['Free', 'Free ','Hate'],[0,0,1]) "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
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        },
        "id": "JuIzyozFkWL4",
        "outputId": "aecc46bd-222a-4de9-d653-0e114cd3ba8c"
      },
      "outputs": [
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              "    <tr>\n",
              "      <th>997</th>\n",
              "      <td>ክክክክክ የሱዳን ዜጋ ናችሁ እንዴ ባለ ነጠላ ጎጃሜ ሁላ</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>998</th>\n",
              "      <td>የምን ማለቃቀስ ነው ምድረ ፎካሪ ሁላ</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>999</th>\n",
              "      <td>መግስት ሀልባ አገር</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1000 rows × 1 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-acbc0cd7-4049-4ec3-8b57-f857e1310cb4')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      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",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-acbc0cd7-4049-4ec3-8b57-f857e1310cb4 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-acbc0cd7-4049-4ec3-8b57-f857e1310cb4');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                                                  post\n",
              "0    አስቀድሜ ጥያቄዬ በጨዋነት በውስጥ መስመር እንዲደርስዎ አድርጌ ፍትህን ለ...\n",
              "1     እነዚህን ወሳኝ ጉዳዮችን የሚያስፈፅም አካል እንዲቋቋምና ክትትል እንዲደ...\n",
              "2    የአማራ ህዝብ በአእምሮ ክንፉ ያልበረረበት ጥበብና ፍልስፍና ያልከፈተው የ...\n",
              "3    ከአማራ ህዝብ የሀገሪቱ ዘርፈ ብዙ እውቀት መንጭቶ የሞላበትከሙላቱም በመል...\n",
              "4    ዛሬ በየትኛውም መለኪያ ይሁን መመዘኛ ኢትዮጵያዊነት የሚንፀባረቀው በአማራ...\n",
              "..                                                 ...\n",
              "995  መፈንቅለመንግስት ከሽፏልእንዴ የየክልሉ ህዝብ ምን ይጠብቃል ሆ ብሎ ሄዶ ...\n",
              "996  ሰው በሩን እንደፈለገ መክፈት እንደፈለገ መዝጋት ይችላል  የምን አውቅልሻ...\n",
              "997                ክክክክክ የሱዳን ዜጋ ናችሁ እንዴ ባለ ነጠላ ጎጃሜ ሁላ\n",
              "998                            የምን ማለቃቀስ ነው ምድረ ፎካሪ ሁላ\n",
              "999                                       መግስት ሀልባ አገር\n",
              "\n",
              "[1000 rows x 1 columns]"
            ]
          },
          "execution_count": 56,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#check the Amharic data\n",
        "Posts.head(1000)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hh3MnC_O6E-4"
      },
      "outputs": [],
      "source": [
        "#merge the datasets\n",
        "Frames = [Labels, Posts]\n",
        "Merged = pd.concat(Frames, axis=1)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "rmeOZhfb2REM",
        "outputId": "2c0098d1-550a-4398-ede4-60d43d45beec"
      },
      "outputs": [
        {
          "data": {
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>አስቀድሜ ጥያቄዬ በጨዋነት በውስጥ መስመር እንዲደርስዎ አድርጌ ፍትህን ለ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>እነዚህን ወሳኝ ጉዳዮችን የሚያስፈፅም አካል እንዲቋቋምና ክትትል እንዲደ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>የአማራ ህዝብ በአእምሮ ክንፉ ያልበረረበት ጥበብና ፍልስፍና ያልከፈተው የ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>ከአማራ ህዝብ የሀገሪቱ ዘርፈ ብዙ እውቀት መንጭቶ የሞላበትከሙላቱም በመል...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>ዛሬ በየትኛውም መለኪያ ይሁን መመዘኛ ኢትዮጵያዊነት የሚንፀባረቀው በአማራ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29995</th>\n",
              "      <td>1</td>\n",
              "      <td>በአሉ የሁሉም ኢትዮጵያዊ ስላልሆነ በኦሮምኛው ቢለፋደድ ምን አገባን</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29996</th>\n",
              "      <td>0</td>\n",
              "      <td>ተባረክ አብቹ ፈር ቀዳጅ ስለሆንህ መጋረጃው መቀደድ ስለጀመረ</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29997</th>\n",
              "      <td>0</td>\n",
              "      <td>እስከ አሁን አንተ ብቻ ነው በ   መፅሀፍ ያልቻልከው  አንተም ታሪክ እ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29998</th>\n",
              "      <td>1</td>\n",
              "      <td>ህገወጥት ጠቅላይ ሚንስትር ፅቤት የተፈቀደ ሆኖ ህዝብን እንዴት ህግ አክ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29999</th>\n",
              "      <td>1</td>\n",
              "      <td>ደነዙ ጠቅላይ ሚንስትር ፅቤት ህገመንግስት ሳይሻሻል በህግ የተወሰነዉን የ...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>30000 rows × 2 columns</p>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
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              "      display:flex;\n",
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              "    .colab-df-convert {\n",
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              "      border: none;\n",
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              "    .colab-df-convert:hover {\n",
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              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-0c3db6b9-8bf0-4013-9acd-e51ee0a3fad9 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-0c3db6b9-8bf0-4013-9acd-e51ee0a3fad9');\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",
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              "        }\n",
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              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "       labels                                               post\n",
              "0           0  አስቀድሜ ጥያቄዬ በጨዋነት በውስጥ መስመር እንዲደርስዎ አድርጌ ፍትህን ለ...\n",
              "1           0   እነዚህን ወሳኝ ጉዳዮችን የሚያስፈፅም አካል እንዲቋቋምና ክትትል እንዲደ...\n",
              "2           0  የአማራ ህዝብ በአእምሮ ክንፉ ያልበረረበት ጥበብና ፍልስፍና ያልከፈተው የ...\n",
              "3           0  ከአማራ ህዝብ የሀገሪቱ ዘርፈ ብዙ እውቀት መንጭቶ የሞላበትከሙላቱም በመል...\n",
              "4           1  ዛሬ በየትኛውም መለኪያ ይሁን መመዘኛ ኢትዮጵያዊነት የሚንፀባረቀው በአማራ...\n",
              "...       ...                                                ...\n",
              "29995       1         በአሉ የሁሉም ኢትዮጵያዊ ስላልሆነ በኦሮምኛው ቢለፋደድ ምን አገባን\n",
              "29996       0             ተባረክ አብቹ ፈር ቀዳጅ ስለሆንህ መጋረጃው መቀደድ ስለጀመረ\n",
              "29997       0   እስከ አሁን አንተ ብቻ ነው በ   መፅሀፍ ያልቻልከው  አንተም ታሪክ እ...\n",
              "29998       1   ህገወጥት ጠቅላይ ሚንስትር ፅቤት የተፈቀደ ሆኖ ህዝብን እንዴት ህግ አክ...\n",
              "29999       1  ደነዙ ጠቅላይ ሚንስትር ፅቤት ህገመንግስት ሳይሻሻል በህግ የተወሰነዉን የ...\n",
              "\n",
              "[30000 rows x 2 columns]"
            ]
          },
          "execution_count": 58,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#preview of merged data\n",
        "Merged"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zf4paAEe1eYX",
        "outputId": "5b083cf2-e066-44fe-89fa-42d160725f67"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Training dataset shape:  (21600, 2)\n",
            "Validation dataset shape:  (2400, 2)\n",
            "Testing dataset shape:  (6000, 2)\n"
          ]
        }
      ],
      "source": [
        "train_val_df, test_dataset = train_test_split(Merged, test_size=0.20, random_state=42)\n",
        "train_dataset, evaluation_dataset = train_test_split(train_val_df, test_size=0.10, random_state=42)\n",
        "print('Training dataset shape: ', train_dataset.shape)\n",
        "print('Validation dataset shape: ', evaluation_dataset.shape)\n",
        "print('Testing dataset shape: ', test_dataset.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZVfzGbMLJqq4"
      },
      "outputs": [],
      "source": [
        "#Divide the dataset into train and test categories \n",
        "msk = np.random.rand(len(Merged)) < 0.8\n",
        "train_dataset = Merged[msk]\n",
        "test_dataset = Merged[~msk]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oFPHgSEM1Dk7"
      },
      "outputs": [],
      "source": [
        "#convert format of the dataset to HuggingFace Dataset from Pandas DataFrame\n",
        "test_dataset=Dataset.from_pandas(test_dataset)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8AjJWpMzNY5O"
      },
      "outputs": [],
      "source": [
        "#convert the format of the dataset to HuggingFace Dataset from Pandas DataFrame\n",
        "train_dataset=Dataset.from_pandas(train_dataset)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BKcbCLrb6_Fb"
      },
      "outputs": [],
      "source": [
        "#convert the format of the dataset to HuggingFace Dataset from Pandas DataFrame\n",
        "evaluation_dataset=Dataset.from_pandas(evaluation_dataset)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5tjTu_uTMbH2",
        "outputId": "8f39f319-6492-4f4a-cd95-8322f70a1f5b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['labels', 'post', '__index_level_0__'],\n",
              "    num_rows: 5956\n",
              "})"
            ]
          },
          "execution_count": 64,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#preview of the dataset after conversion\n",
        "(test_dataset)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8lmUdwFjkEvS"
      },
      "outputs": [],
      "source": [
        "#remove unnecessary column\n",
        "test_dataset=test_dataset.remove_columns(\"__index_level_0__\")\n",
        "train_dataset=train_dataset.remove_columns(\"__index_level_0__\")\n",
        "evaluation_dataset=evaluation_dataset.remove_columns(\"__index_level_0__\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xMd8ZWLwOacB"
      },
      "outputs": [],
      "source": [
        "#combine the train and test dataset into one datset\n",
        "main_dataset= datasets.DatasetDict({\n",
        "    'train': train_dataset,\n",
        "    'test': test_dataset,\n",
        "    'evaluate': evaluation_dataset\n",
        "})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FQphi9G4Qnca",
        "outputId": "82e4a9fd-4c3a-4ce0-85ab-c9316189d227"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['labels', 'post'],\n",
              "        num_rows: 24044\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['labels', 'post'],\n",
              "        num_rows: 5956\n",
              "    })\n",
              "    evaluate: Dataset({\n",
              "        features: ['labels', 'post'],\n",
              "        num_rows: 2400\n",
              "    })\n",
              "})"
            ]
          },
          "execution_count": 67,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#preview of the dataset after merging\n",
        "main_dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nid9zbCnJ-M5"
      },
      "outputs": [],
      "source": [
        "# training and testing data size\n",
        "training_data_size = main_dataset['train'].num_rows\n",
        "testing_data_size = main_dataset['test'].num_rows\n",
        "evaluation_data_size = main_dataset['evaluate'].num_rows"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### *Step 5: Tokenizing Dataset*"
      ],
      "metadata": {
        "id": "giodioN63Udc"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "A Tokenizer is used to translate text into data that can be processed by the model. Models can only process numbers, so tokenizers need to convert our text inputs to numerical data.\n",
        "\n",
        "In this case, the tokenizer used is an AutoTokenizer from the fine-tuned mBERT model made available by Hugging face [here](https://huggingface.co/Davlan/bert-base-multilingual-cased-finetuned-amharic)\n"
      ],
      "metadata": {
        "id": "yn_t_kj61y5g"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "In this phase, we have the following tasks:\n",
        "* Load the tokenizer\n",
        "* Create a tokenizer function that takes the dataset in batches and tokenize them using the tokenizer loaded from the model\n",
        "* Call the tokenizer function on the whole dataset"
      ],
      "metadata": {
        "id": "89DUH3zn2cao"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g-arFaACbefl",
        "outputId": "ea4f7431-ee5f-48a2-d9e7-7e6ab72425bb"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--Davlan--bert-base-multilingual-cased-finetuned-amharic/snapshots/90283498970edd0fe6af2f094f5f25d61e525ebd/config.json\n",
            "Model config BertConfig {\n",
            "  \"_name_or_path\": \"Davlan/bert-base-multilingual-cased-finetuned-amharic\",\n",
            "  \"architectures\": [\n",
            "    \"BertForMaskedLM\"\n",
            "  ],\n",
            "  \"attention_probs_dropout_prob\": 0.1,\n",
            "  \"classifier_dropout\": null,\n",
            "  \"directionality\": \"bidi\",\n",
            "  \"gradient_checkpointing\": false,\n",
            "  \"hidden_act\": \"gelu\",\n",
            "  \"hidden_dropout_prob\": 0.1,\n",
            "  \"hidden_size\": 768,\n",
            "  \"initializer_range\": 0.02,\n",
            "  \"intermediate_size\": 3072,\n",
            "  \"layer_norm_eps\": 1e-12,\n",
            "  \"max_position_embeddings\": 512,\n",
            "  \"model_type\": \"bert\",\n",
            "  \"num_attention_heads\": 12,\n",
            "  \"num_hidden_layers\": 12,\n",
            "  \"pad_token_id\": 0,\n",
            "  \"pooler_fc_size\": 768,\n",
            "  \"pooler_num_attention_heads\": 12,\n",
            "  \"pooler_num_fc_layers\": 3,\n",
            "  \"pooler_size_per_head\": 128,\n",
            "  \"pooler_type\": \"first_token_transform\",\n",
            "  \"position_embedding_type\": \"absolute\",\n",
            "  \"transformers_version\": \"4.24.0\",\n",
            "  \"type_vocab_size\": 2,\n",
            "  \"use_cache\": true,\n",
            "  \"vocab_size\": 119547\n",
            "}\n",
            "\n",
            "loading file vocab.txt from cache at /root/.cache/huggingface/hub/models--Davlan--bert-base-multilingual-cased-finetuned-amharic/snapshots/90283498970edd0fe6af2f094f5f25d61e525ebd/vocab.txt\n",
            "loading file tokenizer.json from cache at None\n",
            "loading file added_tokens.json from cache at None\n",
            "loading file special_tokens_map.json from cache at /root/.cache/huggingface/hub/models--Davlan--bert-base-multilingual-cased-finetuned-amharic/snapshots/90283498970edd0fe6af2f094f5f25d61e525ebd/special_tokens_map.json\n",
            "loading file tokenizer_config.json from cache at /root/.cache/huggingface/hub/models--Davlan--bert-base-multilingual-cased-finetuned-amharic/snapshots/90283498970edd0fe6af2f094f5f25d61e525ebd/tokenizer_config.json\n",
            "loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--Davlan--bert-base-multilingual-cased-finetuned-amharic/snapshots/90283498970edd0fe6af2f094f5f25d61e525ebd/config.json\n",
            "Model config BertConfig {\n",
            "  \"_name_or_path\": \"Davlan/bert-base-multilingual-cased-finetuned-amharic\",\n",
            "  \"architectures\": [\n",
            "    \"BertForMaskedLM\"\n",
            "  ],\n",
            "  \"attention_probs_dropout_prob\": 0.1,\n",
            "  \"classifier_dropout\": null,\n",
            "  \"directionality\": \"bidi\",\n",
            "  \"gradient_checkpointing\": false,\n",
            "  \"hidden_act\": \"gelu\",\n",
            "  \"hidden_dropout_prob\": 0.1,\n",
            "  \"hidden_size\": 768,\n",
            "  \"initializer_range\": 0.02,\n",
            "  \"intermediate_size\": 3072,\n",
            "  \"layer_norm_eps\": 1e-12,\n",
            "  \"max_position_embeddings\": 512,\n",
            "  \"model_type\": \"bert\",\n",
            "  \"num_attention_heads\": 12,\n",
            "  \"num_hidden_layers\": 12,\n",
            "  \"pad_token_id\": 0,\n",
            "  \"pooler_fc_size\": 768,\n",
            "  \"pooler_num_attention_heads\": 12,\n",
            "  \"pooler_num_fc_layers\": 3,\n",
            "  \"pooler_size_per_head\": 128,\n",
            "  \"pooler_type\": \"first_token_transform\",\n",
            "  \"position_embedding_type\": \"absolute\",\n",
            "  \"transformers_version\": \"4.24.0\",\n",
            "  \"type_vocab_size\": 2,\n",
            "  \"use_cache\": true,\n",
            "  \"vocab_size\": 119547\n",
            "}\n",
            "\n",
            "loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--Davlan--bert-base-multilingual-cased-finetuned-amharic/snapshots/90283498970edd0fe6af2f094f5f25d61e525ebd/config.json\n",
            "Model config BertConfig {\n",
            "  \"_name_or_path\": \"Davlan/bert-base-multilingual-cased-finetuned-amharic\",\n",
            "  \"architectures\": [\n",
            "    \"BertForMaskedLM\"\n",
            "  ],\n",
            "  \"attention_probs_dropout_prob\": 0.1,\n",
            "  \"classifier_dropout\": null,\n",
            "  \"directionality\": \"bidi\",\n",
            "  \"gradient_checkpointing\": false,\n",
            "  \"hidden_act\": \"gelu\",\n",
            "  \"hidden_dropout_prob\": 0.1,\n",
            "  \"hidden_size\": 768,\n",
            "  \"initializer_range\": 0.02,\n",
            "  \"intermediate_size\": 3072,\n",
            "  \"layer_norm_eps\": 1e-12,\n",
            "  \"max_position_embeddings\": 512,\n",
            "  \"model_type\": \"bert\",\n",
            "  \"num_attention_heads\": 12,\n",
            "  \"num_hidden_layers\": 12,\n",
            "  \"pad_token_id\": 0,\n",
            "  \"pooler_fc_size\": 768,\n",
            "  \"pooler_num_attention_heads\": 12,\n",
            "  \"pooler_num_fc_layers\": 3,\n",
            "  \"pooler_size_per_head\": 128,\n",
            "  \"pooler_type\": \"first_token_transform\",\n",
            "  \"position_embedding_type\": \"absolute\",\n",
            "  \"transformers_version\": \"4.24.0\",\n",
            "  \"type_vocab_size\": 2,\n",
            "  \"use_cache\": true,\n",
            "  \"vocab_size\": 119547\n",
            "}\n",
            "\n"
          ]
        }
      ],
      "source": [
        "#loading a tokenizer from the pretrained model\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"Davlan/bert-base-multilingual-cased-finetuned-amharic\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ceAd2-Svu-Kb"
      },
      "outputs": [],
      "source": [
        "#Have a tokenizer function that uses the tokenizer \n",
        "def tokenize_function(data):\n",
        "    return tokenizer(data[\"post\"], padding=\"max_length\", truncation=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
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        },
        "id": "Lwb78oGCRkeG",
        "outputId": "cc855fc6-0370-42db-eeec-a343d3b8d4d2"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1212a21978524994a822f4af35da380a",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/24044 [00:00<?, ?ex/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "91b9453e235049a1b3a4eb49a40a1aea",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/5956 [00:00<?, ?ex/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ce44d67b7e2146b6be1769a463da1f7a",
              "version_major": 2,
              "version_minor": 0
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            "text/plain": [
              "  0%|          | 0/2400 [00:00<?, ?ex/s]"
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          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "#Tokenize all the data using the mapping functionality\n",
        "tokenized_datasets = main_dataset.map(tokenize_function)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2-JJ9yRaOVGC"
      },
      "outputs": [],
      "source": [
        "#empty cache\n",
        "torch.cuda.empty_cache()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### *Step 6: Prepare the tokenized Dataset*"
      ],
      "metadata": {
        "id": "bM0mYmMq3Dsn"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "In this phase, we do the following tasks:\n",
        "\n",
        "* Remove unnecessary columns such as the \"posts\" column from the tokenized dataset as we no longer need them\n",
        "* Change the format of the tokenized dataset into pytorch since we are using pytorch \n",
        "* Load the dataset using DataLoader with the proper batch size\n",
        "* Preview the features of the dataset to make sure everything is okay"
      ],
      "metadata": {
        "id": "M9dIIouL3L63"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kbrulB12Oycv"
      },
      "outputs": [],
      "source": [
        "#remove the posts column as it is no longer needed\n",
        "tokenized_datasets = tokenized_datasets.remove_columns([\"post\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zHQL4mR3PDXD"
      },
      "outputs": [],
      "source": [
        "#changing the format of the tokenized dataset to torch\n",
        "tokenized_datasets.set_format(\"torch\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AAtKn5fMPJX4"
      },
      "outputs": [],
      "source": [
        "#shuffeling and selecting the needed size of dataset for training and evaluating the model\n",
        "small_train_dataset = tokenized_datasets[\"train\"].shuffle(seed=42).select(range(training_data_size))\n",
        "small_test_dataset = tokenized_datasets[\"test\"].shuffle(seed=42).select(range(testing_data_size))\n",
        "small_eval_dataset = tokenized_datasets[\"evaluate\"].shuffle(seed=42).select(range(evaluation_data_size))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iiiKJ7QNR8gd",
        "outputId": "fd341d66-334a-4984-cb67-4c41d0c0cc7f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['labels', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
              "    num_rows: 2400\n",
              "})"
            ]
          },
          "execution_count": 76,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# preview of the shuffeled and selected evaluation dataset\n",
        "small_eval_dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Iw2fYFD1SFe_",
        "outputId": "7eb74858-8201-4fcf-d0fb-88e37082141f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['labels', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
              "    num_rows: 24044\n",
              "})"
            ]
          },
          "execution_count": 77,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# preview of the shuffeled and selected training dataset\n",
        "small_train_dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JzrnrKZq77Wk",
        "outputId": "af48251e-d385-4aea-a54a-8f2bbed1a390"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['labels', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
              "    num_rows: 5956\n",
              "})"
            ]
          },
          "execution_count": 78,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# preview of the shuffeled and selected testing dataset\n",
        "small_test_dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Qs3RA2VkRcNL"
      },
      "outputs": [],
      "source": [
        "#load the dataset using DataLoader\n",
        "train_dataloader = DataLoader(small_train_dataset, shuffle=True, batch_size=4)\n",
        "eval_dataloader = DataLoader(small_eval_dataset, batch_size=4)\n",
        "test_dataloader = DataLoader(small_test_dataset, batch_size=4)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### *Step 7: Fine-tune the model*"
      ],
      "metadata": {
        "id": "5WbBDNx04wD7"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "This phase has the following steps:\n",
        "* Load the model\n",
        "* Specify the computing metric\n",
        "* Specify the Training/fine-tuning arguments\n",
        "* Load the Trainer class\n",
        "* Fine-tune the model"
      ],
      "metadata": {
        "id": "svV79SsK49f5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**7.1 Load the model**<br>\n",
        "We load the fine-tuned mBERT mode in this step"
      ],
      "metadata": {
        "id": "eoZCgreV7-Bo"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UiIYAeKoRlid",
        "outputId": "1b201e0c-5f9b-42cb-8673-ad8b3a6b9918"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--Davlan--bert-base-multilingual-cased-finetuned-amharic/snapshots/90283498970edd0fe6af2f094f5f25d61e525ebd/config.json\n",
            "Model config BertConfig {\n",
            "  \"_name_or_path\": \"Davlan/bert-base-multilingual-cased-finetuned-amharic\",\n",
            "  \"architectures\": [\n",
            "    \"BertForMaskedLM\"\n",
            "  ],\n",
            "  \"attention_probs_dropout_prob\": 0.1,\n",
            "  \"classifier_dropout\": null,\n",
            "  \"directionality\": \"bidi\",\n",
            "  \"gradient_checkpointing\": false,\n",
            "  \"hidden_act\": \"gelu\",\n",
            "  \"hidden_dropout_prob\": 0.1,\n",
            "  \"hidden_size\": 768,\n",
            "  \"initializer_range\": 0.02,\n",
            "  \"intermediate_size\": 3072,\n",
            "  \"layer_norm_eps\": 1e-12,\n",
            "  \"max_position_embeddings\": 512,\n",
            "  \"model_type\": \"bert\",\n",
            "  \"num_attention_heads\": 12,\n",
            "  \"num_hidden_layers\": 12,\n",
            "  \"pad_token_id\": 0,\n",
            "  \"pooler_fc_size\": 768,\n",
            "  \"pooler_num_attention_heads\": 12,\n",
            "  \"pooler_num_fc_layers\": 3,\n",
            "  \"pooler_size_per_head\": 128,\n",
            "  \"pooler_type\": \"first_token_transform\",\n",
            "  \"position_embedding_type\": \"absolute\",\n",
            "  \"transformers_version\": \"4.24.0\",\n",
            "  \"type_vocab_size\": 2,\n",
            "  \"use_cache\": true,\n",
            "  \"vocab_size\": 119547\n",
            "}\n",
            "\n",
            "loading weights file pytorch_model.bin from cache at /root/.cache/huggingface/hub/models--Davlan--bert-base-multilingual-cased-finetuned-amharic/snapshots/90283498970edd0fe6af2f094f5f25d61e525ebd/pytorch_model.bin\n",
            "Some weights of the model checkpoint at Davlan/bert-base-multilingual-cased-finetuned-amharic were not used when initializing BertForSequenceClassification: ['cls.predictions.bias', 'cls.predictions.decoder.weight', 'cls.predictions.decoder.bias', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.weight']\n",
            "- This IS expected if you are initializing BertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing BertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at Davlan/bert-base-multilingual-cased-finetuned-amharic and are newly initialized: ['bert.pooler.dense.weight', 'classifier.bias', 'bert.pooler.dense.bias', 'classifier.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ],
      "source": [
        "#Load auto mode classifier from the pretrained model \n",
        "from transformers import AutoModelForSequenceClassification\n",
        "model = AutoModelForSequenceClassification.from_pretrained(\"Davlan/bert-base-multilingual-cased-finetuned-amharic\", num_labels=2)\n",
        "# model = AutoModelForSequenceClassification.from_pretrained(\"Davlan/\", num_labels=2)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**7.2 Computing Metrics** <br>\n",
        "In this stage, we load the computing metrics. The computing metrics used in this phase are the f1-score and the accuracy. These computing metrics are used during the validation and testing phase  "
      ],
      "metadata": {
        "id": "E2M6UT3p8SCG"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hHOIwoGTnKnu"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "from datasets import load_metric\n",
        "\n",
        "metric = load_metric(\"f1\",\"accuracy\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sRyD2M95nRuB"
      },
      "outputs": [],
      "source": [
        "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)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**7.3 Specify the training arguments** <br>\n",
        "This phase includes loading the training parameters and hyperparameters. \n",
        "It also specifies the validation interval during the fine-tuning process."
      ],
      "metadata": {
        "id": "z2YmCHO99Is2"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mRY3ntTtCSJf",
        "outputId": "b3251fa0-0bde-40ca-9dcb-ef69dba11879"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "PyTorch: setting up devices\n",
            "The default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).\n"
          ]
        }
      ],
      "source": [
        "# #load an optimizer\n",
        "# optimizer = AdamW(model.parameters(), lr=5e-5)\n",
        "from transformers import TrainingArguments\n",
        "\n",
        "training_args = TrainingArguments(output_dir=\"test_trainer\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "piRD3_5-n2_O"
      },
      "outputs": [],
      "source": [
        "from transformers import TrainingArguments, Trainer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pUBDrhFBdsSB"
      },
      "outputs": [],
      "source": [
        "from transformers import EarlyStoppingCallback, IntervalStrategy"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W9DEZ5Vxc4qn",
        "outputId": "9d748d2a-5f64-4311-9697-5f5856a53e6e"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "PyTorch: setting up devices\n",
            "The default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).\n"
          ]
        }
      ],
      "source": [
        "training_args = TrainingArguments(\n",
        "   f\"training_with_callbacks\",\n",
        "   evaluation_strategy = IntervalStrategy.STEPS, # \"steps\"\n",
        "   warmup_steps=500,                # number of warmup steps for learning rate  \n",
        "   save_steps=2000,\n",
        "   eval_steps = 2000, # Evaluation and Save happens every 50 steps\n",
        "   save_total_limit = 3, # Only last 5 models are saved. Older ones are deleted.\n",
        "   learning_rate=1e-5,\n",
        "   per_device_train_batch_size=4,\n",
        "   per_device_eval_batch_size=4,\n",
        "   num_train_epochs=10,\n",
        "   weight_decay=0.01,\n",
        "   push_to_hub=False,\n",
        "   metric_for_best_model = 'f1',\n",
        "   load_best_model_at_end=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**7.4 Load the Trainer class**<br>\n",
        "In the trainer class, early stopping strategy is called. Early Stopping is a an optimization technique used to reduce overfitting without compromising on model accuracy. It allows to specify an arbitrary large number of training epochs and stop training once the model performance stops improving on a hold out validation dataset. For this model, the early stopping patience used is 10 epoches."
      ],
      "metadata": {
        "id": "Ar5jaQ1y-ie-"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2bFFRWbLn99O"
      },
      "outputs": [],
      "source": [
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    train_dataset=small_train_dataset,\n",
        "    eval_dataset=small_eval_dataset,\n",
        "    compute_metrics=compute_metrics,\n",
        "    callbacks = [EarlyStoppingCallback(early_stopping_patience=10)],\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**7.5 Fine-tune the model** <br>\n",
        "Fine-tuning process embbeds the validation within itself. After every 2000 steps of finetuning, the model is validated on the loaded computing metrics to modify the hyperparameters to make the model perform well "
      ],
      "metadata": {
        "id": "N9CgMIfb_d-F"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/",
          "height": 404
        },
        "id": "h4n7RGgUoB13",
        "outputId": "4571397d-022a-48fe-8e90-235985acc3f0"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/transformers/optimization.py:310: 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",
            "  FutureWarning,\n",
            "***** Running training *****\n",
            "  Num examples = 24044\n",
            "  Num Epochs = 10\n",
            "  Instantaneous batch size per device = 4\n",
            "  Total train batch size (w. parallel, distributed & accumulation) = 4\n",
            "  Gradient Accumulation steps = 1\n",
            "  Total optimization steps = 60110\n",
            "  Number of trainable parameters = 177854978\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='60110' max='60110' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [60110/60110 7:55:48, Epoch 10/10]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>F1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>2000</td>\n",
              "      <td>0.677800</td>\n",
              "      <td>0.663232</td>\n",
              "      <td>0.679496</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4000</td>\n",
              "      <td>0.662000</td>\n",
              "      <td>0.648701</td>\n",
              "      <td>0.644918</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6000</td>\n",
              "      <td>0.659100</td>\n",
              "      <td>0.607161</td>\n",
              "      <td>0.719093</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8000</td>\n",
              "      <td>0.627400</td>\n",
              "      <td>0.566045</td>\n",
              "      <td>0.729037</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10000</td>\n",
              "      <td>0.624700</td>\n",
              "      <td>0.549193</td>\n",
              "      <td>0.718121</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>12000</td>\n",
              "      <td>0.617400</td>\n",
              "      <td>0.522069</td>\n",
              "      <td>0.783697</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>14000</td>\n",
              "      <td>0.599300</td>\n",
              "      <td>0.681426</td>\n",
              "      <td>0.708698</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>16000</td>\n",
              "      <td>0.569400</td>\n",
              "      <td>0.532305</td>\n",
              "      <td>0.800330</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>18000</td>\n",
              "      <td>0.570000</td>\n",
              "      <td>0.461551</td>\n",
              "      <td>0.794043</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>20000</td>\n",
              "      <td>0.590100</td>\n",
              "      <td>0.705450</td>\n",
              "      <td>0.787668</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>22000</td>\n",
              "      <td>0.604200</td>\n",
              "      <td>0.553620</td>\n",
              "      <td>0.864472</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>24000</td>\n",
              "      <td>0.608700</td>\n",
              "      <td>0.414158</td>\n",
              "      <td>0.874660</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>26000</td>\n",
              "      <td>0.519100</td>\n",
              "      <td>0.658930</td>\n",
              "      <td>0.846416</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>28000</td>\n",
              "      <td>0.548300</td>\n",
              "      <td>0.500406</td>\n",
              "      <td>0.884321</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>30000</td>\n",
              "      <td>0.550700</td>\n",
              "      <td>0.473687</td>\n",
              "      <td>0.886978</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>32000</td>\n",
              "      <td>0.396900</td>\n",
              "      <td>0.535753</td>\n",
              "      <td>0.891870</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>34000</td>\n",
              "      <td>0.393300</td>\n",
              "      <td>0.504377</td>\n",
              "      <td>0.902613</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>36000</td>\n",
              "      <td>0.480200</td>\n",
              "      <td>0.475147</td>\n",
              "      <td>0.905081</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>38000</td>\n",
              "      <td>0.392600</td>\n",
              "      <td>0.508723</td>\n",
              "      <td>0.897959</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>40000</td>\n",
              "      <td>0.390400</td>\n",
              "      <td>0.514351</td>\n",
              "      <td>0.910406</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>42000</td>\n",
              "      <td>0.413800</td>\n",
              "      <td>0.484568</td>\n",
              "      <td>0.904568</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>44000</td>\n",
              "      <td>0.303600</td>\n",
              "      <td>0.549458</td>\n",
              "      <td>0.908868</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>46000</td>\n",
              "      <td>0.342800</td>\n",
              "      <td>0.524430</td>\n",
              "      <td>0.913271</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>48000</td>\n",
              "      <td>0.321800</td>\n",
              "      <td>0.584195</td>\n",
              "      <td>0.905019</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>50000</td>\n",
              "      <td>0.238600</td>\n",
              "      <td>0.569831</td>\n",
              "      <td>0.911465</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>52000</td>\n",
              "      <td>0.263200</td>\n",
              "      <td>0.576466</td>\n",
              "      <td>0.908419</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>54000</td>\n",
              "      <td>0.252700</td>\n",
              "      <td>0.550676</td>\n",
              "      <td>0.915049</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>56000</td>\n",
              "      <td>0.200100</td>\n",
              "      <td>0.589616</td>\n",
              "      <td>0.915712</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>58000</td>\n",
              "      <td>0.285900</td>\n",
              "      <td>0.555592</td>\n",
              "      <td>0.917269</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>60000</td>\n",
              "      <td>0.231600</td>\n",
              "      <td>0.564297</td>\n",
              "      <td>0.913821</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
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          },
          "metadata": {},
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        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "***** Running Evaluation *****\n",
            "  Num examples = 2400\n",
            "  Batch size = 4\n",
            "Saving model checkpoint to training_with_callbacks/checkpoint-2000\n",
            "Configuration saved in training_with_callbacks/checkpoint-2000/config.json\n",
            "Model weights saved in training_with_callbacks/checkpoint-2000/pytorch_model.bin\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 2400\n",
            "  Batch size = 4\n",
            "Saving model checkpoint to training_with_callbacks/checkpoint-4000\n",
            "Configuration saved in training_with_callbacks/checkpoint-4000/config.json\n",
            "Model weights saved in training_with_callbacks/checkpoint-4000/pytorch_model.bin\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 2400\n",
            "  Batch size = 4\n",
            "Saving model checkpoint to training_with_callbacks/checkpoint-6000\n",
            "Configuration saved in training_with_callbacks/checkpoint-6000/config.json\n",
            "Model weights saved in training_with_callbacks/checkpoint-6000/pytorch_model.bin\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 2400\n",
            "  Batch size = 4\n",
            "Saving model checkpoint to training_with_callbacks/checkpoint-8000\n",
            "Configuration saved in training_with_callbacks/checkpoint-8000/config.json\n",
            "Model weights saved in training_with_callbacks/checkpoint-8000/pytorch_model.bin\n",
            "Deleting older checkpoint [training_with_callbacks/checkpoint-2000] due to args.save_total_limit\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 2400\n",
            "  Batch size = 4\n",
            "Saving model checkpoint to training_with_callbacks/checkpoint-10000\n",
            "Configuration saved in training_with_callbacks/checkpoint-10000/config.json\n",
            "Model weights saved in training_with_callbacks/checkpoint-10000/pytorch_model.bin\n",
            "Deleting older checkpoint [training_with_callbacks/checkpoint-4000] due to args.save_total_limit\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 2400\n",
            "  Batch size = 4\n",
            "Saving model checkpoint to training_with_callbacks/checkpoint-12000\n",
            "Configuration saved in training_with_callbacks/checkpoint-12000/config.json\n",
            "Model weights saved in training_with_callbacks/checkpoint-12000/pytorch_model.bin\n",
            "Deleting older checkpoint [training_with_callbacks/checkpoint-6000] due to args.save_total_limit\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 2400\n",
            "  Batch size = 4\n",
            "Saving model checkpoint to training_with_callbacks/checkpoint-14000\n",
            "Configuration saved in training_with_callbacks/checkpoint-14000/config.json\n",
            "Model weights saved in training_with_callbacks/checkpoint-14000/pytorch_model.bin\n",
            "Deleting older checkpoint [training_with_callbacks/checkpoint-8000] due to args.save_total_limit\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 2400\n",
            "  Batch size = 4\n",
            "Saving model checkpoint to training_with_callbacks/checkpoint-16000\n",
            "Configuration saved in training_with_callbacks/checkpoint-16000/config.json\n",
            "Model weights saved in training_with_callbacks/checkpoint-16000/pytorch_model.bin\n",
            "Deleting older checkpoint [training_with_callbacks/checkpoint-10000] due to args.save_total_limit\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 2400\n",
            "  Batch size = 4\n",
            "Saving model checkpoint to training_with_callbacks/checkpoint-18000\n",
            "Configuration saved in training_with_callbacks/checkpoint-18000/config.json\n",
            "Model weights saved in training_with_callbacks/checkpoint-18000/pytorch_model.bin\n",
            "Deleting older checkpoint [training_with_callbacks/checkpoint-12000] due to args.save_total_limit\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 2400\n",
            "  Batch size = 4\n",
            "Saving model checkpoint to training_with_callbacks/checkpoint-20000\n",
            "Configuration saved in training_with_callbacks/checkpoint-20000/config.json\n",
            "Model weights saved in training_with_callbacks/checkpoint-20000/pytorch_model.bin\n",
            "Deleting older checkpoint [training_with_callbacks/checkpoint-14000] due to args.save_total_limit\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 2400\n",
            "  Batch size = 4\n",
            "Saving model checkpoint to training_with_callbacks/checkpoint-22000\n",
            "Configuration saved in training_with_callbacks/checkpoint-22000/config.json\n",
            "Model weights saved in training_with_callbacks/checkpoint-22000/pytorch_model.bin\n",
            "Deleting older checkpoint [training_with_callbacks/checkpoint-16000] due to args.save_total_limit\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 2400\n",
            "  Batch size = 4\n",
            "Saving model checkpoint to training_with_callbacks/checkpoint-24000\n",
            "Configuration saved in training_with_callbacks/checkpoint-24000/config.json\n",
            "Model weights saved in training_with_callbacks/checkpoint-24000/pytorch_model.bin\n",
            "Deleting older checkpoint [training_with_callbacks/checkpoint-18000] due to args.save_total_limit\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 2400\n",
            "  Batch size = 4\n",
            "Saving model checkpoint to training_with_callbacks/checkpoint-26000\n",
            "Configuration saved in training_with_callbacks/checkpoint-26000/config.json\n",
            "Model weights saved in training_with_callbacks/checkpoint-26000/pytorch_model.bin\n",
            "Deleting older checkpoint [training_with_callbacks/checkpoint-20000] due to args.save_total_limit\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 2400\n",
            "  Batch size = 4\n",
            "Saving model checkpoint to training_with_callbacks/checkpoint-28000\n",
            "Configuration saved in training_with_callbacks/checkpoint-28000/config.json\n",
            "Model weights saved in training_with_callbacks/checkpoint-28000/pytorch_model.bin\n",
            "Deleting older checkpoint [training_with_callbacks/checkpoint-22000] due to args.save_total_limit\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 2400\n",
            "  Batch size = 4\n",
            "Saving model checkpoint to training_with_callbacks/checkpoint-30000\n",
            "Configuration saved in training_with_callbacks/checkpoint-30000/config.json\n",
            "Model weights saved in training_with_callbacks/checkpoint-30000/pytorch_model.bin\n",
            "Deleting older checkpoint [training_with_callbacks/checkpoint-24000] due to args.save_total_limit\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 2400\n",
            "  Batch size = 4\n",
            "Saving model checkpoint to training_with_callbacks/checkpoint-32000\n",
            "Configuration saved in training_with_callbacks/checkpoint-32000/config.json\n",
            "Model weights saved in training_with_callbacks/checkpoint-32000/pytorch_model.bin\n",
            "Deleting older checkpoint [training_with_callbacks/checkpoint-26000] due to args.save_total_limit\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 2400\n",
            "  Batch size = 4\n",
            "Saving model checkpoint to training_with_callbacks/checkpoint-34000\n",
            "Configuration saved in training_with_callbacks/checkpoint-34000/config.json\n",
            "Model weights saved in training_with_callbacks/checkpoint-34000/pytorch_model.bin\n",
            "Deleting older checkpoint [training_with_callbacks/checkpoint-28000] due to args.save_total_limit\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 2400\n",
            "  Batch size = 4\n",
            "Saving model checkpoint to training_with_callbacks/checkpoint-36000\n",
            "Configuration saved in training_with_callbacks/checkpoint-36000/config.json\n",
            "Model weights saved in training_with_callbacks/checkpoint-36000/pytorch_model.bin\n",
            "Deleting older checkpoint [training_with_callbacks/checkpoint-30000] due to args.save_total_limit\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 2400\n",
            "  Batch size = 4\n",
            "Saving model checkpoint to training_with_callbacks/checkpoint-38000\n",
            "Configuration saved in training_with_callbacks/checkpoint-38000/config.json\n",
            "Model weights saved in training_with_callbacks/checkpoint-38000/pytorch_model.bin\n",
            "Deleting older checkpoint [training_with_callbacks/checkpoint-32000] due to args.save_total_limit\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 2400\n",
            "  Batch size = 4\n",
            "Saving model checkpoint to training_with_callbacks/checkpoint-40000\n",
            "Configuration saved in training_with_callbacks/checkpoint-40000/config.json\n",
            "Model weights saved in training_with_callbacks/checkpoint-40000/pytorch_model.bin\n",
            "Deleting older checkpoint [training_with_callbacks/checkpoint-34000] due to args.save_total_limit\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 2400\n",
            "  Batch size = 4\n",
            "Saving model checkpoint to training_with_callbacks/checkpoint-42000\n",
            "Configuration saved in training_with_callbacks/checkpoint-42000/config.json\n",
            "Model weights saved in training_with_callbacks/checkpoint-42000/pytorch_model.bin\n",
            "Deleting older checkpoint [training_with_callbacks/checkpoint-36000] due to args.save_total_limit\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 2400\n",
            "  Batch size = 4\n",
            "Saving model checkpoint to training_with_callbacks/checkpoint-44000\n",
            "Configuration saved in training_with_callbacks/checkpoint-44000/config.json\n",
            "Model weights saved in training_with_callbacks/checkpoint-44000/pytorch_model.bin\n",
            "Deleting older checkpoint [training_with_callbacks/checkpoint-38000] due to args.save_total_limit\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 2400\n",
            "  Batch size = 4\n",
            "Saving model checkpoint to training_with_callbacks/checkpoint-46000\n",
            "Configuration saved in training_with_callbacks/checkpoint-46000/config.json\n",
            "Model weights saved in training_with_callbacks/checkpoint-46000/pytorch_model.bin\n",
            "Deleting older checkpoint [training_with_callbacks/checkpoint-40000] due to args.save_total_limit\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 2400\n",
            "  Batch size = 4\n",
            "Saving model checkpoint to training_with_callbacks/checkpoint-48000\n",
            "Configuration saved in training_with_callbacks/checkpoint-48000/config.json\n",
            "Model weights saved in training_with_callbacks/checkpoint-48000/pytorch_model.bin\n",
            "Deleting older checkpoint [training_with_callbacks/checkpoint-42000] due to args.save_total_limit\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 2400\n",
            "  Batch size = 4\n",
            "Saving model checkpoint to training_with_callbacks/checkpoint-50000\n",
            "Configuration saved in training_with_callbacks/checkpoint-50000/config.json\n",
            "Model weights saved in training_with_callbacks/checkpoint-50000/pytorch_model.bin\n",
            "Deleting older checkpoint [training_with_callbacks/checkpoint-44000] due to args.save_total_limit\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 2400\n",
            "  Batch size = 4\n",
            "Saving model checkpoint to training_with_callbacks/checkpoint-52000\n",
            "Configuration saved in training_with_callbacks/checkpoint-52000/config.json\n",
            "Model weights saved in training_with_callbacks/checkpoint-52000/pytorch_model.bin\n",
            "Deleting older checkpoint [training_with_callbacks/checkpoint-48000] due to args.save_total_limit\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 2400\n",
            "  Batch size = 4\n",
            "Saving model checkpoint to training_with_callbacks/checkpoint-54000\n",
            "Configuration saved in training_with_callbacks/checkpoint-54000/config.json\n",
            "Model weights saved in training_with_callbacks/checkpoint-54000/pytorch_model.bin\n",
            "Deleting older checkpoint [training_with_callbacks/checkpoint-46000] due to args.save_total_limit\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 2400\n",
            "  Batch size = 4\n",
            "Saving model checkpoint to training_with_callbacks/checkpoint-56000\n",
            "Configuration saved in training_with_callbacks/checkpoint-56000/config.json\n",
            "Model weights saved in training_with_callbacks/checkpoint-56000/pytorch_model.bin\n",
            "Deleting older checkpoint [training_with_callbacks/checkpoint-50000] due to args.save_total_limit\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 2400\n",
            "  Batch size = 4\n",
            "Saving model checkpoint to training_with_callbacks/checkpoint-58000\n",
            "Configuration saved in training_with_callbacks/checkpoint-58000/config.json\n",
            "Model weights saved in training_with_callbacks/checkpoint-58000/pytorch_model.bin\n",
            "Deleting older checkpoint [training_with_callbacks/checkpoint-52000] due to args.save_total_limit\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 2400\n",
            "  Batch size = 4\n",
            "Saving model checkpoint to training_with_callbacks/checkpoint-60000\n",
            "Configuration saved in training_with_callbacks/checkpoint-60000/config.json\n",
            "Model weights saved in training_with_callbacks/checkpoint-60000/pytorch_model.bin\n",
            "Deleting older checkpoint [training_with_callbacks/checkpoint-54000] due to args.save_total_limit\n",
            "\n",
            "\n",
            "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
            "\n",
            "\n",
            "Loading best model from training_with_callbacks/checkpoint-58000 (score: 0.9172690763052209).\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "TrainOutput(global_step=60110, training_loss=0.4620399525549899, metrics={'train_runtime': 28552.2813, 'train_samples_per_second': 8.421, 'train_steps_per_second': 2.105, 'total_flos': 6.32624221507584e+16, 'train_loss': 0.4620399525549899, 'epoch': 10.0})"
            ]
          },
          "execution_count": 89,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "trainer.train()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### *Step 8: Test the model*"
      ],
      "metadata": {
        "id": "qxeQt3ntAjgM"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "In this stage the model is tested on the testing dataset. This dataset isn't seen by the model during the finetuning process. "
      ],
      "metadata": {
        "id": "-jt2JhsSAqWm"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4uK2afSroMcn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 193
        },
        "outputId": "a347a1fe-58c8-4817-8088-56f700779da2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "***** Running Evaluation *****\n",
            "  Num examples = 2400\n",
            "  Batch size = 4\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='2089' max='1489' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [1489/1489 3:10:33]\n",
              "    </div>\n",
              "    "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'eval_loss': 0.5555920004844666,\n",
              " 'eval_f1': 0.9172690763052209,\n",
              " 'eval_runtime': 84.2989,\n",
              " 'eval_samples_per_second': 28.47,\n",
              " 'eval_steps_per_second': 7.118,\n",
              " 'epoch': 10.0}"
            ]
          },
          "metadata": {},
          "execution_count": 105
        }
      ],
      "source": [
        "trainer.evaluate(small_test_dataset)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### *Step 9: Push the model to Huggingface Hub*"
      ],
      "metadata": {
        "id": "hotgvaDVA2AX"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "One of the aims of this research/project is to contribute to the IT community in the sector of NLP tasks on low-resourced languages. Hence, the final model was pushed and made publicly available on Huggingface. You can find the model on huggingface [here](https://huggingface.co/amengemeda/improved-amharic-hate-speech-detection-mBERT)\n"
      ],
      "metadata": {
        "id": "rFDVDG6hA8Xl"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "fmvb9G-tq1wb",
        "outputId": "fb423f85-5462-4fe5-e7d0-994b8aa3fab9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: huggingface_hub in /usr/local/lib/python3.7/dist-packages (0.11.1)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.7/dist-packages (from huggingface_hub) (4.1.1)\n",
            "Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from huggingface_hub) (4.13.0)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from huggingface_hub) (3.8.0)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from huggingface_hub) (4.64.1)\n",
            "Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.7/dist-packages (from huggingface_hub) (21.3)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from huggingface_hub) (2.23.0)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.7/dist-packages (from huggingface_hub) (6.0)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=20.9->huggingface_hub) (3.0.9)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->huggingface_hub) (3.10.0)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->huggingface_hub) (1.25.11)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->huggingface_hub) (2.10)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->huggingface_hub) (3.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->huggingface_hub) (2022.9.24)\n"
          ]
        }
      ],
      "source": [
        "#install huggingface_hub package to interact with huggingface platform\n",
        "!pip install huggingface_hub"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vYz3n9rlMu7Q"
      },
      "outputs": [],
      "source": [
        "from huggingface_hub import notebook_login"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "K3tx48fLq5BX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 331,
          "referenced_widgets": [
            "9bce1f7370fd4790850d7727207eb334",
            "63f40be1b8c0418e82ba302a5f22cb36",
            "1cc463575aee4549884458edbe49e1f4",
            "917a6c1cc0b2473bb92547b64213b901",
            "6bbe45d6c08f4a2e81f88367ca65d05a",
            "02fc02628b4c4fa899a810dd3e5239aa",
            "3e8305e73dea44db9bb4b555242f9b03",
            "c04e9851447a4f2092d920a32067339c",
            "ba9a6349f2d9414195103dd1b164594e",
            "ae572d6d518d4b2085ca987062a207b0",
            "5611628cd27e41b4a7c58f983a0fe97e",
            "01d1ee171dd245cfb1bb8cd52a084915",
            "dfd61cff5f104898baa3ebcb199ea3bd",
            "0e4dbba6c175477d94572b75de1cb27c",
            "eb1b0813dbb243ae9ab99fb9ca04f51f",
            "36f9d970ba5741d0941db47597324d95",
            "b13694e633f04f8ea9b397a812d88fd4"
          ]
        },
        "outputId": "2a71ffcd-662e-405c-d1a3-157dbad24079"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Token is valid.\n",
            "Your token has been saved in your configured git credential helpers (store).\n",
            "Your token has been saved to /root/.huggingface/token\n",
            "Login successful\n"
          ]
        }
      ],
      "source": [
        "#login to huggingface\n",
        "notebook_login()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#loading a tokenizer from the pretrained model\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"Davlan/bert-base-multilingual-cased-finetuned-amharic\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
            "56346ee2666e4596bc7b45c2c72b5233",
            "a02a7657549e498d97b04d542b27124b",
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    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoModelForSequenceClassification"
      ],
      "metadata": {
        "id": "M2pNNx-rtuy9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = AutoModelForSequenceClassification.from_pretrained(\"/content/drive/MyDrive/Machine Learning/fine-tuned_BERT/model_3\")"
      ],
      "metadata": {
        "id": "yIK9Xb74s44F"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_F8ILd6Rrzdh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f03a0153-557b-4ad8-be8e-584f14b6d32f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('path/to/amharic-hate-speech-detection-mBERT/tokenizer_config.json',\n",
              " 'path/to/amharic-hate-speech-detection-mBERT/special_tokens_map.json',\n",
              " 'path/to/amharic-hate-speech-detection-mBERT/vocab.txt',\n",
              " 'path/to/amharic-hate-speech-detection-mBERT/added_tokens.json',\n",
              " 'path/to/amharic-hate-speech-detection-mBERT/tokenizer.json')"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "#specify the path for the trainde model and tokenizer to huggingface repository\n",
        "model.save_pretrained(\"path/to/amharic-hate-speech-detection-mBERT\")\n",
        "tokenizer.save_pretrained(\"path/to/amharic-hate-speech-detection-mBERT\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "S0bd_ZLtut3Y",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "11ba5a1b-f0d6-4c0e-9c62-fdc75f5fdc18"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CommitInfo(commit_url='https://huggingface.co/amengemeda/amharic-hate-speech-detection-mBERT/commit/e883a0bb0320e316167eedad955f5a34a03458b8', commit_message='Upload BertForSequenceClassification', commit_description='', oid='e883a0bb0320e316167eedad955f5a34a03458b8', pr_url=None, pr_revision=None, pr_num=None)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "#push the trained model to huggingface repository\n",
        "model.push_to_hub(\"amharic-hate-speech-detection-mBERT\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MVJTbIC0x39L",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6b3e25f5-b68c-4615-ea57-20862e2f4758"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CommitInfo(commit_url='https://huggingface.co/amengemeda/amharic-hate-speech-detection-mBERT/commit/69ed3b88b0d5b4bb21d1f0359cda79864466a4cc', commit_message='Upload tokenizer', commit_description='', oid='69ed3b88b0d5b4bb21d1f0359cda79864466a4cc', pr_url=None, pr_revision=None, pr_num=None)"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "#push the tokenizer to huggingface repository\n",
        "tokenizer.push_to_hub(\"amharic-hate-speech-detection-mBERT\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vl1L-xqeR7CS"
      },
      "outputs": [],
      "source": [
        "model.save_pretrained(\"/content/drive/MyDrive/Machine Learning/fine-tuned_BERT/model_3\")"
      ]
    }
  ],
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