NER_ANER.ipynb

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    "accelerator": "GPU"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/omarhatem97/ANER_DEV/blob/main/NER_ANER.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_RPpBgnURDWY"
      },
      "source": [
        "# Fine Tuning Arabert Model to do NER Task"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ml3jN74SzSkA"
      },
      "source": [
        "### Environment setup"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "amGAPB1SRlOL",
        "outputId": "9692e59b-2049-4adf-bf33-e3dd3624546a"
      },
      "source": [
        "import torch\n",
        "\n",
        "# If there's a GPU available...\n",
        "if torch.cuda.is_available():    \n",
        "\n",
        "    # Tell PyTorch to use the GPU.    \n",
        "    device = torch.device(\"cuda\")\n",
        "\n",
        "    print('There are %d GPU(s) available.' % torch.cuda.device_count())\n",
        "\n",
        "    print('We will use the GPU:', torch.cuda.get_device_name(0))\n",
        "    !nvidia-smi\n",
        "\n",
        "# If not...\n",
        "else:\n",
        "    print('No GPU available, using the CPU instead.')\n",
        "    device = torch.device(\"cpu\")"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "There are 1 GPU(s) available.\n",
            "We will use the GPU: Tesla T4\n",
            "Sun Jul 25 00:04:52 2021       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 470.42.01    Driver Version: 460.32.03    CUDA Version: 11.2     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   40C    P8     9W /  70W |      3MiB / 15109MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BKo_mTT8Q_wK",
        "outputId": "240b23f4-3a0d-498d-c62e-8a5f8fbda447"
      },
      "source": [
        "\n",
        "# get the dataset \n",
        "!gdown --id  1bYDqmEYKpVmVyGc9Vmz3W_2bsPMel6UC\n",
        "\n",
        "\n",
        "# The label_lsit file\n",
        "!gdown --id  1th3j28peQf-asgodeaGo-04aIARN0QoL\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "!pip install transformers \n",
        "!pip install pyarabic\n",
        "!pip install seqeval\n",
        "!git clone https://github.com/aub-mind/arabert"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1bYDqmEYKpVmVyGc9Vmz3W_2bsPMel6UC\n",
            "To: /content/dataset_1.0.txt\n",
            "6.03MB [00:00, 28.3MB/s]\n",
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1th3j28peQf-asgodeaGo-04aIARN0QoL\n",
            "To: /content/label_list.txt\n",
            "100% 1.22k/1.22k [00:00<00:00, 2.00MB/s]\n",
            "Collecting transformers\n",
            "  Downloading transformers-4.9.0-py3-none-any.whl (2.6 MB)\n",
            "\u001b[K     |████████████████████████████████| 2.6 MB 8.8 MB/s \n",
            "\u001b[?25hRequirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (2019.12.20)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from transformers) (2.23.0)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from transformers) (21.0)\n",
            "Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from transformers) (4.6.1)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (1.19.5)\n",
            "Collecting sacremoses\n",
            "  Downloading sacremoses-0.0.45-py3-none-any.whl (895 kB)\n",
            "\u001b[K     |████████████████████████████████| 895 kB 42.7 MB/s \n",
            "\u001b[?25hCollecting huggingface-hub==0.0.12\n",
            "  Downloading huggingface_hub-0.0.12-py3-none-any.whl (37 kB)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.7/dist-packages (from transformers) (4.41.1)\n",
            "Collecting pyyaml>=5.1\n",
            "  Downloading PyYAML-5.4.1-cp37-cp37m-manylinux1_x86_64.whl (636 kB)\n",
            "\u001b[K     |████████████████████████████████| 636 kB 59.6 MB/s \n",
            "\u001b[?25hCollecting tokenizers<0.11,>=0.10.1\n",
            "  Downloading tokenizers-0.10.3-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (3.3 MB)\n",
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            "Installing collected packages: tokenizers, sacremoses, pyyaml, huggingface-hub, transformers\n",
            "  Attempting uninstall: pyyaml\n",
            "    Found existing installation: PyYAML 3.13\n",
            "    Uninstalling PyYAML-3.13:\n",
            "      Successfully uninstalled PyYAML-3.13\n",
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            "Collecting seqeval\n",
            "  Downloading seqeval-1.2.2.tar.gz (43 kB)\n",
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            "Building wheels for collected packages: seqeval\n",
            "  Building wheel for seqeval (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for seqeval: filename=seqeval-1.2.2-py3-none-any.whl size=16181 sha256=d965725be1f61d2e10f4f7a4dcdfaef7098cc3bbaaf9f5c68dfb39432dae96c4\n",
            "  Stored in directory: /root/.cache/pip/wheels/05/96/ee/7cac4e74f3b19e3158dce26a20a1c86b3533c43ec72a549fd7\n",
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            "Installing collected packages: seqeval\n",
            "Successfully installed seqeval-1.2.2\n",
            "Cloning into 'arabert'...\n",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "omPkPFWOR4yI"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.metrics import classification_report\n",
        "from  tqdm.notebook import tqdm\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "from collections import Counter\n"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9O5GEuy9zLdb"
      },
      "source": [
        "from arabert.preprocess import ArabertPreprocessor\n",
        "import numpy as np\n",
        "\n",
        "from seqeval.metrics import accuracy_score, f1_score, precision_score, recall_score\n",
        "from transformers import AutoConfig, AutoModelForTokenClassification, AutoTokenizer, BertForTokenClassification, AdamW, get_linear_schedule_with_warmup\n",
        "from transformers import Trainer , TrainingArguments\n",
        "from transformers.trainer_utils import EvaluationStrategy\n",
        "from transformers.data.processors.utils import InputFeatures\n",
        "from torch.utils.data import Dataset\n",
        "from torch.utils.data import DataLoader\n",
        "from sklearn.utils import resample\n",
        "import logging\n",
        "import torch\n",
        "from torch import nn\n",
        "\n",
        "%load_ext google.colab.data_table"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QKvjJKH_SLxY"
      },
      "source": [
        "## Preprocessing\n",
        "\n",
        "In this part, we take the dataset, and convert it to list of sentences using the newline token to get the end of each sentence\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LvseDejXSRa5"
      },
      "source": [
        "This function will make a dataset of sentences \n",
        "\n",
        "returns: list of shape: \n",
        "  [no_sentences] *  [ [words] , [tags] ]"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YGKoNP2iSA8j"
      },
      "source": [
        "def read_dataset(path):\n",
        "  with open(path,'r',encoding='utf-8') as f:\n",
        "    data = []\n",
        "    sentence = []\n",
        "    label = []\n",
        "    for line in f:\n",
        "      # line = line.replace(u'\\u200e','')\n",
        "      # line = line.replace(u'\\ufeff','')\n",
        "      if '[NEWLINE]' in line:\n",
        "        if len(sentence) > 0:\n",
        "          data.append([sentence,label])\n",
        "          sentence = []\n",
        "          label = []\n",
        "        continue\n",
        "      line = line.strip('\\n')\n",
        "      splits = line.split(' ')\n",
        "      sentence.append(splits[0])\n",
        "      label.append(splits[1])\n",
        "    if len(sentence) > 0:\n",
        "      data.append([sentence,label])\n",
        "  return data"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wn5LyEZ7ZQ1S"
      },
      "source": [
        "Here we can see after converting the dataset into sentences, that we have 15763 Sentence"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uKlNr89VSbf2",
        "outputId": "9dfb1ce7-6df7-4b29-dd99-64f3a809285d"
      },
      "source": [
        "\n",
        "data = read_dataset('dataset_1.0.txt')\n",
        "print(len(data))\n"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "15763\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 126
        },
        "id": "_yjFiUUWSz-e",
        "outputId": "d99be02c-2a36-4ba5-9d34-a383c968cf80"
      },
      "source": [
        "df = pd.DataFrame(data, columns=['text', 'tags'])\n",
        "df.head(1)\n"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.module+javascript": "\n      import \"https://ssl.gstatic.com/colaboratory/data_table/a6224c040fa35dcf/data_table.js\";\n\n      window.createDataTable({\n        data: [[{\n            'v': 0,\n            'f': \"0\",\n        },\n[\"\\u062c\\u0645\\u0647\\u0648\\u0631\\u064a\\u0629\", \"\\u062c\\u0646\\u0648\\u0628\", \"\\u0627\\u0644\\u0633\\u0648\\u062f\\u0627\\u0646\", \"\\u060c\", \"\\u0647\\u064a\", \"\\u062f\\u0648\\u0644\\u0629\", \"\\u062a\\u0642\\u0639\", \"\\u0641\\u064a\", \"\\u0634\\u0631\\u0642\", \"\\u0627\\u0641\\u0631\\u064a\\u0642\\u064a\\u0627\", \"\\u060c\", \"\\u0648\", \"\\u062a\\u0639\\u062a\\u0628\\u0631\", \"\\u0645\\u062f\\u064a\\u0646\\u0629\", \"\\u062c\\u0648\\u0628\\u0627\", \"\\u0639\\u0627\\u0635\\u0645\\u0629\", \"\\u0647\\u0627\", \"\\u0648\", \"\\u0627\\u0643\\u0628\\u0631\", \"\\u0645\\u062f\\u0646\", \"\\u0647\\u0627\", \"\\u060c\", \"\\u0648\", \"\\u0645\\u0646\", \"\\u0627\\u0643\\u0628\\u0631\", \"\\u0645\\u062f\\u0646\", \"\\u0647\\u0627\", \"\\u0647\\u064a\", \"\\u0648\\u0627\\u0648\", \"\\u0648\", \"\\u0645\\u0644\\u0643\\u0627\\u0644\", \"\\u0648\", \"\\u0631\\u0648\\u0645\\u0628\\u064a\\u0643\", \"\\u0648\", \"\\u0627\\u0648\\u064a\\u0644\", \"\\u0648\\u064a\\u0627\\u064a\", \".\"],\n[\"B-Nation\", \"I-Nation\", \"I-Nation\", \"O\", \"O\", \"O\", \"O\", \"O\", \"O\", \"B-Continent\", \"O\", \"O\", \"O\", \"O\", \"B-Population-Center\", \"O\", \"O\", \"O\", \"O\", \"O\", \"O\", \"O\", \"O\", \"O\", \"O\", \"O\", \"O\", \"O\", \"B-Population-Center\", \"O\", \"B-Population-Center\", \"O\", \"B-Population-Center\", \"O\", \"B-Population-Center\", \"I-Population-Center\", \"O\"]]],\n        columns: [[\"number\", \"index\"], [\"string\", \"text\"], [\"string\", \"tags\"]],\n        columnOptions: [{\"width\": \"1px\", \"className\": \"index_column\"}],\n        rowsPerPage: 25,\n        helpUrl: \"https://colab.research.google.com/notebooks/data_table.ipynb\",\n        suppressOutputScrolling: true,\n        minimumWidth: undefined,\n      });\n    ",
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>text</th>\n",
              "      <th>tags</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>[جمهورية, جنوب, السودان, ،, هي, دولة, تقع, في,...</td>\n",
              "      <td>[B-Nation, I-Nation, I-Nation, O, O, O, O, O, ...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                text                                               tags\n",
              "0  [جمهورية, جنوب, السودان, ،, هي, دولة, تقع, في,...  [B-Nation, I-Nation, I-Nation, O, O, O, O, O, ..."
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WxJw40QPZuo7"
      },
      "source": [
        "Here we get the list of all classes we have"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "krfSvW1YGQ1d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c85da2dd-bab3-4e54-9967-0cdbca7a6ad4"
      },
      "source": [
        "label_list = list(pd.read_csv('label_list.txt', header=None, index_col=0).T)\n",
        "\n",
        "label_map = { v:index for index, v in enumerate(label_list) }\n",
        "inv_label_map = {i: label for i, label in enumerate(label_list)}\n",
        "\n",
        "print(label_map)\n",
        "print(inv_label_map)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'O': 0, 'B-Artist': 1, 'I-Artist': 2, 'B-Sound': 3, 'I-Sound': 4, 'B-Educational': 5, 'I-Educational': 6, 'B-Building-Grounds': 7, 'I-Building-Grounds': 8, 'B-Population-Center': 9, 'B-Nation': 10, 'B-State-or-Province': 11, 'I-State-or-Province': 12, 'B-Water-Body': 13, 'I-Water-Body': 14, 'B-Land-Region-Natural': 15, 'I-Land-Region-Natural': 16, 'B-Software': 17, 'I-Software': 18, 'B-Scientist': 19, 'B-Book': 20, 'I-Book': 21, 'I-Scientist': 22, 'B-Group': 23, 'B-Celestial': 24, 'B-Police': 25, 'I-Police': 26, 'I-Population-Center': 27, 'I-Celestial': 28, 'B-Engineer': 29, 'I-Engineer': 30, 'B-Projectile': 31, 'B-Government': 32, 'I-Government': 33, 'B-Commercial': 34, 'I-Commercial': 35, 'B-Continent': 36, 'B-Air': 37, 'I-Air': 38, 'B-Other_PER': 39, 'I-Other_PER': 40, 'I-Group': 41, 'B-Politician': 42, 'I-Politician': 43, 'B-Athlete': 44, 'I-Athlete': 45, 'B-Religious_ORG': 46, 'I-Religious_ORG': 47, 'B-Path': 48, 'I-Path': 49, 'B-Media': 50, 'I-Media': 51, 'B-Non-Governmental': 52, 'I-Non-Governmental': 53, 'B-County-or-District': 54, 'I-County-or-District': 55, 'B-Businessperson': 56, 'B-Lawyer': 57, 'I-Lawyer': 58, 'B-GPE-Cluster': 59, 'I-GPE-Cluster': 60, 'I-Nation': 61, 'B-Religious_PER': 62, 'I-Religious_PER': 63, 'I-Businessperson': 64, 'B-Medical-Science': 65, 'I-Medical-Science': 66, 'B-Movie': 67, 'I-Movie': 68, 'B-Water': 69, 'I-Water': 70, 'B-Drug': 71, 'B-Hardware': 72, 'I-Hardware': 73, 'B-Subarea-Facility': 74, 'I-Subarea-Facility': 75, 'B-Blunt': 76, 'B-Airport': 77, 'I-Blunt': 78, 'I-Drug': 79, 'B-Sports': 80, 'I-Sports': 81, 'B-Shooting': 82, 'I-Shooting': 83, 'B-Food': 84, 'I-Food': 85, 'I-Continent': 86, 'B-Nuclear': 87, 'I-Nuclear': 88, 'B-Entertainment': 89, 'I-Entertainment': 90, 'I-Projectile': 91, 'B-Land': 92, 'B-Sharp': 93, 'I-Airport': 94, 'I-Land': 95, 'B-Plant': 96, 'I-Plant': 97, 'B-Exploding': 98, 'I-Exploding': 99, 'B-Chemical': 100, 'I-Chemical': 101}\n",
            "{0: 'O', 1: 'B-Artist', 2: 'I-Artist', 3: 'B-Sound', 4: 'I-Sound', 5: 'B-Educational', 6: 'I-Educational', 7: 'B-Building-Grounds', 8: 'I-Building-Grounds', 9: 'B-Population-Center', 10: 'B-Nation', 11: 'B-State-or-Province', 12: 'I-State-or-Province', 13: 'B-Water-Body', 14: 'I-Water-Body', 15: 'B-Land-Region-Natural', 16: 'I-Land-Region-Natural', 17: 'B-Software', 18: 'I-Software', 19: 'B-Scientist', 20: 'B-Book', 21: 'I-Book', 22: 'I-Scientist', 23: 'B-Group', 24: 'B-Celestial', 25: 'B-Police', 26: 'I-Police', 27: 'I-Population-Center', 28: 'I-Celestial', 29: 'B-Engineer', 30: 'I-Engineer', 31: 'B-Projectile', 32: 'B-Government', 33: 'I-Government', 34: 'B-Commercial', 35: 'I-Commercial', 36: 'B-Continent', 37: 'B-Air', 38: 'I-Air', 39: 'B-Other_PER', 40: 'I-Other_PER', 41: 'I-Group', 42: 'B-Politician', 43: 'I-Politician', 44: 'B-Athlete', 45: 'I-Athlete', 46: 'B-Religious_ORG', 47: 'I-Religious_ORG', 48: 'B-Path', 49: 'I-Path', 50: 'B-Media', 51: 'I-Media', 52: 'B-Non-Governmental', 53: 'I-Non-Governmental', 54: 'B-County-or-District', 55: 'I-County-or-District', 56: 'B-Businessperson', 57: 'B-Lawyer', 58: 'I-Lawyer', 59: 'B-GPE-Cluster', 60: 'I-GPE-Cluster', 61: 'I-Nation', 62: 'B-Religious_PER', 63: 'I-Religious_PER', 64: 'I-Businessperson', 65: 'B-Medical-Science', 66: 'I-Medical-Science', 67: 'B-Movie', 68: 'I-Movie', 69: 'B-Water', 70: 'I-Water', 71: 'B-Drug', 72: 'B-Hardware', 73: 'I-Hardware', 74: 'B-Subarea-Facility', 75: 'I-Subarea-Facility', 76: 'B-Blunt', 77: 'B-Airport', 78: 'I-Blunt', 79: 'I-Drug', 80: 'B-Sports', 81: 'I-Sports', 82: 'B-Shooting', 83: 'I-Shooting', 84: 'B-Food', 85: 'I-Food', 86: 'I-Continent', 87: 'B-Nuclear', 88: 'I-Nuclear', 89: 'B-Entertainment', 90: 'I-Entertainment', 91: 'I-Projectile', 92: 'B-Land', 93: 'B-Sharp', 94: 'I-Airport', 95: 'I-Land', 96: 'B-Plant', 97: 'I-Plant', 98: 'B-Exploding', 99: 'I-Exploding', 100: 'B-Chemical', 101: 'I-Chemical'}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "szAgZufweY6j"
      },
      "source": [
        "## Dataset splits\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pZ71g7W3Z9SD"
      },
      "source": [
        "We splitthe Dataset into:  90% Train, 10% Valdiation, and 10% Test"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hOpLO-H4eYKe"
      },
      "source": [
        " train_data, test_data = train_test_split(data,test_size= 0.2)\n",
        "\n",
        " test_data , eval_data = train_test_split(test_data,test_size= 0.5)"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YEi_vRw0HtUD",
        "outputId": "4ce31864-fb9b-4b0d-c241-908ae9af6bfa"
      },
      "source": [
        "print(len(train_data))\n",
        "print(len(test_data))\n",
        "print(len(eval_data))"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "12610\n",
            "1576\n",
            "1577\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ViJtYz8GaVop"
      },
      "source": [
        "Here we get the frequency of each class in dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kmfXS6QOWYZ0",
        "outputId": "0c79c07e-e3d0-432a-8806-384d982b7c4c"
      },
      "source": [
        "print(Counter([ label for sentence in train_data for label in sentence[1]]))\n",
        "print(Counter([ label for sentence in test_data for label in sentence[1]]))\n",
        "\n"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Counter({'O': 338776, 'B-Population-Center': 4335, 'B-Nation': 4134, 'I-Politician': 2876, 'B-Group': 2361, 'B-Politician': 2256, 'I-Religious_PER': 1566, 'B-Water-Body': 1304, 'B-State-or-Province': 1149, 'I-Artist': 1062, 'I-Water-Body': 969, 'B-Artist': 940, 'B-Other_PER': 936, 'I-Building-Grounds': 933, 'I-Scientist': 931, 'I-Population-Center': 926, 'I-Non-Governmental': 911, 'I-Commercial': 893, 'I-Government': 842, 'I-Other_PER': 825, 'B-Commercial': 824, 'I-Nation': 816, 'B-Scientist': 761, 'B-Building-Grounds': 722, 'B-Religious_PER': 704, 'B-Celestial': 700, 'I-Book': 632, 'B-Non-Governmental': 630, 'I-Athlete': 618, 'I-Air': 617, 'B-Government': 594, 'B-Police': 589, 'B-Athlete': 559, 'I-Educational': 558, 'I-Media': 537, 'I-Software': 515, 'B-Continent': 493, 'I-Sports': 484, 'B-Air': 482, 'I-Group': 476, 'I-Police': 474, 'B-Land-Region-Natural': 430, 'I-Hardware': 426, 'B-Media': 425, 'B-Sports': 424, 'I-State-or-Province': 414, 'B-Software': 400, 'B-Educational': 386, 'I-Land-Region-Natural': 364, 'I-Path': 359, 'B-Businessperson': 328, 'B-Book': 324, 'B-Path': 284, 'B-Hardware': 264, 'I-Businessperson': 263, 'I-GPE-Cluster': 259, 'B-GPE-Cluster': 256, 'I-Water': 247, 'I-Movie': 236, 'I-Celestial': 196, 'B-Movie': 190, 'B-County-or-District': 188, 'I-Sound': 172, 'B-Water': 141, 'I-County-or-District': 116, 'B-Sound': 107, 'I-Engineer': 97, 'I-Airport': 97, 'B-Engineer': 96, 'I-Religious_ORG': 95, 'I-Medical-Science': 93, 'I-Projectile': 82, 'B-Food': 80, 'I-Subarea-Facility': 76, 'B-Drug': 70, 'B-Projectile': 66, 'I-Entertainment': 66, 'I-Continent': 65, 'B-Airport': 61, 'B-Land': 60, 'B-Religious_ORG': 59, 'B-Subarea-Facility': 58, 'B-Medical-Science': 52, 'B-Entertainment': 47, 'B-Lawyer': 44, 'I-Food': 43, 'I-Plant': 40, 'I-Land': 36, 'I-Lawyer': 36, 'I-Nuclear': 35, 'B-Blunt': 31, 'B-Plant': 27, 'B-Nuclear': 25, 'I-Exploding': 23, 'B-Shooting': 20, 'I-Shooting': 20, 'I-Blunt': 10, 'B-Exploding': 8, 'I-Drug': 5, 'B-Chemical': 4, 'B-Sharp': 4, 'I-Chemical': 1})\n",
            "Counter({'O': 42622, 'B-Population-Center': 592, 'B-Nation': 465, 'I-Politician': 320, 'B-Group': 281, 'B-Politician': 257, 'B-State-or-Province': 182, 'B-Water-Body': 159, 'I-Non-Governmental': 147, 'I-Building-Grounds': 142, 'B-Other_PER': 136, 'I-Population-Center': 135, 'I-Artist': 135, 'I-Water-Body': 133, 'B-Artist': 122, 'I-Scientist': 119, 'I-Air': 101, 'I-Book': 99, 'I-Commercial': 99, 'B-Non-Governmental': 98, 'I-Educational': 98, 'B-Building-Grounds': 97, 'I-Religious_PER': 96, 'B-Commercial': 95, 'B-Scientist': 92, 'I-Nation': 84, 'I-Government': 82, 'I-Group': 80, 'I-State-or-Province': 77, 'B-Air': 74, 'B-Educational': 71, 'I-Other_PER': 69, 'B-Police': 66, 'I-Police': 63, 'B-Government': 60, 'B-Religious_PER': 60, 'B-Businessperson': 52, 'I-Software': 51, 'B-Celestial': 49, 'B-Continent': 48, 'B-Software': 48, 'I-Water': 47, 'I-Media': 44, 'I-Sports': 44, 'I-Businessperson': 40, 'B-Media': 38, 'B-Book': 38, 'B-Sports': 36, 'I-Hardware': 36, 'I-Land-Region-Natural': 36, 'B-Athlete': 36, 'B-Land-Region-Natural': 34, 'B-Hardware': 29, 'B-GPE-Cluster': 27, 'B-Water': 26, 'B-County-or-District': 26, 'I-Path': 25, 'I-GPE-Cluster': 24, 'I-Airport': 23, 'I-Athlete': 20, 'B-Path': 18, 'B-Movie': 17, 'I-Projectile': 17, 'I-Movie': 16, 'I-Entertainment': 15, 'B-Food': 15, 'B-Drug': 14, 'I-Engineer': 14, 'I-Celestial': 14, 'B-Entertainment': 13, 'I-Subarea-Facility': 13, 'B-Engineer': 12, 'B-Airport': 10, 'I-Lawyer': 10, 'B-Subarea-Facility': 10, 'I-Sound': 10, 'I-County-or-District': 9, 'B-Projectile': 9, 'I-Medical-Science': 8, 'B-Lawyer': 8, 'I-Continent': 7, 'B-Sound': 7, 'I-Food': 7, 'B-Religious_ORG': 6, 'I-Religious_ORG': 6, 'B-Land': 5, 'I-Land': 5, 'B-Medical-Science': 4, 'I-Drug': 4, 'I-Nuclear': 3, 'B-Nuclear': 2, 'B-Shooting': 1, 'B-Sharp': 1, 'B-Plant': 1, 'I-Plant': 1})\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N9At1fRxaaFa"
      },
      "source": [
        "Here we get a histogram of number of tokens in each sentence, to choose  a suitiable size for BERT MAX_LEN"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 581
        },
        "id": "jqqERV0Dd9ep",
        "outputId": "1a789c59-f9e9-484b-ed50-ff4cbca55a32"
      },
      "source": [
        "print(\"Training Sentence Lengths: \")\n",
        "plt.hist([ len(sentence[0]) for sentence in train_data],bins=range(0,256,2))\n",
        "plt.show()\n",
        "print(sum([len(sentence[0]) > 256 for sentence in train_data]))\n",
        "\n",
        "print(\"Testing Sentence Lengths: \")\n",
        "plt.hist([ len(sentence[0]) for sentence in test_data],bins=range(0,256,2))\n",
        "plt.show()\n",
        "print(sum([len(sentence[0]) > 256 for sentence in test_data]))"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training Sentence Lengths: \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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N+u9zpXbj+AtkrR8wG+X7dtca4Eu1X+mqqI388ttO3xc8qV90mzFz/wBwW1X9u7b988C7q+pDi9odB463zXcAL6zzJW8A/nKdx+5Ejrdvjrdv4x7vP6+q3UvtmNjVMlV1Eji50Z+TZHa5P0t65Hj75nj7tpXj3YyrZS4AB4a297c6SdIW2Yxw/xJwMMmNSa4B7gZOb8LrSJKWMfZlmar6bpIPAf+TwaWQn6qqZ8f9OkM2vLSzwzjevjnevm3ZeMf+hqokafL8hKokdchwl6QO7ehwT3JbkheSzCWZmXR/NkOSl5N8LcnTSWZb3XVJHk/yYnu+dtL9XK8kn0pyKckzQ3VLji8D97fzfTbJocn1fH2WGe9vJLnQzvHTSQ4P7ftoG+8LSf7NZHq9PkkOJPlikueSPJvkw62+y/O7wngnc36rakc+GLxZ+3XgR4BrgK8CN026X5swzpeBGxbV/SYw08ozwMcm3c8NjO+9wCHgmdXGBxwG/hAIcAvw5KT7P6bx/gbwS0u0van9u34zcGP7937VpMewhrHuBQ618lsZ3Jbkpl7P7wrjncj53ckz9+/d5qCq/g5YuM3BleAIcKqVTwF3TrAvG1JVfwp8c1H1cuM7AjxUA08Au5Ls3Zqejscy413OEeCzVfW3VfUXwByDf/c7QlVdrKovt/JfAc8D++j0/K4w3uVs6vndyeG+Dzg3tH2elf9D7lQF/HGSp9otGwD2VNXFVn4V2DOZrm2a5cbX8zn/UFuK+NTQMls3400yBbwTeJIr4PwuGi9M4Pzu5HC/Urynqg4BtwP3JXnv8M4a/H3X7fWsvY+veQD4F8DNwEXgtybbnfFK8sPA7wEfqarvDO/r8fwuMd6JnN+dHO5XxG0OqupCe74EfJ7Bn22vLfy52p4vTa6Hm2K58XV5zqvqtar6+6r6B+C/8o9/mu/48SZ5E4Og+3RV/X6r7vb8LjXeSZ3fnRzu3d/mIMlbkrx1oQz8FPAMg3Eebc2OAo9MpoebZrnxnQbuaVdV3AK8PvTn/Y61aF35ZxmcYxiM9+4kb05yI3AQ+LOt7t96JQnwIPB8Vf320K4uz+9y453Y+Z30O8wbfHf6MIN3pL8O/Nqk+7MJ4/sRBu+mfxV4dmGMwPXAGeBF4H8B1026rxsY42cY/Kn6/xisOR5bbnwMrqL4L+18fw2YnnT/xzTe32njOdv+h9871P7X2nhfAG6fdP/XONb3MFhyOQs83R6Hez2/K4x3IufX2w9IUod28rKMJGkZhrskdchwl6QOGe6S1CHDXZI6ZLhLUocMd0nq0P8Hgv2pTSz3NBoAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "6\n",
            "Testing Sentence Lengths: \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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kuEtSgwx3SWqQ4S5JDTLcJalBhrskNeh/AOOpO7W44VE+AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bmGGv2yNyvCj"
      },
      "source": [
        "## Training\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5qVNRCj4wg1a"
      },
      "source": [
        "### Model Config\n",
        "\n",
        "Here, we define some constants to use "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZCkpPVJOyuQZ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 262,
          "referenced_widgets": [
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            "ee93224422e247529572a99a3b9faf31",
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            "2f90bd8525a246debce870c4488ffba1",
            "74dd006e712c40089231491000ac2de1",
            "7dafcfb178c644938de35aa24319f52a",
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            "0e75feee29f84b99b59128b37f9d6452",
            "0ed6467b527c4fc4919f2adf72296567",
            "d796337f0ce54f4e8ff518a8b5e9ac9e",
            "551ef152852146ed8996f87d1b4a0564",
            "f18a6143154447aeb99767a3221a7f00",
            "bd24cd5ef3c94a478511ffbfca58c177",
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            "9cf1df4f0bf44d1e899342e3be5f5d25",
            "b76871d98cd1498c934b53375b754e0d",
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            "59f11cf863d74f0bb097674df1d16e21",
            "1a5b604b661344a0bfe1f6b1c1f34a7f"
          ]
        },
        "outputId": "3860eb4a-b2bd-45a2-d75c-a7aa30c898cd"
      },
      "source": [
        "DATASET_NAME = 'Fine_GRATED'\n",
        "MODEL_NAME = 'aubmindlab/bert-base-arabertv02'\n",
        "TASK_NAME = 'tokenclassification'\n",
        "\n",
        "#######  TODO: Make it 256 again\n",
        "MAX_LEN = 256\n",
        "TRAIN_BATCH_SIZE = 16\n",
        "VALID_BATCH_SIZE = 16\n",
        "TEST_BATCH_SIZE = 16\n",
        "EPOCHS = 10\n",
        "MODEL_PATH = \"model\"\n",
        "\n",
        "TOKENIZER = AutoTokenizer.from_pretrained(MODEL_NAME)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9cba8c8439584bfdaf7a3bbbf37bd9e2",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=381.0, style=ProgressStyle(description_…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e427c23cb3b34db082ddec072219c3a7",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=384.0, style=ProgressStyle(description_…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2f90bd8525a246debce870c4488ffba1",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=824793.0, style=ProgressStyle(descripti…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f18a6143154447aeb99767a3221a7f00",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=2642362.0, style=ProgressStyle(descript…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6bcbcadcb5294c489209c20544fa5cc3",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=112.0, style=ProgressStyle(description_…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ffD7OI29wp5g"
      },
      "source": [
        "### Dataset\n",
        "\n",
        "\n",
        "Here we define the Dataset class, and how to process batches before training\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "htos49v6hlzW"
      },
      "source": [
        "class NERDataset:\n",
        "  def __init__(self, texts, tags, label_list, model_name, max_length):\n",
        "    self.texts = texts\n",
        "    self.tags = tags\n",
        "    self.label_map = {label: i for i, label in enumerate(label_list)}\n",
        "    self.preprocessor = ArabertPreprocessor(model_name.split(\"/\")[-1])    \n",
        "    self.pad_token_label_id = torch.nn.CrossEntropyLoss().ignore_index\n",
        "    # Use cross entropy ignore_index as padding label id so that only\n",
        "    # real label ids contribute to the loss later.\n",
        "    self.tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
        "    self.max_length = max_length\n",
        "\n",
        "     \n",
        "  def __len__(self):\n",
        "    return len(self.texts)\n",
        "  \n",
        "  def __getitem__(self, item):\n",
        "    textlist = self.texts[item]\n",
        "    tags = self.tags[item]\n",
        "\n",
        "    tokens = []\n",
        "    label_ids = []\n",
        "    for word, label in zip(textlist, tags):      \n",
        "      clean_word = self.preprocessor.preprocess(word)  \n",
        "      word_tokens = self.tokenizer.tokenize(clean_word)\n",
        "\n",
        "  \n",
        "      if len(word_tokens) > 0:\n",
        "        tokens.extend(word_tokens)    \n",
        "        # Use the real label id for the first token of the word, and padding ids for the remaining tokens\n",
        "        label_ids.extend([self.label_map[label]] + [self.pad_token_label_id] * (len(word_tokens) - 1))\n",
        " \n",
        "    # Account for [CLS] and [SEP] with \"- 2\" and with \"- 3\" for RoBERTa.\n",
        "    special_tokens_count = self.tokenizer.num_special_tokens_to_add()\n",
        "    if len(tokens) > self.max_length - special_tokens_count:\n",
        "      tokens = tokens[: (self.max_length - special_tokens_count)]\n",
        "      label_ids = label_ids[: (self.max_length - special_tokens_count)]\n",
        "  \n",
        "    #Add the [SEP] token\n",
        "    tokens += [self.tokenizer.sep_token]\n",
        "    label_ids += [self.pad_token_label_id]\n",
        "    token_type_ids = [0] * len(tokens)\n",
        "\n",
        "    #Add the [CLS] TOKEN\n",
        "    tokens = [self.tokenizer.cls_token] + tokens\n",
        "    label_ids = [self.pad_token_label_id] + label_ids\n",
        "    token_type_ids = [0] + token_type_ids\n",
        "\n",
        "    input_ids = self.tokenizer.convert_tokens_to_ids(tokens)\n",
        "\n",
        "    # The mask has 1 for real tokens and 0 for padding tokens. Only real\n",
        "    # tokens are attended to.\n",
        "    attention_mask = [1] * len(input_ids)\n",
        "\n",
        "    # ana we ant {pad}\n",
        "    # 1    1  1   0  0\n",
        "    # [cls] B-loc O O B-loc [sep] [pad]\n",
        "    #  -100    2   0  0      -100 \n",
        "\n",
        "    # Zero-pad up to the sequence length.\n",
        "    padding_length = self.max_length - len(input_ids)\n",
        "\n",
        "    input_ids += [self.tokenizer.pad_token_id] * padding_length\n",
        "    attention_mask += [0] * padding_length\n",
        "    token_type_ids += [0] * padding_length\n",
        "    label_ids += [self.pad_token_label_id] * padding_length\n",
        "\n",
        "    assert len(input_ids) == self.max_length\n",
        "    assert len(attention_mask) == self.max_length\n",
        "    assert len(token_type_ids) == self.max_length\n",
        "    assert len(label_ids) == self.max_length\n",
        "\n",
        "    # if item < 5:\n",
        "    #   print(\"*** Example ***\")\n",
        "    #   print(\"tokens:\", \" \".join([str(x) for x in tokens]))\n",
        "    #   print(\"input_ids:\", \" \".join([str(x) for x in input_ids]))\n",
        "    #   print(\"attention_mask:\", \" \".join([str(x) for x in attention_mask]))\n",
        "    #   print(\"token_type_ids:\", \" \".join([str(x) for x in token_type_ids]))\n",
        "    #   print(\"label_ids:\", \" \".join([str(x) for x in label_ids]))\n",
        "    \n",
        "    return {\n",
        "        'input_ids' : torch.tensor(input_ids, dtype=torch.long),\n",
        "        'attention_mask' : torch.tensor(attention_mask, dtype=torch.long),\n",
        "        'token_type_ids' : torch.tensor(token_type_ids, dtype=torch.long),\n",
        "        'labels' : torch.tensor(label_ids, dtype=torch.long)       \n",
        "    }"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z6MC0SRIb-Pr"
      },
      "source": [
        "Here we create The datasets for train,val, and test"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HGBV47Nz1HwU"
      },
      "source": [
        "\n",
        "\n",
        "train_dataset = NERDataset(\n",
        "    texts=[x[0] for x in train_data],\n",
        "    tags=[x[1] for x in train_data],\n",
        "    label_list=label_list,\n",
        "    model_name=MODEL_NAME,\n",
        "    max_length=MAX_LEN\n",
        "    )\n",
        "\n",
        "test_dataset = NERDataset(\n",
        "    texts=[x[0] for x in test_data],\n",
        "    tags=[x[1] for x in test_data],\n",
        "    label_list=label_list,\n",
        "    model_name=MODEL_NAME,\n",
        "    max_length=MAX_LEN\n",
        "    )\n",
        "\n",
        "eval_dataset = NERDataset(\n",
        "    texts=[x[0] for x in eval_data],\n",
        "    tags=[x[1] for x in eval_data],\n",
        "    label_list=label_list,\n",
        "    model_name=MODEL_NAME,\n",
        "    max_length=MAX_LEN\n",
        "    )"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7J4XmsTRw4HF"
      },
      "source": [
        "### Model init and Data Loaders \n",
        "\n",
        "Here we initalize the model as a Brt model for token classification"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yNRpiuLQFNIU"
      },
      "source": [
        "\n",
        "def model_init():\n",
        "    return BertForTokenClassification.from_pretrained(MODEL_NAME,\n",
        "                                                      return_dict=True,\n",
        "                                                      num_labels=len(label_map),output_attentions = False,\n",
        "                                                      output_hidden_states = False)\n",
        "\n",
        "\n",
        "\n"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 171,
          "referenced_widgets": [
            "fe93e214276f4b7f90fb8c73686479da",
            "7b9abb7d11f142ea90924b3323f5d8ef",
            "d604bf010f9d4611af206fa5fe167dc3",
            "bb9cd94f7c93417889de05d2d0ebf5ab",
            "f09444f7609340359f30b5ca00b9228e",
            "7cd5c6e523f64cd79bc82f15879379f7",
            "0eb3b22a8b78490bbc10eda5c9bcfc61",
            "320e0b8104294eaea134329794e63c9d"
          ]
        },
        "id": "XdWKbbci6lro",
        "outputId": "a8b2069f-d15f-4ca1-d434-b2c5a7b2c7d3"
      },
      "source": [
        "my_model = model_init()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "fe93e214276f4b7f90fb8c73686479da",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=543490667.0, style=ProgressStyle(descri…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Some weights of the model checkpoint at aubmindlab/bert-base-arabertv02 were not used when initializing BertForTokenClassification: ['cls.predictions.transform.dense.weight', 'cls.predictions.bias', 'cls.predictions.decoder.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.dense.bias', 'cls.seq_relationship.weight', 'cls.predictions.decoder.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.seq_relationship.bias']\n",
            "- This IS expected if you are initializing BertForTokenClassification 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 BertForTokenClassification 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 BertForTokenClassification were not initialized from the model checkpoint at aubmindlab/bert-base-arabertv02 and are newly initialized: ['classifier.weight', 'classifier.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fs-4YX3e6rN1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "04e6e3c3-9aeb-4e45-cdcf-2c42d284eff6"
      },
      "source": [
        "print(my_model)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "BertForTokenClassification(\n",
            "  (bert): BertModel(\n",
            "    (embeddings): BertEmbeddings(\n",
            "      (word_embeddings): Embedding(64000, 768, padding_idx=0)\n",
            "      (position_embeddings): Embedding(512, 768)\n",
            "      (token_type_embeddings): Embedding(2, 768)\n",
            "      (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
            "      (dropout): Dropout(p=0.1, inplace=False)\n",
            "    )\n",
            "    (encoder): BertEncoder(\n",
            "      (layer): ModuleList(\n",
            "        (0): BertLayer(\n",
            "          (attention): BertAttention(\n",
            "            (self): BertSelfAttention(\n",
            "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
            "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
            "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
            "              (dropout): Dropout(p=0.1, inplace=False)\n",
            "            )\n",
            "            (output): BertSelfOutput(\n",
            "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
            "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
            "              (dropout): Dropout(p=0.1, inplace=False)\n",
            "            )\n",
            "          )\n",
            "          (intermediate): BertIntermediate(\n",
            "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
            "          )\n",
            "          (output): BertOutput(\n",
            "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
            "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
            "            (dropout): Dropout(p=0.1, inplace=False)\n",
            "          )\n",
            "        )\n",
            "        (1): BertLayer(\n",
            "          (attention): BertAttention(\n",
            "            (self): BertSelfAttention(\n",
            "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
            "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
            "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
            "              (dropout): Dropout(p=0.1, inplace=False)\n",
            "            )\n",
            "            (output): BertSelfOutput(\n",
            "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
            "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
            "              (dropout): Dropout(p=0.1, inplace=False)\n",
            "            )\n",
            "          )\n",
            "          (intermediate): BertIntermediate(\n",
            "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
            "          )\n",
            "          (output): BertOutput(\n",
            "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
            "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
            "            (dropout): Dropout(p=0.1, inplace=False)\n",
            "          )\n",
            "        )\n",
            "        (2): BertLayer(\n",
            "          (attention): BertAttention(\n",
            "            (self): BertSelfAttention(\n",
            "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
            "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
            "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
            "              (dropout): Dropout(p=0.1, inplace=False)\n",
            "            )\n",
            "            (output): BertSelfOutput(\n",
            "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
            "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
            "              (dropout): Dropout(p=0.1, inplace=False)\n",
            "            )\n",
            "          )\n",
            "          (intermediate): BertIntermediate(\n",
            "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
            "          )\n",
            "          (output): BertOutput(\n",
            "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
            "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
            "            (dropout): Dropout(p=0.1, inplace=False)\n",
            "          )\n",
            "        )\n",
            "        (3): BertLayer(\n",
            "          (attention): BertAttention(\n",
            "            (self): BertSelfAttention(\n",
            "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
            "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
            "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
            "              (dropout): Dropout(p=0.1, inplace=False)\n",
            "            )\n",
            "            (output): BertSelfOutput(\n",
            "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
            "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
            "              (dropout): Dropout(p=0.1, inplace=False)\n",
            "            )\n",
            "          )\n",
            "          (intermediate): BertIntermediate(\n",
            "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
            "          )\n",
            "          (output): BertOutput(\n",
            "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
            "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
            "            (dropout): Dropout(p=0.1, inplace=False)\n",
            "          )\n",
            "        )\n",
            "        (4): BertLayer(\n",
            "          (attention): BertAttention(\n",
            "            (self): BertSelfAttention(\n",
            "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
            "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
            "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
            "              (dropout): Dropout(p=0.1, inplace=False)\n",
            "            )\n",
            "            (output): BertSelfOutput(\n",
            "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
            "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
            "              (dropout): Dropout(p=0.1, inplace=False)\n",
            "            )\n",
            "          )\n",
            "          (intermediate): BertIntermediate(\n",
            "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
            "          )\n",
            "          (output): BertOutput(\n",
            "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
            "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
            "            (dropout): Dropout(p=0.1, inplace=False)\n",
            "          )\n",
            "        )\n",
            "        (5): BertLayer(\n",
            "          (attention): BertAttention(\n",
            "            (self): BertSelfAttention(\n",
            "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
            "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
            "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
            "              (dropout): Dropout(p=0.1, inplace=False)\n",
            "            )\n",
            "            (output): BertSelfOutput(\n",
            "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
            "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
            "              (dropout): Dropout(p=0.1, inplace=False)\n",
            "            )\n",
            "          )\n",
            "          (intermediate): BertIntermediate(\n",
            "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
            "          )\n",
            "          (output): BertOutput(\n",
            "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
            "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
            "            (dropout): Dropout(p=0.1, inplace=False)\n",
            "          )\n",
            "        )\n",
            "        (6): BertLayer(\n",
            "          (attention): BertAttention(\n",
            "            (self): BertSelfAttention(\n",
            "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
            "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
            "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
            "              (dropout): Dropout(p=0.1, inplace=False)\n",
            "            )\n",
            "            (output): BertSelfOutput(\n",
            "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
            "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
            "              (dropout): Dropout(p=0.1, inplace=False)\n",
            "            )\n",
            "          )\n",
            "          (intermediate): BertIntermediate(\n",
            "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
            "          )\n",
            "          (output): BertOutput(\n",
            "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
            "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
            "            (dropout): Dropout(p=0.1, inplace=False)\n",
            "          )\n",
            "        )\n",
            "        (7): BertLayer(\n",
            "          (attention): BertAttention(\n",
            "            (self): BertSelfAttention(\n",
            "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
            "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
            "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
            "              (dropout): Dropout(p=0.1, inplace=False)\n",
            "            )\n",
            "            (output): BertSelfOutput(\n",
            "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
            "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
            "              (dropout): Dropout(p=0.1, inplace=False)\n",
            "            )\n",
            "          )\n",
            "          (intermediate): BertIntermediate(\n",
            "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
            "          )\n",
            "          (output): BertOutput(\n",
            "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
            "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
            "            (dropout): Dropout(p=0.1, inplace=False)\n",
            "          )\n",
            "        )\n",
            "        (8): BertLayer(\n",
            "          (attention): BertAttention(\n",
            "            (self): BertSelfAttention(\n",
            "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
            "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
            "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
            "              (dropout): Dropout(p=0.1, inplace=False)\n",
            "            )\n",
            "            (output): BertSelfOutput(\n",
            "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
            "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
            "              (dropout): Dropout(p=0.1, inplace=False)\n",
            "            )\n",
            "          )\n",
            "          (intermediate): BertIntermediate(\n",
            "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
            "          )\n",
            "          (output): BertOutput(\n",
            "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
            "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
            "            (dropout): Dropout(p=0.1, inplace=False)\n",
            "          )\n",
            "        )\n",
            "        (9): BertLayer(\n",
            "          (attention): BertAttention(\n",
            "            (self): BertSelfAttention(\n",
            "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
            "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
            "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
            "              (dropout): Dropout(p=0.1, inplace=False)\n",
            "            )\n",
            "            (output): BertSelfOutput(\n",
            "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
            "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
            "              (dropout): Dropout(p=0.1, inplace=False)\n",
            "            )\n",
            "          )\n",
            "          (intermediate): BertIntermediate(\n",
            "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
            "          )\n",
            "          (output): BertOutput(\n",
            "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
            "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
            "            (dropout): Dropout(p=0.1, inplace=False)\n",
            "          )\n",
            "        )\n",
            "        (10): BertLayer(\n",
            "          (attention): BertAttention(\n",
            "            (self): BertSelfAttention(\n",
            "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
            "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
            "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
            "              (dropout): Dropout(p=0.1, inplace=False)\n",
            "            )\n",
            "            (output): BertSelfOutput(\n",
            "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
            "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
            "              (dropout): Dropout(p=0.1, inplace=False)\n",
            "            )\n",
            "          )\n",
            "          (intermediate): BertIntermediate(\n",
            "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
            "          )\n",
            "          (output): BertOutput(\n",
            "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
            "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
            "            (dropout): Dropout(p=0.1, inplace=False)\n",
            "          )\n",
            "        )\n",
            "        (11): BertLayer(\n",
            "          (attention): BertAttention(\n",
            "            (self): BertSelfAttention(\n",
            "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
            "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
            "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
            "              (dropout): Dropout(p=0.1, inplace=False)\n",
            "            )\n",
            "            (output): BertSelfOutput(\n",
            "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
            "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
            "              (dropout): Dropout(p=0.1, inplace=False)\n",
            "            )\n",
            "          )\n",
            "          (intermediate): BertIntermediate(\n",
            "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
            "          )\n",
            "          (output): BertOutput(\n",
            "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
            "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
            "            (dropout): Dropout(p=0.1, inplace=False)\n",
            "          )\n",
            "        )\n",
            "      )\n",
            "    )\n",
            "  )\n",
            "  (dropout): Dropout(p=0.1, inplace=False)\n",
            "  (classifier): Linear(in_features=768, out_features=102, bias=True)\n",
            ")\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KwPHlLOwPGhy"
      },
      "source": [
        "train_data_loader = DataLoader(dataset=train_dataset,batch_size=TRAIN_BATCH_SIZE,shuffle= True)\n",
        "eval_data_loader = DataLoader(dataset=eval_dataset,batch_size=VALID_BATCH_SIZE,shuffle= True)\n",
        "test_data_loader = DataLoader(dataset=test_dataset,batch_size=TEST_BATCH_SIZE,shuffle= True)"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ll13A3prxJzV"
      },
      "source": [
        "### Train, Eval, Test Functions"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G_AeVh6DeJqa"
      },
      "source": [
        "#### Helper Funvtions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-ux11kosx37u"
      },
      "source": [
        "\n",
        "def align_predictions(predictions, label_ids):\n",
        "    \"\"\"\n",
        "        Takes batch of senteces, and the logits for every word in every sentences \n",
        "        it will exclude the padding tokens \n",
        "        \n",
        "        predictions:  [ [ [logits] ] ]\n",
        "        label_ids: [[Sentence1], [Sentence2]]\n",
        "        \n",
        "        returns : predicition label list of shape (no_sentences, no_words_in_sentece)\n",
        "                  true label list of shape (no_sentences, no_words_in_sentece)\n",
        "                  \n",
        "                  output example: y_true = [['O', 'O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]\n",
        "                                  y_pred = [['O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]\n",
        "    \"\"\"\n",
        "   \n",
        "    preds = np.argmax(predictions, axis=2)\n",
        "    \n",
        "   \n",
        "\n",
        "    batch_size, seq_len = preds.shape\n",
        "\n",
        "    assert(preds.shape == label_ids.shape)\n",
        "    \n",
        "    out_label_list = [[] for _ in range(batch_size)]\n",
        "    preds_list = [[] for _ in range(batch_size)]\n",
        "\n",
        "    for i in range(batch_size):\n",
        "        for j in range(seq_len):\n",
        "            \n",
        "            if label_ids[i] [j] != torch.nn.CrossEntropyLoss().ignore_index:\n",
        "                \n",
        "                out_label_list[i].append(inv_label_map[label_ids[i][j]])\n",
        "                preds_list[i].append(inv_label_map[preds[i][j]])\n",
        "\n",
        "    return preds_list, out_label_list"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_JUuvw2g4JDg"
      },
      "source": [
        "def compute_metrics(predictions, labels , generate_report = False):\n",
        "    \n",
        "    preds_list, out_label_list = align_predictions(predictions,labels)\n",
        "    \n",
        "    \n",
        "    if generate_report:\n",
        "        try:         \n",
        "            print(classification_report(out_label_list, preds_list))\n",
        "        except:\n",
        "            print('There Was an error while generating the classification report!')\n",
        "            \n",
        "    return {\n",
        "        \"accuracy_score\": accuracy_score(out_label_list, preds_list),\n",
        "        \"precision\": precision_score(out_label_list, preds_list),\n",
        "        \"recall\": recall_score(out_label_list, preds_list),\n",
        "        \"f1\": f1_score(out_label_list, preds_list),\n",
        "    }\n"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jhOn1FP0dlQN"
      },
      "source": [
        "#### Train Function"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9bMFyymI2JyC"
      },
      "source": [
        "def train_epoch(train_dl, model, optimizer,scheduler,device):\n",
        "\n",
        "    ###########################################\n",
        "    #                                         #\n",
        "    #           TRAINING STARTS HERE          #\n",
        "    #                                         #\n",
        "    ###########################################\n",
        "\n",
        "    model.train()\n",
        "\n",
        "    final_loss = 0\n",
        "\n",
        "    i = 0\n",
        "\n",
        "    for data in tqdm(train_dl, total = len(train_dl)):\n",
        "\n",
        "        # BATCH\n",
        "\n",
        "        # Send Data to GPU\n",
        "        input_ids = data['input_ids'].to(device)\n",
        "        attention_mask = data['attention_mask'].to(device)\n",
        "        token_type_ids = data['token_type_ids'].to(device)\n",
        "        labels = data['labels'].to(device)\n",
        "\n",
        "\n",
        "        optimizer.zero_grad()\n",
        "\n",
        "\n",
        "\n",
        "        # we can also pass token_type_ids\n",
        "\n",
        "        outputs = model(input_ids = input_ids,\n",
        "                        token_type_ids=token_type_ids,\n",
        "                        attention_mask=attention_mask,\n",
        "                        labels=labels)\n",
        "\n",
        "        #### For watching training \n",
        "        # if i==2:\n",
        "        #     a= []\n",
        "        #     b = []\n",
        "        #     print(outputs.logits[5].shape)\n",
        "        #     print(labels[5].shape)\n",
        "        #     for word in outputs.logits[5]:\n",
        "        #         a.append(np.argmax(word.to('cpu').detach().numpy()))\n",
        "        #     for l in labels[5]:\n",
        "        #         b.append(l)\n",
        "        #     print(len(a), len(b))\n",
        "        #     print  ( ' CORRECT:  ',torch.sum(torch.tensor(a) == torch.tensor(b)) )\n",
        "        #     for aa,bb in zip(a,b):\n",
        "        #         print(f' predicted : {aa} ,   Label:  {bb}')\n",
        "\n",
        "        # i+=1\n",
        "\n",
        "        #print(outputs)\n",
        "\n",
        "        # TODO: Watchout forthis \n",
        "        loss = outputs.loss\n",
        "\n",
        "        loss.backward()\n",
        "\n",
        "        # Do we need it ? \n",
        "        nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)\n",
        "\n",
        "        optimizer.step()\n",
        "        scheduler.step()\n",
        "\n",
        "        final_loss += loss.item()\n",
        "\n",
        "\n",
        "\n",
        "    print(final_loss)\n",
        "\n",
        "    return final_loss / len(train_dl)"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TZk2w6Bjdmhd"
      },
      "source": [
        "#### Validation Function"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hAA13r5cDiJr"
      },
      "source": [
        "def eval_epoch(eval_dl, model,device, generate_report = False):\n",
        "\n",
        "  ###########################################\n",
        "  #                                         #\n",
        "  #           EVALUATION STARTS HERE        #\n",
        "  #                                         #\n",
        "  ###########################################\n",
        "\n",
        "    model.eval()\n",
        "\n",
        "    final_loss = 0\n",
        "\n",
        "    # 3D array to store every sentence and its logits (2D array)\n",
        "    all_preds = []\n",
        "    \n",
        "    # 2D array to store every sentence and its labels\n",
        "    all_labels = []\n",
        "\n",
        "    for data in tqdm(eval_dl, total = len(eval_dl)):\n",
        "\n",
        "        # BATCH\n",
        "\n",
        "        # Send Data to GPU\n",
        "        input_ids = data['input_ids'].to(device)\n",
        "        attention_mask = data['attention_mask'].to(device)\n",
        "        token_type_ids = data['token_type_ids'].to(device)\n",
        "        labels = data['labels'].to(device)\n",
        "\n",
        "\n",
        "\n",
        "        outputs = model(input_ids = input_ids,\n",
        "                        token_type_ids=token_type_ids,\n",
        "                        attention_mask=attention_mask,\n",
        "                        labels=labels)\n",
        "\n",
        "        \n",
        "        loss = outputs[0]\n",
        "\n",
        "        final_loss += loss.item()\n",
        "        \n",
        "        logits = outputs.logits.detach().cpu().numpy()\n",
        "        labels = labels.to('cpu').numpy()\n",
        "        \n",
        "        \n",
        "        \n",
        "        \n",
        "        all_preds.extend(logits)\n",
        "        all_labels.extend(labels)\n",
        "      \n",
        "        \n",
        " \n",
        "    all_preds = np.array(all_preds)\n",
        "    all_labels = np.asarray(all_labels)\n",
        "    \n",
        "   \n",
        "   \n",
        "    # this will first clean the data from the padding tokens, then will calcuate the metrics\n",
        "    metrics = compute_metrics(all_preds, all_labels, generate_report)\n",
        "    \n",
        "\n",
        "    final_loss = final_loss / len(eval_dl)\n",
        "\n",
        "\n",
        "    \n",
        "    return final_loss, metrics"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PsQxjGC5dqb8"
      },
      "source": [
        "#### Test Function"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U2K4mVzK8h-x"
      },
      "source": [
        "def model_test(test_dl,model,device):\n",
        "\n",
        "    ###########################################\n",
        "    #                                         #\n",
        "    #           TEST STARTS HERE              #\n",
        "    #                                         #\n",
        "    ###########################################\n",
        "    with torch.no_grad():\n",
        "        model.to(device)\n",
        "        model.eval()\n",
        "        final_loss = 0\n",
        "        all_predictions = []\n",
        "        all_labels = []\n",
        "        for data in tqdm(test_dl, total = len(test_dl)):\n",
        "\n",
        "            input_ids = data['input_ids'].to(device)\n",
        "            attention_mask = data['attention_mask'].to(device)\n",
        "            token_type_ids = data['token_type_ids'].to(device)\n",
        "            labels = data['labels'].to(device)\n",
        "\n",
        "\n",
        "\n",
        "            outputs = model(input_ids = input_ids,\n",
        "                            token_type_ids=token_type_ids,\n",
        "                            attention_mask=attention_mask,\n",
        "                            labels=labels)\n",
        "            \n",
        "            \n",
        "            loss = outputs.loss\n",
        "\n",
        "            predictions = outputs.logits\n",
        "\n",
        "            all_labels.extend(labels.to('cpu').numpy())\n",
        "            all_predictions.extend(outputs.logits.to('cpu').numpy())\n",
        "\n",
        "            final_loss += loss.item()\n",
        "\n",
        "            #print(np.array(all_predictions).shape)\n",
        "\n",
        "            # all_labels=  torch.tensor(all_labels).to('cpu').numpy()\n",
        "            # all_predictions = torch.tensor(all_predictions).to('cpu').numpy()\n",
        "\n",
        "            \n",
        "            \n",
        "        metrics = \\\n",
        "        compute_metrics(predictions=np.asarray(all_predictions), labels=np.asarray(all_labels))\n",
        "\n",
        "        accuracy_score = metrics['accuracy_score']\n",
        "        precision= metrics['precision']\n",
        "        recall= metrics['recall']\n",
        "        f1= metrics['f1']\n",
        "\n",
        "        print(f' Accuracy: {accuracy_score}')\n",
        "        print(f' Precision: {precision}')\n",
        "        print(f' Recall: {recall}')\n",
        "        print(f' F1: {f1}')\n",
        "\n",
        "\n",
        "        final_loss /= len(test_dl) \n",
        "\n",
        "        print(f'Final Test Loss is: {final_loss}' )\n"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b-iOQ0g1xRvI"
      },
      "source": [
        "### Train Loop"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pjFRVxb_jx1B"
      },
      "source": [
        "This part is in case we wann continue from a  trined model, so we load the state dict and continue upon it "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v69lxYL768Vm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "65b3f3ae-8c73-4db8-e233-8da5d1575957"
      },
      "source": [
        "\n",
        "\n",
        "! cp drive/MyDrive/Models/model_v1 .\n",
        "\n",
        "model = model_init().to(device)\n",
        "model.load_state_dict(torch.load('model_v1', map_location='cpu'))"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Some weights of the model checkpoint at aubmindlab/bert-base-arabertv02 were not used when initializing BertForTokenClassification: ['cls.predictions.transform.dense.weight', 'cls.predictions.bias', 'cls.predictions.decoder.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.dense.bias', 'cls.seq_relationship.weight', 'cls.predictions.decoder.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.seq_relationship.bias']\n",
            "- This IS expected if you are initializing BertForTokenClassification 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 BertForTokenClassification 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 BertForTokenClassification were not initialized from the model checkpoint at aubmindlab/bert-base-arabertv02 and are newly initialized: ['classifier.weight', 'classifier.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<All keys matched successfully>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JvPsQI8NjeQH"
      },
      "source": [
        "Here we configure an optimizer and a scheduler"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WTVRGMh9NCKV"
      },
      "source": [
        "optimizer = AdamW(model.parameters(), lr=5e-5, correct_bias=False)\n",
        "total_steps = len(train_data_loader) * EPOCHS\n",
        "\n",
        "scheduler = get_linear_schedule_with_warmup(\n",
        "  optimizer,\n",
        "  num_warmup_steps=0,\n",
        "  num_training_steps=total_steps\n",
        ")"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PiZTFoiEnrMU"
      },
      "source": [
        "#### Train Loop"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "akKFfQvyONGr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
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          ]
        },
        "outputId": "0787f8c5-ad8e-43cd-ac1d-89c7213014ee"
      },
      "source": [
        "for epoch in range(EPOCHS):\n",
        "\n",
        "    print(f'Epoch {epoch + 1}/{EPOCHS}')\n",
        "    print('-' * 10)\n",
        "\n",
        "\n",
        "    train_loss = train_epoch(\n",
        "                            model = model,\n",
        "                            train_dl = train_data_loader ,    \n",
        "                            device = device,\n",
        "                            optimizer =optimizer, \n",
        "                            scheduler = scheduler, \n",
        "\n",
        "    )\n",
        "    print(f'Train loss {train_loss}')\n",
        "\n",
        "\n",
        "    eval_loss, metrics = eval_epoch(\n",
        "                                model = model,\n",
        "                                eval_dl = eval_data_loader ,    \n",
        "                                device = device,\n",
        "                                generate_report = False\n",
        "    )\n",
        "\n",
        "    print(f'Eval loss: {eval_loss}')\n",
        "    print(f'Eval Metrics:  {metrics}')\n",
        "    print('--------------------------------------------------------------------')"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "----------\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "315d399226054309b02d8d0d5fce9504",
              "version_minor": 0,
              "version_major": 2
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            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=789.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "105.94783063046634\n",
            "Train loss 0.13428115415775202\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "8f05f77b52bd4d5b8c77095d6c869ec6",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=99.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Eval loss: 0.07898006904305833\n",
            "Eval Metrics:  {'accuracy_score': 0.9767887436383594, 'precision': 0.8130479628674575, 'recall': 0.874133629054616, 'f1': 0.8424849699398798}\n",
            "--------------------------------------------------------------------\n",
            "Epoch 2/10\n",
            "----------\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6576ae806ad14e0fae79bbe630c04f1e",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=789.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "41.181812111870386\n",
            "Train loss 0.052194945642421275\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "461201312d3d417c91d99910cb799719",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=99.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Eval loss: 0.0798633253164213\n",
            "Eval Metrics:  {'accuracy_score': 0.9782856002394971, 'precision': 0.8562791975817532, 'recall': 0.8638757970612697, 'f1': 0.8600607231576041}\n",
            "--------------------------------------------------------------------\n",
            "Epoch 3/10\n",
            "----------\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c2776da4bfc749c08d94da8f1fe4e20b",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=789.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "21.24124811787624\n",
            "Train loss 0.026921733989703725\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "dce534d722f14551acbdf8b67ce8d1e1",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=99.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Eval loss: 0.08164469362001377\n",
            "Eval Metrics:  {'accuracy_score': 0.980301367129029, 'precision': 0.8490516332982087, 'recall': 0.893540338231217, 'f1': 0.8707280832095097}\n",
            "--------------------------------------------------------------------\n",
            "Epoch 4/10\n",
            "----------\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1e76120653f24598a6dff450cd1d71bf",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=789.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "11.36844716736232\n",
            "Train loss 0.014408678285630316\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e0a2a0cc654144c8aaaa7d6f017451ae",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=99.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Eval loss: 0.08789288026346552\n",
            "Eval Metrics:  {'accuracy_score': 0.9798622891926954, 'precision': 0.8485564304461942, 'recall': 0.8963127252564458, 'f1': 0.8717810435486045}\n",
            "--------------------------------------------------------------------\n",
            "Epoch 5/10\n",
            "----------\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "41e61515a47f4a09a63af9f99cf8d96b",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=789.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "7.420482853645808\n",
            "Train loss 0.009404921234025105\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "245229d3b86e458eb504a8dbb201d645",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=99.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Eval loss: 0.08959614255964155\n",
            "Eval Metrics:  {'accuracy_score': 0.980301367129029, 'precision': 0.8564864864864865, 'recall': 0.878569448294982, 'f1': 0.8673874367045299}\n",
            "--------------------------------------------------------------------\n",
            "Epoch 6/10\n",
            "----------\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "91fed2cdc48246f3aaaedf721ebb2a0a",
              "version_minor": 0,
              "version_major": 2
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            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=789.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "4.441823933593696\n",
            "Train loss 0.0056296881287626055\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "db2b1534be8b4855913ab619151cd331",
              "version_minor": 0,
              "version_major": 2
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            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=99.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Eval loss: 0.0970855683955244\n",
            "Eval Metrics:  {'accuracy_score': 0.9805608222732263, 'precision': 0.8595505617977528, 'recall': 0.8907679512059884, 'f1': 0.8748808713410483}\n",
            "--------------------------------------------------------------------\n",
            "Epoch 7/10\n",
            "----------\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d9d40bd9fcd741979073c698f60da933",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=789.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "3.228431094692496\n",
            "Train loss 0.004091801133957536\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "7d14b350fab34d7baa4b6012d95ebc99",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=99.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Eval loss: 0.10069075637647525\n",
            "Eval Metrics:  {'accuracy_score': 0.9808801516814689, 'precision': 0.8740178813329721, 'recall': 0.8943720543387857, 'f1': 0.8840778295423404}\n",
            "--------------------------------------------------------------------\n",
            "Epoch 8/10\n",
            "----------\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "942aa24b126b4c27a4f64e1549272a0d",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=789.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "1.6342345748853404\n",
            "Train loss 0.002071273225456705\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "38e50f1602a74596bcb1d10421892ae4",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=99.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Eval loss: 0.10467533023106028\n",
            "Eval Metrics:  {'accuracy_score': 0.9804809899211655, 'precision': 0.8669170914944996, 'recall': 0.8957582478514, 'f1': 0.881101718025634}\n",
            "--------------------------------------------------------------------\n",
            "Epoch 9/10\n",
            "----------\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c405b0b40dbd48fbbf5c32085ab6d9a6",
              "version_minor": 0,
              "version_major": 2
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            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=789.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "1.2822555266029667\n",
            "Train loss 0.0016251654329568653\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "8915cc93fd034a7e89830948faf7648b",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=99.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Eval loss: 0.10596334810822619\n",
            "Eval Metrics:  {'accuracy_score': 0.9815786847619998, 'precision': 0.8661858974358975, 'recall': 0.8990851122816745, 'f1': 0.8823289348387975}\n",
            "--------------------------------------------------------------------\n",
            "Epoch 10/10\n",
            "----------\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b2a9938cc2124b84bc8af5256220b15f",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=789.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "0.8250579643299716\n",
            "Train loss 0.0010457008419898245\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "de25018390124a19a03338f410df8676",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=99.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Eval loss: 0.10414021417311618\n",
            "Eval Metrics:  {'accuracy_score': 0.9816984332900908, 'precision': 0.8714859437751004, 'recall': 0.902411976711949, 'f1': 0.8866793789158267}\n",
            "--------------------------------------------------------------------\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NVXEVsTsxxx1"
      },
      "source": [
        "### Saving Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vlcdIA7v_UwB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "12ccd48f-1cc4-4a98-fdee-f29f228d80ac"
      },
      "source": [
        "\n",
        "save_model_name = 'full_model_v4'\n",
        "\n",
        "\n",
        "torch.save(model, save_model_name)\n",
        "\n",
        "!cp full_model_v2 drive/MyDrive/Models/"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "cp: cannot stat 'full_model_v2': No such file or directory\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FP53AdCsyKTt"
      },
      "source": [
        "### Testing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OXQRksMkG4-9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "300078dd-96eb-4f39-8781-116f9a183644"
      },
      "source": [
        "! cp drive/MyDrive/Models/full_model_v4 ."
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "cp: cannot stat 'drive/MyDrive/Models/full_model_v4': No such file or directory\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x2taxTzeGOb7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 151,
          "referenced_widgets": [
            "c6338ee449104a439cd81c29e2f2ec90",
            "99ae38f0efed4f3192db4c216c77ecbb",
            "a9c2e4c6024041ca8d1106abcbad4036",
            "b52de1f83a4c4a8d974f2e8624ff1527",
            "01d720f2afae4ac8802d91acfad416ba",
            "2194fc0580fc4814b49fe8bf29b53170",
            "ffc6f251c1c449d1bcfae3240061e52a",
            "6bfb2ce31f404254af678bb7f053377a"
          ]
        },
        "outputId": "7531e388-bdc2-484c-a549-1ba301b5b0ec"
      },
      "source": [
        "############ TESTING ###########\n",
        "\n",
        "\n",
        "# test_model = model_init()\n",
        "# test_model.load_state_dict(torch.load('model_v1', map_location='cpu'))\n",
        "\n",
        "test_model = torch.load('full_model_v4' ,map_location='cpu')\n",
        "\n",
        "model_test(test_dl= test_data_loader,model=test_model, device= device)"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c6338ee449104a439cd81c29e2f2ec90",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=99.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            " Accuracy: 0.9826782008342408\n",
            " Precision: 0.8702497285559175\n",
            " Recall: 0.9053939565094606\n",
            " F1: 0.8874740484429066\n",
            "Final Test Loss is: 0.10402907690223226\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bBbYx4Vh7-O_"
      },
      "source": [
        " BEST Testing result \n",
        "\n",
        "\n",
        "Accuracy: 0.9967066235732112\n",
        " Precision: 0.9761073825503356\n",
        " Recall: 0.9811117107393416\n",
        " F1: 0.9786031489705288\n",
        "Final Test Loss is: 0.018308890143107603"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hcROXvlQyzhl"
      },
      "source": [
        "### Test on one sentence "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5r1Uuioxpptp"
      },
      "source": [
        "This function is to test our trained model, on a sentence "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RIO5gxu7JstD"
      },
      "source": [
        "def predict_sent(sentences):\n",
        "\n",
        "    input_ids  = TOKENIZER.encode(sentences, return_tensors='pt')\n",
        "\n",
        "    #print(input_ids)\n",
        "\n",
        "    with torch.no_grad():\n",
        "        test_model.to('cpu')\n",
        "        output = test_model(input_ids)\n",
        "    label_indices = np.argmax(output[0].to('cpu').numpy(), axis=2)\n",
        "\n",
        "    #print(label_indices)\n",
        "\n",
        "    tokens = TOKENIZER.convert_ids_to_tokens(input_ids.to('cpu').numpy()[0])\n",
        "# hoda m7 ##md\n",
        "\n",
        "    # print(tokens)\n",
        "    # print(len(tokens) )\n",
        "\n",
        "    # print(len(label_indices[0]))\n",
        "\n",
        "\n",
        "    # for a,b in zip(tokens, label_indices[0]):\n",
        "    #     print(a , inv_label_map[b])\n",
        "\n",
        "\n",
        "    new_tokens, new_labels = [], []\n",
        "    for token, label_idx in zip(tokens, label_indices[0]):\n",
        "        if token.startswith(\"##\"):\n",
        "            new_tokens[-1] = new_tokens[-1] + token[2:]\n",
        "        else:\n",
        "            new_labels.append(inv_label_map[label_idx])\n",
        "            new_tokens.append(token)\n",
        "\n",
        "\n",
        "    for token, label in zip(new_tokens, new_labels):\n",
        "        print(\"{}\\t{}\".format(label, token))"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pkJsF_zLJ5ls",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e36d94ce-33e9-419f-bbb6-c9fd4ca39968"
      },
      "source": [
        "\n",
        "sentences = 'من اشهر الجبال في المحافظة هو جبل اكاجي و جبل هارونا'\n",
        "\n",
        "\n",
        "sentence2 = \" النجم محمد صلاح لاعب المنتخب المصري يعيش في مصر بالتحديد من المنصوره\"\n",
        "\n",
        "predict_sent(sentence2)"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "O\t[CLS]\n",
            "O\tالنجم\n",
            "B-Athlete\tمحمد\n",
            "I-Athlete\tصلاح\n",
            "O\tلاعب\n",
            "B-Sports\tالمنتخب\n",
            "I-Sports\tالمصري\n",
            "O\tيعيش\n",
            "O\tفي\n",
            "B-Nation\tمصر\n",
            "O\tبالتحديد\n",
            "O\tمن\n",
            "B-Population-Center\tالمنصوره\n",
            "O\t[SEP]\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}